bytes:\n\n \"\"\"simple docstring\"\"\"\n\n\n\n return baseaa.baaencode(string.encode(\"utf-8\"\t\t\t)\t\t\t)\ndef \t\t\t\t\tUpperCamelCase\t\t( UpperCAmelCase\t\t\t)\t\t->str:\n\n \"\"\"simple docstring\"\"\"\n\n\n\n return baseaa.baadecode(UpperCAmelCase\t\t\t).decode(\"utf-8\"\t\t\t)\n\n\nif __name__ == \"__main__\":\n UpperCamelCase_\t\t\t\t\t\t\t=\t\t\t\t\t'Hello World!'\n UpperCamelCase_\t\t\t\t\t\t\t=\t\t\t\t\tbaseaa_encode(test)\n print(encoded)\n\n UpperCamelCase_\t\t\t\t\t\t\t=\t\t\t\t\tbaseaa_decode(encoded)\n print(decoded)"},"style_context_codestyle":{"kind":"number","value":243,"string":"243"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":503,"cells":{"code":{"kind":"string","value":"\n\n\nimport math\nfrom typing import Optional\n\nimport numpy as np\n\nfrom ...configuration_utils import PretrainedConfig\nfrom ...utils import logging\n\n\n_snake_case\t\t\t\t\t:\t\tUnion[str, Any] = logging.get_logger(__name__)\n\n_snake_case\t\t\t\t\t:\t\tList[str] = {\n \"facebook/encodec_24khz\": \"https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json\",\n \"facebook/encodec_48khz\": \"https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json\",\n}\n\nclass a\t\t(_lowerCAmelCase\t\t\t\t):\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t__UpperCAmelCase :\t\t\t\t\t\t\tTuple\t\t\t\t\t = \"encodec\"\n\n\t\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself\t: Any , lowerCamelCase\t: Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowerCamelCase\t: List[str]=24000 , lowerCamelCase\t: int=1 , lowerCamelCase\t: Optional[int]=False , lowerCamelCase\t: Dict=None , lowerCamelCase\t: Tuple=None , lowerCamelCase\t: Optional[int]=128 , lowerCamelCase\t: Optional[int]=32 , lowerCamelCase\t: List[str]=1 , lowerCamelCase\t: str=[8, 5, 4, 2] , lowerCamelCase\t: List[str]=\"weight_norm\" , lowerCamelCase\t: Any=7 , lowerCamelCase\t: Tuple=7 , lowerCamelCase\t: int=3 , lowerCamelCase\t: int=2 , lowerCamelCase\t: Union[str, Any]=True , lowerCamelCase\t: List[Any]=\"reflect\" , lowerCamelCase\t: Union[str, Any]=2 , lowerCamelCase\t: Optional[int]=2 , lowerCamelCase\t: int=1.0 , lowerCamelCase\t: Optional[Any]=1024 , lowerCamelCase\t: Optional[Any]=None , lowerCamelCase\t: str=True , **lowerCamelCase\t: Dict , ) -> Any:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Tuple\t\t\t\t\t\t\t\t\t\t= target_bandwidths\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= sampling_rate\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= audio_channels\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Dict\t\t\t\t\t\t\t\t\t\t= normalize\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[Any]\t\t\t\t\t\t\t\t\t\t= chunk_length_s\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Tuple\t\t\t\t\t\t\t\t\t\t= overlap\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[int]\t\t\t\t\t\t\t\t\t\t= hidden_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[Any]\t\t\t\t\t\t\t\t\t\t= num_filters\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= num_residual_layers\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[int]\t\t\t\t\t\t\t\t\t\t= upsampling_ratios\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[str]\t\t\t\t\t\t\t\t\t\t= norm_type\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[int]\t\t\t\t\t\t\t\t\t\t= kernel_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Dict\t\t\t\t\t\t\t\t\t\t= last_kernel_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Tuple\t\t\t\t\t\t\t\t\t\t= residual_kernel_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[Any]\t\t\t\t\t\t\t\t\t\t= dilation_growth_rate\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[int]\t\t\t\t\t\t\t\t\t\t= use_causal_conv\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Tuple\t\t\t\t\t\t\t\t\t\t= pad_mode\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= compress\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= num_lstm_layers\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: int\t\t\t\t\t\t\t\t\t\t= trim_right_ratio\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Tuple\t\t\t\t\t\t\t\t\t\t= codebook_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t= codebook_dim if codebook_dim is not None else hidden_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: int\t\t\t\t\t\t\t\t\t\t= use_conv_shortcut\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif self.norm_type not in [\"weight_norm\", \"time_group_norm\"]:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F'self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}' )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(**lowerCamelCase )\n\n\t\t\t\t\t\t\t@property\n\t\t\t\t\t\t\tdef __snake_case (\t\t\t\t\t\t\tself\t: int ) -> Optional[int]:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif self.chunk_length_s is None:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn None\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn int(self.chunk_length_s * self.sampling_rate )\n\n\t\t\t\t\t\t\t@property\n\t\t\t\t\t\t\tdef __snake_case (\t\t\t\t\t\t\tself\t: Union[str, Any] ) -> Optional[int]:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif self.chunk_length_s is None or self.overlap is None:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn None\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn max(1 , int((1.0 - self.overlap) * self.chunk_length ) )\n\n\t\t\t\t\t\t\t@property\n\t\t\t\t\t\t\tdef __snake_case (\t\t\t\t\t\t\tself\t: Optional[Any] ) -> int:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= np.prod(self.upsampling_ratios )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn math.ceil(self.sampling_rate / hop_length )\n\n\n\n\n\n\n\t\t\t\t\t\t\t@property\n\t\t\t\t\t\t\tdef __snake_case (\t\t\t\t\t\t\tself\t: Optional[Any] ) -> int:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )\n\n"},"code_codestyle":{"kind":"number","value":134,"string":"134"},"style_context":{"kind":"string","value":"\n\n\nfrom collections import OrderedDict\nfrom typing import Mapping\n\nfrom packaging import version\n\nfrom ...configuration_utils import PretrainedConfig\nfrom ...onnx import OnnxConfig\nfrom ...utils import logging\n\n\n_snake_case\t\t\t\t\t:\t\tint = logging.get_logger(__name__)\n\n_snake_case\t\t\t\t\t:\t\tint = {\n \"microsoft/beit-base-patch16-224-pt22k\": (\n \"https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json\"\n ),\n # See all BEiT models at https://huggingface.co/models?filter=beit\n}\n\nclass a\t\t(_lowerCAmelCase\t\t\t\t):\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t__UpperCAmelCase :\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t = \"beit\"\n\n\t\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself\t: Union[str, Any] , lowerCamelCase\t: Any=8192 , lowerCamelCase\t: Dict=768 , lowerCamelCase\t: int=12 , lowerCamelCase\t: Optional[Any]=12 , lowerCamelCase\t: List[str]=3072 , lowerCamelCase\t: Tuple=\"gelu\" , lowerCamelCase\t: Union[str, Any]=0.0 , lowerCamelCase\t: int=0.0 , lowerCamelCase\t: Dict=0.02 , lowerCamelCase\t: List[str]=1E-12 , lowerCamelCase\t: Optional[Any]=224 , lowerCamelCase\t: Optional[int]=16 , lowerCamelCase\t: Any=3 , lowerCamelCase\t: Optional[int]=False , lowerCamelCase\t: Any=False , lowerCamelCase\t: Optional[Any]=False , lowerCamelCase\t: int=False , lowerCamelCase\t: Any=0.1 , lowerCamelCase\t: Tuple=0.1 , lowerCamelCase\t: Optional[int]=True , lowerCamelCase\t: int=[3, 5, 7, 11] , lowerCamelCase\t: str=[1, 2, 3, 6] , lowerCamelCase\t: int=True , lowerCamelCase\t: List[Any]=0.4 , lowerCamelCase\t: int=256 , lowerCamelCase\t: str=1 , lowerCamelCase\t: List[str]=False , lowerCamelCase\t: List[str]=255 , **lowerCamelCase\t: Dict , ) -> int:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(**lowerCamelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Any\t\t\t\t\t\t\t\t\t\t= vocab_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[str]\t\t\t\t\t\t\t\t\t\t= hidden_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[Any]\t\t\t\t\t\t\t\t\t\t= num_hidden_layers\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Tuple\t\t\t\t\t\t\t\t\t\t= num_attention_heads\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Dict\t\t\t\t\t\t\t\t\t\t= intermediate_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= hidden_act\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t= hidden_dropout_prob\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[int]\t\t\t\t\t\t\t\t\t\t= attention_probs_dropout_prob\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= initializer_range\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: str\t\t\t\t\t\t\t\t\t\t= layer_norm_eps\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t= image_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[str]\t\t\t\t\t\t\t\t\t\t= patch_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t= num_channels\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Any\t\t\t\t\t\t\t\t\t\t= use_mask_token\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[str]\t\t\t\t\t\t\t\t\t\t= use_absolute_position_embeddings\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[Any]\t\t\t\t\t\t\t\t\t\t= use_relative_position_bias\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: str\t\t\t\t\t\t\t\t\t\t= use_shared_relative_position_bias\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: str\t\t\t\t\t\t\t\t\t\t= layer_scale_init_value\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Any\t\t\t\t\t\t\t\t\t\t= drop_path_rate\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: int\t\t\t\t\t\t\t\t\t\t= use_mean_pooling\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# decode head attributes (semantic segmentation)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t= out_indices\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[str]\t\t\t\t\t\t\t\t\t\t= pool_scales\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# auxiliary head attributes (semantic segmentation)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: int\t\t\t\t\t\t\t\t\t\t= use_auxiliary_head\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: int\t\t\t\t\t\t\t\t\t\t= auxiliary_loss_weight\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: Optional[int]\t\t\t\t\t\t\t\t\t\t= auxiliary_channels\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: int\t\t\t\t\t\t\t\t\t\t= auxiliary_num_convs\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: str\t\t\t\t\t\t\t\t\t\t= auxiliary_concat_input\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__snake_case\t\t: List[str]\t\t\t\t\t\t\t\t\t\t= semantic_loss_ignore_index\n\nclass a\t\t(_lowerCAmelCase\t\t\t\t):\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t__UpperCAmelCase :\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t = version.parse(\"1.11\"\t\t\t\t)\n\n\t\t\t\t\t\t\t@property\n\t\t\t\t\t\t\tdef __snake_case (\t\t\t\t\t\t\tself\t: Dict ) -> Mapping[str, Mapping[int, str]]:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn OrderedDict(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t [\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t (\"pixel_values\", {0: \"batch\", 1: \"num_channels\", 2: \"height\", 3: \"width\"}),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t ] )\n\n\n\n\n\n\n\t\t\t\t\t\t\t@property\n\t\t\t\t\t\t\tdef __snake_case (\t\t\t\t\t\t\tself\t: str ) -> float:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn 1E-4\n\n"},"style_context_codestyle":{"kind":"number","value":134,"string":"134"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":504,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\r'''simple docstring'''\r\r\r\r\r\r\r\rimport argparse\rimport os\rimport re\r\r\rlowerCAmelCase_\t\t\t\t\t: Optional[int] \t=\t\t\t'src/transformers'\r\r# Pattern that looks at the indentation in a line.\rlowerCAmelCase_\t\t\t\t\t: Union[str, Any] \t=\t\t\tre.compile(R'^(\\s*)\\S')\r# Pattern that matches `\"key\":\" and puts `key` in group 0.\rlowerCAmelCase_\t\t\t\t\t: Union[str, Any] \t=\t\t\tre.compile(R'^\\s*\"([^\"]+)\":')\r# Pattern that matches `_import_structure[\"key\"]` and puts `key` in group 0.\rlowerCAmelCase_\t\t\t\t\t: Any \t=\t\t\tre.compile(R'^\\s*_import_structure\\[\"([^\"]+)\"\\]')\r# Pattern that matches `\"key\",` and puts `key` in group 0.\rlowerCAmelCase_\t\t\t\t\t: Optional[int] \t=\t\t\tre.compile(R'^\\s*\"([^\"]+)\",\\s*$')\r# Pattern that matches any `[stuff]` and puts `stuff` in group 0.\rlowerCAmelCase_\t\t\t\t\t: Union[str, Any] \t=\t\t\tre.compile(R'\\[([^\\]]+)\\]')\r\r\r\r\r\r\rdef _lowerCamelCase\t\t\t\t( lowercase\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t)\t->\tAny:\r\t\t\t\t_a\t\t\t\t\t\t\t\t= _re_indent.search(lowercase\t\t\t\t\t\t)\r\t\t\t\treturn \"\" if search is None else search.groups()[0]\r\r\r\r\r\r\rdef _lowerCamelCase\t\t\t\t( lowercase\t\t\t\t\t\t: Dict ,\t\t\t\t\t\t\tlowercase\t\t\t\t\t\t: Union[str, Any]=\"\" ,\t\t\t\t\t\t\tlowercase\t\t\t\t\t\t: Tuple=None ,\t\t\t\t\t\t\tlowercase\t\t\t\t\t\t: List[Any]=None\t\t\t\t\t\t)\t->\tstr:\r\t\t\t\t_a\t\t\t\t\t\t\t\t= 0\r\t\t\t\t_a\t\t\t\t\t\t\t\t= code.split(\"\\n\"\t\t\t\t\t\t)\r\t\t\t\tif start_prompt is not None:\r\t\t\t\t\t\t\t\twhile not lines[index].startswith(lowercase\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\tindex += 1\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [\"\\n\".join(lines[:index]\t\t\t\t\t\t)]\r\t\t\t\telse:\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= []\r\r\t\t\t\t# We split into blocks until we get to the `end_prompt` (or the end of the block).\r\t\t\t\t_a\t\t\t\t\t\t\t\t= [lines[index]]\r\t\t\t\tindex += 1\r\t\t\t\twhile index < len(lowercase\t\t\t\t\t\t) and (end_prompt is None or not lines[index].startswith(lowercase\t\t\t\t\t\t)):\r\t\t\t\t\t\t\t\tif len(lines[index]\t\t\t\t\t\t) > 0 and get_indent(lines[index]\t\t\t\t\t\t) == indent_level:\r\t\t\t\t\t\t\t\t\t\t\t\tif len(lowercase\t\t\t\t\t\t) > 0 and get_indent(current_block[-1]\t\t\t\t\t\t).startswith(indent_level + \" \"\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcurrent_block.append(lines[index]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tblocks.append(\"\\n\".join(lowercase\t\t\t\t\t\t)\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif index < len(lowercase\t\t\t\t\t\t) - 1:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [lines[index + 1]]\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tindex += 1\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= []\r\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tblocks.append(\"\\n\".join(lowercase\t\t\t\t\t\t)\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [lines[index]]\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\tcurrent_block.append(lines[index]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\tindex += 1\r\r\t\t\t\t# Adds current block if it's nonempty.\r\t\t\t\tif len(lowercase\t\t\t\t\t\t) > 0:\r\t\t\t\t\t\t\t\tblocks.append(\"\\n\".join(lowercase\t\t\t\t\t\t)\t\t\t\t\t\t)\r\r\t\t\t\t# Add final block after end_prompt if provided.\r\t\t\t\tif end_prompt is not None and index < len(lowercase\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\tblocks.append(\"\\n\".join(lines[index:]\t\t\t\t\t\t)\t\t\t\t\t\t)\r\r\t\t\t\treturn blocks\r\r\r\r\r\r\rdef _lowerCamelCase\t\t\t\t( lowercase\t\t\t\t\t\t: str\t\t\t\t\t\t)\t->\tint:\r\r\t\t\t\tdef _inner(lowercase\t\t\t\t\t\t: Dict\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\treturn key(lowercase\t\t\t\t\t\t).lower().replace(\"_\" ,\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t)\r\r\t\t\t\treturn _inner\r\r\r\r\r\r\rdef _lowerCamelCase\t\t\t\t( lowercase\t\t\t\t\t\t: str ,\t\t\t\t\t\t\tlowercase\t\t\t\t\t\t: Tuple=None\t\t\t\t\t\t)\t->\tOptional[int]:\r\r\t\t\t\t# If no key is provided, we use a noop.\r\t\t\t\tdef noop(lowercase\t\t\t\t\t\t: List[str]\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\treturn x\r\r\t\t\t\tif key is None:\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= noop\r\t\t\t\t# Constants are all uppercase, they go first.\r\t\t\t\t_a\t\t\t\t\t\t\t\t= [obj for obj in objects if key(lowercase\t\t\t\t\t\t).isupper()]\r\t\t\t\t# Classes are not all uppercase but start with a capital, they go second.\r\t\t\t\t_a\t\t\t\t\t\t\t\t= [obj for obj in objects if key(lowercase\t\t\t\t\t\t)[0].isupper() and not key(lowercase\t\t\t\t\t\t).isupper()]\r\t\t\t\t# Functions begin with a lowercase, they go last.\r\t\t\t\t_a\t\t\t\t\t\t\t\t= [obj for obj in objects if not key(lowercase\t\t\t\t\t\t)[0].isupper()]\r\r\t\t\t\t_a\t\t\t\t\t\t\t\t= ignore_underscore(lowercase\t\t\t\t\t\t)\r\t\t\t\treturn sorted(lowercase ,\t\t\t\t\t\t\tkey=lowercase\t\t\t\t\t\t) + sorted(lowercase ,\t\t\t\t\t\t\tkey=lowercase\t\t\t\t\t\t) + sorted(lowercase ,\t\t\t\t\t\t\tkey=lowercase\t\t\t\t\t\t)\r\r\r\r\r\r\rdef _lowerCamelCase\t\t\t\t( lowercase\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t)\t->\tstr:\r\r\t\t\t\t# This inner function sort imports between [ ].\r\t\t\t\tdef _replace(lowercase\t\t\t\t\t\t: List[str]\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= match.groups()[0]\r\t\t\t\t\t\t\t\tif \",\" not in imports:\r\t\t\t\t\t\t\t\t\t\t\t\treturn F'[{imports}]'\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [part.strip().replace(\"\\\"\" ,\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t) for part in imports.split(\",\"\t\t\t\t\t\t)]\r\t\t\t\t\t\t\t\t# We will have a final empty element if the line finished with a comma.\r\t\t\t\t\t\t\t\tif len(keys[-1]\t\t\t\t\t\t) == 0:\r\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= keys[:-1]\r\t\t\t\t\t\t\t\treturn \"[\" + \", \".join([F'\"{k}\"' for k in sort_objects(lowercase\t\t\t\t\t\t)]\t\t\t\t\t\t) + \"]\"\r\r\t\t\t\t_a\t\t\t\t\t\t\t\t= import_statement.split(\"\\n\"\t\t\t\t\t\t)\r\t\t\t\tif len(lowercase\t\t\t\t\t\t) > 3:\r\t\t\t\t\t\t\t\t# Here we have to sort internal imports that are on several lines (one per name):\r\t\t\t\t\t\t\t\t# key: [\r\t\t\t\t\t\t\t\t# \"object1\",\r\t\t\t\t\t\t\t\t# \"object2\",\r\t\t\t\t\t\t\t\t# ...\r\t\t\t\t\t\t\t\t# ]\r\r\t\t\t\t\t\t\t\t# We may have to ignore one or two lines on each side.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= 2 if lines[1].strip() == \"[\" else 1\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [(i, _re_strip_line.search(lowercase\t\t\t\t\t\t).groups()[0]) for i, line in enumerate(lines[idx:-idx]\t\t\t\t\t\t)]\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= sort_objects(lowercase ,\t\t\t\t\t\t\tkey=lambda lowercase\t\t\t\t\t\t: x[1]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [lines[x[0] + idx] for x in sorted_indices]\r\t\t\t\t\t\t\t\treturn \"\\n\".join(lines[:idx] + sorted_lines + lines[-idx:]\t\t\t\t\t\t)\r\t\t\t\telif len(lowercase\t\t\t\t\t\t) == 3:\r\t\t\t\t\t\t\t\t# Here we have to sort internal imports that are on one separate line:\r\t\t\t\t\t\t\t\t# key: [\r\t\t\t\t\t\t\t\t# \"object1\", \"object2\", ...\r\t\t\t\t\t\t\t\t# ]\r\t\t\t\t\t\t\t\tif _re_bracket_content.search(lines[1]\t\t\t\t\t\t) is not None:\r\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= _re_bracket_content.sub(_replace ,\t\t\t\t\t\t\tlines[1]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [part.strip().replace(\"\\\"\" ,\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t) for part in lines[1].split(\",\"\t\t\t\t\t\t)]\r\t\t\t\t\t\t\t\t\t\t\t\t# We will have a final empty element if the line finished with a comma.\r\t\t\t\t\t\t\t\t\t\t\t\tif len(keys[-1]\t\t\t\t\t\t) == 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= keys[:-1]\r\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= get_indent(lines[1]\t\t\t\t\t\t) + \", \".join([F'\"{k}\"' for k in sort_objects(lowercase\t\t\t\t\t\t)]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\treturn \"\\n\".join(lowercase\t\t\t\t\t\t)\r\t\t\t\telse:\r\t\t\t\t\t\t\t\t# Finally we have to deal with imports fitting on one line\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= _re_bracket_content.sub(_replace ,\t\t\t\t\t\t\tlowercase\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\treturn import_statement\r\r\r\r\r\r\rdef _lowerCamelCase\t\t\t\t( lowercase\t\t\t\t\t\t: Tuple ,\t\t\t\t\t\t\tlowercase\t\t\t\t\t\t: List[Any]=True\t\t\t\t\t\t)\t->\tstr:\r\t\t\t\twith open(lowercase ,\t\t\t\t\t\t\tencoding=\"utf-8\"\t\t\t\t\t\t) as f:\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= f.read()\r\r\t\t\t\tif \"_import_structure\" not in code:\r\t\t\t\t\t\t\t\treturn\r\r\t\t\t\t# Blocks of indent level 0\r\t\t\t\t_a\t\t\t\t\t\t\t\t= split_code_in_indented_blocks(\r\t\t\t\t lowercase ,\t\t\t\t\t\t\tstart_prompt=\"_import_structure = {\" ,\t\t\t\t\t\t\tend_prompt=\"if TYPE_CHECKING:\"\t\t\t\t\t\t)\r\r\t\t\t\t# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).\r\t\t\t\tfor block_idx in range(1 ,\t\t\t\t\t\t\tlen(lowercase\t\t\t\t\t\t) - 1\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t# Check if the block contains some `_import_structure`s thingy to sort.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= main_blocks[block_idx]\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= block.split(\"\\n\"\t\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t# Get to the start of the imports.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= 0\r\t\t\t\t\t\t\t\twhile line_idx < len(lowercase\t\t\t\t\t\t) and \"_import_structure\" not in block_lines[line_idx]:\r\t\t\t\t\t\t\t\t\t\t\t\t# Skip dummy import blocks\r\t\t\t\t\t\t\t\t\t\t\t\tif \"import dummy\" in block_lines[line_idx]:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= len(lowercase\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tline_idx += 1\r\t\t\t\t\t\t\t\tif line_idx >= len(lowercase\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\tcontinue\r\r\t\t\t\t\t\t\t\t# Ignore beginning and last line: they don't contain anything.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= \"\\n\".join(block_lines[line_idx:-1]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= get_indent(block_lines[1]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t# Slit the internal block into blocks of indent level 1.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= split_code_in_indented_blocks(lowercase ,\t\t\t\t\t\t\tindent_level=lowercase\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t# We have two categories of import key: list or _import_structure[key].append/extend\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= _re_direct_key if \"_import_structure = {\" in block_lines[0] else _re_indirect_key\r\t\t\t\t\t\t\t\t# Grab the keys, but there is a trap: some lines are empty or just comments.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [(pattern.search(lowercase\t\t\t\t\t\t).groups()[0] if pattern.search(lowercase\t\t\t\t\t\t) is not None else None) for b in internal_blocks]\r\t\t\t\t\t\t\t\t# We only sort the lines with a key.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [(i, key) for i, key in enumerate(lowercase\t\t\t\t\t\t) if key is not None]\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [x[0] for x in sorted(lowercase ,\t\t\t\t\t\t\tkey=lambda lowercase\t\t\t\t\t\t: x[1]\t\t\t\t\t\t)]\r\r\t\t\t\t\t\t\t\t# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= 0\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= []\r\t\t\t\t\t\t\t\tfor i in range(len(lowercase\t\t\t\t\t\t)\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\tif keys[i] is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treorderded_blocks.append(internal_blocks[i]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= sort_objects_in_import(internal_blocks[sorted_indices[count]]\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treorderded_blocks.append(lowercase\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcount += 1\r\r # And we put our main block back together with its first and last line.\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= \"\\n\".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]\t\t\t\t\t\t)\r\r\t\t\t\tif code != \"\\n\".join(lowercase\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\tif check_only:\r\t\t\t\t\t\t\t\t\t\t\t\treturn True\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\tprint(F'Overwriting {file}.'\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\twith open(lowercase ,\t\t\t\t\t\t\t\"w\" ,\t\t\t\t\t\t\tencoding=\"utf-8\"\t\t\t\t\t\t) as f:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tf.write(\"\\n\".join(lowercase\t\t\t\t\t\t)\t\t\t\t\t\t)\r\r\r\r\r\r\rdef _lowerCamelCase\t\t\t\t( lowercase\t\t\t\t\t\t: List[str]=True\t\t\t\t\t\t)\t->\tList[str]:\r\t\t\t\t_a\t\t\t\t\t\t\t\t= []\r\t\t\t\tfor root, _, files in os.walk(lowercase\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\tif \"__init__.py\" in files:\r\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= sort_imports(os.path.join(lowercase ,\t\t\t\t\t\t\t\"__init__.py\"\t\t\t\t\t\t) ,\t\t\t\t\t\t\tcheck_only=lowercase\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\tif result:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [os.path.join(lowercase ,\t\t\t\t\t\t\t\"__init__.py\"\t\t\t\t\t\t)]\r\t\t\t\tif len(lowercase\t\t\t\t\t\t) > 0:\r\t\t\t\t\t\t\t\traise ValueError(F'Would overwrite {len(lowercase\t\t\t\t\t\t)} files, run `make style`.'\t\t\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\tlowerCAmelCase_\t\t\t\t\t: Optional[Any] \t=\t\t\targparse.ArgumentParser()\r\t\t\t\t\tparser.add_argument('--check_only', action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true", help='Whether to only check or fix style.')\r\t\t\t\t\tlowerCAmelCase_\t\t\t\t\t: List[str] \t=\t\t\tparser.parse_args()\r\r\t\t\t\t\tsort_imports_in_all_inits(check_only=args.check_only)\r"},"code_codestyle":{"kind":"number","value":63,"string":"63"},"style_context":{"kind":"string","value":"\n\nimport gc\nimport unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom diffusers import AutoencoderKL\nfrom diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import enable_full_determinism\n\nfrom .test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nenable_full_determinism()\n\nclass a__ ( snake_case\t\t\t\t\t\t\t,\t\tsnake_case\t\t\t\t\t\t\t,\t\tunittest.TestCase\t):\n\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\t\t\t__lowerCamelCase \t\t\t\t=\t\t\t\tAutoencoderKL\n\t\t\t__lowerCamelCase \t\t\t\t=\t\t\t\t'sample'\n\t\t\t__lowerCamelCase \t\t\t\t=\t\t\t\t1e-2\n\n\n\t\t\t@property\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Optional[Any]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\t4\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\t3\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\t(32, 32)\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tfloats_tensor((batch_size, num_channels) + sizes\t\t\t\t).to(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\treturn {\"sample\": image}\n\n\n\t\t\t@property\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Optional[Any]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\treturn (3, 32, 32)\n\n\n\t\t\t@property\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Optional[Any]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\treturn (3, 32, 32)\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Optional[Any]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\t{\n\t\t\t\t\t\t\t \"block_out_channels\": [32, 64],\n\t\t\t\t\t\t\t \"in_channels\": 3,\n\t\t\t\t\t\t\t \"out_channels\": 3,\n\t\t\t\t\t\t\t \"down_block_types\": [\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n\t\t\t\t\t\t\t \"up_block_types\": [\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n\t\t\t\t\t\t\t \"latent_channels\": 4,\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.dummy_input\n\t\t\t\t\t\t\treturn init_dict, inputs_dict\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Tuple:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tpass\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Any:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tpass\n\n\n\t\t\t@unittest.skipIf(torch_device == \"mps\"\t\t\t\t\t,\t\t\t\t\t\"Gradient checkpointing skipped on MPS\"\t\t\t\t)\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Tuple:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t, A__\t\t\t\t\t\t=\tself.prepare_init_args_and_inputs_for_common()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.model_class(**lowercase\t\t\t\t)\n\t\t\t\t\t\t\tmodel.to(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\tassert not model.is_gradient_checkpointing and model.training\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel(**lowercase\t\t\t\t).sample\n\t\t\t\t\t\t\t# run the backwards pass on the model. For backwards pass, for simplicity purpose,\n\t\t\t\t\t\t\t# we won't calculate the loss and rather backprop on out.sum()\n\t\t\t\t\t\t\tmodel.zero_grad()\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.randn_like(lowercase\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\t(out - labels).mean()\n\t\t\t\t\t\t\tloss.backward()\n\n\t\t\t\t\t\t\t# re-instantiate the model now enabling gradient checkpointing\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.model_class(**lowercase\t\t\t\t)\n\t\t\t\t\t\t\t# clone model\n\t\t\t\t\t\t\tmodel_a.load_state_dict(model.state_dict()\t\t\t\t)\n\t\t\t\t\t\t\tmodel_a.to(lowercase\t\t\t\t)\n\t\t\t\t\t\t\tmodel_a.enable_gradient_checkpointing()\n\n\t\t\t\t\t\t\tassert model_a.is_gradient_checkpointing and model_a.training\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel_a(**lowercase\t\t\t\t).sample\n\t\t\t\t\t\t\t# run the backwards pass on the model. For backwards pass, for simplicity purpose,\n\t\t\t\t\t\t\t# we won't calculate the loss and rather backprop on out.sum()\n\t\t\t\t\t\t\tmodel_a.zero_grad()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\t(out_a - labels).mean()\n\t\t\t\t\t\t\tloss_a.backward()\n\n\t\t\t\t\t\t\t# compare the output and parameters gradients\n\t\t\t\t\t\t\tself.assertTrue((loss - loss_a).abs() < 1e-5\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tdict(model.named_parameters()\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tdict(model_a.named_parameters()\t\t\t\t)\n\t\t\t\t\t\t\tfor name, param in named_params.items():\n\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(torch_all_close(param.grad.data\t\t\t\t\t,\t\t\t\t\tnamed_params_a[name].grad.data\t\t\t\t\t,\t\t\t\t\tatol=5e-5\t\t\t\t)\t\t\t\t)\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Optional[int]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t, A__\t\t\t\t\t\t=\tAutoencoderKL.from_pretrained(\"fusing/autoencoder-kl-dummy\"\t\t\t\t\t,\t\t\t\t\toutput_loading_info=lowercase\t\t\t\t)\n\t\t\t\t\t\t\tself.assertIsNotNone(lowercase\t\t\t\t)\n\t\t\t\t\t\t\tself.assertEqual(len(loading_info[\"missing_keys\"]\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t0\t\t\t\t)\n\n\t\t\t\t\t\t\tmodel.to(lowercase\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel(**self.dummy_input\t\t\t\t)\n\n\t\t\t\t\t\t\tassert image is not None, \"Make sure output is not None\"\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Any:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tAutoencoderKL.from_pretrained(\"fusing/autoencoder-kl-dummy\"\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel.to(lowercase\t\t\t\t)\n\t\t\t\t\t\t\tmodel.eval()\n\n\t\t\t\t\t\t\tif torch_device == \"mps\":\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.manual_seed(0\t\t\t\t)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.Generator(device=lowercase\t\t\t\t).manual_seed(0\t\t\t\t)\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.randn(\n\t\t\t\t\t\t\t 1\t\t\t\t\t,\t\t\t\t\tmodel.config.in_channels\t\t\t\t\t,\t\t\t\t\tmodel.config.sample_size\t\t\t\t\t,\t\t\t\t\tmodel.config.sample_size\t\t\t\t\t,\t\t\t\t\tgenerator=torch.manual_seed(0\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\timage.to(lowercase\t\t\t\t)\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel(lowercase\t\t\t\t\t,\t\t\t\t\tsample_posterior=lowercase\t\t\t\t\t,\t\t\t\t\tgenerator=lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\toutput[0, -1, -3:, -3:].flatten().cpu()\n\n\t\t\t\t\t\t\t# Since the VAE Gaussian prior's generator is seeded on the appropriate device,\n\t\t\t\t\t\t\t# the expected output slices are not the same for CPU and GPU.\n\t\t\t\t\t\t\tif torch_device == \"mps\":\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(\n\t\t\t\t\t\t\t\t\t\t\t [\n\t\t\t\t\t\t\t\t\t\t\t -4.00_78e-01,\n\t\t\t\t\t\t\t\t\t\t\t -3.83_23e-04,\n\t\t\t\t\t\t\t\t\t\t\t -1.26_81e-01,\n\t\t\t\t\t\t\t\t\t\t\t -1.14_62e-01,\n\t\t\t\t\t\t\t\t\t\t\t 2.00_95e-01,\n\t\t\t\t\t\t\t\t\t\t\t 1.08_93e-01,\n\t\t\t\t\t\t\t\t\t\t\t -8.82_47e-02,\n\t\t\t\t\t\t\t\t\t\t\t -3.03_61e-01,\n\t\t\t\t\t\t\t\t\t\t\t -9.86_44e-03,\n\t\t\t\t\t\t\t\t\t\t\t ]\t\t\t\t)\n\t\t\t\t\t\t\telif torch_device == \"cpu\":\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(\n\t\t\t\t\t\t\t\t\t\t\t [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]\t\t\t\t)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(\n\t\t\t\t\t\t\t\t\t\t\t [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]\t\t\t\t)\n\n\t\t\t\t\t\t\tself.assertTrue(torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\trtol=1e-2\t\t\t\t)\t\t\t\t)\n\n@slow\nclass a__ ( unittest.TestCase\t):\n\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> str:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\treturn F'gaussian_noise_s={seed}_shape={\"_\".join([str(lowercase\t\t\t\t) for s in shape]\t\t\t\t)}.npy'\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t) -> Optional[int]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tsuper().tearDown()\n\t\t\t\t\t\t\tgc.collect()\n\t\t\t\t\t\t\ttorch.cuda.empty_cache()\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase=0\t\t\t\t\t,\t\t\t\t\tlowercase=(4, 3, 512, 512)\t\t\t\t\t,\t\t\t\t\tlowercase=False\t\t\t\t) -> Optional[int]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.floataa if fpaa else torch.floataa\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.from_numpy(load_hf_numpy(self.get_file_format(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t)\t\t\t\t)\t\t\t\t).to(lowercase\t\t\t\t).to(lowercase\t\t\t\t)\n\t\t\t\t\t\t\treturn image\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase=\"CompVis/stable-diffusion-v1-4\"\t\t\t\t\t,\t\t\t\t\tlowercase=False\t\t\t\t) -> Any:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\t\"fp16\" if fpaa else None\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.floataa if fpaa else torch.floataa\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tAutoencoderKL.from_pretrained(\n\t\t\t\t\t\t\t lowercase\t\t\t\t\t,\t\t\t\t\tsubfolder=\"vae\"\t\t\t\t\t,\t\t\t\t\ttorch_dtype=lowercase\t\t\t\t\t,\t\t\t\t\trevision=lowercase\t\t\t\t\t,\t\t\t\t\t)\n\t\t\t\t\t\t\tmodel.to(lowercase\t\t\t\t).eval()\n\n\t\t\t\t\t\t\treturn model\n\n\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase=0\t\t\t\t) -> List[str]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tif torch_device == \"mps\":\n\t\t\t\t\t\t\t\t\t\t\treturn torch.manual_seed(lowercase\t\t\t\t)\n\t\t\t\t\t\t\treturn torch.Generator(device=lowercase\t\t\t\t).manual_seed(lowercase\t\t\t\t)\n\n\n\t\t\t@parameterized.expand(\n\t\t\t [\n\t\t\t # fmt: off\n\t\t\t [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],\n\t\t\t [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],\n\t\t\t # fmt: on\n\t\t\t ]\t\t\t\t)\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> int:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_vae_model()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_image(lowercase\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_generator(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel(lowercase\t\t\t\t\t,\t\t\t\t\tgenerator=lowercase\t\t\t\t\t,\t\t\t\t\tsample_posterior=lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tassert sample.shape == image.shape\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tsample[-1, -2:, -2:, :2].flatten().float().cpu()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(expected_slice_mps if torch_device == \"mps\" else expected_slice\t\t\t\t)\n\n\t\t\t\t\t\t\tassert torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tatol=3e-3\t\t\t\t)\n\n\n\t\t\t@parameterized.expand(\n\t\t\t [\n\t\t\t # fmt: off\n\t\t\t [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],\n\t\t\t [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],\n\t\t\t # fmt: on\n\t\t\t ]\t\t\t\t)\n\t\t\t@require_torch_gpu\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> List[Any]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_vae_model(fpaa=lowercase\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_image(lowercase\t\t\t\t\t,\t\t\t\t\tfpaa=lowercase\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_generator(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel(lowercase\t\t\t\t\t,\t\t\t\t\tgenerator=lowercase\t\t\t\t\t,\t\t\t\t\tsample_posterior=lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tassert sample.shape == image.shape\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tsample[-1, -2:, :2, -2:].flatten().float().cpu()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\tassert torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tatol=1e-2\t\t\t\t)\n\n\n\t\t\t@parameterized.expand(\n\t\t\t [\n\t\t\t # fmt: off\n\t\t\t [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],\n\t\t\t [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],\n\t\t\t # fmt: on\n\t\t\t ]\t\t\t\t)\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> Dict:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_vae_model()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_image(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel(lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tassert sample.shape == image.shape\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tsample[-1, -2:, -2:, :2].flatten().float().cpu()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(expected_slice_mps if torch_device == \"mps\" else expected_slice\t\t\t\t)\n\n\t\t\t\t\t\t\tassert torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tatol=3e-3\t\t\t\t)\n\n\n\t\t\t@parameterized.expand(\n\t\t\t [\n\t\t\t # fmt: off\n\t\t\t [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],\n\t\t\t [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],\n\t\t\t # fmt: on\n\t\t\t ]\t\t\t\t)\n\t\t\t@require_torch_gpu\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> Tuple:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_vae_model()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_image(lowercase\t\t\t\t\t,\t\t\t\t\tshape=(3, 4, 64, 64)\t\t\t\t)\n\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel.decode(lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tassert list(sample.shape\t\t\t\t) == [3, 3, 512, 512]\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tsample[-1, -2:, :2, -2:].flatten().cpu()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\tassert torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tatol=1e-3\t\t\t\t)\n\n\n\t\t\t@parameterized.expand(\n\t\t\t [\n\t\t\t # fmt: off\n\t\t\t [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],\n\t\t\t [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],\n\t\t\t # fmt: on\n\t\t\t ]\t\t\t\t)\n\t\t\t@require_torch_gpu\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> Union[str, Any]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_vae_model(fpaa=lowercase\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_image(lowercase\t\t\t\t\t,\t\t\t\t\tshape=(3, 4, 64, 64)\t\t\t\t\t,\t\t\t\t\tfpaa=lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel.decode(lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tassert list(sample.shape\t\t\t\t) == [3, 3, 512, 512]\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tsample[-1, -2:, :2, -2:].flatten().float().cpu()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\tassert torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tatol=5e-3\t\t\t\t)\n\n\n\t\t\t@parameterized.expand([(13,), (16,), (27,)]\t\t\t\t)\n\t\t\t@require_torch_gpu\n\t\t\t@unittest.skipIf(not is_xformers_available()\t\t\t\t\t,\t\t\t\t\treason=\"xformers is not required when using PyTorch 2.0.\"\t\t\t\t)\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> Optional[Any]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_vae_model(fpaa=lowercase\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_image(lowercase\t\t\t\t\t,\t\t\t\t\tshape=(3, 4, 64, 64)\t\t\t\t\t,\t\t\t\t\tfpaa=lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel.decode(lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tmodel.enable_xformers_memory_efficient_attention()\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel.decode(lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tassert list(sample.shape\t\t\t\t) == [3, 3, 512, 512]\n\n\t\t\t\t\t\t\tassert torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tatol=1e-1\t\t\t\t)\n\n\n\t\t\t@parameterized.expand([(13,), (16,), (37,)]\t\t\t\t)\n\t\t\t@require_torch_gpu\n\t\t\t@unittest.skipIf(not is_xformers_available()\t\t\t\t\t,\t\t\t\t\treason=\"xformers is not required when using PyTorch 2.0.\"\t\t\t\t)\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> List[str]:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_vae_model()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_image(lowercase\t\t\t\t\t,\t\t\t\t\tshape=(3, 4, 64, 64)\t\t\t\t)\n\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel.decode(lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tmodel.enable_xformers_memory_efficient_attention()\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel.decode(lowercase\t\t\t\t).sample\n\n\t\t\t\t\t\t\tassert list(sample.shape\t\t\t\t) == [3, 3, 512, 512]\n\n\t\t\t\t\t\t\tassert torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tatol=1e-2\t\t\t\t)\n\n\n\t\t\t@parameterized.expand(\n\t\t\t [\n\t\t\t # fmt: off\n\t\t\t [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],\n\t\t\t [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],\n\t\t\t # fmt: on\n\t\t\t ]\t\t\t\t)\n\t\t\tdef UpperCamelCase\t\t\t\t\t\t(\t\t\tself\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t) -> str:\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_vae_model()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_sd_image(lowercase\t\t\t\t)\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tself.get_generator(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\twith torch.no_grad():\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tmodel.encode(lowercase\t\t\t\t).latent_dist\n\t\t\t\t\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tdist.sample(generator=lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\tassert list(sample.shape\t\t\t\t) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\tsample[0, -1, -3:, -3:].flatten().cpu()\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\ttorch.tensor(lowercase\t\t\t\t)\n\n\t\t\t\t\t\t\tA__\t\t\t\t\t\t=\t3e-3 if torch_device != \"mps\" else 1e-2\n\t\t\t\t\t\t\tassert torch_all_close(lowercase\t\t\t\t\t,\t\t\t\t\tlowercase\t\t\t\t\t,\t\t\t\t\tatol=lowercase\t\t\t\t)\n\n"},"style_context_codestyle":{"kind":"number","value":68,"string":"68"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":505,"cells":{"code":{"kind":"string","value":"\n\n\n\n\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\nimport os\nimport re\nfrom shutil import copyfile\nfrom typing import List, Optional, Tuple\n\nfrom ...tokenization_utils import PreTrainedTokenizer\nfrom ...utils import logging\n\n\nlowerCAmelCase__ = logging.get_logger(__name__)\n\nlowerCAmelCase__ = {\n '''vocab_file''': '''vocab.txt''',\n '''merges_file''': '''bpe.codes''',\n}\n\nlowerCAmelCase__ = {\n '''vocab_file''': {\n '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',\n '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',\n },\n '''merges_file''': {\n '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',\n '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',\n },\n}\n\nlowerCAmelCase__ = {\n '''vinai/phobert-base''': 256,\n '''vinai/phobert-large''': 256,\n}\n\n\n\n\n\n\ndef \tsnake_case_ (\t\t\t\tA_ :\t\tDict\t\t\t\t\t\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n _lowerCamelCase\t\t\t\t\t\t: Optional[int] =\t\t\tset()\n _lowerCamelCase\t\t\t\t\t\t: List[Any] =\t\t\tword[0]\n for char in word[1:]:\n pairs.add((prev_char, char)\t\t\t\t\t\t)\n _lowerCamelCase\t\t\t\t\t\t: Tuple =\t\t\tchar\n\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\tset(A_\t\t\t\t\t\t)\n return pairs\n\n\n\n\n\n\n\nclass \t__snake_case\t( _lowercase):\n snake_case__ : str =\t\t\t\t\t\tVOCAB_FILES_NAMES\n snake_case__ : Tuple =\t\t\t\t\t\tPRETRAINED_VOCAB_FILES_MAP\n snake_case__ : int =\t\t\t\t\t\tPRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\n\n\n\n\n def __init__(\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[Any]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tTuple=\"\"\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tstr=\"\"\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tOptional[int]=\"\"\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[str]=\"\"\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tstr=\"\"\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[str]=\"\"\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tAny=\"\"\t\t\t\t\t\t, **__lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[Any]\t\t\t\t\t\t, ):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n super().__init__(\n bos_token=__lowerCAmelCase\t\t\t\t\t\t, eos_token=__lowerCAmelCase\t\t\t\t\t\t, unk_token=__lowerCAmelCase\t\t\t\t\t\t, sep_token=__lowerCAmelCase\t\t\t\t\t\t, cls_token=__lowerCAmelCase\t\t\t\t\t\t, pad_token=__lowerCAmelCase\t\t\t\t\t\t, mask_token=__lowerCAmelCase\t\t\t\t\t\t, **__lowerCAmelCase\t\t\t\t\t\t, )\n\n _lowerCamelCase\t\t\t\t\t\t: str =\t\t\tvocab_file\n _lowerCamelCase\t\t\t\t\t\t: List[str] =\t\t\tmerges_file\n\n _lowerCamelCase\t\t\t\t\t\t: Tuple =\t\t\t{}\n _lowerCamelCase\t\t\t\t\t\t: int =\t\t\t0\n _lowerCamelCase\t\t\t\t\t\t: List[Any] =\t\t\t1\n _lowerCamelCase\t\t\t\t\t\t: str =\t\t\t2\n _lowerCamelCase\t\t\t\t\t\t: int =\t\t\t3\n\n self.add_from_file(__lowerCAmelCase\t\t)\n\n _lowerCamelCase\t\t\t\t\t\t: Any =\t\t\t{v: k for k, v in self.encoder.items()}\n\n with open(__lowerCAmelCase\t\t\t\t\t\t, encoding='''utf-8'''\t\t) as merges_handle:\n _lowerCamelCase\t\t\t\t\t\t: str =\t\t\tmerges_handle.read().split('''\\n'''\t\t)[:-1]\n _lowerCamelCase\t\t\t\t\t\t: str =\t\t\t[tuple(merge.split()[:-1]\t\t) for merge in merges]\n _lowerCamelCase\t\t\t\t\t\t: Tuple =\t\t\tdict(zip(__lowerCAmelCase\t\t\t\t\t\t, range(len(__lowerCAmelCase\t\t)\t\t)\t\t)\t\t)\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\t{}\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tint\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[int]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tOptional[List[int]] = None\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n if token_ids_a is None:\n return [self.cls_token_id] + token_ids_a + [self.sep_token_id]\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\t[self.cls_token_id]\n _lowerCamelCase\t\t\t\t\t\t: Any =\t\t\t[self.sep_token_id]\n return cls + token_ids_a + sep + sep + token_ids_a + sep\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[int]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tOptional[List[int]] = None\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tbool = False\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n if already_has_special_tokens:\n return super().get_special_tokens_mask(\n token_ids_a=__lowerCAmelCase\t\t\t\t\t\t, token_ids_a=__lowerCAmelCase\t\t\t\t\t\t, already_has_special_tokens=__lowerCAmelCase\t\t)\n\n if token_ids_a is None:\n return [1] + ([0] * len(__lowerCAmelCase\t\t)) + [1]\n return [1] + ([0] * len(__lowerCAmelCase\t\t)) + [1, 1] + ([0] * len(__lowerCAmelCase\t\t)) + [1]\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tstr\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[int]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tOptional[List[int]] = None\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n _lowerCamelCase\t\t\t\t\t\t: int =\t\t\t[self.sep_token_id]\n _lowerCamelCase\t\t\t\t\t\t: Union[str, Any] =\t\t\t[self.cls_token_id]\n\n if token_ids_a is None:\n return len(cls + token_ids_a + sep\t\t) * [0]\n return len(cls + token_ids_a + sep + sep + token_ids_a + sep\t\t) * [0]\n\n\n\n\n @property\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tOptional[Any]\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n return len(self.encoder\t\t)\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tOptional[Any]\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n return dict(self.encoder\t\t\t\t\t\t, **self.added_tokens_encoder\t\t)\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tAny\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tDict\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n if token in self.cache:\n return self.cache[token]\n _lowerCamelCase\t\t\t\t\t\t: Dict =\t\t\ttuple(__lowerCAmelCase\t\t)\n _lowerCamelCase\t\t\t\t\t\t: Optional[int] =\t\t\ttuple(list(word[:-1]\t\t) + [word[-1] + '''''']\t\t)\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\tget_pairs(__lowerCAmelCase\t\t)\n\n if not pairs:\n return token\n\n while True:\n _lowerCamelCase\t\t\t\t\t\t: List[Any] =\t\t\tmin(__lowerCAmelCase\t\t\t\t\t\t, key=lambda __lowerCAmelCase\t\t: self.bpe_ranks.get(__lowerCAmelCase\t\t\t\t\t\t, float('''inf'''\t\t)\t\t)\t\t)\n if bigram not in self.bpe_ranks:\n break\n _lowerCamelCase , _lowerCamelCase\t\t\t\t\t\t: Any =\t\t\tbigram\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\t[]\n _lowerCamelCase\t\t\t\t\t\t: str =\t\t\t0\n while i < len(__lowerCAmelCase\t\t):\n try:\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\tword.index(__lowerCAmelCase\t\t\t\t\t\t, __lowerCAmelCase\t\t)\n except ValueError:\n new_word.extend(word[i:]\t\t)\n break\n else:\n new_word.extend(word[i:j]\t\t)\n _lowerCamelCase\t\t\t\t\t\t: List[Any] =\t\t\tj\n\n if word[i] == first and i < len(__lowerCAmelCase\t\t) - 1 and word[i + 1] == second:\n new_word.append(first + second\t\t)\n i += 2\n else:\n new_word.append(word[i]\t\t)\n i += 1\n _lowerCamelCase\t\t\t\t\t\t: Any =\t\t\ttuple(__lowerCAmelCase\t\t)\n _lowerCamelCase\t\t\t\t\t\t: List[Any] =\t\t\tnew_word\n if len(__lowerCAmelCase\t\t) == 1:\n break\n else:\n _lowerCamelCase\t\t\t\t\t\t: int =\t\t\tget_pairs(__lowerCAmelCase\t\t)\n _lowerCamelCase\t\t\t\t\t\t: int =\t\t\t'''@@ '''.join(__lowerCAmelCase\t\t)\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\tword[:-4]\n _lowerCamelCase\t\t\t\t\t\t: List[Any] =\t\t\tword\n return word\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tTuple\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[Any]\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n _lowerCamelCase\t\t\t\t\t\t: Tuple =\t\t\t[]\n\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\tre.findall(R'''\\S+\\n?'''\t\t\t\t\t\t, __lowerCAmelCase\t\t)\n\n for token in words:\n split_tokens.extend(list(self.bpe(__lowerCAmelCase\t\t).split(''' '''\t\t)\t\t)\t\t)\n return split_tokens\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tint\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tDict\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n return self.encoder.get(__lowerCAmelCase\t\t\t\t\t\t, self.encoder.get(self.unk_token\t\t)\t\t)\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tint\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n return self.decoder.get(__lowerCAmelCase\t\t\t\t\t\t, self.unk_token\t\t)\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tUnion[str, Any]\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n _lowerCamelCase\t\t\t\t\t\t: str =\t\t\t''' '''.join(__lowerCAmelCase\t\t).replace('''@@ '''\t\t\t\t\t\t, ''''''\t\t).strip()\n return out_string\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tTuple\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tstr\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tOptional[str] = None\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n if not os.path.isdir(__lowerCAmelCase\t\t):\n logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''\t\t)\n return\n _lowerCamelCase\t\t\t\t\t\t: List[str] =\t\t\tos.path.join(\n __lowerCAmelCase\t\t\t\t\t\t, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']\t\t)\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\tos.path.join(\n __lowerCAmelCase\t\t\t\t\t\t, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']\t\t)\n\n if os.path.abspath(self.vocab_file\t\t) != os.path.abspath(__lowerCAmelCase\t\t):\n copyfile(self.vocab_file\t\t\t\t\t\t, __lowerCAmelCase\t\t)\n\n if os.path.abspath(self.merges_file\t\t) != os.path.abspath(__lowerCAmelCase\t\t):\n copyfile(self.merges_file\t\t\t\t\t\t, __lowerCAmelCase\t\t)\n\n return out_vocab_file, out_merge_file\n\n\n\n\n def \t\t\t\t\t\tSCREAMING_SNAKE_CASE (\t\t\tself\t\t\t\t\t:\t\t\t\t\t\tstr\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t:\t\t\t\t\t\tList[str]\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n if isinstance(__lowerCAmelCase\t\t\t\t\t\t, __lowerCAmelCase\t\t):\n try:\n with open(__lowerCAmelCase\t\t\t\t\t\t, '''r'''\t\t\t\t\t\t, encoding='''utf-8'''\t\t) as fd:\n self.add_from_file(__lowerCAmelCase\t\t)\n except FileNotFoundError as fnfe:\n raise fnfe\n except UnicodeError:\n raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''\t\t)\n return\n\n _lowerCamelCase\t\t\t\t\t\t: List[str] =\t\t\tf.readlines()\n for lineTmp in lines:\n _lowerCamelCase\t\t\t\t\t\t: str =\t\t\tlineTmp.strip()\n _lowerCamelCase\t\t\t\t\t\t: Optional[Any] =\t\t\tline.rfind(''' '''\t\t)\n if idx == -1:\n raise ValueError('''Incorrect dictionary format, expected \\' \\''''\t\t)\n _lowerCamelCase\t\t\t\t\t\t: int =\t\t\tline[:idx]\n _lowerCamelCase\t\t\t\t\t\t: Any =\t\t\tlen(self.encoder\t\t)\n\n\n"},"code_codestyle":{"kind":"number","value":175,"string":"175"},"style_context":{"kind":"string","value":"\n\n\n\n\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\nimport argparse\n\n\nlowerCAmelCase__ = '''docs/source/_static/js/custom.js'''\n\n\n\n\n\n\ndef \tsnake_case_ (\t\t\t\tA_ :\t\tList[str]\t\t\t\t\t\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n with open(A_, encoding='''utf-8''', newline='''\\n'''\t\t\t\t\t\t) as f:\n _lowerCamelCase\t\t\t\t\t\t: int =\t\t\tf.readlines()\n _lowerCamelCase\t\t\t\t\t\t: List[str] =\t\t\t0\n\n # First let's put the right version\n while not lines[index].startswith('''const stableVersion ='''\t\t\t\t\t\t):\n index += 1\n _lowerCamelCase\t\t\t\t\t\t: List[Any] =\t\t\tF'''const stableVersion = \"v{version}\"\\n'''\n\n # Then update the dictionary\n while not lines[index].startswith('''const versionMapping = {'''\t\t\t\t\t\t):\n index += 1\n\n # We go until the end\n while not lines[index].startswith('''}'''\t\t\t\t\t\t):\n index += 1\n # We add the new version at the end\n lines[index - 1] += F''' \"v{version}\": \"v{version}\",\\n'''\n\n with open(A_, '''w''', encoding='''utf-8''', newline='''\\n'''\t\t\t\t\t\t) as f:\n f.writelines(A_\t\t\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n lowerCAmelCase__ = argparse.ArgumentParser()\n parser.add_argument('''--version''', help='''Release version.''')\n lowerCAmelCase__ = parser.parse_args()\n update_custom_js(args.version)\n\n\n"},"style_context_codestyle":{"kind":"number","value":175,"string":"175"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":506,"cells":{"code":{"kind":"string","value":"import json\nimport logging\nimport os\nimport sys\nfrom time import time\nfrom unittest.mock import patch\n\nfrom transformers.testing_utils import TestCasePlus, require_torch_tpu\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nA : Union[str, Any] =\t\t\t\tlogging.getLogger()\n\n\ndef a__ (\t\t\t\t\t\t__UpperCamelCase ):\n SCREAMING_SNAKE_CASE_ = {}\n SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase ,\t\t\t\t\t\"all_results.json\" )\n if os.path.exists(__UpperCamelCase ):\n with open(__UpperCamelCase ,\t\t\t\t\t\"r\" ) as f:\n SCREAMING_SNAKE_CASE_ = json.load(__UpperCamelCase )\n else:\n raise ValueError(F'''can\\'t find {path}''' )\n return results\n\n\nA : Tuple =\t\t\t\tlogging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\n\n\n\n@require_torch_tpu\nclass lowerCamelCase (SCREAMING_SNAKE_CASE__ ):\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tList[str] ) ->\t\tUnion[str, Any]:\n import xla_spawn\n\n SCREAMING_SNAKE_CASE_ = self.get_auto_remove_tmp_dir()\n SCREAMING_SNAKE_CASE_ = F'''\n ./examples/pytorch/text-classification/run_glue.py\n --num_cores=8\n ./examples/pytorch/text-classification/run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --do_train\n --do_eval\n --debug tpu_metrics_debug\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --max_steps=10\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n '''.split()\n\n with patch.object(__magic_name__ , \"argv\" , __magic_name__ ):\n SCREAMING_SNAKE_CASE_ = time()\n xla_spawn.main()\n SCREAMING_SNAKE_CASE_ = time()\n\n SCREAMING_SNAKE_CASE_ = get_results(__magic_name__ )\n self.assertGreaterEqual(result[\"eval_accuracy\"] , 0.75 )\n\n # Assert that the script takes less than 500 seconds to make sure it doesn't hang.\n self.assertLess(end - start , 500 )\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tUnion[str, Any] ) ->\t\tAny:\n import xla_spawn\n\n SCREAMING_SNAKE_CASE_ = \"\\n ./tests/test_trainer_tpu.py\\n --num_cores=8\\n ./tests/test_trainer_tpu.py\\n \".split()\n with patch.object(__magic_name__ , \"argv\" , __magic_name__ ):\n xla_spawn.main()\n\n\n"},"code_codestyle":{"kind":"number","value":118,"string":"118"},"style_context":{"kind":"string","value":"import inspect\nimport unittest\n\nfrom huggingface_hub import hf_hub_download\n\nfrom transformers import ASTConfig\nfrom transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device\nfrom transformers.utils import cached_property, is_torch_available, is_torchaudio_available\n\nfrom ...test_configuration_common import ConfigTester\nfrom ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor\nfrom ...test_pipeline_mixin import PipelineTesterMixin\n\n\nif is_torch_available():\n import torch\n from torch import nn\n\n from transformers import ASTForAudioClassification, ASTModel\n from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (\n AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,\n )\n\n\nif is_torchaudio_available():\n import torchaudio\n\n from transformers import ASTFeatureExtractor\n\n\nclass lowerCamelCase :\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n def __init__(\t\t\t\t\t\t\tself :\t\t\t\tList[Any] , __magic_name__ :\t\t\t\tAny , __magic_name__ :\t\t\t\tList[Any]=13 , __magic_name__ :\t\t\t\tList[Any]=2 , __magic_name__ :\t\t\t\tTuple=24 , __magic_name__ :\t\t\t\tList[str]=16 , __magic_name__ :\t\t\t\tDict=True , __magic_name__ :\t\t\t\tList[Any]=True , __magic_name__ :\t\t\t\tOptional[int]=32 , __magic_name__ :\t\t\t\tTuple=5 , __magic_name__ :\t\t\t\tint=4 , __magic_name__ :\t\t\t\tTuple=37 , __magic_name__ :\t\t\t\tList[str]=\"gelu\" , __magic_name__ :\t\t\t\tTuple=0.1 , __magic_name__ :\t\t\t\tTuple=0.1 , __magic_name__ :\t\t\t\tUnion[str, Any]=10 , __magic_name__ :\t\t\t\tTuple=0.02 , __magic_name__ :\t\t\t\tTuple=None , __magic_name__ :\t\t\t\tAny=2 , __magic_name__ :\t\t\t\tDict=2 , ) ->\t\tint:\n SCREAMING_SNAKE_CASE_ = parent\n SCREAMING_SNAKE_CASE_ = batch_size\n SCREAMING_SNAKE_CASE_ = patch_size\n SCREAMING_SNAKE_CASE_ = max_length\n SCREAMING_SNAKE_CASE_ = num_mel_bins\n SCREAMING_SNAKE_CASE_ = is_training\n SCREAMING_SNAKE_CASE_ = use_labels\n SCREAMING_SNAKE_CASE_ = hidden_size\n SCREAMING_SNAKE_CASE_ = num_hidden_layers\n SCREAMING_SNAKE_CASE_ = num_attention_heads\n SCREAMING_SNAKE_CASE_ = intermediate_size\n SCREAMING_SNAKE_CASE_ = hidden_act\n SCREAMING_SNAKE_CASE_ = hidden_dropout_prob\n SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob\n SCREAMING_SNAKE_CASE_ = type_sequence_label_size\n SCREAMING_SNAKE_CASE_ = initializer_range\n SCREAMING_SNAKE_CASE_ = scope\n SCREAMING_SNAKE_CASE_ = frequency_stride\n SCREAMING_SNAKE_CASE_ = time_stride\n\n # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)\n SCREAMING_SNAKE_CASE_ = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1\n SCREAMING_SNAKE_CASE_ = (self.max_length - self.patch_size) // self.time_stride + 1\n SCREAMING_SNAKE_CASE_ = frequency_out_dimension * time_out_dimension\n SCREAMING_SNAKE_CASE_ = num_patches + 2\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tAny ) ->\t\tAny:\n SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )\n\n SCREAMING_SNAKE_CASE_ = None\n if self.use_labels:\n SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )\n\n SCREAMING_SNAKE_CASE_ = self.get_config()\n\n return config, input_values, labels\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tAny ) ->\t\tDict:\n return ASTConfig(\n patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__magic_name__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tList[Any] , __magic_name__ :\t\t\t\tList[str] , __magic_name__ :\t\t\t\tstr , __magic_name__ :\t\t\t\tList[str] ) ->\t\tUnion[str, Any]:\n SCREAMING_SNAKE_CASE_ = ASTModel(config=__magic_name__ )\n model.to(__magic_name__ )\n model.eval()\n SCREAMING_SNAKE_CASE_ = model(__magic_name__ )\n self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tTuple ) ->\t\tstr:\n SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()\n (\n (\n SCREAMING_SNAKE_CASE_\n ) , (\n SCREAMING_SNAKE_CASE_\n ) , (\n SCREAMING_SNAKE_CASE_\n ) , \n ) = config_and_inputs\n SCREAMING_SNAKE_CASE_ = {\"input_values\": input_values}\n return config, inputs_dict\n\n\n\n@require_torch\nclass lowerCamelCase (SCREAMING_SNAKE_CASE__ ,\t\t\t\t\tSCREAMING_SNAKE_CASE__ ,\t\t\t\t\tunittest.TestCase ):\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n lowerCamelCase__ = (\n (\n ASTModel,\n ASTForAudioClassification,\n )\n if is_torch_available()\n else ()\n )\n lowerCamelCase__ = (\n {'''audio-classification''': ASTForAudioClassification, '''feature-extraction''': ASTModel}\n if is_torch_available()\n else {}\n )\n lowerCamelCase__ = False\n lowerCamelCase__ = False\n lowerCamelCase__ = False\n lowerCamelCase__ = False\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tTuple , __magic_name__ :\t\t\t\tUnion[str, Any] , __magic_name__ :\t\t\t\tOptional[Any] , __magic_name__ :\t\t\t\tDict , __magic_name__ :\t\t\t\tOptional[int] , __magic_name__ :\t\t\t\tTuple ) ->\t\tTuple:\n if pipeline_test_casse_name == \"AudioClassificationPipelineTests\":\n return True\n\n return False\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tOptional[Any] ) ->\t\tOptional[Any]:\n SCREAMING_SNAKE_CASE_ = ASTModelTester(self )\n SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tUnion[str, Any] ) ->\t\tDict:\n self.config_tester.run_common_tests()\n\n\n\n\n\n\n\n @unittest.skip(reason=\"AST does not use inputs_embeds\" )\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tOptional[Any] ) ->\t\tTuple:\n pass\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tint ) ->\t\tint:\n SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()\n\n for model_class in self.all_model_classes:\n SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )\n self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )\n SCREAMING_SNAKE_CASE_ = model.get_output_embeddings()\n self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tList[Any] ) ->\t\tstr:\n SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()\n\n for model_class in self.all_model_classes:\n SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )\n SCREAMING_SNAKE_CASE_ = inspect.signature(model.forward )\n # signature.parameters is an OrderedDict => so arg_names order is deterministic\n SCREAMING_SNAKE_CASE_ = [*signature.parameters.keys()]\n\n SCREAMING_SNAKE_CASE_ = [\"input_values\"]\n self.assertListEqual(arg_names[:1] , __magic_name__ )\n\n\n\n\n\n\n\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tint ) ->\t\tAny:\n SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()\n self.model_tester.create_and_check_model(*__magic_name__ )\n\n\n\n\n\n\n\n @slow\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tint ) ->\t\tint:\n for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\n SCREAMING_SNAKE_CASE_ = ASTModel.from_pretrained(__magic_name__ )\n self.assertIsNotNone(__magic_name__ )\n\n\n\ndef a__ (\t\t\t\t\t\t):\n SCREAMING_SNAKE_CASE_ = hf_hub_download(\n repo_id=\"nielsr/audio-spectogram-transformer-checkpoint\" ,\t\t\t\t\tfilename=\"sample_audio.flac\" ,\t\t\t\t\trepo_type=\"dataset\" )\n\n SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = torchaudio.load(__UpperCamelCase )\n\n return audio, sampling_rate\n\n\n\n\n\n@require_torch\n@require_torchaudio\nclass lowerCamelCase (unittest.TestCase ):\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n @cached_property\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tList[Any] ) ->\t\tList[Any]:\n return (\n ASTFeatureExtractor.from_pretrained(\"MIT/ast-finetuned-audioset-10-10-0.4593\" )\n if is_torchaudio_available()\n else None\n )\n\n\n\n\n\n\n\n @slow\n def __A (\t\t\t\t\t\t\tself :\t\t\t\tUnion[str, Any] ) ->\t\tOptional[int]:\n SCREAMING_SNAKE_CASE_ = self.default_feature_extractor\n SCREAMING_SNAKE_CASE_ = ASTForAudioClassification.from_pretrained(\"MIT/ast-finetuned-audioset-10-10-0.4593\" ).to(__magic_name__ )\n\n SCREAMING_SNAKE_CASE_ = self.default_feature_extractor\n SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = prepare_audio()\n SCREAMING_SNAKE_CASE_ = audio.squeeze().numpy()\n SCREAMING_SNAKE_CASE_ = feature_extractor(__magic_name__ , sampling_rate=__magic_name__ , return_tensors=\"pt\" ).to(__magic_name__ )\n\n # forward pass\n with torch.no_grad():\n SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )\n\n # verify the logits\n SCREAMING_SNAKE_CASE_ = torch.Size((1, 527) )\n self.assertEqual(outputs.logits.shape , __magic_name__ )\n\n SCREAMING_SNAKE_CASE_ = torch.tensor([-0.8760, -7.0042, -8.6602] ).to(__magic_name__ )\n\n self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )\n\n\n"},"style_context_codestyle":{"kind":"number","value":118,"string":"118"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":507,"cells":{"code":{"kind":"string","value":"import tempfile\r\n\r\nimport torch\r\n\r\nfrom diffusers import PNDMScheduler\r\n\r\nfrom .test_schedulers import SchedulerCommonTest\r\n\r\n\r\n\r\n\r\n\r\nclass UpperCAmelCase\t\t\t\t\t\t( __A ):\r\n\r\n\r\n\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tlowerCamelCase_ = (PNDMScheduler,)\r\n\t\t\t\tlowerCamelCase_ = (('''num_inference_steps''', 5_0),)\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself\t\t, **lowercase ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: int =\t\t\t\t{\r\n\t\t\t\t\t\t 'num_train_timesteps': 1_0_0_0,\r\n\t\t\t\t\t\t 'beta_start': 0.0001,\r\n\t\t\t\t\t\t 'beta_end': 0.02,\r\n\t\t\t\t\t\t 'beta_schedule': 'linear',\r\n\t\t\t\t\t\t}\r\n\r\n\t\t\t\t\t\tconfig.update(**lowercase )\r\n\t\t\t\t\t\treturn config\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself\t\t, lowercase=0\t\t, **lowercase ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tdict(self.forward_default_kwargs )\r\n\t\t\t\t\t\tA_\t\t: int =\t\t\t\tkwargs.pop('num_inference_steps'\t\t, lowercase )\r\n\t\t\t\t\t\tA_\t\t: int =\t\t\t\tself.dummy_sample\r\n\t\t\t\t\t\tA_\t\t: Optional[Any] =\t\t\t\t0.1 * sample\r\n\t\t\t\t\t\tA_\t\t: Dict =\t\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\n\r\n\t\t\t\t\t\tfor scheduler_class in self.scheduler_classes:\r\n\t\t\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tself.get_scheduler_config(**lowercase )\r\n\t\t\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tscheduler_class(**lowercase )\r\n\t\t\t\t\t\t\t\tscheduler.set_timesteps(lowercase )\r\n\t\t\t\t\t\t\t\t# copy over dummy past residuals\r\n\t\t\t\t\t\t\t\tA_\t\t: List[Any] =\t\t\t\tdummy_past_residuals[:]\r\n\r\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\r\n\t\t\t\t\t\t\t\t\t\tscheduler.save_config(lowercase )\r\n\t\t\t\t\t\t\t\t\t\tA_\t\t: Optional[int] =\t\t\t\tscheduler_class.from_pretrained(lowercase )\r\n\t\t\t\t\t\t\t\t\t\tnew_scheduler.set_timesteps(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t# copy over dummy past residuals\r\n\t\t\t\t\t\t\t\t\t\tA_\t\t: str =\t\t\t\tdummy_past_residuals[:]\r\n\r\n\t\t\t\t\t\t\t\tA_\t\t: Tuple =\t\t\t\tscheduler.step_prk(lowercase\t\t, lowercase\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\t\t\t\t\t\t\t\tA_\t\t: int =\t\t\t\tnew_scheduler.step_prk(lowercase\t\t, lowercase\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\r\n\t\t\t\t\t\t\t\tassert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\n\r\n\t\t\t\t\t\t\t\tA_\t\t: Tuple =\t\t\t\tscheduler.step_plms(lowercase\t\t, lowercase\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\t\t\t\t\t\t\t\tA_\t\t: Optional[Any] =\t\t\t\tnew_scheduler.step_plms(lowercase\t\t, lowercase\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\r\n\t\t\t\t\t\t\t\tassert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tpass\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself\t\t, lowercase=0\t\t, **lowercase ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: Dict =\t\t\t\tdict(self.forward_default_kwargs )\r\n\t\t\t\t\t\tA_\t\t: Optional[Any] =\t\t\t\tkwargs.pop('num_inference_steps'\t\t, lowercase )\r\n\t\t\t\t\t\tA_\t\t: List[str] =\t\t\t\tself.dummy_sample\r\n\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\t0.1 * sample\r\n\t\t\t\t\t\tA_\t\t: Dict =\t\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\n\r\n\t\t\t\t\t\tfor scheduler_class in self.scheduler_classes:\r\n\t\t\t\t\t\t\t\tA_\t\t: List[Any] =\t\t\t\tself.get_scheduler_config()\r\n\t\t\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tscheduler_class(**lowercase )\r\n\t\t\t\t\t\t\t\tscheduler.set_timesteps(lowercase )\r\n\r\n\t\t\t\t\t\t\t\t# copy over dummy past residuals (must be after setting timesteps)\r\n\t\t\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tdummy_past_residuals[:]\r\n\r\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\r\n\t\t\t\t\t\t\t\t\t\tscheduler.save_config(lowercase )\r\n\t\t\t\t\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tscheduler_class.from_pretrained(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t# copy over dummy past residuals\r\n\t\t\t\t\t\t\t\t\t\tnew_scheduler.set_timesteps(lowercase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t# copy over dummy past residual (must be after setting timesteps)\r\n\t\t\t\t\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tdummy_past_residuals[:]\r\n\r\n\t\t\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tscheduler.step_prk(lowercase\t\t, lowercase\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\t\t\t\t\t\t\t\tA_\t\t: List[Any] =\t\t\t\tnew_scheduler.step_prk(lowercase\t\t, lowercase\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\r\n\t\t\t\t\t\t\t\tassert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\n\r\n\t\t\t\t\t\t\t\tA_\t\t: List[Any] =\t\t\t\tscheduler.step_plms(lowercase\t\t, lowercase\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\t\t\t\t\t\t\t\tA_\t\t: str =\t\t\t\tnew_scheduler.step_plms(lowercase\t\t, lowercase\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\r\n\t\t\t\t\t\t\t\tassert torch.sum(torch.abs(output - new_output ) ) < 1E-5, \"Scheduler outputs are not identical\"\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself\t\t, **lowercase ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tself.scheduler_classes[0]\r\n\t\t\t\t\t\tA_\t\t: Optional[Any] =\t\t\t\tself.get_scheduler_config(**lowercase )\r\n\t\t\t\t\t\tA_\t\t: Optional[int] =\t\t\t\tscheduler_class(**lowercase )\r\n\r\n\t\t\t\t\t\tA_\t\t: str =\t\t\t\t1_0\r\n\t\t\t\t\t\tA_\t\t: str =\t\t\t\tself.dummy_model()\r\n\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tself.dummy_sample_deter\r\n\t\t\t\t\t\tscheduler.set_timesteps(lowercase )\r\n\r\n\t\t\t\t\t\tfor i, t in enumerate(scheduler.prk_timesteps ):\r\n\t\t\t\t\t\t\t\tA_\t\t: Optional[Any] =\t\t\t\tmodel(lowercase\t\t, lowercase )\r\n\t\t\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tscheduler.step_prk(lowercase\t\t, lowercase\t\t, lowercase ).prev_sample\r\n\r\n\t\t\t\t\t\tfor i, t in enumerate(scheduler.plms_timesteps ):\r\n\t\t\t\t\t\t\t\tA_\t\t: List[str] =\t\t\t\tmodel(lowercase\t\t, lowercase )\r\n\t\t\t\t\t\t\t\tA_\t\t: List[str] =\t\t\t\tscheduler.step_plms(lowercase\t\t, lowercase\t\t, lowercase ).prev_sample\r\n\r\n\t\t\t\t\t\treturn sample\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: Dict =\t\t\t\tdict(self.forward_default_kwargs )\r\n\r\n\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tkwargs.pop('num_inference_steps'\t\t, lowercase )\r\n\r\n\t\t\t\t\t\tfor scheduler_class in self.scheduler_classes:\r\n\t\t\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tself.get_scheduler_config()\r\n\t\t\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tscheduler_class(**lowercase )\r\n\r\n\t\t\t\t\t\t\t\tA_\t\t: Any =\t\t\t\tself.dummy_sample\r\n\t\t\t\t\t\t\t\tA_\t\t: Dict =\t\t\t\t0.1 * sample\r\n\r\n\t\t\t\t\t\t\t\tif num_inference_steps is not None and hasattr(lowercase\t\t, 'set_timesteps' ):\r\n\t\t\t\t\t\t\t\t\t\tscheduler.set_timesteps(lowercase )\r\n\t\t\t\t\t\t\t\telif num_inference_steps is not None and not hasattr(lowercase\t\t, 'set_timesteps' ):\r\n\t\t\t\t\t\t\t\t\t\tA_\t\t: Optional[Any] =\t\t\t\tnum_inference_steps\r\n\r\n\t\t\t\t\t\t\t\t# copy over dummy past residuals (must be done after set_timesteps)\r\n\t\t\t\t\t\t\t\tA_\t\t: int =\t\t\t\t[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\r\n\t\t\t\t\t\t\t\tA_\t\t: int =\t\t\t\tdummy_past_residuals[:]\r\n\r\n\t\t\t\t\t\t\t\tA_\t\t: int =\t\t\t\tscheduler.step_prk(lowercase\t\t, 0\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\t\t\t\t\t\t\t\tA_\t\t: str =\t\t\t\tscheduler.step_prk(lowercase\t\t, 1\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\r\n\t\t\t\t\t\t\t\tself.assertEqual(output_a.shape\t\t, sample.shape )\r\n\t\t\t\t\t\t\t\tself.assertEqual(output_a.shape\t\t, output_a.shape )\r\n\r\n\t\t\t\t\t\t\t\tA_\t\t: Tuple =\t\t\t\tscheduler.step_plms(lowercase\t\t, 0\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\t\t\t\t\t\t\t\tA_\t\t: int =\t\t\t\tscheduler.step_plms(lowercase\t\t, 1\t\t, lowercase\t\t, **lowercase ).prev_sample\r\n\r\n\t\t\t\t\t\t\t\tself.assertEqual(output_a.shape\t\t, sample.shape )\r\n\t\t\t\t\t\t\t\tself.assertEqual(output_a.shape\t\t, output_a.shape )\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tfor timesteps in [1_0_0, 1_0_0_0]:\r\n\t\t\t\t\t\t\t\tself.check_over_configs(num_train_timesteps=lowercase )\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tfor steps_offset in [0, 1]:\r\n\t\t\t\t\t\t\t\tself.check_over_configs(steps_offset=lowercase )\r\n\r\n\t\t\t\t\t\tA_\t\t: List[str] =\t\t\t\tself.scheduler_classes[0]\r\n\t\t\t\t\t\tA_\t\t: str =\t\t\t\tself.get_scheduler_config(steps_offset=1 )\r\n\t\t\t\t\t\tA_\t\t: Dict =\t\t\t\tscheduler_class(**lowercase )\r\n\t\t\t\t\t\tscheduler.set_timesteps(1_0 )\r\n\t\t\t\t\t\tassert torch.equal(\r\n\t\t\t\t\t\t scheduler.timesteps\t\t, torch.LongTensor(\r\n\t\t\t\t\t\t [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1] )\t\t, )\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tfor beta_start, beta_end in zip([0.0001, 0.001]\t\t, [0.002, 0.02] ):\r\n\t\t\t\t\t\t\t\tself.check_over_configs(beta_start=lowercase\t\t, beta_end=lowercase )\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tfor schedule in [\"linear\", \"squaredcos_cap_v2\"]:\r\n\t\t\t\t\t\t\t\tself.check_over_configs(beta_schedule=lowercase )\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tfor prediction_type in [\"epsilon\", \"v_prediction\"]:\r\n\t\t\t\t\t\t\t\tself.check_over_configs(prediction_type=lowercase )\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tfor t in [1, 5, 1_0]:\r\n\t\t\t\t\t\t\t\tself.check_over_forward(time_step=lowercase )\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tfor t, num_inference_steps in zip([1, 5, 1_0]\t\t, [1_0, 5_0, 1_0_0] ):\r\n\t\t\t\t\t\t\t\tself.check_over_forward(num_inference_steps=lowercase )\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: Dict =\t\t\t\t2_7\r\n\r\n\t\t\t\t\t\tfor scheduler_class in self.scheduler_classes:\r\n\t\t\t\t\t\t\t\tA_\t\t: List[Any] =\t\t\t\tself.dummy_sample\r\n\t\t\t\t\t\t\t\tA_\t\t: Optional[Any] =\t\t\t\t0.1 * sample\r\n\r\n\t\t\t\t\t\t\t\tA_\t\t: List[str] =\t\t\t\tself.get_scheduler_config()\r\n\t\t\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tscheduler_class(**lowercase )\r\n\r\n\t\t\t\t\t\t\t\tscheduler.set_timesteps(lowercase )\r\n\r\n\t\t\t\t\t\t\t\t# before power of 3 fix, would error on first step, so we only need to do two\r\n\t\t\t\t\t\t\t\tfor i, t in enumerate(scheduler.prk_timesteps[:2] ):\r\n\t\t\t\t\t\t\t\t\t\tA_\t\t: List[str] =\t\t\t\tscheduler.step_prk(lowercase\t\t, lowercase\t\t, lowercase ).prev_sample\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\twith self.assertRaises(lowercase ):\r\n\t\t\t\t\t\t\t\tA_\t\t: str =\t\t\t\tself.scheduler_classes[0]\r\n\t\t\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tself.get_scheduler_config()\r\n\t\t\t\t\t\t\t\tA_\t\t: List[str] =\t\t\t\tscheduler_class(**lowercase )\r\n\r\n\t\t\t\t\t\t\t\tscheduler.step_plms(self.dummy_sample\t\t, 1\t\t, self.dummy_sample ).prev_sample\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: Dict =\t\t\t\tself.full_loop()\r\n\t\t\t\t\t\tA_\t\t: List[Any] =\t\t\t\ttorch.sum(torch.abs(lowercase ) )\r\n\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\ttorch.mean(torch.abs(lowercase ) )\r\n\r\n\t\t\t\t\t\tassert abs(result_sum.item() - 198.1318 ) < 1E-2\r\n\t\t\t\t\t\tassert abs(result_mean.item() - 0.2580 ) < 1E-3\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: Optional[Any] =\t\t\t\tself.full_loop(prediction_type='v_prediction' )\r\n\t\t\t\t\t\tA_\t\t: Optional[int] =\t\t\t\ttorch.sum(torch.abs(lowercase ) )\r\n\t\t\t\t\t\tA_\t\t: int =\t\t\t\ttorch.mean(torch.abs(lowercase ) )\r\n\r\n\t\t\t\t\t\tassert abs(result_sum.item() - 67.3986 ) < 1E-2\r\n\t\t\t\t\t\tassert abs(result_mean.item() - 0.0878 ) < 1E-3\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\tself.full_loop(set_alpha_to_one=lowercase\t\t, beta_start=0.01 )\r\n\t\t\t\t\t\tA_\t\t: Tuple =\t\t\t\ttorch.sum(torch.abs(lowercase ) )\r\n\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\ttorch.mean(torch.abs(lowercase ) )\r\n\r\n\t\t\t\t\t\tassert abs(result_sum.item() - 230.0399 ) < 1E-2\r\n\t\t\t\t\t\tassert abs(result_mean.item() - 0.2995 ) < 1E-3\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef lowerCAmelCase_ (\t\t\t\t\t\tself ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tA_\t\t: List[Any] =\t\t\t\tself.full_loop(set_alpha_to_one=lowercase\t\t, beta_start=0.01 )\r\n\t\t\t\t\t\tA_\t\t: Union[str, Any] =\t\t\t\ttorch.sum(torch.abs(lowercase ) )\r\n\t\t\t\t\t\tA_\t\t: Tuple =\t\t\t\ttorch.mean(torch.abs(lowercase ) )\r\n\r\n\t\t\t\t\t\tassert abs(result_sum.item() - 186.9482 ) < 1E-2\r\n\t\t\t\t\t\tassert abs(result_mean.item() - 0.2434 ) < 1E-3\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":351,"string":"351"},"style_context":{"kind":"string","value":"import darl # noqa\r\nimport gym\r\nimport tqdm\r\nfrom diffusers.experimental import ValueGuidedRLPipeline\r\n\r\n\r\n_UpperCAmelCase =\t\t{\r\n \"\"\"n_samples\"\"\": 64,\r\n \"\"\"horizon\"\"\": 32,\r\n \"\"\"num_inference_steps\"\"\": 20,\r\n \"\"\"n_guide_steps\"\"\": 2, # can set to 0 for faster sampling, does not use value network\r\n \"\"\"scale_grad_by_std\"\"\": True,\r\n \"\"\"scale\"\"\": 0.1,\r\n \"\"\"eta\"\"\": 0.0,\r\n \"\"\"t_grad_cutoff\"\"\": 2,\r\n \"\"\"device\"\"\": \"\"\"cpu\"\"\",\r\n}\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t_UpperCAmelCase =\t\t\"\"\"hopper-medium-v2\"\"\"\r\n\t_UpperCAmelCase =\t\tgym.make(env_name)\r\n\r\n\t_UpperCAmelCase =\t\tValueGuidedRLPipeline.from_pretrained(\r\n\t \"\"\"bglick13/hopper-medium-v2-value-function-hor32\"\"\",\r\n\t env=env,\r\n\t)\r\n\r\n\tenv.seed(0)\r\n\t_UpperCAmelCase =\t\tenv.reset()\r\n\t_UpperCAmelCase =\t\t0\r\n\t_UpperCAmelCase =\t\t0\r\n\t_UpperCAmelCase =\t\t1000\r\n\t_UpperCAmelCase =\t\t[obs.copy()]\r\n\ttry:\r\n\t\tfor t in tqdm.tqdm(range(T)):\r\n\t\t\t# call the policy\r\n\t\t\t_UpperCAmelCase =\t\tpipeline(obs, planning_horizon=32)\r\n\r\n\t\t\t# execute action in environment\r\n\t\t\t_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase =\t\tenv.step(denorm_actions)\r\n\t\t\t_UpperCAmelCase =\t\tenv.get_normalized_score(total_reward)\r\n\r\n\t\t\t# update return\r\n\t\t\ttotal_reward += reward\r\n\t\t\ttotal_score += score\r\n\t\t\tprint(\r\n\t\t\t F\"\"\"Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:\"\"\"\r\n\t\t\t F\"\"\" {total_score}\"\"\"\r\n\t\t\t)\r\n\r\n\t\t\t# save observations for rendering\r\n\t\t\trollout.append(next_observation.copy())\r\n\r\n\t\t\t_UpperCAmelCase =\t\tnext_observation\r\n\texcept KeyboardInterrupt:\r\n\t\tpass\r\n\r\n\tprint(F\"\"\"Total reward: {total_reward}\"\"\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":192,"string":"192"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":508,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available\r\r\r__A\t\t\t\t\t\t\t\t\t=\t\t\t\t{\r \"configuration_xlm_roberta_xl\": [\r \"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP\",\r \"XLMRobertaXLConfig\",\r \"XLMRobertaXLOnnxConfig\",\r ],\r}\r\rtry:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r __A\t\t\t\t\t\t\t\t\t=\t\t\t\t[\r \"XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST\",\r \"XLMRobertaXLForCausalLM\",\r \"XLMRobertaXLForMaskedLM\",\r \"XLMRobertaXLForMultipleChoice\",\r \"XLMRobertaXLForQuestionAnswering\",\r \"XLMRobertaXLForSequenceClassification\",\r \"XLMRobertaXLForTokenClassification\",\r \"XLMRobertaXLModel\",\r \"XLMRobertaXLPreTrainedModel\",\r ]\r\rif TYPE_CHECKING:\r from .configuration_xlm_roberta_xl import (\r XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,\r XLMRobertaXLConfig,\r XLMRobertaXLOnnxConfig,\r )\r\r try:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_xlm_roberta_xl import (\r XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,\r XLMRobertaXLForCausalLM,\r XLMRobertaXLForMaskedLM,\r XLMRobertaXLForMultipleChoice,\r XLMRobertaXLForQuestionAnswering,\r XLMRobertaXLForSequenceClassification,\r XLMRobertaXLForTokenClassification,\r XLMRobertaXLModel,\r XLMRobertaXLPreTrainedModel,\r )\r\relse:\r import sys\r\r __A\t\t\t\t\t\t\t\t\t=\t\t\t\t_LazyModule(__name__, globals()[\"__file__\"], _import_structure)\r\r"},"code_codestyle":{"kind":"number","value":10,"string":"10"},"style_context":{"kind":"string","value":"\r\r\r\r\r\r'''simple docstring'''\r\r\rimport argparse\r\rfrom transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta\rfrom transformers.utils import logging\r\r\rlogging.set_verbosity_info()\r\r\rdef \t\t__a(SCREAMING_SNAKE_CASE_ : Union[str, Any]\t\t\t\t\t\t,\t\t\t\tSCREAMING_SNAKE_CASE_ : Tuple\t\t\t\t\t\t,\t\t\t\tSCREAMING_SNAKE_CASE_ : int\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r _lowerCAmelCase\t\t\t=\t\t\t\t\tTaConfig.from_json_file(SCREAMING_SNAKE_CASE_\t\t\t)\r print(F'''Building PyTorch model from configuration: {config}'''\t\t\t)\r _lowerCAmelCase\t\t\t=\t\t\t\t\tTaForConditionalGeneration(SCREAMING_SNAKE_CASE_\t\t\t)\r\r # Load weights from tf checkpoint\r load_tf_weights_in_ta(SCREAMING_SNAKE_CASE_\t\t\t\t\t\t,\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t\t\t\t,\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t)\r\r # Save pytorch-model\r print(F'''Save PyTorch model to {pytorch_dump_path}'''\t\t\t)\r model.save_pretrained(SCREAMING_SNAKE_CASE_\t\t\t)\r\r\rif __name__ == \"__main__\":\r _SCREAMING_SNAKE_CASE\t\t =\t\t\t\t\t\t\targparse.ArgumentParser()\r # Required parameters\r parser.add_argument(\r \"--tf_checkpoint_path\", default=None, type=str, required=True, help=\"Path to the TensorFlow checkpoint path.\"\r )\r parser.add_argument(\r \"--config_file\",\r default=None,\r type=str,\r required=True,\r help=(\r \"The config json file corresponding to the pre-trained T5 model. \\nThis specifies the model architecture.\"\r ),\r )\r parser.add_argument(\r \"--pytorch_dump_path\", default=None, type=str, required=True, help=\"Path to the output PyTorch model.\"\r )\r _SCREAMING_SNAKE_CASE\t\t =\t\t\t\t\t\t\tparser.parse_args()\r convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)\r\r\r"},"style_context_codestyle":{"kind":"number","value":158,"string":"158"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":509,"cells":{"code":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\rimport warnings\r\rfrom ...utils import logging\rfrom .image_processing_beit import BeitImageProcessor\r\r\rA: Any =\t\tlogging.get_logger(__name__)\r\r\r\rclass \t\t\t\tSCREAMING_SNAKE_CASE__ ( UpperCAmelCase__\t\t\t):\r\r def __init__( self\t\t\t\t,\t\t\t\t\t*_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t**_SCREAMING_SNAKE_CASE\t\t\t\t\t\t) ->\t\t\t\tNone:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r warnings.warn(\r \"\"\"The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please\"\"\"\r \"\"\" use BeitImageProcessor instead.\"\"\"\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t)\r super().__init__(*_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t**_SCREAMING_SNAKE_CASE\t\t\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":356,"string":"356"},"style_context":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\rdef \t\t\t\t_snake_case\t\t\t\t\t\t(\t\t\t\t\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint ,\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint\t\t\t\t\t\t):\r return number | (1 << position)\r\rdef \t\t\t\t_snake_case\t\t\t\t\t\t(\t\t\t\t\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint ,\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint\t\t\t\t\t\t):\r return number & ~(1 << position)\r\rdef \t\t\t\t_snake_case\t\t\t\t\t\t(\t\t\t\t\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint ,\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint\t\t\t\t\t\t):\r return number ^ (1 << position)\r\rdef \t\t\t\t_snake_case\t\t\t\t\t\t(\t\t\t\t\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint ,\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint\t\t\t\t\t\t):\r return ((number >> position) & 1) == 1\r\rdef \t\t\t\t_snake_case\t\t\t\t\t\t(\t\t\t\t\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint ,\t\tUpperCamelCase\t\t\t\t\t\t:\t\tint\t\t\t\t\t\t):\r return int((number & (1 << position)) != 0\t\t\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"style_context_codestyle":{"kind":"number","value":76,"string":"76"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":510,"cells":{"code":{"kind":"string","value":"\r\r\r\"\"\"simple docstring\"\"\"\r\r\rfrom math import ceil\r\r\rdef _lowerCamelCase\t(\t\t_UpperCamelCase , _UpperCamelCase ):\r\r\t\t\t'''simple docstring'''\r\r\r\r\r\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tlist(range(0 , lowercase_ ) )\r\r\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t[item for sublist in list(device_map.values() ) for item in sublist]\r\r\t\t\t# Duplicate check\r\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t[]\r\t\t\tfor i in device_map_blocks:\r\t\t\t\t\t\tif device_map_blocks.count(lowercase_ ) > 1 and i not in duplicate_blocks:\r\t\t\t\t\t\t\t\t\tduplicate_blocks.append(lowercase_ )\r # Missing blocks\r\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t[i for i in blocks if i not in device_map_blocks]\r\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t[i for i in device_map_blocks if i not in blocks]\r\r\t\t\tif len(lowercase_ ) != 0:\r\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t \"Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.\"\r\t\t\t\t\t\t \" These attention blocks were specified more than once: \" + str(lowercase_ ) )\r\t\t\tif len(lowercase_ ) != 0:\r\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t \"There are attention blocks for this model that are not specified in the device_map. Add these attention \"\r\t\t\t\t\t\t \"blocks to a device on the device_map: \" + str(lowercase_ ) )\r\t\t\tif len(lowercase_ ) != 0:\r\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t \"The device_map contains more attention blocks than this model has. Remove these from the device_map:\"\r\t\t\t\t\t\t + str(lowercase_ ) )\r\r\rdef _lowerCamelCase\t(\t\t_UpperCamelCase , _UpperCamelCase ):\r\r\t\t\t'''simple docstring'''\r\r\r\r\r\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tlist(range(lowercase_ ) )\r\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tint(ceil(n_layers / len(lowercase_ ) ) )\r\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t[layers[i : i + n_blocks] for i in range(0 , lowercase_ , lowercase_ )]\r\r\t\t\treturn dict(zip(lowercase_ , lowercase_ ) )\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":57,"string":"57"},"style_context":{"kind":"string","value":"\r\r\r\r\rimport copy\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\r_lowerCamelCase\t:\t\tAny\t\t\t\t\t\t\t = logging.get_logger(__name__)\r\r\rclass \t\t\t\tUpperCamelCase_ ( UpperCAmelCase__ ):\r\r '''simple docstring'''\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t\t = '''encoder-decoder'''\r UpperCAmelCase__\t\t\t\t\t\t\t = True\r\r\r def __init__(\t\t\tself :\tList[str]\t\t\t\t\t, **UpperCAmelCase__ :\tUnion[str, Any])\t\t\t\t\t\t->List[Any]:\r\r\r\r\r\r '''simple docstring'''\r\r super().__init__(**UpperCAmelCase__)\r assert (\r \"encoder\" in kwargs and \"decoder\" in kwargs\r ), \"Config has to be initialized with encoder and decoder config\"\r A__ = kwargs.pop('''encoder''')\r A__ = encoder_config.pop('''model_type''')\r A__ = kwargs.pop('''decoder''')\r A__ = decoder_config.pop('''model_type''')\r\r from ..auto.configuration_auto import AutoConfig\r\r A__ = AutoConfig.for_model(UpperCAmelCase__\t\t\t\t\t, **UpperCAmelCase__)\r A__ = AutoConfig.for_model(UpperCAmelCase__\t\t\t\t\t, **UpperCAmelCase__)\r A__ = True\r\r\r @classmethod\r def SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\t\t\tcls :\tUnion[str, Any]\t\t\t\t\t, UpperCAmelCase__ :\tPretrainedConfig\t\t\t\t\t, UpperCAmelCase__ :\tPretrainedConfig\t\t\t\t\t, **UpperCAmelCase__ :\tUnion[str, Any])\t\t\t\t\t\t->PretrainedConfig:\r\r\r\r\r\r '''simple docstring'''\r\r logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')\r A__ = True\r A__ = True\r\r return cls(encoder=encoder_config.to_dict()\t\t\t\t\t, decoder=decoder_config.to_dict()\t\t\t\t\t, **UpperCAmelCase__)\r\r\r\r\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\t\t\tself :\tstr)\t\t\t\t\t\t->Optional[Any]:\r\r\r\r\r\r '''simple docstring'''\r\r A__ = copy.deepcopy(self.__dict__)\r A__ = self.encoder.to_dict()\r A__ = self.decoder.to_dict()\r A__ = self.__class__.model_type\r return output\r\r\r"},"style_context_codestyle":{"kind":"number","value":14,"string":"14"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":511,"cells":{"code":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rfrom math import factorial\r\rSCREAMING_SNAKE_CASE\t\t\t: dict[str, int] =\t\t{str(digit): factorial(digit) for digit in range(1_0)}\r\r\r\r\r\r\rdef \t\t__UpperCAmelCase ( snake_case_\t\t\t\t\t:\t\t\t\t\tint\t\t\t\t)\t\t\t\t\t\t\t->\t\tint:\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r if not isinstance(snake_case_\t\t\t\t,\t\tsnake_case_\t\t\t\t):\r raise TypeError(\"\"\"Parameter number must be int\"\"\"\t\t\t\t)\r\r if number < 0:\r raise ValueError(\"\"\"Parameter number must be greater than or equal to 0\"\"\"\t\t\t\t)\r\r # Converts number in string to iterate on its digits and adds its factorial.\r return sum(DIGIT_FACTORIAL[digit] for digit in str(snake_case_\t\t\t\t)\t\t\t\t)\r\r\r\r\r\r\rdef \t\t__UpperCAmelCase ( snake_case_\t\t\t\t\t:\t\t\t\t\tint = 60\t\t\t\t,\t\tsnake_case_\t\t\t\t\t:\t\t\t\t\tint = 1000000\t\t\t\t)\t\t\t\t\t\t\t->\t\tint:\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r if not isinstance(snake_case_\t\t\t\t,\t\tsnake_case_\t\t\t\t) or not isinstance(snake_case_\t\t\t\t,\t\tsnake_case_\t\t\t\t):\r raise TypeError(\"\"\"Parameters chain_length and number_limit must be int\"\"\"\t\t\t\t)\r\r if chain_length <= 0 or number_limit <= 0:\r raise ValueError(\r \"\"\"Parameters chain_length and number_limit must be greater than 0\"\"\"\t\t\t\t)\r\r # the counter for the chains with the exact desired length\r _lowerCAmelCase =\t\t\t\t\t\t0\r # the cached sizes of the previous chains\r _lowerCAmelCase =\t\t\t\t\t\t{}\r\r for start_chain_element in range(1\t\t\t\t,\t\tsnake_case_\t\t\t\t):\r # The temporary set will contain the elements of the chain\r _lowerCAmelCase =\t\t\t\t\t\tset()\r _lowerCAmelCase =\t\t\t\t\t\t0\r\r # Stop computing the chain when you find a cached size, a repeating item or the\r # length is greater then the desired one.\r _lowerCAmelCase =\t\t\t\t\t\tstart_chain_element\r while (\r chain_element not in chain_sets_lengths\r and chain_element not in chain_set\r and chain_set_length <= chain_length\r ):\r chain_set.add(snake_case_\t\t\t\t)\r chain_set_length += 1\r _lowerCAmelCase =\t\t\t\t\t\tdigit_factorial_sum(snake_case_\t\t\t\t)\r\r if chain_element in chain_sets_lengths:\r chain_set_length += chain_sets_lengths[chain_element]\r\r _lowerCAmelCase =\t\t\t\t\t\tchain_set_length\r\r # If chain contains the exact amount of elements increase the counter\r if chain_set_length == chain_length:\r chains_counter += 1\r\r return chains_counter\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r print(F'{solution()}')"},"code_codestyle":{"kind":"number","value":369,"string":"369"},"style_context":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rimport unittest\r\rimport numpy as np\rimport torch\r\rfrom diffusers import VersatileDiffusionImageVariationPipeline\rfrom diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device\r\r\rSCREAMING_SNAKE_CASE\t\t\t: List[str] =\t\tFalse\r\r\rclass __lowerCamelCase\t\t(\t\t\t\tunittest.TestCase ):\r pass\r\r\r\r\r\r@slow\r@require_torch_gpu\rclass __lowerCamelCase\t\t(\t\t\t\tunittest.TestCase ):\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tA__ (self\t\t\t):\r\r\r\r\r '''simple docstring'''\r\r\r\r\r _lowerCAmelCase =\t\t\t\t\t\tVersatileDiffusionImageVariationPipeline.from_pretrained(\"\"\"shi-labs/versatile-diffusion\"\"\"\t\t\t)\r pipe.to(lowerCamelCase\t\t\t)\r pipe.set_progress_bar_config(disable=lowerCamelCase\t\t\t)\r\r _lowerCAmelCase =\t\t\t\t\t\tload_image(\r \"\"\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg\"\"\"\t\t\t)\r _lowerCAmelCase =\t\t\t\t\t\ttorch.manual_seed(0\t\t\t)\r _lowerCAmelCase =\t\t\t\t\t\tpipe(\r image=lowerCamelCase ,\t\tgenerator=lowerCamelCase ,\t\tguidance_scale=7.5 ,\t\tnum_inference_steps=50 ,\t\toutput_type=\"\"\"numpy\"\"\" ,\t\t).images\r\r _lowerCAmelCase =\t\t\t\t\t\timage[0, 253:256, 253:256, -1]\r\r assert image.shape == (1, 512, 512, 3)\r _lowerCAmelCase =\t\t\t\t\t\tnp.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]\t\t\t)\r\r assert np.abs(image_slice.flatten() - expected_slice\t\t\t).max() < 1e-2"},"style_context_codestyle":{"kind":"number","value":317,"string":"317"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":512,"cells":{"code":{"kind":"string","value":"from typing import List, Optional, Union\r\rimport numpy as np\rimport torch\rimport torchaudio.compliance.kaldi as ta_kaldi\r\rfrom ...feature_extraction_sequence_utils import SequenceFeatureExtractor\rfrom ...feature_extraction_utils import BatchFeature\rfrom ...utils import PaddingStrategy, TensorType, logging\r\r\r__lowerCamelCase\t\t\t\t\t:\tDict = logging.get_logger(__name__)\r\r\r\r\r\r\rclass \t\ta__\t\t\t\t\t\t(\t\t\t\t\tlowercase__\t\t\t\t\t):\r\t\t\t\t\tA\t =\t\t\t\t\t\t\t['input_features', 'attention_mask']\r\r\t\t\t\t\tdef __init__( self : int,_A : int=80,_A : str=1_6000,_A : Optional[Any]=80,_A : List[Any]=0.0,_A : Union[str, Any]=True,_A : Tuple=True,_A : Union[str, Any]=True,**_A : List[Any],):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\t\t\t\t\t\t\tsuper().__init__(feature_size=__lowercase,sampling_rate=__lowercase,padding_value=__lowercase,**__lowercase )\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tAny\t\t\t\t = num_mel_bins\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[Any]\t\t\t\t = do_ceptral_normalize\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[str]\t\t\t\t = normalize_means\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[Any]\t\t\t\t = normalize_vars\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[str]\t\t\t\t = True\r\r\t\t\t\t\tdef \t\t\t\t__UpperCamelCase ( self : int,_A : np.ndarray,):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tUnion[str, Any]\t\t\t\t = waveform * (2**15) # Kaldi compliance: 16-bit signed integers\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tOptional[Any]\t\t\t\t = torch.from_numpy(__lowercase ).unsqueeze(0 )\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[str]\t\t\t\t = ta_kaldi.fbank(__lowercase,num_mel_bins=self.num_mel_bins,sample_frequency=self.sampling_rate )\r\t\t\t\t\t\t\treturn features.numpy()\r\r\t\t\t\t\t@staticmethod\r\t\t\t\t\tdef \t\t\t\t__UpperCamelCase ( _A : np.ndarray,_A : int,_A : Optional[bool] = True,_A : Optional[bool] = True,_A : float = 0.0,):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\t\t\t\t\t\t\tif normalize_means:\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tstr\t\t\t\t = x[:input_length].mean(axis=0 )\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tint\t\t\t\t = np.subtract(__lowercase,__lowercase )\r\t\t\t\t\t\t\tif normalize_vars:\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tUnion[str, Any]\t\t\t\t = x[:input_length].std(axis=0 )\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tint\t\t\t\t = np.divide(__lowercase,__lowercase )\r\r\t\t\t\t\t\t\tif input_length < x.shape[0]:\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tTuple\t\t\t\t = padding_value\r\r\t\t\t\t\t\t\t# make sure array is in float32\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tint\t\t\t\t = x.astype(np.floataa )\r\r\t\t\t\t\t\t\treturn x\r\r\t\t\t\t\tdef \t\t\t\t__UpperCamelCase ( self : Tuple,_A : List[np.ndarray],_A : Optional[np.ndarray] = None ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[Any]\t\t\t\t = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]\r\t\t\t\t\t\t\treturn [\r\t\t\t\t\t\t\t self.utterance_cmvn(__lowercase,__lowercase,self.normalize_means,self.normalize_vars,self.padding_value )\r\t\t\t\t\t\t\t for x, n in zip(__lowercase,__lowercase )\r\t\t\t\t\t\t\t]\r\r\r\r\r\r\r\t\t\t\t\tdef __call__( self : int,_A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],_A : Union[bool, str, PaddingStrategy] = False,_A : Optional[int] = None,_A : bool = False,_A : Optional[int] = None,_A : Optional[Union[str, TensorType]] = None,_A : Optional[int] = None,_A : Optional[bool] = None,**_A : int,):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\t\t\t\t\t\t\tif sampling_rate is not None:\r\t\t\t\t\t\t\t\t\tif sampling_rate != self.sampling_rate:\r\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'\r\t\t\t\t\t\t\t\t\t\t\t F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'\r\t\t\t\t\t\t\t\t\t\t\t F' {self.sampling_rate} and not {sampling_rate}.' )\r\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\tlogger.warning(\r\t\t\t\t\t\t\t\t\t \"It is strongly recommended to pass the `sampling_rate` argument to this function. \"\r\t\t\t\t\t\t\t\t\t \"Failing to do so can result in silent errors that might be hard to debug.\" )\r\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[str]\t\t\t\t = isinstance(__lowercase,np.ndarray ) and len(raw_speech.shape ) > 1\r\t\t\t\t\t\t\tif is_batched_numpy and len(raw_speech.shape ) > 2:\r\t\t\t\t\t\t\t\t\traise ValueError(F'Only mono-channel audio is supported for input to {self}' )\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tDict\t\t\t\t = is_batched_numpy or (\r\t\t\t\t\t\t\t isinstance(__lowercase,(list, tuple) ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list) ))\r\t\t\t\t\t\t\t)\r\r\t\t\t\t\t\t\tif is_batched:\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tAny\t\t\t\t = [np.asarray(__lowercase,dtype=np.floataa ) for speech in raw_speech]\r\t\t\t\t\t\t\telif not is_batched and not isinstance(__lowercase,np.ndarray ):\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tint\t\t\t\t = np.asarray(__lowercase,dtype=np.floataa )\r\t\t\t\t\t\t\telif isinstance(__lowercase,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tOptional[int]\t\t\t\t = raw_speech.astype(np.floataa )\r\r\t\t\t\t\t\t\t# always return batch\r\t\t\t\t\t\t\tif not is_batched:\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tint\t\t\t\t = [raw_speech]\r\r\t\t\t\t\t\t\t# extract fbank features\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tDict\t\t\t\t = [self._extract_fbank_features(__lowercase ) for waveform in raw_speech]\r\r\t\t\t\t\t\t\t# convert into correct format for padding\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[Any]\t\t\t\t = BatchFeature({\"input_features\": features} )\r\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tOptional[int]\t\t\t\t = self.pad(\r\t\t\t\t\t\t\t __lowercase,padding=__lowercase,max_length=__lowercase,truncation=__lowercase,pad_to_multiple_of=__lowercase,return_attention_mask=__lowercase,**__lowercase,)\r\r\t\t\t\t\t\t\t# make sure list is in array format\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tOptional[Any]\t\t\t\t = padded_inputs.get(\"input_features\" )\r\t\t\t\t\t\t\tif isinstance(input_features[0],__lowercase ):\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tTuple\t\t\t\t = [np.asarray(__lowercase,dtype=np.floataa ) for feature in input_features]\r\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tTuple\t\t\t\t = padded_inputs.get(\"attention_mask\" )\r\t\t\t\t\t\t\tif attention_mask is not None:\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[Any]\t\t\t\t = [np.asarray(__lowercase,dtype=np.intaa ) for array in attention_mask]\r\r\t\t\t\t\t\t\t# Utterance-level cepstral mean and variance normalization\r\t\t\t\t\t\t\tif self.do_ceptral_normalize:\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tTuple\t\t\t\t = (\r\t\t\t\t\t\t\t\t\t np.array(__lowercase,dtype=np.intaa )\r\t\t\t\t\t\t\t\t\t if self._get_padding_strategies(__lowercase,max_length=__lowercase ) is not PaddingStrategy.DO_NOT_PAD\r\t\t\t\t\t\t\t\t\t else None\r\t\t\t\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tstr\t\t\t\t = self.normalize(\r\t\t\t\t\t\t\t\t\t padded_inputs[\"input_features\"],attention_mask=__lowercase )\r\r\t\t\t\t\t\t\tif return_tensors is not None:\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\t\tList[str]\t\t\t\t = padded_inputs.convert_to_tensors(__lowercase )\r\r\t\t\t\t\t\t\treturn padded_inputs\r"},"code_codestyle":{"kind":"number","value":18,"string":"18"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom ...configuration_utils import PretrainedConfig\r\nfrom ...utils import logging\r\n\r\n\r\na\t\t: Optional[int]\t\t\t = logging.get_logger(__name__)\r\n\r\na\t\t: List[Any]\t\t\t = {\r\n \"facebook/xglm-564M\": \"https://huggingface.co/facebook/xglm-564M/resolve/main/config.json\",\r\n # See all XGLM models at https://huggingface.co/models?filter=xglm\r\n}\r\nclass a ( lowercase__ ):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n a : List[Any]\t\t\t\t\t\t\t =\t'xglm'\r\n a : str\t\t\t\t\t\t\t =\t['past_key_values']\r\n\r\n a : Any\t\t\t\t\t\t\t =\t{\r\n 'num_attention_heads': 'attention_heads',\r\n 'hidden_size': 'd_model',\r\n 'num_hidden_layers': 'num_layers',\r\n }\r\n def __init__( self\t\t\t\t\t:\t\t\tOptional[int]\t\t, __lowercase\t\t\t\t\t:\t\t\tint=256008\t\t, __lowercase\t\t\t\t\t:\t\t\tTuple=2048\t\t, __lowercase\t\t\t\t\t:\t\t\tList[Any]=1024\t\t, __lowercase\t\t\t\t\t:\t\t\tstr=4096\t\t, __lowercase\t\t\t\t\t:\t\t\tOptional[Any]=24\t\t, __lowercase\t\t\t\t\t:\t\t\tOptional[int]=16\t\t, __lowercase\t\t\t\t\t:\t\t\tList[Any]=\"gelu\"\t\t, __lowercase\t\t\t\t\t:\t\t\tstr=0.1\t\t, __lowercase\t\t\t\t\t:\t\t\tDict=0.1\t\t, __lowercase\t\t\t\t\t:\t\t\tTuple=0.0\t\t, __lowercase\t\t\t\t\t:\t\t\tOptional[int]=0.0\t\t, __lowercase\t\t\t\t\t:\t\t\tDict=0.02\t\t, __lowercase\t\t\t\t\t:\t\t\tOptional[int]=True\t\t, __lowercase\t\t\t\t\t:\t\t\tAny=True\t\t, __lowercase\t\t\t\t\t:\t\t\tDict=2\t\t, __lowercase\t\t\t\t\t:\t\t\tOptional[Any]=1\t\t, __lowercase\t\t\t\t\t:\t\t\tList[Any]=0\t\t, __lowercase\t\t\t\t\t:\t\t\tOptional[Any]=2\t\t, **__lowercase\t\t\t\t\t:\t\t\tList[str]\t\t, )\t\t\t\t\t\t\t-> Optional[int]:\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tList[str] = vocab_size\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tOptional[Any] = max_position_embeddings\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tOptional[Any] = d_model\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tstr = ffn_dim\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tList[str] = num_layers\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tDict = attention_heads\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tstr = activation_function\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tOptional[Any] = dropout\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tAny = attention_dropout\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tint = activation_dropout\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tTuple = layerdrop\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tTuple = init_std\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tList[str] = scale_embedding # scale factor will be sqrt(d_model) if True\r\n __UpperCAmelCase\t\t\t\t\t\t\t:\tUnion[str, Any] = use_cache\r\n\r\n super().__init__(\r\n pad_token_id=__lowercase\t\t, bos_token_id=__lowercase\t\t, eos_token_id=__lowercase\t\t, decoder_start_token_id=__lowercase\t\t, **__lowercase\t\t, )\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":114,"string":"114"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":513,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\r\rimport argparse\r\rimport torch\r\rfrom transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel\rfrom transformers.utils import logging\r\r\rlogging.set_verbosity_info()\r\r\r\r\r\rdef __a ( _UpperCamelCase: Dict ,\t\t\t\t\t\t_UpperCamelCase: Tuple ,\t\t\t\t\t\t_UpperCamelCase: int ,\t\t\t\t\t\t_UpperCamelCase: List[str] )\t\t\t\t\t\t->\t\t\tAny:\r\r\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\t\t\t\t\t_snake_case\t\t\t\t\t\t\t=\t\t\t\tFunnelConfig.from_json_file(lowerCAmelCase_ )\r\t\t\t\t\tprint(F\"\"\"Building PyTorch model from configuration: {config}\"\"\" )\r\t\t\t\t\t_snake_case\t\t\t\t\t\t\t=\t\t\t\tFunnelBaseModel(lowerCAmelCase_ ) if base_model else FunnelModel(lowerCAmelCase_ )\r\r\t\t\t\t\t# Load weights from tf checkpoint\r\t\t\t\t\tload_tf_weights_in_funnel(lowerCAmelCase_ ,\t\t\t\t\t\tlowerCAmelCase_ ,\t\t\t\t\t\tlowerCAmelCase_ )\r\r\t\t\t\t\t# Save pytorch-model\r\t\t\t\t\tprint(F\"\"\"Save PyTorch model to {pytorch_dump_path}\"\"\" )\r\t\t\t\t\ttorch.save(model.state_dict() ,\t\t\t\t\t\tlowerCAmelCase_ )\r\r\rif __name__ == \"__main__\":\r\t\t\tUpperCamelCase_\t\t\t\t\t\t: List[str] =\t\t\t\t\t\targparse.ArgumentParser()\r\t\t\t# Required parameters\r\t\t\tparser.add_argument(\r\t\t\t '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''\r\t\t\t)\r\t\t\tparser.add_argument(\r\t\t\t '''--config_file''',\r\t\t\t default=None,\r\t\t\t type=str,\r\t\t\t required=True,\r\t\t\t help='''The config json file corresponding to the pre-trained model. \\nThis specifies the model architecture.''',\r\t\t\t)\r\t\t\tparser.add_argument(\r\t\t\t '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''\r\t\t\t)\r\t\t\tparser.add_argument(\r\t\t\t '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''\r\t\t\t)\r\t\t\tUpperCamelCase_\t\t\t\t\t\t: Dict =\t\t\t\t\t\tparser.parse_args()\r\t\t\tconvert_tf_checkpoint_to_pytorch(\r\t\t\t args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model\r\t\t\t)\r\r\r"},"code_codestyle":{"kind":"number","value":369,"string":"369"},"style_context":{"kind":"string","value":"\r\r\r\r\r'''simple docstring'''\r\r\r\r\r\rimport pprint\r\rimport requests\r\rUpperCamelCase_\t\t\t\t\t\t: Tuple =\t\t\t\t\t\t'''https://zenquotes.io/api'''\r\r\r\r\r\rdef __a ( )\t\t\t\t\t\t->\t\t\tlist:\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r return requests.get(API_ENDPOINT_URL + \"/today\" ).json()\r\r\r\r\r\rdef __a ( )\t\t\t\t\t\t->\t\t\tlist:\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r return requests.get(API_ENDPOINT_URL + \"/random\" ).json()\r\r\rif __name__ == \"__main__\":\r UpperCamelCase_\t\t\t\t\t\t: Any =\t\t\t\t\t\trandom_quotes()\r pprint.pprint(response)\r\r\r"},"style_context_codestyle":{"kind":"number","value":142,"string":"142"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":514,"cells":{"code":{"kind":"string","value":"\n\n'''simple docstring'''\nimport importlib\n\nimport torch\nimport yaml\nfrom omegaconf import OmegaConf\nfrom taming.models.vqgan import VQModel\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case=False )\t\t\t->\tList[Any]:\n _lowercase\t\t\t: Union[str, Any]\t\t\t = OmegaConf.load(snake_case )\n if display:\n print(yaml.dump(OmegaConf.to_container(snake_case ) ) )\n return config\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case=None\t\t,\t\t\t\t\t\t\tsnake_case=None )\t\t\t->\tOptional[int]:\n if conf_path is None:\n _lowercase\t\t\t: str\t\t\t = \"./model_checkpoints/vqgan_only.yaml\"\n _lowercase\t\t\t: List[Any]\t\t\t = load_config(snake_case\t\t,\t\t\t\t\t\t\tdisplay=snake_case )\n _lowercase\t\t\t: Optional[Any]\t\t\t = VQModel(**config.model.params )\n if ckpt_path is None:\n _lowercase\t\t\t: str\t\t\t = \"./model_checkpoints/vqgan_only.pt\"\n _lowercase\t\t\t: Dict\t\t\t = torch.load(snake_case\t\t,\t\t\t\t\t\t\tmap_location=snake_case )\n if \".ckpt\" in ckpt_path:\n _lowercase\t\t\t: Union[str, Any]\t\t\t = sd[\"state_dict\"]\n model.load_state_dict(snake_case\t\t,\t\t\t\t\t\t\tstrict=snake_case )\n model.to(snake_case )\n del sd\n return model\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case )\t\t\t->\tUnion[str, Any]:\n _lowercase , _lowercase , _lowercase\t\t\t: Union[str, Any]\t\t\t = model.encode(snake_case )\n print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''' )\n _lowercase\t\t\t: int\t\t\t = model.decode(snake_case )\n return xrec\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case=False )\t\t\t->\tUnion[str, Any]:\n _lowercase , _lowercase\t\t\t: Optional[Any]\t\t\t = string.rsplit(\".\"\t\t,\t\t\t\t\t\t\t1 )\n if reload:\n _lowercase\t\t\t: Union[str, Any]\t\t\t = importlib.import_module(snake_case )\n importlib.reload(snake_case )\n return getattr(importlib.import_module(snake_case\t\t,\t\t\t\t\t\t\tpackage=snake_case )\t\t,\t\t\t\t\t\t\tcls )\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tOptional[Any]:\n if \"target\" not in config:\n raise KeyError(\"Expected key `target` to instantiate.\" )\n return get_obj_from_str(config[\"target\"] )(**config.get(\"params\"\t\t,\t\t\t\t\t\t\t{} ) )\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case=True\t\t,\t\t\t\t\t\t\tsnake_case=True )\t\t\t->\tUnion[str, Any]:\n _lowercase\t\t\t: Tuple\t\t\t = instantiate_from_config(snake_case )\n if sd is not None:\n model.load_state_dict(snake_case )\n if gpu:\n model.cuda()\n if eval_mode:\n model.eval()\n return {\"model\": model}\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case )\t\t\t->\tstr:\n # load the specified checkpoint\n if ckpt:\n _lowercase\t\t\t: Optional[Any]\t\t\t = torch.load(snake_case\t\t,\t\t\t\t\t\t\tmap_location=\"cpu\" )\n _lowercase\t\t\t: Dict\t\t\t = pl_sd[\"global_step\"]\n print(F'''loaded model from global step {global_step}.''' )\n else:\n _lowercase\t\t\t: int\t\t\t = {\"state_dict\": None}\n _lowercase\t\t\t: int\t\t\t = None\n _lowercase\t\t\t: str\t\t\t = load_model_from_config(config.model\t\t,\t\t\t\t\t\t\tpl_sd[\"state_dict\"]\t\t,\t\t\t\t\t\t\tgpu=snake_case\t\t,\t\t\t\t\t\t\teval_mode=snake_case )[\"model\"]\n return model, global_step\n\n\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":250,"string":"250"},"style_context":{"kind":"string","value":"\n\n'''simple docstring'''\nfrom __future__ import annotations\n\nimport requests\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tdict:\n _lowercase\t\t\t: Dict\t\t\t = F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''\n return requests.get(snake_case ).json()\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case = 10 )\t\t\t->\tlist[dict]:\n _lowercase\t\t\t: List[Any]\t\t\t = \"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty\"\n _lowercase\t\t\t: List[str]\t\t\t = requests.get(snake_case ).json()[:max_stories]\n return [get_hackernews_story(snake_case ) for story_id in story_ids]\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case = 10 )\t\t\t->\tstr:\n _lowercase\t\t\t: Union[str, Any]\t\t\t = hackernews_top_stories(snake_case )\n return \"\\n\".join(\"* [{title}]({url})\".format(**snake_case ) for story in stories )\n\n\nif __name__ == \"__main__\":\n print(hackernews_top_stories_as_markdown())\n\n\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":250,"string":"250"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":515,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\nimport argparse\nimport copy\n\n\n\ndef \t\tA_ (\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tAny:\n UpperCamelCase : Any\t = {}\n\n with open(_lowerCAmelCase\t\t\t\t\t) as f:\n for line in f:\n if line.split()[0] not in dict_of_neighbours:\n UpperCamelCase : Any\t = []\n _list.append([line.split()[1], line.split()[2]]\t\t\t\t\t)\n UpperCamelCase : Any\t = _list\n else:\n dict_of_neighbours[line.split()[0]].append(\n [line.split()[1], line.split()[2]]\t\t\t\t\t)\n if line.split()[1] not in dict_of_neighbours:\n UpperCamelCase : str\t = []\n _list.append([line.split()[0], line.split()[2]]\t\t\t\t\t)\n UpperCamelCase : List[Any]\t = _list\n else:\n dict_of_neighbours[line.split()[1]].append(\n [line.split()[0], line.split()[2]]\t\t\t\t\t)\n\n return dict_of_neighbours\n\n\n\ndef \t\tA_ (\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tList[str]:\n\n with open(_lowerCAmelCase\t\t\t\t\t) as f:\n UpperCamelCase : Optional[Any]\t = f.read(1\t\t\t\t\t)\n UpperCamelCase : Tuple\t = start_node\n\n UpperCamelCase : Any\t = []\n\n UpperCamelCase : int\t = start_node\n\n UpperCamelCase : List[str]\t = 0\n while visiting not in first_solution:\n UpperCamelCase : Optional[int]\t = 1_0000\n for k in dict_of_neighbours[visiting]:\n if int(k[1]\t\t\t\t\t) < int(_lowerCAmelCase\t\t\t\t\t) and k[0] not in first_solution:\n UpperCamelCase : Any\t = k[1]\n UpperCamelCase : List[str]\t = k[0]\n\n first_solution.append(_lowerCAmelCase\t\t\t\t\t)\n UpperCamelCase : Optional[Any]\t = distance_of_first_solution + int(_lowerCAmelCase\t\t\t\t\t)\n UpperCamelCase : List[Any]\t = best_node\n\n first_solution.append(_lowerCAmelCase\t\t\t\t\t)\n\n UpperCamelCase : str\t = 0\n for k in dict_of_neighbours[first_solution[-2]]:\n if k[0] == start_node:\n break\n position += 1\n\n UpperCamelCase : int\t = (\n distance_of_first_solution\n + int(dict_of_neighbours[first_solution[-2]][position][1]\t\t\t\t\t)\n - 1_0000\n )\n return first_solution, distance_of_first_solution\n\n\n\ndef \t\tA_ (\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tOptional[Any]:\n UpperCamelCase : Optional[int]\t = []\n\n for n in solution[1:-1]:\n UpperCamelCase : str\t = solution.index(_lowerCAmelCase\t\t\t\t\t)\n for kn in solution[1:-1]:\n UpperCamelCase : List[str]\t = solution.index(_lowerCAmelCase\t\t\t\t\t)\n if n == kn:\n continue\n\n UpperCamelCase : Union[str, Any]\t = copy.deepcopy(_lowerCAmelCase\t\t\t\t\t)\n UpperCamelCase : Tuple\t = kn\n UpperCamelCase : str\t = n\n\n UpperCamelCase : Union[str, Any]\t = 0\n\n for k in _tmp[:-1]:\n UpperCamelCase : List[Any]\t = _tmp[_tmp.index(_lowerCAmelCase\t\t\t\t\t) + 1]\n for i in dict_of_neighbours[k]:\n if i[0] == next_node:\n UpperCamelCase : List[str]\t = distance + int(i[1]\t\t\t\t\t)\n _tmp.append(_lowerCAmelCase\t\t\t\t\t)\n\n if _tmp not in neighborhood_of_solution:\n neighborhood_of_solution.append(_tmp\t\t\t\t\t)\n\n UpperCamelCase : Dict\t = len(neighborhood_of_solution[0]\t\t\t\t\t) - 1\n\n neighborhood_of_solution.sort(key=lambda _lowerCAmelCase\t\t\t\t\t: x[index_of_last_item_in_the_list]\t\t\t\t\t)\n return neighborhood_of_solution\n\n\n\ndef \t\tA_ (\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tOptional[Any]:\n UpperCamelCase : Dict\t = 1\n UpperCamelCase : Optional[int]\t = first_solution\n UpperCamelCase : List[str]\t = []\n UpperCamelCase : Any\t = distance_of_first_solution\n UpperCamelCase : List[Any]\t = solution\n\n while count <= iters:\n UpperCamelCase : Union[str, Any]\t = find_neighborhood(_lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t)\n UpperCamelCase : Optional[Any]\t = 0\n UpperCamelCase : Optional[int]\t = neighborhood[index_of_best_solution]\n UpperCamelCase : Dict\t = len(_lowerCAmelCase\t\t\t\t\t) - 1\n\n UpperCamelCase : Union[str, Any]\t = False\n while not found:\n UpperCamelCase : Optional[int]\t = 0\n while i < len(_lowerCAmelCase\t\t\t\t\t):\n if best_solution[i] != solution[i]:\n UpperCamelCase : Optional[Any]\t = best_solution[i]\n UpperCamelCase : Dict\t = solution[i]\n break\n UpperCamelCase : List[Any]\t = i + 1\n\n if [first_exchange_node, second_exchange_node] not in tabu_list and [\n second_exchange_node,\n first_exchange_node,\n ] not in tabu_list:\n tabu_list.append([first_exchange_node, second_exchange_node]\t\t\t\t\t)\n UpperCamelCase : Optional[Any]\t = True\n UpperCamelCase : Union[str, Any]\t = best_solution[:-1]\n UpperCamelCase : List[str]\t = neighborhood[index_of_best_solution][best_cost_index]\n if cost < best_cost:\n UpperCamelCase : int\t = cost\n UpperCamelCase : Any\t = solution\n else:\n UpperCamelCase : List[str]\t = index_of_best_solution + 1\n UpperCamelCase : Optional[int]\t = neighborhood[index_of_best_solution]\n\n if len(_lowerCAmelCase\t\t\t\t\t) >= size:\n tabu_list.pop(0\t\t\t\t\t)\n\n UpperCamelCase : Optional[int]\t = count + 1\n\n return best_solution_ever, best_cost\n\n\n\ndef \t\tA_ (\t\t\t\t\t_lowerCAmelCase=None\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tOptional[Any]:\n UpperCamelCase : Union[str, Any]\t = generate_neighbours(args.File\t\t\t\t\t)\n\n UpperCamelCase , UpperCamelCase : Any\t = generate_first_solution(\n args.File\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t)\n\n UpperCamelCase , UpperCamelCase : List[Any]\t = tabu_search(\n _lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t\t\t, _lowerCAmelCase\t\t\t\t\t\t\t, args.Iterations\t\t\t\t\t\t\t, args.Size\t\t\t\t\t\t\t, )\n\n print(F\"\"\"Best solution: {best_sol}, with total distance: {best_cost}.\"\"\"\t\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n __lowerCamelCase\t:\t\t\t\t\tList[str]\t\t\t =\t\t\t\t\targparse.ArgumentParser(description=\"\"\"Tabu Search\"\"\")\n parser.add_argument(\n \"\"\"-f\"\"\",\n \"\"\"--File\"\"\",\n type=str,\n help=\"\"\"Path to the file containing the data\"\"\",\n required=True,\n )\n parser.add_argument(\n \"\"\"-i\"\"\",\n \"\"\"--Iterations\"\"\",\n type=int,\n help=\"\"\"How many iterations the algorithm should perform\"\"\",\n required=True,\n )\n parser.add_argument(\n \"\"\"-s\"\"\", \"\"\"--Size\"\"\", type=int, help=\"\"\"Size of the tabu list\"\"\", required=True\n )\n\n # Pass the arguments to main method\n main(parser.parse_args())\n"},"code_codestyle":{"kind":"number","value":140,"string":"140"},"style_context":{"kind":"string","value":"\n\n\n\n\n\nfrom ...configuration_utils import PretrainedConfig\nfrom ...utils import logging\n\n\n__lowerCamelCase\t:\t\t\t\t\tTuple\t\t\t =\t\t\t\t\tlogging.get_logger(__name__)\n\n\n\nclass A__ (\t__snake_case ):\n _UpperCAmelCase :List[Any]\t\t\t\t\t\t = 'timm_backbone'\n\n\n\n\n\n def __init__(\t\t\tself\t,\t\t\t\t\t\t\tA_=None\t,\t\t\t\t\t\t\tA_=3\t,\t\t\t\t\t\t\tA_=True\t,\t\t\t\t\t\t\tA_=True\t,\t\t\t\t\t\t\tA_=None\t,\t\t\t\t\t\t\t**A_\t,\t\t\t\t\t\t\t):\n '''simple docstring'''\n\n\n\n\n super().__init__(**A_ )\n UpperCamelCase : Tuple\t = backbone\n UpperCamelCase : Dict\t = num_channels\n UpperCamelCase : Tuple\t = features_only\n UpperCamelCase : Optional[int]\t = use_pretrained_backbone\n UpperCamelCase : Dict\t = True\n UpperCamelCase : List[str]\t = out_indices if out_indices is not None else (-1,)\n"},"style_context_codestyle":{"kind":"number","value":140,"string":"140"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":516,"cells":{"code":{"kind":"string","value":"\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport operator as op\r\n\r\n\r\n__UpperCAmelCase = 'scaler.pt'\r\n__UpperCAmelCase = 'pytorch_model'\r\n__UpperCAmelCase = 'random_states'\r\n__UpperCAmelCase = 'optimizer'\r\n__UpperCAmelCase = 'scheduler'\r\n__UpperCAmelCase = 'pytorch_model.bin'\r\n__UpperCAmelCase = 'pytorch_model.bin.index.json'\r\n__UpperCAmelCase = 'model.safetensors'\r\n__UpperCAmelCase = 'model.safetensors.index.json'\r\n__UpperCAmelCase = '1.10.2'\r\n__UpperCAmelCase = 'py38'\r\n__UpperCAmelCase = '4.17.0'\r\n__UpperCAmelCase = ['ml.p3.16xlarge', 'ml.p3dn.24xlarge', 'ml.p4dn.24xlarge']\r\n__UpperCAmelCase = ['FULL_SHARD', 'SHARD_GRAD_OP', 'NO_SHARD', 'HYBRID_SHARD', 'HYBRID_SHARD_ZERO2']\r\n__UpperCAmelCase = ['TRANSFORMER_BASED_WRAP', 'SIZE_BASED_WRAP', 'NO_WRAP']\r\n__UpperCAmelCase = ['BACKWARD_PRE', 'BACKWARD_POST', 'NO_PREFETCH']\r\n__UpperCAmelCase = ['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT']\r\n__UpperCAmelCase = '2.0.1'\r\n__UpperCAmelCase = ['pdsh', 'standard', 'openmpi', 'mvapich']\r\n__UpperCAmelCase = ['default', 'reduce-overhead', 'max-autotune']\r\n\r\n__UpperCAmelCase = {'>': op.gt, '>=': op.ge, '==': op.eq, '!=': op.ne, '<=': op.le, '<': op.lt}\r\n\r\n# These are the args for `torch.distributed.launch` for pytorch < 1.9\r\n__UpperCAmelCase = [\r\n 'nnodes',\r\n 'nproc_per_node',\r\n 'rdzv_backend',\r\n 'rdzv_endpoint',\r\n 'rdzv_id',\r\n 'rdzv_conf',\r\n 'standalone',\r\n 'max_restarts',\r\n 'monitor_interval',\r\n 'start_method',\r\n 'role',\r\n 'module',\r\n 'm',\r\n 'no_python',\r\n 'run_path',\r\n 'log_dir',\r\n 'r',\r\n 'redirects',\r\n 't',\r\n 'tee',\r\n 'node_rank',\r\n 'master_addr',\r\n 'master_port',\r\n]\r\n\r\n__UpperCAmelCase = ['DEEPSPEED', 'MULTI_GPU', 'FSDP', 'MEGATRON_LM']\r\n__UpperCAmelCase = ['DEEPSPEED', 'MULTI_XPU', 'FSDP']\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":84,"string":"84"},"style_context":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\nfrom __future__ import annotations\r\n\r\nimport unittest\r\n\r\nfrom transformers import is_tf_available\r\nfrom transformers.testing_utils import require_tf, slow\r\n\r\nfrom ...test_configuration_common import ConfigTester\r\nfrom ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask\r\nfrom ...test_pipeline_mixin import PipelineTesterMixin\r\n\r\n\r\nif is_tf_available():\r\n\t\t\t\timport numpy\r\n\t\t\t\timport tensorflow as tf\r\n\r\n\t\t\t\tfrom transformers import (\r\n\t\t\t\t TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\t\t\t\t TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\t\t\t\t TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\t\t\t\t BertConfig,\r\n\t\t\t\t DPRConfig,\r\n\t\t\t\t TFDPRContextEncoder,\r\n\t\t\t\t TFDPRQuestionEncoder,\r\n\t\t\t\t TFDPRReader,\r\n\t\t\t\t)\r\nclass \t\t\tUpperCamelCase__:\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef __init__(\t\t\t\tself\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase=13\t\t\t\t\t\t,__UpperCAmelCase=7\t\t\t\t\t\t,__UpperCAmelCase=True\t\t\t\t\t\t,__UpperCAmelCase=True\t\t\t\t\t\t,__UpperCAmelCase=True\t\t\t\t\t\t,__UpperCAmelCase=True\t\t\t\t\t\t,__UpperCAmelCase=99\t\t\t\t\t\t,__UpperCAmelCase=32\t\t\t\t\t\t,__UpperCAmelCase=2\t\t\t\t\t\t,__UpperCAmelCase=4\t\t\t\t\t\t,__UpperCAmelCase=37\t\t\t\t\t\t,__UpperCAmelCase=\"gelu\"\t\t\t\t\t\t,__UpperCAmelCase=0.1\t\t\t\t\t\t,__UpperCAmelCase=0.1\t\t\t\t\t\t,__UpperCAmelCase=5_12\t\t\t\t\t\t,__UpperCAmelCase=16\t\t\t\t\t\t,__UpperCAmelCase=2\t\t\t\t\t\t,__UpperCAmelCase=0.0_2\t\t\t\t\t\t,__UpperCAmelCase=3\t\t\t\t\t\t,__UpperCAmelCase=4\t\t\t\t\t\t,__UpperCAmelCase=None\t\t\t\t\t\t,__UpperCAmelCase=0\t\t\t\t\t\t,) -> Dict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tparent\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tbatch_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tseq_length\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tis_training\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tuse_input_mask\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tuse_token_type_ids\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tuse_labels\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tvocab_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\thidden_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tnum_hidden_layers\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tnum_attention_heads\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tintermediate_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\thidden_act\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\thidden_dropout_prob\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tattention_probs_dropout_prob\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmax_position_embeddings\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\ttype_vocab_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\ttype_sequence_label_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tinitializer_range\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tnum_labels\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tnum_choices\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tscope\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tprojection_dim\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> Optional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tids_tensor([self.batch_size, self.seq_length]\t\t\t\t\t\t,self.vocab_size )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tNone\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.use_input_mask:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# follow test_modeling_tf_ctrl.py\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\trandom_attention_mask([self.batch_size, self.seq_length] )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tNone\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.use_token_type_ids:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tids_tensor([self.batch_size, self.seq_length]\t\t\t\t\t\t,self.type_vocab_size )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tNone\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tNone\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tNone\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.use_labels:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tids_tensor([self.batch_size]\t\t\t\t\t\t,self.type_sequence_label_size )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tids_tensor([self.batch_size, self.seq_length]\t\t\t\t\t\t,self.num_labels )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tids_tensor([self.batch_size]\t\t\t\t\t\t,self.num_choices )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tBertConfig(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t vocab_size=self.vocab_size\t\t\t\t\t\t,hidden_size=self.hidden_size\t\t\t\t\t\t,num_hidden_layers=self.num_hidden_layers\t\t\t\t\t\t,num_attention_heads=self.num_attention_heads\t\t\t\t\t\t,intermediate_size=self.intermediate_size\t\t\t\t\t\t,hidden_act=self.hidden_act\t\t\t\t\t\t,hidden_dropout_prob=self.hidden_dropout_prob\t\t\t\t\t\t,attention_probs_dropout_prob=self.attention_probs_dropout_prob\t\t\t\t\t\t,max_position_embeddings=self.max_position_embeddings\t\t\t\t\t\t,type_vocab_size=self.type_vocab_size\t\t\t\t\t\t,is_decoder=__UpperCAmelCase\t\t\t\t\t\t,initializer_range=self.initializer_range\t\t\t\t\t\t,)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tDPRConfig(projection_dim=self.projection_dim\t\t\t\t\t\t,**config.to_dict() )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase ) -> Tuple:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRContextEncoder(config=__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmodel(__UpperCAmelCase\t\t\t\t\t\t,attention_mask=__UpperCAmelCase\t\t\t\t\t\t,token_type_ids=__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmodel(__UpperCAmelCase\t\t\t\t\t\t,token_type_ids=__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmodel(__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(result.pooler_output.shape\t\t\t\t\t\t,(self.batch_size, self.projection_dim or self.hidden_size) )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase ) -> Union[str, Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRQuestionEncoder(config=__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmodel(__UpperCAmelCase\t\t\t\t\t\t,attention_mask=__UpperCAmelCase\t\t\t\t\t\t,token_type_ids=__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmodel(__UpperCAmelCase\t\t\t\t\t\t,token_type_ids=__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmodel(__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(result.pooler_output.shape\t\t\t\t\t\t,(self.batch_size, self.projection_dim or self.hidden_size) )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase\t\t\t\t\t\t,__UpperCAmelCase ) -> Optional[int]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRReader(config=__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmodel(__UpperCAmelCase\t\t\t\t\t\t,attention_mask=__UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(result.start_logits.shape\t\t\t\t\t\t,(self.batch_size, self.seq_length) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(result.end_logits.shape\t\t\t\t\t\t,(self.batch_size, self.seq_length) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(result.relevance_logits.shape\t\t\t\t\t\t,(self.batch_size,) )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> int:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tself.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t (\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t A__\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t) , (\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t A__\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t) , (\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t A__\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t) , (\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t A__\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t) , (\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t A__\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t) , (\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t A__\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t) , (\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t A__\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t) , \r\n\t\t\t\t\t\t\t\t\t\t\t\t\t)\t\t =\t\t\t\t\t\t\tconfig_and_inputs\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\t{'input_ids': input_ids}\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn config, inputs_dict\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_tf\r\nclass \t\t\tUpperCamelCase__(\t__A\t\t\t\t\t\t, __A\t\t\t\t\t\t, unittest.TestCase ):\r\n\t\t\t\t\t\tlowerCAmelCase__ : Optional[int] \t\t\t\t\t\t\t=\t\t\t\t\t\t(\r\n\t\t\t\t\t\t (\r\n\t\t\t\t\t\t TFDPRContextEncoder,\r\n\t\t\t\t\t\t TFDPRQuestionEncoder,\r\n\t\t\t\t\t\t TFDPRReader,\r\n\t\t\t\t\t\t )\r\n\t\t\t\t\t\t if is_tf_available()\r\n\t\t\t\t\t\t else ()\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t\tlowerCAmelCase__ : List[str] \t\t\t\t\t\t\t=\t\t\t\t\t\t{'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}\r\n\r\n\t\t\t\t\t\tlowerCAmelCase__ : Tuple \t\t\t\t\t\t\t=\t\t\t\t\t\tFalse\r\n\t\t\t\t\t\tlowerCAmelCase__ : Optional[int] \t\t\t\t\t\t\t=\t\t\t\t\t\tFalse\r\n\t\t\t\t\t\tlowerCAmelCase__ : List[str] \t\t\t\t\t\t\t=\t\t\t\t\t\tFalse\r\n\t\t\t\t\t\tlowerCAmelCase__ : int \t\t\t\t\t\t\t=\t\t\t\t\t\tFalse\r\n\t\t\t\t\t\tlowerCAmelCase__ : str \t\t\t\t\t\t\t=\t\t\t\t\t\tFalse\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> str:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRModelTester(self )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tConfigTester(self\t\t\t\t\t\t,config_class=__UpperCAmelCase\t\t\t\t\t\t,hidden_size=37 )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> Optional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.config_tester.run_common_tests()\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> int:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tself.model_tester.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.model_tester.create_and_check_dpr_context_encoder(*__UpperCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> Optional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tself.model_tester.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.model_tester.create_and_check_dpr_question_encoder(*__UpperCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> List[str]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tself.model_tester.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.model_tester.create_and_check_dpr_reader(*__UpperCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@slow\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> int:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRContextEncoder.from_pretrained(__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsNotNone(__UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRContextEncoder.from_pretrained(__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsNotNone(__UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRQuestionEncoder.from_pretrained(__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsNotNone(__UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRReader.from_pretrained(__UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsNotNone(__UpperCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_tf\r\nclass \t\t\tUpperCamelCase__(\tunittest.TestCase ):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@slow\r\n\t\t\t\t\t\tdef snake_case__\t\t(\t\t\t\tself ) -> Optional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tTFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base' )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\ttf.constant(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]] ) # [CLS] hello, is my dog cute? [SEP]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\tmodel(__UpperCAmelCase )[0] # embedding shape = (1, 768)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# compare the actual values for a slice.\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tA__\t\t =\t\t\t\t\t\t\ttf.constant(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.0_3_2_3_6_2_5_3,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.1_2_7_5_3_3_3_5,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.1_6_8_1_8_5_0_9,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.0_0_2_7_9_7_8_6,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.3_8_9_6_9_3_3,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.2_4_2_6_4_9_4_5,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.2_1_7_8_9_7_1,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -0.0_2_3_3_5_2_2_7,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -0.0_8_4_8_1_9_5_9,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -0.1_4_3_2_4_1_1_7,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ] )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(numpy.allclose(output[:, :10].numpy()\t\t\t\t\t\t,expected_slice.numpy()\t\t\t\t\t\t,atol=1e-4 ) )\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":221,"string":"221"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":517,"cells":{"code":{"kind":"string","value":"\n\n\n\nimport unittest\n\nfrom transformers import is_torch_available\nfrom transformers.testing_utils import require_torch\n\n\nif is_torch_available():\n\t\t\t\t\t\t\timport torch\n\n\t\t\t\t\t\t\tfrom transformers.activations import gelu_new, gelu_python, get_activation\n@require_torch\nclass \t\t\t_snake_case ( unittest.TestCase):\n\n\n\n\n\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self : Any ):\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\ttorch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\tget_activation(\"gelu\" )\n\t\t\t\t\t\t\tself.assertTrue(torch.allclose(gelu_python(__A ), torch_builtin(__A ) ) )\n\t\t\t\t\t\t\tself.assertFalse(torch.allclose(gelu_python(__A ), gelu_new(__A ) ) )\n\n\n\n\n\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self : Tuple ):\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\ttorch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\tget_activation(\"gelu\" )\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\tget_activation(\"gelu_10\" )\n\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\ttorch_builtin(__A )\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\tgeluaa(__A )\n\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\ttorch.where(y_gelu_aa < 10.0, 1, 0 )\n\n\t\t\t\t\t\t\tself.assertTrue(torch.max(__A ).item() == 10.0 )\n\t\t\t\t\t\t\tself.assertTrue(torch.allclose(y_gelu * clipped_mask, y_gelu_aa * clipped_mask ) )\n\n\n\n\n\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self : List[str] ):\n\t\t\t\t\t\t\tget_activation(\"gelu\" )\n\t\t\t\t\t\t\tget_activation(\"gelu_10\" )\n\t\t\t\t\t\t\tget_activation(\"gelu_fast\" )\n\t\t\t\t\t\t\tget_activation(\"gelu_new\" )\n\t\t\t\t\t\t\tget_activation(\"gelu_python\" )\n\t\t\t\t\t\t\tget_activation(\"gelu_pytorch_tanh\" )\n\t\t\t\t\t\t\tget_activation(\"linear\" )\n\t\t\t\t\t\t\tget_activation(\"mish\" )\n\t\t\t\t\t\t\tget_activation(\"quick_gelu\" )\n\t\t\t\t\t\t\tget_activation(\"relu\" )\n\t\t\t\t\t\t\tget_activation(\"sigmoid\" )\n\t\t\t\t\t\t\tget_activation(\"silu\" )\n\t\t\t\t\t\t\tget_activation(\"swish\" )\n\t\t\t\t\t\t\tget_activation(\"tanh\" )\n\t\t\t\t\t\t\twith self.assertRaises(__A ):\n\t\t\t\t\t\t\t\t\t\tget_activation(\"bogus\" )\n\t\t\t\t\t\t\twith self.assertRaises(__A ):\n\t\t\t\t\t\t\t\t\t\tget_activation(__A )\n\n\n\n\n\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self : Union[str, Any] ):\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\tget_activation(\"gelu\" )\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\t1\n\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\tget_activation(\"gelu\" )\n\t\t\t\t\t\t\tself.assertEqual(acta.a, 1 )\n\t\t\t\t\t\t\twith self.assertRaises(__A ):\n\t\t\t\t\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\tacta.a\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":351,"string":"351"},"style_context":{"kind":"string","value":"\n\n\n\ndef __lowerCAmelCase\t( SCREAMING_SNAKE_CASE_ ,\t\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t\t\t):\n\t\t\tlowercase__ \t\t\t\t\t=\t\t\t1 # To kept the Calculated Value\n\t\t\t# Since C(n, k) = C(n, n-k)\n\t\t\tif k > (n - k):\n\t\t\t\t\t\tlowercase__ \t\t\t\t\t=\t\t\tn - k\n\t\t\t# Calculate C(n,k)\n\t\t\tfor i in range(SCREAMING_SNAKE_CASE_\t\t\t\t\t):\n\t\t\t\t\t\tresult *= n - i\n\t\t\t\t\t\tresult //= i + 1\n\t\t\treturn result\n\n\n\ndef __lowerCAmelCase\t( SCREAMING_SNAKE_CASE_\t\t\t\t\t):\n\t\t\treturn binomial_coefficient(2 * node_count ,\t\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t\t\t) // (node_count + 1)\n\n\n\ndef __lowerCAmelCase\t( SCREAMING_SNAKE_CASE_\t\t\t\t\t):\n\t\t\tif n < 0:\n\t\t\t\t\t\traise ValueError(\"factorial() not defined for negative values\"\t\t\t\t\t)\n\t\t\tlowercase__ \t\t\t\t\t=\t\t\t1\n\t\t\tfor i in range(1 ,\t\t\t\t\tn + 1\t\t\t\t\t):\n\t\t\t\t\t\tresult *= i\n\t\t\treturn result\n\n\n\ndef __lowerCAmelCase\t( SCREAMING_SNAKE_CASE_\t\t\t\t\t):\n\t\t\treturn catalan_number(SCREAMING_SNAKE_CASE_\t\t\t\t\t) * factorial(SCREAMING_SNAKE_CASE_\t\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n\t\t\t\t\t\t\tlowercase_\t\t\t\t\t\t= int(input(\"\"\"Enter the number of nodes: \"\"\").strip() or 0)\n\t\t\t\t\t\t\tif node_count <= 0:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\"\"\"We need some nodes to work with.\"\"\")\n\t\t\t\t\t\t\tprint(\n\t\t\t\t\t\t\t F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '\n\t\t\t\t\t\t\t F'binary trees and {catalan_number(node_count)} binary search trees.'\n\t\t\t\t\t\t\t)\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":224,"string":"224"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":518,"cells":{"code":{"kind":"string","value":"\r\rfrom dataclasses import dataclass\rfrom typing import Optional\r\rimport numpy as np\rimport torch\rimport torch.nn as nn\r\rfrom ..utils import BaseOutput, is_torch_version, randn_tensor\rfrom .attention_processor import SpatialNorm\rfrom .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block\r\r\r\r\r\r\r@dataclass\rclass UpperCAmelCase__\t\t( _a ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r a =\t\t\t\t\t42\r\r\r\r\r\r\rclass UpperCAmelCase__\t\t( nn.Module ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r def __init__(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tList[str]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tAny=3\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]=3\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[str]=(\"DownEncoderBlock2D\",)\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[Any]=(64,)\t, __lowerCamelCase\t\t\t\t\t\t:\t\tDict=2\t, __lowerCamelCase\t\t\t\t\t\t:\t\tUnion[str, Any]=32\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[str]=\"silu\"\t, __lowerCamelCase\t\t\t\t\t\t:\t\tUnion[str, Any]=True\t, )\t\t->\t\t\t\t\t\t\tOptional[Any]:\r super().__init__()\r SCREAMING_SNAKE_CASE__ = layers_per_block\r\r SCREAMING_SNAKE_CASE__ = torch.nn.Convad(\r lowercase__\t, block_out_channels[0]\t, kernel_size=3\t, stride=1\t, padding=1\t, )\r\r SCREAMING_SNAKE_CASE__ = None\r SCREAMING_SNAKE_CASE__ = nn.ModuleList([]\t\t\t\t\t)\r\r # down\r SCREAMING_SNAKE_CASE__ = block_out_channels[0]\r for i, down_block_type in enumerate(lowercase__\t\t\t\t\t):\r SCREAMING_SNAKE_CASE__ = output_channel\r SCREAMING_SNAKE_CASE__ = block_out_channels[i]\r SCREAMING_SNAKE_CASE__ = i == len(lowercase__\t\t\t\t\t) - 1\r\r SCREAMING_SNAKE_CASE__ = get_down_block(\r lowercase__\t, num_layers=self.layers_per_block\t, in_channels=lowercase__\t, out_channels=lowercase__\t, add_downsample=not is_final_block\t, resnet_eps=1e-6\t, downsample_padding=0\t, resnet_act_fn=lowercase__\t, resnet_groups=lowercase__\t, attention_head_dim=lowercase__\t, temb_channels=lowercase__\t, )\r self.down_blocks.append(lowercase__\t\t\t\t\t)\r\r # mid\r SCREAMING_SNAKE_CASE__ = UNetMidBlockaD(\r in_channels=block_out_channels[-1]\t, resnet_eps=1e-6\t, resnet_act_fn=lowercase__\t, output_scale_factor=1\t, resnet_time_scale_shift='''default'''\t, attention_head_dim=block_out_channels[-1]\t, resnet_groups=lowercase__\t, temb_channels=lowercase__\t, )\r\r # out\r SCREAMING_SNAKE_CASE__ = nn.GroupNorm(num_channels=block_out_channels[-1]\t, num_groups=lowercase__\t, eps=1e-6\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = nn.SiLU()\r\r SCREAMING_SNAKE_CASE__ = 2 * out_channels if double_z else out_channels\r SCREAMING_SNAKE_CASE__ = nn.Convad(block_out_channels[-1]\t, lowercase__\t, 3\t, padding=1\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__ = False\r\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tAny\t, __lowerCamelCase\t\t\t\t\t\t:\t\tint\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tAny:\r SCREAMING_SNAKE_CASE__ = x\r SCREAMING_SNAKE_CASE__ = self.conv_in(lowercase__\t\t\t\t\t)\r\r if self.training and self.gradient_checkpointing:\r\r def create_custom_forward(__lowerCamelCase\t\t\t\t\t\t:\t\tDict\t\t\t\t\t):\r def custom_forward(*__lowerCamelCase\t\t\t\t\t\t:\t\tDict\t\t\t\t\t):\r return module(*lowercase__\t\t\t\t\t)\r\r return custom_forward\r\r # down\r if is_torch_version('''>='''\t, '''1.11.0'''\t\t\t\t\t):\r for down_block in self.down_blocks:\r SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(\r create_custom_forward(lowercase__\t\t\t\t\t)\t, lowercase__\t, use_reentrant=lowercase__\t\t\t\t\t)\r # middle\r SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(\r create_custom_forward(self.mid_block\t\t\t\t\t)\t, lowercase__\t, use_reentrant=lowercase__\t\t\t\t\t)\r else:\r for down_block in self.down_blocks:\r SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(create_custom_forward(lowercase__\t\t\t\t\t)\t, lowercase__\t\t\t\t\t)\r # middle\r SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block\t\t\t\t\t)\t, lowercase__\t\t\t\t\t)\r\r else:\r # down\r for down_block in self.down_blocks:\r SCREAMING_SNAKE_CASE__ = down_block(lowercase__\t\t\t\t\t)\r\r # middle\r SCREAMING_SNAKE_CASE__ = self.mid_block(lowercase__\t\t\t\t\t)\r\r # post-process\r SCREAMING_SNAKE_CASE__ = self.conv_norm_out(lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = self.conv_act(lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = self.conv_out(lowercase__\t\t\t\t\t)\r\r return sample\r\r\r\r\r\r\rclass UpperCAmelCase__\t\t( nn.Module ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r def __init__(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tOptional[int]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tint=3\t, __lowerCamelCase\t\t\t\t\t\t:\t\tTuple=3\t, __lowerCamelCase\t\t\t\t\t\t:\t\tAny=(\"UpDecoderBlock2D\",)\t, __lowerCamelCase\t\t\t\t\t\t:\t\tAny=(64,)\t, __lowerCamelCase\t\t\t\t\t\t:\t\tTuple=2\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[str]=32\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[str]=\"silu\"\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]=\"group\"\t, )\t\t->\t\t\t\t\t\t\tOptional[Any]:\r super().__init__()\r SCREAMING_SNAKE_CASE__ = layers_per_block\r\r SCREAMING_SNAKE_CASE__ = nn.Convad(\r lowercase__\t, block_out_channels[-1]\t, kernel_size=3\t, stride=1\t, padding=1\t, )\r\r SCREAMING_SNAKE_CASE__ = None\r SCREAMING_SNAKE_CASE__ = nn.ModuleList([]\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__ = in_channels if norm_type == '''spatial''' else None\r\r # mid\r SCREAMING_SNAKE_CASE__ = UNetMidBlockaD(\r in_channels=block_out_channels[-1]\t, resnet_eps=1e-6\t, resnet_act_fn=lowercase__\t, output_scale_factor=1\t, resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type\t, attention_head_dim=block_out_channels[-1]\t, resnet_groups=lowercase__\t, temb_channels=lowercase__\t, )\r\r # up\r SCREAMING_SNAKE_CASE__ = list(reversed(lowercase__\t\t\t\t\t)\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = reversed_block_out_channels[0]\r for i, up_block_type in enumerate(lowercase__\t\t\t\t\t):\r SCREAMING_SNAKE_CASE__ = output_channel\r SCREAMING_SNAKE_CASE__ = reversed_block_out_channels[i]\r\r SCREAMING_SNAKE_CASE__ = i == len(lowercase__\t\t\t\t\t) - 1\r\r SCREAMING_SNAKE_CASE__ = get_up_block(\r lowercase__\t, num_layers=self.layers_per_block + 1\t, in_channels=lowercase__\t, out_channels=lowercase__\t, prev_output_channel=lowercase__\t, add_upsample=not is_final_block\t, resnet_eps=1e-6\t, resnet_act_fn=lowercase__\t, resnet_groups=lowercase__\t, attention_head_dim=lowercase__\t, temb_channels=lowercase__\t, resnet_time_scale_shift=lowercase__\t, )\r self.up_blocks.append(lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = output_channel\r\r # out\r if norm_type == \"spatial\":\r SCREAMING_SNAKE_CASE__ = SpatialNorm(block_out_channels[0]\t, lowercase__\t\t\t\t\t)\r else:\r SCREAMING_SNAKE_CASE__ = nn.GroupNorm(num_channels=block_out_channels[0]\t, num_groups=lowercase__\t, eps=1e-6\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = nn.SiLU()\r SCREAMING_SNAKE_CASE__ = nn.Convad(block_out_channels[0]\t, lowercase__\t, 3\t, padding=1\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__ = False\r\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tList[str]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tUnion[str, Any]=None\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tint:\r SCREAMING_SNAKE_CASE__ = z\r SCREAMING_SNAKE_CASE__ = self.conv_in(lowercase__\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__ = next(iter(self.up_blocks.parameters()\t\t\t\t\t)\t\t\t\t\t).dtype\r if self.training and self.gradient_checkpointing:\r\r def create_custom_forward(__lowerCamelCase\t\t\t\t\t\t:\t\tAny\t\t\t\t\t):\r def custom_forward(*__lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]\t\t\t\t\t):\r return module(*lowercase__\t\t\t\t\t)\r\r return custom_forward\r\r if is_torch_version('''>='''\t, '''1.11.0'''\t\t\t\t\t):\r # middle\r SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(\r create_custom_forward(self.mid_block\t\t\t\t\t)\t, lowercase__\t, lowercase__\t, use_reentrant=lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = sample.to(lowercase__\t\t\t\t\t)\r\r # up\r for up_block in self.up_blocks:\r SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(\r create_custom_forward(lowercase__\t\t\t\t\t)\t, lowercase__\t, lowercase__\t, use_reentrant=lowercase__\t\t\t\t\t)\r else:\r # middle\r SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(\r create_custom_forward(self.mid_block\t\t\t\t\t)\t, lowercase__\t, lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = sample.to(lowercase__\t\t\t\t\t)\r\r # up\r for up_block in self.up_blocks:\r SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(create_custom_forward(lowercase__\t\t\t\t\t)\t, lowercase__\t, lowercase__\t\t\t\t\t)\r else:\r # middle\r SCREAMING_SNAKE_CASE__ = self.mid_block(lowercase__\t, lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = sample.to(lowercase__\t\t\t\t\t)\r\r # up\r for up_block in self.up_blocks:\r SCREAMING_SNAKE_CASE__ = up_block(lowercase__\t, lowercase__\t\t\t\t\t)\r\r # post-process\r if latent_embeds is None:\r SCREAMING_SNAKE_CASE__ = self.conv_norm_out(lowercase__\t\t\t\t\t)\r else:\r SCREAMING_SNAKE_CASE__ = self.conv_norm_out(lowercase__\t, lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = self.conv_act(lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = self.conv_out(lowercase__\t\t\t\t\t)\r\r return sample\r\r\r\r\r\r\rclass UpperCAmelCase__\t\t( nn.Module ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r def __init__(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tAny\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tAny\t, __lowerCamelCase\t\t\t\t\t\t:\t\tint\t, __lowerCamelCase\t\t\t\t\t\t:\t\tTuple=None\t, __lowerCamelCase\t\t\t\t\t\t:\t\tstr=\"random\"\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]=False\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[Any]=True\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tTuple:\r super().__init__()\r SCREAMING_SNAKE_CASE__ = n_e\r SCREAMING_SNAKE_CASE__ = vq_embed_dim\r SCREAMING_SNAKE_CASE__ = beta\r SCREAMING_SNAKE_CASE__ = legacy\r\r SCREAMING_SNAKE_CASE__ = nn.Embedding(self.n_e\t, self.vq_embed_dim\t\t\t\t\t)\r self.embedding.weight.data.uniform_(-1.0 / self.n_e\t, 1.0 / self.n_e\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__ = remap\r if self.remap is not None:\r self.register_buffer('''used'''\t, torch.tensor(np.load(self.remap\t\t\t\t\t)\t\t\t\t\t)\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = self.used.shape[0]\r SCREAMING_SNAKE_CASE__ = unknown_index # \"random\" or \"extra\" or integer\r if self.unknown_index == \"extra\":\r SCREAMING_SNAKE_CASE__ = self.re_embed\r SCREAMING_SNAKE_CASE__ = self.re_embed + 1\r print(\r f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''\r f'''Using {self.unknown_index} for unknown indices.'''\t\t\t\t\t)\r else:\r SCREAMING_SNAKE_CASE__ = n_e\r\r SCREAMING_SNAKE_CASE__ = sane_index_shape\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tList[Any]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tDict:\r SCREAMING_SNAKE_CASE__ = inds.shape\r assert len(lowercase__\t\t\t\t\t) > 1\r SCREAMING_SNAKE_CASE__ = inds.reshape(ishape[0]\t, -1\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = self.used.to(lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = (inds[:, :, None] == used[None, None, ...]).long()\r SCREAMING_SNAKE_CASE__ = match.argmax(-1\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = match.sum(2\t\t\t\t\t) < 1\r if self.unknown_index == \"random\":\r SCREAMING_SNAKE_CASE__ = torch.randint(0\t, self.re_embed\t, size=new[unknown].shape\t\t\t\t\t).to(device=new.device\t\t\t\t\t)\r else:\r SCREAMING_SNAKE_CASE__ = self.unknown_index\r return new.reshape(lowercase__\t\t\t\t\t)\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tDict\t, __lowerCamelCase\t\t\t\t\t\t:\t\tTuple\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tAny:\r SCREAMING_SNAKE_CASE__ = inds.shape\r assert len(lowercase__\t\t\t\t\t) > 1\r SCREAMING_SNAKE_CASE__ = inds.reshape(ishape[0]\t, -1\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = self.used.to(lowercase__\t\t\t\t\t)\r if self.re_embed > self.used.shape[0]: # extra token\r SCREAMING_SNAKE_CASE__ = 0 # simply set to zero\r SCREAMING_SNAKE_CASE__ = torch.gather(used[None, :][inds.shape[0] * [0], :]\t, 1\t, lowercase__\t\t\t\t\t)\r return back.reshape(lowercase__\t\t\t\t\t)\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tList[Any]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tstr\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tAny:\r # reshape z -> (batch, height, width, channel) and flatten\r SCREAMING_SNAKE_CASE__ = z.permute(0\t, 2\t, 3\t, 1\t\t\t\t\t).contiguous()\r SCREAMING_SNAKE_CASE__ = z.view(-1\t, self.vq_embed_dim\t\t\t\t\t)\r\r # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z\r SCREAMING_SNAKE_CASE__ = torch.argmin(torch.cdist(lowercase__\t, self.embedding.weight\t\t\t\t\t)\t, dim=1\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__ = self.embedding(lowercase__\t\t\t\t\t).view(z.shape\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = None\r SCREAMING_SNAKE_CASE__ = None\r\r # compute loss for embedding\r if not self.legacy:\r SCREAMING_SNAKE_CASE__ = self.beta * torch.mean((z_q.detach() - z) ** 2\t\t\t\t\t) + torch.mean((z_q - z.detach()) ** 2\t\t\t\t\t)\r else:\r SCREAMING_SNAKE_CASE__ = torch.mean((z_q.detach() - z) ** 2\t\t\t\t\t) + self.beta * torch.mean((z_q - z.detach()) ** 2\t\t\t\t\t)\r\r # preserve gradients\r SCREAMING_SNAKE_CASE__ = z + (z_q - z).detach()\r\r # reshape back to match original input shape\r SCREAMING_SNAKE_CASE__ = z_q.permute(0\t, 3\t, 1\t, 2\t\t\t\t\t).contiguous()\r\r if self.remap is not None:\r SCREAMING_SNAKE_CASE__ = min_encoding_indices.reshape(z.shape[0]\t, -1\t\t\t\t\t) # add batch axis\r SCREAMING_SNAKE_CASE__ = self.remap_to_used(lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = min_encoding_indices.reshape(-1\t, 1\t\t\t\t\t) # flatten\r\r if self.sane_index_shape:\r SCREAMING_SNAKE_CASE__ = min_encoding_indices.reshape(z_q.shape[0]\t, z_q.shape[2]\t, z_q.shape[3]\t\t\t\t\t)\r\r return z_q, loss, (perplexity, min_encodings, min_encoding_indices)\r\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tAny\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tAny\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tDict:\r # shape specifying (batch, height, width, channel)\r if self.remap is not None:\r SCREAMING_SNAKE_CASE__ = indices.reshape(shape[0]\t, -1\t\t\t\t\t) # add batch axis\r SCREAMING_SNAKE_CASE__ = self.unmap_to_all(lowercase__\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = indices.reshape(-1\t\t\t\t\t) # flatten again\r\r # get quantized latent vectors\r SCREAMING_SNAKE_CASE__ = self.embedding(lowercase__\t\t\t\t\t)\r\r if shape is not None:\r SCREAMING_SNAKE_CASE__ = z_q.view(lowercase__\t\t\t\t\t)\r # reshape back to match original input shape\r SCREAMING_SNAKE_CASE__ = z_q.permute(0\t, 3\t, 1\t, 2\t\t\t\t\t).contiguous()\r\r return z_q\r\r\r\r\r\r\rclass UpperCAmelCase__\t\t( _a ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r def __init__(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tUnion[str, Any]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[Any]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tTuple=False\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tTuple:\r SCREAMING_SNAKE_CASE__ = parameters\r SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = torch.chunk(lowercase__\t, 2\t, dim=1\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = torch.clamp(self.logvar\t, -30.0\t, 20.0\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = deterministic\r SCREAMING_SNAKE_CASE__ = torch.exp(0.5 * self.logvar\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = torch.exp(self.logvar\t\t\t\t\t)\r if self.deterministic:\r SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = torch.zeros_like(\r self.mean\t, device=self.parameters.device\t, dtype=self.parameters.dtype\t\t\t\t\t)\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tList[str]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tUnion[str, Any] = None\t\t\t\t\t)\t\t->\t\t\t\t\t\t\ttorch.FloatTensor:\r # make sure sample is on the same device as the parameters and has same dtype\r SCREAMING_SNAKE_CASE__ = randn_tensor(\r self.mean.shape\t, generator=lowercase__\t, device=self.parameters.device\t, dtype=self.parameters.dtype\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = self.mean + self.std * sample\r return x\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tList[str]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tDict=None\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tAny:\r if self.deterministic:\r return torch.Tensor([0.0]\t\t\t\t\t)\r else:\r if other is None:\r return 0.5 * torch.sum(torch.pow(self.mean\t, 2\t\t\t\t\t) + self.var - 1.0 - self.logvar\t, dim=[1, 2, 3]\t\t\t\t\t)\r else:\r return 0.5 * torch.sum(\r torch.pow(self.mean - other.mean\t, 2\t\t\t\t\t) / other.var\r + self.var / other.var\r - 1.0\r - self.logvar\r + other.logvar\t, dim=[1, 2, 3]\t, )\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tUnion[str, Any]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tAny\t, __lowerCamelCase\t\t\t\t\t\t:\t\tTuple=[1, 2, 3]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tOptional[Any]:\r if self.deterministic:\r return torch.Tensor([0.0]\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = np.log(2.0 * np.pi\t\t\t\t\t)\r return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean\t, 2\t\t\t\t\t) / self.var\t, dim=lowercase__\t\t\t\t\t)\r\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tOptional[Any]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tList[str]:\r return self.mean\r\r\r"},"code_codestyle":{"kind":"number","value":314,"string":"314"},"style_context":{"kind":"string","value":"\r\n\r\nA_\t\t\t\t\t\t:\tList[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}\r\nA_\t\t\t\t\t\t:\tint = ['a', 'b', 'c', 'd', 'e']\r\n\r\n\r\ndef \t\t\t\t\t__a\t\t\t\t( SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t\t)\t\t\t\t\t-> List[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n __UpperCAmelCase =\tstart\r\n # add current to visited\r\n visited.append(SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\n __UpperCAmelCase =\tedges[current]\r\n for neighbor in neighbors:\r\n # if neighbor not in visited, visit\r\n if neighbor not in visited:\r\n __UpperCAmelCase =\ttopological_sort(SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\n # if all neighbors visited add current to sort\r\n sort.append(SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\n # if all vertices haven't been visited select a new one to visit\r\n if len(SCREAMING_SNAKE_CASE\t\t\t\t\t) != len(SCREAMING_SNAKE_CASE\t\t\t\t\t):\r\n for vertice in vertices:\r\n if vertice not in visited:\r\n __UpperCAmelCase =\ttopological_sort(SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\n # return sort\r\n return sort\r\n\r\n\r\nif __name__ == \"__main__\":\r\n A_\t\t\t\t\t\t:\tTuple = topological_sort('a', [], [])\r\n print(sort)\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":333,"string":"333"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":519,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\rimport re\rimport string\r\rimport numpy as np\r\rimport datasets\r\r\r_SCREAMING_SNAKE_CASE \t\t=\t\t\t\t\t\t\"\"\"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n\"\"\"\r\r_SCREAMING_SNAKE_CASE \t\t=\t\t\t\t\t\t\"\"\"\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\\\"exact_match\\\")\n >>> refs = [\\\"the cat\\\", \\\"theater\\\", \\\"YELLING\\\", \\\"agent007\\\"]\n >>> preds = [\\\"cat?\\\", \\\"theater\\\", \\\"yelling\\\", \\\"agent\\\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\\\"exact_match\\\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\\\"exact_match\\\")\n >>> refs = [\\\"the cat\\\", \\\"theater\\\", \\\"YELLING\\\", \\\"agent007\\\"]\n >>> preds = [\\\"cat?\\\", \\\"theater\\\", \\\"yelling\\\", \\\"agent\\\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\\\"the \\\", \\\"yell\\\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\\\"exact_match\\\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\\\"exact_match\\\")\n >>> refs = [\\\"the cat\\\", \\\"theater\\\", \\\"YELLING\\\", \\\"agent007\\\"]\n >>> preds = [\\\"cat?\\\", \\\"theater\\\", \\\"yelling\\\", \\\"agent\\\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\\\"the \\\", \\\"yell\\\", \\\"YELL\\\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\\\"exact_match\\\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\\\"exact_match\\\")\n >>> refs = [\\\"the cat\\\", \\\"theater\\\", \\\"YELLING\\\", \\\"agent007\\\"]\n >>> preds = [\\\"cat?\\\", \\\"theater\\\", \\\"yelling\\\", \\\"agent\\\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\\\"the \\\", \\\"yell\\\", \\\"YELL\\\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\\\"exact_match\\\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\\\"exact_match\\\")\n >>> refs = [\\\"The cat sat on the mat.\\\", \\\"Theaters are great.\\\", \\\"It's like comparing oranges and apples.\\\"]\n >>> preds = [\\\"The cat sat on the mat?\\\", \\\"Theaters are great.\\\", \\\"It's like comparing apples and oranges.\\\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\\\"exact_match\\\"], 1))\n 33.3\n\n\"\"\"\r\r_SCREAMING_SNAKE_CASE \t\t=\t\t\t\t\t\t\"\"\"\n\"\"\"\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,\t\t\t_KWARGS_DESCRIPTION )\rclass SCREAMING_SNAKE_CASE_ ( datasets.Metric ):\r\r\r\r\r\r\r\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t( self : Union[str, Any]\t\t)\t\t\t\t\t\t\t-> Optional[Any]:\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\t\t\t\t\t\t\treturn datasets.MetricInfo(\r\t\t\t\t\t\t\t description=_DESCRIPTION\t\t\t\t\t\t,\t\t\t\tcitation=_CITATION\t\t\t\t\t\t,\t\t\t\tinputs_description=_KWARGS_DESCRIPTION\t\t\t\t\t\t,\t\t\t\tfeatures=datasets.Features(\r\t\t\t\t\t\t\t {\r\t\t\t\t\t\t\t 'predictions': datasets.Value('string'\t\t\t\t\t\t,\t\t\t\tid='sequence'\t\t),\r\t\t\t\t\t\t\t 'references': datasets.Value('string'\t\t\t\t\t\t,\t\t\t\tid='sequence'\t\t),\r\t\t\t\t\t\t\t }\t\t)\t\t\t\t\t\t,\t\t\t\treference_urls=[]\t\t\t\t\t\t,\t\t\t\t)\r\r\r\r\r\r\r\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t( self : List[Any]\t\t\t\t\t\t,\t\t\t\t_A : Optional[Any]\t\t\t\t\t\t,\t\t\t\t_A : Optional[int]\t\t\t\t\t\t,\t\t\t\t_A : Optional[int]=None\t\t\t\t\t\t,\t\t\t\t_A : Dict=False\t\t\t\t\t\t,\t\t\t\t_A : Dict=False\t\t\t\t\t\t,\t\t\t\t_A : Optional[Any]=False\t\t\t\t\t\t,\t\t\t\t)\t\t\t\t\t\t\t-> List[str]:\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\t\t\t\t\t\t\tif regexes_to_ignore is not None:\r\t\t\t\t\t\t\t\tfor s in regexes_to_ignore:\r\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: List[str] =\t\tnp.array([re.sub(_A\t\t\t\t\t\t,\t\t\t\t''\t\t\t\t\t\t,\t\t\t\t_A\t\t) for x in predictions]\t\t)\r\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: int =\t\tnp.array([re.sub(_A\t\t\t\t\t\t,\t\t\t\t''\t\t\t\t\t\t,\t\t\t\t_A\t\t) for x in references]\t\t)\r\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: Optional[Any] =\t\tnp.asarray(_A\t\t)\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: Optional[Any] =\t\tnp.asarray(_A\t\t)\r\r\t\t\t\t\t\t\tif ignore_case:\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: int =\t\tnp.char.lower(_A\t\t)\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: List[str] =\t\tnp.char.lower(_A\t\t)\r\r\t\t\t\t\t\t\tif ignore_punctuation:\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: str =\t\tstring.punctuation.maketrans(''\t\t\t\t\t\t,\t\t\t\t''\t\t\t\t\t\t,\t\t\t\tstring.punctuation\t\t)\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: str =\t\tnp.char.translate(_A\t\t\t\t\t\t,\t\t\t\ttable=_A\t\t)\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: Any =\t\tnp.char.translate(_A\t\t\t\t\t\t,\t\t\t\ttable=_A\t\t)\r\r\t\t\t\t\t\t\tif ignore_numbers:\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: int =\t\tstring.digits.maketrans(''\t\t\t\t\t\t,\t\t\t\t''\t\t\t\t\t\t,\t\t\t\tstring.digits\t\t)\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: Tuple =\t\tnp.char.translate(_A\t\t\t\t\t\t,\t\t\t\ttable=_A\t\t)\r\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: Optional[Any] =\t\tnp.char.translate(_A\t\t\t\t\t\t,\t\t\t\ttable=_A\t\t)\r\r\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t: Optional[Any] =\t\tpredictions == references\r\r\t\t\t\t\t\t\treturn {\"exact_match\": np.mean(_A\t\t) * 100}\r\r"},"code_codestyle":{"kind":"number","value":368,"string":"368"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\nfrom __future__ import annotations\r\n\r\nimport math\r\n\r\n\r\ndef \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ ( __a\t\t\t\t,\t__a\t\t\t\t):\r\n\tsnake_case_\t\t\t\t\t\t: Optional[int] =\t\tu\r\n\tfor i in range(1\t\t\t\t,\t__a\t\t\t\t):\r\n\t\tsnake_case_\t\t\t\t\t\t: Optional[Any] =\t\ttemp * (u - i)\r\n\treturn temp\r\n\r\n\r\ndef \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ ( ):\r\n\tsnake_case_\t\t\t\t\t\t: Dict =\t\tint(input('enter the numbers of values: '\t\t\t\t)\t\t\t\t)\r\n\tsnake_case_\t\t\t\t\t\t: list[list[float]] =\t\t[]\r\n\tfor _ in range(__a\t\t\t\t):\r\n\t\ty.append([]\t\t\t\t)\r\n\tfor i in range(__a\t\t\t\t):\r\n\t\tfor j in range(__a\t\t\t\t):\r\n\t\t\ty[i].append(__a\t\t\t\t)\r\n\t\t\tsnake_case_\t\t\t\t\t\t: str =\t\t0\r\n\r\n\tprint('enter the values of parameters in a list: '\t\t\t\t)\r\n\tsnake_case_\t\t\t\t\t\t: int =\t\tlist(map(__a\t\t\t\t,\tinput().split()\t\t\t\t)\t\t\t\t)\r\n\r\n\tprint('enter the values of corresponding parameters: '\t\t\t\t)\r\n\tfor i in range(__a\t\t\t\t):\r\n\t\tsnake_case_\t\t\t\t\t\t: Union[str, Any] =\t\tfloat(input()\t\t\t\t)\r\n\r\n\tsnake_case_\t\t\t\t\t\t: int =\t\tint(input('enter the value to interpolate: '\t\t\t\t)\t\t\t\t)\r\n\tsnake_case_\t\t\t\t\t\t: List[Any] =\t\t(value - x[0]) / (x[1] - x[0])\r\n\r\n\t# for calculating forward difference table\r\n\r\n\tfor i in range(1\t\t\t\t,\t__a\t\t\t\t):\r\n\t\tfor j in range(n - i\t\t\t\t):\r\n\t\t\tsnake_case_\t\t\t\t\t\t: int =\t\ty[j + 1][i - 1] - y[j][i - 1]\r\n\r\n\tsnake_case_\t\t\t\t\t\t: str =\t\ty[0][0]\r\n\tfor i in range(1\t\t\t\t,\t__a\t\t\t\t):\r\n\t\tsumm += (ucal(__a\t\t\t\t,\t__a\t\t\t\t) * y[0][i]) / math.factorial(__a\t\t\t\t)\r\n\r\n\tprint(f\"\"\"the value at {value} is {summ}\"\"\"\t\t\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\t\t\t\tmain()\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":88,"string":"88"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":520,"cells":{"code":{"kind":"string","value":"\r\r\r\rimport math\rimport unittest\r\r\r\r\r\r\rdef \t\t\t\tA\t\t\t\t(\t\t\t\t\t_lowerCamelCase ):\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\t\t\t\t\t\t\tassert isinstance(_lowerCamelCase\t\t\t\t, _lowerCamelCase ) and (\r\t\t\t\t\t\t\t number >= 0\r\t\t\t\t\t\t\t), \"'number' must been an int and positive\"\r\r\t\t\t\t\t\t\tif 1 < number < 4:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t# 2 and 3 are primes\r\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn True\r\t\t\t\t\t\t\telif number < 2 or number % 2 == 0 or number % 3 == 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes\r\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn False\r\r\t\t\t\t\t\t\t# All primes number are in format of 6k +/- 1\r\t\t\t\t\t\t\tfor i in range(5\t\t\t\t, int(math.sqrt(_lowerCamelCase ) + 1 )\t\t\t\t, 6 ):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tif number % i == 0 or number % (i + 2) == 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn False\r\t\t\t\t\t\t\treturn True\r\r\r\rclass UpperCAmelCase_ ( unittest.TestCase):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\tdef snake_case__ ( self):\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(2))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(3))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(5))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(7))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(11))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(13))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(17))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(19))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(23))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(is_prime(29))\r\r\r\r\r\r\r\r\t\t\t\t\t\t\tdef snake_case__ ( self):\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\twith self.assertRaises(__a):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tis_prime(-19)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t is_prime(0),\t\t\t\"Zero doesn't have any positive factors, primes must have exactly two.\",\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t is_prime(1),\t\t\t\"One only has 1 positive factor, primes must have exactly two.\",\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(is_prime(2 * 2))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(is_prime(2 * 3))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(is_prime(3 * 3))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(is_prime(3 * 5))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(is_prime(3 * 5 * 7))\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\t\tunittest.main()\r"},"code_codestyle":{"kind":"number","value":36,"string":"36"},"style_context":{"kind":"string","value":"\r\r\r\"\"\"simple docstring\"\"\"\r\r\rimport copy\rimport json\rimport os\rimport tempfile\r\rfrom transformers import is_torch_available\r\rfrom .test_configuration_utils import config_common_kwargs\rclass _UpperCamelCase ( lowerCAmelCase__\t\t):\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t\t\t\t\t\tdef __init__( self , __a , __a=None , __a=True , __a=None , **__a\t):\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tparent\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tconfig_class\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\thas_text_modality\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tkwargs\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tcommon_properties\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class(**self.inputs_dict\t)\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t(\r\t\t\t\t\t\t\t\t\t\t [\"hidden_size\", \"num_attention_heads\", \"num_hidden_layers\"]\r\t\t\t\t\t\t\t\t\t\t if self.common_properties is None\r\t\t\t\t\t\t\t\t\t\t else self.common_properties\r\t\t\t\t\t\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t\t# Add common fields for text models\r\t\t\t\t\t\t\t\t\t\tif self.has_text_modality:\r\t\t\t\t\t\t\t\t\t\t\t\t\tcommon_properties.extend([\"vocab_size\"]\t)\r\r\t\t\t\t\t\t\t\t\t\t# Test that config has the common properties as getters\r\t\t\t\t\t\t\t\t\t\tfor prop in common_properties:\r\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertTrue(hasattr(__a , __a\t) , msg=f\"`{prop}` does not exist\"\t)\r\r\t\t\t\t\t\t\t\t\t\t# Test that config has the common properties as setter\r\t\t\t\t\t\t\t\t\t\tfor idx, name in enumerate(__a\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsetattr(__a , __a , __a\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t getattr(__a , __a\t) , __a , msg=f\"`{name} value {idx} expected, but was {getattr(__a , __a\t)}\"\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\texcept NotImplementedError:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Some models might not be able to implement setters for common_properties\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# In that case, a NotImplementedError is raised\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\r # Test if config class can be called with Config(prop_name=..)\r\t\t\t\t\t\t\t\t\t\tfor idx, name in enumerate(__a\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class(**{name: idx}\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t getattr(__a , __a\t) , __a , msg=f\"`{name} value {idx} expected, but was {getattr(__a , __a\t)}\"\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\texcept NotImplementedError:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Some models might not be able to implement setters for common_properties\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# In that case, a NotImplementedError is raised\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class(**self.inputs_dict\t)\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tjson.loads(config.to_json_string()\t)\r\t\t\t\t\t\t\t\t\t\tfor key, value in self.inputs_dict.items():\r\t\t\t\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(obj[key] , __a\t)\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class(**self.inputs_dict\t)\r\r\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\r\t\t\t\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tos.path.join(__a , \"config.json\"\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\tconfig_first.to_json_file(__a\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class.from_json_file(__a\t)\r\r\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(config_second.to_dict() , config_first.to_dict()\t)\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class(**self.inputs_dict\t)\r\r\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\r\t\t\t\t\t\t\t\t\t\t\t\t\tconfig_first.save_pretrained(__a\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class.from_pretrained(__a\t)\r\r\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(config_second.to_dict() , config_first.to_dict()\t)\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class(**self.inputs_dict\t)\r\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t\"test\"\r\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\r\t\t\t\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tos.path.join(__a , __a\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\tconfig_first.save_pretrained(__a\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class.from_pretrained(__a , subfolder=__a\t)\r\r\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(config_second.to_dict() , config_first.to_dict()\t)\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class(**self.inputs_dict , num_labels=5\t)\r\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(len(config.idalabel\t) , 5\t)\r\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(len(config.labelaid\t) , 5\t)\r\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t3\r\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(len(config.idalabel\t) , 3\t)\r\t\t\t\t\t\t\t\t\t\tself.parent.assertEqual(len(config.labelaid\t) , 3\t)\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\tif self.config_class.is_composition:\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class()\r\t\t\t\t\t\t\t\t\t\tself.parent.assertIsNotNone(__a\t)\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tcopy.deepcopy(__a\t)\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\tself.config_class(**__a\t)\r\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t[]\r\t\t\t\t\t\t\t\t\t\tfor key, value in config_common_kwargs.items():\r\t\t\t\t\t\t\t\t\t\t\t\t\tif key == \"torch_dtype\":\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not is_torch_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcontinue\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\timport torch\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif config.torch_dtype != torch.floataa:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twrong_values.append((\"torch_dtype\", config.torch_dtype, torch.floataa)\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\telif getattr(__a , __a\t) != value:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twrong_values.append((key, getattr(__a , __a\t), value)\t)\r\r\t\t\t\t\t\t\t\t\t\tif len(__a\t) > 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t__lowerCAmelCase\t\t\t\t =\t\t\t\"\\n\".join([f\"- {v[0]}: got {v[1]} instead of {v[2]}\" for v in wrong_values]\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(f\"The following keys were not properly set in the config:\\n{errors}\"\t)\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tsnake_case\t\t\t\t( self\t):\r\t\t\t\t\t\t\t\t\t\tself.create_and_test_config_common_properties()\r\t\t\t\t\t\t\t\t\t\tself.create_and_test_config_to_json_string()\r\t\t\t\t\t\t\t\t\t\tself.create_and_test_config_to_json_file()\r\t\t\t\t\t\t\t\t\t\tself.create_and_test_config_from_and_save_pretrained()\r\t\t\t\t\t\t\t\t\t\tself.create_and_test_config_from_and_save_pretrained_subfolder()\r\t\t\t\t\t\t\t\t\t\tself.create_and_test_config_with_num_labels()\r\t\t\t\t\t\t\t\t\t\tself.check_config_can_be_init_without_params()\r\t\t\t\t\t\t\t\t\t\tself.check_config_arguments_init()\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":57,"string":"57"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":521,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\n\nimport argparse\nimport collections\n\nimport torch\nfrom flax import traverse_util\nfrom tax import checkpoints\n\nfrom transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration\nfrom transformers.utils import logging\n\n\nlogging.set_verbosity_info()\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase=\"attention\"\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n UpperCAmelCase\t\t = params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']\n UpperCAmelCase\t\t = params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']\n UpperCAmelCase\t\t = params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']\n UpperCAmelCase\t\t = params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']\n return k, o, q, v\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase=False\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n if split_mlp_wi:\n UpperCAmelCase\t\t = params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']\n UpperCAmelCase\t\t = params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']\n UpperCAmelCase\t\t = (wi_a, wi_a)\n else:\n UpperCAmelCase\t\t = params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']\n\n UpperCAmelCase\t\t = params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']\n return wi, wo\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t, *, lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n UpperCAmelCase\t\t = traverse_util.flatten_dict(variables[\"\"\"target\"\"\"]\t\t\t\t\t\t)\n UpperCAmelCase\t\t = {\"\"\"/\"\"\".join(lowerCAmelCase\t\t\t\t\t\t): v for k, v in old.items()}\n\n # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi\n UpperCAmelCase\t\t = \"\"\"encoder/layers_0/mlp/wi_0/kernel\"\"\" in old\n print(\"\"\"Split MLP:\"\"\"\t\t, lowerCAmelCase\t\t\t\t\t\t)\n\n UpperCAmelCase\t\t = collections.OrderedDict()\n\n # Shared embeddings.\n UpperCAmelCase\t\t = old[\"\"\"token_embedder/embedding\"\"\"]\n\n # Encoder.\n for i in range(lowerCAmelCase\t\t\t\t\t\t):\n # Block i, layer 0 (Self Attention).\n UpperCAmelCase\t\t = tax_layer_norm_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"encoder\"\"\"\t\t, \"\"\"pre_attention_layer_norm\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase\t\t = tax_attention_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"encoder\"\"\"\t\t, \"\"\"attention\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase\t\t = layer_norm\n UpperCAmelCase\t\t = k.T\n UpperCAmelCase\t\t = o.T\n UpperCAmelCase\t\t = q.T\n UpperCAmelCase\t\t = v.T\n\n # Block i, layer 1 (MLP).\n UpperCAmelCase\t\t = tax_layer_norm_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"encoder\"\"\"\t\t, \"\"\"pre_mlp_layer_norm\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase , UpperCAmelCase\t\t = tax_mlp_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"encoder\"\"\"\t\t, lowerCAmelCase\t\t\t\t\t\t)\n UpperCAmelCase\t\t = layer_norm\n if split_mlp_wi:\n UpperCAmelCase\t\t = wi[0].T\n UpperCAmelCase\t\t = wi[1].T\n else:\n UpperCAmelCase\t\t = wi.T\n UpperCAmelCase\t\t = wo.T\n\n UpperCAmelCase\t\t = old[\n \"\"\"encoder/relpos_bias/rel_embedding\"\"\"\n ].T\n UpperCAmelCase\t\t = old[\"\"\"encoder/encoder_norm/scale\"\"\"]\n\n if not is_encoder_only:\n # Decoder.\n for i in range(lowerCAmelCase\t\t\t\t\t\t):\n # Block i, layer 0 (Self Attention).\n UpperCAmelCase\t\t = tax_layer_norm_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"decoder\"\"\"\t\t, \"\"\"pre_self_attention_layer_norm\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase\t\t = tax_attention_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"decoder\"\"\"\t\t, \"\"\"self_attention\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase\t\t = layer_norm\n UpperCAmelCase\t\t = k.T\n UpperCAmelCase\t\t = o.T\n UpperCAmelCase\t\t = q.T\n UpperCAmelCase\t\t = v.T\n\n # Block i, layer 1 (Cross Attention).\n UpperCAmelCase\t\t = tax_layer_norm_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"decoder\"\"\"\t\t, \"\"\"pre_cross_attention_layer_norm\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase\t\t = tax_attention_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"decoder\"\"\"\t\t, \"\"\"encoder_decoder_attention\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase\t\t = layer_norm\n UpperCAmelCase\t\t = k.T\n UpperCAmelCase\t\t = o.T\n UpperCAmelCase\t\t = q.T\n UpperCAmelCase\t\t = v.T\n\n # Block i, layer 2 (MLP).\n UpperCAmelCase\t\t = tax_layer_norm_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"decoder\"\"\"\t\t, \"\"\"pre_mlp_layer_norm\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase , UpperCAmelCase\t\t = tax_mlp_lookup(lowerCAmelCase\t\t, lowerCAmelCase\t\t, \"\"\"decoder\"\"\"\t\t, lowerCAmelCase\t\t\t\t\t\t)\n UpperCAmelCase\t\t = layer_norm\n if split_mlp_wi:\n UpperCAmelCase\t\t = wi[0].T\n UpperCAmelCase\t\t = wi[1].T\n else:\n UpperCAmelCase\t\t = wi.T\n UpperCAmelCase\t\t = wo.T\n\n UpperCAmelCase\t\t = old[\"\"\"decoder/decoder_norm/scale\"\"\"]\n UpperCAmelCase\t\t = old[\n \"\"\"decoder/relpos_bias/rel_embedding\"\"\"\n ].T\n\n # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)\n if \"decoder/logits_dense/kernel\" in old:\n UpperCAmelCase\t\t = old[\"\"\"decoder/logits_dense/kernel\"\"\"].T\n\n return new\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n UpperCAmelCase\t\t = collections.OrderedDict([(k, torch.from_numpy(v.copy()\t\t\t\t\t\t)) for (k, v) in converted_params.items()]\t\t\t\t\t\t)\n\n # Add what is missing.\n if \"encoder.embed_tokens.weight\" not in state_dict:\n UpperCAmelCase\t\t = state_dict[\"\"\"shared.weight\"\"\"]\n\n if not is_encoder_only:\n if \"decoder.embed_tokens.weight\" not in state_dict:\n UpperCAmelCase\t\t = state_dict[\"\"\"shared.weight\"\"\"]\n\n if \"lm_head.weight\" not in state_dict: # For old 1.0 models.\n print(\"\"\"Using shared word embeddings as lm_head.\"\"\"\t\t\t\t\t\t)\n UpperCAmelCase\t\t = state_dict[\"\"\"shared.weight\"\"\"]\n\n return state_dict\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n UpperCAmelCase\t\t = checkpoints.load_tax_checkpoint(lowerCAmelCase\t\t\t\t\t\t)\n UpperCAmelCase\t\t = convert_tax_to_pytorch(lowerCAmelCase\t\t, num_layers=config.num_layers\t\t, is_encoder_only=lowerCAmelCase\t\t\t\t\t\t)\n UpperCAmelCase\t\t = make_state_dict(lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t)\n model.load_state_dict(lowerCAmelCase\t\t, strict=lowerCAmelCase\t\t\t\t\t\t)\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase = False\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n UpperCAmelCase\t\t = TaConfig.from_json_file(lowerCAmelCase\t\t\t\t\t\t)\n print(F'''Building PyTorch model from configuration: {config}'''\t\t\t\t\t\t)\n # Non-v1.1 checkpoints could also use T5Model, but this works for all.\n # The v1.0 checkpoints will simply have an LM head that is the word embeddings.\n if is_encoder_only:\n UpperCAmelCase\t\t = TaEncoderModel(lowerCAmelCase\t\t\t\t\t\t)\n else:\n UpperCAmelCase\t\t = TaForConditionalGeneration(lowerCAmelCase\t\t\t\t\t\t)\n\n # Load weights from tf checkpoint\n load_tax_weights_in_ta(lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t)\n\n # Save pytorch-model\n print(F'''Save PyTorch model to {pytorch_dump_path}'''\t\t\t\t\t\t)\n model.save_pretrained(lowerCAmelCase\t\t\t\t\t\t)\n\n # Verify that we can load the checkpoint.\n model.from_pretrained(lowerCAmelCase\t\t\t\t\t\t)\n print(\"\"\"Done\"\"\"\t\t\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n lowerCAmelCase_\t\t: Tuple = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')\n # Required parameters\n parser.add_argument(\n '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''\n )\n parser.add_argument(\n '''--config_file''',\n default=None,\n type=str,\n required=True,\n help='''The config json file corresponding to the pre-trained T5 model.\\nThis specifies the model architecture.''',\n )\n parser.add_argument(\n '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''\n )\n parser.add_argument(\n '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False\n )\n lowerCAmelCase_\t\t: Tuple = parser.parse_args()\n convert_tax_checkpoint_to_pytorch(\n args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only\n )\n"},"code_codestyle":{"kind":"number","value":371,"string":"371"},"style_context":{"kind":"string","value":"\n\n\n\n\n\n\n\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\nimport numpy as np\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\nfrom sklearn.model_selection import train_test_split\nfrom xgboost import XGBRegressor\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n return (data[\"data\"], data[\"target\"])\n\n\n\n\n\n\ndef _lowerCAmelCase ( lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n UpperCAmelCase\t\t = XGBRegressor(verbosity=0\t\t, random_state=42\t\t\t\t\t\t)\n xgb.fit(lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t)\n # Predict target for test data\n UpperCAmelCase\t\t = xgb.predict(lowerCAmelCase\t\t\t\t\t\t)\n UpperCAmelCase\t\t = predictions.reshape(len(lowerCAmelCase\t\t\t\t\t\t)\t\t, 1\t\t\t\t\t\t)\n return predictions\n\n\n\n\n\n\ndef _lowerCAmelCase ( ):\n\n\n\n\n\n\n\n '''simple docstring'''\n UpperCAmelCase\t\t = fetch_california_housing()\n UpperCAmelCase , UpperCAmelCase\t\t = data_handling(lowerCAmelCase\t\t\t\t\t\t)\n UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase\t\t = train_test_split(\n lowerCAmelCase\t\t, lowerCAmelCase\t\t, test_size=0.25\t\t, random_state=1\t\t\t\t\t\t)\n UpperCAmelCase\t\t = xgboost(lowerCAmelCase\t\t, lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t)\n # Error printing\n print(F'''Mean Absolute Error : {mean_absolute_error(lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t)}'''\t\t\t\t\t\t)\n print(F'''Mean Square Error : {mean_squared_error(lowerCAmelCase\t\t, lowerCAmelCase\t\t\t\t\t\t)}'''\t\t\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n import doctest\n\n doctest.testmod(verbose=True)\n main()\n"},"style_context_codestyle":{"kind":"number","value":248,"string":"248"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":522,"cells":{"code":{"kind":"string","value":"\r\r\rfrom __future__ import annotations\r\rfrom collections import deque\rfrom collections.abc import Iterator\rfrom dataclasses import dataclass\r\r\r\r\r\r\r@dataclass\rclass UpperCAmelCase__ :\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r UpperCAmelCase__\t\t\t\t\t\t:\t\tOptional[int] = 4_2\r UpperCAmelCase__\t\t\t\t\t\t:\t\tList[Any] = 4_2\r\r\r\r\r\r\rclass UpperCAmelCase__ :\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r def __init__(\t\t\tself\t\t\t\t\t, A_ ) -> Optional[Any]:\r __UpperCamelCase \t\t\t\t\t\t\t=[[] for _ in range(A_ )]\r __UpperCamelCase \t\t\t\t\t\t\t=size\r\r\r\r\r\r\r def __getitem__(\t\t\tself\t\t\t\t\t, A_ ) -> Any:\r return iter(self._graph[vertex] )\r\r\r\r\r\r\r @property\r def _a\t\t\t\t\t(\t\t\tself ) -> Optional[Any]:\r return self._size\r\r\r\r\r\r\r def _a\t\t\t\t\t(\t\t\tself\t\t\t\t\t, A_\t\t\t\t\t, A_\t\t\t\t\t, A_ ) -> str:\r if weight not in (0, 1):\r raise ValueError('Edge weight must be either 0 or 1.' )\r\r if to_vertex < 0 or to_vertex >= self.size:\r raise ValueError('Vertex indexes must be in [0; size).' )\r\r self._graph[from_vertex].append(Edge(A_\t\t\t\t\t, A_ ) )\r\r\r\r\r\r\r def _a\t\t\t\t\t(\t\t\tself\t\t\t\t\t, A_\t\t\t\t\t, A_ ) -> Union[str, Any]:\r __UpperCamelCase \t\t\t\t\t\t\t=deque([start_vertex] )\r __UpperCamelCase \t\t\t\t\t\t\t=[None] * self.size\r __UpperCamelCase \t\t\t\t\t\t\t=0\r\r while queue:\r __UpperCamelCase \t\t\t\t\t\t\t=queue.popleft()\r __UpperCamelCase \t\t\t\t\t\t\t=distances[current_vertex]\r if current_distance is None:\r continue\r\r for edge in self[current_vertex]:\r __UpperCamelCase \t\t\t\t\t\t\t=current_distance + edge.weight\r __UpperCamelCase \t\t\t\t\t\t\t=distances[edge.destination_vertex]\r if (\r isinstance(A_\t\t\t\t\t, A_ )\r and new_distance >= dest_vertex_distance\r ):\r continue\r __UpperCamelCase \t\t\t\t\t\t\t=new_distance\r if edge.weight == 0:\r queue.appendleft(edge.destination_vertex )\r else:\r queue.append(edge.destination_vertex )\r\r if distances[finish_vertex] is None:\r raise ValueError('No path from start_vertex to finish_vertex.' )\r\r return distances[finish_vertex]\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":62,"string":"62"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom __future__ import annotations\r\n\r\nfrom collections.abc import Iterator\r\nfrom typing import Any\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass lowerCAmelCase__\t:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\tdef __init__( self , lowercase ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = data\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tNode | None = None\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass lowerCAmelCase__\t:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\tdef __init__( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = None\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = None\r\n\t\t\tdef __iter__( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = self.head\r\n\t\t\t\t\t\t\twhile self.head:\r\n\t\t\t\t\t\t\t\t\t\t\tyield node.data\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = node.next\r\n\t\t\t\t\t\t\t\t\t\t\tif node == self.head:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbreak\r\n\t\t\tdef __len__( self ):\r\n\t\t\t\t\t\t\treturn sum(1 for _ in self )\r\n\t\t\tdef __repr__( self ):\r\n\t\t\t\t\t\t\treturn \"->\".join(str(lowercase ) for item in iter(self ) )\r\n\t\t\tdef \t\t\t\t\tA_ ( self , lowercase ):\r\n\t\t\t\t\t\t\tself.insert_nth(len(self ) , lowercase )\r\n\t\t\tdef \t\t\t\t\tA_ ( self , lowercase ):\r\n\t\t\t\t\t\t\tself.insert_nth(0 , lowercase )\r\n\t\t\tdef \t\t\t\t\tA_ ( self , lowercase , lowercase ):\r\n\t\t\t\t\t\t\tif index < 0 or index > len(self ):\r\n\t\t\t\t\t\t\t\t\t\t\traise IndexError('list index out of range.' )\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = Node(lowercase )\r\n\t\t\t\t\t\t\tif self.head is None:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = new_node # first node points itself\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = new_node\r\n\t\t\t\t\t\t\telif index == 0: # insert at head\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = self.head\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = new_node\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = self.head\r\n\t\t\t\t\t\t\t\t\t\t\tfor _ in range(index - 1 ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = temp.next\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = temp.next\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = new_node\r\n\t\t\t\t\t\t\t\t\t\t\tif index == len(self ) - 1: # insert at tail\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = new_node\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\treturn self.delete_nth(0 )\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\treturn self.delete_nth(len(self ) - 1 )\r\n\t\t\tdef \t\t\t\t\tA_ ( self , lowercase = 0 ):\r\n\t\t\t\t\t\t\tif not 0 <= index < len(self ):\r\n\t\t\t\t\t\t\t\t\t\t\traise IndexError('list index out of range.' )\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = self.head\r\n\t\t\t\t\t\t\tif self.head == self.tail: # just one node\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = None\r\n\t\t\t\t\t\t\telif index == 0: # delete head node\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = self.tail.next.next\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = self.head.next\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = self.head\r\n\t\t\t\t\t\t\t\t\t\t\tfor _ in range(index - 1 ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = temp.next\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = temp.next\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = temp.next.next\r\n\t\t\t\t\t\t\t\t\t\t\tif index == len(self ) - 1: # delete at tail\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = temp\r\n\t\t\t\t\t\t\treturn delete_node.data\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\treturn len(self ) == 0\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\t):\r\n\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = CircularLinkedList()\r\n\t\t\t\tassert len(lowercase__\t\t) == 0\r\n\t\t\t\tassert circular_linked_list.is_empty() is True\r\n\t\t\t\tassert str(lowercase__\t\t) == \"\"\r\n\r\n\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tcircular_linked_list.delete_front()\r\n\t\t\t\t\t\t\t\traise AssertionError # This should not happen\r\n\t\t\t\texcept IndexError:\r\n\t\t\t\t\t\t\t\tassert True # This should happen\r\n\r\n\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tcircular_linked_list.delete_tail()\r\n\t\t\t\t\t\t\t\traise AssertionError # This should not happen\r\n\t\t\t\texcept IndexError:\r\n\t\t\t\t\t\t\t\tassert True # This should happen\r\n\r\n\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tcircular_linked_list.delete_nth(-1\t\t)\r\n\t\t\t\t\t\t\t\traise AssertionError\r\n\t\t\t\texcept IndexError:\r\n\t\t\t\t\t\t\t\tassert True\r\n\r\n\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tcircular_linked_list.delete_nth(0\t\t)\r\n\t\t\t\t\t\t\t\traise AssertionError\r\n\t\t\t\texcept IndexError:\r\n\t\t\t\t\t\t\t\tassert True\r\n\r\n\t\t\t\tassert circular_linked_list.is_empty() is True\r\n\t\t\t\tfor i in range(5\t\t):\r\n\t\t\t\t\t\t\t\tassert len(lowercase__\t\t) == i\r\n\t\t\t\t\t\t\t\tcircular_linked_list.insert_nth(lowercase__\t\t\t\t\t\t\t, i + 1\t\t)\r\n\t\t\t\tassert str(lowercase__\t\t) == \"->\".join(str(lowercase__\t\t) for i in range(1\t\t\t\t\t\t\t, 6\t\t)\t\t)\r\n\r\n\t\t\t\tcircular_linked_list.insert_tail(6\t\t)\r\n\t\t\t\tassert str(lowercase__\t\t) == \"->\".join(str(lowercase__\t\t) for i in range(1\t\t\t\t\t\t\t, 7\t\t)\t\t)\r\n\t\t\t\tcircular_linked_list.insert_head(0\t\t)\r\n\t\t\t\tassert str(lowercase__\t\t) == \"->\".join(str(lowercase__\t\t) for i in range(0\t\t\t\t\t\t\t, 7\t\t)\t\t)\r\n\r\n\t\t\t\tassert circular_linked_list.delete_front() == 0\r\n\t\t\t\tassert circular_linked_list.delete_tail() == 6\r\n\t\t\t\tassert str(lowercase__\t\t) == \"->\".join(str(lowercase__\t\t) for i in range(1\t\t\t\t\t\t\t, 6\t\t)\t\t)\r\n\t\t\t\tassert circular_linked_list.delete_nth(2\t\t) == 3\r\n\r\n\t\t\t\tcircular_linked_list.insert_nth(2\t\t\t\t\t\t\t, 3\t\t)\r\n\t\t\t\tassert str(lowercase__\t\t) == \"->\".join(str(lowercase__\t\t) for i in range(1\t\t\t\t\t\t\t, 6\t\t)\t\t)\r\n\r\n\t\t\t\tassert circular_linked_list.is_empty() is False\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\timport doctest\r\n\r\n\t\t\tdoctest.testmod()"},"style_context_codestyle":{"kind":"number","value":96,"string":"96"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":523,"cells":{"code":{"kind":"string","value":"\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\nfrom typing import TYPE_CHECKING\r\n\r\nfrom ...utils import (\r\n OptionalDependencyNotAvailable,\r\n _LazyModule,\r\n is_flax_available,\r\n is_tf_available,\r\n is_torch_available,\r\n)\r\n\r\n\r\n_lowerCAmelCase : List[str] = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}\r\n\r\ntry:\r\n\t\t\t\t\t\tif not is_torch_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\tpass\r\nelse:\r\n\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = ['''EncoderDecoderModel''']\r\n\r\ntry:\r\n\t\t\t\t\t\tif not is_tf_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\tpass\r\nelse:\r\n\t\t\t\t\t\t_lowerCAmelCase : Any = ['''TFEncoderDecoderModel''']\r\n\r\ntry:\r\n\t\t\t\t\t\tif not is_flax_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\tpass\r\nelse:\r\n\t\t\t\t\t\t_lowerCAmelCase : Union[str, Any] = ['''FlaxEncoderDecoderModel''']\r\n\r\nif TYPE_CHECKING:\r\n\t\t\t\t\t\tfrom .configuration_encoder_decoder import EncoderDecoderConfig\r\n\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif not is_torch_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\n\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\t\t\t\t\t\t\tpass\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_encoder_decoder import EncoderDecoderModel\r\n\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif not is_tf_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\n\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\t\t\t\t\t\t\tpass\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_tf_encoder_decoder import TFEncoderDecoderModel\r\n\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif not is_flax_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\n\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\t\t\t\t\t\t\tpass\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel\r\n\r\nelse:\r\n\t\t\t\t\t\timport sys\r\n\r\n\t\t\t\t\t\t_lowerCAmelCase : Dict = _LazyModule(__name__, globals()[\"__file__\"], _import_structure, module_spec=__spec__)\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":371,"string":"371"},"style_context":{"kind":"string","value":"\r\n\r\nfrom __future__ import annotations\r\n\r\nimport matplotlib.pyplot as plt # type: ignore\r\nimport numpy\r\n\r\n# initial triangle of Koch snowflake\r\n_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])\r\n_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])\r\n_lowerCAmelCase : Any = numpy.array([1, 0])\r\n_lowerCAmelCase : int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \tUpperCamelCase_(\t\t\t\t\t\t\t_snake_case : list[numpy.ndarray]\t,\t\t\t\t\t_snake_case : int ):\r\n\r\n\r\n\r\n\r\n\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t__a \t\t\t\t=initial_vectors\r\n\t\t\tfor _ in range(_snake_case ):\r\n\t\t\t\t\t\t__a \t\t\t\t=iteration_step(_snake_case )\r\n\t\t\treturn vectors\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \tUpperCamelCase_(\t\t\t\t\t\t\t_snake_case : list[numpy.ndarray] ):\r\n\r\n\r\n\r\n\r\n\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t__a \t\t\t\t=[]\r\n\t\t\tfor i, start_vector in enumerate(vectors[:-1] ):\r\n\t\t\t\t\t\t__a \t\t\t\t=vectors[i + 1]\r\n\t\t\t\t\t\tnew_vectors.append(_snake_case )\r\n\t\t\t\t\t\t__a \t\t\t\t=end_vector - start_vector\r\n\t\t\t\t\t\tnew_vectors.append(start_vector + difference_vector / 3 )\r\n\t\t\t\t\t\tnew_vectors.append(\r\n\t\t\t\t\t\t start_vector + difference_vector / 3 + rotate(difference_vector / 3\t,\t\t\t\t\t60 ) )\r\n\t\t\t\t\t\tnew_vectors.append(start_vector + difference_vector * 2 / 3 )\r\n\t\t\tnew_vectors.append(vectors[-1] )\r\n\t\t\treturn new_vectors\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \tUpperCamelCase_(\t\t\t\t\t\t\t_snake_case : numpy.ndarray\t,\t\t\t\t\t_snake_case : float ):\r\n\r\n\r\n\r\n\r\n\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t__a \t\t\t\t=numpy.radians(_snake_case )\r\n\t\t\t__a\t\t\t\t, __a \t\t\t\t=numpy.cos(_snake_case ), numpy.sin(_snake_case )\r\n\t\t\t__a \t\t\t\t=numpy.array(((c, -s), (s, c)) )\r\n\t\t\treturn numpy.dot(_snake_case\t,\t\t\t\t\t_snake_case )\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \tUpperCamelCase_(\t\t\t\t\t\t\t_snake_case : list[numpy.ndarray] ):\r\n\r\n\r\n\r\n\r\n\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t__a \t\t\t\t=plt.gca()\r\n\t\t\taxes.set_aspect('equal' )\r\n\r\n\t\t\t# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all\r\n\t\t\t# y-coordinates as inputs, which are constructed from the vector-list using\r\n\t\t\t# zip()\r\n\t\t\t__a\t\t\t\t, __a \t\t\t\t=zip(*_snake_case )\r\n\t\t\tplt.plot(_snake_case\t,\t\t\t\t\t_snake_case )\r\n\t\t\tplt.show()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\t\t\t\timport doctest\r\n\r\n\t\t\t\t\t\tdoctest.testmod()\r\n\r\n\t\t\t\t\t\t_lowerCAmelCase : List[Any] = iterate(INITIAL_VECTORS, 5)\r\n\t\t\t\t\t\tplot(processed_vectors)\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":308,"string":"308"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":524,"cells":{"code":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\rimport json\rimport os\rimport unittest\r\rfrom transformers.models.roc_bert.tokenization_roc_bert import (\r VOCAB_FILES_NAMES,\r RoCBertBasicTokenizer,\r RoCBertTokenizer,\r RoCBertWordpieceTokenizer,\r _is_control,\r _is_punctuation,\r _is_whitespace,\r)\rfrom transformers.testing_utils import require_tokenizers, slow\r\rfrom ...test_tokenization_common import TokenizerTesterMixin, filter_non_english\r\r\r@require_tokenizers\rclass UpperCAmelCase\t\t\t\t\t\t(_UpperCAmelCase ,unittest.TestCase ):\r\r\r\r \"\"\"simple docstring\"\"\"\r\r _UpperCAmelCase\t:Tuple\t\t\t\t\t\t = RoCBertTokenizer\r _UpperCAmelCase\t:Tuple\t\t\t\t\t\t = None\r _UpperCAmelCase\t:Optional[int]\t\t\t\t\t\t = False\r _UpperCAmelCase\t:str\t\t\t\t\t\t = True\r _UpperCAmelCase\t:Union[str, Any]\t\t\t\t\t\t = filter_non_english\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r super().setUp()\r\r lowercase__: Optional[Any]\t\t\t\t\t= [\"[UNK]\", \"[CLS]\", \"[SEP]\", \"[PAD]\", \"[MASK]\", \"你\", \"好\", \"是\", \"谁\", \"a\", \"b\", \"c\", \"d\"]\r lowercase__: Optional[Any]\t\t\t\t\t= {}\r lowercase__: Optional[Any]\t\t\t\t\t= {}\r for i, value in enumerate(__lowerCamelCase\t\t\t\t\t\t\t):\r lowercase__: Any\t\t\t\t\t= i\r lowercase__: Optional[Any]\t\t\t\t\t= i\r lowercase__: Union[str, Any]\t\t\t\t\t= os.path.join(self.tmpdirname ,\t\t\tVOCAB_FILES_NAMES['''vocab_file''']\t\t\t\t\t\t\t)\r lowercase__: Union[str, Any]\t\t\t\t\t= os.path.join(self.tmpdirname ,\t\t\tVOCAB_FILES_NAMES['''word_shape_file''']\t\t\t\t\t\t\t)\r lowercase__: Tuple\t\t\t\t\t= os.path.join(self.tmpdirname ,\t\t\tVOCAB_FILES_NAMES['''word_pronunciation_file''']\t\t\t\t\t\t\t)\r with open(self.vocab_file ,\t\t\t'''w''' ,\t\t\tencoding='''utf-8'''\t\t\t\t\t\t\t) as vocab_writer:\r vocab_writer.write(''''''.join([x + '''\\n''' for x in vocab_tokens]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r with open(self.word_shape_file ,\t\t\t'''w''' ,\t\t\tencoding='''utf-8'''\t\t\t\t\t\t\t) as word_shape_writer:\r json.dump(__lowerCamelCase ,\t\t\t__lowerCamelCase ,\t\t\tensure_ascii=__lowerCamelCase\t\t\t\t\t\t\t)\r with open(self.word_pronunciation_file ,\t\t\t'''w''' ,\t\t\tencoding='''utf-8'''\t\t\t\t\t\t\t) as word_pronunciation_writer:\r json.dump(__lowerCamelCase ,\t\t\t__lowerCamelCase ,\t\t\tensure_ascii=__lowerCamelCase\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: List[Any]\t\t\t\t\t= self.tokenizer_class(self.vocab_file ,\t\t\tself.word_shape_file ,\t\t\tself.word_pronunciation_file\t\t\t\t\t\t\t)\r\r lowercase__: Optional[Any]\t\t\t\t\t= tokenizer.tokenize('''你好[SEP]你是谁'''\t\t\t\t\t\t\t)\r self.assertListEqual(__lowerCamelCase ,\t\t\t['''你''', '''好''', '''[SEP]''', '''你''', '''是''', '''谁''']\t\t\t\t\t\t\t)\r self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase\t\t\t\t\t\t\t) ,\t\t\t[5, 6, 2, 5, 7, 8]\t\t\t\t\t\t\t)\r self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__lowerCamelCase\t\t\t\t\t\t\t) ,\t\t\t[5, 6, 2, 5, 7, 8]\t\t\t\t\t\t\t)\r self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__lowerCamelCase\t\t\t\t\t\t\t) ,\t\t\t[5, 6, 2, 5, 7, 8]\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Tuple\t\t\t\t\t= RoCBertBasicTokenizer()\r\r self.assertListEqual(tokenizer.tokenize('''ah\\u535A\\u63A8zz'''\t\t\t\t\t\t\t) ,\t\t\t['''ah''', '''\\u535A''', '''\\u63A8''', '''zz''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Dict\t\t\t\t\t= RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase\t\t\t\t\t\t\t)\r\r self.assertListEqual(\r tokenizer.tokenize(''' \\tHeLLo!how \\n Are yoU? '''\t\t\t\t\t\t\t) ,\t\t\t['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']\t\t\t\t\t\t\t)\r self.assertListEqual(tokenizer.tokenize('''H\\u00E9llo'''\t\t\t\t\t\t\t) ,\t\t\t['''hello''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: List[Any]\t\t\t\t\t= RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase ,\t\t\tstrip_accents=__lowerCamelCase\t\t\t\t\t\t\t)\r\r self.assertListEqual(\r tokenizer.tokenize(''' \\tHäLLo!how \\n Are yoU? '''\t\t\t\t\t\t\t) ,\t\t\t['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']\t\t\t\t\t\t\t)\r self.assertListEqual(tokenizer.tokenize('''H\\u00E9llo'''\t\t\t\t\t\t\t) ,\t\t\t['''h\\u00E9llo''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: List[str]\t\t\t\t\t= RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase ,\t\t\tstrip_accents=__lowerCamelCase\t\t\t\t\t\t\t)\r\r self.assertListEqual(\r tokenizer.tokenize(''' \\tHäLLo!how \\n Are yoU? '''\t\t\t\t\t\t\t) ,\t\t\t['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']\t\t\t\t\t\t\t)\r self.assertListEqual(tokenizer.tokenize('''H\\u00E9llo'''\t\t\t\t\t\t\t) ,\t\t\t['''hello''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: int\t\t\t\t\t= RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase\t\t\t\t\t\t\t)\r\r self.assertListEqual(\r tokenizer.tokenize(''' \\tHäLLo!how \\n Are yoU? '''\t\t\t\t\t\t\t) ,\t\t\t['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']\t\t\t\t\t\t\t)\r self.assertListEqual(tokenizer.tokenize('''H\\u00E9llo'''\t\t\t\t\t\t\t) ,\t\t\t['''hello''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Optional[Any]\t\t\t\t\t= RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase\t\t\t\t\t\t\t)\r\r self.assertListEqual(\r tokenizer.tokenize(''' \\tHeLLo!how \\n Are yoU? '''\t\t\t\t\t\t\t) ,\t\t\t['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Tuple\t\t\t\t\t= RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase ,\t\t\tstrip_accents=__lowerCamelCase\t\t\t\t\t\t\t)\r\r self.assertListEqual(\r tokenizer.tokenize(''' \\tHäLLo!how \\n Are yoU? '''\t\t\t\t\t\t\t) ,\t\t\t['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Optional[Any]\t\t\t\t\t= RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase ,\t\t\tstrip_accents=__lowerCamelCase\t\t\t\t\t\t\t)\r\r self.assertListEqual(\r tokenizer.tokenize(''' \\tHäLLo!how \\n Are yoU? '''\t\t\t\t\t\t\t) ,\t\t\t['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Union[str, Any]\t\t\t\t\t= RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase ,\t\t\tnever_split=['''[UNK]''']\t\t\t\t\t\t\t)\r\r self.assertListEqual(\r tokenizer.tokenize(''' \\tHeLLo!how \\n Are yoU? [UNK]'''\t\t\t\t\t\t\t) ,\t\t\t['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Union[str, Any]\t\t\t\t\t= [\"[UNK]\", \"[CLS]\", \"[SEP]\", \"want\", \"##want\", \"##ed\", \"wa\", \"un\", \"runn\", \"##ing\"]\r\r lowercase__: Optional[int]\t\t\t\t\t= {}\r for i, token in enumerate(__lowerCamelCase\t\t\t\t\t\t\t):\r lowercase__: List[str]\t\t\t\t\t= i\r lowercase__: Union[str, Any]\t\t\t\t\t= RoCBertWordpieceTokenizer(vocab=__lowerCamelCase ,\t\t\tunk_token='''[UNK]'''\t\t\t\t\t\t\t)\r\r self.assertListEqual(tokenizer.tokenize(''''''\t\t\t\t\t\t\t) ,\t\t\t[]\t\t\t\t\t\t\t)\r\r self.assertListEqual(tokenizer.tokenize('''unwanted running'''\t\t\t\t\t\t\t) ,\t\t\t['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']\t\t\t\t\t\t\t)\r\r self.assertListEqual(tokenizer.tokenize('''unwantedX running'''\t\t\t\t\t\t\t) ,\t\t\t['''[UNK]''', '''runn''', '''##ing''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r self.assertTrue(_is_whitespace(''' '''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertTrue(_is_whitespace('''\\t'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertTrue(_is_whitespace('''\\r'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertTrue(_is_whitespace('''\\n'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertTrue(_is_whitespace('''\\u00A0'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r self.assertFalse(_is_whitespace('''A'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertFalse(_is_whitespace('''-'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r self.assertTrue(_is_control('''\\u0005'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r self.assertFalse(_is_control('''A'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertFalse(_is_control(''' '''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertFalse(_is_control('''\\t'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertFalse(_is_control('''\\r'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r self.assertTrue(_is_punctuation('''-'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertTrue(_is_punctuation('''$'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertTrue(_is_punctuation('''`'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertTrue(_is_punctuation('''.'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r self.assertFalse(_is_punctuation('''A'''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertFalse(_is_punctuation(''' '''\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Dict\t\t\t\t\t= self.get_tokenizer()\r\r # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340\r self.assertListEqual([tokenizer.tokenize(__lowerCamelCase\t\t\t\t\t\t\t) for t in ['''Test''', '''\\xad''', '''test''']] ,\t\t\t[['''[UNK]'''], [], ['''[UNK]''']]\t\t\t\t\t\t\t)\r\r if self.test_rust_tokenizer:\r lowercase__: List[Any]\t\t\t\t\t= self.get_rust_tokenizer()\r self.assertListEqual(\r [rust_tokenizer.tokenize(__lowerCamelCase\t\t\t\t\t\t\t) for t in ['''Test''', '''\\xad''', '''test''']] ,\t\t\t[['''[UNK]'''], [], ['''[UNK]''']]\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r for tokenizer, pretrained_name, kwargs in self.tokenizers_list:\r with self.subTest(F\"\"\"{tokenizer.__class__.__name__} ({pretrained_name})\"\"\"\t\t\t\t\t\t\t):\r lowercase__: Union[str, Any]\t\t\t\t\t= self.rust_tokenizer_class.from_pretrained(__lowerCamelCase ,\t\t\t**__lowerCamelCase\t\t\t\t\t\t\t)\r\r lowercase__: int\t\t\t\t\t= F\"\"\"A, naïve {tokenizer_r.mask_token} AllenNLP sentence.\"\"\"\r lowercase__: Optional[int]\t\t\t\t\t= tokenizer_r.encode_plus(\r __lowerCamelCase ,\t\t\treturn_attention_mask=__lowerCamelCase ,\t\t\treturn_token_type_ids=__lowerCamelCase ,\t\t\treturn_offsets_mapping=__lowerCamelCase ,\t\t\tadd_special_tokens=__lowerCamelCase ,\t\t\t)\r\r lowercase__: Tuple\t\t\t\t\t= tokenizer_r.do_lower_case if hasattr(__lowerCamelCase ,\t\t\t'''do_lower_case'''\t\t\t\t\t\t\t) else False\r lowercase__: Tuple\t\t\t\t\t= (\r [\r ((0, 0), tokenizer_r.cls_token),\r ((0, 1), \"A\"),\r ((1, 2), \",\"),\r ((3, 5), \"na\"),\r ((5, 6), \"##ï\"),\r ((6, 8), \"##ve\"),\r ((9, 15), tokenizer_r.mask_token),\r ((16, 21), \"Allen\"),\r ((21, 23), \"##NL\"),\r ((23, 24), \"##P\"),\r ((25, 33), \"sentence\"),\r ((33, 34), \".\"),\r ((0, 0), tokenizer_r.sep_token),\r ]\r if not do_lower_case\r else [\r ((0, 0), tokenizer_r.cls_token),\r ((0, 1), \"a\"),\r ((1, 2), \",\"),\r ((3, 8), \"naive\"),\r ((9, 15), tokenizer_r.mask_token),\r ((16, 21), \"allen\"),\r ((21, 23), \"##nl\"),\r ((23, 24), \"##p\"),\r ((25, 33), \"sentence\"),\r ((33, 34), \".\"),\r ((0, 0), tokenizer_r.sep_token),\r ]\r )\r\r self.assertEqual(\r [e[1] for e in expected_results] ,\t\t\ttokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r self.assertEqual([e[0] for e in expected_results] ,\t\t\ttokens['''offset_mapping''']\t\t\t\t\t\t\t)\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Tuple\t\t\t\t\t= [\"的\", \"人\", \"有\"]\r lowercase__: int\t\t\t\t\t= \"\".join(__lowerCamelCase\t\t\t\t\t\t\t)\r for tokenizer, pretrained_name, kwargs in self.tokenizers_list:\r with self.subTest(F\"\"\"{tokenizer.__class__.__name__} ({pretrained_name})\"\"\"\t\t\t\t\t\t\t):\r lowercase__: Union[str, Any]\t\t\t\t\t= True\r lowercase__: Tuple\t\t\t\t\t= self.tokenizer_class.from_pretrained(__lowerCamelCase ,\t\t\t**__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: str\t\t\t\t\t= self.rust_tokenizer_class.from_pretrained(__lowerCamelCase ,\t\t\t**__lowerCamelCase\t\t\t\t\t\t\t)\r\r lowercase__: List[Any]\t\t\t\t\t= tokenizer_p.encode(__lowerCamelCase ,\t\t\tadd_special_tokens=__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: Tuple\t\t\t\t\t= tokenizer_r.encode(__lowerCamelCase ,\t\t\tadd_special_tokens=__lowerCamelCase\t\t\t\t\t\t\t)\r\r lowercase__: Union[str, Any]\t\t\t\t\t= tokenizer_r.convert_ids_to_tokens(__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: Dict\t\t\t\t\t= tokenizer_p.convert_ids_to_tokens(__lowerCamelCase\t\t\t\t\t\t\t)\r\r # it is expected that each Chinese character is not preceded by \"##\"\r self.assertListEqual(__lowerCamelCase ,\t\t\t__lowerCamelCase\t\t\t\t\t\t\t)\r self.assertListEqual(__lowerCamelCase ,\t\t\t__lowerCamelCase\t\t\t\t\t\t\t)\r\r lowercase__: Union[str, Any]\t\t\t\t\t= False\r lowercase__: List[Any]\t\t\t\t\t= self.rust_tokenizer_class.from_pretrained(__lowerCamelCase ,\t\t\t**__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: str\t\t\t\t\t= self.tokenizer_class.from_pretrained(__lowerCamelCase ,\t\t\t**__lowerCamelCase\t\t\t\t\t\t\t)\r\r lowercase__: Dict\t\t\t\t\t= tokenizer_r.encode(__lowerCamelCase ,\t\t\tadd_special_tokens=__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: Optional[Any]\t\t\t\t\t= tokenizer_p.encode(__lowerCamelCase ,\t\t\tadd_special_tokens=__lowerCamelCase\t\t\t\t\t\t\t)\r\r lowercase__: Dict\t\t\t\t\t= tokenizer_r.convert_ids_to_tokens(__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: int\t\t\t\t\t= tokenizer_p.convert_ids_to_tokens(__lowerCamelCase\t\t\t\t\t\t\t)\r\r # it is expected that only the first Chinese character is not preceded by \"##\".\r lowercase__: Tuple\t\t\t\t\t= [\r F\"\"\"##{token}\"\"\" if idx != 0 else token for idx, token in enumerate(__lowerCamelCase\t\t\t\t\t\t\t)\r ]\r self.assertListEqual(__lowerCamelCase ,\t\t\t__lowerCamelCase\t\t\t\t\t\t\t)\r self.assertListEqual(__lowerCamelCase ,\t\t\t__lowerCamelCase\t\t\t\t\t\t\t)\r\r\r\r\r\r\r @slow\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Optional[int]\t\t\t\t\t= self.tokenizer_class(self.vocab_file ,\t\t\tself.word_shape_file ,\t\t\tself.word_pronunciation_file\t\t\t\t\t\t\t)\r\r lowercase__: Optional[int]\t\t\t\t\t= tokenizer.encode('''你好''' ,\t\t\tadd_special_tokens=__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: List[Any]\t\t\t\t\t= tokenizer.encode('''你是谁''' ,\t\t\tadd_special_tokens=__lowerCamelCase\t\t\t\t\t\t\t)\r\r lowercase__: Union[str, Any]\t\t\t\t\t= tokenizer.build_inputs_with_special_tokens(__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: int\t\t\t\t\t= tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,\t\t\t__lowerCamelCase\t\t\t\t\t\t\t)\r\r assert encoded_sentence == [1] + text + [2]\r assert encoded_pair == [1] + text + [2] + text_a + [2]\r\r\r\r\r\r\r def _snake_case\t\t\t( self\t\t\t\t\t\t\t):\r lowercase__: Tuple\t\t\t\t\t= self.get_tokenizers(do_lower_case=__lowerCamelCase\t\t\t\t\t\t\t)\r for tokenizer in tokenizers:\r with self.subTest(F\"\"\"{tokenizer.__class__.__name__}\"\"\"\t\t\t\t\t\t\t):\r lowercase__: Any\t\t\t\t\t= \"你好，你是谁\"\r lowercase__: Dict\t\t\t\t\t= tokenizer.tokenize(__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: Any\t\t\t\t\t= tokenizer.convert_tokens_to_ids(__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: Optional[Any]\t\t\t\t\t= tokenizer.convert_tokens_to_shape_ids(__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: str\t\t\t\t\t= tokenizer.convert_tokens_to_pronunciation_ids(__lowerCamelCase\t\t\t\t\t\t\t)\r lowercase__: Tuple\t\t\t\t\t= tokenizer.prepare_for_model(\r __lowerCamelCase ,\t\t\t__lowerCamelCase ,\t\t\t__lowerCamelCase ,\t\t\tadd_special_tokens=__lowerCamelCase\t\t\t\t\t\t\t)\r\r lowercase__: List[Any]\t\t\t\t\t= tokenizer.encode_plus(__lowerCamelCase ,\t\t\tadd_special_tokens=__lowerCamelCase\t\t\t\t\t\t\t)\r\r self.assertEqual(__lowerCamelCase ,\t\t\t__lowerCamelCase\t\t\t\t\t\t\t)\r"},"code_codestyle":{"kind":"number","value":177,"string":"177"},"style_context":{"kind":"string","value":"\n\n\ndef \t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t(UpperCamelCase__ : Optional[Any]\t\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__ : Union[str, Any]\t\t\t\t\t\t):\n # \"extended trapezoidal rule\"\n # int(f) = dx/2 * (f1 + 2f2 + ... + fn)\n _A : int\t\t\t\t = (boundary[1] - boundary[0]) / steps\n _A : Any\t\t\t\t = boundary[0]\n _A : List[Any]\t\t\t\t = boundary[1]\n _A : str\t\t\t\t = make_points(UpperCamelCase__\t\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__\t\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__\t\t\t\t\t\t)\n _A : str\t\t\t\t = 0.0\n y += (h / 2.0) * f(UpperCamelCase__\t\t\t\t\t\t)\n for i in x_i:\n # print(i)\n y += h * f(UpperCamelCase__\t\t\t\t\t\t)\n y += (h / 2.0) * f(UpperCamelCase__\t\t\t\t\t\t)\n return y\ndef \t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t(UpperCamelCase__ : List[str]\t\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__ : Optional[int]\t\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__ : Any\t\t\t\t\t\t):\n _A : Optional[int]\t\t\t\t = a + h\n while x < (b - h):\n yield x\n _A : Dict\t\t\t\t = x + h\ndef \t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t(UpperCamelCase__ : Optional[int]\t\t\t\t\t\t): # enter your function here\n _A : Any\t\t\t\t = (x - 0) * (x - 0)\n return y\ndef \t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t():\n _A : Optional[Any]\t\t\t\t = 0.0 # Lower bound of integration\n _A : Optional[int]\t\t\t\t = 1.0 # Upper bound of integration\n _A : List[Any]\t\t\t\t = 10.0 # define number of steps or resolution\n _A : Any\t\t\t\t = [a, b] # define boundary of integration\n _A : Tuple\t\t\t\t = method_a(UpperCamelCase__\t\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__\t\t\t\t\t\t)\n print(f\"y = {y}\"\t\t\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n main()\n"},"style_context_codestyle":{"kind":"number","value":11,"string":"11"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":525,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\r# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:\r# hack it in for now:\rimport sys\rfrom pathlib import Path\r\r\r__A = Path(__file__).resolve().parents[3] / \"src\"\rsys.path.insert(1, str(git_repo_path))\r\rimport dataclasses # noqa\rimport io # noqa\rimport itertools # noqa\rimport json # noqa\rimport os # noqa\rimport unittest # noqa\rfrom copy import deepcopy # noqa\r\rfrom parameterized import parameterized # noqa\rfrom transformers import TrainingArguments, is_torch_available # noqa\rfrom transformers.deepspeed import is_deepspeed_available # noqa\rfrom transformers.file_utils import WEIGHTS_NAME # noqa\rfrom transformers.testing_utils import ( # noqa\r CaptureLogger,\r ExtendSysPath,\r TestCasePlus,\r execute_subprocess_async,\r get_gpu_count,\r mockenv_context,\r require_deepspeed,\r require_torch_gpu,\r require_torch_multi_gpu,\r slow,\r)\rfrom transformers.trainer_utils import set_seed # noqa\r\r\rset_seed(42)\r\r__A = {\"base\": \"patrickvonplaten/wav2vec2_tiny_random\", \"robust\": \"patrickvonplaten/wav2vec2_tiny_random_robust\"}\r\r__A = \"zero2\"\r__A = \"zero3\"\r__A = [ZEROa, ZEROa]\r\r\r\r\r\rdef \t\t\t\t\tlowerCAmelCase_\t\t\t(\t__a , __a , __a\t\t)\t\t\t\t\t->\t\tTuple:\r\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r lowerCamelCase__:\t\t\t\tList[Any] =parameterized.to_safe_name(\"_\".join(str(__a\t\t) for x in param.args\t\t)\t\t)\r return F\"\"\"{func.__name__}_{param_based_name}\"\"\"\r\r\r# Cartesian-product of zero stages with models to test\r__A = list(itertools.product(stages, models.keys()))\r\r\r\r\r\r\r@slow\r@require_deepspeed\r@require_torch_gpu\rclass _SCREAMING_SNAKE_CASE\t\t\t\t\t\t( __SCREAMING_SNAKE_CASE ):\r\r\r\r\r\r\r\r '''simple docstring'''\r\r @parameterized.expand(UpperCAmelCase_\t,\t\t\tname_func=UpperCAmelCase_)\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Any\t,\t\t\tUpperCAmelCase_ : Optional[int]\t,\t\t\tUpperCAmelCase_ : int)\t->str:\r\r\r\r '''simple docstring'''\r\r\r\r self.run_and_check(\r stage=UpperCAmelCase_\t,\t\t\tmodel=UpperCAmelCase_\t,\t\t\tdistributed=UpperCAmelCase_\t,\t\t\tfpaa=UpperCAmelCase_\t,\t\t\t)\r\r @require_torch_multi_gpu\r @parameterized.expand(UpperCAmelCase_\t,\t\t\tname_func=UpperCAmelCase_)\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Optional[int]\t,\t\t\tUpperCAmelCase_ : str\t,\t\t\tUpperCAmelCase_ : List[str])\t->str:\r\r\r\r '''simple docstring'''\r\r\r\r self.run_and_check(\r stage=UpperCAmelCase_\t,\t\t\tmodel=UpperCAmelCase_\t,\t\t\tdistributed=UpperCAmelCase_\t,\t\t\tfpaa=UpperCAmelCase_\t,\t\t\t)\r\r @parameterized.expand(UpperCAmelCase_\t,\t\t\tname_func=UpperCAmelCase_)\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Union[str, Any]\t,\t\t\tUpperCAmelCase_ : Union[str, Any]\t,\t\t\tUpperCAmelCase_ : Any)\t->List[str]:\r\r\r\r '''simple docstring'''\r\r\r\r self.run_and_check(\r stage=UpperCAmelCase_\t,\t\t\tmodel=UpperCAmelCase_\t,\t\t\tdistributed=UpperCAmelCase_\t,\t\t\tfpaa=UpperCAmelCase_\t,\t\t\t)\r\r @require_torch_multi_gpu\r @parameterized.expand(UpperCAmelCase_\t,\t\t\tname_func=UpperCAmelCase_)\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : List[Any]\t,\t\t\tUpperCAmelCase_ : List[Any]\t,\t\t\tUpperCAmelCase_ : Optional[Any])\t->Dict:\r\r\r\r '''simple docstring'''\r\r\r\r self.run_and_check(\r stage=UpperCAmelCase_\t,\t\t\tmodel=UpperCAmelCase_\t,\t\t\tdistributed=UpperCAmelCase_\t,\t\t\tfpaa=UpperCAmelCase_\t,\t\t\t)\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Any\t,\t\t\tUpperCAmelCase_ : str)\t->Dict:\r\r\r\r '''simple docstring'''\r\r\r\r pass\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Tuple\t,\t\t\tUpperCAmelCase_ : str\t,\t\t\tUpperCAmelCase_ : str\t,\t\t\tUpperCAmelCase_ : int = 10\t,\t\t\tUpperCAmelCase_ : bool = True\t,\t\t\tUpperCAmelCase_ : bool = True\t,\t\t\tUpperCAmelCase_ : bool = True\t,\t\t\t)\t->Optional[int]:\r\r\r\r '''simple docstring'''\r\r\r\r lowerCamelCase__:\t\t\t\tList[Any] =models[model]\r\r lowerCamelCase__:\t\t\t\tDict =self.run_trainer(\r stage=UpperCAmelCase_\t,\t\t\tmodel_name=UpperCAmelCase_\t,\t\t\teval_steps=UpperCAmelCase_\t,\t\t\tnum_train_epochs=1\t,\t\t\tdistributed=UpperCAmelCase_\t,\t\t\tfpaa=UpperCAmelCase_\t,\t\t\t)\r\r self.do_checks(UpperCAmelCase_)\r\r return output_dir\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : List[str]\t,\t\t\tUpperCAmelCase_ : str\t,\t\t\tUpperCAmelCase_ : str\t,\t\t\tUpperCAmelCase_ : int = 10\t,\t\t\tUpperCAmelCase_ : int = 1\t,\t\t\tUpperCAmelCase_ : bool = True\t,\t\t\tUpperCAmelCase_ : bool = True\t,\t\t\t)\t->Union[str, Any]:\r\r\r\r '''simple docstring'''\r\r\r\r lowerCamelCase__:\t\t\t\tOptional[Any] =self.get_auto_remove_tmp_dir(\"./xxx\"\t,\t\t\tafter=UpperCAmelCase_)\r lowerCamelCase__:\t\t\t\tDict =F\"\"\"\n --model_name_or_path {model_name}\n --dataset_name hf-internal-testing/librispeech_asr_dummy\n --dataset_config_name clean\n --train_split_name validation\n --validation_split_name validation\n --output_dir {output_dir}\n --num_train_epochs {str(UpperCAmelCase_)}\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 2\n --evaluation_strategy steps\n --learning_rate 5e-4\n --warmup_steps 8\n --orthography timit\n --preprocessing_num_workers 1\n --group_by_length\n --freeze_feature_extractor\n --report_to none\n --save_steps 0\n --eval_steps {eval_steps}\n --report_to none\n \"\"\".split()\r\r if fpaa:\r args.extend([\"--fp16\"])\r\r # currently ds_config_wav2vec2_zero.json requires \"zero_optimization.find_unused_parameters\": true,\r # hence the separate config files\r lowerCamelCase__:\t\t\t\tDict =F\"\"\"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json\"\"\".split()\r lowerCamelCase__:\t\t\t\tList[Any] =[F\"\"\"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py\"\"\"]\r lowerCamelCase__:\t\t\t\tDict =self.get_launcher(UpperCAmelCase_)\r\r lowerCamelCase__:\t\t\t\tOptional[int] =launcher + script + args + ds_args\r # keep for quick debug\r # print(\" \".join([f\"\\nPYTHONPATH={self.src_dir_str}\"] +cmd)); die\r execute_subprocess_async(UpperCAmelCase_\t,\t\t\tenv=self.get_env())\r\r return output_dir\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Dict\t,\t\t\tUpperCAmelCase_ : Tuple=False)\t->Optional[int]:\r\r\r\r '''simple docstring'''\r\r\r\r lowerCamelCase__:\t\t\t\tOptional[int] =min(2\t,\t\t\tget_gpu_count()) if distributed else 1\r return F\"\"\"deepspeed --num_nodes 1 --num_gpus {num_gpus}\"\"\".split()\r\r"},"code_codestyle":{"kind":"number","value":273,"string":"273"},"style_context":{"kind":"string","value":"\r\r\r\r\r\rfrom pathlib import Path\rfrom typing import List\r\rfrom transformers import is_torch_available, is_vision_available\rfrom transformers.testing_utils import get_tests_dir, is_tool_test\rfrom transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText\r\r\rif is_torch_available():\r import torch\r\rif is_vision_available():\r from PIL import Image\r\r\r__A = [\"text\", \"image\", \"audio\"]\r\r\r\r\r\rdef \t\t\t\t\tlowerCAmelCase_\t\t\t(\t__a\t\t)\t\t\t\t\t->\t\tOptional[Any]:\r\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r lowerCamelCase__:\t\t\t\tTuple =[]\r\r for input_type in input_types:\r if input_type == \"text\":\r inputs.append(\"Text input\"\t\t)\r elif input_type == \"image\":\r inputs.append(\r Image.open(Path(get_tests_dir(\"fixtures/tests_samples/COCO\"\t\t)\t\t) / \"000000039769.png\"\t\t).resize((512, 512)\t\t)\t\t)\r elif input_type == \"audio\":\r inputs.append(torch.ones(3000\t\t)\t\t)\r elif isinstance(__a , __a\t\t):\r inputs.append(create_inputs(__a\t\t)\t\t)\r else:\r raise ValueError(F\"\"\"Invalid type requested: {input_type}\"\"\"\t\t)\r\r return inputs\r\r\r\r\r\rdef \t\t\t\t\tlowerCAmelCase_\t\t\t(\t__a\t\t)\t\t\t\t\t->\t\tUnion[str, Any]:\r\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r lowerCamelCase__:\t\t\t\tUnion[str, Any] =[]\r\r for output in outputs:\r if isinstance(__a , (str, AgentText)\t\t):\r output_types.append(\"text\"\t\t)\r elif isinstance(__a , (Image.Image, AgentImage)\t\t):\r output_types.append(\"image\"\t\t)\r elif isinstance(__a , (torch.Tensor, AgentAudio)\t\t):\r output_types.append(\"audio\"\t\t)\r else:\r raise ValueError(F\"\"\"Invalid output: {output}\"\"\"\t\t)\r\r return output_types\r\r\r\r\r\r\r@is_tool_test\rclass _SCREAMING_SNAKE_CASE\t\t\t\t\t\t:\r\r\r\r\r\r\r\r '''simple docstring'''\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : List[str])\t->Dict:\r\r\r\r '''simple docstring'''\r\r\r\r self.assertTrue(hasattr(self.tool\t,\t\t\t\"inputs\"))\r self.assertTrue(hasattr(self.tool\t,\t\t\t\"outputs\"))\r\r lowerCamelCase__:\t\t\t\tTuple =self.tool.inputs\r for _input in inputs:\r if isinstance(_input\t,\t\t\tUpperCAmelCase_):\r for __input in _input:\r self.assertTrue(__input in authorized_types)\r else:\r self.assertTrue(_input in authorized_types)\r\r lowerCamelCase__:\t\t\t\tOptional[Any] =self.tool.outputs\r for _output in outputs:\r self.assertTrue(_output in authorized_types)\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Optional[Any])\t->str:\r\r\r\r '''simple docstring'''\r\r\r\r lowerCamelCase__:\t\t\t\tList[str] =create_inputs(self.tool.inputs)\r lowerCamelCase__:\t\t\t\tstr =self.tool(*UpperCAmelCase_)\r\r # There is a single output\r if len(self.tool.outputs) == 1:\r lowerCamelCase__:\t\t\t\tOptional[Any] =[outputs]\r\r self.assertListEqual(output_types(UpperCAmelCase_)\t,\t\t\tself.tool.outputs)\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Dict)\t->Any:\r\r\r\r '''simple docstring'''\r\r\r\r self.assertTrue(hasattr(self.tool\t,\t\t\t\"description\"))\r self.assertTrue(hasattr(self.tool\t,\t\t\t\"default_checkpoint\"))\r self.assertTrue(self.tool.description.startswith(\"This is a tool that\"))\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Union[str, Any])\t->Optional[int]:\r\r\r\r '''simple docstring'''\r\r\r\r lowerCamelCase__:\t\t\t\tstr =create_inputs(self.tool.inputs)\r lowerCamelCase__:\t\t\t\tDict =self.tool(*UpperCAmelCase_)\r\r if not isinstance(UpperCAmelCase_\t,\t\t\tUpperCAmelCase_):\r lowerCamelCase__:\t\t\t\tTuple =[outputs]\r\r self.assertEqual(len(UpperCAmelCase_)\t,\t\t\tlen(self.tool.outputs))\r\r for output, output_type in zip(UpperCAmelCase_\t,\t\t\tself.tool.outputs):\r lowerCamelCase__:\t\t\t\tAny =AGENT_TYPE_MAPPING[output_type]\r self.assertTrue(isinstance(UpperCAmelCase_\t,\t\t\tUpperCAmelCase_))\r\r\r\r\r\r\r\r def \t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (self : Dict)\t->str:\r\r\r\r '''simple docstring'''\r\r\r\r lowerCamelCase__:\t\t\t\tAny =create_inputs(self.tool.inputs)\r\r lowerCamelCase__:\t\t\t\tint =[]\r\r for _input, input_type in zip(UpperCAmelCase_\t,\t\t\tself.tool.inputs):\r if isinstance(UpperCAmelCase_\t,\t\t\tUpperCAmelCase_):\r _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])\r else:\r _inputs.append(AGENT_TYPE_MAPPING[input_type](_input))\r\r # Should not raise an error\r lowerCamelCase__:\t\t\t\tUnion[str, Any] =self.tool(*UpperCAmelCase_)\r\r if not isinstance(UpperCAmelCase_\t,\t\t\tUpperCAmelCase_):\r lowerCamelCase__:\t\t\t\tstr =[outputs]\r\r self.assertEqual(len(UpperCAmelCase_)\t,\t\t\tlen(self.tool.outputs))\r\r"},"style_context_codestyle":{"kind":"number","value":273,"string":"273"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":526,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\r'''simple docstring'''\r\rimport collections.abc\rfrom typing import Optional, Tuple, Union\r\rimport torch\rimport torch.utils.checkpoint\rfrom torch import nn\rfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\r\rfrom ...activations import ACTaFN\rfrom ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention\rfrom ...modeling_utils import PreTrainedModel\rfrom ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging\rfrom .configuration_poolformer import PoolFormerConfig\r\r\rlowercase_\t\t\t\t\t= logging.get_logger(__name__)\r\r# General docstring\rlowercase_\t\t\t\t\t= \"PoolFormerConfig\"\r\r# Base docstring\rlowercase_\t\t\t\t\t= \"sail/poolformer_s12\"\rlowercase_\t\t\t\t\t= [1, 512, 7, 7]\r\r# Image classification docstring\rlowercase_\t\t\t\t\t= \"sail/poolformer_s12\"\rlowercase_\t\t\t\t\t= \"tabby, tabby cat\"\r\rlowercase_\t\t\t\t\t= [\r \"sail/poolformer_s12\",\r # See all PoolFormer models at https://huggingface.co/models?filter=poolformer\r]\r\r\r\r\r\rdef lowerCamelCase\t\t\t\t\t\t( __lowerCamelCase : Dict\t\t\t\t\t, __lowerCamelCase : float = 0.0\t\t\t\t\t, __lowerCamelCase : bool = False )\t\t\t\t\t\t->Dict:\r\r\r\r\r\r\r if drop_prob == 0.0 or not training:\r return input\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t1 - drop_prob\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t(input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tkeep_prob + torch.rand(__lowerCamelCase\t\t\t\t\t, dtype=input.dtype\t\t\t\t\t, device=input.device )\r random_tensor.floor_() # binarize\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tinput.div(__lowerCamelCase ) * random_tensor\r return output\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tnn.Module ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA = None )\t\t-> None:\r super().__init__()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tdrop_prob\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA )\t\t-> torch.Tensor:\r return drop_path(a__ ,\t\tself.drop_prob ,\t\tself.training )\r\r\r\r\r\r def \t\t\tsnake_case_(\t\tself )\t\t-> str:\r return \"p={}\".format(self.drop_prob )\r\r\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tnn.Module ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA ,\t\tA ,\t\tA ,\t\tA ,\t\tA ,\t\tA=None )\t\t-> Tuple:\r super().__init__()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tpatch_size if isinstance(a__ ,\t\tcollections.abc.Iterable ) else (patch_size, patch_size)\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tstride if isinstance(a__ ,\t\tcollections.abc.Iterable ) else (stride, stride)\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tpadding if isinstance(a__ ,\t\tcollections.abc.Iterable ) else (padding, padding)\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.Convad(a__ ,\t\ta__ ,\t\tkernel_size=a__ ,\t\tstride=a__ ,\t\tpadding=a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnorm_layer(a__ ) if norm_layer else nn.Identity()\r\r\r\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA )\t\t-> str:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.projection(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.norm(a__ )\r return embeddings\r\r\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tnn.GroupNorm ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA ,\t\t**A )\t\t-> List[str]:\r super().__init__(1 ,\t\ta__ ,\t\t**a__ )\r\r\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tnn.Module ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA )\t\t-> List[Any]:\r super().__init__()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.AvgPoolad(a__ ,\t\tstride=1 ,\t\tpadding=pool_size // 2 ,\t\tcount_include_pad=a__ )\r\r\r\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA )\t\t-> Dict:\r return self.pool(a__ ) - hidden_states\r\r\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tnn.Module ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA ,\t\tA ,\t\tA ,\t\tA )\t\t-> Optional[Any]:\r super().__init__()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.Convad(a__ ,\t\ta__ ,\t\t1 )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.Convad(a__ ,\t\ta__ ,\t\t1 )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerDropPath(a__ )\r if isinstance(config.hidden_act ,\t\ta__ ):\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tACTaFN[config.hidden_act]\r else:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tconfig.hidden_act\r\r\r\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA )\t\t-> Dict:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.conva(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.act_fn(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.drop(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.conva(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.drop(a__ )\r\r return hidden_states\r\r\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tnn.Module ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA ,\t\tA ,\t\tA ,\t\tA ,\t\tA ,\t\tA )\t\t-> Dict:\r super().__init__()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerPooling(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerOutput(a__ ,\t\ta__ ,\t\ta__ ,\t\ta__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerGroupNorm(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerGroupNorm(a__ )\r\r # Useful for training neural nets\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerDropPath(a__ ) if drop_path > 0.0 else nn.Identity()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tconfig.use_layer_scale\r if config.use_layer_scale:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.Parameter(\r config.layer_scale_init_value * torch.ones((a__) ) ,\t\trequires_grad=a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.Parameter(\r config.layer_scale_init_value * torch.ones((a__) ) ,\t\trequires_grad=a__ )\r\r\r\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA )\t\t-> List[Any]:\r\r\r\r\r if self.use_layer_scale:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.pooling(self.before_norm(a__ ) )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output\r # First residual connection\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\thidden_states + self.drop_path(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t()\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.output(self.after_norm(a__ ) )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output\r # Second residual connection\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\thidden_states + self.drop_path(a__ )\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t(output,) + outputs\r return outputs\r\r else:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.drop_path(self.pooling(self.before_norm(a__ ) ) )\r # First residual connection\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tpooling_output + hidden_states\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t()\r\r # Second residual connection inside the PoolFormerOutput block\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.drop_path(self.output(self.after_norm(a__ ) ) )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\thidden_states + layer_output\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t(output,) + outputs\r return outputs\r\r\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tnn.Module ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA )\t\t-> Tuple:\r super().__init__()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tconfig\r # stochastic depth decay rule\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t[x.item() for x in torch.linspace(0 ,\t\tconfig.drop_path_rate ,\t\tsum(config.depths ) )]\r\r # patch embeddings\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t[]\r for i in range(config.num_encoder_blocks ):\r embeddings.append(\r PoolFormerEmbeddings(\r patch_size=config.patch_sizes[i] ,\t\tstride=config.strides[i] ,\t\tpadding=config.padding[i] ,\t\tnum_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] ,\t\thidden_size=config.hidden_sizes[i] ,\t\t) )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.ModuleList(a__ )\r\r # Transformer blocks\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t[]\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t0\r for i in range(config.num_encoder_blocks ):\r # each block consists of layers\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t[]\r if i != 0:\r cur += config.depths[i - 1]\r for j in range(config.depths[i] ):\r layers.append(\r PoolFormerLayer(\r a__ ,\t\tnum_channels=config.hidden_sizes[i] ,\t\tpool_size=config.pool_size ,\t\thidden_size=config.hidden_sizes[i] ,\t\tintermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) ,\t\tdrop_path=dpr[cur + j] ,\t\t) )\r blocks.append(nn.ModuleList(a__ ) )\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.ModuleList(a__ )\r\r\r\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA ,\t\tA=False ,\t\tA=True )\t\t-> Tuple:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t() if output_hidden_states else None\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tpixel_values\r for idx, layers in enumerate(zip(self.patch_embeddings ,\t\tself.block ) ):\r _SCREAMING_SNAKE_CASE\t\t\t\t, _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tlayers\r # Get patch embeddings from hidden_states\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tembedding_layer(a__ )\r # Send the embeddings through the blocks\r for _, blk in enumerate(a__ ):\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tblk(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tlayer_outputs[0]\r\r if output_hidden_states:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tall_hidden_states + (hidden_states,)\r\r if not return_dict:\r return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )\r\r return BaseModelOutputWithNoAttention(last_hidden_state=a__ ,\t\thidden_states=a__ )\r\r\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tlowercase_ ):\r\r '''simple docstring'''\r UpperCamelCase = PoolFormerConfig\r UpperCamelCase = \"poolformer\"\r UpperCamelCase = \"pixel_values\"\r UpperCamelCase = True\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA )\t\t-> List[Any]:\r\r\r\r\r if isinstance(a__ ,\t\t(nn.Linear, nn.Convad) ):\r module.weight.data.normal_(mean=0.0 ,\t\tstd=self.config.initializer_range )\r if module.bias is not None:\r module.bias.data.zero_()\r elif isinstance(a__ ,\t\tnn.LayerNorm ):\r module.bias.data.zero_()\r module.weight.data.fill_(1.0 )\r\r\r\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA ,\t\tA=False )\t\t-> Dict:\r\r\r\r\r if isinstance(a__ ,\t\ta__ ):\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tvalue\r\r\r\r\rlowercase_\t\t\t\t\t= r\"\\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\\n behavior.\\n\\n Parameters:\\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\\n Initializing with a config file does not load the weights associated with the model, only the\\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\\n\"\r\rlowercase_\t\t\t\t\t= r\"\\n Args:\\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\\n [`PoolFormerImageProcessor.__call__`] for details.\\n\"\r\r\r@add_start_docstrings(\r '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.''' , lowercase_ , )\rclass \t\t\t\t\ta_ (\t\t\t\tlowercase_ ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA )\t\t-> str:\r super().__init__(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tconfig\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerEncoder(a__ )\r\r # Initialize weights and apply final processing\r self.post_init()\r\r\r def \t\t\tsnake_case_(\t\tself )\t\t-> Dict:\r return self.embeddings.patch_embeddings\r\r\r\r\r\r @add_start_docstrings_to_model_forward(a__ )\r @add_code_sample_docstrings(\r checkpoint=_CHECKPOINT_FOR_DOC ,\t\toutput_type=a__ ,\t\tconfig_class=_CONFIG_FOR_DOC ,\t\tmodality=\"\"\"vision\"\"\" ,\t\texpected_output=_EXPECTED_OUTPUT_SHAPE ,\t\t)\r def \t\t\tsnake_case_(\t\tself ,\t\tA = None ,\t\tA = None ,\t\tA = None ,\t\t)\t\t-> Union[Tuple, BaseModelOutputWithNoAttention]:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t(\r output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\r )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\treturn_dict if return_dict is not None else self.config.use_return_dict\r\r if pixel_values is None:\r raise ValueError(\"\"\"You have to specify pixel_values\"\"\" )\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.encoder(\r a__ ,\t\toutput_hidden_states=a__ ,\t\treturn_dict=a__ ,\t\t)\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tencoder_outputs[0]\r\r if not return_dict:\r return (sequence_output, None) + encoder_outputs[1:]\r\r return BaseModelOutputWithNoAttention(\r last_hidden_state=a__ ,\t\thidden_states=encoder_outputs.hidden_states ,\t\t)\r\r\r\r\rclass \t\t\t\t\ta_ (\t\t\t\tnn.Module ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA )\t\t-> int:\r super().__init__()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tnn.Linear(config.hidden_size ,\t\tconfig.hidden_size )\r\r\r\r\r\r def \t\t\tsnake_case_(\t\tself ,\t\tA )\t\t-> List[str]:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.dense(a__ )\r return output\r\r\r\r\r@add_start_docstrings(\r '''\\n PoolFormer Model transformer with an image classification head on top\\n ''' , lowercase_ , )\rclass \t\t\t\t\ta_ (\t\t\t\tlowercase_ ):\r\r '''simple docstring'''\r\r\r def __init__(\t\tself ,\t\tA )\t\t-> List[Any]:\r super().__init__(a__ )\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tconfig.num_labels\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerModel(a__ )\r\r # Final norm\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tPoolFormerGroupNorm(config.hidden_sizes[-1] )\r # Classifier head\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t(\r nn.Linear(config.hidden_sizes[-1] ,\t\tconfig.num_labels ) if config.num_labels > 0 else nn.Identity()\r )\r\r # Initialize weights and apply final processing\r self.post_init()\r\r\r\r\r\r @add_start_docstrings_to_model_forward(a__ )\r @add_code_sample_docstrings(\r checkpoint=_IMAGE_CLASS_CHECKPOINT ,\t\toutput_type=a__ ,\t\tconfig_class=_CONFIG_FOR_DOC ,\t\texpected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,\t\t)\r def \t\t\tsnake_case_(\t\tself ,\t\tA = None ,\t\tA = None ,\t\tA = None ,\t\tA = None ,\t\t)\t\t-> Union[Tuple, ImageClassifierOutputWithNoAttention]:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\treturn_dict if return_dict is not None else self.config.use_return_dict\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.poolformer(\r a__ ,\t\toutput_hidden_states=a__ ,\t\treturn_dict=a__ ,\t\t)\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\toutputs[0]\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tself.classifier(self.norm(a__ ).mean([-2, -1] ) )\r\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tNone\r if labels is not None:\r if self.config.problem_type is None:\r if self.num_labels == 1:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t\"\"\"regression\"\"\"\r elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t\"\"\"single_label_classification\"\"\"\r else:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t\"\"\"multi_label_classification\"\"\"\r\r if self.config.problem_type == \"regression\":\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tMSELoss()\r if self.num_labels == 1:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tloss_fct(logits.squeeze() ,\t\tlabels.squeeze() )\r else:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tloss_fct(a__ ,\t\ta__ )\r elif self.config.problem_type == \"single_label_classification\":\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tCrossEntropyLoss()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tloss_fct(logits.view(-1 ,\t\tself.num_labels ) ,\t\tlabels.view(-1 ) )\r elif self.config.problem_type == \"multi_label_classification\":\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tBCEWithLogitsLoss()\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\tloss_fct(a__ ,\t\ta__ )\r\r if not return_dict:\r _SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t\t\t=\t\t\t(logits,) + outputs[2:]\r return ((loss,) + output) if loss is not None else output\r\r return ImageClassifierOutputWithNoAttention(loss=a__ ,\t\tlogits=a__ ,\t\thidden_states=outputs.hidden_states )\r\r\r\r"},"code_codestyle":{"kind":"number","value":58,"string":"58"},"style_context":{"kind":"string","value":"\n\n\n\n\n'''simple docstring'''\n\n\n\n\n\nfrom __future__ import annotations\n\n\ndef UpperCamelCase_(\tsnake_case\t\t\t\t\t\t\t: list[int] ):\n\n\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\t\t\t\t\t\t\treturn len(set(snake_case ) ) == len(snake_case )\n\n\nif __name__ == \"__main__\":\n\t\timport doctest\n\n\t\tdoctest.testmod()\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":85,"string":"85"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":527,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\nimport pickle\r\nimport unittest\r\n\r\nimport torch\r\n\r\nfrom accelerate import Accelerator\r\nfrom accelerate.state import AcceleratorState\r\nfrom accelerate.test_utils import require_cpu\r\n\r\n@require_cpu\r\nclass \t\tlowerCamelCase_ (\tunittest.TestCase ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef SCREAMING_SNAKE_CASE__\t\t\t\t\t\t(\t\tself : int\t) ->\t\t\tList[str]:\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: List[str] = torch.nn.Linear(10\t\t,\t\t10\t)\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Optional[Any] = torch.optim.SGD(model.parameters()\t\t,\t\t0.1\t)\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Optional[int] = Accelerator()\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Tuple = accelerator.prepare(__lowerCamelCase\t)\r\n\t\t\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\t\t\tpickle.loads(pickle.dumps(__lowerCamelCase\t)\t)\r\n\t\t\t\t\t\t\t\t\texcept Exception as e:\r\n\t\t\t\t\t\t\t\t\t\t\t\tself.fail(F\"\"\"Accelerated optimizer pickling failed with {e}\"\"\"\t)\r\n\t\t\t\t\t\t\t\t\tAcceleratorState._reset_state()\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":369,"string":"369"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\nimport json\r\nfrom typing import List, Optional, Tuple\r\n\r\nfrom tokenizers import normalizers\r\n\r\nfrom ...tokenization_utils_fast import PreTrainedTokenizerFast\r\nfrom .tokenization_electra import ElectraTokenizer\r\n\r\n\r\n__SCREAMING_SNAKE_CASE =\t\t\t\t\t{\"\"\"vocab_file\"\"\": \"\"\"vocab.txt\"\"\", \"\"\"tokenizer_file\"\"\": \"\"\"tokenizer.json\"\"\"}\r\n\r\n__SCREAMING_SNAKE_CASE =\t\t\t\t\t{\r\n \"\"\"vocab_file\"\"\": {\r\n \"\"\"google/electra-small-generator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt\"\"\"\r\n ),\r\n \"\"\"google/electra-base-generator\"\"\": \"\"\"https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt\"\"\",\r\n \"\"\"google/electra-large-generator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt\"\"\"\r\n ),\r\n \"\"\"google/electra-small-discriminator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt\"\"\"\r\n ),\r\n \"\"\"google/electra-base-discriminator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt\"\"\"\r\n ),\r\n \"\"\"google/electra-large-discriminator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt\"\"\"\r\n ),\r\n },\r\n \"\"\"tokenizer_file\"\"\": {\r\n \"\"\"google/electra-small-generator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json\"\"\"\r\n ),\r\n \"\"\"google/electra-base-generator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json\"\"\"\r\n ),\r\n \"\"\"google/electra-large-generator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json\"\"\"\r\n ),\r\n \"\"\"google/electra-small-discriminator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json\"\"\"\r\n ),\r\n \"\"\"google/electra-base-discriminator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json\"\"\"\r\n ),\r\n \"\"\"google/electra-large-discriminator\"\"\": (\r\n \"\"\"https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json\"\"\"\r\n ),\r\n },\r\n}\r\n\r\n__SCREAMING_SNAKE_CASE =\t\t\t\t\t{\r\n \"\"\"google/electra-small-generator\"\"\": 512,\r\n \"\"\"google/electra-base-generator\"\"\": 512,\r\n \"\"\"google/electra-large-generator\"\"\": 512,\r\n \"\"\"google/electra-small-discriminator\"\"\": 512,\r\n \"\"\"google/electra-base-discriminator\"\"\": 512,\r\n \"\"\"google/electra-large-discriminator\"\"\": 512,\r\n}\r\n\r\n__SCREAMING_SNAKE_CASE =\t\t\t\t\t{\r\n \"\"\"google/electra-small-generator\"\"\": {\"\"\"do_lower_case\"\"\": True},\r\n \"\"\"google/electra-base-generator\"\"\": {\"\"\"do_lower_case\"\"\": True},\r\n \"\"\"google/electra-large-generator\"\"\": {\"\"\"do_lower_case\"\"\": True},\r\n \"\"\"google/electra-small-discriminator\"\"\": {\"\"\"do_lower_case\"\"\": True},\r\n \"\"\"google/electra-base-discriminator\"\"\": {\"\"\"do_lower_case\"\"\": True},\r\n \"\"\"google/electra-large-discriminator\"\"\": {\"\"\"do_lower_case\"\"\": True},\r\n}\r\n\r\nclass \t\tlowerCamelCase_ (\t_A ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t'''simple docstring'''\r\n\t\t\t\t\t\ta__\t\t\t\t=\t\t\t\t\tVOCAB_FILES_NAMES\r\n\t\t\t\t\t\ta__\t\t\t\t=\t\t\t\t\tPRETRAINED_VOCAB_FILES_MAP\r\n\t\t\t\t\t\ta__\t\t\t\t=\t\t\t\t\tPRETRAINED_INIT_CONFIGURATION\r\n\t\t\t\t\t\ta__\t\t\t\t=\t\t\t\t\tPRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r\n\t\t\t\t\t\ta__\t\t\t\t=\t\t\t\t\tElectraTokenizer\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef __init__(\t\tself : int\t\t,\t\t__lowerCamelCase : str=None\t\t,\t\t__lowerCamelCase : Optional[int]=None\t\t,\t\t__lowerCamelCase : Tuple=True\t\t,\t\t__lowerCamelCase : int=\"[UNK]\"\t\t,\t\t__lowerCamelCase : Any=\"[SEP]\"\t\t,\t\t__lowerCamelCase : Union[str, Any]=\"[PAD]\"\t\t,\t\t__lowerCamelCase : str=\"[CLS]\"\t\t,\t\t__lowerCamelCase : Tuple=\"[MASK]\"\t\t,\t\t__lowerCamelCase : Union[str, Any]=True\t\t,\t\t__lowerCamelCase : str=None\t\t,\t\t**__lowerCamelCase : str\t\t,\t\t) ->\t\t\tList[str]:\r\n\t\t\t\t\t\t\t\t\tsuper().__init__(\r\n\t\t\t\t\t\t\t\t\t __lowerCamelCase\t\t,\t\ttokenizer_file=__lowerCamelCase\t\t,\t\tdo_lower_case=__lowerCamelCase\t\t,\t\tunk_token=__lowerCamelCase\t\t,\t\tsep_token=__lowerCamelCase\t\t,\t\tpad_token=__lowerCamelCase\t\t,\t\tcls_token=__lowerCamelCase\t\t,\t\tmask_token=__lowerCamelCase\t\t,\t\ttokenize_chinese_chars=__lowerCamelCase\t\t,\t\tstrip_accents=__lowerCamelCase\t\t,\t\t**__lowerCamelCase\t\t,\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Dict = json.loads(self.backend_tokenizer.normalizer.__getstate__()\t)\r\n\t\t\t\t\t\t\t\t\tif (\r\n\t\t\t\t\t\t\t\t\t normalizer_state.get(\"lowercase\"\t\t,\t\t__lowerCamelCase\t) != do_lower_case\r\n\t\t\t\t\t\t\t\t\t or normalizer_state.get(\"strip_accents\"\t\t,\t\t__lowerCamelCase\t) != strip_accents\r\n\t\t\t\t\t\t\t\t\t or normalizer_state.get(\"handle_chinese_chars\"\t\t,\t\t__lowerCamelCase\t) != tokenize_chinese_chars\r\n\t\t\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Union[str, Any] = getattr(__lowerCamelCase\t\t,\t\tnormalizer_state.pop(\"type\"\t)\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: List[Any] = do_lower_case\r\n\t\t\t\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Tuple = strip_accents\r\n\t\t\t\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Any = tokenize_chinese_chars\r\n\t\t\t\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Tuple = normalizer_class(**__lowerCamelCase\t)\r\n\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Optional[Any] = do_lower_case\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef SCREAMING_SNAKE_CASE__\t\t\t\t\t\t(\t\tself : Optional[int]\t\t,\t\t__lowerCamelCase : Optional[Any]\t\t,\t\t__lowerCamelCase : Dict=None\t) ->\t\t\tList[Any]:\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Tuple = [self.cls_token_id] + token_ids_a + [self.sep_token_id]\r\n\r\n\t\t\t\t\t\t\t\t\tif token_ids_a:\r\n\t\t\t\t\t\t\t\t\t\t\t\toutput += token_ids_a + [self.sep_token_id]\r\n\r\n\t\t\t\t\t\t\t\t\treturn output\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef SCREAMING_SNAKE_CASE__\t\t\t\t\t\t(\t\tself : Tuple\t\t,\t\t__lowerCamelCase : List[int]\t\t,\t\t__lowerCamelCase : Optional[List[int]] = None\t) ->\t\t\tList[int]:\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: int = [self.sep_token_id]\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: Optional[int] = [self.cls_token_id]\r\n\t\t\t\t\t\t\t\t\tif token_ids_a is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn len(cls + token_ids_a + sep\t) * [0]\r\n\t\t\t\t\t\t\t\t\treturn len(cls + token_ids_a + sep\t) * [0] + len(token_ids_a + sep\t) * [1]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef SCREAMING_SNAKE_CASE__\t\t\t\t\t\t(\t\tself : Any\t\t,\t\t__lowerCamelCase : str\t\t,\t\t__lowerCamelCase : Optional[str] = None\t) ->\t\t\tTuple[str]:\r\n\t\t\t\t\t\t\t\t\tA\t\t\t\t\t\t\t: List[Any] = self._tokenizer.model.save(__lowerCamelCase\t\t,\t\tname=__lowerCamelCase\t)\r\n\t\t\t\t\t\t\t\t\treturn tuple(__lowerCamelCase\t)"},"style_context_codestyle":{"kind":"number","value":256,"string":"256"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":528,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\nfrom argparse import ArgumentParser\r\n\r\nfrom .env import EnvironmentCommand\r\n\r\n\r\n\r\n\r\ndef snake_case_ ( ) ->\t\t\t\tList[Any]:\r\n lowercase__: Optional[int] \t\t\t= ArgumentParser('Diffusers CLI tool'\t\t\t,\t\t\t\t\t\t\tusage='diffusers-cli []' )\r\n lowercase__: Union[str, Any] \t\t\t= parser.add_subparsers(help='diffusers-cli command helpers' )\r\n\r\n # Register commands\r\n EnvironmentCommand.register_subcommand(snake_case )\r\n\r\n # Let's go\r\n lowercase__: str \t\t\t= parser.parse_args()\r\n\r\n if not hasattr(snake_case\t\t\t,\t\t\t\t\t\t\t'func' ):\r\n parser.print_help()\r\n exit(1 )\r\n\r\n # Run\r\n lowercase__: Any \t\t\t= args.func(snake_case )\r\n service.run()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":196,"string":"196"},"style_context":{"kind":"string","value":"\r\n\r\n\r\nfrom typing import Optional\r\n\r\nimport torch\r\nimport torch.utils.checkpoint\r\nfrom torch import Tensor, nn\r\nfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\r\n\r\nfrom ...activations import ACTaFN\r\nfrom ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward\r\nfrom ...modeling_outputs import (\r\n BaseModelOutputWithNoAttention,\r\n BaseModelOutputWithPoolingAndNoAttention,\r\n ImageClassifierOutputWithNoAttention,\r\n)\r\nfrom ...modeling_utils import PreTrainedModel\r\nfrom ...utils import logging\r\nfrom .configuration_regnet import RegNetConfig\r\n\r\n\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\tlogging.get_logger(__name__)\r\n\r\n# General docstring\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\t'''RegNetConfig'''\r\n\r\n# Base docstring\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\t'''facebook/regnet-y-040'''\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\t[1, 10_88, 7, 7]\r\n\r\n# Image classification docstring\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\t'''facebook/regnet-y-040'''\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\t'''tabby, tabby cat'''\r\n\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\t[\r\n '''facebook/regnet-y-040''',\r\n # See all regnet models at https://huggingface.co/models?filter=regnet\r\n]\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( nn.Module ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ = 3\t\t\t\t, lowerCAmelCase__ = 1\t\t\t\t, lowerCAmelCase__ = 1\t\t\t\t, lowerCAmelCase__ = \"relu\"\t\t\t\t, )\t\t\t\t\t->\t\t\t\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__()\r\n lowercase__: Any \t\t\t= nn.Convad(\r\n lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, kernel_size=lowerCAmelCase__\t\t\t\t, stride=lowerCAmelCase__\t\t\t\t, padding=kernel_size // 2\t\t\t\t, groups=lowerCAmelCase__\t\t\t\t, bias=lowerCAmelCase__\t\t\t\t, )\r\n lowercase__: str \t\t\t= nn.BatchNormad(lowerCAmelCase__ )\r\n lowercase__: Union[str, Any] \t\t\t= ACTaFN[activation] if activation is not None else nn.Identity()\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tDict:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: List[str] \t\t\t= self.convolution(lowerCAmelCase__ )\r\n lowercase__: Optional[Any] \t\t\t= self.normalization(lowerCAmelCase__ )\r\n lowercase__: Union[str, Any] \t\t\t= self.activation(lowerCAmelCase__ )\r\n return hidden_state\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( nn.Module ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tUnion[str, Any]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__()\r\n lowercase__: Dict \t\t\t= RegNetConvLayer(\r\n config.num_channels\t\t\t\t, config.embedding_size\t\t\t\t, kernel_size=3\t\t\t\t, stride=2\t\t\t\t, activation=config.hidden_act )\r\n lowercase__: Dict \t\t\t= config.num_channels\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tint:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: Tuple \t\t\t= pixel_values.shape[1]\r\n if num_channels != self.num_channels:\r\n raise ValueError(\r\n 'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )\r\n lowercase__: Optional[int] \t\t\t= self.embedder(lowerCAmelCase__ )\r\n return hidden_state\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( nn.Module ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ = 2 )\t\t\t\t\t->\t\t\t\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__()\r\n lowercase__: Optional[Any] \t\t\t= nn.Convad(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, kernel_size=1\t\t\t\t, stride=lowerCAmelCase__\t\t\t\t, bias=lowerCAmelCase__ )\r\n lowercase__: Union[str, Any] \t\t\t= nn.BatchNormad(lowerCAmelCase__ )\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tTensor:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: Any \t\t\t= self.convolution(lowerCAmelCase__ )\r\n lowercase__: str \t\t\t= self.normalization(lowerCAmelCase__ )\r\n return hidden_state\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( nn.Module ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tList[str]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__()\r\n\r\n lowercase__: Any \t\t\t= nn.AdaptiveAvgPoolad((1, 1) )\r\n lowercase__: str \t\t\t= nn.Sequential(\r\n nn.Convad(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, kernel_size=1 )\t\t\t\t, nn.ReLU()\t\t\t\t, nn.Convad(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, kernel_size=1 )\t\t\t\t, nn.Sigmoid()\t\t\t\t, )\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n # b c h w -> b c 1 1\r\n lowercase__: str \t\t\t= self.pooler(lowerCAmelCase__ )\r\n lowercase__: List[str] \t\t\t= self.attention(lowerCAmelCase__ )\r\n lowercase__: List[Any] \t\t\t= hidden_state * attention\r\n return hidden_state\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( nn.Module ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ = 1 )\t\t\t\t\t->\t\t\t\tDict:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__()\r\n lowercase__: str \t\t\t= in_channels != out_channels or stride != 1\r\n lowercase__: Optional[int] \t\t\t= max(1\t\t\t\t, out_channels // config.groups_width )\r\n lowercase__: Union[str, Any] \t\t\t= (\r\n RegNetShortCut(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()\r\n )\r\n lowercase__: Dict \t\t\t= nn.Sequential(\r\n RegNetConvLayer(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, kernel_size=1\t\t\t\t, activation=config.hidden_act )\t\t\t\t, RegNetConvLayer(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, stride=lowerCAmelCase__\t\t\t\t, groups=lowerCAmelCase__\t\t\t\t, activation=config.hidden_act )\t\t\t\t, RegNetConvLayer(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, kernel_size=1\t\t\t\t, activation=lowerCAmelCase__ )\t\t\t\t, )\r\n lowercase__: Tuple \t\t\t= ACTaFN[config.hidden_act]\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tint:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: Dict \t\t\t= hidden_state\r\n lowercase__: Union[str, Any] \t\t\t= self.layer(lowerCAmelCase__ )\r\n lowercase__: int \t\t\t= self.shortcut(lowerCAmelCase__ )\r\n hidden_state += residual\r\n lowercase__: Optional[int] \t\t\t= self.activation(lowerCAmelCase__ )\r\n return hidden_state\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( nn.Module ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ = 1 )\t\t\t\t\t->\t\t\t\tDict:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__()\r\n lowercase__: Optional[int] \t\t\t= in_channels != out_channels or stride != 1\r\n lowercase__: List[str] \t\t\t= max(1\t\t\t\t, out_channels // config.groups_width )\r\n lowercase__: Any \t\t\t= (\r\n RegNetShortCut(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()\r\n )\r\n lowercase__: str \t\t\t= nn.Sequential(\r\n RegNetConvLayer(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, kernel_size=1\t\t\t\t, activation=config.hidden_act )\t\t\t\t, RegNetConvLayer(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, stride=lowerCAmelCase__\t\t\t\t, groups=lowerCAmelCase__\t\t\t\t, activation=config.hidden_act )\t\t\t\t, RegNetSELayer(lowerCAmelCase__\t\t\t\t, reduced_channels=int(round(in_channels / 4 ) ) )\t\t\t\t, RegNetConvLayer(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, kernel_size=1\t\t\t\t, activation=lowerCAmelCase__ )\t\t\t\t, )\r\n lowercase__: Union[str, Any] \t\t\t= ACTaFN[config.hidden_act]\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tList[str]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: Optional[Any] \t\t\t= hidden_state\r\n lowercase__: Optional[int] \t\t\t= self.layer(lowerCAmelCase__ )\r\n lowercase__: str \t\t\t= self.shortcut(lowerCAmelCase__ )\r\n hidden_state += residual\r\n lowercase__: Optional[int] \t\t\t= self.activation(lowerCAmelCase__ )\r\n return hidden_state\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( nn.Module ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ = 2\t\t\t\t, lowerCAmelCase__ = 2\t\t\t\t, )\t\t\t\t\t->\t\t\t\tTuple:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__()\r\n\r\n lowercase__: Optional[int] \t\t\t= RegNetXLayer if config.layer_type == 'x' else RegNetYLayer\r\n\r\n lowercase__: str \t\t\t= nn.Sequential(\r\n # downsampling is done in the first layer with stride of 2\r\n layer(\r\n lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, stride=lowerCAmelCase__\t\t\t\t, )\t\t\t\t, *[layer(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ ) for _ in range(depth - 1 )]\t\t\t\t, )\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: str \t\t\t= self.layers(lowerCAmelCase__ )\r\n return hidden_state\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( nn.Module ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tDict:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__()\r\n lowercase__: int \t\t\t= nn.ModuleList([] )\r\n # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input\r\n self.stages.append(\r\n RegNetStage(\r\n lowerCAmelCase__\t\t\t\t, config.embedding_size\t\t\t\t, config.hidden_sizes[0]\t\t\t\t, stride=2 if config.downsample_in_first_stage else 1\t\t\t\t, depth=config.depths[0]\t\t\t\t, ) )\r\n lowercase__: int \t\t\t= zip(config.hidden_sizes\t\t\t\t, config.hidden_sizes[1:] )\r\n for (in_channels, out_channels), depth in zip(lowerCAmelCase__\t\t\t\t, config.depths[1:] ):\r\n self.stages.append(RegNetStage(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__\t\t\t\t, depth=lowerCAmelCase__ ) )\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ = False\t\t\t\t, lowerCAmelCase__ = True )\t\t\t\t\t->\t\t\t\tBaseModelOutputWithNoAttention:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: List[str] \t\t\t= () if output_hidden_states else None\r\n\r\n for stage_module in self.stages:\r\n if output_hidden_states:\r\n lowercase__: Optional[Any] \t\t\t= hidden_states + (hidden_state,)\r\n\r\n lowercase__: List[Any] \t\t\t= stage_module(lowerCAmelCase__ )\r\n\r\n if output_hidden_states:\r\n lowercase__: Optional[Any] \t\t\t= hidden_states + (hidden_state,)\r\n\r\n if not return_dict:\r\n return tuple(v for v in [hidden_state, hidden_states] if v is not None )\r\n\r\n return BaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__\t\t\t\t, hidden_states=lowerCAmelCase__ )\r\n\r\n\r\n\r\n\r\n\r\nclass __a\t\t\t( __UpperCamelCase ):\r\n __lowercase :\t\t\t\t\t\t\tDict = RegNetConfig\r\n __lowercase :\t\t\t\t\t\t\tDict = 'regnet'\r\n __lowercase :\t\t\t\t\t\t\tstr = 'pixel_values'\r\n __lowercase :\t\t\t\t\t\t\tList[str] = True\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tList[str]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n if isinstance(lowerCAmelCase__\t\t\t\t, nn.Convad ):\r\n nn.init.kaiming_normal_(module.weight\t\t\t\t, mode='fan_out'\t\t\t\t, nonlinearity='relu' )\r\n elif isinstance(lowerCAmelCase__\t\t\t\t, (nn.BatchNormad, nn.GroupNorm) ):\r\n nn.init.constant_(module.weight\t\t\t\t, 1 )\r\n nn.init.constant_(module.bias\t\t\t\t, 0 )\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__=False )\t\t\t\t\t->\t\t\t\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n if isinstance(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ ):\r\n lowercase__: Any \t\t\t= value\r\n\r\n\r\n\r\n\r\n\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\tr'''\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'''\r\n\r\n__lowerCAmelCase\t\t =\t\t\t\t\t\t\tr'''\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'''\r\n\r\n\r\n\r\n\r\n\r\n@add_start_docstrings(\r\n 'The bare RegNet model outputting raw features without any specific head on top.' , __UpperCamelCase , )\r\n# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet\r\nclass __a\t\t\t( __UpperCamelCase ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tList[str]:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__(lowerCAmelCase__ )\r\n lowercase__: Tuple \t\t\t= config\r\n lowercase__: List[str] \t\t\t= RegNetEmbeddings(lowerCAmelCase__ )\r\n lowercase__: Optional[int] \t\t\t= RegNetEncoder(lowerCAmelCase__ )\r\n lowercase__: Optional[Any] \t\t\t= nn.AdaptiveAvgPoolad((1, 1) )\r\n # Initialize weights and apply final processing\r\n self.post_init()\r\n @add_start_docstrings_to_model_forward(lowerCAmelCase__ )\r\n @add_code_sample_docstrings(\r\n checkpoint=_CHECKPOINT_FOR_DOC\t\t\t\t, output_type=lowerCAmelCase__\t\t\t\t, config_class=_CONFIG_FOR_DOC\t\t\t\t, modality='vision'\t\t\t\t, expected_output=_EXPECTED_OUTPUT_SHAPE\t\t\t\t, )\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ = None\t\t\t\t, lowerCAmelCase__ = None )\t\t\t\t\t->\t\t\t\tBaseModelOutputWithPoolingAndNoAttention:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: List[Any] \t\t\t= (\r\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\r\n )\r\n lowercase__: Union[str, Any] \t\t\t= return_dict if return_dict is not None else self.config.use_return_dict\r\n\r\n lowercase__: Any \t\t\t= self.embedder(lowerCAmelCase__ )\r\n\r\n lowercase__: List[Any] \t\t\t= self.encoder(\r\n lowerCAmelCase__\t\t\t\t, output_hidden_states=lowerCAmelCase__\t\t\t\t, return_dict=lowerCAmelCase__ )\r\n\r\n lowercase__: Optional[Any] \t\t\t= encoder_outputs[0]\r\n\r\n lowercase__: Optional[int] \t\t\t= self.pooler(lowerCAmelCase__ )\r\n\r\n if not return_dict:\r\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\r\n\r\n return BaseModelOutputWithPoolingAndNoAttention(\r\n last_hidden_state=lowerCAmelCase__\t\t\t\t, pooler_output=lowerCAmelCase__\t\t\t\t, hidden_states=encoder_outputs.hidden_states\t\t\t\t, )\r\n\r\n\r\n\r\n\r\n\r\n@add_start_docstrings(\r\n '\\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\\n ImageNet.\\n ' , __UpperCamelCase , )\r\n# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet\r\nclass __a\t\t\t( __UpperCamelCase ):\r\n def __init__( self\t\t\t\t, lowerCAmelCase__ )\t\t\t\t\t->\t\t\t\tstr:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n super().__init__(lowerCAmelCase__ )\r\n lowercase__: Dict \t\t\t= config.num_labels\r\n lowercase__: Dict \t\t\t= RegNetModel(lowerCAmelCase__ )\r\n # classification head\r\n lowercase__: str \t\t\t= nn.Sequential(\r\n nn.Flatten()\t\t\t\t, nn.Linear(config.hidden_sizes[-1]\t\t\t\t, config.num_labels ) if config.num_labels > 0 else nn.Identity()\t\t\t\t, )\r\n # initialize weights and apply final processing\r\n self.post_init()\r\n @add_start_docstrings_to_model_forward(lowerCAmelCase__ )\r\n @add_code_sample_docstrings(\r\n checkpoint=_IMAGE_CLASS_CHECKPOINT\t\t\t\t, output_type=lowerCAmelCase__\t\t\t\t, config_class=_CONFIG_FOR_DOC\t\t\t\t, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT\t\t\t\t, )\r\n def \t\tSCREAMING_SNAKE_CASE__ ( self\t\t\t\t, lowerCAmelCase__ = None\t\t\t\t, lowerCAmelCase__ = None\t\t\t\t, lowerCAmelCase__ = None\t\t\t\t, lowerCAmelCase__ = None\t\t\t\t, )\t\t\t\t\t->\t\t\t\tImageClassifierOutputWithNoAttention:\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n lowercase__: str \t\t\t= return_dict if return_dict is not None else self.config.use_return_dict\r\n\r\n lowercase__: Optional[int] \t\t\t= self.regnet(lowerCAmelCase__\t\t\t\t, output_hidden_states=lowerCAmelCase__\t\t\t\t, return_dict=lowerCAmelCase__ )\r\n\r\n lowercase__: Dict \t\t\t= outputs.pooler_output if return_dict else outputs[1]\r\n\r\n lowercase__: List[str] \t\t\t= self.classifier(lowerCAmelCase__ )\r\n\r\n lowercase__: Optional[Any] \t\t\t= None\r\n\r\n if labels is not None:\r\n if self.config.problem_type is None:\r\n if self.num_labels == 1:\r\n lowercase__: Dict \t\t\t= 'regression'\r\n elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\r\n lowercase__: Optional[int] \t\t\t= 'single_label_classification'\r\n else:\r\n lowercase__: Tuple \t\t\t= 'multi_label_classification'\r\n if self.config.problem_type == \"regression\":\r\n lowercase__: List[Any] \t\t\t= MSELoss()\r\n if self.num_labels == 1:\r\n lowercase__: Optional[int] \t\t\t= loss_fct(logits.squeeze()\t\t\t\t, labels.squeeze() )\r\n else:\r\n lowercase__: int \t\t\t= loss_fct(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ )\r\n elif self.config.problem_type == \"single_label_classification\":\r\n lowercase__: Dict \t\t\t= CrossEntropyLoss()\r\n lowercase__: Optional[int] \t\t\t= loss_fct(logits.view(-1\t\t\t\t, self.num_labels )\t\t\t\t, labels.view(-1 ) )\r\n elif self.config.problem_type == \"multi_label_classification\":\r\n lowercase__: List[Any] \t\t\t= BCEWithLogitsLoss()\r\n lowercase__: Any \t\t\t= loss_fct(lowerCAmelCase__\t\t\t\t, lowerCAmelCase__ )\r\n\r\n if not return_dict:\r\n lowercase__: int \t\t\t= (logits,) + outputs[2:]\r\n return (loss,) + output if loss is not None else output\r\n\r\n return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase__\t\t\t\t, logits=lowerCAmelCase__\t\t\t\t, hidden_states=outputs.hidden_states )\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":196,"string":"196"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":529,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\nfrom .configuration_bert_masked import MaskedBertConfig\nfrom .modeling_bert_masked import (\n MaskedBertForMultipleChoice,\n MaskedBertForQuestionAnswering,\n MaskedBertForSequenceClassification,\n MaskedBertForTokenClassification,\n MaskedBertModel,\n)\nfrom .modules import *\n\n\n\n"},"code_codestyle":{"kind":"number","value":351,"string":"351"},"style_context":{"kind":"string","value":"\n\n\n\n\n\nfrom collections import OrderedDict\nfrom typing import Any, Mapping, Optional\n\nfrom ... import PreTrainedTokenizer, TensorType, is_torch_available\nfrom ...configuration_utils import PretrainedConfig\nfrom ...onnx import OnnxConfigWithPast\nfrom ...utils import logging\n\n\nUpperCAmelCase_ \t\t\t\t\t\t\t= logging.get_logger(__name__)\n\nUpperCAmelCase_ \t\t\t\t\t\t\t= {\n \"\"\"EleutherAI/gpt-neo-1.3B\"\"\": \"\"\"https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json\"\"\",\n # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo\n}\nclass UpperCamelCase_\t\t\t\t\t\t(\t\t\t\t\t_lowerCamelCase ):\n\t\t\t\t\tlowerCAmelCase_\t\t\t\t\t\t\t=\t\t'''gpt_neo'''\n\t\t\t\t\tlowerCAmelCase_\t\t\t\t\t\t\t=\t\t['''past_key_values''']\n\t\t\t\t\tlowerCAmelCase_\t\t\t\t\t\t\t=\t\t{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}\n\n\n\n\n\t\t\t\t\tdef __init__(\t\t\t\t\t\tself\t\t\t, lowerCAmelCase_=5_0257\t\t\t, lowerCAmelCase_=2048\t\t\t, lowerCAmelCase_=2048\t\t\t, lowerCAmelCase_=24\t\t\t, lowerCAmelCase_=[[[\"global\", \"local\"], 12]]\t\t\t, lowerCAmelCase_=16\t\t\t, lowerCAmelCase_=None\t\t\t, lowerCAmelCase_=256\t\t\t, lowerCAmelCase_=\"gelu_new\"\t\t\t, lowerCAmelCase_=0.0\t\t\t, lowerCAmelCase_=0.0\t\t\t, lowerCAmelCase_=0.0\t\t\t, lowerCAmelCase_=0.1\t\t\t, lowerCAmelCase_=1E-5\t\t\t, lowerCAmelCase_=0.02\t\t\t, lowerCAmelCase_=True\t\t\t, lowerCAmelCase_=5_0256\t\t\t, lowerCAmelCase_=5_0256\t\t\t, **lowerCAmelCase_\t\t\t, )\t\t\t\t\t\t->\t\t\t\t\t\t\tTuple:\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tvocab_size\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tmax_position_embeddings\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\thidden_size\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tnum_layers\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tnum_heads\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tintermediate_size\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\twindow_size\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tactivation_function\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tresid_dropout\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tembed_dropout\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tattention_dropout\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tclassifier_dropout\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tlayer_norm_epsilon\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tuse_cache\n\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tbos_token_id\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\teos_token_id\n\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tattention_types\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.expand_attention_types_params(lowerCAmelCase_\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\tif len(self.attention_layers\t\t\t\t\t) != self.num_layers:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'Configuration for convolutional module is incorrect. '\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'It is required that `len(config.attention_layers)` == `config.num_layers` '\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F'''but is `len(config.attention_layers) = {len(self.attention_layers\t\t\t\t\t)}`, '''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F'''`config.num_layers = {self.num_layers}`. '''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '`config.attention_layers` is prepared using `config.attention_types`. '\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'Please verify the value of `config.attention_types` argument.'\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\tsuper().__init__(bos_token_id=lowerCAmelCase_\t\t\t, eos_token_id=lowerCAmelCase_\t\t\t, **lowerCAmelCase_\t\t\t\t\t)\n\n\n\n\n\n\n\t\t\t\t\t@staticmethod\n\t\t\t\t\tdef \t\tlowerCAmelCase (\t\t\t\t\t\tlowerCAmelCase_\t\t\t\t\t)\t\t\t\t\t\t->\t\t\t\t\t\t\tAny:\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t[]\n\t\t\t\t\t\t\t\t\t\tfor item in attention_types:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor _ in range(item[1]\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tattentions.extend(item[0]\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\treturn attentions\n\n\n\n\n\ndef lowerCamelCase__ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) ->\tAny:\n\n\n\n\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\timport torch\n\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tinput.size()\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tlen(UpperCamelCase__ )\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tshape[dimension]\n\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttorch.arange(0 , UpperCamelCase__ , UpperCamelCase__ )\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttorch.div(sizedim - size , UpperCamelCase__ , rounding_mode='floor' ) + 1\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttorch.arange(UpperCamelCase__ ) + low_indices[:min_length][:, None]\n\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t[slice(UpperCamelCase__ )] * rank\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tindices\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tinput[s]\n\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tlist(range(0 , rank + 1 ) )\n\t\t\t\t\tperm.append(perm.pop(dimension + 1 ) )\n\n\t\t\t\t\treturn sliced.permute(UpperCamelCase__ )\n\n\n\n\n\ndef lowerCamelCase__ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict ) ->\tstr:\n\n\n\n\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\timport torch\n\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttorch.arange(1 , UpperCamelCase__ )\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttorch.remainder(UpperCamelCase__ , UpperCamelCase__ )\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tremainders == 0\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tcandidates[divisor_indices]\n\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttorch.max(UpperCamelCase__ )\n\t\t\t\t\treturn largest_divisor, torch.div(UpperCamelCase__ , UpperCamelCase__ , rounding_mode='floor' )\n\n\n\n\nclass UpperCamelCase_\t\t\t\t\t\t(\t\t\t\t\t_lowerCamelCase ):\n\n\n\n\n\t\t\t\t\t@property\n\t\t\t\t\tdef \t\tlowerCAmelCase (\t\t\t\t\t\tself\t\t\t\t\t)\t\t\t\t\t\t->\t\t\t\t\t\t\tMapping[str, Mapping[int, str]]:\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tOrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\tif self.use_past:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.fill_with_past_key_values_(lowerCAmelCase_\t\t\t, direction='inputs'\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t{0: 'batch', 1: 'past_sequence + sequence'}\n\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t{0: 'batch', 1: 'sequence'}\n\n\t\t\t\t\t\t\t\t\t\treturn common_inputs\n\n\n\n\n\t\t\t\t\t@property\n\t\t\t\t\tdef \t\tlowerCAmelCase (\t\t\t\t\t\tself\t\t\t\t\t)\t\t\t\t\t\t->\t\t\t\t\t\t\tint:\n\t\t\t\t\t\t\t\t\t\treturn self._config.num_heads\n\n\n\n\n\t\t\t\t\tdef \t\tlowerCAmelCase (\t\t\t\t\t\tself\t\t\t, lowerCAmelCase_\t\t\t, lowerCAmelCase_ = -1\t\t\t, lowerCAmelCase_ = -1\t\t\t, lowerCAmelCase_ = False\t\t\t, lowerCAmelCase_ = None\t\t\t, )\t\t\t\t\t\t->\t\t\t\t\t\t\tMapping[str, Any]:\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tsuper(lowerCAmelCase_\t\t\t, self\t\t\t\t\t).generate_dummy_inputs(\n\t\t\t\t\t\t\t\t\t\t lowerCAmelCase_\t\t\t, batch_size=lowerCAmelCase_\t\t\t, seq_length=lowerCAmelCase_\t\t\t, is_pair=lowerCAmelCase_\t\t\t, framework=lowerCAmelCase_\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t# We need to order the input in the way they appears in the forward()\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tOrderedDict({'input_ids': common_inputs['input_ids']}\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t# Need to add the past_keys\n\t\t\t\t\t\t\t\t\t\tif self.use_past:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not is_torch_available():\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\timport torch\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tcommon_inputs['input_ids'].shape\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Not using the same length for past_key_values\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tseqlen + 2\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t batch,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.num_attention_heads,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t past_key_values_length,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self._config.hidden_size // self.num_attention_heads,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t[\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t (torch.zeros(lowerCAmelCase_\t\t\t\t\t), torch.zeros(lowerCAmelCase_\t\t\t\t\t)) for _ in range(self.num_layers\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\n\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tcommon_inputs['attention_mask']\n\t\t\t\t\t\t\t\t\t\tif self.use_past:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tordered_inputs['attention_mask'].dtype\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_snake_case\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttorch.cat(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t [ordered_inputs['attention_mask'], torch.ones(lowerCAmelCase_\t\t\t, lowerCAmelCase_\t\t\t, dtype=lowerCAmelCase_\t\t\t\t\t)]\t\t\t, dim=1\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\treturn ordered_inputs\n\n\n\n\n\n\n\t\t\t\t\t@property\n\t\t\t\t\tdef \t\tlowerCAmelCase (\t\t\t\t\t\tself\t\t\t\t\t)\t\t\t\t\t\t->\t\t\t\t\t\t\tint:\n\t\t\t\t\t\t\t\t\t\treturn 13\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":295,"string":"295"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":530,"cells":{"code":{"kind":"string","value":"\r\r\r\r'''simple docstring'''\r\r\r\r\r\rfrom typing import Dict\r\rimport numpy as np\r\rfrom ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging\rfrom .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException\r\r\rif is_tf_available():\r import tensorflow as tf\r\r from ..tf_utils import stable_softmax\r\r\rif is_torch_available():\r import torch\r\r\r__a =\t\tlogging.get_logger(__name__)\r\r\r\r\r\r\r\r@add_end_docstrings(\r _a\t\t,\t\tr\"\\n top_k (`int`, defaults to 5):\\n The number of predictions to return.\\n targets (`str` or `List[str]`, *optional*):\\n When passed, the model will limit the scores to the passed targets instead of looking up in the whole\\n vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\\n token will be used (with a warning, and that might be slower).\\n\\n \"\t\t,\t\t)\rclass UpperCAmelCase_ ( _a\t):\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\r def lowerCamelCase ( self\t\t\t\t\t: Union[str, Any] , snake_case_\t\t\t\t\t: GenericTensor\t\t\t\t\t\t):\r if self.framework == \"tf\":\r snake_case__ : Optional[Any] \t\t\t\t\t\t= tf.where(input_ids == self.tokenizer.mask_token_id\t\t\t\t\t\t).numpy()\r elif self.framework == \"pt\":\r snake_case__ : Tuple \t\t\t\t\t\t= torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=snake_case_\t\t\t\t\t\t)\r else:\r raise ValueError(\"\"\"Unsupported framework\"\"\"\t\t\t\t\t\t)\r return masked_index\r\r\r\r\r def lowerCamelCase ( self\t\t\t\t\t: Optional[Any] , snake_case_\t\t\t\t\t: GenericTensor\t\t\t\t\t\t):\r snake_case__ : List[Any] \t\t\t\t\t\t= self.get_masked_index(snake_case_\t\t\t\t\t\t)\r snake_case__ : List[Any] \t\t\t\t\t\t= np.prod(masked_index.shape\t\t\t\t\t\t)\r if numel < 1:\r raise PipelineException(\r \"\"\"fill-mask\"\"\" , self.model.base_model_prefix , f\"No mask_token ({self.tokenizer.mask_token}) found on the input\" , )\r\r\r\r\r def lowerCamelCase ( self\t\t\t\t\t: Tuple , snake_case_\t\t\t\t\t: GenericTensor\t\t\t\t\t\t):\r if isinstance(snake_case_ , snake_case_\t\t\t\t\t\t):\r for model_input in model_inputs:\r self._ensure_exactly_one_mask_token(model_input[\"\"\"input_ids\"\"\"][0]\t\t\t\t\t\t)\r else:\r for input_ids in model_inputs[\"input_ids\"]:\r self._ensure_exactly_one_mask_token(snake_case_\t\t\t\t\t\t)\r\r\r\r\r def lowerCamelCase ( self\t\t\t\t\t: List[Any] , snake_case_\t\t\t\t\t: Any , snake_case_\t\t\t\t\t: Optional[int]=None , **snake_case_\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t):\r if return_tensors is None:\r snake_case__ : Tuple \t\t\t\t\t\t= self.framework\r snake_case__ : Optional[Any] \t\t\t\t\t\t= self.tokenizer(snake_case_ , return_tensors=snake_case_\t\t\t\t\t\t)\r self.ensure_exactly_one_mask_token(snake_case_\t\t\t\t\t\t)\r return model_inputs\r\r\r\r\r def lowerCamelCase ( self\t\t\t\t\t: str , snake_case_\t\t\t\t\t: str\t\t\t\t\t\t):\r snake_case__ : Union[str, Any] \t\t\t\t\t\t= self.model(**snake_case_\t\t\t\t\t\t)\r snake_case__ : Dict \t\t\t\t\t\t= model_inputs[\"\"\"input_ids\"\"\"]\r return model_outputs\r\r\r\r\r def lowerCamelCase ( self\t\t\t\t\t: Union[str, Any] , snake_case_\t\t\t\t\t: Union[str, Any] , snake_case_\t\t\t\t\t: str=5 , snake_case_\t\t\t\t\t: List[Any]=None\t\t\t\t\t\t):\r # Cap top_k if there are targets\r if target_ids is not None and target_ids.shape[0] < top_k:\r snake_case__ : Any \t\t\t\t\t\t= target_ids.shape[0]\r snake_case__ : List[Any] \t\t\t\t\t\t= model_outputs[\"\"\"input_ids\"\"\"][0]\r snake_case__ : Optional[Any] \t\t\t\t\t\t= model_outputs[\"\"\"logits\"\"\"]\r\r if self.framework == \"tf\":\r snake_case__ : Optional[Any] \t\t\t\t\t\t= tf.where(input_ids == self.tokenizer.mask_token_id\t\t\t\t\t\t).numpy()[:, 0]\r\r snake_case__ : Optional[int] \t\t\t\t\t\t= outputs.numpy()\r\r snake_case__ : Optional[int] \t\t\t\t\t\t= outputs[0, masked_index, :]\r snake_case__ : Optional[int] \t\t\t\t\t\t= stable_softmax(snake_case_ , axis=-1\t\t\t\t\t\t)\r if target_ids is not None:\r snake_case__ : Optional[Any] \t\t\t\t\t\t= tf.gather_nd(tf.squeeze(snake_case_ , 0\t\t\t\t\t\t) , target_ids.reshape(-1 , 1\t\t\t\t\t\t)\t\t\t\t\t\t)\r snake_case__ : Optional[int] \t\t\t\t\t\t= tf.expand_dims(snake_case_ , 0\t\t\t\t\t\t)\r\r snake_case__ : int \t\t\t\t\t\t= tf.math.top_k(snake_case_ , k=snake_case_\t\t\t\t\t\t)\r snake_case__\t\t\t\t\t\t,\t\t\t\tsnake_case__ : Any \t\t\t\t\t\t= topk.values.numpy(), topk.indices.numpy()\r else:\r snake_case__ : List[Any] \t\t\t\t\t\t= torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=snake_case_\t\t\t\t\t\t).squeeze(-1\t\t\t\t\t\t)\r # Fill mask pipeline supports only one ${mask_token} per sample\r\r snake_case__ : Tuple \t\t\t\t\t\t= outputs[0, masked_index, :]\r snake_case__ : Tuple \t\t\t\t\t\t= logits.softmax(dim=-1\t\t\t\t\t\t)\r if target_ids is not None:\r snake_case__ : List[str] \t\t\t\t\t\t= probs[..., target_ids]\r\r snake_case__\t\t\t\t\t\t,\t\t\t\tsnake_case__ : List[str] \t\t\t\t\t\t= probs.topk(snake_case_\t\t\t\t\t\t)\r\r snake_case__ : Tuple \t\t\t\t\t\t= []\r snake_case__ : List[str] \t\t\t\t\t\t= values.shape[0] == 1\r for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist()\t\t\t\t\t\t)\t\t\t\t\t\t):\r snake_case__ : Union[str, Any] \t\t\t\t\t\t= []\r for v, p in zip(_values , _predictions\t\t\t\t\t\t):\r # Copy is important since we're going to modify this array in place\r snake_case__ : Dict \t\t\t\t\t\t= input_ids.numpy().copy()\r if target_ids is not None:\r snake_case__ : Any \t\t\t\t\t\t= target_ids[p].tolist()\r\r snake_case__ : Union[str, Any] \t\t\t\t\t\t= p\r # Filter padding out:\r snake_case__ : List[str] \t\t\t\t\t\t= tokens[np.where(tokens != self.tokenizer.pad_token_id\t\t\t\t\t\t)]\r # Originally we skip special tokens to give readable output.\r # For multi masks though, the other [MASK] would be removed otherwise\r # making the output look odd, so we add them back\r snake_case__ : str \t\t\t\t\t\t= self.tokenizer.decode(snake_case_ , skip_special_tokens=snake_case_\t\t\t\t\t\t)\r snake_case__ : Union[str, Any] \t\t\t\t\t\t= {\"\"\"score\"\"\": v, \"\"\"token\"\"\": p, \"\"\"token_str\"\"\": self.tokenizer.decode([p]\t\t\t\t\t\t), \"\"\"sequence\"\"\": sequence}\r row.append(snake_case_\t\t\t\t\t\t)\r result.append(snake_case_\t\t\t\t\t\t)\r if single_mask:\r return result[0]\r return result\r\r\r\r\r def lowerCamelCase ( self\t\t\t\t\t: int , snake_case_\t\t\t\t\t: Any , snake_case_\t\t\t\t\t: str=None\t\t\t\t\t\t):\r if isinstance(snake_case_ , snake_case_\t\t\t\t\t\t):\r snake_case__ : Union[str, Any] \t\t\t\t\t\t= [targets]\r try:\r snake_case__ : Any \t\t\t\t\t\t= self.tokenizer.get_vocab()\r except Exception:\r snake_case__ : str \t\t\t\t\t\t= {}\r snake_case__ : List[Any] \t\t\t\t\t\t= []\r for target in targets:\r snake_case__ : List[str] \t\t\t\t\t\t= vocab.get(snake_case_ , snake_case_\t\t\t\t\t\t)\r if id_ is None:\r snake_case__ : int \t\t\t\t\t\t= self.tokenizer(\r snake_case_ , add_special_tokens=snake_case_ , return_attention_mask=snake_case_ , return_token_type_ids=snake_case_ , max_length=1 , truncation=snake_case_ , )[\"\"\"input_ids\"\"\"]\r if len(snake_case_\t\t\t\t\t\t) == 0:\r logger.warning(\r f\"The specified target token `{target}` does not exist in the model vocabulary. \"\r \"\"\"We cannot replace it with anything meaningful, ignoring it\"\"\"\t\t\t\t\t\t)\r continue\r snake_case__ : Optional[Any] \t\t\t\t\t\t= input_ids[0]\r # XXX: If users encounter this pass\r # it becomes pretty slow, so let's make sure\r # The warning enables them to fix the input to\r # get faster performance.\r logger.warning(\r f\"The specified target token `{target}` does not exist in the model vocabulary. \"\r f\"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_\t\t\t\t\t\t)}`.\"\t\t\t\t\t\t)\r target_ids.append(id_\t\t\t\t\t\t)\r snake_case__ : Optional[Any] \t\t\t\t\t\t= list(set(snake_case_\t\t\t\t\t\t)\t\t\t\t\t\t)\r if len(snake_case_\t\t\t\t\t\t) == 0:\r raise ValueError(\"\"\"At least one target must be provided when passed.\"\"\"\t\t\t\t\t\t)\r snake_case__ : Dict \t\t\t\t\t\t= np.array(snake_case_\t\t\t\t\t\t)\r return target_ids\r\r\r\r\r def lowerCamelCase ( self\t\t\t\t\t: Union[str, Any] , snake_case_\t\t\t\t\t: Tuple=None , snake_case_\t\t\t\t\t: Union[str, Any]=None\t\t\t\t\t\t):\r snake_case__ : Union[str, Any] \t\t\t\t\t\t= {}\r\r if targets is not None:\r snake_case__ : List[str] \t\t\t\t\t\t= self.get_target_ids(snake_case_ , snake_case_\t\t\t\t\t\t)\r snake_case__ : Union[str, Any] \t\t\t\t\t\t= target_ids\r\r if top_k is not None:\r snake_case__ : Optional[int] \t\t\t\t\t\t= top_k\r\r if self.tokenizer.mask_token_id is None:\r raise PipelineException(\r \"\"\"fill-mask\"\"\" , self.model.base_model_prefix , \"\"\"The tokenizer does not define a `mask_token`.\"\"\"\t\t\t\t\t\t)\r return {}, {}, postprocess_params\r\r\r\r\r def __call__( self\t\t\t\t\t: List[str] , snake_case_\t\t\t\t\t: Union[str, Any] , *snake_case_\t\t\t\t\t: Tuple , **snake_case_\t\t\t\t\t: List[Any]\t\t\t\t\t\t):\r snake_case__ : Optional[int] \t\t\t\t\t\t= super().__call__(snake_case_ , **snake_case_\t\t\t\t\t\t)\r if isinstance(snake_case_ , snake_case_\t\t\t\t\t\t) and len(snake_case_\t\t\t\t\t\t) == 1:\r return outputs[0]\r return outputs\r"},"code_codestyle":{"kind":"number","value":35,"string":"35"},"style_context":{"kind":"string","value":"\r\rdef \t\t\t\t\t__lowerCamelCase\t( ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\treturn [list(range(1000 - i ,\t\t\t\t\t\t\t-1000 - i ,\t\t\t\t\t\t\t-1 ) ) for i in range(1000 )]\r\r\r_UpperCAmelCase\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t= generate_large_matrix()\r_UpperCAmelCase\t\t: Tuple\t\t\t\t\t\t\t\t\t\t= (\r [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],\r [[3, 2], [1, 0]],\r [[7, 7, 6]],\r [[7, 7, 6], [-1, -2, -3]],\r grid,\r)\r\r\r\r\r\rdef \t\t\t\t\t__lowerCamelCase\t( UpperCamelCase__ ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\tassert all(row == sorted(UpperCamelCase__ ,\t\t\t\t\t\t\treverse=UpperCamelCase__ ) for row in grid )\r\t\t\t\t\t\t\tassert all(list(UpperCamelCase__ ) == sorted(UpperCamelCase__ ,\t\t\t\t\t\t\treverse=UpperCamelCase__ ) for col in zip(*UpperCamelCase__ ) )\r\r\r\r\r\rdef \t\t\t\t\t__lowerCamelCase\t( UpperCamelCase__ ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= 0\r\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= len(UpperCamelCase__ ) - 1\r\r\t\t\t\t\t\t\t# Edge cases such as no values or all numbers are negative.\r\t\t\t\t\t\t\tif not array or array[0] < 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn 0\r\r\t\t\t\t\t\t\twhile right + 1 > left:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= (left + right) // 2\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= array[mid]\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Num must be negative and the index must be greater than or equal to 0.\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tif num < 0 and array[mid - 1] >= 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn mid\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tif num >= 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= mid + 1\r\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= mid - 1\r # No negative numbers so return the last index of the array + 1 which is the length.\r\t\t\t\t\t\t\treturn len(UpperCamelCase__ )\r\r\r\r\r\rdef \t\t\t\t\t__lowerCamelCase\t( UpperCamelCase__ ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= 0\r\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= len(grid[0] )\r\r\t\t\t\t\t\t\tfor i in range(len(UpperCamelCase__ ) ):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= find_negative_index(grid[i][:bound] )\r\t\t\t\t\t\t\t\t\t\t\t\t\t\ttotal += bound\r\t\t\t\t\t\t\treturn (len(UpperCamelCase__ ) * len(grid[0] )) - total\r\r\r\r\r\rdef \t\t\t\t\t__lowerCamelCase\t( UpperCamelCase__ ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\treturn len([number for row in grid for number in row if number < 0] )\r\r\r\r\r\rdef \t\t\t\t\t__lowerCamelCase\t( UpperCamelCase__ ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= 0\r\t\t\t\t\t\t\tfor row in grid:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor i, number in enumerate(UpperCamelCase__ ):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif number < 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttotal += len(UpperCamelCase__ ) - i\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbreak\r\t\t\t\t\t\t\treturn total\r\r\r\r\r\rdef \t\t\t\t\t__lowerCamelCase\t( ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\tfrom timeit import timeit\r\r\t\t\t\t\t\t\tprint('Running benchmarks' )\r\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= (\r\t\t\t\t\t\t\t 'from __main__ import count_negatives_binary_search, '\r\t\t\t\t\t\t\t 'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'\r\t\t\t\t\t\t\t)\r\t\t\t\t\t\t\tfor func in (\r\t\t\t\t\t\t\t \"count_negatives_binary_search\", # took 0.7727 seconds\r\t\t\t\t\t\t\t \"count_negatives_brute_force_with_break\", # took 4.6505 seconds\r\t\t\t\t\t\t\t \"count_negatives_brute_force\", # took 12.8160 seconds\r\t\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= timeit(F'''{func}(grid=grid)''' ,\t\t\t\t\t\t\tsetup=UpperCamelCase__ ,\t\t\t\t\t\t\tnumber=500 )\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tprint(F'''{func}() took {time:0.4f} seconds''' )\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\timport doctest\r\r\t\t\t\t\tdoctest.testmod()\r\t\t\t\t\tbenchmark()\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":285,"string":"285"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":531,"cells":{"code":{"kind":"string","value":"\r\r\rimport gc\rimport unittest\r\rimport numpy as np\rimport torch\rfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\r\rfrom diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline\rfrom diffusers.pipelines.shap_e import ShapERenderer\rfrom diffusers.utils import load_numpy, slow\rfrom diffusers.utils.testing_utils import require_torch_gpu, torch_device\r\rfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\r\r\r\r\r\r\r\rclass __lowerCamelCase ( snake_case_ , unittest.TestCase ):\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\t\tlowerCAmelCase__ =\t\t\t\t\t\t\tShapEPipeline\r\t\t\t\t\t\t\tlowerCAmelCase__ =\t\t\t\t\t\t\t[\"prompt\"]\r\t\t\t\t\t\t\tlowerCAmelCase__ =\t\t\t\t\t\t\t[\"prompt\"]\r\t\t\t\t\t\t\tlowerCAmelCase__ =\t\t\t\t\t\t\t[\r\t\t\t\t\t\t\t \"num_images_per_prompt\",\r\t\t\t\t\t\t\t \"num_inference_steps\",\r\t\t\t\t\t\t\t \"generator\",\r\t\t\t\t\t\t\t \"latents\",\r\t\t\t\t\t\t\t \"guidance_scale\",\r\t\t\t\t\t\t\t \"frame_size\",\r\t\t\t\t\t\t\t \"output_type\",\r\t\t\t\t\t\t\t \"return_dict\",\r\t\t\t\t\t\t\t]\r\t\t\t\t\t\t\tlowerCAmelCase__ =\t\t\t\t\t\t\tFalse\r\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Optional[int]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\treturn 32\r\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> int:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\treturn 32\r\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Optional[Any]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\treturn self.time_input_dim * 4\r\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Optional[int]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\treturn 8\r\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Tuple:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tCLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\" )\r\t\t\t\t\t\t\t\treturn tokenizer\r\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> int:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\ttorch.manual_seed(0 )\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tCLIPTextConfig(\r\t\t\t\t\t\t\t\t bos_token_id=0\t\t, eos_token_id=2\t\t, hidden_size=self.text_embedder_hidden_size\t\t, projection_dim=self.text_embedder_hidden_size\t\t, intermediate_size=37\t\t, layer_norm_eps=1e-05\t\t, num_attention_heads=4\t\t, num_hidden_layers=5\t\t, pad_token_id=1\t\t, vocab_size=1000\t\t, )\r\t\t\t\t\t\t\t\treturn CLIPTextModelWithProjection(UpperCAmelCase )\r\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> List[str]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\ttorch.manual_seed(0 )\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\t{\r\t\t\t\t\t\t\t\t \"num_attention_heads\": 2,\r\t\t\t\t\t\t\t\t \"attention_head_dim\": 16,\r\t\t\t\t\t\t\t\t \"embedding_dim\": self.time_input_dim,\r\t\t\t\t\t\t\t\t \"num_embeddings\": 32,\r\t\t\t\t\t\t\t\t \"embedding_proj_dim\": self.text_embedder_hidden_size,\r\t\t\t\t\t\t\t\t \"time_embed_dim\": self.time_embed_dim,\r\t\t\t\t\t\t\t\t \"num_layers\": 1,\r\t\t\t\t\t\t\t\t \"clip_embed_dim\": self.time_input_dim * 2,\r\t\t\t\t\t\t\t\t \"additional_embeddings\": 0,\r\t\t\t\t\t\t\t\t \"time_embed_act_fn\": \"gelu\",\r\t\t\t\t\t\t\t\t \"norm_in_type\": \"layer\",\r\t\t\t\t\t\t\t\t \"encoder_hid_proj_type\": None,\r\t\t\t\t\t\t\t\t \"added_emb_type\": None,\r\t\t\t\t\t\t\t\t}\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tPriorTransformer(**UpperCAmelCase )\r\t\t\t\t\t\t\t\treturn model\r\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Optional[Any]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\ttorch.manual_seed(0 )\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\t{\r\t\t\t\t\t\t\t\t \"param_shapes\": (\r\t\t\t\t\t\t\t\t (self.renderer_dim, 93),\r\t\t\t\t\t\t\t\t (self.renderer_dim, 8),\r\t\t\t\t\t\t\t\t (self.renderer_dim, 8),\r\t\t\t\t\t\t\t\t (self.renderer_dim, 8),\r\t\t\t\t\t\t\t\t ),\r\t\t\t\t\t\t\t\t \"d_latent\": self.time_input_dim,\r\t\t\t\t\t\t\t\t \"d_hidden\": self.renderer_dim,\r\t\t\t\t\t\t\t\t \"n_output\": 12,\r\t\t\t\t\t\t\t\t \"background\": (\r\t\t\t\t\t\t\t\t 0.1,\r\t\t\t\t\t\t\t\t 0.1,\r\t\t\t\t\t\t\t\t 0.1,\r\t\t\t\t\t\t\t\t ),\r\t\t\t\t\t\t\t\t}\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tShapERenderer(**UpperCAmelCase )\r\t\t\t\t\t\t\t\treturn model\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> int:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.dummy_prior\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.dummy_text_encoder\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.dummy_tokenizer\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.dummy_renderer\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tHeunDiscreteScheduler(\r\t\t\t\t\t\t\t\t beta_schedule=\"exp\"\t\t, num_train_timesteps=1024\t\t, prediction_type=\"sample\"\t\t, use_karras_sigmas=UpperCAmelCase\t\t, clip_sample=UpperCAmelCase\t\t, clip_sample_range=1.0\t\t, )\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\t{\r\t\t\t\t\t\t\t\t \"prior\": prior,\r\t\t\t\t\t\t\t\t \"text_encoder\": text_encoder,\r\t\t\t\t\t\t\t\t \"tokenizer\": tokenizer,\r\t\t\t\t\t\t\t\t \"renderer\": renderer,\r\t\t\t\t\t\t\t\t \"scheduler\": scheduler,\r\t\t\t\t\t\t\t\t}\r\r\t\t\t\t\t\t\t\treturn components\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self\t\t, UpperCAmelCase\t\t, UpperCAmelCase=0 ) -> Optional[int]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tif str(UpperCAmelCase ).startswith(\"mps\" ):\r\t\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\ttorch.manual_seed(UpperCAmelCase )\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\ttorch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\t{\r\t\t\t\t\t\t\t\t \"prompt\": \"horse\",\r\t\t\t\t\t\t\t\t \"generator\": generator,\r\t\t\t\t\t\t\t\t \"num_inference_steps\": 1,\r\t\t\t\t\t\t\t\t \"frame_size\": 32,\r\t\t\t\t\t\t\t\t \"output_type\": \"np\",\r\t\t\t\t\t\t\t\t}\r\t\t\t\t\t\t\t\treturn inputs\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Tuple:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\t\"cpu\"\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.get_dummy_components()\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.pipeline_class(**UpperCAmelCase )\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tpipe.to(UpperCAmelCase )\r\r\t\t\t\t\t\t\t\tpipe.set_progress_bar_config(disable=UpperCAmelCase )\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tpipe(**self.get_dummy_inputs(UpperCAmelCase ) )\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\toutput.images[0]\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\timage[0, -3:, -3:, -1]\r\r\t\t\t\t\t\t\t\tassert image.shape == (20, 32, 32, 3)\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tnp.array(\r\t\t\t\t\t\t\t\t [\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t 0.00039216,\r\t\t\t\t\t\t\t\t ] )\r\r\t\t\t\t\t\t\t\tassert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Union[str, Any]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tself._test_inference_batch_consistent(batch_sizes=[1, 2] )\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Optional[int]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\ttorch_device == \"cpu\"\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tTrue\r\r\t\t\t\t\t\t\t\tself._test_inference_batch_single_identical(\r\t\t\t\t\t\t\t\t batch_size=2\t\t, test_max_difference=UpperCAmelCase\t\t, relax_max_difference=UpperCAmelCase\t\t, )\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Dict:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.get_dummy_components()\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.pipeline_class(**UpperCAmelCase )\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tpipe.to(UpperCAmelCase )\r\t\t\t\t\t\t\t\tpipe.set_progress_bar_config(disable=UpperCAmelCase )\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\t1\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\t2\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tself.get_dummy_inputs(UpperCAmelCase )\r\r\t\t\t\t\t\t\t\tfor key in inputs.keys():\r\t\t\t\t\t\t\t\t\tif key in self.batch_params:\r\t\t\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tbatch_size * [inputs[key]]\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tpipe(**UpperCAmelCase\t\t, num_images_per_prompt=UpperCAmelCase )[0]\r\r\t\t\t\t\t\t\t\tassert images.shape[0] == batch_size * num_images_per_prompt\r\r\r\r\r\r\r\r@slow\r@require_torch_gpu\rclass __lowerCamelCase ( unittest.TestCase ):\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Optional[Any]:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tsuper().tearDown()\r\t\t\t\t\t\t\t\tgc.collect()\r\t\t\t\t\t\t\t\ttorch.cuda.empty_cache()\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> Any:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tload_numpy(\r\t\t\t\t\t\t\t\t \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\r\t\t\t\t\t\t\t\t \"/shap_e/test_shap_e_np_out.npy\" )\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tShapEPipeline.from_pretrained(\"openai/shap-e\" )\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tpipe.to(UpperCAmelCase )\r\t\t\t\t\t\t\t\tpipe.set_progress_bar_config(disable=UpperCAmelCase )\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\ttorch.Generator(device=UpperCAmelCase ).manual_seed(0 )\r\r\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tpipe(\r\t\t\t\t\t\t\t\t \"a shark\"\t\t, generator=UpperCAmelCase\t\t, guidance_scale=15.0\t\t, num_inference_steps=64\t\t, frame_size=64\t\t, output_type=\"np\"\t\t, ).images[0]\r\r\t\t\t\t\t\t\t\tassert images.shape == (20, 64, 64, 3)\r\r\t\t\t\t\t\t\t\tassert_mean_pixel_difference(UpperCAmelCase\t\t, UpperCAmelCase )\r\r"},"code_codestyle":{"kind":"number","value":297,"string":"297"},"style_context":{"kind":"string","value":"\r\r\rdef SCREAMING_SNAKE_CASE_ (\t\t\t\t\t\t\t__lowerCamelCase:\t\t\t\t\t\tfloat ):\r\r\r\r\r\r\r\r\t'''simple docstring'''\r\r\r\r\treturn 10 - x * x\r\r\r\r\rdef SCREAMING_SNAKE_CASE_ (\t\t\t\t\t\t\t__lowerCamelCase:\t\t\t\t\t\tfloat ,\t__lowerCamelCase:\t\t\t\t\t\tfloat ):\r\r\r\r\r\r\r\r\t'''simple docstring'''\r\r\r\r\tif equation(__lowerCamelCase ) * equation(__lowerCamelCase ) >= 0:\r\t\traise ValueError(\"Wrong space!\" )\r\r\tlowercase_\t\t\t\t\t=\t\t\t\ta\r\twhile (b - a) >= 0.01:\r\t\t# Find middle point\r\t\tlowercase_\t\t\t\t\t=\t\t\t\t(a + b) / 2\r\t\t# Check if middle point is root\r\t\tif equation(__lowerCamelCase ) == 0.0:\r\t\t\tbreak\r\t\t# Decide the side to repeat the steps\r\t\tif equation(__lowerCamelCase ) * equation(__lowerCamelCase ) < 0:\r\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tc\r\t\telse:\r\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tc\r\treturn c\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\t\t\timport doctest\r\r\t\t\t\t\t\t\tdoctest.testmod()\r\r\t\t\t\t\t\t\tprint(bisection(-2, 5))\r\t\t\t\t\t\t\tprint(bisection(0, 6))\r\r"},"style_context_codestyle":{"kind":"number","value":297,"string":"297"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":532,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\nimport warnings\r\nfrom collections import OrderedDict\r\nfrom typing import Mapping\r\n\r\nfrom packaging import version\r\n\r\nfrom ...configuration_utils import PretrainedConfig\r\nfrom ...onnx import OnnxConfig\r\nfrom ...utils import logging\r\n\r\n\r\nsnake_case_\t\t\t\t = logging.get_logger(__name__)\r\n\r\nsnake_case_\t\t\t\t = {\r\n \"\"\"nvidia/segformer-b0-finetuned-ade-512-512\"\"\": (\r\n \"\"\"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json\"\"\"\r\n ),\r\n # See all SegFormer models at https://huggingface.co/models?filter=segformer\r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass A_\t\t\t( SCREAMING_SNAKE_CASE_\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t=\t\t\t\t\t\t\t\"\"\"segformer\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef __init__(\t\t\t\t\tself :Optional[int] , lowercase_ :Any=3 , lowercase_ :Tuple=4 , lowercase_ :Optional[Any]=[2, 2, 2, 2] , lowercase_ :Optional[int]=[8, 4, 2, 1] , lowercase_ :str=[32, 64, 1_60, 2_56] , lowercase_ :Dict=[7, 3, 3, 3] , lowercase_ :List[str]=[4, 2, 2, 2] , lowercase_ :Tuple=[1, 2, 5, 8] , lowercase_ :str=[4, 4, 4, 4] , lowercase_ :Tuple=\"gelu\" , lowercase_ :Tuple=0.0 , lowercase_ :Tuple=0.0 , lowercase_ :Optional[int]=0.1 , lowercase_ :List[str]=0.02 , lowercase_ :Tuple=0.1 , lowercase_ :str=1E-6 , lowercase_ :int=2_56 , lowercase_ :List[Any]=2_55 , **lowercase_ :Any , )\t\t\t\t->\t\t\tstr:\r\n\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(**lowercase_\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif \"reshape_last_stage\" in kwargs and kwargs[\"reshape_last_stage\"] is False:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twarnings.warn(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ' removed, as the behaviour will default to that of reshape_last_stage = True.' , lowercase_ , )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = num_channels\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = num_encoder_blocks\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = depths\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = sr_ratios\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = hidden_sizes\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = patch_sizes\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = strides\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = mlp_ratios\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = num_attention_heads\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = hidden_act\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = hidden_dropout_prob\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = attention_probs_dropout_prob\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = classifier_dropout_prob\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = initializer_range\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = drop_path_rate\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = layer_norm_eps\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = decoder_hidden_size\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = kwargs.get('reshape_last_stage' , lowercase_\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = semantic_loss_ignore_index\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass A_\t\t\t( SCREAMING_SNAKE_CASE_\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t=\t\t\t\t\t\t\tversion.parse(\"\"\"1.11\"\"\"\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@property\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :Tuple\t\t\t\t\t)\t\t\t\t->\t\t\tMapping[str, Mapping[int, str]]:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn OrderedDict(\r\n\t\t\t\t\t\t\t\t\t\t\t\t [\r\n\t\t\t\t\t\t\t\t\t\t\t\t ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),\r\n\t\t\t\t\t\t\t\t\t\t\t\t ]\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@property\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :str\t\t\t\t\t)\t\t\t\t->\t\t\tfloat:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn 1E-4\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@property\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :Dict\t\t\t\t\t)\t\t\t\t->\t\t\tint:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn 12\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":78,"string":"78"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport collections\r\nimport inspect\r\nimport unittest\r\nfrom typing import Dict, List, Tuple\r\n\r\nfrom transformers import MaskFormerSwinConfig\r\nfrom transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device\r\nfrom transformers.utils import is_torch_available\r\n\r\nfrom ...test_backbone_common import BackboneTesterMixin\r\nfrom ...test_configuration_common import ConfigTester\r\nfrom ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor\r\nfrom ...test_pipeline_mixin import PipelineTesterMixin\r\n\r\n\r\nif is_torch_available():\r\n\t\timport torch\r\n\t\tfrom torch import nn\r\n\r\n\t\tfrom transformers import MaskFormerSwinBackbone\r\n\t\tfrom transformers.models.maskformer import MaskFormerSwinModel\r\n\r\n\r\n\r\n\r\n\r\nclass __lowerCamelCase :\r\n\r\n\r\n\r\n\r\n\r\n\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tdef __init__(\t\t\t\t\t\t\tself , UpperCAmelCase , UpperCAmelCase=13 , UpperCAmelCase=32 , UpperCAmelCase=2 , UpperCAmelCase=3 , UpperCAmelCase=16 , UpperCAmelCase=[1, 2, 1] , UpperCAmelCase=[2, 2, 4] , UpperCAmelCase=2 , UpperCAmelCase=2.0 , UpperCAmelCase=True , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase=\"gelu\" , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=0.02 , UpperCAmelCase=1e-5 , UpperCAmelCase=True , UpperCAmelCase=None , UpperCAmelCase=True , UpperCAmelCase=10 , UpperCAmelCase=8 , UpperCAmelCase=[\"stage1\", \"stage2\", \"stage3\"] , UpperCAmelCase=[1, 2, 3] , ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tparent\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tbatch_size\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\timage_size\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tpatch_size\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tnum_channels\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tembed_dim\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tdepths\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tnum_heads\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\twindow_size\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmlp_ratio\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tqkv_bias\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\thidden_dropout_prob\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tattention_probs_dropout_prob\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tdrop_path_rate\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\thidden_act\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tuse_absolute_embeddings\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tpatch_norm\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tlayer_norm_eps\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tinitializer_range\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tis_training\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tscope\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tuse_labels\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\ttype_sequence_label_size\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tencoder_stride\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tout_features\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tout_indices\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tfloats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tNone\r\n\t\t\t\t\t\t\tif self.use_labels:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tids_tensor([self.batch_size] , self.type_sequence_label_size )\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.get_config()\r\n\r\n\t\t\t\t\t\t\treturn config, pixel_values, labels\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\treturn MaskFormerSwinConfig(\r\n\t\t\t\t\t\t\t image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tMaskFormerSwinModel(config=UpperCAmelCase )\r\n\t\t\t\t\t\t\tmodel.to(UpperCAmelCase )\r\n\t\t\t\t\t\t\tmodel.eval()\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel(UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tint(config.embed_dim * 2 ** (len(config.depths ) - 1) )\r\n\r\n\t\t\t\t\t\t\tself.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tMaskFormerSwinBackbone(config=UpperCAmelCase )\r\n\t\t\t\t\t\t\tmodel.to(UpperCAmelCase )\r\n\t\t\t\t\t\t\tmodel.eval()\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel(UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t# verify feature maps\r\n\t\t\t\t\t\t\tself.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )\r\n\t\t\t\t\t\t\tself.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )\r\n\r\n\t\t\t\t\t\t\t# verify channels\r\n\t\t\t\t\t\t\tself.parent.assertEqual(len(model.channels ) , len(config.out_features ) )\r\n\t\t\t\t\t\t\tself.parent.assertListEqual(model.channels , [16, 32, 64] )\r\n\r\n\t\t\t\t\t\t\t# verify ValueError\r\n\t\t\t\t\t\t\twith self.parent.assertRaises(UpperCAmelCase ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t['stem']\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tMaskFormerSwinBackbone(config=UpperCAmelCase )\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tconfig_and_inputs\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t{'pixel_values': pixel_values}\r\n\t\t\t\t\t\t\treturn config, inputs_dict\r\n\r\n\r\n\r\n\r\n\r\n@require_torch\r\nclass __lowerCamelCase ( snake_case__ , snake_case__ , unittest.TestCase):\r\n\r\n\r\n\r\n\r\n\r\n\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= (\r\n\t (\r\n\t MaskFormerSwinModel,\r\n\t MaskFormerSwinBackbone,\r\n\t )\r\n\t if is_torch_available()\r\n\t else ()\r\n\t)\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= {\"feature-extraction\": MaskFormerSwinModel} if is_torch_available() else {}\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= False\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= False\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= False\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= False\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= False\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tMaskFormerSwinModelTester(self )\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tConfigTester(self , config_class=UpperCAmelCase , embed_dim=37 )\r\n\r\n\t@require_torch_multi_gpu\r\n\t@unittest.skip(\r\n\t reason=(\r\n\t '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\\'t work well with'\r\n\t ' `nn.DataParallel`'\r\n\t ) )\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tpass\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tself.create_and_test_config_common_properties()\r\n\t\t\t\t\t\t\tself.config_tester.create_and_test_config_to_json_string()\r\n\t\t\t\t\t\t\tself.config_tester.create_and_test_config_to_json_file()\r\n\t\t\t\t\t\t\tself.config_tester.create_and_test_config_from_and_save_pretrained()\r\n\t\t\t\t\t\t\tself.config_tester.create_and_test_config_with_num_labels()\r\n\t\t\t\t\t\t\tself.config_tester.check_config_can_be_init_without_params()\r\n\t\t\t\t\t\t\tself.config_tester.check_config_arguments_init()\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\treturn\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\tself.model_tester.create_and_check_model(*UpperCAmelCase )\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\tself.model_tester.create_and_check_backbone(*UpperCAmelCase )\r\n\r\n\t@unittest.skip('Swin does not use inputs_embeds' )\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tpass\r\n\r\n\t@unittest.skip('Swin does not support feedforward chunking' )\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tpass\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\n\r\n\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel_class(UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(model.get_input_embeddings() , (nn.Module) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel.get_output_embeddings()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(x is None or isinstance(UpperCAmelCase , nn.Linear ) )\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\n\r\n\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel_class(UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tinspect.signature(model.forward )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# signature.parameters is an OrderedDict => so arg_names order is deterministic\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t[*signature.parameters.keys()]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t['pixel_values']\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(arg_names[:1] , UpperCAmelCase )\r\n\r\n\t@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\\'t support output_attentions' )\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tpass\r\n\r\n\t@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tpass\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel_class(UpperCAmelCase )\r\n\t\t\t\t\t\t\tmodel.to(UpperCAmelCase )\r\n\t\t\t\t\t\t\tmodel.eval()\r\n\r\n\t\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\toutputs.hidden_states\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tgetattr(\r\n\t\t\t\t\t\t\t self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )\r\n\t\t\t\t\t\t\tself.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t# Swin has a different seq_length\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t(\r\n\t\t\t\t\t\t\t config.patch_size\r\n\t\t\t\t\t\t\t if isinstance(config.patch_size , collections.abc.Iterable )\r\n\t\t\t\t\t\t\t else (config.patch_size, config.patch_size)\r\n\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])\r\n\r\n\t\t\t\t\t\t\tself.assertListEqual(\r\n\t\t\t\t\t\t\t list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t(\r\n\t\t\t\t\t\t\t self.model_tester.image_size\r\n\t\t\t\t\t\t\t if isinstance(self.model_tester.image_size , collections.abc.Iterable )\r\n\t\t\t\t\t\t\t else (self.model_tester.image_size, self.model_tester.image_size)\r\n\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tTrue\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# check that output_hidden_states also work using config\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tdel inputs_dict[\"output_hidden_states\"]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tTrue\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t3\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t(\r\n\t\t\t\t\t\t\t self.model_tester.image_size\r\n\t\t\t\t\t\t\t if isinstance(self.model_tester.image_size , collections.abc.Iterable )\r\n\t\t\t\t\t\t\t else (self.model_tester.image_size, self.model_tester.image_size)\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t(\r\n\t\t\t\t\t\t\t config.patch_size\r\n\t\t\t\t\t\t\t if isinstance(config.patch_size , collections.abc.Iterable )\r\n\t\t\t\t\t\t\t else (config.patch_size, config.patch_size)\r\n\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\timage_size[0] + patch_size[0] - (image_size[0] % patch_size[0])\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\timage_size[1] + patch_size[1] - (image_size[1] % patch_size[1])\r\n\r\n\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tTrue\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width) )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# check that output_hidden_states also work using config\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tdel inputs_dict[\"output_hidden_states\"]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tTrue\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width) )\r\n\r\n\t@unittest.skip(reason='MaskFormerSwin doesn\\'t have pretrained checkpoints' )\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tpass\r\n\r\n\t@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tpass\r\n\r\n\t@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tpass\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\n\r\n\t\t\t\t\t\t\tdef set_nan_tensor_to_zero(UpperCAmelCase ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\t0\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn t\r\n\r\n\t\t\t\t\t\t\tdef check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase={} ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel(**UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel(**UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase ).to_tuple()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef recursive_check(UpperCAmelCase , UpperCAmelCase ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif isinstance(UpperCAmelCase , (List, Tuple) ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase , UpperCAmelCase ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trecursive_check(UpperCAmelCase , UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif isinstance(UpperCAmelCase , UpperCAmelCase ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor tuple_iterable_value, dict_iterable_value in zip(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t tuple_object.values() , dict_object.values() ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trecursive_check(UpperCAmelCase , UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif tuple_object is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t torch.allclose(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t set_nan_tensor_to_zero(UpperCAmelCase ) , set_nan_tensor_to_zero(UpperCAmelCase ) , atol=1e-5 ) , msg=(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'Tuple and dict output are not equal. Difference:'\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\"\"\" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:\"\"\"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\"\"\" {torch.isnan(UpperCAmelCase ).any()} and `inf`: {torch.isinf(UpperCAmelCase )}. Dict has\"\"\"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\"\"\" `nan`: {torch.isnan(UpperCAmelCase ).any()} and `inf`: {torch.isinf(UpperCAmelCase )}.\"\"\"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ) , )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trecursive_check(UpperCAmelCase , UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tmodel_class(UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.to(UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.eval()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(UpperCAmelCase , UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(UpperCAmelCase , UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tcheck_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tcheck_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(UpperCAmelCase , UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(UpperCAmelCase , UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tcheck_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {'output_hidden_states': True} )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tcheck_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {'output_hidden_states': True} )\r\n\r\n\r\n\r\n\r\n\r\n@require_torch\r\nclass __lowerCamelCase ( unittest.TestCase , snake_case__):\r\n\r\n\r\n\r\n\r\n\r\n\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= (MaskFormerSwinBackbone,) if is_torch_available() else ()\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= MaskFormerSwinConfig\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tMaskFormerSwinModelTester(self )\r\n\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tself.model_tester.prepare_config_and_inputs_for_common()\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tinputs_dict['pixel_values'].shape[0]\r\n\r\n\t\t\t\t\t\t\tfor backbone_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tbackbone_class(UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tbackbone.to(UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tbackbone.eval()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tbackbone(**UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Test default outputs and verify feature maps\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(outputs.feature_maps , UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsNone(outputs.hidden_states )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsNone(outputs.attentions )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Test output_hidden_states=True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tbackbone(**UpperCAmelCase , output_hidden_states=UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsNotNone(outputs.hidden_states )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# We skip the stem layer\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor hidden_state in hidden_states:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Hidden states are in the format (batch_size, (height * width), n_channels)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t, _UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\thidden_state.shape\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )\r\n\r\n # Test output_attentions=True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.has_attentions:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tbackbone(**UpperCAmelCase , output_attentions=UpperCAmelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsNotNone(outputs.attentions )\r\n"},"style_context_codestyle":{"kind":"number","value":39,"string":"39"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":533,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n# tests directory-specific settings - this file is run automatically\r\n# by pytest before any tests are run\r\n\r\nimport sys\r\nimport warnings\r\nfrom os.path import abspath, dirname, join\r\n\r\n\r\n# allow having multiple repository checkouts and not needing to remember to rerun\r\n# 'pip install -e .[dev]' when switching between checkouts and running tests.\r\nUpperCamelCase__ : Any \t\t\t\t\t\t= abspath(join(dirname(dirname(__file__)), \"\"\"src\"\"\"))\r\nsys.path.insert(1, git_repo_path)\r\n\r\n# silence FutureWarning warnings in tests since often we can't act on them until\r\n# they become normal warnings - i.e. the tests still need to test the current functionality\r\nwarnings.simplefilter(action=\"\"\"ignore\"\"\", category=FutureWarning)\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t\tSCREAMING_SNAKE_CASE__ (\t\t\t\tsnake_case_\t\t\t\t\t)\t\t\t\t->\t\t\t\t\tList[str]:\r\n\r\n\r\n\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\tfrom diffusers.utils.testing_utils import pytest_addoption_shared\r\n\r\n\tpytest_addoption_shared(snake_case_\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t\tSCREAMING_SNAKE_CASE__ (\t\t\t\tsnake_case_\t\t\t\t\t)\t\t\t\t->\t\t\t\t\tstr:\r\n\r\n\r\n\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\tfrom diffusers.utils.testing_utils import pytest_terminal_summary_main\r\n\r\n\ta\t\t\t\t= terminalreporter.config.getoption('''--make-reports'''\t\t\t\t\t)\r\n\tif make_reports:\r\n\t\tpytest_terminal_summary_main(snake_case_,\t\t\t\t\tid=snake_case_\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":355,"string":"355"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom ...configuration_utils import PretrainedConfig\r\nfrom ...utils import logging\r\n\r\n\r\nUpperCamelCase__ : str \t\t\t\t\t\t= logging.get_logger(__name__)\r\n\r\nUpperCamelCase__ : Optional[int] \t\t\t\t\t\t= {\r\n \"\"\"studio-ousia/luke-base\"\"\": \"\"\"https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json\"\"\",\r\n \"\"\"studio-ousia/luke-large\"\"\": \"\"\"https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json\"\"\",\r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass \t\t\tlowerCamelCase_ (\t\ta_ ):\r\n\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ = 'luke'\r\n\t\t\t\t\t\t\tdef __init__( self\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Optional[Any]=5_02_67\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: str=50_00_00\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Any=7_68\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: int=2_56\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Optional[int]=12\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Tuple=12\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Any=30_72\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Any=\"gelu\"\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Any=0.1\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Tuple=0.1\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Tuple=5_12\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: int=2\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Optional[int]=0.02\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: List[Any]=1e-12\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Dict=True\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Tuple=None\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Any=1\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Dict=0\t\t\t\t\t\t\t,__lowerCamelCase\t\t\t\t\t\t: Any=2\t\t\t\t\t\t\t,**__lowerCamelCase\t\t\t\t\t\t: str\t\t\t\t\t\t\t,):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\tsuper().__init__(pad_token_id=__lowerCamelCase\t\t\t\t\t\t\t,bos_token_id=__lowerCamelCase\t\t\t\t\t\t\t,eos_token_id=__lowerCamelCase\t\t\t\t\t\t\t,**__lowerCamelCase )\r\n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= vocab_size\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= entity_vocab_size\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= hidden_size\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= entity_emb_size\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= num_hidden_layers\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= num_attention_heads\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= hidden_act\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= intermediate_size\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= hidden_dropout_prob\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= attention_probs_dropout_prob\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= max_position_embeddings\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= type_vocab_size\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= initializer_range\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= layer_norm_eps\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= use_entity_aware_attention\r\n\t\t\t\t\t\t\t\ta\t\t\t\t= classifier_dropout\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":330,"string":"330"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":534,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\ndef __lowercase ( _SCREAMING_SNAKE_CASE\t) -> Optional[Any]:\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n stooge(_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t0 ,\t\t\t\t\t\tlen(_SCREAMING_SNAKE_CASE\t) - 1\t)\r\n return arr\r\n\r\n\r\n\r\ndef __lowercase ( _SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t_SCREAMING_SNAKE_CASE\t) -> str:\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n if i >= h:\r\n return\r\n\r\n # If first element is smaller than the last then swap them\r\n if arr[i] > arr[h]:\r\n SCREAMING_SNAKE_CASE,\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t=\t\t\t\t\t\tarr[h], arr[i]\r\n\r\n # If there are more than 2 elements in the array\r\n if h - i + 1 > 2:\r\n SCREAMING_SNAKE_CASE\t\t=\t\t\t\t\t\t(int)((h - i + 1) / 3\t)\r\n\r\n # Recursively sort first 2/3 elements\r\n stooge(_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t(h - t)\t)\r\n\r\n # Recursively sort last 2/3 elements\r\n stooge(_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\ti + t ,\t\t\t\t\t\t(_SCREAMING_SNAKE_CASE)\t)\r\n\r\n # Recursively sort first 2/3 elements\r\n stooge(_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t(h - t)\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n SCREAMING_SNAKE_CASE_\t\t\t\t\t\t = input(\"\"\"Enter numbers separated by a comma:\\n\"\"\").strip()\r\n SCREAMING_SNAKE_CASE_\t\t\t\t\t\t = [int(item) for item in user_input.split(\"\"\",\"\"\")]\r\n print(stooge_sort(unsorted))\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":296,"string":"296"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\nimport os\r\nfrom distutils.util import strtobool\r\n\r\n\r\n\r\ndef __lowercase ( _SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t_SCREAMING_SNAKE_CASE\t) -> Tuple:\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n for e in env_keys:\r\n SCREAMING_SNAKE_CASE\t\t=\t\t\t\t\t\tint(os.environ.get(_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t-1\t)\t)\r\n if val >= 0:\r\n return val\r\n return default\r\n\r\n\r\n\r\ndef __lowercase ( _SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t_SCREAMING_SNAKE_CASE=False\t) -> Optional[int]:\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n SCREAMING_SNAKE_CASE\t\t=\t\t\t\t\t\tos.environ.get(_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\tstr(_SCREAMING_SNAKE_CASE\t)\t)\r\n return strtobool(_SCREAMING_SNAKE_CASE\t) == 1 # As its name indicates `strtobool` actually returns an int...\r\n\r\n\r\n\r\ndef __lowercase ( _SCREAMING_SNAKE_CASE ,\t\t\t\t\t\t_SCREAMING_SNAKE_CASE=\"no\"\t) -> Any:\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n SCREAMING_SNAKE_CASE\t\t=\t\t\t\t\t\tos.environ.get(_SCREAMING_SNAKE_CASE ,\t\t\t\t\t\tstr(_SCREAMING_SNAKE_CASE\t)\t)\r\n return value\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":296,"string":"296"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":535,"cells":{"code":{"kind":"string","value":"\n'''simple docstring'''\n\n\n\n\n\n\n\nimport unittest\n\nfrom transformers import XLMConfig, is_torch_available\nfrom transformers.testing_utils import require_torch, slow, torch_device\n\nfrom ...generation.test_utils import GenerationTesterMixin\nfrom ...test_configuration_common import ConfigTester\nfrom ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask\nfrom ...test_pipeline_mixin import PipelineTesterMixin\n\n\nif is_torch_available():\n import torch\n\n from transformers import (\n XLMForMultipleChoice,\n XLMForQuestionAnswering,\n XLMForQuestionAnsweringSimple,\n XLMForSequenceClassification,\n XLMForTokenClassification,\n XLMModel,\n XLMWithLMHeadModel,\n )\n from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST\n\n\n\n\n\n\n\nclass UpperCAmelCase__ :\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n def __init__(\t\tself\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]=13\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]=7\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr=True\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]=True\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]=True\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]=True\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]=True\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]=False\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]=False\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]=False\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr=2\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]=99\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple=0\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]=32\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict=5\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]=4\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]=0.1\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple=0.1\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]=512\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]=2\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny=0.02\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]=2\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict=4\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint=\"last\"\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]=True\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr=None\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr=0\t\t\t\t\t\t\t,):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Dict = parent\n _a : Tuple = batch_size\n _a : List[Any] = seq_length\n _a : Any = is_training\n _a : List[Any] = use_input_lengths\n _a : Optional[Any] = use_token_type_ids\n _a : Tuple = use_labels\n _a : Dict = gelu_activation\n _a : int = sinusoidal_embeddings\n _a : int = causal\n _a : Any = asm\n _a : Dict = n_langs\n _a : str = vocab_size\n _a : Optional[int] = n_special\n _a : List[Any] = hidden_size\n _a : Any = num_hidden_layers\n _a : List[Any] = num_attention_heads\n _a : Optional[Any] = hidden_dropout_prob\n _a : List[Any] = attention_probs_dropout_prob\n _a : Dict = max_position_embeddings\n _a : List[str] = type_sequence_label_size\n _a : List[str] = initializer_range\n _a : Any = num_labels\n _a : Optional[int] = num_choices\n _a : List[str] = summary_type\n _a : Optional[Any] = use_proj\n _a : Dict = scope\n _a : List[Any] = bos_token_id\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tOptional[Any]\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Optional[Any] = ids_tensor([self.batch_size, self.seq_length]\t\t\t\t\t\t\t,self.vocab_size\t)\n _a : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length]\t)\n\n _a : int = None\n if self.use_input_lengths:\n _a : Union[str, Any] = (\n ids_tensor([self.batch_size]\t\t\t\t\t\t\t,vocab_size=2\t) + self.seq_length - 2\n ) # small variation of seq_length\n\n _a : Optional[Any] = None\n if self.use_token_type_ids:\n _a : Optional[int] = ids_tensor([self.batch_size, self.seq_length]\t\t\t\t\t\t\t,self.n_langs\t)\n\n _a : Any = None\n _a : List[Any] = None\n _a : Any = None\n if self.use_labels:\n _a : Dict = ids_tensor([self.batch_size]\t\t\t\t\t\t\t,self.type_sequence_label_size\t)\n _a : str = ids_tensor([self.batch_size, self.seq_length]\t\t\t\t\t\t\t,self.num_labels\t)\n _a : Optional[Any] = ids_tensor([self.batch_size]\t\t\t\t\t\t\t,2\t).float()\n _a : List[Any] = ids_tensor([self.batch_size]\t\t\t\t\t\t\t,self.num_choices\t)\n\n _a : str = self.get_config()\n\n return (\n config,\n input_ids,\n token_type_ids,\n input_lengths,\n sequence_labels,\n token_labels,\n is_impossible_labels,\n choice_labels,\n input_mask,\n )\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tList[Any]\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n return XLMConfig(\n vocab_size=self.vocab_size\t\t\t\t\t\t\t,n_special=self.n_special\t\t\t\t\t\t\t,emb_dim=self.hidden_size\t\t\t\t\t\t\t,n_layers=self.num_hidden_layers\t\t\t\t\t\t\t,n_heads=self.num_attention_heads\t\t\t\t\t\t\t,dropout=self.hidden_dropout_prob\t\t\t\t\t\t\t,attention_dropout=self.attention_probs_dropout_prob\t\t\t\t\t\t\t,gelu_activation=self.gelu_activation\t\t\t\t\t\t\t,sinusoidal_embeddings=self.sinusoidal_embeddings\t\t\t\t\t\t\t,asm=self.asm\t\t\t\t\t\t\t,causal=self.causal\t\t\t\t\t\t\t,n_langs=self.n_langs\t\t\t\t\t\t\t,max_position_embeddings=self.max_position_embeddings\t\t\t\t\t\t\t,initializer_range=self.initializer_range\t\t\t\t\t\t\t,summary_type=self.summary_type\t\t\t\t\t\t\t,use_proj=self.use_proj\t\t\t\t\t\t\t,num_labels=self.num_labels\t\t\t\t\t\t\t,bos_token_id=self.bos_token_id\t\t\t\t\t\t\t,)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t,):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : List[str] = XLMModel(config=_a\t)\n model.to(_a\t)\n model.eval()\n _a : Union[str, Any] = model(_a\t\t\t\t\t\t\t,lengths=_a\t\t\t\t\t\t\t,langs=_a\t)\n _a : List[str] = model(_a\t\t\t\t\t\t\t,langs=_a\t)\n _a : Any = model(_a\t)\n self.parent.assertEqual(result.last_hidden_state.shape\t\t\t\t\t\t\t,(self.batch_size, self.seq_length, self.hidden_size)\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr\t\t\t\t\t\t\t,):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Union[str, Any] = XLMWithLMHeadModel(_a\t)\n model.to(_a\t)\n model.eval()\n\n _a : Optional[int] = model(_a\t\t\t\t\t\t\t,token_type_ids=_a\t\t\t\t\t\t\t,labels=_a\t)\n self.parent.assertEqual(result.loss.shape\t\t\t\t\t\t\t,()\t)\n self.parent.assertEqual(result.logits.shape\t\t\t\t\t\t\t,(self.batch_size, self.seq_length, self.vocab_size)\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : str = XLMForQuestionAnsweringSimple(_a\t)\n model.to(_a\t)\n model.eval()\n\n _a : int = model(_a\t)\n\n _a : int = model(_a\t\t\t\t\t\t\t,start_positions=_a\t\t\t\t\t\t\t,end_positions=_a\t)\n _a : Optional[int] = outputs\n self.parent.assertEqual(result.start_logits.shape\t\t\t\t\t\t\t,(self.batch_size, self.seq_length)\t)\n self.parent.assertEqual(result.end_logits.shape\t\t\t\t\t\t\t,(self.batch_size, self.seq_length)\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t,):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Tuple = XLMForQuestionAnswering(_a\t)\n model.to(_a\t)\n model.eval()\n\n _a : Optional[int] = model(_a\t)\n\n _a : Tuple = model(\n _a\t\t\t\t\t\t\t,start_positions=_a\t\t\t\t\t\t\t,end_positions=_a\t\t\t\t\t\t\t,cls_index=_a\t\t\t\t\t\t\t,is_impossible=_a\t\t\t\t\t\t\t,p_mask=_a\t\t\t\t\t\t\t,)\n\n _a : Tuple = model(\n _a\t\t\t\t\t\t\t,start_positions=_a\t\t\t\t\t\t\t,end_positions=_a\t\t\t\t\t\t\t,cls_index=_a\t\t\t\t\t\t\t,is_impossible=_a\t\t\t\t\t\t\t,)\n\n ((_a), ) : str = result_with_labels.to_tuple()\n\n _a : int = model(_a\t\t\t\t\t\t\t,start_positions=_a\t\t\t\t\t\t\t,end_positions=_a\t)\n\n ((_a), ) : int = result_with_labels.to_tuple()\n\n self.parent.assertEqual(result_with_labels.loss.shape\t\t\t\t\t\t\t,()\t)\n self.parent.assertEqual(result.start_top_log_probs.shape\t\t\t\t\t\t\t,(self.batch_size, model.config.start_n_top)\t)\n self.parent.assertEqual(result.start_top_index.shape\t\t\t\t\t\t\t,(self.batch_size, model.config.start_n_top)\t)\n self.parent.assertEqual(\n result.end_top_log_probs.shape\t\t\t\t\t\t\t,(self.batch_size, model.config.start_n_top * model.config.end_n_top)\t)\n self.parent.assertEqual(\n result.end_top_index.shape\t\t\t\t\t\t\t,(self.batch_size, model.config.start_n_top * model.config.end_n_top)\t)\n self.parent.assertEqual(result.cls_logits.shape\t\t\t\t\t\t\t,(self.batch_size,)\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr\t\t\t\t\t\t\t,):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Dict = XLMForSequenceClassification(_a\t)\n model.to(_a\t)\n model.eval()\n\n _a : Any = model(_a\t)\n _a : Optional[int] = model(_a\t\t\t\t\t\t\t,labels=_a\t)\n self.parent.assertEqual(result.loss.shape\t\t\t\t\t\t\t,()\t)\n self.parent.assertEqual(result.logits.shape\t\t\t\t\t\t\t,(self.batch_size, self.type_sequence_label_size)\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tstr\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : List[str] = self.num_labels\n _a : Optional[int] = XLMForTokenClassification(_a\t)\n model.to(_a\t)\n model.eval()\n\n _a : Tuple = model(_a\t\t\t\t\t\t\t,attention_mask=_a\t\t\t\t\t\t\t,labels=_a\t)\n self.parent.assertEqual(result.logits.shape\t\t\t\t\t\t\t,(self.batch_size, self.seq_length, self.num_labels)\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Tuple = self.num_choices\n _a : Optional[Any] = XLMForMultipleChoice(config=_a\t)\n model.to(_a\t)\n model.eval()\n _a : Tuple = input_ids.unsqueeze(1\t).expand(-1\t\t\t\t\t\t\t,self.num_choices\t\t\t\t\t\t\t,-1\t).contiguous()\n _a : int = token_type_ids.unsqueeze(1\t).expand(-1\t\t\t\t\t\t\t,self.num_choices\t\t\t\t\t\t\t,-1\t).contiguous()\n _a : List[Any] = input_mask.unsqueeze(1\t).expand(-1\t\t\t\t\t\t\t,self.num_choices\t\t\t\t\t\t\t,-1\t).contiguous()\n _a : Optional[int] = model(\n _a\t\t\t\t\t\t\t,attention_mask=_a\t\t\t\t\t\t\t,token_type_ids=_a\t\t\t\t\t\t\t,labels=_a\t\t\t\t\t\t\t,)\n self.parent.assertEqual(result.logits.shape\t\t\t\t\t\t\t,(self.batch_size, self.num_choices)\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tAny\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Dict = self.prepare_config_and_inputs()\n (\n (\n _a\n ), (\n _a\n ), (\n _a\n ), (\n _a\n ), (\n _a\n ), (\n _a\n ), (\n _a\n ), (\n _a\n ), (\n _a\n ), \n ) : List[str] = config_and_inputs\n _a : Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}\n return config, inputs_dict\n\n\n\n\n\n\n\n@require_torch\nclass UpperCAmelCase__ (\t\t\tlowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n __UpperCAmelCase : Union[str, Any] \t\t\t= (\n (\n XLMModel,\n XLMWithLMHeadModel,\n XLMForQuestionAnswering,\n XLMForSequenceClassification,\n XLMForQuestionAnsweringSimple,\n XLMForTokenClassification,\n XLMForMultipleChoice,\n )\n if is_torch_available()\n else ()\n )\n __UpperCAmelCase : Any \t\t\t= (\n (XLMWithLMHeadModel,) if is_torch_available() else ()\n ) # TODO (PVP): Check other models whether language generation is also applicable\n __UpperCAmelCase : Any \t\t\t= (\n {\n '''feature-extraction''': XLMModel,\n '''fill-mask''': XLMWithLMHeadModel,\n '''question-answering''': XLMForQuestionAnsweringSimple,\n '''text-classification''': XLMForSequenceClassification,\n '''text-generation''': XLMWithLMHeadModel,\n '''token-classification''': XLMForTokenClassification,\n '''zero-shot''': XLMForSequenceClassification,\n }\n if is_torch_available()\n else {}\n )\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tAny\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n if (\n pipeline_test_casse_name == \"QAPipelineTests\"\n and tokenizer_name is not None\n and not tokenizer_name.endswith('Fast'\t)\n ):\n # `QAPipelineTests` fails for a few models when the slower tokenizer are used.\n # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)\n # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer\n return True\n\n return False\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[Any]=False\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Union[str, Any] = super()._prepare_for_class(_a\t\t\t\t\t\t\t,_a\t\t\t\t\t\t\t,return_labels=_a\t)\n\n if return_labels:\n if model_class.__name__ == \"XLMForQuestionAnswering\":\n _a : Tuple = torch.zeros(\n self.model_tester.batch_size\t\t\t\t\t\t\t,dtype=torch.long\t\t\t\t\t\t\t,device=_a\t)\n _a : Any = torch.zeros(\n self.model_tester.batch_size\t\t\t\t\t\t\t,dtype=torch.long\t\t\t\t\t\t\t,device=_a\t)\n\n return inputs_dict\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tList[Any]\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Dict = XLMModelTester(self\t)\n _a : Union[str, Any] = ConfigTester(self\t\t\t\t\t\t\t,config_class=_a\t\t\t\t\t\t\t,emb_dim=37\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tstr\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n self.config_tester.run_common_tests()\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tTuple\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : str = self.model_tester.prepare_config_and_inputs()\n self.model_tester.create_and_check_xlm_model(*_a\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tstr\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : str = self.model_tester.prepare_config_and_inputs()\n self.model_tester.create_and_check_xlm_lm_head(*_a\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : int = self.model_tester.prepare_config_and_inputs()\n self.model_tester.create_and_check_xlm_simple_qa(*_a\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tTuple\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Dict = self.model_tester.prepare_config_and_inputs()\n self.model_tester.create_and_check_xlm_qa(*_a\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tDict\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : List[str] = self.model_tester.prepare_config_and_inputs()\n self.model_tester.create_and_check_xlm_sequence_classif(*_a\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tOptional[Any]\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()\n self.model_tester.create_and_check_xlm_token_classif(*_a\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tint\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : List[Any] = self.model_tester.prepare_config_and_inputs()\n self.model_tester.create_and_check_xlm_for_multiple_choice(*_a\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tDict\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]=False\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tstr=1\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n self.assertIsInstance(_a\t\t\t\t\t\t\t,_a\t)\n self.assertListEqual(\n [isinstance(_a\t\t\t\t\t\t\t,_a\t) for iter_attentions in attentions]\t\t\t\t\t\t\t,[True] * len(_a\t)\t)\n self.assertEqual(len(_a\t)\t\t\t\t\t\t\t,(max_length - min_length) * num_beam_groups\t)\n\n for idx, iter_attentions in enumerate(_a\t):\n # adds PAD dummy token\n _a : Optional[Any] = min_length + idx + 1\n _a : Optional[Any] = min_length + idx + 1\n\n _a : List[Any] = (\n batch_size * num_beam_groups,\n config.num_attention_heads,\n tgt_len,\n src_len,\n )\n # check attn size\n self.assertListEqual(\n [layer_attention.shape for layer_attention in iter_attentions]\t\t\t\t\t\t\t,[expected_shape] * len(_a\t)\t)\n\n\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tint\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tOptional[int]=False\t\t\t\t\t\t\t,_a\t\t\t\t:\t\t\t\t\tList[Any]=1\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n self.assertIsInstance(_a\t\t\t\t\t\t\t,_a\t)\n self.assertListEqual(\n [isinstance(_a\t\t\t\t\t\t\t,_a\t) for iter_hidden_states in hidden_states]\t\t\t\t\t\t\t,[True] * len(_a\t)\t\t\t\t\t\t\t,)\n self.assertEqual(len(_a\t)\t\t\t\t\t\t\t,(max_length - min_length) * num_beam_groups\t)\n\n for idx, iter_hidden_states in enumerate(_a\t):\n # adds PAD dummy token\n _a : Dict = min_length + idx + 1\n _a : int = (batch_size * num_beam_groups, seq_len, config.hidden_size)\n # check hidden size\n self.assertListEqual(\n [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]\t\t\t\t\t\t\t,[expected_shape] * len(_a\t)\t\t\t\t\t\t\t,)\n pass\n\n\n @slow\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tOptional[Any]\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\n _a : Union[str, Any] = XLMModel.from_pretrained(_a\t)\n self.assertIsNotNone(_a\t)\n\n\n\n\n\n\n\n@require_torch\nclass UpperCAmelCase__ (\t\t\tunittest.TestCase ):\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n @slow\n def __lowercase\t\t(\t\tself\t\t\t\t:\t\t\t\t\tAny\t):\n\n\n '''simple docstring'''\n\n\n\n\n\n\n _a : Optional[Any] = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'\t)\n model.to(_a\t)\n _a : Optional[int] = torch.tensor([[14, 447]]\t\t\t\t\t\t\t,dtype=torch.long\t\t\t\t\t\t\t,device=_a\t) # the president\n _a : int = [\n 14,\n 447,\n 14,\n 447,\n 14,\n 447,\n 14,\n 447,\n 14,\n 447,\n 14,\n 447,\n 14,\n 447,\n 14,\n 447,\n 14,\n 447,\n 14,\n 447,\n ] # the president the president the president the president the president the president the president the president the president the president\n # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference\n _a : Dict = model.generate(_a\t\t\t\t\t\t\t,do_sample=_a\t)\n self.assertListEqual(output_ids[0].cpu().numpy().tolist()\t\t\t\t\t\t\t,_a\t)\n\n\n\n"},"code_codestyle":{"kind":"number","value":5,"string":"5"},"style_context":{"kind":"string","value":"\n'''simple docstring'''\n\n\n\n\n\n\n\nimport sys\n\n\n\n\n\ndef UpperCAmelCase_\t\t(__a\t\t\t\t: List[str]\t\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n _a : List[str] = len(__a\t\t\t)\n _a : Dict = [[0 for x in range(__a\t\t\t)] for x in range(__a\t\t\t)]\n _a : Union[str, Any] = [[0 for x in range(__a\t\t\t)] for x in range(__a\t\t\t)]\n\n for chain_length in range(2\t\t\t, __a\t\t\t):\n for a in range(1\t\t\t, n - chain_length + 1\t\t\t):\n _a : Tuple = a + chain_length - 1\n\n _a : Any = sys.maxsize\n for c in range(__a\t\t\t, __a\t\t\t):\n _a : Optional[Any] = (\n matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]\n )\n if cost < matrix[a][b]:\n _a : Dict = cost\n _a : Any = c\n return matrix, sol\n\n\n\n\n\ndef UpperCAmelCase_\t\t(__a\t\t\t\t: Tuple\t\t\t, __a\t\t\t\t: List[str]\t\t\t, __a\t\t\t\t: Dict\t\t\t):\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n if i == j:\n print('A' + str(__a\t\t\t)\t\t\t, end=' '\t\t\t)\n else:\n print('('\t\t\t, end=' '\t\t\t)\n print_optiomal_solution(__a\t\t\t, __a\t\t\t, optimal_solution[i][j]\t\t\t)\n print_optiomal_solution(__a\t\t\t, optimal_solution[i][j] + 1\t\t\t, __a\t\t\t)\n print(')'\t\t\t, end=' '\t\t\t)\n\n\n\n\n\ndef UpperCAmelCase_\t\t():\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n _a : Any = [3_0, 3_5, 1_5, 5, 1_0, 2_0, 2_5]\n _a : Any = len(__a\t\t\t)\n # Size of matrix created from above array will be\n # 30*35 35*15 15*5 5*10 10*20 20*25\n _a, _a : Union[str, Any] = matrix_chain_order(__a\t\t\t)\n\n print('No. of Operation required: ' + str(matrix[1][n - 1]\t\t\t)\t\t\t)\n print_optiomal_solution(__a\t\t\t, 1\t\t\t, n - 1\t\t\t)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":5,"string":"5"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":536,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom collections import OrderedDict\r\nfrom typing import Mapping\r\n\r\nfrom packaging import version\r\n\r\nfrom ...configuration_utils import PretrainedConfig\r\nfrom ...onnx import OnnxConfig\r\nfrom ...utils import logging\r\nfrom ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices\r\n\r\n\r\nA__\t\t\t\t: Optional[Any] =logging.get_logger(__name__)\r\n\r\nA__\t\t\t\t: str ={\r\n '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',\r\n}\r\n\r\n\r\n\r\n\r\nclass UpperCAmelCase (\t\t\tsnake_case_\t\t\t\t\t\t\t,\t\tsnake_case_ ):\r\n _lowercase:\t\t\tstr\t\t\t\t\t\t = '''resnet'''\r\n _lowercase:\t\t\tstr\t\t\t\t\t\t = ['''basic''', '''bottleneck''']\r\n\r\n\r\n\r\n\r\n def __init__(\t\t\tself\t\t\t: Optional[int]\t\t\t,\t\t\t__snake_case\t\t\t: Tuple=3\t\t\t,\t\t\t__snake_case\t\t\t: List[str]=64\t\t\t,\t\t\t__snake_case\t\t\t: Optional[Any]=[2_56, 5_12, 10_24, 20_48]\t\t\t,\t\t\t__snake_case\t\t\t: str=[3, 4, 6, 3]\t\t\t,\t\t\t__snake_case\t\t\t: int=\"bottleneck\"\t\t\t,\t\t\t__snake_case\t\t\t: Optional[Any]=\"relu\"\t\t\t,\t\t\t__snake_case\t\t\t: int=False\t\t\t,\t\t\t__snake_case\t\t\t: int=None\t\t\t,\t\t\t__snake_case\t\t\t: Union[str, Any]=None\t\t\t,\t\t\t**__snake_case\t\t\t: Any\t\t\t,\t\t\t)\t\t\t\t-> int:\r\n super().__init__(**__snake_case )\r\n if layer_type not in self.layer_types:\r\n raise ValueError(f\"layer_type={layer_type} is not one of {','.join(self.layer_types )}\" )\r\n _lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\tnum_channels\r\n _lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\tembedding_size\r\n _lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\thidden_sizes\r\n _lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\tdepths\r\n _lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\tlayer_type\r\n _lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\thidden_act\r\n _lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\tdownsample_in_first_stage\r\n _lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\t[\"\"\"stem\"\"\"] + [f\"stage{idx}\" for idx in range(1\t\t\t,\t\t\tlen(__snake_case ) + 1 )]\r\n _lowerCAmelCase ,\t\t\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t\t =\t\t\t\t\t\t\tget_aligned_output_features_output_indices(\r\n out_features=__snake_case\t\t\t,\t\t\tout_indices=__snake_case\t\t\t,\t\t\tstage_names=self.stage_names )\r\n\r\n\r\n\r\n\r\nclass UpperCAmelCase (\t\t\tsnake_case_ ):\r\n _lowercase:\t\t\tOptional[int]\t\t\t\t\t\t = version.parse('''1.11''' )\r\n\r\n\r\n\r\n\r\n @property\r\n def \t\t\t\tlowercase__ (\t\t\tself\t\t\t: Dict )\t\t\t\t-> Mapping[str, Mapping[int, str]]:\r\n return OrderedDict(\r\n [\r\n (\"\"\"pixel_values\"\"\", {0: \"\"\"batch\"\"\", 1: \"\"\"num_channels\"\"\", 2: \"\"\"height\"\"\", 3: \"\"\"width\"\"\"}),\r\n ] )\r\n\r\n\r\n\r\n\r\n @property\r\n def \t\t\t\tlowercase__ (\t\t\tself\t\t\t: Optional[int] )\t\t\t\t-> float:\r\n return 1E-3\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":70,"string":"70"},"style_context":{"kind":"string","value":"\r\r\r\rimport argparse\rimport os\rimport re\r\rimport packaging.version\r\r\rA__ : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t'''examples/'''\rA__ : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t{\r '''examples''': (re.compile(R'''^check_min_version\\(\"[^\"]+\"\\)\\s*$''', re.MULTILINE), '''check_min_version(\"VERSION\")\\n'''),\r '''init''': (re.compile(R'''^__version__\\s+=\\s+\"([^\"]+)\"\\s*$''', re.MULTILINE), '''__version__ = \"VERSION\"\\n'''),\r '''setup''': (re.compile(R'''^(\\s*)version\\s*=\\s*\"[^\"]+\",''', re.MULTILINE), R'''\\1version=\"VERSION\",'''),\r '''doc''': (re.compile(R'''^(\\s*)release\\s*=\\s*\"[^\"]+\"$''', re.MULTILINE), '''release = \"VERSION\"\\n'''),\r}\rA__ : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t{\r '''init''': '''src/transformers/__init__.py''',\r '''setup''': '''setup.py''',\r}\rA__ : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t'''README.md'''\r\r\rdef \t\t\tUpperCamelCase( __UpperCamelCase\t\t\t\t\t\t\t:\t\tint ,__UpperCamelCase\t\t\t\t\t\t\t:\t\tList[Any] ,__UpperCamelCase\t\t\t\t\t\t\t:\t\tList[Any] ):\r\t\twith open(__UpperCamelCase ,'''r''' ,encoding='''utf-8''' ,newline='''\\n''' ) as f:\r\t\t\t\tlowerCAmelCase_\t\t\t: Tuple\t\t\t\t\t\t\t\t\t= f.read()\r\t\tlowerCAmelCase_\t\t\t\t\t, lowerCAmelCase_\t\t\t: Dict\t\t\t\t\t\t\t\t\t= REPLACE_PATTERNS[pattern]\r\t\tlowerCAmelCase_\t\t\t: Tuple\t\t\t\t\t\t\t\t\t= replace.replace('''VERSION''' ,__UpperCamelCase )\r\t\tlowerCAmelCase_\t\t\t: Optional[int]\t\t\t\t\t\t\t\t\t= re_pattern.sub(__UpperCamelCase ,__UpperCamelCase )\r\t\twith open(__UpperCamelCase ,'''w''' ,encoding='''utf-8''' ,newline='''\\n''' ) as f:\r\t\t\t\tf.write(__UpperCamelCase )\r\r\rdef \t\t\tUpperCamelCase( __UpperCamelCase\t\t\t\t\t\t\t:\t\tUnion[str, Any] ):\r\t\tfor folder, directories, fnames in os.walk(__UpperCamelCase ):\r\t\t\t\t# Removing some of the folders with non-actively maintained examples from the walk\r\t\t\t\tif \"research_projects\" in directories:\r\t\t\t\t\t\tdirectories.remove('''research_projects''' )\r\t\t\t\tif \"legacy\" in directories:\r\t\t\t\t\t\tdirectories.remove('''legacy''' )\r\t\t\t\tfor fname in fnames:\r\t\t\t\t\t\tif fname.endswith('''.py''' ):\r\t\t\t\t\t\t\t\tupdate_version_in_file(os.path.join(__UpperCamelCase ,__UpperCamelCase ) ,__UpperCamelCase ,pattern='''examples''' )\r\r\rdef \t\t\tUpperCamelCase( __UpperCamelCase\t\t\t\t\t\t\t:\t\tint ,__UpperCamelCase\t\t\t\t\t\t\t:\t\tList[Any]=False ):\r\t\tfor pattern, fname in REPLACE_FILES.items():\r\t\t\t\tupdate_version_in_file(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )\r\t\tif not patch:\r\t\t\t\tupdate_version_in_examples(__UpperCamelCase )\r\r\rdef \t\t\tUpperCamelCase( ):\r\t\tlowerCAmelCase_\t\t\t: List[str]\t\t\t\t\t\t\t\t\t= '''🤗 Transformers currently provides the following architectures'''\r\t\tlowerCAmelCase_\t\t\t: List[Any]\t\t\t\t\t\t\t\t\t= '''1. Want to contribute a new model?'''\r\t\twith open(__UpperCamelCase ,'''r''' ,encoding='''utf-8''' ,newline='''\\n''' ) as f:\r\t\t\t\tlowerCAmelCase_\t\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t= f.readlines()\r\r\t\t# Find the start of the list.\r\t\tlowerCAmelCase_\t\t\t: int\t\t\t\t\t\t\t\t\t= 0\r\t\twhile not lines[start_index].startswith(_start_prompt ):\r\t\t\t\tstart_index += 1\r\t\tstart_index += 1\r\r\t\tlowerCAmelCase_\t\t\t: str\t\t\t\t\t\t\t\t\t= start_index\r\t\t# Update the lines in the model list.\r\t\twhile not lines[index].startswith(_end_prompt ):\r\t\t\t\tif lines[index].startswith('''1.''' ):\r\t\t\t\t\t\tlowerCAmelCase_\t\t\t: int\t\t\t\t\t\t\t\t\t= lines[index].replace(\r\t\t\t\t\t\t '''https://huggingface.co/docs/transformers/main/model_doc''' ,'''https://huggingface.co/docs/transformers/model_doc''' ,)\r\t\t\t\tindex += 1\r\r\t\twith open(__UpperCamelCase ,'''w''' ,encoding='''utf-8''' ,newline='''\\n''' ) as f:\r\t\t\t\tf.writelines(__UpperCamelCase )\r\r\rdef \t\t\tUpperCamelCase( ):\r\t\twith open(REPLACE_FILES['''init'''] ,'''r''' ) as f:\r\t\t\t\tlowerCAmelCase_\t\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t= f.read()\r\t\tlowerCAmelCase_\t\t\t: Dict\t\t\t\t\t\t\t\t\t= REPLACE_PATTERNS['''init'''][0].search(__UpperCamelCase ).groups()[0]\r\t\treturn packaging.version.parse(__UpperCamelCase )\r\r\rdef \t\t\tUpperCamelCase( __UpperCamelCase\t\t\t\t\t\t\t:\t\tDict=False ):\r\t\tlowerCAmelCase_\t\t\t: Union[str, Any]\t\t\t\t\t\t\t\t\t= get_version()\r\t\tif patch and default_version.is_devrelease:\r\t\t\t\traise ValueError('''Can\\'t create a patch version from the dev branch, checkout a released version!''' )\r\t\tif default_version.is_devrelease:\r\t\t\t\tlowerCAmelCase_\t\t\t: List[str]\t\t\t\t\t\t\t\t\t= default_version.base_version\r\t\telif patch:\r\t\t\t\tlowerCAmelCase_\t\t\t: int\t\t\t\t\t\t\t\t\t= f\"\"\"{default_version.major}.{default_version.minor}.{default_version.micro + 1}\"\"\"\r\t\telse:\r\t\t\t\tlowerCAmelCase_\t\t\t: int\t\t\t\t\t\t\t\t\t= f\"\"\"{default_version.major}.{default_version.minor + 1}.0\"\"\"\r\r\t\t# Now let's ask nicely if that's the right one.\r\t\tlowerCAmelCase_\t\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t= input(f\"\"\"Which version are you releasing? [{default_version}]\"\"\" )\r\t\tif len(__UpperCamelCase ) == 0:\r\t\t\t\tlowerCAmelCase_\t\t\t: List[str]\t\t\t\t\t\t\t\t\t= default_version\r\r\t\tprint(f\"\"\"Updating version to {version}.\"\"\" )\r\t\tglobal_version_update(__UpperCamelCase ,patch=__UpperCamelCase )\r\t\tif not patch:\r\t\t\t\tprint('''Cleaning main README, don\\'t forget to run `make fix-copies`.''' )\r\t\t\t\tclean_main_ref_in_model_list()\r\r\rdef \t\t\tUpperCamelCase( ):\r\t\tlowerCAmelCase_\t\t\t: Any\t\t\t\t\t\t\t\t\t= get_version()\r\t\tlowerCAmelCase_\t\t\t: int\t\t\t\t\t\t\t\t\t= f\"\"\"{current_version.major}.{current_version.minor + 1}.0.dev0\"\"\"\r\t\tlowerCAmelCase_\t\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t= current_version.base_version\r\r\t\t# Check with the user we got that right.\r\t\tlowerCAmelCase_\t\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t= input(f\"\"\"Which version are we developing now? [{dev_version}]\"\"\" )\r\t\tif len(__UpperCamelCase ) == 0:\r\t\t\t\tlowerCAmelCase_\t\t\t: int\t\t\t\t\t\t\t\t\t= dev_version\r\r\t\tprint(f\"\"\"Updating version to {version}.\"\"\" )\r\t\tglobal_version_update(__UpperCamelCase )\r\t\tprint('''Cleaning main README, don\\'t forget to run `make fix-copies`.''' )\r\t\tclean_main_ref_in_model_list()\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\t\tA__ : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\targparse.ArgumentParser()\r\t\t\t\t\t\tparser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')\r\t\t\t\t\t\tparser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')\r\t\t\t\t\t\tA__ : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\tparser.parse_args()\r\t\t\t\t\t\tif not args.post_release:\r\t\t\t\t\t\t\t\t\t\t\t\tpre_release_work(patch=args.patch)\r\t\t\t\t\t\telif args.patch:\r\t\t\t\t\t\t\t\t\t\t\t\tprint('''Nothing to do after a patch :-)''')\r\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\tpost_release_work()\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":103,"string":"103"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":537,"cells":{"code":{"kind":"string","value":"'''simple docstring'''\n\n\n\n\n\n\nfrom typing import List, Optional, Union\n\nfrom ...configuration_utils import PretrainedConfig\nfrom ...utils import logging\n\n\n__a\t\t\t\t\t\t = logging.get_logger(__name__)\n\n__a\t\t\t\t\t\t = {\n 'huggingface/time-series-transformer-tourism-monthly': (\n 'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'\n ),\n # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer\n}\n\n\n\n\n\nclass A__\t\t\t\t\t\t\t(\tUpperCamelCase ):\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n UpperCamelCase_\t:\t\t\t\t\t\tTuple =\t\t\t\t\t'''time_series_transformer'''\n UpperCamelCase_\t:\t\t\t\t\t\tOptional[Any] =\t\t\t\t\t{\n '''hidden_size''': '''d_model''',\n '''num_attention_heads''': '''encoder_attention_heads''',\n '''num_hidden_layers''': '''encoder_layers''',\n }\n\n\n\n\n def __init__(\t\t\tself\t\t\t\t\t:\t\tOptional[int]\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tOptional[int] = None\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tOptional[int] = None\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tstr = \"student_t\"\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tstr = \"nll\"\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 1\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tList[int] = [1, 2, 3, 4, 5, 6, 7]\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tOptional[Union[str, bool]] = \"mean\"\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 0\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 0\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 0\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 0\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tOptional[List[int]] = None\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tOptional[List[int]] = None\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 3_2\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 3_2\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 2\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 2\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 2\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 2\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tbool = True\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tstr = \"gelu\"\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 6_4\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tfloat = 0.1\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tfloat = 0.1\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tfloat = 0.1\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tfloat = 0.1\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tfloat = 0.1\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tint = 1_0_0\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tfloat = 0.02\t\t\t\t\t\t,\t\t\t\t\tlowerCAmelCase__\t\t\t\t\t:\t\tDict=True\t\t\t\t\t\t,\t\t\t\t\t**lowerCAmelCase__\t\t\t\t\t:\t\tTuple\t\t\t\t\t\t,\t\t\t\t\t)\t-> Tuple:\n\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= prediction_length\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= context_length or prediction_length\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= distribution_output\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= loss\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= input_size\n _UpperCAmelCase\t\t\t\t\t\t\t: int\t\t\t\t\t\t\t= num_time_features\n _UpperCAmelCase\t\t\t\t\t\t\t: Any\t\t\t\t\t\t\t= lags_sequence\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= scaling\n _UpperCAmelCase\t\t\t\t\t\t\t: Tuple\t\t\t\t\t\t\t= num_dynamic_real_features\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= num_static_real_features\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= num_static_categorical_features\n if cardinality and num_static_categorical_features > 0:\n if len(lowerCAmelCase__ ) != num_static_categorical_features:\n raise ValueError(\n \"The cardinality should be a list of the same length as `num_static_categorical_features`\" )\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= cardinality\n else:\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= [0]\n if embedding_dimension and num_static_categorical_features > 0:\n if len(lowerCAmelCase__ ) != num_static_categorical_features:\n raise ValueError(\n \"The embedding dimension should be a list of the same length as `num_static_categorical_features`\" )\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= embedding_dimension\n else:\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= [min(5_0\t\t\t\t\t\t,\t\t\t\t\t(cat + 1) // 2 ) for cat in self.cardinality]\n _UpperCAmelCase\t\t\t\t\t\t\t: str\t\t\t\t\t\t\t= num_parallel_samples\n\n # Transformer architecture configuration\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= input_size * len(lowerCAmelCase__ ) + self._number_of_features\n _UpperCAmelCase\t\t\t\t\t\t\t: str\t\t\t\t\t\t\t= d_model\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= encoder_attention_heads\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= decoder_attention_heads\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= encoder_ffn_dim\n _UpperCAmelCase\t\t\t\t\t\t\t: str\t\t\t\t\t\t\t= decoder_ffn_dim\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= encoder_layers\n _UpperCAmelCase\t\t\t\t\t\t\t: str\t\t\t\t\t\t\t= decoder_layers\n\n _UpperCAmelCase\t\t\t\t\t\t\t: Any\t\t\t\t\t\t\t= dropout\n _UpperCAmelCase\t\t\t\t\t\t\t: str\t\t\t\t\t\t\t= attention_dropout\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= activation_dropout\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= encoder_layerdrop\n _UpperCAmelCase\t\t\t\t\t\t\t: Any\t\t\t\t\t\t\t= decoder_layerdrop\n\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= activation_function\n _UpperCAmelCase\t\t\t\t\t\t\t: Tuple\t\t\t\t\t\t\t= init_std\n\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= use_cache\n\n super().__init__(is_encoder_decoder=lowerCAmelCase__\t\t\t\t\t\t,\t\t\t\t\t**lowerCAmelCase__ )\n\n\n\n\n @property\n def _lowerCAmelCase\t\t(\t\t\tself\t\t\t\t\t:\t\tstr )\t-> int:\n\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n return (\n sum(self.embedding_dimension )\n + self.num_dynamic_real_features\n + self.num_time_features\n + self.num_static_real_features\n + self.input_size * 2 # the log1p(abs(loc)) and log(scale) features\n )"},"code_codestyle":{"kind":"number","value":363,"string":"363"},"style_context":{"kind":"string","value":"'''simple docstring'''\n\n\n\n\n\n\nimport argparse\nfrom collections import OrderedDict\nfrom pathlib import Path\n\nimport requests\nimport torch\nfrom PIL import Image\n\nfrom transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor\nfrom transformers.utils import logging\n\n\nlogging.set_verbosity_info()\n__a\t\t\t\t\t\t = logging.get_logger(__name__)\n\n\n\n\n\n\ndef __UpperCAmelCase ( a_:\tList[str]\t\t\t\t\t\t\t):\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= OrderedDict()\n for key, value in state_dict.items():\n if key.startswith(\"module.encoder\"\t\t\t\t\t\t\t):\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= key.replace(\"module.encoder\", \"glpn.encoder\"\t\t\t\t\t\t\t)\n if key.startswith(\"module.decoder\"\t\t\t\t\t\t\t):\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= key.replace(\"module.decoder\", \"decoder.stages\"\t\t\t\t\t\t\t)\n if \"patch_embed\" in key:\n # replace for example patch_embed1 by patch_embeddings.0\n _UpperCAmelCase\t\t\t\t\t\t\t: int\t\t\t\t\t\t\t= key[key.find(\"patch_embed\"\t\t\t\t\t\t\t) + len(\"patch_embed\"\t\t\t\t\t\t\t)]\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= key.replace(f\"\"\"patch_embed{idx}\"\"\", f\"\"\"patch_embeddings.{int(a_\t\t\t\t\t\t\t)-1}\"\"\"\t\t\t\t\t\t\t)\n if \"norm\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= key.replace(\"norm\", \"layer_norm\"\t\t\t\t\t\t\t)\n if \"glpn.encoder.layer_norm\" in key:\n # replace for example layer_norm1 by layer_norm.0\n _UpperCAmelCase\t\t\t\t\t\t\t: str\t\t\t\t\t\t\t= key[key.find(\"glpn.encoder.layer_norm\"\t\t\t\t\t\t\t) + len(\"glpn.encoder.layer_norm\"\t\t\t\t\t\t\t)]\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= key.replace(f\"\"\"layer_norm{idx}\"\"\", f\"\"\"layer_norm.{int(a_\t\t\t\t\t\t\t)-1}\"\"\"\t\t\t\t\t\t\t)\n if \"layer_norm1\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= key.replace(\"layer_norm1\", \"layer_norm_1\"\t\t\t\t\t\t\t)\n if \"layer_norm2\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= key.replace(\"layer_norm2\", \"layer_norm_2\"\t\t\t\t\t\t\t)\n if \"block\" in key:\n # replace for example block1 by block.0\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= key[key.find(\"block\"\t\t\t\t\t\t\t) + len(\"block\"\t\t\t\t\t\t\t)]\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= key.replace(f\"\"\"block{idx}\"\"\", f\"\"\"block.{int(a_\t\t\t\t\t\t\t)-1}\"\"\"\t\t\t\t\t\t\t)\n if \"attn.q\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= key.replace(\"attn.q\", \"attention.self.query\"\t\t\t\t\t\t\t)\n if \"attn.proj\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= key.replace(\"attn.proj\", \"attention.output.dense\"\t\t\t\t\t\t\t)\n if \"attn\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= key.replace(\"attn\", \"attention.self\"\t\t\t\t\t\t\t)\n if \"fc1\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= key.replace(\"fc1\", \"dense1\"\t\t\t\t\t\t\t)\n if \"fc2\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= key.replace(\"fc2\", \"dense2\"\t\t\t\t\t\t\t)\n if \"linear_pred\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Any\t\t\t\t\t\t\t= key.replace(\"linear_pred\", \"classifier\"\t\t\t\t\t\t\t)\n if \"linear_fuse\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= key.replace(\"linear_fuse.conv\", \"linear_fuse\"\t\t\t\t\t\t\t)\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= key.replace(\"linear_fuse.bn\", \"batch_norm\"\t\t\t\t\t\t\t)\n if \"linear_c\" in key:\n # replace for example linear_c4 by linear_c.3\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= key[key.find(\"linear_c\"\t\t\t\t\t\t\t) + len(\"linear_c\"\t\t\t\t\t\t\t)]\n _UpperCAmelCase\t\t\t\t\t\t\t: Tuple\t\t\t\t\t\t\t= key.replace(f\"\"\"linear_c{idx}\"\"\", f\"\"\"linear_c.{int(a_\t\t\t\t\t\t\t)-1}\"\"\"\t\t\t\t\t\t\t)\n if \"bot_conv\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= key.replace(\"bot_conv\", \"0.convolution\"\t\t\t\t\t\t\t)\n if \"skip_conv1\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= key.replace(\"skip_conv1\", \"1.convolution\"\t\t\t\t\t\t\t)\n if \"skip_conv2\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= key.replace(\"skip_conv2\", \"2.convolution\"\t\t\t\t\t\t\t)\n if \"fusion1\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= key.replace(\"fusion1\", \"1.fusion\"\t\t\t\t\t\t\t)\n if \"fusion2\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= key.replace(\"fusion2\", \"2.fusion\"\t\t\t\t\t\t\t)\n if \"fusion3\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= key.replace(\"fusion3\", \"3.fusion\"\t\t\t\t\t\t\t)\n if \"fusion\" in key and \"conv\" in key:\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= key.replace(\"conv\", \"convolutional_layer\"\t\t\t\t\t\t\t)\n if key.startswith(\"module.last_layer_depth\"\t\t\t\t\t\t\t):\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= key.replace(\"module.last_layer_depth\", \"head.head\"\t\t\t\t\t\t\t)\n _UpperCAmelCase\t\t\t\t\t\t\t: int\t\t\t\t\t\t\t= value\n\n return new_state_dict\n\n\n\n\n\n\ndef __UpperCAmelCase ( a_:\tstr, a_:\tList[Any]\t\t\t\t\t\t\t):\n # for each of the encoder blocks:\n for i in range(config.num_encoder_blocks\t\t\t\t\t\t\t):\n for j in range(config.depths[i]\t\t\t\t\t\t\t):\n # read in weights + bias of keys and values (which is a single matrix in the original implementation)\n _UpperCAmelCase\t\t\t\t\t\t\t: Tuple\t\t\t\t\t\t\t= state_dict.pop(f\"\"\"glpn.encoder.block.{i}.{j}.attention.self.kv.weight\"\"\"\t\t\t\t\t\t\t)\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= state_dict.pop(f\"\"\"glpn.encoder.block.{i}.{j}.attention.self.kv.bias\"\"\"\t\t\t\t\t\t\t)\n # next, add keys and values (in that order) to the state dict\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= kv_weight[\n : config.hidden_sizes[i], :\n ]\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= kv_bias[: config.hidden_sizes[i]]\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= kv_weight[\n config.hidden_sizes[i] :, :\n ]\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= kv_bias[config.hidden_sizes[i] :]\n\n\n\n\n\n\ndef __UpperCAmelCase ( ):\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= Image.open(requests.get(a_, stream=a_\t\t\t\t\t\t\t).raw\t\t\t\t\t\t\t)\n\n return image\n\n\n\n\n\n\n@torch.no_grad()\ndef __UpperCAmelCase ( a_:\tTuple, a_:\tAny, a_:\tOptional[Any]=False, a_:\tList[Any]=None\t\t\t\t\t\t\t):\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= GLPNConfig(hidden_sizes=[64, 128, 320, 512], decoder_hidden_size=64, depths=[3, 8, 27, 3]\t\t\t\t\t\t\t)\n\n # load image processor (only resize + rescale)\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= GLPNImageProcessor()\n\n # prepare image\n _UpperCAmelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t= prepare_img()\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t= image_processor(images=a_, return_tensors=\"pt\"\t\t\t\t\t\t\t).pixel_values\n\n logger.info(\"Converting model...\"\t\t\t\t\t\t\t)\n\n # load original state dict\n _UpperCAmelCase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t= torch.load(a_, map_location=torch.device(\"cpu\"\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\n\n # rename keys\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= rename_keys(a_\t\t\t\t\t\t\t)\n\n # key and value matrices need special treatment\n read_in_k_v(a_, a_\t\t\t\t\t\t\t)\n\n # create HuggingFace model and load state dict\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= GLPNForDepthEstimation(a_\t\t\t\t\t\t\t)\n model.load_state_dict(a_\t\t\t\t\t\t\t)\n model.eval()\n\n # forward pass\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= model(a_\t\t\t\t\t\t\t)\n _UpperCAmelCase\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t= outputs.predicted_depth\n\n # verify output\n if model_name is not None:\n if \"nyu\" in model_name:\n _UpperCAmelCase\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t= torch.tensor(\n [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]\t\t\t\t\t\t\t)\n elif \"kitti\" in model_name:\n _UpperCAmelCase\t\t\t\t\t\t\t: Tuple\t\t\t\t\t\t\t= torch.tensor(\n [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]\t\t\t\t\t\t\t)\n else:\n raise ValueError(f\"\"\"Unknown model name: {model_name}\"\"\"\t\t\t\t\t\t\t)\n\n _UpperCAmelCase\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t= torch.Size([1, 480, 640]\t\t\t\t\t\t\t)\n\n assert predicted_depth.shape == expected_shape\n assert torch.allclose(predicted_depth[0, :3, :3], a_, atol=1e-4\t\t\t\t\t\t\t)\n print(\"Looks ok!\"\t\t\t\t\t\t\t)\n\n # finally, push to hub if required\n if push_to_hub:\n logger.info(\"Pushing model and image processor to the hub...\"\t\t\t\t\t\t\t)\n model.push_to_hub(\n repo_path_or_name=Path(a_, a_\t\t\t\t\t\t\t), organization=\"nielsr\", commit_message=\"Add model\", use_temp_dir=a_, )\n image_processor.push_to_hub(\n repo_path_or_name=Path(a_, a_\t\t\t\t\t\t\t), organization=\"nielsr\", commit_message=\"Add image processor\", use_temp_dir=a_, )\n\n\nif __name__ == \"__main__\":\n __a\t\t\t\t\t\t = argparse.ArgumentParser()\n\n parser.add_argument(\n '--checkpoint_path',\n default=None,\n type=str,\n help='Path to the original PyTorch checkpoint (.pth file).',\n )\n parser.add_argument(\n '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'\n )\n parser.add_argument(\n '--push_to_hub', action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true", help='Whether to upload the model to the HuggingFace hub.'\n )\n parser.add_argument(\n '--model_name',\n default='glpn-kitti',\n type=str,\n help='Name of the model in case you\\'re pushing to the hub.',\n )\n __a\t\t\t\t\t\t = parser.parse_args()\n convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)"},"style_context_codestyle":{"kind":"number","value":17,"string":"17"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":538,"cells":{"code":{"kind":"string","value":"\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rdef _A ( UpperCamelCase_ : float,\t\t\tUpperCamelCase_ : float)\t->\t\t\t\t\t\tfloat:\r '''simple docstring'''\r\r\r if mass < 0:\r raise ValueError(\"The mass of a body cannot be negative\")\r return 0.5 * mass * abs(UpperCamelCase_) * abs(UpperCamelCase_)\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod(verbose=True)\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":17,"string":"17"},"style_context":{"kind":"string","value":"\r\r\r\r\r\r'''simple docstring'''\r\rimport argparse\rimport shlex\r\rimport runhouse as rh\r\r\rif __name__ == \"__main__\":\r # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access\r # setup instructions, if using on-demand hardware\r\r # If user passes --user --host --key_path , fill them in as BYO cluster\r # If user passes --instance --provider , fill them in as on-demand cluster\r # Throw an error if user passes both BYO and on-demand cluster args\r # Otherwise, use default values\r lowercase_\t\t\t\t\t= argparse.ArgumentParser()\r parser.add_argument(\"\"\"--user\"\"\", type=str, default=\"\"\"ubuntu\"\"\")\r parser.add_argument(\"\"\"--host\"\"\", type=str, default=\"\"\"localhost\"\"\")\r parser.add_argument(\"\"\"--key_path\"\"\", type=str, default=None)\r parser.add_argument(\"\"\"--instance\"\"\", type=str, default=\"\"\"V100:1\"\"\")\r parser.add_argument(\"\"\"--provider\"\"\", type=str, default=\"\"\"cheapest\"\"\")\r parser.add_argument(\"\"\"--use_spot\"\"\", type=bool, default=False)\r parser.add_argument(\"\"\"--example\"\"\", type=str, default=\"\"\"pytorch/text-generation/run_generation.py\"\"\")\r lowercase_\t\t\t\t,\t\t\tlowercase_\t\t\t\t\t= parser.parse_known_args()\r if args.host != \"localhost\":\r if args.instance != \"V100:1\" or args.provider != \"cheapest\":\r raise ValueError(\"\"\"Cannot specify both BYO and on-demand cluster args\"\"\")\r lowercase_\t\t\t\t\t= rh.cluster(\r name=\"\"\"rh-cluster\"\"\", ips=[args.host], ssh_creds={\"\"\"ssh_user\"\"\": args.user, \"\"\"ssh_private_key\"\"\": args.key_path}\r )\r else:\r lowercase_\t\t\t\t\t= rh.cluster(\r name=\"\"\"rh-cluster\"\"\", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot\r )\r lowercase_\t\t\t\t\t= args.example.rsplit(\"\"\"/\"\"\", 1)[0]\r\r # Set up remote environment\r cluster.install_packages([\"\"\"pip:./\"\"\"]) # Installs transformers from local source\r # Note transformers is copied into the home directory on the remote machine, so we can install from there\r cluster.run([f\"\"\"pip install -r transformers/examples/{example_dir}/requirements.txt\"\"\"])\r cluster.run([\"\"\"pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117\"\"\"])\r\r # Run example. You can bypass the CLI wrapper and paste your own code here.\r cluster.run([f\"\"\"python transformers/examples/{args.example} {\" \".join(shlex.quote(arg) for arg in unknown)}\"\"\"])\r\r # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):\r # from my_script... import train\r # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']\r # launch_train_gpu = rh.function(fn=train,\r # system=gpu,\r # reqs=reqs,\r # name='train_bert_glue')\r #\r # We can pass in arguments just like we would to a function:\r # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16\r # stream_logs=True)\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":58,"string":"58"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":539,"cells":{"code":{"kind":"string","value":"\r\r\r# Copyright 2021 The HuggingFace Team. All rights reserved.\r#\r# Licensed under the Apache License, Version 2.0 (the \"License\");\r# you may not use this file except in compliance with the License.\r# You may obtain a copy of the License at\r#\r# http://www.apache.org/licenses/LICENSE-2.0\r#\r# Unless required by applicable law or agreed to in writing, software\r# distributed under the License is distributed on an \"AS IS\" BASIS,\r# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r# See the License for the specific language governing permissions and\r# limitations under the License.\r\rimport argparse\rimport os\r\rfrom accelerate.utils import ComputeEnvironment\r\rfrom .cluster import get_cluster_input\rfrom .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401\rfrom .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401\rfrom .sagemaker import get_sagemaker_input\r\r\rlowerCAmelCase \t=\t\t'''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''\r\r\r\r\r\r\r\rdef \t_lowerCamelCase( )\t\t\t\t\t\t-> str:\r\r\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t__lowercase= _ask_options(\r\t\t 'In which compute environment are you running?' ,\t\t\t['This machine', 'AWS (Amazon SageMaker)'] ,\t\t\t_convert_compute_environment ,\t\t\t)\r\t\tif compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:\r\t\t\t\t__lowercase= get_sagemaker_input()\r\t\telse:\r\t\t\t\t__lowercase= get_cluster_input()\r\t\treturn config\r\r\r\r\r\r\r\rdef \t_lowerCamelCase( lowercase__=None )\t\t\t\t\t\t-> List[str]:\r\r\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\tif subparsers is not None:\r\t\t\t\t__lowercase= subparsers.add_parser('config' ,\t\t\tdescription=lowercase__ )\r\t\telse:\r\t\t\t\t__lowercase= argparse.ArgumentParser('Accelerate config command' ,\t\t\tdescription=lowercase__ )\r\r\t\tparser.add_argument(\r\t\t '--config_file' ,\t\t\tdefault=lowercase__ ,\t\t\thelp=(\r\t\t 'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '\r\t\t 'location, which is the content of the environment `HF_HOME` suffixed with \\'accelerate\\', or if you don\\'t have '\r\t\t 'such an environment variable, your cache directory (\\'~/.cache\\' or the content of `XDG_CACHE_HOME`) suffixed '\r\t\t 'with \\'huggingface\\'.'\r\t\t ) ,\t\t\t)\r\r\t\tif subparsers is not None:\r\t\t\t\tparser.set_defaults(func=lowercase__ )\r\t\treturn parser\r\r\r\r\r\r\r\rdef \t_lowerCamelCase( lowercase__ )\t\t\t\t\t\t-> Tuple:\r\r\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t__lowercase= get_user_input()\r\t\tif args.config_file is not None:\r\t\t\t\t__lowercase= args.config_file\r\t\telse:\r\t\t\t\tif not os.path.isdir(lowercase__ ):\r\t\t\t\t\t\tos.makedirs(lowercase__ )\r\t\t\t\t__lowercase= default_yaml_config_file\r\r\t\tif config_file.endswith('.json' ):\r\t\t\t\tconfig.to_json_file(lowercase__ )\r\t\telse:\r\t\t\t\tconfig.to_yaml_file(lowercase__ )\r\t\tprint(F'accelerate configuration saved at {config_file}' )\r\r\r\r\r\r\r\rdef \t_lowerCamelCase( )\t\t\t\t\t\t-> Union[str, Any]:\r\r\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t__lowercase= config_command_parser()\r\t\t__lowercase= parser.parse_args()\r\t\tconfig_command(lowercase__ )\r\r\rif __name__ == \"__main__\":\r\tmain()\r\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":304,"string":"304"},"style_context":{"kind":"string","value":"\r\r\rdef \t_lowerCamelCase( lowercase__ = 1_0_0_0 )\t\t\t\t\t\t-> int:\r\r\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t__lowercase= 2**power\r\t\t__lowercase= str(lowercase__ )\r\t\t__lowercase= list(lowercase__ )\r\t\t__lowercase= 0\r\r\t\tfor i in list_num:\r\t\t\t\tsum_of_num += int(lowercase__ )\r\r\t\treturn sum_of_num\r\r\rif __name__ == \"__main__\":\r\tlowerCAmelCase \t=\t\tint(input('''Enter the power of 2: ''').strip())\r\tprint('''2 ^ ''', power, ''' = ''', 2**power)\r\tlowerCAmelCase \t=\t\tsolution(power)\r\tprint('''Sum of the digits is: ''', result)\r\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":304,"string":"304"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":540,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\nfrom __future__ import annotations\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _SCREAMING_SNAKE_CASE\t\t(\t_lowercase\t\t\t\t\t\t: float , _lowercase\t\t\t\t\t\t: float , _lowercase\t\t\t\t\t\t: float , ) ->tuple[str, float]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tif (stress, tangential_force, area).count(0\t\t\t\t\t) != 1:\r\n\t\traise ValueError(\"You cannot supply more or less than 2 values\"\t\t\t\t\t)\r\n\telif stress < 0:\r\n\t\traise ValueError(\"Stress cannot be negative\"\t\t\t\t\t)\r\n\telif tangential_force < 0:\r\n\t\traise ValueError(\"Tangential Force cannot be negative\"\t\t\t\t\t)\r\n\telif area < 0:\r\n\t\traise ValueError(\"Area cannot be negative\"\t\t\t\t\t)\r\n\telif stress == 0:\r\n\t\treturn (\r\n\t\t \"stress\",\r\n\t\t tangential_force / area,\r\n\t\t)\r\n\telif tangential_force == 0:\r\n\t\treturn (\r\n\t\t \"tangential_force\",\r\n\t\t stress * area,\r\n\t\t)\r\n\telse:\r\n\t\treturn (\r\n\t\t \"area\",\r\n\t\t tangential_force / stress,\r\n\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\timport doctest\r\n\r\n\t\tdoctest.testmod()\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":105,"string":"105"},"style_context":{"kind":"string","value":"\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rimport torch\r\rfrom diffusers import DDIMParallelScheduler\r\rfrom .test_schedulers import SchedulerCommonTest\r\r\rclass A_\t(lowercase__ ):\r\r\r\r\r '''simple docstring'''\r SCREAMING_SNAKE_CASE__\t\t\t:\t\t\t\t\t\tOptional[int] =\t\t(DDIMParallelScheduler,)\r SCREAMING_SNAKE_CASE__\t\t\t:\t\t\t\t\t\tOptional[Any] =\t\t((\"\"\"eta\"\"\", 0.0), (\"\"\"num_inference_steps\"\"\", 50))\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself\t\t\t\t, **lowercase_ ):\r\r \"\"\"simple docstring\"\"\"\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tint\t\t\t = {\r \"num_train_timesteps\": 1000,\r \"beta_start\": 0.00_01,\r \"beta_end\": 0.02,\r \"beta_schedule\": \"linear\",\r \"clip_sample\": True,\r }\r\r config.update(**lowercase_ )\r return config\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself\t\t\t\t, **lowercase_ ):\r\r \"\"\"simple docstring\"\"\"\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tDict\t\t\t = self.scheduler_classes[0]\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t = self.get_scheduler_config(**lowercase_ )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tint\t\t\t = scheduler_class(**lowercase_ )\r\r UpperCAmelCase_ , UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tstr\t\t\t = 10, 0.0\r\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t = self.dummy_model()\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tstr\t\t\t = self.dummy_sample_deter\r\r scheduler.set_timesteps(lowercase_ )\r\r for t in scheduler.timesteps:\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tDict\t\t\t = model(lowercase_\t\t\t\t, lowercase_ )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tDict\t\t\t = scheduler.step(lowercase_\t\t\t\t, lowercase_\t\t\t\t, lowercase_\t\t\t\t, lowercase_ ).prev_sample\r\r return sample\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for timesteps in [100, 500, 1000]:\r self.check_over_configs(num_train_timesteps=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for steps_offset in [0, 1]:\r self.check_over_configs(steps_offset=lowercase_ )\r\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tstr\t\t\t = self.scheduler_classes[0]\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = self.get_scheduler_config(steps_offset=1 )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = scheduler_class(**lowercase_ )\r scheduler.set_timesteps(5 )\r assert torch.equal(scheduler.timesteps\t\t\t\t, torch.LongTensor([801, 601, 401, 201, 1] ) )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]\t\t\t\t, [0.0_02, 0.02, 0.2, 2] ):\r self.check_over_configs(beta_start=lowercase_\t\t\t\t, beta_end=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\r self.check_over_configs(beta_schedule=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for prediction_type in [\"epsilon\", \"v_prediction\"]:\r self.check_over_configs(prediction_type=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for clip_sample in [True, False]:\r self.check_over_configs(clip_sample=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for timestep_spacing in [\"trailing\", \"leading\"]:\r self.check_over_configs(timestep_spacing=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for rescale_betas_zero_snr in [True, False]:\r self.check_over_configs(rescale_betas_zero_snr=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r self.check_over_configs(thresholding=lowercase_ )\r for threshold in [0.5, 1.0, 2.0]:\r for prediction_type in [\"epsilon\", \"v_prediction\"]:\r self.check_over_configs(\r thresholding=lowercase_\t\t\t\t, prediction_type=lowercase_\t\t\t\t, sample_max_value=lowercase_\t\t\t\t, )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for t in [1, 10, 49]:\r self.check_over_forward(time_step=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for t, num_inference_steps in zip([1, 10, 50]\t\t\t\t, [10, 50, 500] ):\r self.check_over_forward(time_step=lowercase_\t\t\t\t, num_inference_steps=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r for t, eta in zip([1, 10, 49]\t\t\t\t, [0.0, 0.5, 1.0] ):\r self.check_over_forward(time_step=lowercase_\t\t\t\t, eta=lowercase_ )\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t = self.scheduler_classes[0]\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = self.get_scheduler_config()\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = scheduler_class(**lowercase_ )\r\r assert torch.sum(torch.abs(scheduler._get_variance(0\t\t\t\t, 0 ) - 0.0 ) ) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(420\t\t\t\t, 400 ) - 0.1_47_71 ) ) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(980\t\t\t\t, 960 ) - 0.3_24_60 ) ) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(0\t\t\t\t, 0 ) - 0.0 ) ) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(487\t\t\t\t, 486 ) - 0.0_09_79 ) ) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(999\t\t\t\t, 998 ) - 0.02 ) ) < 1E-5\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tTuple\t\t\t = self.scheduler_classes[0]\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t = self.get_scheduler_config()\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = scheduler_class(**lowercase_ )\r\r UpperCAmelCase_ , UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tTuple\t\t\t = 10, 0.0\r scheduler.set_timesteps(lowercase_ )\r\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t = self.dummy_model()\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = self.dummy_sample_deter\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tAny\t\t\t = self.dummy_sample_deter + 0.1\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tint\t\t\t = self.dummy_sample_deter - 0.1\r\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t = samplea.shape[0]\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tint\t\t\t = torch.stack([samplea, samplea, samplea]\t\t\t\t, dim=0 )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tint\t\t\t = torch.arange(lowercase_ )[0:3, None].repeat(1\t\t\t\t, lowercase_ )\r\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tint\t\t\t = model(samples.flatten(0\t\t\t\t, 1 )\t\t\t\t, timesteps.flatten(0\t\t\t\t, 1 ) )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t = scheduler.batch_step_no_noise(lowercase_\t\t\t\t, timesteps.flatten(0\t\t\t\t, 1 )\t\t\t\t, samples.flatten(0\t\t\t\t, 1 )\t\t\t\t, lowercase_ )\r\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t = torch.sum(torch.abs(lowercase_ ) )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tstr\t\t\t = torch.mean(torch.abs(lowercase_ ) )\r\r assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2\r assert abs(result_mean.item() - 0.49_82 ) < 1E-3\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tTuple\t\t\t = self.full_loop()\r\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tint\t\t\t = torch.sum(torch.abs(lowercase_ ) )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = torch.mean(torch.abs(lowercase_ ) )\r\r assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2\r assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = self.full_loop(prediction_type=\"v_prediction\" )\r\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tstr\t\t\t = torch.sum(torch.abs(lowercase_ ) )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tDict\t\t\t = torch.mean(torch.abs(lowercase_ ) )\r\r assert abs(result_sum.item() - 52.53_02 ) < 1E-2\r assert abs(result_mean.item() - 0.06_84 ) < 1E-3\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r # We specify different beta, so that the first alpha is 0.99\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t = self.full_loop(set_alpha_to_one=lowercase_\t\t\t\t, beta_start=0.01 )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tDict\t\t\t = torch.sum(torch.abs(lowercase_ ) )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tTuple\t\t\t = torch.mean(torch.abs(lowercase_ ) )\r\r assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2\r assert abs(result_mean.item() - 0.19_51 ) < 1E-3\r\r\r\r\r\r\r\r def UpperCamelCase__\t\t\t\t\t(\t\tself ):\r\r \"\"\"simple docstring\"\"\"\r # We specify different beta, so that the first alpha is 0.99\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tint\t\t\t = self.full_loop(set_alpha_to_one=lowercase_\t\t\t\t, beta_start=0.01 )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tList[Any]\t\t\t = torch.sum(torch.abs(lowercase_ ) )\r UpperCAmelCase_\t\t\t\t\t:\t\t\t\t\tDict\t\t\t = torch.mean(torch.abs(lowercase_ ) )\r\r assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2\r assert abs(result_mean.item() - 0.19_41 ) < 1E-3\r\r\r"},"style_context_codestyle":{"kind":"number","value":61,"string":"61"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":541,"cells":{"code":{"kind":"string","value":"\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\r__magic_name__\t= logging.get_logger(__name__)\r\r__magic_name__\t= {\r \"google/switch-base-8\": \"https://huggingface.co/google/switch-base-8/blob/main/config.json\",\r}\rclass \t\t\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t(\t\t\t\t\t\t__a\t\t\t):\r\r\r\r\r\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\t\t__lowercase\t\t\t\t\t\t\t: str\t\t\t\t\t\t =\t\t'''switch_transformers'''\r\t\t__lowercase\t\t\t\t\t\t\t: str\t\t\t\t\t\t =\t\t['''past_key_values''']\r\t\t__lowercase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t =\t\t{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}\r\r\r\r\r\t\tdef __init__(\t\t\t\t\tself\t\t\t\t\t\t\t, lowerCAmelCase__=3_2_1_2_8\t\t\t\t\t\t\t, lowerCAmelCase__=7_6_8\t\t\t\t\t\t\t, lowerCAmelCase__=6_4\t\t\t\t\t\t\t, lowerCAmelCase__=2_0_4_8\t\t\t\t\t\t\t, lowerCAmelCase__=6_4\t\t\t\t\t\t\t, lowerCAmelCase__=1_2\t\t\t\t\t\t\t, lowerCAmelCase__=3\t\t\t\t\t\t\t, lowerCAmelCase__=1_2\t\t\t\t\t\t\t, lowerCAmelCase__=3\t\t\t\t\t\t\t, lowerCAmelCase__=1_2\t\t\t\t\t\t\t, lowerCAmelCase__=8\t\t\t\t\t\t\t, lowerCAmelCase__=False\t\t\t\t\t\t\t, lowerCAmelCase__=0.01\t\t\t\t\t\t\t, lowerCAmelCase__=\"float32\"\t\t\t\t\t\t\t, lowerCAmelCase__=False\t\t\t\t\t\t\t, lowerCAmelCase__=3_2\t\t\t\t\t\t\t, lowerCAmelCase__=1_2_8\t\t\t\t\t\t\t, lowerCAmelCase__=0.1\t\t\t\t\t\t\t, lowerCAmelCase__=1E-6\t\t\t\t\t\t\t, lowerCAmelCase__=0.0_01\t\t\t\t\t\t\t, lowerCAmelCase__=0.0_01\t\t\t\t\t\t\t, lowerCAmelCase__=1.0\t\t\t\t\t\t\t, lowerCAmelCase__=\"relu\"\t\t\t\t\t\t\t, lowerCAmelCase__=True\t\t\t\t\t\t\t, lowerCAmelCase__=False\t\t\t\t\t\t\t, lowerCAmelCase__=True\t\t\t\t\t\t\t, lowerCAmelCase__=0\t\t\t\t\t\t\t, lowerCAmelCase__=1\t\t\t\t\t\t\t, **lowerCAmelCase__\t\t\t\t\t\t\t, ):\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tvocab_size\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\td_model\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\td_kv\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\td_ff\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tnum_sparse_encoder_layers\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tnum_layers\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\t(\r\t\t\t\t num_decoder_layers if num_decoder_layers is not None else self.num_layers\r\t\t\t\t) # default = symmetry\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tnum_sparse_decoder_layers\r\r\t\t\t\t# This tells us, each how many encoder layer we'll have to set a sparse layer.\r\t\t\t\tif self.num_sparse_encoder_layers > 0:\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.num_layers // self.num_sparse_encoder_layers\r\t\t\t\telse:\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.num_layers # HACK: this will create 0 sparse layers\r\r\t\t\t\t# This tells us, each how many encoder layer we'll have to set a sparse layer.\r\t\t\t\tif self.num_sparse_decoder_layers > 0:\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.num_decoder_layers // self.num_sparse_decoder_layers\r\t\t\t\telse:\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.num_decoder_layers # HACK: this will create 0 sparse layers\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tnum_heads\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tnum_experts\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\texpert_capacity\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trouter_bias\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trouter_jitter_noise\r\t\t\t\tif router_dtype not in [\"float32\", \"float16\", \"bfloat16\"]:\r\t\t\t\t\t\traise ValueError(f\"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}\")\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trouter_dtype\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trouter_ignore_padding_tokens\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trelative_attention_num_buckets\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trelative_attention_max_distance\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tdropout_rate\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tlayer_norm_epsilon\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tinitializer_factor\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tfeed_forward_proj\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tuse_cache\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tadd_router_probs\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trouter_z_loss_coef\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trouter_aux_loss_coef\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.feed_forward_proj.split(\"\"\"-\"\"\")\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tact_info[-1]\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tact_info[0] == \"\"\"gated\"\"\"\r\r\t\t\t\tif len(lowerCAmelCase__) > 1 and act_info[0] != \"gated\" or len(lowerCAmelCase__) > 2:\r\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t f\"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.\"\r\t\t\t\t\t\t \"\"\"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. \"\"\"\r\t\t\t\t\t\t \"\"\"'gated-gelu' or 'relu'\"\"\")\r\r\t\t\t\t# for backwards compatibility\r\t\t\t\tif feed_forward_proj == \"gated-gelu\":\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\t\"\"\"gelu_new\"\"\"\r\r\t\t\t\tsuper().__init__(\r\t\t\t\t pad_token_id=lowerCAmelCase__\t\t\t\t\t\t\t, eos_token_id=lowerCAmelCase__\t\t\t\t\t\t\t, is_encoder_decoder=lowerCAmelCase__\t\t\t\t\t\t\t, **lowerCAmelCase__\t\t\t\t\t\t\t, )\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":255,"string":"255"},"style_context":{"kind":"string","value":"\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rimport unittest\rfrom typing import Dict, List, Optional, Union\r\rimport numpy as np\r\rfrom transformers.testing_utils import require_torch, require_vision\rfrom transformers.utils import is_torch_available, is_vision_available\r\rfrom ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs\r\r\rif is_torch_available():\r\t\t\t\t\t\t\timport torch\r\rif is_vision_available():\r\t\t\t\t\t\t\tfrom PIL import Image\r\r\t\t\t\t\t\t\tfrom transformers import BridgeTowerImageProcessor\rclass \t\t\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t(\t\t\t\t\t\tunittest.TestCase\t\t\t):\r\r\r\r\r\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\t\tdef __init__(\t\t\t\t\tself\t\t\t\t\t\t\t, lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__ = True\t\t\t\t\t\t\t, lowerCAmelCase__ = None\t\t\t\t\t\t\t, lowerCAmelCase__ = 3_2\t\t\t\t\t\t\t, lowerCAmelCase__ = True\t\t\t\t\t\t\t, lowerCAmelCase__ = 1 / 2_5_5\t\t\t\t\t\t\t, lowerCAmelCase__ = True\t\t\t\t\t\t\t, lowerCAmelCase__ = True\t\t\t\t\t\t\t, lowerCAmelCase__ = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]\t\t\t\t\t\t\t, lowerCAmelCase__ = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]\t\t\t\t\t\t\t, lowerCAmelCase__ = True\t\t\t\t\t\t\t, lowerCAmelCase__=7\t\t\t\t\t\t\t, lowerCAmelCase__=3_0\t\t\t\t\t\t\t, lowerCAmelCase__=4_0_0\t\t\t\t\t\t\t, lowerCAmelCase__=3\t\t\t\t\t\t\t, ):\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tparent\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tdo_resize\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tsize if size is not None else {\"\"\"shortest_edge\"\"\": 2_8_8}\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tsize_divisor\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tdo_rescale\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\trescale_factor\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tdo_normalize\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tdo_center_crop\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_mean\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_std\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tdo_pad\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tbatch_size\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tnum_channels\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tmin_resolution\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tmax_resolution\r\r\r\r\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself):\r\t\t\t\treturn {\r\t\t\t\t \"image_mean\": self.image_mean,\r\t\t\t\t \"image_std\": self.image_std,\r\t\t\t\t \"do_normalize\": self.do_normalize,\r\t\t\t\t \"do_resize\": self.do_resize,\r\t\t\t\t \"size\": self.size,\r\t\t\t\t \"size_divisor\": self.size_divisor,\r\t\t\t\t}\r\r\r\r\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself\t\t\t\t\t\t\t, lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__=False):\r\t\t\t\tif not batched:\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.size[\"\"\"shortest_edge\"\"\"]\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_inputs[0]\r\t\t\t\t\t\tif isinstance(lowerCAmelCase__\t\t\t\t\t\t\t, Image.Image):\r\t\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage.size\r\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage.shape[1], image.shape[2]\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tsize / min(lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__)\r\t\t\t\t\t\tif h < w:\r\t\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tsize, scale * w\r\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tscale * h, size\r\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tint((1_3_3_3 / 8_0_0) * size)\r\t\t\t\t\t\tif max(lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__) > max_size:\r\t\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tmax_size / max(lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__)\r\t\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tnewh * scale\r\t\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tneww * scale\r\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tint(newh + 0.5), int(neww + 0.5)\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\t(\r\t\t\t\t\t\t newh // self.size_divisor * self.size_divisor,\r\t\t\t\t\t\t neww // self.size_divisor * self.size_divisor,\r\t\t\t\t\t\t)\r\r\t\t\t\telse:\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\t[]\r\t\t\t\t\t\tfor image in image_inputs:\r\t\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.get_expected_values([image])\r\t\t\t\t\t\t\t\texpected_values.append((expected_height, expected_width))\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tmax(lowerCAmelCase__\t\t\t\t\t\t\t, key=lambda lowerCAmelCase__: item[0])[0]\r\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tmax(lowerCAmelCase__\t\t\t\t\t\t\t, key=lambda lowerCAmelCase__: item[1])[1]\r\r\t\t\t\treturn expected_height, expected_width\r\r\r\r@require_torch\r@require_vision\rclass \t\t\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t(\t\t\t\t\t\t__a\t\t\t\t\t, unittest.TestCase\t\t\t):\r\r\r\r\r\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\t\t__lowercase\t\t\t\t\t\t\t: Tuple\t\t\t\t\t\t =\t\tBridgeTowerImageProcessor if is_vision_available() else None\r\r\r\r\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself):\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tBridgeTowerImageProcessingTester(self)\r\r\r\r\r\t\t@property\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself):\r\t\t\t\treturn self.image_processor_tester.prepare_image_processor_dict()\r\r\r\r\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself):\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processing_class(**self.image_processor_dict)\r\t\t\t\tself.assertTrue(hasattr(lowerCAmelCase__\t\t\t\t\t\t\t, \"\"\"image_mean\"\"\"))\r\t\t\t\tself.assertTrue(hasattr(lowerCAmelCase__\t\t\t\t\t\t\t, \"\"\"image_std\"\"\"))\r\t\t\t\tself.assertTrue(hasattr(lowerCAmelCase__\t\t\t\t\t\t\t, \"\"\"do_normalize\"\"\"))\r\t\t\t\tself.assertTrue(hasattr(lowerCAmelCase__\t\t\t\t\t\t\t, \"\"\"do_resize\"\"\"))\r\t\t\t\tself.assertTrue(hasattr(lowerCAmelCase__\t\t\t\t\t\t\t, \"\"\"size\"\"\"))\r\t\t\t\tself.assertTrue(hasattr(lowerCAmelCase__\t\t\t\t\t\t\t, \"\"\"size_divisor\"\"\"))\r\r\r\r\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself):\r\t\t\t\tpass\r\r\r\r\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself):\r\t\t\t\t# Initialize image processor\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processing_class(**self.image_processor_dict)\r\t\t\t\t# create random PIL images\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tprepare_image_inputs(self.image_processor_tester\t\t\t\t\t\t\t, equal_resolution=lowerCAmelCase__)\r\t\t\t\tfor image in image_inputs:\r\t\t\t\t\t\tself.assertIsInstance(lowerCAmelCase__\t\t\t\t\t\t\t, Image.Image)\r\r\t\t\t\t# Test not batched input\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_processing(image_inputs[0]\t\t\t\t\t\t\t, return_tensors=\"\"\"pt\"\"\").pixel_values\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processor_tester.get_expected_values(lowerCAmelCase__)\r\t\t\t\tself.assertEqual(\r\t\t\t\t encoded_images.shape\t\t\t\t\t\t\t, (1, self.image_processor_tester.num_channels, expected_height, expected_width)\t\t\t\t\t\t\t, )\r\r\t\t\t\t# Test batched\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_processing(lowerCAmelCase__\t\t\t\t\t\t\t, return_tensors=\"\"\"pt\"\"\").pixel_values\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processor_tester.get_expected_values(lowerCAmelCase__\t\t\t\t\t\t\t, batched=lowerCAmelCase__)\r\t\t\t\tself.assertEqual(\r\t\t\t\t encoded_images.shape\t\t\t\t\t\t\t, (\r\t\t\t\t self.image_processor_tester.batch_size,\r\t\t\t\t self.image_processor_tester.num_channels,\r\t\t\t\t expected_height,\r\t\t\t\t expected_width,\r\t\t\t\t )\t\t\t\t\t\t\t, )\r\r\r\r\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself):\r\t\t\t\t# Initialize image processor\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processing_class(**self.image_processor_dict)\r\t\t\t\t# create random numpy tensors\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tprepare_image_inputs(self.image_processor_tester\t\t\t\t\t\t\t, equal_resolution=lowerCAmelCase__\t\t\t\t\t\t\t, numpify=lowerCAmelCase__)\r\t\t\t\tfor image in image_inputs:\r\t\t\t\t\t\tself.assertIsInstance(lowerCAmelCase__\t\t\t\t\t\t\t, np.ndarray)\r\r\t\t\t\t# Test not batched input\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_processing(image_inputs[0]\t\t\t\t\t\t\t, return_tensors=\"\"\"pt\"\"\").pixel_values\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processor_tester.get_expected_values(lowerCAmelCase__)\r\t\t\t\tself.assertEqual(\r\t\t\t\t encoded_images.shape\t\t\t\t\t\t\t, (1, self.image_processor_tester.num_channels, expected_height, expected_width)\t\t\t\t\t\t\t, )\r\r\t\t\t\t# Test batched\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_processing(lowerCAmelCase__\t\t\t\t\t\t\t, return_tensors=\"\"\"pt\"\"\").pixel_values\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processor_tester.get_expected_values(lowerCAmelCase__\t\t\t\t\t\t\t, batched=lowerCAmelCase__)\r\t\t\t\tself.assertEqual(\r\t\t\t\t encoded_images.shape\t\t\t\t\t\t\t, (\r\t\t\t\t self.image_processor_tester.batch_size,\r\t\t\t\t self.image_processor_tester.num_channels,\r\t\t\t\t expected_height,\r\t\t\t\t expected_width,\r\t\t\t\t )\t\t\t\t\t\t\t, )\r\r\r\r\r\t\tdef snake_case_\t\t\t\t\t(\t\t\t\t\tself):\r\t\t\t\t# Initialize image processor\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processing_class(**self.image_processor_dict)\r\t\t\t\t# create random PyTorch tensors\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tprepare_image_inputs(self.image_processor_tester\t\t\t\t\t\t\t, equal_resolution=lowerCAmelCase__\t\t\t\t\t\t\t, torchify=lowerCAmelCase__)\r\t\t\t\tfor image in image_inputs:\r\t\t\t\t\t\tself.assertIsInstance(lowerCAmelCase__\t\t\t\t\t\t\t, torch.Tensor)\r\r\t\t\t\t# Test not batched input\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_processing(image_inputs[0]\t\t\t\t\t\t\t, return_tensors=\"\"\"pt\"\"\").pixel_values\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processor_tester.get_expected_values(lowerCAmelCase__)\r\t\t\t\tself.assertEqual(\r\t\t\t\t encoded_images.shape\t\t\t\t\t\t\t, (1, self.image_processor_tester.num_channels, expected_height, expected_width)\t\t\t\t\t\t\t, )\r\r\t\t\t\t# Test batched\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\timage_processing(lowerCAmelCase__\t\t\t\t\t\t\t, return_tensors=\"\"\"pt\"\"\").pixel_values\r\r\t\t\t\t__SCREAMING_SNAKE_CASE\t,__SCREAMING_SNAKE_CASE\t\t\t\t\t\t=\t\t\tself.image_processor_tester.get_expected_values(lowerCAmelCase__\t\t\t\t\t\t\t, batched=lowerCAmelCase__)\r\t\t\t\tself.assertEqual(\r\t\t\t\t encoded_images.shape\t\t\t\t\t\t\t, (\r\t\t\t\t self.image_processor_tester.batch_size,\r\t\t\t\t self.image_processor_tester.num_channels,\r\t\t\t\t expected_height,\r\t\t\t\t expected_width,\r\t\t\t\t )\t\t\t\t\t\t\t, )\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":255,"string":"255"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":542,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\nlowercase : Any\t\t\t\t\t\t\t\t\t= \"\"\"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/\"\"\"\n\n\n\ndef A_\t(\t\tA__\t\t\t\t) ->\t\tbytes:\n # Make sure the supplied data is a bytes-like object\n if not isinstance(A__\t\t, A__\t\t\t\t):\n a__\t\t\t\t\t\t:\tList[Any] =\t\t\t\tF'a bytes-like object is required, not \\'{data.__class__.__name__}\\''\n raise TypeError(A__\t\t\t\t)\n\n a__\t\t\t\t\t\t:\tstr =\t\t\t\t''.join(bin(A__\t\t\t\t)[2:].zfill(8\t\t\t\t) for byte in data\t\t\t\t)\n\n a__\t\t\t\t\t\t:\tList[Any] =\t\t\t\tlen(A__\t\t\t\t) % 6 != 0\n\n if padding_needed:\n # The padding that will be added later\n a__\t\t\t\t\t\t:\tstr =\t\t\t\tB'=' * ((6 - len(A__\t\t\t\t) % 6) // 2)\n\n # Append binary_stream with arbitrary binary digits (0's by default) to make its\n # length a multiple of 6.\n binary_stream += \"0\" * (6 - len(A__\t\t\t\t) % 6)\n else:\n a__\t\t\t\t\t\t:\tAny =\t\t\t\tB''\n\n # Encode every 6 binary digits to their corresponding Base64 character\n return (\n \"\".join(\n B64_CHARSET[int(binary_stream[index : index + 6]\t\t, 2\t\t\t\t)]\n for index in range(0\t\t, len(A__\t\t\t\t)\t\t, 6\t\t\t\t)\t\t\t\t).encode()\n + padding\n )\n\n\n\ndef A_\t(\t\tA__\t\t\t\t) ->\t\tbytes:\n # Make sure encoded_data is either a string or a bytes-like object\n if not isinstance(A__\t\t, A__\t\t\t\t) and not isinstance(A__\t\t, A__\t\t\t\t):\n a__\t\t\t\t\t\t:\tint =\t\t\t\t(\n 'argument should be a bytes-like object or ASCII string, '\n F'not \\'{encoded_data.__class__.__name__}\\''\n )\n raise TypeError(A__\t\t\t\t)\n\n # In case encoded_data is a bytes-like object, make sure it contains only\n # ASCII characters so we convert it to a string object\n if isinstance(A__\t\t, A__\t\t\t\t):\n try:\n a__\t\t\t\t\t\t:\tOptional[int] =\t\t\t\tencoded_data.decode('utf-8'\t\t\t\t)\n except UnicodeDecodeError:\n raise ValueError('base64 encoded data should only contain ASCII characters'\t\t\t\t)\n\n a__\t\t\t\t\t\t:\tList[Any] =\t\t\t\tencoded_data.count('='\t\t\t\t)\n\n # Check if the encoded string contains non base64 characters\n if padding:\n assert all(\n char in B64_CHARSET for char in encoded_data[:-padding]\t\t\t\t), \"Invalid base64 character(s) found.\"\n else:\n assert all(\n char in B64_CHARSET for char in encoded_data\t\t\t\t), \"Invalid base64 character(s) found.\"\n\n # Check the padding\n assert len(A__\t\t\t\t) % 4 == 0 and padding < 3, \"Incorrect padding\"\n\n if padding:\n # Remove padding if there is one\n a__\t\t\t\t\t\t:\tOptional[int] =\t\t\t\tencoded_data[:-padding]\n\n a__\t\t\t\t\t\t:\tOptional[int] =\t\t\t\t''.join(\n bin(B64_CHARSET.index(A__\t\t\t\t)\t\t\t\t)[2:].zfill(6\t\t\t\t) for char in encoded_data\t\t\t\t)[: -padding * 2]\n else:\n a__\t\t\t\t\t\t:\tOptional[int] =\t\t\t\t''.join(\n bin(B64_CHARSET.index(A__\t\t\t\t)\t\t\t\t)[2:].zfill(6\t\t\t\t) for char in encoded_data\t\t\t\t)\n\n a__\t\t\t\t\t\t:\tAny =\t\t\t\t[\n int(binary_stream[index : index + 8]\t\t, 2\t\t\t\t)\n for index in range(0\t\t, len(A__\t\t\t\t)\t\t, 8\t\t\t\t)\n ]\n\n return bytes(A__\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n import doctest\n\n doctest.testmod()\n\n\n"},"code_codestyle":{"kind":"number","value":99,"string":"99"},"style_context":{"kind":"string","value":"\r\n'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\r\n\r\nfrom dataclasses import dataclass\r\nfrom typing import Optional, Tuple, Union\r\n\r\nimport flax\r\nimport jax\r\nimport jax.numpy as jnp\r\n\r\nfrom ..configuration_utils import ConfigMixin, register_to_config\r\nfrom .scheduling_utils_flax import (\r\n CommonSchedulerState,\r\n FlaxKarrasDiffusionSchedulers,\r\n FlaxSchedulerMixin,\r\n FlaxSchedulerOutput,\r\n add_noise_common,\r\n get_velocity_common,\r\n)\r\n\r\n\r\n\r\n\r\n\r\n\r\n@flax.struct.dataclass\r\nclass lowerCAmelCase__ :\r\n\t\t\t\t\tlowerCAmelCase_ = 42\r\n\r\n\t\t\t\t\t# setable values\r\n\t\t\t\t\tlowerCAmelCase_ = 42\r\n\t\t\t\t\tlowerCAmelCase_ = 42\r\n\t\t\t\t\tlowerCAmelCase_ = None\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t@classmethod\r\n\t\t\t\t\tdef \t_snake_case ( cls\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn cls(common=__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\tinit_noise_sigma=__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\ttimesteps=__SCREAMING_SNAKE_CASE\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n@dataclass\r\nclass lowerCAmelCase__ ( lowerCamelCase_ ):\r\n\t\t\t\t\tlowerCAmelCase_ = 42\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass lowerCAmelCase__ ( lowerCamelCase_ ,\t\t\t\t\t\tlowerCamelCase_ ):\r\n\t\t\t\t\tlowerCAmelCase_ = [e.name for e in FlaxKarrasDiffusionSchedulers]\r\n\r\n\t\t\t\t\tlowerCAmelCase_ = 42\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t@property\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn True\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t@register_to_config\r\n\t\t\t\t\tdef __init__( self\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = 10_00\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = 0.0_001\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = 0.02\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = \"linear\"\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = None\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = \"fixed_small\"\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = True\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = \"epsilon\"\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = jnp.floataa\t\t\t\t,\t\t\t\t\t\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Dict\t\t\t\t\t\t\t\t\t\t\t\t\t= dtype\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = None\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tif common is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Tuple\t\t\t\t\t\t\t\t\t\t\t\t\t= CommonSchedulerState.create(self\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# standard deviation of the initial noise distribution\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.array(1.0\t\t\t\t,\t\t\t\t\t\t\tdtype=self.dtype\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.arange(0\t\t\t\t,\t\t\t\t\t\t\tself.config.num_train_timesteps\t\t).round()[::-1]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn DDPMSchedulerState.create(\r\n\t\t\t\t\t\t\t\t\t\t\t common=__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\tinit_noise_sigma=__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\ttimesteps=__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = None\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn sample\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = ()\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= self.config.num_train_timesteps // num_inference_steps\r\n\t\t\t\t\t\t\t\t\t\t\t# creates integer timesteps by multiplying by ratio\r\n\t\t\t\t\t\t\t\t\t\t\t# rounding to avoid issues when num_inference_step is power of 3\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t= (jnp.arange(0\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t) * step_ratio).round()[::-1]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn state.replace(\r\n\t\t\t\t\t\t\t\t\t\t\t num_inference_steps=__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\ttimesteps=__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE=None\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE=None\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= state.common.alphas_cumprod[t]\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: str\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.where(t > 0\t\t\t\t,\t\t\t\t\t\t\tstate.common.alphas_cumprod[t - 1]\t\t\t\t,\t\t\t\t\t\t\tjnp.array(1.0\t\t\t\t,\t\t\t\t\t\t\tdtype=self.dtype\t\t)\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)\r\n\t\t\t\t\t\t\t\t\t\t\t# and sample from it to get previous sample\r\n\t\t\t\t\t\t\t\t\t\t\t# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t= (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tif variance_type is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: str\t\t\t\t\t\t\t\t\t\t\t\t\t= self.config.variance_type\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# hacks - were probably added for training stability\r\n\t\t\t\t\t\t\t\t\t\t\tif variance_type == \"fixed_small\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.clip(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\ta_min=1E-2_0\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t# for rl-diffuser https://arxiv.org/abs/2205.09991\r\n\t\t\t\t\t\t\t\t\t\t\telif variance_type == \"fixed_small_log\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[str]\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\ta_min=1E-2_0\t\t)\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\telif variance_type == \"fixed_large\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= state.common.betas[t]\r\n\t\t\t\t\t\t\t\t\t\t\telif variance_type == \"fixed_large_log\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Glide max_log\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.log(state.common.betas[t]\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\telif variance_type == \"learned\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn predicted_variance\r\n\t\t\t\t\t\t\t\t\t\t\telif variance_type == \"learned_range\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= variance\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= state.common.betas[t]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= (predicted_variance + 1) / 2\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Any\t\t\t\t\t\t\t\t\t\t\t\t\t= frac * max_log + (1 - frac) * min_log\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn variance\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = None\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE = True\t\t\t\t,\t\t\t\t\t\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t= timestep\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tif key is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t= jax.random.PRNGKey(0\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tif model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in [\"learned\", \"learned_range\"]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t\t, lowercase_\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.split(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\tsample.shape[1]\t\t\t\t,\t\t\t\t\t\t\taxis=1\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t= None\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# 1. compute alphas, betas\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Any\t\t\t\t\t\t\t\t\t\t\t\t\t= state.common.alphas_cumprod[t]\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.where(t > 0\t\t\t\t,\t\t\t\t\t\t\tstate.common.alphas_cumprod[t - 1]\t\t\t\t,\t\t\t\t\t\t\tjnp.array(1.0\t\t\t\t,\t\t\t\t\t\t\tdtype=self.dtype\t\t)\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t= 1 - alpha_prod_t\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: str\t\t\t\t\t\t\t\t\t\t\t\t\t= 1 - alpha_prod_t_prev\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# 2. compute predicted original sample from predicted noise also called\r\n\t\t\t\t\t\t\t\t\t\t\t# \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\r\n\t\t\t\t\t\t\t\t\t\t\tif self.config.prediction_type == \"epsilon\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Tuple\t\t\t\t\t\t\t\t\t\t\t\t\t= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5\r\n\t\t\t\t\t\t\t\t\t\t\telif self.config.prediction_type == \"sample\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Any\t\t\t\t\t\t\t\t\t\t\t\t\t= model_output\r\n\t\t\t\t\t\t\t\t\t\t\telif self.config.prediction_type == \"v_prediction\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\r\n\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ''' for the FlaxDDPMScheduler.'''\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# 3. Clip \"predicted x_0\"\r\n\t\t\t\t\t\t\t\t\t\t\tif self.config.clip_sample:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.clip(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t-1\t\t\t\t,\t\t\t\t\t\t\t1\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t\r\n\t\t\t\t\t\t\t\t\t\t\t# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# 5. Compute predicted previous sample µ_t\r\n\t\t\t\t\t\t\t\t\t\t\t# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# 6. Add noise\r\n\t\t\t\t\t\t\t\t\t\t\tdef random_variance():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: str\t\t\t\t\t\t\t\t\t\t\t\t\t= jax.random.split(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\tnum=1\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= jax.random.normal(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\tshape=model_output.shape\t\t\t\t,\t\t\t\t\t\t\tdtype=self.dtype\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn (self._get_variance(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\tpredicted_variance=__SCREAMING_SNAKE_CASE\t\t) ** 0.5) * noise\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= jnp.where(t > 0\t\t\t\t,\t\t\t\t\t\t\trandom_variance()\t\t\t\t,\t\t\t\t\t\t\tjnp.zeros(model_output.shape\t\t\t\t,\t\t\t\t\t\t\tdtype=self.dtype\t\t)\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Any\t\t\t\t\t\t\t\t\t\t\t\t\t= pred_prev_sample + variance\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tif not return_dict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn (pred_prev_sample, state)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\tstate=__SCREAMING_SNAKE_CASE\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn add_noise_common(state.common\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn get_velocity_common(state.common\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef __len__( self\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\treturn self.config.num_train_timesteps\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":93,"string":"93"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":543,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n'''simple docstring'''\r\n\r\n\r\n\r\ndef _a (\t\t\t\t\t\t_lowercase : str ):\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )\r\n\r\n\r\n\r\n\r\ndef _a (\t\t\t\t\t\t_lowercase : str ):\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n __UpperCAmelCase :\t\t\tint \t\t\t\t\t\t\t= credit_card_number\r\n __UpperCAmelCase :\t\t\tUnion[str, Any] \t\t\t\t\t\t\t= 0\r\n __UpperCAmelCase :\t\t\tOptional[Any] \t\t\t\t\t\t\t= len(_lowercase ) - 2\r\n for i in range(_lowercase , -1 , -2 ):\r\n # double the value of every second digit\r\n __UpperCAmelCase :\t\t\tTuple \t\t\t\t\t\t\t= int(cc_number[i] )\r\n digit *= 2\r\n # If doubling of a number results in a two digit number\r\n # i.e greater than 9(e.g., 6 × 2 = 12),\r\n # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),\r\n # to get a single digit number.\r\n if digit > 9:\r\n digit %= 10\r\n digit += 1\r\n __UpperCAmelCase :\t\t\tList[Any] \t\t\t\t\t\t\t= cc_number[:i] + str(_lowercase ) + cc_number[i + 1 :]\r\n total += digit\r\n\r\n # Sum up the remaining digits\r\n for i in range(len(_lowercase ) - 1 , -1 , -2 ):\r\n total += int(cc_number[i] )\r\n\r\n return total % 10 == 0\r\n\r\n\r\n\r\n\r\ndef _a (\t\t\t\t\t\t_lowercase : str ):\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n __UpperCAmelCase :\t\t\tTuple \t\t\t\t\t\t\t= F'{credit_card_number} is an invalid credit card number because'\r\n if not credit_card_number.isdigit():\r\n print(F'{error_message} it has nonnumerical characters.' )\r\n return False\r\n\r\n if not 13 <= len(_lowercase ) <= 16:\r\n print(F'{error_message} of its length.' )\r\n return False\r\n\r\n if not validate_initial_digits(_lowercase ):\r\n print(F'{error_message} of its first two digits.' )\r\n return False\r\n\r\n if not luhn_validation(_lowercase ):\r\n print(F'{error_message} it fails the Luhn check.' )\r\n return False\r\n\r\n print(F'{credit_card_number} is a valid credit card number.' )\r\n return True\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import doctest\r\n\r\n doctest.testmod()\r\n validate_credit_card_number(\"4111111111111111\")\r\n validate_credit_card_number(\"32323\")"},"code_codestyle":{"kind":"number","value":240,"string":"240"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n'''simple docstring'''\r\n\r\n\r\n\r\nimport argparse\r\n\r\nimport torch\r\n\r\nfrom transformers import BlenderbotConfig, BlenderbotForConditionalGeneration\r\nfrom transformers.utils import logging\r\n\r\n\r\nlogging.set_verbosity_info()\r\n__UpperCAmelCase :Any\t\t\t = logging.get_logger(__name__)\r\n\r\n__UpperCAmelCase :Dict\t\t\t = [\r\n [\"attention\", \"attn\"],\r\n [\"encoder_attention\", \"encoder_attn\"],\r\n [\"q_lin\", \"q_proj\"],\r\n [\"k_lin\", \"k_proj\"],\r\n [\"v_lin\", \"v_proj\"],\r\n [\"out_lin\", \"out_proj\"],\r\n [\"norm_embeddings\", \"layernorm_embedding\"],\r\n [\"position_embeddings\", \"embed_positions\"],\r\n [\"embeddings\", \"embed_tokens\"],\r\n [\"ffn.lin\", \"fc\"],\r\n]\r\n\r\n\r\n\r\n\r\ndef _a (\t\t\t\t\t\t_lowercase : Tuple ):\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n if k == \"embeddings.weight\":\r\n return \"shared.weight\"\r\n\r\n for parlai_name, hf_name in PATTERNS:\r\n __UpperCAmelCase :\t\t\tAny \t\t\t\t\t\t\t= k.replace(_lowercase , _lowercase )\r\n\r\n if k.startswith('''encoder''' ):\r\n __UpperCAmelCase :\t\t\tstr \t\t\t\t\t\t\t= k.replace('''.attn''' , '''.self_attn''' )\r\n __UpperCAmelCase :\t\t\tAny \t\t\t\t\t\t\t= k.replace('''norm1''' , '''self_attn_layer_norm''' )\r\n __UpperCAmelCase :\t\t\tList[str] \t\t\t\t\t\t\t= k.replace('''norm2''' , '''final_layer_norm''' )\r\n elif k.startswith('''decoder''' ):\r\n __UpperCAmelCase :\t\t\tint \t\t\t\t\t\t\t= k.replace('''norm1''' , '''self_attn_layer_norm''' )\r\n __UpperCAmelCase :\t\t\tUnion[str, Any] \t\t\t\t\t\t\t= k.replace('''norm2''' , '''encoder_attn_layer_norm''' )\r\n __UpperCAmelCase :\t\t\tList[Any] \t\t\t\t\t\t\t= k.replace('''norm3''' , '''final_layer_norm''' )\r\n return k\r\n\r\n\r\n\r\n\r\ndef _a (\t\t\t\t\t\t_lowercase : Union[str, Any] ):\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n __UpperCAmelCase :\t\t\tint \t\t\t\t\t\t\t= [\r\n '''model.encoder.layernorm_embedding.weight''',\r\n '''model.encoder.layernorm_embedding.bias''',\r\n '''model.decoder.layernorm_embedding.weight''',\r\n '''model.decoder.layernorm_embedding.bias''',\r\n ]\r\n for k in keys:\r\n __UpperCAmelCase :\t\t\tAny \t\t\t\t\t\t\t= sd.pop(_lowercase )\r\n __UpperCAmelCase :\t\t\tOptional[int] \t\t\t\t\t\t\t= k.replace('''layernorm_embedding''' , '''layer_norm''' )\r\n assert new_k not in sd\r\n __UpperCAmelCase :\t\t\tList[str] \t\t\t\t\t\t\t= v\r\n\r\n\r\n__UpperCAmelCase :str\t\t\t = [\"START\"]\r\n\r\n\r\n\r\n\r\n@torch.no_grad()\r\ndef _a (\t\t\t\t\t\t_lowercase : Optional[int] , _lowercase : Optional[int] , _lowercase : str ):\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n __UpperCAmelCase :\t\t\tAny \t\t\t\t\t\t\t= torch.load(_lowercase , map_location='''cpu''' )\r\n __UpperCAmelCase :\t\t\tList[str] \t\t\t\t\t\t\t= model['''model''']\r\n __UpperCAmelCase :\t\t\tOptional[Any] \t\t\t\t\t\t\t= BlenderbotConfig.from_json_file(_lowercase )\r\n __UpperCAmelCase :\t\t\tOptional[Any] \t\t\t\t\t\t\t= BlenderbotForConditionalGeneration(_lowercase )\r\n __UpperCAmelCase :\t\t\tOptional[Any] \t\t\t\t\t\t\t= m.model.state_dict().keys()\r\n __UpperCAmelCase :\t\t\tint \t\t\t\t\t\t\t= []\r\n __UpperCAmelCase :\t\t\tList[str] \t\t\t\t\t\t\t= {}\r\n for k, v in sd.items():\r\n if k in IGNORE_KEYS:\r\n continue\r\n\r\n __UpperCAmelCase :\t\t\tint \t\t\t\t\t\t\t= rename_state_dict_key(_lowercase )\r\n if new_k not in valid_keys:\r\n failures.append([k, new_k] )\r\n else:\r\n __UpperCAmelCase :\t\t\tUnion[str, Any] \t\t\t\t\t\t\t= v\r\n if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm\r\n rename_layernorm_keys(_lowercase )\r\n m.model.load_state_dict(_lowercase , strict=_lowercase )\r\n m.half()\r\n m.save_pretrained(_lowercase )\r\n\r\n\r\nif __name__ == \"__main__\":\r\n __UpperCAmelCase :Optional[int]\t\t\t = argparse.ArgumentParser()\r\n # Required parameters\r\n parser.add_argument(\"--src_path\", type=str, help=\"like blenderbot-model.bin\")\r\n parser.add_argument(\"--save_dir\", default=\"hf_blenderbot\", type=str, help=\"Where to save converted model.\")\r\n parser.add_argument(\r\n \"--hf_config_json\", default=\"blenderbot-3b-config.json\", type=str, help=\"Path to config to use\"\r\n )\r\n __UpperCAmelCase :Tuple\t\t\t = parser.parse_args()\r\n convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)"},"style_context_codestyle":{"kind":"number","value":240,"string":"240"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":544,"cells":{"code":{"kind":"string","value":"\n\n\n\nfrom collections.abc import Sequence\n\n\ndef \t\t_UpperCAmelCase\t\t\t\t( snake_case\t,\t\t\t\t\t\tsnake_case = False\t\t\t):\n\n\n\n\n\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\t\t\t\t\tif not arr:\n\t\t\t\t\t\t\t\t\t\treturn 0\n\n\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t= 0 if allow_empty_subarrays else float(\"\"\"-inf\"\"\"\t\t\t)\n\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t= 0.0\n\t\t\t\t\tfor num in arr:\n\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t= max(0 if allow_empty_subarrays else num\t,\t\t\t\t\t\tcurr_sum + num\t\t\t)\n\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t= max(snake_case\t,\t\t\t\t\t\tsnake_case\t\t\t)\n\n\t\t\t\t\treturn max_sum\n\n\nif __name__ == \"__main__\":\n\tfrom doctest import testmod\n\n\ttestmod()\n\n\tA__\t = [-2, 1, -3, 4, -1, 2, 1, -5, 4]\n\tprint(f\"{max_subarray_sum(nums) = }\")\n\n\n\n"},"code_codestyle":{"kind":"number","value":82,"string":"82"},"style_context":{"kind":"string","value":"\r\n\r\nfrom __future__ import annotations\r\n\r\nimport numpy as np\r\nfrom numpy import floataa\r\nfrom numpy.typing import NDArray\r\n\r\ndef \t__snake_case\t\t( __UpperCamelCase : NDArray[floataa]\t\t\t\t\t\t\t,__UpperCamelCase : NDArray[floataa]\t\t\t\t\t\t\t,__UpperCamelCase : list[int]\t\t\t\t\t\t\t,__UpperCamelCase : int\t\t\t\t\t\t\t,):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n A_ ,\t\t\tA_\t\t =\t\t\t\t\t\t\tcoefficient_matrix.shape\r\n A_ ,\t\t\tA_\t\t =\t\t\t\t\t\t\tconstant_matrix.shape\r\n\r\n if rowsa != colsa:\r\n A_\t\t =\t\t\t\t\t\t\tf'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''\r\n raise ValueError(__UpperCamelCase )\r\n\r\n if colsa != 1:\r\n A_\t\t =\t\t\t\t\t\t\tf'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''\r\n raise ValueError(__UpperCamelCase )\r\n\r\n if rowsa != rowsa:\r\n A_\t\t =\t\t\t\t\t\t\t(\r\n \"Coefficient and constant matrices dimensions must be nxn and nx1 but \"\r\n f'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''\r\n )\r\n raise ValueError(__UpperCamelCase )\r\n\r\n if len(__UpperCamelCase ) != rowsa:\r\n A_\t\t =\t\t\t\t\t\t\t(\r\n \"Number of initial values must be equal to number of rows in coefficient \"\r\n f'''matrix but received {len(__UpperCamelCase )} and {rowsa}'''\r\n )\r\n raise ValueError(__UpperCamelCase )\r\n\r\n if iterations <= 0:\r\n raise ValueError(\"Iterations must be at least 1\" )\r\n\r\n A_\t\t =\t\t\t\t\t\t\tnp.concatenate(\r\n (coefficient_matrix, constant_matrix)\t\t\t\t\t\t\t,axis=1 )\r\n\r\n A_ ,\t\t\tA_\t\t =\t\t\t\t\t\t\ttable.shape\r\n\r\n strictly_diagonally_dominant(__UpperCamelCase )\r\n\r\n # Iterates the whole matrix for given number of times\r\n for _ in range(__UpperCamelCase ):\r\n A_\t\t =\t\t\t\t\t\t\t[]\r\n for row in range(__UpperCamelCase ):\r\n A_\t\t =\t\t\t\t\t\t\t0\r\n for col in range(__UpperCamelCase ):\r\n if col == row:\r\n A_\t\t =\t\t\t\t\t\t\ttable[row][col]\r\n elif col == cols - 1:\r\n A_\t\t =\t\t\t\t\t\t\ttable[row][col]\r\n else:\r\n temp += (-1) * table[row][col] * init_val[col]\r\n A_\t\t =\t\t\t\t\t\t\t(temp + val) / denom\r\n new_val.append(__UpperCamelCase )\r\n A_\t\t =\t\t\t\t\t\t\tnew_val\r\n\r\n return [float(__UpperCamelCase ) for i in new_val]\r\n\r\ndef \t__snake_case\t\t( __UpperCamelCase : NDArray[floataa] ):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n A_ ,\t\t\tA_\t\t =\t\t\t\t\t\t\ttable.shape\r\n\r\n A_\t\t =\t\t\t\t\t\t\tTrue\r\n\r\n for i in range(0\t\t\t\t\t\t\t,__UpperCamelCase ):\r\n A_\t\t =\t\t\t\t\t\t\t0\r\n for j in range(0\t\t\t\t\t\t\t,cols - 1 ):\r\n if i == j:\r\n continue\r\n else:\r\n total += table[i][j]\r\n\r\n if table[i][i] <= total:\r\n raise ValueError(\"Coefficient matrix is not strictly diagonally dominant\" )\r\n\r\n return is_diagonally_dominant\r\n\r\n\r\n# Test Cases\r\nif __name__ == \"__main__\":\r\n import doctest\r\n\r\n doctest.testmod()"},"style_context_codestyle":{"kind":"number","value":312,"string":"312"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":545,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport inspect\r\nimport os\r\nimport unittest\r\n\r\nimport torch\r\n\r\nimport accelerate\r\nfrom accelerate import Accelerator\r\nfrom accelerate.test_utils import execute_subprocess_async, require_multi_gpu\r\nfrom accelerate.utils import patch_environment\r\n\r\n\r\n\r\n\r\n\r\nclass __a ( unittest.TestCase\t):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __lowercase (\t\tself : List[Any]\t\t\t\t\t\t):\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n UpperCamelCase__ : Dict\t = inspect.getfile(accelerate.test_utils\t\t\t\t\t\t)\r\n UpperCamelCase__ : Dict\t = os.path.sep.join(mod_file.split(os.path.sep\t\t\t\t\t\t)[:-1] + [\"scripts\", \"test_script.py\"]\t\t\t\t\t\t)\r\n UpperCamelCase__ : List[str]\t = os.path.sep.join(\r\n mod_file.split(os.path.sep\t\t\t\t\t\t)[:-1] + [\"scripts\", \"test_distributed_data_loop.py\"]\t\t\t\t\t\t)\r\n UpperCamelCase__ : int\t = os.path.sep.join(mod_file.split(os.path.sep\t\t\t\t\t\t)[:-1] + [\"scripts\", \"test_ops.py\"]\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n @require_multi_gpu\r\n def __lowercase (\t\tself : Tuple\t\t\t\t\t\t):\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n print(F'Found {torch.cuda.device_count()} devices.'\t\t\t\t\t\t)\r\n UpperCamelCase__ : Dict\t = [\"torchrun\", F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]\r\n with patch_environment(omp_num_threads=1\t\t\t\t\t\t):\r\n execute_subprocess_async(SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t, env=os.environ.copy()\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n @require_multi_gpu\r\n def __lowercase (\t\tself : int\t\t\t\t\t\t):\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n print(F'Found {torch.cuda.device_count()} devices.'\t\t\t\t\t\t)\r\n UpperCamelCase__ : int\t = [\"torchrun\", F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]\r\n print(F'Command: {cmd}'\t\t\t\t\t\t)\r\n with patch_environment(omp_num_threads=1\t\t\t\t\t\t):\r\n execute_subprocess_async(SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t, env=os.environ.copy()\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n @require_multi_gpu\r\n def __lowercase (\t\tself : Any\t\t\t\t\t\t):\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n UpperCamelCase__ : Optional[Any]\t = [\"torchrun\", F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__\t\t\t\t\t\t)]\r\n with patch_environment(omp_num_threads=1\t\t\t\t\t\t):\r\n execute_subprocess_async(SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t, env=os.environ.copy()\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n @require_multi_gpu\r\n def __lowercase (\t\tself : Dict\t\t\t\t\t\t):\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'\t\t\t\t\t\t)\r\n UpperCamelCase__ : Tuple\t = [\"torchrun\", F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]\r\n with patch_environment(omp_num_threads=1\t\t\t\t\t\t\t, cuda_visible_devices=\"0,1\"\t\t\t\t\t\t):\r\n execute_subprocess_async(SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t, env=os.environ.copy()\t\t\t\t\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n lowerCamelCase :\t\t\t\t\t\t\tList[Any] =Accelerator()\r\n lowerCamelCase :\t\t\t\t\t\t\tDict =(accelerator.state.process_index + 2, 10)\r\n lowerCamelCase :\t\t\t\t\t\t\tint =torch.randint(0, 10, shape).to(accelerator.device)\r\n\r\n lowerCamelCase :\t\t\t\t\t\t\tTuple =''''''\r\n\r\n lowerCamelCase :\t\t\t\t\t\t\tOptional[int] =accelerator.pad_across_processes(tensor)\r\n if tensora.shape[0] != accelerator.state.num_processes + 1:\r\n error_msg += F\"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0.\"\r\n if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):\r\n error_msg += \"Tensors have different values.\"\r\n if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):\r\n error_msg += \"Padding was not done with the right value (0).\"\r\n\r\n lowerCamelCase :\t\t\t\t\t\t\tstr =accelerator.pad_across_processes(tensor, pad_first=True)\r\n if tensora.shape[0] != accelerator.state.num_processes + 1:\r\n error_msg += F\"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0.\"\r\n lowerCamelCase :\t\t\t\t\t\t\tOptional[int] =accelerator.state.num_processes - accelerator.state.process_index - 1\r\n if not torch.equal(tensora[index:], tensor):\r\n error_msg += \"Tensors have different values.\"\r\n if not torch.all(tensora[:index] == 0):\r\n error_msg += \"Padding was not done with the right value (0).\"\r\n\r\n # Raise error at the end to make sure we don't stop at the first failure.\r\n if len(error_msg) > 0:\r\n raise ValueError(error_msg)"},"code_codestyle":{"kind":"number","value":196,"string":"196"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport argparse\r\nimport os\r\nimport re\r\n\r\nimport packaging.version\r\n\r\n\r\nlowerCamelCase :\t\t\t\t\t\t\tOptional[Any] ='''examples/'''\r\nlowerCamelCase :\t\t\t\t\t\t\tList[Any] ={\r\n '''examples''': (re.compile(R'''^check_min_version\\(\"[^\"]+\"\\)\\s*$''', re.MULTILINE), '''check_min_version(\"VERSION\")\\n'''),\r\n '''init''': (re.compile(R'''^__version__\\s+=\\s+\"([^\"]+)\"\\s*$''', re.MULTILINE), '''__version__ = \"VERSION\"\\n'''),\r\n '''setup''': (re.compile(R'''^(\\s*)version\\s*=\\s*\"[^\"]+\",''', re.MULTILINE), R'''\\1version=\"VERSION\",'''),\r\n '''doc''': (re.compile(R'''^(\\s*)release\\s*=\\s*\"[^\"]+\"$''', re.MULTILINE), '''release = \"VERSION\"\\n'''),\r\n}\r\nlowerCamelCase :\t\t\t\t\t\t\tList[str] ={\r\n '''init''': '''src/transformers/__init__.py''',\r\n '''setup''': '''setup.py''',\r\n}\r\nlowerCamelCase :\t\t\t\t\t\t\tint ='''README.md'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )\t\t\t-> Optional[Any]:\r\n with open(__lowerCAmelCase , \"r\" , encoding=\"utf-8\" , newline=\"\\n\" ) as f:\r\n UpperCamelCase__ : List[Any]\t = f.read()\r\n UpperCamelCase__ ,\t\t\t\t\tUpperCamelCase__ : List[str]\t = REPLACE_PATTERNS[pattern]\r\n UpperCamelCase__ : Union[str, Any]\t = replace.replace(\"VERSION\" , __lowerCAmelCase )\r\n UpperCamelCase__ : Tuple\t = re_pattern.sub(__lowerCAmelCase , __lowerCAmelCase )\r\n with open(__lowerCAmelCase , \"w\" , encoding=\"utf-8\" , newline=\"\\n\" ) as f:\r\n f.write(__lowerCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t( __lowerCAmelCase )\t\t\t-> Union[str, Any]:\r\n for folder, directories, fnames in os.walk(__lowerCAmelCase ):\r\n # Removing some of the folders with non-actively maintained examples from the walk\r\n if \"research_projects\" in directories:\r\n directories.remove(\"research_projects\" )\r\n if \"legacy\" in directories:\r\n directories.remove(\"legacy\" )\r\n for fname in fnames:\r\n if fname.endswith(\".py\" ):\r\n update_version_in_file(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , pattern=\"examples\" )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t( __lowerCAmelCase , __lowerCAmelCase=False )\t\t\t-> Optional[int]:\r\n for pattern, fname in REPLACE_FILES.items():\r\n update_version_in_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )\r\n if not patch:\r\n update_version_in_examples(__lowerCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t( )\t\t\t-> Optional[Any]:\r\n UpperCamelCase__ : Tuple\t = \"🤗 Transformers currently provides the following architectures\"\r\n UpperCamelCase__ : Tuple\t = \"1. Want to contribute a new model?\"\r\n with open(__lowerCAmelCase , \"r\" , encoding=\"utf-8\" , newline=\"\\n\" ) as f:\r\n UpperCamelCase__ : Optional[int]\t = f.readlines()\r\n\r\n # Find the start of the list.\r\n UpperCamelCase__ : List[Any]\t = 0\r\n while not lines[start_index].startswith(_start_prompt ):\r\n start_index += 1\r\n start_index += 1\r\n\r\n UpperCamelCase__ : Dict\t = start_index\r\n # Update the lines in the model list.\r\n while not lines[index].startswith(_end_prompt ):\r\n if lines[index].startswith(\"1.\" ):\r\n UpperCamelCase__ : str\t = lines[index].replace(\r\n \"https://huggingface.co/docs/transformers/main/model_doc\" , \"https://huggingface.co/docs/transformers/model_doc\" , )\r\n index += 1\r\n\r\n with open(__lowerCAmelCase , \"w\" , encoding=\"utf-8\" , newline=\"\\n\" ) as f:\r\n f.writelines(__lowerCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t( )\t\t\t-> Tuple:\r\n with open(REPLACE_FILES[\"init\"] , \"r\" ) as f:\r\n UpperCamelCase__ : str\t = f.read()\r\n UpperCamelCase__ : Dict\t = REPLACE_PATTERNS[\"init\"][0].search(__lowerCAmelCase ).groups()[0]\r\n return packaging.version.parse(__lowerCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t( __lowerCAmelCase=False )\t\t\t-> Optional[int]:\r\n UpperCamelCase__ : Dict\t = get_version()\r\n if patch and default_version.is_devrelease:\r\n raise ValueError(\"Can't create a patch version from the dev branch, checkout a released version!\" )\r\n if default_version.is_devrelease:\r\n UpperCamelCase__ : List[str]\t = default_version.base_version\r\n elif patch:\r\n UpperCamelCase__ : int\t = f'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'\r\n else:\r\n UpperCamelCase__ : Tuple\t = f'{default_version.major}.{default_version.minor + 1}.0'\r\n\r\n # Now let's ask nicely if that's the right one.\r\n UpperCamelCase__ : Tuple\t = input(f'Which version are you releasing? [{default_version}]' )\r\n if len(__lowerCAmelCase ) == 0:\r\n UpperCamelCase__ : Any\t = default_version\r\n\r\n print(f'Updating version to {version}.' )\r\n global_version_update(__lowerCAmelCase , patch=__lowerCAmelCase )\r\n if not patch:\r\n print(\"Cleaning main README, don't forget to run `make fix-copies`.\" )\r\n clean_main_ref_in_model_list()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t( )\t\t\t-> int:\r\n UpperCamelCase__ : str\t = get_version()\r\n UpperCamelCase__ : Dict\t = f'{current_version.major}.{current_version.minor + 1}.0.dev0'\r\n UpperCamelCase__ : int\t = current_version.base_version\r\n\r\n # Check with the user we got that right.\r\n UpperCamelCase__ : List[str]\t = input(f'Which version are we developing now? [{dev_version}]' )\r\n if len(__lowerCAmelCase ) == 0:\r\n UpperCamelCase__ : Optional[Any]\t = dev_version\r\n\r\n print(f'Updating version to {version}.' )\r\n global_version_update(__lowerCAmelCase )\r\n print(\"Cleaning main README, don't forget to run `make fix-copies`.\" )\r\n clean_main_ref_in_model_list()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n lowerCamelCase :\t\t\t\t\t\t\tList[Any] =argparse.ArgumentParser()\r\n parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')\r\n parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')\r\n lowerCamelCase :\t\t\t\t\t\t\tOptional[Any] =parser.parse_args()\r\n if not args.post_release:\r\n pre_release_work(patch=args.patch)\r\n elif args.patch:\r\n print('''Nothing to do after a patch :-)''')\r\n else:\r\n post_release_work()"},"style_context_codestyle":{"kind":"number","value":196,"string":"196"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":546,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n'''simple docstring'''\r\n\r\nimport copy\r\nfrom typing import Any, Dict, List, Optional, Union\r\n\r\nimport numpy as np\r\n\r\nfrom ...audio_utils import mel_filter_bank, spectrogram, window_function\r\nfrom ...feature_extraction_sequence_utils import SequenceFeatureExtractor\r\nfrom ...feature_extraction_utils import BatchFeature\r\nfrom ...utils import TensorType, logging\r\n\r\n\r\na_ :\t\t\t\tUnion[str, Any]\t\t\t\t\t\t= logging.get_logger(__name__)\r\n\r\n\r\n\r\n\r\n\r\nclass snake_case ( __lowerCAmelCase\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n _lowerCamelCase\t\t = [\"input_features\"]\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __init__( self\t\t\t, UpperCamelCase=80\t\t\t, UpperCamelCase=1_6000\t\t\t, UpperCamelCase=160\t\t\t, UpperCamelCase=30\t\t\t, UpperCamelCase=400\t\t\t, UpperCamelCase=0.0\t\t\t, UpperCamelCase=False\t\t\t, **UpperCamelCase\t\t\t, ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n super().__init__(\r\n feature_size=UpperCamelCase\t\t\t, sampling_rate=UpperCamelCase\t\t\t, padding_value=UpperCamelCase\t\t\t, return_attention_mask=UpperCamelCase\t\t\t, **UpperCamelCase\t\t\t, )\r\n lowerCamelCase_ = n_fft\r\n lowerCamelCase_ = hop_length\r\n lowerCamelCase_ = chunk_length\r\n lowerCamelCase_ = chunk_length * sampling_rate\r\n lowerCamelCase_ = self.n_samples // hop_length\r\n lowerCamelCase_ = sampling_rate\r\n lowerCamelCase_ = mel_filter_bank(\r\n num_frequency_bins=1 + n_fft // 2\t\t\t, num_mel_filters=UpperCamelCase\t\t\t, min_frequency=0.0\t\t\t, max_frequency=8_000.0\t\t\t, sampling_rate=UpperCamelCase\t\t\t, norm=\"slaney\"\t\t\t, mel_scale=\"slaney\"\t\t\t, )\r\n\r\n\r\n\r\n\r\n\r\n\r\n def snake_case\t\t( self\t\t\t, UpperCamelCase ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n lowerCamelCase_ = spectrogram(\r\n UpperCamelCase\t\t\t, window_function(self.n_fft\t\t\t, \"hann\" )\t\t\t, frame_length=self.n_fft\t\t\t, hop_length=self.hop_length\t\t\t, power=2.0\t\t\t, mel_filters=self.mel_filters\t\t\t, log_mel=\"log10\"\t\t\t, )\r\n lowerCamelCase_ = log_spec[:, :-1]\r\n lowerCamelCase_ = np.maximum(UpperCamelCase\t\t\t, log_spec.max() - 8.0 )\r\n lowerCamelCase_ = (log_spec + 4.0) / 4.0\r\n return log_spec\r\n\r\n\r\n\r\n\r\n\r\n\r\n @staticmethod\r\n # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm\r\n def snake_case\t\t( UpperCamelCase\t\t\t, UpperCamelCase\t\t\t, UpperCamelCase = 0.0 ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n if attention_mask is not None:\r\n lowerCamelCase_ = np.array(UpperCamelCase\t\t\t, np.intaa )\r\n lowerCamelCase_ = []\r\n\r\n for vector, length in zip(UpperCamelCase\t\t\t, attention_mask.sum(-1 ) ):\r\n lowerCamelCase_ = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )\r\n if length < normed_slice.shape[0]:\r\n lowerCamelCase_ = padding_value\r\n\r\n normed_input_values.append(UpperCamelCase )\r\n else:\r\n lowerCamelCase_ = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]\r\n\r\n return normed_input_values\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __call__( self\t\t\t, UpperCamelCase\t\t\t, UpperCamelCase = True\t\t\t, UpperCamelCase = None\t\t\t, UpperCamelCase = None\t\t\t, UpperCamelCase = None\t\t\t, UpperCamelCase = \"max_length\"\t\t\t, UpperCamelCase = None\t\t\t, UpperCamelCase = None\t\t\t, UpperCamelCase = None\t\t\t, **UpperCamelCase\t\t\t, ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n if sampling_rate is not None:\r\n if sampling_rate != self.sampling_rate:\r\n raise ValueError(\r\n f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''\r\n f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''\r\n f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )\r\n else:\r\n logger.warning(\r\n \"It is strongly recommended to pass the `sampling_rate` argument to this function. \"\r\n \"Failing to do so can result in silent errors that might be hard to debug.\" )\r\n\r\n lowerCamelCase_ = isinstance(UpperCamelCase\t\t\t, np.ndarray ) and len(raw_speech.shape ) > 1\r\n if is_batched_numpy and len(raw_speech.shape ) > 2:\r\n raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )\r\n lowerCamelCase_ = is_batched_numpy or (\r\n isinstance(UpperCamelCase\t\t\t, (list, tuple) ) and (isinstance(raw_speech[0]\t\t\t, (np.ndarray, tuple, list) ))\r\n )\r\n\r\n if is_batched:\r\n lowerCamelCase_ = [np.asarray([speech]\t\t\t, dtype=np.floataa ).T for speech in raw_speech]\r\n elif not is_batched and not isinstance(UpperCamelCase\t\t\t, np.ndarray ):\r\n lowerCamelCase_ = np.asarray(UpperCamelCase\t\t\t, dtype=np.floataa )\r\n elif isinstance(UpperCamelCase\t\t\t, np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):\r\n lowerCamelCase_ = raw_speech.astype(np.floataa )\r\n\r\n # always return batch\r\n if not is_batched:\r\n lowerCamelCase_ = [np.asarray([raw_speech] ).T]\r\n\r\n lowerCamelCase_ = BatchFeature({\"input_features\": raw_speech} )\r\n\r\n # convert into correct format for padding\r\n\r\n lowerCamelCase_ = self.pad(\r\n UpperCamelCase\t\t\t, padding=UpperCamelCase\t\t\t, max_length=max_length if max_length else self.n_samples\t\t\t, truncation=UpperCamelCase\t\t\t, pad_to_multiple_of=UpperCamelCase\t\t\t, return_attention_mask=return_attention_mask or do_normalize\t\t\t, )\r\n\r\n # zero-mean and unit-variance normalization\r\n if do_normalize:\r\n lowerCamelCase_ = self.zero_mean_unit_var_norm(\r\n padded_inputs[\"input_features\"]\t\t\t, attention_mask=padded_inputs[\"attention_mask\"]\t\t\t, padding_value=self.padding_value\t\t\t, )\r\n lowerCamelCase_ = np.stack(padded_inputs[\"input_features\"]\t\t\t, axis=0 )\r\n\r\n # make sure list is in array format\r\n lowerCamelCase_ = padded_inputs.get(\"input_features\" ).transpose(2\t\t\t, 0\t\t\t, 1 )\r\n\r\n lowerCamelCase_ = [self._np_extract_fbank_features(UpperCamelCase ) for waveform in input_features[0]]\r\n\r\n if isinstance(input_features[0]\t\t\t, UpperCamelCase ):\r\n lowerCamelCase_ = [np.asarray(UpperCamelCase\t\t\t, dtype=np.floataa ) for feature in input_features]\r\n else:\r\n lowerCamelCase_ = input_features\r\n\r\n if return_attention_mask:\r\n # rescale from sample (48000) to feature (3000)\r\n lowerCamelCase_ = padded_inputs['attention_mask'][:, :: self.hop_length]\r\n\r\n if return_tensors is not None:\r\n lowerCamelCase_ = padded_inputs.convert_to_tensors(UpperCamelCase )\r\n\r\n return padded_inputs\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def snake_case\t\t( self ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n lowerCamelCase_ = copy.deepcopy(self.__dict__ )\r\n lowerCamelCase_ = self.__class__.__name__\r\n if \"mel_filters\" in output:\r\n del output[\"mel_filters\"]\r\n return output\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":55,"string":"55"},"style_context":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\nimport os\nfrom typing import Dict, List, Tuple, TypeVar, Union\n\n\nlowercase__\t\t\t\t\t =\tTypeVar('T')\n\nlowercase__\t\t\t\t\t =\tUnion[List[T], Tuple[T, ...]]\nlowercase__\t\t\t\t\t =\tUnion[T, List[T], Dict[str, T]]\nlowercase__\t\t\t\t\t =\tUnion[str, bytes, os.PathLike]\n\n\n"},"style_context_codestyle":{"kind":"number","value":290,"string":"290"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":547,"cells":{"code":{"kind":"string","value":"import json\r\nimport unittest\r\n\r\nimport numpy as np\r\nfrom huggingface_hub import hf_hub_download\r\n\r\nfrom transformers.testing_utils import require_torch, require_vision\r\nfrom transformers.utils import is_torch_available, is_vision_available\r\n\r\nfrom ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs\r\n\r\n\r\nif is_torch_available():\r\n import torch\r\n\r\n if is_vision_available():\r\n from transformers import OneFormerImageProcessor\r\n from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle\r\n from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput\r\n\r\nif is_vision_available():\r\n from PIL import Image\r\n\r\n\r\n\r\ndef __SCREAMING_SNAKE_CASE ( __UpperCamelCase :\tAny\t\t\t,\t\t\t\t\t__UpperCamelCase :\tTuple=\"shi-labs/oneformer_demo\" )\t\t\t\t\t\t\t-> Tuple:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n with open(hf_hub_download(__UpperCamelCase\t\t\t,\t\t\t\t\t__UpperCamelCase\t\t\t,\t\t\t\t\trepo_type=\"\"\"dataset\"\"\" )\t\t\t,\t\t\t\t\t\"\"\"r\"\"\" ) as f:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tjson.load(__UpperCamelCase )\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t{}\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t[]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t[]\r\n for key, info in class_info.items():\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tinfo[\"\"\"name\"\"\"]\r\n class_names.append(info[\"\"\"name\"\"\"] )\r\n if info[\"isthing\"]:\r\n thing_ids.append(int(__UpperCamelCase ) )\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tthing_ids\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tclass_names\r\n return metadata\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass __snake_case (\t\t\t\t\t\tunittest.TestCase\t\t):\r\n\r\n\r\n\r\n\r\n def __init__( self : List[Any]\t, _lowercase : Optional[Any]\t, _lowercase : int=7\t, _lowercase : Any=3\t, _lowercase : int=30\t, _lowercase : List[Any]=4_00\t, _lowercase : Union[str, Any]=None\t, _lowercase : Dict=True\t, _lowercase : Tuple=True\t, _lowercase : int=[0.5, 0.5, 0.5]\t, _lowercase : List[str]=[0.5, 0.5, 0.5]\t, _lowercase : str=10\t, _lowercase : Union[str, Any]=False\t, _lowercase : int=2_55\t, _lowercase : List[str]=\"shi-labs/oneformer_demo\"\t, _lowercase : Any=\"ade20k_panoptic.json\"\t, _lowercase : Any=10\t, ):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tparent\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tbatch_size\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tnum_channels\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tmin_resolution\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tmax_resolution\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tdo_resize\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t{\"\"\"shortest_edge\"\"\": 32, \"\"\"longest_edge\"\"\": 13_33} if size is None else size\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tdo_normalize\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_mean\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_std\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tclass_info_file\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tprepare_metadata(_lowercase\t, _lowercase\t\t\t\t)\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tnum_text\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\trepo_path\r\n\r\n # for the post_process_functions\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t2\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t10\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t10\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t3\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t4\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tnum_labels\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tdo_reduce_labels\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tignore_index\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Optional[int]\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n return {\r\n \"do_resize\": self.do_resize,\r\n \"size\": self.size,\r\n \"do_normalize\": self.do_normalize,\r\n \"image_mean\": self.image_mean,\r\n \"image_std\": self.image_std,\r\n \"num_labels\": self.num_labels,\r\n \"do_reduce_labels\": self.do_reduce_labels,\r\n \"ignore_index\": self.ignore_index,\r\n \"class_info_file\": self.class_info_file,\r\n \"metadata\": self.metadata,\r\n \"num_text\": self.num_text,\r\n }\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : List[Any]\t, _lowercase : Any\t, _lowercase : Optional[Any]=False\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n if not batched:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_inputs[0]\r\n if isinstance(_lowercase\t, Image.Image\t\t\t\t):\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage.size\r\n else:\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage.shape[1], image.shape[2]\r\n if w < h:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tint(self.size[\"\"\"shortest_edge\"\"\"] * h / w\t\t\t\t)\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.size[\"\"\"shortest_edge\"\"\"]\r\n elif w > h:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.size[\"\"\"shortest_edge\"\"\"]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tint(self.size[\"\"\"shortest_edge\"\"\"] * w / h\t\t\t\t)\r\n else:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.size[\"\"\"shortest_edge\"\"\"]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.size[\"\"\"shortest_edge\"\"\"]\r\n\r\n else:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t[]\r\n for image in image_inputs:\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.get_expected_values([image]\t\t\t\t)\r\n expected_values.append((expected_height, expected_width)\t\t\t\t)\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tmax(_lowercase\t, key=lambda _lowercase\t\t\t\t: item[0]\t\t\t\t)[0]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tmax(_lowercase\t, key=lambda _lowercase\t\t\t\t: item[1]\t\t\t\t)[1]\r\n\r\n return expected_height, expected_width\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Dict\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n return OneFormerForUniversalSegmentationOutput(\r\n # +1 for null class\r\n class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)\t\t\t\t)\t, masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)\t\t\t\t)\t, )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_torch\r\n@require_vision\r\nclass __snake_case (\t\t\t\t\t\tlowerCamelCase_ ,\t\tunittest.TestCase\t\t):\r\n lowerCAmelCase_\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tOneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None\r\n # only for test_image_processing_common.test_image_proc_to_json_string\r\n lowerCAmelCase_\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\timage_processing_class\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : str\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tOneFormerImageProcessorTester(self\t\t\t\t)\r\n\r\n\r\n\r\n\r\n @property\r\n def __a\t\t\t\t\t\t( self : str\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n return self.image_processing_tester.prepare_image_processor_dict()\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : str\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"image_mean\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"image_std\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"do_normalize\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"do_resize\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"size\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"ignore_index\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"class_info_file\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"num_text\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"repo_path\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"metadata\"\"\"\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_lowercase\t, \"\"\"do_reduce_labels\"\"\"\t\t\t\t)\t\t\t\t)\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Any\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n pass\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Optional[int]\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n # create random PIL images\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tprepare_image_inputs(self.image_processing_tester\t, equal_resolution=_lowercase\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(_lowercase\t, Image.Image\t\t\t\t)\r\n\r\n # Test not batched input\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor(image_inputs[0]\t, [\"\"\"semantic\"\"\"]\t, return_tensors=\"\"\"pt\"\"\"\t\t\t\t).pixel_values\r\n\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_expected_values(_lowercase\t\t\t\t)\r\n\r\n self.assertEqual(\r\n encoded_images.shape\t, (1, self.image_processing_tester.num_channels, expected_height, expected_width)\t, )\r\n\r\n # Test batched\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_expected_values(_lowercase\t, batched=_lowercase\t\t\t\t)\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor(\r\n _lowercase\t, [\"\"\"semantic\"\"\"] * len(_lowercase\t\t\t\t)\t, return_tensors=\"\"\"pt\"\"\"\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t, (\r\n self.image_processing_tester.batch_size,\r\n self.image_processing_tester.num_channels,\r\n expected_height,\r\n expected_width,\r\n )\t, )\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : int\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n # create random numpy tensors\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tprepare_image_inputs(self.image_processing_tester\t, equal_resolution=_lowercase\t, numpify=_lowercase\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(_lowercase\t, np.ndarray\t\t\t\t)\r\n\r\n # Test not batched input\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor(image_inputs[0]\t, [\"\"\"semantic\"\"\"]\t, return_tensors=\"\"\"pt\"\"\"\t\t\t\t).pixel_values\r\n\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_expected_values(_lowercase\t\t\t\t)\r\n\r\n self.assertEqual(\r\n encoded_images.shape\t, (1, self.image_processing_tester.num_channels, expected_height, expected_width)\t, )\r\n\r\n # Test batched\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_expected_values(_lowercase\t, batched=_lowercase\t\t\t\t)\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor(\r\n _lowercase\t, [\"\"\"semantic\"\"\"] * len(_lowercase\t\t\t\t)\t, return_tensors=\"\"\"pt\"\"\"\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t, (\r\n self.image_processing_tester.batch_size,\r\n self.image_processing_tester.num_channels,\r\n expected_height,\r\n expected_width,\r\n )\t, )\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : str\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n # create random PyTorch tensors\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tprepare_image_inputs(self.image_processing_tester\t, equal_resolution=_lowercase\t, torchify=_lowercase\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(_lowercase\t, torch.Tensor\t\t\t\t)\r\n\r\n # Test not batched input\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor(image_inputs[0]\t, [\"\"\"semantic\"\"\"]\t, return_tensors=\"\"\"pt\"\"\"\t\t\t\t).pixel_values\r\n\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_expected_values(_lowercase\t\t\t\t)\r\n\r\n self.assertEqual(\r\n encoded_images.shape\t, (1, self.image_processing_tester.num_channels, expected_height, expected_width)\t, )\r\n\r\n # Test batched\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t,\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_expected_values(_lowercase\t, batched=_lowercase\t\t\t\t)\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor(\r\n _lowercase\t, [\"\"\"semantic\"\"\"] * len(_lowercase\t\t\t\t)\t, return_tensors=\"\"\"pt\"\"\"\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t, (\r\n self.image_processing_tester.batch_size,\r\n self.image_processing_tester.num_channels,\r\n expected_height,\r\n expected_width,\r\n )\t, )\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Tuple\t, _lowercase : Optional[int]=False\t, _lowercase : Any=False\t, _lowercase : List[str]=\"np\"\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n # prepare image and target\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.num_labels\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tNone\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tNone\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tprepare_image_inputs(self.image_processing_tester\t, equal_resolution=_lowercase\t\t\t\t)\r\n if with_segmentation_maps:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tnum_labels\r\n if is_instance_map:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tlist(range(_lowercase\t\t\t\t)\t\t\t\t) * 2\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tdict(enumerate(_lowercase\t\t\t\t)\t\t\t\t)\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t[\r\n np.random.randint(0\t, high * 2\t, (img.size[1], img.size[0])\t\t\t\t).astype(np.uinta\t\t\t\t) for img in image_inputs\r\n ]\r\n if segmentation_type == \"pil\":\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t[Image.fromarray(_lowercase\t\t\t\t) for annotation in annotations]\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor(\r\n _lowercase\t, [\"\"\"semantic\"\"\"] * len(_lowercase\t\t\t\t)\t, _lowercase\t, return_tensors=\"\"\"pt\"\"\"\t, instance_id_to_semantic_id=_lowercase\t, pad_and_return_pixel_mask=_lowercase\t, )\r\n\r\n return inputs\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : str\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n pass\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Tuple\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n def common(_lowercase : Optional[int]=False\t, _lowercase : Union[str, Any]=None\t\t\t\t):\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.comm_get_image_processor_inputs(\r\n with_segmentation_maps=_lowercase\t, is_instance_map=_lowercase\t, segmentation_type=_lowercase\t\t\t\t)\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tinputs[\"\"\"mask_labels\"\"\"]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tinputs[\"\"\"class_labels\"\"\"]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tinputs[\"\"\"pixel_values\"\"\"]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tinputs[\"\"\"text_inputs\"\"\"]\r\n\r\n # check the batch_size\r\n for mask_label, class_label, text_input in zip(_lowercase\t, _lowercase\t, _lowercase\t\t\t\t):\r\n self.assertEqual(mask_label.shape[0]\t, class_label.shape[0]\t\t\t\t)\r\n # this ensure padding has happened\r\n self.assertEqual(mask_label.shape[1:]\t, pixel_values.shape[2:]\t\t\t\t)\r\n self.assertEqual(len(_lowercase\t\t\t\t)\t, self.image_processing_tester.num_text\t\t\t\t)\r\n\r\n common()\r\n common(is_instance_map=_lowercase\t\t\t\t)\r\n common(is_instance_map=_lowercase\t, segmentation_type=\"\"\"pil\"\"\"\t\t\t\t)\r\n common(is_instance_map=_lowercase\t, segmentation_type=\"\"\"pil\"\"\"\t\t\t\t)\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Any\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tnp.zeros((20, 50)\t\t\t\t)\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t1\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t1\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t1\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tbinary_mask_to_rle(_lowercase\t\t\t\t)\r\n self.assertEqual(len(_lowercase\t\t\t\t)\t, 4\t\t\t\t)\r\n self.assertEqual(rle[0]\t, 21\t\t\t\t)\r\n self.assertEqual(rle[1]\t, 45\t\t\t\t)\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Dict\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_class(\r\n num_labels=self.image_processing_tester.num_classes\t, max_seq_length=77\t, task_seq_length=77\t, class_info_file=\"\"\"ade20k_panoptic.json\"\"\"\t, num_text=self.image_processing_tester.num_text\t, repo_path=\"\"\"shi-labs/oneformer_demo\"\"\"\t, )\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_fake_oneformer_outputs()\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tfature_extractor.post_process_semantic_segmentation(_lowercase\t\t\t\t)\r\n\r\n self.assertEqual(len(_lowercase\t\t\t\t)\t, self.image_processing_tester.batch_size\t\t\t\t)\r\n self.assertEqual(\r\n segmentation[0].shape\t, (\r\n self.image_processing_tester.height,\r\n self.image_processing_tester.width,\r\n )\t, )\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t[(1, 4) for i in range(self.image_processing_tester.batch_size\t\t\t\t)]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tfature_extractor.post_process_semantic_segmentation(_lowercase\t, target_sizes=_lowercase\t\t\t\t)\r\n\r\n self.assertEqual(segmentation[0].shape\t, target_sizes[0]\t\t\t\t)\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : List[str]\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_class(\r\n num_labels=self.image_processing_tester.num_classes\t, max_seq_length=77\t, task_seq_length=77\t, class_info_file=\"\"\"ade20k_panoptic.json\"\"\"\t, num_text=self.image_processing_tester.num_text\t, repo_path=\"\"\"shi-labs/oneformer_demo\"\"\"\t, )\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_fake_oneformer_outputs()\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor.post_process_instance_segmentation(_lowercase\t, threshold=0\t\t\t\t)\r\n\r\n self.assertTrue(len(_lowercase\t\t\t\t) == self.image_processing_tester.batch_size\t\t\t\t)\r\n for el in segmentation:\r\n self.assertTrue(\"\"\"segmentation\"\"\" in el\t\t\t\t)\r\n self.assertTrue(\"\"\"segments_info\"\"\" in el\t\t\t\t)\r\n self.assertEqual(type(el[\"\"\"segments_info\"\"\"]\t\t\t\t)\t, _lowercase\t\t\t\t)\r\n self.assertEqual(\r\n el[\"\"\"segmentation\"\"\"].shape\t, (self.image_processing_tester.height, self.image_processing_tester.width)\t\t\t\t)\r\n\r\n\r\n\r\n\r\n def __a\t\t\t\t\t\t( self : Optional[Any]\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_class(\r\n num_labels=self.image_processing_tester.num_classes\t, max_seq_length=77\t, task_seq_length=77\t, class_info_file=\"\"\"ade20k_panoptic.json\"\"\"\t, num_text=self.image_processing_tester.num_text\t, repo_path=\"\"\"shi-labs/oneformer_demo\"\"\"\t, )\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tself.image_processing_tester.get_fake_oneformer_outputs()\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timage_processor.post_process_panoptic_segmentation(_lowercase\t, threshold=0\t\t\t\t)\r\n\r\n self.assertTrue(len(_lowercase\t\t\t\t) == self.image_processing_tester.batch_size\t\t\t\t)\r\n for el in segmentation:\r\n self.assertTrue(\"\"\"segmentation\"\"\" in el\t\t\t\t)\r\n self.assertTrue(\"\"\"segments_info\"\"\" in el\t\t\t\t)\r\n self.assertEqual(type(el[\"\"\"segments_info\"\"\"]\t\t\t\t)\t, _lowercase\t\t\t\t)\r\n self.assertEqual(\r\n el[\"\"\"segmentation\"\"\"].shape\t, (self.image_processing_tester.height, self.image_processing_tester.width)\t\t\t\t)\r\n"},"code_codestyle":{"kind":"number","value":204,"string":"204"},"style_context":{"kind":"string","value":"import PIL.Image\r\nimport PIL.ImageOps\r\nfrom packaging import version\r\nfrom PIL import Image\r\n\r\n\r\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):\r\n __lowerCamelCase :\t\t\tOptional[Any]\t\t\t\t\t\t\t =\t\t{\r\n '''linear''': PIL.Image.Resampling.BILINEAR,\r\n '''bilinear''': PIL.Image.Resampling.BILINEAR,\r\n '''bicubic''': PIL.Image.Resampling.BICUBIC,\r\n '''lanczos''': PIL.Image.Resampling.LANCZOS,\r\n '''nearest''': PIL.Image.Resampling.NEAREST,\r\n }\r\nelse:\r\n __lowerCamelCase :\t\t\tint\t\t\t\t\t\t\t =\t\t{\r\n '''linear''': PIL.Image.LINEAR,\r\n '''bilinear''': PIL.Image.BILINEAR,\r\n '''bicubic''': PIL.Image.BICUBIC,\r\n '''lanczos''': PIL.Image.LANCZOS,\r\n '''nearest''': PIL.Image.NEAREST,\r\n }\r\n\r\n\r\n\r\ndef __SCREAMING_SNAKE_CASE ( __UpperCamelCase :\tint )\t\t\t\t\t\t\t-> Optional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t(images / 2 + 0.5).clamp(0\t\t\t,\t\t\t\t\t1 )\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timages.cpu().permute(0\t\t\t,\t\t\t\t\t2\t\t\t,\t\t\t\t\t3\t\t\t,\t\t\t\t\t1 ).float().numpy()\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\tnumpy_to_pil(__UpperCamelCase )\r\n return images\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef __SCREAMING_SNAKE_CASE ( __UpperCamelCase :\tint )\t\t\t\t\t\t\t-> int:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n if images.ndim == 3:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\timages[None, ...]\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t(images * 2_55).round().astype(\"\"\"uint8\"\"\" )\r\n if images.shape[-1] == 1:\r\n # special case for grayscale (single channel) images\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t[Image.fromarray(image.squeeze()\t\t\t,\t\t\t\t\tmode=\"\"\"L\"\"\" ) for image in images]\r\n else:\r\n SCREAMING_SNAKE_CASE__ \t\t\t\t\t\t\t=\t\t\t\t[Image.fromarray(__UpperCamelCase ) for image in images]\r\n\r\n return pil_images\r\n"},"style_context_codestyle":{"kind":"number","value":204,"string":"204"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":548,"cells":{"code":{"kind":"string","value":"\r\r\r\rimport gc\rimport importlib.metadata\rimport tempfile\rimport unittest\r\rfrom packaging import version\r\rfrom transformers import (\r AutoModel,\r AutoModelForCausalLM,\r AutoModelForSeqaSeqLM,\r AutoModelForSequenceClassification,\r AutoTokenizer,\r BitsAndBytesConfig,\r pipeline,\r)\rfrom transformers.testing_utils import (\r is_torch_available,\r require_accelerate,\r require_bitsandbytes,\r require_torch,\r require_torch_gpu,\r require_torch_multi_gpu,\r slow,\r)\r\r\r\r\r\r\rdef a( A : List[str] )\t\t-> List[str]:\r\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\t\t\t\t\tif model.config.model_type == \"gpt2\":\r\t\t\t\t\t\t\t\t\t\treturn model.transformer.h[0].mlp.c_fc\r\t\t\t\t\treturn model.transformer.h[0].mlp.dense_ah_to_h\r\r\rif is_torch_available():\r\t\t\t\timport torch\r\t\t\t\timport torch.nn as nn\r\r\r\r\t\t\t\tclass \t_lowercase\t\t\t\t\t\t\t( nn.Module\t):\r\r\r\r\r\r\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\t\t\t\t\tdef __init__(self\t\t, lowerCamelCase_\t\t, lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\t\t\t\t\tsuper().__init__()\r\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = module\r\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = nn.Sequential(\r\t\t\t\t\t\t\t\t\t\t nn.Linear(module.in_features\t\t, lowerCamelCase_\t\t, bias=lowerCamelCase_\t\t)\t\t, nn.Linear(lowerCamelCase_\t\t, module.out_features\t\t, bias=lowerCamelCase_\t\t)\t\t, )\r\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = (2.0 / (5 * min(module.in_features\t\t, module.out_features\t\t))) ** 0.5\r\t\t\t\t\t\t\t\t\t\tnn.init.normal_(self.adapter[0].weight\t\t, std=lowerCamelCase_\t\t)\r\t\t\t\t\t\t\t\t\t\tnn.init.zeros_(self.adapter[1].weight\t\t)\r\t\t\t\t\t\t\t\t\t\tself.adapter.to(module.weight.device\t\t)\r\r\r\r\r\t\t\t\t\tdef UpperCamelCase_ (self\t\t, lowerCamelCase_\t\t, *lowerCamelCase_\t\t, **lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\t\t\t\t\treturn self.module(lowerCamelCase_\t\t, *lowerCamelCase_\t\t, **lowerCamelCase_\t\t) + self.adapter(lowerCamelCase_\t\t)\r\r\r\r@require_bitsandbytes\r@require_accelerate\r@require_torch\r@require_torch_gpu\r@slow\rclass \t_lowercase\t\t\t\t\t\t\t( unittest.TestCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\t# We keep the constants inside the init function and model loading inside setUp function\r\r\t# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)\r\t# Therefore here we use only bloom-1b3 to test our module\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\t\"bigscience/bloom-1b7\"\r\r\t# Constant values\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\t2.109_659_552_692_574\r\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\t\"Hello my name is\"\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\tset()\r\tEXPECTED_OUTPUTS.add(\"Hello my name is John and I am a professional photographer. I\"\t)\r\tEXPECTED_OUTPUTS.add(\"Hello my name is John.\\nI am a friend of your father.\\n\"\t)\r\tEXPECTED_OUTPUTS.add(\"Hello my name is John Doe, I am a student at the University\"\t)\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\t10\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = AutoTokenizer.from_pretrained(self.model_name\t\t)\r\r\r\r\rclass \t_lowercase\t\t\t\t\t\t\t( lowerCAmelCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tsuper().setUp()\r\r\t\t\t\t\t\t# Models and tokenizer\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForCausalLM.from_pretrained(\r\t\t\t\t\t\t self.model_name\t\t, torch_dtype=torch.floataa\t\t, device_map=\"auto\"\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForCausalLM.from_pretrained(self.model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tdel self.model_fpaa\r\t\t\t\t\t\tdel self.model_abit\r\r\t\t\t\t\t\tgc.collect()\r\t\t\t\t\t\ttorch.cuda.empty_cache()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_abit.config\r\r\t\t\t\t\t\tself.assertTrue(hasattr(lowerCamelCase_\t\t, \"quantization_config\"\t\t)\t\t)\r\r\t\t\t\t\t\ta\t\t\t\t\t = config.to_dict()\r\t\t\t\t\t\ta\t\t\t\t\t = config.to_diff_dict()\r\r\t\t\t\t\t\ta\t\t\t\t\t = config.to_json_string()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tfrom bitsandbytes.nn import Paramsabit\r\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_fpaa.get_memory_footprint()\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_abit.get_memory_footprint()\r\r\t\t\t\t\t\tself.assertAlmostEqual(mem_fpaa / mem_abit\t\t, self.EXPECTED_RELATIVE_DIFFERENCE\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = get_some_linear_layer(self.model_abit\t\t)\r\t\t\t\t\t\tself.assertTrue(linear.weight.__class__ == Paramsabit\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tfrom transformers import TaPreTrainedModel\r\r\t\t\t\t\t\tself.model_fpaa.get_memory_footprint()\r\t\t\t\t\t\tself.model_abit.get_memory_footprint()\r\r\t\t\t\t\t\tfor name, module in self.model_abit.named_modules():\r\t\t\t\t\t\t\t\t\t\t\tif isinstance(lowerCamelCase_\t\t, torch.nn.Linear\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif name not in [\"lm_head\"] + TaPreTrainedModel._keep_in_fpaa_modules:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# 4-bit parameters are packed in uint8 variables\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(module.weight.dtype == torch.uinta\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(self.input_text\t\t, return_tensors=\"pt\"\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_abit.generate(input_ids=encoded_input[\"input_ids\"].to(0\t\t)\t\t, max_new_tokens=10\t\t)\r\r\t\t\t\t\t\tself.assertIn(self.tokenizer.decode(output_sequences[0]\t\t, skip_special_tokens=lowerCamelCase_\t\t)\t\t, self.EXPECTED_OUTPUTS\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = BitsAndBytesConfig()\r\t\t\t\t\t\ta\t\t\t\t\t = True\r\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForCausalLM.from_pretrained(\r\t\t\t\t\t\t self.model_name\t\t, quantization_config=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(self.input_text\t\t, return_tensors=\"pt\"\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = model_abit_from_config.generate(\r\t\t\t\t\t\t input_ids=encoded_input[\"input_ids\"].to(0\t\t)\t\t, max_new_tokens=10\t\t)\r\r\t\t\t\t\t\tself.assertIn(self.tokenizer.decode(output_sequences[0]\t\t, skip_special_tokens=lowerCamelCase_\t\t)\t\t, self.EXPECTED_OUTPUTS\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\twith self.assertRaises(lowerCamelCase_\t\t), tempfile.TemporaryDirectory() as tmpdirname:\r\t\t\t\t\t\t\t\t\t\t\tself.model_abit.save_pretrained(lowerCamelCase_\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = BitsAndBytesConfig()\r\r\t\t\t\t\t\twith self.assertRaises(lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForCausalLM.from_pretrained(\r\t\t\t\t\t\t\t\t\t\t\t self.model_name\t\t, quantization_config=lowerCamelCase_\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t, bnb_abit_quant_type=\"nf4\"\t\t, )\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\twith self.assertRaises(lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\t# Tries with `str`\r\t\t\t\t\t\t\t\t\t\t\tself.model_abit.to(\"cpu\"\t\t)\r\r\t\t\t\t\t\twith self.assertRaises(lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\t# Tries with a `dtype``\r\t\t\t\t\t\t\t\t\t\t\tself.model_abit.to(torch.floataa\t\t)\r\r\t\t\t\t\t\twith self.assertRaises(lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\t# Tries with a `device`\r\t\t\t\t\t\t\t\t\t\t\tself.model_abit.to(torch.device(\"cuda:0\"\t\t)\t\t)\r\r\t\t\t\t\t\twith self.assertRaises(lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\t# Tries with a `device`\r\t\t\t\t\t\t\t\t\t\t\tself.model_abit.float()\r\r\t\t\t\t\t\twith self.assertRaises(lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\t# Tries with a `device`\r\t\t\t\t\t\t\t\t\t\t\tself.model_abit.half()\r\r\t\t\t\t\t\t# Test if we did not break anything\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(self.input_text\t\t, return_tensors=\"pt\"\t\t)\r\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_fpaa.to(torch.floataa\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_fpaa.generate(input_ids=encoded_input[\"input_ids\"].to(0\t\t)\t\t, max_new_tokens=10\t\t)\r\r\t\t\t\t\t\t# Check this does not throw an error\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_fpaa.to(\"cpu\"\t\t)\r\r\t\t\t\t\t\t# Check this does not throw an error\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_fpaa.half()\r\r\t\t\t\t\t\t# Check this does not throw an error\r\t\t\t\t\t\ta\t\t\t\t\t = self.model_fpaa.float()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForSeqaSeqLM.from_pretrained(\"t5-small\"\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\t\t\t\t\t\tself.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa\t\t)\r\r\r\r\r@require_bitsandbytes\r@require_accelerate\r@require_torch\r@require_torch_gpu\r@slow\rclass \t_lowercase\t\t\t\t\t\t\t( unittest.TestCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\t@classmethod\r\tdef UpperCamelCase_ (cls\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = \"t5-small\"\r\t\t\t\t\t\ta\t\t\t\t\t = \"google/flan-t5-small\" # flan-t5 uses dense-act instead of dense-relu-dense\r\t\t\t\t\t\ta\t\t\t\t\t = AutoTokenizer.from_pretrained(cls.model_name\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = \"Translate in German: Hello, my dog is cute\"\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tgc.collect()\r\t\t\t\t\t\ttorch.cuda.empty_cache()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tfrom transformers import TaForConditionalGeneration\r\r\t\t\t\t\t\ta\t\t\t\t\t = TaForConditionalGeneration._keep_in_fpaa_modules\r\t\t\t\t\t\ta\t\t\t\t\t = None\r\r\t\t\t\t\t\t# test with `t5-small`\r\t\t\t\t\t\ta\t\t\t\t\t = TaForConditionalGeneration.from_pretrained(self.model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(self.input_text\t\t, return_tensors=\"pt\"\t\t).to(0\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = model.generate(**lowerCamelCase_\t\t)\r\r\t\t\t\t\t\t# test with `flan-t5-small`\r\t\t\t\t\t\ta\t\t\t\t\t = TaForConditionalGeneration.from_pretrained(\r\t\t\t\t\t\t self.dense_act_model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(self.input_text\t\t, return_tensors=\"pt\"\t\t).to(0\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = model.generate(**lowerCamelCase_\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = modules\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\timport bitsandbytes as bnb\r\r\t\t\t\t\t\tfrom transformers import TaForConditionalGeneration\r\r\t\t\t\t\t\t# test with `t5-small`\r\t\t\t\t\t\ta\t\t\t\t\t = TaForConditionalGeneration.from_pretrained(self.model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\r\t\t\t\t\t\t# there was a bug with decoders - this test checks that it is fixed\r\t\t\t\t\t\tself.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q\t\t, bnb.nn.Linearabit\t\t)\t\t)\r\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(self.input_text\t\t, return_tensors=\"pt\"\t\t).to(0\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = model.generate(**lowerCamelCase_\t\t)\r\r\t\t\t\t\t\t# test with `flan-t5-small`\r\t\t\t\t\t\ta\t\t\t\t\t = TaForConditionalGeneration.from_pretrained(\r\t\t\t\t\t\t self.dense_act_model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(self.input_text\t\t, return_tensors=\"pt\"\t\t).to(0\t\t)\r\t\t\t\t\t\ta\t\t\t\t\t = model.generate(**lowerCamelCase_\t\t)\r\r\r\r\rclass \t_lowercase\t\t\t\t\t\t\t( lowerCAmelCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tsuper().setUp()\r\t\t\t\t\t\t# model_name\r\t\t\t\t\t\ta\t\t\t\t\t = \"bigscience/bloom-560m\"\r\t\t\t\t\t\ta\t\t\t\t\t = \"t5-small\"\r\r\t\t\t\t\t\t# Different types of model\r\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModel.from_pretrained(self.model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\t\t\t\t\t\t# Sequence classification model\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForSequenceClassification.from_pretrained(\r\t\t\t\t\t\t self.model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\t\t\t\t\t\t# CausalLM model\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForCausalLM.from_pretrained(self.model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\t\t\t\t\t\t# Seq2seq model\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForSeqaSeqLM.from_pretrained(\r\t\t\t\t\t\t self.seq_to_seq_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"auto\"\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tdel self.base_model\r\t\t\t\t\t\tdel self.sequence_model\r\t\t\t\t\t\tdel self.model_abit\r\t\t\t\t\t\tdel self.seq_to_seq_model\r\r\t\t\t\t\t\tgc.collect()\r\t\t\t\t\t\ttorch.cuda.empty_cache()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tfrom bitsandbytes.nn import Paramsabit\r\r\t\t\t\t\t\tself.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit\t\t)\r\r\t\t\t\t\t\t# Other heads should be nn.Parameter\r\t\t\t\t\t\tself.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter\t\t)\r\t\t\t\t\t\tself.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter\t\t)\r\t\t\t\t\t\tself.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter\t\t)\r\r\r\r\rclass \t_lowercase\t\t\t\t\t\t\t( lowerCAmelCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tsuper().setUp()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tdel self.pipe\r\r\t\t\t\t\t\tgc.collect()\r\t\t\t\t\t\ttorch.cuda.empty_cache()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = pipeline(\r\t\t\t\t\t\t \"text-generation\"\t\t, model=self.model_name\t\t, model_kwargs={\"device_map\": \"auto\", \"load_in_4bit\": True, \"torch_dtype\": torch.floataa}\t\t, max_new_tokens=self.MAX_NEW_TOKENS\t\t, )\r\r\t\t\t\t\t\t# Real second forward pass\r\t\t\t\t\t\ta\t\t\t\t\t = self.pipe(self.input_text\t\t)\r\t\t\t\t\t\tself.assertIn(pipeline_output[0][\"generated_text\"]\t\t, self.EXPECTED_OUTPUTS\t\t)\r\r\r\r\r@require_torch_multi_gpu\rclass \t_lowercase\t\t\t\t\t\t\t( lowerCAmelCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tsuper().setUp()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForCausalLM.from_pretrained(\r\t\t\t\t\t\t self.model_name\t\t, load_in_abit=lowerCamelCase_\t\t, device_map=\"balanced\"\t\t)\r\r\t\t\t\t\t\t# Check correct device map\r\t\t\t\t\t\tself.assertEqual(set(model_parallel.hf_device_map.values()\t\t)\t\t, {0, 1}\t\t)\r\r\t\t\t\t\t\t# Check that inference pass works on the model\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(self.input_text\t\t, return_tensors=\"pt\"\t\t)\r\r\t\t\t\t\t\t# Second real batch\r\t\t\t\t\t\ta\t\t\t\t\t = model_parallel.generate(input_ids=encoded_input[\"input_ids\"].to(0\t\t)\t\t, max_new_tokens=10\t\t)\r\t\t\t\t\t\tself.assertIn(self.tokenizer.decode(output_parallel[0]\t\t, skip_special_tokens=lowerCamelCase_\t\t)\t\t, self.EXPECTED_OUTPUTS\t\t)\r\r\r\r\rclass \t_lowercase\t\t\t\t\t\t\t( lowerCAmelCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = \"facebook/opt-350m\"\r\t\t\t\t\t\tsuper().setUp()\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tif version.parse(importlib.metadata.version(\"bitsandbytes\"\t\t)\t\t) < version.parse(\"0.37.0\"\t\t):\r\t\t\t\t\t\t\t\t\t\t\treturn\r\r\t\t\t\t\t\t# Step 1: freeze all parameters\r\t\t\t\t\t\ta\t\t\t\t\t = AutoModelForCausalLM.from_pretrained(self.model_name\t\t, load_in_abit=lowerCamelCase_\t\t)\r\r\t\t\t\t\t\tself.assertEqual(set(model.hf_device_map.values()\t\t)\t\t, {torch.cuda.current_device()}\t\t)\r\r\t\t\t\t\t\tfor param in model.parameters():\r\t\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = False # freeze the model - train adapters later\r\t\t\t\t\t\t\t\t\t\t\tif param.ndim == 1:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# cast the small parameters (e.g. layernorm) to fp32 for stability\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = param.data.to(torch.floataa\t\t)\r\r # Step 2: add adapters\r\t\t\t\t\t\tfor _, module in model.named_modules():\r\t\t\t\t\t\t\t\t\t\t\tif \"OPTAttention\" in repr(type(lowerCamelCase_\t\t)\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = LoRALayer(module.q_proj\t\t, rank=16\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = LoRALayer(module.k_proj\t\t, rank=16\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = LoRALayer(module.v_proj\t\t, rank=16\t\t)\r\r # Step 3: dummy batch\r\t\t\t\t\t\ta\t\t\t\t\t = self.tokenizer(\"Test batch \"\t\t, return_tensors=\"pt\"\t\t).to(0\t\t)\r\r\t\t\t\t\t\t# Step 4: Check if the gradient is not None\r\t\t\t\t\t\twith torch.cuda.amp.autocast():\r\t\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t = model.forward(**lowerCamelCase_\t\t)\r\t\t\t\t\t\t\t\t\t\t\tout.logits.norm().backward()\r\r\t\t\t\t\t\tfor module in model.modules():\r\t\t\t\t\t\t\t\t\t\t\tif isinstance(lowerCamelCase_\t\t, lowerCamelCase_\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(module.adapter[1].weight.grad is not None\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(module.adapter[1].weight.grad.norm().item() > 0\t\t)\r\t\t\t\t\t\t\t\t\t\t\telif isinstance(lowerCamelCase_\t\t, nn.Embedding\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(module.weight.grad is None\t\t)\r\r\r\r\rclass \t_lowercase\t\t\t\t\t\t\t( lowerCAmelCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\t\"gpt2-xl\"\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\t3.3_191_854_854_152_187\r"},"code_codestyle":{"kind":"number","value":227,"string":"227"},"style_context":{"kind":"string","value":"\r\r\r\rimport unittest\r\rfrom transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer\rfrom transformers.testing_utils import get_tests_dir, require_sentencepiece, slow\rfrom transformers.utils import cached_property\r\rfrom ...test_tokenization_common import TokenizerTesterMixin\r\r\r_lowercase: Optional[int] \t\t\t=\t\tget_tests_dir(\"fixtures/test_sentencepiece.model\")\r\r\r\r@require_sentencepiece\rclass \t_lowercase\t\t\t\t\t\t\t( lowerCAmelCase, unittest.TestCase\t):\r\r\r\r\r\r\t\"\"\"simple docstring\"\"\"\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\tXLMProphetNetTokenizer\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\tFalse\r\t__A\t\t\t\t\t\t\t=\t\t\t\t\t\t\tTrue\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tsuper().setUp()\r\r\t\t\t\t\t\t# We have a SentencePiece fixture for testing\r\t\t\t\t\t\ta\t\t\t\t\t = XLMProphetNetTokenizer(lowerCamelCase_\t\t, keep_accents=lowerCamelCase_\t\t)\r\t\t\t\t\t\ttokenizer.save_pretrained(self.tmpdirname\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = \"[PAD]\"\r\t\t\t\t\t\ta\t\t\t\t\t = 0\r\r\t\t\t\t\t\tself.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_\t\t)\t\t, lowerCamelCase_\t\t)\r\t\t\t\t\t\tself.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_\t\t)\t\t, lowerCamelCase_\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = list(self.get_tokenizer().get_vocab().keys()\t\t)\r\r\t\t\t\t\t\tself.assertEqual(vocab_keys[0]\t\t, \"[PAD]\"\t\t)\r\t\t\t\t\t\tself.assertEqual(vocab_keys[1]\t\t, \"[CLS]\"\t\t)\r\t\t\t\t\t\tself.assertEqual(vocab_keys[-1]\t\t, \"j\"\t\t)\r\t\t\t\t\t\tself.assertEqual(len(lowerCamelCase_\t\t)\t\t, 1012\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\tself.assertEqual(self.get_tokenizer().vocab_size\t\t, 1012\t\t)\r\r\r\r\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = XLMProphetNetTokenizer(lowerCamelCase_\t\t, keep_accents=lowerCamelCase_\t\t)\r\r\t\t\t\t\t\ta\t\t\t\t\t = tokenizer.tokenize(\"This is a test\"\t\t)\r\t\t\t\t\t\tself.assertListEqual(lowerCamelCase_\t\t, [\"▁This\", \"▁is\", \"▁a\", \"▁t\", \"est\"]\t\t)\r\r\t\t\t\t\t\tself.assertListEqual(\r\t\t\t\t\t\t tokenizer.convert_tokens_to_ids(lowerCamelCase_\t\t)\t\t, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]\t\t, )\r\r\t\t\t\t\t\ta\t\t\t\t\t = tokenizer.tokenize(\"I was born in 92000, and this is falsé.\"\t\t)\r\t\t\t\t\t\tself.assertListEqual(\r\t\t\t\t\t\t lowerCamelCase_\t\t, [\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"I\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"was\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"b\",\r\t\t\t\t\t\t \"or\",\r\t\t\t\t\t\t \"n\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"in\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"\",\r\t\t\t\t\t\t \"9\",\r\t\t\t\t\t\t \"2\",\r\t\t\t\t\t\t \"0\",\r\t\t\t\t\t\t \"0\",\r\t\t\t\t\t\t \"0\",\r\t\t\t\t\t\t \",\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"and\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"this\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"is\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"f\",\r\t\t\t\t\t\t \"al\",\r\t\t\t\t\t\t \"s\",\r\t\t\t\t\t\t \"é\",\r\t\t\t\t\t\t \".\",\r\t\t\t\t\t\t ]\t\t, )\r\t\t\t\t\t\ta\t\t\t\t\t = tokenizer.convert_tokens_to_ids(lowerCamelCase_\t\t)\r\t\t\t\t\t\tself.assertListEqual(\r\t\t\t\t\t\t lowerCamelCase_\t\t, [\r\t\t\t\t\t\t value + tokenizer.fairseq_offset\r\t\t\t\t\t\t for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]\r\t\t\t\t\t\t ]\t\t, )\r\r\t\t\t\t\t\ta\t\t\t\t\t = tokenizer.convert_ids_to_tokens(lowerCamelCase_\t\t)\r\t\t\t\t\t\tself.assertListEqual(\r\t\t\t\t\t\t lowerCamelCase_\t\t, [\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"I\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"was\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"b\",\r\t\t\t\t\t\t \"or\",\r\t\t\t\t\t\t \"n\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"in\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"\",\r\t\t\t\t\t\t \"[UNK]\",\r\t\t\t\t\t\t \"2\",\r\t\t\t\t\t\t \"0\",\r\t\t\t\t\t\t \"0\",\r\t\t\t\t\t\t \"0\",\r\t\t\t\t\t\t \",\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"and\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"this\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"is\",\r\t\t\t\t\t\t SPIECE_UNDERLINE + \"f\",\r\t\t\t\t\t\t \"al\",\r\t\t\t\t\t\t \"s\",\r\t\t\t\t\t\t \"[UNK]\",\r\t\t\t\t\t\t \".\",\r\t\t\t\t\t\t ]\t\t, )\r\r\r\r\r\t@cached_property\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\treturn XLMProphetNetTokenizer.from_pretrained(\"microsoft/xprophetnet-large-wiki100-cased\"\t\t)\r\r\r\r\r\t@slow\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = \"Hello World!\"\r\t\t\t\t\t\ta\t\t\t\t\t = [35389, 6672, 49, 2]\r\t\t\t\t\t\tself.assertListEqual(lowerCamelCase_\t\t, self.big_tokenizer.encode(lowerCamelCase_\t\t)\t\t)\r\r\r\r\r\t@slow\r\tdef UpperCamelCase_ (self\t\t):\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\ta\t\t\t\t\t = {\"input_ids\": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], \"attention_mask\": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501\r\t\t\t\t\t\t# fmt: on\r\r\t\t\t\t\t\tself.tokenizer_integration_test_util(\r\t\t\t\t\t\t expected_encoding=lowerCamelCase_\t\t, model_name=\"microsoft/xprophetnet-large-wiki100-cased\"\t\t, revision=\"1acad1643ddd54a44df6a1b797ada8373685d90e\"\t\t, )\r"},"style_context_codestyle":{"kind":"number","value":227,"string":"227"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":549,"cells":{"code":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available\r\r\rSCREAMING_SNAKE_CASE\t\t\t: Optional[Any] =\t\t{\r '''configuration_pix2struct''': [\r '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',\r '''Pix2StructConfig''',\r '''Pix2StructTextConfig''',\r '''Pix2StructVisionConfig''',\r ],\r '''processing_pix2struct''': ['''Pix2StructProcessor'''],\r}\r\rtry:\r if not is_vision_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r SCREAMING_SNAKE_CASE\t\t\t: List[Any] =\t\t['''Pix2StructImageProcessor''']\r\r\rtry:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r SCREAMING_SNAKE_CASE\t\t\t: Tuple =\t\t[\r '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',\r '''Pix2StructPreTrainedModel''',\r '''Pix2StructForConditionalGeneration''',\r '''Pix2StructVisionModel''',\r '''Pix2StructTextModel''',\r ]\r\rif TYPE_CHECKING:\r from .configuration_pixastruct import (\r PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,\r PixaStructConfig,\r PixaStructTextConfig,\r PixaStructVisionConfig,\r )\r from .processing_pixastruct import PixaStructProcessor\r\r try:\r if not is_vision_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .image_processing_pixastruct import PixaStructImageProcessor\r\r try:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_pixastruct import (\r PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,\r PixaStructForConditionalGeneration,\r PixaStructPreTrainedModel,\r PixaStructTextModel,\r PixaStructVisionModel,\r )\r\relse:\r import sys\r\r SCREAMING_SNAKE_CASE\t\t\t: Any =\t\t_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)"},"code_codestyle":{"kind":"number","value":317,"string":"317"},"style_context":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rfrom typing import TYPE_CHECKING\r\r# rely on isort to merge the imports\rfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available\r\r\rSCREAMING_SNAKE_CASE\t\t\t: List[Any] =\t\t{'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}\r\r\rtry:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r pass\relse:\r SCREAMING_SNAKE_CASE\t\t\t: Union[str, Any] =\t\t[\r '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',\r '''FocalNetForImageClassification''',\r '''FocalNetForMaskedImageModeling''',\r '''FocalNetBackbone''',\r '''FocalNetModel''',\r '''FocalNetPreTrainedModel''',\r ]\r\rif TYPE_CHECKING:\r from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig\r\r try:\r if not is_torch_available():\r raise OptionalDependencyNotAvailable()\r except OptionalDependencyNotAvailable:\r pass\r else:\r from .modeling_focalnet import (\r FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,\r FocalNetBackbone,\r FocalNetForImageClassification,\r FocalNetForMaskedImageModeling,\r FocalNetModel,\r FocalNetPreTrainedModel,\r )\r\relse:\r import sys\r\r SCREAMING_SNAKE_CASE\t\t\t: Optional[Any] =\t\t_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)"},"style_context_codestyle":{"kind":"number","value":317,"string":"317"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":550,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport copy\r\nimport inspect\r\nimport unittest\r\n\r\nimport numpy as np\r\nfrom huggingface_hub import hf_hub_download\r\n\r\nfrom transformers import TimesformerConfig\r\nfrom transformers.models.auto import get_values\r\nfrom transformers.testing_utils import require_torch, require_vision, slow, torch_device\r\nfrom transformers.utils import cached_property, is_torch_available, is_vision_available\r\n\r\nfrom ...test_configuration_common import ConfigTester\r\nfrom ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor\r\nfrom ...test_pipeline_mixin import PipelineTesterMixin\r\n\r\n\r\nif is_torch_available():\r\n\t\t\timport torch\r\n\t\t\tfrom torch import nn\r\n\r\n\t\t\tfrom transformers import (\r\n\t\t\t MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,\r\n\t\t\t TimesformerForVideoClassification,\r\n\t\t\t TimesformerModel,\r\n\t\t\t)\r\n\t\t\tfrom transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST\r\n\r\n\r\nif is_vision_available():\r\n\t\t\tfrom transformers import VideoMAEImageProcessor\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass lowerCAmelCase__\t:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\tdef __init__( self , lowercase , lowercase=13 , lowercase=10 , lowercase=3 , lowercase=2 , lowercase=2 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase=\"gelu\" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.02 , lowercase=\"divided_space_time\" , lowercase=None , ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tTuple = parent\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = batch_size\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tTuple = image_size\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = num_channels\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = patch_size\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = num_frames\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = is_training\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = use_labels\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = hidden_size\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = num_hidden_layers\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = num_attention_heads\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = intermediate_size\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = hidden_act\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = hidden_dropout_prob\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = attention_probs_dropout_prob\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = attention_type\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = initializer_range\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = scope\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = num_labels\r\n\r\n\t\t\t\t\t\t\t# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = (image_size // patch_size) ** 2\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = (num_frames) * self.num_patches_per_frame + 1\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = floats_tensor(\r\n\t\t\t\t\t\t\t [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )\r\n\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = None\r\n\t\t\t\t\t\t\tif self.use_labels:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = ids_tensor([self.batch_size] , self.num_labels )\r\n\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = self.get_config()\r\n\r\n\t\t\t\t\t\t\treturn config, pixel_values, labels\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = TimesformerConfig(\r\n\t\t\t\t\t\t\t image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = self.num_labels\r\n\t\t\t\t\t\t\treturn config\r\n\t\t\tdef \t\t\t\t\tA_ ( self , lowercase , lowercase , lowercase ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = TimesformerModel(config=lowercase )\r\n\t\t\t\t\t\t\tmodel.to(lowercase )\r\n\t\t\t\t\t\t\tmodel.eval()\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = model(lowercase )\r\n\t\t\t\t\t\t\tself.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )\r\n\t\t\tdef \t\t\t\t\tA_ ( self , lowercase , lowercase , lowercase ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = TimesformerForVideoClassification(lowercase )\r\n\t\t\t\t\t\t\tmodel.to(lowercase )\r\n\t\t\t\t\t\t\tmodel.eval()\r\n\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = model(lowercase )\r\n\r\n\t\t\t\t\t\t\t# verify the logits shape\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = torch.Size((self.batch_size, self.num_labels) )\r\n\t\t\t\t\t\t\tself.parent.assertEqual(result.logits.shape , lowercase )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = self.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\t_lowerCamelCase, _lowerCamelCase, _lowerCamelCase :\t\t\tList[str] = config_and_inputs\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = {'pixel_values': pixel_values}\r\n\t\t\t\t\t\t\treturn config, inputs_dict\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_torch\r\nclass lowerCAmelCase__\t( lowercase,\tlowercase,\tunittest.TestCase\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\tlowerCamelCase__ =\t\t\t(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()\r\n\t\t\tlowerCamelCase__ =\t\t\t(\r\n\t\t\t {\"\"\"feature-extraction\"\"\": TimesformerModel, \"\"\"video-classification\"\"\": TimesformerForVideoClassification}\r\n\t\t\t if is_torch_available()\r\n\t\t\t else {}\r\n\t\t\t)\r\n\r\n\t\t\tlowerCamelCase__ =\t\t\tFalse\r\n\t\t\tlowerCamelCase__ =\t\t\tFalse\r\n\t\t\tlowerCamelCase__ =\t\t\tFalse\r\n\t\t\tlowerCamelCase__ =\t\t\tFalse\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = TimesformerModelTester(self )\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = ConfigTester(\r\n\t\t\t\t\t\t\t self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )\r\n\t\t\tdef \t\t\t\t\tA_ ( self , lowercase , lowercase , lowercase=False ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tTuple = copy.deepcopy(lowercase )\r\n\r\n\t\t\t\t\t\t\tif return_labels:\r\n\t\t\t\t\t\t\t\t\t\t\tif model_class in get_values(lowercase ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = torch.zeros(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.model_tester.batch_size , dtype=torch.long , device=lowercase )\r\n\r\n\t\t\t\t\t\t\treturn inputs_dict\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\tself.config_tester.run_common_tests()\r\n\t\t\t@unittest.skip(reason='TimeSformer does not use inputs_embeds' )\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\tpass\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase, _lowerCamelCase :\t\t\tDict = self.model_tester.prepare_config_and_inputs_for_common()\r\n\r\n\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = model_class(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(model.get_input_embeddings() , (nn.Module) )\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = model.get_output_embeddings()\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(x is None or isinstance(lowercase , nn.Linear ) )\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase, _lowerCamelCase :\t\t\tTuple = self.model_tester.prepare_config_and_inputs_for_common()\r\n\r\n\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = model_class(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = inspect.signature(model.forward )\r\n\t\t\t\t\t\t\t\t\t\t\t# signature.parameters is an OrderedDict => so arg_names order is deterministic\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = [*signature.parameters.keys()]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = ['pixel_values']\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(arg_names[:1] , lowercase )\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = self.model_tester.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\tself.model_tester.create_and_check_model(*lowercase )\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = self.model_tester.prepare_config_and_inputs()\r\n\t\t\t\t\t\t\tself.model_tester.create_and_check_for_video_classification(*lowercase )\r\n\t\t\t@slow\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\tfor model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = TimesformerModel.from_pretrained(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertIsNotNone(lowercase )\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\tif not self.has_attentions:\r\n\t\t\t\t\t\t\t\t\t\t\tpass\r\n\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase, _lowerCamelCase :\t\t\tList[Any] = self.model_tester.prepare_config_and_inputs_for_common()\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = True\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = self.model_tester.seq_length\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = self.model_tester.num_frames\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = False\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = model_class(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.to(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.eval()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = model(**self._prepare_for_class(lowercase , lowercase ) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = outputs.attentions\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# check that output_attentions also work using config\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel inputs_dict[\"output_attentions\"]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = model_class(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.to(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.eval()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = model(**self._prepare_for_class(lowercase , lowercase ) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = outputs.attentions\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = len(lowercase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Check attention is always last and order is fine\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = model_class(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.to(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.eval()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = model(**self._prepare_for_class(lowercase , lowercase ) )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(out_len + 1 , len(lowercase ) )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = outputs.attentions\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\tdef check_hidden_states_output(lowercase , lowercase , lowercase ):\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = model_class(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\tmodel.to(lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\tmodel.eval()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = model(**self._prepare_for_class(lowercase , lowercase ) )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = outputs.hidden_states\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = self.model_tester.num_hidden_layers + 1\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(len(lowercase ) , lowercase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = self.model_tester.seq_length\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )\r\n\r\n\t\t\t\t\t\t\t_lowerCamelCase, _lowerCamelCase :\t\t\tAny = self.model_tester.prepare_config_and_inputs_for_common()\r\n\r\n\t\t\t\t\t\t\tfor model_class in self.all_model_classes:\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tTuple = True\r\n\t\t\t\t\t\t\t\t\t\t\tcheck_hidden_states_output(lowercase , lowercase , lowercase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# check that output_hidden_states also work using config\r\n\t\t\t\t\t\t\t\t\t\t\tdel inputs_dict[\"output_hidden_states\"]\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = True\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tcheck_hidden_states_output(lowercase , lowercase , lowercase )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\t):\r\n\t\t\t\t_lowerCamelCase :\t\t\tstr = hf_hub_download(\r\n\t\t\t\t repo_id='hf-internal-testing/spaghetti-video'\t\t\t\t\t\t\t, filename='eating_spaghetti.npy'\t\t\t\t\t\t\t, repo_type='dataset'\t\t)\r\n\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = np.load(lowercase__\t\t)\r\n\t\t\t\treturn list(lowercase__\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_torch\r\n@require_vision\r\nclass lowerCAmelCase__\t( unittest.TestCase\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t@cached_property\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t# logits were tested with a different mean and std, so we use the same here\r\n\t\t\t\t\t\t\treturn (\r\n\t\t\t\t\t\t\t VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )\r\n\t\t\t\t\t\t\t if is_vision_available()\r\n\t\t\t\t\t\t\t else None\r\n\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t@slow\r\n\t\t\tdef \t\t\t\t\tA_ ( self ):\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400' ).to(\r\n\t\t\t\t\t\t\t lowercase )\r\n\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = self.default_image_processor\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = prepare_video()\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = image_processor(video[:8] , return_tensors='pt' ).to(lowercase )\r\n\r\n\t\t\t\t\t\t\t# forward pass\r\n\t\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = model(**lowercase )\r\n\r\n\t\t\t\t\t\t\t# verify the logits\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = torch.Size((1, 400) )\r\n\t\t\t\t\t\t\tself.assertEqual(outputs.logits.shape , lowercase )\r\n\r\n\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = torch.tensor([-0.30_16, -0.77_13, -0.42_05] ).to(lowercase )\r\n\r\n\t\t\t\t\t\t\tself.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1E-4 ) )"},"code_codestyle":{"kind":"number","value":96,"string":"96"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\nclass SCREAMING_SNAKE_CASE__ :\r\n\r\n\r\n\r\n def __init__(\t\t\t\t\t\t\tself,__lowerCamelCase ):\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tset_counts\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tmax(__lowerCamelCase )\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tlen(__lowerCamelCase )\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t[1] * num_sets\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tlist(range(__lowerCamelCase ) )\r\n\r\n\r\n\r\n def \t\t\t\tUpperCamelCase\t\t\t(\t\t\t\t\t\t\tself,__lowerCamelCase,__lowerCamelCase ):\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tself.get_parent(__lowerCamelCase )\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tself.get_parent(__lowerCamelCase )\r\n\r\n if src_parent == dst_parent:\r\n return False\r\n\r\n if self.ranks[dst_parent] >= self.ranks[src_parent]:\r\n self.set_counts[dst_parent] += self.set_counts[src_parent]\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t0\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tdst_parent\r\n if self.ranks[dst_parent] == self.ranks[src_parent]:\r\n self.ranks[dst_parent] += 1\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tself.set_counts[dst_parent]\r\n else:\r\n self.set_counts[src_parent] += self.set_counts[dst_parent]\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t0\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tsrc_parent\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tself.set_counts[src_parent]\r\n\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tmax(self.max_set,__lowerCamelCase )\r\n return True\r\n\r\n\r\n\r\n\r\n def \t\t\t\tUpperCamelCase\t\t\t(\t\t\t\t\t\t\tself,__lowerCamelCase ):\r\n if self.parents[disj_set] == disj_set:\r\n return disj_set\r\n A__\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tself.get_parent(self.parents[disj_set] )\r\n return self.parents[disj_set]\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":193,"string":"193"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":551,"cells":{"code":{"kind":"string","value":"\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\nimport warnings\r\nfrom typing import List, Optional, Union\r\n\r\nfrom ...processing_utils import ProcessorMixin\r\nfrom ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy\r\nfrom ...utils import TensorType\r\n\r\n\r\n\r\nclass a ( _lowerCamelCase\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n UpperCAmelCase \t= [\"image_processor\", \"tokenizer\"]\r\n UpperCAmelCase \t= \"LayoutLMv3ImageProcessor\"\r\n UpperCAmelCase \t= (\"LayoutLMv3Tokenizer\", \"LayoutLMv3TokenizerFast\")\r\n\r\n def __init__( self:\tTuple , UpperCamelCase:\tUnion[str, Any]=None , UpperCamelCase:\tTuple=None , **UpperCamelCase:\tDict ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tNone\r\n if \"feature_extractor\" in kwargs:\r\n warnings.warn(\r\n \"\"\"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`\"\"\"\r\n \"\"\" instead.\"\"\" , lowercase_ , )\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tkwargs.pop(\"\"\"feature_extractor\"\"\" )\r\n\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\timage_processor if image_processor is not None else feature_extractor\r\n if image_processor is None:\r\n raise ValueError(\"\"\"You need to specify an `image_processor`.\"\"\" )\r\n if tokenizer is None:\r\n raise ValueError(\"\"\"You need to specify a `tokenizer`.\"\"\" )\r\n\r\n super().__init__(lowercase_ , lowercase_ )\r\n\r\n def __call__( self:\tOptional[Any] , UpperCamelCase:\tAny , UpperCamelCase:\tUnion[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCamelCase:\tOptional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , UpperCamelCase:\tUnion[List[List[int]], List[List[List[int]]]] = None , UpperCamelCase:\tOptional[Union[List[int], List[List[int]]]] = None , UpperCamelCase:\tbool = True , UpperCamelCase:\tUnion[bool, str, PaddingStrategy] = False , UpperCamelCase:\tUnion[bool, str, TruncationStrategy] = None , UpperCamelCase:\tOptional[int] = None , UpperCamelCase:\tint = 0 , UpperCamelCase:\tOptional[int] = None , UpperCamelCase:\tOptional[bool] = None , UpperCamelCase:\tOptional[bool] = None , UpperCamelCase:\tbool = False , UpperCamelCase:\tbool = False , UpperCamelCase:\tbool = False , UpperCamelCase:\tbool = False , UpperCamelCase:\tbool = True , UpperCamelCase:\tOptional[Union[str, TensorType]] = None , **UpperCamelCase:\tList[str] , ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n if self.image_processor.apply_ocr and (boxes is not None):\r\n raise ValueError(\r\n \"\"\"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.\"\"\" )\r\n\r\n if self.image_processor.apply_ocr and (word_labels is not None):\r\n raise ValueError(\r\n \"\"\"You cannot provide word labels if you initialized the image processor with apply_ocr set to True.\"\"\" )\r\n\r\n # first, apply the image processor\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tself.image_processor(images=lowercase_ , return_tensors=lowercase_ )\r\n\r\n # second, apply the tokenizer\r\n if text is not None and self.image_processor.apply_ocr and text_pair is None:\r\n if isinstance(lowercase_ , lowercase_ ):\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\t[text] # add batch dimension (as the image processor always adds a batch dimension)\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tfeatures[\"\"\"words\"\"\"]\r\n\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tself.tokenizer(\r\n text=text if text is not None else features[\"\"\"words\"\"\"] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features[\"\"\"boxes\"\"\"] , word_labels=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_token_type_ids=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )\r\n\r\n # add pixel values\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tfeatures.pop(\"\"\"pixel_values\"\"\" )\r\n if return_overflowing_tokens is True:\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tself.get_overflowing_images(lowercase_ , encoded_inputs[\"\"\"overflow_to_sample_mapping\"\"\"] )\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\timages\r\n\r\n return encoded_inputs\r\n\r\n def \t\t\tUpperCamelCase\t\t( self:\tstr , UpperCamelCase:\tList[str] , UpperCamelCase:\tList[str] ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\t[]\r\n for sample_idx in overflow_to_sample_mapping:\r\n images_with_overflow.append(images[sample_idx] )\r\n\r\n if len(lowercase_ ) != len(lowercase_ ):\r\n raise ValueError(\r\n \"\"\"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got\"\"\"\r\n f\"\"\" {len(lowercase_ )} and {len(lowercase_ )}\"\"\" )\r\n\r\n return images_with_overflow\r\n\r\n def \t\t\tUpperCamelCase\t\t( self:\tint , *UpperCamelCase:\tTuple , **UpperCamelCase:\tTuple ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )\r\n\r\n def \t\t\tUpperCamelCase\t\t( self:\tAny , *UpperCamelCase:\tUnion[str, Any] , **UpperCamelCase:\tDict ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n return self.tokenizer.decode(*lowercase_ , **lowercase_ )\r\n\r\n @property\r\n def \t\t\tUpperCamelCase\t\t( self:\tOptional[Any] ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n return [\"input_ids\", \"bbox\", \"attention_mask\", \"pixel_values\"]\r\n\r\n @property\r\n def \t\t\tUpperCamelCase\t\t( self:\tint ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n warnings.warn(\r\n \"\"\"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.\"\"\" , lowercase_ , )\r\n return self.image_processor_class\r\n\r\n\r\n\r\n\r\n\r\n @property\r\n def \t\t\tUpperCamelCase\t\t( self:\tAny ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n warnings.warn(\r\n \"\"\"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.\"\"\" , lowercase_ , )\r\n return self.image_processor\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":369,"string":"369"},"style_context":{"kind":"string","value":"\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\nfrom typing import List, Union\r\n\r\nfrom ..utils import (\r\n add_end_docstrings,\r\n is_tf_available,\r\n is_torch_available,\r\n is_vision_available,\r\n logging,\r\n requires_backends,\r\n)\r\nfrom .base import PIPELINE_INIT_ARGS, Pipeline\r\n\r\n\r\nif is_vision_available():\r\n from PIL import Image\r\n\r\n from ..image_utils import load_image\r\n\r\nif is_tf_available():\r\n import tensorflow as tf\r\n\r\n from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r\n from ..tf_utils import stable_softmax\r\n\r\nif is_torch_available():\r\n from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r\n\r\nSCREAMING_SNAKE_CASE_ :\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t= logging.get_logger(__name__)\r\n\r\n\r\n\r\n@add_end_docstrings(_lowerCamelCase\t\t\t)\r\nclass a ( _lowerCamelCase\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n def __init__( self:\tUnion[str, Any] , *UpperCamelCase:\tList[str] , **UpperCamelCase:\tUnion[str, Any] ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n super().__init__(*UpperCamelCase , **UpperCamelCase )\r\n requires_backends(self , \"\"\"vision\"\"\" )\r\n self.check_model_type(\r\n TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\r\n if self.framework == \"\"\"tf\"\"\"\r\n else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )\r\n\r\n def \t\t\tUpperCamelCase\t\t( self:\tList[str] , UpperCamelCase:\tAny=None ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\t{}\r\n if top_k is not None:\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\ttop_k\r\n return {}, {}, postprocess_params\r\n\r\n def __call__( self:\tUnion[str, Any] , UpperCamelCase:\tUnion[str, List[str], \"Image.Image\", List[\"Image.Image\"]] , **UpperCamelCase:\tDict ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n return super().__call__(UpperCamelCase , **UpperCamelCase )\r\n\r\n def \t\t\tUpperCamelCase\t\t( self:\tAny , UpperCamelCase:\tint ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tload_image(UpperCamelCase )\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tself.image_processor(images=UpperCamelCase , return_tensors=self.framework )\r\n return model_inputs\r\n\r\n def \t\t\tUpperCamelCase\t\t( self:\tList[Any] , UpperCamelCase:\tAny ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tself.model(**UpperCamelCase )\r\n return model_outputs\r\n\r\n\r\n\r\n\r\n\r\n def \t\t\tUpperCamelCase\t\t( self:\tAny , UpperCamelCase:\tOptional[Any] , UpperCamelCase:\tint=5 ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n if top_k > self.model.config.num_labels:\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tself.model.config.num_labels\r\n\r\n if self.framework == \"pt\":\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tmodel_outputs.logits.softmax(-1 )[0]\r\n A__ ,\t\t\tA__\t\t\t\t\t\t\t =\t\t\t\t\tprobs.topk(UpperCamelCase )\r\n elif self.framework == \"tf\":\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tstable_softmax(model_outputs.logits , axis=-1 )[0]\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\ttf.math.top_k(UpperCamelCase , k=UpperCamelCase )\r\n A__ ,\t\t\tA__\t\t\t\t\t\t\t =\t\t\t\t\ttopk.values.numpy(), topk.indices.numpy()\r\n else:\r\n raise ValueError(f\"\"\"Unsupported framework: {self.framework}\"\"\" )\r\n\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tscores.tolist()\r\n A__\t\t\t\t\t\t\t =\t\t\t\t\tids.tolist()\r\n return [{\"score\": score, \"label\": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase , UpperCamelCase )]\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":69,"string":"69"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":552,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport unittest\r\n\r\nimport torch\r\n\r\nfrom diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel\r\nfrom diffusers.training_utils import set_seed\r\nfrom diffusers.utils.testing_utils import slow\r\n\r\n\r\n_A\t:\t\t\tUnion[str, Any] =False\r\n\r\nclass _lowercase\t(\t\t\t\tunittest.TestCase\t\t):\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tdef \t\tlowerCamelCase_\t\t\t\t(\t\t\t\t\t\t\tself:\t\t\t\tint\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__:\t\t\t\tDict=32 ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tset_seed(0 )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tstr = UNetaDModel(sample_size=UpperCamelCase__\t\t\t,\t\t\t\t\t\t\tin_channels=3\t\t\t,\t\t\t\t\t\t\tout_channels=3 )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tDict = torch.optim.SGD(model.parameters()\t\t\t,\t\t\t\t\t\t\tlr=0.0_001 )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn model, optimizer\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t@slow\r\n\t\t\t\t\t\t\tdef \t\tlowerCamelCase_\t\t\t\t(\t\t\t\t\t\t\tself:\t\t\t\tDict ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tAny = \"\"\"cpu\"\"\" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tint = DDPMScheduler(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t num_train_timesteps=1_000\t\t\t,\t\t\t\t\t\t\tbeta_start=0.0_001\t\t\t,\t\t\t\t\t\t\tbeta_end=0.02\t\t\t,\t\t\t\t\t\t\tbeta_schedule=\"\"\"linear\"\"\"\t\t\t,\t\t\t\t\t\t\tclip_sample=UpperCamelCase__\t\t\t,\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tint = DDIMScheduler(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t num_train_timesteps=1_000\t\t\t,\t\t\t\t\t\t\tbeta_start=0.0_001\t\t\t,\t\t\t\t\t\t\tbeta_end=0.02\t\t\t,\t\t\t\t\t\t\tbeta_schedule=\"\"\"linear\"\"\"\t\t\t,\t\t\t\t\t\t\tclip_sample=UpperCamelCase__\t\t\t,\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tassert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# shared batches for DDPM and DDIM\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tset_seed(0 )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tTuple = [torch.randn((4, 3, 32, 32) ).clip(-1\t\t\t,\t\t\t\t\t\t\t1 ).to(UpperCamelCase__ ) for _ in range(4 )]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tstr = [torch.randn((4, 3, 32, 32) ).to(UpperCamelCase__ ) for _ in range(4 )]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tTuple = [torch.randint(0\t\t\t,\t\t\t\t\t\t\t1_000\t\t\t,\t\t\t\t\t\t\t(4,) ).long().to(UpperCamelCase__ ) for _ in range(4 )]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# train with a DDPM scheduler\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__ ,\t\tlowerCamelCase__\t\t\t\t\t:\tint = self.get_model_optimizer(resolution=32 )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.train().to(UpperCamelCase__ )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor i in range(4 ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\toptimizer.zero_grad()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tAny = ddpm_scheduler.add_noise(clean_images[i]\t\t\t,\t\t\t\t\t\t\tnoise[i]\t\t\t,\t\t\t\t\t\t\ttimesteps[i] )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tList[Any] = model(UpperCamelCase__\t\t\t,\t\t\t\t\t\t\ttimesteps[i] ).sample\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tUnion[str, Any] = torch.nn.functional.mse_loss(UpperCamelCase__\t\t\t,\t\t\t\t\t\t\tnoise[i] )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tloss.backward()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\toptimizer.step()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel model, optimizer\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# recreate the model and optimizer, and retry with DDIM\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__ ,\t\tlowerCamelCase__\t\t\t\t\t:\tAny = self.get_model_optimizer(resolution=32 )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.train().to(UpperCamelCase__ )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor i in range(4 ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\toptimizer.zero_grad()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tUnion[str, Any] = ddim_scheduler.add_noise(clean_images[i]\t\t\t,\t\t\t\t\t\t\tnoise[i]\t\t\t,\t\t\t\t\t\t\ttimesteps[i] )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tList[Any] = model(UpperCamelCase__\t\t\t,\t\t\t\t\t\t\ttimesteps[i] ).sample\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t\t\t\t:\tint = torch.nn.functional.mse_loss(UpperCamelCase__\t\t\t,\t\t\t\t\t\t\tnoise[i] )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tloss.backward()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\toptimizer.step()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel model, optimizer\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(torch.allclose(UpperCamelCase__\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__\t\t\t,\t\t\t\t\t\t\tatol=1e-5 ) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(torch.allclose(UpperCamelCase__\t\t\t,\t\t\t\t\t\t\tUpperCamelCase__\t\t\t,\t\t\t\t\t\t\tatol=1e-5 ) )\r\n\r\n"},"code_codestyle":{"kind":"number","value":41,"string":"41"},"style_context":{"kind":"string","value":"import torch\n\nfrom diffusers import KDPMaDiscreteScheduler\nfrom diffusers.utils import torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\n\n\n\n\n\nclass \t__snake_case ( lowerCamelCase__ ):\n __lowerCamelCase\t\t\t: Optional[int] =\t\t\t\t\t(KDPMaDiscreteScheduler,)\n __lowerCamelCase\t\t\t: List[str] =\t\t\t\t\t10\n def \t\tUpperCAmelCase__\t\t\t\t( self\t\t\t\t\t\t, **snake_case__ )\t\t->\t\t\t\t\t\t\tstr:\n '''simple docstring'''\n\n\n\n\n\n UpperCAmelCase\t\t\t: int\t\t\t ={\n '''num_train_timesteps''': 1100,\n '''beta_start''': 0.0001,\n '''beta_end''': 0.02,\n '''beta_schedule''': '''linear''',\n }\n\n config.update(**snake_case__ )\n return config\n def \t\tUpperCAmelCase__\t\t\t\t( self )\t\t->\t\t\t\t\t\t\tTuple:\n '''simple docstring'''\n\n\n\n\n\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=snake_case__ )\n def \t\tUpperCAmelCase__\t\t\t\t( self )\t\t->\t\t\t\t\t\t\tOptional[int]:\n '''simple docstring'''\n\n\n\n\n\n for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]\t\t\t\t\t\t, [0.0002, 0.002, 0.02] ):\n self.check_over_configs(beta_start=snake_case__\t\t\t\t\t\t, beta_end=snake_case__ )\n def \t\tUpperCAmelCase__\t\t\t\t( self )\t\t->\t\t\t\t\t\t\tstr:\n '''simple docstring'''\n\n\n\n\n\n for schedule in [\"linear\", \"scaled_linear\"]:\n self.check_over_configs(beta_schedule=snake_case__ )\n def \t\tUpperCAmelCase__\t\t\t\t( self )\t\t->\t\t\t\t\t\t\tDict:\n '''simple docstring'''\n\n\n\n\n\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=snake_case__ )\n def \t\tUpperCAmelCase__\t\t\t\t( self )\t\t->\t\t\t\t\t\t\tstr:\n '''simple docstring'''\n\n\n\n\n\n UpperCAmelCase\t\t\t: Optional[Any]\t\t\t =self.scheduler_classes[0]\n UpperCAmelCase\t\t\t: Optional[int]\t\t\t =self.get_scheduler_config(prediction_type='''v_prediction''' )\n UpperCAmelCase\t\t\t: Optional[Any]\t\t\t =scheduler_class(**snake_case__ )\n\n scheduler.set_timesteps(self.num_inference_steps )\n\n UpperCAmelCase\t\t\t: str\t\t\t =self.dummy_model()\n UpperCAmelCase\t\t\t: Optional[Any]\t\t\t =self.dummy_sample_deter * scheduler.init_noise_sigma\n UpperCAmelCase\t\t\t: Union[str, Any]\t\t\t =sample.to(snake_case__ )\n\n for i, t in enumerate(scheduler.timesteps ):\n UpperCAmelCase\t\t\t: str\t\t\t =scheduler.scale_model_input(snake_case__\t\t\t\t\t\t, snake_case__ )\n\n UpperCAmelCase\t\t\t: Any\t\t\t =model(snake_case__\t\t\t\t\t\t, snake_case__ )\n\n UpperCAmelCase\t\t\t: Union[str, Any]\t\t\t =scheduler.step(snake_case__\t\t\t\t\t\t, snake_case__\t\t\t\t\t\t, snake_case__ )\n UpperCAmelCase\t\t\t: int\t\t\t =output.prev_sample\n\n UpperCAmelCase\t\t\t: Dict\t\t\t =torch.sum(torch.abs(snake_case__ ) )\n UpperCAmelCase\t\t\t: Optional[Any]\t\t\t =torch.mean(torch.abs(snake_case__ ) )\n\n if torch_device in [\"cpu\", \"mps\"]:\n assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2\n assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3\n else:\n # CUDA\n assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2\n assert abs(result_mean.item() - 0.0002 ) < 1e-3\n def \t\tUpperCAmelCase__\t\t\t\t( self )\t\t->\t\t\t\t\t\t\tint:\n '''simple docstring'''\n\n\n\n\n\n if torch_device == \"mps\":\n return\n UpperCAmelCase\t\t\t: Any\t\t\t =self.scheduler_classes[0]\n UpperCAmelCase\t\t\t: Optional[int]\t\t\t =self.get_scheduler_config()\n UpperCAmelCase\t\t\t: Optional[Any]\t\t\t =scheduler_class(**snake_case__ )\n\n scheduler.set_timesteps(self.num_inference_steps )\n\n UpperCAmelCase\t\t\t: Optional[int]\t\t\t =self.dummy_model()\n UpperCAmelCase\t\t\t: Union[str, Any]\t\t\t =self.dummy_sample_deter * scheduler.init_noise_sigma\n UpperCAmelCase\t\t\t: str\t\t\t =sample.to(snake_case__ )\n\n for i, t in enumerate(scheduler.timesteps ):\n UpperCAmelCase\t\t\t: Dict\t\t\t =scheduler.scale_model_input(snake_case__\t\t\t\t\t\t, snake_case__ )\n\n UpperCAmelCase\t\t\t: Union[str, Any]\t\t\t =model(snake_case__\t\t\t\t\t\t, snake_case__ )\n\n UpperCAmelCase\t\t\t: List[str]\t\t\t =scheduler.step(snake_case__\t\t\t\t\t\t, snake_case__\t\t\t\t\t\t, snake_case__ )\n UpperCAmelCase\t\t\t: Optional[int]\t\t\t =output.prev_sample\n\n UpperCAmelCase\t\t\t: Any\t\t\t =torch.sum(torch.abs(snake_case__ ) )\n UpperCAmelCase\t\t\t: Union[str, Any]\t\t\t =torch.mean(torch.abs(snake_case__ ) )\n\n if torch_device in [\"cpu\", \"mps\"]:\n assert abs(result_sum.item() - 20.4125 ) < 1e-2\n assert abs(result_mean.item() - 0.0266 ) < 1e-3\n else:\n # CUDA\n assert abs(result_sum.item() - 20.4125 ) < 1e-2\n assert abs(result_mean.item() - 0.0266 ) < 1e-3\n\n\n\n\n\n\n def \t\tUpperCAmelCase__\t\t\t\t( self )\t\t->\t\t\t\t\t\t\tstr:\n '''simple docstring'''\n\n\n\n\n\n if torch_device == \"mps\":\n return\n UpperCAmelCase\t\t\t: List[Any]\t\t\t =self.scheduler_classes[0]\n UpperCAmelCase\t\t\t: Dict\t\t\t =self.get_scheduler_config()\n UpperCAmelCase\t\t\t: List[str]\t\t\t =scheduler_class(**snake_case__ )\n\n scheduler.set_timesteps(self.num_inference_steps\t\t\t\t\t\t, device=snake_case__ )\n\n UpperCAmelCase\t\t\t: int\t\t\t =self.dummy_model()\n UpperCAmelCase\t\t\t: Tuple\t\t\t =self.dummy_sample_deter.to(snake_case__ ) * scheduler.init_noise_sigma\n\n for t in scheduler.timesteps:\n UpperCAmelCase\t\t\t: Optional[Any]\t\t\t =scheduler.scale_model_input(snake_case__\t\t\t\t\t\t, snake_case__ )\n\n UpperCAmelCase\t\t\t: int\t\t\t =model(snake_case__\t\t\t\t\t\t, snake_case__ )\n\n UpperCAmelCase\t\t\t: str\t\t\t =scheduler.step(snake_case__\t\t\t\t\t\t, snake_case__\t\t\t\t\t\t, snake_case__ )\n UpperCAmelCase\t\t\t: List[str]\t\t\t =output.prev_sample\n\n UpperCAmelCase\t\t\t: List[str]\t\t\t =torch.sum(torch.abs(snake_case__ ) )\n UpperCAmelCase\t\t\t: Dict\t\t\t =torch.mean(torch.abs(snake_case__ ) )\n\n if str(snake_case__ ).startswith('''cpu''' ):\n # The following sum varies between 148 and 156 on mps. Why?\n assert abs(result_sum.item() - 20.4125 ) < 1e-2\n assert abs(result_mean.item() - 0.0266 ) < 1e-3\n else:\n # CUDA\n assert abs(result_sum.item() - 20.4125 ) < 1e-2\n assert abs(result_mean.item() - 0.0266 ) < 1e-3\n"},"style_context_codestyle":{"kind":"number","value":348,"string":"348"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":553,"cells":{"code":{"kind":"string","value":"\r\nfrom multiprocessing import Lock, Pipe, Process\r\n\r\n# lock used to ensure that two processes do not access a pipe at the same time\r\n_A =\t\t\tLock()\r\n\r\n\r\n\r\ndef \t\t\t\t\t\tlowercase_ ( A__\t\t\t,\t\tA__\t\t\t,\t\tA__\t\t\t,\t\tA__\t\t\t,\t\tA__\t\t\t,\t\tA__\t\t\t,\t\tA__\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tAny:\r\n \"\"\"simple docstring\"\"\"\r\n\r\n global process_lock\r\n\r\n # we perform n swaps since after n swaps we know we are sorted\r\n # we *could* stop early if we are sorted already, but it takes as long to\r\n # find out we are sorted as it does to sort the list with this algorithm\r\n for i in range(0\t\t\t,\t\t10\t\t):\r\n if (i + position) % 2 == 0 and r_send is not None:\r\n # send your value to your right neighbor\r\n process_lock.acquire()\r\n r_send[1].send(__a\t\t)\r\n process_lock.release()\r\n\r\n # receive your right neighbor's value\r\n process_lock.acquire()\r\n snake_case\t\t\t\t\t\t\t\t= rr_cv[0].recv()\r\n process_lock.release()\r\n\r\n # take the lower value since you are on the left\r\n snake_case\t\t\t\t\t\t\t\t= min(__a\t\t\t,\t\t__a\t\t)\r\n elif (i + position) % 2 != 0 and l_send is not None:\r\n # send your value to your left neighbor\r\n process_lock.acquire()\r\n l_send[1].send(__a\t\t)\r\n process_lock.release()\r\n\r\n # receive your left neighbor's value\r\n process_lock.acquire()\r\n snake_case\t\t\t\t\t\t\t\t= lr_cv[0].recv()\r\n process_lock.release()\r\n\r\n # take the higher value since you are on the right\r\n snake_case\t\t\t\t\t\t\t\t= max(__a\t\t\t,\t\t__a\t\t)\r\n # after all swaps are performed, send the values back to main\r\n result_pipe[1].send(__a\t\t)\r\n\r\n\r\n\r\ndef \t\t\t\t\t\tlowercase_ ( A__\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tOptional[Any]:\r\n \"\"\"simple docstring\"\"\"\r\n\r\n snake_case\t\t\t\t\t\t\t\t= []\r\n snake_case\t\t\t\t\t\t\t\t= []\r\n # initialize the list of pipes where the values will be retrieved\r\n for _ in arr:\r\n result_pipe.append(Pipe()\t\t)\r\n # creates the processes\r\n # the first and last process only have one neighbor so they are made outside\r\n # of the loop\r\n snake_case\t\t\t\t\t\t\t\t= Pipe()\r\n snake_case\t\t\t\t\t\t\t\t= Pipe()\r\n process_array_.append(\r\n Process(\r\n target=__a\t\t\t,\t\targs=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])\t\t\t,\t\t)\t\t)\r\n snake_case\t\t\t\t\t\t\t\t= temp_rs\r\n snake_case\t\t\t\t\t\t\t\t= temp_rr\r\n\r\n for i in range(1\t\t\t,\t\tlen(__a\t\t) - 1\t\t):\r\n snake_case\t\t\t\t\t\t\t\t= Pipe()\r\n snake_case\t\t\t\t\t\t\t\t= Pipe()\r\n process_array_.append(\r\n Process(\r\n target=__a\t\t\t,\t\targs=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])\t\t\t,\t\t)\t\t)\r\n snake_case\t\t\t\t\t\t\t\t= temp_rs\r\n snake_case\t\t\t\t\t\t\t\t= temp_rr\r\n\r\n process_array_.append(\r\n Process(\r\n target=__a\t\t\t,\t\targs=(\r\n len(__a\t\t) - 1,\r\n arr[len(__a\t\t) - 1],\r\n temp_ls,\r\n None,\r\n temp_lr,\r\n None,\r\n result_pipe[len(__a\t\t) - 1],\r\n )\t\t\t,\t\t)\t\t)\r\n\r\n # start the processes\r\n for p in process_array_:\r\n p.start()\r\n\r\n # wait for the processes to end and write their values to the list\r\n for p in range(0\t\t\t,\t\tlen(__a\t\t)\t\t):\r\n snake_case\t\t\t\t\t\t\t\t= result_pipe[p][0].recv()\r\n process_array_[p].join()\r\n return arr\r\n\r\n\r\n\r\ndef \t\t\t\t\t\tlowercase_ ( )\t\t\t\t\t\t\t->\t\t\t\t\t\t\tUnion[str, Any]:\r\n \"\"\"simple docstring\"\"\"\r\n\r\n snake_case\t\t\t\t\t\t\t\t= list(range(10\t\t\t,\t\t0\t\t\t,\t\t-1\t\t)\t\t)\r\n print(\"Initial List\"\t\t)\r\n print(*__a\t\t)\r\n snake_case\t\t\t\t\t\t\t\t= odd_even_transposition(__a\t\t)\r\n print(\"Sorted List\\n\"\t\t)\r\n print(*__a\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":355,"string":"355"},"style_context":{"kind":"string","value":"\r\nfrom collections import defaultdict\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass \t\t\t\t\tlowerCamelCase\t\t:\r\n\r\n\r\n\r\n\r\n\r\n def __init__(self\t\t\t\t: Tuple , _A\t\t\t\t: Optional[int] , _A\t\t\t\t: List[str] )\t\t\t\t\t\t->\t\t\t\t\t\tUnion[str, Any]:\r\n snake_case\t\t\t\t\t\t\t\t= total # total no of tasks (N)\r\n\r\n # DP table will have a dimension of (2^M)*N\r\n # initially all values are set to -1\r\n snake_case\t\t\t\t\t\t\t\t= [\r\n [-1 for i in range(total + 1 )] for j in range(2 ** len(_A ) )\r\n ]\r\n\r\n snake_case\t\t\t\t\t\t\t\t= defaultdict(_A ) # stores the list of persons for each task\r\n\r\n # final_mask is used to check if all persons are included by setting all bits\r\n # to 1\r\n snake_case\t\t\t\t\t\t\t\t= (1 << len(_A )) - 1\r\n\r\n\r\n\r\n\r\n\r\n def \t\tUpperCAmelCase(self\t\t\t\t: str , _A\t\t\t\t: Optional[Any] , _A\t\t\t\t: List[Any] )\t\t\t\t\t\t->\t\t\t\t\t\tstr:\r\n # if mask == self.finalmask all persons are distributed tasks, return 1\r\n if mask == self.final_mask:\r\n return 1\r\n\r\n # if not everyone gets the task and no more tasks are available, return 0\r\n if task_no > self.total_tasks:\r\n return 0\r\n\r\n # if case already considered\r\n if self.dp[mask][task_no] != -1:\r\n return self.dp[mask][task_no]\r\n\r\n # Number of ways when we don't this task in the arrangement\r\n snake_case\t\t\t\t\t\t\t\t= self.count_ways_until(_A , task_no + 1 )\r\n\r\n # now assign the tasks one by one to all possible persons and recursively\r\n # assign for the remaining tasks.\r\n if task_no in self.task:\r\n for p in self.task[task_no]:\r\n # if p is already given a task\r\n if mask & (1 << p):\r\n continue\r\n\r\n # assign this task to p and change the mask value. And recursively\r\n # assign tasks with the new mask value.\r\n total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )\r\n\r\n # save the value.\r\n snake_case\t\t\t\t\t\t\t\t= total_ways_util\r\n\r\n return self.dp[mask][task_no]\r\n\r\n\r\n\r\n\r\n\r\n def \t\tUpperCAmelCase(self\t\t\t\t: Any , _A\t\t\t\t: Dict )\t\t\t\t\t\t->\t\t\t\t\t\tOptional[Any]:\r\n # Store the list of persons for each task\r\n for i in range(len(_A ) ):\r\n for j in task_performed[i]:\r\n self.task[j].append(_A )\r\n\r\n # call the function to fill the DP table, final answer is stored in dp[0][1]\r\n return self.count_ways_until(0 , 1 )\r\n\r\n\r\nif __name__ == \"__main__\":\r\n _A =\t\t\t5 # total no of tasks (the value of N)\r\n\r\n # the list of tasks that can be done by M persons.\r\n _A =\t\t\t[[1, 3, 4], [1, 2, 5], [3, 4]]\r\n print(\r\n AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(\r\n task_performed\r\n )\r\n )\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":137,"string":"137"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":554,"cells":{"code":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\n\n\ndef \t\t\t\t\tUpperCAmelCase (\t\tUpperCAmelCase\t\t\t\t\t)\t\t\t\t-> Dict:\n snake_case_\t\t\t\t\t =\t\t\t\t\tlen(UpperCAmelCase\t\t\t\t\t)\n snake_case_\t\t\t\t\t =\t\t\t\t\tsum(UpperCAmelCase\t\t\t\t\t)\n\n snake_case_\t\t\t\t\t =\t\t\t\t\t[[False for x in range(s + 1\t\t\t\t\t)] for y in range(n + 1\t\t\t\t\t)]\n\n for i in range(1\t\t\t\t\t\t,\t\t\t\t\tn + 1\t\t\t\t\t):\n snake_case_\t\t\t\t\t =\t\t\t\t\tTrue\n\n for i in range(1\t\t\t\t\t\t,\t\t\t\t\ts + 1\t\t\t\t\t):\n snake_case_\t\t\t\t\t =\t\t\t\t\tFalse\n\n for i in range(1\t\t\t\t\t\t,\t\t\t\t\tn + 1\t\t\t\t\t):\n for j in range(1\t\t\t\t\t\t,\t\t\t\t\ts + 1\t\t\t\t\t):\n snake_case_\t\t\t\t\t =\t\t\t\t\tdp[i][j - 1]\n\n if arr[i - 1] <= j:\n snake_case_\t\t\t\t\t =\t\t\t\t\tdp[i][j] or dp[i - 1][j - arr[i - 1]]\n\n for j in range(int(s / 2\t\t\t\t\t)\t\t\t\t\t\t,\t\t\t\t\t-1\t\t\t\t\t\t,\t\t\t\t\t-1\t\t\t\t\t):\n if dp[n][j] is True:\n snake_case_\t\t\t\t\t =\t\t\t\t\ts - 2 * j\n break\n\n return diff\n\n"},"code_codestyle":{"kind":"number","value":69,"string":"69"},"style_context":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\n\n\nimport torch\nimport torch.nn as nn\nfrom transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel\n\nfrom ...utils import logging\n\n\n__UpperCamelCase\t\t\t\t\t\t\t\t\t=\t\t\tlogging.get_logger(__name__)\n\n\n\n\n\n\n\ndef \t\t\t\t\tUpperCAmelCase (\t\tUpperCAmelCase\t\t\t\t\t\t,\t\t\t\t\tUpperCAmelCase\t\t\t\t\t)\t\t\t\t-> int:\n snake_case_\t\t\t\t\t =\t\t\t\t\tnn.functional.normalize(UpperCAmelCase\t\t\t\t\t)\n snake_case_\t\t\t\t\t =\t\t\t\t\tnn.functional.normalize(UpperCAmelCase\t\t\t\t\t)\n return torch.mm(UpperCAmelCase\t\t\t\t\t\t,\t\t\t\t\tnormalized_text_embeds.t()\t\t\t\t\t)\n\n\n\n\nclass UpperCamelCase\t\t\t\t\t(\t\t\t\tlowerCAmelCase__ ):\n SCREAMING_SNAKE_CASE_\t\t\t= CLIPConfig\n\n SCREAMING_SNAKE_CASE_\t\t\t= [\"CLIPEncoderLayer\"]\n\n\n\n\n\n\n\n def __init__( self,\t\t\t\t\t\t\tlowerCAmelCase__) -> Optional[int]:\n super().__init__(lowerCAmelCase__)\n\n snake_case_\t\t\t\t\t =\t\t\t\t\tCLIPVisionModel(config.vision_config)\n snake_case_\t\t\t\t\t =\t\t\t\t\tnn.Linear(config.vision_config.hidden_size,\t\t\t\t\t\t\tconfig.projection_dim,\t\t\t\t\t\t\tbias=lowerCAmelCase__)\n\n snake_case_\t\t\t\t\t =\t\t\t\t\tnn.Parameter(torch.ones(17,\t\t\t\t\t\t\tconfig.projection_dim),\t\t\t\t\t\t\trequires_grad=lowerCAmelCase__)\n snake_case_\t\t\t\t\t =\t\t\t\t\tnn.Parameter(torch.ones(3,\t\t\t\t\t\t\tconfig.projection_dim),\t\t\t\t\t\t\trequires_grad=lowerCAmelCase__)\n\n snake_case_\t\t\t\t\t =\t\t\t\t\tnn.Parameter(torch.ones(17),\t\t\t\t\t\t\trequires_grad=lowerCAmelCase__)\n snake_case_\t\t\t\t\t =\t\t\t\t\tnn.Parameter(torch.ones(3),\t\t\t\t\t\t\trequires_grad=lowerCAmelCase__)\n\n\n\n\n\n\n\n @torch.no_grad()\n def \t\ta_\t\t\t\t\t\t( self,\t\t\t\t\t\t\tlowerCAmelCase__,\t\t\t\t\t\t\tlowerCAmelCase__) -> Tuple:\n snake_case_\t\t\t\t\t =\t\t\t\t\tself.vision_model(lowerCAmelCase__)[1] # pooled_output\n snake_case_\t\t\t\t\t =\t\t\t\t\tself.visual_projection(lowerCAmelCase__)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n snake_case_\t\t\t\t\t =\t\t\t\t\tcosine_distance(lowerCAmelCase__,\t\t\t\t\t\t\tself.special_care_embeds).cpu().float().numpy()\n snake_case_\t\t\t\t\t =\t\t\t\t\tcosine_distance(lowerCAmelCase__,\t\t\t\t\t\t\tself.concept_embeds).cpu().float().numpy()\n\n snake_case_\t\t\t\t\t =\t\t\t\t\t[]\n snake_case_\t\t\t\t\t =\t\t\t\t\timage_embeds.shape[0]\n for i in range(lowerCAmelCase__):\n snake_case_\t\t\t\t\t =\t\t\t\t\t{'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}\n\n # increase this value to create a stronger `nfsw` filter\n # at the cost of increasing the possibility of filtering benign images\n snake_case_\t\t\t\t\t =\t\t\t\t\t0.0\n\n for concept_idx in range(len(special_cos_dist[0])):\n snake_case_\t\t\t\t\t =\t\t\t\t\tspecial_cos_dist[i][concept_idx]\n snake_case_\t\t\t\t\t =\t\t\t\t\tself.special_care_embeds_weights[concept_idx].item()\n snake_case_\t\t\t\t\t =\t\t\t\t\tround(concept_cos - concept_threshold + adjustment,\t\t\t\t\t\t\t3)\n if result_img[\"special_scores\"][concept_idx] > 0:\n result_img[\"special_care\"].append({concept_idx, result_img['special_scores'][concept_idx]})\n snake_case_\t\t\t\t\t =\t\t\t\t\t0.01\n\n for concept_idx in range(len(cos_dist[0])):\n snake_case_\t\t\t\t\t =\t\t\t\t\tcos_dist[i][concept_idx]\n snake_case_\t\t\t\t\t =\t\t\t\t\tself.concept_embeds_weights[concept_idx].item()\n snake_case_\t\t\t\t\t =\t\t\t\t\tround(concept_cos - concept_threshold + adjustment,\t\t\t\t\t\t\t3)\n if result_img[\"concept_scores\"][concept_idx] > 0:\n result_img[\"bad_concepts\"].append(lowerCAmelCase__)\n\n result.append(lowerCAmelCase__)\n\n snake_case_\t\t\t\t\t =\t\t\t\t\t[len(res['bad_concepts']) > 0 for res in result]\n\n return images, has_nsfw_concepts\n\n\n\n\n\n\n\n @torch.no_grad()\n def \t\ta_\t\t\t\t\t\t( self,\t\t\t\t\t\t\tlowerCAmelCase__,\t\t\t\t\t\t\tlowerCAmelCase__) -> Optional[int]:\n snake_case_\t\t\t\t\t =\t\t\t\t\tself.vision_model(lowerCAmelCase__)[1] # pooled_output\n snake_case_\t\t\t\t\t =\t\t\t\t\tself.visual_projection(lowerCAmelCase__)\n\n snake_case_\t\t\t\t\t =\t\t\t\t\tcosine_distance(lowerCAmelCase__,\t\t\t\t\t\t\tself.special_care_embeds)\n snake_case_\t\t\t\t\t =\t\t\t\t\tcosine_distance(lowerCAmelCase__,\t\t\t\t\t\t\tself.concept_embeds)\n\n # increase this value to create a stronger `nsfw` filter\n # at the cost of increasing the possibility of filtering benign images\n snake_case_\t\t\t\t\t =\t\t\t\t\t0.0\n\n snake_case_\t\t\t\t\t =\t\t\t\t\tspecial_cos_dist - self.special_care_embeds_weights + adjustment\n # special_scores = special_scores.round(decimals=3)\n snake_case_\t\t\t\t\t =\t\t\t\t\ttorch.any(special_scores > 0,\t\t\t\t\t\t\tdim=1)\n snake_case_\t\t\t\t\t =\t\t\t\t\tspecial_care * 0.01\n snake_case_\t\t\t\t\t =\t\t\t\t\tspecial_adjustment.unsqueeze(1).expand(-1,\t\t\t\t\t\t\tcos_dist.shape[1])\n\n snake_case_\t\t\t\t\t =\t\t\t\t\t(cos_dist - self.concept_embeds_weights) + special_adjustment\n # concept_scores = concept_scores.round(decimals=3)\n snake_case_\t\t\t\t\t =\t\t\t\t\ttorch.any(concept_scores > 0,\t\t\t\t\t\t\tdim=1)\n\n return images, has_nsfw_concepts\n\n"},"style_context_codestyle":{"kind":"number","value":69,"string":"69"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":555,"cells":{"code":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom typing import TYPE_CHECKING\r\n\r\nfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available\r\n\r\n\r\n_a\t\t\t\t\t:\t\t\t\t\tDict=\t\t\t{\"configuration_swin\": [\"SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP\", \"SwinConfig\", \"SwinOnnxConfig\"]}\r\n\r\n\r\ntry:\r\n\tif not is_torch_available():\r\n\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\tpass\r\nelse:\r\n\t_a\t\t\t\t\t:\t\t\t\t\tList[str]=\t\t\t[\r\n\t \"SWIN_PRETRAINED_MODEL_ARCHIVE_LIST\",\r\n\t \"SwinForImageClassification\",\r\n\t \"SwinForMaskedImageModeling\",\r\n\t \"SwinModel\",\r\n\t \"SwinPreTrainedModel\",\r\n\t \"SwinBackbone\",\r\n\t]\r\n\r\ntry:\r\n\tif not is_tf_available():\r\n\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\tpass\r\nelse:\r\n\t_a\t\t\t\t\t:\t\t\t\t\tList[Any]=\t\t\t[\r\n\t \"TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST\",\r\n\t \"TFSwinForImageClassification\",\r\n\t \"TFSwinForMaskedImageModeling\",\r\n\t \"TFSwinModel\",\r\n\t \"TFSwinPreTrainedModel\",\r\n\t]\r\n\r\nif TYPE_CHECKING:\r\n\tfrom .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig\r\n\r\n\ttry:\r\n\t\tif not is_torch_available():\r\n\t\t\traise OptionalDependencyNotAvailable()\r\n\texcept OptionalDependencyNotAvailable:\r\n\t\tpass\r\n\telse:\r\n\t\tfrom .modeling_swin import (\r\n\t\t SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\t\t SwinBackbone,\r\n\t\t SwinForImageClassification,\r\n\t\t SwinForMaskedImageModeling,\r\n\t\t SwinModel,\r\n\t\t SwinPreTrainedModel,\r\n\t\t)\r\n\r\n\ttry:\r\n\t\tif not is_tf_available():\r\n\t\t\traise OptionalDependencyNotAvailable()\r\n\texcept OptionalDependencyNotAvailable:\r\n\t\tpass\r\n\telse:\r\n\t\tfrom .modeling_tf_swin import (\r\n\t\t TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\t\t TFSwinForImageClassification,\r\n\t\t TFSwinForMaskedImageModeling,\r\n\t\t TFSwinModel,\r\n\t\t TFSwinPreTrainedModel,\r\n\t\t)\r\n\r\nelse:\r\n\timport sys\r\n\r\n\t_a\t\t\t\t\t:\t\t\t\t\tTuple=\t\t\t_LazyModule(__name__, globals()[\"__file__\"], _import_structure, module_spec=__spec__)\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":363,"string":"363"},"style_context":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport argparse\r\nimport json\r\nfrom dataclasses import dataclass, field\r\nfrom functools import partial\r\nfrom pathlib import Path\r\nfrom typing import List\r\n\r\nimport timm\r\nimport torch\r\nimport torch.nn as nn\r\nfrom huggingface_hub import hf_hub_download\r\nfrom torch import Tensor\r\n\r\nfrom transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification\r\nfrom transformers.utils import logging\r\n\r\n\r\nlogging.set_verbosity_info()\r\n_a\t\t\t\t\t:\t\t\t\t\tOptional[int]=\t\t\tlogging.get_logger()\r\n\r\n@dataclass\r\nclass \t\tUpperCamelCase :\r\n\t\t\t\tUpperCAmelCase :\t\t\t\tnn.Module\r\n\t\t\t\tUpperCAmelCase :\t\t\t\tList[nn.Module] =\t\tfield(default_factory=lowercase\t\t\t\t\t\t)\r\n\t\t\t\tUpperCAmelCase :\t\t\t\tlist =\t\tfield(default_factory=lowercase\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\t\t\t\tdef _lowercase\t\t\t\t\t(self :\t\t\t\tstr , _A :\t\t\t\tOptional[Any] , _A :\t\t\t\tTensor , _A :\t\t\t\tTensor) -> Any:\r\n\t\t\t\t\t__snake_case :\t\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\tlen(list(m.modules())) == 1 or isinstance(_A , nn.Convad) or isinstance(_A , nn.BatchNormad)\r\n\t\t\t\t\tif has_not_submodules:\r\n\t\t\t\t\t\tself.traced.append(_A)\r\n\r\n\r\n\r\n\t\t\t\tdef __call__(self :\t\t\t\tDict , _A :\t\t\t\tTensor) -> Optional[Any]:\r\n\t\t\t\t\tfor m in self.module.modules():\r\n\t\t\t\t\t\tself.handles.append(m.register_forward_hook(self._forward_hook))\r\n\t\t\t\t\tself.module(_A)\r\n\t\t\t\t\t[x.remove() for x in self.handles]\r\n\t\t\t\t\treturn self\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t@property\r\n\t\t\t\tdef _lowercase\t\t\t\t\t(self :\t\t\t\tUnion[str, Any]) -> List[str]:\r\n\t\t\t\t\t# check the len of the state_dict keys to see if we have learnable params\r\n\t\t\t\t\treturn list(filter(lambda _A: len(list(x.state_dict().keys())) > 0 , self.traced))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n@dataclass\r\nclass \t\tUpperCamelCase :\r\n\t\t\t\tUpperCAmelCase :\t\t\t\tnn.Module\r\n\t\t\t\tUpperCAmelCase :\t\t\t\tnn.Module\r\n\t\t\t\tUpperCAmelCase :\t\t\t\tint =\t\t0\r\n\t\t\t\tUpperCAmelCase :\t\t\t\tList =\t\tfield(default_factory=lowercase\t\t\t\t\t\t)\r\n\t\t\t\tUpperCAmelCase :\t\t\t\tList =\t\tfield(default_factory=lowercase\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\t\t\t\tdef __call__(self :\t\t\t\tList[str] , _A :\t\t\t\tTensor) -> List[Any]:\r\n\t\t\t\t\t__snake_case :\t\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\tTracker(self.dest)(_A).parametrized\r\n\t\t\t\t\t__snake_case :\t\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\tTracker(self.src)(_A).parametrized\r\n\r\n\t\t\t\t\t__snake_case :\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t=\t\t\tlist(filter(lambda _A: type(_A) not in self.src_skip , _A))\r\n\t\t\t\t\t__snake_case :\t\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\tlist(filter(lambda _A: type(_A) not in self.dest_skip , _A))\r\n\r\n\t\t\t\t\tif len(_A) != len(_A):\r\n\t\t\t\t\t\traise Exception(\r\n\t\t\t\t\t\t f\"Numbers of operations are different. Source module has {len(_A)} operations while\"\r\n\t\t\t\t\t\t f\" destination module has {len(_A)}.\")\r\n\r\n\t\t\t\t\tfor dest_m, src_m in zip(_A , _A):\r\n\t\t\t\t\t\tdest_m.load_state_dict(src_m.state_dict())\r\n\t\t\t\t\t\tif self.verbose == 1:\r\n\t\t\t\t\t\t\tprint(f\"Transfered from={src_m} to={dest_m}\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef __UpperCAmelCase ( UpperCAmelCase_ : str ,\t\t\t\t\t\t\tUpperCAmelCase_ : ResNetConfig ,\t\t\t\t\t\t\tUpperCAmelCase_ : Path ,\t\t\t\t\t\t\tUpperCAmelCase_ : bool = True )\t\t\t\t\t\t\t-> List[str]:\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\tprint(F\"Converting {name}...\" )\r\n\twith torch.no_grad():\r\n\t\t__snake_case :\t\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\ttimm.create_model(UpperCAmelCase_ ,\t\t\t\t\t\t\tpretrained=UpperCAmelCase_ ).eval()\r\n\t\t__snake_case :\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t=\t\t\tResNetForImageClassification(UpperCAmelCase_ ).eval()\r\n\t\t__snake_case :\t\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\tModuleTransfer(src=UpperCAmelCase_ ,\t\t\t\t\t\t\tdest=UpperCAmelCase_ )\r\n\t\t__snake_case :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\ttorch.randn((1, 3, 2_24, 2_24) )\r\n\t\tmodule_transfer(UpperCAmelCase_ )\r\n\r\n\tassert torch.allclose(from_model(UpperCAmelCase_ ) ,\t\t\t\t\t\t\tour_model(UpperCAmelCase_ ).logits ), \"The model logits don't match the original one.\"\r\n\r\n\t__snake_case :\t\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\tF\"resnet{'-'.join(name.split('resnet' ) )}\"\r\n\tprint(UpperCAmelCase_ )\r\n\r\n\tif push_to_hub:\r\n\t\tour_model.push_to_hub(\r\n\t\t repo_path_or_name=save_directory / checkpoint_name ,\t\t\t\t\t\t\tcommit_message='Add model' ,\t\t\t\t\t\t\tuse_temp_dir=UpperCAmelCase_ ,\t\t\t\t\t\t\t)\r\n\r\n\t\t# we can use the convnext one\r\n\t\t__snake_case :\t\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\tAutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )\r\n\t\timage_processor.push_to_hub(\r\n\t\t repo_path_or_name=save_directory / checkpoint_name ,\t\t\t\t\t\t\tcommit_message='Add image processor' ,\t\t\t\t\t\t\tuse_temp_dir=UpperCAmelCase_ ,\t\t\t\t\t\t\t)\r\n\r\n\t\tprint(F\"Pushed {checkpoint_name}\" )\r\ndef __UpperCAmelCase ( UpperCAmelCase_ : Path ,\t\t\t\t\t\t\tUpperCAmelCase_ : str = None ,\t\t\t\t\t\t\tUpperCAmelCase_ : bool = True )\t\t\t\t\t\t\t-> Union[str, Any]:\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t__snake_case :\t\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t'imagenet-1k-id2label.json'\r\n\t__snake_case :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\t10_00\r\n\t__snake_case :\t\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\t(1, num_labels)\r\n\r\n\t__snake_case :\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t=\t\t\t'huggingface/label-files'\r\n\t__snake_case :\t\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\tnum_labels\r\n\t__snake_case :\t\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\tjson.load(open(hf_hub_download(UpperCAmelCase_ ,\t\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\t\trepo_type='dataset' ) ,\t\t\t\t\t\t\t'r' ) )\r\n\t__snake_case :\t\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\t{int(UpperCAmelCase_ ): v for k, v in idalabel.items()}\r\n\r\n\t__snake_case :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\tidalabel\r\n\t__snake_case :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\t{v: k for k, v in idalabel.items()}\r\n\r\n\t__snake_case :\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t=\t\t\tpartial(UpperCAmelCase_ ,\t\t\t\t\t\t\tnum_labels=UpperCAmelCase_ ,\t\t\t\t\t\t\tidalabel=UpperCAmelCase_ ,\t\t\t\t\t\t\tlabelaid=UpperCAmelCase_ )\r\n\r\n\t__snake_case :\t\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t{\r\n\t 'resnet18': ImageNetPreTrainedConfig(\r\n\t depths=[2, 2, 2, 2] ,\t\t\t\t\t\t\thidden_sizes=[64, 1_28, 2_56, 5_12] ,\t\t\t\t\t\t\tlayer_type='basic' ),\r\n\t 'resnet26': ImageNetPreTrainedConfig(\r\n\t depths=[2, 2, 2, 2] ,\t\t\t\t\t\t\thidden_sizes=[2_56, 5_12, 10_24, 20_48] ,\t\t\t\t\t\t\tlayer_type='bottleneck' ),\r\n\t 'resnet34': ImageNetPreTrainedConfig(\r\n\t depths=[3, 4, 6, 3] ,\t\t\t\t\t\t\thidden_sizes=[64, 1_28, 2_56, 5_12] ,\t\t\t\t\t\t\tlayer_type='basic' ),\r\n\t 'resnet50': ImageNetPreTrainedConfig(\r\n\t depths=[3, 4, 6, 3] ,\t\t\t\t\t\t\thidden_sizes=[2_56, 5_12, 10_24, 20_48] ,\t\t\t\t\t\t\tlayer_type='bottleneck' ),\r\n\t 'resnet101': ImageNetPreTrainedConfig(\r\n\t depths=[3, 4, 23, 3] ,\t\t\t\t\t\t\thidden_sizes=[2_56, 5_12, 10_24, 20_48] ,\t\t\t\t\t\t\tlayer_type='bottleneck' ),\r\n\t 'resnet152': ImageNetPreTrainedConfig(\r\n\t depths=[3, 8, 36, 3] ,\t\t\t\t\t\t\thidden_sizes=[2_56, 5_12, 10_24, 20_48] ,\t\t\t\t\t\t\tlayer_type='bottleneck' ),\r\n\t}\r\n\r\n\tif model_name:\r\n\t\tconvert_weight_and_push(UpperCAmelCase_ ,\t\t\t\t\t\t\tnames_to_config[model_name] ,\t\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\t\tUpperCAmelCase_ )\r\n\telse:\r\n\t\tfor model_name, config in names_to_config.items():\r\n\t\t\tconvert_weight_and_push(UpperCAmelCase_ ,\t\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\t\tUpperCAmelCase_ ,\t\t\t\t\t\t\tUpperCAmelCase_ )\r\n\treturn config, expected_shape\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t_a\t\t\t\t\t:\t\t\t\t\tOptional[Any]=\t\t\targparse.ArgumentParser()\r\n\t# Required parameters\r\n\tparser.add_argument(\r\n\t \"--model_name\",\r\n\t default=None,\r\n\t type=str,\r\n\t help=(\r\n\t \"The name of the model you wish to convert, it must be one of the supported resnet* architecture,\"\r\n\t \" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.\"\r\n\t ),\r\n\t)\r\n\tparser.add_argument(\r\n\t \"--pytorch_dump_folder_path\",\r\n\t default=None,\r\n\t type=Path,\r\n\t required=True,\r\n\t help=\"Path to the output PyTorch model directory.\",\r\n\t)\r\n\tparser.add_argument(\r\n\t \"--push_to_hub\",\r\n\t default=True,\r\n\t type=bool,\r\n\t required=False,\r\n\t help=\"If True, push model and image processor to the hub.\",\r\n\t)\r\n\r\n\t_a\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]=\t\t\tparser.parse_args()\r\n\t_a\t\t\t\t\t:\t\t\t\t\tPath=\t\t\targs.pytorch_dump_folder_path\r\n\tpytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)\r\n\tconvert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":95,"string":"95"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":556,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\nimport copy\nimport os\nfrom typing import Union\n\nfrom ...configuration_utils import PretrainedConfig\nfrom ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES\nfrom ...utils import logging\nfrom ..auto import CONFIG_MAPPING\n\n\n_a \t\t\t\t\t\t= logging.get_logger(__name__)\n\n_a \t\t\t\t\t\t= {\n \"salesforce/blip2-opt-2.7b\": \"https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json\",\n}\n\n\n\n\n\n\nclass \t\t\t\t\t__A ( lowerCAmelCase ):\n\n\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\tlowerCAmelCase_\t\t\t\t\t\t\t =\t\t\t\t\t\t\"\"\"blip_2_vision_model\"\"\"\n\n\n\n\n\n\t\t\tdef __init__( self , __lowerCAmelCase=1_4_0_8 , __lowerCAmelCase=6_1_4_4 , __lowerCAmelCase=3_9 , __lowerCAmelCase=1_6 , __lowerCAmelCase=2_2_4 , __lowerCAmelCase=1_4 , __lowerCAmelCase=\"gelu\" , __lowerCAmelCase=0.0_0001 , __lowerCAmelCase=0.0 , __lowerCAmelCase=1E-10 , __lowerCAmelCase=True , **__lowerCAmelCase , ):\n\n\n\n\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\tsuper().__init__(**__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\thidden_size\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tintermediate_size\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tnum_hidden_layers\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tnum_attention_heads\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tpatch_size\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\timage_size\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tinitializer_range\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tattention_dropout\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tlayer_norm_eps\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\thidden_act\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tqkv_bias\n\n\n\n\n\n\t\t\t@classmethod\n\t\t\tdef \t\t__lowerCamelCase\t\t\t\t( cls , __lowerCAmelCase , **__lowerCAmelCase\t\t\t\t\t\t\t):\n\n\n\n\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\tcls._set_token_in_kwargs(__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\t\t\t\tlowerCamelCase__\t\t\t\t,\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tcls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\t\t\t\t# get the vision config dict if we are loading from Blip2Config\n\t\t\t\tif config_dict.get('''model_type'''\t\t\t\t\t\t\t) == \"blip-2\":\n\t\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tconfig_dict['''vision_config''']\n\n\t\t\t\tif \"model_type\" in config_dict and hasattr(cls , '''model_type'''\t\t\t\t\t\t\t) and config_dict[\"model_type\"] != cls.model_type:\n\t\t\t\t\tlogger.warning(\n\t\t\t\t\t F'You are using a model of type {config_dict[\"model_type\"]} to instantiate a model of type '\n\t\t\t\t\t F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'\t\t\t\t\t\t\t)\n\n\t\t\t\treturn cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\n\n\n\n\nclass \t\t\t\t\t__A ( lowerCAmelCase ):\n\n\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\tlowerCAmelCase_\t\t\t\t\t\t\t =\t\t\t\t\t\t\"\"\"blip_2_qformer\"\"\"\n\n\n\n\n\n\t\t\tdef __init__( self , __lowerCAmelCase=3_0_5_2_2 , __lowerCAmelCase=7_6_8 , __lowerCAmelCase=1_2 , __lowerCAmelCase=1_2 , __lowerCAmelCase=3_0_7_2 , __lowerCAmelCase=\"gelu\" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-12 , __lowerCAmelCase=0 , __lowerCAmelCase=\"absolute\" , __lowerCAmelCase=2 , __lowerCAmelCase=1_4_0_8 , **__lowerCAmelCase , ):\n\n\n\n\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\tsuper().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tvocab_size\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\thidden_size\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tnum_hidden_layers\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tnum_attention_heads\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\thidden_act\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tintermediate_size\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\thidden_dropout_prob\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tattention_probs_dropout_prob\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tmax_position_embeddings\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tinitializer_range\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tlayer_norm_eps\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tposition_embedding_type\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tcross_attention_frequency\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tencoder_hidden_size\n\n\n\n\n\n\t\t\t@classmethod\n\t\t\tdef \t\t__lowerCamelCase\t\t\t\t( cls , __lowerCAmelCase , **__lowerCAmelCase\t\t\t\t\t\t\t):\n\n\n\n\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\tcls._set_token_in_kwargs(__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\t\t\t\tlowerCamelCase__\t\t\t\t,\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tcls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\t\t\t\t# get the qformer config dict if we are loading from Blip2Config\n\t\t\t\tif config_dict.get('''model_type'''\t\t\t\t\t\t\t) == \"blip-2\":\n\t\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tconfig_dict['''qformer_config''']\n\n\t\t\t\tif \"model_type\" in config_dict and hasattr(cls , '''model_type'''\t\t\t\t\t\t\t) and config_dict[\"model_type\"] != cls.model_type:\n\t\t\t\t\tlogger.warning(\n\t\t\t\t\t F'You are using a model of type {config_dict[\"model_type\"]} to instantiate a model of type '\n\t\t\t\t\t F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'\t\t\t\t\t\t\t)\n\n\t\t\t\treturn cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\n\n\n\n\nclass \t\t\t\t\t__A ( lowerCAmelCase ):\n\n\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\tlowerCAmelCase_\t\t\t\t\t\t\t =\t\t\t\t\t\t\"\"\"blip-2\"\"\"\n\t\t\tlowerCAmelCase_\t\t\t\t\t\t\t =\t\t\t\t\t\tTrue\n\n\n\n\n\n\t\t\tdef __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=3_2 , **__lowerCAmelCase\t\t\t\t\t\t\t):\n\n\n\n\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\tsuper().__init__(**__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\t\t\t\tif vision_config is None:\n\t\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{}\n\t\t\t\t\tlogger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.'''\t\t\t\t\t\t\t)\n\n\t\t\t\tif qformer_config is None:\n\t\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{}\n\t\t\t\t\tlogger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.'''\t\t\t\t\t\t\t)\n\n\t\t\t\tif text_config is None:\n\t\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{}\n\t\t\t\t\tlogger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''\t\t\t\t\t\t\t)\n\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tBlipaVisionConfig(**__lowerCAmelCase\t\t\t\t\t\t\t)\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tBlipaQFormerConfig(**__lowerCAmelCase\t\t\t\t\t\t\t)\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\ttext_config['''model_type'''] if '''model_type''' in text_config else '''opt'''\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tCONFIG_MAPPING[text_model_type](**__lowerCAmelCase\t\t\t\t\t\t\t)\n\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tself.text_config.tie_word_embeddings\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tself.text_config.is_encoder_decoder\n\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tnum_query_tokens\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tself.vision_config.hidden_size\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tself.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t1.0\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t0.02\n\n\n\n\n\n\t\t\t@classmethod\n\t\t\tdef \t\t__lowerCamelCase\t\t\t\t( cls , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase , ):\n\n\n\n\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\treturn cls(\n\t\t\t\t vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__lowerCAmelCase , )\n\n\n\n\n\n\t\t\tdef \t\t__lowerCamelCase\t\t\t\t( self\t\t\t\t\t\t\t):\n\n\n\n\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tcopy.deepcopy(self.__dict__\t\t\t\t\t\t\t)\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tself.vision_config.to_dict()\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tself.qformer_config.to_dict()\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tself.text_config.to_dict()\n\t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\tself.__class__.model_type\n\t\t\t\treturn output\n\n\n\n"},"code_codestyle":{"kind":"number","value":209,"string":"209"},"style_context":{"kind":"string","value":"\n\n\n\n\n\nimport pytest\n\nfrom datasets.parallel import ParallelBackendConfig, parallel_backend\nfrom datasets.utils.py_utils import map_nested\n\nfrom .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows\n\n\n\n\n\ndef lowerCAmelCase__(__snake_case\t\t\t\t\t\t\t)\t\t\t\t-> int: # picklable for multiprocessing\n\n\n\n\n\n\n\n\t'''simple docstring'''\n\n\n\n\n\n\n\n\treturn i + 1\n\n\n\n\n\n@require_dill_gt_0_3_2\n@require_joblibspark\n@require_not_windows\ndef lowerCAmelCase__()\t\t\t\t-> Any:\n\n\n\n\n\n\n\n\t'''simple docstring'''\n\n\n\n\n\n\n\n\twith parallel_backend('''spark'''\t\t\t\t\t\t\t):\n\t\tassert ParallelBackendConfig.backend_name == \"spark\"\n\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t[1, 2, 3]\n\twith pytest.raises(__snake_case\t\t\t\t\t\t\t):\n\t\twith parallel_backend('''unsupported backend'''\t\t\t\t\t\t\t):\n\t\t\tmap_nested(__snake_case ,__snake_case ,num_proc=2\t\t\t\t\t\t\t)\n\n\twith pytest.raises(__snake_case\t\t\t\t\t\t\t):\n\t\twith parallel_backend('''unsupported backend'''\t\t\t\t\t\t\t):\n\t\t\tmap_nested(__snake_case ,__snake_case ,num_proc=-1\t\t\t\t\t\t\t)\n\n\n\n\n\n@require_dill_gt_0_3_2\n@require_joblibspark\n@require_not_windows\n@pytest.mark.parametrize('''num_proc''' ,[2, -1]\t\t\t\t\t\t\t)\ndef lowerCAmelCase__(__snake_case\t\t\t\t\t\t\t)\t\t\t\t-> Tuple:\n\n\n\n\n\n\n\n\t'''simple docstring'''\n\n\n\n\n\n\n\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t[1, 2]\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{'''a''': 1, '''b''': 2}\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{'''a''': [1, 2], '''b''': [3, 4]}\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{'''a''': {'''1''': 1}, '''b''': 2}\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t[2, 3]\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{'''a''': 2, '''b''': 3}\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{'''a''': [2, 3], '''b''': [4, 5]}\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{'''a''': {'''1''': 2}, '''b''': 3}\n\tlowerCamelCase__\t\t\t\t\t\t\t\t=\t\t{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}\n\n\twith parallel_backend('''spark'''\t\t\t\t\t\t\t):\n\t\tassert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case\t\t\t\t\t\t\t) == expected_map_nested_sa\n\t\tassert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case\t\t\t\t\t\t\t) == expected_map_nested_sa\n\t\tassert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case\t\t\t\t\t\t\t) == expected_map_nested_sa\n\t\tassert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case\t\t\t\t\t\t\t) == expected_map_nested_sa\n\t\tassert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case\t\t\t\t\t\t\t) == expected_map_nested_sa\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":209,"string":"209"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":557,"cells":{"code":{"kind":"string","value":"\r'''simple docstring'''\r\r\r\rdef A\t\t\t\t(__lowerCamelCase\t\t\t\t:str\t\t\t, __lowerCamelCase\t\t\t\t:str\t\t):\r\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tlen(__lowerCamelCase\t\t) + 1\r\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tlen(__lowerCamelCase\t\t) + 1\r\r\t\t\t\t\t\t# dp is a 2d matrix where dp[i][j] denotes whether prefix string of\r\t\t\t\t\t\t# length i of input_string matches with prefix string of length j of\r\t\t\t\t\t\t# given pattern.\r\t\t\t\t\t\t# \"dp\" stands for dynamic programming.\r\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\t[[0 for i in range(__lowerCamelCase\t\t)] for j in range(__lowerCamelCase\t\t)]\r\r\t\t\t\t\t\t# since string of zero length match pattern of zero length\r\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\t1\r\r\t\t\t\t\t\t# since pattern of zero length will never match with string of non-zero length\r\t\t\t\t\t\tfor i in range(1\t\t\t, __lowerCamelCase\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\t0\r\r\t\t\t\t\t\t# since string of zero length will match with pattern where there\r\t\t\t\t\t\t# is at least one * alternatively\r\t\t\t\t\t\tfor j in range(1\t\t\t, __lowerCamelCase\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tdp[0][j - 2] if pattern[j - 1] == \"\"\"*\"\"\" else 0\r\r\t\t\t\t\t\t# now using bottom-up approach to find for all remaining lengths\r\t\t\t\t\t\tfor i in range(1\t\t\t, __lowerCamelCase\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\tfor j in range(1\t\t\t, __lowerCamelCase\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == \".\":\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tdp[i - 1][j - 1]\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif pattern[j - 1] == \"*\":\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif dp[i][j - 2] == 1:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\t1\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif pattern[j - 2] in (input_string[i - 1], \".\"):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tdp[i - 1][j]\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\t0\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\t0\r\r\t\t\t\t\t\treturn bool(dp[-1][-1]\t\t)\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\t\timport doctest\r\r\t\t\t\t\t\tdoctest.testmod()\r\t\t\t\t\t\t# inputing the strings\r\t\t\t\t\t\t# input_string = input(\"input a string :\")\r\t\t\t\t\t\t# pattern = input(\"input a pattern :\")\r\r\t\t\t\t\t\t_lowercase =\t\t\"\"\"aab\"\"\"\r\t\t\t\t\t\t_lowercase =\t\t\"\"\"c*a*b\"\"\"\r\r\t\t\t\t\t\t# using function to check whether given string matches the given pattern\r\t\t\t\t\t\tif match_pattern(input_string, pattern):\r\t\t\t\t\t\t\t\t\t\t\t\tprint(F\"\"\"{input_string} matches the given pattern {pattern}\"\"\")\r\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\tprint(F\"\"\"{input_string} does not match with the given pattern {pattern}\"\"\")\r"},"code_codestyle":{"kind":"number","value":229,"string":"229"},"style_context":{"kind":"string","value":"\r'''simple docstring'''\r\r\r\rimport logging\r\rfrom transformers import PretrainedConfig\r\r\r_lowercase =\t\tlogging.getLogger(__name__)\r\r\r_lowercase =\t\t{\r \"\"\"bertabs-finetuned-cnndm\"\"\": \"\"\"https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json\"\"\",\r}\r\r\r\r\rclass UpperCAmelCase_\t\t\t\t\t(\t\t_SCREAMING_SNAKE_CASE\t\t\t):\r\r\r\r\r\r\r\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t_lowercase\t\t:\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t= '''bertabs'''\r\r\r\r\r\t\t\t\t\t\tdef __init__( self , _lowercase=30_522 , _lowercase=512 , _lowercase=6 , _lowercase=512 , _lowercase=8 , _lowercase=512 , _lowercase=0.2 , _lowercase=6 , _lowercase=768 , _lowercase=8 , _lowercase=2_048 , _lowercase=0.2 , **_lowercase , ):\r\r\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(**_lowercase\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tvocab_size\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tmax_pos\r\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tenc_layers\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tenc_hidden_size\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tenc_heads\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tenc_ff_size\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tenc_dropout\r\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tdec_layers\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tdec_hidden_size\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tdec_heads\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tdec_ff_size\r\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase \t\t\t\t\t\t=\t\t\tdec_dropout\r"},"style_context_codestyle":{"kind":"number","value":229,"string":"229"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":558,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\nfrom typing import List, Optional, Union\r\n\r\nimport torch\r\nfrom transformers import (\r\n XLMRobertaTokenizer,\r\n)\r\n\r\nfrom ...models import UNetaDConditionModel, VQModel\r\nfrom ...pipelines import DiffusionPipeline\r\nfrom ...pipelines.pipeline_utils import ImagePipelineOutput\r\nfrom ...schedulers import DDIMScheduler, DDPMScheduler\r\nfrom ...utils import (\r\n is_accelerate_available,\r\n is_accelerate_version,\r\n logging,\r\n randn_tensor,\r\n replace_example_docstring,\r\n)\r\nfrom .text_encoder import MultilingualCLIP\r\n\r\n\r\nlowerCAmelCase =\t\t\t\t\t\t\tlogging.get_logger(__name__) # pylint: disable=invalid-name\r\n\r\nlowerCAmelCase =\t\t\t\t\t\t\t'''\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"red cat, 4k photo\"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n >>> pipe.to(\"cuda\")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save(\"cat.png\")\n ```\n'''\r\ndef \t\t\t_a ( SCREAMING_SNAKE_CASE\t\t\t\t\t\t,\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t,\t\t\t\tSCREAMING_SNAKE_CASE=8 ):\r\n\r\n\r\n\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\tlowercase__ = h // scale_factor**2\r\n\t\tif h % scale_factor**2 != 0:\r\n\t\t\t\tnew_h += 1\r\n\t\tlowercase__ = w // scale_factor**2\r\n\t\tif w % scale_factor**2 != 0:\r\n\t\t\t\tnew_w += 1\r\n\t\treturn new_h * scale_factor, new_w * scale_factor\r\n\r\n\r\nclass _a\t\t\t\t\t\t\t(\t\t\tA__\t\t\t):\r\n\r\n\t\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself:\tTuple ,\t\t\t\t\t\tUpperCamelCase_:\tMultilingualCLIP ,\t\t\t\t\t\tUpperCamelCase_:\tXLMRobertaTokenizer ,\t\t\t\t\t\tUpperCamelCase_:\tUNetaDConditionModel ,\t\t\t\t\t\tUpperCamelCase_:\tUnion[DDIMScheduler, DDPMScheduler] ,\t\t\t\t\t\tUpperCamelCase_:\tVQModel ,\t\t\t\t\t\t)\t\t\t-> List[str]:\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\tsuper().__init__()\r\n\r\n\t\t\t\t\t\t\t\t\tself.register_modules(\r\n\t\t\t\t\t\t\t\t\t text_encoder=UpperCamelCase_ ,\t\t\t\t\t\ttokenizer=UpperCamelCase_ ,\t\t\t\t\t\tunet=UpperCamelCase_ ,\t\t\t\t\t\tscheduler=UpperCamelCase_ ,\t\t\t\t\t\tmovq=UpperCamelCase_ ,\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\tlowercase__ = 2 ** (len(self.movq.config.block_out_channels\t\t\t\t\t\t\t) - 1)\r\n\r\n\t\t\t\t\t\t\tdef lowerCamelCase_ (\t\t\t\t\t\t\tself:\tint ,\t\t\t\t\t\tUpperCamelCase_:\tOptional[Any] ,\t\t\t\t\t\tUpperCamelCase_:\tint ,\t\t\t\t\t\tUpperCamelCase_:\tAny ,\t\t\t\t\t\tUpperCamelCase_:\tDict ,\t\t\t\t\t\tUpperCamelCase_:\tstr ,\t\t\t\t\t\tUpperCamelCase_:\tstr\t\t\t\t\t\t\t)\t\t\t-> Tuple:\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\tif latents is None:\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = randn_tensor(UpperCamelCase_ ,\t\t\t\t\t\tgenerator=UpperCamelCase_ ,\t\t\t\t\t\tdevice=UpperCamelCase_ ,\t\t\t\t\t\tdtype=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\tif latents.shape != shape:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = latents.to(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = latents * scheduler.init_noise_sigma\r\n\t\t\t\t\t\t\t\t\treturn latents\r\n\r\n\t\t\t\t\t\t\tdef lowerCamelCase_ (\t\t\t\t\t\t\tself:\tDict ,\t\t\t\t\t\tUpperCamelCase_:\tOptional[int] ,\t\t\t\t\t\tUpperCamelCase_:\tDict ,\t\t\t\t\t\tUpperCamelCase_:\tList[Any] ,\t\t\t\t\t\tUpperCamelCase_:\tstr ,\t\t\t\t\t\tUpperCamelCase_:\tUnion[str, Any]=None ,\t\t\t\t\t\t)\t\t\t-> List[str]:\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = len(UpperCamelCase_\t\t\t\t\t\t\t) if isinstance(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t) else 1\r\n\t\t\t\t\t\t\t\t\t# get prompt text embeddings\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self.tokenizer(\r\n\t\t\t\t\t\t\t\t\t UpperCamelCase_ ,\t\t\t\t\t\tpadding='''max_length''' ,\t\t\t\t\t\ttruncation=UpperCamelCase_ ,\t\t\t\t\t\tmax_length=77 ,\t\t\t\t\t\treturn_attention_mask=UpperCamelCase_ ,\t\t\t\t\t\tadd_special_tokens=UpperCamelCase_ ,\t\t\t\t\t\treturn_tensors='''pt''' ,\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = text_inputs.input_ids\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self.tokenizer(UpperCamelCase_ ,\t\t\t\t\t\tpadding='''longest''' ,\t\t\t\t\t\treturn_tensors='''pt'''\t\t\t\t\t\t\t).input_ids\r\n\r\n\t\t\t\t\t\t\t\t\tif untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlogger.warning(\r\n\t\t\t\t\t\t\t\t\t\t\t '''The following part of your input was truncated because CLIP can only handle sequences up to'''\r\n\t\t\t\t\t\t\t\t\t\t\t f' {self.tokenizer.model_max_length} tokens: {removed_text}'\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = text_input_ids.to(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\tlowercase__ = text_inputs.attention_mask.to(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self.text_encoder(\r\n\t\t\t\t\t\t\t\t\t input_ids=UpperCamelCase_ ,\t\t\t\t\t\tattention_mask=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = prompt_embeds.repeat_interleave(UpperCamelCase_ ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\tlowercase__ = text_encoder_hidden_states.repeat_interleave(UpperCamelCase_ ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\tlowercase__ = text_mask.repeat_interleave(UpperCamelCase_ ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tif do_classifier_free_guidance:\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = 42\r\n\t\t\t\t\t\t\t\t\t\t\tif negative_prompt is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = [\"\"] * batch_size\r\n\t\t\t\t\t\t\t\t\t\t\telif type(UpperCamelCase_\t\t\t\t\t\t\t) is not type(UpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\traise TypeError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t f'`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase_\t\t\t\t\t\t\t)} !='\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t f' {type(UpperCamelCase_\t\t\t\t\t\t\t)}.'\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\telif isinstance(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = [negative_prompt]\r\n\t\t\t\t\t\t\t\t\t\t\telif batch_size != len(UpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t f'`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase_\t\t\t\t\t\t\t)}, but `prompt`:'\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ''' the batch size of `prompt`.'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = negative_prompt\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = self.tokenizer(\r\n\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase_ ,\t\t\t\t\t\tpadding='''max_length''' ,\t\t\t\t\t\tmax_length=77 ,\t\t\t\t\t\ttruncation=UpperCamelCase_ ,\t\t\t\t\t\treturn_attention_mask=UpperCamelCase_ ,\t\t\t\t\t\tadd_special_tokens=UpperCamelCase_ ,\t\t\t\t\t\treturn_tensors='''pt''' ,\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = uncond_input.input_ids.to(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = uncond_input.attention_mask.to(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = self.text_encoder(\r\n\t\t\t\t\t\t\t\t\t\t\t input_ids=UpperCamelCase_ ,\t\t\t\t\t\tattention_mask=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# duplicate unconditional embeddings for each generation per prompt, using mps friendly method\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = negative_prompt_embeds.shape[1]\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = negative_prompt_embeds.repeat(1 ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = negative_prompt_embeds.view(batch_size * num_images_per_prompt ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = uncond_text_encoder_hidden_states.shape[1]\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = uncond_text_encoder_hidden_states.repeat(1 ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\t1\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = uncond_text_encoder_hidden_states.view(\r\n\t\t\t\t\t\t\t\t\t\t\t batch_size * num_images_per_prompt ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\t-1\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = uncond_text_mask.repeat_interleave(UpperCamelCase_ ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# done duplicates\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# For classifier free guidance, we need to do two forward passes.\r\n\t\t\t\t\t\t\t\t\t\t\t# Here we concatenate the unconditional and text embeddings into a single batch\r\n\t\t\t\t\t\t\t\t\t\t\t# to avoid doing two forward passes\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = torch.cat([negative_prompt_embeds, prompt_embeds]\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = torch.cat([uncond_text_mask, text_mask]\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\treturn prompt_embeds, text_encoder_hidden_states, text_mask\r\n\r\n\t\t\t\t\t\t\tdef lowerCamelCase_ (\t\t\t\t\t\t\tself:\tAny ,\t\t\t\t\t\tUpperCamelCase_:\tList[str]=0\t\t\t\t\t\t\t)\t\t\t-> Dict:\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\tif is_accelerate_available():\r\n\t\t\t\t\t\t\t\t\t\t\tfrom accelerate import cpu_offload\r\n\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\traise ImportError('''Please install accelerate via `pip install accelerate`'''\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = torch.device(f'cuda:{gpu_id}'\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = [\r\n\t\t\t\t\t\t\t\t\t self.unet,\r\n\t\t\t\t\t\t\t\t\t self.text_encoder,\r\n\t\t\t\t\t\t\t\t\t self.movq,\r\n\t\t\t\t\t\t\t\t\t]\r\n\t\t\t\t\t\t\t\t\tfor cpu_offloaded_model in models:\r\n\t\t\t\t\t\t\t\t\t\t\tif cpu_offloaded_model is not None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tcpu_offload(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\tdef lowerCamelCase_ (\t\t\t\t\t\t\tself:\tTuple ,\t\t\t\t\t\tUpperCamelCase_:\tTuple=0\t\t\t\t\t\t\t)\t\t\t-> Tuple:\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\tif is_accelerate_available() and is_accelerate_version('''>=''' ,\t\t\t\t\t\t'''0.17.0.dev0'''\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\tfrom accelerate import cpu_offload_with_hook\r\n\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\traise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = torch.device(f'cuda:{gpu_id}'\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tif self.device.type != \"cpu\":\r\n\t\t\t\t\t\t\t\t\t\t\tself.to('''cpu''' ,\t\t\t\t\t\tsilence_dtype_warnings=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\ttorch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = None\r\n\t\t\t\t\t\t\t\t\tfor cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = cpu_offload_with_hook(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tprev_module_hook=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tif self.safety_checker is not None:\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = cpu_offload_with_hook(self.safety_checker ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tprev_module_hook=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t# We'll offload the last model manually.\r\n\t\t\t\t\t\t\t\t\tlowercase__ = hook\r\n\r\n\t\t\t\t\t\t\t@property\r\n\t\t\t\t\t\t\t# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device\r\n\t\t\t\t\t\t\tdef lowerCamelCase_ (\t\t\t\t\t\t\tself:\tList[Any]\t\t\t\t\t\t\t)\t\t\t-> List[str]:\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\tif not hasattr(self.unet ,\t\t\t\t\t\t'''_hf_hook'''\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\treturn self.device\r\n\t\t\t\t\t\t\t\t\tfor module in self.unet.modules():\r\n\t\t\t\t\t\t\t\t\t\t\tif (\r\n\t\t\t\t\t\t\t\t\t\t\t hasattr(UpperCamelCase_ ,\t\t\t\t\t\t'''_hf_hook'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t and hasattr(module._hf_hook ,\t\t\t\t\t\t'''execution_device'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t and module._hf_hook.execution_device is not None\r\n\t\t\t\t\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn torch.device(module._hf_hook.execution_device\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\treturn self.device\r\n\r\n\r\n\t\t\t\t\t\t\t@torch.no_grad()\r\n\t\t\t\t\t\t\t@replace_example_docstring(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\tdef __call__(\t\t\t\t\t\t\tself:\tstr ,\t\t\t\t\t\tUpperCamelCase_:\tUnion[str, List[str]] ,\t\t\t\t\t\tUpperCamelCase_:\tUnion[torch.FloatTensor, List[torch.FloatTensor]] ,\t\t\t\t\t\tUpperCamelCase_:\tUnion[torch.FloatTensor, List[torch.FloatTensor]] ,\t\t\t\t\t\tUpperCamelCase_:\tOptional[Union[str, List[str]]] = None ,\t\t\t\t\t\tUpperCamelCase_:\tint = 512 ,\t\t\t\t\t\tUpperCamelCase_:\tint = 512 ,\t\t\t\t\t\tUpperCamelCase_:\tint = 100 ,\t\t\t\t\t\tUpperCamelCase_:\tfloat = 4.0 ,\t\t\t\t\t\tUpperCamelCase_:\tint = 1 ,\t\t\t\t\t\tUpperCamelCase_:\tOptional[Union[torch.Generator, List[torch.Generator]]] = None ,\t\t\t\t\t\tUpperCamelCase_:\tOptional[torch.FloatTensor] = None ,\t\t\t\t\t\tUpperCamelCase_:\tOptional[str] = \"pil\" ,\t\t\t\t\t\tUpperCamelCase_:\tbool = True ,\t\t\t\t\t\t)\t\t\t-> Tuple:\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\tif isinstance(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = 1\r\n\t\t\t\t\t\t\t\t\telif isinstance(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = len(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\traise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_\t\t\t\t\t\t\t)}'\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self._execution_device\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = batch_size * num_images_per_prompt\r\n\t\t\t\t\t\t\t\t\tlowercase__ = guidance_scale > 1.0\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self._encode_prompt(\r\n\t\t\t\t\t\t\t\t\t UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tif isinstance(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = torch.cat(UpperCamelCase_ ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\tif isinstance(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = torch.cat(UpperCamelCase_ ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tif do_classifier_free_guidance:\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = image_embeds.repeat_interleave(UpperCamelCase_ ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = negative_image_embeds.repeat_interleave(UpperCamelCase_ ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = torch.cat([negative_image_embeds, image_embeds] ,\t\t\t\t\t\tdim=0\t\t\t\t\t\t\t).to(\r\n\t\t\t\t\t\t\t\t\t dtype=prompt_embeds.dtype ,\t\t\t\t\t\tdevice=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tself.scheduler.set_timesteps(UpperCamelCase_ ,\t\t\t\t\t\tdevice=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self.scheduler.timesteps\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self.unet.config.in_channels\r\n\r\n\t\t\t\t\t\t\t\t\tlowercase__ = get_new_h_w(UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tself.movq_scale_factor\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t# create initial latent\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self.prepare_latents(\r\n\t\t\t\t\t\t\t\t\t (batch_size, num_channels_latents, height, width) ,\t\t\t\t\t\ttext_encoder_hidden_states.dtype ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tself.scheduler ,\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tfor i, t in enumerate(self.progress_bar(UpperCamelCase_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t# expand the latents if we are doing classifier free guidance\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = torch.cat([latents] * 2\t\t\t\t\t\t\t) if do_classifier_free_guidance else latents\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = {\"text_embeds\": prompt_embeds, \"image_embeds\": image_embeds}\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = self.unet(\r\n\t\t\t\t\t\t\t\t\t\t\t sample=UpperCamelCase_ ,\t\t\t\t\t\ttimestep=UpperCamelCase_ ,\t\t\t\t\t\tencoder_hidden_states=UpperCamelCase_ ,\t\t\t\t\t\tadded_cond_kwargs=UpperCamelCase_ ,\t\t\t\t\t\treturn_dict=UpperCamelCase_ ,\t\t\t\t\t\t)[0]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tif do_classifier_free_guidance:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = noise_pred.split(latents.shape[1] ,\t\t\t\t\t\tdim=1\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = noise_pred.chunk(2\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = variance_pred.chunk(2\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = torch.cat([noise_pred, variance_pred_text] ,\t\t\t\t\t\tdim=1\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tif not (\r\n\t\t\t\t\t\t\t\t\t\t\t hasattr(self.scheduler.config ,\t\t\t\t\t\t'''variance_type'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\r\n\t\t\t\t\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = noise_pred.split(latents.shape[1] ,\t\t\t\t\t\tdim=1\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t# compute the previous noisy sample x_t -> x_t-1\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = self.scheduler.step(\r\n\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tUpperCamelCase_ ,\t\t\t\t\t\tgenerator=UpperCamelCase_ ,\t\t\t\t\t\t).prev_sample\r\n\t\t\t\t\t\t\t\t\t# post-processing\r\n\t\t\t\t\t\t\t\t\tlowercase__ = self.movq.decode(UpperCamelCase_ ,\t\t\t\t\t\tforce_not_quantize=UpperCamelCase_\t\t\t\t\t\t\t)[\"sample\"]\r\n\r\n\t\t\t\t\t\t\t\t\tif output_type not in [\"pt\", \"np\", \"pil\"]:\r\n\t\t\t\t\t\t\t\t\t\t\traise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tif output_type in [\"np\", \"pil\"]:\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = image * 0.5 + 0.5\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = image.clamp(0 ,\t\t\t\t\t\t1\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = image.cpu().permute(0 ,\t\t\t\t\t\t2 ,\t\t\t\t\t\t3 ,\t\t\t\t\t\t1\t\t\t\t\t\t\t).float().numpy()\r\n\r\n\t\t\t\t\t\t\t\t\tif output_type == \"pil\":\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase__ = self.numpy_to_pil(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\tif not return_dict:\r\n\t\t\t\t\t\t\t\t\t\t\treturn (image,)\r\n\r\n\t\t\t\t\t\t\t\t\treturn ImagePipelineOutput(images=UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":110,"string":"110"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom typing import TYPE_CHECKING\r\n\r\nfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available\r\n\r\n\r\nlowerCamelCase\t:\t\t\t\t\t\t\tint\t\t\t\t={\r\n '''configuration_audio_spectrogram_transformer''': [\r\n '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',\r\n '''ASTConfig''',\r\n ]\r\n}\r\n\r\ntry:\r\n if not is_torch_available():\r\n raise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n pass\r\nelse:\r\n lowerCamelCase\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t=[\r\n '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',\r\n '''ASTForAudioClassification''',\r\n '''ASTModel''',\r\n '''ASTPreTrainedModel''',\r\n ]\r\n\r\ntry:\r\n if not is_speech_available():\r\n raise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n pass\r\nelse:\r\n lowerCamelCase\t:\t\t\t\t\t\t\tOptional[int]\t\t\t\t=['''ASTFeatureExtractor''']\r\n\r\nif TYPE_CHECKING:\r\n from .configuration_audio_spectrogram_transformer import (\r\n AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,\r\n ASTConfig,\r\n )\r\n\r\n try:\r\n if not is_torch_available():\r\n raise OptionalDependencyNotAvailable()\r\n except OptionalDependencyNotAvailable:\r\n pass\r\n else:\r\n from .modeling_audio_spectrogram_transformer import (\r\n AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n ASTForAudioClassification,\r\n ASTModel,\r\n ASTPreTrainedModel,\r\n )\r\n\r\n try:\r\n if not is_speech_available():\r\n raise OptionalDependencyNotAvailable()\r\n except OptionalDependencyNotAvailable:\r\n pass\r\n else:\r\n from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor\r\n\r\n\r\nelse:\r\n import sys\r\n\r\n lowerCamelCase\t:\t\t\t\t\t\t\tOptional[int]\t\t\t\t=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)"},"style_context_codestyle":{"kind":"number","value":189,"string":"189"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":559,"cells":{"code":{"kind":"string","value":"\r\r\rimport warnings\r\rfrom ...utils import logging\rfrom .image_processing_imagegpt import ImageGPTImageProcessor\r\r\rlowerCAmelCase \t=\t\tlogging.get_logger(__name__)\r\r\r\r\r\rclass \t\t\t\tA ( A_\t\t\t):\r\r\r\t\t\t\t\t\t\tdef __init__(self\t\t\t\t, *lowerCAmelCase\t\t\t\t, **lowerCAmelCase ):\r\t\t\t\t\t\t\t\t\twarnings.warn(\r\t\t\t\t\t\t\t\t\t 'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'\r\t\t\t\t\t\t\t\t\t ' Please use ImageGPTImageProcessor instead.'\t\t\t\t, lowerCAmelCase\t\t\t\t, )\r\t\t\t\t\t\t\t\t\tsuper().__init__(*lowerCAmelCase\t\t\t\t, **lowerCAmelCase )\r\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":304,"string":"304"},"style_context":{"kind":"string","value":"\r\r\rimport math\rfrom datetime import datetime, timedelta\r\r\r\r\r\r\r\rdef \t_lowerCamelCase( lowercase__ )\t\t\t\t\t\t-> datetime:\r\r\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t__lowercase= year % 1_9\r\t\t__lowercase= year % 4\r\t\t__lowercase= year % 7\r\t\t__lowercase= math.floor(year / 1_0_0 )\r\t\t__lowercase= math.floor((1_3 + 8 * leap_day_inhibits) / 2_5 )\r\t\t__lowercase= leap_day_inhibits / 4\r\t\t__lowercase= (\r\t\t 1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number\r\t\t) % 3_0\r\t\t__lowercase= (4 + leap_day_inhibits - leap_day_reinstall_number) % 7\r\r\t\t# days to be added to March 21\r\t\t__lowercase= (1_9 * metonic_cycle + secular_moon_shift) % 3_0\r\r\t\t# PHM -> Paschal Full Moon\r\t\t__lowercase= (\r\t\t 2 * julian_leap_year\r\t\t + 4 * non_leap_year\r\t\t + 6 * days_to_add\r\t\t + century_starting_point\r\t\t) % 7\r\r\t\tif days_to_add == 2_9 and days_from_phm_to_sunday == 6:\r\t\t\t\treturn datetime(lowercase__ ,\t\t\t4 ,\t\t\t1_9 )\r\t\telif days_to_add == 2_8 and days_from_phm_to_sunday == 6:\r\t\t\t\treturn datetime(lowercase__ ,\t\t\t4 ,\t\t\t1_8 )\r\t\telse:\r\t\t\t\treturn datetime(lowercase__ ,\t\t\t3 ,\t\t\t2_2 ) + timedelta(\r\t\t\t\t days=int(days_to_add + days_from_phm_to_sunday ) )\r\r\rif __name__ == \"__main__\":\r\tfor year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):\r\t\tlowerCAmelCase \t=\t\t'''will be''' if year > datetime.now().year else '''was'''\r\t\tprint(F'Easter in {year} {tense} {gauss_easter(year)}')\r\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":304,"string":"304"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":560,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\rimport gc\rimport unittest\r\rfrom parameterized import parameterized\r\rfrom diffusers import FlaxUNetaDConditionModel\rfrom diffusers.utils import is_flax_available\rfrom diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow\r\r\rif is_flax_available():\r\t\t\t\t\t\timport jax\r\t\t\t\t\t\timport jax.numpy as jnp\r\r\r\r\r\r\r\r@slow\r@require_flax\rclass lowercase__\t\t\t(\t\t\t\t\t\t\tunittest.TestCase ):\r\r\r\t\t\tdef \tUpperCAmelCase__\t\t\t\t\t\t( self : int , snake_case__ : List[str] , snake_case__ : List[str]\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\treturn F\"\"\"gaussian_noise_s={seed}_shape={'_'.join([str(__SCREAMING_SNAKE_CASE\t\t\t\t\t) for s in shape]\t\t\t\t\t)}.npy\"\"\"\r\r\r\t\t\tdef \tUpperCAmelCase__\t\t\t\t\t\t( self : List[Any]\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\tsuper().tearDown()\r\t\t\t\t\t\t\t\t\t\tgc.collect()\r\r\r\t\t\tdef \tUpperCAmelCase__\t\t\t\t\t\t( self : Optional[int] , snake_case__ : Tuple=0 , snake_case__ : List[Any]=(4, 4, 64, 64) , snake_case__ : List[Any]=False\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t\t=jnp.bfloataa if fpaa else jnp.floataa\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t\t=jnp.array(load_hf_numpy(self.get_file_format(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE\t\t\t\t\t)\t\t\t\t\t) , dtype=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\treturn image\r\r\r\t\t\tdef \tUpperCAmelCase__\t\t\t\t\t\t( self : List[Any] , snake_case__ : Tuple=False , snake_case__ : Union[str, Any]=\"CompVis/stable-diffusion-v1-4\"\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t=jnp.bfloataa if fpaa else jnp.floataa\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t='''bf16''' if fpaa else None\r\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t=FlaxUNetaDConditionModel.from_pretrained(\r\t\t\t\t\t\t\t\t\t\t __SCREAMING_SNAKE_CASE , subfolder=\"unet\" , dtype=__SCREAMING_SNAKE_CASE , revision=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\treturn model, params\r\r\r\t\t\tdef \tUpperCAmelCase__\t\t\t\t\t\t( self : Dict , snake_case__ : str=0 , snake_case__ : List[Any]=(4, 77, 768) , snake_case__ : Any=False\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t=jnp.bfloataa if fpaa else jnp.floataa\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t\t=jnp.array(load_hf_numpy(self.get_file_format(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE\t\t\t\t\t)\t\t\t\t\t) , dtype=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\treturn hidden_states\r\r\r\t\t\t@parameterized.expand(\r\t\t\t [\r\t\t\t # fmt: off\r\t\t\t [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],\r\t\t\t [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],\r\t\t\t [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],\r\t\t\t [3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],\r\t\t\t # fmt: on\r\t\t\t ]\t\t\t\t\t)\r\t\t\tdef \tUpperCAmelCase__\t\t\t\t\t\t( self : Dict , snake_case__ : List[str] , snake_case__ : Optional[Any] , snake_case__ : Union[str, Any]\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tTuple\t\t\t\t\t\t\t\t\t=self.get_unet_model(model_id=\"CompVis/stable-diffusion-v1-4\" , fpaa=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t\t=self.get_latents(__SCREAMING_SNAKE_CASE , fpaa=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t=self.get_encoder_hidden_states(__SCREAMING_SNAKE_CASE , fpaa=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t=model.apply(\r\t\t\t\t\t\t\t\t\t\t {\"params\": params} , __SCREAMING_SNAKE_CASE , jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.intaa\t\t\t\t\t) , encoder_hidden_states=__SCREAMING_SNAKE_CASE , ).sample\r\r\t\t\t\t\t\t\t\t\t\tassert sample.shape == latents.shape\r\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t=jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())\t\t\t\t\t) , dtype=jnp.floataa\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tTuple\t\t\t\t\t\t\t\t\t=jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.floataa\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t\t# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware\r\t\t\t\t\t\t\t\t\t\tassert jnp.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1E-2\t\t\t\t\t)\r\r\r\t\t\t@parameterized.expand(\r\t\t\t [\r\t\t\t # fmt: off\r\t\t\t [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],\r\t\t\t [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],\r\t\t\t [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],\r\t\t\t [3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],\r\t\t\t # fmt: on\r\t\t\t ]\t\t\t\t\t)\r\t\t\tdef \tUpperCAmelCase__\t\t\t\t\t\t( self : Tuple , snake_case__ : int , snake_case__ : List[str] , snake_case__ : Any\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t=self.get_unet_model(model_id=\"stabilityai/stable-diffusion-2\" , fpaa=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t\t=self.get_latents(__SCREAMING_SNAKE_CASE , shape=(4, 4, 96, 96) , fpaa=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t=self.get_encoder_hidden_states(__SCREAMING_SNAKE_CASE , shape=(4, 77, 1024) , fpaa=__SCREAMING_SNAKE_CASE\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t=model.apply(\r\t\t\t\t\t\t\t\t\t\t {\"params\": params} , __SCREAMING_SNAKE_CASE , jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.intaa\t\t\t\t\t) , encoder_hidden_states=__SCREAMING_SNAKE_CASE , ).sample\r\r\t\t\t\t\t\t\t\t\t\tassert sample.shape == latents.shape\r\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t=jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())\t\t\t\t\t) , dtype=jnp.floataa\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tlowerCamelCase_ :\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t\t=jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.floataa\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t\t# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware\r\t\t\t\t\t\t\t\t\t\tassert jnp.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1E-2\t\t\t\t\t)\r\r\r\r"},"code_codestyle":{"kind":"number","value":144,"string":"144"},"style_context":{"kind":"string","value":"\r\n'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport unittest\r\n\r\nfrom knapsack import greedy_knapsack as kp\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass lowerCAmelCase__ ( unittest.TestCase ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: List[str]\t\t\t\t\t\t\t\t\t\t\t\t\t= [10, 20, 30, 40, 50, 60]\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= [2, 4, 6, 8, 10, 12]\r\n\t\t\t\t\t\t\t\t\t\t\tlowercase_\t: Union[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t= 1_00\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(kp.calc_profit(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE\t\t)\t\t\t\t,\t\t\t\t\t\t\t2_10\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertRaisesRegex(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t'''max_weight must greater than zero.'''\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertRaisesRegex(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t'''Weight can not be negative.'''\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertRaisesRegex(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t'''Profit can not be negative.'''\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertRaisesRegex(__SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t'''max_weight must greater than zero.'''\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef \t_snake_case ( self\t\t):\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\tself.assertRaisesRegex(\r\n\t\t\t\t\t\t\t\t\t\t\t __SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t\t\t'''The length of profit and weight must be same.'''\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\t\t\t\t\tunittest.main()\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":93,"string":"93"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":561,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\n\n\"\"\"simple docstring\"\"\"\n\n\nimport unittest\n\nimport numpy as np\n\nfrom transformers import DistilBertConfig, is_flax_available\nfrom transformers.testing_utils import require_flax, slow\n\nfrom ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask\n\n\nif is_flax_available():\n import jax.numpy as jnp\n\n from transformers.models.distilbert.modeling_flax_distilbert import (\n FlaxDistilBertForMaskedLM,\n FlaxDistilBertForMultipleChoice,\n FlaxDistilBertForQuestionAnswering,\n FlaxDistilBertForSequenceClassification,\n FlaxDistilBertForTokenClassification,\n FlaxDistilBertModel,\n )\n\n\n\n\n\n\nclass \t\t\t\t\t\tsnake_case ( unittest.TestCase\t):\n\n\n\n\n\n def __init__(\t\t\t\t\t\t\tself : List[Any] ,\t\tA : Dict ,\t\tA : Optional[int]=1_3 ,\t\tA : Dict=7 ,\t\tA : Union[str, Any]=True ,\t\tA : Union[str, Any]=True ,\t\tA : Optional[Any]=True ,\t\tA : str=True ,\t\tA : Any=9_9 ,\t\tA : Dict=3_2 ,\t\tA : Union[str, Any]=5 ,\t\tA : Tuple=4 ,\t\tA : List[Any]=3_7 ,\t\tA : Tuple=\"gelu\" ,\t\tA : str=0.1 ,\t\tA : Union[str, Any]=0.1 ,\t\tA : Optional[int]=5_1_2 ,\t\tA : int=1_6 ,\t\tA : Any=2 ,\t\tA : List[str]=0.02 ,\t\tA : int=4 ,\t\t):\n\n\n '''simple docstring'''\n\n\n\n\n a\t\t\t\t\t: Dict = parent\n a\t\t\t\t\t: List[Any] = batch_size\n a\t\t\t\t\t: Optional[Any] = seq_length\n a\t\t\t\t\t: Tuple = is_training\n a\t\t\t\t\t: int = use_attention_mask\n a\t\t\t\t\t: Optional[int] = use_token_type_ids\n a\t\t\t\t\t: Any = use_labels\n a\t\t\t\t\t: List[Any] = vocab_size\n a\t\t\t\t\t: Optional[int] = hidden_size\n a\t\t\t\t\t: List[Any] = num_hidden_layers\n a\t\t\t\t\t: Union[str, Any] = num_attention_heads\n a\t\t\t\t\t: Any = intermediate_size\n a\t\t\t\t\t: Tuple = hidden_act\n a\t\t\t\t\t: Optional[int] = hidden_dropout_prob\n a\t\t\t\t\t: Dict = attention_probs_dropout_prob\n a\t\t\t\t\t: Any = max_position_embeddings\n a\t\t\t\t\t: Tuple = type_vocab_size\n a\t\t\t\t\t: Any = type_sequence_label_size\n a\t\t\t\t\t: str = initializer_range\n a\t\t\t\t\t: Dict = num_choices\n\n\n\n\n\n def lowerCamelCase__\t\t\t\t(\t\t\t\t\t\t\tself : Tuple ):\n\n\n '''simple docstring'''\n\n\n\n\n a\t\t\t\t\t: Dict = ids_tensor([self.batch_size, self.seq_length] ,\t\tself.vocab_size )\n\n a\t\t\t\t\t: List[str] = None\n if self.use_attention_mask:\n a\t\t\t\t\t: List[Any] = random_attention_mask([self.batch_size, self.seq_length] )\n\n a\t\t\t\t\t: Optional[Any] = DistilBertConfig(\n vocab_size=self.vocab_size ,\t\tdim=self.hidden_size ,\t\tn_layers=self.num_hidden_layers ,\t\tn_heads=self.num_attention_heads ,\t\thidden_dim=self.intermediate_size ,\t\thidden_act=self.hidden_act ,\t\tdropout=self.hidden_dropout_prob ,\t\tattention_dropout=self.attention_probs_dropout_prob ,\t\tmax_position_embeddings=self.max_position_embeddings ,\t\tinitializer_range=self.initializer_range ,\t\ttie_weights_=_UpperCAmelCase ,\t\t)\n\n return config, input_ids, attention_mask\n\n\n\n\n\n def lowerCamelCase__\t\t\t\t(\t\t\t\t\t\t\tself : List[Any] ):\n\n\n '''simple docstring'''\n\n\n\n\n a\t\t\t\t\t: Union[str, Any] = self.prepare_config_and_inputs()\n a,\t\t\t\t\ta,\t\t\t\t\ta\t\t\t\t\t: Optional[int] = config_and_inputs\n a\t\t\t\t\t: int = {'input_ids': input_ids, 'attention_mask': attention_mask}\n return config, inputs_dict\n\n\n\n\n\n\n@require_flax\nclass \t\t\t\t\t\tsnake_case ( lowerCamelCase_ ,\t\t\tunittest.TestCase\t):\n __magic_name__ \t\t\t\t\t=\t\t\t\t\t(\n (\n FlaxDistilBertModel,\n FlaxDistilBertForMaskedLM,\n FlaxDistilBertForMultipleChoice,\n FlaxDistilBertForQuestionAnswering,\n FlaxDistilBertForSequenceClassification,\n FlaxDistilBertForTokenClassification,\n FlaxDistilBertForQuestionAnswering,\n )\n if is_flax_available()\n else ()\n )\n\n\n\n\n\n def lowerCamelCase__\t\t\t\t(\t\t\t\t\t\t\tself : Any ):\n\n\n '''simple docstring'''\n\n\n\n\n a\t\t\t\t\t: Any = FlaxDistilBertModelTester(self )\n\n\n\n\n\n @slow\n def lowerCamelCase__\t\t\t\t(\t\t\t\t\t\t\tself : int ):\n\n\n '''simple docstring'''\n\n\n\n\n for model_class_name in self.all_model_classes:\n a\t\t\t\t\t: List[Any] = model_class_name.from_pretrained('distilbert-base-uncased' )\n a\t\t\t\t\t: Optional[Any] = model(np.ones((1, 1) ) )\n self.assertIsNotNone(_UpperCAmelCase )\n\n\n\n\n\n\n@require_flax\nclass \t\t\t\t\t\tsnake_case ( unittest.TestCase\t):\n\n\n\n\n\n @slow\n def lowerCamelCase__\t\t\t\t(\t\t\t\t\t\t\tself : Optional[int] ):\n\n\n '''simple docstring'''\n\n\n\n\n a\t\t\t\t\t: Union[str, Any] = FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )\n a\t\t\t\t\t: List[Any] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )\n a\t\t\t\t\t: Tuple = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )\n a\t\t\t\t\t: List[str] = model(_UpperCAmelCase ,\t\tattention_mask=_UpperCAmelCase )[0]\n a\t\t\t\t\t: List[str] = (1, 1_1, 7_6_8)\n self.assertEqual(output.shape ,\t\t_UpperCAmelCase )\n a\t\t\t\t\t: Optional[int] = np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]] )\n\n self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,\t\t_UpperCAmelCase ,\t\tatol=1E-4 ) )\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":367,"string":"367"},"style_context":{"kind":"string","value":"\n\n\n\n\n\n\n\"\"\"simple docstring\"\"\"\n\n\nimport argparse\nfrom collections import defaultdict\n\nimport yaml\n\n\n_UpperCamelCase : int \t\t\t=\t\t\t\t\t\t'docs/source/en/_toctree.yml'\n\n\n\n\ndef snake_case (A_\t\t\t\t:Optional[Any]\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n\n a\t\t\t\t\t: List[Any] = defaultdict(A_\t\t\t\t)\n for doc in model_doc:\n counts[doc[\"local\"]] += 1\n a\t\t\t\t\t: Optional[Any] = [key for key, value in counts.items() if value > 1]\n\n a\t\t\t\t\t: List[str] = []\n for duplicate_key in duplicates:\n a\t\t\t\t\t: int = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}\t\t\t\t)\n if len(A_\t\t\t\t) > 1:\n raise ValueError(\n f'''{duplicate_key} is present several times in the documentation table of content at '''\n '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '\n 'others.'\t\t\t\t)\n # Only add this once\n new_doc.append({'local': duplicate_key, 'title': titles[0]}\t\t\t\t)\n\n # Add none duplicate-keys\n new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]\t\t\t\t)\n\n # Sort\n return sorted(A_ , key=lambda A_\t\t\t\t: s[\"title\"].lower()\t\t\t\t)\n\n\n\n\ndef snake_case (A_\t\t\t\t:List[str]=False\t\t\t\t):\n\n\n\n\n\n\n\n '''simple docstring'''\n\n with open(A_ , encoding='utf-8'\t\t\t\t) as f:\n a\t\t\t\t\t: Dict = yaml.safe_load(f.read()\t\t\t\t)\n\n # Get to the API doc\n a\t\t\t\t\t: Optional[Any] = 0\n while content[api_idx][\"title\"] != \"API\":\n api_idx += 1\n a\t\t\t\t\t: List[str] = content[api_idx]['sections']\n\n # Then to the model doc\n a\t\t\t\t\t: Optional[int] = 0\n while api_doc[model_idx][\"title\"] != \"Models\":\n model_idx += 1\n\n a\t\t\t\t\t: Optional[Any] = api_doc[model_idx]['sections']\n\n a\t\t\t\t\t: Dict = [(idx, section) for idx, section in enumerate(A_\t\t\t\t) if 'sections' in section]\n a\t\t\t\t\t: List[str] = False\n for idx, modality_doc in modalities_docs:\n a\t\t\t\t\t: str = modality_doc['sections']\n a\t\t\t\t\t: str = clean_model_doc_toc(A_\t\t\t\t)\n\n if old_modality_doc != new_modality_doc:\n a\t\t\t\t\t: str = True\n if overwrite:\n a\t\t\t\t\t: Any = new_modality_doc\n\n if diff:\n if overwrite:\n a\t\t\t\t\t: Any = model_doc\n a\t\t\t\t\t: str = api_doc\n with open(A_ , 'w' , encoding='utf-8'\t\t\t\t) as f:\n f.write(yaml.dump(A_ , allow_unicode=A_\t\t\t\t)\t\t\t\t)\n else:\n raise ValueError(\n 'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n _UpperCamelCase : Optional[int] \t\t\t=\t\t\t\t\t\targparse.ArgumentParser()\n parser.add_argument('--fix_and_overwrite', action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true", help='Whether to fix inconsistencies.')\n _UpperCamelCase : Any \t\t\t=\t\t\t\t\t\tparser.parse_args()\n\n check_model_doc(args.fix_and_overwrite)\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":186,"string":"186"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":562,"cells":{"code":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef __UpperCAmelCase\t\t\t\t( UpperCAmelCase_ :\t\t\t\tdict )\t\t\t\t\t\t\t->\t\t\tbool:\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t__snake_case\t\t\t:\t\t\t\t\t\t\tset[int] \t\t= set()\r\n\t# To detect a back edge, keep track of vertices currently in the recursion stack\r\n\t__snake_case\t\t\t:\t\t\t\t\t\t\tset[int] \t\t= set()\r\n\treturn any(\r\n\t node not in visited and depth_first_search(UpperCAmelCase_\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_ )\r\n\t for node in graph )\r\ndef __UpperCAmelCase\t\t\t\t( UpperCAmelCase_ :\t\t\t\tdict\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_ :\t\t\t\tint\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_ :\t\t\t\tset\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_ :\t\t\t\tset )\t\t\t\t\t\t\t->\t\t\tbool:\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\tvisited.add(UpperCAmelCase_ )\r\n\trec_stk.add(UpperCAmelCase_ )\r\n\r\n\tfor node in graph[vertex]:\r\n\t\tif node not in visited:\r\n\t\t\tif depth_first_search(UpperCAmelCase_\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_\t\t\t,\t\t\t\t\t\t\tUpperCAmelCase_ ):\r\n\t\t\t\treturn True\r\n\t\telif node in rec_stk:\r\n\t\t\treturn True\r\n\r\n # The node needs to be removed from recursion stack before function ends\r\n\trec_stk.remove(UpperCAmelCase_ )\r\n\treturn False\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\tfrom doctest import testmod\r\n\r\n\ttestmod()\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":172,"string":"172"},"style_context":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport os\r\nfrom argparse import ArgumentParser, Namespace\r\n\r\nfrom ..data import SingleSentenceClassificationProcessor as Processor\r\nfrom ..pipelines import TextClassificationPipeline\r\nfrom ..utils import is_tf_available, is_torch_available, logging\r\nfrom . import BaseTransformersCLICommand\r\n\r\n\r\nif not is_tf_available() and not is_torch_available():\r\n\traise RuntimeError(\"At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training\")\r\n\r\n# TF training parameters\r\n_a : Optional[int]= False\r\n_a : int= False\r\ndef __UpperCAmelCase\t\t\t\t( UpperCAmelCase_ :\t\t\t\tNamespace )\t\t\t\t\t\t\t->\t\t\tOptional[Any]:\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\treturn TrainCommand(UpperCAmelCase_ )\r\n\r\nclass \t\tUpperCamelCase\t\t\t( lowercase\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t@staticmethod\r\n\t\t\t\tdef \t\t\t\t\t_lowercase (_A\t\t\t\t: ArgumentParser) ->\tAny:\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tAny \t\t= parser.add_parser('train'\t, help='CLI tool to train a model on a task.')\r\n\r\n\t\t\t\t\ttrain_parser.add_argument(\r\n\t\t\t\t\t '--train_data'\t, type=_A\t, required=_A\t, help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'\t, )\r\n\t\t\t\t\ttrain_parser.add_argument(\r\n\t\t\t\t\t '--column_label'\t, type=_A\t, default=0\t, help='Column of the dataset csv file with example labels.')\r\n\t\t\t\t\ttrain_parser.add_argument(\r\n\t\t\t\t\t '--column_text'\t, type=_A\t, default=1\t, help='Column of the dataset csv file with example texts.')\r\n\t\t\t\t\ttrain_parser.add_argument(\r\n\t\t\t\t\t '--column_id'\t, type=_A\t, default=2\t, help='Column of the dataset csv file with example ids.')\r\n\t\t\t\t\ttrain_parser.add_argument(\r\n\t\t\t\t\t '--skip_first_row'\t, action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true"\t, help='Skip the first row of the csv file (headers).')\r\n\r\n\t\t\t\t\ttrain_parser.add_argument('--validation_data'\t, type=_A\t, default=''\t, help='path to validation dataset.')\r\n\t\t\t\t\ttrain_parser.add_argument(\r\n\t\t\t\t\t '--validation_split'\t, type=_A\t, default=0.1\t, help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'\t, )\r\n\r\n\t\t\t\t\ttrain_parser.add_argument('--output'\t, type=_A\t, default='./'\t, help='path to saved the trained model.')\r\n\r\n\t\t\t\t\ttrain_parser.add_argument(\r\n\t\t\t\t\t '--task'\t, type=_A\t, default='text_classification'\t, help='Task to train the model on.')\r\n\t\t\t\t\ttrain_parser.add_argument(\r\n\t\t\t\t\t '--model'\t, type=_A\t, default='bert-base-uncased'\t, help='Model\\'s name or path to stored model.')\r\n\t\t\t\t\ttrain_parser.add_argument('--train_batch_size'\t, type=_A\t, default=32\t, help='Batch size for training.')\r\n\t\t\t\t\ttrain_parser.add_argument('--valid_batch_size'\t, type=_A\t, default=64\t, help='Batch size for validation.')\r\n\t\t\t\t\ttrain_parser.add_argument('--learning_rate'\t, type=_A\t, default=3E-5\t, help='Learning rate.')\r\n\t\t\t\t\ttrain_parser.add_argument('--adam_epsilon'\t, type=_A\t, default=1E-08\t, help='Epsilon for Adam optimizer.')\r\n\t\t\t\t\ttrain_parser.set_defaults(func=_A)\r\n\r\n\r\n\r\n\t\t\t\tdef __init__(self\t\t\t\t: int\t, _A\t\t\t\t: Namespace) ->\tTuple:\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tOptional[int] \t\t= logging.get_logger('transformers-cli/training')\r\n\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tOptional[int] \t\t= 'tf' if is_tf_available() else 'torch'\r\n\r\n\t\t\t\t\tos.makedirs(args.output\t, exist_ok=_A)\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tList[Any] \t\t= args.output\r\n\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tAny \t\t= args.column_label\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tstr \t\t= args.column_text\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tAny \t\t= args.column_id\r\n\r\n\t\t\t\t\tself.logger.info(f\"Loading {args.task} pipeline for {args.model}\")\r\n\t\t\t\t\tif args.task == \"text_classification\":\r\n\t\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tList[str] \t\t= TextClassificationPipeline.from_pretrained(args.model)\r\n\t\t\t\t\telif args.task == \"token_classification\":\r\n\t\t\t\t\t\traise NotImplementedError\r\n\t\t\t\t\telif args.task == \"question_answering\":\r\n\t\t\t\t\t\traise NotImplementedError\r\n\r\n\t\t\t\t\tself.logger.info(f\"Loading dataset from {args.train_data}\")\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tList[Any] \t\t= Processor.create_from_csv(\r\n\t\t\t\t\t args.train_data\t, column_label=args.column_label\t, column_text=args.column_text\t, column_id=args.column_id\t, skip_first_row=args.skip_first_row\t, )\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tList[str] \t\t= None\r\n\t\t\t\t\tif args.validation_data:\r\n\t\t\t\t\t\tself.logger.info(f\"Loading validation dataset from {args.validation_data}\")\r\n\t\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tDict \t\t= Processor.create_from_csv(\r\n\t\t\t\t\t\t args.validation_data\t, column_label=args.column_label\t, column_text=args.column_text\t, column_id=args.column_id\t, skip_first_row=args.skip_first_row\t, )\r\n\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tList[str] \t\t= args.validation_split\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tstr \t\t= args.train_batch_size\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tAny \t\t= args.valid_batch_size\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tUnion[str, Any] \t\t= args.learning_rate\r\n\t\t\t\t\t__snake_case\t\t\t:\t\t\t\t\t\t\tstr \t\t= args.adam_epsilon\r\n\r\n\r\n\r\n\t\t\t\tdef \t\t\t\t\t_lowercase (self\t\t\t\t: List[str]) ->\tstr:\r\n\t\t\t\t\tif self.framework == \"tf\":\r\n\t\t\t\t\t\treturn self.run_tf()\r\n\t\t\t\t\treturn self.run_torch()\r\n\r\n\r\n\r\n\t\t\t\tdef \t\t\t\t\t_lowercase (self\t\t\t\t: str) ->\tint:\r\n\t\t\t\t\traise NotImplementedError\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef \t\t\t\t\t_lowercase (self\t\t\t\t: Union[str, Any]) ->\tOptional[Any]:\r\n\t\t\t\t\tself.pipeline.fit(\r\n\t\t\t\t\t self.train_dataset\t, validation_data=self.valid_dataset\t, validation_split=self.validation_split\t, learning_rate=self.learning_rate\t, adam_epsilon=self.adam_epsilon\t, train_batch_size=self.train_batch_size\t, valid_batch_size=self.valid_batch_size\t, )\r\n\r\n\t\t\t\t\t# Save trained pipeline\r\n\t\t\t\t\tself.pipeline.save_pretrained(self.output)\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":172,"string":"172"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":563,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom dataclasses import dataclass\r\nfrom typing import Tuple\r\n\r\nimport numpy as np\r\nimport torch\r\n@dataclass\r\nclass \t__A\t:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\tlowerCAmelCase : torch.Tensor # [batch_size x 3]\r\n\t\tlowerCAmelCase : torch.Tensor # [batch_size x 3]\r\n\t\tlowerCAmelCase : torch.Tensor # [batch_size x 3]\r\n\t\tlowerCAmelCase : torch.Tensor # [batch_size x 3]\r\n\t\tlowerCAmelCase : int\r\n\t\tlowerCAmelCase : int\r\n\t\tlowerCAmelCase : float\r\n\t\tlowerCAmelCase : float\r\n\t\tlowerCAmelCase : Tuple[int]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\tdef UpperCAmelCase ( self\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t) ->\t\tTuple:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\t\t\t\tassert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]\r\n\t\t\t\tassert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3\r\n\t\t\t\tassert len(self.x.shape\t\t\t\t\t\t) == len(self.y.shape\t\t\t\t\t\t) == len(self.z.shape\t\t\t\t\t\t) == len(self.origin.shape\t\t\t\t\t\t) == 2\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\tdef UpperCAmelCase ( self\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t) ->\t\tint:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\t\t\t\treturn torch.from_numpy(np.array([self.width, self.height] ,dtype=np.floataa\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\tdef UpperCAmelCase ( self\t\t\t\t\t\t: List[str]\t\t\t\t\t\t) ->\t\tList[str]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\t\t\t\treturn torch.from_numpy(np.array([self.x_fov, self.y_fov] ,dtype=np.floataa\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\tdef UpperCAmelCase ( self\t\t\t\t\t\t: Dict\t\t\t\t\t\t) ->\t\ttorch.Tensor:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\t\t\t\tlowercase__ : Optional[Any]\t\t\t\t= torch.arange(self.height * self.width\t\t\t\t\t\t)\r\n\t\t\t\tlowercase__ : Optional[int]\t\t\t\t= torch.stack(\r\n\t\t\t\t [\r\n\t\t\t\t pixel_indices % self.width,\r\n\t\t\t\t torch.div(_snake_case ,self.width ,rounding_mode='''trunc'''\t\t\t\t\t\t),\r\n\t\t\t\t ] ,axis=1 ,)\r\n\t\t\t\treturn coords\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t@property\r\n\t\tdef UpperCAmelCase ( self\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t) ->\t\tList[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\t\t\t\tlowercase__\t\t, *lowercase__ : Dict\t\t\t\t= self.shape\r\n\t\t\t\tlowercase__ : Union[str, Any]\t\t\t\t= int(np.prod(_snake_case\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\r\n\t\t\t\tlowercase__ : str\t\t\t\t= self.get_image_coords()\r\n\t\t\t\tlowercase__ : Tuple\t\t\t\t= torch.broadcast_to(coords.unsqueeze(0\t\t\t\t\t\t) ,[batch_size * inner_batch_size, *coords.shape]\t\t\t\t\t\t)\r\n\t\t\t\tlowercase__ : Union[str, Any]\t\t\t\t= self.get_camera_rays(_snake_case\t\t\t\t\t\t)\r\n\r\n\t\t\t\tlowercase__ : str\t\t\t\t= rays.view(_snake_case ,inner_batch_size * self.height * self.width ,2 ,3\t\t\t\t\t\t)\r\n\r\n\t\t\t\treturn rays\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\tdef UpperCAmelCase ( self\t\t\t\t\t\t: Any ,_snake_case\t\t\t\t\t\t: torch.Tensor\t\t\t\t\t\t) ->\t\ttorch.Tensor:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\t\t\t\tlowercase__\t\t, *lowercase__\t\t, lowercase__ : Any\t\t\t\t= coords.shape\r\n\t\t\t\tassert n_coords == 2\r\n\t\t\t\tassert batch_size == self.origin.shape[0]\r\n\r\n\t\t\t\tlowercase__ : int\t\t\t\t= coords.view(_snake_case ,-1 ,2\t\t\t\t\t\t)\r\n\r\n\t\t\t\tlowercase__ : Optional[Any]\t\t\t\t= self.resolution()\r\n\t\t\t\tlowercase__ : List[str]\t\t\t\t= self.fov()\r\n\r\n\t\t\t\tlowercase__ : Dict\t\t\t\t= (flat.float() / (res - 1)) * 2 - 1\r\n\t\t\t\tlowercase__ : List[str]\t\t\t\t= fracs * torch.tan(fov / 2\t\t\t\t\t\t)\r\n\r\n\t\t\t\tlowercase__ : Dict\t\t\t\t= fracs.view(_snake_case ,-1 ,2\t\t\t\t\t\t)\r\n\t\t\t\tlowercase__ : Tuple\t\t\t\t= (\r\n\t\t\t\t self.z.view(_snake_case ,1 ,3\t\t\t\t\t\t)\r\n\t\t\t\t + self.x.view(_snake_case ,1 ,3\t\t\t\t\t\t) * fracs[:, :, :1]\r\n\t\t\t\t + self.y.view(_snake_case ,1 ,3\t\t\t\t\t\t) * fracs[:, :, 1:]\r\n\t\t\t\t)\r\n\t\t\t\tlowercase__ : Tuple\t\t\t\t= directions / directions.norm(dim=-1 ,keepdim=_snake_case\t\t\t\t\t\t)\r\n\t\t\t\tlowercase__ : Tuple\t\t\t\t= torch.stack(\r\n\t\t\t\t [\r\n\t\t\t\t torch.broadcast_to(self.origin.view(_snake_case ,1 ,3\t\t\t\t\t\t) ,[batch_size, directions.shape[1], 3]\t\t\t\t\t\t),\r\n\t\t\t\t directions,\r\n\t\t\t\t ] ,dim=2 ,)\r\n\t\t\t\treturn rays.view(_snake_case ,*_snake_case ,2 ,3\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\tdef UpperCAmelCase ( self\t\t\t\t\t\t: int ,_snake_case\t\t\t\t\t\t: int ,_snake_case\t\t\t\t\t\t: int\t\t\t\t\t\t) ->\t\t\"DifferentiableProjectiveCamera\":\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\t\t\t\tassert width * self.height == height * self.width, \"The aspect ratio should not change.\"\r\n\t\t\t\treturn DifferentiableProjectiveCamera(\r\n\t\t\t\t origin=self.origin ,x=self.x ,y=self.y ,z=self.z ,width=_snake_case ,height=_snake_case ,x_fov=self.x_fov ,y_fov=self.y_fov ,)\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t\t__UpperCAmelCase\t\t\t\t( __lowerCamelCase )\t\t\t\t\t\t\t->\t\t\t\tDifferentiableProjectiveCamera:\r\n\t\tlowercase__ : List[str]\t\t\t\t= []\r\n\t\tlowercase__ : Dict\t\t\t\t= []\r\n\t\tlowercase__ : Dict\t\t\t\t= []\r\n\t\tlowercase__ : Dict\t\t\t\t= []\r\n\t\tfor theta in np.linspace(0\t\t\t\t\t\t,\t\t\t\t\t2 * np.pi\t\t\t\t\t\t,\t\t\t\t\tnum=20 ):\r\n\t\t\t\tlowercase__ : Any\t\t\t\t= np.array([np.sin(__lowerCamelCase ), np.cos(__lowerCamelCase ), -0.5] )\r\n\t\t\t\tz /= np.sqrt(np.sum(z**2 ) )\r\n\t\t\t\tlowercase__ : int\t\t\t\t= -z * 4\r\n\t\t\t\tlowercase__ : int\t\t\t\t= np.array([np.cos(__lowerCamelCase ), -np.sin(__lowerCamelCase ), 0.0] )\r\n\t\t\t\tlowercase__ : Any\t\t\t\t= np.cross(__lowerCamelCase\t\t\t\t\t\t,\t\t\t\t\t__lowerCamelCase )\r\n\t\t\t\torigins.append(__lowerCamelCase )\r\n\t\t\t\txs.append(__lowerCamelCase )\r\n\t\t\t\tys.append(__lowerCamelCase )\r\n\t\t\t\tzs.append(__lowerCamelCase )\r\n\t\treturn DifferentiableProjectiveCamera(\r\n\t\t origin=torch.from_numpy(np.stack(__lowerCamelCase\t\t\t\t\t\t,\t\t\t\t\taxis=0 ) ).float()\t\t\t\t\t\t,\t\t\t\t\tx=torch.from_numpy(np.stack(__lowerCamelCase\t\t\t\t\t\t,\t\t\t\t\taxis=0 ) ).float()\t\t\t\t\t\t,\t\t\t\t\ty=torch.from_numpy(np.stack(__lowerCamelCase\t\t\t\t\t\t,\t\t\t\t\taxis=0 ) ).float()\t\t\t\t\t\t,\t\t\t\t\tz=torch.from_numpy(np.stack(__lowerCamelCase\t\t\t\t\t\t,\t\t\t\t\taxis=0 ) ).float()\t\t\t\t\t\t,\t\t\t\t\twidth=__lowerCamelCase\t\t\t\t\t\t,\t\t\t\t\theight=__lowerCamelCase\t\t\t\t\t\t,\t\t\t\t\tx_fov=0.7\t\t\t\t\t\t,\t\t\t\t\ty_fov=0.7\t\t\t\t\t\t,\t\t\t\t\tshape=(1, len(__lowerCamelCase ))\t\t\t\t\t\t,\t\t\t\t\t)\r\n"},"code_codestyle":{"kind":"number","value":302,"string":"302"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport unittest\r\n\r\nfrom transformers import AutoTokenizer, is_flax_available\r\nfrom transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow\r\n\r\n\r\nif is_flax_available():\r\n\t\t\t\t\t\t\timport jax.numpy as jnp\r\n\r\n\t\t\t\t\t\t\tfrom transformers import FlaxXLMRobertaModel\r\n@require_sentencepiece\r\n@require_tokenizers\r\n@require_flax\r\nclass \t__A\t( unittest.TestCase ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t@slow\r\n\t\tdef UpperCAmelCase ( self\t\t\t\t\t\t: List[str]\t\t\t\t\t\t) ->\t\tAny:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\t\t\t\tlowercase__ : List[str]\t\t\t\t= FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base'''\t\t\t\t\t\t)\r\n\t\t\t\tlowercase__ : List[str]\t\t\t\t= AutoTokenizer.from_pretrained('''xlm-roberta-base'''\t\t\t\t\t\t)\r\n\t\t\t\tlowercase__ : List[str]\t\t\t\t= '''The dog is cute and lives in the garden house'''\r\n\t\t\t\tlowercase__ : int\t\t\t\t= jnp.array([tokenizer.encode(_snake_case\t\t\t\t\t\t)]\t\t\t\t\t\t)\r\n\r\n\t\t\t\tlowercase__ : Any\t\t\t\t= (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim\r\n\t\t\t\tlowercase__ : Tuple\t\t\t\t= jnp.array(\r\n\t\t\t\t [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]\t\t\t\t\t\t)\r\n\r\n\t\t\t\tlowercase__ : Optional[Any]\t\t\t\t= model(_snake_case\t\t\t\t\t\t)['''last_hidden_state''']\r\n\t\t\t\tself.assertEqual(output.shape ,_snake_case\t\t\t\t\t\t)\r\n\t\t\t\t# compare the actual values for a slice of last dim\r\n\t\t\t\tself.assertTrue(jnp.allclose(output[:, :, -1] ,_snake_case ,atol=1e-3\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n"},"style_context_codestyle":{"kind":"number","value":302,"string":"302"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":564,"cells":{"code":{"kind":"string","value":"\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\rimport os\rimport unittest\r\rfrom transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer\r\rfrom ...test_tokenization_common import TokenizerTesterMixin\rclass \t\t\t\t\t\tSCREAMING_SNAKE_CASE_ (\t\t\t\t\t\t__a ,\t\t\t\tunittest.TestCase ):\r\r\r\r\r\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\t\t__lowercase\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t =\t\tPhobertTokenizer\r\t\t__lowercase\t\t\t\t\t\t\t: Any\t\t\t\t\t\t =\t\tFalse\r\r\r\r\r\t\tdef \tsnake_case_ ( self):\r\t\t\t\tsuper().setUp()\r\r\t\t\t\t# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt\r\t\t\t\t__SCREAMING_SNAKE_CASE = [\"\"\"T@@\"\"\", \"\"\"i\"\"\", \"\"\"I\"\"\", \"\"\"R@@\"\"\", \"\"\"r\"\"\", \"\"\"e@@\"\"\"]\r\t\t\t\t__SCREAMING_SNAKE_CASE = dict(zip(lowerCAmelCase__\t\t\t\t\t\t\t, range(len(lowerCAmelCase__))))\r\t\t\t\t__SCREAMING_SNAKE_CASE = [\"\"\"#version: 0.2\"\"\", \"\"\"l à\"\"\"]\r\t\t\t\t__SCREAMING_SNAKE_CASE = {\"\"\"unk_token\"\"\": \"\"\"\"\"\"}\r\r\t\t\t\t__SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname\t\t\t\t\t\t\t, VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"])\r\t\t\t\t__SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname\t\t\t\t\t\t\t, VOCAB_FILES_NAMES[\"\"\"merges_file\"\"\"])\r\r\t\t\t\twith open(self.vocab_file\t\t\t\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t\t\t\t, encoding=\"\"\"utf-8\"\"\") as fp:\r\t\t\t\t\t\tfor token in vocab_tokens:\r\t\t\t\t\t\t\t\tfp.write(f\"{token} {vocab_tokens[token]}\\n\")\r\t\t\t\twith open(self.merges_file\t\t\t\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t\t\t\t, encoding=\"\"\"utf-8\"\"\") as fp:\r\t\t\t\t\t\tfp.write(\"\"\"\\n\"\"\".join(lowerCAmelCase__))\r\r\r\r\r\t\tdef \tsnake_case_ ( self\t\t\t\t\t\t\t, **lowerCAmelCase__):\r\t\t\t\tkwargs.update(self.special_tokens_map)\r\t\t\t\treturn PhobertTokenizer.from_pretrained(self.tmpdirname\t\t\t\t\t\t\t, **lowerCAmelCase__)\r\r\r\r\r\t\tdef \tsnake_case_ ( self\t\t\t\t\t\t\t, lowerCAmelCase__):\r\t\t\t\t__SCREAMING_SNAKE_CASE = \"\"\"Tôi là VinAI Research\"\"\"\r\t\t\t\t__SCREAMING_SNAKE_CASE = \"\"\"T i I Re e \"\"\"\r\t\t\t\treturn input_text, output_text\r\r\r\r\r\t\tdef \tsnake_case_ ( self):\r\t\t\t\t__SCREAMING_SNAKE_CASE = PhobertTokenizer(self.vocab_file\t\t\t\t\t\t\t, self.merges_file\t\t\t\t\t\t\t, **self.special_tokens_map)\r\t\t\t\t__SCREAMING_SNAKE_CASE = \"\"\"Tôi là VinAI Research\"\"\"\r\t\t\t\t__SCREAMING_SNAKE_CASE = \"\"\"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h\"\"\".split()\r\t\t\t\t__SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCAmelCase__)\r\t\t\t\tprint(lowerCAmelCase__)\r\t\t\t\tself.assertListEqual(lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__)\r\r\t\t\t\t__SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]\r\r\t\t\t\t__SCREAMING_SNAKE_CASE = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]\r\t\t\t\tself.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__)\t\t\t\t\t\t\t, lowerCAmelCase__)\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":100,"string":"100"},"style_context":{"kind":"string","value":"\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\r__magic_name__ =\t\t\t\"Tobias Carryer\"\r\rfrom time import time\rclass \t\t\t\t\t\tSCREAMING_SNAKE_CASE_ :\r\r\r\r\r\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\t\tdef __init__( self\t\t\t\t\t\t\t, lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__\t\t\t\t\t\t\t, lowerCAmelCase__=int(time())): # noqa: B008\r\t\t\t\t__SCREAMING_SNAKE_CASE = multiplier\r\t\t\t\t__SCREAMING_SNAKE_CASE = increment\r\t\t\t\t__SCREAMING_SNAKE_CASE = modulo\r\t\t\t\t__SCREAMING_SNAKE_CASE = seed\r\r\r\r\r\t\tdef \tsnake_case_ ( self):\r\t\t\t\t__SCREAMING_SNAKE_CASE = (self.multiplier * self.seed + self.increment) % self.modulo\r\t\t\t\treturn self.seed\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\t\t\t# Show the LCG in action.\r\t\t\t\t\t\t\t__magic_name__ =\t\t\tLinearCongruentialGenerator(1664525, 1013904223, 2 << 31)\r\t\t\t\t\t\t\twhile True:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tprint(lcg.next_number())\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":100,"string":"100"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":565,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n'''simple docstring'''\n\n\n\nlowerCamelCase_\t\t\t=\t\t\t\t\t\t{str(digit): digit**5 for digit in range(10)}\ndef \t\t\tSCREAMING_SNAKE_CASE_\t\t\t(\t\t\t\t\t\t\t__A :\t\t\tint )\t\t\t->\t\t\tint:\n return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__A ) )\ndef \t\t\tSCREAMING_SNAKE_CASE_\t\t\t(\t\t\t\t\t\t\t)\t\t\t->\t\t\tint:\n return sum(\n number\n for number in range(10_00\t,\t\t\t\t\t1_00_00_00 )\n if number == digits_fifth_powers_sum(__A ) )\n\n\nif __name__ == \"__main__\":\n print(solution())\n\n"},"code_codestyle":{"kind":"number","value":111,"string":"111"},"style_context":{"kind":"string","value":"\n\n\n\n\n'''simple docstring'''\n\n\n\nfrom collections import OrderedDict\nfrom typing import TYPE_CHECKING, Any, Mapping, Optional, Union\n\nfrom ...configuration_utils import PretrainedConfig\nfrom ...onnx import OnnxConfig\nfrom ...utils import logging\n\n\nif TYPE_CHECKING:\n from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType\n\n\nlowerCamelCase_\t\t\t=\t\t\t\t\t\tlogging.get_logger(__name__)\n\nlowerCamelCase_\t\t\t=\t\t\t\t\t\t{\n 'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',\n 'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',\n 'microsoft/deberta-v2-xlarge-mnli': (\n 'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'\n ),\n 'microsoft/deberta-v2-xxlarge-mnli': (\n 'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'\n ),\n}\n\n\n\n\n\n\n\nclass \t\tlowercase_ (\t\t\tA ):\n\n\n \"\"\"simple docstring\"\"\"\n\n lowerCamelCase_ =\t\t\t\t\t\t'''deberta-v2'''\n\n\n\n def __init__( self : str ,\t\t\t\t__lowerCamelCase : Union[str, Any]=1_2_8_1_0_0 ,\t\t\t\t__lowerCamelCase : Optional[int]=1_5_3_6 ,\t\t\t\t__lowerCamelCase : Optional[int]=2_4 ,\t\t\t\t__lowerCamelCase : Optional[int]=2_4 ,\t\t\t\t__lowerCamelCase : Tuple=6_1_4_4 ,\t\t\t\t__lowerCamelCase : List[str]=\"gelu\" ,\t\t\t\t__lowerCamelCase : int=0.1 ,\t\t\t\t__lowerCamelCase : Optional[Any]=0.1 ,\t\t\t\t__lowerCamelCase : Union[str, Any]=5_1_2 ,\t\t\t\t__lowerCamelCase : Optional[Any]=0 ,\t\t\t\t__lowerCamelCase : str=0.0_2 ,\t\t\t\t__lowerCamelCase : int=1e-7 ,\t\t\t\t__lowerCamelCase : Any=False ,\t\t\t\t__lowerCamelCase : Any=-1 ,\t\t\t\t__lowerCamelCase : Tuple=0 ,\t\t\t\t__lowerCamelCase : str=True ,\t\t\t\t__lowerCamelCase : List[Any]=None ,\t\t\t\t__lowerCamelCase : Optional[int]=0 ,\t\t\t\t__lowerCamelCase : Any=\"gelu\" ,\t\t\t\t**__lowerCamelCase : Union[str, Any] ,\t\t\t\t):\n\n \"\"\"simple docstring\"\"\"\n super().__init__(**__lowerCamelCase )\n\n _SCREAMING_SNAKE_CASE\t=\t\t\t\thidden_size\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tnum_hidden_layers\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tnum_attention_heads\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tintermediate_size\n _SCREAMING_SNAKE_CASE\t=\t\t\t\thidden_act\n _SCREAMING_SNAKE_CASE\t=\t\t\t\thidden_dropout_prob\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tattention_probs_dropout_prob\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tmax_position_embeddings\n _SCREAMING_SNAKE_CASE\t=\t\t\t\ttype_vocab_size\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tinitializer_range\n _SCREAMING_SNAKE_CASE\t=\t\t\t\trelative_attention\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tmax_relative_positions\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tpad_token_id\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tposition_biased_input\n\n # Backwards compatibility\n if type(__lowerCamelCase ) == str:\n _SCREAMING_SNAKE_CASE\t=\t\t\t\t[x.strip() for x in pos_att_type.lower().split(\"|\" )]\n\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tpos_att_type\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tvocab_size\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tlayer_norm_eps\n\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tkwargs.get(\"pooler_hidden_size\" ,\t\t\t\t__lowerCamelCase )\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tpooler_dropout\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tpooler_hidden_act\n\n\n\n\n\n\n\nclass \t\tlowercase_ (\t\t\tA ):\n\n\n \"\"\"simple docstring\"\"\"\n\n @property\n def \t\t\t\t\t\t\tlowerCAmelCase_ ( self : List[Any] ):\n\n \"\"\"simple docstring\"\"\"\n if self.task == \"multiple-choice\":\n _SCREAMING_SNAKE_CASE\t=\t\t\t\t{0: \"batch\", 1: \"choice\", 2: \"sequence\"}\n else:\n _SCREAMING_SNAKE_CASE\t=\t\t\t\t{0: \"batch\", 1: \"sequence\"}\n if self._config.type_vocab_size > 0:\n return OrderedDict(\n [(\"input_ids\", dynamic_axis), (\"attention_mask\", dynamic_axis), (\"token_type_ids\", dynamic_axis)] )\n else:\n return OrderedDict([(\"input_ids\", dynamic_axis), (\"attention_mask\", dynamic_axis)] )\n\n\n\n @property\n def \t\t\t\t\t\t\tlowerCAmelCase_ ( self : List[str] ):\n\n \"\"\"simple docstring\"\"\"\n return 1_2\n\n\n\n def \t\t\t\t\t\t\tlowerCAmelCase_ ( self : List[str] ,\t\t\t\t__lowerCamelCase : Union[\"PreTrainedTokenizerBase\", \"FeatureExtractionMixin\"] ,\t\t\t\t__lowerCamelCase : int = -1 ,\t\t\t\t__lowerCamelCase : int = -1 ,\t\t\t\t__lowerCamelCase : int = -1 ,\t\t\t\t__lowerCamelCase : bool = False ,\t\t\t\t__lowerCamelCase : Optional[\"TensorType\"] = None ,\t\t\t\t__lowerCamelCase : int = 3 ,\t\t\t\t__lowerCamelCase : int = 4_0 ,\t\t\t\t__lowerCamelCase : int = 4_0 ,\t\t\t\t__lowerCamelCase : \"PreTrainedTokenizerBase\" = None ,\t\t\t\t):\n\n \"\"\"simple docstring\"\"\"\n _SCREAMING_SNAKE_CASE\t=\t\t\t\tsuper().generate_dummy_inputs(preprocessor=__lowerCamelCase ,\t\t\t\tframework=__lowerCamelCase )\n if self._config.type_vocab_size == 0 and \"token_type_ids\" in dummy_inputs:\n del dummy_inputs[\"token_type_ids\"]\n return dummy_inputs\n\n"},"style_context_codestyle":{"kind":"number","value":111,"string":"111"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":566,"cells":{"code":{"kind":"string","value":"\nimport itertools\nimport random\nimport unittest\n\nimport numpy as np\n\nfrom transformers import ASTFeatureExtractor\nfrom transformers.testing_utils import require_torch, require_torchaudio\nfrom transformers.utils.import_utils import is_torch_available\n\nfrom ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin\n\n\nSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t=\t\t\t\trandom.Random()\n\nif is_torch_available():\n\t\t\t\t\t\t\timport torch\n\n\n\n\ndef \t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE (\t\tSCREAMING_SNAKE_CASE : Union[str, Any] ,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE : str=1.0 ,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE : Optional[Any]=None ,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE : Union[str, Any]=None\t\t\t) ->\t\t\t\t\t\tUnion[str, Any]:\n\t\t\t\tif rng is None:\n\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tglobal_rng\n\n\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\t[]\n\t\t\t\tfor batch_idx in range(shape[0]\t\t\t):\n\t\t\t\t\t\t\t\tvalues.append([]\t\t\t)\n\t\t\t\t\t\t\t\tfor _ in range(shape[1]\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\tvalues[-1].append(rng.random() * scale\t\t\t)\n\n\t\t\t\treturn values\nclass \tA__ (\t\t\t\t\t\tunittest.TestCase\t\t\t\t):\n\n\n\n\n\n\t\t\t\t\t\t\tdef __init__( self : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=7 , _UpperCAmelCase : Dict=4_00 , _UpperCAmelCase : Optional[int]=20_00 , _UpperCAmelCase : Any=1 , _UpperCAmelCase : List[str]=0.0 , _UpperCAmelCase : Union[str, Any]=1_60_00 , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Union[str, Any]=True , )\t\t\t\t\t\t->\t\tList[Any]:\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tparent\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tbatch_size\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tmin_seq_length\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tmax_seq_length\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\t(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeature_size\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tpadding_value\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tsampling_rate\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\treturn_attention_mask\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tdo_normalize\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\ta__ ( self : Optional[int]\t\t\t\t)\t\t\t\t\t\t->\t\tDict:\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t\t\t\t\treturn {\n\t\t\t\t\t\t\t\t\t\t\t \"feature_size\": self.feature_size,\n\t\t\t\t\t\t\t\t\t\t\t \"padding_value\": self.padding_value,\n\t\t\t\t\t\t\t\t\t\t\t \"sampling_rate\": self.sampling_rate,\n\t\t\t\t\t\t\t\t\t\t\t \"return_attention_mask\": self.return_attention_mask,\n\t\t\t\t\t\t\t\t\t\t\t \"do_normalize\": self.do_normalize,\n\t\t\t\t\t\t\t\t\t\t\t}\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\ta__ ( self : str , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : int=False\t\t\t\t)\t\t\t\t\t\t->\t\tAny:\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t\t\t\t\tdef _flatten(_UpperCAmelCase : Union[str, Any]\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn list(itertools.chain(*_SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\tif equal_length:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfloats_list((self.batch_size, self.max_seq_length)\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# make sure that inputs increase in size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\t[\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t _flatten(floats_list((x, self.feature_size)\t\t\t\t)\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\n\t\t\t\t\t\t\t\t\t\t\tif numpify:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\t[np.asarray(_SCREAMING_SNAKE_CASE\t\t\t\t) for x in speech_inputs]\n\n\t\t\t\t\t\t\t\t\t\t\treturn speech_inputs\n\n\n\n\n\n\n@require_torch\n@require_torchaudio\nclass \tA__ (\t\t\t\t\t\t__SCREAMING_SNAKE_CASE ,\t\t\t\t\t\tunittest.TestCase\t\t\t\t):\n\t\t\t\t\t\t\tlowerCAmelCase__\t\t:\t\t\t\t\t\tint \t\t\t=\t\t\t\t\t\t\tASTFeatureExtractor\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\ta__ ( self : int\t\t\t\t)\t\t\t\t\t\t->\t\tList[str]:\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tASTFeatureExtractionTester(self\t\t\t\t)\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\ta__ ( self : Optional[int]\t\t\t\t)\t\t\t\t\t\t->\t\tOptional[int]:\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t# create three inputs of length 800, 1000, and 1200\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\t[floats_list((1, x)\t\t\t\t)[0] for x in range(8_00 , 14_00 , 2_00\t\t\t\t)]\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\t[np.asarray(_SCREAMING_SNAKE_CASE\t\t\t\t) for speech_input in speech_inputs]\n\n\t\t\t\t\t\t\t\t\t\t\t# Test not batched input\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeat_extract(speech_inputs[0] , return_tensors='np'\t\t\t\t).input_values\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeat_extract(np_speech_inputs[0] , return_tensors='np'\t\t\t\t).input_values\n\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3\t\t\t\t)\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t# Test batched\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeat_extract(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='np'\t\t\t\t).input_values\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeat_extract(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='np'\t\t\t\t).input_values\n\t\t\t\t\t\t\t\t\t\t\tfor enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3\t\t\t\t)\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t# Test 2-D numpy arrays are batched.\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\t[floats_list((1, x)\t\t\t\t)[0] for x in (8_00, 8_00, 8_00)]\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tnp.asarray(_SCREAMING_SNAKE_CASE\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeat_extract(_SCREAMING_SNAKE_CASE , return_tensors='np'\t\t\t\t).input_values\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeat_extract(_SCREAMING_SNAKE_CASE , return_tensors='np'\t\t\t\t).input_values\n\t\t\t\t\t\t\t\t\t\t\tfor enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3\t\t\t\t)\t\t\t\t)\n\n\n\n\n\n\t\t\t\t\t\t\t@require_torch\n\t\t\t\t\t\t\tdef \t\t\ta__ ( self : Dict\t\t\t\t)\t\t\t\t\t\t->\t\tTuple:\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t\t\t\t\timport torch\n\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tnp.random.rand(1_00\t\t\t\t).astype(np.floataa\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tnp_speech_inputs.tolist()\n\n\t\t\t\t\t\t\t\t\t\t\tfor inputs in [py_speech_inputs, np_speech_inputs]:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeature_extractor.pad([{'input_values': inputs}] , return_tensors='np'\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(np_processed.input_values.dtype == np.floataa\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeature_extractor.pad([{'input_values': inputs}] , return_tensors='pt'\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(pt_processed.input_values.dtype == torch.floataa\t\t\t\t)\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\ta__ ( self : Dict , _UpperCAmelCase : Optional[int]\t\t\t\t)\t\t\t\t\t\t->\t\tint:\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t\t\t\t\tfrom datasets import load_dataset\n\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tload_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation'\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t# automatic decoding with librispeech\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tds.sort('id'\t\t\t\t).select(range(_SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t)[:num_samples][\"audio\"]\n\n\t\t\t\t\t\t\t\t\t\t\treturn [x[\"array\"] for x in speech_samples]\n\n\n\n\n\n\t\t\t\t\t\t\t@require_torch\n\t\t\t\t\t\t\tdef \t\t\ta__ ( self : Dict\t\t\t\t)\t\t\t\t\t\t->\t\tDict:\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\ttorch.tensor(\n\t\t\t\t\t\t\t\t\t\t\t [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,\n\t\t\t\t\t\t\t\t\t\t\t -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,\n\t\t\t\t\t\t\t\t\t\t\t -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,\n\t\t\t\t\t\t\t\t\t\t\t -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869]\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t# fmt: on\n\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tself._load_datasamples(1\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tASTFeatureExtractor()\n\t\t\t\t\t\t\t\t\t\t\t__lowercase\t\t\t\t\t\t\t\t=\t\t\t\t\tfeature_extractor(_SCREAMING_SNAKE_CASE , return_tensors='pt'\t\t\t\t).input_values\n\t\t\t\t\t\t\t\t\t\t\tself.assertEquals(input_values.shape , (1, 10_24, 1_28)\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(torch.allclose(input_values[0, 0, :30] , _SCREAMING_SNAKE_CASE , atol=1e-4\t\t\t\t)\t\t\t\t)\n\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":325,"string":"325"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport numpy as np\r\nfrom scipy.spatial.distance import cdist\r\nfrom sklearn.metrics import fa_score\r\n\r\nimport datasets\r\n\r\n\r\n__snake_case\t\t\t\t\t\t\t: Optional[int] = '\\\\n @inproceedings{kakwani2020indicnlpsuite,\\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\\n author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\\n year={2020},\\n booktitle={Findings of EMNLP},\\n}\\n'\r\n\r\n__snake_case\t\t\t\t\t\t\t: str = '\\\\n IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\\n variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\\n'\r\n\r\n__snake_case\t\t\t\t\t\t\t: str = '\\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\\nArgs:\\n predictions: list of predictions to score (as int64),\\n except for \\'cvit-mkb-clsr\\' where each prediction is a vector (of float32).\\n references: list of ground truth labels corresponding to the predictions (as int64),\\n except for \\'cvit-mkb-clsr\\' where each reference is a vector (of float32).\\nReturns: depending on the IndicGLUE subset, one or several of:\\n \"accuracy\": Accuracy\\n \"f1\": F1 score\\n \"precision\": Precision@10\\nExamples:\\n\\n >>> indic_glue_metric = datasets.load_metric(\\'indic_glue\\', \\'wnli\\') # \\'wnli\\' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\\n >>> references = [0, 1]\\n >>> predictions = [0, 1]\\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\\n >>> print(results)\\n {\\'accuracy\\': 1.0}\\n\\n >>> indic_glue_metric = datasets.load_metric(\\'indic_glue\\', \\'wiki-ner\\')\\n >>> references = [0, 1]\\n >>> predictions = [0, 1]\\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\\n >>> print(results)\\n {\\'accuracy\\': 1.0, \\'f1\\': 1.0}\\n\\n >>> indic_glue_metric = datasets.load_metric(\\'indic_glue\\', \\'cvit-mkb-clsr\\')\\n >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\\n >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\\n >>> print(results)\\n {\\'precision@10\\': 1.0}\\n\\n'\r\n\r\n\r\ndef \t\t\t_lowercase\t\t\t\t( __snake_case\t\t\t\t\t,__snake_case\t)\t\t\t\t\t\t-> Union[str, Any]:\r\n\t\t\treturn float((preds == labels).mean()\t)\r\n\r\n\r\ndef \t\t\t_lowercase\t\t\t\t( __snake_case\t\t\t\t\t,__snake_case\t)\t\t\t\t\t\t-> str:\r\n\t\t\t__lowerCAmelCase : str\t\t\t\t\t\t\t\t\t\t\t\t= simple_accuracy(__snake_case\t\t\t\t\t,__snake_case\t)\r\n\t\t\t__lowerCAmelCase : Any\t\t\t\t\t\t\t\t\t\t\t\t= float(fa_score(y_true=__snake_case\t\t\t\t\t,y_pred=__snake_case\t)\t)\r\n\t\t\treturn {\r\n\t\t\t \"accuracy\": acc,\r\n\t\t\t \"f1\": fa,\r\n\t\t\t}\r\n\r\n\r\ndef \t\t\t_lowercase\t\t\t\t( __snake_case\t\t\t\t\t,__snake_case\t)\t\t\t\t\t\t-> int:\r\n\t\t\t__lowerCAmelCase : Union[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t= np.array(__snake_case\t)\r\n\t\t\t__lowerCAmelCase : Tuple\t\t\t\t\t\t\t\t\t\t\t\t= np.array(__snake_case\t)\r\n\t\t\t__lowerCAmelCase : List[Any]\t\t\t\t\t\t\t\t\t\t\t\t= en_sentvecs.shape[0]\r\n\r\n\t\t\t# mean centering\r\n\t\t\t__lowerCAmelCase : Union[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t= en_sentvecs - np.mean(__snake_case\t\t\t\t\t,axis=0\t)\r\n\t\t\t__lowerCAmelCase : int\t\t\t\t\t\t\t\t\t\t\t\t= in_sentvecs - np.mean(__snake_case\t\t\t\t\t,axis=0\t)\r\n\r\n\t\t\t__lowerCAmelCase : Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t= cdist(__snake_case\t\t\t\t\t,__snake_case\t\t\t\t\t,\"cosine\"\t)\r\n\t\t\t__lowerCAmelCase : int\t\t\t\t\t\t\t\t\t\t\t\t= np.array(range(__snake_case\t)\t)\r\n\t\t\t__lowerCAmelCase : int\t\t\t\t\t\t\t\t\t\t\t\t= sim.argsort(axis=1\t)[:, :10]\r\n\t\t\t__lowerCAmelCase : Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t= np.any(preds == actual[:, None]\t\t\t\t\t,axis=1\t)\r\n\t\t\treturn float(matches.mean()\t)\r\n\r\n\r\n\r\n\r\n\r\n@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,\t\t_KWARGS_DESCRIPTION\t\t\t\t\t\t\t)\r\nclass \t\t\tA__ ( datasets.Metric\t\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\tdef \t\t\t\t\t\t\t_SCREAMING_SNAKE_CASE ( self:\t\tint)\t\t\t\t-> str:\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\tif self.config_name not in [\r\n\t\t\t\t \"wnli\",\r\n\t\t\t\t \"copa\",\r\n\t\t\t\t \"sna\",\r\n\t\t\t\t \"csqa\",\r\n\t\t\t\t \"wstp\",\r\n\t\t\t\t \"inltkh\",\r\n\t\t\t\t \"bbca\",\r\n\t\t\t\t \"cvit-mkb-clsr\",\r\n\t\t\t\t \"iitp-mr\",\r\n\t\t\t\t \"iitp-pr\",\r\n\t\t\t\t \"actsa-sc\",\r\n\t\t\t\t \"md\",\r\n\t\t\t\t \"wiki-ner\",\r\n\t\t\t\t]:\r\n\t\t\t\t\t\t\traise KeyError(\r\n\t\t\t\t\t\t\t \"You should supply a configuration name selected in \"\r\n\t\t\t\t\t\t\t \"[\\\"wnli\\\", \\\"copa\\\", \\\"sna\\\", \\\"csqa\\\", \\\"wstp\\\", \\\"inltkh\\\", \\\"bbca\\\", \"\r\n\t\t\t\t\t\t\t \"\\\"cvit-mkb-clsr\\\", \\\"iitp-mr\\\", \\\"iitp-pr\\\", \\\"actsa-sc\\\", \\\"md\\\", \"\r\n\t\t\t\t\t\t\t \"\\\"wiki-ner\\\"]\")\r\n\t\t\t\treturn datasets.MetricInfo(\r\n\t\t\t\t description=_DESCRIPTION ,\t\t\tcitation=_CITATION ,\t\t\tinputs_description=_KWARGS_DESCRIPTION ,\t\t\tfeatures=datasets.Features(\r\n\t\t\t\t {\r\n\t\t\t\t \"predictions\": datasets.Value(\"int64\")\r\n\t\t\t\t if self.config_name != \"cvit-mkb-clsr\"\r\n\t\t\t\t else datasets.Sequence(datasets.Value(\"float32\")),\r\n\t\t\t\t \"references\": datasets.Value(\"int64\")\r\n\t\t\t\t if self.config_name != \"cvit-mkb-clsr\"\r\n\t\t\t\t else datasets.Sequence(datasets.Value(\"float32\")),\r\n\t\t\t\t }) ,\t\t\tcodebase_urls=[] ,\t\t\treference_urls=[] ,\t\t\tformat=\"numpy\" if self.config_name != \"cvit-mkb-clsr\" else None ,\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tdef \t\t\t\t\t\t\t_SCREAMING_SNAKE_CASE ( self:\t\tList[str] ,\t\t\t_SCREAMING_SNAKE_CASE:\t\tint ,\t\t\t_SCREAMING_SNAKE_CASE:\t\tOptional[Any])\t\t\t\t-> int:\r\n\r\n\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\tif self.config_name == \"cvit-mkb-clsr\":\r\n\t\t\t\t\t\t\treturn {\"precision@10\": precision_at_aa(_SCREAMING_SNAKE_CASE ,\t\t\t_SCREAMING_SNAKE_CASE)}\r\n\t\t\t\telif self.config_name in [\"wiki-ner\"]:\r\n\t\t\t\t\t\t\treturn acc_and_fa(_SCREAMING_SNAKE_CASE ,\t\t\t_SCREAMING_SNAKE_CASE)\r\n\t\t\t\telif self.config_name in [\r\n\t\t\t\t \"wnli\",\r\n\t\t\t\t \"copa\",\r\n\t\t\t\t \"sna\",\r\n\t\t\t\t \"csqa\",\r\n\t\t\t\t \"wstp\",\r\n\t\t\t\t \"inltkh\",\r\n\t\t\t\t \"bbca\",\r\n\t\t\t\t \"iitp-mr\",\r\n\t\t\t\t \"iitp-pr\",\r\n\t\t\t\t \"actsa-sc\",\r\n\t\t\t\t \"md\",\r\n\t\t\t\t]:\r\n\t\t\t\t\t\t\treturn {\"accuracy\": simple_accuracy(_SCREAMING_SNAKE_CASE ,\t\t\t_SCREAMING_SNAKE_CASE)}\r\n\t\t\t\telse:\r\n\t\t\t\t\t\t\traise KeyError(\r\n\t\t\t\t\t\t\t \"You should supply a configuration name selected in \"\r\n\t\t\t\t\t\t\t \"[\\\"wnli\\\", \\\"copa\\\", \\\"sna\\\", \\\"csqa\\\", \\\"wstp\\\", \\\"inltkh\\\", \\\"bbca\\\", \"\r\n\t\t\t\t\t\t\t \"\\\"cvit-mkb-clsr\\\", \\\"iitp-mr\\\", \\\"iitp-pr\\\", \\\"actsa-sc\\\", \\\"md\\\", \"\r\n\t\t\t\t\t\t\t \"\\\"wiki-ner\\\"]\")"},"style_context_codestyle":{"kind":"number","value":269,"string":"269"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":567,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\ndef lowerCamelCase_\t\t( lowerCAmelCase:\t\tint ,\t\t\t\t\t\t\tlowerCAmelCase:\t\tint\t\t\t)->\t\t\t\t\t\tint:\n while a != 0:\n _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t:\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\tb % a, a\n return b\n\n\n\n\n\n\n\ndef lowerCamelCase_\t\t( lowerCAmelCase:\t\tint ,\t\t\t\t\t\t\tlowerCAmelCase:\t\tint\t\t\t)->\t\t\t\t\t\tint:\n if gcd(lowerCAmelCase ,\t\t\t\t\t\t\tlowerCAmelCase\t\t\t) != 1:\n _snake_case\t\t\t\t:\t\t\tAny\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\tF\"\"\"mod inverse of {a!r} and {m!r} does not exist\"\"\"\n raise ValueError(lowerCAmelCase\t\t\t)\n _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t:\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t1, 0, a\n _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t:\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t0, 1, m\n while va != 0:\n _snake_case\t\t\t\t:\t\t\tDict\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\tua // va\n _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t\t\t\t, _snake_case\t\t\t\t:\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t(ua - q * va), (ua - q * va), (ua - q * va), va, va, va\n return ua % m\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":260,"string":"260"},"style_context":{"kind":"string","value":"\n\n\n\n\n\nimport argparse\n\nimport torch\n\nfrom transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert\nfrom transformers.utils import logging\n\n\nlogging.set_verbosity_info()\n\n\ndef lowerCamelCase_\t\t( lowerCAmelCase:\t\tList[str] ,\t\t\t\t\t\t\tlowerCAmelCase:\t\tDict ,\t\t\t\t\t\t\tlowerCAmelCase:\t\tstr\t\t\t)->\t\t\t\t\t\tList[str]:\n # Initialise PyTorch model\n _snake_case\t\t\t\t:\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\tMobileBertConfig.from_json_file(lowerCAmelCase\t\t\t)\n print(F\"\"\"Building PyTorch model from configuration: {config}\"\"\"\t\t\t)\n _snake_case\t\t\t\t:\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\tMobileBertForPreTraining(lowerCAmelCase\t\t\t)\n # Load weights from tf checkpoint\n _snake_case\t\t\t\t:\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\tload_tf_weights_in_mobilebert(lowerCAmelCase ,\t\t\t\t\t\t\tlowerCAmelCase ,\t\t\t\t\t\t\tlowerCAmelCase\t\t\t)\n # Save pytorch-model\n print(F\"\"\"Save PyTorch model to {pytorch_dump_path}\"\"\"\t\t\t)\n torch.save(model.state_dict() ,\t\t\t\t\t\t\tlowerCAmelCase\t\t\t)\n\n\nif __name__ == \"__main__\":\n lowerCAmelCase_ \t\t\t\t= argparse.ArgumentParser()\n # Required parameters\n parser.add_argument(\n \"\"\"--tf_checkpoint_path\"\"\", default=None, type=str, required=True, help=\"\"\"Path to the TensorFlow checkpoint path.\"\"\"\n )\n parser.add_argument(\n \"\"\"--mobilebert_config_file\"\"\",\n default=None,\n type=str,\n required=True,\n help=(\n \"\"\"The config json file corresponding to the pre-trained MobileBERT model. \\n\"\"\"\n \"\"\"This specifies the model architecture.\"\"\"\n ),\n )\n parser.add_argument(\n \"\"\"--pytorch_dump_path\"\"\", default=None, type=str, required=True, help=\"\"\"Path to the output PyTorch model.\"\"\"\n )\n lowerCAmelCase_ \t\t\t\t= parser.parse_args()\n convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":260,"string":"260"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":568,"cells":{"code":{"kind":"string","value":"\r\n\r\nfrom collections import deque\r\nfrom math import floor\r\nfrom random import random\r\nfrom time import time\r\n\r\n\r\nclass A\t\t\t\t\t\t:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n def __init__( self : List[Any]\t\t)-> Optional[int]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = {}\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Dict,lowercase_ : Union[str, Any],lowercase_ : str,lowercase_ : List[str]=1\t\t)-> Optional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if self.graph.get(lowercase_\t\t):\r\n if self.graph[u].count([w, v]\t\t) == 0:\r\n self.graph[u].append([w, v]\t\t)\r\n else:\r\n A__ = [[w, v]]\r\n if not self.graph.get(lowercase_\t\t):\r\n A__ = []\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : List[Any]\t\t)-> str:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n return list(self.graph\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Dict,lowercase_ : Optional[int],lowercase_ : List[Any]\t\t)-> List[Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if self.graph.get(lowercase_\t\t):\r\n for _ in self.graph[u]:\r\n if _[1] == v:\r\n self.graph[u].remove(lowercase_\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : List[Any],lowercase_ : Optional[int]=-2,lowercase_ : str=-1\t\t)-> List[Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if s == d:\r\n return []\r\n A__ = []\r\n A__ = []\r\n if s == -2:\r\n A__ = list(self.graph\t\t)[0]\r\n stack.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n A__ = s\r\n\r\n while True:\r\n # check if there is any non isolated nodes\r\n if len(self.graph[s]\t\t) != 0:\r\n A__ = s\r\n for node in self.graph[s]:\r\n if visited.count(node[1]\t\t) < 1:\r\n if node[1] == d:\r\n visited.append(lowercase_\t\t)\r\n return visited\r\n else:\r\n stack.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n A__ = node[1]\r\n break\r\n\r\n # check if all the children are visited\r\n if s == ss:\r\n stack.pop()\r\n if len(lowercase_\t\t) != 0:\r\n A__ = stack[len(lowercase_\t\t) - 1]\r\n else:\r\n A__ = ss\r\n\r\n # check if se have reached the starting point\r\n if len(lowercase_\t\t) == 0:\r\n return visited\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : List[Any],lowercase_ : str=-1\t\t)-> str:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if c == -1:\r\n A__ = floor(random() * 1_0_0_0_0\t\t) + 1_0\r\n for i in range(lowercase_\t\t):\r\n # every vertex has max 100 edges\r\n for _ in range(floor(random() * 1_0_2\t\t) + 1\t\t):\r\n A__ = floor(random() * c\t\t) + 1\r\n if n != i:\r\n self.add_pair(lowercase_,lowercase_,1\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Dict,lowercase_ : Dict=-2\t\t)-> int:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = deque()\r\n A__ = []\r\n if s == -2:\r\n A__ = list(self.graph\t\t)[0]\r\n d.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n while d:\r\n A__ = d.popleft()\r\n if len(self.graph[s]\t\t) != 0:\r\n for node in self.graph[s]:\r\n if visited.count(node[1]\t\t) < 1:\r\n d.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n return visited\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Optional[Any],lowercase_ : Optional[int]\t\t)-> Optional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = 0\r\n for x in self.graph:\r\n for y in self.graph[x]:\r\n if y[1] == u:\r\n count += 1\r\n return count\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Tuple,lowercase_ : Optional[Any]\t\t)-> Any:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n return len(self.graph[u]\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Union[str, Any],lowercase_ : int=-2\t\t)-> int:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = []\r\n A__ = []\r\n if s == -2:\r\n A__ = list(self.graph\t\t)[0]\r\n stack.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n A__ = s\r\n A__ = []\r\n\r\n while True:\r\n # check if there is any non isolated nodes\r\n if len(self.graph[s]\t\t) != 0:\r\n A__ = s\r\n for node in self.graph[s]:\r\n if visited.count(node[1]\t\t) < 1:\r\n stack.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n A__ = node[1]\r\n break\r\n\r\n # check if all the children are visited\r\n if s == ss:\r\n sorted_nodes.append(stack.pop()\t\t)\r\n if len(lowercase_\t\t) != 0:\r\n A__ = stack[len(lowercase_\t\t) - 1]\r\n else:\r\n A__ = ss\r\n\r\n # check if se have reached the starting point\r\n if len(lowercase_\t\t) == 0:\r\n return sorted_nodes\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : int\t\t)-> Optional[int]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = []\r\n A__ = []\r\n A__ = list(self.graph\t\t)[0]\r\n stack.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n A__ = -2\r\n A__ = []\r\n A__ = s\r\n A__ = False\r\n A__ = set()\r\n\r\n while True:\r\n # check if there is any non isolated nodes\r\n if len(self.graph[s]\t\t) != 0:\r\n A__ = s\r\n for node in self.graph[s]:\r\n if (\r\n visited.count(node[1]\t\t) > 0\r\n and node[1] != parent\r\n and indirect_parents.count(node[1]\t\t) > 0\r\n and not on_the_way_back\r\n ):\r\n A__ = len(lowercase_\t\t) - 1\r\n while len_stack >= 0:\r\n if stack[len_stack] == node[1]:\r\n anticipating_nodes.add(node[1]\t\t)\r\n break\r\n else:\r\n anticipating_nodes.add(stack[len_stack]\t\t)\r\n len_stack -= 1\r\n if visited.count(node[1]\t\t) < 1:\r\n stack.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n A__ = node[1]\r\n break\r\n\r\n # check if all the children are visited\r\n if s == ss:\r\n stack.pop()\r\n A__ = True\r\n if len(lowercase_\t\t) != 0:\r\n A__ = stack[len(lowercase_\t\t) - 1]\r\n else:\r\n A__ = False\r\n indirect_parents.append(lowercase_\t\t)\r\n A__ = s\r\n A__ = ss\r\n\r\n # check if se have reached the starting point\r\n if len(lowercase_\t\t) == 0:\r\n return list(lowercase_\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : List[Any]\t\t)-> Union[str, Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = []\r\n A__ = []\r\n A__ = list(self.graph\t\t)[0]\r\n stack.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n A__ = -2\r\n A__ = []\r\n A__ = s\r\n A__ = False\r\n A__ = set()\r\n\r\n while True:\r\n # check if there is any non isolated nodes\r\n if len(self.graph[s]\t\t) != 0:\r\n A__ = s\r\n for node in self.graph[s]:\r\n if (\r\n visited.count(node[1]\t\t) > 0\r\n and node[1] != parent\r\n and indirect_parents.count(node[1]\t\t) > 0\r\n and not on_the_way_back\r\n ):\r\n A__ = len(lowercase_\t\t) - 1\r\n while len_stack_minus_one >= 0:\r\n if stack[len_stack_minus_one] == node[1]:\r\n anticipating_nodes.add(node[1]\t\t)\r\n break\r\n else:\r\n return True\r\n if visited.count(node[1]\t\t) < 1:\r\n stack.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n A__ = node[1]\r\n break\r\n\r\n # check if all the children are visited\r\n if s == ss:\r\n stack.pop()\r\n A__ = True\r\n if len(lowercase_\t\t) != 0:\r\n A__ = stack[len(lowercase_\t\t) - 1]\r\n else:\r\n A__ = False\r\n indirect_parents.append(lowercase_\t\t)\r\n A__ = s\r\n A__ = ss\r\n\r\n # check if se have reached the starting point\r\n if len(lowercase_\t\t) == 0:\r\n return False\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Tuple,lowercase_ : List[Any]=-2,lowercase_ : Optional[int]=-1\t\t)-> int:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = time()\r\n self.dfs(lowercase_,lowercase_\t\t)\r\n A__ = time()\r\n return end - begin\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : int,lowercase_ : List[str]=-2\t\t)-> Union[str, Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = time()\r\n self.bfs(lowercase_\t\t)\r\n A__ = time()\r\n return end - begin\r\n\r\n\r\n\r\n\r\nclass A\t\t\t\t\t\t:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n def __init__( self : Tuple\t\t)-> Optional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = {}\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : str,lowercase_ : Optional[Any],lowercase_ : str,lowercase_ : Any=1\t\t)-> Union[str, Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if self.graph.get(lowercase_\t\t):\r\n # if there already is a edge\r\n if self.graph[u].count([w, v]\t\t) == 0:\r\n self.graph[u].append([w, v]\t\t)\r\n else:\r\n # if u does not exist\r\n A__ = [[w, v]]\r\n # add the other way\r\n if self.graph.get(lowercase_\t\t):\r\n # if there already is a edge\r\n if self.graph[v].count([w, u]\t\t) == 0:\r\n self.graph[v].append([w, u]\t\t)\r\n else:\r\n # if u does not exist\r\n A__ = [[w, u]]\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : List[str],lowercase_ : Optional[int],lowercase_ : Optional[int]\t\t)-> List[Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if self.graph.get(lowercase_\t\t):\r\n for _ in self.graph[u]:\r\n if _[1] == v:\r\n self.graph[u].remove(lowercase_\t\t)\r\n # the other way round\r\n if self.graph.get(lowercase_\t\t):\r\n for _ in self.graph[v]:\r\n if _[1] == u:\r\n self.graph[v].remove(lowercase_\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Dict,lowercase_ : Any=-2,lowercase_ : List[str]=-1\t\t)-> Any:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if s == d:\r\n return []\r\n A__ = []\r\n A__ = []\r\n if s == -2:\r\n A__ = list(self.graph\t\t)[0]\r\n stack.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n A__ = s\r\n\r\n while True:\r\n # check if there is any non isolated nodes\r\n if len(self.graph[s]\t\t) != 0:\r\n A__ = s\r\n for node in self.graph[s]:\r\n if visited.count(node[1]\t\t) < 1:\r\n if node[1] == d:\r\n visited.append(lowercase_\t\t)\r\n return visited\r\n else:\r\n stack.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n A__ = node[1]\r\n break\r\n\r\n # check if all the children are visited\r\n if s == ss:\r\n stack.pop()\r\n if len(lowercase_\t\t) != 0:\r\n A__ = stack[len(lowercase_\t\t) - 1]\r\n else:\r\n A__ = ss\r\n\r\n # check if se have reached the starting point\r\n if len(lowercase_\t\t) == 0:\r\n return visited\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Tuple,lowercase_ : Any=-1\t\t)-> Optional[int]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if c == -1:\r\n A__ = floor(random() * 1_0_0_0_0\t\t) + 1_0\r\n for i in range(lowercase_\t\t):\r\n # every vertex has max 100 edges\r\n for _ in range(floor(random() * 1_0_2\t\t) + 1\t\t):\r\n A__ = floor(random() * c\t\t) + 1\r\n if n != i:\r\n self.add_pair(lowercase_,lowercase_,1\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Union[str, Any],lowercase_ : List[Any]=-2\t\t)-> Union[str, Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = deque()\r\n A__ = []\r\n if s == -2:\r\n A__ = list(self.graph\t\t)[0]\r\n d.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n while d:\r\n A__ = d.popleft()\r\n if len(self.graph[s]\t\t) != 0:\r\n for node in self.graph[s]:\r\n if visited.count(node[1]\t\t) < 1:\r\n d.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n return visited\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Optional[Any],lowercase_ : int\t\t)-> Optional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n return len(self.graph[u]\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Union[str, Any]\t\t)-> str:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = []\r\n A__ = []\r\n A__ = list(self.graph\t\t)[0]\r\n stack.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n A__ = -2\r\n A__ = []\r\n A__ = s\r\n A__ = False\r\n A__ = set()\r\n\r\n while True:\r\n # check if there is any non isolated nodes\r\n if len(self.graph[s]\t\t) != 0:\r\n A__ = s\r\n for node in self.graph[s]:\r\n if (\r\n visited.count(node[1]\t\t) > 0\r\n and node[1] != parent\r\n and indirect_parents.count(node[1]\t\t) > 0\r\n and not on_the_way_back\r\n ):\r\n A__ = len(lowercase_\t\t) - 1\r\n while len_stack >= 0:\r\n if stack[len_stack] == node[1]:\r\n anticipating_nodes.add(node[1]\t\t)\r\n break\r\n else:\r\n anticipating_nodes.add(stack[len_stack]\t\t)\r\n len_stack -= 1\r\n if visited.count(node[1]\t\t) < 1:\r\n stack.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n A__ = node[1]\r\n break\r\n\r\n # check if all the children are visited\r\n if s == ss:\r\n stack.pop()\r\n A__ = True\r\n if len(lowercase_\t\t) != 0:\r\n A__ = stack[len(lowercase_\t\t) - 1]\r\n else:\r\n A__ = False\r\n indirect_parents.append(lowercase_\t\t)\r\n A__ = s\r\n A__ = ss\r\n\r\n # check if se have reached the starting point\r\n if len(lowercase_\t\t) == 0:\r\n return list(lowercase_\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Optional[Any]\t\t)-> Tuple:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = []\r\n A__ = []\r\n A__ = list(self.graph\t\t)[0]\r\n stack.append(lowercase_\t\t)\r\n visited.append(lowercase_\t\t)\r\n A__ = -2\r\n A__ = []\r\n A__ = s\r\n A__ = False\r\n A__ = set()\r\n\r\n while True:\r\n # check if there is any non isolated nodes\r\n if len(self.graph[s]\t\t) != 0:\r\n A__ = s\r\n for node in self.graph[s]:\r\n if (\r\n visited.count(node[1]\t\t) > 0\r\n and node[1] != parent\r\n and indirect_parents.count(node[1]\t\t) > 0\r\n and not on_the_way_back\r\n ):\r\n A__ = len(lowercase_\t\t) - 1\r\n while len_stack_minus_one >= 0:\r\n if stack[len_stack_minus_one] == node[1]:\r\n anticipating_nodes.add(node[1]\t\t)\r\n break\r\n else:\r\n return True\r\n if visited.count(node[1]\t\t) < 1:\r\n stack.append(node[1]\t\t)\r\n visited.append(node[1]\t\t)\r\n A__ = node[1]\r\n break\r\n\r\n # check if all the children are visited\r\n if s == ss:\r\n stack.pop()\r\n A__ = True\r\n if len(lowercase_\t\t) != 0:\r\n A__ = stack[len(lowercase_\t\t) - 1]\r\n else:\r\n A__ = False\r\n indirect_parents.append(lowercase_\t\t)\r\n A__ = s\r\n A__ = ss\r\n\r\n # check if se have reached the starting point\r\n if len(lowercase_\t\t) == 0:\r\n return False\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Dict\t\t)-> List[str]:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n return list(self.graph\t\t)\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Dict,lowercase_ : Union[str, Any]=-2,lowercase_ : str=-1\t\t)-> str:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = time()\r\n self.dfs(lowercase_,lowercase_\t\t)\r\n A__ = time()\r\n return end - begin\r\n\r\n\r\n def \t\t\t\t\t\tsnake_case__ ( self : Optional[int],lowercase_ : List[Any]=-2\t\t)-> Dict:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = time()\r\n self.bfs(lowercase_\t\t)\r\n A__ = time()\r\n return end - begin\r\n\r\n"},"code_codestyle":{"kind":"number","value":7,"string":"7"},"style_context":{"kind":"string","value":"\r\n\r\nimport os\r\n\r\n# Precomputes a list of the 100 first triangular numbers\r\nlowercase_ \t\t= [int(0.5 * n * (n + 1)) for n in range(1, 101)]\r\ndef \t\t\t\t\t\t_snake_case( )\t\t\t\t\t\t\t-> int:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__ ) )\r\n A__ = os.path.join(SCREAMING_SNAKE_CASE__ ,\t\t\t'words.txt' )\r\n\r\n A__ = ''\r\n with open(SCREAMING_SNAKE_CASE__ ) as f:\r\n A__ = f.readline()\r\n\r\n A__ = [word.strip('\"' ) for word in words.strip('\\r\\n' ).split(',' )]\r\n A__ = [\r\n word\r\n for word in [sum(ord(SCREAMING_SNAKE_CASE__ ) - 64 for x in word ) for word in words]\r\n if word in TRIANGULAR_NUMBERS\r\n ]\r\n return len(SCREAMING_SNAKE_CASE__ )\r\n\r\n\r\nif __name__ == \"__main__\":\r\n print(solution())\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":7,"string":"7"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":569,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\nfrom ...configuration_utils import PretrainedConfig\r\nfrom ...utils import logging\r\n\r\n\r\n__magic_name__ \t\t\t\t\t= logging.get_logger(__name__)\r\n\r\n__magic_name__ \t\t\t\t\t= {\r\n \"facebook/nllb-moe-54B\": \"https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json\",\r\n}\r\n\r\nclass lowercase\t\t\t\t\t\t( A__\t\t\t):\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n __SCREAMING_SNAKE_CASE\t\t\t\t\t\t = \"\"\"nllb-moe\"\"\"\r\n __SCREAMING_SNAKE_CASE\t\t\t\t\t\t = [\"\"\"past_key_values\"\"\"]\r\n __SCREAMING_SNAKE_CASE\t\t\t\t\t\t = {\"\"\"num_attention_heads\"\"\": \"\"\"encoder_attention_heads\"\"\", \"\"\"hidden_size\"\"\": \"\"\"d_model\"\"\"}\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __init__(\t\t\t\t\t\tself\t\t,\t_snake_case=12_8112\t\t,\t_snake_case=1024\t\t,\t_snake_case=12\t\t,\t_snake_case=4096\t\t,\t_snake_case=16\t\t,\t_snake_case=12\t\t,\t_snake_case=4096\t\t,\t_snake_case=16\t\t,\t_snake_case=0.05\t\t,\t_snake_case=0.05\t\t,\t_snake_case=True\t\t,\t_snake_case=True\t\t,\t_snake_case=\"relu\"\t\t,\t_snake_case=1024\t\t,\t_snake_case=0.1\t\t,\t_snake_case=0.1\t\t,\t_snake_case=0.0\t\t,\t_snake_case=0.02\t\t,\t_snake_case=2\t\t,\t_snake_case=True\t\t,\t_snake_case=False\t\t,\t_snake_case=\"float32\"\t\t,\t_snake_case=False\t\t,\t_snake_case=128\t\t,\t_snake_case=64\t\t,\t_snake_case=4\t\t,\t_snake_case=4\t\t,\t_snake_case=0.001\t\t,\t_snake_case=0.001\t\t,\t_snake_case=\"all\"\t\t,\t_snake_case=False\t\t,\t_snake_case=False\t\t,\t_snake_case=1.0\t\t,\t_snake_case=0.2\t\t,\t_snake_case=1\t\t,\t_snake_case=0\t\t,\t_snake_case=2\t\t,\t_snake_case=False\t\t,\t**_snake_case\t\t,\t) ->\t\tList[str]:\r\n\r\n \"\"\"simple docstring\"\"\"\r\n UpperCAmelCase = vocab_size\r\n UpperCAmelCase = max_position_embeddings\r\n UpperCAmelCase = d_model\r\n UpperCAmelCase = encoder_ffn_dim\r\n UpperCAmelCase = encoder_layers\r\n UpperCAmelCase = encoder_attention_heads\r\n UpperCAmelCase = decoder_ffn_dim\r\n UpperCAmelCase = decoder_layers\r\n UpperCAmelCase = decoder_attention_heads\r\n UpperCAmelCase = dropout\r\n UpperCAmelCase = attention_dropout\r\n UpperCAmelCase = activation_dropout\r\n UpperCAmelCase = activation_function\r\n UpperCAmelCase = init_std\r\n UpperCAmelCase = encoder_layerdrop\r\n UpperCAmelCase = decoder_layerdrop\r\n UpperCAmelCase = use_cache\r\n UpperCAmelCase = encoder_layers\r\n UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True\r\n UpperCAmelCase = router_z_loss_coef\r\n UpperCAmelCase = router_aux_loss_coef\r\n UpperCAmelCase = decoder_sparse_step\r\n UpperCAmelCase = encoder_sparse_step\r\n UpperCAmelCase = num_experts\r\n UpperCAmelCase = expert_capacity\r\n UpperCAmelCase = router_bias\r\n if router_dtype not in [\"float32\", \"float16\", \"bfloat16\"]:\r\n raise ValueError(f\"\"\"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}\"\"\" )\r\n UpperCAmelCase = router_dtype\r\n\r\n UpperCAmelCase = router_ignore_padding_tokens\r\n UpperCAmelCase = batch_prioritized_routing\r\n UpperCAmelCase = second_expert_policy\r\n UpperCAmelCase = normalize_router_prob_before_dropping\r\n UpperCAmelCase = moe_eval_capacity_token_fraction\r\n UpperCAmelCase = moe_token_dropout\r\n UpperCAmelCase = output_router_logits\r\n super().__init__(\r\n pad_token_id=_snake_case\t\t,\tbos_token_id=_snake_case\t\t,\teos_token_id=_snake_case\t\t,\tis_encoder_decoder=_snake_case\t\t,\tdecoder_start_token_id=_snake_case\t\t,\t**_snake_case\t\t,\t)\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":152,"string":"152"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\nfrom ...utils import (\r\n OptionalDependencyNotAvailable,\r\n is_torch_available,\r\n is_transformers_available,\r\n is_transformers_version,\r\n)\r\n\r\n\r\ntry:\r\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.25.0\")):\r\n raise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline\r\nelse:\r\n from .pipeline_unclip import UnCLIPPipeline\r\n from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline\r\n from .text_proj import UnCLIPTextProjModel\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":152,"string":"152"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":570,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\n\nfrom __future__ import annotations\n\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\ttuple[int, int, int]\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\ttuple[str, str, str]\n\n\n# used alphabet --------------------------\n# from string.ascii_uppercase\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n\n# -------------------------- default selection --------------------------\n# rotors --------------------------\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"EGZWVONAHDCLFQMSIPJBYUKXTR\"\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"FOBHMDKEXQNRAULPGSJVTYICZW\"\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"ZJXESIUQLHAVRMDOYGTNFWPBKC\"\n# reflector --------------------------\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t{\n \"A\": \"N\",\n \"N\": \"A\",\n \"B\": \"O\",\n \"O\": \"B\",\n \"C\": \"P\",\n \"P\": \"C\",\n \"D\": \"Q\",\n \"Q\": \"D\",\n \"E\": \"R\",\n \"R\": \"E\",\n \"F\": \"S\",\n \"S\": \"F\",\n \"G\": \"T\",\n \"T\": \"G\",\n \"H\": \"U\",\n \"U\": \"H\",\n \"I\": \"V\",\n \"V\": \"I\",\n \"J\": \"W\",\n \"W\": \"J\",\n \"K\": \"X\",\n \"X\": \"K\",\n \"L\": \"Y\",\n \"Y\": \"L\",\n \"M\": \"Z\",\n \"Z\": \"M\",\n}\n\n# -------------------------- extra rotors --------------------------\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"RMDJXFUWGISLHVTCQNKYPBEZOA\"\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"SGLCPQWZHKXAREONTFBVIYJUDM\"\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"HVSICLTYKQUBXDWAJZOMFGPREN\"\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"RZWQHFMVDBKICJLNTUXAGYPSOE\"\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"LFKIJODBEGAMQPXVUHYSTCZRWN\"\nlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"KOAEGVDHXPQZMLFTYWJNBRCIUS\"\n\n\n\n\n\ndef \t\t\t\t\t\t__UpperCAmelCase\t\t\t( a_\t\t\t\t\t\t\t, a_\t\t\t\t\t\t\t, a_):\n\t\t\t\t\t\t\t# Checks if there are 3 unique rotors\n\n\t\t\t\t\t\t\tif (unique_rotsel := len(set(a_))) < 3:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tf'''Please use 3 unique rotors (not {unique_rotsel})'''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise Exception(a_)\n\n\t\t\t\t\t\t\t# Checks if rotor positions are valid\n\t\t\t\t\t\t\tsnake_case_\t\t\t,\t\tsnake_case_\t\t\t,\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\trotpos\n\t\t\t\t\t\t\tif not 0 < rotorposa <= len(a_):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tf'''First rotor position is not within range of 1..26 ({rotorposa}'''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(a_)\n\t\t\t\t\t\t\tif not 0 < rotorposa <= len(a_):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tf'''Second rotor position is not within range of 1..26 ({rotorposa})'''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(a_)\n\t\t\t\t\t\t\tif not 0 < rotorposa <= len(a_):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tf'''Third rotor position is not within range of 1..26 ({rotorposa})'''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(a_)\n\n\t\t\t\t\t\t\t# Validates string and returns dict\n\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\t_plugboard(a_)\n\n\t\t\t\t\t\t\treturn rotpos, rotsel, pbdict\n\n\n\n\n\ndef \t\t\t\t\t\t__UpperCAmelCase\t\t\t( a_):\n\n\t\t\t\t\t\t\t# tests the input string if it\n\t\t\t\t\t\t\t# a) is type string\n\t\t\t\t\t\t\t# b) has even length (so pairs can be made)\n\t\t\t\t\t\t\tif not isinstance(a_\t\t\t\t\t\t\t, a_):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tf'''Plugboard setting isn\\'t type string ({type(a_)})'''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise TypeError(a_)\n\t\t\t\t\t\t\telif len(a_) % 2 != 0:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tf'''Odd number of symbols ({len(a_)})'''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise Exception(a_)\n\t\t\t\t\t\t\telif pbstring == \"\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn {}\n\n\t\t\t\t\t\t\tpbstring.replace(' '\t\t\t\t\t\t\t, '')\n\n\t\t\t\t\t\t\t# Checks if all characters are unique\n\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tset()\n\t\t\t\t\t\t\tfor i in pbstring:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif i not in abc:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tf'''\\'{i}\\' not in list of symbols'''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise Exception(a_)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telif i in tmppbl:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tf'''Duplicate symbol ({i})'''\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise Exception(a_)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttmppbl.add(a_)\n\t\t\t\t\t\t\tdel tmppbl\n\n\t\t\t\t\t\t\t# Created the dictionary\n\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\t{}\n\t\t\t\t\t\t\tfor j in range(0\t\t\t\t\t\t\t, len(a_) - 1\t\t\t\t\t\t\t, 2):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tpbstring[j + 1]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tpbstring[j]\n\n\t\t\t\t\t\t\treturn pb\n\n\n\n\n\ndef \t\t\t\t\t\t__UpperCAmelCase\t\t\t( a_\t\t\t\t\t\t\t, a_\t\t\t\t\t\t\t, a_ = (rotora, rotora, rotora)\t\t\t\t\t\t\t, a_ = \"\"\t\t\t\t\t\t\t, ):\n\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\ttext.upper()\n\t\t\t\t\t\t\tsnake_case_\t\t\t,\t\tsnake_case_\t\t\t,\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\t_validator(\n\t\t\t\t\t\t\t a_\t\t\t\t\t\t\t, a_\t\t\t\t\t\t\t, plugb.upper())\n\n\t\t\t\t\t\t\tsnake_case_\t\t\t,\t\tsnake_case_\t\t\t,\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\trotor_position\n\t\t\t\t\t\t\tsnake_case_\t\t\t,\t\tsnake_case_\t\t\t,\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\trotor_selection\n\t\t\t\t\t\t\trotorposa -= 1\n\t\t\t\t\t\t\trotorposa -= 1\n\t\t\t\t\t\t\trotorposa -= 1\n\n\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\t[]\n\n\t\t\t\t\t\t\t# encryption/decryption process --------------------------\n\t\t\t\t\t\t\tfor symbol in text:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif symbol in abc:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# 1st plugboard --------------------------\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif symbol in plugboard:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tplugboard[symbol]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# rotor ra --------------------------\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tabc.index(a_) + rotorposa\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\trotora[index % len(a_)]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# rotor rb --------------------------\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tabc.index(a_) + rotorposa\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\trotora[index % len(a_)]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# rotor rc --------------------------\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tabc.index(a_) + rotorposa\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\trotora[index % len(a_)]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# reflector --------------------------\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# this is the reason you don't need another machine to decipher\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\treflector[symbol]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# 2nd rotors\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tabc[rotora.index(a_) - rotorposa]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tabc[rotora.index(a_) - rotorposa]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tabc[rotora.index(a_) - rotorposa]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# 2nd plugboard\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif symbol in plugboard:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tplugboard[symbol]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# moves/resets rotor positions\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trotorposa += 1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif rotorposa >= len(a_):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trotorposa += 1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif rotorposa >= len(a_):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\t0\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trotorposa += 1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif rotorposa >= len(a_):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\t0\n\n # else:\n # pass\n # Error could be also raised\n # raise ValueError(\n # 'Invalid symbol('+repr(symbol)+')')\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tresult.append(a_)\n\n\t\t\t\t\t\t\treturn \"\".join(a_)\n\n\nif __name__ == \"__main__\":\n\tlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"This is my Python script that emulates the Enigma machine from WWII.\"\n\tlowercase \t\t\t\t\t\t\t=\t\t\t\t\t(1, 1, 1)\n\tlowercase \t\t\t\t\t\t\t=\t\t\t\t\t\"pictures\"\n\tlowercase \t\t\t\t\t\t\t=\t\t\t\t\t(rotora, rotora, rotora)\n\tlowercase \t\t\t\t\t\t\t=\t\t\t\t\tenigma(message, rotor_pos, rotor_sel, pb)\n\n\tprint(\"Encrypted message:\", en)\n\tprint(\"Decrypted message:\", enigma(en, rotor_pos, rotor_sel, pb))\n\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":178,"string":"178"},"style_context":{"kind":"string","value":"\n\n\n\n\n\n\nfrom typing import List, Optional, Union\n\nfrom ...image_utils import ImageInput\nfrom ...processing_utils import ProcessorMixin\nfrom ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy\nfrom ...utils import TensorType\n\n\n\n\nclass UpperCamelCase_ (\t\t\tsnake_case_\t\t\t\t):\n\n\n\n\t'''simple docstring'''\n\n\n\n\n\tlowerCAmelCase\t\t\t\t\t\t\t\t\t\t\t\t\t= ['''image_processor''', '''tokenizer''']\n\tlowerCAmelCase\t\t\t\t\t\t\t\t\t\t\t\t\t= '''BlipImageProcessor'''\n\tlowerCAmelCase\t\t\t\t\t\t\t\t\t\t\t\t\t= ('''BertTokenizer''', '''BertTokenizerFast''')\n\n\n\n\n\tdef __init__( self ,\t\t\ta ,\t\t\ta\t\t\t\t\t) -> Tuple:\n\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tFalse\n\t\t\t\t\t\t\t\tsuper().__init__(a ,\t\t\ta\t\t\t\t\t)\n\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tself.image_processor\n\n\n\n\n\tdef __call__( self ,\t\t\ta = None ,\t\t\ta = None ,\t\t\ta = True ,\t\t\ta = False ,\t\t\ta = None ,\t\t\ta = None ,\t\t\ta = 0 ,\t\t\ta = None ,\t\t\ta = None ,\t\t\ta = False ,\t\t\ta = False ,\t\t\ta = False ,\t\t\ta = False ,\t\t\ta = False ,\t\t\ta = True ,\t\t\ta = None ,\t\t\t**a ,\t\t\t) -> BatchEncoding:\n\t\t\t\t\t\t\t\tif images is None and text is None:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('You have to specify either images or text.'\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t# Get only text\n\t\t\t\t\t\t\t\tif images is None:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tself.tokenizer\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tself.tokenizer(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t text=a ,\t\t\tadd_special_tokens=a ,\t\t\tpadding=a ,\t\t\ttruncation=a ,\t\t\tmax_length=a ,\t\t\tstride=a ,\t\t\tpad_to_multiple_of=a ,\t\t\treturn_attention_mask=a ,\t\t\treturn_overflowing_tokens=a ,\t\t\treturn_special_tokens_mask=a ,\t\t\treturn_offsets_mapping=a ,\t\t\treturn_token_type_ids=a ,\t\t\treturn_length=a ,\t\t\tverbose=a ,\t\t\treturn_tensors=a ,\t\t\t**a ,\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn text_encoding\n\n\t\t\t\t\t\t\t\t# add pixel_values\n\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tself.image_processor(a ,\t\t\treturn_tensors=a\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\tif text is not None:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tself.tokenizer(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t text=a ,\t\t\tadd_special_tokens=a ,\t\t\tpadding=a ,\t\t\ttruncation=a ,\t\t\tmax_length=a ,\t\t\tstride=a ,\t\t\tpad_to_multiple_of=a ,\t\t\treturn_attention_mask=a ,\t\t\treturn_overflowing_tokens=a ,\t\t\treturn_special_tokens_mask=a ,\t\t\treturn_offsets_mapping=a ,\t\t\treturn_token_type_ids=a ,\t\t\treturn_length=a ,\t\t\tverbose=a ,\t\t\treturn_tensors=a ,\t\t\t**a ,\t\t\t)\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tNone\n\n\t\t\t\t\t\t\t\tif text_encoding is not None:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tencoding_image_processor.update(a\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\treturn encoding_image_processor\n\n\n\n\n\tdef \t\t\t\t\t\t\t_UpperCamelCase\t\t\t( self ,\t\t\t*a ,\t\t\t**a\t\t\t\t\t) -> int:\n\t\t\t\t\t\t\t\treturn self.tokenizer.batch_decode(*a ,\t\t\t**a\t\t\t\t\t)\n\n\n\n\n\tdef \t\t\t\t\t\t\t_UpperCamelCase\t\t\t( self ,\t\t\t*a ,\t\t\t**a\t\t\t\t\t) -> Any:\n\t\t\t\t\t\t\t\treturn self.tokenizer.decode(*a ,\t\t\t**a\t\t\t\t\t)\n\n\n\n\n\n\n\t@property\n\tdef \t\t\t\t\t\t\t_UpperCamelCase\t\t\t( self\t\t\t\t\t) -> List[str]:\n\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tself.tokenizer.model_input_names\n\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t=\t\t\t\t\t\t\tself.image_processor.model_input_names\n\t\t\t\t\t\t\t\treturn list(dict.fromkeys(tokenizer_input_names + image_processor_input_names\t\t\t\t\t)\t\t\t\t\t)\n\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":178,"string":"178"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":571,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\nimport unittest\r\n\r\nfrom accelerate import debug_launcher\r\nfrom accelerate.test_utils import require_cpu, test_ops, test_script\r\n\r\n@require_cpu\r\nclass \t\t__lowerCAmelCase ( unittest.TestCase\t\t\t):\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:List[Any] ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\tdebug_launcher(test_script.main )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:Tuple ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\tdebug_launcher(test_ops.main )\r\n\r\n"},"code_codestyle":{"kind":"number","value":347,"string":"347"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\nimport warnings\r\nfrom typing import Dict, List, Optional, Tuple\r\n\r\nfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\r\nfrom ...utils import logging\r\n\r\n\r\n__UpperCamelCase :\tDict =\t\t\t\tlogging.get_logger(__name__)\r\n\r\nclass \t\t__lowerCAmelCase ( __magic_name__\t\t\t):\r\n\t\t\t\t\t\t\tUpperCamelCase__ \t\t\t\t\t= ['''input_ids''', '''attention_mask''']\r\n\t\t\t\t\t\t\tdef __init__(\t\t\t\t\t\tself\t\t\t\t\t\t:List[str]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:int=\"\"\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:List[Any]=\"\"\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:Optional[Any]=\"\"\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:Optional[int]=125\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:List[str]=None\t\t,\t\t\t\t**__magic_name__\t\t\t\t\t\t:List[str]\t\t,\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\tif extra_ids > 0 and additional_special_tokens is None:\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\t[F'' for i in range(__magic_name__ )]\r\n\t\t\t\t\t\t\t\telif extra_ids > 0 and additional_special_tokens is not None:\r\n\t\t\t\t\t\t\t\t\t# Check that we have the right number of extra_id special tokens\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tlen(set(filter(lambda __magic_name__ : bool(\"\"\"extra_id\"\"\" in str(__magic_name__ ) )\t\t,\t\t\t\t__magic_name__ ) ) )\r\n\t\t\t\t\t\t\t\t\tif extra_tokens != extra_ids:\r\n\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'\r\n\t\t\t\t\t\t\t\t\t\t \"\"\" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the\"\"\"\r\n\t\t\t\t\t\t\t\t\t\t \"\"\" extra_ids tokens\"\"\" )\r\n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tAddedToken(__magic_name__\t\t,\t\t\t\tlstrip=__magic_name__\t\t,\t\t\t\trstrip=__magic_name__ ) if isinstance(__magic_name__\t\t,\t\t\t\t__magic_name__ ) else pad_token\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tAddedToken(__magic_name__\t\t,\t\t\t\tlstrip=__magic_name__\t\t,\t\t\t\trstrip=__magic_name__ ) if isinstance(__magic_name__\t\t,\t\t\t\t__magic_name__ ) else eos_token\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tAddedToken(__magic_name__\t\t,\t\t\t\tlstrip=__magic_name__\t\t,\t\t\t\trstrip=__magic_name__ ) if isinstance(__magic_name__\t\t,\t\t\t\t__magic_name__ ) else unk_token\r\n\r\n\t\t\t\t\t\t\t\tsuper().__init__(\r\n\t\t\t\t\t\t\t\t eos_token=__magic_name__\t\t,\t\t\t\tunk_token=__magic_name__\t\t,\t\t\t\tpad_token=__magic_name__\t\t,\t\t\t\textra_ids=__magic_name__\t\t,\t\t\t\tadditional_special_tokens=__magic_name__\t\t,\t\t\t\t**__magic_name__\t\t,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\textra_ids\r\n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\t2**8 # utf is 8 bits\r\n\r\n\t\t\t\t\t\t\t\t# define special tokens dict\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\t{\r\n\t\t\t\t\t\t\t\t self.pad_token: 0,\r\n\t\t\t\t\t\t\t\t self.eos_token: 1,\r\n\t\t\t\t\t\t\t\t self.unk_token: 2,\r\n\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tlen(self.special_tokens_encoder )\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tlen(__magic_name__ )\r\n\t\t\t\t\t\t\t\tfor i, token in enumerate(__magic_name__ ):\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself.vocab_size + i - n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\t{v: k for k, v in self.special_tokens_encoder.items()}\r\n\t\t\t\t\t\t\t@property\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:List[Any] ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\treturn self._utf_vocab_size + self._num_special_tokens + self._extra_ids\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:Any\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:List[int]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:Optional[List[int]] = None\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:bool = False ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\tif already_has_special_tokens:\r\n\t\t\t\t\t\t\t\t\treturn super().get_special_tokens_mask(\r\n\t\t\t\t\t\t\t\t\t token_ids_a=__magic_name__\t\t,\t\t\t\ttoken_ids_a=__magic_name__\t\t,\t\t\t\talready_has_special_tokens=__magic_name__ )\r\n\r\n\t\t\t\t\t\t\t\t# normal case: some special tokens\r\n\t\t\t\t\t\t\t\tif token_ids_a is None:\r\n\t\t\t\t\t\t\t\t\treturn ([0] * len(__magic_name__ )) + [1]\r\n\t\t\t\t\t\t\t\treturn ([0] * len(__magic_name__ )) + [1] + ([0] * len(__magic_name__ )) + [1]\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:str\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:List[int] ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\tif len(__magic_name__ ) > 0 and token_ids[-1] == self.eos_token_id:\r\n\t\t\t\t\t\t\t\t\twarnings.warn(\r\n\t\t\t\t\t\t\t\t\t F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'\r\n\t\t\t\t\t\t\t\t\t \"\"\" eos tokens being added.\"\"\" )\r\n\t\t\t\t\t\t\t\t\treturn token_ids\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\treturn token_ids + [self.eos_token_id]\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:Union[str, Any]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:List[int]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:Optional[List[int]] = None ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\t[self.eos_token_id]\r\n\r\n\t\t\t\t\t\t\t\tif token_ids_a is None:\r\n\t\t\t\t\t\t\t\t\treturn len(token_ids_a + eos ) * [0]\r\n\t\t\t\t\t\t\t\treturn len(token_ids_a + eos + token_ids_a + eos ) * [0]\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:Union[str, Any]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:List[int]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:Optional[List[int]] = None ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself._add_eos_if_not_present(__magic_name__ )\r\n\t\t\t\t\t\t\t\tif token_ids_a is None:\r\n\t\t\t\t\t\t\t\t\treturn token_ids_a\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself._add_eos_if_not_present(__magic_name__ )\r\n\t\t\t\t\t\t\t\t\treturn token_ids_a + token_ids_a\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:List[str]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:str ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\t[chr(__magic_name__ ) for i in text.encode(\"\"\"utf-8\"\"\" )]\r\n\t\t\t\t\t\t\t\treturn tokens\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:Tuple\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:str ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\tif token in self.special_tokens_encoder:\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself.special_tokens_encoder[token]\r\n\t\t\t\t\t\t\t\telif token in self.added_tokens_encoder:\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself.added_tokens_encoder[token]\r\n\t\t\t\t\t\t\t\telif len(__magic_name__ ) != 1:\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself.unk_token_id\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tord(__magic_name__ ) + self._num_special_tokens\r\n\t\t\t\t\t\t\t\treturn token_id\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:List[str]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:Dict ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\tif index in self.special_tokens_decoder:\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself.special_tokens_decoder[index]\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tchr(index - self._num_special_tokens )\r\n\t\t\t\t\t\t\t\treturn token\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:Tuple\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:Optional[int] ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tb\"\"\"\"\"\"\r\n\t\t\t\t\t\t\t\tfor token in tokens:\r\n\t\t\t\t\t\t\t\t\tif token in self.special_tokens_decoder:\r\n\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself.special_tokens_decoder[token].encode(\"\"\"utf-8\"\"\" )\r\n\t\t\t\t\t\t\t\t\telif token in self.added_tokens_decoder:\r\n\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tself.special_tokens_decoder[token].encode(\"\"\"utf-8\"\"\" )\r\n\t\t\t\t\t\t\t\t\telif token in self.special_tokens_encoder:\r\n\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\ttoken.encode(\"\"\"utf-8\"\"\" )\r\n\t\t\t\t\t\t\t\t\telif token in self.added_tokens_encoder:\r\n\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\ttoken.encode(\"\"\"utf-8\"\"\" )\r\n\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tbytes([ord(__magic_name__ )] )\r\n\t\t\t\t\t\t\t\t\tbstring += tok_string\r\n\t\t\t\t\t\t\t\ta\t\t\t\t\t\t =\t\t\t\t\t\t\tbstring.decode(\"\"\"utf-8\"\"\"\t\t,\t\t\t\terrors=\"\"\"ignore\"\"\" )\r\n\t\t\t\t\t\t\t\treturn string\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tdef \t\t\t\tlowerCamelCase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t:Optional[Any]\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:str\t\t,\t\t\t\t__magic_name__\t\t\t\t\t\t:Optional[str] = None ):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\treturn ()\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":347,"string":"347"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":572,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n'''simple docstring'''\n\n\n\n\n\n\n\nimport logging\nimport os\nfrom dataclasses import dataclass, field\nfrom typing import Dict, Optional\n\nimport numpy as np\nfrom utils_multiple_choice import MultipleChoiceDataset, Split, processors\n\nimport transformers\nfrom transformers import (\n AutoConfig,\n AutoModelForMultipleChoice,\n AutoTokenizer,\n DataCollatorWithPadding,\n EvalPrediction,\n HfArgumentParser,\n Trainer,\n TrainingArguments,\n set_seed,\n)\nfrom transformers.trainer_utils import is_main_process\n\n\nA =logging.getLogger(__name__)\n\n\n\n\n\ndef \t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t(_a\t:\t\t\t\t\t\t\tDict , _a\t:\t\t\t\t\t\t\tUnion[str, Any] ):\n return (preds == labels).mean()\n\n\n@dataclass\nclass \t\t\t_a :\n __a\t\t\t\t: str \t\t\t\t=\t\t\t\t\tfield(\n metadata={\"\"\"help\"\"\": \"\"\"Path to pretrained model or model identifier from huggingface.co/models\"\"\"} )\n __a\t\t\t\t: Optional[str] \t\t\t\t=\t\t\t\t\tfield(\n default=__a\t\t\t\t\t\t\t,\t\t\t\t\t\t\tmetadata={\"\"\"help\"\"\": \"\"\"Pretrained config name or path if not the same as model_name\"\"\"} )\n __a\t\t\t\t: Optional[str] \t\t\t\t=\t\t\t\t\tfield(\n default=__a\t\t\t\t\t\t\t,\t\t\t\t\t\t\tmetadata={\"\"\"help\"\"\": \"\"\"Pretrained tokenizer name or path if not the same as model_name\"\"\"} )\n __a\t\t\t\t: Optional[str] \t\t\t\t=\t\t\t\t\tfield(\n default=__a\t\t\t\t\t\t\t,\t\t\t\t\t\t\tmetadata={\"\"\"help\"\"\": \"\"\"Where do you want to store the pretrained models downloaded from huggingface.co\"\"\"}\t\t\t\t\t\t\t,\t\t\t\t\t\t\t)\n\n\n\n\n\n\n@dataclass\nclass \t\t\t_a :\n __a\t\t\t\t: str \t\t\t\t=\t\t\t\t\tfield(metadata={\"\"\"help\"\"\": \"\"\"The name of the task to train on: \"\"\" + \"\"\", \"\"\".join(processors.keys() )} )\n __a\t\t\t\t: str \t\t\t\t=\t\t\t\t\tfield(metadata={\"\"\"help\"\"\": \"\"\"Should contain the data files for the task.\"\"\"} )\n __a\t\t\t\t: int \t\t\t\t=\t\t\t\t\tfield(\n default=128\t\t\t\t\t\t\t,\t\t\t\t\t\t\tmetadata={\n \"\"\"help\"\"\": (\n \"\"\"The maximum total input sequence length after tokenization. Sequences longer \"\"\"\n \"\"\"than this will be truncated, sequences shorter will be padded.\"\"\"\n )\n }\t\t\t\t\t\t\t,\t\t\t\t\t\t\t)\n __a\t\t\t\t: bool \t\t\t\t=\t\t\t\t\tfield(\n default=__a\t\t\t\t\t\t\t,\t\t\t\t\t\t\tmetadata={\"\"\"help\"\"\": \"\"\"Overwrite the cached training and evaluation sets\"\"\"} )\n\n\n\n\n\n\ndef \t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t():\n # See all possible arguments in src/transformers/training_args.py\n # or by passing the --help flag to this script.\n # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n UpperCAmelCase\t\t\t =\t\tHfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )\n UpperCAmelCase , UpperCAmelCase , UpperCAmelCase\t\t\t =\t\tparser.parse_args_into_dataclasses()\n\n if (\n os.path.exists(training_args.output_dir )\n and os.listdir(training_args.output_dir )\n and training_args.do_train\n and not training_args.overwrite_output_dir\n ):\n raise ValueError(\n F\"Output directory ({training_args.output_dir}) already exists and is not empty. Use\"\n ''' --overwrite_output_dir to overcome.''' )\n\n # Setup logging\n logging.basicConfig(\n format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )\n logger.warning(\n '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )\n # Set the verbosity to info of the Transformers logger (on main process only):\n if is_main_process(training_args.local_rank ):\n transformers.utils.logging.set_verbosity_info()\n transformers.utils.logging.enable_default_handler()\n transformers.utils.logging.enable_explicit_format()\n logger.info('''Training/evaluation parameters %s''' , _a )\n\n # Set seed\n set_seed(training_args.seed )\n\n try:\n UpperCAmelCase\t\t\t =\t\tprocessors[data_args.task_name]()\n UpperCAmelCase\t\t\t =\t\tprocessor.get_labels()\n UpperCAmelCase\t\t\t =\t\tlen(_a )\n except KeyError:\n raise ValueError('''Task not found: %s''' % (data_args.task_name) )\n\n # Load pretrained model and tokenizer\n #\n # Distributed training:\n # The .from_pretrained methods guarantee that only one local process can concurrently\n # download model & vocab.\n\n UpperCAmelCase\t\t\t =\t\tAutoConfig.from_pretrained(\n model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_a , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )\n UpperCAmelCase\t\t\t =\t\tAutoTokenizer.from_pretrained(\n model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )\n UpperCAmelCase\t\t\t =\t\tAutoModelForMultipleChoice.from_pretrained(\n model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_a , cache_dir=model_args.cache_dir , )\n\n # Get datasets\n UpperCAmelCase\t\t\t =\t\t(\n MultipleChoiceDataset(\n data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )\n if training_args.do_train\n else None\n )\n UpperCAmelCase\t\t\t =\t\t(\n MultipleChoiceDataset(\n data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )\n if training_args.do_eval\n else None\n )\n\n def compute_metrics(_a\t:\t\t\t\t\t\t\tEvalPrediction ) -> Dict:\n UpperCAmelCase\t\t\t =\t\tnp.argmax(p.predictions , axis=1 )\n return {\"acc\": simple_accuracy(_a , p.label_ids )}\n\n # Data collator\n UpperCAmelCase\t\t\t =\t\tDataCollatorWithPadding(_a , pad_to_multiple_of=8 ) if training_args.fpaa else None\n\n # Initialize our Trainer\n UpperCAmelCase\t\t\t =\t\tTrainer(\n model=_a , args=_a , train_dataset=_a , eval_dataset=_a , compute_metrics=_a , data_collator=_a , )\n\n # Training\n if training_args.do_train:\n trainer.train(\n model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )\n trainer.save_model()\n # For convenience, we also re-save the tokenizer to the same directory,\n # so that you can share your model easily on huggingface.co/models =)\n if trainer.is_world_master():\n tokenizer.save_pretrained(training_args.output_dir )\n\n # Evaluation\n UpperCAmelCase\t\t\t =\t\t{}\n if training_args.do_eval:\n logger.info('''*** Evaluate ***''' )\n\n UpperCAmelCase\t\t\t =\t\ttrainer.evaluate()\n\n UpperCAmelCase\t\t\t =\t\tos.path.join(training_args.output_dir , '''eval_results.txt''' )\n if trainer.is_world_master():\n with open(_a , '''w''' ) as writer:\n logger.info('''***** Eval results *****''' )\n for key, value in result.items():\n logger.info(''' %s = %s''' , _a , _a )\n writer.write('''%s = %s\\n''' % (key, value) )\n\n results.update(_a )\n\n return results\n\n\n\n\n\ndef \t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t(_a\t:\t\t\t\t\t\t\tOptional[int] ):\n # For xla_spawn (TPUs)\n main()\n\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":34,"string":"34"},"style_context":{"kind":"string","value":"\n\n\n\n\n\n'''simple docstring'''\n\n\n\n\ndef \tUpperCAmelCase_\t\t\t\t\t\t\t(\t\t\t\t\t__lowercase\t\t\t\t\t\t\t: int )\t\t-> int:\n\n\n '''simple docstring'''\n\n\n\n if not isinstance(__lowercase\t\t\t, __lowercase ) or number < 0:\n raise ValueError(\"Input must be a non-negative integer\" )\n\n _UpperCAmelCase \t\t\t\t\t\t\t= 0\n while number:\n # This way we arrive at next set bit (next 1) instead of looping\n # through each bit and checking for 1s hence the\n # loop won't run 32 times it will only run the number of `1` times\n number &= number - 1\n count += 1\n return count\n\n\nif __name__ == \"__main__\":\n import doctest\n\n doctest.testmod()\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":22,"string":"22"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":573,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\n\nfrom math import loga\n\n\n\n\ndef __A ( _lowercase\t):\n\n\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\t\tif a < 0:\n\t\t\t\t\t\t\t\t\t\traise ValueError('''Input value must be a positive integer'''\t)\n\t\t\t\t\telif isinstance(_lowercase , _lowercase\t):\n\t\t\t\t\t\t\t\t\t\traise TypeError('''Input value must be a \\'int\\' type'''\t)\n\t\t\t\t\treturn 0 if (a == 0) else int(loga(a & -a\t)\t)\n\n\nif __name__ == \"__main__\":\n\t\t\t\timport doctest\n\n\t\t\t\tdoctest.testmod()\n\n\n\n"},"code_codestyle":{"kind":"number","value":75,"string":"75"},"style_context":{"kind":"string","value":"\n\n\n\n\n\n\nimport warnings\n\nfrom ...utils import logging\nfrom .image_processing_dpt import DPTImageProcessor\n\n\n__A \t=\t\t\tlogging.get_logger(__name__)\n\n\n\n\n\nclass SCREAMING_SNAKE_CASE\t\t\t(\tsnake_case ):\n\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\t\t\t\t\t\t\tdef __init__(\t\t\t\t\tself:\t\t\t\tList[Any]\t\t\t\t\t\t, *__A:\t\t\t\tUnion[str, Any]\t\t\t\t\t\t, **__A:\t\t\t\tOptional[Any]\t\t\t\t) ->\t\t\tNone:\n\t\t\t\t\t\t\t\t\t\t\t\twarnings.warn(\n\t\t\t\t\t\t\t\t\t\t\t\t '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''\n\t\t\t\t\t\t\t\t\t\t\t\t ''' use DPTImageProcessor instead.'''\t\t\t\t\t\t, __A\t\t\t\t\t\t, )\n\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(*__A\t\t\t\t\t\t, **__A\t\t\t\t)\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":75,"string":"75"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":574,"cells":{"code":{"kind":"string","value":"\r\r\rimport sys\rimport tempfile\rimport unittest\rimport unittest.mock as mock\rfrom pathlib import Path\r\rfrom huggingface_hub import HfFolder, delete_repo\rfrom requests.exceptions import HTTPError\r\rfrom transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor\rfrom transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test\r\r\rsys.path.append(str(Path(__file__).parent.parent / '''utils'''))\r\rfrom test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402\r\r\rlowerCAmelCase =\t\tget_tests_dir('''fixtures''')\r\r\r\r\r\rclass A ( unittest.TestCase ):\r\r\r\t\t\t\t\t\t\tdef \t\t\t_A (self\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t# A mock response for an HTTP head request to emulate server down\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tmock.Mock()\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\t5_0_0\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\t{}\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tHTTPError\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\t{}\r\r\t\t\t\t\t\t\t\t\t# Download this model to make sure it's in the cache.\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t# Under the mock environment we get a 500 error when trying to reach the model.\r\t\t\t\t\t\t\t\t\twith mock.patch('requests.Session.request' ,\t\t\t\treturn_value=_lowerCAmelCase\t\t\t\t\t) as mock_head:\r\t\t\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t# This check we did call the fake head request\r\t\t\t\t\t\t\t\t\t\t\tmock_head.assert_called()\r\r\r\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t_A (self\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t# This test is for deprecated behavior and can be removed in v5\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained(\r\t\t\t\t\t\t\t\t\t 'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'\t\t\t\t\t)\r\r\r\r\r\r@is_staging_test\rclass A ( unittest.TestCase ):\r\r\r\t\t\t\t\t\t\t@classmethod\r\t\t\t\t\t\t\tdef \t\t\t_A (cls\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tTOKEN\r\t\t\t\t\t\t\t\t\tHfFolder.save_token(_lowerCAmelCase\t\t\t\t\t)\r\r\r\t\t\t\t\t\t\t@classmethod\r\t\t\t\t\t\t\tdef \t\t\t_A (cls\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\tdelete_repo(token=cls._token ,\t\t\t\trepo_id='test-feature-extractor'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\texcept HTTPError:\r\t\t\t\t\t\t\t\t\t\t\tpass\r\r\t\t\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\tdelete_repo(token=cls._token ,\t\t\t\trepo_id='valid_org/test-feature-extractor-org'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\texcept HTTPError:\r\t\t\t\t\t\t\t\t\t\t\tpass\r\r\t\t\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\tdelete_repo(token=cls._token ,\t\t\t\trepo_id='test-dynamic-feature-extractor'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\texcept HTTPError:\r\t\t\t\t\t\t\t\t\t\t\tpass\r\r\r\t\t\t\t\t\t\tdef \t\t\t_A (self\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\tfeature_extractor.push_to_hub('test-feature-extractor' ,\t\t\t\tuse_auth_token=self._token\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\tfor k, v in feature_extractor.__dict__.items():\r\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(_lowerCAmelCase ,\t\t\t\tgetattr(_lowerCAmelCase ,\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t# Reset repo\r\t\t\t\t\t\t\t\t\tdelete_repo(token=self._token ,\t\t\t\trepo_id='test-feature-extractor'\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t# Push to hub via save_pretrained\r\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\r\t\t\t\t\t\t\t\t\t\t\tfeature_extractor.save_pretrained(\r\t\t\t\t\t\t\t\t\t\t\t _lowerCAmelCase ,\t\t\t\trepo_id='test-feature-extractor' ,\t\t\t\tpush_to_hub=_lowerCAmelCase ,\t\t\t\tuse_auth_token=self._token\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\tfor k, v in feature_extractor.__dict__.items():\r\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(_lowerCAmelCase ,\t\t\t\tgetattr(_lowerCAmelCase ,\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t)\r\r\r\t\t\t\t\t\t\tdef \t\t\t_A (self\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\tfeature_extractor.push_to_hub('valid_org/test-feature-extractor' ,\t\t\t\tuse_auth_token=self._token\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\tfor k, v in feature_extractor.__dict__.items():\r\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(_lowerCAmelCase ,\t\t\t\tgetattr(_lowerCAmelCase ,\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t# Reset repo\r\t\t\t\t\t\t\t\t\tdelete_repo(token=self._token ,\t\t\t\trepo_id='valid_org/test-feature-extractor'\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t# Push to hub via save_pretrained\r\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\r\t\t\t\t\t\t\t\t\t\t\tfeature_extractor.save_pretrained(\r\t\t\t\t\t\t\t\t\t\t\t _lowerCAmelCase ,\t\t\t\trepo_id='valid_org/test-feature-extractor-org' ,\t\t\t\tpush_to_hub=_lowerCAmelCase ,\t\t\t\tuse_auth_token=self._token\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tWavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\tfor k, v in feature_extractor.__dict__.items():\r\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(_lowerCAmelCase ,\t\t\t\tgetattr(_lowerCAmelCase ,\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t)\r\r\r\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t_A (self\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\tCustomFeatureExtractor.register_for_auto_class()\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tCustomFeatureExtractor.from_pretrained(_lowerCAmelCase\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\tfeature_extractor.push_to_hub('test-dynamic-feature-extractor' ,\t\t\t\tuse_auth_token=self._token\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t# This has added the proper auto_map field to the config\r\t\t\t\t\t\t\t\t\tself.assertDictEqual(\r\t\t\t\t\t\t\t\t\t feature_extractor.auto_map ,\t\t\t\t{'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'} ,\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\t__lowercase=\t\t\tAutoFeatureExtractor.from_pretrained(\r\t\t\t\t\t\t\t\t\t f'{USER}/test-dynamic-feature-extractor' ,\t\t\t\ttrust_remote_code=_lowerCAmelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module\r\t\t\t\t\t\t\t\t\tself.assertEqual(new_feature_extractor.__class__.__name__ ,\t\t\t\t'CustomFeatureExtractor'\t\t\t\t\t)\r\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":295,"string":"295"},"style_context":{"kind":"string","value":"\n\n\n\n\n\n'''simple docstring'''\n\n\nimport time\nfrom dataclasses import dataclass\nfrom multiprocessing import Pool\nfrom unittest import TestCase\nfrom unittest.mock import patch\n\nimport multiprocess\nimport numpy as np\nimport pytest\n\nfrom datasets.utils.py_utils import (\n NestedDataStructure,\n asdict,\n iflatmap_unordered,\n map_nested,\n temp_seed,\n temporary_assignment,\n zip_dict,\n)\n\nfrom .utils import require_tf, require_torch\n\n\n\n\ndef __a ( UpperCAmelCase\t\t\t)\t\t\t\t\t->Tuple: # picklable for multiprocessing\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\treturn x.sum()\n\n\n\n\ndef __a ( UpperCAmelCase\t\t\t)\t\t\t\t\t->int: # picklable for multiprocessing\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\treturn i + 1\n\n\n\n@dataclass\nclass \t\t\t\t\t\t\t__UpperCAmelCase :\n\n\n\n\n\n\n\n\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t__lowerCAmelCase =\t\t\t42\n\t\t__lowerCAmelCase =\t\t\t42\n\n\n\n\n\n\nclass \t\t\t\t\t\t\t__UpperCAmelCase (\t\t\t\tA__ ):\n\n\n\n\n\n\n\n\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\tdef \t\t\t\tA (self :\t\t\t\t\tTuple\t\t\t\t\t):\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t[]\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t1\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t[1, 2]\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": 1, \"\"\"b\"\"\": 2}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": [1, 2], \"\"\"b\"\"\": [3, 4]}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": {\"\"\"1\"\"\": 1}, \"\"\"b\"\"\": 2}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": 1, \"\"\"b\"\"\": 2, \"\"\"c\"\"\": 3, \"\"\"d\"\"\": 4}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t[]\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t2\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t[2, 3]\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": 2, \"\"\"b\"\"\": 3}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": [2, 3], \"\"\"b\"\"\": [4, 5]}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": {\"\"\"1\"\"\": 2}, \"\"\"b\"\"\": 3}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": 2, \"\"\"b\"\"\": 3, \"\"\"c\"\"\": 4, \"\"\"d\"\"\": 5}\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t2\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": np.eye(2\t\t\t\t\t), \"\"\"b\"\"\": np.zeros(3\t\t\t\t\t), \"\"\"c\"\"\": np.ones(2\t\t\t\t\t)}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": 2, \"\"\"b\"\"\": 0, \"\"\"c\"\"\": 2}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\n\t\t\t \"\"\"a\"\"\": np.eye(2\t\t\t\t\t).astype(_lowerCAmelCase\t\t\t\t\t),\n\t\t\t \"\"\"b\"\"\": np.zeros(3\t\t\t\t\t).astype(_lowerCAmelCase\t\t\t\t\t),\n\t\t\t \"\"\"c\"\"\": np.ones(2\t\t\t\t\t).astype(_lowerCAmelCase\t\t\t\t\t),\n\t\t\t}\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tmap_numpy=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(\n\t\t\t {k: v.tolist() for k, v in map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tmap_numpy=_lowerCAmelCase\t\t\t\t\t).items()}\t\t\t\t\t,\t\t\t\t\t{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}\t\t\t\t\t,\t\t\t\t\t)\n\t\t\tself.assertEqual(map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tmap_numpy=_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertEqual(\n\t\t\t {k: v.tolist() for k, v in map_nested(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tmap_numpy=_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t).items()}\t\t\t\t\t,\t\t\t\t\t{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}\t\t\t\t\t,\t\t\t\t\t)\n\t\t\twith self.assertRaises(_lowerCAmelCase\t\t\t\t\t): # can't pickle a local lambda\n\t\t\t\tmap_nested(lambda _lowerCAmelCase\t\t\t\t\t: x + 1\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\tnum_proc=_lowerCAmelCase\t\t\t\t\t)\n\n\n\n\n\n\n\n\t\tdef \t\t\t\tA (self :\t\t\t\t\tList[Any]\t\t\t\t\t):\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": 1, \"\"\"b\"\"\": 2}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": 3, \"\"\"b\"\"\": 4}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": 5, \"\"\"b\"\"\": 6}\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tsorted([(\"\"\"a\"\"\", (1, 3, 5)), (\"\"\"b\"\"\", (2, 4, 6))]\t\t\t\t\t)\n\t\t\tself.assertEqual(sorted(zip_dict(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\n\n\n\n\n\n\n\t\tdef \t\t\t\tA (self :\t\t\t\t\tUnion[str, Any]\t\t\t\t\t):\n\n\n\n\t\t\tclass \t\t\t\t\t\t\t__UpperCAmelCase :\n\n\n\n\n\n\n\n\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t\t\t\t__lowerCAmelCase =\t\t\t'''bar'''\n\n\n\n\n\n\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tFoo()\n\t\t\tself.assertEqual(foo.my_attr\t\t\t\t\t,\t\t\t\t\t\"\"\"bar\"\"\"\t\t\t\t\t)\n\t\t\twith temporary_assignment(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t\"\"\"my_attr\"\"\"\t\t\t\t\t,\t\t\t\t\t\"\"\"BAR\"\"\"\t\t\t\t\t):\n\t\t\t\tself.assertEqual(foo.my_attr\t\t\t\t\t,\t\t\t\t\t\"\"\"BAR\"\"\"\t\t\t\t\t)\n\t\t\tself.assertEqual(foo.my_attr\t\t\t\t\t,\t\t\t\t\t\"\"\"bar\"\"\"\t\t\t\t\t)\n\n\n\n\n\n\n@pytest.mark.parametrize(\n \"\"\"iterable_length, num_proc, expected_num_proc\"\"\"\t\t\t\t,\t[\n (1, None, 1),\n (1, 1, 1),\n (2, None, 1),\n (2, 1, 1),\n (2, 2, 1),\n (2, 3, 1),\n (3, 2, 1),\n (16, 16, 16),\n (16, 17, 16),\n (17, 16, 16),\n ]\t\t\t\t,\t)\ndef __a ( UpperCAmelCase\t\t\t\t,\tUpperCAmelCase\t\t\t\t,\tUpperCAmelCase\t\t\t)\t\t\t\t\t->Any:\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\twith patch(\"\"\"datasets.utils.py_utils._single_map_nested\"\"\"\t\t\t) as mock_single_map_nested, patch(\n\t \"\"\"datasets.parallel.parallel.Pool\"\"\"\t\t\t) as mock_multiprocessing_pool:\n\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t{f\"\"\"{i}\"\"\": i for i in range(UpperCAmelCase\t\t\t)}\n\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tmap_nested(lambda UpperCAmelCase\t\t\t: x + 10\t\t\t\t,\tUpperCAmelCase\t\t\t\t,\tnum_proc=UpperCAmelCase\t\t\t\t,\tparallel_min_length=16\t\t\t)\n\t\tif expected_num_proc == 1:\n\t\t\tassert mock_single_map_nested.called\n\t\t\tassert not mock_multiprocessing_pool.called\n\t\telse:\n\t\t\tassert not mock_single_map_nested.called\n\t\t\tassert mock_multiprocessing_pool.called\n\t\t\tassert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc\n\n\n\nclass \t\t\t\t\t\t\t__UpperCAmelCase (\t\t\t\tA__ ):\n\n\n\n\n\n\n\n\t\t'''simple docstring'''\n\n\n\n\n\n\n\n\t\t@require_tf\n\t\tdef \t\t\t\tA (self :\t\t\t\t\tDict\t\t\t\t\t):\n\t\t\timport tensorflow as tf\n\t\t\tfrom tensorflow.keras import layers\n\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tlayers.Dense(2\t\t\t\t\t)\n\n\t\t\tdef gen_random_output():\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\ttf.random.uniform((1, 3)\t\t\t\t\t)\n\t\t\t\treturn model(_lowerCAmelCase\t\t\t\t\t).numpy()\n\n\t\t\twith temp_seed(42\t\t\t\t\t,\t\t\t\t\tset_tensorflow=_lowerCAmelCase\t\t\t\t\t):\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\t\t\twith temp_seed(42\t\t\t\t\t,\t\t\t\t\tset_tensorflow=_lowerCAmelCase\t\t\t\t\t):\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\n\t\t\tnp.testing.assert_equal(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertGreater(np.abs(outa - outa\t\t\t\t\t).sum()\t\t\t\t\t,\t\t\t\t\t0\t\t\t\t\t)\n\n\n\n\n\n\n\n\t\t@require_torch\n\t\tdef \t\t\t\tA (self :\t\t\t\t\tTuple\t\t\t\t\t):\n\t\t\timport torch\n\n\t\t\tdef gen_random_output():\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\ttorch.nn.Linear(3\t\t\t\t\t,\t\t\t\t\t2\t\t\t\t\t)\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\ttorch.rand(1\t\t\t\t\t,\t\t\t\t\t3\t\t\t\t\t)\n\t\t\t\treturn model(_lowerCAmelCase\t\t\t\t\t).detach().numpy()\n\n\t\t\twith temp_seed(42\t\t\t\t\t,\t\t\t\t\tset_pytorch=_lowerCAmelCase\t\t\t\t\t):\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\t\t\twith temp_seed(42\t\t\t\t\t,\t\t\t\t\tset_pytorch=_lowerCAmelCase\t\t\t\t\t):\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\n\t\t\tnp.testing.assert_equal(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertGreater(np.abs(outa - outa\t\t\t\t\t).sum()\t\t\t\t\t,\t\t\t\t\t0\t\t\t\t\t)\n\n\n\n\n\n\n\n\t\tdef \t\t\t\tA (self :\t\t\t\t\tstr\t\t\t\t\t):\n\t\t\tdef gen_random_output():\n\t\t\t\treturn np.random.rand(1\t\t\t\t\t,\t\t\t\t\t3\t\t\t\t\t)\n\n\t\t\twith temp_seed(42\t\t\t\t\t):\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\t\t\twith temp_seed(42\t\t\t\t\t):\n\t\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\t\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tgen_random_output()\n\n\t\t\tnp.testing.assert_equal(_lowerCAmelCase\t\t\t\t\t,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t)\n\t\t\tself.assertGreater(np.abs(outa - outa\t\t\t\t\t).sum()\t\t\t\t\t,\t\t\t\t\t0\t\t\t\t\t)\n\n\n\n\n\n\n@pytest.mark.parametrize(\"\"\"input_data\"\"\"\t\t\t\t,\t[{}]\t\t\t)\ndef __a ( UpperCAmelCase\t\t\t)\t\t\t\t\t->List[str]:\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\tA\t\t\t\t\t\t\t =\t\t\t\t\tNestedDataStructure(UpperCAmelCase\t\t\t).data\n\tassert output_data == input_data\n\n\n\n\n@pytest.mark.parametrize(\n \"\"\"data, expected_output\"\"\"\t\t\t\t,\t[\n ({}, []),\n ([], []),\n (\"\"\"foo\"\"\", [\"\"\"foo\"\"\"]),\n ([\"\"\"foo\"\"\", \"\"\"bar\"\"\"], [\"\"\"foo\"\"\", \"\"\"bar\"\"\"]),\n ([[\"\"\"foo\"\"\", \"\"\"bar\"\"\"]], [\"\"\"foo\"\"\", \"\"\"bar\"\"\"]),\n ([[[\"\"\"foo\"\"\"], [\"\"\"bar\"\"\"]]], [\"\"\"foo\"\"\", \"\"\"bar\"\"\"]),\n ([[[\"\"\"foo\"\"\"], \"\"\"bar\"\"\"]], [\"\"\"foo\"\"\", \"\"\"bar\"\"\"]),\n ({\"\"\"a\"\"\": 1, \"\"\"b\"\"\": 2}, [1, 2]),\n ({\"\"\"a\"\"\": [1, 2], \"\"\"b\"\"\": [3, 4]}, [1, 2, 3, 4]),\n ({\"\"\"a\"\"\": [[1, 2]], \"\"\"b\"\"\": [[3, 4]]}, [1, 2, 3, 4]),\n ({\"\"\"a\"\"\": [[1, 2]], \"\"\"b\"\"\": [3, 4]}, [1, 2, 3, 4]),\n ({\"\"\"a\"\"\": [[[1], [2]]], \"\"\"b\"\"\": [[[3], [4]]]}, [1, 2, 3, 4]),\n ({\"\"\"a\"\"\": [[[1], [2]]], \"\"\"b\"\"\": [[3, 4]]}, [1, 2, 3, 4]),\n ({\"\"\"a\"\"\": [[[1], [2]]], \"\"\"b\"\"\": [3, 4]}, [1, 2, 3, 4]),\n ({\"\"\"a\"\"\": [[[1], [2]]], \"\"\"b\"\"\": [3, [4]]}, [1, 2, 3, 4]),\n ({\"\"\"a\"\"\": {\"\"\"1\"\"\": 1}, \"\"\"b\"\"\": 2}, [1, 2]),\n ({\"\"\"a\"\"\": {\"\"\"1\"\"\": [1]}, \"\"\"b\"\"\": 2}, [1, 2]),\n ({\"\"\"a\"\"\": {\"\"\"1\"\"\": [1]}, \"\"\"b\"\"\": [2]}, [1, 2]),\n ]\t\t\t\t,\t)\ndef __a ( UpperCAmelCase\t\t\t\t,\tUpperCAmelCase\t\t\t)\t\t\t\t\t->List[Any]:\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\tA\t\t\t\t\t\t\t =\t\t\t\t\tNestedDataStructure(UpperCAmelCase\t\t\t).flatten()\n\tassert output == expected_output\n\n\n\n\ndef __a ( )\t\t\t\t\t->Optional[Any]:\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\tA\t\t\t\t\t\t\t =\t\t\t\t\tA(x=1\t\t\t\t,\ty=\"\"\"foobar\"\"\"\t\t\t)\n\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"x\"\"\": 1, \"\"\"y\"\"\": \"\"\"foobar\"\"\"}\n\tassert asdict(UpperCAmelCase\t\t\t) == expected_output\n\n\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": {\"\"\"b\"\"\": A(x=10\t\t\t\t,\ty=\"\"\"foo\"\"\"\t\t\t)}, \"\"\"c\"\"\": [A(x=20\t\t\t\t,\ty=\"\"\"bar\"\"\"\t\t\t)]}\n\tA\t\t\t\t\t\t\t =\t\t\t\t\t{\"\"\"a\"\"\": {\"\"\"b\"\"\": {\"\"\"x\"\"\": 10, \"\"\"y\"\"\": \"\"\"foo\"\"\"}}, \"\"\"c\"\"\": [{\"\"\"x\"\"\": 20, \"\"\"y\"\"\": \"\"\"bar\"\"\"}]}\n\tassert asdict(UpperCAmelCase\t\t\t) == expected_output\n\n\twith pytest.raises(UpperCAmelCase\t\t\t):\n\t\tasdict([1, A(x=10\t\t\t\t,\ty=\"\"\"foo\"\"\"\t\t\t)]\t\t\t)\n\n\n\n\ndef __a ( UpperCAmelCase\t\t\t)\t\t\t\t\t->Tuple:\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\treturn text.split()\n\n\n\n\ndef __a ( UpperCAmelCase\t\t\t)\t\t\t\t\t->List[str]:\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\tyield (time.time(), content)\n\ttime.sleep(2\t\t\t)\n\tyield (time.time(), content)\n\n\n\n\ndef __a ( )\t\t\t\t\t->Optional[int]:\n\n\n\n\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n\n\twith Pool(2\t\t\t) as pool:\n\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tlist(iflatmap_unordered(UpperCAmelCase\t\t\t\t,\t_split_text\t\t\t\t,\tkwargs_iterable=[{\"\"\"text\"\"\": \"\"\"hello there\"\"\"}] * 10\t\t\t)\t\t\t)\n\t\tassert out.count(\"\"\"hello\"\"\"\t\t\t) == 10\n\t\tassert out.count(\"\"\"there\"\"\"\t\t\t) == 10\n\t\tassert len(UpperCAmelCase\t\t\t) == 20\n\n\t# check multiprocess from pathos (uses dill for pickling)\n\twith multiprocess.Pool(2\t\t\t) as pool:\n\t\tA\t\t\t\t\t\t\t =\t\t\t\t\tlist(iflatmap_unordered(UpperCAmelCase\t\t\t\t,\t_split_text\t\t\t\t,\tkwargs_iterable=[{\"\"\"text\"\"\": \"\"\"hello there\"\"\"}] * 10\t\t\t)\t\t\t)\n\t\tassert out.count(\"\"\"hello\"\"\"\t\t\t) == 10\n\t\tassert out.count(\"\"\"there\"\"\"\t\t\t) == 10\n\t\tassert len(UpperCAmelCase\t\t\t) == 20\n\n\t# check that we get items as fast as possible\n\twith Pool(2\t\t\t) as pool:\n\t\tA\t\t\t\t\t\t\t =\t\t\t\t\t[]\n\t\tfor yield_time, content in iflatmap_unordered(\n\t\t UpperCAmelCase\t\t\t\t,\t_aseconds_generator_of_aitems_with_timing\t\t\t\t,\tkwargs_iterable=[{\"\"\"content\"\"\": \"\"\"a\"\"\"}, {\"\"\"content\"\"\": \"\"\"b\"\"\"}]\t\t\t):\n\t\t\tassert yield_time < time.time() + 0.1, \"we should each item directly after it was yielded\"\n\t\t\tout.append(UpperCAmelCase\t\t\t)\n\t\tassert out.count(\"\"\"a\"\"\"\t\t\t) == 2\n\t\tassert out.count(\"\"\"b\"\"\"\t\t\t) == 2\n\t\tassert len(UpperCAmelCase\t\t\t) == 4\n\n"},"style_context_codestyle":{"kind":"number","value":258,"string":"258"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":575,"cells":{"code":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\rimport os\rfrom shutil import copyfile\rfrom typing import List, Optional, Tuple\r\rfrom ...tokenization_utils import AddedToken\rfrom ...tokenization_utils_fast import PreTrainedTokenizerFast\rfrom ...utils import is_sentencepiece_available, logging\r\r\rif is_sentencepiece_available():\r from .tokenization_rembert import RemBertTokenizer\relse:\r A \t\t\t\t\t\t\t=\t\tNone\r\rA \t\t\t\t\t\t\t=\t\tlogging.get_logger(__name__)\rA \t\t\t\t\t\t\t=\t\t{\"vocab_file\": \"sentencepiece.model\", \"tokenizer_file\": \"tokenizer.json\"}\r\rA \t\t\t\t\t\t\t=\t\t{\r \"vocab_file\": {\r \"google/rembert\": \"https://huggingface.co/google/rembert/resolve/main/sentencepiece.model\",\r },\r \"tokenizer_file\": {\r \"google/rembert\": \"https://huggingface.co/google/rembert/resolve/main/tokenizer.json\",\r },\r}\r\rA \t\t\t\t\t\t\t=\t\t{\r \"google/rembert\": 2_5_6,\r}\r\rA \t\t\t\t\t\t\t=\t\t\"▁\"\r\r\r\rclass \t\t\t\tSCREAMING_SNAKE_CASE__ ( UpperCAmelCase__\t\t\t):\r __lowerCAmelCase : str\t\t\t\t\t\t=\t\t\t\t\t\tVOCAB_FILES_NAMES\r __lowerCAmelCase : List[str]\t\t\t\t\t\t=\t\t\t\t\t\tPRETRAINED_VOCAB_FILES_MAP\r __lowerCAmelCase : List[Any]\t\t\t\t\t\t=\t\t\t\t\t\tPRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r __lowerCAmelCase : Union[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\tRemBertTokenizer\r\r def __init__( self\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=None\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=None\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=True\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=True\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=False\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=\"[CLS]\"\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=\"[SEP]\"\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=\"\"\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=\"[SEP]\"\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=\"\"\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=\"[CLS]\"\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE=\"[MASK]\"\t\t\t\t,\t\t\t\t\t**_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t) ->\t\t\t\tList[Any]:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r UpperCAmelCase : List[str] = AddedToken(_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tlstrip=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\trstrip=_SCREAMING_SNAKE_CASE\t\t\t\t\t\t) if isinstance(_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t\t\t) else mask_token\r\r super().__init__(\r _SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\ttokenizer_file=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tdo_lower_case=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tremove_space=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tkeep_accents=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tbos_token=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\teos_token=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tunk_token=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tsep_token=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tpad_token=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tcls_token=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\tmask_token=_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t**_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t)\r\r UpperCAmelCase : Dict = do_lower_case\r UpperCAmelCase : Dict = remove_space\r UpperCAmelCase : Any = keep_accents\r UpperCAmelCase : Optional[Any] = vocab_file\r UpperCAmelCase : List[str] = False if not self.vocab_file else True\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE = None\t\t\t\t\t\t) ->\t\t\t\tList[int]:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r UpperCAmelCase : List[str] = [self.sep_token_id]\r UpperCAmelCase : str = [self.cls_token_id]\r if token_ids_a is None:\r return cls + token_ids_a + sep\r return cls + token_ids_a + sep + token_ids_a + sep\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE = None\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE = False\t\t\t\t\t\t) ->\t\t\t\tList[int]:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r if already_has_special_tokens:\r if token_ids_a is not None:\r raise ValueError(\r \"\"\"You should not supply a second sequence if the provided sequence of \"\"\"\r \"\"\"ids is already formatted with special tokens for the model.\"\"\"\t\t\t\t\t\t)\r return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]\r\r if token_ids_a is not None:\r return [1] + ([0] * len(_SCREAMING_SNAKE_CASE\t\t\t\t\t\t)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE\t\t\t\t\t\t)) + [1]\r return [1] + ([0] * len(_SCREAMING_SNAKE_CASE\t\t\t\t\t\t)) + [1]\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE = None\t\t\t\t\t\t) ->\t\t\t\tList[int]:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r UpperCAmelCase : str = [self.sep_token_id]\r UpperCAmelCase : Any = [self.cls_token_id]\r\r if token_ids_a is None:\r return len(cls + token_ids_a + sep\t\t\t\t\t\t) * [0]\r return len(cls + token_ids_a + sep\t\t\t\t\t\t) * [0] + len(token_ids_a + sep\t\t\t\t\t\t) * [1]\r\r\r\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE = None\t\t\t\t\t\t) ->\t\t\t\tTuple[str]:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r if not os.path.isdir(_SCREAMING_SNAKE_CASE\t\t\t\t\t\t):\r logger.error(\"\"\"Vocabulary path ({}) should be a directory\"\"\".format(_SCREAMING_SNAKE_CASE\t\t\t\t\t\t)\t\t\t\t\t\t)\r return\r UpperCAmelCase : Dict = os.path.join(\r _SCREAMING_SNAKE_CASE\t\t\t\t,\t\t\t\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"]\t\t\t\t\t\t)\r\r if os.path.abspath(self.vocab_file\t\t\t\t\t\t) != os.path.abspath(_SCREAMING_SNAKE_CASE\t\t\t\t\t\t):\r copyfile(self.vocab_file\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t\t\t)\r\r return (out_vocab_file,)\r"},"code_codestyle":{"kind":"number","value":371,"string":"371"},"style_context":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\rfrom __future__ import annotations\r\rfrom typing import Any\r\r\r\rclass \t\t\t\tSCREAMING_SNAKE_CASE__ :\r\r def __init__( self\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE = 6\t\t\t\t\t\t) ->\t\t\t\tNone:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r UpperCAmelCase : Node | None = None\r UpperCAmelCase : Node | None = None\r self.create_linked_list(_SCREAMING_SNAKE_CASE\t\t\t\t\t\t)\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t\t\t) ->\t\t\t\tNone:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r UpperCAmelCase : Union[str, Any] = Node()\r UpperCAmelCase : Dict = current_node\r UpperCAmelCase : Any = current_node\r UpperCAmelCase : Optional[int] = current_node\r for _ in range(1\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t\t\t):\r UpperCAmelCase : Optional[Any] = Node()\r UpperCAmelCase : Tuple = current_node\r UpperCAmelCase : Any = previous_node\r UpperCAmelCase : List[Any] = current_node\r UpperCAmelCase : List[str] = self.front\r UpperCAmelCase : Tuple = previous_node\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t\t\t) ->\t\t\t\tbool:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r return (\r self.front == self.rear\r and self.front is not None\r and self.front.data is None\r )\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t\t\t) ->\t\t\t\tAny | None:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r self.check_can_perform_operation()\r return self.front.data if self.front else None\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t,\t\t\t\t\t_SCREAMING_SNAKE_CASE\t\t\t\t\t\t) ->\t\t\t\tNone:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r if self.rear is None:\r return\r\r self.check_is_full()\r if not self.is_empty():\r UpperCAmelCase : Optional[Any] = self.rear.next\r if self.rear:\r UpperCAmelCase : Optional[int] = data\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t\t\t) ->\t\t\t\tAny:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r self.check_can_perform_operation()\r if self.rear is None or self.front is None:\r return None\r if self.front == self.rear:\r UpperCAmelCase : Tuple = self.front.data\r UpperCAmelCase : int = None\r return data\r\r UpperCAmelCase : Dict = self.front\r UpperCAmelCase : Tuple = old_front.next\r UpperCAmelCase : str = old_front.data\r UpperCAmelCase : int = None\r return data\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t\t\t) ->\t\t\t\tNone:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r if self.is_empty():\r raise Exception(\"\"\"Empty Queue\"\"\"\t\t\t\t\t\t)\r\r\r\r\r def SCREAMING_SNAKE_CASE\t\t\t\t\t( self\t\t\t\t\t\t) ->\t\t\t\tNone:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r if self.rear and self.rear.next == self.front:\r raise Exception(\"\"\"Full Queue\"\"\"\t\t\t\t\t\t)\r\r\r\rclass \t\t\t\tSCREAMING_SNAKE_CASE__ :\r\r def __init__( self\t\t\t\t\t\t) ->\t\t\t\tNone:\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r UpperCAmelCase : Any | None = None\r UpperCAmelCase : Node | None = None\r UpperCAmelCase : Node | None = None\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"style_context_codestyle":{"kind":"number","value":76,"string":"76"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":576,"cells":{"code":{"kind":"string","value":"\nimport argparse\nimport os\nfrom pathlib import Path\nfrom typing import Dict\n\nimport tensorflow as tf\nimport torch\nfrom tqdm import tqdm\n\nfrom transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer\nfrom transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params\n\n\na__\t\t\t\t\t\t\t:\tOptional[Any] \t\t\t\t\t\t=\t\t\t[\n # replace left string with right string to get the relevant state_dict key (identical state dict to bart)\n ['''memory_attention''', '''encoder_attn'''],\n ['''attention''', '''attn'''],\n ['''/''', '''.'''],\n ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],\n ['''.LayerNorm.beta''', '''_layer_norm.bias'''],\n ['''r.layer_''', '''r.layers.'''],\n ['''output_proj''', '''out_proj'''],\n ['''ffn.dense_1.''', '''fc2.'''],\n ['''ffn.dense.''', '''fc1.'''],\n ['''ffn_layer_norm''', '''final_layer_norm'''],\n ['''kernel''', '''weight'''],\n ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],\n ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],\n ['''embeddings.weights''', '''shared.weight'''],\n]\n\n\n\n\n\n\n\ndef \t\t\t\t\t\t\tUpperCAmelCase_( a__\t\t\t\t\t):\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n for pegasus_name, hf_name in PATTERNS:\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t= k.replace(a__ ,\t\ta__\t\t\t\t\t)\n return k\n\n\n\n\n\n\n\ndef \t\t\t\t\t\t\tUpperCAmelCase_( a__ ,\t\ta__\t\t\t\t\t):\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tTuple\t\t\t\t\t\t\t\t= DEFAULTS.copy()\n cfg_kwargs.update(a__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t= PegasusConfig(**a__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t= PegasusForConditionalGeneration(a__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t= torch_model.model.state_dict()\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= {}\n for k, v in tf_weights.items():\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t= rename_state_dict_key(a__\t\t\t\t\t)\n if new_k not in sd:\n raise ValueError(F\"\"\"could not find new key {new_k} in state dict. (converted from {k})\"\"\"\t\t\t\t\t)\n\n if \"dense\" in k or \"proj\" in new_k:\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t= v.T\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tTuple\t\t\t\t\t\t\t\t= torch.tensor(a__ ,\t\tdtype=sd[new_k].dtype\t\t\t\t\t)\n assert v.shape == sd[new_k].shape, F\"\"\"{new_k}, {k}, {v.shape}, {sd[new_k].shape}\"\"\"\n # make sure embedding.padding_idx is respected\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tTuple\t\t\t\t\t\t\t\t= torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t= mapping['''shared.weight''']\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t= mapping['''shared.weight''']\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t= {k: torch.zeros_like(a__\t\t\t\t\t) for k, v in sd.items() if k.endswith('''bias'''\t\t\t\t\t) and k not in mapping}\n mapping.update(**a__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t,\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t= torch_model.model.load_state_dict(a__ ,\t\tstrict=a__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t= [\n k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']\n ]\n assert unexpected_missing == [], F\"\"\"no matches found for the following torch keys {unexpected_missing}\"\"\"\n assert extra == [], F\"\"\"no matches found for the following tf keys {extra}\"\"\"\n return torch_model\n\n\n\n\n\n\n\ndef \t\t\t\t\t\t\tUpperCAmelCase_( a__=\"./ckpt/aeslc/model.ckpt-32000\"\t\t\t\t\t):\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= tf.train.list_variables(a__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= {}\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t= ['''Adafactor''', '''global_step''']\n for name, shape in tqdm(a__ ,\t\tdesc='''converting tf checkpoint to dict'''\t\t\t\t\t):\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t= any(pat in name for pat in ignore_name\t\t\t\t\t)\n if skip_key:\n continue\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t= tf.train.load_variable(a__ ,\t\ta__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t= array\n return tf_weights\n\n\n\n\n\n\n\ndef \t\t\t\t\t\t\tUpperCAmelCase_( a__ ,\t\ta__\t\t\t\t\t):\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= Path(a__\t\t\t\t\t).parent.name\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t= task_specific_params[F\"\"\"summarization_{dataset}\"\"\"]['''max_position_embeddings''']\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t= PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' ,\t\tmodel_max_length=a__\t\t\t\t\t)\n assert tok.model_max_length == desired_max_model_length\n tok.save_pretrained(a__\t\t\t\t\t)\n\n # convert model\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t= get_tf_weights_as_numpy(a__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= task_specific_params[F\"\"\"summarization_{dataset}\"\"\"]\n if dataset == \"large\":\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t= task_specific_params\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= convert_pegasus(a__ ,\t\ta__\t\t\t\t\t)\n torch_model.save_pretrained(a__\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t= torch_model.state_dict()\n sd.pop('''model.decoder.embed_positions.weight'''\t\t\t\t\t)\n sd.pop('''model.encoder.embed_positions.weight'''\t\t\t\t\t)\n torch.save(a__ ,\t\tPath(a__\t\t\t\t\t) / '''pytorch_model.bin'''\t\t\t\t\t)\n\n\nif __name__ == \"__main__\":\n a__\t\t\t\t\t\t\t:\tUnion[str, Any] \t\t\t\t\t\t=\t\t\targparse.ArgumentParser()\n # Required parameters\n parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')\n parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')\n a__\t\t\t\t\t\t\t:\tList[str] \t\t\t\t\t\t=\t\t\tparser.parse_args()\n if args.save_dir is None:\n a__\t\t\t\t\t\t\t:\tAny \t\t\t\t\t\t=\t\t\tPath(args.tf_ckpt_path).parent.name\n a__\t\t\t\t\t\t\t:\tint \t\t\t\t\t\t=\t\t\tos.path.join('''pegasus''', dataset)\n convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)\n"},"code_codestyle":{"kind":"number","value":313,"string":"313"},"style_context":{"kind":"string","value":"\nfrom typing import TYPE_CHECKING\n\nfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available\n\n\na__\t\t\t\t\t\t\t:\tTuple \t\t\t\t\t\t=\t\t\t{'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}\n\ntry:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n pass\nelse:\n a__\t\t\t\t\t\t\t:\tDict \t\t\t\t\t\t=\t\t\t[\n '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',\n '''WavLMForAudioFrameClassification''',\n '''WavLMForCTC''',\n '''WavLMForSequenceClassification''',\n '''WavLMForXVector''',\n '''WavLMModel''',\n '''WavLMPreTrainedModel''',\n ]\n\nif TYPE_CHECKING:\n from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig\n\n try:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n pass\n else:\n from .modeling_wavlm import (\n WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,\n WavLMForAudioFrameClassification,\n WavLMForCTC,\n WavLMForSequenceClassification,\n WavLMForXVector,\n WavLMModel,\n WavLMPreTrainedModel,\n )\n\nelse:\n import sys\n\n a__\t\t\t\t\t\t\t:\tDict \t\t\t\t\t\t=\t\t\t_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)\n"},"style_context_codestyle":{"kind":"number","value":313,"string":"313"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":577,"cells":{"code":{"kind":"string","value":"\n\n\n\n\nfrom sklearn.metrics import fa_score, matthews_corrcoef\n\nimport datasets\n\nfrom .record_evaluation import evaluate as evaluate_record\n\n\n_lowerCamelCase :\t\t\t\t\tOptional[int] = \"\\\\n@article{wang2019superglue,\\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\\n journal={arXiv preprint arXiv:1905.00537},\\n year={2019}\\n}\\n\"\n\n_lowerCamelCase :\t\t\t\t\tList[str] = \"\\\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\\nGLUE with a new set of more difficult language understanding tasks, improved\\nresources, and a new public leaderboard.\\n\"\n\n_lowerCamelCase :\t\t\t\t\tOptional[int] = \"\\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\\nArgs:\\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\\n - for 'record': list of question-answer dictionaries with the following keys:\\n - 'idx': index of the question as specified by the dataset\\n - 'prediction_text': the predicted answer text\\n - for 'multirc': list of question-answer dictionaries with the following keys:\\n - 'idx': index of the question-answer pair as specified by the dataset\\n - 'prediction': the predicted answer label\\n - otherwise: list of predicted labels\\n references: list of reference labels. Depending on the SuperGLUE subset:\\n - for 'record': list of question-answers dictionaries with the following keys:\\n - 'idx': index of the question as specified by the dataset\\n - 'answers': list of possible answers\\n - otherwise: list of reference labels\\nReturns: depending on the SuperGLUE subset:\\n - for 'record':\\n - 'exact_match': Exact match between answer and gold answer\\n - 'f1': F1 score\\n - for 'multirc':\\n - 'exact_match': Exact match between answer and gold answer\\n - 'f1_m': Per-question macro-F1 score\\n - 'f1_a': Average F1 score over all answers\\n - for 'axb':\\n 'matthews_correlation': Matthew Correlation\\n - for 'cb':\\n - 'accuracy': Accuracy\\n - 'f1': F1 score\\n - for all others:\\n - 'accuracy': Accuracy\\nExamples:\\n\\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\\\"copa\\\", \\\"rte\\\", \\\"wic\\\", \\\"wsc\\\", \\\"wsc.fixed\\\", \\\"boolq\\\", \\\"axg\\\"]\\n >>> predictions = [0, 1]\\n >>> references = [0, 1]\\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\\n >>> print(results)\\n {'accuracy': 1.0}\\n\\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\\n >>> predictions = [0, 1]\\n >>> references = [0, 1]\\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\\n >>> print(results)\\n {'accuracy': 1.0, 'f1': 1.0}\\n\\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\\n >>> print(results)\\n {'exact_match': 1.0, 'f1': 1.0}\\n\\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\\n >>> references = [0, 1]\\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\\n >>> print(results)\\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\\n\\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\\n >>> references = [0, 1]\\n >>> predictions = [0, 1]\\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\\n >>> print(results)\\n {'matthews_correlation': 1.0}\\n\"\n\n\n\n\ndef \t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t(UpperCamelCase_\t:\tDict , UpperCamelCase_\t:\tint ):\n\t\t\t\t'''simple docstring'''\n\t\t\t\treturn float((preds == labels).mean() )\n\n\n\n\ndef \t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t(UpperCamelCase_\t:\tList[str] , UpperCamelCase_\t:\tDict , UpperCamelCase_\t:\tDict=\"binary\" ):\n\t\t\t\t'''simple docstring'''\n\t\t\t\t_lowerCAmelCase : str = simple_accuracy(UpperCamelCase_ , UpperCamelCase_ )\n\t\t\t\t_lowerCAmelCase : Tuple = float(fa_score(y_true=UpperCamelCase_ , y_pred=UpperCamelCase_ , average=UpperCamelCase_ ) )\n\t\t\t\treturn {\n\t\t\t\t \"accuracy\": acc,\n\t\t\t\t \"f1\": fa,\n\t\t\t\t}\n\n\n\n\ndef \t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t(UpperCamelCase_\t:\tOptional[Any] , UpperCamelCase_\t:\tTuple ):\n\t\t\t\t'''simple docstring'''\n\t\t\t\t_lowerCAmelCase : Optional[Any] = {}\n\t\t\t\tfor id_pred, label in zip(UpperCamelCase_ , UpperCamelCase_ ):\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = F\"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}\"\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[Any] = id_pred[\"\"\"prediction\"\"\"]\n\t\t\t\t\t\t\t\tif question_id in question_map:\n\t\t\t\t\t\t\t\t\t\t\t\tquestion_map[question_id].append((pred, label) )\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Tuple = [(pred, label)]\n\t\t\t\t_lowerCAmelCase : List[str] = [], []\n\t\t\t\tfor question, preds_labels in question_map.items():\n\t\t\t\t\t\t\t\t_lowerCAmelCase : List[Any] = zip(*UpperCamelCase_ )\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Dict = fa_score(y_true=UpperCamelCase_ , y_pred=UpperCamelCase_ , average=\"\"\"macro\"\"\" )\n\t\t\t\t\t\t\t\tfas.append(UpperCamelCase_ )\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[Any] = int(sum(pred == label for pred, label in preds_labels ) == len(UpperCamelCase_ ) )\n\t\t\t\t\t\t\t\tems.append(UpperCamelCase_ )\n\t\t\t\t_lowerCAmelCase : List[Any] = float(sum(UpperCamelCase_ ) / len(UpperCamelCase_ ) )\n\t\t\t\t_lowerCAmelCase : Union[str, Any] = sum(UpperCamelCase_ ) / len(UpperCamelCase_ )\n\t\t\t\t_lowerCAmelCase : Any = float(fa_score(y_true=UpperCamelCase_ , y_pred=[id_pred[\"\"\"prediction\"\"\"] for id_pred in ids_preds] ) )\n\t\t\t\treturn {\"exact_match\": em, \"f1_m\": fa_m, \"f1_a\": fa_a}\n\n\n\n\n\n@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,\t\t_KWARGS_DESCRIPTION\t\t\t\t\t\t)\nclass __snake_case\t\t\t\t\t(datasets.Metric\t\t\t\t\t\t):\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tint )\t\t\t\t\t-> Union[str, Any]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\tif self.config_name not in [\n\t\t\t\t\t\t\t\t \"boolq\",\n\t\t\t\t\t\t\t\t \"cb\",\n\t\t\t\t\t\t\t\t \"copa\",\n\t\t\t\t\t\t\t\t \"multirc\",\n\t\t\t\t\t\t\t\t \"record\",\n\t\t\t\t\t\t\t\t \"rte\",\n\t\t\t\t\t\t\t\t \"wic\",\n\t\t\t\t\t\t\t\t \"wsc\",\n\t\t\t\t\t\t\t\t \"wsc.fixed\",\n\t\t\t\t\t\t\t\t \"axb\",\n\t\t\t\t\t\t\t\t \"axg\",\n\t\t\t\t\t\t\t\t]:\n\t\t\t\t\t\t\t\t\t\t\t\traise KeyError(\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"You should supply a configuration name selected in \"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"[\\\"boolq\\\", \\\"cb\\\", \\\"copa\\\", \\\"multirc\\\", \\\"record\\\", \\\"rte\\\", \\\"wic\\\", \\\"wsc\\\", \\\"wsc.fixed\\\", \\\"axb\\\", \\\"axg\\\",]\"\"\" )\n\t\t\t\t\t\t\t\treturn datasets.MetricInfo(\n\t\t\t\t\t\t\t\t description=_DESCRIPTION\t\t\t\t\t\t,\t\tcitation=_CITATION\t\t\t\t\t\t,\t\tinputs_description=_KWARGS_DESCRIPTION\t\t\t\t\t\t,\t\tfeatures=datasets.Features(self._get_feature_types() )\t\t\t\t\t\t,\t\tcodebase_urls=[]\t\t\t\t\t\t,\t\treference_urls=[]\t\t\t\t\t\t,\t\tformat=\"\"\"numpy\"\"\" if not self.config_name == \"\"\"record\"\"\" and not self.config_name == \"\"\"multirc\"\"\" else None\t\t\t\t\t\t,\t\t)\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tTuple )\t\t\t\t\t-> Optional[Any]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\tif self.config_name == \"record\":\n\t\t\t\t\t\t\t\t\t\t\t\treturn {\n\t\t\t\t\t\t\t\t\t\t\t\t \"predictions\": {\n\t\t\t\t\t\t\t\t\t\t\t\t \"idx\": {\n\t\t\t\t\t\t\t\t\t\t\t\t \"passage\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t \"query\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t },\n\t\t\t\t\t\t\t\t\t\t\t\t \"prediction_text\": datasets.Value(\"\"\"string\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t },\n\t\t\t\t\t\t\t\t\t\t\t\t \"references\": {\n\t\t\t\t\t\t\t\t\t\t\t\t \"idx\": {\n\t\t\t\t\t\t\t\t\t\t\t\t \"passage\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t \"query\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t },\n\t\t\t\t\t\t\t\t\t\t\t\t \"answers\": datasets.Sequence(datasets.Value(\"\"\"string\"\"\" ) ),\n\t\t\t\t\t\t\t\t\t\t\t\t },\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\telif self.config_name == \"multirc\":\n\t\t\t\t\t\t\t\t\t\t\t\treturn {\n\t\t\t\t\t\t\t\t\t\t\t\t \"predictions\": {\n\t\t\t\t\t\t\t\t\t\t\t\t \"idx\": {\n\t\t\t\t\t\t\t\t\t\t\t\t \"answer\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t \"paragraph\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t \"question\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t },\n\t\t\t\t\t\t\t\t\t\t\t\t \"prediction\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t },\n\t\t\t\t\t\t\t\t\t\t\t\t \"references\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\treturn {\n\t\t\t\t\t\t\t\t\t\t\t\t \"predictions\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t \"references\": datasets.Value(\"\"\"int64\"\"\" ),\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tDict\t\t\t\t\t\t,\t\t_UpperCAmelCase :\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t,\t\t_UpperCAmelCase :\t\t\t\t\t\tList[str] )\t\t\t\t\t-> Optional[int]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\tif self.config_name == \"axb\":\n\t\t\t\t\t\t\t\t\t\t\t\treturn {\"matthews_correlation\": matthews_corrcoef(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )}\n\t\t\t\t\t\t\t\telif self.config_name == \"cb\":\n\t\t\t\t\t\t\t\t\t\t\t\treturn acc_and_fa(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase\t\t\t\t\t\t,\t\tfa_avg=\"\"\"macro\"\"\" )\n\t\t\t\t\t\t\t\telif self.config_name == \"record\":\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Any = [\n\t\t\t\t\t\t\t\t\t\t\t\t {\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"qas\"\"\": [\n\t\t\t\t\t\t\t\t\t\t\t\t {\"\"\"id\"\"\": ref[\"\"\"idx\"\"\"][\"\"\"query\"\"\"], \"\"\"answers\"\"\": [{\"\"\"text\"\"\": ans} for ans in ref[\"\"\"answers\"\"\"]]}\n\t\t\t\t\t\t\t\t\t\t\t\t for ref in references\n\t\t\t\t\t\t\t\t\t\t\t\t ]\n\t\t\t\t\t\t\t\t\t\t\t\t }\n\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Dict = {pred[\"\"\"idx\"\"\"][\"\"\"query\"\"\"]: pred[\"\"\"prediction_text\"\"\"] for pred in predictions}\n\t\t\t\t\t\t\t\t\t\t\t\treturn evaluate_record(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )[0]\n\t\t\t\t\t\t\t\telif self.config_name == \"multirc\":\n\t\t\t\t\t\t\t\t\t\t\t\treturn evaluate_multirc(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\t\t\t\t\t\t\t\telif self.config_name in [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]:\n\t\t\t\t\t\t\t\t\t\t\t\treturn {\"accuracy\": simple_accuracy(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )}\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\traise KeyError(\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"You should supply a configuration name selected in \"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"[\\\"boolq\\\", \\\"cb\\\", \\\"copa\\\", \\\"multirc\\\", \\\"record\\\", \\\"rte\\\", \\\"wic\\\", \\\"wsc\\\", \\\"wsc.fixed\\\", \\\"axb\\\", \\\"axg\\\",]\"\"\" )\n\n"},"code_codestyle":{"kind":"number","value":359,"string":"359"},"style_context":{"kind":"string","value":"\n\n\n\n\nimport json\nimport os\nimport sys\nimport tempfile\nimport unittest\nfrom pathlib import Path\nfrom shutil import copyfile\n\nfrom huggingface_hub import HfFolder, Repository, create_repo, delete_repo\nfrom requests.exceptions import HTTPError\n\nimport transformers\nfrom transformers import (\n CONFIG_MAPPING,\n FEATURE_EXTRACTOR_MAPPING,\n PROCESSOR_MAPPING,\n TOKENIZER_MAPPING,\n AutoConfig,\n AutoFeatureExtractor,\n AutoProcessor,\n AutoTokenizer,\n BertTokenizer,\n ProcessorMixin,\n WavaVecaConfig,\n WavaVecaFeatureExtractor,\n WavaVecaProcessor,\n)\nfrom transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test\nfrom transformers.tokenization_utils import TOKENIZER_CONFIG_FILE\nfrom transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available\n\n\nsys.path.append(str(Path(__file__).parent.parent.parent.parent / \"utils\"))\n\nfrom test_module.custom_configuration import CustomConfig # noqa E402\nfrom test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402\nfrom test_module.custom_processing import CustomProcessor # noqa E402\nfrom test_module.custom_tokenization import CustomTokenizer # noqa E402\n\n\n_lowerCamelCase :\t\t\t\t\tTuple = get_tests_dir(\"fixtures/dummy_feature_extractor_config.json\")\n_lowerCamelCase :\t\t\t\t\tList[str] = get_tests_dir(\"fixtures/vocab.json\")\n_lowerCamelCase :\t\t\t\t\tstr = get_tests_dir(\"fixtures\")\nclass __snake_case\t\t\t\t\t(unittest.TestCase\t\t\t\t\t\t):\n\t\t\t\tlowerCAmelCase__ \t\t= [\"[UNK]\", \"[CLS]\", \"[SEP]\", \"[PAD]\", \"[MASK]\", \"bla\", \"blou\"]\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tDict )\t\t\t\t\t-> Any:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Any = 0\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tTuple )\t\t\t\t\t-> Any:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained(\"\"\"facebook/wav2vec2-base-960h\"\"\" )\n\t\t\t\t\t\t\t\tself.assertIsInstance(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tAny )\t\t\t\t\t-> List[Any]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : List[Any] = WavaVecaConfig()\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : str = AutoProcessor.from_pretrained(\"\"\"facebook/wav2vec2-base-960h\"\"\" )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# save in new folder\n\t\t\t\t\t\t\t\t\t\t\t\tmodel_config.save_pretrained(_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tprocessor.save_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Any = AutoProcessor.from_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\tself.assertIsInstance(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tint )\t\t\t\t\t-> Union[str, Any]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\n\t\t\t\t\t\t\t\t\t\t\t\t# copy relevant files\n\t\t\t\t\t\t\t\t\t\t\t\tcopyfile(_UpperCAmelCase\t\t\t\t\t\t,\t\tos.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase ) )\n\t\t\t\t\t\t\t\t\t\t\t\tcopyfile(_UpperCAmelCase\t\t\t\t\t\t,\t\tos.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"vocab.json\"\"\" ) )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\tself.assertIsInstance(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tOptional[int] )\t\t\t\t\t-> Optional[Any]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Any = WavaVecaFeatureExtractor()\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(\"\"\"facebook/wav2vec2-base-960h\"\"\" )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = WavaVecaProcessor(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# save in new folder\n\t\t\t\t\t\t\t\t\t\t\t\tprocessor.save_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# drop `processor_class` in tokenizer\n\t\t\t\t\t\t\t\t\t\t\t\twith open(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\t\t\t\t\t\t,\t\t\"\"\"r\"\"\" ) as f:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Union[str, Any] = json.load(_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tconfig_dict.pop(\"\"\"processor_class\"\"\" )\n\n\t\t\t\t\t\t\t\t\t\t\t\twith open(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\t\t\t\t\t\t,\t\t\"\"\"w\"\"\" ) as f:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tf.write(json.dumps(_UpperCAmelCase ) )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : List[Any] = AutoProcessor.from_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\tself.assertIsInstance(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tint )\t\t\t\t\t-> Optional[Any]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Dict = WavaVecaFeatureExtractor()\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained(\"\"\"facebook/wav2vec2-base-960h\"\"\" )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : str = WavaVecaProcessor(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# save in new folder\n\t\t\t\t\t\t\t\t\t\t\t\tprocessor.save_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# drop `processor_class` in feature extractor\n\t\t\t\t\t\t\t\t\t\t\t\twith open(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\t\t\t\t\t\t,\t\t\"\"\"r\"\"\" ) as f:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : str = json.load(_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tconfig_dict.pop(\"\"\"processor_class\"\"\" )\n\n\t\t\t\t\t\t\t\t\t\t\t\twith open(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\t\t\t\t\t\t,\t\t\"\"\"w\"\"\" ) as f:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tf.write(json.dumps(_UpperCAmelCase ) )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\tself.assertIsInstance(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tTuple )\t\t\t\t\t-> str:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Tuple = WavaVecaConfig(processor_class=\"\"\"Wav2Vec2Processor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\tmodel_config.save_pretrained(_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\t# copy relevant files\n\t\t\t\t\t\t\t\t\t\t\t\tcopyfile(_UpperCAmelCase\t\t\t\t\t\t,\t\tos.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"vocab.json\"\"\" ) )\n\t\t\t\t\t\t\t\t\t\t\t\t# create emtpy sample processor\n\t\t\t\t\t\t\t\t\t\t\t\twith open(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\t\t\t\t\t\t,\t\t\"\"\"w\"\"\" ) as f:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tf.write(\"\"\"{}\"\"\" )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\tself.assertIsInstance(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tOptional[int] )\t\t\t\t\t-> List[str]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\twith self.assertRaises(_UpperCAmelCase ):\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Any = AutoProcessor.from_pretrained(\"\"\"hf-internal-testing/test_dynamic_processor\"\"\" )\n\t\t\t\t\t\t\t\t# If remote code is disabled, we can't load this config.\n\t\t\t\t\t\t\t\twith self.assertRaises(_UpperCAmelCase ):\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = AutoProcessor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_processor\"\"\"\t\t\t\t\t\t,\t\ttrust_remote_code=_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(\"\"\"hf-internal-testing/test_dynamic_processor\"\"\"\t\t\t\t\t\t,\t\ttrust_remote_code=_UpperCAmelCase )\n\t\t\t\t\t\t\t\tself.assertTrue(processor.special_attribute_present )\n\t\t\t\t\t\t\t\tself.assertEqual(processor.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"NewProcessor\"\"\" )\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = processor.feature_extractor\n\t\t\t\t\t\t\t\tself.assertTrue(feature_extractor.special_attribute_present )\n\t\t\t\t\t\t\t\tself.assertEqual(feature_extractor.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"NewFeatureExtractor\"\"\" )\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Union[str, Any] = processor.tokenizer\n\t\t\t\t\t\t\t\tself.assertTrue(tokenizer.special_attribute_present )\n\t\t\t\t\t\t\t\tif is_tokenizers_available():\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(tokenizer.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"NewTokenizerFast\"\"\" )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# Test we can also load the slow version\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_processor\"\"\"\t\t\t\t\t\t,\t\ttrust_remote_code=_UpperCAmelCase\t\t\t\t\t\t,\t\tuse_fast=_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = new_processor.tokenizer\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(new_tokenizer.special_attribute_present )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(new_tokenizer.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"NewTokenizer\"\"\" )\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(tokenizer.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"NewTokenizer\"\"\" )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tstr )\t\t\t\t\t-> Union[str, Any]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\t\tAutoConfig.register(\"\"\"custom\"\"\"\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tAutoFeatureExtractor.register(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tAutoTokenizer.register(_UpperCAmelCase\t\t\t\t\t\t,\t\tslow_tokenizer_class=_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tAutoProcessor.register(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\t# Trying to register something existing in the Transformers library will raise an error\n\t\t\t\t\t\t\t\t\t\t\t\twith self.assertRaises(_UpperCAmelCase ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAutoProcessor.register(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# Now that the config is registered, it can be used as any other config with the auto-API\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = CustomFeatureExtractor.from_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Tuple = os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"vocab.txt\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith open(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"w\"\"\"\t\t\t\t\t\t,\t\tencoding=\"\"\"utf-8\"\"\" ) as vocab_writer:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvocab_writer.write(\"\"\"\"\"\".join([x + \"\"\"\\n\"\"\" for x in self.vocab_tokens] ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : str = CustomTokenizer(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = CustomProcessor(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tprocessor.save_pretrained(_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained(_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\tfinally:\n\t\t\t\t\t\t\t\t\t\t\t\tif \"custom\" in CONFIG_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel CONFIG_MAPPING._extra_content[\"custom\"]\n\t\t\t\t\t\t\t\t\t\t\t\tif CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]\n\t\t\t\t\t\t\t\t\t\t\t\tif CustomConfig in TOKENIZER_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel TOKENIZER_MAPPING._extra_content[CustomConfig]\n\t\t\t\t\t\t\t\t\t\t\t\tif CustomConfig in PROCESSOR_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel PROCESSOR_MAPPING._extra_content[CustomConfig]\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tAny )\t\t\t\t\t-> Optional[int]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\tclass __snake_case\t\t\t\t\t(_a\t\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ \t\t= False\n\n\n\n\n\n\n\t\t\t\t\t\t\t\tclass __snake_case\t\t\t\t\t(_a\t\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ \t\t= False\n\n\n\n\n\n\n\t\t\t\t\t\t\t\tclass __snake_case\t\t\t\t\t(_a\t\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ \t\t= \"AutoFeatureExtractor\"\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ \t\t= \"AutoTokenizer\"\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ \t\t= False\n\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\t\tAutoConfig.register(\"\"\"custom\"\"\"\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tAutoFeatureExtractor.register(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tAutoTokenizer.register(_UpperCAmelCase\t\t\t\t\t\t,\t\tslow_tokenizer_class=_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tAutoProcessor.register(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\t# If remote code is not set, the default is to use local classes.\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained(\"\"\"hf-internal-testing/test_dynamic_processor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(processor.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"NewProcessor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(processor.special_attribute_present )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(processor.feature_extractor.special_attribute_present )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(processor.tokenizer.special_attribute_present )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# If remote code is disabled, we load the local ones.\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : str = AutoProcessor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_processor\"\"\"\t\t\t\t\t\t,\t\ttrust_remote_code=_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(processor.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"NewProcessor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(processor.special_attribute_present )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(processor.feature_extractor.special_attribute_present )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertFalse(processor.tokenizer.special_attribute_present )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# If remote is enabled, we load from the Hub.\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_processor\"\"\"\t\t\t\t\t\t,\t\ttrust_remote_code=_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(processor.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"NewProcessor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(processor.special_attribute_present )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(processor.feature_extractor.special_attribute_present )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(processor.tokenizer.special_attribute_present )\n\n\t\t\t\t\t\t\t\tfinally:\n\t\t\t\t\t\t\t\t\t\t\t\tif \"custom\" in CONFIG_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel CONFIG_MAPPING._extra_content[\"custom\"]\n\t\t\t\t\t\t\t\t\t\t\t\tif CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]\n\t\t\t\t\t\t\t\t\t\t\t\tif CustomConfig in TOKENIZER_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel TOKENIZER_MAPPING._extra_content[CustomConfig]\n\t\t\t\t\t\t\t\t\t\t\t\tif CustomConfig in PROCESSOR_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel PROCESSOR_MAPPING._extra_content[CustomConfig]\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tDict )\t\t\t\t\t-> Union[str, Any]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained(\"\"\"hf-internal-testing/tiny-random-bert\"\"\" )\n\t\t\t\t\t\t\t\tself.assertEqual(processor.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"BertTokenizerFast\"\"\" )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tList[Any] )\t\t\t\t\t-> Dict:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = AutoProcessor.from_pretrained(\"\"\"hf-internal-testing/tiny-random-convnext\"\"\" )\n\t\t\t\t\t\t\t\tself.assertEqual(processor.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"ConvNextImageProcessor\"\"\" )\n\n\n\n\n\n\n@is_staging_test\nclass __snake_case\t\t\t\t\t(unittest.TestCase\t\t\t\t\t\t):\n\t\t\t\tlowerCAmelCase__ \t\t= [\"[UNK]\", \"[CLS]\", \"[SEP]\", \"[PAD]\", \"[MASK]\", \"bla\", \"blou\"]\n\n\n\n\t\t\t\t@classmethod\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( cls :\t\t\t\t\t\tint )\t\t\t\t\t-> Any:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = TOKEN\n\t\t\t\t\t\t\t\tHfFolder.save_token(_UpperCAmelCase )\n\n\n\n\t\t\t\t@classmethod\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( cls :\t\t\t\t\t\tTuple )\t\t\t\t\t-> Optional[int]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\t\tdelete_repo(token=cls._token\t\t\t\t\t\t,\t\trepo_id=\"\"\"test-processor\"\"\" )\n\t\t\t\t\t\t\t\texcept HTTPError:\n\t\t\t\t\t\t\t\t\t\t\t\tpass\n\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\t\tdelete_repo(token=cls._token\t\t\t\t\t\t,\t\trepo_id=\"\"\"valid_org/test-processor-org\"\"\" )\n\t\t\t\t\t\t\t\texcept HTTPError:\n\t\t\t\t\t\t\t\t\t\t\t\tpass\n\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\t\tdelete_repo(token=cls._token\t\t\t\t\t\t,\t\trepo_id=\"\"\"test-dynamic-processor\"\"\" )\n\t\t\t\t\t\t\t\texcept HTTPError:\n\t\t\t\t\t\t\t\t\t\t\t\tpass\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tDict )\t\t\t\t\t-> str:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Optional[int] = WavaVecaProcessor.from_pretrained(_UpperCAmelCase )\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\n\t\t\t\t\t\t\t\t\t\t\t\tprocessor.save_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"test-processor\"\"\" )\t\t\t\t\t\t,\t\tpush_to_hub=_UpperCAmelCase\t\t\t\t\t\t,\t\tuse_auth_token=self._token )\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : str = WavaVecaProcessor.from_pretrained(f\"{USER}/test-processor\" )\n\t\t\t\t\t\t\t\t\t\t\t\tfor k, v in processor.feature_extractor.__dict__.items():\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(_UpperCAmelCase\t\t\t\t\t\t,\t\tgetattr(new_processor.feature_extractor\t\t\t\t\t\t,\t\t_UpperCAmelCase ) )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertDictEqual(new_processor.tokenizer.get_vocab()\t\t\t\t\t\t,\t\tprocessor.tokenizer.get_vocab() )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tDict )\t\t\t\t\t-> int:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : int = WavaVecaProcessor.from_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\n\t\t\t\t\t\t\t\t\t\t\t\tprocessor.save_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"test-processor-org\"\"\" )\t\t\t\t\t\t,\t\tpush_to_hub=_UpperCAmelCase\t\t\t\t\t\t,\t\tuse_auth_token=self._token\t\t\t\t\t\t,\t\torganization=\"\"\"valid_org\"\"\"\t\t\t\t\t\t,\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : str = WavaVecaProcessor.from_pretrained(\"\"\"valid_org/test-processor-org\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\tfor k, v in processor.feature_extractor.__dict__.items():\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(_UpperCAmelCase\t\t\t\t\t\t,\t\tgetattr(new_processor.feature_extractor\t\t\t\t\t\t,\t\t_UpperCAmelCase ) )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertDictEqual(new_processor.tokenizer.get_vocab()\t\t\t\t\t\t,\t\tprocessor.tokenizer.get_vocab() )\n\n\n\n\t\t\t\tdef SCREAMING_SNAKE_CASE ( self :\t\t\t\t\t\tOptional[Any] )\t\t\t\t\t-> List[str]:\n\n\n\n\n\n\t\t\t\t\t\t\t\t'''simple docstring'''\n\n\n\n\n\t\t\t\t\t\t\t\tCustomFeatureExtractor.register_for_auto_class()\n\t\t\t\t\t\t\t\tCustomTokenizer.register_for_auto_class()\n\t\t\t\t\t\t\t\tCustomProcessor.register_for_auto_class()\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Any = CustomFeatureExtractor.from_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : int = os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"vocab.txt\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\twith open(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"w\"\"\"\t\t\t\t\t\t,\t\tencoding=\"\"\"utf-8\"\"\" ) as vocab_writer:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvocab_writer.write(\"\"\"\"\"\".join([x + \"\"\"\\n\"\"\" for x in self.vocab_tokens] ) )\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = CustomTokenizer(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : List[str] = CustomProcessor(_UpperCAmelCase\t\t\t\t\t\t,\t\t_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\n\t\t\t\t\t\t\t\t\t\t\t\tcreate_repo(f\"{USER}/test-dynamic-processor\"\t\t\t\t\t\t,\t\ttoken=self._token )\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : Union[str, Any] = Repository(_UpperCAmelCase\t\t\t\t\t\t,\t\tclone_from=f\"{USER}/test-dynamic-processor\"\t\t\t\t\t\t,\t\ttoken=self._token )\n\t\t\t\t\t\t\t\t\t\t\t\tprocessor.save_pretrained(_UpperCAmelCase )\n\n\t\t\t\t\t\t\t\t\t\t\t\t# This has added the proper auto_map field to the feature extractor config\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertDictEqual(\n\t\t\t\t\t\t\t\t\t\t\t\t processor.feature_extractor.auto_map\t\t\t\t\t\t,\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"AutoFeatureExtractor\"\"\": \"\"\"custom_feature_extraction.CustomFeatureExtractor\"\"\",\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"AutoProcessor\"\"\": \"\"\"custom_processing.CustomProcessor\"\"\",\n\t\t\t\t\t\t\t\t\t\t\t\t }\t\t\t\t\t\t,\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t# This has added the proper auto_map field to the tokenizer config\n\t\t\t\t\t\t\t\t\t\t\t\twith open(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"tokenizer_config.json\"\"\" ) ) as f:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCAmelCase : str = json.load(_UpperCAmelCase )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertDictEqual(\n\t\t\t\t\t\t\t\t\t\t\t\t tokenizer_config[\"\"\"auto_map\"\"\"]\t\t\t\t\t\t,\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"AutoTokenizer\"\"\": [\"\"\"custom_tokenization.CustomTokenizer\"\"\", None],\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"AutoProcessor\"\"\": \"\"\"custom_processing.CustomProcessor\"\"\",\n\t\t\t\t\t\t\t\t\t\t\t\t }\t\t\t\t\t\t,\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t# The code has been copied from fixtures\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"custom_feature_extraction.py\"\"\" ) ) )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"custom_tokenization.py\"\"\" ) ) )\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase\t\t\t\t\t\t,\t\t\"\"\"custom_processing.py\"\"\" ) ) )\n\n\t\t\t\t\t\t\t\t\t\t\t\trepo.push_to_hub()\n\n\t\t\t\t\t\t\t\t_lowerCAmelCase : Tuple = AutoProcessor.from_pretrained(f\"{USER}/test-dynamic-processor\"\t\t\t\t\t\t,\t\ttrust_remote_code=_UpperCAmelCase )\n\t\t\t\t\t\t\t\t# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module\n\t\t\t\t\t\t\t\tself.assertEqual(new_processor.__class__.__name__\t\t\t\t\t\t,\t\t\"\"\"CustomProcessor\"\"\" )\n\n"},"style_context_codestyle":{"kind":"number","value":159,"string":"159"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":578,"cells":{"code":{"kind":"string","value":"\n'''simple docstring'''\n\ndef \t\t_UpperCAmelCase ( _lowerCamelCase :\t\t\t\tTuple ,\t\t\t\t\t_lowerCamelCase :\t\t\t\tList[str]\t\t\t\t\t\t)\t\t\t-> Union[str, Any]:\n if not isinstance(_lowerCAmelCase ,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t):\n raise ValueError(\"\"\"iterations must be defined as integers\"\"\"\t\t\t\t\t\t)\n if not isinstance(_lowerCAmelCase ,\t\t\t\t\t_lowerCAmelCase\t\t\t\t\t\t) or not number >= 1:\n raise ValueError(\n \"\"\"starting number must be\n and integer and be more than 0\"\"\"\t\t\t\t\t\t)\n if not iterations >= 1:\n raise ValueError(\"\"\"Iterations must be done more than 0 times to play FizzBuzz\"\"\"\t\t\t\t\t\t)\n\n _lowerCAmelCase\t\t\t\t: Optional[int] = \"\"\"\"\"\"\n while number <= iterations:\n if number % 3 == 0:\n out += \"Fizz\"\n if number % 5 == 0:\n out += \"Buzz\"\n if 0 not in (number % 3, number % 5):\n out += str(_lowerCAmelCase\t\t\t\t\t\t)\n\n # print(out)\n number += 1\n out += \" \"\n return out\n\n\nif __name__ == \"__main__\":\n import doctest\n\n doctest.testmod()\n\n\n"},"code_codestyle":{"kind":"number","value":309,"string":"309"},"style_context":{"kind":"string","value":"\r\n\"\"\"simple docstring\"\"\"\r\n# This is the module that test_patching.py uses to test patch_submodule()\r\n\r\nimport os # noqa: this is just for tests\r\nimport os as renamed_os # noqa: this is just for tests\r\nfrom os import path # noqa: this is just for tests\r\nfrom os import path as renamed_path # noqa: this is just for tests\r\nfrom os.path import join # noqa: this is just for tests\r\nfrom os.path import join as renamed_join # noqa: this is just for tests\r\n\r\n\r\nSCREAMING_SNAKE_CASE_ \t\t\t= open # noqa: we just need to have a builtin inside this module to test it properly\r\n"},"style_context_codestyle":{"kind":"number","value":301,"string":"301"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":579,"cells":{"code":{"kind":"string","value":"\rfrom __future__ import annotations\r\rimport time\r\rimport numpy as np\r\rlowercase : Optional[Any]\t\t\t\t\t\t =\t\t\t\t\t\t\t[8, 5, 9, 7]\rlowercase : Union[str, Any]\t\t\t\t\t\t =\t\t\t\t\t\t\t[\r [2, 0, 1, 1],\r [0, 1, 2, 1],\r [4, 0, 0, 3],\r [0, 2, 1, 0],\r [1, 0, 3, 0],\r]\rlowercase : Optional[Any]\t\t\t\t\t\t =\t\t\t\t\t\t\t[\r [3, 2, 1, 4],\r [0, 2, 5, 2],\r [5, 1, 0, 5],\r [1, 5, 3, 0],\r [3, 0, 3, 3],\r]\r\rclass __snake_case\t\t:\r def __init__( self\t\t,snake_case\t\t,snake_case\t\t,snake_case\t\t,):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tAny\t =\t\t\t\tclaim_vector\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t =\t\t\t\tallocated_resources_table\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\tmaximum_claim_table\r def \t\t\t\t_SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return [\r sum(p_item[i] for p_item in self.__allocated_resources_table\t\t\t\t)\r for i in range(len(self.__allocated_resources_table[0]\t\t\t\t)\t\t\t\t)\r ]\r def \t\t\t\t_SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return np.array(self.__claim_vector\t\t\t\t) - np.array(\r self.__processes_resource_summation()\t\t\t\t)\r def \t\t\t\t_SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return [\r list(np.array(self.__maximum_claim_table[i]\t\t\t\t) - np.array(snake_case\t\t\t\t)\t\t\t\t)\r for i, allocated_resource in enumerate(self.__allocated_resources_table\t\t\t\t)\r ]\r def \t\t\t\t_SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return {self.__need().index(snake_case\t\t\t\t): i for i in self.__need()}\r def \t\t\t\t_SCREAMING_SNAKE_CASE\t( self\t\t,**snake_case\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tAny\t =\t\t\t\tself.__need()\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[str]\t =\t\t\t\tself.__allocated_resources_table\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tint\t =\t\t\t\tself.__available_resources()\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tint\t =\t\t\t\tself.__need_index_manager()\r for kw, val in kwargs.items():\r if kw and val is True:\r self.__pretty_data()\r print(\"\"\"_\"\"\" * 50 + \"\"\"\\n\"\"\"\t\t\t\t)\r while need_list:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[str]\t =\t\t\t\tFalse\r for each_need in need_list:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tTrue\r for index, need in enumerate(snake_case\t\t\t\t):\r if need > available_resources[index]:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tFalse\r break\r if execution:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tTrue\r # get the original index of the process from ind_ctrl db\r for original_need_index, need_clone in need_index_manager.items():\r if each_need == need_clone:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t =\t\t\t\toriginal_need_index\r print(f\"Process {process_number + 1} is executing.\"\t\t\t\t)\r # remove the process run from stack\r need_list.remove(snake_case\t\t\t\t)\r # update available/freed resources stack\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\tnp.array(snake_case\t\t\t\t) + np.array(\r alloc_resources_table[process_number]\t\t\t\t)\r print(\r \"\"\"Updated available resource stack for processes: \"\"\"\r + \"\"\" \"\"\".join([str(snake_case\t\t\t\t) for x in available_resources]\t\t\t\t)\t\t\t\t)\r break\r if safe:\r print(\"\"\"The process is in a safe state.\\n\"\"\"\t\t\t\t)\r else:\r print(\"\"\"System in unsafe state. Aborting...\\n\"\"\"\t\t\t\t)\r break\r def \t\t\t\t_SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r print(\"\"\" \"\"\" * 9 + \"\"\"Allocated Resource Table\"\"\"\t\t\t\t)\r for item in self.__allocated_resources_table:\r print(\r f\"P{self.__allocated_resources_table.index(snake_case\t\t\t\t) + 1}\"\r + \"\"\" \"\"\".join(f\"{it:>8}\" for it in item\t\t\t\t)\r + \"\"\"\\n\"\"\"\t\t\t\t)\r print(\"\"\" \"\"\" * 9 + \"\"\"System Resource Table\"\"\"\t\t\t\t)\r for item in self.__maximum_claim_table:\r print(\r f\"P{self.__maximum_claim_table.index(snake_case\t\t\t\t) + 1}\"\r + \"\"\" \"\"\".join(f\"{it:>8}\" for it in item\t\t\t\t)\r + \"\"\"\\n\"\"\"\t\t\t\t)\r print(\r \"\"\"Current Usage by Active Processes: \"\"\"\r + \"\"\" \"\"\".join(str(snake_case\t\t\t\t) for x in self.__claim_vector\t\t\t\t)\t\t\t\t)\r print(\r \"\"\"Initial Available Resources: \"\"\"\r + \"\"\" \"\"\".join(str(snake_case\t\t\t\t) for x in self.__available_resources()\t\t\t\t)\t\t\t\t)\r time.sleep(1\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"code_codestyle":{"kind":"number","value":285,"string":"285"},"style_context":{"kind":"string","value":"\rfrom __future__ import annotations\r\rfrom itertools import permutations\rfrom random import randint\rfrom timeit import repeat\r\r\r\r\r\r\r\rdef \t\t\t\t\t\t_snake_case( )\t\t\t-> tuple[list[int], int]:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[Any]\t =\t\t\t\t[randint(-1_000 , 1_000 ) for i in range(10 )]\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tTuple\t =\t\t\t\trandint(-5_000 , 5_000 )\r return (arr, r)\r\r\rlowercase : List[Any]\t\t\t\t\t\t =\t\t\t\t\t\t\tmake_dataset()\r\r\r\r\r\r\r\rdef \t\t\t\t\t\t_snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )\t\t\t-> tuple[int, ...]:\r for triplet in permutations(SCREAMING_SNAKE_CASE__ , 3 ):\r if sum(SCREAMING_SNAKE_CASE__ ) == target:\r return tuple(sorted(SCREAMING_SNAKE_CASE__ ) )\r return (0, 0, 0)\r\r\r\r\r\r\r\rdef \t\t\t\t\t\t_snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )\t\t\t-> tuple[int, int, int]:\r arr.sort()\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int]\t =\t\t\t\tlen(SCREAMING_SNAKE_CASE__ )\r for i in range(n - 1 ):\r lowercase , lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\ti + 1, n - 1\r while left < right:\r if arr[i] + arr[left] + arr[right] == target:\r return (arr[i], arr[left], arr[right])\r elif arr[i] + arr[left] + arr[right] < target:\r left += 1\r elif arr[i] + arr[left] + arr[right] > target:\r right -= 1\r return (0, 0, 0)\r\r\r\r\r\r\r\rdef \t\t\t\t\t\t_snake_case( )\t\t\t-> tuple[float, float]:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\t\"\"\"\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n\"\"\"\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tTuple\t =\t\t\t\t\"\"\"\ntriplet_sum1(*dataset)\n\"\"\"\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tint\t =\t\t\t\t\"\"\"\ntriplet_sum2(*dataset)\n\"\"\"\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tstr\t =\t\t\t\trepeat(setup=SCREAMING_SNAKE_CASE__ , stmt=SCREAMING_SNAKE_CASE__ , repeat=5 , number=10_000 )\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\trepeat(setup=SCREAMING_SNAKE_CASE__ , stmt=SCREAMING_SNAKE_CASE__ , repeat=5 , number=10_000 )\r return (min(SCREAMING_SNAKE_CASE__ ), min(SCREAMING_SNAKE_CASE__ ))\r\r\rif __name__ == \"__main__\":\r from doctest import testmod\r\r testmod()\r lowercase : Union[str, Any]\t\t\t\t\t\t =\t\t\t\t\t\t\tsolution_times()\r print(F'''The time for naive implementation is {times[0]}.''')\r print(F'''The time for optimized implementation is {times[1]}.''')\r"},"style_context_codestyle":{"kind":"number","value":285,"string":"285"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":580,"cells":{"code":{"kind":"string","value":"\n\n\n\nimport itertools\nimport os\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\n\nfrom transformers import TvltFeatureExtractor, is_datasets_available\nfrom transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio\nfrom transformers.utils.import_utils import is_torch_available\n\nfrom ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin\n\n\nif is_torch_available():\n import torch\n\nif is_datasets_available():\n from datasets import load_dataset\n\nSCREAMING_SNAKE_CASE_:int =\t\t\t\t\t\trandom.Random()\n\n\n\n\ndef \t__UpperCamelCase\t( _lowerCAmelCase , _lowerCAmelCase=1.0 , _lowerCAmelCase=None , _lowerCAmelCase=None\t\t) -> Union[str, Any]:\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n if rng is None:\n A : str = global_rng\n\n A : str = []\n for batch_idx in range(shape[0]\t\t):\n values.append([]\t\t)\n for _ in range(shape[1]\t\t):\n values[-1].append(rng.random() * scale\t\t)\n\n return values\n\n\n\n\n\n\nclass SCREAMING_SNAKE_CASE__ (\t\tunittest.TestCase ):\n\n\n\n\n '''simple docstring'''\n\n\n\n\n\n\n def __init__( self,\t\tlowerCamelCase__,\t\tlowerCamelCase__=7,\t\tlowerCamelCase__=400,\t\tlowerCamelCase__=2000,\t\tlowerCamelCase__=2048,\t\tlowerCamelCase__=128,\t\tlowerCamelCase__=1,\t\tlowerCamelCase__=512,\t\tlowerCamelCase__=30,\t\tlowerCamelCase__=4_4100,\t\t):\n A : str = parent\n A : Optional[int] = batch_size\n A : str = min_seq_length\n A : int = max_seq_length\n A : Any = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)\n A : Tuple = spectrogram_length\n A : Optional[Any] = feature_size\n A : Any = num_audio_channels\n A : str = hop_length\n A : int = chunk_length\n A : Optional[Any] = sampling_rate\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self\t\t):\n return {\n \"spectrogram_length\": self.spectrogram_length,\n \"feature_size\": self.feature_size,\n \"num_audio_channels\": self.num_audio_channels,\n \"hop_length\": self.hop_length,\n \"chunk_length\": self.chunk_length,\n \"sampling_rate\": self.sampling_rate,\n }\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self,\t\tlowerCamelCase__=False,\t\tlowerCamelCase__=False\t\t):\n def _flatten(lowerCamelCase__\t\t):\n return list(itertools.chain(*UpperCAmelCase_\t\t)\t\t)\n\n if equal_length:\n A : List[str] = [floats_list((self.max_seq_length, self.feature_size)\t\t) for _ in range(self.batch_size\t\t)]\n else:\n # make sure that inputs increase in size\n A : int = [\n floats_list((x, self.feature_size)\t\t)\n for x in range(self.min_seq_length,\t\tself.max_seq_length,\t\tself.seq_length_diff\t\t)\n ]\n if numpify:\n A : Dict = [np.asarray(UpperCAmelCase_\t\t) for x in speech_inputs]\n return speech_inputs\n\n\n\n\n\n\n@require_torch\n@require_torchaudio\nclass SCREAMING_SNAKE_CASE__ (\t\tSCREAMING_SNAKE_CASE__ ,\t\t\t\t\t\tunittest.TestCase ):\n\n\n\n\n '''simple docstring'''\n\n\n\n\n\n\n __lowerCamelCase\t\t\t: Optional[int]\t\t\t\t\t= TvltFeatureExtractor\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self\t\t):\n A : Dict = TvltFeatureExtractionTester(self\t\t)\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self\t\t):\n A : List[str] = self.feature_extraction_class(**self.feat_extract_dict\t\t)\n self.assertTrue(hasattr(UpperCAmelCase_,\t\t\"\"\"spectrogram_length\"\"\"\t\t)\t\t)\n self.assertTrue(hasattr(UpperCAmelCase_,\t\t\"\"\"feature_size\"\"\"\t\t)\t\t)\n self.assertTrue(hasattr(UpperCAmelCase_,\t\t\"\"\"num_audio_channels\"\"\"\t\t)\t\t)\n self.assertTrue(hasattr(UpperCAmelCase_,\t\t\"\"\"hop_length\"\"\"\t\t)\t\t)\n self.assertTrue(hasattr(UpperCAmelCase_,\t\t\"\"\"chunk_length\"\"\"\t\t)\t\t)\n self.assertTrue(hasattr(UpperCAmelCase_,\t\t\"\"\"sampling_rate\"\"\"\t\t)\t\t)\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self\t\t):\n A : List[Any] = self.feature_extraction_class(**self.feat_extract_dict\t\t)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n A : Optional[int] = feat_extract_first.save_pretrained(UpperCAmelCase_\t\t)[0]\n check_json_file_has_correct_format(UpperCAmelCase_\t\t)\n A : Any = self.feature_extraction_class.from_pretrained(UpperCAmelCase_\t\t)\n\n A : Union[str, Any] = feat_extract_first.to_dict()\n A : List[Any] = feat_extract_second.to_dict()\n A : Any = dict_first.pop(\"\"\"mel_filters\"\"\"\t\t)\n A : str = dict_second.pop(\"\"\"mel_filters\"\"\"\t\t)\n self.assertTrue(np.allclose(UpperCAmelCase_,\t\tUpperCAmelCase_\t\t)\t\t)\n self.assertEqual(UpperCAmelCase_,\t\tUpperCAmelCase_\t\t)\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self\t\t):\n A : Any = self.feature_extraction_class(**self.feat_extract_dict\t\t)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n A : List[str] = os.path.join(UpperCAmelCase_,\t\t\"\"\"feat_extract.json\"\"\"\t\t)\n feat_extract_first.to_json_file(UpperCAmelCase_\t\t)\n A : Union[str, Any] = self.feature_extraction_class.from_json_file(UpperCAmelCase_\t\t)\n\n A : List[Any] = feat_extract_first.to_dict()\n A : Dict = feat_extract_second.to_dict()\n A : Optional[Any] = dict_first.pop(\"\"\"mel_filters\"\"\"\t\t)\n A : Optional[Any] = dict_second.pop(\"\"\"mel_filters\"\"\"\t\t)\n self.assertTrue(np.allclose(UpperCAmelCase_,\t\tUpperCAmelCase_\t\t)\t\t)\n self.assertEqual(UpperCAmelCase_,\t\tUpperCAmelCase_\t\t)\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self\t\t):\n # Initialize feature_extractor\n A : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict\t\t)\n\n # create three inputs of length 800, 1000, and 1200\n A : Optional[Any] = [floats_list((1, x)\t\t)[0] for x in range(800,\t\t1400,\t\t200\t\t)]\n A : Dict = [np.asarray(UpperCAmelCase_\t\t) for speech_input in speech_inputs]\n\n # Test not batched input\n A : List[Any] = feature_extractor(np_speech_inputs[0],\t\treturn_tensors=\"\"\"np\"\"\",\t\tsampling_rate=4_4100\t\t).audio_values\n\n self.assertTrue(encoded_audios.ndim == 4\t\t)\n self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size\t\t)\n self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length\t\t)\n self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels\t\t)\n\n # Test batched\n A : List[str] = feature_extractor(UpperCAmelCase_,\t\treturn_tensors=\"\"\"np\"\"\",\t\tsampling_rate=4_4100\t\t).audio_values\n\n self.assertTrue(encoded_audios.ndim == 4\t\t)\n self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size\t\t)\n self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length\t\t)\n self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels\t\t)\n\n # Test audio masking\n A : Tuple = feature_extractor(\n UpperCAmelCase_,\t\treturn_tensors=\"\"\"np\"\"\",\t\tsampling_rate=4_4100,\t\tmask_audio=UpperCAmelCase_\t\t).audio_values\n\n self.assertTrue(encoded_audios.ndim == 4\t\t)\n self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size\t\t)\n self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length\t\t)\n self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels\t\t)\n\n # Test 2-D numpy arrays are batched.\n A : Tuple = [floats_list((1, x)\t\t)[0] for x in (800, 800, 800)]\n A : Optional[int] = np.asarray(UpperCAmelCase_\t\t)\n A : List[str] = feature_extractor(UpperCAmelCase_,\t\treturn_tensors=\"\"\"np\"\"\",\t\tsampling_rate=4_4100\t\t).audio_values\n self.assertTrue(encoded_audios.ndim == 4\t\t)\n self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size\t\t)\n self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length\t\t)\n self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels\t\t)\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self,\t\tlowerCamelCase__\t\t):\n A : Optional[int] = load_dataset(\"\"\"hf-internal-testing/librispeech_asr_dummy\"\"\",\t\t\"\"\"clean\"\"\",\t\tsplit=\"\"\"validation\"\"\"\t\t)\n # automatic decoding with librispeech\n A : List[str] = ds.sort(\"\"\"id\"\"\"\t\t).select(range(UpperCAmelCase_\t\t)\t\t)[:num_samples]['audio']\n\n return [x[\"array\"] for x in speech_samples]\n\n\n\n\n\n\n def \t\t\t_lowerCAmelCase\t\t\t\t\t( self\t\t):\n A : List[Any] = self._load_datasamples(1\t\t)\n A : Union[str, Any] = TvltFeatureExtractor()\n A : List[Any] = feature_extractor(UpperCAmelCase_,\t\treturn_tensors=\"\"\"pt\"\"\"\t\t).audio_values\n\n self.assertEquals(audio_values.shape,\t\t(1, 1, 192, 128)\t\t)\n\n A : List[Any] = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]\t\t)\n self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],\t\tUpperCAmelCase_,\t\tatol=1e-4\t\t)\t\t)\n\n\n"},"code_codestyle":{"kind":"number","value":116,"string":"116"},"style_context":{"kind":"string","value":"\r\rfrom datetime import datetime as dt\rimport os\r\rfrom github import Github\r\r\r__A : Dict =\t[\r '''good first issue''',\r '''good second issue''',\r '''good difficult issue''',\r '''feature request''',\r '''new model''',\r '''wip''',\r]\rdef \t\t\t\t\t\tSCREAMING_SNAKE_CASE__ (\t\t\t\t\t\t\t) -> int:\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r lowerCAmelCase :\tOptional[Any]\t\t\t\t=\t\t\t\t\tGithub(os.environ['GITHUB_TOKEN'] )\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tg.get_repo('huggingface/transformers' )\r lowerCAmelCase :\tTuple\t\t\t\t=\t\t\t\t\trepo.get_issues(state='open' )\r\r for issue in open_issues:\r lowerCAmelCase :\tDict\t\t\t\t=\t\t\t\t\tsorted([comment for comment in issue.get_comments()],\t\t\t\t\tkey=lambda _UpperCAmelCase : i.created_at,\t\t\t\t\treverse=_UpperCAmelCase )\r lowerCAmelCase :\tOptional[Any]\t\t\t\t=\t\t\t\t\tcomments[0] if len(_UpperCAmelCase ) > 0 else None\r if (\r last_comment is not None\r and last_comment.user.login == \"github-actions[bot]\"\r and (dt.utcnow() - issue.updated_at).days > 7\r and (dt.utcnow() - issue.created_at).days >= 30\r and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )\r ):\r # print(f\"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.\")\r issue.edit(state='closed' )\r elif (\r (dt.utcnow() - issue.updated_at).days > 23\r and (dt.utcnow() - issue.created_at).days >= 30\r and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )\r ):\r # print(f\"Would add stale comment to {issue.number}\")\r issue.create_comment(\r 'This issue has been automatically marked as stale because it has not had '\r 'recent activity. If you think this still needs to be addressed '\r 'please comment on this thread.\\n\\nPlease note that issues that do not follow the '\r '[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '\r 'are likely to be ignored.' )\r\r\rif __name__ == \"__main__\":\r main()\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":138,"string":"138"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":581,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\n\nimport json\nimport os\nfrom collections import Counter\n\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom torch import nn\nfrom torch.utils.data import Dataset\n\n\n__snake_case : Optional[int] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}\n\n\nclass __SCREAMING_SNAKE_CASE (\t\t\t\tnn.Module):\n\n\n\n\n\n\n\t\t\t\t\t\tdef __init__(\t\tself , _UpperCamelCase\t\t\t\t):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__()\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = torchvision.models.resnetaaa(pretrained=_UpperCamelCase\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = list(model.children()\t\t\t\t)[:-2]\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = nn.Sequential(*_UpperCamelCase\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]\t\t\t\t)\n\n\n\n\n\n\n\n\t\t\t\t\t\tdef UpperCamelCase__ (\t\tself , _UpperCamelCase\t\t\t\t):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = self.pool(self.model(_UpperCamelCase\t\t\t\t)\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = torch.flatten(_UpperCamelCase , start_dim=2\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = out.transpose(1 , 2\t\t\t\t).contiguous()\n\t\t\t\t\t\t\t\t\t\t\t\treturn out # BxNx2048\n\n\n\n\nclass __SCREAMING_SNAKE_CASE (\t\t\t\t__lowercase):\n\n\n\n\n\n\n\t\t\t\t\t\tdef __init__(\t\tself , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase\t\t\t\t):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = [json.loads(_UpperCamelCase\t\t\t\t) for l in open(_UpperCamelCase\t\t\t\t)]\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = os.path.dirname(_UpperCamelCase\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = tokenizer\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = labels\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = len(_UpperCamelCase\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = max_seq_length\n\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = transforms\n\n\n\n\n\n\n\t\t\t\t\t\tdef __len__(\t\tself\t\t\t\t):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\treturn len(self.data\t\t\t\t)\n\n\n\n\n\n\n\t\t\t\t\t\tdef __getitem__(\t\tself , _UpperCamelCase\t\t\t\t):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] , add_special_tokens=_UpperCamelCase\t\t\t\t)\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = sentence[0], sentence[1:-1], sentence[-1]\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = sentence[: self.max_seq_length]\n\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = torch.zeros(self.n_classes\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = 1\n\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = Image.open(os.path.join(self.data_dir , self.data[index]['img']\t\t\t\t)\t\t\t\t).convert('RGB'\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = self.transforms(_UpperCamelCase\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\treturn {\n\t\t\t\t\t\t\t\t\t\t\t\t \"image_start_token\": start_token,\n\t\t\t\t\t\t\t\t\t\t\t\t \"image_end_token\": end_token,\n\t\t\t\t\t\t\t\t\t\t\t\t \"sentence\": sentence,\n\t\t\t\t\t\t\t\t\t\t\t\t \"image\": image,\n\t\t\t\t\t\t\t\t\t\t\t\t \"label\": label,\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\n\n\n\n\n\n\n\t\t\t\t\t\tdef UpperCamelCase__ (\t\tself\t\t\t\t):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = Counter()\n\t\t\t\t\t\t\t\t\t\t\t\tfor row in self.data:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlabel_freqs.update(row['label']\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\treturn label_freqs\n\n\n\n\n\n\n\ndef \t\t_UpperCamelCase\t\t\t\t( UpperCamelCase_ : List[Any]\t\t\t\t\t\t)\t\t\t\t\t->\t\t\t\tTuple:\n\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\t\t\t\t\t\tlowerCAmelCase__ = [len(row['sentence']\t\t\t\t\t\t) for row in batch]\n\t\t\t\t\t\tlowerCAmelCase__ , lowerCAmelCase__ = len(UpperCamelCase_\t\t\t\t\t\t), max(UpperCamelCase_\t\t\t\t\t\t)\n\n\t\t\t\t\t\tlowerCAmelCase__ = torch.zeros(UpperCamelCase_ , UpperCamelCase_ , dtype=torch.long\t\t\t\t\t\t)\n\t\t\t\t\t\tlowerCAmelCase__ = torch.zeros(UpperCamelCase_ , UpperCamelCase_ , dtype=torch.long\t\t\t\t\t\t)\n\n\t\t\t\t\t\tfor i_batch, (input_row, length) in enumerate(zip(UpperCamelCase_ , UpperCamelCase_\t\t\t\t\t\t)\t\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = input_row['sentence']\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ = 1\n\n\t\t\t\t\t\tlowerCAmelCase__ = torch.stack([row['image'] for row in batch]\t\t\t\t\t\t)\n\t\t\t\t\t\tlowerCAmelCase__ = torch.stack([row['label'] for row in batch]\t\t\t\t\t\t)\n\t\t\t\t\t\tlowerCAmelCase__ = torch.stack([row['image_start_token'] for row in batch]\t\t\t\t\t\t)\n\t\t\t\t\t\tlowerCAmelCase__ = torch.stack([row['image_end_token'] for row in batch]\t\t\t\t\t\t)\n\n\t\t\t\t\t\treturn text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor\n\n\n\n\n\n\n\ndef \t\t_UpperCamelCase\t\t\t\t( )\t\t\t\t\t->\t\t\t\tOptional[int]:\n\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\t\t\t\t\t\treturn [\n\t\t\t\t\t\t \"Crime\",\n\t\t\t\t\t\t \"Drama\",\n\t\t\t\t\t\t \"Thriller\",\n\t\t\t\t\t\t \"Action\",\n\t\t\t\t\t\t \"Comedy\",\n\t\t\t\t\t\t \"Romance\",\n\t\t\t\t\t\t \"Documentary\",\n\t\t\t\t\t\t \"Short\",\n\t\t\t\t\t\t \"Mystery\",\n\t\t\t\t\t\t \"History\",\n\t\t\t\t\t\t \"Family\",\n\t\t\t\t\t\t \"Adventure\",\n\t\t\t\t\t\t \"Fantasy\",\n\t\t\t\t\t\t \"Sci-Fi\",\n\t\t\t\t\t\t \"Western\",\n\t\t\t\t\t\t \"Horror\",\n\t\t\t\t\t\t \"Sport\",\n\t\t\t\t\t\t \"War\",\n\t\t\t\t\t\t \"Music\",\n\t\t\t\t\t\t \"Musical\",\n\t\t\t\t\t\t \"Animation\",\n\t\t\t\t\t\t \"Biography\",\n\t\t\t\t\t\t \"Film-Noir\",\n\t\t\t\t\t\t]\n\n\n\n\n\n\n\ndef \t\t_UpperCamelCase\t\t\t\t( )\t\t\t\t\t->\t\t\t\tOptional[int]:\n\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\t\t\t\t\t\treturn transforms.Compose(\n\t\t\t\t\t\t [\n\t\t\t\t\t\t transforms.Resize(256\t\t\t\t\t\t),\n\t\t\t\t\t\t transforms.CenterCrop(224\t\t\t\t\t\t),\n\t\t\t\t\t\t transforms.ToTensor(),\n\t\t\t\t\t\t transforms.Normalize(\n\t\t\t\t\t\t mean=[0.4677_7044, 0.4453_1429, 0.4066_1017] , std=[0.1222_1994, 0.1214_5835, 0.1438_0469] , ),\n\t\t\t\t\t\t ]\t\t\t\t\t\t)\n\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":122,"string":"122"},"style_context":{"kind":"string","value":"\n\n\n\n\n\n\nfrom typing import TYPE_CHECKING\n\n# rely on isort to merge the imports\nfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available\n\n\n__snake_case : Dict = {\"\"\"configuration_focalnet\"\"\": [\"\"\"FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP\"\"\", \"\"\"FocalNetConfig\"\"\"]}\n\n\ntry:\n\t\t\t\t\tif not is_torch_available():\n\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n\t\t\t\t\tpass\nelse:\n\t\t\t\t\t__snake_case : str = [\n\t\t\t\t\t \"\"\"FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST\"\"\",\n\t\t\t\t\t \"\"\"FocalNetForImageClassification\"\"\",\n\t\t\t\t\t \"\"\"FocalNetForMaskedImageModeling\"\"\",\n\t\t\t\t\t \"\"\"FocalNetBackbone\"\"\",\n\t\t\t\t\t \"\"\"FocalNetModel\"\"\",\n\t\t\t\t\t \"\"\"FocalNetPreTrainedModel\"\"\",\n\t\t\t\t\t]\n\nif TYPE_CHECKING:\n\t\t\t\t\tfrom .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig\n\n\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\tif not is_torch_available():\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\n\t\t\t\t\texcept OptionalDependencyNotAvailable:\n\t\t\t\t\t\t\t\t\t\tpass\n\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tfrom .modeling_focalnet import (\n\t\t\t\t\t\t\t\t\t\t FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,\n\t\t\t\t\t\t\t\t\t\t FocalNetBackbone,\n\t\t\t\t\t\t\t\t\t\t FocalNetForImageClassification,\n\t\t\t\t\t\t\t\t\t\t FocalNetForMaskedImageModeling,\n\t\t\t\t\t\t\t\t\t\t FocalNetModel,\n\t\t\t\t\t\t\t\t\t\t FocalNetPreTrainedModel,\n\t\t\t\t\t\t\t\t\t\t)\n\nelse:\n\t\t\t\t\timport sys\n\n\t\t\t\t\t__snake_case : List[Any] = _LazyModule(__name__, globals()[\"\"\"__file__\"\"\"], _import_structure, module_spec=__spec__)\n\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":122,"string":"122"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":582,"cells":{"code":{"kind":"string","value":"\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\rlowerCAmelCase__ \t\t\t\t= range(2, 20 + 1)\rlowerCAmelCase__ \t\t\t\t= [10**k for k in range(ks[-1] + 1)]\rlowerCAmelCase__ \t\t\t\t= {}\rdef \t\t\t\t\ta__\t\t\t(\t\t\t\tSCREAMING_SNAKE_CASE : str\t\t\t\t, SCREAMING_SNAKE_CASE : Optional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE : Tuple\t\t\t\t, SCREAMING_SNAKE_CASE : List[str]\t\t\t\t):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase : Union[str, Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tsum(a_i[j] for j in range(SCREAMING_SNAKE_CASE\t\t\t\t, len(SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t)\t\t\t\t)\r lowerCAmelCase : int\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tsum(a_i[j] * base[j] for j in range(min(len(SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t)\t\t\t\t)\r\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : List[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t0, 0\r lowerCAmelCase : int\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tn - i\r\r lowerCAmelCase : Optional[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tmemo.get(SCREAMING_SNAKE_CASE\t\t\t\t)\r\r if sub_memo is not None:\r lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tsub_memo.get(SCREAMING_SNAKE_CASE\t\t\t\t)\r\r if jumps is not None and len(SCREAMING_SNAKE_CASE\t\t\t\t) > 0:\r # find and make the largest jump without going over\r lowerCAmelCase : int\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t-1\r for _k in range(len(SCREAMING_SNAKE_CASE\t\t\t\t) - 1\t\t\t\t, -1\t\t\t\t, -1\t\t\t\t):\r if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:\r lowerCAmelCase : str\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t_k\r break\r\r if max_jump >= 0:\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tjumps[max_jump]\r # since the difference between jumps is cached, add c\r lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tdiff + c\r for j in range(min(SCREAMING_SNAKE_CASE\t\t\t\t, len(SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t)\t\t\t\t):\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tdivmod(SCREAMING_SNAKE_CASE\t\t\t\t, 1_0\t\t\t\t)\r if new_c > 0:\r add(SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t)\r\r else:\r lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t[]\r else:\r lowerCAmelCase : Union[str, Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t{c: []}\r lowerCAmelCase : List[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tsub_memo\r\r if dn >= max_dn or c + diff >= base[k]:\r return diff, dn\r\r if k > ks[0]:\r while True:\r # keep doing smaller jumps\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tnext_term(SCREAMING_SNAKE_CASE\t\t\t\t, k - 1\t\t\t\t, i + dn\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t)\r diff += _diff\r dn += terms_jumped\r\r if dn >= max_dn or c + diff >= base[k]:\r break\r else:\r # would be too small a jump, just compute sequential terms instead\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tcompute(SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t, i + dn\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t)\r diff += _diff\r dn += terms_jumped\r\r lowerCAmelCase : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tsub_memo[c]\r\r # keep jumps sorted by # of terms skipped\r lowerCAmelCase : Optional[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t0\r while j < len(SCREAMING_SNAKE_CASE\t\t\t\t):\r if jumps[j][1] > dn:\r break\r j += 1\r\r # cache the jump for this value digitsum(b) and c\r sub_memo[c].insert(SCREAMING_SNAKE_CASE\t\t\t\t, (diff, dn, k)\t\t\t\t)\r return (diff, dn)\rdef \t\t\t\t\ta__\t\t\t(\t\t\t\tSCREAMING_SNAKE_CASE : Union[str, Any]\t\t\t\t, SCREAMING_SNAKE_CASE : Dict\t\t\t\t, SCREAMING_SNAKE_CASE : Optional[int]\t\t\t\t, SCREAMING_SNAKE_CASE : Optional[Any]\t\t\t\t):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r if i >= n:\r return 0, i\r if k > len(SCREAMING_SNAKE_CASE\t\t\t\t):\r a_i.extend([0 for _ in range(k - len(SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t)]\t\t\t\t)\r\r # note: a_i -> b * 10^k + c\r # ds_b -> digitsum(b)\r # ds_c -> digitsum(c)\r lowerCAmelCase : int\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\ti\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : List[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t0, 0, 0\r for j in range(len(SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t):\r if j >= k:\r ds_b += a_i[j]\r else:\r ds_c += a_i[j]\r\r while i < n:\r i += 1\r lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tds_c + ds_b\r diff += addend\r lowerCAmelCase : str\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t0\r for j in range(SCREAMING_SNAKE_CASE\t\t\t\t):\r lowerCAmelCase : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\ta_i[j] + addend\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tdivmod(SCREAMING_SNAKE_CASE\t\t\t\t, 1_0\t\t\t\t)\r\r ds_c += a_i[j]\r\r if addend > 0:\r break\r\r if addend > 0:\r add(SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t)\r return diff, i - start_i\rdef \t\t\t\t\ta__\t\t\t(\t\t\t\tSCREAMING_SNAKE_CASE : Any\t\t\t\t, SCREAMING_SNAKE_CASE : Optional[Any]\t\t\t\t, SCREAMING_SNAKE_CASE : Tuple\t\t\t\t):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r for j in range(SCREAMING_SNAKE_CASE\t\t\t\t, len(SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t):\r lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tdigits[j] + addend\r if s >= 1_0:\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tdivmod(SCREAMING_SNAKE_CASE\t\t\t\t, 1_0\t\t\t\t)\r lowerCAmelCase : str\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\taddend // 1_0 + quotient\r else:\r lowerCAmelCase : List[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\ts\r lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\taddend // 1_0\r\r if addend == 0:\r break\r\r while addend > 0:\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : List[str]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tdivmod(SCREAMING_SNAKE_CASE\t\t\t\t, 1_0\t\t\t\t)\r digits.append(SCREAMING_SNAKE_CASE\t\t\t\t)\rdef \t\t\t\t\ta__\t\t\t(\t\t\t\tSCREAMING_SNAKE_CASE : int = 1_0**1_5\t\t\t\t):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowerCAmelCase : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t[1]\r lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t1\r lowerCAmelCase : Optional[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t0\r while True:\r lowerCAmelCase\t\t\t\t\t\t, lowerCAmelCase : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tnext_term(SCREAMING_SNAKE_CASE\t\t\t\t, 2_0\t\t\t\t, i + dn\t\t\t\t, SCREAMING_SNAKE_CASE\t\t\t\t)\r dn += terms_jumped\r if dn == n - i:\r break\r\r lowerCAmelCase : Any\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t0\r for j in range(len(SCREAMING_SNAKE_CASE\t\t\t\t)\t\t\t\t):\r a_n += digits[j] * 1_0**j\r return a_n\r\r\rif __name__ == \"__main__\":\r print(F\"{solution() = }\")\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":108,"string":"108"},"style_context":{"kind":"string","value":"\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\rimport json\rimport os\rimport shutil\rimport tempfile\rfrom unittest import TestCase\r\rfrom transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast\rfrom transformers.models.bart.configuration_bart import BartConfig\rfrom transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES\rfrom transformers.models.dpr.configuration_dpr import DPRConfig\rfrom transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES\rfrom transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow\rfrom transformers.utils import is_datasets_available, is_faiss_available, is_torch_available\r\r\rif is_torch_available() and is_datasets_available() and is_faiss_available():\r from transformers.models.rag.configuration_rag import RagConfig\r from transformers.models.rag.tokenization_rag import RagTokenizer\r\r\r@require_faiss\r@require_torch\rclass SCREAMING_SNAKE_CASE__\t\t\t\t(\t\tlowercase\t\t\t\t):\r \"\"\"simple docstring\"\"\"\r\r\r def lowercase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\ttempfile.mkdtemp()\r lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t8\r\r # DPR tok\r lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t[\r \"[UNK]\",\r \"[CLS]\",\r \"[SEP]\",\r \"[PAD]\",\r \"[MASK]\",\r \"want\",\r \"##want\",\r \"##ed\",\r \"wa\",\r \"un\",\r \"runn\",\r \"##ing\",\r \",\",\r \"low\",\r \"lowest\",\r ]\r lowerCAmelCase : List[str]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tos.path.join(self.tmpdirname\t\t, \"dpr_tokenizer\"\t\t\t\t\t\t)\r os.makedirs(snake_case__\t\t, exist_ok=snake_case__\t\t\t\t\t\t)\r lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tos.path.join(snake_case__\t\t, DPR_VOCAB_FILES_NAMES[\"vocab_file\"]\t\t\t\t\t\t)\r with open(self.vocab_file\t\t, \"w\"\t\t, encoding=\"utf-8\"\t\t\t\t\t\t) as vocab_writer:\r vocab_writer.write(\"\".join([x + \"\\n\" for x in vocab_tokens]\t\t\t\t\t\t)\t\t\t\t\t\t)\r\r # BART tok\r lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t[\r \"l\",\r \"o\",\r \"w\",\r \"e\",\r \"r\",\r \"s\",\r \"t\",\r \"i\",\r \"d\",\r \"n\",\r \"\\u0120\",\r \"\\u0120l\",\r \"\\u0120n\",\r \"\\u0120lo\",\r \"\\u0120low\",\r \"er\",\r \"\\u0120lowest\",\r \"\\u0120newer\",\r \"\\u0120wider\",\r \"\",\r ]\r lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tdict(zip(snake_case__\t\t, range(len(snake_case__\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\r lowerCAmelCase : List[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t[\"#version: 0.2\", \"\\u0120 l\", \"\\u0120l o\", \"\\u0120lo w\", \"e r\", \"\"]\r lowerCAmelCase : str\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t{\"unk_token\": \"\"}\r\r lowerCAmelCase : int\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tos.path.join(self.tmpdirname\t\t, \"bart_tokenizer\"\t\t\t\t\t\t)\r os.makedirs(snake_case__\t\t, exist_ok=snake_case__\t\t\t\t\t\t)\r lowerCAmelCase : int\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tos.path.join(snake_case__\t\t, BART_VOCAB_FILES_NAMES[\"vocab_file\"]\t\t\t\t\t\t)\r lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tos.path.join(snake_case__\t\t, BART_VOCAB_FILES_NAMES[\"merges_file\"]\t\t\t\t\t\t)\r with open(self.vocab_file\t\t, \"w\"\t\t, encoding=\"utf-8\"\t\t\t\t\t\t) as fp:\r fp.write(json.dumps(snake_case__\t\t\t\t\t\t) + \"\\n\"\t\t\t\t\t\t)\r with open(self.merges_file\t\t, \"w\"\t\t, encoding=\"utf-8\"\t\t\t\t\t\t) as fp:\r fp.write(\"\\n\".join(snake_case__\t\t\t\t\t\t)\t\t\t\t\t\t)\r\r def lowercase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname\t\t, \"dpr_tokenizer\"\t\t\t\t\t\t)\t\t\t\t\t\t)\r\r def lowercase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname\t\t, \"bart_tokenizer\"\t\t\t\t\t\t)\t\t\t\t\t\t)\r\r def lowercase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r shutil.rmtree(self.tmpdirname\t\t\t\t\t\t)\r\r @require_tokenizers\r def lowercase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r lowerCAmelCase : str\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tos.path.join(self.tmpdirname\t\t, \"rag_tokenizer\"\t\t\t\t\t\t)\r lowerCAmelCase : List[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tRagConfig(question_encoder=DPRConfig().to_dict()\t\t, generator=BartConfig().to_dict()\t\t\t\t\t\t)\r lowerCAmelCase : Optional[Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tRagTokenizer(question_encoder=self.get_dpr_tokenizer()\t\t, generator=self.get_bart_tokenizer()\t\t\t\t\t\t)\r rag_config.save_pretrained(snake_case__\t\t\t\t\t\t)\r rag_tokenizer.save_pretrained(snake_case__\t\t\t\t\t\t)\r lowerCAmelCase : List[str]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tRagTokenizer.from_pretrained(snake_case__\t\t, config=snake_case__\t\t\t\t\t\t)\r self.assertIsInstance(new_rag_tokenizer.question_encoder\t\t, snake_case__\t\t\t\t\t\t)\r self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()\t\t, rag_tokenizer.question_encoder.get_vocab()\t\t\t\t\t\t)\r self.assertIsInstance(new_rag_tokenizer.generator\t\t, snake_case__\t\t\t\t\t\t)\r self.assertEqual(new_rag_tokenizer.generator.get_vocab()\t\t, rag_tokenizer.generator.get_vocab()\t\t\t\t\t\t)\r\r @slow\r def lowercase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r lowerCAmelCase : Optional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tRagTokenizer.from_pretrained(\"facebook/rag-token-nq\"\t\t\t\t\t\t)\r lowerCAmelCase : Dict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t[\r \"who got the first nobel prize in physics\",\r \"when is the next deadpool movie being released\",\r \"which mode is used for short wave broadcast service\",\r \"who is the owner of reading football club\",\r \"when is the next scandal episode coming out\",\r \"when is the last time the philadelphia won the superbowl\",\r \"what is the most current adobe flash player version\",\r \"how many episodes are there in dragon ball z\",\r \"what is the first step in the evolution of the eye\",\r \"where is gall bladder situated in human body\",\r \"what is the main mineral in lithium batteries\",\r \"who is the president of usa right now\",\r \"where do the greasers live in the outsiders\",\r \"panda is a national animal of which country\",\r \"what is the name of manchester united stadium\",\r ]\r lowerCAmelCase : Union[str, Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\ttokenizer(snake_case__\t\t\t\t\t\t)\r self.assertIsNotNone(snake_case__\t\t\t\t\t\t)\r\r\r\r\r\r\r\r @slow\r def lowercase__\t\t\t\t\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r\r\r\r lowerCAmelCase : List[str]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tRagTokenizer.from_pretrained(\"facebook/rag-sequence-nq\"\t\t\t\t\t\t)\r lowerCAmelCase : List[str]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t[\r \"who got the first nobel prize in physics\",\r \"when is the next deadpool movie being released\",\r \"which mode is used for short wave broadcast service\",\r \"who is the owner of reading football club\",\r \"when is the next scandal episode coming out\",\r \"when is the last time the philadelphia won the superbowl\",\r \"what is the most current adobe flash player version\",\r \"how many episodes are there in dragon ball z\",\r \"what is the first step in the evolution of the eye\",\r \"where is gall bladder situated in human body\",\r \"what is the main mineral in lithium batteries\",\r \"who is the president of usa right now\",\r \"where do the greasers live in the outsiders\",\r \"panda is a national animal of which country\",\r \"what is the name of manchester united stadium\",\r ]\r lowerCAmelCase : str\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\ttokenizer(snake_case__\t\t\t\t\t\t)\r self.assertIsNotNone(snake_case__\t\t\t\t\t\t)\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":108,"string":"108"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":583,"cells":{"code":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\nimport math\r\nfrom datetime import datetime, timedelta\r\n\r\n\r\ndef lowerCAmelCase\t(__UpperCamelCase : int\t):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=year % 1_9\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=year % 4\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=year % 7\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=math.floor(year / 1_0_0\t)\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=math.floor((1_3 + 8 * leap_day_inhibits) / 2_5\t)\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=leap_day_inhibits / 4\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=(\r\n\t\t\t\t\t\t\t 1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number\r\n\t\t\t\t\t\t\t) % 3_0\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=(4 + leap_day_inhibits - leap_day_reinstall_number) % 7\r\n\r\n\t\t\t\t\t\t\t# days to be added to March 21\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=(1_9 * metonic_cycle + secular_moon_shift) % 3_0\r\n\r\n\t\t\t\t\t\t\t# PHM -> Paschal Full Moon\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=(\r\n\t\t\t\t\t\t\t 2 * julian_leap_year\r\n\t\t\t\t\t\t\t + 4 * non_leap_year\r\n\t\t\t\t\t\t\t + 6 * days_to_add\r\n\t\t\t\t\t\t\t + century_starting_point\r\n\t\t\t\t\t\t\t) % 7\r\n\r\n\t\t\t\t\t\t\tif days_to_add == 2_9 and days_from_phm_to_sunday == 6:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn datetime(__UpperCamelCase ,\t\t\t4 ,\t\t\t1_9\t)\r\n\t\t\t\t\t\t\telif days_to_add == 2_8 and days_from_phm_to_sunday == 6:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn datetime(__UpperCamelCase ,\t\t\t4 ,\t\t\t1_8\t)\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn datetime(__UpperCamelCase ,\t\t\t3 ,\t\t\t2_2\t) + timedelta(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t days=int(days_to_add + days_from_phm_to_sunday\t)\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\t\tfor year in (1_994, 2_000, 2_010, 2_021, 2_023):\r\n\t\t\t\t\t\t\t\t__lowercase\t\t\t\t =\t\t\t\t\t\t\t'''will be''' if year > datetime.now().year else '''was'''\r\n\t\t\t\t\t\t\t\tprint(f'''Easter in {year} {tense} {gauss_easter(year)}''')\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":85,"string":"85"},"style_context":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\nimport itertools\r\nimport json\r\nimport os\r\nimport unittest\r\n\r\nfrom transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast\r\nfrom transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES\r\nfrom transformers.testing_utils import require_tokenizers, slow\r\n\r\nfrom ...test_tokenization_common import TokenizerTesterMixin\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_tokenizers\r\nclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\t__a\t\t\t,\t\t\tunittest.TestCase ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tlowercase__ = LongformerTokenizer\r\n\t\t\t\t\t\tlowercase__ = True\r\n\t\t\t\t\t\tlowercase__ = LongformerTokenizerFast\r\n\t\t\t\t\t\tlowercase__ = True\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Optional[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tAny:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tsuper().setUp()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''l''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''o''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''w''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''e''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''r''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''s''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''t''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''i''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''d''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''n''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''\\u0120''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''\\u0120l''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''\\u0120n''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''\\u0120lo''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''\\u0120low''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''er''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''\\u0120lowest''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''\\u0120newer''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''\\u0120wider''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=dict(zip(UpperCamelCase__ ,\t\t\t\trange(len(UpperCamelCase__\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=['''#version: 0.2''', '''\\u0120 l''', '''\\u0120l o''', '''\\u0120lo w''', '''e r''', '''''']\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t={'''unk_token''': ''''''}\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=os.path.join(self.tmpdirname ,\t\t\t\tVOCAB_FILES_NAMES['''vocab_file''']\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=os.path.join(self.tmpdirname ,\t\t\t\tVOCAB_FILES_NAMES['''merges_file''']\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith open(self.vocab_file ,\t\t\t\t'''w''' ,\t\t\t\tencoding='''utf-8'''\t\t\t\t\t\t\t) as fp:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfp.write(json.dumps(UpperCamelCase__\t\t\t\t\t\t\t) + '''\\n'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith open(self.merges_file ,\t\t\t\t'''w''' ,\t\t\t\tencoding='''utf-8'''\t\t\t\t\t\t\t) as fp:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfp.write('''\\n'''.join(UpperCamelCase__\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Optional[int] ,\t\t\t\t**UpperCamelCase__ : str\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tkwargs.update(self.special_tokens_map\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn self.tokenizer_class.from_pretrained(self.tmpdirname ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : List[str] ,\t\t\t\t**UpperCamelCase__ : Optional[int]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tUnion[str, Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tkwargs.update(self.special_tokens_map\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : List[str] ,\t\t\t\tUpperCamelCase__ : List[str]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tOptional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''lower newer'''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''lower newer'''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn input_text, output_text\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : int\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tList[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.tokenizer_class(self.vocab_file ,\t\t\t\tself.merges_file ,\t\t\t\t**self.special_tokens_map\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''lower newer'''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=['''l''', '''o''', '''w''', '''er''', '''\\u0120''', '''n''', '''e''', '''w''', '''er''']\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.tokenize(UpperCamelCase__\t\t\t\t\t\t\t) # , add_prefix_space=True)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokens + [tokenizer.unk_token]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__\t\t\t\t\t\t\t) ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Union[str, Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tint:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.get_tokenizer()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(tokenizer.encode('''Hello world!''' ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t) ,\t\t\t\t[0, 31414, 232, 328, 2]\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t tokenizer.encode('''Hello world! cécé herlolip 418''' ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t) ,\t\t\t\t[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2] ,\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@slow\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Tuple\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tOptional[int]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode('''sequence builders''' ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode('''multi-sequence build''' ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''sequence builders''' ,\t\t\t\tadd_special_tokens=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''sequence builders''' ,\t\t\t\t'''multi-sequence build''' ,\t\t\t\tadd_special_tokens=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.build_inputs_with_special_tokens(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.build_inputs_with_special_tokens(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tassert encoded_sentence == encoded_text_from_decode\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tassert encoded_pair == encoded_pair_from_decode\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : int\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.get_tokenizer()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''Encode this sequence.'''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.byte_encoder[''' '''.encode('''utf-8'''\t\t\t\t\t\t\t)[0]]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Testing encoder arguments\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode(UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.convert_ids_to_tokens(encoded[0]\t\t\t\t\t\t\t)[0]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertNotEqual(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode(UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.convert_ids_to_tokens(encoded[0]\t\t\t\t\t\t\t)[0]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\ttokenizer.add_special_tokens({'''bos_token''': ''''''}\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode(UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.convert_ids_to_tokens(encoded[1]\t\t\t\t\t\t\t)[0]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertNotEqual(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Testing spaces after special tokens\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=''''''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\ttokenizer.add_special_tokens(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t {'''mask_token''': AddedToken(UpperCamelCase__ ,\t\t\t\tlstrip=UpperCamelCase__ ,\t\t\t\trstrip=UpperCamelCase__\t\t\t\t\t\t\t)}\t\t\t\t\t\t\t) # mask token has a left space\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.convert_tokens_to_ids(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''Encode sequence'''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''Encode sequence'''\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=encoded.index(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]\t\t\t\t\t\t\t)[0]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.encode(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=encoded.index(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]\t\t\t\t\t\t\t)[0]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertNotEqual(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : int\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Union[str, Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tList[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor tokenizer, pretrained_name, kwargs in self.tokenizers_list:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith self.subTest(f\"\"\"{tokenizer.__class__.__name__} ({pretrained_name})\"\"\"\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(UpperCamelCase__ ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.tokenizer_class.from_pretrained(UpperCamelCase__ ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''A, AllenNLP sentence.'''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r.encode_plus(UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__ ,\t\t\t\treturn_token_type_ids=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_p.encode_plus(UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__ ,\t\t\t\treturn_token_type_ids=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# token_type_ids should put 0 everywhere\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(sum(tokens_r['''token_type_ids''']\t\t\t\t\t\t\t) ,\t\t\t\tsum(tokens_p['''token_type_ids''']\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# attention_mask should put 1 everywhere, so sum over length should be 1\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t sum(tokens_r['''attention_mask''']\t\t\t\t\t\t\t) / len(tokens_r['''attention_mask''']\t\t\t\t\t\t\t) ,\t\t\t\tsum(tokens_p['''attention_mask''']\t\t\t\t\t\t\t) / len(tokens_p['''attention_mask''']\t\t\t\t\t\t\t) ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Rust correctly handles the space before the mask while python doesnt\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertSequenceEqual(tokens_p['''input_ids'''] ,\t\t\t\t[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertSequenceEqual(tokens_r['''input_ids'''] ,\t\t\t\t[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertSequenceEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\t['''''', '''A''', ''',''', '''''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''''']\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertSequenceEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\t['''''', '''A''', ''',''', '''''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''''']\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Union[str, Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tOptional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor trim_offsets, add_prefix_space in itertools.product([True, False] ,\t\t\t\trepeat=2\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.tmpdirname ,\t\t\t\tuse_fast=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__ ,\t\t\t\ttrim_offsets=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(pre_tokenizer_state['''add_prefix_space'''] ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(post_processor_state['''add_prefix_space'''] ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(post_processor_state['''trim_offsets'''] ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : List[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tint:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor tokenizer, pretrained_name, kwargs in self.tokenizers_list:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith self.subTest(f\"\"\"{tokenizer.__class__.__name__} ({pretrained_name})\"\"\"\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''hello''' # `hello` is a token in the vocabulary of `pretrained_name`\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=f\"\"\"{text_of_1_token} {text_of_1_token}\"\"\"\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\tuse_fast=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__ ,\t\t\t\ttrim_offsets=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r(UpperCamelCase__ ,\t\t\t\treturn_offsets_mapping=UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(encoding.offset_mapping[0] ,\t\t\t\t(0, len(UpperCamelCase__\t\t\t\t\t\t\t))\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t encoding.offset_mapping[1] ,\t\t\t\t(len(UpperCamelCase__\t\t\t\t\t\t\t) + 1, len(UpperCamelCase__\t\t\t\t\t\t\t) + 1 + len(UpperCamelCase__\t\t\t\t\t\t\t)) ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\tuse_fast=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__ ,\t\t\t\ttrim_offsets=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r(UpperCamelCase__ ,\t\t\t\treturn_offsets_mapping=UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(encoding.offset_mapping[0] ,\t\t\t\t(0, len(UpperCamelCase__\t\t\t\t\t\t\t))\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t encoding.offset_mapping[1] ,\t\t\t\t(len(UpperCamelCase__\t\t\t\t\t\t\t) + 1, len(UpperCamelCase__\t\t\t\t\t\t\t) + 1 + len(UpperCamelCase__\t\t\t\t\t\t\t)) ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\tuse_fast=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__ ,\t\t\t\ttrim_offsets=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r(UpperCamelCase__ ,\t\t\t\treturn_offsets_mapping=UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(encoding.offset_mapping[0] ,\t\t\t\t(0, len(UpperCamelCase__\t\t\t\t\t\t\t))\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t encoding.offset_mapping[1] ,\t\t\t\t(len(UpperCamelCase__\t\t\t\t\t\t\t), len(UpperCamelCase__\t\t\t\t\t\t\t) + 1 + len(UpperCamelCase__\t\t\t\t\t\t\t)) ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\tuse_fast=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__ ,\t\t\t\ttrim_offsets=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r(UpperCamelCase__ ,\t\t\t\treturn_offsets_mapping=UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(encoding.offset_mapping[0] ,\t\t\t\t(0, len(UpperCamelCase__\t\t\t\t\t\t\t))\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t encoding.offset_mapping[1] ,\t\t\t\t(len(UpperCamelCase__\t\t\t\t\t\t\t), len(UpperCamelCase__\t\t\t\t\t\t\t) + 1 + len(UpperCamelCase__\t\t\t\t\t\t\t)) ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=f\"\"\" {text}\"\"\"\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# tokenizer_r = self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# self.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# encoding.offset_mapping[1],\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\tuse_fast=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__ ,\t\t\t\ttrim_offsets=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r(UpperCamelCase__ ,\t\t\t\treturn_offsets_mapping=UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(encoding.offset_mapping[0] ,\t\t\t\t(1, 1 + len(UpperCamelCase__\t\t\t\t\t\t\t))\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t encoding.offset_mapping[1] ,\t\t\t\t(1 + len(UpperCamelCase__\t\t\t\t\t\t\t) + 1, 1 + len(UpperCamelCase__\t\t\t\t\t\t\t) + 1 + len(UpperCamelCase__\t\t\t\t\t\t\t)) ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\tuse_fast=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__ ,\t\t\t\ttrim_offsets=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r(UpperCamelCase__ ,\t\t\t\treturn_offsets_mapping=UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(encoding.offset_mapping[0] ,\t\t\t\t(0, 1 + len(UpperCamelCase__\t\t\t\t\t\t\t))\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t encoding.offset_mapping[1] ,\t\t\t\t(1 + len(UpperCamelCase__\t\t\t\t\t\t\t), 1 + len(UpperCamelCase__\t\t\t\t\t\t\t) + 1 + len(UpperCamelCase__\t\t\t\t\t\t\t)) ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.rust_tokenizer_class.from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\tuse_fast=UpperCamelCase__ ,\t\t\t\tadd_prefix_space=UpperCamelCase__ ,\t\t\t\ttrim_offsets=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer_r(UpperCamelCase__ ,\t\t\t\treturn_offsets_mapping=UpperCamelCase__ ,\t\t\t\tadd_special_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(encoding.offset_mapping[0] ,\t\t\t\t(0, 1 + len(UpperCamelCase__\t\t\t\t\t\t\t))\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t encoding.offset_mapping[1] ,\t\t\t\t(1 + len(UpperCamelCase__\t\t\t\t\t\t\t), 1 + len(UpperCamelCase__\t\t\t\t\t\t\t) + 1 + len(UpperCamelCase__\t\t\t\t\t\t\t)) ,\t\t\t\t)\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":85,"string":"85"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":584,"cells":{"code":{"kind":"string","value":"\rimport itertools\rimport random\rimport unittest\r\rimport numpy as np\r\rfrom transformers import BatchFeature, SpeechTaFeatureExtractor\rfrom transformers.testing_utils import require_torch\rfrom transformers.utils.import_utils import is_torch_available\r\rfrom ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin\r\r\rif is_torch_available():\r import torch\r\r\r_A\t\t = random.Random()\r\r\r\r\rdef lowerCamelCase__ ( a__ : List[str]\t\t,\t\t\t\t\ta__ : int=1.0\t\t,\t\t\t\t\ta__ : Any=None\t\t,\t\t\t\t\ta__ : Dict=None )\t\t\t\t\t\t->\t\tList[Any]:\r if rng is None:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tglobal_rng\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[]\r for batch_idx in range(shape[0] ):\r values.append([] )\r for _ in range(shape[1] ):\r values[-1].append(rng.random() * scale )\r\r return values\r\r@require_torch\rclass \t\t\t\t\t\tlowercase_\t\t\t\t(\t\t\t\tunittest.TestCase ):\r\r def __init__(\t\t\t\t\tself ,\t\t__UpperCamelCase ,\t\t__UpperCamelCase=7 ,\t\t__UpperCamelCase=4_0_0 ,\t\t__UpperCamelCase=2_0_0_0 ,\t\t__UpperCamelCase=1 ,\t\t__UpperCamelCase=0.0 ,\t\t__UpperCamelCase=1_6_0_0_0 ,\t\t__UpperCamelCase=True ,\t\t__UpperCamelCase=8_0 ,\t\t__UpperCamelCase=1_6 ,\t\t__UpperCamelCase=6_4 ,\t\t__UpperCamelCase=\"hann_window\" ,\t\t__UpperCamelCase=8_0 ,\t\t__UpperCamelCase=7_6_0_0 ,\t\t__UpperCamelCase=1e-10 ,\t\t__UpperCamelCase=True ,\t\t):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tparent\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tbatch_size\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tmin_seq_length\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tmax_seq_length\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_size\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tpadding_value\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tsampling_rate\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tdo_normalize\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tnum_mel_bins\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\thop_length\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\twin_length\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\twin_function\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfmin\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfmax\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tmel_floor\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\treturn_attention_mask\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r return {\r \"feature_size\": self.feature_size,\r \"padding_value\": self.padding_value,\r \"sampling_rate\": self.sampling_rate,\r \"do_normalize\": self.do_normalize,\r \"num_mel_bins\": self.num_mel_bins,\r \"hop_length\": self.hop_length,\r \"win_length\": self.win_length,\r \"win_function\": self.win_function,\r \"fmin\": self.fmin,\r \"fmax\": self.fmax,\r \"mel_floor\": self.mel_floor,\r \"return_attention_mask\": self.return_attention_mask,\r }\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase=False ,\t\t__UpperCamelCase=False ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r def _flatten(__UpperCamelCase ):\r return list(itertools.chain(*__UpperCamelCase ) )\r\r if equal_length:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfloats_list((self.batch_size, self.max_seq_length) )\r else:\r # make sure that inputs increase in size\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[\r _flatten(floats_list((x, self.feature_size) ) )\r for x in range(self.min_seq_length ,\t\tself.max_seq_length ,\t\tself.seq_length_diff )\r ]\r\r if numpify:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[np.asarray(__UpperCamelCase ) for x in speech_inputs]\r\r return speech_inputs\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase=False ,\t\t__UpperCamelCase=False ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r if equal_length:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]\r else:\r # make sure that inputs increase in size\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[\r floats_list((x, self.num_mel_bins) )\r for x in range(self.min_seq_length ,\t\tself.max_seq_length ,\t\tself.seq_length_diff )\r ]\r\r if numpify:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[np.asarray(__UpperCamelCase ) for x in speech_inputs]\r\r return speech_inputs\r\r\r\r@require_torch\rclass \t\t\t\t\t\tlowercase_\t\t\t\t(\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t,\t\tunittest.TestCase ):\r A__ : Union[str, Any] \t\t\t\t\t=\t\t\tSpeechTaFeatureExtractor\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tSpeechTaFeatureExtractionTester(self )\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r self.assertTrue(np.all(np.mean(__UpperCamelCase ,\t\taxis=0 ) < 1e-3 ) )\r self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase ,\t\taxis=0 ) - 1 ) < 1e-3 ) )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )\r # create three inputs of length 800, 1000, and 1200\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((1, x) )[0] for x in range(8_0_0 ,\t\t1_4_0_0 ,\t\t2_0_0 )]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]\r\r # Test not batched input\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(speech_inputs[0] ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(np_speech_inputs[0] ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r self.assertTrue(np.allclose(__UpperCamelCase ,\t\t__UpperCamelCase ,\t\tatol=1e-3 ) )\r\r # Test batched\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r for enc_seq_a, enc_seq_a in zip(__UpperCamelCase ,\t\t__UpperCamelCase ):\r self.assertTrue(np.allclose(__UpperCamelCase ,\t\t__UpperCamelCase ,\t\tatol=1e-3 ) )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((1, x) )[0] for x in range(8_0_0 ,\t\t1_4_0_0 ,\t\t2_0_0 )]\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[\"\"\"longest\"\"\", \"\"\"max_length\"\"\", \"\"\"do_not_pad\"\"\"]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[None, 1_6_0_0, None]\r for max_length, padding in zip(__UpperCamelCase ,\t\t__UpperCamelCase ):\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(__UpperCamelCase ,\t\tpadding=__UpperCamelCase ,\t\tmax_length=__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tprocessed.input_values\r\r self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )\r self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6 )\r self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )\r self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6 )\r self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\trange(8_0_0 ,\t\t1_4_0_0 ,\t\t2_0_0 )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((1, x) )[0] for x in lengths]\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[\"\"\"longest\"\"\", \"\"\"max_length\"\"\", \"\"\"do_not_pad\"\"\"]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[None, 1_6_0_0, None]\r\r for max_length, padding in zip(__UpperCamelCase ,\t\t__UpperCamelCase ):\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(__UpperCamelCase ,\t\tmax_length=__UpperCamelCase ,\t\tpadding=__UpperCamelCase )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tprocessed.input_values\r\r self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )\r self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )\r self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((1, x) )[0] for x in range(8_0_0 ,\t\t1_4_0_0 ,\t\t2_0_0 )]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(\r __UpperCamelCase ,\t\ttruncation=__UpperCamelCase ,\t\tmax_length=1_0_0_0 ,\t\tpadding=\"\"\"max_length\"\"\" ,\t\treturn_tensors=\"\"\"np\"\"\" )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tprocessed.input_values\r\r self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )\r self._check_zero_mean_unit_variance(input_values[1] )\r self._check_zero_mean_unit_variance(input_values[2] )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((1, x) )[0] for x in range(8_0_0 ,\t\t1_4_0_0 ,\t\t2_0_0 )]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(\r __UpperCamelCase ,\t\ttruncation=__UpperCamelCase ,\t\tmax_length=1_0_0_0 ,\t\tpadding=\"\"\"longest\"\"\" ,\t\treturn_tensors=\"\"\"np\"\"\" )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tprocessed.input_values\r\r self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )\r self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )\r self._check_zero_mean_unit_variance(input_values[2] )\r\r # make sure that if max_length < longest -> then pad to max_length\r self.assertTrue(input_values.shape == (3, 1_0_0_0) )\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((1, x) )[0] for x in range(8_0_0 ,\t\t1_4_0_0 ,\t\t2_0_0 )]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract(\r __UpperCamelCase ,\t\ttruncation=__UpperCamelCase ,\t\tmax_length=2_0_0_0 ,\t\tpadding=\"\"\"longest\"\"\" ,\t\treturn_tensors=\"\"\"np\"\"\" )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tprocessed.input_values\r\r self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )\r self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )\r self._check_zero_mean_unit_variance(input_values[2] )\r\r # make sure that if max_length > longest -> then pad to longest\r self.assertTrue(input_values.shape == (3, 1_2_0_0) )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tnp.random.rand(1_0_0 ).astype(np.floataa )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tnp_speech_inputs.tolist()\r\r for inputs in [py_speech_inputs, np_speech_inputs]:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor.pad([{\"\"\"input_values\"\"\": inputs}] ,\t\treturn_tensors=\"\"\"np\"\"\" )\r self.assertTrue(np_processed.input_values.dtype == np.floataa )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor.pad([{\"\"\"input_values\"\"\": inputs}] ,\t\treturn_tensors=\"\"\"pt\"\"\" )\r self.assertTrue(pt_processed.input_values.dtype == torch.floataa )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )\r # create three inputs of length 800, 1000, and 1200\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((1, x) )[0] for x in range(8_0_0 ,\t\t1_4_0_0 ,\t\t2_0_0 )]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]\r\r # Test feature size\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(audio_target=__UpperCamelCase ,\t\tpadding=__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r self.assertTrue(input_values.ndim == 3 )\r self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )\r\r # Test not batched input\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(speech_inputs[0] ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(np_speech_inputs[0] ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r self.assertTrue(np.allclose(__UpperCamelCase ,\t\t__UpperCamelCase ,\t\tatol=1e-3 ) )\r\r # Test batched\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r for enc_seq_a, enc_seq_a in zip(__UpperCamelCase ,\t\t__UpperCamelCase ):\r self.assertTrue(np.allclose(__UpperCamelCase ,\t\t__UpperCamelCase ,\t\tatol=1e-3 ) )\r\r # Test 2-D numpy arrays are batched.\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tnp.asarray(__UpperCamelCase )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" ).input_values\r for enc_seq_a, enc_seq_a in zip(__UpperCamelCase ,\t\t__UpperCamelCase ):\r self.assertTrue(np.allclose(__UpperCamelCase ,\t\t__UpperCamelCase ,\t\tatol=1e-3 ) )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feat_extract_tester.prepare_inputs_for_target()\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_dict )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.model_input_names[0]\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tBatchFeature({input_name: speech_inputs} )\r\r self.assertTrue(all(len(__UpperCamelCase ) == len(__UpperCamelCase ) for x, y in zip(__UpperCamelCase ,\t\tprocessed_features[input_name] ) ) )\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCamelCase )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tBatchFeature({input_name: speech_inputs} ,\t\ttensor_type=\"\"\"np\"\"\" )\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tprocessed_features[input_name]\r\r if len(batch_features_input.shape ) < 3:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tbatch_features_input[:, :, None]\r\r self.assertTrue(\r batch_features_input.shape\r == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )\r\r @require_torch\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCamelCase )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_dict )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.model_input_names[0]\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tBatchFeature({input_name: speech_inputs} ,\t\ttensor_type=\"\"\"pt\"\"\" )\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tprocessed_features[input_name]\r\r if len(batch_features_input.shape ) < 3:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tbatch_features_input[:, :, None]\r\r self.assertTrue(\r batch_features_input.shape\r == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )\r\r @require_torch\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**self.feat_extract_dict )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feat_extract_tester.prepare_inputs_for_target()\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.model_input_names[0]\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tBatchFeature({input_name: speech_inputs} )\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.num_mel_bins # hack!\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.pad(__UpperCamelCase ,\t\tpadding=\"\"\"longest\"\"\" ,\t\treturn_tensors=\"\"\"np\"\"\" )[input_name]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.pad(__UpperCamelCase ,\t\tpadding=\"\"\"longest\"\"\" ,\t\treturn_tensors=\"\"\"pt\"\"\" )[input_name]\r\r self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feat_extract_dict\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tTrue\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**__UpperCamelCase )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feat_extract_tester.prepare_inputs_for_target()\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[len(__UpperCamelCase ) for x in speech_inputs]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.model_input_names[0]\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tBatchFeature({input_name: speech_inputs} )\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.num_mel_bins # hack!\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.pad(__UpperCamelCase ,\t\tpadding=\"\"\"longest\"\"\" ,\t\treturn_tensors=\"\"\"np\"\"\" )\r self.assertIn(\"\"\"attention_mask\"\"\" ,\t\t__UpperCamelCase )\r self.assertListEqual(list(processed.attention_mask.shape ) ,\t\tlist(processed[input_name].shape[:2] ) )\r self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,\t\t__UpperCamelCase )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feat_extract_dict\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tTrue\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feature_extraction_class(**__UpperCamelCase )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.feat_extract_tester.prepare_inputs_for_target()\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[len(__UpperCamelCase ) for x in speech_inputs]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.model_input_names[0]\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tBatchFeature({input_name: speech_inputs} )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tmin(__UpperCamelCase )\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.num_mel_bins # hack!\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeat_extract.pad(\r __UpperCamelCase ,\t\tpadding=\"\"\"max_length\"\"\" ,\t\tmax_length=__UpperCamelCase ,\t\ttruncation=__UpperCamelCase ,\t\treturn_tensors=\"\"\"np\"\"\" )\r self.assertIn(\"\"\"attention_mask\"\"\" ,\t\t__UpperCamelCase )\r self.assertListEqual(\r list(processed_pad.attention_mask.shape ) ,\t\t[processed_pad[input_name].shape[0], max_length] )\r self.assertListEqual(\r processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,\t\t[max_length for x in speech_inputs] )\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r from datasets import load_dataset\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tload_dataset(\"\"\"hf-internal-testing/librispeech_asr_dummy\"\"\" ,\t\t\"\"\"clean\"\"\" ,\t\tsplit=\"\"\"validation\"\"\" )\r # automatic decoding with librispeech\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tds.sort(\"\"\"id\"\"\" ).select(range(__UpperCamelCase ) )[:num_samples][\"\"\"audio\"\"\"]\r\r return [x[\"array\"] for x in speech_samples]\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\ttorch.tensor(\r [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,\r 3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,\r 2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,\r 4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,\r 7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,\r 4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03] )\r # fmt: on\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself._load_datasamples(1 )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tSpeechTaFeatureExtractor()\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(__UpperCamelCase ,\t\treturn_tensors=\"\"\"pt\"\"\" ).input_values\r self.assertEquals(input_values.shape ,\t\t(1, 9_3_6_8_0) )\r self.assertTrue(torch.allclose(input_values[0, :3_0] ,\t\t__UpperCamelCase ,\t\tatol=1e-6 ) )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\ttorch.tensor(\r [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,\r -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,\r -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,\r -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998] )\r # fmt: on\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself._load_datasamples(1 )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tSpeechTaFeatureExtractor()\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfeature_extractor(audio_target=__UpperCamelCase ,\t\treturn_tensors=\"\"\"pt\"\"\" ).input_values\r self.assertEquals(input_values.shape ,\t\t(1, 3_6_6, 8_0) )\r self.assertTrue(torch.allclose(input_values[0, 0, :3_0] ,\t\t__UpperCamelCase ,\t\tatol=1e-4 ) )\r\r\r\r"},"code_codestyle":{"kind":"number","value":122,"string":"122"},"style_context":{"kind":"string","value":"\rimport os\rfrom shutil import copyfile\rfrom typing import Any, Dict, List, Optional, Tuple\r\rimport sentencepiece as spm\r\rfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\rfrom ...utils import logging\r\r\r_A\t\t = logging.get_logger(__name__)\r\r_A\t\t = '''▁'''\r\r_A\t\t = {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}\r\r_A\t\t = {\r '''vocab_file''': {\r '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',\r },\r '''monolingual_vocab_file''': {\r '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',\r },\r}\r\r_A\t\t = {'''vinai/bartpho-syllable''': 1_024}\r\rclass \t\t\t\t\t\tlowercase_\t\t\t\t(\t\t\t\t__SCREAMING_SNAKE_CASE ):\r A__ : List[Any] \t\t\t\t\t=\t\t\tVOCAB_FILES_NAMES\r A__ : Optional[int] \t\t\t\t\t=\t\t\tPRETRAINED_VOCAB_FILES_MAP\r A__ : Optional[int] \t\t\t\t\t=\t\t\tPRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r A__ : Union[str, Any] \t\t\t\t\t=\t\t\t[\"\"\"input_ids\"\"\", \"\"\"attention_mask\"\"\"]\r\r def __init__(\t\t\t\t\tself ,\t\t__UpperCamelCase ,\t\t__UpperCamelCase ,\t\t__UpperCamelCase=\"\" ,\t\t__UpperCamelCase=\"\" ,\t\t__UpperCamelCase=\"\" ,\t\t__UpperCamelCase=\"\" ,\t\t__UpperCamelCase=\"\" ,\t\t__UpperCamelCase=\"\" ,\t\t__UpperCamelCase=\"\" ,\t\t__UpperCamelCase = None ,\t\t**__UpperCamelCase ,\t\t):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tAddedToken(__UpperCamelCase ,\t\tlstrip=__UpperCamelCase ,\t\trstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,\t\t__UpperCamelCase ) else mask_token\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\r super().__init__(\r bos_token=__UpperCamelCase ,\t\teos_token=__UpperCamelCase ,\t\tunk_token=__UpperCamelCase ,\t\tsep_token=__UpperCamelCase ,\t\tcls_token=__UpperCamelCase ,\t\tpad_token=__UpperCamelCase ,\t\tmask_token=__UpperCamelCase ,\t\tsp_model_kwargs=self.sp_model_kwargs ,\t\t**__UpperCamelCase ,\t\t)\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tvocab_file\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tmonolingual_vocab_file\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.Load(str(__UpperCamelCase ) )\r\r # Load the reduced vocab\r\r # Keep order of special tokens for backward compatibility\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t{}\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t0\r for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:\r if str(__UpperCamelCase ) not in self.fairseq_tokens_to_ids:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tcnt\r cnt += 1\r with open(__UpperCamelCase ,\t\t\"\"\"r\"\"\" ,\t\tencoding=\"\"\"utf-8\"\"\" ) as f:\r for line in f.readlines():\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tline.strip().split()[0]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tlen(self.fairseq_tokens_to_ids )\r if str(__UpperCamelCase ) not in self.fairseq_tokens_to_ids:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tlen(self.fairseq_tokens_to_ids )\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t{v: k for k, v in self.fairseq_tokens_to_ids.items()}\r\r def __getstate__(\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.__dict__.copy()\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tNone\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.sp_model.serialized_model_proto()\r return state\r\r def __setstate__(\t\t\t\t\tself ,\t\t__UpperCamelCase ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\td\r\r # for backward compatibility\r if not hasattr(self ,\t\t\"\"\"sp_model_kwargs\"\"\" ):\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t{}\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs )\r self.sp_model.LoadFromSerializedProto(self.sp_model_proto )\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ,\t\t__UpperCamelCase = None ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r if token_ids_a is None:\r return [self.cls_token_id] + token_ids_a + [self.sep_token_id]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[self.cls_token_id]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[self.sep_token_id]\r return cls + token_ids_a + sep + sep + token_ids_a + sep\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ,\t\t__UpperCamelCase = None ,\t\t__UpperCamelCase = False ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r if already_has_special_tokens:\r return super().get_special_tokens_mask(\r token_ids_a=__UpperCamelCase ,\t\ttoken_ids_a=__UpperCamelCase ,\t\talready_has_special_tokens=__UpperCamelCase )\r\r if token_ids_a is None:\r return [1] + ([0] * len(__UpperCamelCase )) + [1]\r return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1]\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ,\t\t__UpperCamelCase = None ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[self.sep_token_id]\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t[self.cls_token_id]\r\r if token_ids_a is None:\r return len(cls + token_ids_a + sep ) * [0]\r return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]\r\r @property\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r return len(self.fairseq_ids_to_tokens )\r\r def lowerCamelCase_ (\t\t\t\t\tself ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t{self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}\r vocab.update(self.added_tokens_encoder )\r return vocab\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r return self.sp_model.encode(__UpperCamelCase ,\t\tout_type=__UpperCamelCase )\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r if token in self.fairseq_tokens_to_ids:\r return self.fairseq_tokens_to_ids[token]\r else:\r return self.unk_token_id\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r return self.fairseq_ids_to_tokens[index]\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\"\"\"\"\"\".join(__UpperCamelCase ).replace(__UpperCamelCase ,\t\t\"\"\" \"\"\" ).strip()\r return out_string\r\r def lowerCamelCase_ (\t\t\t\t\tself ,\t\t__UpperCamelCase ,\t\t__UpperCamelCase = None ):\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r if not os.path.isdir(__UpperCamelCase ):\r logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )\r return\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tos.path.join(\r __UpperCamelCase ,\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"vocab_file\"\"\"] )\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tos.path.join(\r __UpperCamelCase ,\t\t(filename_prefix + \"\"\"-\"\"\" if filename_prefix else \"\"\"\"\"\") + VOCAB_FILES_NAMES[\"\"\"monolingual_vocab_file\"\"\"] ,\t\t)\r\r if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ) and os.path.isfile(self.vocab_file ):\r copyfile(self.vocab_file ,\t\t__UpperCamelCase )\r elif not os.path.isfile(self.vocab_file ):\r with open(__UpperCamelCase ,\t\t\"\"\"wb\"\"\" ) as fi:\r UpperCamelCase_\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.sp_model.serialized_model_proto()\r fi.write(__UpperCamelCase )\r\r if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(\r __UpperCamelCase ) and os.path.isfile(self.monolingual_vocab_file ):\r copyfile(self.monolingual_vocab_file ,\t\t__UpperCamelCase )\r elif not os.path.isfile(self.monolingual_vocab_file ):\r with open(__UpperCamelCase ,\t\t\"\"\"w\"\"\" ,\t\tencoding=\"\"\"utf-8\"\"\" ) as fp:\r for token in self.fairseq_tokens_to_ids:\r if token not in self.all_special_tokens:\r fp.write(f'''{str(__UpperCamelCase )} \\n''' )\r\r return out_vocab_file, out_monolingual_vocab_file\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":122,"string":"122"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":585,"cells":{"code":{"kind":"string","value":"\n\n\n\n\nimport json\nimport sys\nimport tempfile\nimport unittest\nfrom pathlib import Path\n\nimport transformers\nfrom transformers import (\n CONFIG_MAPPING,\n FEATURE_EXTRACTOR_MAPPING,\n AutoConfig,\n AutoFeatureExtractor,\n WavaVecaConfig,\n WavaVecaFeatureExtractor,\n)\nfrom transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir\n\n\nsys.path.append(str(Path(__file__).parent.parent.parent.parent / \"utils\"))\n\nfrom test_module.custom_configuration import CustomConfig # noqa E402\nfrom test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402\n\n\n_snake_case =\tget_tests_dir(\"fixtures\")\n_snake_case =\tget_tests_dir(\"fixtures/dummy_feature_extractor_config.json\")\n_snake_case =\tget_tests_dir(\"fixtures/dummy-config.json\")\n\n\n\n\n\n\n\nclass \t\t\t\t\t\tlowercase\t\t\t\t\t\t\t( unittest.TestCase\t\t):\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tList[str]:\n\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int] =\t\t\t\t\t\t0\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tList[str]:\n\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tint =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\"\"\"facebook/wav2vec2-base-960h\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(_a\t\t,\t\t\t\t\t_a )\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tTuple:\n\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tDict =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(_a )\n\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(_a\t\t,\t\t\t\t\t_a )\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tDict:\n\t\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmpdirname:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tTuple =\t\t\t\t\t\tWavaVecaConfig()\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tstr =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(_a ).to_dict()\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tconfig_dict.pop(\"\"\"feature_extractor_type\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int] =\t\t\t\t\t\tWavaVecaFeatureExtractor(**_a )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# save in new folder\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel_config.save_pretrained(_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tconfig.save_pretrained(_a )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any] =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(_a )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# make sure private variable is not incorrectly saved\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tList[str] =\t\t\t\t\t\tjson.loads(config.to_json_string() )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(\"\"\"_processor_class\"\"\" not in dict_as_saved )\n\n\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(_a\t\t,\t\t\t\t\t_a )\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tOptional[Any]:\n\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tint =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(_a )\n\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(_a\t\t,\t\t\t\t\t_a )\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tint:\n\t\t\t\t\t\t\t\t\t\t\twith self.assertRaisesRegex(\n\t\t\t\t\t\t\t\t\t\t\t _a\t\t,\t\t\t\t\t\"\"\"bert-base is not a local folder and is not a valid model identifier\"\"\" ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any] =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\"\"\"bert-base\"\"\" )\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tstr:\n\t\t\t\t\t\t\t\t\t\t\twith self.assertRaisesRegex(\n\t\t\t\t\t\t\t\t\t\t\t _a\t\t,\t\t\t\t\tR\"\"\"aaaaaa is not a valid git identifier \\(branch name, tag name or commit id\\)\"\"\" ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tTuple =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(_a\t\t,\t\t\t\t\trevision=\"\"\"aaaaaa\"\"\" )\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tAny:\n\t\t\t\t\t\t\t\t\t\t\twith self.assertRaisesRegex(\n\t\t\t\t\t\t\t\t\t\t\t _a\t\t,\t\t\t\t\t\"\"\"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.\"\"\"\t\t,\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tint =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\"\"\"hf-internal-testing/config-no-model\"\"\" )\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tAny:\n\t\t\t\t\t\t\t\t\t\t\t# If remote code is not set, we will time out when asking whether to load the model.\n\t\t\t\t\t\t\t\t\t\t\twith self.assertRaises(_a ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tTuple =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_feature_extractor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t# If remote code is disabled, we can't load this config.\n\t\t\t\t\t\t\t\t\t\t\twith self.assertRaises(_a ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tDict =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_feature_extractor\"\"\"\t\t,\t\t\t\t\ttrust_remote_code=_a )\n\n\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tDict =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_feature_extractor\"\"\"\t\t,\t\t\t\t\ttrust_remote_code=_a )\n\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(feature_extractor.__class__.__name__\t\t,\t\t\t\t\t\"\"\"NewFeatureExtractor\"\"\" )\n\n\t\t\t\t\t\t\t\t\t\t\t# Test feature extractor can be reloaded.\n\t\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfeature_extractor.save_pretrained(_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tList[str] =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(_a\t\t,\t\t\t\t\ttrust_remote_code=_a )\n\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(reloaded_feature_extractor.__class__.__name__\t\t,\t\t\t\t\t\"\"\"NewFeatureExtractor\"\"\" )\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tList[Any]:\n\t\t\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAutoConfig.register(\"\"\"custom\"\"\"\t\t,\t\t\t\t\t_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAutoFeatureExtractor.register(_a\t\t,\t\t\t\t\t_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Trying to register something existing in the Transformers library will raise an error\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith self.assertRaises(_a ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAutoFeatureExtractor.register(_a\t\t,\t\t\t\t\t_a )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Now that the config is registered, it can be used as any other config with the auto-API\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tList[str] =\t\t\t\t\t\tCustomFeatureExtractor.from_pretrained(_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfeature_extractor.save_pretrained(_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tList[Any] =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertIsInstance(_a\t\t,\t\t\t\t\t_a )\n\n\t\t\t\t\t\t\t\t\t\t\tfinally:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif \"custom\" in CONFIG_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel CONFIG_MAPPING._extra_content[\"custom\"]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]\n\n\n\n\t\t\t\t\tdef a__ ( self )\t\t\t\t\t\t->\t\t\t\t\t\t\tList[Any]:\n\n\n\n\n\n\n\n\t\t\t\t\t\t\t\t\t\t\tclass \t\t\t\t\t\tlowercase\t\t\t\t\t\t\t( UpperCamelCase__\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tTrue\n\n\t\t\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAutoConfig.register(\"\"\"custom\"\"\"\t\t,\t\t\t\t\t_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAutoFeatureExtractor.register(_a\t\t,\t\t\t\t\t_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# If remote code is not set, the default is to use local\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tList[Any] =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_feature_extractor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(feature_extractor.__class__.__name__\t\t,\t\t\t\t\t\"\"\"NewFeatureExtractor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(feature_extractor.is_local )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# If remote code is disabled, we load the local one.\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tList[str] =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_feature_extractor\"\"\"\t\t,\t\t\t\t\ttrust_remote_code=_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(feature_extractor.__class__.__name__\t\t,\t\t\t\t\t\"\"\"NewFeatureExtractor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(feature_extractor.is_local )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# If remote is enabled, we load from the Hub\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_A\t\t\t\t\t\t\t:\t\t\t\t\tList[Any] =\t\t\t\t\t\tAutoFeatureExtractor.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"hf-internal-testing/test_dynamic_feature_extractor\"\"\"\t\t,\t\t\t\t\ttrust_remote_code=_a )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(feature_extractor.__class__.__name__\t\t,\t\t\t\t\t\"\"\"NewFeatureExtractor\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(not hasattr(_a\t\t,\t\t\t\t\t\"\"\"is_local\"\"\" ) )\n\n\t\t\t\t\t\t\t\t\t\t\tfinally:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif \"custom\" in CONFIG_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel CONFIG_MAPPING._extra_content[\"custom\"]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdel FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]\n\n"},"code_codestyle":{"kind":"number","value":366,"string":"366"},"style_context":{"kind":"string","value":"\n\n\n\n\nfrom __future__ import annotations\n\nfrom collections.abc import Callable\n\n_snake_case =\tlist[list[float | int]]\n\n\n\n\n\n\n\ndef lowerCAmelCase_ (\t\t\t\t\tsnake_case_,snake_case_\t\t\t\t\t):\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint =\t\t\t\t\t\tlen(snake_case_\t\t\t\t\t)\n _A\t\t\t\t\t\t\t:\t\t\t\t\tMatrix =\t\t\t\t\t\t[[0 for _ in range(size + 1\t\t\t\t\t)] for _ in range(snake_case_\t\t\t\t\t)]\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n _A\t\t\t\t\t\t\t:\t\t\t\t\tfloat\n\n for row in range(snake_case_\t\t\t\t\t):\n for col in range(snake_case_\t\t\t\t\t):\n _A\t\t\t\t\t\t\t:\t\t\t\t\tDict =\t\t\t\t\t\tmatrix[row][col]\n\n _A\t\t\t\t\t\t\t:\t\t\t\t\tList[Any] =\t\t\t\t\t\tvector[row][0]\n\n _A\t\t\t\t\t\t\t:\t\t\t\t\tList[Any] =\t\t\t\t\t\t0\n _A\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any] =\t\t\t\t\t\t0\n while row < size and col < size:\n # pivoting\n _A\t\t\t\t\t\t\t:\t\t\t\t\tAny =\t\t\t\t\t\tmax((abs(augmented[rowa][col]\t\t\t\t\t), rowa) for rowa in range(snake_case_,snake_case_\t\t\t\t\t)\t\t\t\t\t)[\n 1\n ]\n if augmented[pivot_row][col] == 0:\n col += 1\n continue\n else:\n _A , _A\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any] =\t\t\t\t\t\taugmented[pivot_row], augmented[row]\n\n for rowa in range(row + 1,snake_case_\t\t\t\t\t):\n _A\t\t\t\t\t\t\t:\t\t\t\t\tstr =\t\t\t\t\t\taugmented[rowa][col] / augmented[row][col]\n _A\t\t\t\t\t\t\t:\t\t\t\t\tList[Any] =\t\t\t\t\t\t0\n for cola in range(col + 1,size + 1\t\t\t\t\t):\n augmented[rowa][cola] -= augmented[row][cola] * ratio\n\n row += 1\n col += 1\n\n # back substitution\n for col in range(1,snake_case_\t\t\t\t\t):\n for row in range(snake_case_\t\t\t\t\t):\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint =\t\t\t\t\t\taugmented[row][col] / augmented[col][col]\n for cola in range(snake_case_,size + 1\t\t\t\t\t):\n augmented[row][cola] -= augmented[col][cola] * ratio\n\n # round to get rid of numbers like 2.000000000000004\n return [\n [round(augmented[row][size] / augmented[row][row],10\t\t\t\t\t)] for row in range(snake_case_\t\t\t\t\t)\n ]\n\n\n\n\n\n\n\ndef lowerCAmelCase_ (\t\t\t\t\tsnake_case_\t\t\t\t\t):\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint =\t\t\t\t\t\tlen(snake_case_\t\t\t\t\t)\n _A\t\t\t\t\t\t\t:\t\t\t\t\tMatrix =\t\t\t\t\t\t[[0 for _ in range(snake_case_\t\t\t\t\t)] for _ in range(snake_case_\t\t\t\t\t)]\n _A\t\t\t\t\t\t\t:\t\t\t\t\tMatrix =\t\t\t\t\t\t[[0] for _ in range(snake_case_\t\t\t\t\t)]\n _A\t\t\t\t\t\t\t:\t\t\t\t\tMatrix\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n\n for x_val, y_val in enumerate(snake_case_\t\t\t\t\t):\n for col in range(snake_case_\t\t\t\t\t):\n _A\t\t\t\t\t\t\t:\t\t\t\t\tstr =\t\t\t\t\t\t(x_val + 1) ** (size - col - 1)\n _A\t\t\t\t\t\t\t:\t\t\t\t\tList[str] =\t\t\t\t\t\ty_val\n\n _A\t\t\t\t\t\t\t:\t\t\t\t\tAny =\t\t\t\t\t\tsolve(snake_case_,snake_case_\t\t\t\t\t)\n\n def interpolated_func(snake_case_\t\t\t\t\t) -> int:\n return sum(\n round(coeffs[x_val][0]\t\t\t\t\t) * (var ** (size - x_val - 1))\n for x_val in range(snake_case_\t\t\t\t\t)\t\t\t\t\t)\n\n return interpolated_func\n\n\n\n\n\n\n\ndef lowerCAmelCase_ (\t\t\t\t\tsnake_case_\t\t\t\t\t):\n return (\n 1\n - variable\n + variable**2\n - variable**3\n + variable**4\n - variable**5\n + variable**6\n - variable**7\n + variable**8\n - variable**9\n + variable**10\n )\n\n\n\n\n\n\n\ndef lowerCAmelCase_ (\t\t\t\t\tsnake_case_ = question_function,snake_case_ = 10\t\t\t\t\t):\n _A\t\t\t\t\t\t\t:\t\t\t\t\tlist[int] =\t\t\t\t\t\t[func(snake_case_\t\t\t\t\t) for x_val in range(1,order + 1\t\t\t\t\t)]\n\n _A\t\t\t\t\t\t\t:\t\t\t\t\tlist[Callable[[int], int]] =\t\t\t\t\t\t[\n interpolate(data_points[:max_coeff]\t\t\t\t\t) for max_coeff in range(1,order + 1\t\t\t\t\t)\n ]\n\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint =\t\t\t\t\t\t0\n _A\t\t\t\t\t\t\t:\t\t\t\t\tCallable[[int], int]\n _A\t\t\t\t\t\t\t:\t\t\t\t\tint\n\n for poly in polynomials:\n _A\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int] =\t\t\t\t\t\t1\n while func(snake_case_\t\t\t\t\t) == poly(snake_case_\t\t\t\t\t):\n x_val += 1\n\n ret += poly(snake_case_\t\t\t\t\t)\n\n return ret\n\n\nif __name__ == \"__main__\":\n print(f\"\"\"{solution() = }\"\"\")\n\n"},"style_context_codestyle":{"kind":"number","value":343,"string":"343"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":586,"cells":{"code":{"kind":"string","value":"\n\nimport collections\nimport os\nfrom shutil import copyfile\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom ...tokenization_utils import PreTrainedTokenizer\nfrom ...utils import logging\n\n\n_lowerCamelCase\t\t\t\t: int\t\t\t\t\t =\t\t\t\t\t\t\tlogging.get_logger(__name__)\n\n_lowerCamelCase\t\t\t\t: List[str]\t\t\t\t\t =\t\t\t\t\t\t\t\"▁\"\n\n_lowerCamelCase\t\t\t\t: Optional[int]\t\t\t\t\t =\t\t\t\t\t\t\t{\"vocab_file\": \"prophetnet.tokenizer\"}\n\n_lowerCamelCase\t\t\t\t: Optional[Any]\t\t\t\t\t =\t\t\t\t\t\t\t{\n \"vocab_file\": {\n \"microsoft/xprophetnet-large-wiki100-cased\": (\n \"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer\"\n ),\n }\n}\n\n_lowerCamelCase\t\t\t\t: Optional[Any]\t\t\t\t\t =\t\t\t\t\t\t\t{\n \"microsoft/xprophetnet-large-wiki100-cased\": {\"do_lower_case\": False},\n}\n\n_lowerCamelCase\t\t\t\t: Optional[Any]\t\t\t\t\t =\t\t\t\t\t\t\t{\n \"microsoft/xprophetnet-large-wiki100-cased\": 5_1_2,\n}\n\ndef \t\t\ta__ (\t\tUpperCAmelCase\t\t\t\t\t\t\t:\t\t\tint\t\t\t) ->\t\t\t\t\t\tList[str]:\n UpperCAmelCase :\t\t\t\t\t\t\tint = collections.OrderedDict()\n with open(lowercase__\t\t\t\t, '''r'''\t\t\t\t, encoding='''utf-8'''\t\t\t) as reader:\n UpperCAmelCase :\t\t\t\t\t\t\tOptional[Any] = reader.readlines()\n for index, token in enumerate(lowercase__\t\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tTuple = token.rstrip('''\\n'''\t\t\t)\n UpperCAmelCase :\t\t\t\t\t\t\tOptional[int] = index\n return vocab\n\n\n\n\n\n\nclass \t\t\t\t\t__UpperCAmelCase ( __UpperCAmelCase\t\t\t\t\t\t):\n UpperCamelCase = VOCAB_FILES_NAMES\n UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP\n UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\n UpperCamelCase = [\"\"\"input_ids\"\"\", \"\"\"attention_mask\"\"\"]\n\n\n\n def __init__(\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Optional[Any],\t\t__A\t\t\t\t\t\t\t: List[str],\t\t__A\t\t\t\t\t\t\t: Tuple=\"[SEP]\",\t\t__A\t\t\t\t\t\t\t: Union[str, Any]=\"[SEP]\",\t\t__A\t\t\t\t\t\t\t: Tuple=\"[SEP]\",\t\t__A\t\t\t\t\t\t\t: Union[str, Any]=\"[UNK]\",\t\t__A\t\t\t\t\t\t\t: Tuple=\"[PAD]\",\t\t__A\t\t\t\t\t\t\t: List[Any]=\"[CLS]\",\t\t__A\t\t\t\t\t\t\t: List[str]=\"[MASK]\",\t\t__A\t\t\t\t\t\t\t: Tuple = None,\t\t**__A\t\t\t\t\t\t\t: Optional[Any],\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tOptional[int] = {} if sp_model_kwargs is None else sp_model_kwargs\n\n super().__init__(\n bos_token=lowerCamelCase__,\t\teos_token=lowerCamelCase__,\t\tsep_token=lowerCamelCase__,\t\tunk_token=lowerCamelCase__,\t\tpad_token=lowerCamelCase__,\t\tcls_token=lowerCamelCase__,\t\tmask_token=lowerCamelCase__,\t\tsp_model_kwargs=self.sp_model_kwargs,\t\t**lowerCamelCase__,\t\t)\n\n try:\n import sentencepiece as spm\n except ImportError:\n logger.warning(\n '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''\n ''' pip install sentencepiece'''\t\t\t\t\t\t)\n raise\n\n UpperCAmelCase :\t\t\t\t\t\t\tDict = spm.SentencePieceProcessor(**self.sp_model_kwargs\t\t\t\t\t\t)\n self.sp_model.Load(str(lowerCamelCase__\t\t\t\t\t\t)\t\t\t\t\t\t)\n UpperCAmelCase :\t\t\t\t\t\t\tDict = vocab_file\n\n # Original fairseq vocab and spm vocab must be \"aligned\":\n # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9\n # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----\n # fairseq | '' | '' | '' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-'\n # spm | '' | '' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'\n\n # put special tokens and [unused] tokens into the vocab\n UpperCAmelCase :\t\t\t\t\t\t\tDict = {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}\n\n for i in range(1_0\t\t\t\t\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tDict = F'''[unused{i}]'''\n UpperCAmelCase :\t\t\t\t\t\t\tOptional[Any] = 5 + i\n\n # The first \"real\" token \",\" has position 15 in the embedding vocab and position 3 in the spm vocab\n UpperCAmelCase :\t\t\t\t\t\t\tint = 1_2\n UpperCAmelCase :\t\t\t\t\t\t\tList[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}\n for k in self.fairseq_tokens_to_ids.keys():\n self.unique_no_split_tokens.append(lowerCamelCase__\t\t\t\t\t\t)\n\n\n\n def __getstate__(\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tOptional[int] = self.__dict__.copy()\n UpperCAmelCase :\t\t\t\t\t\t\tDict = None\n return state\n\n\n\n def __setstate__(\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: List[Any],\t\t__A\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tUnion[str, Any] = d\n try:\n import sentencepiece as spm\n except ImportError:\n logger.warning(\n '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''\n ''' pip install sentencepiece'''\t\t\t\t\t\t)\n raise\n\n # for backward compatibility\n if not hasattr(self,\t\t'''sp_model_kwargs'''\t\t\t\t\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tUnion[str, Any] = {}\n\n UpperCAmelCase :\t\t\t\t\t\t\tOptional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs\t\t\t\t\t\t)\n self.sp_model.Load(self.vocab_file\t\t\t\t\t\t)\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: List[Any],\t\t__A\t\t\t\t\t\t\t: Tuple,\t\t__A\t\t\t\t\t\t\t: str = None,\t\t__A\t\t\t\t\t\t\t: int = False\t\t\t\t\t\t):\n\n\n\n if already_has_special_tokens:\n return super().get_special_tokens_mask(\n token_ids_a=lowerCamelCase__,\t\ttoken_ids_a=lowerCamelCase__,\t\talready_has_special_tokens=lowerCamelCase__\t\t\t\t\t\t)\n\n if token_ids_a is None:\n return ([0] * len(lowerCamelCase__\t\t\t\t\t\t)) + [1]\n return ([0] * len(lowerCamelCase__\t\t\t\t\t\t)) + [1] + ([0] * len(lowerCamelCase__\t\t\t\t\t\t)) + [1]\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Dict,\t\t__A\t\t\t\t\t\t\t: Dict,\t\t__A\t\t\t\t\t\t\t: Any = None\t\t\t\t\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tOptional[int] = [self.sep_token_id]\n\n if token_ids_a is None:\n return len(token_ids_a + sep\t\t\t\t\t\t) * [0]\n return len(token_ids_a + sep + sep + token_ids_a + sep\t\t\t\t\t\t) * [0]\n\n\n\n @property\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t):\n return len(self.sp_model\t\t\t\t\t\t) + self.fairseq_offset\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tstr = {self.convert_ids_to_tokens(lowerCamelCase__\t\t\t\t\t\t): i for i in range(self.vocab_size\t\t\t\t\t\t)}\n vocab.update(self.added_tokens_encoder\t\t\t\t\t\t)\n return vocab\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: List[Any],\t\t__A\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t):\n return self.sp_model.encode(lowerCamelCase__,\t\tout_type=lowerCamelCase__\t\t\t\t\t\t)\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Tuple,\t\t__A\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t):\n\n\n\n if token in self.fairseq_tokens_to_ids:\n return self.fairseq_tokens_to_ids[token]\n UpperCAmelCase :\t\t\t\t\t\t\tList[Any] = self.sp_model.PieceToId(lowerCamelCase__\t\t\t\t\t\t)\n\n # Need to return unknown token if the SP model returned 0\n return spm_id + self.fairseq_offset if spm_id else self.unk_token_id\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Optional[Any],\t\t__A\t\t\t\t\t\t\t: str\t\t\t\t\t\t):\n\n\n\n if index in self.fairseq_ids_to_tokens:\n return self.fairseq_ids_to_tokens[index]\n return self.sp_model.IdToPiece(index - self.fairseq_offset\t\t\t\t\t\t)\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Union[str, Any],\t\t__A\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t):\n UpperCAmelCase :\t\t\t\t\t\t\tOptional[Any] = ''''''.join(lowerCamelCase__\t\t\t\t\t\t).replace(lowerCamelCase__,\t\t''' '''\t\t\t\t\t\t).strip()\n return out_string\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: Any,\t\t__A\t\t\t\t\t\t\t: Any,\t\t__A\t\t\t\t\t\t\t: Any = None\t\t\t\t\t\t):\n\n\n\n if not os.path.isdir(lowerCamelCase__\t\t\t\t\t\t):\n logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''\t\t\t\t\t\t)\n return\n UpperCAmelCase :\t\t\t\t\t\t\tUnion[str, Any] = os.path.join(\n lowerCamelCase__,\t\t(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']\t\t\t\t\t\t)\n\n if os.path.abspath(self.vocab_file\t\t\t\t\t\t) != os.path.abspath(lowerCamelCase__\t\t\t\t\t\t) and os.path.isfile(self.vocab_file\t\t\t\t\t\t):\n copyfile(self.vocab_file,\t\tlowerCamelCase__\t\t\t\t\t\t)\n elif not os.path.isfile(self.vocab_file\t\t\t\t\t\t):\n with open(lowerCamelCase__,\t\t'''wb'''\t\t\t\t\t\t) as fi:\n UpperCAmelCase :\t\t\t\t\t\t\tint = self.sp_model.serialized_model_proto()\n fi.write(lowerCamelCase__\t\t\t\t\t\t)\n\n return (out_vocab_file,)\n\n\n\n\n\n def __magic_name__ (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t: str,\t\t__A\t\t\t\t\t\t\t: int,\t\t__A\t\t\t\t\t\t\t: Optional[int] = None\t\t\t\t\t\t):\n\n\n\n if token_ids_a is None:\n return token_ids_a + [self.sep_token_id]\n UpperCAmelCase :\t\t\t\t\t\t\tstr = [self.sep_token_id]\n return token_ids_a + sep + token_ids_a + sep\n\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":336,"string":"336"},"style_context":{"kind":"string","value":"\r'''simple docstring'''\r\r\r\rimport heapq as hq\rimport math\rfrom collections.abc import Iterator\r\r\r\r\r\rclass \tA\t\t\t\t\t\t\t:\r\r\r\r\t\t\tdef __init__( self\t\t\t\t, lowerCamelCase__\t\t\t\t)\t->\t\t\t\t\t\tOptional[Any]:\r\r\r\r\r\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tstr(id_\t\t\t\t)\r\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tNone\r\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tNone\r\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\t[]\r\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\t{} # {vertex:distance}\r\r\r\r\t\t\tdef __lt__( self\t\t\t\t, lowerCamelCase__\t\t\t\t)\t->\t\t\t\t\t\tUnion[str, Any]:\r\r\r\r\r\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\t\t\t\t\t\t\t\t\treturn self.key < other.key\r\r\r\r\t\t\tdef __repr__( self\t\t\t\t)\t->\t\t\t\t\t\tOptional[Any]:\r\r\r\r\r\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\t\t\t\t\t\t\t\t\treturn self.id\r\r\r\r\t\t\tdef A__ ( self\t\t\t\t, lowerCamelCase__\t\t\t\t)\t->\t\t\t\t\t\tDict:\r\r\r\r\r\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\t\t\t\t\t\t\t\t\tself.neighbors.append(lowerCamelCase__\t\t\t\t)\r\r\r\r\t\t\tdef A__ ( self\t\t\t\t, lowerCamelCase__\t\t\t\t, lowerCamelCase__\t\t\t\t)\t->\t\t\t\t\t\tList[Any]:\r\r\r\r\r\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tweight\r\r\r\r\r\rdef _A\t\t\t\t\t\t(\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t,\t\t\t\t\t\tlowercase__\t\t\t\t\t\t,\t\t\t\t\t\tlowercase__\t\t\t\t\t\t,\t\t\t\t\t\tlowercase__\t\t):\r\t\t\t\t\t\t# add the neighbors:\r\t\t\t\t\t\tgraph[a - 1].add_neighbor(graph[b - 1]\t\t)\r\t\t\t\t\t\tgraph[b - 1].add_neighbor(graph[a - 1]\t\t)\r\t\t\t\t\t\t# add the edges:\r\t\t\t\t\t\tgraph[a - 1].add_edge(graph[b - 1]\t\t\t\t\t\t,\t\t\t\t\t\tlowercase__\t\t)\r\t\t\t\t\t\tgraph[b - 1].add_edge(graph[a - 1]\t\t\t\t\t\t,\t\t\t\t\t\tlowercase__\t\t)\r\r\r\r\r\rdef _A\t\t\t\t\t\t(\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t,\t\t\t\t\t\tlowercase__\t\t):\r\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\t[]\r\t\t\t\t\t\tfor u in graph:\r\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tmath.inf\r\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tNone\r\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\t0\r\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tgraph[:]\r\t\t\t\t\t\twhile q:\r\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tmin(lowercase__\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\tq.remove(lowercase__\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\tfor v in u.neighbors:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif (v in q) and (u.edges[v.id] < v.key):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tu\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tu.edges[v.id]\r\t\t\t\t\t\tfor i in range(1\t\t\t\t\t\t,\t\t\t\t\t\tlen(lowercase__\t\t)\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\ta.append((int(graph[i].id\t\t) + 1, int(graph[i].pi.id\t\t) + 1)\t\t)\r\t\t\t\t\t\treturn a\r\r\r\r\r\rdef _A\t\t\t\t\t\t(\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t,\t\t\t\t\t\tlowercase__\t\t):\r\t\t\t\t\t\tfor u in graph:\r\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tmath.inf\r\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tNone\r\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\t0\r\r\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tlist(lowercase__\t\t)\r\t\t\t\t\t\thq.heapify(lowercase__\t\t)\r\r\t\t\t\t\t\twhile h:\r\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\thq.heappop(lowercase__\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\tfor v in u.neighbors:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif (v in h) and (u.edges[v.id] < v.key):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tu\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowercase__\t\t\t\t\t\t\t =\t\t\t\tu.edges[v.id]\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\thq.heapify(lowercase__\t\t)\r\r\t\t\t\t\t\tfor i in range(1\t\t\t\t\t\t,\t\t\t\t\t\tlen(lowercase__\t\t)\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\tyield (int(graph[i].id\t\t) + 1, int(graph[i].pi.id\t\t) + 1)\r\r\r\r\r\rdef _A\t\t\t\t\t\t(\t\t\t\t\t\t\t):\r\t\t\t\t\t\tpass\r\r\rif __name__ == \"__main__\":\r\t\t\t\timport doctest\r\r\t\t\t\tdoctest.testmod()\r\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":164,"string":"164"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":587,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\nprint((lambda quine: quine % quine)(\"print((lambda quine: quine %% quine)(%r))\"))\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":359,"string":"359"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport argparse\r\nimport io\r\n\r\nimport requests\r\nimport torch\r\nfrom omegaconf import OmegaConf\r\n\r\nfrom diffusers import AutoencoderKL\r\nfrom diffusers.pipelines.stable_diffusion.convert_from_ckpt import (\r\n assign_to_checkpoint,\r\n conv_attn_to_linear,\r\n create_vae_diffusers_config,\r\n renew_vae_attention_paths,\r\n renew_vae_resnet_paths,\r\n)\r\ndef \t\t\t\t\t\t_a (\t\t\t\ta\t\t\t:Union[str, Any] ,\t\t\t\t\t\t\ta\t\t\t:List[Any]\t\t\t)\t\t\t\t\t\t\t-> List[Any]:\r\n a\t\t =\t\t\t\tcheckpoint\r\n\r\n a\t\t =\t\t\t\t{}\r\n\r\n a\t\t =\t\t\t\tvae_state_dict['''encoder.conv_in.weight''']\r\n a\t\t =\t\t\t\tvae_state_dict['''encoder.conv_in.bias''']\r\n a\t\t =\t\t\t\tvae_state_dict['''encoder.conv_out.weight''']\r\n a\t\t =\t\t\t\tvae_state_dict['''encoder.conv_out.bias''']\r\n a\t\t =\t\t\t\tvae_state_dict['''encoder.norm_out.weight''']\r\n a\t\t =\t\t\t\tvae_state_dict['''encoder.norm_out.bias''']\r\n\r\n a\t\t =\t\t\t\tvae_state_dict['''decoder.conv_in.weight''']\r\n a\t\t =\t\t\t\tvae_state_dict['''decoder.conv_in.bias''']\r\n a\t\t =\t\t\t\tvae_state_dict['''decoder.conv_out.weight''']\r\n a\t\t =\t\t\t\tvae_state_dict['''decoder.conv_out.bias''']\r\n a\t\t =\t\t\t\tvae_state_dict['''decoder.norm_out.weight''']\r\n a\t\t =\t\t\t\tvae_state_dict['''decoder.norm_out.bias''']\r\n\r\n a\t\t =\t\t\t\tvae_state_dict['''quant_conv.weight''']\r\n a\t\t =\t\t\t\tvae_state_dict['''quant_conv.bias''']\r\n a\t\t =\t\t\t\tvae_state_dict['''post_quant_conv.weight''']\r\n a\t\t =\t\t\t\tvae_state_dict['''post_quant_conv.bias''']\r\n\r\n # Retrieves the keys for the encoder down blocks only\r\n a\t\t =\t\t\t\tlen({'''.'''.join(layer.split('''.'''\t\t\t)[:3]\t\t\t) for layer in vae_state_dict if '''encoder.down''' in layer}\t\t\t)\r\n a\t\t =\t\t\t\t{\r\n layer_id: [key for key in vae_state_dict if F\"\"\"down.{layer_id}\"\"\" in key] for layer_id in range(a\t\t\t)\r\n }\r\n\r\n # Retrieves the keys for the decoder up blocks only\r\n a\t\t =\t\t\t\tlen({'''.'''.join(layer.split('''.'''\t\t\t)[:3]\t\t\t) for layer in vae_state_dict if '''decoder.up''' in layer}\t\t\t)\r\n a\t\t =\t\t\t\t{\r\n layer_id: [key for key in vae_state_dict if F\"\"\"up.{layer_id}\"\"\" in key] for layer_id in range(a\t\t\t)\r\n }\r\n\r\n for i in range(a\t\t\t):\r\n a\t\t =\t\t\t\t[key for key in down_blocks[i] if F\"\"\"down.{i}\"\"\" in key and F\"\"\"down.{i}.downsample\"\"\" not in key]\r\n\r\n if F\"\"\"encoder.down.{i}.downsample.conv.weight\"\"\" in vae_state_dict:\r\n a\t\t =\t\t\t\tvae_state_dict.pop(\r\n F\"\"\"encoder.down.{i}.downsample.conv.weight\"\"\"\t\t\t)\r\n a\t\t =\t\t\t\tvae_state_dict.pop(\r\n F\"\"\"encoder.down.{i}.downsample.conv.bias\"\"\"\t\t\t)\r\n\r\n a\t\t =\t\t\t\trenew_vae_resnet_paths(a\t\t\t)\r\n a\t\t =\t\t\t\t{'''old''': F\"\"\"down.{i}.block\"\"\", '''new''': F\"\"\"down_blocks.{i}.resnets\"\"\"}\r\n assign_to_checkpoint(a ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\tadditional_replacements=[meta_path] ,\t\t\t\t\t\t\tconfig=a\t\t\t)\r\n\r\n a\t\t =\t\t\t\t[key for key in vae_state_dict if '''encoder.mid.block''' in key]\r\n a\t\t =\t\t\t\t2\r\n for i in range(1 ,\t\t\t\t\t\t\tnum_mid_res_blocks + 1\t\t\t):\r\n a\t\t =\t\t\t\t[key for key in mid_resnets if F\"\"\"encoder.mid.block_{i}\"\"\" in key]\r\n\r\n a\t\t =\t\t\t\trenew_vae_resnet_paths(a\t\t\t)\r\n a\t\t =\t\t\t\t{'''old''': F\"\"\"mid.block_{i}\"\"\", '''new''': F\"\"\"mid_block.resnets.{i - 1}\"\"\"}\r\n assign_to_checkpoint(a ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\tadditional_replacements=[meta_path] ,\t\t\t\t\t\t\tconfig=a\t\t\t)\r\n\r\n a\t\t =\t\t\t\t[key for key in vae_state_dict if '''encoder.mid.attn''' in key]\r\n a\t\t =\t\t\t\trenew_vae_attention_paths(a\t\t\t)\r\n a\t\t =\t\t\t\t{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}\r\n assign_to_checkpoint(a ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\tadditional_replacements=[meta_path] ,\t\t\t\t\t\t\tconfig=a\t\t\t)\r\n conv_attn_to_linear(a\t\t\t)\r\n\r\n for i in range(a\t\t\t):\r\n a\t\t =\t\t\t\tnum_up_blocks - 1 - i\r\n a\t\t =\t\t\t\t[\r\n key for key in up_blocks[block_id] if F\"\"\"up.{block_id}\"\"\" in key and F\"\"\"up.{block_id}.upsample\"\"\" not in key\r\n ]\r\n\r\n if F\"\"\"decoder.up.{block_id}.upsample.conv.weight\"\"\" in vae_state_dict:\r\n a\t\t =\t\t\t\tvae_state_dict[\r\n F\"\"\"decoder.up.{block_id}.upsample.conv.weight\"\"\"\r\n ]\r\n a\t\t =\t\t\t\tvae_state_dict[\r\n F\"\"\"decoder.up.{block_id}.upsample.conv.bias\"\"\"\r\n ]\r\n\r\n a\t\t =\t\t\t\trenew_vae_resnet_paths(a\t\t\t)\r\n a\t\t =\t\t\t\t{'''old''': F\"\"\"up.{block_id}.block\"\"\", '''new''': F\"\"\"up_blocks.{i}.resnets\"\"\"}\r\n assign_to_checkpoint(a ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\tadditional_replacements=[meta_path] ,\t\t\t\t\t\t\tconfig=a\t\t\t)\r\n\r\n a\t\t =\t\t\t\t[key for key in vae_state_dict if '''decoder.mid.block''' in key]\r\n a\t\t =\t\t\t\t2\r\n for i in range(1 ,\t\t\t\t\t\t\tnum_mid_res_blocks + 1\t\t\t):\r\n a\t\t =\t\t\t\t[key for key in mid_resnets if F\"\"\"decoder.mid.block_{i}\"\"\" in key]\r\n\r\n a\t\t =\t\t\t\trenew_vae_resnet_paths(a\t\t\t)\r\n a\t\t =\t\t\t\t{'''old''': F\"\"\"mid.block_{i}\"\"\", '''new''': F\"\"\"mid_block.resnets.{i - 1}\"\"\"}\r\n assign_to_checkpoint(a ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\tadditional_replacements=[meta_path] ,\t\t\t\t\t\t\tconfig=a\t\t\t)\r\n\r\n a\t\t =\t\t\t\t[key for key in vae_state_dict if '''decoder.mid.attn''' in key]\r\n a\t\t =\t\t\t\trenew_vae_attention_paths(a\t\t\t)\r\n a\t\t =\t\t\t\t{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}\r\n assign_to_checkpoint(a ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\ta ,\t\t\t\t\t\t\tadditional_replacements=[meta_path] ,\t\t\t\t\t\t\tconfig=a\t\t\t)\r\n conv_attn_to_linear(a\t\t\t)\r\n return new_checkpoint\r\ndef \t\t\t\t\t\t_a (\t\t\t\ta\t\t\t:str ,\t\t\t\t\t\t\ta\t\t\t:str ,\t\t\t\t\t\t\t)\t\t\t\t\t\t\t-> List[str]:\r\n # Only support V1\r\n a\t\t =\t\t\t\trequests.get(\r\n ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'''\t\t\t)\r\n a\t\t =\t\t\t\tio.BytesIO(r.content\t\t\t)\r\n\r\n a\t\t =\t\t\t\tOmegaConf.load(a\t\t\t)\r\n a\t\t =\t\t\t\t512\r\n a\t\t =\t\t\t\t'''cuda''' if torch.cuda.is_available() else '''cpu'''\r\n if checkpoint_path.endswith('''safetensors'''\t\t\t):\r\n from safetensors import safe_open\r\n\r\n a\t\t =\t\t\t\t{}\r\n with safe_open(a ,\t\t\t\t\t\t\tframework='''pt''' ,\t\t\t\t\t\t\tdevice='''cpu'''\t\t\t) as f:\r\n for key in f.keys():\r\n a\t\t =\t\t\t\tf.get_tensor(a\t\t\t)\r\n else:\r\n a\t\t =\t\t\t\ttorch.load(a ,\t\t\t\t\t\t\tmap_location=a\t\t\t)['''state_dict''']\r\n\r\n # Convert the VAE model.\r\n a\t\t =\t\t\t\tcreate_vae_diffusers_config(a ,\t\t\t\t\t\t\timage_size=a\t\t\t)\r\n a\t\t =\t\t\t\tcustom_convert_ldm_vae_checkpoint(a ,\t\t\t\t\t\t\ta\t\t\t)\r\n\r\n a\t\t =\t\t\t\tAutoencoderKL(**a\t\t\t)\r\n vae.load_state_dict(a\t\t\t)\r\n vae.save_pretrained(a\t\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n UpperCAmelCase__ =\t\targparse.ArgumentParser()\r\n\r\n parser.add_argument(\"--vae_pt_path\", default=None, type=str, required=True, help=\"Path to the VAE.pt to convert.\")\r\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the VAE.pt to convert.\")\r\n\r\n UpperCAmelCase__ =\t\tparser.parse_args()\r\n\r\n vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":26,"string":"26"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":588,"cells":{"code":{"kind":"string","value":"\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\r_UpperCAmelCase\t\t: str\t\t\t\t\t\t\t\t\t\t= logging.get_logger(__name__)\r\r_UpperCAmelCase\t\t: str\t\t\t\t\t\t\t\t\t\t= {}\r\r\r\r\r\r\r\rclass lowercase\t\t\t\t( lowercase_\t\t\t):\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t: Optional[int] \t\t\t=\t\t\t'''llama'''\r\t\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t\t: str \t\t\t=\t\t\t['''past_key_values''']\r\r\r\r\r\t\t\t\tdef __init__(\t\t\t\t\tself , snake_case=3_2000 , snake_case=4096 , snake_case=1_1008 , snake_case=32 , snake_case=32 , snake_case=None , snake_case=\"silu\" , snake_case=2048 , snake_case=0.02 , snake_case=1e-6 , snake_case=True , snake_case=0 , snake_case=1 , snake_case=2 , snake_case=1 , snake_case=False , snake_case=None , **snake_case , ):\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= vocab_size\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= max_position_embeddings\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= hidden_size\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= intermediate_size\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= num_hidden_layers\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= num_attention_heads\r\r\t\t\t\t\t\t\t\t\t\t\t# for backward compatibility\r\t\t\t\t\t\t\t\t\t\t\tif num_key_value_heads is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= num_attention_heads\r\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= num_key_value_heads\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= hidden_act\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= initializer_range\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= rms_norm_eps\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= pretraining_tp\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= use_cache\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= rope_scaling\r\t\t\t\t\t\t\t\t\t\t\tself._rope_scaling_validation()\r\r\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(\r\t\t\t\t\t\t\t\t\t\t\t pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , tie_word_embeddings=snake_case , **snake_case , )\r\r\r\r\r\t\t\t\tdef a\t\t\t\t\t\t\t(\t\t\t\t\tself ):\r\t\t\t\t\t\t\t\t\t\t\tif self.rope_scaling is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn\r\r\t\t\t\t\t\t\t\t\t\t\tif not isinstance(self.rope_scaling , snake_case ) or len(self.rope_scaling ) != 2:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F'''got {self.rope_scaling}''' )\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= self.rope_scaling.get('type' , snake_case )\r\t\t\t\t\t\t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t= self.rope_scaling.get('factor' , snake_case )\r\t\t\t\t\t\t\t\t\t\t\tif rope_scaling_type is None or rope_scaling_type not in [\"linear\", \"dynamic\"]:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F'''`rope_scaling`\\'s name field must be one of [\\'linear\\', \\'dynamic\\'], got {rope_scaling_type}''' )\r\t\t\t\t\t\t\t\t\t\t\tif rope_scaling_factor is None or not isinstance(snake_case , snake_case ) or rope_scaling_factor <= 1.0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(F'''`rope_scaling`\\'s factor field must be an float > 1, got {rope_scaling_factor}''' )\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":285,"string":"285"},"style_context":{"kind":"string","value":"\r\rdef \t\t\t\t\t__lowerCamelCase\t( UpperCamelCase__ ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\tif edge <= 0 or not isinstance(UpperCamelCase__ ,\t\t\t\t\t\t\tUpperCamelCase__ ):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('Length must be a positive.' )\r\t\t\t\t\t\t\treturn 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)\r\r\r\r\r\rdef \t\t\t\t\t__lowerCamelCase\t( UpperCamelCase__ ):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\tif edge <= 0 or not isinstance(UpperCamelCase__ ,\t\t\t\t\t\t\tUpperCamelCase__ ):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('Length must be a positive.' )\r\t\t\t\t\t\t\treturn ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\timport doctest\r\r\t\t\t\t\tdoctest.testmod()\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":285,"string":"285"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":589,"cells":{"code":{"kind":"string","value":"\rimport fire\r\rfrom utils import calculate_rouge, save_json\r\r\r\r\r\r\r\rdef A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=None , **lowerCamelCase )\t\t\t\t\t->\t\tAny:\r\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tAny\t\t= [x.strip() for x in open(lowerCamelCase ).readlines()]\r\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tTuple\t\t= [x.strip() for x in open(lowerCamelCase ).readlines()][: len(lowerCamelCase )]\r\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tOptional[Any]\t\t= calculate_rouge(lowerCamelCase , lowerCamelCase , **lowerCamelCase )\r\t\t\t\tif save_path is not None:\r\t\t\t\t\t\t\t\tsave_json(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase )\r\t\t\t\treturn metrics # these print nicely\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\t\t\tfire.Fire(calculate_rouge_path)\r\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":223,"string":"223"},"style_context":{"kind":"string","value":"\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rlowerCamelCase_ :\tOptional[int] \t\t\t\t=\t\t\t\t\tlogging.get_logger(__name__)\r\rlowerCamelCase_ :\tOptional[int] \t\t\t\t=\t\t\t\t\t{\"\"\"ctrl\"\"\": \"\"\"https://huggingface.co/ctrl/resolve/main/config.json\"\"\"}\r\rclass _UpperCamelCase\t\t\t( _A\t\t\t\t\t):\r\r\r\r\r\r\r\t\t\t'''simple docstring'''\r\r\t\t\t__UpperCamelCase : int =\t\"\"\"ctrl\"\"\"\r\t\t\t__UpperCamelCase : Dict =\t[\"\"\"past_key_values\"\"\"]\r\t\t\t__UpperCamelCase : List[str] =\t{\r\t\t\t \"\"\"max_position_embeddings\"\"\": \"\"\"n_positions\"\"\",\r\t\t\t \"\"\"hidden_size\"\"\": \"\"\"n_embd\"\"\",\r\t\t\t \"\"\"num_attention_heads\"\"\": \"\"\"n_head\"\"\",\r\t\t\t \"\"\"num_hidden_layers\"\"\": \"\"\"n_layer\"\"\",\r\t\t\t}\r\r\t\t\tdef __init__( self\t: Dict ,\t\tsnake_case_\t: Any=24_6534 ,\t\tsnake_case_\t: Dict=256 ,\t\tsnake_case_\t: str=1280 ,\t\tsnake_case_\t: Optional[int]=8192 ,\t\tsnake_case_\t: Union[str, Any]=48 ,\t\tsnake_case_\t: Any=16 ,\t\tsnake_case_\t: Optional[int]=0.1 ,\t\tsnake_case_\t: Any=0.1 ,\t\tsnake_case_\t: Any=1e-6 ,\t\tsnake_case_\t: Optional[Any]=0.02 ,\t\tsnake_case_\t: Optional[int]=True ,\t\t**snake_case_\t: Union[str, Any] ,\t\t):\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tUnion[str, Any]\t\t= vocab_size\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tUnion[str, Any]\t\t= n_positions\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tOptional[int]\t\t= n_embd\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tint\t\t= n_layer\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tstr\t\t= n_head\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tOptional[int]\t\t= dff\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tOptional[Any]\t\t= resid_pdrop\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tUnion[str, Any]\t\t= embd_pdrop\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tList[str]\t\t= layer_norm_epsilon\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tOptional[Any]\t\t= initializer_range\r\r\t\t\t\t\t\t\tUpperCamelCase_:\t\t\t\t\t\t\tOptional[Any]\t\t= use_cache\r\r\t\t\t\t\t\t\tsuper().__init__(**snake_case_ )\r\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":223,"string":"223"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":590,"cells":{"code":{"kind":"string","value":"import json\r\nimport os\r\nimport unittest\r\n\r\nfrom transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast\r\nfrom transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES\r\nfrom transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers\r\n\r\nfrom ...test_tokenization_common import TokenizerTesterMixin\r\n\r\n\r\n\r\n\r\n@require_tokenizers\r\nclass _snake_case ( _lowercase ,\t\t\t\t\tunittest.TestCase ):\r\n\t\t\t\t\tlowerCamelCase__:\t\t\t\t\t\tList[str] = OpenAIGPTTokenizer\r\n\t\t\t\t\tlowerCamelCase__:\t\t\t\t\t\tstr = OpenAIGPTTokenizerFast\r\n\t\t\t\t\tlowerCamelCase__:\t\t\t\t\t\tDict = True\r\n\t\t\t\t\tlowerCamelCase__:\t\t\t\t\t\tList[Any] = False\r\n\t\t\t\t\tdef _lowerCamelCase ( self: Any )\t\t\t-> Dict:\r\n\t\t\t\t\t\t\t\t\t\t\t\tsuper().setUp()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tDict \t\t\t= [\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"l\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"o\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"w\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"e\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"r\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"s\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"t\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"i\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"d\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"n\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"w\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"r\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"t\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"lo\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"low\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"er\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"low\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"lowest\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"newer\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"wider\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t]\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tList[str] \t\t\t= dict(zip(__lowerCamelCase\t\t, range(len(__lowerCamelCase ) ) ) )\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[Any] \t\t\t= [\"#version: 0.2\", \"l o\", \"lo w\", \"e r\", \"\"]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tList[str] \t\t\t= os.path.join(self.tmpdirname\t\t, VOCAB_FILES_NAMES[\"vocab_file\"] )\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tint \t\t\t= os.path.join(self.tmpdirname\t\t, VOCAB_FILES_NAMES[\"merges_file\"] )\r\n\t\t\t\t\t\t\t\t\t\t\t\twith open(self.vocab_file\t\t, \"w\" ) as fp:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfp.write(json.dumps(__lowerCamelCase ) )\r\n\t\t\t\t\t\t\t\t\t\t\t\twith open(self.merges_file\t\t, \"w\" ) as fp:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfp.write(\"\\n\".join(__lowerCamelCase ) )\r\n\t\t\t\t\tdef _lowerCamelCase ( self: Optional[int]\t\t, __lowerCamelCase: List[Any] )\t\t\t-> Tuple:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn \"lower newer\", \"lower newer\"\r\n\t\t\t\t\tdef _lowerCamelCase ( self: int )\t\t\t-> List[str]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[Any] \t\t\t= OpenAIGPTTokenizer(self.vocab_file\t\t, self.merges_file )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tAny \t\t\t= \"lower\"\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tList[Any] \t\t\t= [\"low\", \"er\"]\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tint \t\t\t= tokenizer.tokenize(__lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(__lowerCamelCase\t\t, __lowerCamelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tTuple \t\t\t= tokens + [\"\"]\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tList[str] \t\t\t= [14, 15, 20]\r\n\t\t\t\t\t\t\t\t\t\t\t\tself.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase )\t\t, __lowerCamelCase )\r\n\t\t\t\t\tdef _lowerCamelCase ( self: Optional[Any]\t\t, __lowerCamelCase: Optional[Any]=15 )\t\t\t-> str:\r\n\t\t\t\t\t\t\t\t\t\t\t\tfor tokenizer, pretrained_name, kwargs in self.tokenizers_list:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[int] \t\t\t= self.rust_tokenizer_class.from_pretrained(__lowerCamelCase\t\t, **__lowerCamelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Simple input\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tint \t\t\t= \"This is a simple input\"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tstr \t\t\t= [\"This is a simple input 1\", \"This is a simple input 2\"]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tint \t\t\t= (\"This is a simple input\", \"This is a pair\")\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tint \t\t\t= [\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t (\"This is a simple input 1\", \"This is a simple input 2\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t (\"This is a simple pair 1\", \"This is a simple pair 2\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Simple input tests\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertRaises(__lowerCamelCase\t\t, tokenizer_r.encode\t\t, __lowerCamelCase\t\t, max_length=__lowerCamelCase\t\t, padding=\"max_length\" )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Simple input\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertRaises(__lowerCamelCase\t\t, tokenizer_r.encode_plus\t\t, __lowerCamelCase\t\t, max_length=__lowerCamelCase\t\t, padding=\"max_length\" )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Simple input\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertRaises(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t __lowerCamelCase\t\t, tokenizer_r.batch_encode_plus\t\t, __lowerCamelCase\t\t, max_length=__lowerCamelCase\t\t, padding=\"max_length\"\t\t, )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Pair input\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertRaises(__lowerCamelCase\t\t, tokenizer_r.encode\t\t, __lowerCamelCase\t\t, max_length=__lowerCamelCase\t\t, padding=\"max_length\" )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Pair input\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertRaises(__lowerCamelCase\t\t, tokenizer_r.encode_plus\t\t, __lowerCamelCase\t\t, max_length=__lowerCamelCase\t\t, padding=\"max_length\" )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Pair input\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertRaises(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t __lowerCamelCase\t\t, tokenizer_r.batch_encode_plus\t\t, __lowerCamelCase\t\t, max_length=__lowerCamelCase\t\t, padding=\"max_length\"\t\t, )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tdef _lowerCamelCase ( self: Union[str, Any] )\t\t\t-> Optional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\tpass\r\n\r\n\r\n\r\n\r\n@require_ftfy\r\n@require_spacy\r\n@require_tokenizers\r\nclass _snake_case ( _lowercase ):\r\n\t\t\t\t\tpass\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":157,"string":"157"},"style_context":{"kind":"string","value":"import os\r\nimport shutil\r\nfrom pathlib import Path\r\nfrom typing import Optional, Union\r\n\r\nimport numpy as np\r\nfrom huggingface_hub import hf_hub_download\r\n\r\nfrom ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging\r\n\r\n\r\nif is_onnx_available():\r\n\timport onnxruntime as ort\r\n\r\n\r\n_snake_case\t\t\t\t\t\t\t\t= logging.get_logger(__name__)\r\n\r\n_snake_case\t\t\t\t\t\t\t\t= {\r\n '''tensor(bool)''': np.bool_,\r\n '''tensor(int8)''': np.inta,\r\n '''tensor(uint8)''': np.uinta,\r\n '''tensor(int16)''': np.intaa,\r\n '''tensor(uint16)''': np.uintaa,\r\n '''tensor(int32)''': np.intaa,\r\n '''tensor(uint32)''': np.uintaa,\r\n '''tensor(int64)''': np.intaa,\r\n '''tensor(uint64)''': np.uintaa,\r\n '''tensor(float16)''': np.floataa,\r\n '''tensor(float)''': np.floataa,\r\n '''tensor(double)''': np.floataa,\r\n}\r\n\r\n\r\n\r\n\r\nclass _snake_case :\r\n\t\t\t\t\tdef __init__( self: Tuple\t\t, __lowerCamelCase: Tuple=None\t\t, **__lowerCamelCase: Union[str, Any] )\t\t\t-> Dict:\r\n\t\t\t\t\t\t\t\t\t\t\t\tlogger.info(\"`diffusers.OnnxRuntimeModel` is experimental and might change in the future.\" )\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tUnion[str, Any] \t\t\t= model\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[Any] \t\t\t= kwargs.get(\"model_save_dir\"\t\t, __lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tstr \t\t\t= kwargs.get(\"latest_model_name\"\t\t, __lowerCamelCase )\r\n\t\t\t\t\tdef __call__( self: int\t\t, **__lowerCamelCase: Optional[Any] )\t\t\t-> int:\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[Any] \t\t\t= {k: np.array(__lowerCamelCase ) for k, v in kwargs.items()}\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self.model.run(__lowerCamelCase\t\t, __lowerCamelCase )\r\n\t\t\t\t\t@staticmethod\r\n\t\t\t\t\tdef _lowerCamelCase ( __lowerCamelCase: Union[str, Path]\t\t, __lowerCamelCase: Union[str, Any]=None\t\t, __lowerCamelCase: Tuple=None )\t\t\t-> List[str]:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif provider is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.info(\"No onnxruntime provider specified, using CPUExecutionProvider\" )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tAny \t\t\t= \"CPUExecutionProvider\"\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn ort.InferenceSession(__lowerCamelCase\t\t, providers=[provider]\t\t, sess_options=__lowerCamelCase )\r\n\t\t\t\t\tdef _lowerCamelCase ( self: Dict\t\t, __lowerCamelCase: Union[str, Path]\t\t, __lowerCamelCase: Optional[str] = None\t\t, **__lowerCamelCase: Union[str, Any] )\t\t\t-> Optional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tTuple \t\t\t= file_name if file_name is not None else ONNX_WEIGHTS_NAME\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tstr \t\t\t= self.model_save_dir.joinpath(self.latest_model_name )\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tAny \t\t\t= Path(__lowerCamelCase ).joinpath(__lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tshutil.copyfile(__lowerCamelCase\t\t, __lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\texcept shutil.SameFileError:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t# copy external weights (for models >2GB)\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tstr \t\t\t= self.model_save_dir.joinpath(__lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\tif src_path.exists():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tList[str] \t\t\t= Path(__lowerCamelCase ).joinpath(__lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tshutil.copyfile(__lowerCamelCase\t\t, __lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texcept shutil.SameFileError:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\n\t\t\t\t\tdef _lowerCamelCase ( self: Any\t\t, __lowerCamelCase: Union[str, os.PathLike]\t\t, **__lowerCamelCase: Any\t\t, )\t\t\t-> List[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif os.path.isfile(__lowerCamelCase ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.error(f'''Provided path ({save_directory}) should be a directory, not a file''' )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tos.makedirs(__lowerCamelCase\t\t, exist_ok=__lowerCamelCase )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t# saving model weights/files\r\n\t\t\t\t\t\t\t\t\t\t\t\tself._save_pretrained(__lowerCamelCase\t\t, **__lowerCamelCase )\r\n\t\t\t\t\t@classmethod\r\n\t\t\t\t\tdef _lowerCamelCase ( cls: Optional[Any]\t\t, __lowerCamelCase: Union[str, Path]\t\t, __lowerCamelCase: Optional[Union[bool, str, None]] = None\t\t, __lowerCamelCase: Optional[Union[str, None]] = None\t\t, __lowerCamelCase: bool = False\t\t, __lowerCamelCase: Optional[str] = None\t\t, __lowerCamelCase: Optional[str] = None\t\t, __lowerCamelCase: Optional[str] = None\t\t, __lowerCamelCase: Optional[\"ort.SessionOptions\"] = None\t\t, **__lowerCamelCase: Union[str, Any]\t\t, )\t\t\t-> Optional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tTuple \t\t\t= file_name if file_name is not None else ONNX_WEIGHTS_NAME\r\n\t\t\t\t\t\t\t\t\t\t\t\t# load model from local directory\r\n\t\t\t\t\t\t\t\t\t\t\t\tif os.path.isdir(__lowerCamelCase ):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[int] \t\t\t= OnnxRuntimeModel.load_model(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t os.path.join(__lowerCamelCase\t\t, __lowerCamelCase )\t\t, provider=__lowerCamelCase\t\t, sess_options=__lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tUnion[str, Any] \t\t\t= Path(__lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\t# load model from hub\r\n\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# download model\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[Any] \t\t\t= hf_hub_download(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t repo_id=__lowerCamelCase\t\t, filename=__lowerCamelCase\t\t, use_auth_token=__lowerCamelCase\t\t, revision=__lowerCamelCase\t\t, cache_dir=__lowerCamelCase\t\t, force_download=__lowerCamelCase\t\t, )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tAny \t\t\t= Path(__lowerCamelCase ).parent\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tList[Any] \t\t\t= Path(__lowerCamelCase ).name\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tDict \t\t\t= OnnxRuntimeModel.load_model(__lowerCamelCase\t\t, provider=__lowerCamelCase\t\t, sess_options=__lowerCamelCase )\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn cls(model=__lowerCamelCase\t\t, **__lowerCamelCase )\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t@classmethod\r\n\t\t\t\t\tdef _lowerCamelCase ( cls: Optional[int]\t\t, __lowerCamelCase: Union[str, Path]\t\t, __lowerCamelCase: bool = True\t\t, __lowerCamelCase: Optional[str] = None\t\t, __lowerCamelCase: Optional[str] = None\t\t, **__lowerCamelCase: Tuple\t\t, )\t\t\t-> Optional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tint \t\t\t= None\r\n\t\t\t\t\t\t\t\t\t\t\t\tif len(str(__lowerCamelCase ).split(\"@\" ) ) == 2:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase\t\t\t,\t\t__UpperCAmelCase :\tAny \t\t\t= model_id.split(\"@\" )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn cls._from_pretrained(\r\n\t\t\t\t\t\t\t\t\t\t\t\t model_id=__lowerCamelCase\t\t, revision=__lowerCamelCase\t\t, cache_dir=__lowerCamelCase\t\t, force_download=__lowerCamelCase\t\t, use_auth_token=__lowerCamelCase\t\t, **__lowerCamelCase\t\t, )\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":157,"string":"157"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":591,"cells":{"code":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\rfrom __future__ import annotations\r\rfrom collections.abc import Iterable, Iterator\rfrom dataclasses import dataclass\r\r_lowercase\t\t\t:\t\t\tstr\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t(3, 9, -1_1, 0, 7, 5, 1, -1)\r_lowercase\t\t\t:\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t(4, 6, 2, 0, 8, 1_0, 3, -2)\r\r@dataclass\rclass \t__SCREAMING_SNAKE_CASE :\r\r\r\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\t\t\t\t\t_a =\t\t\t\t\t\t\t42\r\t\t\t\t\t_a =\t\t\t\t\t\t\t42\r\r\r\r\r\rclass \t__SCREAMING_SNAKE_CASE :\r\r\r\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\t\t\t\t\tdef __init__(\tself\t\t\t\t\t\t\t: str, lowerCamelCase\t\t\t\t\t\t\t: Iterable[int]\t\t\t\t\t)-> None:\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tNode | None\t\t =None\r\t\t\t\t\t\t\t\tfor i in sorted(lowerCamelCase, reverse=lowerCamelCase\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tDict\t\t =Node(lowerCamelCase, self.head\t\t\t\t\t)\r\r\r\r\t\t\t\t\tdef __iter__(\tself\t\t\t\t\t\t\t: int\t\t\t\t\t)-> Iterator[int]:\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tint\t\t =self.head\r\t\t\t\t\t\t\t\twhile node:\r\t\t\t\t\t\t\t\t\t\t\tyield node.data\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tOptional[int]\t\t =node.next_node\r\r\r\r\t\t\t\t\tdef __len__(\tself\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t)-> int:\r\t\t\t\t\t\t\t\treturn sum(1 for _ in self\t\t\t\t\t)\r\r\r\r\r\t\t\t\t\tdef __str__(\tself\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t)-> str:\r\t\t\t\t\t\t\t\treturn \" -> \".join([str(lowerCamelCase\t\t\t\t\t) for node in self]\t\t\t\t\t)\r\r\r\r\r\r\rdef \t\t\t\t\tsnake_case__ (\t\t__lowerCamelCase : SortedLinkedList\t\t\t\t\t\t\t,\t\t__lowerCamelCase : SortedLinkedList ):\r\t\t\t\"\"\"simple docstring\"\"\"\r\t\t\treturn SortedLinkedList(list(__lowerCamelCase ) + list(__lowerCamelCase ) )\r\r\rif __name__ == \"__main__\":\r\t\timport doctest\r\r\t\tdoctest.testmod()\r\t\t_lowercase\t\t\t:\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\tSortedLinkedList\r\t\tprint(merge_lists(SSL(test_data_odd), SSL(test_data_even)))\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":272,"string":"272"},"style_context":{"kind":"string","value":"\r\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\rfrom ...processing_utils import ProcessorMixin\r\rclass \t__SCREAMING_SNAKE_CASE (\t\t\t\tlowerCAmelCase_ ):\r\r\r\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\t\t\t\t\t_a =\t\t\t\t\t\t\t'SpeechT5FeatureExtractor'\r\t\t\t\t\t_a =\t\t\t\t\t\t\t'SpeechT5Tokenizer'\r\r\r\r\t\t\t\t\tdef __init__(\tself\t\t\t\t\t\t\t: Dict, lowerCamelCase\t\t\t\t\t\t\t: Optional[int], lowerCamelCase\t\t\t\t\t\t\t: str\t\t\t\t\t)-> Any:\r\t\t\t\t\t\t\t\tsuper().__init__(lowerCamelCase, lowerCamelCase\t\t\t\t\t)\r\r\r\r\t\t\t\t\tdef __call__(\tself\t\t\t\t\t\t\t: Tuple, *lowerCamelCase\t\t\t\t\t\t\t: List[str], **lowerCamelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t)-> List[str]:\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[Any]\t\t =kwargs.pop('''audio''', lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[str]\t\t =kwargs.pop('''text''', lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tint\t\t =kwargs.pop('''text_target''', lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tDict\t\t =kwargs.pop('''audio_target''', lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tAny\t\t =kwargs.pop('''sampling_rate''', lowerCamelCase\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\tif audio is not None and text is not None:\r\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''\t\t\t\t\t)\r\t\t\t\t\t\t\t\tif audio_target is not None and text_target is not None:\r\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''\t\t\t\t\t)\r\t\t\t\t\t\t\t\tif audio is None and audio_target is None and text is None and text_target is None:\r\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\tif audio is not None:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tUnion[str, Any]\t\t =self.feature_extractor(lowerCamelCase, *lowerCamelCase, sampling_rate=lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\telif text is not None:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[Any]\t\t =self.tokenizer(lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tAny\t\t =None\r\r\t\t\t\t\t\t\t\tif audio_target is not None:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[str]\t\t =self.feature_extractor(audio_target=lowerCamelCase, *lowerCamelCase, sampling_rate=lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tTuple\t\t =targets['''input_values''']\r\t\t\t\t\t\t\t\telif text_target is not None:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tDict\t\t =self.tokenizer(lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tint\t\t =targets['''input_ids''']\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[str]\t\t =None\r\r\t\t\t\t\t\t\t\tif inputs is None:\r\t\t\t\t\t\t\t\t\t\t\treturn targets\r\r\t\t\t\t\t\t\t\tif targets is not None:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tDict\t\t =labels\r\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tAny\t\t =targets.get('''attention_mask'''\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\tif decoder_attention_mask is not None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tDict\t\t =decoder_attention_mask\r\r\t\t\t\t\t\t\t\treturn inputs\r\r\r\r\t\t\t\t\tdef \t\tsnake_case\t(\tself\t\t\t\t\t\t\t: int, *lowerCamelCase\t\t\t\t\t\t\t: Optional[Any], **lowerCamelCase\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t)-> Optional[Any]:\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[Any]\t\t =kwargs.pop('''input_values''', lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tUnion[str, Any]\t\t =kwargs.pop('''input_ids''', lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tOptional[Any]\t\t =kwargs.pop('''labels''', lowerCamelCase\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\tif input_values is not None and input_ids is not None:\r\t\t\t\t\t\t\t\t\t\t\traise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''\t\t\t\t\t)\r\t\t\t\t\t\t\t\tif input_values is None and input_ids is None and labels is None:\r\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\tif input_values is not None:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[str]\t\t =self.feature_extractor.pad(lowerCamelCase, *lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\telif input_ids is not None:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tTuple\t\t =self.tokenizer.pad(lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tAny\t\t =None\r\r\t\t\t\t\t\t\t\tif labels is not None:\r\t\t\t\t\t\t\t\t\t\t\tif \"input_ids\" in labels or (isinstance(lowerCamelCase, lowerCamelCase\t\t\t\t\t) and \"input_ids\" in labels[0]):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tstr\t\t =self.tokenizer.pad(lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[Any]\t\t =targets['''input_ids''']\r\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tAny\t\t =self.feature_extractor.feature_size\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tOptional[Any]\t\t =self.feature_extractor.num_mel_bins\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tOptional[int]\t\t =self.feature_extractor.pad(lowerCamelCase, *lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tList[Any]\t\t =feature_size_hack\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tTuple\t\t =targets['''input_values''']\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tOptional[Any]\t\t =None\r\r\t\t\t\t\t\t\t\tif inputs is None:\r\t\t\t\t\t\t\t\t\t\t\treturn targets\r\r\t\t\t\t\t\t\t\tif targets is not None:\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tTuple\t\t =labels\r\r\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tOptional[int]\t\t =targets.get('''attention_mask'''\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\tif decoder_attention_mask is not None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCamelCase__\t\t:\t\tOptional[Any]\t\t =decoder_attention_mask\r\r\t\t\t\t\t\t\t\treturn inputs\r\r\r\r\t\t\t\t\tdef \t\tsnake_case\t(\tself\t\t\t\t\t\t\t: List[str], *lowerCamelCase\t\t\t\t\t\t\t: Union[str, Any], **lowerCamelCase\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t)-> List[Any]:\r\t\t\t\t\t\t\t\treturn self.tokenizer.batch_decode(*lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\r\r\r\r\t\t\t\t\tdef \t\tsnake_case\t(\tself\t\t\t\t\t\t\t: List[str], *lowerCamelCase\t\t\t\t\t\t\t: List[Any], **lowerCamelCase\t\t\t\t\t\t\t: Tuple\t\t\t\t\t)-> int:\r\t\t\t\t\t\t\t\treturn self.tokenizer.decode(*lowerCamelCase, **lowerCamelCase\t\t\t\t\t)\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":272,"string":"272"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":592,"cells":{"code":{"kind":"string","value":"\r\rimport os\rimport re\rimport warnings\rfrom shutil import copyfile\rfrom typing import List, Optional, Tuple\r\rfrom ...tokenization_utils_fast import PreTrainedTokenizerFast\rfrom ...utils import is_sentencepiece_available, logging\r\r\rif is_sentencepiece_available():\r\t\t\t\t\t\tfrom .tokenization_ta import TaTokenizer\relse:\r\t\t\t\t\t\tlowerCAmelCase\t\t\t\t\t\t\t: List[Any] \t=\t\t\t\t\t\tNone\r\r\rlowerCAmelCase\t\t\t\t\t\t\t: str \t=\t\t\t\t\t\tlogging.get_logger(__name__)\r\rlowerCAmelCase\t\t\t\t\t\t\t: Union[str, Any] \t=\t\t\t\t\t\t{\"\"\"vocab_file\"\"\": \"\"\"spiece.model\"\"\", \"\"\"tokenizer_file\"\"\": \"\"\"tokenizer.json\"\"\"}\r\rlowerCAmelCase\t\t\t\t\t\t\t: Optional[Any] \t=\t\t\t\t\t\t{\r \"\"\"vocab_file\"\"\": {\r \"\"\"t5-small\"\"\": \"\"\"https://huggingface.co/t5-small/resolve/main/spiece.model\"\"\",\r \"\"\"t5-base\"\"\": \"\"\"https://huggingface.co/t5-base/resolve/main/spiece.model\"\"\",\r \"\"\"t5-large\"\"\": \"\"\"https://huggingface.co/t5-large/resolve/main/spiece.model\"\"\",\r \"\"\"t5-3b\"\"\": \"\"\"https://huggingface.co/t5-3b/resolve/main/spiece.model\"\"\",\r \"\"\"t5-11b\"\"\": \"\"\"https://huggingface.co/t5-11b/resolve/main/spiece.model\"\"\",\r },\r \"\"\"tokenizer_file\"\"\": {\r \"\"\"t5-small\"\"\": \"\"\"https://huggingface.co/t5-small/resolve/main/tokenizer.json\"\"\",\r \"\"\"t5-base\"\"\": \"\"\"https://huggingface.co/t5-base/resolve/main/tokenizer.json\"\"\",\r \"\"\"t5-large\"\"\": \"\"\"https://huggingface.co/t5-large/resolve/main/tokenizer.json\"\"\",\r \"\"\"t5-3b\"\"\": \"\"\"https://huggingface.co/t5-3b/resolve/main/tokenizer.json\"\"\",\r \"\"\"t5-11b\"\"\": \"\"\"https://huggingface.co/t5-11b/resolve/main/tokenizer.json\"\"\",\r },\r}\r\r\r# TODO(PVP) - this should be removed in Transformers v5\rlowerCAmelCase\t\t\t\t\t\t\t: str \t=\t\t\t\t\t\t{\r \"\"\"t5-small\"\"\": 512,\r \"\"\"t5-base\"\"\": 512,\r \"\"\"t5-large\"\"\": 512,\r \"\"\"t5-3b\"\"\": 512,\r \"\"\"t5-11b\"\"\": 512,\r}\r\r\r\rclass __lowercase\t\t\t\t( UpperCAmelCase_\t):\r\r\r\r\r\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\r\t\t\t_UpperCAmelCase :\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t\t\t= VOCAB_FILES_NAMES\r\t\t\t_UpperCAmelCase :\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t\t= PRETRAINED_VOCAB_FILES_MAP\r\t\t\t_UpperCAmelCase :\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t\t\t= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r\t\t\t_UpperCAmelCase :\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t\t\t= ['''input_ids''', '''attention_mask''']\r\t\t\t_UpperCAmelCase :\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t= TaTokenizer\r\r\t\t\t_UpperCAmelCase :\t\t\t\t\t\t\tList[int]\t\t\t\t\t\t\t\t\t\t= []\r\r\r\r\t\t\tdef __init__( self : Any ,\t\t\t\t\t\tlowerCAmelCase__ : Optional[int]=None ,\t\t\t\t\t\tlowerCAmelCase__ : Dict=None ,\t\t\t\t\t\tlowerCAmelCase__ : int=\"\" ,\t\t\t\t\t\tlowerCAmelCase__ : List[Any]=\"\" ,\t\t\t\t\t\tlowerCAmelCase__ : str=\"\" ,\t\t\t\t\t\tlowerCAmelCase__ : List[Any]=100 ,\t\t\t\t\t\tlowerCAmelCase__ : List[Any]=None ,\t\t\t\t\t\t**lowerCAmelCase__ : Tuple ,\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t# Add extra_ids to the special token list\r\t\t\t\t\t\t\t\t\tif extra_ids > 0 and additional_special_tokens is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: Optional[int] \t\t= [F\"\" for i in range(lowerCAmelCase__)]\r\t\t\t\t\t\t\t\t\telif extra_ids > 0 and additional_special_tokens is not None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Check that we have the right number of extra special tokens\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: List[str] \t\t= len(set(filter(lambda lowerCAmelCase__: bool(\"extra_id_\" in str(lowerCAmelCase__)) ,\t\t\t\t\t\tlowerCAmelCase__)))\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif extra_tokens != extra_ids:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \" tokens\")\r\r\t\t\t\t\t\t\t\t\tsuper().__init__(\r\t\t\t\t\t\t\t\t\t lowerCAmelCase__ ,\t\t\t\t\t\ttokenizer_file=lowerCAmelCase__ ,\t\t\t\t\t\teos_token=lowerCAmelCase__ ,\t\t\t\t\t\tunk_token=lowerCAmelCase__ ,\t\t\t\t\t\tpad_token=lowerCAmelCase__ ,\t\t\t\t\t\textra_ids=lowerCAmelCase__ ,\t\t\t\t\t\tadditional_special_tokens=lowerCAmelCase__ ,\t\t\t\t\t\t**lowerCAmelCase__ ,\t\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: Dict \t\t= vocab_file\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: Dict \t\t= False if not self.vocab_file else True\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: List[str] \t\t= extra_ids\r\r\r\r\t\t\t@staticmethod\r\t\t\tdef _SCREAMING_SNAKE_CASE\t\t\t( lowerCAmelCase__ : List[str] ,\t\t\t\t\t\tlowerCAmelCase__ : Dict ,\t\t\t\t\t\tlowerCAmelCase__ : Any):\r\t\t\t\t\t\t\t\t\tif pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: List[str] \t\t= TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif init_max_model_length is not None and init_max_model_length != max_model_length:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn init_max_model_length\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif init_max_model_length is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twarnings.warn(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"This tokenizer was incorrectly instantiated with a model max length of\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\" {deprecated_max_model_length} which will be corrected in Transformers v5.\\nFor now, this\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \" behavior is kept to avoid breaking backwards compatibility when padding/encoding with\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \" `truncation is True`.\\n- Be aware that you SHOULD NOT rely on\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\" {pretrained_model_name_or_path} automatically truncating your input to\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\" {deprecated_max_model_length} when padding/encoding.\\n- If you want to encode/pad to sequences\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \" `model_max_length` or pass `max_length` when encoding/padding.\\n- To avoid this warning, please\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \" instantiate this tokenizer with `model_max_length` set to your preferred value.\" ,\t\t\t\t\t\tlowerCAmelCase__ ,\t\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t\treturn max_model_length\r\r\r\r\t\t\tdef _SCREAMING_SNAKE_CASE\t\t\t( self : Any ,\t\t\t\t\t\tlowerCAmelCase__ : str ,\t\t\t\t\t\tlowerCAmelCase__ : Optional[str] = None):\r\t\t\t\t\t\t\t\t\tif not self.can_save_slow_tokenizer:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow \"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"tokenizer.\")\r\r\t\t\t\t\t\t\t\t\tif not os.path.isdir(lowerCAmelCase__):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.error(F\"Vocabulary path ({save_directory}) should be a directory\")\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: Tuple \t\t= os.path.join(\r\t\t\t\t\t\t\t\t\t lowerCAmelCase__ ,\t\t\t\t\t\t(filename_prefix + \"-\" if filename_prefix else \"\") + VOCAB_FILES_NAMES[\"vocab_file\"])\r\r\t\t\t\t\t\t\t\t\tif os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcopyfile(self.vocab_file ,\t\t\t\t\t\tlowerCAmelCase__)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.info(F\"Copy vocab file to {out_vocab_file}\")\r\r\t\t\t\t\t\t\t\t\treturn (out_vocab_file,)\r\r\r\r\t\t\tdef _SCREAMING_SNAKE_CASE\t\t\t( self : str ,\t\t\t\t\t\tlowerCAmelCase__ : List[int] ,\t\t\t\t\t\tlowerCAmelCase__ : Optional[List[int]] = None):\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: Optional[int] \t\t= token_ids_a + [self.eos_token_id]\r\t\t\t\t\t\t\t\t\tif token_ids_a is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn self.prefix_tokens + token_ids_a\r\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: Optional[int] \t\t= token_ids_a + [self.eos_token_id]\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn self.prefix_tokens + token_ids_a + token_ids_a\r\r\r\r\t\t\tdef _SCREAMING_SNAKE_CASE\t\t\t( self : Union[str, Any] ,\t\t\t\t\t\tlowerCAmelCase__ : List[int] ,\t\t\t\t\t\tlowerCAmelCase__ : Optional[List[int]] = None):\r\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_: List[Any] \t\t= [self.eos_token_id]\r\r\t\t\t\t\t\t\t\t\tif token_ids_a is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn len(token_ids_a + eos) * [0]\r\t\t\t\t\t\t\t\t\treturn len(token_ids_a + eos + token_ids_a + eos) * [0]\r\r\r\r\t\t\tdef _SCREAMING_SNAKE_CASE\t\t\t( self : List[str]):\r\t\t\t\t\t\t\t\t\treturn list(\r\t\t\t\t\t\t\t\t\t set(filter(lambda lowerCAmelCase__: bool(re.search(R\"\" ,\t\t\t\t\t\tlowerCAmelCase__)) is not None ,\t\t\t\t\t\tself.additional_special_tokens)))\r\r\r\r\r\t\t\tdef _SCREAMING_SNAKE_CASE\t\t\t( self : Union[str, Any]):\r\t\t\t\t\t\t\t\t\treturn [self.convert_tokens_to_ids(lowerCAmelCase__) for token in self.get_sentinel_tokens()]\r\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":13,"string":"13"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n'''simple docstring'''\r\n\r\n\r\nimport json\r\nimport os\r\nfrom functools import lru_cache\r\nfrom typing import List, Optional, Tuple\r\n\r\nimport regex as re\r\n\r\nfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\r\nfrom ...utils import logging\r\n\r\n\r\nUpperCamelCase \t\t\t= logging.get_logger(__name__)\r\n\r\n\r\nUpperCamelCase \t\t\t= {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}\r\n\r\nUpperCamelCase \t\t\t= {\r\n '''vocab_file''': {\r\n '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',\r\n '''allenai/longformer-large-4096''': (\r\n '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''\r\n ),\r\n '''allenai/longformer-large-4096-finetuned-triviaqa''': (\r\n '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''\r\n ),\r\n '''allenai/longformer-base-4096-extra.pos.embd.only''': (\r\n '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''\r\n ),\r\n '''allenai/longformer-large-4096-extra.pos.embd.only''': (\r\n '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''\r\n ),\r\n },\r\n '''merges_file''': {\r\n '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',\r\n '''allenai/longformer-large-4096''': (\r\n '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''\r\n ),\r\n '''allenai/longformer-large-4096-finetuned-triviaqa''': (\r\n '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''\r\n ),\r\n '''allenai/longformer-base-4096-extra.pos.embd.only''': (\r\n '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''\r\n ),\r\n '''allenai/longformer-large-4096-extra.pos.embd.only''': (\r\n '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''\r\n ),\r\n },\r\n}\r\n\r\nUpperCamelCase \t\t\t= {\r\n '''allenai/longformer-base-4096''': 4096,\r\n '''allenai/longformer-large-4096''': 4096,\r\n '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,\r\n '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,\r\n '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,\r\n}\r\n@lru_cache()\r\n# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode\r\ndef SCREAMING_SNAKE_CASE(\t\t\t\t\t) -> Dict:\r\n A:\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\t\t\t\t(\r\n list(range(ord('''!''' )\t\t\t\t\t\t,\t\t\t\t\t\t\tord('''~''' ) + 1 ) ) + list(range(ord('''¡''' )\t\t\t\t\t\t,\t\t\t\t\t\t\tord('''¬''' ) + 1 ) ) + list(range(ord('''®''' )\t\t\t\t\t\t,\t\t\t\t\t\t\tord('''ÿ''' ) + 1 ) )\r\n )\r\n A:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\tbs[:]\r\n A:\t\t\t\t\t\tList[str]\t\t\t\t\t\t=\t\t\t\t\t\t0\r\n for b in range(2**8 ):\r\n if b not in bs:\r\n bs.append(__lowercase )\r\n cs.append(2**8 + n )\r\n n += 1\r\n A:\t\t\t\t\t\tList[Any]\t\t\t\t\t\t=\t\t\t\t\t\t[chr(__lowercase ) for n in cs]\r\n return dict(zip(__lowercase\t\t\t\t\t\t,\t\t\t\t\t\t\t__lowercase ) )\r\ndef SCREAMING_SNAKE_CASE(\t\t\t\t\t__lowercase ) -> Optional[int]:\r\n A:\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\t\t\t\tset()\r\n A:\t\t\t\t\t\tTuple\t\t\t\t\t\t=\t\t\t\t\t\tword[0]\r\n for char in word[1:]:\r\n pairs.add((prev_char, char) )\r\n A:\t\t\t\t\t\tList[Any]\t\t\t\t\t\t=\t\t\t\t\t\tchar\r\n return pairs\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass \t\tlowerCAmelCase_ ( UpperCAmelCase_\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n UpperCamelCase_ :\tint\t\t\t\t =\t\t\t\t\t\t\tVOCAB_FILES_NAMES\r\n UpperCamelCase_ :\tint\t\t\t\t =\t\t\t\t\t\t\tPRETRAINED_VOCAB_FILES_MAP\r\n UpperCamelCase_ :\tList[str]\t\t\t\t =\t\t\t\t\t\t\tPRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r\n UpperCamelCase_ :\tint\t\t\t\t =\t\t\t\t\t\t\t[\"\"\"input_ids\"\"\", \"\"\"attention_mask\"\"\"]\r\n\r\n\r\n\r\n def __init__( self\t\t\t\t\t\t\t:\t\t\t\tint , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[int] , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tstr , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tstr=\"replace\" , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tstr=\"\" , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tAny=\"\" , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tint=\"\" , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tList[Any]=\"\" , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tstr=\"\" , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tDict=\"\" , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tDict=\"\" , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tUnion[str, Any]=False , **SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tTuple , ) ->\t\t\t\t\t\tList[str]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n A:\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\t\t\t\tAddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token\r\n A:\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\t\t\t\tAddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token\r\n A:\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\t\t\t\tAddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token\r\n A:\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\t\t\t\tAddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token\r\n A:\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\t\t\t\tAddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\tAddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token\r\n\r\n # Mask token behave like a normal word, i.e. include the space before it\r\n A:\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\t\t\t\tAddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token\r\n\r\n super().__init__(\r\n errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )\r\n\r\n with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as vocab_handle:\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\tjson.load(SCREAMING_SNAKE_CASE_ )\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\t{v: k for k, v in self.encoder.items()}\r\n A:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\terrors # how to handle errors in decoding\r\n A:\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t=\t\t\t\t\t\tbytes_to_unicode()\r\n A:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\t{v: k for k, v in self.byte_encoder.items()}\r\n with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as merges_handle:\r\n A:\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\t\t\t\tmerges_handle.read().split('''\\n''' )[1:-1]\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\t[tuple(merge.split() ) for merge in bpe_merges]\r\n A:\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\t\t\t\tdict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )\r\n A:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\t{}\r\n A:\t\t\t\t\t\tTuple\t\t\t\t\t\t=\t\t\t\t\t\tadd_prefix_space\r\n\r\n # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions\r\n A:\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\t\t\t\tre.compile(R'''\\'s|\\'t|\\'re|\\'ve|\\'m|\\'ll|\\'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+''' )\r\n\r\n\r\n\r\n @property\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tint ) ->\t\t\t\t\t\tList[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n return len(self.encoder )\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tOptional[Any] ) ->\t\t\t\t\t\tint:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n return dict(self.encoder , **self.added_tokens_encoder )\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tstr , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[int] ) ->\t\t\t\t\t\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n if token in self.cache:\r\n return self.cache[token]\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\ttuple(SCREAMING_SNAKE_CASE_ )\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\tget_pairs(SCREAMING_SNAKE_CASE_ )\r\n\r\n if not pairs:\r\n return token\r\n\r\n while True:\r\n A:\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\t\t\t\tmin(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('''inf''' ) ) )\r\n if bigram not in self.bpe_ranks:\r\n break\r\n A\t\t\t, A:\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\t\t\t\tbigram\r\n A:\t\t\t\t\t\tTuple\t\t\t\t\t\t=\t\t\t\t\t\t[]\r\n A:\t\t\t\t\t\tList[Any]\t\t\t\t\t\t=\t\t\t\t\t\t0\r\n while i < len(SCREAMING_SNAKE_CASE_ ):\r\n try:\r\n A:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\tword.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )\r\n except ValueError:\r\n new_word.extend(word[i:] )\r\n break\r\n else:\r\n new_word.extend(word[i:j] )\r\n A:\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\t\t\t\tj\r\n\r\n if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:\r\n new_word.append(first + second )\r\n i += 2\r\n else:\r\n new_word.append(word[i] )\r\n i += 1\r\n A:\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\t\t\t\ttuple(SCREAMING_SNAKE_CASE_ )\r\n A:\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\t\t\t\tnew_word\r\n if len(SCREAMING_SNAKE_CASE_ ) == 1:\r\n break\r\n else:\r\n A:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\tget_pairs(SCREAMING_SNAKE_CASE_ )\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\t''' '''.join(SCREAMING_SNAKE_CASE_ )\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\tword\r\n return word\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tUnion[str, Any] , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[Any] ) ->\t\t\t\t\t\tOptional[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n A:\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\t\t\t\t[]\r\n for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):\r\n A:\t\t\t\t\t\tTuple\t\t\t\t\t\t=\t\t\t\t\t\t''''''.join(\r\n self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)\r\n bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(''' ''' ) )\r\n return bpe_tokens\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tList[Any] , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[Any] ) ->\t\t\t\t\t\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tTuple , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[Any] ) ->\t\t\t\t\t\tstr:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n return self.decoder.get(SCREAMING_SNAKE_CASE_ )\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tUnion[str, Any] , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[int] ) ->\t\t\t\t\t\tTuple:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n A:\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t=\t\t\t\t\t\t''''''.join(SCREAMING_SNAKE_CASE_ )\r\n A:\t\t\t\t\t\tTuple\t\t\t\t\t\t=\t\t\t\t\t\tbytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )\r\n return text\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tint , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tstr , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[str] = None ) ->\t\t\t\t\t\tTuple[str]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):\r\n logger.error(f\"\"\"Vocabulary path ({save_directory}) should be a directory\"\"\" )\r\n return\r\n A:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\tos.path.join(\r\n SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )\r\n A:\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\t\t\t\tos.path.join(\r\n SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )\r\n\r\n with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as f:\r\n f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '''\\n''' )\r\n\r\n A:\t\t\t\t\t\tAny\t\t\t\t\t\t=\t\t\t\t\t\t0\r\n with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as writer:\r\n writer.write('''#version: 0.2\\n''' )\r\n for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):\r\n if index != token_index:\r\n logger.warning(\r\n f\"\"\"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.\"\"\"\r\n ''' Please check that the tokenizer is not corrupted!''' )\r\n A:\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t=\t\t\t\t\t\ttoken_index\r\n writer.write(''' '''.join(SCREAMING_SNAKE_CASE_ ) + '''\\n''' )\r\n index += 1\r\n\r\n return vocab_file, merge_file\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tList[Any] , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tList[int] , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[List[int]] = None ) ->\t\t\t\t\t\tList[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n if token_ids_a is None:\r\n return [self.cls_token_id] + token_ids_a + [self.sep_token_id]\r\n A:\t\t\t\t\t\tint\t\t\t\t\t\t=\t\t\t\t\t\t[self.cls_token_id]\r\n A:\t\t\t\t\t\tstr\t\t\t\t\t\t=\t\t\t\t\t\t[self.sep_token_id]\r\n return cls + token_ids_a + sep + sep + token_ids_a + sep\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tTuple , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tList[int] , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[List[int]] = None , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tbool = False ) ->\t\t\t\t\t\tList[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n if already_has_special_tokens:\r\n return super().get_special_tokens_mask(\r\n token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )\r\n\r\n if token_ids_a is None:\r\n return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]\r\n return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tTuple , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tList[int] , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[List[int]] = None ) ->\t\t\t\t\t\tList[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n A:\t\t\t\t\t\tDict\t\t\t\t\t\t=\t\t\t\t\t\t[self.sep_token_id]\r\n A:\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\t\t\t\t[self.cls_token_id]\r\n\r\n if token_ids_a is None:\r\n return len(cls + token_ids_a + sep ) * [0]\r\n return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]\r\n\r\n\r\n\r\n\r\n def _snake_case\t\t\t( self\t\t\t\t\t\t\t:\t\t\t\tint , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tint , SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tDict=False , **SCREAMING_SNAKE_CASE_\t\t\t\t\t\t\t:\t\t\t\tOptional[int] ) ->\t\t\t\t\t\tint:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n A:\t\t\t\t\t\tTuple\t\t\t\t\t\t=\t\t\t\t\t\tkwargs.pop('''add_prefix_space''' , self.add_prefix_space )\r\n if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):\r\n A:\t\t\t\t\t\tList[Any]\t\t\t\t\t\t=\t\t\t\t\t\t''' ''' + text\r\n return (text, kwargs)\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":319,"string":"319"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":593,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\nimport os\r\nfrom shutil import copyfile\r\nfrom typing import Any, Dict, List, Optional, Tuple\r\n\r\nimport sentencepiece as spm\r\n\r\nfrom ...tokenization_utils import PreTrainedTokenizer\r\nfrom ...utils import logging\r\n\r\n\r\nUpperCAmelCase_\t\t\t\t:\t\t\tstr =\t'▁'\r\n\r\nUpperCAmelCase_\t\t\t\t:\t\t\tDict =\t{'vocab_file': 'spiece.model'}\r\n\r\nUpperCAmelCase_\t\t\t\t:\t\t\tstr =\t{\r\n 'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}\r\n}\r\n\r\nUpperCAmelCase_\t\t\t\t:\t\t\tList[str] =\t{\r\n 'google/pegasus-xsum': 512,\r\n}\r\n\r\n\r\nUpperCAmelCase_\t\t\t\t:\t\t\tstr =\tlogging.get_logger(__name__)\r\nclass \t\t\tSCREAMING_SNAKE_CASE__ ( lowercase__ ):\r\n snake_case__ :\t\t\t\t\tList[Any]\t\t\t\t\t =\t\t\t\t\t\tVOCAB_FILES_NAMES\r\n\r\n snake_case__ :\t\t\t\t\tTuple\t\t\t\t\t =\t\t\t\t\t\tVOCAB_FILES_NAMES\r\n snake_case__ :\t\t\t\t\tUnion[str, Any]\t\t\t\t\t =\t\t\t\t\t\tPRETRAINED_VOCAB_FILES_MAP\r\n snake_case__ :\t\t\t\t\tAny\t\t\t\t\t =\t\t\t\t\t\tPRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r\n snake_case__ :\t\t\t\t\tTuple\t\t\t\t\t =\t\t\t\t\t\t['''input_ids''', '''attention_mask''']\r\n def __init__(\tself :\tDict\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tAny\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tUnion[str, Any]=\"\"\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tTuple=\"\"\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tstr=\"\"\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tOptional[int]=\"\"\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tOptional[int]=\"\"\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tstr=None\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tint=1_0_3\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tOptional[Dict[str, Any]] = None\t\t\t\t\t,\t\t\t\t\t\t\t**SCREAMING_SNAKE_CASE__ :\tList[Any]\t\t\t\t\t,\t\t\t\t\t\t\t)\t\t-> None:\r\n a_ :\t\tList[str]\t\t\t\t\t\t\t= offset\r\n if additional_special_tokens is not None:\r\n if not isinstance(SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t):\r\n raise TypeError(\r\n F\"\"\"additional_special_tokens should be of type {type(SCREAMING_SNAKE_CASE__\t\t\t\t\t)}, but is\"\"\"\r\n F\"\"\" {type(SCREAMING_SNAKE_CASE__\t\t\t\t\t)}\"\"\"\t\t\t\t\t)\r\n\r\n a_ :\t\tint\t\t\t\t\t\t\t= (\r\n ([mask_token_sent] + additional_special_tokens)\r\n if mask_token_sent not in additional_special_tokens and mask_token_sent is not None\r\n else additional_special_tokens\r\n )\r\n # fill additional tokens with ..., in case not all additional tokens are already taken\r\n additional_special_tokens_extended += [\r\n F\"\"\"\"\"\" for i in range(len(SCREAMING_SNAKE_CASE__\t\t\t\t\t)\t\t\t\t\t,\t\t\t\t\t\t\tself.offset - 1\t\t\t\t\t)\r\n ]\r\n\r\n if len(set(SCREAMING_SNAKE_CASE__\t\t\t\t\t)\t\t\t\t\t) != len(SCREAMING_SNAKE_CASE__\t\t\t\t\t):\r\n raise ValueError(\r\n 'Please make sure that the provided additional_special_tokens do not contain an incorrectly'\r\n F\"\"\" shifted list of tokens. Found {additional_special_tokens_extended}.\"\"\"\t\t\t\t\t)\r\n a_ :\t\tDict\t\t\t\t\t\t\t= additional_special_tokens_extended\r\n else:\r\n a_ :\t\tUnion[str, Any]\t\t\t\t\t\t\t= [mask_token_sent] if mask_token_sent is not None else []\r\n additional_special_tokens += [F\"\"\"\"\"\" for i in range(2\t\t\t\t\t,\t\t\t\t\t\t\tself.offset\t\t\t\t\t)]\r\n\r\n a_ :\t\tOptional[int]\t\t\t\t\t\t\t= {} if sp_model_kwargs is None else sp_model_kwargs\r\n\r\n super().__init__(\r\n eos_token=SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\tunk_token=SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\tmask_token=SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\tpad_token=SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\tmask_token_sent=SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\toffset=SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\tadditional_special_tokens=SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\tsp_model_kwargs=self.sp_model_kwargs\t\t\t\t\t,\t\t\t\t\t\t\t**SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\t)\r\n a_ :\t\tOptional[Any]\t\t\t\t\t\t\t= mask_token_sent\r\n a_ :\t\tOptional[int]\t\t\t\t\t\t\t= vocab_file\r\n a_ :\t\tAny\t\t\t\t\t\t\t= spm.SentencePieceProcessor(**self.sp_model_kwargs\t\t\t\t\t)\r\n self.sp_model.Load(SCREAMING_SNAKE_CASE__\t\t\t\t\t)\r\n\r\n # add special tokens to encoder dict\r\n a_ :\t\tDict[int, str]\t\t\t\t\t\t\t= {\r\n 0: self.pad_token,\r\n 1: self.eos_token,\r\n }\r\n\r\n if self.mask_token_sent is not None:\r\n self.encoder.update(\r\n {\r\n 2: self.mask_token_sent,\r\n 3: self.mask_token,\r\n }\t\t\t\t\t)\r\n\r\n if self.offset > 0:\r\n # entries 2-104 are only used for pretraining and called , , unk_2, ...unk_102\r\n # mask_token_sent is already added to list -> so start at 1\r\n self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1\t\t\t\t\t,\t\t\t\t\t\t\tself.offset - 1\t\t\t\t\t)}\t\t\t\t\t)\r\n\r\n a_ :\t\tDict[str, int]\t\t\t\t\t\t\t= {v: k for k, v in self.encoder.items()}\r\n @property\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tOptional[int]\t\t\t\t\t)\t\t-> int:\r\n return len(self.sp_model\t\t\t\t\t) + self.offset\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tAny\t\t\t\t\t)\t\t-> Dict[str, int]:\r\n a_ :\t\tTuple\t\t\t\t\t\t\t= {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__\t\t\t\t\t): i for i in range(self.vocab_size\t\t\t\t\t)}\r\n vocab.update(self.added_tokens_encoder\t\t\t\t\t)\r\n return vocab\r\n def __getstate__(\tself :\tTuple\t\t\t\t\t)\t\t-> Tuple:\r\n a_ :\t\tint\t\t\t\t\t\t\t= self.__dict__.copy()\r\n a_ :\t\tOptional[Any]\t\t\t\t\t\t\t= None\r\n return state\r\n def __setstate__(\tself :\tint\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tAny\t\t\t\t\t)\t\t-> List[str]:\r\n a_ :\t\tOptional[int]\t\t\t\t\t\t\t= d\r\n\r\n # for backward compatibility\r\n if not hasattr(self\t\t\t\t\t,\t\t\t\t\t\t\t'sp_model_kwargs'\t\t\t\t\t):\r\n a_ :\t\tList[Any]\t\t\t\t\t\t\t= {}\r\n\r\n a_ :\t\tOptional[int]\t\t\t\t\t\t\t= spm.SentencePieceProcessor(**self.sp_model_kwargs\t\t\t\t\t)\r\n self.sp_model.Load(self.vocab_file\t\t\t\t\t)\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tint\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tstr\t\t\t\t\t)\t\t-> List[str]:\r\n return self.sp_model.encode(SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\tout_type=SCREAMING_SNAKE_CASE__\t\t\t\t\t)\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tList[str]\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tstr\t\t\t\t\t)\t\t-> int:\r\n if token in self.decoder:\r\n return self.decoder[token]\r\n elif token in self.added_tokens_decoder:\r\n return self.added_tokens_decoder[token]\r\n a_ :\t\tstr\t\t\t\t\t\t\t= self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__\t\t\t\t\t)\r\n return sp_id + self.offset\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tList[str]\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tint\t\t\t\t\t)\t\t-> str:\r\n if index in self.encoder:\r\n return self.encoder[index]\r\n elif index in self.added_tokens_encoder:\r\n return self.added_tokens_encoder[index]\r\n else:\r\n a_ :\t\tDict\t\t\t\t\t\t\t= self.sp_model.IdToPiece(index - self.offset\t\t\t\t\t)\r\n return token\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tDict\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tList[Any]\t\t\t\t\t)\t\t-> List[Any]:\r\n a_ :\t\tstr\t\t\t\t\t\t\t= []\r\n a_ :\t\tTuple\t\t\t\t\t\t\t= ''\r\n for token in tokens:\r\n # make sure that special tokens are not decoded using sentencepiece model\r\n if token in self.all_special_tokens:\r\n out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__\t\t\t\t\t) + token\r\n a_ :\t\tint\t\t\t\t\t\t\t= []\r\n else:\r\n current_sub_tokens.append(SCREAMING_SNAKE_CASE__\t\t\t\t\t)\r\n out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__\t\t\t\t\t)\r\n return out_string.strip()\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tstr\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tint=False\t\t\t\t\t)\t\t-> Any:\r\n return 1\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tList[str]\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tDict\t\t\t\t\t)\t\t-> Union[str, Any]:\r\n a_ :\t\tUnion[str, Any]\t\t\t\t\t\t\t= set(self.all_special_ids\t\t\t\t\t) # call it once instead of inside list comp\r\n all_special_ids.remove(self.unk_token_id\t\t\t\t\t) # is only sometimes special\r\n\r\n return [1 if x in all_special_ids else 0 for x in seq]\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tAny\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tList\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tOptional[List] = None\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tbool = False\t\t\t\t\t)\t\t-> List[int]:\r\n if already_has_special_tokens:\r\n return self._special_token_mask(SCREAMING_SNAKE_CASE__\t\t\t\t\t)\r\n elif token_ids_a is None:\r\n return self._special_token_mask(SCREAMING_SNAKE_CASE__\t\t\t\t\t) + [1]\r\n else:\r\n return self._special_token_mask(token_ids_a + token_ids_a\t\t\t\t\t) + [1]\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tint\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tAny\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tOptional[int]=None\t\t\t\t\t)\t\t-> List[int]:\r\n if token_ids_a is None:\r\n return token_ids_a + [self.eos_token_id]\r\n # We don't expect to process pairs, but leave the pair logic for API consistency\r\n return token_ids_a + token_ids_a + [self.eos_token_id]\r\n\r\n def \tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t(\tself :\tUnion[str, Any]\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tstr\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ :\tOptional[str] = None\t\t\t\t\t)\t\t-> Tuple[str]:\r\n if not os.path.isdir(SCREAMING_SNAKE_CASE__\t\t\t\t\t):\r\n logger.error(F\"\"\"Vocabulary path ({save_directory}) should be a directory\"\"\"\t\t\t\t\t)\r\n return\r\n a_ :\t\tOptional[int]\t\t\t\t\t\t\t= os.path.join(\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\t(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']\t\t\t\t\t)\r\n\r\n if os.path.abspath(self.vocab_file\t\t\t\t\t) != os.path.abspath(SCREAMING_SNAKE_CASE__\t\t\t\t\t) and os.path.isfile(self.vocab_file\t\t\t\t\t):\r\n copyfile(self.vocab_file\t\t\t\t\t,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t)\r\n elif not os.path.isfile(self.vocab_file\t\t\t\t\t):\r\n with open(SCREAMING_SNAKE_CASE__\t\t\t\t\t,\t\t\t\t\t\t\t'wb'\t\t\t\t\t) as fi:\r\n a_ :\t\tUnion[str, Any]\t\t\t\t\t\t\t= self.sp_model.serialized_model_proto()\r\n fi.write(SCREAMING_SNAKE_CASE__\t\t\t\t\t)\r\n\r\n return (out_vocab_file,)\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":371,"string":"371"},"style_context":{"kind":"string","value":"\r\n\r\n\r\ndef \tSCREAMING_SNAKE_CASE_\t\t\t\t\t\t( __A\t\t\t: int ) ->\t\tint:\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n if not isinstance(__A , __A ):\r\n raise ValueError('Input must be an integer' )\r\n if input_num <= 0:\r\n raise ValueError('Input must be positive' )\r\n return sum(\r\n divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import doctest\r\n\r\n doctest.testmod()\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":120,"string":"120"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":594,"cells":{"code":{"kind":"string","value":"\n\n\n'''simple docstring'''\n\n\n\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport torch\n\nfrom ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available\n\n\n\n\n\n@dataclass\nclass \t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t(\t\tlowerCAmelCase_\t\t\t\t\t):\n\tA_\t\t\t\t= 42\n\n\ntry:\n\tif not (is_transformers_available() and is_torch_available()):\n\t\traise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n\tfrom ...utils.dummy_torch_and_transformers_objects import * # noqa F403\nelse:\n\tfrom .pipeline_text_to_video_synth import TextToVideoSDPipeline\n\tfrom .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401\n\tfrom .pipeline_text_to_video_zero import TextToVideoZeroPipeline\n\n\n\n"},"code_codestyle":{"kind":"number","value":27,"string":"27"},"style_context":{"kind":"string","value":"\r\r\r\r\r\rimport re\r\rfrom filelock import FileLock\r\r\rtry:\r import nltk\r\r _snake_case : Any\t\t\t\t\t\t = True\rexcept (ImportError, ModuleNotFoundError):\r _snake_case : Union[str, Any]\t\t\t\t\t\t = False\r\rif NLTK_AVAILABLE:\r with FileLock('.lock') as lock:\r nltk.download('punkt', quiet=True)\r\rdef a_ ( lowerCAmelCase_ :\t\t\tstr\t):\r re.sub('',\t'',\tlowerCAmelCase_\t) # remove pegasus newline char\r assert NLTK_AVAILABLE, \"nltk must be installed to separate newlines between sentences. (pip install nltk)\"\r return \"\\n\".join(nltk.sent_tokenize(lowerCAmelCase_\t)\t)\r\r"},"style_context_codestyle":{"kind":"number","value":284,"string":"284"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":595,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\nimport unittest\r\n\r\nimport numpy as np\r\n\r\nfrom transformers.testing_utils import require_torch, require_vision\r\nfrom transformers.utils import is_torch_available, is_vision_available\r\n\r\nfrom ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs\r\n\r\n\r\nif is_torch_available():\r\n import torch\r\n\r\nif is_vision_available():\r\n from PIL import Image\r\n\r\n from transformers import MobileViTImageProcessor\r\nclass \t\t\t\t\t\t_A ( unittest.TestCase\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __init__( self\t\t: Dict\t\t\t\t,\t\t\t\t\t\t_A\t\t: Union[str, Any]\t\t\t\t,\t\t\t\t\t\t_A\t\t: str=7\t\t\t\t,\t\t\t\t\t\t_A\t\t: Any=3\t\t\t\t,\t\t\t\t\t\t_A\t\t: Optional[int]=18\t\t\t\t,\t\t\t\t\t\t_A\t\t: Optional[int]=30\t\t\t\t,\t\t\t\t\t\t_A\t\t: Optional[Any]=400\t\t\t\t,\t\t\t\t\t\t_A\t\t: Optional[Any]=True\t\t\t\t,\t\t\t\t\t\t_A\t\t: List[str]=None\t\t\t\t,\t\t\t\t\t\t_A\t\t: List[Any]=True\t\t\t\t,\t\t\t\t\t\t_A\t\t: Dict=None\t\t\t\t,\t\t\t\t\t\t_A\t\t: Optional[Any]=True\t\t\t\t,\t\t\t\t\t\t)\t\t-> Any:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n lowercase : Union[str, Any] =\t\t\tsize if size is not None else {'''shortest_edge''': 20}\r\n lowercase : Dict =\t\t\tcrop_size if crop_size is not None else {'''height''': 18, '''width''': 18}\r\n lowercase : List[Any] =\t\t\tparent\r\n lowercase : Union[str, Any] =\t\t\tbatch_size\r\n lowercase : Union[str, Any] =\t\t\tnum_channels\r\n lowercase : Optional[Any] =\t\t\timage_size\r\n lowercase : Dict =\t\t\tmin_resolution\r\n lowercase : Tuple =\t\t\tmax_resolution\r\n lowercase : Optional[int] =\t\t\tdo_resize\r\n lowercase : int =\t\t\tsize\r\n lowercase : int =\t\t\tdo_center_crop\r\n lowercase : str =\t\t\tcrop_size\r\n lowercase : Tuple =\t\t\tdo_flip_channel_order\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __a ( self\t\t: Any\t\t\t\t)\t\t-> int:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n return {\r\n \"do_resize\": self.do_resize,\r\n \"size\": self.size,\r\n \"do_center_crop\": self.do_center_crop,\r\n \"crop_size\": self.crop_size,\r\n \"do_flip_channel_order\": self.do_flip_channel_order,\r\n }\r\n\r\n\r\n@require_torch\r\n@require_vision\r\nclass \t\t\t\t\t\t_A ( _lowerCamelCase , unittest.TestCase\t\t\t\t):\r\n _UpperCamelCase : Dict\t\t\t= MobileViTImageProcessor if is_vision_available() else None\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __a ( self\t\t: Dict\t\t\t\t)\t\t-> Tuple:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n lowercase : List[Any] =\t\t\tMobileViTImageProcessingTester(self\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n @property\r\n def __a ( self\t\t: List[Any]\t\t\t\t)\t\t-> str:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n return self.image_processor_tester.prepare_image_processor_dict()\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __a ( self\t\t: Dict\t\t\t\t)\t\t-> Dict:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n lowercase : Tuple =\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n self.assertTrue(hasattr(_A\t\t\t\t,\t\t\t\t\t\t'''do_resize'''\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_A\t\t\t\t,\t\t\t\t\t\t'''size'''\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_A\t\t\t\t,\t\t\t\t\t\t'''do_center_crop'''\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_A\t\t\t\t,\t\t\t\t\t\t'''center_crop'''\t\t\t\t)\t\t\t\t)\r\n self.assertTrue(hasattr(_A\t\t\t\t,\t\t\t\t\t\t'''do_flip_channel_order'''\t\t\t\t)\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __a ( self\t\t: Union[str, Any]\t\t\t\t)\t\t-> Dict:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n lowercase : List[str] =\t\t\tself.image_processing_class.from_dict(self.image_processor_dict\t\t\t\t)\r\n self.assertEqual(image_processor.size\t\t\t\t,\t\t\t\t\t\t{'''shortest_edge''': 20}\t\t\t\t)\r\n self.assertEqual(image_processor.crop_size\t\t\t\t,\t\t\t\t\t\t{'''height''': 18, '''width''': 18}\t\t\t\t)\r\n\r\n lowercase : List[Any] =\t\t\tself.image_processing_class.from_dict(self.image_processor_dict\t\t\t\t,\t\t\t\t\t\tsize=42\t\t\t\t,\t\t\t\t\t\tcrop_size=84\t\t\t\t)\r\n self.assertEqual(image_processor.size\t\t\t\t,\t\t\t\t\t\t{'''shortest_edge''': 42}\t\t\t\t)\r\n self.assertEqual(image_processor.crop_size\t\t\t\t,\t\t\t\t\t\t{'''height''': 84, '''width''': 84}\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __a ( self\t\t: Union[str, Any]\t\t\t\t)\t\t-> List[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n pass\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __a ( self\t\t: int\t\t\t\t)\t\t-> str:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n lowercase : Union[str, Any] =\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n # create random PIL images\r\n lowercase : Any =\t\t\tprepare_image_inputs(self.image_processor_tester\t\t\t\t,\t\t\t\t\t\tequal_resolution=_A\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(_A\t\t\t\t,\t\t\t\t\t\tImage.Image\t\t\t\t)\r\n\r\n # Test not batched input\r\n lowercase : List[str] =\t\t\timage_processing(image_inputs[0]\t\t\t\t,\t\t\t\t\t\treturn_tensors='''pt'''\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t\t\t,\t\t\t\t\t\t(\r\n 1,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size['''height'''],\r\n self.image_processor_tester.crop_size['''width'''],\r\n )\t\t\t\t,\t\t\t\t\t\t)\r\n\r\n # Test batched\r\n lowercase : Any =\t\t\timage_processing(_A\t\t\t\t,\t\t\t\t\t\treturn_tensors='''pt'''\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t\t\t,\t\t\t\t\t\t(\r\n self.image_processor_tester.batch_size,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size['''height'''],\r\n self.image_processor_tester.crop_size['''width'''],\r\n )\t\t\t\t,\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __a ( self\t\t: List[str]\t\t\t\t)\t\t-> Optional[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n lowercase : Dict =\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n # create random numpy tensors\r\n lowercase : Dict =\t\t\tprepare_image_inputs(self.image_processor_tester\t\t\t\t,\t\t\t\t\t\tequal_resolution=_A\t\t\t\t,\t\t\t\t\t\tnumpify=_A\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(_A\t\t\t\t,\t\t\t\t\t\tnp.ndarray\t\t\t\t)\r\n\r\n # Test not batched input\r\n lowercase : int =\t\t\timage_processing(image_inputs[0]\t\t\t\t,\t\t\t\t\t\treturn_tensors='''pt'''\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t\t\t,\t\t\t\t\t\t(\r\n 1,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size['''height'''],\r\n self.image_processor_tester.crop_size['''width'''],\r\n )\t\t\t\t,\t\t\t\t\t\t)\r\n\r\n # Test batched\r\n lowercase : Tuple =\t\t\timage_processing(_A\t\t\t\t,\t\t\t\t\t\treturn_tensors='''pt'''\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t\t\t,\t\t\t\t\t\t(\r\n self.image_processor_tester.batch_size,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size['''height'''],\r\n self.image_processor_tester.crop_size['''width'''],\r\n )\t\t\t\t,\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __a ( self\t\t: Optional[int]\t\t\t\t)\t\t-> Union[str, Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n lowercase : Any =\t\t\tself.image_processing_class(**self.image_processor_dict\t\t\t\t)\r\n # create random PyTorch tensors\r\n lowercase : Optional[int] =\t\t\tprepare_image_inputs(self.image_processor_tester\t\t\t\t,\t\t\t\t\t\tequal_resolution=_A\t\t\t\t,\t\t\t\t\t\ttorchify=_A\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(_A\t\t\t\t,\t\t\t\t\t\ttorch.Tensor\t\t\t\t)\r\n\r\n # Test not batched input\r\n lowercase : Union[str, Any] =\t\t\timage_processing(image_inputs[0]\t\t\t\t,\t\t\t\t\t\treturn_tensors='''pt'''\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t\t\t,\t\t\t\t\t\t(\r\n 1,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size['''height'''],\r\n self.image_processor_tester.crop_size['''width'''],\r\n )\t\t\t\t,\t\t\t\t\t\t)\r\n\r\n # Test batched\r\n lowercase : Optional[Any] =\t\t\timage_processing(_A\t\t\t\t,\t\t\t\t\t\treturn_tensors='''pt'''\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t\t\t,\t\t\t\t\t\t(\r\n self.image_processor_tester.batch_size,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size['''height'''],\r\n self.image_processor_tester.crop_size['''width'''],\r\n )\t\t\t\t,\t\t\t\t\t\t)"},"code_codestyle":{"kind":"number","value":116,"string":"116"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\nfrom string import ascii_uppercase\r\n\r\nlowerCAmelCase_\t\t\t\t\t\t=\t\t\t{char: i for i, char in enumerate(ascii_uppercase)}\r\nlowerCAmelCase_\t\t\t\t\t\t=\t\t\tdict(enumerate(ascii_uppercase))\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef snake_case( __magic_name__\t\t\t,\t\t__magic_name__ )\t\t\t->\t\t\t\t\t\t\tstr:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n lowercase : Optional[Any] =\t\t\tlen(__magic_name__ )\r\n lowercase : Any =\t\t\t0\r\n while True:\r\n if x == i:\r\n lowercase : Any =\t\t\t0\r\n if len(__magic_name__ ) == len(__magic_name__ ):\r\n break\r\n key += key[i]\r\n i += 1\r\n return key\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef snake_case( __magic_name__\t\t\t,\t\t__magic_name__ )\t\t\t->\t\t\t\t\t\t\tstr:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n lowercase : str =\t\t\t''''''\r\n lowercase : Dict =\t\t\t0\r\n for letter in message:\r\n if letter == \" \":\r\n cipher_text += \" \"\r\n else:\r\n lowercase : Dict =\t\t\t(dicta[letter] - dicta[key_new[i]]) % 26\r\n i += 1\r\n cipher_text += dicta[x]\r\n return cipher_text\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef snake_case( __magic_name__\t\t\t,\t\t__magic_name__ )\t\t\t->\t\t\t\t\t\t\tstr:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n lowercase : Any =\t\t\t''''''\r\n lowercase : str =\t\t\t0\r\n for letter in cipher_text:\r\n if letter == \" \":\r\n or_txt += \" \"\r\n else:\r\n lowercase : Any =\t\t\t(dicta[letter] + dicta[key_new[i]] + 26) % 26\r\n i += 1\r\n or_txt += dicta[x]\r\n return or_txt\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef snake_case( )\t\t\t->\t\t\t\t\t\t\tNone:\r\n\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n lowercase : Dict =\t\t\t'''THE GERMAN ATTACK'''\r\n lowercase : Dict =\t\t\t'''SECRET'''\r\n lowercase : Union[str, Any] =\t\t\tgenerate_key(__magic_name__\t\t\t,\t\t__magic_name__ )\r\n lowercase : List[str] =\t\t\tcipher_text(__magic_name__\t\t\t,\t\t__magic_name__ )\r\n print(F\"\"\"Encrypted Text = {s}\"\"\" )\r\n print(F\"\"\"Original Text = {original_text(__magic_name__\t\t\t,\t\t__magic_name__ )}\"\"\" )\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import doctest\r\n\r\n doctest.testmod()\r\n main()"},"style_context_codestyle":{"kind":"number","value":116,"string":"116"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":596,"cells":{"code":{"kind":"string","value":"\r\nfrom typing import TYPE_CHECKING\r\n\r\nfrom ...utils import (\r\n OptionalDependencyNotAvailable,\r\n _LazyModule,\r\n is_tf_available,\r\n is_tokenizers_available,\r\n is_torch_available,\r\n)\r\n\r\n\r\nlowerCamelCase__ =\t\t{\r\n \"\"\"configuration_mobilebert\"\"\": [\r\n \"\"\"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP\"\"\",\r\n \"\"\"MobileBertConfig\"\"\",\r\n \"\"\"MobileBertOnnxConfig\"\"\",\r\n ],\r\n \"\"\"tokenization_mobilebert\"\"\": [\"\"\"MobileBertTokenizer\"\"\"],\r\n}\r\n\r\ntry:\r\n\t\t\t\t\tif not is_tokenizers_available():\r\n\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\tpass\r\nelse:\r\n\t\t\t\t\tlowerCamelCase__ =\t\t[\"\"\"MobileBertTokenizerFast\"\"\"]\r\n\r\ntry:\r\n\t\t\t\t\tif not is_torch_available():\r\n\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\tpass\r\nelse:\r\n\t\t\t\t\tlowerCamelCase__ =\t\t[\r\n\t\t\t\t\t \"\"\"MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertForMaskedLM\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertForMultipleChoice\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertForNextSentencePrediction\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertForPreTraining\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertForQuestionAnswering\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertForSequenceClassification\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertForTokenClassification\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertLayer\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertModel\"\"\",\r\n\t\t\t\t\t \"\"\"MobileBertPreTrainedModel\"\"\",\r\n\t\t\t\t\t \"\"\"load_tf_weights_in_mobilebert\"\"\",\r\n\t\t\t\t\t]\r\n\r\ntry:\r\n\t\t\t\t\tif not is_tf_available():\r\n\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\tpass\r\nelse:\r\n\t\t\t\t\tlowerCamelCase__ =\t\t[\r\n\t\t\t\t\t \"\"\"TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertForMaskedLM\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertForMultipleChoice\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertForNextSentencePrediction\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertForPreTraining\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertForQuestionAnswering\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertForSequenceClassification\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertForTokenClassification\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertMainLayer\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertModel\"\"\",\r\n\t\t\t\t\t \"\"\"TFMobileBertPreTrainedModel\"\"\",\r\n\t\t\t\t\t]\r\n\r\n\r\nif TYPE_CHECKING:\r\n\t\t\t\t\tfrom .configuration_mobilebert import (\r\n\t\t\t\t\t MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,\r\n\t\t\t\t\t MobileBertConfig,\r\n\t\t\t\t\t MobileBertOnnxConfig,\r\n\t\t\t\t\t)\r\n\t\t\t\t\tfrom .tokenization_mobilebert import MobileBertTokenizer\r\n\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\tif not is_tokenizers_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\n\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\t\t\t\t\tpass\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\tfrom .tokenization_mobilebert_fast import MobileBertTokenizerFast\r\n\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\tif not is_torch_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\n\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\t\t\t\t\tpass\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\tfrom .modeling_mobilebert import (\r\n\t\t\t\t\t\t\t\t\t\t MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertForMaskedLM,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertForMultipleChoice,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertForNextSentencePrediction,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertForPreTraining,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertForQuestionAnswering,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertForSequenceClassification,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertForTokenClassification,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertLayer,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertModel,\r\n\t\t\t\t\t\t\t\t\t\t MobileBertPreTrainedModel,\r\n\t\t\t\t\t\t\t\t\t\t load_tf_weights_in_mobilebert,\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\tif not is_tf_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\n\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\t\t\t\t\tpass\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\tfrom .modeling_tf_mobilebert import (\r\n\t\t\t\t\t\t\t\t\t\t TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertForMaskedLM,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertForMultipleChoice,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertForNextSentencePrediction,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertForPreTraining,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertForQuestionAnswering,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertForSequenceClassification,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertForTokenClassification,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertMainLayer,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertModel,\r\n\t\t\t\t\t\t\t\t\t\t TFMobileBertPreTrainedModel,\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\r\nelse:\r\n\t\t\t\t\timport sys\r\n\r\n\t\t\t\t\tlowerCamelCase__ =\t\t_LazyModule(__name__, globals()[\"\"\"__file__\"\"\"], _import_structure, module_spec=__spec__)"},"code_codestyle":{"kind":"number","value":212,"string":"212"},"style_context":{"kind":"string","value":"\r\ndef lowerCAmelCase__\t\t\t( SCREAMING_SNAKE_CASE_ )\t\t\t\t->\t\tlist:\r\n\r\n\t\t\tif len(SCREAMING_SNAKE_CASE_ ) <= 1:\r\n\t\t\t\t\t\treturn [tuple(SCREAMING_SNAKE_CASE_ )]\r\n\r\n\t\t\tlowerCAmelCase__ : Optional[Any] =\t\t[]\r\n\r\n\t\t\tdef generate(SCREAMING_SNAKE_CASE_ ,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ ):\r\n\t\t\t\t\t\tif k == 1:\r\n\t\t\t\t\t\t\t\t\tres.append(tuple(arr[:] ) )\r\n\t\t\t\t\t\t\t\t\treturn\r\n\r\n\t\t\t\t\t\tgenerate(k - 1 ,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ )\r\n\r\n\t\t\t\t\t\tfor i in range(k - 1 ):\r\n\t\t\t\t\t\t\t\t\tif k % 2 == 0: # k is even\r\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ ,\t\t\t\tlowerCAmelCase__ : str =\t\tarr[k - 1], arr[i]\r\n\t\t\t\t\t\t\t\t\telse: # k is odd\r\n\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase__ ,\t\t\t\tlowerCAmelCase__ : Union[str, Any] =\t\tarr[k - 1], arr[0]\r\n\t\t\t\t\t\t\t\t\tgenerate(k - 1 ,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ )\r\n\r\n\t\t\tgenerate(len(SCREAMING_SNAKE_CASE_ ) ,\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_ )\r\n\t\t\treturn res\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\t\t\tlowerCamelCase__ =\t\tinput(\"\"\"Enter numbers separated by a comma:\\n\"\"\").strip()\r\n\t\t\t\t\tlowerCamelCase__ =\t\t[int(item) for item in user_input.split(\"\"\",\"\"\")]\r\n\t\t\t\t\tprint(heaps(arr))"},"style_context_codestyle":{"kind":"number","value":212,"string":"212"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":597,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n'''simple docstring'''\r\nfrom random import randint\r\nfrom tempfile import TemporaryFile\r\n\r\nimport numpy as np\r\n\r\n\r\n\r\n\r\n\r\ndef \t_a(\t\t\t\t\tUpperCamelCase__\t\t\t: str,\t\tUpperCamelCase__\t\t\t: List[str],\t\tUpperCamelCase__\t\t\t: List[Any]\t\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tDict\t\t\t\t\t\t\t\t\t\t\t\t\t=0\r\n if start < end:\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=randint(UpperCamelCase__,\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=a[end]\r\n SCREAMING_SNAKE_CASE__ :\t\t\tDict\t\t\t\t\t\t\t\t\t\t\t\t\t=a[pivot]\r\n SCREAMING_SNAKE_CASE__ :\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=temp\r\n\r\n SCREAMING_SNAKE_CASE__\t\t\t\t\t\t,\t\tSCREAMING_SNAKE_CASE__ :\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=_in_place_partition(UpperCamelCase__,\t\tUpperCamelCase__,\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n count += _in_place_quick_sort(UpperCamelCase__,\t\tUpperCamelCase__,\t\tp - 1\t\t\t\t\t\t\t)\r\n count += _in_place_quick_sort(UpperCamelCase__,\t\tp + 1,\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n return count\r\n\r\n\r\n\r\n\r\n\r\ndef \t_a(\t\t\t\t\tUpperCamelCase__\t\t\t: Union[str, Any],\t\tUpperCamelCase__\t\t\t: Optional[int],\t\tUpperCamelCase__\t\t\t: Union[str, Any]\t\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=0\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t=randint(UpperCamelCase__,\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n SCREAMING_SNAKE_CASE__ :\t\t\tAny\t\t\t\t\t\t\t\t\t\t\t\t\t=a[end]\r\n SCREAMING_SNAKE_CASE__ :\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=a[pivot]\r\n SCREAMING_SNAKE_CASE__ :\t\t\tAny\t\t\t\t\t\t\t\t\t\t\t\t\t=temp\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[str]\t\t\t\t\t\t\t\t\t\t\t\t\t=start - 1\r\n for index in range(UpperCamelCase__,\t\tUpperCamelCase__\t\t\t\t\t\t\t):\r\n count += 1\r\n if a[index] < a[end]: # check if current val is less than pivot value\r\n SCREAMING_SNAKE_CASE__ :\t\t\tDict\t\t\t\t\t\t\t\t\t\t\t\t\t=new_pivot_index + 1\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=a[new_pivot_index]\r\n SCREAMING_SNAKE_CASE__ :\t\t\tDict\t\t\t\t\t\t\t\t\t\t\t\t\t=a[index]\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t=temp\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[str]\t\t\t\t\t\t\t\t\t\t\t\t\t=a[new_pivot_index + 1]\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=a[end]\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t=temp\r\n return new_pivot_index + 1, count\r\n\r\n\r\na_\t\t\t\t\t=\tTemporaryFile()\r\na_\t\t\t\t\t=\t1_0_0 # 1000 elements are to be sorted\r\n\r\n\r\na_\t\t,\t\t\ta_\t\t\t\t\t=\t0, 1 # mean and standard deviation\r\na_\t\t\t\t\t=\tnp.random.normal(mu, sigma, p)\r\nnp.save(outfile, X)\r\nprint('The array is')\r\nprint(X)\r\n\r\n\r\noutfile.seek(0) # using the same array\r\na_\t\t\t\t\t=\tnp.load(outfile)\r\na_\t\t\t\t\t=\tlen(M) - 1\r\na_\t\t\t\t\t=\t_in_place_quick_sort(M, 0, r)\r\n\r\nprint(\r\n 'No of Comparisons for 100 elements selected from a standard normal distribution'\r\n 'is :'\r\n)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nprint(z)"},"code_codestyle":{"kind":"number","value":222,"string":"222"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\n'''simple docstring'''\r\nimport gc\r\nimport unittest\r\n\r\nimport numpy as np\r\nimport torch\r\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\r\n\r\nfrom diffusers import (\r\n AutoencoderKL,\r\n DDIMScheduler,\r\n StableDiffusionSAGPipeline,\r\n UNetaDConditionModel,\r\n)\r\nfrom diffusers.utils import slow, torch_device\r\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu\r\n\r\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\r\nfrom ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin\r\n\r\n\r\nenable_full_determinism()\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass __SCREAMING_SNAKE_CASE ( lowerCamelCase\t\t,\t\t\tlowerCamelCase\t\t,\t\t\tunittest.TestCase\t\t\t):\r\n snake_case_\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tStableDiffusionSAGPipeline\r\n snake_case_\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tTEXT_TO_IMAGE_PARAMS\r\n snake_case_\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tTEXT_TO_IMAGE_BATCH_PARAMS\r\n snake_case_\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tTEXT_TO_IMAGE_IMAGE_PARAMS\r\n snake_case_\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tTEXT_TO_IMAGE_IMAGE_PARAMS\r\n snake_case_\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tFalse\r\n\r\n\r\n\r\n\r\n def __magic_name__\t\t( self\t\t:\t\t\t\t\t\t\tstr )\t\t-> Union[str, Any]:\r\n torch.manual_seed(0 )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=UNetaDConditionModel(\r\n block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=DDIMScheduler(\r\n beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__lowercase , set_alpha_to_one=__lowercase , )\r\n torch.manual_seed(0 )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=AutoencoderKL(\r\n block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )\r\n torch.manual_seed(0 )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=CLIPTextConfig(\r\n bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=CLIPTextModel(__lowercase )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[str]\t\t\t\t\t\t\t\t\t\t\t\t\t=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t={\r\n '''unet''': unet,\r\n '''scheduler''': scheduler,\r\n '''vae''': vae,\r\n '''text_encoder''': text_encoder,\r\n '''tokenizer''': tokenizer,\r\n '''safety_checker''': None,\r\n '''feature_extractor''': None,\r\n }\r\n return components\r\n\r\n\r\n\r\n\r\n def __magic_name__\t\t( self\t\t:\t\t\t\t\t\t\tint , __lowercase\t\t:\t\t\t\t\t\t\tUnion[str, Any] , __lowercase\t\t:\t\t\t\t\t\t\tAny=0 )\t\t-> Optional[Any]:\r\n if str(__lowercase ).startswith('''mps''' ):\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t=torch.manual_seed(__lowercase )\r\n else:\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t=torch.Generator(device=__lowercase ).manual_seed(__lowercase )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tDict\t\t\t\t\t\t\t\t\t\t\t\t\t={\r\n '''prompt''': '''.''',\r\n '''generator''': generator,\r\n '''num_inference_steps''': 2,\r\n '''guidance_scale''': 1.0,\r\n '''sag_scale''': 1.0,\r\n '''output_type''': '''numpy''',\r\n }\r\n return inputs\r\n\r\n\r\n\r\n\r\n def __magic_name__\t\t( self\t\t:\t\t\t\t\t\t\tint )\t\t-> str:\r\n super().test_inference_batch_single_identical(expected_max_diff=3e-3 )\r\n\r\n\r\n\r\n\r\n\r\n\r\n@slow\r\n@require_torch_gpu\r\nclass __SCREAMING_SNAKE_CASE ( unittest.TestCase\t\t\t):\r\n\r\n\r\n\r\n\r\n def __magic_name__\t\t( self\t\t:\t\t\t\t\t\t\tint )\t\t-> Optional[int]:\r\n # clean up the VRAM after each test\r\n super().tearDown()\r\n gc.collect()\r\n torch.cuda.empty_cache()\r\n\r\n\r\n\r\n\r\n def __magic_name__\t\t( self\t\t:\t\t\t\t\t\t\tint )\t\t-> Optional[Any]:\r\n SCREAMING_SNAKE_CASE__ :\t\t\tAny\t\t\t\t\t\t\t\t\t\t\t\t\t=StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=sag_pipe.to(__lowercase )\r\n sag_pipe.set_progress_bar_config(disable=__lowercase )\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t='''.'''\r\n SCREAMING_SNAKE_CASE__ :\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=torch.manual_seed(0 )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=sag_pipe(\r\n [prompt] , generator=__lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' )\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tint\t\t\t\t\t\t\t\t\t\t\t\t\t=output.images\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tint\t\t\t\t\t\t\t\t\t\t\t\t\t=image[0, -3:, -3:, -1]\r\n\r\n assert image.shape == (1, 5_12, 5_12, 3)\r\n SCREAMING_SNAKE_CASE__ :\t\t\tstr\t\t\t\t\t\t\t\t\t\t\t\t\t=np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )\r\n\r\n assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2\r\n\r\n\r\n\r\n\r\n def __magic_name__\t\t( self\t\t:\t\t\t\t\t\t\tList[Any] )\t\t-> Any:\r\n SCREAMING_SNAKE_CASE__ :\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=sag_pipe.to(__lowercase )\r\n sag_pipe.set_progress_bar_config(disable=__lowercase )\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[str]\t\t\t\t\t\t\t\t\t\t\t\t\t='''.'''\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=torch.manual_seed(0 )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=sag_pipe(\r\n [prompt] , generator=__lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' )\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=output.images\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=image[0, -3:, -3:, -1]\r\n\r\n assert image.shape == (1, 5_12, 5_12, 3)\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )\r\n\r\n assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2\r\n\r\n\r\n\r\n\r\n def __magic_name__\t\t( self\t\t:\t\t\t\t\t\t\tstr )\t\t-> Any:\r\n SCREAMING_SNAKE_CASE__ :\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t=sag_pipe.to(__lowercase )\r\n sag_pipe.set_progress_bar_config(disable=__lowercase )\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t='''.'''\r\n SCREAMING_SNAKE_CASE__ :\t\t\tDict\t\t\t\t\t\t\t\t\t\t\t\t\t=torch.manual_seed(0 )\r\n SCREAMING_SNAKE_CASE__ :\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=sag_pipe(\r\n [prompt] , width=7_68 , height=5_12 , generator=__lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' , )\r\n\r\n SCREAMING_SNAKE_CASE__ :\t\t\tAny\t\t\t\t\t\t\t\t\t\t\t\t\t=output.images\r\n\r\n assert image.shape == (1, 5_12, 7_68, 3)"},"style_context_codestyle":{"kind":"number","value":222,"string":"222"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":598,"cells":{"code":{"kind":"string","value":"\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom __future__ import annotations\r\n\r\nimport unittest\r\n\r\nfrom transformers import is_tf_available\r\nfrom transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow\r\n\r\n\r\nif is_tf_available():\r\n\t\t\t\t\t\timport tensorflow as tf\r\n\r\n\t\t\t\t\t\tfrom transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_tf\r\n@require_sentencepiece\r\n@require_tokenizers\r\nclass \t\t\t_UpperCAmelCase\t(\t\t\t\t\t\tunittest.TestCase):\r\n\t\t\t@slow\r\n\t\t\tdef \t\t\t\t\t\t\t__snake_case (\t\t\t\t\t\tself )\t\t\t->\t\t\tDict:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = TFAutoModelForSeqaSeqLM.from_pretrained(\"\"\"google/mt5-small\"\"\" )\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = AutoTokenizer.from_pretrained(\"\"\"google/mt5-small\"\"\" )\r\n\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = tokenizer(\"\"\"Hello there\"\"\"\t\t\t\t\t,\t\t\t\t\t\treturn_tensors=\"\"\"tf\"\"\" ).input_ids\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = tokenizer(\"\"\"Hi I am\"\"\"\t\t\t\t\t,\t\t\t\t\t\treturn_tensors=\"\"\"tf\"\"\" ).input_ids\r\n\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tDict\t = model(_A\t\t\t\t\t,\t\t\t\t\t\tlabels=_A ).loss\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = -tf.math.reduce_mean(_A ).numpy()\r\n\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = -21.228168\r\n\t\t\t\tself.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2e-4 )\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":246,"string":"246"},"style_context":{"kind":"string","value":"\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport collections\r\nimport gzip\r\nimport os\r\nimport urllib\r\n\r\nimport numpy\r\nfrom tensorflow.python.framework import dtypes, random_seed\r\nfrom tensorflow.python.platform import gfile\r\nfrom tensorflow.python.util.deprecation import deprecated\r\n\r\nlowerCamelCase__ : str\t\t\t\t\t\t\t = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])\r\n\r\n# CVDF mirror of http://yann.lecun.com/exdb/mnist/\r\nlowerCamelCase__ : Union[str, Any]\t\t\t\t\t\t\t = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''\r\ndef \t\t\t\t\tUpperCamelCase (\t_lowerCAmelCase : List[str] ) ->\tOptional[Any]:\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = numpy.dtype(numpy.uintaa ).newbyteorder(\"\"\">\"\"\" )\r\n\treturn numpy.frombuffer(bytestream.read(4 ), dtype=_lowerCAmelCase )[0]\r\n@deprecated(_lowerCAmelCase, \"\"\"Please use tf.data to implement this functionality.\"\"\" )\r\ndef \t\t\t\t\tUpperCamelCase (\t_lowerCAmelCase : int ) ->\tOptional[Any]:\r\n\tprint(\"\"\"Extracting\"\"\", f.name )\r\n\twith gzip.GzipFile(fileobj=_lowerCAmelCase ) as bytestream:\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = _readaa(_lowerCAmelCase )\r\n\t\tif magic != 2051:\r\n\t\t\traise ValueError(\r\n\t\t\t \"\"\"Invalid magic number %d in MNIST image file: %s\"\"\" % (magic, f.name) )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = _readaa(_lowerCAmelCase )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t = _readaa(_lowerCAmelCase )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[str]\t = _readaa(_lowerCAmelCase )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[str]\t = bytestream.read(rows * cols * num_images )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t = numpy.frombuffer(_lowerCAmelCase, dtype=numpy.uinta )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tAny\t = data.reshape(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, 1 )\r\n\t\treturn data\r\n@deprecated(_lowerCAmelCase, \"\"\"Please use tf.one_hot on tensors.\"\"\" )\r\ndef \t\t\t\t\tUpperCamelCase (\t_lowerCAmelCase : Tuple, _lowerCAmelCase : Tuple ) ->\tUnion[str, Any]:\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tint\t = labels_dense.shape[0]\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tAny\t = numpy.arange(_lowerCAmelCase ) * num_classes\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = numpy.zeros((num_labels, num_classes) )\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tDict\t = 1\r\n\treturn labels_one_hot\r\n@deprecated(_lowerCAmelCase, \"\"\"Please use tf.data to implement this functionality.\"\"\" )\r\ndef \t\t\t\t\tUpperCamelCase (\t_lowerCAmelCase : Optional[int], _lowerCAmelCase : Optional[int]=False, _lowerCAmelCase : Optional[Any]=10 ) ->\tUnion[str, Any]:\r\n\tprint(\"\"\"Extracting\"\"\", f.name )\r\n\twith gzip.GzipFile(fileobj=_lowerCAmelCase ) as bytestream:\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = _readaa(_lowerCAmelCase )\r\n\t\tif magic != 2049:\r\n\t\t\traise ValueError(\r\n\t\t\t \"\"\"Invalid magic number %d in MNIST label file: %s\"\"\" % (magic, f.name) )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = _readaa(_lowerCAmelCase )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tDict\t = bytestream.read(_lowerCAmelCase )\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t = numpy.frombuffer(_lowerCAmelCase, dtype=numpy.uinta )\r\n\t\tif one_hot:\r\n\t\t\treturn _dense_to_one_hot(_lowerCAmelCase, _lowerCAmelCase )\r\n\t\treturn labels\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass \t\t\t_UpperCAmelCase\t:\r\n\t\t\t@deprecated(\r\n\t\t\t _A\t\t\t\t\t,\t\t\t\t\t\t\"\"\"Please use alternatives such as official/mnist/_DataSet.py\"\"\"\r\n\t\t\t \"\"\" from tensorflow/models.\"\"\"\t\t\t\t\t,\t\t\t\t\t\t)\r\n\t\t\tdef __init__(\t\t\t\t\t\tself\t\t\t\t\t,\t\t\t\t\t\t_A\t\t\t\t\t,\t\t\t\t\t\t_A\t\t\t\t\t,\t\t\t\t\t\t_A=False\t\t\t\t\t,\t\t\t\t\t\t_A=False\t\t\t\t\t,\t\t\t\t\t\t_A=dtypes.floataa\t\t\t\t\t,\t\t\t\t\t\t_A=True\t\t\t\t\t,\t\t\t\t\t\t_A=None\t\t\t\t\t,\t\t\t\t\t\t)\t\t\t->\t\t\tstr:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t_UpperCAmelCase , _UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tint\t = random_seed.get_seed(_A )\r\n\t\t\t\t# If op level seed is not set, use whatever graph level seed is returned\r\n\t\t\t\tnumpy.random.seed(seeda if seed is None else seeda )\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = dtypes.as_dtype(_A ).base_dtype\r\n\t\t\t\tif dtype not in (dtypes.uinta, dtypes.floataa):\r\n\t\t\t\t\traise TypeError(\"\"\"Invalid image dtype %r, expected uint8 or float32\"\"\" % dtype )\r\n\t\t\t\tif fake_data:\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t = 1_00_00\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t = one_hot\r\n\t\t\t\telse:\r\n\t\t\t\t\tassert (\r\n\t\t\t\t\t images.shape[0] == labels.shape[0]\r\n\t\t\t\t\t), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tAny\t = images.shape[0]\r\n\r\n\t\t\t\t\t# Convert shape from [num examples, rows, columns, depth]\r\n\t\t\t\t\t# to [num examples, rows*columns] (assuming depth == 1)\r\n\t\t\t\t\tif reshape:\r\n\t\t\t\t\t\tassert images.shape[3] == 1\r\n\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tint\t = images.reshape(\r\n\t\t\t\t\t\t images.shape[0]\t\t\t\t\t,\t\t\t\t\t\timages.shape[1] * images.shape[2] )\r\n\t\t\t\t\tif dtype == dtypes.floataa:\r\n\t\t\t\t\t\t# Convert from [0, 255] -> [0.0, 1.0].\r\n\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tDict\t = images.astype(numpy.floataa )\r\n\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tAny\t = numpy.multiply(_A\t\t\t\t\t,\t\t\t\t\t\t1.0 / 255.0 )\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t = images\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = labels\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t = 0\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t = 0\r\n\t\t\t@property\r\n\t\t\tdef \t\t\t\t\t\t\t__snake_case (\t\t\t\t\t\tself )\t\t\t->\t\t\tOptional[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\treturn self._images\r\n\t\t\t@property\r\n\t\t\tdef \t\t\t\t\t\t\t__snake_case (\t\t\t\t\t\tself )\t\t\t->\t\t\tAny:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\treturn self._labels\r\n\t\t\t@property\r\n\t\t\tdef \t\t\t\t\t\t\t__snake_case (\t\t\t\t\t\tself )\t\t\t->\t\t\tUnion[str, Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\treturn self._num_examples\r\n\t\t\t@property\r\n\t\t\tdef \t\t\t\t\t\t\t__snake_case (\t\t\t\t\t\tself )\t\t\t->\t\t\tOptional[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\treturn self._epochs_completed\r\n\t\t\tdef \t\t\t\t\t\t\t__snake_case (\t\t\t\t\t\tself\t\t\t\t\t,\t\t\t\t\t\t_A\t\t\t\t\t,\t\t\t\t\t\t_A=False\t\t\t\t\t,\t\t\t\t\t\t_A=True )\t\t\t->\t\t\tTuple:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tif fake_data:\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tint\t = [1] * 7_84\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = [1] + [0] * 9 if self.one_hot else 0\r\n\t\t\t\t\treturn (\r\n\t\t\t\t\t [fake_image for _ in range(_A )],\r\n\t\t\t\t\t [fake_label for _ in range(_A )],\r\n\t\t\t\t\t)\r\n\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = self._index_in_epoch\r\n\t\t\t\t# Shuffle for the first epoch\r\n\t\t\t\tif self._epochs_completed == 0 and start == 0 and shuffle:\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = numpy.arange(self._num_examples )\r\n\t\t\t\t\tnumpy.random.shuffle(_A )\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = self.images[perma]\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t = self.labels[perma]\r\n\t\t\t\t# Go to the next epoch\r\n\t\t\t\tif start + batch_size > self._num_examples:\r\n\t\t\t\t\t# Finished epoch\r\n\t\t\t\t\tself._epochs_completed += 1\r\n\t\t\t\t\t# Get the rest examples in this epoch\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = self._num_examples - start\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = self._images[start : self._num_examples]\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[str]\t = self._labels[start : self._num_examples]\r\n\t\t\t\t\t# Shuffle the data\r\n\t\t\t\t\tif shuffle:\r\n\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = numpy.arange(self._num_examples )\r\n\t\t\t\t\t\tnumpy.random.shuffle(_A )\r\n\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = self.images[perm]\r\n\t\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = self.labels[perm]\r\n\t\t\t\t\t# Start next epoch\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = 0\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = batch_size - rest_num_examples\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t = self._index_in_epoch\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = self._images[start:end]\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = self._labels[start:end]\r\n\t\t\t\t\treturn (\r\n\t\t\t\t\t numpy.concatenate((images_rest_part, images_new_part)\t\t\t\t\t,\t\t\t\t\t\taxis=0 ),\r\n\t\t\t\t\t numpy.concatenate((labels_rest_part, labels_new_part)\t\t\t\t\t,\t\t\t\t\t\taxis=0 ),\r\n\t\t\t\t\t)\r\n\t\t\t\telse:\r\n\t\t\t\t\tself._index_in_epoch += batch_size\r\n\t\t\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = self._index_in_epoch\r\n\t\t\t\t\treturn self._images[start:end], self._labels[start:end]\r\n@deprecated(_lowerCAmelCase, \"\"\"Please write your own downloading logic.\"\"\" )\r\ndef \t\t\t\t\tUpperCamelCase (\t_lowerCAmelCase : int, _lowerCAmelCase : List[Any], _lowerCAmelCase : Optional[Any] ) ->\tUnion[str, Any]:\r\n\tif not gfile.Exists(_lowerCAmelCase ):\r\n\t\tgfile.MakeDirs(_lowerCAmelCase )\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = os.path.join(_lowerCAmelCase, _lowerCAmelCase )\r\n\tif not gfile.Exists(_lowerCAmelCase ):\r\n\t\turllib.request.urlretrieve(_lowerCAmelCase, _lowerCAmelCase ) # noqa: S310\r\n\t\twith gfile.GFile(_lowerCAmelCase ) as f:\r\n\t\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = f.size()\r\n\t\tprint(\"\"\"Successfully downloaded\"\"\", _lowerCAmelCase, _lowerCAmelCase, \"\"\"bytes.\"\"\" )\r\n\treturn filepath\r\n\r\n\r\n\r\n\r\n\r\n@deprecated(\r\n _lowerCAmelCase, \"\"\"Please use alternatives such as:\"\"\" \"\"\" tensorflow_datasets.load('mnist')\"\"\" )\r\ndef \t\t\t\t\tUpperCamelCase (\t_lowerCAmelCase : Tuple, _lowerCAmelCase : str=False, _lowerCAmelCase : List[str]=False, _lowerCAmelCase : Tuple=dtypes.floataa, _lowerCAmelCase : List[str]=True, _lowerCAmelCase : Union[str, Any]=5000, _lowerCAmelCase : Optional[Any]=None, _lowerCAmelCase : int=DEFAULT_SOURCE_URL, ) ->\tOptional[Any]:\r\n\tif fake_data:\r\n\r\n\t\tdef fake():\r\n\t\t\treturn _DataSet(\r\n\t\t\t [], [], fake_data=_lowerCAmelCase, one_hot=_lowerCAmelCase, dtype=_lowerCAmelCase, seed=_lowerCAmelCase )\r\n\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = fake()\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tint\t = fake()\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tAny\t = fake()\r\n\t\treturn _Datasets(train=_lowerCAmelCase, validation=_lowerCAmelCase, test=_lowerCAmelCase )\r\n\r\n\tif not source_url: # empty string check\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t = DEFAULT_SOURCE_URL\r\n\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = \"\"\"train-images-idx3-ubyte.gz\"\"\"\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tint\t = \"\"\"train-labels-idx1-ubyte.gz\"\"\"\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t = \"\"\"t10k-images-idx3-ubyte.gz\"\"\"\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = \"\"\"t10k-labels-idx1-ubyte.gz\"\"\"\r\n\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = _maybe_download(\r\n\t _lowerCAmelCase, _lowerCAmelCase, source_url + train_images_file )\r\n\twith gfile.Open(_lowerCAmelCase, \"\"\"rb\"\"\" ) as f:\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = _extract_images(_lowerCAmelCase )\r\n\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tAny\t = _maybe_download(\r\n\t _lowerCAmelCase, _lowerCAmelCase, source_url + train_labels_file )\r\n\twith gfile.Open(_lowerCAmelCase, \"\"\"rb\"\"\" ) as f:\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = _extract_labels(_lowerCAmelCase, one_hot=_lowerCAmelCase )\r\n\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = _maybe_download(\r\n\t _lowerCAmelCase, _lowerCAmelCase, source_url + test_images_file )\r\n\twith gfile.Open(_lowerCAmelCase, \"\"\"rb\"\"\" ) as f:\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t = _extract_images(_lowerCAmelCase )\r\n\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = _maybe_download(\r\n\t _lowerCAmelCase, _lowerCAmelCase, source_url + test_labels_file )\r\n\twith gfile.Open(_lowerCAmelCase, \"\"\"rb\"\"\" ) as f:\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = _extract_labels(_lowerCAmelCase, one_hot=_lowerCAmelCase )\r\n\r\n\tif not 0 <= validation_size <= len(_lowerCAmelCase ):\r\n\t\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tint\t = (\r\n\t\t \"\"\"Validation size should be between 0 and \"\"\"\r\n\t\t f'''{len(_lowerCAmelCase )}. Received: {validation_size}.'''\r\n\t\t)\r\n\t\traise ValueError(_lowerCAmelCase )\r\n\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tstr\t = train_images[:validation_size]\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t = train_labels[:validation_size]\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t = train_images[validation_size:]\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = train_labels[validation_size:]\r\n\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tOptional[int]\t = {\"\"\"dtype\"\"\": dtype, \"\"\"reshape\"\"\": reshape, \"\"\"seed\"\"\": seed}\r\n\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tTuple\t = _DataSet(_lowerCAmelCase, _lowerCAmelCase, **_lowerCAmelCase )\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tDict\t = _DataSet(_lowerCAmelCase, _lowerCAmelCase, **_lowerCAmelCase )\r\n\t_UpperCAmelCase\t\t\t:\t\t\t\t\t\t\tList[Any]\t = _DataSet(_lowerCAmelCase, _lowerCAmelCase, **_lowerCAmelCase )\r\n\r\n\treturn _Datasets(train=_lowerCAmelCase, validation=_lowerCAmelCase, test=_lowerCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":246,"string":"246"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":599,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\r\rimport pytest\r\rfrom datasets.parallel import ParallelBackendConfig, parallel_backend\rfrom datasets.utils.py_utils import map_nested\r\rfrom .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ): # picklable for multiprocessing\r return i + 1\r\r\r\r\r\r\r@require_dill_gt_0_3_2\r@require_joblibspark\r@require_not_windows\rdef \tlowerCamelCase_ (\t\t\t\t\t\t):\r with parallel_backend('spark' ):\r assert ParallelBackendConfig.backend_name == \"spark\"\r\r lowerCamelCase__\t\t\t\t\t: Dict\t\t\t\t\t =\t\t\t\t\t\t[1, 2, 3]\r with pytest.raises(_lowerCamelCase ):\r with parallel_backend('unsupported backend' ):\r map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=2 )\r\r with pytest.raises(_lowerCamelCase ):\r with parallel_backend('unsupported backend' ):\r map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=-1 )\r\r\r\r\r\r\r@require_dill_gt_0_3_2\r@require_joblibspark\r@require_not_windows\r@pytest.mark.parametrize('num_proc' , [2, -1] )\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r lowerCamelCase__\t\t\t\t\t: str\t\t\t\t\t =\t\t\t\t\t\t[1, 2]\r lowerCamelCase__\t\t\t\t\t: Tuple\t\t\t\t\t =\t\t\t\t\t\t{'a': 1, 'b': 2}\r lowerCamelCase__\t\t\t\t\t: List[Any]\t\t\t\t\t =\t\t\t\t\t\t{'a': [1, 2], 'b': [3, 4]}\r lowerCamelCase__\t\t\t\t\t: Optional[int]\t\t\t\t\t =\t\t\t\t\t\t{'a': {'1': 1}, 'b': 2}\r lowerCamelCase__\t\t\t\t\t: List[Any]\t\t\t\t\t =\t\t\t\t\t\t{'a': 1, 'b': 2, 'c': 3, 'd': 4}\r lowerCamelCase__\t\t\t\t\t: List[str]\t\t\t\t\t =\t\t\t\t\t\t[2, 3]\r lowerCamelCase__\t\t\t\t\t: List[Any]\t\t\t\t\t =\t\t\t\t\t\t{'a': 2, 'b': 3}\r lowerCamelCase__\t\t\t\t\t: Optional[Any]\t\t\t\t\t =\t\t\t\t\t\t{'a': [2, 3], 'b': [4, 5]}\r lowerCamelCase__\t\t\t\t\t: Any\t\t\t\t\t =\t\t\t\t\t\t{'a': {'1': 2}, 'b': 3}\r lowerCamelCase__\t\t\t\t\t: List[Any]\t\t\t\t\t =\t\t\t\t\t\t{'a': 2, 'b': 3, 'c': 4, 'd': 5}\r\r with parallel_backend('spark' ):\r assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa\r assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa\r assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa\r assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa\r assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa\r\r"},"code_codestyle":{"kind":"number","value":363,"string":"363"},"style_context":{"kind":"string","value":"\r\r\r\r\r\r\r\"\"\"simple docstring\"\"\"\rfrom __future__ import annotations\r\rimport queue\r\r\r\rclass \t\ta_\t\t\t\t:\r\r\r\r\r\r\r\r '''simple docstring'''\r\r def __init__(self,\t\t\t\t\tlowerCamelCase_ ):\r\r '''simple docstring'''\r\r\r\r lowerCamelCase__\t\t\t\t\t: Union[str, Any]\t\t\t\t\t =\t\t\t\t\t\tdata\r lowerCamelCase__\t\t\t\t\t: Optional[int]\t\t\t\t\t =\t\t\t\t\t\tNone\r lowerCamelCase__\t\t\t\t\t: List[Any]\t\t\t\t\t =\t\t\t\t\t\tNone\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t):\r print('\\n********Press N to stop entering at any point of time********\\n' )\r lowerCamelCase__\t\t\t\t\t: str\t\t\t\t\t =\t\t\t\t\t\tinput('Enter the value of the root node: ' ).strip().lower()\r lowerCamelCase__\t\t\t\t\t: queue.Queue\t\t\t\t\t =\t\t\t\t\t\tqueue.Queue()\r lowerCamelCase__\t\t\t\t\t: Optional[Any]\t\t\t\t\t =\t\t\t\t\t\tTreeNode(int(_lowerCamelCase ) )\r q.put(_lowerCamelCase )\r while not q.empty():\r lowerCamelCase__\t\t\t\t\t: List[Any]\t\t\t\t\t =\t\t\t\t\t\tq.get()\r lowerCamelCase__\t\t\t\t\t: str\t\t\t\t\t =\t\t\t\t\t\tf'''Enter the left node of {node_found.data}: '''\r lowerCamelCase__\t\t\t\t\t: Dict\t\t\t\t\t =\t\t\t\t\t\tinput(_lowerCamelCase ).strip().lower() or 'n'\r if check == \"n\":\r return tree_node\r lowerCamelCase__\t\t\t\t\t: str\t\t\t\t\t =\t\t\t\t\t\tTreeNode(int(_lowerCamelCase ) )\r lowerCamelCase__\t\t\t\t\t: Dict\t\t\t\t\t =\t\t\t\t\t\tleft_node\r q.put(_lowerCamelCase )\r lowerCamelCase__\t\t\t\t\t: List[str]\t\t\t\t\t =\t\t\t\t\t\tf'''Enter the right node of {node_found.data}: '''\r lowerCamelCase__\t\t\t\t\t: List[str]\t\t\t\t\t =\t\t\t\t\t\tinput(_lowerCamelCase ).strip().lower() or 'n'\r if check == \"n\":\r return tree_node\r lowerCamelCase__\t\t\t\t\t: Optional[int]\t\t\t\t\t =\t\t\t\t\t\tTreeNode(int(_lowerCamelCase ) )\r lowerCamelCase__\t\t\t\t\t: Any\t\t\t\t\t =\t\t\t\t\t\tright_node\r q.put(_lowerCamelCase )\r raise\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node:\r return\r print(node.data , end=',' )\r pre_order(node.left )\r pre_order(node.right )\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node:\r return\r in_order(node.left )\r print(node.data , end=',' )\r in_order(node.right )\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node:\r return\r post_order(node.left )\r post_order(node.right )\r print(node.data , end=',' )\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node:\r return\r lowerCamelCase__\t\t\t\t\t: queue.Queue\t\t\t\t\t =\t\t\t\t\t\tqueue.Queue()\r q.put(_lowerCamelCase )\r while not q.empty():\r lowerCamelCase__\t\t\t\t\t: Any\t\t\t\t\t =\t\t\t\t\t\tq.get()\r print(node_dequeued.data , end=',' )\r if node_dequeued.left:\r q.put(node_dequeued.left )\r if node_dequeued.right:\r q.put(node_dequeued.right )\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node:\r return\r lowerCamelCase__\t\t\t\t\t: queue.Queue\t\t\t\t\t =\t\t\t\t\t\tqueue.Queue()\r q.put(_lowerCamelCase )\r while not q.empty():\r lowerCamelCase__\t\t\t\t\t: List[Any]\t\t\t\t\t =\t\t\t\t\t\t[]\r while not q.empty():\r lowerCamelCase__\t\t\t\t\t: str\t\t\t\t\t =\t\t\t\t\t\tq.get()\r print(node_dequeued.data , end=',' )\r if node_dequeued.left:\r list_.append(node_dequeued.left )\r if node_dequeued.right:\r list_.append(node_dequeued.right )\r print()\r for node in list_:\r q.put(_lowerCamelCase )\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node:\r return\r lowerCamelCase__\t\t\t\t\t: list[TreeNode]\t\t\t\t\t =\t\t\t\t\t\t[]\r lowerCamelCase__\t\t\t\t\t: int\t\t\t\t\t =\t\t\t\t\t\tnode\r while n or stack:\r while n: # start from root node, find its left child\r print(n.data , end=',' )\r stack.append(_lowerCamelCase )\r lowerCamelCase__\t\t\t\t\t: Union[str, Any]\t\t\t\t\t =\t\t\t\t\t\tn.left\r # end of while means current node doesn't have left child\r lowerCamelCase__\t\t\t\t\t: List[Any]\t\t\t\t\t =\t\t\t\t\t\tstack.pop()\r # start to traverse its right child\r lowerCamelCase__\t\t\t\t\t: Optional[Any]\t\t\t\t\t =\t\t\t\t\t\tn.right\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node:\r return\r lowerCamelCase__\t\t\t\t\t: list[TreeNode]\t\t\t\t\t =\t\t\t\t\t\t[]\r lowerCamelCase__\t\t\t\t\t: int\t\t\t\t\t =\t\t\t\t\t\tnode\r while n or stack:\r while n:\r stack.append(_lowerCamelCase )\r lowerCamelCase__\t\t\t\t\t: List[str]\t\t\t\t\t =\t\t\t\t\t\tn.left\r lowerCamelCase__\t\t\t\t\t: Tuple\t\t\t\t\t =\t\t\t\t\t\tstack.pop()\r print(n.data , end=',' )\r lowerCamelCase__\t\t\t\t\t: Union[str, Any]\t\t\t\t\t =\t\t\t\t\t\tn.right\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase ):\r if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node:\r return\r lowerCamelCase__ ,\t\t\t\t\tlowerCamelCase__\t\t\t\t\t: Any\t\t\t\t\t =\t\t\t\t\t\t[], []\r lowerCamelCase__\t\t\t\t\t: int\t\t\t\t\t =\t\t\t\t\t\tnode\r stacka.append(_lowerCamelCase )\r while stacka: # to find the reversed order of post order, store it in stack2\r lowerCamelCase__\t\t\t\t\t: List[str]\t\t\t\t\t =\t\t\t\t\t\tstacka.pop()\r if n.left:\r stacka.append(n.left )\r if n.right:\r stacka.append(n.right )\r stacka.append(_lowerCamelCase )\r while stacka: # pop up from stack2 will be the post order\r print(stacka.pop().data , end=',' )\r\r\r\r\r\r\rdef \tlowerCamelCase_ (\t\t\t\t\t\t_lowerCamelCase = \"\" , _lowerCamelCase=50 , _lowerCamelCase=\"*\" ):\r if not s:\r return \"\\n\" + width * char\r lowerCamelCase__ ,\t\t\t\t\tlowerCamelCase__\t\t\t\t\t: Dict\t\t\t\t\t =\t\t\t\t\t\tdivmod(width - len(_lowerCamelCase ) - 2 , 2 )\r return f'''{left * char} {s} {(left + extra) * char}'''\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r print(prompt(\"Binary Tree Traversals\"))\r\r A_ :\t\t\tTreeNode = build_tree()\r print(prompt(\"Pre Order Traversal\"))\r pre_order(node)\r print(prompt() + \"\\n\")\r\r print(prompt(\"In Order Traversal\"))\r in_order(node)\r print(prompt() + \"\\n\")\r\r print(prompt(\"Post Order Traversal\"))\r post_order(node)\r print(prompt() + \"\\n\")\r\r print(prompt(\"Level Order Traversal\"))\r level_order(node)\r print(prompt() + \"\\n\")\r\r print(prompt(\"Actual Level Order Traversal\"))\r level_order_actual(node)\r print(\"*\" * 50 + \"\\n\")\r\r print(prompt(\"Pre Order Traversal - Iteration Version\"))\r pre_order_iter(node)\r print(prompt() + \"\\n\")\r\r print(prompt(\"In Order Traversal - Iteration Version\"))\r in_order_iter(node)\r print(prompt() + \"\\n\")\r\r print(prompt(\"Post Order Traversal - Iteration Version\"))\r post_order_iter(node)\r print(prompt())\r\r"},"style_context_codestyle":{"kind":"number","value":316,"string":"316"},"label":{"kind":"number","value":0,"string":"0"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":5,"numItemsPerPage":100,"numTotalItems":153992,"offset":500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc1NTcxMzc0OCwic3ViIjoiL2RhdGFzZXRzL2luZmluaXR5b2ZzcGFjZS9weXRob25fY29kZXN0eWxlcy1taXhlZDEtNTAwIiwiZXhwIjoxNzU1NzE3MzQ4LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.DFSR2MLk3t1_jY4ggI1lxENqvSd8JoivTz1qRT2gskgkAqLaFwrw09qgljPuQto0ExmvpyUySenfcqne-DnzBg","displayUrls":true},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">

Datasets:

---

infinityofspace

/

python_codestyles-mixed1-500

like 0

Dataset card Data Studio Files Files and versions Community

1

Subset (1)

default · 182k rows

default (182k rows)

Split (2)

train · 154k rows

train (154k rows)test (28.2k rows)

SQL

Console

code stringlengths 86 54.5k	code_codestyle int64 0 371	style_context stringlengths 87 49.2k	style_context_codestyle int64 0 349	label int64 0 1
"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_pegasus import PegasusTokenizer else: snake_case_ = None snake_case_ = logging.get_logger(__name__) snake_case_ = "▁" snake_case_ = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} snake_case_ = { "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}, "tokenizer_file": { "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json" }, } snake_case_ = { "google/pegasus-xsum": 512, } class A_ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" __UpperCamelCase = VOCAB_FILES_NAMES __UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP __UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES __UpperCamelCase = PegasusTokenizer __UpperCamelCase = ["""input_ids""", """attention_mask"""] def __init__( self :Any , lowercase_ :Any=None , lowercase_ :List[Any]=None , lowercase_ :Dict="<pad>" , lowercase_ :List[Any]="</s>" , lowercase_ :Dict="<unk>" , lowercase_ :Tuple="<mask_2>" , lowercase_ :Union[str, Any]="<mask_1>" , lowercase_ :Union[str, Any]=None , lowercase_ :List[str]=1_03 , **lowercase_ :str , ) -> List[str]: UpperCAmelCase = offset if additional_special_tokens is not None: if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): raise TypeError( f"""additional_special_tokens should be of type {type(SCREAMING_SNAKE_CASE_ )}, but is""" f""" {type(SCREAMING_SNAKE_CASE_ )}""" ) UpperCAmelCase = ( ([mask_token_sent] + additional_special_tokens) if mask_token_sent not in additional_special_tokens and mask_token_sent is not None else additional_special_tokens ) # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken additional_special_tokens_extended += [ f"""<unk_{i}>""" for i in range(len(SCREAMING_SNAKE_CASE_ ) , self.offset - 1 ) ] if len(set(SCREAMING_SNAKE_CASE_ ) ) != len(SCREAMING_SNAKE_CASE_ ): raise ValueError( 'Please make sure that the provided additional_special_tokens do not contain an incorrectly' f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" ) UpperCAmelCase = additional_special_tokens_extended else: UpperCAmelCase = [mask_token_sent] if mask_token_sent is not None else [] additional_special_tokens += [f"""<unk_{i}>""" for i in range(2 , self.offset )] super().__init__( SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , mask_token_sent=SCREAMING_SNAKE_CASE_ , offset=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) UpperCAmelCase = vocab_file UpperCAmelCase = False if not self.vocab_file else True def UpperCAmelCase__ ( self :str , lowercase_ :Optional[Any] ) -> Optional[Any]: UpperCAmelCase = set(self.all_special_ids ) # call it once instead of inside list comp all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ): raise ValueError( 'There should be 3 special tokens: mask_token, pad_token, and eos_token +' f""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" ) return [1 if x in all_special_ids else 0 for x in seq] def UpperCAmelCase__ ( self :Optional[Any] , lowercase_ :Optional[int] , lowercase_ :List[Any] = None , lowercase_ :Any = False ) -> List[int]: if already_has_special_tokens: return self._special_token_mask(SCREAMING_SNAKE_CASE_ ) elif token_ids_a is None: return self._special_token_mask(SCREAMING_SNAKE_CASE_ ) + [1] else: return self._special_token_mask(token_ids_a + token_ids_a ) + [1] def UpperCAmelCase__ ( self :Optional[Any] , lowercase_ :int , lowercase_ :int=None ) -> List[int]: if token_ids_a is None: return token_ids_a + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return token_ids_a + token_ids_a + [self.eos_token_id] def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Any , lowercase_ :Union[str, Any] = None ) -> Tuple[str]: if not self.can_save_slow_tokenizer: raise ValueError( 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow ' 'tokenizer.' ) if not os.path.isdir(SCREAMING_SNAKE_CASE_ ): logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" ) return UpperCAmelCase = os.path.join( SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ): copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ ) return (out_vocab_file,)	78	from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast from ...utils import logging if TYPE_CHECKING: from ...feature_extraction_utils import FeatureExtractionMixin from ...tokenization_utils_base import PreTrainedTokenizerBase from ...utils import TensorType lowercase__ : str = logging.get_logger(__name__) lowercase__ : Union[str, Any] = { "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json", } # fmt: off lowercase__ : str = [ 1, 2, 7, 8, 9, 1_0, 1_4, 2_5, 2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2, 6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5, 7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7, 1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1, 4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6, 1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1, 1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9, 3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1 ] lowercase__ : str = [ 1, 2, 7, 8, 9, 1_0, 1_4, 2_5, 2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2, 6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3, 8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7, 3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7, 7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3, 1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5, 2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5, 4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2 ] class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" _snake_case = 'whisper' _snake_case = ['past_key_values'] _snake_case = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'} def __init__( self , SCREAMING_SNAKE_CASE_=51865 , SCREAMING_SNAKE_CASE_=80 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1536 , SCREAMING_SNAKE_CASE_=1536 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=50257 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0_2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1500 , SCREAMING_SNAKE_CASE_=448 , SCREAMING_SNAKE_CASE_=50256 , SCREAMING_SNAKE_CASE_=50256 , SCREAMING_SNAKE_CASE_=50256 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=[220, 50256] , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=0.0_5 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=7 , **SCREAMING_SNAKE_CASE_ , )-> Union[str, Any]: '''simple docstring''' __UpperCamelCase = vocab_size __UpperCamelCase = num_mel_bins __UpperCamelCase = d_model __UpperCamelCase = encoder_layers __UpperCamelCase = encoder_attention_heads __UpperCamelCase = decoder_layers __UpperCamelCase = decoder_attention_heads __UpperCamelCase = decoder_ffn_dim __UpperCamelCase = encoder_ffn_dim __UpperCamelCase = dropout __UpperCamelCase = attention_dropout __UpperCamelCase = activation_dropout __UpperCamelCase = activation_function __UpperCamelCase = init_std __UpperCamelCase = encoder_layerdrop __UpperCamelCase = decoder_layerdrop __UpperCamelCase = use_cache __UpperCamelCase = encoder_layers __UpperCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True __UpperCamelCase = max_source_positions __UpperCamelCase = max_target_positions # Audio Classification-specific parameters. Feel free to ignore for other classes. __UpperCamelCase = classifier_proj_size __UpperCamelCase = use_weighted_layer_sum # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 __UpperCamelCase = apply_spec_augment __UpperCamelCase = mask_time_prob __UpperCamelCase = mask_time_length __UpperCamelCase = mask_time_min_masks __UpperCamelCase = mask_feature_prob __UpperCamelCase = mask_feature_length __UpperCamelCase = mask_feature_min_masks __UpperCamelCase = median_filter_width super().__init__( pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , suppress_tokens=SCREAMING_SNAKE_CASE_ , begin_suppress_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" @property def A__ ( self )-> Mapping[str, Mapping[int, str]]: '''simple docstring''' __UpperCamelCase = OrderedDict( [ ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}), ] ) if self.use_past: __UpperCamelCase = {0: '''batch'''} else: __UpperCamelCase = {0: '''batch''', 1: '''decoder_sequence'''} if self.use_past: self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ , direction='''inputs''' ) return common_inputs def A__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = -1 , SCREAMING_SNAKE_CASE_ = -1 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 22050 , SCREAMING_SNAKE_CASE_ = 5.0 , SCREAMING_SNAKE_CASE_ = 220 , )-> Mapping[str, Any]: '''simple docstring''' __UpperCamelCase = OrderedDict() __UpperCamelCase = OnnxConfig.generate_dummy_inputs( self , preprocessor=preprocessor.feature_extractor , batch_size=SCREAMING_SNAKE_CASE_ , framework=SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , time_duration=SCREAMING_SNAKE_CASE_ , frequency=SCREAMING_SNAKE_CASE_ , ) __UpperCamelCase = encoder_inputs['''input_features'''].shape[2] __UpperCamelCase = encoder_sequence_length // 2 if self.use_past else seq_length __UpperCamelCase = super().generate_dummy_inputs( preprocessor.tokenizer , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) __UpperCamelCase = encoder_inputs.pop('''input_features''' ) __UpperCamelCase = decoder_inputs.pop('''decoder_input_ids''' ) if "past_key_values" in decoder_inputs: __UpperCamelCase = decoder_inputs.pop('''past_key_values''' ) return dummy_inputs @property def A__ ( self )-> float: '''simple docstring''' return 1E-3	328	0
'''simple docstring''' import tempfile import unittest import numpy as np from diffusers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, OnnxStableDiffusionPipeline, PNDMScheduler, ) from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin if is_onnx_available(): import onnxruntime as ort class __UpperCAmelCase ( _lowerCamelCase , unittest.TestCase ): __lowercase = """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline""" def lowerCamelCase ( self , lowerCAmelCase_=0 ): """simple docstring""" _snake_case = np.random.RandomState(lowerCAmelCase_ ) _snake_case = { 'prompt': 'A painting of a squirrel eating a burger', 'generator': generator, 'num_inference_steps': 2, 'guidance_scale': 7.5, 'output_type': 'numpy', } return inputs def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = self.get_dummy_inputs() _snake_case = pipe(**lowerCAmelCase_ ).images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 1_28, 1_28, 3) _snake_case = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' ) _snake_case = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=lowerCAmelCase_ ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = self.get_dummy_inputs() _snake_case = pipe(**lowerCAmelCase_ ).images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 1_28, 1_28, 3) _snake_case = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' ) _snake_case = LMSDiscreteScheduler.from_config(pipe.scheduler.config ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = self.get_dummy_inputs() _snake_case = pipe(**lowerCAmelCase_ ).images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 1_28, 1_28, 3) _snake_case = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' ) _snake_case = EulerDiscreteScheduler.from_config(pipe.scheduler.config ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = self.get_dummy_inputs() _snake_case = pipe(**lowerCAmelCase_ ).images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 1_28, 1_28, 3) _snake_case = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' ) _snake_case = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = self.get_dummy_inputs() _snake_case = pipe(**lowerCAmelCase_ ).images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 1_28, 1_28, 3) _snake_case = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' ) _snake_case = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = self.get_dummy_inputs() _snake_case = pipe(**lowerCAmelCase_ ).images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 1_28, 1_28, 3) _snake_case = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = self.get_dummy_inputs() _snake_case = 3 * [inputs['prompt']] # forward _snake_case = pipe(**lowerCAmelCase_ ) _snake_case = output.images[0, -3:, -3:, -1] _snake_case = self.get_dummy_inputs() _snake_case = 3 * [inputs.pop('prompt' )] _snake_case = pipe.tokenizer( lowerCAmelCase_ , padding='max_length' , max_length=pipe.tokenizer.model_max_length , truncation=lowerCAmelCase_ , return_tensors='np' , ) _snake_case = text_inputs['input_ids'] _snake_case = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] _snake_case = prompt_embeds # forward _snake_case = pipe(**lowerCAmelCase_ ) _snake_case = output.images[0, -3:, -3:, -1] assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4 def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = self.get_dummy_inputs() _snake_case = 3 * ['this is a negative prompt'] _snake_case = negative_prompt _snake_case = 3 * [inputs['prompt']] # forward _snake_case = pipe(**lowerCAmelCase_ ) _snake_case = output.images[0, -3:, -3:, -1] _snake_case = self.get_dummy_inputs() _snake_case = 3 * [inputs.pop('prompt' )] _snake_case = [] for p in [prompt, negative_prompt]: _snake_case = pipe.tokenizer( lowerCAmelCase_ , padding='max_length' , max_length=pipe.tokenizer.model_max_length , truncation=lowerCAmelCase_ , return_tensors='np' , ) _snake_case = text_inputs['input_ids'] embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] ) _snake_case , _snake_case = embeds # forward _snake_case = pipe(**lowerCAmelCase_ ) _snake_case = output.images[0, -3:, -3:, -1] assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4 @nightly @require_onnxruntime @require_torch_gpu class __UpperCAmelCase ( unittest.TestCase ): @property def lowerCamelCase ( self ): """simple docstring""" return ( "CUDAExecutionProvider", { "gpu_mem_limit": "15000000000", # 15GB "arena_extend_strategy": "kSameAsRequested", }, ) @property def lowerCamelCase ( self ): """simple docstring""" _snake_case = ort.SessionOptions() _snake_case = False return options def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained( 'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=lowerCAmelCase_ , feature_extractor=lowerCAmelCase_ , provider=self.gpu_provider , sess_options=self.gpu_options , ) sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = 'A painting of a squirrel eating a burger' np.random.seed(0 ) _snake_case = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type='np' ) _snake_case = output.images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 5_12, 5_12, 3) _snake_case = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 def lowerCamelCase ( self ): """simple docstring""" _snake_case = DDIMScheduler.from_pretrained( 'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' ) _snake_case = OnnxStableDiffusionPipeline.from_pretrained( 'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCAmelCase_ , safety_checker=lowerCAmelCase_ , feature_extractor=lowerCAmelCase_ , provider=self.gpu_provider , sess_options=self.gpu_options , ) sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = 'open neural network exchange' _snake_case = np.random.RandomState(0 ) _snake_case = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCAmelCase_ , output_type='np' ) _snake_case = output.images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 5_12, 5_12, 3) _snake_case = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 def lowerCamelCase ( self ): """simple docstring""" _snake_case = LMSDiscreteScheduler.from_pretrained( 'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' ) _snake_case = OnnxStableDiffusionPipeline.from_pretrained( 'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=lowerCAmelCase_ , safety_checker=lowerCAmelCase_ , feature_extractor=lowerCAmelCase_ , provider=self.gpu_provider , sess_options=self.gpu_options , ) sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = 'open neural network exchange' _snake_case = np.random.RandomState(0 ) _snake_case = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=lowerCAmelCase_ , output_type='np' ) _snake_case = output.images _snake_case = image[0, -3:, -3:, -1] assert image.shape == (1, 5_12, 5_12, 3) _snake_case = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 def lowerCamelCase ( self ): """simple docstring""" _snake_case = 0 def test_callback_fn(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> None: _snake_case = True nonlocal number_of_steps number_of_steps += 1 if step == 0: assert latents.shape == (1, 4, 64, 64) _snake_case = latents[0, -3:, -3:, -1] _snake_case = np.array( [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167] ) assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3 elif step == 5: assert latents.shape == (1, 4, 64, 64) _snake_case = latents[0, -3:, -3:, -1] _snake_case = np.array( [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875] ) assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3 _snake_case = False _snake_case = OnnxStableDiffusionPipeline.from_pretrained( 'runwayml/stable-diffusion-v1-5' , revision='onnx' , safety_checker=lowerCAmelCase_ , feature_extractor=lowerCAmelCase_ , provider=self.gpu_provider , sess_options=self.gpu_options , ) pipe.set_progress_bar_config(disable=lowerCAmelCase_ ) _snake_case = 'Andromeda galaxy in a bottle' _snake_case = np.random.RandomState(0 ) pipe( prompt=lowerCAmelCase_ , num_inference_steps=5 , guidance_scale=7.5 , generator=lowerCAmelCase_ , callback=lowerCAmelCase_ , callback_steps=1 , ) assert test_callback_fn.has_been_called assert number_of_steps == 6 def lowerCamelCase ( self ): """simple docstring""" _snake_case = OnnxStableDiffusionPipeline.from_pretrained( 'runwayml/stable-diffusion-v1-5' , revision='onnx' , safety_checker=lowerCAmelCase_ , feature_extractor=lowerCAmelCase_ , provider=self.gpu_provider , sess_options=self.gpu_options , ) assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) assert pipe.safety_checker is None _snake_case = pipe('example prompt' , num_inference_steps=2 ).images[0] assert image is not None # check that there's no error when saving a pipeline with one of the models being None with tempfile.TemporaryDirectory() as tmpdirname: pipe.save_pretrained(lowerCAmelCase_ ) _snake_case = OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase_ ) # sanity check that the pipeline still works assert pipe.safety_checker is None _snake_case = pipe('example prompt' , num_inference_steps=2 ).images[0] assert image is not None	160	'''simple docstring''' import json import os import shutil import tempfile import unittest from transformers import BatchEncoding, CanineTokenizer from transformers.testing_utils import require_tokenizers, require_torch from transformers.tokenization_utils import AddedToken from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin class __UpperCAmelCase ( _lowerCamelCase , unittest.TestCase ): __lowercase = CanineTokenizer __lowercase = False def lowerCamelCase ( self ): """simple docstring""" super().setUp() _snake_case = CanineTokenizer() tokenizer.save_pretrained(self.tmpdirname ) @cached_property def lowerCamelCase ( self ): """simple docstring""" return CanineTokenizer.from_pretrained('google/canine-s' ) def lowerCamelCase ( self , **lowerCAmelCase_ ): """simple docstring""" _snake_case = self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase_ ) _snake_case = 10_24 return tokenizer @require_torch def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.canine_tokenizer _snake_case = ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.'] # fmt: off _snake_case = [5_73_44, 76, 1_05, 1_02, 1_01, 32, 1_05, 1_15, 32, 1_08, 1_05, 1_07, 1_01, 32, 97, 32, 98, 1_11, 1_20, 32, 1_11, 1_02, 32, 99, 1_04, 1_11, 99, 1_11, 1_08, 97, 1_16, 1_01, 1_15, 46, 5_73_45, 0, 0, 0, 0] # fmt: on _snake_case = tokenizer(lowerCAmelCase_ , padding=lowerCAmelCase_ , return_tensors='pt' ) self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_ ) _snake_case = list(batch.input_ids.numpy()[0] ) self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ ) self.assertEqual((2, 39) , batch.input_ids.shape ) self.assertEqual((2, 39) , batch.attention_mask.shape ) @require_torch def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.canine_tokenizer _snake_case = ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.'] _snake_case = tokenizer(lowerCAmelCase_ , padding=lowerCAmelCase_ , return_tensors='pt' ) # check if input_ids, attention_mask and token_type_ids are returned self.assertIn('input_ids' , lowerCAmelCase_ ) self.assertIn('attention_mask' , lowerCAmelCase_ ) self.assertIn('token_type_ids' , lowerCAmelCase_ ) @require_torch def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.canine_tokenizer _snake_case = [ 'What\'s the weater?', 'It\'s about 25 degrees.', ] _snake_case = tokenizer( text_target=lowerCAmelCase_ , max_length=32 , padding='max_length' , truncation=lowerCAmelCase_ , return_tensors='pt' ) self.assertEqual(32 , targets['input_ids'].shape[1] ) def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): self.assertNotEqual(tokenizer.model_max_length , 42 ) # Now let's start the test _snake_case = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): # Isolate this from the other tests because we save additional tokens/etc _snake_case = tempfile.mkdtemp() _snake_case = ' He is very happy, UNwant\u00E9d,running' _snake_case = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) tokenizer.save_pretrained(lowerCAmelCase_ ) _snake_case = tokenizer.__class__.from_pretrained(lowerCAmelCase_ ) _snake_case = after_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ ) shutil.rmtree(lowerCAmelCase_ ) _snake_case = self.get_tokenizers(model_max_length=42 ) for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): # Isolate this from the other tests because we save additional tokens/etc _snake_case = tempfile.mkdtemp() _snake_case = ' He is very happy, UNwant\u00E9d,running' _snake_case = tokenizer.additional_special_tokens # We can add a new special token for Canine as follows: _snake_case = chr(0XE_0_0_7 ) additional_special_tokens.append(lowerCAmelCase_ ) tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} ) _snake_case = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) tokenizer.save_pretrained(lowerCAmelCase_ ) _snake_case = tokenizer.__class__.from_pretrained(lowerCAmelCase_ ) _snake_case = after_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ ) self.assertIn(lowerCAmelCase_ , after_tokenizer.additional_special_tokens ) self.assertEqual(after_tokenizer.model_max_length , 42 ) _snake_case = tokenizer.__class__.from_pretrained(lowerCAmelCase_ , model_max_length=43 ) self.assertEqual(tokenizer.model_max_length , 43 ) shutil.rmtree(lowerCAmelCase_ ) def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.get_tokenizers(do_lower_case=lowerCAmelCase_ ) for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): _snake_case , _snake_case = self.get_clean_sequence(lowerCAmelCase_ ) # a special token for Canine can be defined as follows: _snake_case = 0XE_0_0_5 _snake_case = chr(lowerCAmelCase_ ) tokenizer.add_special_tokens({'cls_token': special_token} ) _snake_case = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) self.assertEqual(len(lowerCAmelCase_ ) , 1 ) _snake_case = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=lowerCAmelCase_ ) _snake_case = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) _snake_case = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) _snake_case = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) self.assertEqual(lowerCAmelCase_ , input_encoded + special_token_id ) _snake_case = tokenizer.decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_ ) self.assertTrue(special_token not in decoded ) def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.get_tokenizers(do_lower_case=lowerCAmelCase_ ) for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): _snake_case = chr(0XE_0_0_5 ) _snake_case = chr(0XE_0_0_6 ) # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py) tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=lowerCAmelCase_ ) # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`, # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py) tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]} ) _snake_case = tokenizer.tokenize(lowerCAmelCase_ ) _snake_case = tokenizer.tokenize(lowerCAmelCase_ ) self.assertEqual(len(lowerCAmelCase_ ) , 1 ) self.assertEqual(len(lowerCAmelCase_ ) , 1 ) self.assertEqual(token_a[0] , lowerCAmelCase_ ) self.assertEqual(token_a[0] , lowerCAmelCase_ ) @require_tokenizers def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.get_tokenizers(do_lower_case=lowerCAmelCase_ ) for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): # a special token for Canine can be defined as follows: _snake_case = 0XE_0_0_6 _snake_case = chr(lowerCAmelCase_ ) _snake_case = AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ ) tokenizer.add_special_tokens({'additional_special_tokens': [new_token]} ) with tempfile.TemporaryDirectory() as tmp_dir_name: tokenizer.save_pretrained(lowerCAmelCase_ ) tokenizer.from_pretrained(lowerCAmelCase_ ) def lowerCamelCase ( self ): """simple docstring""" _snake_case = [] if self.test_slow_tokenizer: tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) ) if self.test_rust_tokenizer: tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) ) for tokenizer_class, tokenizer_utils in tokenizer_list: with tempfile.TemporaryDirectory() as tmp_dir: tokenizer_utils.save_pretrained(lowerCAmelCase_ ) with open(os.path.join(lowerCAmelCase_ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file: _snake_case = json.load(lowerCAmelCase_ ) with open(os.path.join(lowerCAmelCase_ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file: _snake_case = json.load(lowerCAmelCase_ ) # a special token for Canine can be defined as follows: _snake_case = 0XE_0_0_6 _snake_case = chr(lowerCAmelCase_ ) _snake_case = [new_token_a] _snake_case = [new_token_a] with open(os.path.join(lowerCAmelCase_ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile: json.dump(lowerCAmelCase_ , lowerCAmelCase_ ) with open(os.path.join(lowerCAmelCase_ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile: json.dump(lowerCAmelCase_ , lowerCAmelCase_ ) # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and # "special_tokens_map.json" files _snake_case = tokenizer_class.from_pretrained(lowerCAmelCase_ , extra_ids=0 ) self.assertIn(lowerCAmelCase_ , tokenizer_without_change_in_init.additional_special_tokens ) # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab self.assertEqual( [new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens( tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , ) _snake_case = 0XE_0_0_7 _snake_case = chr(lowerCAmelCase_ ) # Now we test that we can change the value of additional_special_tokens in the from_pretrained _snake_case = [AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ )] _snake_case = tokenizer_class.from_pretrained( lowerCAmelCase_ , additional_special_tokens=lowerCAmelCase_ , extra_ids=0 ) self.assertIn(lowerCAmelCase_ , tokenizer.additional_special_tokens ) # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab self.assertEqual( [new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) ) @require_tokenizers def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.get_tokenizers(do_lower_case=lowerCAmelCase_ ) for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): _snake_case = 'hello world' if self.space_between_special_tokens: _snake_case = '[CLS] hello world [SEP]' else: _snake_case = input _snake_case = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) _snake_case = tokenizer.decode(lowerCAmelCase_ , spaces_between_special_tokens=self.space_between_special_tokens ) self.assertIn(lowerCAmelCase_ , [output, output.lower()] ) def lowerCamelCase ( self ): """simple docstring""" _snake_case = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): _snake_case = [ 'bos_token', 'eos_token', 'unk_token', 'sep_token', 'pad_token', 'cls_token', 'mask_token', ] _snake_case = 'a' _snake_case = ord(lowerCAmelCase_ ) for attr in attributes_list: setattr(lowerCAmelCase_ , attr + '_id' , lowerCAmelCase_ ) self.assertEqual(getattr(lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ ) self.assertEqual(getattr(lowerCAmelCase_ , attr + '_id' ) , lowerCAmelCase_ ) setattr(lowerCAmelCase_ , attr + '_id' , lowerCAmelCase_ ) self.assertEqual(getattr(lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ ) self.assertEqual(getattr(lowerCAmelCase_ , attr + '_id' ) , lowerCAmelCase_ ) setattr(lowerCAmelCase_ , 'additional_special_tokens_ids' , [] ) self.assertListEqual(getattr(lowerCAmelCase_ , 'additional_special_tokens' ) , [] ) self.assertListEqual(getattr(lowerCAmelCase_ , 'additional_special_tokens_ids' ) , [] ) _snake_case = 0XE_0_0_6 _snake_case = chr(lowerCAmelCase_ ) setattr(lowerCAmelCase_ , 'additional_special_tokens_ids' , [additional_special_token_id] ) self.assertListEqual(getattr(lowerCAmelCase_ , 'additional_special_tokens' ) , [additional_special_token] ) self.assertListEqual(getattr(lowerCAmelCase_ , 'additional_special_tokens_ids' ) , [additional_special_token_id] ) def lowerCamelCase ( self ): """simple docstring""" pass def lowerCamelCase ( self ): """simple docstring""" pass def lowerCamelCase ( self ): """simple docstring""" pass def lowerCamelCase ( self ): """simple docstring""" pass def lowerCamelCase ( self ): """simple docstring""" pass def lowerCamelCase ( self ): """simple docstring""" pass def lowerCamelCase ( self ): """simple docstring""" pass def lowerCamelCase ( self ): """simple docstring""" pass	160	1
"""simple docstring""" import argparse import glob import importlib.util import os import re import black from doc_builder.style_doc import style_docstrings_in_code # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_copies.py UpperCamelCase_ = 'src/diffusers' UpperCamelCase_ = '.' # This is to make sure the diffusers module imported is the one in the repo. UpperCamelCase_ = importlib.util.spec_from_file_location( 'diffusers', os.path.join(DIFFUSERS_PATH, '__init__.py'), submodule_search_locations=[DIFFUSERS_PATH], ) UpperCamelCase_ = spec.loader.load_module() def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->Dict: """simple docstring""" return line.startswith(UpperCAmelCase ) or len(UpperCAmelCase ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$" , UpperCAmelCase ) is not None def UpperCamelCase ( UpperCAmelCase ) ->Any: """simple docstring""" a_ = object_name.split("." ) a_ = 0 # First let's find the module where our object lives. a_ = parts[i] while i < len(UpperCAmelCase ) and not os.path.isfile(os.path.join(UpperCAmelCase , F'''{module}.py''' ) ): i += 1 if i < len(UpperCAmelCase ): a_ = os.path.join(UpperCAmelCase , parts[i] ) if i >= len(UpperCAmelCase ): raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''' ) with open(os.path.join(UpperCAmelCase , F'''{module}.py''' ) , "r" , encoding="utf-8" , newline="\n" ) as f: a_ = f.readlines() # Now let's find the class / func in the code! a_ = "" a_ = 0 for name in parts[i + 1 :]: while ( line_index < len(UpperCAmelCase ) and re.search(rF'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index] ) is None ): line_index += 1 indent += " " line_index += 1 if line_index >= len(UpperCAmelCase ): raise ValueError(F''' {object_name} does not match any function or class in {module}.''' ) # We found the beginning of the class / func, now let's find the end (when the indent diminishes). a_ = line_index while line_index < len(UpperCAmelCase ) and _should_continue(lines[line_index] , UpperCAmelCase ): line_index += 1 # Clean up empty lines at the end (if any). while len(lines[line_index - 1] ) <= 1: line_index -= 1 a_ = lines[start_index:line_index] return "".join(UpperCAmelCase ) UpperCamelCase_ = re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)') UpperCamelCase_ = re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)') UpperCamelCase_ = re.compile(R'<FILL\s+[^>]*>') def UpperCamelCase ( UpperCAmelCase ) ->int: """simple docstring""" a_ = code.split("\n" ) a_ = 0 while idx < len(UpperCAmelCase ) and len(lines[idx] ) == 0: idx += 1 if idx < len(UpperCAmelCase ): return re.search(r"^(\s*)\S" , lines[idx] ).groups()[0] return "" def UpperCamelCase ( UpperCAmelCase ) ->int: """simple docstring""" a_ = len(get_indent(UpperCAmelCase ) ) > 0 if has_indent: a_ = F'''class Bla:\n{code}''' a_ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=UpperCAmelCase ) a_ = black.format_str(UpperCAmelCase , mode=UpperCAmelCase ) a_ , a_ = style_docstrings_in_code(UpperCAmelCase ) return result[len("class Bla:\n" ) :] if has_indent else result def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=False ) ->str: """simple docstring""" with open(UpperCAmelCase , "r" , encoding="utf-8" , newline="\n" ) as f: a_ = f.readlines() a_ = [] a_ = 0 # Not a for loop cause `lines` is going to change (if `overwrite=True`). while line_index < len(UpperCAmelCase ): a_ = _re_copy_warning.search(lines[line_index] ) if search is None: line_index += 1 continue # There is some copied code here, let's retrieve the original. a_ , a_ , a_ = search.groups() a_ = find_code_in_diffusers(UpperCAmelCase ) a_ = get_indent(UpperCAmelCase ) a_ = line_index + 1 if indent == theoretical_indent else line_index + 2 a_ = theoretical_indent a_ = start_index # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment. a_ = True while line_index < len(UpperCAmelCase ) and should_continue: line_index += 1 if line_index >= len(UpperCAmelCase ): break a_ = lines[line_index] a_ = _should_continue(UpperCAmelCase , UpperCAmelCase ) and re.search(F'''^{indent}# End copy''' , UpperCAmelCase ) is None # Clean up empty lines at the end (if any). while len(lines[line_index - 1] ) <= 1: line_index -= 1 a_ = lines[start_index:line_index] a_ = "".join(UpperCAmelCase ) # Remove any nested `Copied from` comments to avoid circular copies a_ = [line for line in theoretical_code.split("\n" ) if _re_copy_warning.search(UpperCAmelCase ) is None] a_ = "\n".join(UpperCAmelCase ) # Before comparing, use the `replace_pattern` on the original code. if len(UpperCAmelCase ) > 0: a_ = replace_pattern.replace("with" , "" ).split("," ) a_ = [_re_replace_pattern.search(UpperCAmelCase ) for p in patterns] for pattern in patterns: if pattern is None: continue a_ , a_ , a_ = pattern.groups() a_ = re.sub(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) if option.strip() == "all-casing": a_ = re.sub(obja.lower() , obja.lower() , UpperCAmelCase ) a_ = re.sub(obja.upper() , obja.upper() , UpperCAmelCase ) # Blackify after replacement. To be able to do that, we need the header (class or function definition) # from the previous line a_ = blackify(lines[start_index - 1] + theoretical_code ) a_ = theoretical_code[len(lines[start_index - 1] ) :] # Test for a diff and act accordingly. if observed_code != theoretical_code: diffs.append([object_name, start_index] ) if overwrite: a_ = lines[:start_index] + [theoretical_code] + lines[line_index:] a_ = start_index + 1 if overwrite and len(UpperCAmelCase ) > 0: # Warn the user a file has been modified. print(F'''Detected changes, rewriting {filename}.''' ) with open(UpperCAmelCase , "w" , encoding="utf-8" , newline="\n" ) as f: f.writelines(UpperCAmelCase ) return diffs def UpperCamelCase ( UpperCAmelCase = False ) ->int: """simple docstring""" a_ = glob.glob(os.path.join(UpperCAmelCase , "**/*.py" ) , recursive=UpperCAmelCase ) a_ = [] for filename in all_files: a_ = is_copy_consistent(UpperCAmelCase , UpperCAmelCase ) diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs] if not overwrite and len(UpperCAmelCase ) > 0: a_ = "\n".join(UpperCAmelCase ) raise Exception( "Found the following copy inconsistencies:\n" + diff + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." ) if __name__ == "__main__": UpperCamelCase_ = argparse.ArgumentParser() parser.add_argument('--fix_and_overwrite', action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true", help='Whether to fix inconsistencies.') UpperCamelCase_ = parser.parse_args() check_copies(args.fix_and_overwrite)	243	"""simple docstring""" import baseaa def UpperCamelCase ( UpperCAmelCase ) ->bytes: """simple docstring""" return baseaa.baaencode(string.encode("utf-8" ) ) def UpperCamelCase ( UpperCAmelCase ) ->str: """simple docstring""" return baseaa.baadecode(UpperCAmelCase ).decode("utf-8" ) if __name__ == "__main__": UpperCamelCase_ = 'Hello World!' UpperCamelCase_ = baseaa_encode(test) print(encoded) UpperCamelCase_ = baseaa_decode(encoded) print(decoded)	243	1
import math from typing import Optional import numpy as np from ...configuration_utils import PretrainedConfig from ...utils import logging _snake_case : Union[str, Any] = logging.get_logger(__name__) _snake_case : List[str] = { "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json", "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json", } class a (_lowerCAmelCase ): """simple docstring""" __UpperCAmelCase : Tuple = "encodec" def __init__( self : Any , lowerCamelCase : Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowerCamelCase : List[str]=24000 , lowerCamelCase : int=1 , lowerCamelCase : Optional[int]=False , lowerCamelCase : Dict=None , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[int]=128 , lowerCamelCase : Optional[int]=32 , lowerCamelCase : List[str]=1 , lowerCamelCase : str=[8, 5, 4, 2] , lowerCamelCase : List[str]="weight_norm" , lowerCamelCase : Any=7 , lowerCamelCase : Tuple=7 , lowerCamelCase : int=3 , lowerCamelCase : int=2 , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[Any]="reflect" , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : int=1.0 , lowerCamelCase : Optional[Any]=1024 , lowerCamelCase : Optional[Any]=None , lowerCamelCase : str=True , **lowerCamelCase : Dict , ) -> Any: __snake_case : Tuple = target_bandwidths __snake_case : Union[str, Any] = sampling_rate __snake_case : Union[str, Any] = audio_channels __snake_case : Dict = normalize __snake_case : List[Any] = chunk_length_s __snake_case : Tuple = overlap __snake_case : Optional[int] = hidden_size __snake_case : List[Any] = num_filters __snake_case : Union[str, Any] = num_residual_layers __snake_case : Optional[int] = upsampling_ratios __snake_case : List[str] = norm_type __snake_case : Optional[int] = kernel_size __snake_case : Dict = last_kernel_size __snake_case : Tuple = residual_kernel_size __snake_case : List[Any] = dilation_growth_rate __snake_case : Optional[int] = use_causal_conv __snake_case : Tuple = pad_mode __snake_case : Union[str, Any] = compress __snake_case : Union[str, Any] = num_lstm_layers __snake_case : int = trim_right_ratio __snake_case : Tuple = codebook_size __snake_case : Optional[Any] = codebook_dim if codebook_dim is not None else hidden_size __snake_case : int = use_conv_shortcut if self.norm_type not in ["weight_norm", "time_group_norm"]: raise ValueError( F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' ) super().__init__(**lowerCamelCase ) @property def __snake_case ( self : int ) -> Optional[int]: if self.chunk_length_s is None: return None else: return int(self.chunk_length_s * self.sampling_rate ) @property def __snake_case ( self : Union[str, Any] ) -> Optional[int]: if self.chunk_length_s is None or self.overlap is None: return None else: return max(1 , int((1.0 - self.overlap) * self.chunk_length ) ) @property def __snake_case ( self : Optional[Any] ) -> int: __snake_case : Union[str, Any] = np.prod(self.upsampling_ratios ) return math.ceil(self.sampling_rate / hop_length ) @property def __snake_case ( self : Optional[Any] ) -> int: return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )	134	from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging _snake_case : int = logging.get_logger(__name__) _snake_case : int = { "microsoft/beit-base-patch16-224-pt22k": ( "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json" ), # See all BEiT models at https://huggingface.co/models?filter=beit } class a (_lowerCAmelCase ): """simple docstring""" __UpperCAmelCase : Union[str, Any] = "beit" def __init__( self : Union[str, Any] , lowerCamelCase : Any=8192 , lowerCamelCase : Dict=768 , lowerCamelCase : int=12 , lowerCamelCase : Optional[Any]=12 , lowerCamelCase : List[str]=3072 , lowerCamelCase : Tuple="gelu" , lowerCamelCase : Union[str, Any]=0.0 , lowerCamelCase : int=0.0 , lowerCamelCase : Dict=0.02 , lowerCamelCase : List[str]=1E-12 , lowerCamelCase : Optional[Any]=224 , lowerCamelCase : Optional[int]=16 , lowerCamelCase : Any=3 , lowerCamelCase : Optional[int]=False , lowerCamelCase : Any=False , lowerCamelCase : Optional[Any]=False , lowerCamelCase : int=False , lowerCamelCase : Any=0.1 , lowerCamelCase : Tuple=0.1 , lowerCamelCase : Optional[int]=True , lowerCamelCase : int=[3, 5, 7, 11] , lowerCamelCase : str=[1, 2, 3, 6] , lowerCamelCase : int=True , lowerCamelCase : List[Any]=0.4 , lowerCamelCase : int=256 , lowerCamelCase : str=1 , lowerCamelCase : List[str]=False , lowerCamelCase : List[str]=255 , **lowerCamelCase : Dict , ) -> int: super().__init__(**lowerCamelCase ) __snake_case : Any = vocab_size __snake_case : List[str] = hidden_size __snake_case : List[Any] = num_hidden_layers __snake_case : Tuple = num_attention_heads __snake_case : Dict = intermediate_size __snake_case : Union[str, Any] = hidden_act __snake_case : Optional[Any] = hidden_dropout_prob __snake_case : Optional[int] = attention_probs_dropout_prob __snake_case : Union[str, Any] = initializer_range __snake_case : str = layer_norm_eps __snake_case : Optional[Any] = image_size __snake_case : List[str] = patch_size __snake_case : Optional[Any] = num_channels __snake_case : Any = use_mask_token __snake_case : List[str] = use_absolute_position_embeddings __snake_case : List[Any] = use_relative_position_bias __snake_case : str = use_shared_relative_position_bias __snake_case : str = layer_scale_init_value __snake_case : Any = drop_path_rate __snake_case : int = use_mean_pooling # decode head attributes (semantic segmentation) __snake_case : Optional[Any] = out_indices __snake_case : List[str] = pool_scales # auxiliary head attributes (semantic segmentation) __snake_case : int = use_auxiliary_head __snake_case : int = auxiliary_loss_weight __snake_case : Optional[int] = auxiliary_channels __snake_case : int = auxiliary_num_convs __snake_case : str = auxiliary_concat_input __snake_case : List[str] = semantic_loss_ignore_index class a (_lowerCAmelCase ): """simple docstring""" __UpperCAmelCase : Union[str, Any] = version.parse("1.11" ) @property def __snake_case ( self : Dict ) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def __snake_case ( self : str ) -> float: return 1E-4	134	1
'''simple docstring''' import argparse import os import re lowerCAmelCase_ : Optional[int] = 'src/transformers' # Pattern that looks at the indentation in a line. lowerCAmelCase_ : Union[str, Any] = re.compile(R'^(\s*)\S') # Pattern that matches `"key":" and puts `key` in group 0. lowerCAmelCase_ : Union[str, Any] = re.compile(R'^\s*"([^"]+)":') # Pattern that matches `_import_structure["key"]` and puts `key` in group 0. lowerCAmelCase_ : Any = re.compile(R'^\s*_import_structure\["([^"]+)"\]') # Pattern that matches `"key",` and puts `key` in group 0. lowerCAmelCase_ : Optional[int] = re.compile(R'^\s*"([^"]+)",\s*$') # Pattern that matches any `[stuff]` and puts `stuff` in group 0. lowerCAmelCase_ : Union[str, Any] = re.compile(R'\[([^\]]+)\]') def _lowerCamelCase ( lowercase : Union[str, Any] ) -> Any: _a = _re_indent.search(lowercase ) return "" if search is None else search.groups()[0] def _lowerCamelCase ( lowercase : Dict , lowercase : Union[str, Any]="" , lowercase : Tuple=None , lowercase : List[Any]=None ) -> str: _a = 0 _a = code.split("\n" ) if start_prompt is not None: while not lines[index].startswith(lowercase ): index += 1 _a = ["\n".join(lines[:index] )] else: _a = [] # We split into blocks until we get to the `end_prompt` (or the end of the block). _a = [lines[index]] index += 1 while index < len(lowercase ) and (end_prompt is None or not lines[index].startswith(lowercase )): if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level: if len(lowercase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ): current_block.append(lines[index] ) blocks.append("\n".join(lowercase ) ) if index < len(lowercase ) - 1: _a = [lines[index + 1]] index += 1 else: _a = [] else: blocks.append("\n".join(lowercase ) ) _a = [lines[index]] else: current_block.append(lines[index] ) index += 1 # Adds current block if it's nonempty. if len(lowercase ) > 0: blocks.append("\n".join(lowercase ) ) # Add final block after end_prompt if provided. if end_prompt is not None and index < len(lowercase ): blocks.append("\n".join(lines[index:] ) ) return blocks def _lowerCamelCase ( lowercase : str ) -> int: def _inner(lowercase : Dict ): return key(lowercase ).lower().replace("_" , "" ) return _inner def _lowerCamelCase ( lowercase : str , lowercase : Tuple=None ) -> Optional[int]: # If no key is provided, we use a noop. def noop(lowercase : List[str] ): return x if key is None: _a = noop # Constants are all uppercase, they go first. _a = [obj for obj in objects if key(lowercase ).isupper()] # Classes are not all uppercase but start with a capital, they go second. _a = [obj for obj in objects if key(lowercase )[0].isupper() and not key(lowercase ).isupper()] # Functions begin with a lowercase, they go last. _a = [obj for obj in objects if not key(lowercase )[0].isupper()] _a = ignore_underscore(lowercase ) return sorted(lowercase , key=lowercase ) + sorted(lowercase , key=lowercase ) + sorted(lowercase , key=lowercase ) def _lowerCamelCase ( lowercase : Union[str, Any] ) -> str: # This inner function sort imports between [ ]. def _replace(lowercase : List[str] ): _a = match.groups()[0] if "," not in imports: return F'[{imports}]' _a = [part.strip().replace("\"" , "" ) for part in imports.split("," )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: _a = keys[:-1] return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowercase )] ) + "]" _a = import_statement.split("\n" ) if len(lowercase ) > 3: # Here we have to sort internal imports that are on several lines (one per name): # key: [ # "object1", # "object2", # ... # ] # We may have to ignore one or two lines on each side. _a = 2 if lines[1].strip() == "[" else 1 _a = [(i, _re_strip_line.search(lowercase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )] _a = sort_objects(lowercase , key=lambda lowercase : x[1] ) _a = [lines[x[0] + idx] for x in sorted_indices] return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] ) elif len(lowercase ) == 3: # Here we have to sort internal imports that are on one separate line: # key: [ # "object1", "object2", ... # ] if _re_bracket_content.search(lines[1] ) is not None: _a = _re_bracket_content.sub(_replace , lines[1] ) else: _a = [part.strip().replace("\"" , "" ) for part in lines[1].split("," )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: _a = keys[:-1] _a = get_indent(lines[1] ) + ", ".join([F'"{k}"' for k in sort_objects(lowercase )] ) return "\n".join(lowercase ) else: # Finally we have to deal with imports fitting on one line _a = _re_bracket_content.sub(_replace , lowercase ) return import_statement def _lowerCamelCase ( lowercase : Tuple , lowercase : List[Any]=True ) -> str: with open(lowercase , encoding="utf-8" ) as f: _a = f.read() if "_import_structure" not in code: return # Blocks of indent level 0 _a = split_code_in_indented_blocks( lowercase , start_prompt="_import_structure = {" , end_prompt="if TYPE_CHECKING:" ) # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt). for block_idx in range(1 , len(lowercase ) - 1 ): # Check if the block contains some `_import_structure`s thingy to sort. _a = main_blocks[block_idx] _a = block.split("\n" ) # Get to the start of the imports. _a = 0 while line_idx < len(lowercase ) and "_import_structure" not in block_lines[line_idx]: # Skip dummy import blocks if "import dummy" in block_lines[line_idx]: _a = len(lowercase ) else: line_idx += 1 if line_idx >= len(lowercase ): continue # Ignore beginning and last line: they don't contain anything. _a = "\n".join(block_lines[line_idx:-1] ) _a = get_indent(block_lines[1] ) # Slit the internal block into blocks of indent level 1. _a = split_code_in_indented_blocks(lowercase , indent_level=lowercase ) # We have two categories of import key: list or _import_structure[key].append/extend _a = _re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key # Grab the keys, but there is a trap: some lines are empty or just comments. _a = [(pattern.search(lowercase ).groups()[0] if pattern.search(lowercase ) is not None else None) for b in internal_blocks] # We only sort the lines with a key. _a = [(i, key) for i, key in enumerate(lowercase ) if key is not None] _a = [x[0] for x in sorted(lowercase , key=lambda lowercase : x[1] )] # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. _a = 0 _a = [] for i in range(len(lowercase ) ): if keys[i] is None: reorderded_blocks.append(internal_blocks[i] ) else: _a = sort_objects_in_import(internal_blocks[sorted_indices[count]] ) reorderded_blocks.append(lowercase ) count += 1 # And we put our main block back together with its first and last line. _a = "\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] ) if code != "\n".join(lowercase ): if check_only: return True else: print(F'Overwriting {file}.' ) with open(lowercase , "w" , encoding="utf-8" ) as f: f.write("\n".join(lowercase ) ) def _lowerCamelCase ( lowercase : List[str]=True ) -> List[str]: _a = [] for root, _, files in os.walk(lowercase ): if "__init__.py" in files: _a = sort_imports(os.path.join(lowercase , "__init__.py" ) , check_only=lowercase ) if result: _a = [os.path.join(lowercase , "__init__.py" )] if len(lowercase ) > 0: raise ValueError(F'Would overwrite {len(lowercase )} files, run `make style`.' ) if __name__ == "__main__": lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser() parser.add_argument('--check_only', action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true", help='Whether to only check or fix style.') lowerCAmelCase_ : List[str] = parser.parse_args() sort_imports_in_all_inits(check_only=args.check_only)	63	import gc import unittest import torch from parameterized import parameterized from diffusers import AutoencoderKL from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import enable_full_determinism from .test_modeling_common import ModelTesterMixin, UNetTesterMixin enable_full_determinism() class a__ ( snake_case , snake_case , unittest.TestCase ): """simple docstring""" __lowerCamelCase = AutoencoderKL __lowerCamelCase = 'sample' __lowerCamelCase = 1e-2 @property def UpperCamelCase ( self ) -> Optional[Any]: '''simple docstring''' A__ = 4 A__ = 3 A__ = (32, 32) A__ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase ) return {"sample": image} @property def UpperCamelCase ( self ) -> Optional[Any]: '''simple docstring''' return (3, 32, 32) @property def UpperCamelCase ( self ) -> Optional[Any]: '''simple docstring''' return (3, 32, 32) def UpperCamelCase ( self ) -> Optional[Any]: '''simple docstring''' A__ = { "block_out_channels": [32, 64], "in_channels": 3, "out_channels": 3, "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"], "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"], "latent_channels": 4, } A__ = self.dummy_input return init_dict, inputs_dict def UpperCamelCase ( self ) -> Tuple: '''simple docstring''' pass def UpperCamelCase ( self ) -> Any: '''simple docstring''' pass @unittest.skipIf(torch_device == "mps" , "Gradient checkpointing skipped on MPS" ) def UpperCamelCase ( self ) -> Tuple: '''simple docstring''' A__ , A__ = self.prepare_init_args_and_inputs_for_common() A__ = self.model_class(**lowercase ) model.to(lowercase ) assert not model.is_gradient_checkpointing and model.training A__ = model(**lowercase ).sample # run the backwards pass on the model. For backwards pass, for simplicity purpose, # we won't calculate the loss and rather backprop on out.sum() model.zero_grad() A__ = torch.randn_like(lowercase ) A__ = (out - labels).mean() loss.backward() # re-instantiate the model now enabling gradient checkpointing A__ = self.model_class(**lowercase ) # clone model model_a.load_state_dict(model.state_dict() ) model_a.to(lowercase ) model_a.enable_gradient_checkpointing() assert model_a.is_gradient_checkpointing and model_a.training A__ = model_a(**lowercase ).sample # run the backwards pass on the model. For backwards pass, for simplicity purpose, # we won't calculate the loss and rather backprop on out.sum() model_a.zero_grad() A__ = (out_a - labels).mean() loss_a.backward() # compare the output and parameters gradients self.assertTrue((loss - loss_a).abs() < 1e-5 ) A__ = dict(model.named_parameters() ) A__ = dict(model_a.named_parameters() ) for name, param in named_params.items(): self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) ) def UpperCamelCase ( self ) -> Optional[int]: '''simple docstring''' A__ , A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" , output_loading_info=lowercase ) self.assertIsNotNone(lowercase ) self.assertEqual(len(loading_info["missing_keys"] ) , 0 ) model.to(lowercase ) A__ = model(**self.dummy_input ) assert image is not None, "Make sure output is not None" def UpperCamelCase ( self ) -> Any: '''simple docstring''' A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" ) A__ = model.to(lowercase ) model.eval() if torch_device == "mps": A__ = torch.manual_seed(0 ) else: A__ = torch.Generator(device=lowercase ).manual_seed(0 ) A__ = torch.randn( 1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , ) A__ = image.to(lowercase ) with torch.no_grad(): A__ = model(lowercase , sample_posterior=lowercase , generator=lowercase ).sample A__ = output[0, -1, -3:, -3:].flatten().cpu() # Since the VAE Gaussian prior's generator is seeded on the appropriate device, # the expected output slices are not the same for CPU and GPU. if torch_device == "mps": A__ = torch.tensor( [ -4.00_78e-01, -3.83_23e-04, -1.26_81e-01, -1.14_62e-01, 2.00_95e-01, 1.08_93e-01, -8.82_47e-02, -3.03_61e-01, -9.86_44e-03, ] ) elif torch_device == "cpu": A__ = torch.tensor( [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] ) else: A__ = torch.tensor( [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] ) self.assertTrue(torch_all_close(lowercase , lowercase , rtol=1e-2 ) ) @slow class a__ ( unittest.TestCase ): """simple docstring""" def UpperCamelCase ( self , lowercase , lowercase ) -> str: '''simple docstring''' return F'gaussian_noise_s={seed}_shape={"_".join([str(lowercase ) for s in shape] )}.npy' def UpperCamelCase ( self ) -> Optional[int]: '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def UpperCamelCase ( self , lowercase=0 , lowercase=(4, 3, 512, 512) , lowercase=False ) -> Optional[int]: '''simple docstring''' A__ = torch.floataa if fpaa else torch.floataa A__ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase , lowercase ) ) ).to(lowercase ).to(lowercase ) return image def UpperCamelCase ( self , lowercase="CompVis/stable-diffusion-v1-4" , lowercase=False ) -> Any: '''simple docstring''' A__ = "fp16" if fpaa else None A__ = torch.floataa if fpaa else torch.floataa A__ = AutoencoderKL.from_pretrained( lowercase , subfolder="vae" , torch_dtype=lowercase , revision=lowercase , ) model.to(lowercase ).eval() return model def UpperCamelCase ( self , lowercase=0 ) -> List[str]: '''simple docstring''' if torch_device == "mps": return torch.manual_seed(lowercase ) return torch.Generator(device=lowercase ).manual_seed(lowercase ) @parameterized.expand( [ # fmt: off [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]], [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]], # fmt: on ] ) def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> int: '''simple docstring''' A__ = self.get_sd_vae_model() A__ = self.get_sd_image(lowercase ) A__ = self.get_generator(lowercase ) with torch.no_grad(): A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample assert sample.shape == image.shape A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu() A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice ) assert torch_all_close(lowercase , lowercase , atol=3e-3 ) @parameterized.expand( [ # fmt: off [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]], [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]], # fmt: on ] ) @require_torch_gpu def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]: '''simple docstring''' A__ = self.get_sd_vae_model(fpaa=lowercase ) A__ = self.get_sd_image(lowercase , fpaa=lowercase ) A__ = self.get_generator(lowercase ) with torch.no_grad(): A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample assert sample.shape == image.shape A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu() A__ = torch.tensor(lowercase ) assert torch_all_close(lowercase , lowercase , atol=1e-2 ) @parameterized.expand( [ # fmt: off [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]], [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]], # fmt: on ] ) def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Dict: '''simple docstring''' A__ = self.get_sd_vae_model() A__ = self.get_sd_image(lowercase ) with torch.no_grad(): A__ = model(lowercase ).sample assert sample.shape == image.shape A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu() A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice ) assert torch_all_close(lowercase , lowercase , atol=3e-3 ) @parameterized.expand( [ # fmt: off [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]], [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]], # fmt: on ] ) @require_torch_gpu def UpperCamelCase ( self , lowercase , lowercase ) -> Tuple: '''simple docstring''' A__ = self.get_sd_vae_model() A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) ) with torch.no_grad(): A__ = model.decode(lowercase ).sample assert list(sample.shape ) == [3, 3, 512, 512] A__ = sample[-1, -2:, :2, -2:].flatten().cpu() A__ = torch.tensor(lowercase ) assert torch_all_close(lowercase , lowercase , atol=1e-3 ) @parameterized.expand( [ # fmt: off [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]], [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]], # fmt: on ] ) @require_torch_gpu def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]: '''simple docstring''' A__ = self.get_sd_vae_model(fpaa=lowercase ) A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase ) with torch.no_grad(): A__ = model.decode(lowercase ).sample assert list(sample.shape ) == [3, 3, 512, 512] A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu() A__ = torch.tensor(lowercase ) assert torch_all_close(lowercase , lowercase , atol=5e-3 ) @parameterized.expand([(13,), (16,), (27,)] ) @require_torch_gpu @unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." ) def UpperCamelCase ( self , lowercase ) -> Optional[Any]: '''simple docstring''' A__ = self.get_sd_vae_model(fpaa=lowercase ) A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase ) with torch.no_grad(): A__ = model.decode(lowercase ).sample model.enable_xformers_memory_efficient_attention() with torch.no_grad(): A__ = model.decode(lowercase ).sample assert list(sample.shape ) == [3, 3, 512, 512] assert torch_all_close(lowercase , lowercase , atol=1e-1 ) @parameterized.expand([(13,), (16,), (37,)] ) @require_torch_gpu @unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." ) def UpperCamelCase ( self , lowercase ) -> List[str]: '''simple docstring''' A__ = self.get_sd_vae_model() A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) ) with torch.no_grad(): A__ = model.decode(lowercase ).sample model.enable_xformers_memory_efficient_attention() with torch.no_grad(): A__ = model.decode(lowercase ).sample assert list(sample.shape ) == [3, 3, 512, 512] assert torch_all_close(lowercase , lowercase , atol=1e-2 ) @parameterized.expand( [ # fmt: off [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]], [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]], # fmt: on ] ) def UpperCamelCase ( self , lowercase , lowercase ) -> str: '''simple docstring''' A__ = self.get_sd_vae_model() A__ = self.get_sd_image(lowercase ) A__ = self.get_generator(lowercase ) with torch.no_grad(): A__ = model.encode(lowercase ).latent_dist A__ = dist.sample(generator=lowercase ) assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]] A__ = sample[0, -1, -3:, -3:].flatten().cpu() A__ = torch.tensor(lowercase ) A__ = 3e-3 if torch_device != "mps" else 1e-2 assert torch_all_close(lowercase , lowercase , atol=lowercase )	68	0
"""simple docstring""" import os import re from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging lowerCAmelCase__ = logging.get_logger(__name__) lowerCAmelCase__ = { '''vocab_file''': '''vocab.txt''', '''merges_file''': '''bpe.codes''', } lowerCAmelCase__ = { '''vocab_file''': { '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''', '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''', }, '''merges_file''': { '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''', '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''', }, } lowerCAmelCase__ = { '''vinai/phobert-base''': 256, '''vinai/phobert-large''': 256, } def snake_case_ ( A_ : Dict ): '''simple docstring''' _lowerCamelCase : Optional[int] = set() _lowerCamelCase : List[Any] = word[0] for char in word[1:]: pairs.add((prev_char, char) ) _lowerCamelCase : Tuple = char _lowerCamelCase : Optional[Any] = set(A_ ) return pairs class __snake_case ( _lowercase): snake_case__ : str = VOCAB_FILES_NAMES snake_case__ : Tuple = PRETRAINED_VOCAB_FILES_MAP snake_case__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple="<s>" , __lowerCAmelCase : str="</s>" , __lowerCAmelCase : Optional[int]="</s>" , __lowerCAmelCase : List[str]="<s>" , __lowerCAmelCase : str="<unk>" , __lowerCAmelCase : List[str]="<pad>" , __lowerCAmelCase : Any="<mask>" , **__lowerCAmelCase : List[Any] , ): """simple docstring""" super().__init__( bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , **__lowerCAmelCase , ) _lowerCamelCase : str = vocab_file _lowerCamelCase : List[str] = merges_file _lowerCamelCase : Tuple = {} _lowerCamelCase : int = 0 _lowerCamelCase : List[Any] = 1 _lowerCamelCase : str = 2 _lowerCamelCase : int = 3 self.add_from_file(__lowerCAmelCase ) _lowerCamelCase : Any = {v: k for k, v in self.encoder.items()} with open(__lowerCAmelCase , encoding='''utf-8''' ) as merges_handle: _lowerCamelCase : str = merges_handle.read().split('''\n''' )[:-1] _lowerCamelCase : str = [tuple(merge.split()[:-1] ) for merge in merges] _lowerCamelCase : Tuple = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) ) _lowerCamelCase : Optional[Any] = {} def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ): """simple docstring""" if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] _lowerCamelCase : Optional[Any] = [self.cls_token_id] _lowerCamelCase : Any = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase ) if token_ids_a is None: return [1] + ([0] * len(__lowerCAmelCase )) + [1] return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1] def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ): """simple docstring""" _lowerCamelCase : int = [self.sep_token_id] _lowerCamelCase : Union[str, Any] = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def SCREAMING_SNAKE_CASE ( self : Optional[Any] ): """simple docstring""" return len(self.encoder ) def SCREAMING_SNAKE_CASE ( self : Optional[Any] ): """simple docstring""" return dict(self.encoder , **self.added_tokens_encoder ) def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : Dict ): """simple docstring""" if token in self.cache: return self.cache[token] _lowerCamelCase : Dict = tuple(__lowerCAmelCase ) _lowerCamelCase : Optional[int] = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] ) _lowerCamelCase : Optional[Any] = get_pairs(__lowerCAmelCase ) if not pairs: return token while True: _lowerCamelCase : List[Any] = min(__lowerCAmelCase , key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase , float('''inf''' ) ) ) if bigram not in self.bpe_ranks: break _lowerCamelCase , _lowerCamelCase : Any = bigram _lowerCamelCase : Optional[Any] = [] _lowerCamelCase : str = 0 while i < len(__lowerCAmelCase ): try: _lowerCamelCase : Optional[Any] = word.index(__lowerCAmelCase , __lowerCAmelCase ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) _lowerCamelCase : List[Any] = j if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 _lowerCamelCase : Any = tuple(__lowerCAmelCase ) _lowerCamelCase : List[Any] = new_word if len(__lowerCAmelCase ) == 1: break else: _lowerCamelCase : int = get_pairs(__lowerCAmelCase ) _lowerCamelCase : int = '''@@ '''.join(__lowerCAmelCase ) _lowerCamelCase : Optional[Any] = word[:-4] _lowerCamelCase : List[Any] = word return word def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : List[Any] ): """simple docstring""" _lowerCamelCase : Tuple = [] _lowerCamelCase : Optional[Any] = re.findall(R'''\S+\n?''' , __lowerCAmelCase ) for token in words: split_tokens.extend(list(self.bpe(__lowerCAmelCase ).split(''' ''' ) ) ) return split_tokens def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : Dict ): """simple docstring""" return self.encoder.get(__lowerCAmelCase , self.encoder.get(self.unk_token ) ) def SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCAmelCase : int ): """simple docstring""" return self.decoder.get(__lowerCAmelCase , self.unk_token ) def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : Union[str, Any] ): """simple docstring""" _lowerCamelCase : str = ''' '''.join(__lowerCAmelCase ).replace('''@@ ''' , '''''' ).strip() return out_string def SCREAMING_SNAKE_CASE ( self : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ): """simple docstring""" if not os.path.isdir(__lowerCAmelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return _lowerCamelCase : List[str] = os.path.join( __lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) _lowerCamelCase : Optional[Any] = os.path.join( __lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ): copyfile(self.vocab_file , __lowerCAmelCase ) if os.path.abspath(self.merges_file ) != os.path.abspath(__lowerCAmelCase ): copyfile(self.merges_file , __lowerCAmelCase ) return out_vocab_file, out_merge_file def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : List[str] ): """simple docstring""" if isinstance(__lowerCAmelCase , __lowerCAmelCase ): try: with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as fd: self.add_from_file(__lowerCAmelCase ) except FileNotFoundError as fnfe: raise fnfe except UnicodeError: raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' ) return _lowerCamelCase : List[str] = f.readlines() for lineTmp in lines: _lowerCamelCase : str = lineTmp.strip() _lowerCamelCase : Optional[Any] = line.rfind(''' ''' ) if idx == -1: raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' ) _lowerCamelCase : int = line[:idx] _lowerCamelCase : Any = len(self.encoder )	175	"""simple docstring""" import argparse lowerCAmelCase__ = '''docs/source/_static/js/custom.js''' def snake_case_ ( A_ : List[str] ): '''simple docstring''' with open(A_, encoding='''utf-8''', newline='''\n''' ) as f: _lowerCamelCase : int = f.readlines() _lowerCamelCase : List[str] = 0 # First let's put the right version while not lines[index].startswith('''const stableVersion =''' ): index += 1 _lowerCamelCase : List[Any] = F'''const stableVersion = "v{version}"\n''' # Then update the dictionary while not lines[index].startswith('''const versionMapping = {''' ): index += 1 # We go until the end while not lines[index].startswith('''}''' ): index += 1 # We add the new version at the end lines[index - 1] += F''' "v{version}": "v{version}",\n''' with open(A_, '''w''', encoding='''utf-8''', newline='''\n''' ) as f: f.writelines(A_ ) if __name__ == "__main__": lowerCAmelCase__ = argparse.ArgumentParser() parser.add_argument('''--version''', help='''Release version.''') lowerCAmelCase__ = parser.parse_args() update_custom_js(args.version)	175	1
import json import logging import os import sys from time import time from unittest.mock import patch from transformers.testing_utils import TestCasePlus, require_torch_tpu logging.basicConfig(level=logging.DEBUG) A : Union[str, Any] = logging.getLogger() def a__ ( __UpperCamelCase ): SCREAMING_SNAKE_CASE_ = {} SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "all_results.json" ) if os.path.exists(__UpperCamelCase ): with open(__UpperCamelCase , "r" ) as f: SCREAMING_SNAKE_CASE_ = json.load(__UpperCamelCase ) else: raise ValueError(F'''can\'t find {path}''' ) return results A : Tuple = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) @require_torch_tpu class lowerCamelCase (SCREAMING_SNAKE_CASE__ ): """simple docstring""" def __A ( self : List[str] ) -> Union[str, Any]: import xla_spawn SCREAMING_SNAKE_CASE_ = self.get_auto_remove_tmp_dir() SCREAMING_SNAKE_CASE_ = F''' ./examples/pytorch/text-classification/run_glue.py --num_cores=8 ./examples/pytorch/text-classification/run_glue.py --model_name_or_path distilbert-base-uncased --output_dir {tmp_dir} --overwrite_output_dir --train_file ./tests/fixtures/tests_samples/MRPC/train.csv --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv --do_train --do_eval --debug tpu_metrics_debug --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --learning_rate=1e-4 --max_steps=10 --warmup_steps=2 --seed=42 --max_seq_length=128 '''.split() with patch.object(__magic_name__ , "argv" , __magic_name__ ): SCREAMING_SNAKE_CASE_ = time() xla_spawn.main() SCREAMING_SNAKE_CASE_ = time() SCREAMING_SNAKE_CASE_ = get_results(__magic_name__ ) self.assertGreaterEqual(result["eval_accuracy"] , 0.75 ) # Assert that the script takes less than 500 seconds to make sure it doesn't hang. self.assertLess(end - start , 500 ) def __A ( self : Union[str, Any] ) -> Any: import xla_spawn SCREAMING_SNAKE_CASE_ = "\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n ".split() with patch.object(__magic_name__ , "argv" , __magic_name__ ): xla_spawn.main()	118	import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ASTConfig from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_torchaudio_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ASTForAudioClassification, ASTModel from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import ( AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ) if is_torchaudio_available(): import torchaudio from transformers import ASTFeatureExtractor class lowerCamelCase : """simple docstring""" def __init__( self : List[Any] , __magic_name__ : Any , __magic_name__ : List[Any]=13 , __magic_name__ : List[Any]=2 , __magic_name__ : Tuple=24 , __magic_name__ : List[str]=16 , __magic_name__ : Dict=True , __magic_name__ : List[Any]=True , __magic_name__ : Optional[int]=32 , __magic_name__ : Tuple=5 , __magic_name__ : int=4 , __magic_name__ : Tuple=37 , __magic_name__ : List[str]="gelu" , __magic_name__ : Tuple=0.1 , __magic_name__ : Tuple=0.1 , __magic_name__ : Union[str, Any]=10 , __magic_name__ : Tuple=0.02 , __magic_name__ : Tuple=None , __magic_name__ : Any=2 , __magic_name__ : Dict=2 , ) -> int: SCREAMING_SNAKE_CASE_ = parent SCREAMING_SNAKE_CASE_ = batch_size SCREAMING_SNAKE_CASE_ = patch_size SCREAMING_SNAKE_CASE_ = max_length SCREAMING_SNAKE_CASE_ = num_mel_bins SCREAMING_SNAKE_CASE_ = is_training SCREAMING_SNAKE_CASE_ = use_labels SCREAMING_SNAKE_CASE_ = hidden_size SCREAMING_SNAKE_CASE_ = num_hidden_layers SCREAMING_SNAKE_CASE_ = num_attention_heads SCREAMING_SNAKE_CASE_ = intermediate_size SCREAMING_SNAKE_CASE_ = hidden_act SCREAMING_SNAKE_CASE_ = hidden_dropout_prob SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob SCREAMING_SNAKE_CASE_ = type_sequence_label_size SCREAMING_SNAKE_CASE_ = initializer_range SCREAMING_SNAKE_CASE_ = scope SCREAMING_SNAKE_CASE_ = frequency_stride SCREAMING_SNAKE_CASE_ = time_stride # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens) SCREAMING_SNAKE_CASE_ = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1 SCREAMING_SNAKE_CASE_ = (self.max_length - self.patch_size) // self.time_stride + 1 SCREAMING_SNAKE_CASE_ = frequency_out_dimension * time_out_dimension SCREAMING_SNAKE_CASE_ = num_patches + 2 def __A ( self : Any ) -> Any: SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] ) SCREAMING_SNAKE_CASE_ = None if self.use_labels: SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) SCREAMING_SNAKE_CASE_ = self.get_config() return config, input_values, labels def __A ( self : Any ) -> Dict: return ASTConfig( patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__magic_name__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , ) def __A ( self : List[Any] , __magic_name__ : List[str] , __magic_name__ : str , __magic_name__ : List[str] ) -> Union[str, Any]: SCREAMING_SNAKE_CASE_ = ASTModel(config=__magic_name__ ) model.to(__magic_name__ ) model.eval() SCREAMING_SNAKE_CASE_ = model(__magic_name__ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def __A ( self : Tuple ) -> str: SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs() ( ( SCREAMING_SNAKE_CASE_ ) , ( SCREAMING_SNAKE_CASE_ ) , ( SCREAMING_SNAKE_CASE_ ) , ) = config_and_inputs SCREAMING_SNAKE_CASE_ = {"input_values": input_values} return config, inputs_dict @require_torch class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = ( ( ASTModel, ASTForAudioClassification, ) if is_torch_available() else () ) lowerCamelCase__ = ( {'''audio-classification''': ASTForAudioClassification, '''feature-extraction''': ASTModel} if is_torch_available() else {} ) lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False def __A ( self : Tuple , __magic_name__ : Union[str, Any] , __magic_name__ : Optional[Any] , __magic_name__ : Dict , __magic_name__ : Optional[int] , __magic_name__ : Tuple ) -> Tuple: if pipeline_test_casse_name == "AudioClassificationPipelineTests": return True return False def __A ( self : Optional[Any] ) -> Optional[Any]: SCREAMING_SNAKE_CASE_ = ASTModelTester(self ) SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 ) def __A ( self : Union[str, Any] ) -> Dict: self.config_tester.run_common_tests() @unittest.skip(reason="AST does not use inputs_embeds" ) def __A ( self : Optional[Any] ) -> Tuple: pass def __A ( self : int ) -> int: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) SCREAMING_SNAKE_CASE_ = model.get_output_embeddings() self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) ) def __A ( self : List[Any] ) -> str: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ ) SCREAMING_SNAKE_CASE_ = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic SCREAMING_SNAKE_CASE_ = [*signature.parameters.keys()] SCREAMING_SNAKE_CASE_ = ["input_values"] self.assertListEqual(arg_names[:1] , __magic_name__ ) def __A ( self : int ) -> Any: SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__magic_name__ ) @slow def __A ( self : int ) -> int: for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE_ = ASTModel.from_pretrained(__magic_name__ ) self.assertIsNotNone(__magic_name__ ) def a__ ( ): SCREAMING_SNAKE_CASE_ = hf_hub_download( repo_id="nielsr/audio-spectogram-transformer-checkpoint" , filename="sample_audio.flac" , repo_type="dataset" ) SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = torchaudio.load(__UpperCamelCase ) return audio, sampling_rate @require_torch @require_torchaudio class lowerCamelCase (unittest.TestCase ): """simple docstring""" @cached_property def __A ( self : List[Any] ) -> List[Any]: return ( ASTFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593" ) if is_torchaudio_available() else None ) @slow def __A ( self : Union[str, Any] ) -> Optional[int]: SCREAMING_SNAKE_CASE_ = self.default_feature_extractor SCREAMING_SNAKE_CASE_ = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593" ).to(__magic_name__ ) SCREAMING_SNAKE_CASE_ = self.default_feature_extractor SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = prepare_audio() SCREAMING_SNAKE_CASE_ = audio.squeeze().numpy() SCREAMING_SNAKE_CASE_ = feature_extractor(__magic_name__ , sampling_rate=__magic_name__ , return_tensors="pt" ).to(__magic_name__ ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ) # verify the logits SCREAMING_SNAKE_CASE_ = torch.Size((1, 527) ) self.assertEqual(outputs.logits.shape , __magic_name__ ) SCREAMING_SNAKE_CASE_ = torch.tensor([-0.8760, -7.0042, -8.6602] ).to(__magic_name__ ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )	118	1
import tempfile import torch from diffusers import PNDMScheduler from .test_schedulers import SchedulerCommonTest class UpperCAmelCase ( __A ): '''simple docstring''' lowerCamelCase_ = (PNDMScheduler,) lowerCamelCase_ = (('''num_inference_steps''', 5_0),) def lowerCAmelCase_ ( self , **lowercase ): """simple docstring""" A_ : int = { 'num_train_timesteps': 1_0_0_0, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', } config.update(**lowercase ) return config def lowerCAmelCase_ ( self , lowercase=0 , **lowercase ): """simple docstring""" A_ : Union[str, Any] = dict(self.forward_default_kwargs ) A_ : int = kwargs.pop('num_inference_steps' , lowercase ) A_ : int = self.dummy_sample A_ : Optional[Any] = 0.1 * sample A_ : Dict = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: A_ : Union[str, Any] = self.get_scheduler_config(**lowercase ) A_ : Any = scheduler_class(**lowercase ) scheduler.set_timesteps(lowercase ) # copy over dummy past residuals A_ : List[Any] = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(lowercase ) A_ : Optional[int] = scheduler_class.from_pretrained(lowercase ) new_scheduler.set_timesteps(lowercase ) # copy over dummy past residuals A_ : str = dummy_past_residuals[:] A_ : Tuple = scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample A_ : int = new_scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" A_ : Tuple = scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample A_ : Optional[Any] = new_scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def lowerCAmelCase_ ( self ): """simple docstring""" pass def lowerCAmelCase_ ( self , lowercase=0 , **lowercase ): """simple docstring""" A_ : Dict = dict(self.forward_default_kwargs ) A_ : Optional[Any] = kwargs.pop('num_inference_steps' , lowercase ) A_ : List[str] = self.dummy_sample A_ : Union[str, Any] = 0.1 * sample A_ : Dict = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: A_ : List[Any] = self.get_scheduler_config() A_ : Any = scheduler_class(**lowercase ) scheduler.set_timesteps(lowercase ) # copy over dummy past residuals (must be after setting timesteps) A_ : Union[str, Any] = dummy_past_residuals[:] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(lowercase ) A_ : Any = scheduler_class.from_pretrained(lowercase ) # copy over dummy past residuals new_scheduler.set_timesteps(lowercase ) # copy over dummy past residual (must be after setting timesteps) A_ : Union[str, Any] = dummy_past_residuals[:] A_ : Any = scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample A_ : List[Any] = new_scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" A_ : List[Any] = scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample A_ : str = new_scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def lowerCAmelCase_ ( self , **lowercase ): """simple docstring""" A_ : Union[str, Any] = self.scheduler_classes[0] A_ : Optional[Any] = self.get_scheduler_config(**lowercase ) A_ : Optional[int] = scheduler_class(**lowercase ) A_ : str = 1_0 A_ : str = self.dummy_model() A_ : Any = self.dummy_sample_deter scheduler.set_timesteps(lowercase ) for i, t in enumerate(scheduler.prk_timesteps ): A_ : Optional[Any] = model(lowercase , lowercase ) A_ : Any = scheduler.step_prk(lowercase , lowercase , lowercase ).prev_sample for i, t in enumerate(scheduler.plms_timesteps ): A_ : List[str] = model(lowercase , lowercase ) A_ : List[str] = scheduler.step_plms(lowercase , lowercase , lowercase ).prev_sample return sample def lowerCAmelCase_ ( self ): """simple docstring""" A_ : Dict = dict(self.forward_default_kwargs ) A_ : Union[str, Any] = kwargs.pop('num_inference_steps' , lowercase ) for scheduler_class in self.scheduler_classes: A_ : Any = self.get_scheduler_config() A_ : Any = scheduler_class(**lowercase ) A_ : Any = self.dummy_sample A_ : Dict = 0.1 * sample if num_inference_steps is not None and hasattr(lowercase , 'set_timesteps' ): scheduler.set_timesteps(lowercase ) elif num_inference_steps is not None and not hasattr(lowercase , 'set_timesteps' ): A_ : Optional[Any] = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) A_ : int = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] A_ : int = dummy_past_residuals[:] A_ : int = scheduler.step_prk(lowercase , 0 , lowercase , **lowercase ).prev_sample A_ : str = scheduler.step_prk(lowercase , 1 , lowercase , **lowercase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) A_ : Tuple = scheduler.step_plms(lowercase , 0 , lowercase , **lowercase ).prev_sample A_ : int = scheduler.step_plms(lowercase , 1 , lowercase , **lowercase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def lowerCAmelCase_ ( self ): """simple docstring""" for timesteps in [1_0_0, 1_0_0_0]: self.check_over_configs(num_train_timesteps=lowercase ) def lowerCAmelCase_ ( self ): """simple docstring""" for steps_offset in [0, 1]: self.check_over_configs(steps_offset=lowercase ) A_ : List[str] = self.scheduler_classes[0] A_ : str = self.get_scheduler_config(steps_offset=1 ) A_ : Dict = scheduler_class(**lowercase ) scheduler.set_timesteps(1_0 ) assert torch.equal( scheduler.timesteps , torch.LongTensor( [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1] ) , ) def lowerCAmelCase_ ( self ): """simple docstring""" for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ): self.check_over_configs(beta_start=lowercase , beta_end=lowercase ) def lowerCAmelCase_ ( self ): """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=lowercase ) def lowerCAmelCase_ ( self ): """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=lowercase ) def lowerCAmelCase_ ( self ): """simple docstring""" for t in [1, 5, 1_0]: self.check_over_forward(time_step=lowercase ) def lowerCAmelCase_ ( self ): """simple docstring""" for t, num_inference_steps in zip([1, 5, 1_0] , [1_0, 5_0, 1_0_0] ): self.check_over_forward(num_inference_steps=lowercase ) def lowerCAmelCase_ ( self ): """simple docstring""" A_ : Dict = 2_7 for scheduler_class in self.scheduler_classes: A_ : List[Any] = self.dummy_sample A_ : Optional[Any] = 0.1 * sample A_ : List[str] = self.get_scheduler_config() A_ : Union[str, Any] = scheduler_class(**lowercase ) scheduler.set_timesteps(lowercase ) # before power of 3 fix, would error on first step, so we only need to do two for i, t in enumerate(scheduler.prk_timesteps[:2] ): A_ : List[str] = scheduler.step_prk(lowercase , lowercase , lowercase ).prev_sample def lowerCAmelCase_ ( self ): """simple docstring""" with self.assertRaises(lowercase ): A_ : str = self.scheduler_classes[0] A_ : Union[str, Any] = self.get_scheduler_config() A_ : List[str] = scheduler_class(**lowercase ) scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample def lowerCAmelCase_ ( self ): """simple docstring""" A_ : Dict = self.full_loop() A_ : List[Any] = torch.sum(torch.abs(lowercase ) ) A_ : Union[str, Any] = torch.mean(torch.abs(lowercase ) ) assert abs(result_sum.item() - 198.1318 ) < 1E-2 assert abs(result_mean.item() - 0.2580 ) < 1E-3 def lowerCAmelCase_ ( self ): """simple docstring""" A_ : Optional[Any] = self.full_loop(prediction_type='v_prediction' ) A_ : Optional[int] = torch.sum(torch.abs(lowercase ) ) A_ : int = torch.mean(torch.abs(lowercase ) ) assert abs(result_sum.item() - 67.3986 ) < 1E-2 assert abs(result_mean.item() - 0.0878 ) < 1E-3 def lowerCAmelCase_ ( self ): """simple docstring""" A_ : Union[str, Any] = self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01 ) A_ : Tuple = torch.sum(torch.abs(lowercase ) ) A_ : Union[str, Any] = torch.mean(torch.abs(lowercase ) ) assert abs(result_sum.item() - 230.0399 ) < 1E-2 assert abs(result_mean.item() - 0.2995 ) < 1E-3 def lowerCAmelCase_ ( self ): """simple docstring""" A_ : List[Any] = self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01 ) A_ : Union[str, Any] = torch.sum(torch.abs(lowercase ) ) A_ : Tuple = torch.mean(torch.abs(lowercase ) ) assert abs(result_sum.item() - 186.9482 ) < 1E-2 assert abs(result_mean.item() - 0.2434 ) < 1E-3	351	import darl # noqa import gym import tqdm from diffusers.experimental import ValueGuidedRLPipeline _UpperCAmelCase = { """n_samples""": 64, """horizon""": 32, """num_inference_steps""": 20, """n_guide_steps""": 2, # can set to 0 for faster sampling, does not use value network """scale_grad_by_std""": True, """scale""": 0.1, """eta""": 0.0, """t_grad_cutoff""": 2, """device""": """cpu""", } if __name__ == "__main__": _UpperCAmelCase = """hopper-medium-v2""" _UpperCAmelCase = gym.make(env_name) _UpperCAmelCase = ValueGuidedRLPipeline.from_pretrained( """bglick13/hopper-medium-v2-value-function-hor32""", env=env, ) env.seed(0) _UpperCAmelCase = env.reset() _UpperCAmelCase = 0 _UpperCAmelCase = 0 _UpperCAmelCase = 1000 _UpperCAmelCase = [obs.copy()] try: for t in tqdm.tqdm(range(T)): # call the policy _UpperCAmelCase = pipeline(obs, planning_horizon=32) # execute action in environment _UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase = env.step(denorm_actions) _UpperCAmelCase = env.get_normalized_score(total_reward) # update return total_reward += reward total_score += score print( F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:""" F""" {total_score}""" ) # save observations for rendering rollout.append(next_observation.copy()) _UpperCAmelCase = next_observation except KeyboardInterrupt: pass print(F"""Total reward: {total_reward}""")	192	0
from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available __A = { "configuration_xlm_roberta_xl": [ "XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaXLConfig", "XLMRobertaXLOnnxConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __A = [ "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMRobertaXLForCausalLM", "XLMRobertaXLForMaskedLM", "XLMRobertaXLForMultipleChoice", "XLMRobertaXLForQuestionAnswering", "XLMRobertaXLForSequenceClassification", "XLMRobertaXLForTokenClassification", "XLMRobertaXLModel", "XLMRobertaXLPreTrainedModel", ] if TYPE_CHECKING: from .configuration_xlm_roberta_xl import ( XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaXLConfig, XLMRobertaXLOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_xlm_roberta_xl import ( XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST, XLMRobertaXLForCausalLM, XLMRobertaXLForMaskedLM, XLMRobertaXLForMultipleChoice, XLMRobertaXLForQuestionAnswering, XLMRobertaXLForSequenceClassification, XLMRobertaXLForTokenClassification, XLMRobertaXLModel, XLMRobertaXLPreTrainedModel, ) else: import sys __A = _LazyModule(__name__, globals()["__file__"], _import_structure)	10	'''simple docstring''' import argparse from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta from transformers.utils import logging logging.set_verbosity_info() def __a(SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : int ): '''simple docstring''' _lowerCAmelCase = TaConfig.from_json_file(SCREAMING_SNAKE_CASE_ ) print(F'''Building PyTorch model from configuration: {config}''' ) _lowerCAmelCase = TaForConditionalGeneration(SCREAMING_SNAKE_CASE_ ) # Load weights from tf checkpoint load_tf_weights_in_ta(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) # Save pytorch-model print(F'''Save PyTorch model to {pytorch_dump_path}''' ) model.save_pretrained(SCREAMING_SNAKE_CASE_ ) if __name__ == "__main__": _SCREAMING_SNAKE_CASE = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--config_file", default=None, type=str, required=True, help=( "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture." ), ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) _SCREAMING_SNAKE_CASE = parser.parse_args() convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)	158	0
"""simple docstring""" import warnings from ...utils import logging from .image_processing_beit import BeitImageProcessor A: Any = logging.get_logger(__name__) class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ): def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> None: '''simple docstring''' warnings.warn( """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please""" """ use BeitImageProcessor instead.""" , _SCREAMING_SNAKE_CASE , ) super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )	356	"""simple docstring""" def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ): return number | (1 << position) def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ): return number & ~(1 << position) def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ): return number ^ (1 << position) def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ): return ((number >> position) & 1) == 1 def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ): return int((number & (1 << position)) != 0 ) if __name__ == "__main__": import doctest doctest.testmod()	76	0
"""simple docstring""" from math import ceil def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ): '''simple docstring''' __lowerCAmelCase = list(range(0 , lowercase_ ) ) __lowerCAmelCase = [item for sublist in list(device_map.values() ) for item in sublist] # Duplicate check __lowerCAmelCase = [] for i in device_map_blocks: if device_map_blocks.count(lowercase_ ) > 1 and i not in duplicate_blocks: duplicate_blocks.append(lowercase_ ) # Missing blocks __lowerCAmelCase = [i for i in blocks if i not in device_map_blocks] __lowerCAmelCase = [i for i in device_map_blocks if i not in blocks] if len(lowercase_ ) != 0: raise ValueError( "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device." " These attention blocks were specified more than once: " + str(lowercase_ ) ) if len(lowercase_ ) != 0: raise ValueError( "There are attention blocks for this model that are not specified in the device_map. Add these attention " "blocks to a device on the device_map: " + str(lowercase_ ) ) if len(lowercase_ ) != 0: raise ValueError( "The device_map contains more attention blocks than this model has. Remove these from the device_map:" + str(lowercase_ ) ) def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ): '''simple docstring''' __lowerCAmelCase = list(range(lowercase_ ) ) __lowerCAmelCase = int(ceil(n_layers / len(lowercase_ ) ) ) __lowerCAmelCase = [layers[i : i + n_blocks] for i in range(0 , lowercase_ , lowercase_ )] return dict(zip(lowercase_ , lowercase_ ) )	57	import copy from ...configuration_utils import PretrainedConfig from ...utils import logging _lowerCamelCase : Any = logging.get_logger(__name__) class UpperCamelCase_ ( UpperCAmelCase__ ): '''simple docstring''' UpperCAmelCase__ = '''encoder-decoder''' UpperCAmelCase__ = True def __init__( self : List[str] , **UpperCAmelCase__ : Union[str, Any]) ->List[Any]: '''simple docstring''' super().__init__(**UpperCAmelCase__) assert ( "encoder" in kwargs and "decoder" in kwargs ), "Config has to be initialized with encoder and decoder config" A__ = kwargs.pop('''encoder''') A__ = encoder_config.pop('''model_type''') A__ = kwargs.pop('''decoder''') A__ = decoder_config.pop('''model_type''') from ..auto.configuration_auto import AutoConfig A__ = AutoConfig.for_model(UpperCAmelCase__ , **UpperCAmelCase__) A__ = AutoConfig.for_model(UpperCAmelCase__ , **UpperCAmelCase__) A__ = True @classmethod def SCREAMING_SNAKE_CASE ( cls : Union[str, Any] , UpperCAmelCase__ : PretrainedConfig , UpperCAmelCase__ : PretrainedConfig , **UpperCAmelCase__ : Union[str, Any]) ->PretrainedConfig: '''simple docstring''' logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''') A__ = True A__ = True return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **UpperCAmelCase__) def SCREAMING_SNAKE_CASE ( self : str) ->Optional[Any]: '''simple docstring''' A__ = copy.deepcopy(self.__dict__) A__ = self.encoder.to_dict() A__ = self.decoder.to_dict() A__ = self.__class__.model_type return output	14	0
"""simple docstring""" from math import factorial SCREAMING_SNAKE_CASE : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)} def __UpperCAmelCase ( snake_case_ : int ) -> int: """simple docstring""" if not isinstance(snake_case_ , snake_case_ ): raise TypeError("""Parameter number must be int""" ) if number < 0: raise ValueError("""Parameter number must be greater than or equal to 0""" ) # Converts number in string to iterate on its digits and adds its factorial. return sum(DIGIT_FACTORIAL[digit] for digit in str(snake_case_ ) ) def __UpperCAmelCase ( snake_case_ : int = 60 , snake_case_ : int = 1000000 ) -> int: """simple docstring""" if not isinstance(snake_case_ , snake_case_ ) or not isinstance(snake_case_ , snake_case_ ): raise TypeError("""Parameters chain_length and number_limit must be int""" ) if chain_length <= 0 or number_limit <= 0: raise ValueError( """Parameters chain_length and number_limit must be greater than 0""" ) # the counter for the chains with the exact desired length _lowerCAmelCase = 0 # the cached sizes of the previous chains _lowerCAmelCase = {} for start_chain_element in range(1 , snake_case_ ): # The temporary set will contain the elements of the chain _lowerCAmelCase = set() _lowerCAmelCase = 0 # Stop computing the chain when you find a cached size, a repeating item or the # length is greater then the desired one. _lowerCAmelCase = start_chain_element while ( chain_element not in chain_sets_lengths and chain_element not in chain_set and chain_set_length <= chain_length ): chain_set.add(snake_case_ ) chain_set_length += 1 _lowerCAmelCase = digit_factorial_sum(snake_case_ ) if chain_element in chain_sets_lengths: chain_set_length += chain_sets_lengths[chain_element] _lowerCAmelCase = chain_set_length # If chain contains the exact amount of elements increase the counter if chain_set_length == chain_length: chains_counter += 1 return chains_counter if __name__ == "__main__": import doctest doctest.testmod() print(F'{solution()}')	369	"""simple docstring""" import unittest import numpy as np import torch from diffusers import VersatileDiffusionImageVariationPipeline from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device SCREAMING_SNAKE_CASE : List[str] = False class __lowerCamelCase ( unittest.TestCase ): pass @slow @require_torch_gpu class __lowerCamelCase ( unittest.TestCase ): def A__ (self ): '''simple docstring''' _lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" ) pipe.to(lowerCamelCase ) pipe.set_progress_bar_config(disable=lowerCamelCase ) _lowerCAmelCase = load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" ) _lowerCAmelCase = torch.manual_seed(0 ) _lowerCAmelCase = pipe( image=lowerCamelCase , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images _lowerCAmelCase = image[0, 253:256, 253:256, -1] assert image.shape == (1, 512, 512, 3) _lowerCAmelCase = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2	317	0
from typing import List, Optional, Union import numpy as np import torch import torchaudio.compliance.kaldi as ta_kaldi from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import PaddingStrategy, TensorType, logging __lowerCamelCase : Dict = logging.get_logger(__name__) class a__ ( lowercase__ ): A = ['input_features', 'attention_mask'] def __init__( self : int,_A : int=80,_A : str=1_6000,_A : Optional[Any]=80,_A : List[Any]=0.0,_A : Union[str, Any]=True,_A : Tuple=True,_A : Union[str, Any]=True,**_A : List[Any],): """simple docstring""" super().__init__(feature_size=__lowercase,sampling_rate=__lowercase,padding_value=__lowercase,**__lowercase ) SCREAMING_SNAKE_CASE_ : Any = num_mel_bins SCREAMING_SNAKE_CASE_ : List[Any] = do_ceptral_normalize SCREAMING_SNAKE_CASE_ : List[str] = normalize_means SCREAMING_SNAKE_CASE_ : List[Any] = normalize_vars SCREAMING_SNAKE_CASE_ : List[str] = True def __UpperCamelCase ( self : int,_A : np.ndarray,): """simple docstring""" SCREAMING_SNAKE_CASE_ : Union[str, Any] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers SCREAMING_SNAKE_CASE_ : Optional[Any] = torch.from_numpy(__lowercase ).unsqueeze(0 ) SCREAMING_SNAKE_CASE_ : List[str] = ta_kaldi.fbank(__lowercase,num_mel_bins=self.num_mel_bins,sample_frequency=self.sampling_rate ) return features.numpy() @staticmethod def __UpperCamelCase ( _A : np.ndarray,_A : int,_A : Optional[bool] = True,_A : Optional[bool] = True,_A : float = 0.0,): """simple docstring""" if normalize_means: SCREAMING_SNAKE_CASE_ : str = x[:input_length].mean(axis=0 ) SCREAMING_SNAKE_CASE_ : int = np.subtract(__lowercase,__lowercase ) if normalize_vars: SCREAMING_SNAKE_CASE_ : Union[str, Any] = x[:input_length].std(axis=0 ) SCREAMING_SNAKE_CASE_ : int = np.divide(__lowercase,__lowercase ) if input_length < x.shape[0]: SCREAMING_SNAKE_CASE_ : Tuple = padding_value # make sure array is in float32 SCREAMING_SNAKE_CASE_ : int = x.astype(np.floataa ) return x def __UpperCamelCase ( self : Tuple,_A : List[np.ndarray],_A : Optional[np.ndarray] = None ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[Any] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features] return [ self.utterance_cmvn(__lowercase,__lowercase,self.normalize_means,self.normalize_vars,self.padding_value ) for x, n in zip(__lowercase,__lowercase ) ] def __call__( self : int,_A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],_A : Union[bool, str, PaddingStrategy] = False,_A : Optional[int] = None,_A : bool = False,_A : Optional[int] = None,_A : Optional[Union[str, TensorType]] = None,_A : Optional[int] = None,_A : Optional[bool] = None,**_A : int,): """simple docstring""" if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of' F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with' F' {self.sampling_rate} and not {sampling_rate}.' ) else: logger.warning( "It is strongly recommended to pass the `sampling_rate` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) SCREAMING_SNAKE_CASE_ : List[str] = isinstance(__lowercase,np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(F'Only mono-channel audio is supported for input to {self}' ) SCREAMING_SNAKE_CASE_ : Dict = is_batched_numpy or ( isinstance(__lowercase,(list, tuple) ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list) )) ) if is_batched: SCREAMING_SNAKE_CASE_ : Any = [np.asarray(__lowercase,dtype=np.floataa ) for speech in raw_speech] elif not is_batched and not isinstance(__lowercase,np.ndarray ): SCREAMING_SNAKE_CASE_ : int = np.asarray(__lowercase,dtype=np.floataa ) elif isinstance(__lowercase,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): SCREAMING_SNAKE_CASE_ : Optional[int] = raw_speech.astype(np.floataa ) # always return batch if not is_batched: SCREAMING_SNAKE_CASE_ : int = [raw_speech] # extract fbank features SCREAMING_SNAKE_CASE_ : Dict = [self._extract_fbank_features(__lowercase ) for waveform in raw_speech] # convert into correct format for padding SCREAMING_SNAKE_CASE_ : List[Any] = BatchFeature({"input_features": features} ) SCREAMING_SNAKE_CASE_ : Optional[int] = self.pad( __lowercase,padding=__lowercase,max_length=__lowercase,truncation=__lowercase,pad_to_multiple_of=__lowercase,return_attention_mask=__lowercase,**__lowercase,) # make sure list is in array format SCREAMING_SNAKE_CASE_ : Optional[Any] = padded_inputs.get("input_features" ) if isinstance(input_features[0],__lowercase ): SCREAMING_SNAKE_CASE_ : Tuple = [np.asarray(__lowercase,dtype=np.floataa ) for feature in input_features] SCREAMING_SNAKE_CASE_ : Tuple = padded_inputs.get("attention_mask" ) if attention_mask is not None: SCREAMING_SNAKE_CASE_ : List[Any] = [np.asarray(__lowercase,dtype=np.intaa ) for array in attention_mask] # Utterance-level cepstral mean and variance normalization if self.do_ceptral_normalize: SCREAMING_SNAKE_CASE_ : Tuple = ( np.array(__lowercase,dtype=np.intaa ) if self._get_padding_strategies(__lowercase,max_length=__lowercase ) is not PaddingStrategy.DO_NOT_PAD else None ) SCREAMING_SNAKE_CASE_ : str = self.normalize( padded_inputs["input_features"],attention_mask=__lowercase ) if return_tensors is not None: SCREAMING_SNAKE_CASE_ : List[str] = padded_inputs.convert_to_tensors(__lowercase ) return padded_inputs	18	from ...configuration_utils import PretrainedConfig from ...utils import logging a : Optional[int] = logging.get_logger(__name__) a : List[Any] = { "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json", # See all XGLM models at https://huggingface.co/models?filter=xglm } class a ( lowercase__ ): """simple docstring""" a : List[Any] = 'xglm' a : str = ['past_key_values'] a : Any = { 'num_attention_heads': 'attention_heads', 'hidden_size': 'd_model', 'num_hidden_layers': 'num_layers', } def __init__( self : Optional[int] , __lowercase : int=256008 , __lowercase : Tuple=2048 , __lowercase : List[Any]=1024 , __lowercase : str=4096 , __lowercase : Optional[Any]=24 , __lowercase : Optional[int]=16 , __lowercase : List[Any]="gelu" , __lowercase : str=0.1 , __lowercase : Dict=0.1 , __lowercase : Tuple=0.0 , __lowercase : Optional[int]=0.0 , __lowercase : Dict=0.02 , __lowercase : Optional[int]=True , __lowercase : Any=True , __lowercase : Dict=2 , __lowercase : Optional[Any]=1 , __lowercase : List[Any]=0 , __lowercase : Optional[Any]=2 , **__lowercase : List[str] , ) -> Optional[int]: __UpperCAmelCase : List[str] = vocab_size __UpperCAmelCase : Optional[Any] = max_position_embeddings __UpperCAmelCase : Optional[Any] = d_model __UpperCAmelCase : str = ffn_dim __UpperCAmelCase : List[str] = num_layers __UpperCAmelCase : Dict = attention_heads __UpperCAmelCase : str = activation_function __UpperCAmelCase : Optional[Any] = dropout __UpperCAmelCase : Any = attention_dropout __UpperCAmelCase : int = activation_dropout __UpperCAmelCase : Tuple = layerdrop __UpperCAmelCase : Tuple = init_std __UpperCAmelCase : List[str] = scale_embedding # scale factor will be sqrt(d_model) if True __UpperCAmelCase : Union[str, Any] = use_cache super().__init__( pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase , decoder_start_token_id=__lowercase , **__lowercase , )	114	0
'''simple docstring''' import argparse import torch from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel from transformers.utils import logging logging.set_verbosity_info() def __a ( _UpperCamelCase: Dict , _UpperCamelCase: Tuple , _UpperCamelCase: int , _UpperCamelCase: List[str] ) -> Any: """simple docstring""" _snake_case = FunnelConfig.from_json_file(lowerCAmelCase_ ) print(F"""Building PyTorch model from configuration: {config}""" ) _snake_case = FunnelBaseModel(lowerCAmelCase_ ) if base_model else FunnelModel(lowerCAmelCase_ ) # Load weights from tf checkpoint load_tf_weights_in_funnel(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) # Save pytorch-model print(F"""Save PyTorch model to {pytorch_dump_path}""" ) torch.save(model.state_dict() , lowerCAmelCase_ ) if __name__ == "__main__": UpperCamelCase_ : List[str] = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.''' ) parser.add_argument( '''--config_file''', default=None, type=str, required=True, help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''', ) parser.add_argument( '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.''' ) parser.add_argument( '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.''' ) UpperCamelCase_ : Dict = parser.parse_args() convert_tf_checkpoint_to_pytorch( args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model )	369	'''simple docstring''' import pprint import requests UpperCamelCase_ : Tuple = '''https://zenquotes.io/api''' def __a ( ) -> list: """simple docstring""" return requests.get(API_ENDPOINT_URL + "/today" ).json() def __a ( ) -> list: """simple docstring""" return requests.get(API_ENDPOINT_URL + "/random" ).json() if __name__ == "__main__": UpperCamelCase_ : Any = random_quotes() pprint.pprint(response)	142	0
'''simple docstring''' import importlib import torch import yaml from omegaconf import OmegaConf from taming.models.vqgan import VQModel def _A ( snake_case , snake_case=False ) -> List[Any]: _lowercase : Union[str, Any] = OmegaConf.load(snake_case ) if display: print(yaml.dump(OmegaConf.to_container(snake_case ) ) ) return config def _A ( snake_case , snake_case=None , snake_case=None ) -> Optional[int]: if conf_path is None: _lowercase : str = "./model_checkpoints/vqgan_only.yaml" _lowercase : List[Any] = load_config(snake_case , display=snake_case ) _lowercase : Optional[Any] = VQModel(**config.model.params ) if ckpt_path is None: _lowercase : str = "./model_checkpoints/vqgan_only.pt" _lowercase : Dict = torch.load(snake_case , map_location=snake_case ) if ".ckpt" in ckpt_path: _lowercase : Union[str, Any] = sd["state_dict"] model.load_state_dict(snake_case , strict=snake_case ) model.to(snake_case ) del sd return model def _A ( snake_case , snake_case ) -> Union[str, Any]: _lowercase , _lowercase , _lowercase : Union[str, Any] = model.encode(snake_case ) print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''' ) _lowercase : int = model.decode(snake_case ) return xrec def _A ( snake_case , snake_case=False ) -> Union[str, Any]: _lowercase , _lowercase : Optional[Any] = string.rsplit("." , 1 ) if reload: _lowercase : Union[str, Any] = importlib.import_module(snake_case ) importlib.reload(snake_case ) return getattr(importlib.import_module(snake_case , package=snake_case ) , cls ) def _A ( snake_case ) -> Optional[Any]: if "target" not in config: raise KeyError("Expected key `target` to instantiate." ) return get_obj_from_str(config["target"] )(**config.get("params" , {} ) ) def _A ( snake_case , snake_case , snake_case=True , snake_case=True ) -> Union[str, Any]: _lowercase : Tuple = instantiate_from_config(snake_case ) if sd is not None: model.load_state_dict(snake_case ) if gpu: model.cuda() if eval_mode: model.eval() return {"model": model} def _A ( snake_case , snake_case , snake_case , snake_case ) -> str: # load the specified checkpoint if ckpt: _lowercase : Optional[Any] = torch.load(snake_case , map_location="cpu" ) _lowercase : Dict = pl_sd["global_step"] print(F'''loaded model from global step {global_step}.''' ) else: _lowercase : int = {"state_dict": None} _lowercase : int = None _lowercase : str = load_model_from_config(config.model , pl_sd["state_dict"] , gpu=snake_case , eval_mode=snake_case )["model"] return model, global_step	250	'''simple docstring''' from __future__ import annotations import requests def _A ( snake_case ) -> dict: _lowercase : Dict = F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty''' return requests.get(snake_case ).json() def _A ( snake_case = 10 ) -> list[dict]: _lowercase : List[Any] = "https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty" _lowercase : List[str] = requests.get(snake_case ).json()[:max_stories] return [get_hackernews_story(snake_case ) for story_id in story_ids] def _A ( snake_case = 10 ) -> str: _lowercase : Union[str, Any] = hackernews_top_stories(snake_case ) return "\n".join("* [{title}]({url})".format(**snake_case ) for story in stories ) if __name__ == "__main__": print(hackernews_top_stories_as_markdown())	250	1
import argparse import copy def A_ ( _lowerCAmelCase ) -> Any: UpperCamelCase : Any = {} with open(_lowerCAmelCase ) as f: for line in f: if line.split()[0] not in dict_of_neighbours: UpperCamelCase : Any = [] _list.append([line.split()[1], line.split()[2]] ) UpperCamelCase : Any = _list else: dict_of_neighbours[line.split()[0]].append( [line.split()[1], line.split()[2]] ) if line.split()[1] not in dict_of_neighbours: UpperCamelCase : str = [] _list.append([line.split()[0], line.split()[2]] ) UpperCamelCase : List[Any] = _list else: dict_of_neighbours[line.split()[1]].append( [line.split()[0], line.split()[2]] ) return dict_of_neighbours def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> List[str]: with open(_lowerCAmelCase ) as f: UpperCamelCase : Optional[Any] = f.read(1 ) UpperCamelCase : Tuple = start_node UpperCamelCase : Any = [] UpperCamelCase : int = start_node UpperCamelCase : List[str] = 0 while visiting not in first_solution: UpperCamelCase : Optional[int] = 1_0000 for k in dict_of_neighbours[visiting]: if int(k[1] ) < int(_lowerCAmelCase ) and k[0] not in first_solution: UpperCamelCase : Any = k[1] UpperCamelCase : List[str] = k[0] first_solution.append(_lowerCAmelCase ) UpperCamelCase : Optional[Any] = distance_of_first_solution + int(_lowerCAmelCase ) UpperCamelCase : List[Any] = best_node first_solution.append(_lowerCAmelCase ) UpperCamelCase : str = 0 for k in dict_of_neighbours[first_solution[-2]]: if k[0] == start_node: break position += 1 UpperCamelCase : int = ( distance_of_first_solution + int(dict_of_neighbours[first_solution[-2]][position][1] ) - 1_0000 ) return first_solution, distance_of_first_solution def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]: UpperCamelCase : Optional[int] = [] for n in solution[1:-1]: UpperCamelCase : str = solution.index(_lowerCAmelCase ) for kn in solution[1:-1]: UpperCamelCase : List[str] = solution.index(_lowerCAmelCase ) if n == kn: continue UpperCamelCase : Union[str, Any] = copy.deepcopy(_lowerCAmelCase ) UpperCamelCase : Tuple = kn UpperCamelCase : str = n UpperCamelCase : Union[str, Any] = 0 for k in _tmp[:-1]: UpperCamelCase : List[Any] = _tmp[_tmp.index(_lowerCAmelCase ) + 1] for i in dict_of_neighbours[k]: if i[0] == next_node: UpperCamelCase : List[str] = distance + int(i[1] ) _tmp.append(_lowerCAmelCase ) if _tmp not in neighborhood_of_solution: neighborhood_of_solution.append(_tmp ) UpperCamelCase : Dict = len(neighborhood_of_solution[0] ) - 1 neighborhood_of_solution.sort(key=lambda _lowerCAmelCase : x[index_of_last_item_in_the_list] ) return neighborhood_of_solution def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]: UpperCamelCase : Dict = 1 UpperCamelCase : Optional[int] = first_solution UpperCamelCase : List[str] = [] UpperCamelCase : Any = distance_of_first_solution UpperCamelCase : List[Any] = solution while count <= iters: UpperCamelCase : Union[str, Any] = find_neighborhood(_lowerCAmelCase , _lowerCAmelCase ) UpperCamelCase : Optional[Any] = 0 UpperCamelCase : Optional[int] = neighborhood[index_of_best_solution] UpperCamelCase : Dict = len(_lowerCAmelCase ) - 1 UpperCamelCase : Union[str, Any] = False while not found: UpperCamelCase : Optional[int] = 0 while i < len(_lowerCAmelCase ): if best_solution[i] != solution[i]: UpperCamelCase : Optional[Any] = best_solution[i] UpperCamelCase : Dict = solution[i] break UpperCamelCase : List[Any] = i + 1 if [first_exchange_node, second_exchange_node] not in tabu_list and [ second_exchange_node, first_exchange_node, ] not in tabu_list: tabu_list.append([first_exchange_node, second_exchange_node] ) UpperCamelCase : Optional[Any] = True UpperCamelCase : Union[str, Any] = best_solution[:-1] UpperCamelCase : List[str] = neighborhood[index_of_best_solution][best_cost_index] if cost < best_cost: UpperCamelCase : int = cost UpperCamelCase : Any = solution else: UpperCamelCase : List[str] = index_of_best_solution + 1 UpperCamelCase : Optional[int] = neighborhood[index_of_best_solution] if len(_lowerCAmelCase ) >= size: tabu_list.pop(0 ) UpperCamelCase : Optional[int] = count + 1 return best_solution_ever, best_cost def A_ ( _lowerCAmelCase=None ) -> Optional[Any]: UpperCamelCase : Union[str, Any] = generate_neighbours(args.File ) UpperCamelCase , UpperCamelCase : Any = generate_first_solution( args.File , _lowerCAmelCase ) UpperCamelCase , UpperCamelCase : List[Any] = tabu_search( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.Iterations , args.Size , ) print(F"""Best solution: {best_sol}, with total distance: {best_cost}.""" ) if __name__ == "__main__": __lowerCamelCase : List[str] = argparse.ArgumentParser(description="""Tabu Search""") parser.add_argument( """-f""", """--File""", type=str, help="""Path to the file containing the data""", required=True, ) parser.add_argument( """-i""", """--Iterations""", type=int, help="""How many iterations the algorithm should perform""", required=True, ) parser.add_argument( """-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True ) # Pass the arguments to main method main(parser.parse_args())	140	from ...configuration_utils import PretrainedConfig from ...utils import logging __lowerCamelCase : Tuple = logging.get_logger(__name__) class A__ ( __snake_case ): _UpperCAmelCase :List[Any] = 'timm_backbone' def __init__( self , A_=None , A_=3 , A_=True , A_=True , A_=None , **A_ , ): '''simple docstring''' super().__init__(**A_ ) UpperCamelCase : Tuple = backbone UpperCamelCase : Dict = num_channels UpperCamelCase : Tuple = features_only UpperCamelCase : Optional[int] = use_pretrained_backbone UpperCamelCase : Dict = True UpperCamelCase : List[str] = out_indices if out_indices is not None else (-1,)	140	1
"""simple docstring""" import operator as op __UpperCAmelCase = 'scaler.pt' __UpperCAmelCase = 'pytorch_model' __UpperCAmelCase = 'random_states' __UpperCAmelCase = 'optimizer' __UpperCAmelCase = 'scheduler' __UpperCAmelCase = 'pytorch_model.bin' __UpperCAmelCase = 'pytorch_model.bin.index.json' __UpperCAmelCase = 'model.safetensors' __UpperCAmelCase = 'model.safetensors.index.json' __UpperCAmelCase = '1.10.2' __UpperCAmelCase = 'py38' __UpperCAmelCase = '4.17.0' __UpperCAmelCase = ['ml.p3.16xlarge', 'ml.p3dn.24xlarge', 'ml.p4dn.24xlarge'] __UpperCAmelCase = ['FULL_SHARD', 'SHARD_GRAD_OP', 'NO_SHARD', 'HYBRID_SHARD', 'HYBRID_SHARD_ZERO2'] __UpperCAmelCase = ['TRANSFORMER_BASED_WRAP', 'SIZE_BASED_WRAP', 'NO_WRAP'] __UpperCAmelCase = ['BACKWARD_PRE', 'BACKWARD_POST', 'NO_PREFETCH'] __UpperCAmelCase = ['FULL_STATE_DICT', 'LOCAL_STATE_DICT', 'SHARDED_STATE_DICT'] __UpperCAmelCase = '2.0.1' __UpperCAmelCase = ['pdsh', 'standard', 'openmpi', 'mvapich'] __UpperCAmelCase = ['default', 'reduce-overhead', 'max-autotune'] __UpperCAmelCase = {'>': op.gt, '>=': op.ge, '==': op.eq, '!=': op.ne, '<=': op.le, '<': op.lt} # These are the args for `torch.distributed.launch` for pytorch < 1.9 __UpperCAmelCase = [ 'nnodes', 'nproc_per_node', 'rdzv_backend', 'rdzv_endpoint', 'rdzv_id', 'rdzv_conf', 'standalone', 'max_restarts', 'monitor_interval', 'start_method', 'role', 'module', 'm', 'no_python', 'run_path', 'log_dir', 'r', 'redirects', 't', 'tee', 'node_rank', 'master_addr', 'master_port', ] __UpperCAmelCase = ['DEEPSPEED', 'MULTI_GPU', 'FSDP', 'MEGATRON_LM'] __UpperCAmelCase = ['DEEPSPEED', 'MULTI_XPU', 'FSDP']	84	"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class UpperCamelCase__: def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase=13 ,__UpperCAmelCase=7 ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=99 ,__UpperCAmelCase=32 ,__UpperCAmelCase=2 ,__UpperCAmelCase=4 ,__UpperCAmelCase=37 ,__UpperCAmelCase="gelu" ,__UpperCAmelCase=0.1 ,__UpperCAmelCase=0.1 ,__UpperCAmelCase=5_12 ,__UpperCAmelCase=16 ,__UpperCAmelCase=2 ,__UpperCAmelCase=0.0_2 ,__UpperCAmelCase=3 ,__UpperCAmelCase=4 ,__UpperCAmelCase=None ,__UpperCAmelCase=0 ,) -> Dict: A__ = parent A__ = batch_size A__ = seq_length A__ = is_training A__ = use_input_mask A__ = use_token_type_ids A__ = use_labels A__ = vocab_size A__ = hidden_size A__ = num_hidden_layers A__ = num_attention_heads A__ = intermediate_size A__ = hidden_act A__ = hidden_dropout_prob A__ = attention_probs_dropout_prob A__ = max_position_embeddings A__ = type_vocab_size A__ = type_sequence_label_size A__ = initializer_range A__ = num_labels A__ = num_choices A__ = scope A__ = projection_dim def snake_case__ ( self ) -> Optional[Any]: A__ = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) A__ = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py A__ = random_attention_mask([self.batch_size, self.seq_length] ) A__ = None if self.use_token_type_ids: A__ = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size ) A__ = None A__ = None A__ = None if self.use_labels: A__ = ids_tensor([self.batch_size] ,self.type_sequence_label_size ) A__ = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels ) A__ = ids_tensor([self.batch_size] ,self.num_choices ) A__ = BertConfig( vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=__UpperCAmelCase ,initializer_range=self.initializer_range ,) A__ = DPRConfig(projection_dim=self.projection_dim ,**config.to_dict() ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple: A__ = TFDPRContextEncoder(config=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,attention_mask=__UpperCAmelCase ,token_type_ids=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,token_type_ids=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ) self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.projection_dim or self.hidden_size) ) def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Union[str, Any]: A__ = TFDPRQuestionEncoder(config=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,attention_mask=__UpperCAmelCase ,token_type_ids=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,token_type_ids=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ) self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.projection_dim or self.hidden_size) ) def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Optional[int]: A__ = TFDPRReader(config=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,attention_mask=__UpperCAmelCase ) self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) ) self.parent.assertEqual(result.relevance_logits.shape ,(self.batch_size,) ) def snake_case__ ( self ) -> int: A__ = self.prepare_config_and_inputs() ( ( A__ ) , ( A__ ) , ( A__ ) , ( A__ ) , ( A__ ) , ( A__ ) , ( A__ ) , ) = config_and_inputs A__ = {'input_ids': input_ids} return config, inputs_dict @require_tf class UpperCamelCase__( __A , __A , unittest.TestCase ): lowerCAmelCase__ : Optional[int] = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) lowerCAmelCase__ : List[str] = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {} lowerCAmelCase__ : Tuple = False lowerCAmelCase__ : Optional[int] = False lowerCAmelCase__ : List[str] = False lowerCAmelCase__ : int = False lowerCAmelCase__ : str = False def snake_case__ ( self ) -> str: A__ = TFDPRModelTester(self ) A__ = ConfigTester(self ,config_class=__UpperCAmelCase ,hidden_size=37 ) def snake_case__ ( self ) -> Optional[Any]: self.config_tester.run_common_tests() def snake_case__ ( self ) -> int: A__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*__UpperCAmelCase ) def snake_case__ ( self ) -> Optional[Any]: A__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*__UpperCAmelCase ) def snake_case__ ( self ) -> List[str]: A__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*__UpperCAmelCase ) @slow def snake_case__ ( self ) -> int: for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: A__ = TFDPRContextEncoder.from_pretrained(__UpperCAmelCase ) self.assertIsNotNone(__UpperCAmelCase ) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: A__ = TFDPRContextEncoder.from_pretrained(__UpperCAmelCase ) self.assertIsNotNone(__UpperCAmelCase ) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: A__ = TFDPRQuestionEncoder.from_pretrained(__UpperCAmelCase ) self.assertIsNotNone(__UpperCAmelCase ) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: A__ = TFDPRReader.from_pretrained(__UpperCAmelCase ) self.assertIsNotNone(__UpperCAmelCase ) @require_tf class UpperCamelCase__( unittest.TestCase ): @slow def snake_case__ ( self ) -> Optional[Any]: A__ = TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base' ) A__ = tf.constant( [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]] ) # [CLS] hello, is my dog cute? [SEP] A__ = model(__UpperCAmelCase )[0] # embedding shape = (1, 768) # compare the actual values for a slice. A__ = tf.constant( [ [ 0.0_3_2_3_6_2_5_3, 0.1_2_7_5_3_3_3_5, 0.1_6_8_1_8_5_0_9, 0.0_0_2_7_9_7_8_6, 0.3_8_9_6_9_3_3, 0.2_4_2_6_4_9_4_5, 0.2_1_7_8_9_7_1, -0.0_2_3_3_5_2_2_7, -0.0_8_4_8_1_9_5_9, -0.1_4_3_2_4_1_1_7, ] ] ) self.assertTrue(numpy.allclose(output[:, :10].numpy() ,expected_slice.numpy() ,atol=1e-4 ) )	221	0
import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch if is_torch_available(): import torch from transformers.activations import gelu_new, gelu_python, get_activation @require_torch class _snake_case ( unittest.TestCase): def A__ ( self : Any ): lowercase__ = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] ) lowercase__ = get_activation("gelu" ) self.assertTrue(torch.allclose(gelu_python(__A ), torch_builtin(__A ) ) ) self.assertFalse(torch.allclose(gelu_python(__A ), gelu_new(__A ) ) ) def A__ ( self : Tuple ): lowercase__ = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] ) lowercase__ = get_activation("gelu" ) lowercase__ = get_activation("gelu_10" ) lowercase__ = torch_builtin(__A ) lowercase__ = geluaa(__A ) lowercase__ = torch.where(y_gelu_aa < 10.0, 1, 0 ) self.assertTrue(torch.max(__A ).item() == 10.0 ) self.assertTrue(torch.allclose(y_gelu * clipped_mask, y_gelu_aa * clipped_mask ) ) def A__ ( self : List[str] ): get_activation("gelu" ) get_activation("gelu_10" ) get_activation("gelu_fast" ) get_activation("gelu_new" ) get_activation("gelu_python" ) get_activation("gelu_pytorch_tanh" ) get_activation("linear" ) get_activation("mish" ) get_activation("quick_gelu" ) get_activation("relu" ) get_activation("sigmoid" ) get_activation("silu" ) get_activation("swish" ) get_activation("tanh" ) with self.assertRaises(__A ): get_activation("bogus" ) with self.assertRaises(__A ): get_activation(__A ) def A__ ( self : Union[str, Any] ): lowercase__ = get_activation("gelu" ) lowercase__ = 1 lowercase__ = get_activation("gelu" ) self.assertEqual(acta.a, 1 ) with self.assertRaises(__A ): lowercase__ = acta.a	351	def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): lowercase__ = 1 # To kept the Calculated Value # Since C(n, k) = C(n, n-k) if k > (n - k): lowercase__ = n - k # Calculate C(n,k) for i in range(SCREAMING_SNAKE_CASE_ ): result *= n - i result //= i + 1 return result def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ): return binomial_coefficient(2 * node_count , SCREAMING_SNAKE_CASE_ ) // (node_count + 1) def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ): if n < 0: raise ValueError("factorial() not defined for negative values" ) lowercase__ = 1 for i in range(1 , n + 1 ): result *= i return result def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ): return catalan_number(SCREAMING_SNAKE_CASE_ ) * factorial(SCREAMING_SNAKE_CASE_ ) if __name__ == "__main__": lowercase_ = int(input("""Enter the number of nodes: """).strip() or 0) if node_count <= 0: raise ValueError("""We need some nodes to work with.""") print( F'Given {node_count} nodes, there are {binary_tree_count(node_count)} ' F'binary trees and {catalan_number(node_count)} binary search trees.' )	224	0
from dataclasses import dataclass from typing import Optional import numpy as np import torch import torch.nn as nn from ..utils import BaseOutput, is_torch_version, randn_tensor from .attention_processor import SpatialNorm from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block @dataclass class UpperCAmelCase__ ( _a ): """simple docstring""" a = 42 class UpperCAmelCase__ ( nn.Module ): """simple docstring""" def __init__( self : List[str] , __lowerCamelCase : Any=3 , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : List[str]=("DownEncoderBlock2D",) , __lowerCamelCase : Optional[Any]=(64,) , __lowerCamelCase : Dict=2 , __lowerCamelCase : Union[str, Any]=32 , __lowerCamelCase : List[str]="silu" , __lowerCamelCase : Union[str, Any]=True , ) -> Optional[Any]: super().__init__() SCREAMING_SNAKE_CASE__ = layers_per_block SCREAMING_SNAKE_CASE__ = torch.nn.Convad( lowercase__ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , ) SCREAMING_SNAKE_CASE__ = None SCREAMING_SNAKE_CASE__ = nn.ModuleList([] ) # down SCREAMING_SNAKE_CASE__ = block_out_channels[0] for i, down_block_type in enumerate(lowercase__ ): SCREAMING_SNAKE_CASE__ = output_channel SCREAMING_SNAKE_CASE__ = block_out_channels[i] SCREAMING_SNAKE_CASE__ = i == len(lowercase__ ) - 1 SCREAMING_SNAKE_CASE__ = get_down_block( lowercase__ , num_layers=self.layers_per_block , in_channels=lowercase__ , out_channels=lowercase__ , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=lowercase__ , resnet_groups=lowercase__ , attention_head_dim=lowercase__ , temb_channels=lowercase__ , ) self.down_blocks.append(lowercase__ ) # mid SCREAMING_SNAKE_CASE__ = UNetMidBlockaD( in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=lowercase__ , output_scale_factor=1 , resnet_time_scale_shift='''default''' , attention_head_dim=block_out_channels[-1] , resnet_groups=lowercase__ , temb_channels=lowercase__ , ) # out SCREAMING_SNAKE_CASE__ = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=lowercase__ , eps=1e-6 ) SCREAMING_SNAKE_CASE__ = nn.SiLU() SCREAMING_SNAKE_CASE__ = 2 * out_channels if double_z else out_channels SCREAMING_SNAKE_CASE__ = nn.Convad(block_out_channels[-1] , lowercase__ , 3 , padding=1 ) SCREAMING_SNAKE_CASE__ = False def lowercase_ ( self : Any , __lowerCamelCase : int ) -> Any: SCREAMING_SNAKE_CASE__ = x SCREAMING_SNAKE_CASE__ = self.conv_in(lowercase__ ) if self.training and self.gradient_checkpointing: def create_custom_forward(__lowerCamelCase : Dict ): def custom_forward(*__lowerCamelCase : Dict ): return module(*lowercase__ ) return custom_forward # down if is_torch_version('''>=''' , '''1.11.0''' ): for down_block in self.down_blocks: SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint( create_custom_forward(lowercase__ ) , lowercase__ , use_reentrant=lowercase__ ) # middle SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint( create_custom_forward(self.mid_block ) , lowercase__ , use_reentrant=lowercase__ ) else: for down_block in self.down_blocks: SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(create_custom_forward(lowercase__ ) , lowercase__ ) # middle SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , lowercase__ ) else: # down for down_block in self.down_blocks: SCREAMING_SNAKE_CASE__ = down_block(lowercase__ ) # middle SCREAMING_SNAKE_CASE__ = self.mid_block(lowercase__ ) # post-process SCREAMING_SNAKE_CASE__ = self.conv_norm_out(lowercase__ ) SCREAMING_SNAKE_CASE__ = self.conv_act(lowercase__ ) SCREAMING_SNAKE_CASE__ = self.conv_out(lowercase__ ) return sample class UpperCAmelCase__ ( nn.Module ): """simple docstring""" def __init__( self : Optional[int] , __lowerCamelCase : int=3 , __lowerCamelCase : Tuple=3 , __lowerCamelCase : Any=("UpDecoderBlock2D",) , __lowerCamelCase : Any=(64,) , __lowerCamelCase : Tuple=2 , __lowerCamelCase : List[str]=32 , __lowerCamelCase : List[str]="silu" , __lowerCamelCase : Optional[int]="group" , ) -> Optional[Any]: super().__init__() SCREAMING_SNAKE_CASE__ = layers_per_block SCREAMING_SNAKE_CASE__ = nn.Convad( lowercase__ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , ) SCREAMING_SNAKE_CASE__ = None SCREAMING_SNAKE_CASE__ = nn.ModuleList([] ) SCREAMING_SNAKE_CASE__ = in_channels if norm_type == '''spatial''' else None # mid SCREAMING_SNAKE_CASE__ = UNetMidBlockaD( in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=lowercase__ , output_scale_factor=1 , resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=lowercase__ , temb_channels=lowercase__ , ) # up SCREAMING_SNAKE_CASE__ = list(reversed(lowercase__ ) ) SCREAMING_SNAKE_CASE__ = reversed_block_out_channels[0] for i, up_block_type in enumerate(lowercase__ ): SCREAMING_SNAKE_CASE__ = output_channel SCREAMING_SNAKE_CASE__ = reversed_block_out_channels[i] SCREAMING_SNAKE_CASE__ = i == len(lowercase__ ) - 1 SCREAMING_SNAKE_CASE__ = get_up_block( lowercase__ , num_layers=self.layers_per_block + 1 , in_channels=lowercase__ , out_channels=lowercase__ , prev_output_channel=lowercase__ , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=lowercase__ , resnet_groups=lowercase__ , attention_head_dim=lowercase__ , temb_channels=lowercase__ , resnet_time_scale_shift=lowercase__ , ) self.up_blocks.append(lowercase__ ) SCREAMING_SNAKE_CASE__ = output_channel # out if norm_type == "spatial": SCREAMING_SNAKE_CASE__ = SpatialNorm(block_out_channels[0] , lowercase__ ) else: SCREAMING_SNAKE_CASE__ = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=lowercase__ , eps=1e-6 ) SCREAMING_SNAKE_CASE__ = nn.SiLU() SCREAMING_SNAKE_CASE__ = nn.Convad(block_out_channels[0] , lowercase__ , 3 , padding=1 ) SCREAMING_SNAKE_CASE__ = False def lowercase_ ( self : List[str] , __lowerCamelCase : List[Any] , __lowerCamelCase : Union[str, Any]=None ) -> int: SCREAMING_SNAKE_CASE__ = z SCREAMING_SNAKE_CASE__ = self.conv_in(lowercase__ ) SCREAMING_SNAKE_CASE__ = next(iter(self.up_blocks.parameters() ) ).dtype if self.training and self.gradient_checkpointing: def create_custom_forward(__lowerCamelCase : Any ): def custom_forward(*__lowerCamelCase : List[Any] ): return module(*lowercase__ ) return custom_forward if is_torch_version('''>=''' , '''1.11.0''' ): # middle SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint( create_custom_forward(self.mid_block ) , lowercase__ , lowercase__ , use_reentrant=lowercase__ ) SCREAMING_SNAKE_CASE__ = sample.to(lowercase__ ) # up for up_block in self.up_blocks: SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint( create_custom_forward(lowercase__ ) , lowercase__ , lowercase__ , use_reentrant=lowercase__ ) else: # middle SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint( create_custom_forward(self.mid_block ) , lowercase__ , lowercase__ ) SCREAMING_SNAKE_CASE__ = sample.to(lowercase__ ) # up for up_block in self.up_blocks: SCREAMING_SNAKE_CASE__ = torch.utils.checkpoint.checkpoint(create_custom_forward(lowercase__ ) , lowercase__ , lowercase__ ) else: # middle SCREAMING_SNAKE_CASE__ = self.mid_block(lowercase__ , lowercase__ ) SCREAMING_SNAKE_CASE__ = sample.to(lowercase__ ) # up for up_block in self.up_blocks: SCREAMING_SNAKE_CASE__ = up_block(lowercase__ , lowercase__ ) # post-process if latent_embeds is None: SCREAMING_SNAKE_CASE__ = self.conv_norm_out(lowercase__ ) else: SCREAMING_SNAKE_CASE__ = self.conv_norm_out(lowercase__ , lowercase__ ) SCREAMING_SNAKE_CASE__ = self.conv_act(lowercase__ ) SCREAMING_SNAKE_CASE__ = self.conv_out(lowercase__ ) return sample class UpperCAmelCase__ ( nn.Module ): """simple docstring""" def __init__( self : Any , __lowerCamelCase : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : int , __lowerCamelCase : Tuple=None , __lowerCamelCase : str="random" , __lowerCamelCase : List[Any]=False , __lowerCamelCase : Optional[Any]=True ) -> Tuple: super().__init__() SCREAMING_SNAKE_CASE__ = n_e SCREAMING_SNAKE_CASE__ = vq_embed_dim SCREAMING_SNAKE_CASE__ = beta SCREAMING_SNAKE_CASE__ = legacy SCREAMING_SNAKE_CASE__ = nn.Embedding(self.n_e , self.vq_embed_dim ) self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e ) SCREAMING_SNAKE_CASE__ = remap if self.remap is not None: self.register_buffer('''used''' , torch.tensor(np.load(self.remap ) ) ) SCREAMING_SNAKE_CASE__ = self.used.shape[0] SCREAMING_SNAKE_CASE__ = unknown_index # "random" or "extra" or integer if self.unknown_index == "extra": SCREAMING_SNAKE_CASE__ = self.re_embed SCREAMING_SNAKE_CASE__ = self.re_embed + 1 print( f'''Remapping {self.n_e} indices to {self.re_embed} indices. ''' f'''Using {self.unknown_index} for unknown indices.''' ) else: SCREAMING_SNAKE_CASE__ = n_e SCREAMING_SNAKE_CASE__ = sane_index_shape def lowercase_ ( self : List[Any] , __lowerCamelCase : Optional[int] ) -> Dict: SCREAMING_SNAKE_CASE__ = inds.shape assert len(lowercase__ ) > 1 SCREAMING_SNAKE_CASE__ = inds.reshape(ishape[0] , -1 ) SCREAMING_SNAKE_CASE__ = self.used.to(lowercase__ ) SCREAMING_SNAKE_CASE__ = (inds[:, :, None] == used[None, None, ...]).long() SCREAMING_SNAKE_CASE__ = match.argmax(-1 ) SCREAMING_SNAKE_CASE__ = match.sum(2 ) < 1 if self.unknown_index == "random": SCREAMING_SNAKE_CASE__ = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device ) else: SCREAMING_SNAKE_CASE__ = self.unknown_index return new.reshape(lowercase__ ) def lowercase_ ( self : Dict , __lowerCamelCase : Tuple ) -> Any: SCREAMING_SNAKE_CASE__ = inds.shape assert len(lowercase__ ) > 1 SCREAMING_SNAKE_CASE__ = inds.reshape(ishape[0] , -1 ) SCREAMING_SNAKE_CASE__ = self.used.to(lowercase__ ) if self.re_embed > self.used.shape[0]: # extra token SCREAMING_SNAKE_CASE__ = 0 # simply set to zero SCREAMING_SNAKE_CASE__ = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , lowercase__ ) return back.reshape(lowercase__ ) def lowercase_ ( self : List[Any] , __lowerCamelCase : str ) -> Any: # reshape z -> (batch, height, width, channel) and flatten SCREAMING_SNAKE_CASE__ = z.permute(0 , 2 , 3 , 1 ).contiguous() SCREAMING_SNAKE_CASE__ = z.view(-1 , self.vq_embed_dim ) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z SCREAMING_SNAKE_CASE__ = torch.argmin(torch.cdist(lowercase__ , self.embedding.weight ) , dim=1 ) SCREAMING_SNAKE_CASE__ = self.embedding(lowercase__ ).view(z.shape ) SCREAMING_SNAKE_CASE__ = None SCREAMING_SNAKE_CASE__ = None # compute loss for embedding if not self.legacy: SCREAMING_SNAKE_CASE__ = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 ) else: SCREAMING_SNAKE_CASE__ = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 ) # preserve gradients SCREAMING_SNAKE_CASE__ = z + (z_q - z).detach() # reshape back to match original input shape SCREAMING_SNAKE_CASE__ = z_q.permute(0 , 3 , 1 , 2 ).contiguous() if self.remap is not None: SCREAMING_SNAKE_CASE__ = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis SCREAMING_SNAKE_CASE__ = self.remap_to_used(lowercase__ ) SCREAMING_SNAKE_CASE__ = min_encoding_indices.reshape(-1 , 1 ) # flatten if self.sane_index_shape: SCREAMING_SNAKE_CASE__ = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] ) return z_q, loss, (perplexity, min_encodings, min_encoding_indices) def lowercase_ ( self : Any , __lowerCamelCase : Optional[int] , __lowerCamelCase : Any ) -> Dict: # shape specifying (batch, height, width, channel) if self.remap is not None: SCREAMING_SNAKE_CASE__ = indices.reshape(shape[0] , -1 ) # add batch axis SCREAMING_SNAKE_CASE__ = self.unmap_to_all(lowercase__ ) SCREAMING_SNAKE_CASE__ = indices.reshape(-1 ) # flatten again # get quantized latent vectors SCREAMING_SNAKE_CASE__ = self.embedding(lowercase__ ) if shape is not None: SCREAMING_SNAKE_CASE__ = z_q.view(lowercase__ ) # reshape back to match original input shape SCREAMING_SNAKE_CASE__ = z_q.permute(0 , 3 , 1 , 2 ).contiguous() return z_q class UpperCAmelCase__ ( _a ): """simple docstring""" def __init__( self : Union[str, Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Tuple=False ) -> Tuple: SCREAMING_SNAKE_CASE__ = parameters SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = torch.chunk(lowercase__ , 2 , dim=1 ) SCREAMING_SNAKE_CASE__ = torch.clamp(self.logvar , -30.0 , 20.0 ) SCREAMING_SNAKE_CASE__ = deterministic SCREAMING_SNAKE_CASE__ = torch.exp(0.5 * self.logvar ) SCREAMING_SNAKE_CASE__ = torch.exp(self.logvar ) if self.deterministic: SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = torch.zeros_like( self.mean , device=self.parameters.device , dtype=self.parameters.dtype ) def lowercase_ ( self : List[str] , __lowerCamelCase : Union[str, Any] = None ) -> torch.FloatTensor: # make sure sample is on the same device as the parameters and has same dtype SCREAMING_SNAKE_CASE__ = randn_tensor( self.mean.shape , generator=lowercase__ , device=self.parameters.device , dtype=self.parameters.dtype ) SCREAMING_SNAKE_CASE__ = self.mean + self.std * sample return x def lowercase_ ( self : List[str] , __lowerCamelCase : Dict=None ) -> Any: if self.deterministic: return torch.Tensor([0.0] ) else: if other is None: return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] ) else: return 0.5 * torch.sum( torch.pow(self.mean - other.mean , 2 ) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar , dim=[1, 2, 3] , ) def lowercase_ ( self : Union[str, Any] , __lowerCamelCase : Any , __lowerCamelCase : Tuple=[1, 2, 3] ) -> Optional[Any]: if self.deterministic: return torch.Tensor([0.0] ) SCREAMING_SNAKE_CASE__ = np.log(2.0 * np.pi ) return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=lowercase__ ) def lowercase_ ( self : Optional[Any] ) -> List[str]: return self.mean	314	A_ : List[Any] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []} A_ : int = ['a', 'b', 'c', 'd', 'e'] def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]: '''simple docstring''' __UpperCAmelCase = start # add current to visited visited.append(SCREAMING_SNAKE_CASE ) __UpperCAmelCase = edges[current] for neighbor in neighbors: # if neighbor not in visited, visit if neighbor not in visited: __UpperCAmelCase = topological_sort(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) # if all neighbors visited add current to sort sort.append(SCREAMING_SNAKE_CASE ) # if all vertices haven't been visited select a new one to visit if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ): for vertice in vertices: if vertice not in visited: __UpperCAmelCase = topological_sort(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) # return sort return sort if __name__ == "__main__": A_ : Tuple = topological_sort('a', [], []) print(sort)	333	0
import re import string import numpy as np import datasets _SCREAMING_SNAKE_CASE = """ Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list. """ _SCREAMING_SNAKE_CASE = """ Args: predictions: List of predicted texts. references: List of reference texts. regexes_to_ignore: List, defaults to None. Regex expressions of characters to ignore when calculating the exact matches. Note: these regexes are removed from the input data before the changes based on the options below (e.g. ignore_case, ignore_punctuation, ignore_numbers) are applied. ignore_case: Boolean, defaults to False. If true, turns everything to lowercase so that capitalization differences are ignored. ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before comparing predictions and references. ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before comparing predictions and references. Returns: exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive. Examples: >>> exact_match = datasets.load_metric(\"exact_match\") >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"] >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"] >>> results = exact_match.compute(references=refs, predictions=preds) >>> print(round(results[\"exact_match\"], 1)) 25.0 >>> exact_match = datasets.load_metric(\"exact_match\") >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"] >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"] >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True) >>> print(round(results[\"exact_match\"], 1)) 50.0 >>> exact_match = datasets.load_metric(\"exact_match\") >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"] >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"] >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True) >>> print(round(results[\"exact_match\"], 1)) 75.0 >>> exact_match = datasets.load_metric(\"exact_match\") >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"] >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"] >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True) >>> print(round(results[\"exact_match\"], 1)) 100.0 >>> exact_match = datasets.load_metric(\"exact_match\") >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"] >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"] >>> results = exact_match.compute(references=refs, predictions=preds) >>> print(round(results[\"exact_match\"], 1)) 33.3 """ _SCREAMING_SNAKE_CASE = """ """ @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class SCREAMING_SNAKE_CASE_ ( datasets.Metric ): def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]: """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { 'predictions': datasets.Value('string' , id='sequence' ), 'references': datasets.Value('string' , id='sequence' ), } ) , reference_urls=[] , ) def UpperCAmelCase_ ( self : List[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Optional[int]=None , _A : Dict=False , _A : Dict=False , _A : Optional[Any]=False , ) -> List[str]: """simple docstring""" if regexes_to_ignore is not None: for s in regexes_to_ignore: snake_case_ : List[str] = np.array([re.sub(_A , '' , _A ) for x in predictions] ) snake_case_ : int = np.array([re.sub(_A , '' , _A ) for x in references] ) else: snake_case_ : Optional[Any] = np.asarray(_A ) snake_case_ : Optional[Any] = np.asarray(_A ) if ignore_case: snake_case_ : int = np.char.lower(_A ) snake_case_ : List[str] = np.char.lower(_A ) if ignore_punctuation: snake_case_ : str = string.punctuation.maketrans('' , '' , string.punctuation ) snake_case_ : str = np.char.translate(_A , table=_A ) snake_case_ : Any = np.char.translate(_A , table=_A ) if ignore_numbers: snake_case_ : int = string.digits.maketrans('' , '' , string.digits ) snake_case_ : Tuple = np.char.translate(_A , table=_A ) snake_case_ : Optional[Any] = np.char.translate(_A , table=_A ) snake_case_ : Optional[Any] = predictions == references return {"exact_match": np.mean(_A ) * 100}	368	from __future__ import annotations import math def SCREAMING_SNAKE_CASE__ ( __a , __a ): snake_case_ : Optional[int] = u for i in range(1 , __a ): snake_case_ : Optional[Any] = temp * (u - i) return temp def SCREAMING_SNAKE_CASE__ ( ): snake_case_ : Dict = int(input('enter the numbers of values: ' ) ) snake_case_ : list[list[float]] = [] for _ in range(__a ): y.append([] ) for i in range(__a ): for j in range(__a ): y[i].append(__a ) snake_case_ : str = 0 print('enter the values of parameters in a list: ' ) snake_case_ : int = list(map(__a , input().split() ) ) print('enter the values of corresponding parameters: ' ) for i in range(__a ): snake_case_ : Union[str, Any] = float(input() ) snake_case_ : int = int(input('enter the value to interpolate: ' ) ) snake_case_ : List[Any] = (value - x[0]) / (x[1] - x[0]) # for calculating forward difference table for i in range(1 , __a ): for j in range(n - i ): snake_case_ : int = y[j + 1][i - 1] - y[j][i - 1] snake_case_ : str = y[0][0] for i in range(1 , __a ): summ += (ucal(__a , __a ) * y[0][i]) / math.factorial(__a ) print(f"""the value at {value} is {summ}""" ) if __name__ == "__main__": main()	88	0
import math import unittest def A ( _lowerCamelCase ): '''simple docstring''' assert isinstance(_lowerCamelCase , _lowerCamelCase ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(math.sqrt(_lowerCamelCase ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True class UpperCAmelCase_ ( unittest.TestCase): def snake_case__ ( self): '''simple docstring''' self.assertTrue(is_prime(2)) self.assertTrue(is_prime(3)) self.assertTrue(is_prime(5)) self.assertTrue(is_prime(7)) self.assertTrue(is_prime(11)) self.assertTrue(is_prime(13)) self.assertTrue(is_prime(17)) self.assertTrue(is_prime(19)) self.assertTrue(is_prime(23)) self.assertTrue(is_prime(29)) def snake_case__ ( self): '''simple docstring''' with self.assertRaises(__a): is_prime(-19) self.assertFalse( is_prime(0), "Zero doesn't have any positive factors, primes must have exactly two.", ) self.assertFalse( is_prime(1), "One only has 1 positive factor, primes must have exactly two.", ) self.assertFalse(is_prime(2 * 2)) self.assertFalse(is_prime(2 * 3)) self.assertFalse(is_prime(3 * 3)) self.assertFalse(is_prime(3 * 5)) self.assertFalse(is_prime(3 * 5 * 7)) if __name__ == "__main__": unittest.main()	36	"""simple docstring""" import copy import json import os import tempfile from transformers import is_torch_available from .test_configuration_utils import config_common_kwargs class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a , __a=None , __a=True , __a=None , **__a ): __lowerCAmelCase = parent __lowerCAmelCase = config_class __lowerCAmelCase = has_text_modality __lowerCAmelCase = kwargs __lowerCAmelCase = common_properties def snake_case ( self ): __lowerCAmelCase = self.config_class(**self.inputs_dict ) __lowerCAmelCase = ( ["hidden_size", "num_attention_heads", "num_hidden_layers"] if self.common_properties is None else self.common_properties ) # Add common fields for text models if self.has_text_modality: common_properties.extend(["vocab_size"] ) # Test that config has the common properties as getters for prop in common_properties: self.parent.assertTrue(hasattr(__a , __a ) , msg=f"`{prop}` does not exist" ) # Test that config has the common properties as setter for idx, name in enumerate(__a ): try: setattr(__a , __a , __a ) self.parent.assertEqual( getattr(__a , __a ) , __a , msg=f"`{name} value {idx} expected, but was {getattr(__a , __a )}" ) except NotImplementedError: # Some models might not be able to implement setters for common_properties # In that case, a NotImplementedError is raised pass # Test if config class can be called with Config(prop_name=..) for idx, name in enumerate(__a ): try: __lowerCAmelCase = self.config_class(**{name: idx} ) self.parent.assertEqual( getattr(__a , __a ) , __a , msg=f"`{name} value {idx} expected, but was {getattr(__a , __a )}" ) except NotImplementedError: # Some models might not be able to implement setters for common_properties # In that case, a NotImplementedError is raised pass def snake_case ( self ): __lowerCAmelCase = self.config_class(**self.inputs_dict ) __lowerCAmelCase = json.loads(config.to_json_string() ) for key, value in self.inputs_dict.items(): self.parent.assertEqual(obj[key] , __a ) def snake_case ( self ): __lowerCAmelCase = self.config_class(**self.inputs_dict ) with tempfile.TemporaryDirectory() as tmpdirname: __lowerCAmelCase = os.path.join(__a , "config.json" ) config_first.to_json_file(__a ) __lowerCAmelCase = self.config_class.from_json_file(__a ) self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() ) def snake_case ( self ): __lowerCAmelCase = self.config_class(**self.inputs_dict ) with tempfile.TemporaryDirectory() as tmpdirname: config_first.save_pretrained(__a ) __lowerCAmelCase = self.config_class.from_pretrained(__a ) self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() ) def snake_case ( self ): __lowerCAmelCase = self.config_class(**self.inputs_dict ) __lowerCAmelCase = "test" with tempfile.TemporaryDirectory() as tmpdirname: __lowerCAmelCase = os.path.join(__a , __a ) config_first.save_pretrained(__a ) __lowerCAmelCase = self.config_class.from_pretrained(__a , subfolder=__a ) self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() ) def snake_case ( self ): __lowerCAmelCase = self.config_class(**self.inputs_dict , num_labels=5 ) self.parent.assertEqual(len(config.idalabel ) , 5 ) self.parent.assertEqual(len(config.labelaid ) , 5 ) __lowerCAmelCase = 3 self.parent.assertEqual(len(config.idalabel ) , 3 ) self.parent.assertEqual(len(config.labelaid ) , 3 ) def snake_case ( self ): if self.config_class.is_composition: return __lowerCAmelCase = self.config_class() self.parent.assertIsNotNone(__a ) def snake_case ( self ): __lowerCAmelCase = copy.deepcopy(__a ) __lowerCAmelCase = self.config_class(**__a ) __lowerCAmelCase = [] for key, value in config_common_kwargs.items(): if key == "torch_dtype": if not is_torch_available(): continue else: import torch if config.torch_dtype != torch.floataa: wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa) ) elif getattr(__a , __a ) != value: wrong_values.append((key, getattr(__a , __a ), value) ) if len(__a ) > 0: __lowerCAmelCase = "\n".join([f"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values] ) raise ValueError(f"The following keys were not properly set in the config:\n{errors}" ) def snake_case ( self ): self.create_and_test_config_common_properties() self.create_and_test_config_to_json_string() self.create_and_test_config_to_json_file() self.create_and_test_config_from_and_save_pretrained() self.create_and_test_config_from_and_save_pretrained_subfolder() self.create_and_test_config_with_num_labels() self.check_config_can_be_init_without_params() self.check_config_arguments_init()	57	0
import argparse import collections import torch from flax import traverse_util from tax import checkpoints from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration from transformers.utils import logging logging.set_verbosity_info() def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase="attention" ): '''simple docstring''' UpperCAmelCase = params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel'''] UpperCAmelCase = params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel'''] UpperCAmelCase = params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel'''] UpperCAmelCase = params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel'''] return k, o, q, v def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=False ): '''simple docstring''' if split_mlp_wi: UpperCAmelCase = params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel'''] UpperCAmelCase = params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel'''] UpperCAmelCase = (wi_a, wi_a) else: UpperCAmelCase = params[F'''{prefix}/layers_{i}/mlp/wi/kernel'''] UpperCAmelCase = params[F'''{prefix}/layers_{i}/mlp/wo/kernel'''] return wi, wo def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ): '''simple docstring''' return params[F'''{prefix}/layers_{i}/{layer_name}/scale'''] def _lowerCAmelCase ( lowerCAmelCase , *, lowerCAmelCase , lowerCAmelCase ): '''simple docstring''' UpperCAmelCase = traverse_util.flatten_dict(variables["""target"""] ) UpperCAmelCase = {"""/""".join(lowerCAmelCase ): v for k, v in old.items()} # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi UpperCAmelCase = """encoder/layers_0/mlp/wi_0/kernel""" in old print("""Split MLP:""" , lowerCAmelCase ) UpperCAmelCase = collections.OrderedDict() # Shared embeddings. UpperCAmelCase = old["""token_embedder/embedding"""] # Encoder. for i in range(lowerCAmelCase ): # Block i, layer 0 (Self Attention). UpperCAmelCase = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , """encoder""" , """pre_attention_layer_norm""" ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = tax_attention_lookup(lowerCAmelCase , lowerCAmelCase , """encoder""" , """attention""" ) UpperCAmelCase = layer_norm UpperCAmelCase = k.T UpperCAmelCase = o.T UpperCAmelCase = q.T UpperCAmelCase = v.T # Block i, layer 1 (MLP). UpperCAmelCase = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , """encoder""" , """pre_mlp_layer_norm""" ) UpperCAmelCase , UpperCAmelCase = tax_mlp_lookup(lowerCAmelCase , lowerCAmelCase , """encoder""" , lowerCAmelCase ) UpperCAmelCase = layer_norm if split_mlp_wi: UpperCAmelCase = wi[0].T UpperCAmelCase = wi[1].T else: UpperCAmelCase = wi.T UpperCAmelCase = wo.T UpperCAmelCase = old[ """encoder/relpos_bias/rel_embedding""" ].T UpperCAmelCase = old["""encoder/encoder_norm/scale"""] if not is_encoder_only: # Decoder. for i in range(lowerCAmelCase ): # Block i, layer 0 (Self Attention). UpperCAmelCase = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , """decoder""" , """pre_self_attention_layer_norm""" ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = tax_attention_lookup(lowerCAmelCase , lowerCAmelCase , """decoder""" , """self_attention""" ) UpperCAmelCase = layer_norm UpperCAmelCase = k.T UpperCAmelCase = o.T UpperCAmelCase = q.T UpperCAmelCase = v.T # Block i, layer 1 (Cross Attention). UpperCAmelCase = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , """decoder""" , """pre_cross_attention_layer_norm""" ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = tax_attention_lookup(lowerCAmelCase , lowerCAmelCase , """decoder""" , """encoder_decoder_attention""" ) UpperCAmelCase = layer_norm UpperCAmelCase = k.T UpperCAmelCase = o.T UpperCAmelCase = q.T UpperCAmelCase = v.T # Block i, layer 2 (MLP). UpperCAmelCase = tax_layer_norm_lookup(lowerCAmelCase , lowerCAmelCase , """decoder""" , """pre_mlp_layer_norm""" ) UpperCAmelCase , UpperCAmelCase = tax_mlp_lookup(lowerCAmelCase , lowerCAmelCase , """decoder""" , lowerCAmelCase ) UpperCAmelCase = layer_norm if split_mlp_wi: UpperCAmelCase = wi[0].T UpperCAmelCase = wi[1].T else: UpperCAmelCase = wi.T UpperCAmelCase = wo.T UpperCAmelCase = old["""decoder/decoder_norm/scale"""] UpperCAmelCase = old[ """decoder/relpos_bias/rel_embedding""" ].T # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead) if "decoder/logits_dense/kernel" in old: UpperCAmelCase = old["""decoder/logits_dense/kernel"""].T return new def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase ): '''simple docstring''' UpperCAmelCase = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] ) # Add what is missing. if "encoder.embed_tokens.weight" not in state_dict: UpperCAmelCase = state_dict["""shared.weight"""] if not is_encoder_only: if "decoder.embed_tokens.weight" not in state_dict: UpperCAmelCase = state_dict["""shared.weight"""] if "lm_head.weight" not in state_dict: # For old 1.0 models. print("""Using shared word embeddings as lm_head.""" ) UpperCAmelCase = state_dict["""shared.weight"""] return state_dict def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ): '''simple docstring''' UpperCAmelCase = checkpoints.load_tax_checkpoint(lowerCAmelCase ) UpperCAmelCase = convert_tax_to_pytorch(lowerCAmelCase , num_layers=config.num_layers , is_encoder_only=lowerCAmelCase ) UpperCAmelCase = make_state_dict(lowerCAmelCase , lowerCAmelCase ) model.load_state_dict(lowerCAmelCase , strict=lowerCAmelCase ) def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = False ): '''simple docstring''' UpperCAmelCase = TaConfig.from_json_file(lowerCAmelCase ) print(F'''Building PyTorch model from configuration: {config}''' ) # Non-v1.1 checkpoints could also use T5Model, but this works for all. # The v1.0 checkpoints will simply have an LM head that is the word embeddings. if is_encoder_only: UpperCAmelCase = TaEncoderModel(lowerCAmelCase ) else: UpperCAmelCase = TaForConditionalGeneration(lowerCAmelCase ) # Load weights from tf checkpoint load_tax_weights_in_ta(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) # Save pytorch-model print(F'''Save PyTorch model to {pytorch_dump_path}''' ) model.save_pretrained(lowerCAmelCase ) # Verify that we can load the checkpoint. model.from_pretrained(lowerCAmelCase ) print("""Done""" ) if __name__ == "__main__": lowerCAmelCase_ : Tuple = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''') # Required parameters parser.add_argument( '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.''' ) parser.add_argument( '''--config_file''', default=None, type=str, required=True, help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''', ) parser.add_argument( '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.''' ) parser.add_argument( '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False ) lowerCAmelCase_ : Tuple = parser.parse_args() convert_tax_checkpoint_to_pytorch( args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only )	371	"""simple docstring""" import numpy as np from sklearn.datasets import fetch_california_housing from sklearn.metrics import mean_absolute_error, mean_squared_error from sklearn.model_selection import train_test_split from xgboost import XGBRegressor def _lowerCAmelCase ( lowerCAmelCase ): '''simple docstring''' return (data["data"], data["target"]) def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ): '''simple docstring''' UpperCAmelCase = XGBRegressor(verbosity=0 , random_state=42 ) xgb.fit(lowerCAmelCase , lowerCAmelCase ) # Predict target for test data UpperCAmelCase = xgb.predict(lowerCAmelCase ) UpperCAmelCase = predictions.reshape(len(lowerCAmelCase ) , 1 ) return predictions def _lowerCAmelCase ( ): '''simple docstring''' UpperCAmelCase = fetch_california_housing() UpperCAmelCase , UpperCAmelCase = data_handling(lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = train_test_split( lowerCAmelCase , lowerCAmelCase , test_size=0.25 , random_state=1 ) UpperCAmelCase = xgboost(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) # Error printing print(F'''Mean Absolute Error : {mean_absolute_error(lowerCAmelCase , lowerCAmelCase )}''' ) print(F'''Mean Square Error : {mean_squared_error(lowerCAmelCase , lowerCAmelCase )}''' ) if __name__ == "__main__": import doctest doctest.testmod(verbose=True) main()	248	0
from __future__ import annotations from collections import deque from collections.abc import Iterator from dataclasses import dataclass @dataclass class UpperCAmelCase__ : """simple docstring""" UpperCAmelCase__ : Optional[int] = 4_2 UpperCAmelCase__ : List[Any] = 4_2 class UpperCAmelCase__ : """simple docstring""" def __init__( self , A_ ) -> Optional[Any]: __UpperCamelCase =[[] for _ in range(A_ )] __UpperCamelCase =size def __getitem__( self , A_ ) -> Any: return iter(self._graph[vertex] ) @property def _a ( self ) -> Optional[Any]: return self._size def _a ( self , A_ , A_ , A_ ) -> str: if weight not in (0, 1): raise ValueError('Edge weight must be either 0 or 1.' ) if to_vertex < 0 or to_vertex >= self.size: raise ValueError('Vertex indexes must be in [0; size).' ) self._graph[from_vertex].append(Edge(A_ , A_ ) ) def _a ( self , A_ , A_ ) -> Union[str, Any]: __UpperCamelCase =deque([start_vertex] ) __UpperCamelCase =[None] * self.size __UpperCamelCase =0 while queue: __UpperCamelCase =queue.popleft() __UpperCamelCase =distances[current_vertex] if current_distance is None: continue for edge in self[current_vertex]: __UpperCamelCase =current_distance + edge.weight __UpperCamelCase =distances[edge.destination_vertex] if ( isinstance(A_ , A_ ) and new_distance >= dest_vertex_distance ): continue __UpperCamelCase =new_distance if edge.weight == 0: queue.appendleft(edge.destination_vertex ) else: queue.append(edge.destination_vertex ) if distances[finish_vertex] is None: raise ValueError('No path from start_vertex to finish_vertex.' ) return distances[finish_vertex] if __name__ == "__main__": import doctest doctest.testmod()	62	"""simple docstring""" from __future__ import annotations from collections.abc import Iterator from typing import Any class lowerCAmelCase__ : '''simple docstring''' def __init__( self , lowercase ): _lowerCamelCase : Any = data _lowerCamelCase : Node | None = None class lowerCAmelCase__ : '''simple docstring''' def __init__( self ): _lowerCamelCase : str = None _lowerCamelCase : str = None def __iter__( self ): _lowerCamelCase : List[str] = self.head while self.head: yield node.data _lowerCamelCase : Optional[int] = node.next if node == self.head: break def __len__( self ): return sum(1 for _ in self ) def __repr__( self ): return "->".join(str(lowercase ) for item in iter(self ) ) def A_ ( self , lowercase ): self.insert_nth(len(self ) , lowercase ) def A_ ( self , lowercase ): self.insert_nth(0 , lowercase ) def A_ ( self , lowercase , lowercase ): if index < 0 or index > len(self ): raise IndexError('list index out of range.' ) _lowerCamelCase : List[Any] = Node(lowercase ) if self.head is None: _lowerCamelCase : str = new_node # first node points itself _lowerCamelCase : Union[str, Any] = new_node elif index == 0: # insert at head _lowerCamelCase : List[str] = self.head _lowerCamelCase : str = new_node else: _lowerCamelCase : Union[str, Any] = self.head for _ in range(index - 1 ): _lowerCamelCase : List[Any] = temp.next _lowerCamelCase : Union[str, Any] = temp.next _lowerCamelCase : List[str] = new_node if index == len(self ) - 1: # insert at tail _lowerCamelCase : Any = new_node def A_ ( self ): return self.delete_nth(0 ) def A_ ( self ): return self.delete_nth(len(self ) - 1 ) def A_ ( self , lowercase = 0 ): if not 0 <= index < len(self ): raise IndexError('list index out of range.' ) _lowerCamelCase : Any = self.head if self.head == self.tail: # just one node _lowerCamelCase : List[str] = None elif index == 0: # delete head node _lowerCamelCase : List[str] = self.tail.next.next _lowerCamelCase : Optional[int] = self.head.next else: _lowerCamelCase : Dict = self.head for _ in range(index - 1 ): _lowerCamelCase : List[Any] = temp.next _lowerCamelCase : int = temp.next _lowerCamelCase : Optional[int] = temp.next.next if index == len(self ) - 1: # delete at tail _lowerCamelCase : List[Any] = temp return delete_node.data def A_ ( self ): return len(self ) == 0 def _snake_case ( ): _lowerCamelCase : Union[str, Any] = CircularLinkedList() assert len(lowercase__ ) == 0 assert circular_linked_list.is_empty() is True assert str(lowercase__ ) == "" try: circular_linked_list.delete_front() raise AssertionError # This should not happen except IndexError: assert True # This should happen try: circular_linked_list.delete_tail() raise AssertionError # This should not happen except IndexError: assert True # This should happen try: circular_linked_list.delete_nth(-1 ) raise AssertionError except IndexError: assert True try: circular_linked_list.delete_nth(0 ) raise AssertionError except IndexError: assert True assert circular_linked_list.is_empty() is True for i in range(5 ): assert len(lowercase__ ) == i circular_linked_list.insert_nth(lowercase__ , i + 1 ) assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) ) circular_linked_list.insert_tail(6 ) assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 7 ) ) circular_linked_list.insert_head(0 ) assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(0 , 7 ) ) assert circular_linked_list.delete_front() == 0 assert circular_linked_list.delete_tail() == 6 assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) ) assert circular_linked_list.delete_nth(2 ) == 3 circular_linked_list.insert_nth(2 , 3 ) assert str(lowercase__ ) == "->".join(str(lowercase__ ) for i in range(1 , 6 ) ) assert circular_linked_list.is_empty() is False if __name__ == "__main__": import doctest doctest.testmod()	96	0
"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _lowerCAmelCase : List[str] = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase : Optional[int] = ['''EncoderDecoderModel'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase : Any = ['''TFEncoderDecoderModel'''] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase : Union[str, Any] = ['''FlaxEncoderDecoderModel'''] if TYPE_CHECKING: from .configuration_encoder_decoder import EncoderDecoderConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_encoder_decoder import EncoderDecoderModel try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_encoder_decoder import TFEncoderDecoderModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel else: import sys _lowerCAmelCase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)	371	from __future__ import annotations import matplotlib.pyplot as plt # type: ignore import numpy # initial triangle of Koch snowflake _lowerCAmelCase : Optional[Any] = numpy.array([0, 0]) _lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254]) _lowerCAmelCase : Any = numpy.array([1, 0]) _lowerCAmelCase : int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1] def UpperCamelCase_( _snake_case : list[numpy.ndarray] , _snake_case : int ): """simple docstring""" __a =initial_vectors for _ in range(_snake_case ): __a =iteration_step(_snake_case ) return vectors def UpperCamelCase_( _snake_case : list[numpy.ndarray] ): """simple docstring""" __a =[] for i, start_vector in enumerate(vectors[:-1] ): __a =vectors[i + 1] new_vectors.append(_snake_case ) __a =end_vector - start_vector new_vectors.append(start_vector + difference_vector / 3 ) new_vectors.append( start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) ) new_vectors.append(start_vector + difference_vector * 2 / 3 ) new_vectors.append(vectors[-1] ) return new_vectors def UpperCamelCase_( _snake_case : numpy.ndarray , _snake_case : float ): """simple docstring""" __a =numpy.radians(_snake_case ) __a , __a =numpy.cos(_snake_case ), numpy.sin(_snake_case ) __a =numpy.array(((c, -s), (s, c)) ) return numpy.dot(_snake_case , _snake_case ) def UpperCamelCase_( _snake_case : list[numpy.ndarray] ): """simple docstring""" __a =plt.gca() axes.set_aspect('equal' ) # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all # y-coordinates as inputs, which are constructed from the vector-list using # zip() __a , __a =zip(*_snake_case ) plt.plot(_snake_case , _snake_case ) plt.show() if __name__ == "__main__": import doctest doctest.testmod() _lowerCAmelCase : List[Any] = iterate(INITIAL_VECTORS, 5) plot(processed_vectors)	308	0
"""simple docstring""" import json import os import unittest from transformers.models.roc_bert.tokenization_roc_bert import ( VOCAB_FILES_NAMES, RoCBertBasicTokenizer, RoCBertTokenizer, RoCBertWordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace, ) from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english @require_tokenizers class UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ): """simple docstring""" _UpperCAmelCase :Tuple = RoCBertTokenizer _UpperCAmelCase :Tuple = None _UpperCAmelCase :Optional[int] = False _UpperCAmelCase :str = True _UpperCAmelCase :Union[str, Any] = filter_non_english def _snake_case ( self ): super().setUp() lowercase__: Optional[Any] = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"] lowercase__: Optional[Any] = {} lowercase__: Optional[Any] = {} for i, value in enumerate(__lowerCamelCase ): lowercase__: Any = i lowercase__: Optional[Any] = i lowercase__: Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] ) lowercase__: Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''word_shape_file'''] ) lowercase__: Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''word_pronunciation_file'''] ) with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer: vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) ) with open(self.word_shape_file , '''w''' , encoding='''utf-8''' ) as word_shape_writer: json.dump(__lowerCamelCase , __lowerCamelCase , ensure_ascii=__lowerCamelCase ) with open(self.word_pronunciation_file , '''w''' , encoding='''utf-8''' ) as word_pronunciation_writer: json.dump(__lowerCamelCase , __lowerCamelCase , ensure_ascii=__lowerCamelCase ) def _snake_case ( self ): lowercase__: List[Any] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file ) lowercase__: Optional[Any] = tokenizer.tokenize('''你好[SEP]你是谁''' ) self.assertListEqual(__lowerCamelCase , ['''你''', '''好''', '''[SEP]''', '''你''', '''是''', '''谁'''] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , [5, 6, 2, 5, 7, 8] ) self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__lowerCamelCase ) , [5, 6, 2, 5, 7, 8] ) self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__lowerCamelCase ) , [5, 6, 2, 5, 7, 8] ) def _snake_case ( self ): lowercase__: Tuple = RoCBertBasicTokenizer() self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ) , ['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] ) def _snake_case ( self ): lowercase__: Dict = RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] ) self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] ) def _snake_case ( self ): lowercase__: List[Any] = RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase , strip_accents=__lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] ) self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''h\u00E9llo'''] ) def _snake_case ( self ): lowercase__: List[str] = RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase , strip_accents=__lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] ) self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] ) def _snake_case ( self ): lowercase__: int = RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] ) self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] ) def _snake_case ( self ): lowercase__: Optional[Any] = RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] ) def _snake_case ( self ): lowercase__: Tuple = RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase , strip_accents=__lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] ) def _snake_case ( self ): lowercase__: Optional[Any] = RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase , strip_accents=__lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] ) def _snake_case ( self ): lowercase__: Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=__lowerCamelCase , never_split=['''[UNK]'''] ) self.assertListEqual( tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? [UNK]''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] ) def _snake_case ( self ): lowercase__: Union[str, Any] = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"] lowercase__: Optional[int] = {} for i, token in enumerate(__lowerCamelCase ): lowercase__: List[str] = i lowercase__: Union[str, Any] = RoCBertWordpieceTokenizer(vocab=__lowerCamelCase , unk_token='''[UNK]''' ) self.assertListEqual(tokenizer.tokenize('''''' ) , [] ) self.assertListEqual(tokenizer.tokenize('''unwanted running''' ) , ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] ) self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ) , ['''[UNK]''', '''runn''', '''##ing'''] ) def _snake_case ( self ): self.assertTrue(_is_whitespace(''' ''' ) ) self.assertTrue(_is_whitespace('''\t''' ) ) self.assertTrue(_is_whitespace('''\r''' ) ) self.assertTrue(_is_whitespace('''\n''' ) ) self.assertTrue(_is_whitespace('''\u00A0''' ) ) self.assertFalse(_is_whitespace('''A''' ) ) self.assertFalse(_is_whitespace('''-''' ) ) def _snake_case ( self ): self.assertTrue(_is_control('''\u0005''' ) ) self.assertFalse(_is_control('''A''' ) ) self.assertFalse(_is_control(''' ''' ) ) self.assertFalse(_is_control('''\t''' ) ) self.assertFalse(_is_control('''\r''' ) ) def _snake_case ( self ): self.assertTrue(_is_punctuation('''-''' ) ) self.assertTrue(_is_punctuation('''$''' ) ) self.assertTrue(_is_punctuation('''`''' ) ) self.assertTrue(_is_punctuation('''.''' ) ) self.assertFalse(_is_punctuation('''A''' ) ) self.assertFalse(_is_punctuation(''' ''' ) ) def _snake_case ( self ): lowercase__: Dict = self.get_tokenizer() # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340 self.assertListEqual([tokenizer.tokenize(__lowerCamelCase ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] ) if self.test_rust_tokenizer: lowercase__: List[Any] = self.get_rust_tokenizer() self.assertListEqual( [rust_tokenizer.tokenize(__lowerCamelCase ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] ) def _snake_case ( self ): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): lowercase__: Union[str, Any] = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase ) lowercase__: int = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence.""" lowercase__: Optional[int] = tokenizer_r.encode_plus( __lowerCamelCase , return_attention_mask=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , add_special_tokens=__lowerCamelCase , ) lowercase__: Tuple = tokenizer_r.do_lower_case if hasattr(__lowerCamelCase , '''do_lower_case''' ) else False lowercase__: Tuple = ( [ ((0, 0), tokenizer_r.cls_token), ((0, 1), "A"), ((1, 2), ","), ((3, 5), "na"), ((5, 6), "##ï"), ((6, 8), "##ve"), ((9, 15), tokenizer_r.mask_token), ((16, 21), "Allen"), ((21, 23), "##NL"), ((23, 24), "##P"), ((25, 33), "sentence"), ((33, 34), "."), ((0, 0), tokenizer_r.sep_token), ] if not do_lower_case else [ ((0, 0), tokenizer_r.cls_token), ((0, 1), "a"), ((1, 2), ","), ((3, 8), "naive"), ((9, 15), tokenizer_r.mask_token), ((16, 21), "allen"), ((21, 23), "##nl"), ((23, 24), "##p"), ((25, 33), "sentence"), ((33, 34), "."), ((0, 0), tokenizer_r.sep_token), ] ) self.assertEqual( [e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) ) self.assertEqual([e[0] for e in expected_results] , tokens['''offset_mapping'''] ) def _snake_case ( self ): lowercase__: Tuple = ["的", "人", "有"] lowercase__: int = "".join(__lowerCamelCase ) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): lowercase__: Union[str, Any] = True lowercase__: Tuple = self.tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase ) lowercase__: str = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase ) lowercase__: List[Any] = tokenizer_p.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) lowercase__: Tuple = tokenizer_r.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) lowercase__: Union[str, Any] = tokenizer_r.convert_ids_to_tokens(__lowerCamelCase ) lowercase__: Dict = tokenizer_p.convert_ids_to_tokens(__lowerCamelCase ) # it is expected that each Chinese character is not preceded by "##" self.assertListEqual(__lowerCamelCase , __lowerCamelCase ) self.assertListEqual(__lowerCamelCase , __lowerCamelCase ) lowercase__: Union[str, Any] = False lowercase__: List[Any] = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase ) lowercase__: str = self.tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase ) lowercase__: Dict = tokenizer_r.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) lowercase__: Optional[Any] = tokenizer_p.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) lowercase__: Dict = tokenizer_r.convert_ids_to_tokens(__lowerCamelCase ) lowercase__: int = tokenizer_p.convert_ids_to_tokens(__lowerCamelCase ) # it is expected that only the first Chinese character is not preceded by "##". lowercase__: Tuple = [ F"""##{token}""" if idx != 0 else token for idx, token in enumerate(__lowerCamelCase ) ] self.assertListEqual(__lowerCamelCase , __lowerCamelCase ) self.assertListEqual(__lowerCamelCase , __lowerCamelCase ) @slow def _snake_case ( self ): lowercase__: Optional[int] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file ) lowercase__: Optional[int] = tokenizer.encode('''你好''' , add_special_tokens=__lowerCamelCase ) lowercase__: List[Any] = tokenizer.encode('''你是谁''' , add_special_tokens=__lowerCamelCase ) lowercase__: Union[str, Any] = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ) lowercase__: int = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase , __lowerCamelCase ) assert encoded_sentence == [1] + text + [2] assert encoded_pair == [1] + text + [2] + text_a + [2] def _snake_case ( self ): lowercase__: Tuple = self.get_tokenizers(do_lower_case=__lowerCamelCase ) for tokenizer in tokenizers: with self.subTest(F"""{tokenizer.__class__.__name__}""" ): lowercase__: Any = "你好，你是谁" lowercase__: Dict = tokenizer.tokenize(__lowerCamelCase ) lowercase__: Any = tokenizer.convert_tokens_to_ids(__lowerCamelCase ) lowercase__: Optional[Any] = tokenizer.convert_tokens_to_shape_ids(__lowerCamelCase ) lowercase__: str = tokenizer.convert_tokens_to_pronunciation_ids(__lowerCamelCase ) lowercase__: Tuple = tokenizer.prepare_for_model( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , add_special_tokens=__lowerCamelCase ) lowercase__: List[Any] = tokenizer.encode_plus(__lowerCamelCase , add_special_tokens=__lowerCamelCase ) self.assertEqual(__lowerCamelCase , __lowerCamelCase )	177	def _UpperCAmelCase (UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] ): # "extended trapezoidal rule" # int(f) = dx/2 * (f1 + 2f2 + ... + fn) _A : int = (boundary[1] - boundary[0]) / steps _A : Any = boundary[0] _A : List[Any] = boundary[1] _A : str = make_points(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) _A : str = 0.0 y += (h / 2.0) * f(UpperCamelCase__ ) for i in x_i: # print(i) y += h * f(UpperCamelCase__ ) y += (h / 2.0) * f(UpperCamelCase__ ) return y def _UpperCAmelCase (UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any ): _A : Optional[int] = a + h while x < (b - h): yield x _A : Dict = x + h def _UpperCAmelCase (UpperCamelCase__ : Optional[int] ): # enter your function here _A : Any = (x - 0) * (x - 0) return y def _UpperCAmelCase (): _A : Optional[Any] = 0.0 # Lower bound of integration _A : Optional[int] = 1.0 # Upper bound of integration _A : List[Any] = 10.0 # define number of steps or resolution _A : Any = [a, b] # define boundary of integration _A : Tuple = method_a(UpperCamelCase__ , UpperCamelCase__ ) print(f"y = {y}" ) if __name__ == "__main__": main()	11	0
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path: # hack it in for now: import sys from pathlib import Path __A = Path(__file__).resolve().parents[3] / "src" sys.path.insert(1, str(git_repo_path)) import dataclasses # noqa import io # noqa import itertools # noqa import json # noqa import os # noqa import unittest # noqa from copy import deepcopy # noqa from parameterized import parameterized # noqa from transformers import TrainingArguments, is_torch_available # noqa from transformers.deepspeed import is_deepspeed_available # noqa from transformers.file_utils import WEIGHTS_NAME # noqa from transformers.testing_utils import ( # noqa CaptureLogger, ExtendSysPath, TestCasePlus, execute_subprocess_async, get_gpu_count, mockenv_context, require_deepspeed, require_torch_gpu, require_torch_multi_gpu, slow, ) from transformers.trainer_utils import set_seed # noqa set_seed(42) __A = {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"} __A = "zero2" __A = "zero3" __A = [ZEROa, ZEROa] def lowerCAmelCase_ ( __a , __a , __a ) -> Tuple: """simple docstring""" lowerCamelCase__: List[Any] =parameterized.to_safe_name("_".join(str(__a ) for x in param.args ) ) return F"""{func.__name__}_{param_based_name}""" # Cartesian-product of zero stages with models to test __A = list(itertools.product(stages, models.keys())) @slow @require_deepspeed @require_torch_gpu class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ): '''simple docstring''' @parameterized.expand(UpperCAmelCase_ , name_func=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Any , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int) ->str: '''simple docstring''' self.run_and_check( stage=UpperCAmelCase_ , model=UpperCAmelCase_ , distributed=UpperCAmelCase_ , fpaa=UpperCAmelCase_ , ) @require_torch_multi_gpu @parameterized.expand(UpperCAmelCase_ , name_func=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : str , UpperCAmelCase_ : List[str]) ->str: '''simple docstring''' self.run_and_check( stage=UpperCAmelCase_ , model=UpperCAmelCase_ , distributed=UpperCAmelCase_ , fpaa=UpperCAmelCase_ , ) @parameterized.expand(UpperCAmelCase_ , name_func=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Any) ->List[str]: '''simple docstring''' self.run_and_check( stage=UpperCAmelCase_ , model=UpperCAmelCase_ , distributed=UpperCAmelCase_ , fpaa=UpperCAmelCase_ , ) @require_torch_multi_gpu @parameterized.expand(UpperCAmelCase_ , name_func=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[Any]) ->Dict: '''simple docstring''' self.run_and_check( stage=UpperCAmelCase_ , model=UpperCAmelCase_ , distributed=UpperCAmelCase_ , fpaa=UpperCAmelCase_ , ) def SCREAMING_SNAKE_CASE_ (self : Any , UpperCAmelCase_ : str) ->Dict: '''simple docstring''' pass def SCREAMING_SNAKE_CASE_ (self : Tuple , UpperCAmelCase_ : str , UpperCAmelCase_ : str , UpperCAmelCase_ : int = 10 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : bool = True , ) ->Optional[int]: '''simple docstring''' lowerCamelCase__: List[Any] =models[model] lowerCamelCase__: Dict =self.run_trainer( stage=UpperCAmelCase_ , model_name=UpperCAmelCase_ , eval_steps=UpperCAmelCase_ , num_train_epochs=1 , distributed=UpperCAmelCase_ , fpaa=UpperCAmelCase_ , ) self.do_checks(UpperCAmelCase_) return output_dir def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : str , UpperCAmelCase_ : str , UpperCAmelCase_ : int = 10 , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : bool = True , ) ->Union[str, Any]: '''simple docstring''' lowerCamelCase__: Optional[Any] =self.get_auto_remove_tmp_dir("./xxx" , after=UpperCAmelCase_) lowerCamelCase__: Dict =F""" --model_name_or_path {model_name} --dataset_name hf-internal-testing/librispeech_asr_dummy --dataset_config_name clean --train_split_name validation --validation_split_name validation --output_dir {output_dir} --num_train_epochs {str(UpperCAmelCase_)} --per_device_train_batch_size 2 --per_device_eval_batch_size 2 --evaluation_strategy steps --learning_rate 5e-4 --warmup_steps 8 --orthography timit --preprocessing_num_workers 1 --group_by_length --freeze_feature_extractor --report_to none --save_steps 0 --eval_steps {eval_steps} --report_to none """.split() if fpaa: args.extend(["--fp16"]) # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true, # hence the separate config files lowerCamelCase__: Dict =F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split() lowerCamelCase__: List[Any] =[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""] lowerCamelCase__: Dict =self.get_launcher(UpperCAmelCase_) lowerCamelCase__: Optional[int] =launcher + script + args + ds_args # keep for quick debug # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die execute_subprocess_async(UpperCAmelCase_ , env=self.get_env()) return output_dir def SCREAMING_SNAKE_CASE_ (self : Dict , UpperCAmelCase_ : Tuple=False) ->Optional[int]: '''simple docstring''' lowerCamelCase__: Optional[int] =min(2 , get_gpu_count()) if distributed else 1 return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()	273	from pathlib import Path from typing import List from transformers import is_torch_available, is_vision_available from transformers.testing_utils import get_tests_dir, is_tool_test from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText if is_torch_available(): import torch if is_vision_available(): from PIL import Image __A = ["text", "image", "audio"] def lowerCAmelCase_ ( __a ) -> Optional[Any]: """simple docstring""" lowerCamelCase__: Tuple =[] for input_type in input_types: if input_type == "text": inputs.append("Text input" ) elif input_type == "image": inputs.append( Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png" ).resize((512, 512) ) ) elif input_type == "audio": inputs.append(torch.ones(3000 ) ) elif isinstance(__a , __a ): inputs.append(create_inputs(__a ) ) else: raise ValueError(F"""Invalid type requested: {input_type}""" ) return inputs def lowerCAmelCase_ ( __a ) -> Union[str, Any]: """simple docstring""" lowerCamelCase__: Union[str, Any] =[] for output in outputs: if isinstance(__a , (str, AgentText) ): output_types.append("text" ) elif isinstance(__a , (Image.Image, AgentImage) ): output_types.append("image" ) elif isinstance(__a , (torch.Tensor, AgentAudio) ): output_types.append("audio" ) else: raise ValueError(F"""Invalid output: {output}""" ) return output_types @is_tool_test class _SCREAMING_SNAKE_CASE : '''simple docstring''' def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Dict: '''simple docstring''' self.assertTrue(hasattr(self.tool , "inputs")) self.assertTrue(hasattr(self.tool , "outputs")) lowerCamelCase__: Tuple =self.tool.inputs for _input in inputs: if isinstance(_input , UpperCAmelCase_): for __input in _input: self.assertTrue(__input in authorized_types) else: self.assertTrue(_input in authorized_types) lowerCamelCase__: Optional[Any] =self.tool.outputs for _output in outputs: self.assertTrue(_output in authorized_types) def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->str: '''simple docstring''' lowerCamelCase__: List[str] =create_inputs(self.tool.inputs) lowerCamelCase__: str =self.tool(*UpperCAmelCase_) # There is a single output if len(self.tool.outputs) == 1: lowerCamelCase__: Optional[Any] =[outputs] self.assertListEqual(output_types(UpperCAmelCase_) , self.tool.outputs) def SCREAMING_SNAKE_CASE_ (self : Dict) ->Any: '''simple docstring''' self.assertTrue(hasattr(self.tool , "description")) self.assertTrue(hasattr(self.tool , "default_checkpoint")) self.assertTrue(self.tool.description.startswith("This is a tool that")) def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Optional[int]: '''simple docstring''' lowerCamelCase__: str =create_inputs(self.tool.inputs) lowerCamelCase__: Dict =self.tool(*UpperCAmelCase_) if not isinstance(UpperCAmelCase_ , UpperCAmelCase_): lowerCamelCase__: Tuple =[outputs] self.assertEqual(len(UpperCAmelCase_) , len(self.tool.outputs)) for output, output_type in zip(UpperCAmelCase_ , self.tool.outputs): lowerCamelCase__: Any =AGENT_TYPE_MAPPING[output_type] self.assertTrue(isinstance(UpperCAmelCase_ , UpperCAmelCase_)) def SCREAMING_SNAKE_CASE_ (self : Dict) ->str: '''simple docstring''' lowerCamelCase__: Any =create_inputs(self.tool.inputs) lowerCamelCase__: int =[] for _input, input_type in zip(UpperCAmelCase_ , self.tool.inputs): if isinstance(UpperCAmelCase_ , UpperCAmelCase_): _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type]) else: _inputs.append(AGENT_TYPE_MAPPING[input_type](_input)) # Should not raise an error lowerCamelCase__: Union[str, Any] =self.tool(*UpperCAmelCase_) if not isinstance(UpperCAmelCase_ , UpperCAmelCase_): lowerCamelCase__: str =[outputs] self.assertEqual(len(UpperCAmelCase_) , len(self.tool.outputs))	273	1
'''simple docstring''' import collections.abc from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACTaFN from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention from ...modeling_utils import PreTrainedModel from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_poolformer import PoolFormerConfig lowercase_ = logging.get_logger(__name__) # General docstring lowercase_ = "PoolFormerConfig" # Base docstring lowercase_ = "sail/poolformer_s12" lowercase_ = [1, 512, 7, 7] # Image classification docstring lowercase_ = "sail/poolformer_s12" lowercase_ = "tabby, tabby cat" lowercase_ = [ "sail/poolformer_s12", # See all PoolFormer models at https://huggingface.co/models?filter=poolformer ] def lowerCamelCase ( __lowerCamelCase : Dict , __lowerCamelCase : float = 0.0 , __lowerCamelCase : bool = False ) ->Dict: if drop_prob == 0.0 or not training: return input _SCREAMING_SNAKE_CASE = 1 - drop_prob _SCREAMING_SNAKE_CASE = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets _SCREAMING_SNAKE_CASE = keep_prob + torch.rand(__lowerCamelCase , dtype=input.dtype , device=input.device ) random_tensor.floor_() # binarize _SCREAMING_SNAKE_CASE = input.div(__lowerCamelCase ) * random_tensor return output class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A = None ) -> None: super().__init__() _SCREAMING_SNAKE_CASE = drop_prob def snake_case_( self , A ) -> torch.Tensor: return drop_path(a__ , self.drop_prob , self.training ) def snake_case_( self ) -> str: return "p={}".format(self.drop_prob ) class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A , A , A , A , A , A=None ) -> Tuple: super().__init__() _SCREAMING_SNAKE_CASE = patch_size if isinstance(a__ , collections.abc.Iterable ) else (patch_size, patch_size) _SCREAMING_SNAKE_CASE = stride if isinstance(a__ , collections.abc.Iterable ) else (stride, stride) _SCREAMING_SNAKE_CASE = padding if isinstance(a__ , collections.abc.Iterable ) else (padding, padding) _SCREAMING_SNAKE_CASE = nn.Convad(a__ , a__ , kernel_size=a__ , stride=a__ , padding=a__ ) _SCREAMING_SNAKE_CASE = norm_layer(a__ ) if norm_layer else nn.Identity() def snake_case_( self , A ) -> str: _SCREAMING_SNAKE_CASE = self.projection(a__ ) _SCREAMING_SNAKE_CASE = self.norm(a__ ) return embeddings class a_ ( nn.GroupNorm ): '''simple docstring''' def __init__( self , A , **A ) -> List[str]: super().__init__(1 , a__ , **a__ ) class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A ) -> List[Any]: super().__init__() _SCREAMING_SNAKE_CASE = nn.AvgPoolad(a__ , stride=1 , padding=pool_size // 2 , count_include_pad=a__ ) def snake_case_( self , A ) -> Dict: return self.pool(a__ ) - hidden_states class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A , A , A , A ) -> Optional[Any]: super().__init__() _SCREAMING_SNAKE_CASE = nn.Convad(a__ , a__ , 1 ) _SCREAMING_SNAKE_CASE = nn.Convad(a__ , a__ , 1 ) _SCREAMING_SNAKE_CASE = PoolFormerDropPath(a__ ) if isinstance(config.hidden_act , a__ ): _SCREAMING_SNAKE_CASE = ACTaFN[config.hidden_act] else: _SCREAMING_SNAKE_CASE = config.hidden_act def snake_case_( self , A ) -> Dict: _SCREAMING_SNAKE_CASE = self.conva(a__ ) _SCREAMING_SNAKE_CASE = self.act_fn(a__ ) _SCREAMING_SNAKE_CASE = self.drop(a__ ) _SCREAMING_SNAKE_CASE = self.conva(a__ ) _SCREAMING_SNAKE_CASE = self.drop(a__ ) return hidden_states class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A , A , A , A , A , A ) -> Dict: super().__init__() _SCREAMING_SNAKE_CASE = PoolFormerPooling(a__ ) _SCREAMING_SNAKE_CASE = PoolFormerOutput(a__ , a__ , a__ , a__ ) _SCREAMING_SNAKE_CASE = PoolFormerGroupNorm(a__ ) _SCREAMING_SNAKE_CASE = PoolFormerGroupNorm(a__ ) # Useful for training neural nets _SCREAMING_SNAKE_CASE = PoolFormerDropPath(a__ ) if drop_path > 0.0 else nn.Identity() _SCREAMING_SNAKE_CASE = config.use_layer_scale if config.use_layer_scale: _SCREAMING_SNAKE_CASE = nn.Parameter( config.layer_scale_init_value * torch.ones((a__) ) , requires_grad=a__ ) _SCREAMING_SNAKE_CASE = nn.Parameter( config.layer_scale_init_value * torch.ones((a__) ) , requires_grad=a__ ) def snake_case_( self , A ) -> List[Any]: if self.use_layer_scale: _SCREAMING_SNAKE_CASE = self.pooling(self.before_norm(a__ ) ) _SCREAMING_SNAKE_CASE = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output # First residual connection _SCREAMING_SNAKE_CASE = hidden_states + self.drop_path(a__ ) _SCREAMING_SNAKE_CASE = () _SCREAMING_SNAKE_CASE = self.output(self.after_norm(a__ ) ) _SCREAMING_SNAKE_CASE = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output # Second residual connection _SCREAMING_SNAKE_CASE = hidden_states + self.drop_path(a__ ) _SCREAMING_SNAKE_CASE = (output,) + outputs return outputs else: _SCREAMING_SNAKE_CASE = self.drop_path(self.pooling(self.before_norm(a__ ) ) ) # First residual connection _SCREAMING_SNAKE_CASE = pooling_output + hidden_states _SCREAMING_SNAKE_CASE = () # Second residual connection inside the PoolFormerOutput block _SCREAMING_SNAKE_CASE = self.drop_path(self.output(self.after_norm(a__ ) ) ) _SCREAMING_SNAKE_CASE = hidden_states + layer_output _SCREAMING_SNAKE_CASE = (output,) + outputs return outputs class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A ) -> Tuple: super().__init__() _SCREAMING_SNAKE_CASE = config # stochastic depth decay rule _SCREAMING_SNAKE_CASE = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )] # patch embeddings _SCREAMING_SNAKE_CASE = [] for i in range(config.num_encoder_blocks ): embeddings.append( PoolFormerEmbeddings( patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) ) _SCREAMING_SNAKE_CASE = nn.ModuleList(a__ ) # Transformer blocks _SCREAMING_SNAKE_CASE = [] _SCREAMING_SNAKE_CASE = 0 for i in range(config.num_encoder_blocks ): # each block consists of layers _SCREAMING_SNAKE_CASE = [] if i != 0: cur += config.depths[i - 1] for j in range(config.depths[i] ): layers.append( PoolFormerLayer( a__ , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) ) blocks.append(nn.ModuleList(a__ ) ) _SCREAMING_SNAKE_CASE = nn.ModuleList(a__ ) def snake_case_( self , A , A=False , A=True ) -> Tuple: _SCREAMING_SNAKE_CASE = () if output_hidden_states else None _SCREAMING_SNAKE_CASE = pixel_values for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ): _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = layers # Get patch embeddings from hidden_states _SCREAMING_SNAKE_CASE = embedding_layer(a__ ) # Send the embeddings through the blocks for _, blk in enumerate(a__ ): _SCREAMING_SNAKE_CASE = blk(a__ ) _SCREAMING_SNAKE_CASE = layer_outputs[0] if output_hidden_states: _SCREAMING_SNAKE_CASE = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None ) return BaseModelOutputWithNoAttention(last_hidden_state=a__ , hidden_states=a__ ) class a_ ( lowercase_ ): '''simple docstring''' UpperCamelCase = PoolFormerConfig UpperCamelCase = "poolformer" UpperCamelCase = "pixel_values" UpperCamelCase = True def snake_case_( self , A ) -> List[Any]: if isinstance(a__ , (nn.Linear, nn.Convad) ): module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range ) if module.bias is not None: module.bias.data.zero_() elif isinstance(a__ , nn.LayerNorm ): module.bias.data.zero_() module.weight.data.fill_(1.0 ) def snake_case_( self , A , A=False ) -> Dict: if isinstance(a__ , a__ ): _SCREAMING_SNAKE_CASE = value lowercase_ = r"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n" lowercase_ = r"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`PoolFormerImageProcessor.__call__`] for details.\n" @add_start_docstrings( '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.''' , lowercase_ , ) class a_ ( lowercase_ ): '''simple docstring''' def __init__( self , A ) -> str: super().__init__(a__ ) _SCREAMING_SNAKE_CASE = config _SCREAMING_SNAKE_CASE = PoolFormerEncoder(a__ ) # Initialize weights and apply final processing self.post_init() def snake_case_( self ) -> Dict: return self.embeddings.patch_embeddings @add_start_docstrings_to_model_forward(a__ ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC , output_type=a__ , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , ) def snake_case_( self , A = None , A = None , A = None , ) -> Union[Tuple, BaseModelOutputWithNoAttention]: _SCREAMING_SNAKE_CASE = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) _SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("""You have to specify pixel_values""" ) _SCREAMING_SNAKE_CASE = self.encoder( a__ , output_hidden_states=a__ , return_dict=a__ , ) _SCREAMING_SNAKE_CASE = encoder_outputs[0] if not return_dict: return (sequence_output, None) + encoder_outputs[1:] return BaseModelOutputWithNoAttention( last_hidden_state=a__ , hidden_states=encoder_outputs.hidden_states , ) class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A ) -> int: super().__init__() _SCREAMING_SNAKE_CASE = nn.Linear(config.hidden_size , config.hidden_size ) def snake_case_( self , A ) -> List[str]: _SCREAMING_SNAKE_CASE = self.dense(a__ ) return output @add_start_docstrings( '''\n PoolFormer Model transformer with an image classification head on top\n ''' , lowercase_ , ) class a_ ( lowercase_ ): '''simple docstring''' def __init__( self , A ) -> List[Any]: super().__init__(a__ ) _SCREAMING_SNAKE_CASE = config.num_labels _SCREAMING_SNAKE_CASE = PoolFormerModel(a__ ) # Final norm _SCREAMING_SNAKE_CASE = PoolFormerGroupNorm(config.hidden_sizes[-1] ) # Classifier head _SCREAMING_SNAKE_CASE = ( nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(a__ ) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=a__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , ) def snake_case_( self , A = None , A = None , A = None , A = None , ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]: _SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict _SCREAMING_SNAKE_CASE = self.poolformer( a__ , output_hidden_states=a__ , return_dict=a__ , ) _SCREAMING_SNAKE_CASE = outputs[0] _SCREAMING_SNAKE_CASE = self.classifier(self.norm(a__ ).mean([-2, -1] ) ) _SCREAMING_SNAKE_CASE = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: _SCREAMING_SNAKE_CASE = """regression""" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): _SCREAMING_SNAKE_CASE = """single_label_classification""" else: _SCREAMING_SNAKE_CASE = """multi_label_classification""" if self.config.problem_type == "regression": _SCREAMING_SNAKE_CASE = MSELoss() if self.num_labels == 1: _SCREAMING_SNAKE_CASE = loss_fct(logits.squeeze() , labels.squeeze() ) else: _SCREAMING_SNAKE_CASE = loss_fct(a__ , a__ ) elif self.config.problem_type == "single_label_classification": _SCREAMING_SNAKE_CASE = CrossEntropyLoss() _SCREAMING_SNAKE_CASE = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) ) elif self.config.problem_type == "multi_label_classification": _SCREAMING_SNAKE_CASE = BCEWithLogitsLoss() _SCREAMING_SNAKE_CASE = loss_fct(a__ , a__ ) if not return_dict: _SCREAMING_SNAKE_CASE = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutputWithNoAttention(loss=a__ , logits=a__ , hidden_states=outputs.hidden_states )	58	'''simple docstring''' from __future__ import annotations def UpperCamelCase_( snake_case : list[int] ): '''simple docstring''' return len(set(snake_case ) ) == len(snake_case ) if __name__ == "__main__": import doctest doctest.testmod()	85	0
import pickle import unittest import torch from accelerate import Accelerator from accelerate.state import AcceleratorState from accelerate.test_utils import require_cpu @require_cpu class lowerCamelCase_ ( unittest.TestCase ): '''simple docstring''' def SCREAMING_SNAKE_CASE__ ( self : int ) -> List[str]: A : List[str] = torch.nn.Linear(10 , 10 ) A : Optional[Any] = torch.optim.SGD(model.parameters() , 0.1 ) A : Optional[int] = Accelerator() A : Tuple = accelerator.prepare(__lowerCamelCase ) try: pickle.loads(pickle.dumps(__lowerCamelCase ) ) except Exception as e: self.fail(F"""Accelerated optimizer pickling failed with {e}""" ) AcceleratorState._reset_state()	369	import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_electra import ElectraTokenizer __SCREAMING_SNAKE_CASE = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} __SCREAMING_SNAKE_CASE = { """vocab_file""": { """google/electra-small-generator""": ( """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt""" ), """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""", """google/electra-large-generator""": ( """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt""" ), """google/electra-small-discriminator""": ( """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt""" ), """google/electra-base-discriminator""": ( """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt""" ), """google/electra-large-discriminator""": ( """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt""" ), }, """tokenizer_file""": { """google/electra-small-generator""": ( """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json""" ), """google/electra-base-generator""": ( """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json""" ), """google/electra-large-generator""": ( """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json""" ), """google/electra-small-discriminator""": ( """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json""" ), """google/electra-base-discriminator""": ( """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json""" ), """google/electra-large-discriminator""": ( """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json""" ), }, } __SCREAMING_SNAKE_CASE = { """google/electra-small-generator""": 512, """google/electra-base-generator""": 512, """google/electra-large-generator""": 512, """google/electra-small-discriminator""": 512, """google/electra-base-discriminator""": 512, """google/electra-large-discriminator""": 512, } __SCREAMING_SNAKE_CASE = { """google/electra-small-generator""": {"""do_lower_case""": True}, """google/electra-base-generator""": {"""do_lower_case""": True}, """google/electra-large-generator""": {"""do_lower_case""": True}, """google/electra-small-discriminator""": {"""do_lower_case""": True}, """google/electra-base-discriminator""": {"""do_lower_case""": True}, """google/electra-large-discriminator""": {"""do_lower_case""": True}, } class lowerCamelCase_ ( _A ): '''simple docstring''' a__ = VOCAB_FILES_NAMES a__ = PRETRAINED_VOCAB_FILES_MAP a__ = PRETRAINED_INIT_CONFIGURATION a__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES a__ = ElectraTokenizer def __init__( self : int , __lowerCamelCase : str=None , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Tuple=True , __lowerCamelCase : int="[UNK]" , __lowerCamelCase : Any="[SEP]" , __lowerCamelCase : Union[str, Any]="[PAD]" , __lowerCamelCase : str="[CLS]" , __lowerCamelCase : Tuple="[MASK]" , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : str=None , **__lowerCamelCase : str , ) -> List[str]: super().__init__( __lowerCamelCase , tokenizer_file=__lowerCamelCase , do_lower_case=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , pad_token=__lowerCamelCase , cls_token=__lowerCamelCase , mask_token=__lowerCamelCase , tokenize_chinese_chars=__lowerCamelCase , strip_accents=__lowerCamelCase , **__lowerCamelCase , ) A : Dict = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("lowercase" , __lowerCamelCase ) != do_lower_case or normalizer_state.get("strip_accents" , __lowerCamelCase ) != strip_accents or normalizer_state.get("handle_chinese_chars" , __lowerCamelCase ) != tokenize_chinese_chars ): A : Union[str, Any] = getattr(__lowerCamelCase , normalizer_state.pop("type" ) ) A : List[Any] = do_lower_case A : Tuple = strip_accents A : Any = tokenize_chinese_chars A : Tuple = normalizer_class(**__lowerCamelCase ) A : Optional[Any] = do_lower_case def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict=None ) -> List[Any]: A : Tuple = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def SCREAMING_SNAKE_CASE__ ( self : Tuple , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ) -> List[int]: A : int = [self.sep_token_id] A : Optional[int] = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def SCREAMING_SNAKE_CASE__ ( self : Any , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ) -> Tuple[str]: A : List[Any] = self._tokenizer.model.save(__lowerCamelCase , name=__lowerCamelCase ) return tuple(__lowerCamelCase )	256	0
from argparse import ArgumentParser from .env import EnvironmentCommand def snake_case_ ( ) -> List[Any]: lowercase__: Optional[int] = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' ) lowercase__: Union[str, Any] = parser.add_subparsers(help='diffusers-cli command helpers' ) # Register commands EnvironmentCommand.register_subcommand(snake_case ) # Let's go lowercase__: str = parser.parse_args() if not hasattr(snake_case , 'func' ): parser.print_help() exit(1 ) # Run lowercase__: Any = args.func(snake_case ) service.run() if __name__ == "__main__": main()	196	from typing import Optional import torch import torch.utils.checkpoint from torch import Tensor, nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACTaFN from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward from ...modeling_outputs import ( BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, ) from ...modeling_utils import PreTrainedModel from ...utils import logging from .configuration_regnet import RegNetConfig __lowerCAmelCase = logging.get_logger(__name__) # General docstring __lowerCAmelCase = '''RegNetConfig''' # Base docstring __lowerCAmelCase = '''facebook/regnet-y-040''' __lowerCAmelCase = [1, 10_88, 7, 7] # Image classification docstring __lowerCAmelCase = '''facebook/regnet-y-040''' __lowerCAmelCase = '''tabby, tabby cat''' __lowerCAmelCase = [ '''facebook/regnet-y-040''', # See all regnet models at https://huggingface.co/models?filter=regnet ] class __a ( nn.Module ): def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 3 , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 1 , lowerCAmelCase__ = "relu" , ) -> Optional[Any]: '''simple docstring''' super().__init__() lowercase__: Any = nn.Convad( lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=lowerCAmelCase__ , stride=lowerCAmelCase__ , padding=kernel_size // 2 , groups=lowerCAmelCase__ , bias=lowerCAmelCase__ , ) lowercase__: str = nn.BatchNormad(lowerCAmelCase__ ) lowercase__: Union[str, Any] = ACTaFN[activation] if activation is not None else nn.Identity() def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Dict: '''simple docstring''' lowercase__: List[str] = self.convolution(lowerCAmelCase__ ) lowercase__: Optional[Any] = self.normalization(lowerCAmelCase__ ) lowercase__: Union[str, Any] = self.activation(lowerCAmelCase__ ) return hidden_state class __a ( nn.Module ): def __init__( self , lowerCAmelCase__ ) -> Union[str, Any]: '''simple docstring''' super().__init__() lowercase__: Dict = RegNetConvLayer( config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act ) lowercase__: Dict = config.num_channels def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> int: '''simple docstring''' lowercase__: Tuple = pixel_values.shape[1] if num_channels != self.num_channels: raise ValueError( 'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' ) lowercase__: Optional[int] = self.embedder(lowerCAmelCase__ ) return hidden_state class __a ( nn.Module ): def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 2 ) -> Optional[Any]: '''simple docstring''' super().__init__() lowercase__: Optional[Any] = nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , stride=lowerCAmelCase__ , bias=lowerCAmelCase__ ) lowercase__: Union[str, Any] = nn.BatchNormad(lowerCAmelCase__ ) def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Tensor: '''simple docstring''' lowercase__: Any = self.convolution(lowerCAmelCase__ ) lowercase__: str = self.normalization(lowerCAmelCase__ ) return hidden_state class __a ( nn.Module ): def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]: '''simple docstring''' super().__init__() lowercase__: Any = nn.AdaptiveAvgPoolad((1, 1) ) lowercase__: str = nn.Sequential( nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 ) , nn.ReLU() , nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 ) , nn.Sigmoid() , ) def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[Any]: '''simple docstring''' # b c h w -> b c 1 1 lowercase__: str = self.pooler(lowerCAmelCase__ ) lowercase__: List[str] = self.attention(lowerCAmelCase__ ) lowercase__: List[Any] = hidden_state * attention return hidden_state class __a ( nn.Module ): def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 1 ) -> Dict: '''simple docstring''' super().__init__() lowercase__: str = in_channels != out_channels or stride != 1 lowercase__: Optional[int] = max(1 , out_channels // config.groups_width ) lowercase__: Union[str, Any] = ( RegNetShortCut(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity() ) lowercase__: Dict = nn.Sequential( RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ , groups=lowerCAmelCase__ , activation=config.hidden_act ) , RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=lowerCAmelCase__ ) , ) lowercase__: Tuple = ACTaFN[config.hidden_act] def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> int: '''simple docstring''' lowercase__: Dict = hidden_state lowercase__: Union[str, Any] = self.layer(lowerCAmelCase__ ) lowercase__: int = self.shortcut(lowerCAmelCase__ ) hidden_state += residual lowercase__: Optional[int] = self.activation(lowerCAmelCase__ ) return hidden_state class __a ( nn.Module ): def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 1 ) -> Dict: '''simple docstring''' super().__init__() lowercase__: Optional[int] = in_channels != out_channels or stride != 1 lowercase__: List[str] = max(1 , out_channels // config.groups_width ) lowercase__: Any = ( RegNetShortCut(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity() ) lowercase__: str = nn.Sequential( RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ , groups=lowerCAmelCase__ , activation=config.hidden_act ) , RegNetSELayer(lowerCAmelCase__ , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=lowerCAmelCase__ ) , ) lowercase__: Union[str, Any] = ACTaFN[config.hidden_act] def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[str]: '''simple docstring''' lowercase__: Optional[Any] = hidden_state lowercase__: Optional[int] = self.layer(lowerCAmelCase__ ) lowercase__: str = self.shortcut(lowerCAmelCase__ ) hidden_state += residual lowercase__: Optional[int] = self.activation(lowerCAmelCase__ ) return hidden_state class __a ( nn.Module ): def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , ) -> Tuple: '''simple docstring''' super().__init__() lowercase__: Optional[int] = RegNetXLayer if config.layer_type == 'x' else RegNetYLayer lowercase__: str = nn.Sequential( # downsampling is done in the first layer with stride of 2 layer( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ , ) , *[layer(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) for _ in range(depth - 1 )] , ) def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Optional[Any]: '''simple docstring''' lowercase__: str = self.layers(lowerCAmelCase__ ) return hidden_state class __a ( nn.Module ): def __init__( self , lowerCAmelCase__ ) -> Dict: '''simple docstring''' super().__init__() lowercase__: int = nn.ModuleList([] ) # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input self.stages.append( RegNetStage( lowerCAmelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) ) lowercase__: int = zip(config.hidden_sizes , config.hidden_sizes[1:] ) for (in_channels, out_channels), depth in zip(lowerCAmelCase__ , config.depths[1:] ): self.stages.append(RegNetStage(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , depth=lowerCAmelCase__ ) ) def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = False , lowerCAmelCase__ = True ) -> BaseModelOutputWithNoAttention: '''simple docstring''' lowercase__: List[str] = () if output_hidden_states else None for stage_module in self.stages: if output_hidden_states: lowercase__: Optional[Any] = hidden_states + (hidden_state,) lowercase__: List[Any] = stage_module(lowerCAmelCase__ ) if output_hidden_states: lowercase__: Optional[Any] = hidden_states + (hidden_state,) if not return_dict: return tuple(v for v in [hidden_state, hidden_states] if v is not None ) return BaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__ , hidden_states=lowerCAmelCase__ ) class __a ( __UpperCamelCase ): __lowercase : Dict = RegNetConfig __lowercase : Dict = 'regnet' __lowercase : str = 'pixel_values' __lowercase : List[str] = True def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[str]: '''simple docstring''' if isinstance(lowerCAmelCase__ , nn.Convad ): nn.init.kaiming_normal_(module.weight , mode='fan_out' , nonlinearity='relu' ) elif isinstance(lowerCAmelCase__ , (nn.BatchNormad, nn.GroupNorm) ): nn.init.constant_(module.weight , 1 ) nn.init.constant_(module.bias , 0 ) def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__=False ) -> Optional[Any]: '''simple docstring''' if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ): lowercase__: Any = value __lowerCAmelCase = r''' This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`RegNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. ''' __lowerCAmelCase = r''' Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConvNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. ''' @add_start_docstrings( 'The bare RegNet model outputting raw features without any specific head on top.' , __UpperCamelCase , ) # Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet class __a ( __UpperCamelCase ): def __init__( self , lowerCAmelCase__ ) -> List[str]: '''simple docstring''' super().__init__(lowerCAmelCase__ ) lowercase__: Tuple = config lowercase__: List[str] = RegNetEmbeddings(lowerCAmelCase__ ) lowercase__: Optional[int] = RegNetEncoder(lowerCAmelCase__ ) lowercase__: Optional[Any] = nn.AdaptiveAvgPoolad((1, 1) ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(lowerCAmelCase__ ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , ) def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None ) -> BaseModelOutputWithPoolingAndNoAttention: '''simple docstring''' lowercase__: List[Any] = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) lowercase__: Union[str, Any] = return_dict if return_dict is not None else self.config.use_return_dict lowercase__: Any = self.embedder(lowerCAmelCase__ ) lowercase__: List[Any] = self.encoder( lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ ) lowercase__: Optional[Any] = encoder_outputs[0] lowercase__: Optional[int] = self.pooler(lowerCAmelCase__ ) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=lowerCAmelCase__ , pooler_output=lowerCAmelCase__ , hidden_states=encoder_outputs.hidden_states , ) @add_start_docstrings( '\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , __UpperCamelCase , ) # Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet class __a ( __UpperCamelCase ): def __init__( self , lowerCAmelCase__ ) -> str: '''simple docstring''' super().__init__(lowerCAmelCase__ ) lowercase__: Dict = config.num_labels lowercase__: Dict = RegNetModel(lowerCAmelCase__ ) # classification head lowercase__: str = nn.Sequential( nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , ) # initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(lowerCAmelCase__ ) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , ) def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , ) -> ImageClassifierOutputWithNoAttention: '''simple docstring''' lowercase__: str = return_dict if return_dict is not None else self.config.use_return_dict lowercase__: Optional[int] = self.regnet(lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ ) lowercase__: Dict = outputs.pooler_output if return_dict else outputs[1] lowercase__: List[str] = self.classifier(lowerCAmelCase__ ) lowercase__: Optional[Any] = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: lowercase__: Dict = 'regression' elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): lowercase__: Optional[int] = 'single_label_classification' else: lowercase__: Tuple = 'multi_label_classification' if self.config.problem_type == "regression": lowercase__: List[Any] = MSELoss() if self.num_labels == 1: lowercase__: Optional[int] = loss_fct(logits.squeeze() , labels.squeeze() ) else: lowercase__: int = loss_fct(lowerCAmelCase__ , lowerCAmelCase__ ) elif self.config.problem_type == "single_label_classification": lowercase__: Dict = CrossEntropyLoss() lowercase__: Optional[int] = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) ) elif self.config.problem_type == "multi_label_classification": lowercase__: List[Any] = BCEWithLogitsLoss() lowercase__: Any = loss_fct(lowerCAmelCase__ , lowerCAmelCase__ ) if not return_dict: lowercase__: int = (logits,) + outputs[2:] return (loss,) + output if loss is not None else output return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase__ , logits=lowerCAmelCase__ , hidden_states=outputs.hidden_states )	196	1
from .configuration_bert_masked import MaskedBertConfig from .modeling_bert_masked import ( MaskedBertForMultipleChoice, MaskedBertForQuestionAnswering, MaskedBertForSequenceClassification, MaskedBertForTokenClassification, MaskedBertModel, ) from .modules import *	351	from collections import OrderedDict from typing import Any, Mapping, Optional from ... import PreTrainedTokenizer, TensorType, is_torch_available from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfigWithPast from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""", # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo } class UpperCamelCase_ ( _lowerCamelCase ): lowerCAmelCase_ = '''gpt_neo''' lowerCAmelCase_ = ['''past_key_values'''] lowerCAmelCase_ = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''} def __init__( self , lowerCAmelCase_=5_0257 , lowerCAmelCase_=2048 , lowerCAmelCase_=2048 , lowerCAmelCase_=24 , lowerCAmelCase_=[[["global", "local"], 12]] , lowerCAmelCase_=16 , lowerCAmelCase_=None , lowerCAmelCase_=256 , lowerCAmelCase_="gelu_new" , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.1 , lowerCAmelCase_=1E-5 , lowerCAmelCase_=0.02 , lowerCAmelCase_=True , lowerCAmelCase_=5_0256 , lowerCAmelCase_=5_0256 , **lowerCAmelCase_ , ) -> Tuple: _snake_case = vocab_size _snake_case = max_position_embeddings _snake_case = hidden_size _snake_case = num_layers _snake_case = num_heads _snake_case = intermediate_size _snake_case = window_size _snake_case = activation_function _snake_case = resid_dropout _snake_case = embed_dropout _snake_case = attention_dropout _snake_case = classifier_dropout _snake_case = layer_norm_epsilon _snake_case = initializer_range _snake_case = use_cache _snake_case = bos_token_id _snake_case = eos_token_id _snake_case = attention_types _snake_case = self.expand_attention_types_params(lowerCAmelCase_ ) if len(self.attention_layers ) != self.num_layers: raise ValueError( 'Configuration for convolutional module is incorrect. ' 'It is required that `len(config.attention_layers)` == `config.num_layers` ' F'''but is `len(config.attention_layers) = {len(self.attention_layers )}`, ''' F'''`config.num_layers = {self.num_layers}`. ''' '`config.attention_layers` is prepared using `config.attention_types`. ' 'Please verify the value of `config.attention_types` argument.' ) super().__init__(bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ ) @staticmethod def lowerCAmelCase ( lowerCAmelCase_ ) -> Any: _snake_case = [] for item in attention_types: for _ in range(item[1] ): attentions.extend(item[0] ) return attentions def lowerCamelCase__ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) -> Any: '''simple docstring''' import torch _snake_case = input.size() _snake_case = len(UpperCamelCase__ ) _snake_case = shape[dimension] _snake_case = torch.arange(0 , UpperCamelCase__ , UpperCamelCase__ ) _snake_case = torch.div(sizedim - size , UpperCamelCase__ , rounding_mode='floor' ) + 1 _snake_case = torch.arange(UpperCamelCase__ ) + low_indices[:min_length][:, None] _snake_case = [slice(UpperCamelCase__ )] * rank _snake_case = indices _snake_case = input[s] _snake_case = list(range(0 , rank + 1 ) ) perm.append(perm.pop(dimension + 1 ) ) return sliced.permute(UpperCamelCase__ ) def lowerCamelCase__ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict ) -> str: '''simple docstring''' import torch _snake_case = torch.arange(1 , UpperCamelCase__ ) _snake_case = torch.remainder(UpperCamelCase__ , UpperCamelCase__ ) _snake_case = remainders == 0 _snake_case = candidates[divisor_indices] _snake_case = torch.max(UpperCamelCase__ ) return largest_divisor, torch.div(UpperCamelCase__ , UpperCamelCase__ , rounding_mode='floor' ) class UpperCamelCase_ ( _lowerCamelCase ): @property def lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]: _snake_case = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} ) if self.use_past: self.fill_with_past_key_values_(lowerCAmelCase_ , direction='inputs' ) _snake_case = {0: 'batch', 1: 'past_sequence + sequence'} else: _snake_case = {0: 'batch', 1: 'sequence'} return common_inputs @property def lowerCAmelCase ( self ) -> int: return self._config.num_heads def lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = -1 , lowerCAmelCase_ = -1 , lowerCAmelCase_ = False , lowerCAmelCase_ = None , ) -> Mapping[str, Any]: _snake_case = super(lowerCAmelCase_ , self ).generate_dummy_inputs( lowerCAmelCase_ , batch_size=lowerCAmelCase_ , seq_length=lowerCAmelCase_ , is_pair=lowerCAmelCase_ , framework=lowerCAmelCase_ ) # We need to order the input in the way they appears in the forward() _snake_case = OrderedDict({'input_ids': common_inputs['input_ids']} ) # Need to add the past_keys if self.use_past: if not is_torch_available(): raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' ) else: import torch _snake_case , _snake_case = common_inputs['input_ids'].shape # Not using the same length for past_key_values _snake_case = seqlen + 2 _snake_case = ( batch, self.num_attention_heads, past_key_values_length, self._config.hidden_size // self.num_attention_heads, ) _snake_case = [ (torch.zeros(lowerCAmelCase_ ), torch.zeros(lowerCAmelCase_ )) for _ in range(self.num_layers ) ] _snake_case = common_inputs['attention_mask'] if self.use_past: _snake_case = ordered_inputs['attention_mask'].dtype _snake_case = torch.cat( [ordered_inputs['attention_mask'], torch.ones(lowerCAmelCase_ , lowerCAmelCase_ , dtype=lowerCAmelCase_ )] , dim=1 ) return ordered_inputs @property def lowerCAmelCase ( self ) -> int: return 13	295	0
'''simple docstring''' from typing import Dict import numpy as np from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException if is_tf_available(): import tensorflow as tf from ..tf_utils import stable_softmax if is_torch_available(): import torch __a = logging.get_logger(__name__) @add_end_docstrings( _a , r"\n top_k (`int`, defaults to 5):\n The number of predictions to return.\n targets (`str` or `List[str]`, *optional*):\n When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n token will be used (with a warning, and that might be slower).\n\n " , ) class UpperCAmelCase_ ( _a ): """simple docstring""" def lowerCamelCase ( self : Union[str, Any] , snake_case_ : GenericTensor ): if self.framework == "tf": snake_case__ : Optional[Any] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy() elif self.framework == "pt": snake_case__ : Tuple = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=snake_case_ ) else: raise ValueError("""Unsupported framework""" ) return masked_index def lowerCamelCase ( self : Optional[Any] , snake_case_ : GenericTensor ): snake_case__ : List[Any] = self.get_masked_index(snake_case_ ) snake_case__ : List[Any] = np.prod(masked_index.shape ) if numel < 1: raise PipelineException( """fill-mask""" , self.model.base_model_prefix , f"No mask_token ({self.tokenizer.mask_token}) found on the input" , ) def lowerCamelCase ( self : Tuple , snake_case_ : GenericTensor ): if isinstance(snake_case_ , snake_case_ ): for model_input in model_inputs: self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0] ) else: for input_ids in model_inputs["input_ids"]: self._ensure_exactly_one_mask_token(snake_case_ ) def lowerCamelCase ( self : List[Any] , snake_case_ : Any , snake_case_ : Optional[int]=None , **snake_case_ : Optional[Any] ): if return_tensors is None: snake_case__ : Tuple = self.framework snake_case__ : Optional[Any] = self.tokenizer(snake_case_ , return_tensors=snake_case_ ) self.ensure_exactly_one_mask_token(snake_case_ ) return model_inputs def lowerCamelCase ( self : str , snake_case_ : str ): snake_case__ : Union[str, Any] = self.model(**snake_case_ ) snake_case__ : Dict = model_inputs["""input_ids"""] return model_outputs def lowerCamelCase ( self : Union[str, Any] , snake_case_ : Union[str, Any] , snake_case_ : str=5 , snake_case_ : List[Any]=None ): # Cap top_k if there are targets if target_ids is not None and target_ids.shape[0] < top_k: snake_case__ : Any = target_ids.shape[0] snake_case__ : List[Any] = model_outputs["""input_ids"""][0] snake_case__ : Optional[Any] = model_outputs["""logits"""] if self.framework == "tf": snake_case__ : Optional[Any] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0] snake_case__ : Optional[int] = outputs.numpy() snake_case__ : Optional[int] = outputs[0, masked_index, :] snake_case__ : Optional[int] = stable_softmax(snake_case_ , axis=-1 ) if target_ids is not None: snake_case__ : Optional[Any] = tf.gather_nd(tf.squeeze(snake_case_ , 0 ) , target_ids.reshape(-1 , 1 ) ) snake_case__ : Optional[int] = tf.expand_dims(snake_case_ , 0 ) snake_case__ : int = tf.math.top_k(snake_case_ , k=snake_case_ ) snake_case__ , snake_case__ : Any = topk.values.numpy(), topk.indices.numpy() else: snake_case__ : List[Any] = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=snake_case_ ).squeeze(-1 ) # Fill mask pipeline supports only one ${mask_token} per sample snake_case__ : Tuple = outputs[0, masked_index, :] snake_case__ : Tuple = logits.softmax(dim=-1 ) if target_ids is not None: snake_case__ : List[str] = probs[..., target_ids] snake_case__ , snake_case__ : List[str] = probs.topk(snake_case_ ) snake_case__ : Tuple = [] snake_case__ : List[str] = values.shape[0] == 1 for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ): snake_case__ : Union[str, Any] = [] for v, p in zip(_values , _predictions ): # Copy is important since we're going to modify this array in place snake_case__ : Dict = input_ids.numpy().copy() if target_ids is not None: snake_case__ : Any = target_ids[p].tolist() snake_case__ : Union[str, Any] = p # Filter padding out: snake_case__ : List[str] = tokens[np.where(tokens != self.tokenizer.pad_token_id )] # Originally we skip special tokens to give readable output. # For multi masks though, the other [MASK] would be removed otherwise # making the output look odd, so we add them back snake_case__ : str = self.tokenizer.decode(snake_case_ , skip_special_tokens=snake_case_ ) snake_case__ : Union[str, Any] = {"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p] ), """sequence""": sequence} row.append(snake_case_ ) result.append(snake_case_ ) if single_mask: return result[0] return result def lowerCamelCase ( self : int , snake_case_ : Any , snake_case_ : str=None ): if isinstance(snake_case_ , snake_case_ ): snake_case__ : Union[str, Any] = [targets] try: snake_case__ : Any = self.tokenizer.get_vocab() except Exception: snake_case__ : str = {} snake_case__ : List[Any] = [] for target in targets: snake_case__ : List[str] = vocab.get(snake_case_ , snake_case_ ) if id_ is None: snake_case__ : int = self.tokenizer( snake_case_ , add_special_tokens=snake_case_ , return_attention_mask=snake_case_ , return_token_type_ids=snake_case_ , max_length=1 , truncation=snake_case_ , )["""input_ids"""] if len(snake_case_ ) == 0: logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " """We cannot replace it with anything meaningful, ignoring it""" ) continue snake_case__ : Optional[Any] = input_ids[0] # XXX: If users encounter this pass # it becomes pretty slow, so let's make sure # The warning enables them to fix the input to # get faster performance. logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`." ) target_ids.append(id_ ) snake_case__ : Optional[Any] = list(set(snake_case_ ) ) if len(snake_case_ ) == 0: raise ValueError("""At least one target must be provided when passed.""" ) snake_case__ : Dict = np.array(snake_case_ ) return target_ids def lowerCamelCase ( self : Union[str, Any] , snake_case_ : Tuple=None , snake_case_ : Union[str, Any]=None ): snake_case__ : Union[str, Any] = {} if targets is not None: snake_case__ : List[str] = self.get_target_ids(snake_case_ , snake_case_ ) snake_case__ : Union[str, Any] = target_ids if top_k is not None: snake_case__ : Optional[int] = top_k if self.tokenizer.mask_token_id is None: raise PipelineException( """fill-mask""" , self.model.base_model_prefix , """The tokenizer does not define a `mask_token`.""" ) return {}, {}, postprocess_params def __call__( self : List[str] , snake_case_ : Union[str, Any] , *snake_case_ : Tuple , **snake_case_ : List[Any] ): snake_case__ : Optional[int] = super().__call__(snake_case_ , **snake_case_ ) if isinstance(snake_case_ , snake_case_ ) and len(snake_case_ ) == 1: return outputs[0] return outputs	35	def __lowerCamelCase ( ): '''simple docstring''' return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )] _UpperCAmelCase : Union[str, Any] = generate_large_matrix() _UpperCAmelCase : Tuple = ( [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]], [[3, 2], [1, 0]], [[7, 7, 6]], [[7, 7, 6], [-1, -2, -3]], grid, ) def __lowerCamelCase ( UpperCamelCase__ ): '''simple docstring''' assert all(row == sorted(UpperCamelCase__ , reverse=UpperCamelCase__ ) for row in grid ) assert all(list(UpperCamelCase__ ) == sorted(UpperCamelCase__ , reverse=UpperCamelCase__ ) for col in zip(*UpperCamelCase__ ) ) def __lowerCamelCase ( UpperCamelCase__ ): '''simple docstring''' snake_case_ = 0 snake_case_ = len(UpperCamelCase__ ) - 1 # Edge cases such as no values or all numbers are negative. if not array or array[0] < 0: return 0 while right + 1 > left: snake_case_ = (left + right) // 2 snake_case_ = array[mid] # Num must be negative and the index must be greater than or equal to 0. if num < 0 and array[mid - 1] >= 0: return mid if num >= 0: snake_case_ = mid + 1 else: snake_case_ = mid - 1 # No negative numbers so return the last index of the array + 1 which is the length. return len(UpperCamelCase__ ) def __lowerCamelCase ( UpperCamelCase__ ): '''simple docstring''' snake_case_ = 0 snake_case_ = len(grid[0] ) for i in range(len(UpperCamelCase__ ) ): snake_case_ = find_negative_index(grid[i][:bound] ) total += bound return (len(UpperCamelCase__ ) * len(grid[0] )) - total def __lowerCamelCase ( UpperCamelCase__ ): '''simple docstring''' return len([number for row in grid for number in row if number < 0] ) def __lowerCamelCase ( UpperCamelCase__ ): '''simple docstring''' snake_case_ = 0 for row in grid: for i, number in enumerate(UpperCamelCase__ ): if number < 0: total += len(UpperCamelCase__ ) - i break return total def __lowerCamelCase ( ): '''simple docstring''' from timeit import timeit print('Running benchmarks' ) snake_case_ = ( 'from __main__ import count_negatives_binary_search, ' 'count_negatives_brute_force, count_negatives_brute_force_with_break, grid' ) for func in ( "count_negatives_binary_search", # took 0.7727 seconds "count_negatives_brute_force_with_break", # took 4.6505 seconds "count_negatives_brute_force", # took 12.8160 seconds ): snake_case_ = timeit(F'''{func}(grid=grid)''' , setup=UpperCamelCase__ , number=500 ) print(F'''{func}() took {time:0.4f} seconds''' ) if __name__ == "__main__": import doctest doctest.testmod() benchmark()	285	0
import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline from diffusers.pipelines.shap_e import ShapERenderer from diffusers.utils import load_numpy, slow from diffusers.utils.testing_utils import require_torch_gpu, torch_device from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference class __lowerCamelCase ( snake_case_ , unittest.TestCase ): """simple docstring""" lowerCAmelCase__ = ShapEPipeline lowerCAmelCase__ = ["prompt"] lowerCAmelCase__ = ["prompt"] lowerCAmelCase__ = [ "num_images_per_prompt", "num_inference_steps", "generator", "latents", "guidance_scale", "frame_size", "output_type", "return_dict", ] lowerCAmelCase__ = False @property def A__ ( self ) -> Optional[int]: '''simple docstring''' return 32 @property def A__ ( self ) -> int: '''simple docstring''' return 32 @property def A__ ( self ) -> Optional[Any]: '''simple docstring''' return self.time_input_dim * 4 @property def A__ ( self ) -> Optional[int]: '''simple docstring''' return 8 @property def A__ ( self ) -> Tuple: '''simple docstring''' lowercase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) return tokenizer @property def A__ ( self ) -> int: '''simple docstring''' torch.manual_seed(0 ) lowercase_ = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) return CLIPTextModelWithProjection(UpperCAmelCase ) @property def A__ ( self ) -> List[str]: '''simple docstring''' torch.manual_seed(0 ) lowercase_ = { "num_attention_heads": 2, "attention_head_dim": 16, "embedding_dim": self.time_input_dim, "num_embeddings": 32, "embedding_proj_dim": self.text_embedder_hidden_size, "time_embed_dim": self.time_embed_dim, "num_layers": 1, "clip_embed_dim": self.time_input_dim * 2, "additional_embeddings": 0, "time_embed_act_fn": "gelu", "norm_in_type": "layer", "encoder_hid_proj_type": None, "added_emb_type": None, } lowercase_ = PriorTransformer(**UpperCAmelCase ) return model @property def A__ ( self ) -> Optional[Any]: '''simple docstring''' torch.manual_seed(0 ) lowercase_ = { "param_shapes": ( (self.renderer_dim, 93), (self.renderer_dim, 8), (self.renderer_dim, 8), (self.renderer_dim, 8), ), "d_latent": self.time_input_dim, "d_hidden": self.renderer_dim, "n_output": 12, "background": ( 0.1, 0.1, 0.1, ), } lowercase_ = ShapERenderer(**UpperCAmelCase ) return model def A__ ( self ) -> int: '''simple docstring''' lowercase_ = self.dummy_prior lowercase_ = self.dummy_text_encoder lowercase_ = self.dummy_tokenizer lowercase_ = self.dummy_renderer lowercase_ = HeunDiscreteScheduler( beta_schedule="exp" , num_train_timesteps=1024 , prediction_type="sample" , use_karras_sigmas=UpperCAmelCase , clip_sample=UpperCAmelCase , clip_sample_range=1.0 , ) lowercase_ = { "prior": prior, "text_encoder": text_encoder, "tokenizer": tokenizer, "renderer": renderer, "scheduler": scheduler, } return components def A__ ( self , UpperCAmelCase , UpperCAmelCase=0 ) -> Optional[int]: '''simple docstring''' if str(UpperCAmelCase ).startswith("mps" ): lowercase_ = torch.manual_seed(UpperCAmelCase ) else: lowercase_ = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase ) lowercase_ = { "prompt": "horse", "generator": generator, "num_inference_steps": 1, "frame_size": 32, "output_type": "np", } return inputs def A__ ( self ) -> Tuple: '''simple docstring''' lowercase_ = "cpu" lowercase_ = self.get_dummy_components() lowercase_ = self.pipeline_class(**UpperCAmelCase ) lowercase_ = pipe.to(UpperCAmelCase ) pipe.set_progress_bar_config(disable=UpperCAmelCase ) lowercase_ = pipe(**self.get_dummy_inputs(UpperCAmelCase ) ) lowercase_ = output.images[0] lowercase_ = image[0, -3:, -3:, -1] assert image.shape == (20, 32, 32, 3) lowercase_ = np.array( [ 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, ] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 def A__ ( self ) -> Union[str, Any]: '''simple docstring''' self._test_inference_batch_consistent(batch_sizes=[1, 2] ) def A__ ( self ) -> Optional[int]: '''simple docstring''' lowercase_ = torch_device == "cpu" lowercase_ = True self._test_inference_batch_single_identical( batch_size=2 , test_max_difference=UpperCAmelCase , relax_max_difference=UpperCAmelCase , ) def A__ ( self ) -> Dict: '''simple docstring''' lowercase_ = self.get_dummy_components() lowercase_ = self.pipeline_class(**UpperCAmelCase ) lowercase_ = pipe.to(UpperCAmelCase ) pipe.set_progress_bar_config(disable=UpperCAmelCase ) lowercase_ = 1 lowercase_ = 2 lowercase_ = self.get_dummy_inputs(UpperCAmelCase ) for key in inputs.keys(): if key in self.batch_params: lowercase_ = batch_size * [inputs[key]] lowercase_ = pipe(**UpperCAmelCase , num_images_per_prompt=UpperCAmelCase )[0] assert images.shape[0] == batch_size * num_images_per_prompt @slow @require_torch_gpu class __lowerCamelCase ( unittest.TestCase ): """simple docstring""" def A__ ( self ) -> Optional[Any]: '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self ) -> Any: '''simple docstring''' lowercase_ = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/test_shap_e_np_out.npy" ) lowercase_ = ShapEPipeline.from_pretrained("openai/shap-e" ) lowercase_ = pipe.to(UpperCAmelCase ) pipe.set_progress_bar_config(disable=UpperCAmelCase ) lowercase_ = torch.Generator(device=UpperCAmelCase ).manual_seed(0 ) lowercase_ = pipe( "a shark" , generator=UpperCAmelCase , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="np" , ).images[0] assert images.shape == (20, 64, 64, 3) assert_mean_pixel_difference(UpperCAmelCase , UpperCAmelCase )	297	def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: float ): '''simple docstring''' return 10 - x * x def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: float , __lowerCamelCase: float ): '''simple docstring''' if equation(__lowerCamelCase ) * equation(__lowerCamelCase ) >= 0: raise ValueError("Wrong space!" ) lowercase_ = a while (b - a) >= 0.01: # Find middle point lowercase_ = (a + b) / 2 # Check if middle point is root if equation(__lowerCamelCase ) == 0.0: break # Decide the side to repeat the steps if equation(__lowerCamelCase ) * equation(__lowerCamelCase ) < 0: lowercase_ = c else: lowercase_ = c return c if __name__ == "__main__": import doctest doctest.testmod() print(bisection(-2, 5)) print(bisection(0, 6))	297	1
"""simple docstring""" import warnings from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging snake_case_ = logging.get_logger(__name__) snake_case_ = { """nvidia/segformer-b0-finetuned-ade-512-512""": ( """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json""" ), # See all SegFormer models at https://huggingface.co/models?filter=segformer } class A_ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" __UpperCamelCase = """segformer""" def __init__( self :Optional[int] , lowercase_ :Any=3 , lowercase_ :Tuple=4 , lowercase_ :Optional[Any]=[2, 2, 2, 2] , lowercase_ :Optional[int]=[8, 4, 2, 1] , lowercase_ :str=[32, 64, 1_60, 2_56] , lowercase_ :Dict=[7, 3, 3, 3] , lowercase_ :List[str]=[4, 2, 2, 2] , lowercase_ :Tuple=[1, 2, 5, 8] , lowercase_ :str=[4, 4, 4, 4] , lowercase_ :Tuple="gelu" , lowercase_ :Tuple=0.0 , lowercase_ :Tuple=0.0 , lowercase_ :Optional[int]=0.1 , lowercase_ :List[str]=0.02 , lowercase_ :Tuple=0.1 , lowercase_ :str=1E-6 , lowercase_ :int=2_56 , lowercase_ :List[Any]=2_55 , **lowercase_ :Any , ) -> str: super().__init__(**lowercase_ ) if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False: warnings.warn( 'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be' ' removed, as the behaviour will default to that of reshape_last_stage = True.' , lowercase_ , ) UpperCAmelCase = num_channels UpperCAmelCase = num_encoder_blocks UpperCAmelCase = depths UpperCAmelCase = sr_ratios UpperCAmelCase = hidden_sizes UpperCAmelCase = patch_sizes UpperCAmelCase = strides UpperCAmelCase = mlp_ratios UpperCAmelCase = num_attention_heads UpperCAmelCase = hidden_act UpperCAmelCase = hidden_dropout_prob UpperCAmelCase = attention_probs_dropout_prob UpperCAmelCase = classifier_dropout_prob UpperCAmelCase = initializer_range UpperCAmelCase = drop_path_rate UpperCAmelCase = layer_norm_eps UpperCAmelCase = decoder_hidden_size UpperCAmelCase = kwargs.get('reshape_last_stage' , lowercase_ ) UpperCAmelCase = semantic_loss_ignore_index class A_ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" __UpperCamelCase = version.parse("""1.11""" ) @property def UpperCAmelCase__ ( self :Tuple ) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}), ] ) @property def UpperCAmelCase__ ( self :str ) -> float: return 1E-4 @property def UpperCAmelCase__ ( self :Dict ) -> int: return 12	78	import collections import inspect import unittest from typing import Dict, List, Tuple from transformers import MaskFormerSwinConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device from transformers.utils import is_torch_available from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import MaskFormerSwinBackbone from transformers.models.maskformer import MaskFormerSwinModel class __lowerCamelCase : """simple docstring""" def __init__( self , UpperCAmelCase , UpperCAmelCase=13 , UpperCAmelCase=32 , UpperCAmelCase=2 , UpperCAmelCase=3 , UpperCAmelCase=16 , UpperCAmelCase=[1, 2, 1] , UpperCAmelCase=[2, 2, 4] , UpperCAmelCase=2 , UpperCAmelCase=2.0 , UpperCAmelCase=True , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase="gelu" , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=0.02 , UpperCAmelCase=1e-5 , UpperCAmelCase=True , UpperCAmelCase=None , UpperCAmelCase=True , UpperCAmelCase=10 , UpperCAmelCase=8 , UpperCAmelCase=["stage1", "stage2", "stage3"] , UpperCAmelCase=[1, 2, 3] , ): """simple docstring""" _UpperCAmelCase = parent _UpperCAmelCase = batch_size _UpperCAmelCase = image_size _UpperCAmelCase = patch_size _UpperCAmelCase = num_channels _UpperCAmelCase = embed_dim _UpperCAmelCase = depths _UpperCAmelCase = num_heads _UpperCAmelCase = window_size _UpperCAmelCase = mlp_ratio _UpperCAmelCase = qkv_bias _UpperCAmelCase = hidden_dropout_prob _UpperCAmelCase = attention_probs_dropout_prob _UpperCAmelCase = drop_path_rate _UpperCAmelCase = hidden_act _UpperCAmelCase = use_absolute_embeddings _UpperCAmelCase = patch_norm _UpperCAmelCase = layer_norm_eps _UpperCAmelCase = initializer_range _UpperCAmelCase = is_training _UpperCAmelCase = scope _UpperCAmelCase = use_labels _UpperCAmelCase = type_sequence_label_size _UpperCAmelCase = encoder_stride _UpperCAmelCase = out_features _UpperCAmelCase = out_indices def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) _UpperCAmelCase = None if self.use_labels: _UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size ) _UpperCAmelCase = self.get_config() return config, pixel_values, labels def UpperCamelCase ( self ): """simple docstring""" return MaskFormerSwinConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , ) def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ): """simple docstring""" _UpperCAmelCase = MaskFormerSwinModel(config=UpperCAmelCase ) model.to(UpperCAmelCase ) model.eval() _UpperCAmelCase = model(UpperCAmelCase ) _UpperCAmelCase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1)) _UpperCAmelCase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) ) def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ): """simple docstring""" _UpperCAmelCase = MaskFormerSwinBackbone(config=UpperCAmelCase ) model.to(UpperCAmelCase ) model.eval() _UpperCAmelCase = model(UpperCAmelCase ) # verify feature maps self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) ) self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] ) # verify channels self.parent.assertEqual(len(model.channels ) , len(config.out_features ) ) self.parent.assertListEqual(model.channels , [16, 32, 64] ) # verify ValueError with self.parent.assertRaises(UpperCAmelCase ): _UpperCAmelCase = ['stem'] _UpperCAmelCase = MaskFormerSwinBackbone(config=UpperCAmelCase ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = self.prepare_config_and_inputs() _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs _UpperCAmelCase = {'pixel_values': pixel_values} return config, inputs_dict @require_torch class __lowerCamelCase ( snake_case__ , snake_case__ , unittest.TestCase): """simple docstring""" UpperCamelCase__ = ( ( MaskFormerSwinModel, MaskFormerSwinBackbone, ) if is_torch_available() else () ) UpperCamelCase__ = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {} UpperCamelCase__ = False UpperCamelCase__ = False UpperCamelCase__ = False UpperCamelCase__ = False UpperCamelCase__ = False def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = MaskFormerSwinModelTester(self ) _UpperCAmelCase = ConfigTester(self , config_class=UpperCAmelCase , embed_dim=37 ) @require_torch_multi_gpu @unittest.skip( reason=( '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with' ' `nn.DataParallel`' ) ) def UpperCamelCase ( self ): """simple docstring""" pass def UpperCamelCase ( self ): """simple docstring""" self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def UpperCamelCase ( self ): """simple docstring""" return def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*UpperCAmelCase ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*UpperCAmelCase ) @unittest.skip('Swin does not use inputs_embeds' ) def UpperCamelCase ( self ): """simple docstring""" pass @unittest.skip('Swin does not support feedforward chunking' ) def UpperCamelCase ( self ): """simple docstring""" pass def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCAmelCase = model_class(UpperCAmelCase ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) _UpperCAmelCase = model.get_output_embeddings() self.assertTrue(x is None or isinstance(UpperCAmelCase , nn.Linear ) ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCAmelCase = model_class(UpperCAmelCase ) _UpperCAmelCase = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCAmelCase = [*signature.parameters.keys()] _UpperCAmelCase = ['pixel_values'] self.assertListEqual(arg_names[:1] , UpperCAmelCase ) @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' ) def UpperCamelCase ( self ): """simple docstring""" pass @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' ) def UpperCamelCase ( self ): """simple docstring""" pass def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ): """simple docstring""" _UpperCAmelCase = model_class(UpperCAmelCase ) model.to(UpperCAmelCase ) model.eval() with torch.no_grad(): _UpperCAmelCase = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) ) _UpperCAmelCase = outputs.hidden_states _UpperCAmelCase = getattr( self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 ) self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase ) # Swin has a different seq_length _UpperCAmelCase = ( config.patch_size if isinstance(config.patch_size , collections.abc.Iterable ) else (config.patch_size, config.patch_size) ) _UpperCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.assertListEqual( list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCAmelCase = ( self.model_tester.image_size if isinstance(self.model_tester.image_size , collections.abc.Iterable ) else (self.model_tester.image_size, self.model_tester.image_size) ) for model_class in self.all_model_classes: _UpperCAmelCase = True self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _UpperCAmelCase = True self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCAmelCase = 3 _UpperCAmelCase = ( self.model_tester.image_size if isinstance(self.model_tester.image_size , collections.abc.Iterable ) else (self.model_tester.image_size, self.model_tester.image_size) ) _UpperCAmelCase = ( config.patch_size if isinstance(config.patch_size , collections.abc.Iterable ) else (config.patch_size, config.patch_size) ) _UpperCAmelCase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0]) _UpperCAmelCase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1]) for model_class in self.all_model_classes: _UpperCAmelCase = True self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width) ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _UpperCAmelCase = True self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width) ) @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' ) def UpperCamelCase ( self ): """simple docstring""" pass @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' ) def UpperCamelCase ( self ): """simple docstring""" pass @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' ) def UpperCamelCase ( self ): """simple docstring""" pass def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(UpperCAmelCase ): _UpperCAmelCase = 0 return t def check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase={} ): with torch.no_grad(): _UpperCAmelCase = model(**UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase ) _UpperCAmelCase = model(**UpperCAmelCase , return_dict=UpperCAmelCase , **UpperCAmelCase ).to_tuple() def recursive_check(UpperCAmelCase , UpperCAmelCase ): if isinstance(UpperCAmelCase , (List, Tuple) ): for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase , UpperCAmelCase ): recursive_check(UpperCAmelCase , UpperCAmelCase ) elif isinstance(UpperCAmelCase , UpperCAmelCase ): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values() , dict_object.values() ): recursive_check(UpperCAmelCase , UpperCAmelCase ) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(UpperCAmelCase ) , set_nan_tensor_to_zero(UpperCAmelCase ) , atol=1e-5 ) , msg=( 'Tuple and dict output are not equal. Difference:' F""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:""" F""" {torch.isnan(UpperCAmelCase ).any()} and `inf`: {torch.isinf(UpperCAmelCase )}. Dict has""" F""" `nan`: {torch.isnan(UpperCAmelCase ).any()} and `inf`: {torch.isinf(UpperCAmelCase )}.""" ) , ) recursive_check(UpperCAmelCase , UpperCAmelCase ) for model_class in self.all_model_classes: _UpperCAmelCase = model_class(UpperCAmelCase ) model.to(UpperCAmelCase ) model.eval() _UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) _UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) _UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase ) _UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase ) check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) _UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) _UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {'output_hidden_states': True} ) _UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase ) _UpperCAmelCase = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase ) check_equivalence(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , {'output_hidden_states': True} ) @require_torch class __lowerCamelCase ( unittest.TestCase , snake_case__): """simple docstring""" UpperCamelCase__ = (MaskFormerSwinBackbone,) if is_torch_available() else () UpperCamelCase__ = MaskFormerSwinConfig def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = MaskFormerSwinModelTester(self ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCAmelCase = inputs_dict['pixel_values'].shape[0] for backbone_class in self.all_model_classes: _UpperCAmelCase = backbone_class(UpperCAmelCase ) backbone.to(UpperCAmelCase ) backbone.eval() _UpperCAmelCase = backbone(**UpperCAmelCase ) # Test default outputs and verify feature maps self.assertIsInstance(outputs.feature_maps , UpperCAmelCase ) self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) ) for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ): self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) ) self.assertIsNone(outputs.hidden_states ) self.assertIsNone(outputs.attentions ) # Test output_hidden_states=True _UpperCAmelCase = backbone(**UpperCAmelCase , output_hidden_states=UpperCAmelCase ) self.assertIsNotNone(outputs.hidden_states ) self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) ) # We skip the stem layer for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ): for hidden_state in hidden_states: # Hidden states are in the format (batch_size, (height * width), n_channels) _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = hidden_state.shape self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) ) # Test output_attentions=True if self.has_attentions: _UpperCAmelCase = backbone(**UpperCAmelCase , output_attentions=UpperCAmelCase ) self.assertIsNotNone(outputs.attentions )	39	0
"""simple docstring""" # tests directory-specific settings - this file is run automatically # by pytest before any tests are run import sys import warnings from os.path import abspath, dirname, join # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. UpperCamelCase__ : Any = abspath(join(dirname(dirname(__file__)), """src""")) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action="""ignore""", category=FutureWarning) def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> List[str]: """simple docstring""" from diffusers.utils.testing_utils import pytest_addoption_shared pytest_addoption_shared(snake_case_ ) def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> str: """simple docstring""" from diffusers.utils.testing_utils import pytest_terminal_summary_main a = terminalreporter.config.getoption('''--make-reports''' ) if make_reports: pytest_terminal_summary_main(snake_case_, id=snake_case_ )	355	from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase__ : str = logging.get_logger(__name__) UpperCamelCase__ : Optional[int] = { """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""", """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""", } class lowerCamelCase_ ( a_ ): SCREAMING_SNAKE_CASE_ = 'luke' def __init__( self : Dict ,__lowerCamelCase : Optional[Any]=5_02_67 ,__lowerCamelCase : str=50_00_00 ,__lowerCamelCase : Any=7_68 ,__lowerCamelCase : int=2_56 ,__lowerCamelCase : Optional[int]=12 ,__lowerCamelCase : Tuple=12 ,__lowerCamelCase : Any=30_72 ,__lowerCamelCase : Any="gelu" ,__lowerCamelCase : Any=0.1 ,__lowerCamelCase : Tuple=0.1 ,__lowerCamelCase : Tuple=5_12 ,__lowerCamelCase : int=2 ,__lowerCamelCase : Optional[int]=0.02 ,__lowerCamelCase : List[Any]=1e-12 ,__lowerCamelCase : Dict=True ,__lowerCamelCase : Tuple=None ,__lowerCamelCase : Any=1 ,__lowerCamelCase : Dict=0 ,__lowerCamelCase : Any=2 ,**__lowerCamelCase : str ,): '''simple docstring''' super().__init__(pad_token_id=__lowerCamelCase ,bos_token_id=__lowerCamelCase ,eos_token_id=__lowerCamelCase ,**__lowerCamelCase ) a = vocab_size a = entity_vocab_size a = hidden_size a = entity_emb_size a = num_hidden_layers a = num_attention_heads a = hidden_act a = intermediate_size a = hidden_dropout_prob a = attention_probs_dropout_prob a = max_position_embeddings a = type_vocab_size a = initializer_range a = layer_norm_eps a = use_entity_aware_attention a = classifier_dropout	330	0
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[Any]: '''simple docstring''' stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 ) return arr def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str: '''simple docstring''' if i >= h: return # If first element is smaller than the last then swap them if arr[i] > arr[h]: SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = arr[h], arr[i] # If there are more than 2 elements in the array if h - i + 1 > 2: SCREAMING_SNAKE_CASE = (int)((h - i + 1) / 3 ) # Recursively sort first 2/3 elements stooge(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (h - t) ) # Recursively sort last 2/3 elements stooge(_SCREAMING_SNAKE_CASE , i + t , (_SCREAMING_SNAKE_CASE) ) # Recursively sort first 2/3 elements stooge(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (h - t) ) if __name__ == "__main__": SCREAMING_SNAKE_CASE_ = input("""Enter numbers separated by a comma:\n""").strip() SCREAMING_SNAKE_CASE_ = [int(item) for item in user_input.split(""",""")] print(stooge_sort(unsorted))	296	import os from distutils.util import strtobool def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple: '''simple docstring''' for e in env_keys: SCREAMING_SNAKE_CASE = int(os.environ.get(_SCREAMING_SNAKE_CASE , -1 ) ) if val >= 0: return val return default def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Optional[int]: '''simple docstring''' SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) ) return strtobool(_SCREAMING_SNAKE_CASE ) == 1 # As its name indicates `strtobool` actually returns an int... def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="no" ) -> Any: '''simple docstring''' SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) ) return value	296	1
'''simple docstring''' import unittest from transformers import XLMConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMWithLMHeadModel, ) from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST class UpperCAmelCase__ : """simple docstring""" def __init__( self : Tuple ,_a : Tuple ,_a : Optional[Any]=13 ,_a : List[str]=7 ,_a : str=True ,_a : List[str]=True ,_a : Optional[int]=True ,_a : Union[str, Any]=True ,_a : Union[str, Any]=True ,_a : Union[str, Any]=False ,_a : Union[str, Any]=False ,_a : List[Any]=False ,_a : str=2 ,_a : List[str]=99 ,_a : Tuple=0 ,_a : List[str]=32 ,_a : Dict=5 ,_a : Union[str, Any]=4 ,_a : Union[str, Any]=0.1 ,_a : Tuple=0.1 ,_a : Union[str, Any]=512 ,_a : Optional[int]=2 ,_a : Any=0.02 ,_a : List[str]=2 ,_a : Dict=4 ,_a : int="last" ,_a : Optional[Any]=True ,_a : str=None ,_a : str=0 ,): '''simple docstring''' _a : Dict = parent _a : Tuple = batch_size _a : List[Any] = seq_length _a : Any = is_training _a : List[Any] = use_input_lengths _a : Optional[Any] = use_token_type_ids _a : Tuple = use_labels _a : Dict = gelu_activation _a : int = sinusoidal_embeddings _a : int = causal _a : Any = asm _a : Dict = n_langs _a : str = vocab_size _a : Optional[int] = n_special _a : List[Any] = hidden_size _a : Any = num_hidden_layers _a : List[Any] = num_attention_heads _a : Optional[Any] = hidden_dropout_prob _a : List[Any] = attention_probs_dropout_prob _a : Dict = max_position_embeddings _a : List[str] = type_sequence_label_size _a : List[str] = initializer_range _a : Any = num_labels _a : Optional[int] = num_choices _a : List[str] = summary_type _a : Optional[Any] = use_proj _a : Dict = scope _a : List[Any] = bos_token_id def __lowercase ( self : Optional[Any] ): '''simple docstring''' _a : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) _a : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] ) _a : int = None if self.use_input_lengths: _a : Union[str, Any] = ( ids_tensor([self.batch_size] ,vocab_size=2 ) + self.seq_length - 2 ) # small variation of seq_length _a : Optional[Any] = None if self.use_token_type_ids: _a : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.n_langs ) _a : Any = None _a : List[Any] = None _a : Any = None if self.use_labels: _a : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size ) _a : str = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels ) _a : Optional[Any] = ids_tensor([self.batch_size] ,2 ).float() _a : List[Any] = ids_tensor([self.batch_size] ,self.num_choices ) _a : str = self.get_config() return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) def __lowercase ( self : List[Any] ): '''simple docstring''' return XLMConfig( vocab_size=self.vocab_size ,n_special=self.n_special ,emb_dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,gelu_activation=self.gelu_activation ,sinusoidal_embeddings=self.sinusoidal_embeddings ,asm=self.asm ,causal=self.causal ,n_langs=self.n_langs ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,summary_type=self.summary_type ,use_proj=self.use_proj ,num_labels=self.num_labels ,bos_token_id=self.bos_token_id ,) def __lowercase ( self : Tuple ,_a : Any ,_a : str ,_a : str ,_a : List[Any] ,_a : Optional[Any] ,_a : Tuple ,_a : List[Any] ,_a : Optional[Any] ,_a : List[Any] ,): '''simple docstring''' _a : List[str] = XLMModel(config=_a ) model.to(_a ) model.eval() _a : Union[str, Any] = model(_a ,lengths=_a ,langs=_a ) _a : List[str] = model(_a ,langs=_a ) _a : Any = model(_a ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) ) def __lowercase ( self : Any ,_a : Any ,_a : int ,_a : str ,_a : Dict ,_a : Union[str, Any] ,_a : Optional[int] ,_a : List[str] ,_a : Dict ,_a : str ,): '''simple docstring''' _a : Union[str, Any] = XLMWithLMHeadModel(_a ) model.to(_a ) model.eval() _a : Optional[int] = model(_a ,token_type_ids=_a ,labels=_a ) self.parent.assertEqual(result.loss.shape ,() ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) ) def __lowercase ( self : List[Any] ,_a : Tuple ,_a : List[str] ,_a : Optional[int] ,_a : Optional[Any] ,_a : Any ,_a : int ,_a : Dict ,_a : Optional[int] ,_a : Union[str, Any] ,): '''simple docstring''' _a : str = XLMForQuestionAnsweringSimple(_a ) model.to(_a ) model.eval() _a : int = model(_a ) _a : int = model(_a ,start_positions=_a ,end_positions=_a ) _a : Optional[int] = outputs self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) ) def __lowercase ( self : Optional[int] ,_a : Optional[Any] ,_a : Optional[int] ,_a : Any ,_a : Tuple ,_a : Optional[int] ,_a : Union[str, Any] ,_a : Union[str, Any] ,_a : str ,_a : List[Any] ,): '''simple docstring''' _a : Tuple = XLMForQuestionAnswering(_a ) model.to(_a ) model.eval() _a : Optional[int] = model(_a ) _a : Tuple = model( _a ,start_positions=_a ,end_positions=_a ,cls_index=_a ,is_impossible=_a ,p_mask=_a ,) _a : Tuple = model( _a ,start_positions=_a ,end_positions=_a ,cls_index=_a ,is_impossible=_a ,) ((_a), ) : str = result_with_labels.to_tuple() _a : int = model(_a ,start_positions=_a ,end_positions=_a ) ((_a), ) : int = result_with_labels.to_tuple() self.parent.assertEqual(result_with_labels.loss.shape ,() ) self.parent.assertEqual(result.start_top_log_probs.shape ,(self.batch_size, model.config.start_n_top) ) self.parent.assertEqual(result.start_top_index.shape ,(self.batch_size, model.config.start_n_top) ) self.parent.assertEqual( result.end_top_log_probs.shape ,(self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual( result.end_top_index.shape ,(self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual(result.cls_logits.shape ,(self.batch_size,) ) def __lowercase ( self : List[str] ,_a : List[str] ,_a : List[str] ,_a : Union[str, Any] ,_a : Dict ,_a : Optional[Any] ,_a : Any ,_a : Union[str, Any] ,_a : Tuple ,_a : str ,): '''simple docstring''' _a : Dict = XLMForSequenceClassification(_a ) model.to(_a ) model.eval() _a : Any = model(_a ) _a : Optional[int] = model(_a ,labels=_a ) self.parent.assertEqual(result.loss.shape ,() ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) ) def __lowercase ( self : str ,_a : Optional[Any] ,_a : int ,_a : Optional[int] ,_a : Dict ,_a : int ,_a : int ,_a : Optional[int] ,_a : Optional[Any] ,_a : Union[str, Any] ,): '''simple docstring''' _a : List[str] = self.num_labels _a : Optional[int] = XLMForTokenClassification(_a ) model.to(_a ) model.eval() _a : Tuple = model(_a ,attention_mask=_a ,labels=_a ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) ) def __lowercase ( self : Any ,_a : List[Any] ,_a : Tuple ,_a : List[Any] ,_a : Optional[Any] ,_a : List[str] ,_a : Any ,_a : Optional[int] ,_a : int ,_a : Tuple ,): '''simple docstring''' _a : Tuple = self.num_choices _a : Optional[Any] = XLMForMultipleChoice(config=_a ) model.to(_a ) model.eval() _a : Tuple = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous() _a : int = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous() _a : List[Any] = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous() _a : Optional[int] = model( _a ,attention_mask=_a ,token_type_ids=_a ,labels=_a ,) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) ) def __lowercase ( self : Any ): '''simple docstring''' _a : Dict = self.prepare_config_and_inputs() ( ( _a ), ( _a ), ( _a ), ( _a ), ( _a ), ( _a ), ( _a ), ( _a ), ( _a ), ) : List[str] = config_and_inputs _a : Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths} return config, inputs_dict @require_torch class UpperCAmelCase__ ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ): """simple docstring""" __UpperCAmelCase : Union[str, Any] = ( ( XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering, XLMForSequenceClassification, XLMForQuestionAnsweringSimple, XLMForTokenClassification, XLMForMultipleChoice, ) if is_torch_available() else () ) __UpperCAmelCase : Any = ( (XLMWithLMHeadModel,) if is_torch_available() else () ) # TODO (PVP): Check other models whether language generation is also applicable __UpperCAmelCase : Any = ( { '''feature-extraction''': XLMModel, '''fill-mask''': XLMWithLMHeadModel, '''question-answering''': XLMForQuestionAnsweringSimple, '''text-classification''': XLMForSequenceClassification, '''text-generation''': XLMWithLMHeadModel, '''token-classification''': XLMForTokenClassification, '''zero-shot''': XLMForSequenceClassification, } if is_torch_available() else {} ) def __lowercase ( self : int ,_a : str ,_a : Union[str, Any] ,_a : Optional[int] ,_a : Any ,_a : Any ): '''simple docstring''' if ( pipeline_test_casse_name == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith('Fast' ) ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False def __lowercase ( self : Union[str, Any] ,_a : Optional[Any] ,_a : Tuple ,_a : Optional[Any]=False ): '''simple docstring''' _a : Union[str, Any] = super()._prepare_for_class(_a ,_a ,return_labels=_a ) if return_labels: if model_class.__name__ == "XLMForQuestionAnswering": _a : Tuple = torch.zeros( self.model_tester.batch_size ,dtype=torch.long ,device=_a ) _a : Any = torch.zeros( self.model_tester.batch_size ,dtype=torch.long ,device=_a ) return inputs_dict def __lowercase ( self : List[Any] ): '''simple docstring''' _a : Dict = XLMModelTester(self ) _a : Union[str, Any] = ConfigTester(self ,config_class=_a ,emb_dim=37 ) def __lowercase ( self : str ): '''simple docstring''' self.config_tester.run_common_tests() def __lowercase ( self : Tuple ): '''simple docstring''' _a : str = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_model(*_a ) def __lowercase ( self : str ): '''simple docstring''' _a : str = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_lm_head(*_a ) def __lowercase ( self : Union[str, Any] ): '''simple docstring''' _a : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_simple_qa(*_a ) def __lowercase ( self : Tuple ): '''simple docstring''' _a : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_qa(*_a ) def __lowercase ( self : Dict ): '''simple docstring''' _a : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_sequence_classif(*_a ) def __lowercase ( self : Optional[Any] ): '''simple docstring''' _a : Union[str, Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_token_classif(*_a ) def __lowercase ( self : int ): '''simple docstring''' _a : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_for_multiple_choice(*_a ) def __lowercase ( self : Tuple ,_a : Tuple ,_a : Dict ,_a : Union[str, Any] ,_a : List[Any] ,_a : Dict ,_a : List[str]=False ,_a : str=1 ): '''simple docstring''' self.assertIsInstance(_a ,_a ) self.assertListEqual( [isinstance(_a ,_a ) for iter_attentions in attentions] ,[True] * len(_a ) ) self.assertEqual(len(_a ) ,(max_length - min_length) * num_beam_groups ) for idx, iter_attentions in enumerate(_a ): # adds PAD dummy token _a : Optional[Any] = min_length + idx + 1 _a : Optional[Any] = min_length + idx + 1 _a : List[Any] = ( batch_size * num_beam_groups, config.num_attention_heads, tgt_len, src_len, ) # check attn size self.assertListEqual( [layer_attention.shape for layer_attention in iter_attentions] ,[expected_shape] * len(_a ) ) def __lowercase ( self : Any ,_a : int ,_a : int ,_a : List[str] ,_a : Tuple ,_a : Union[str, Any] ,_a : Optional[int]=False ,_a : List[Any]=1 ): '''simple docstring''' self.assertIsInstance(_a ,_a ) self.assertListEqual( [isinstance(_a ,_a ) for iter_hidden_states in hidden_states] ,[True] * len(_a ) ,) self.assertEqual(len(_a ) ,(max_length - min_length) * num_beam_groups ) for idx, iter_hidden_states in enumerate(_a ): # adds PAD dummy token _a : Dict = min_length + idx + 1 _a : int = (batch_size * num_beam_groups, seq_len, config.hidden_size) # check hidden size self.assertListEqual( [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] ,[expected_shape] * len(_a ) ,) pass @slow def __lowercase ( self : Optional[Any] ): '''simple docstring''' for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _a : Union[str, Any] = XLMModel.from_pretrained(_a ) self.assertIsNotNone(_a ) @require_torch class UpperCAmelCase__ ( unittest.TestCase ): """simple docstring""" @slow def __lowercase ( self : Any ): '''simple docstring''' _a : Optional[Any] = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' ) model.to(_a ) _a : Optional[int] = torch.tensor([[14, 447]] ,dtype=torch.long ,device=_a ) # the president _a : int = [ 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, ] # the president the president the president the president the president the president the president the president the president the president # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference _a : Dict = model.generate(_a ,do_sample=_a ) self.assertListEqual(output_ids[0].cpu().numpy().tolist() ,_a )	5	'''simple docstring''' import sys def UpperCAmelCase_ (__a : List[str] ): """simple docstring""" _a : List[str] = len(__a ) _a : Dict = [[0 for x in range(__a )] for x in range(__a )] _a : Union[str, Any] = [[0 for x in range(__a )] for x in range(__a )] for chain_length in range(2 , __a ): for a in range(1 , n - chain_length + 1 ): _a : Tuple = a + chain_length - 1 _a : Any = sys.maxsize for c in range(__a , __a ): _a : Optional[Any] = ( matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b] ) if cost < matrix[a][b]: _a : Dict = cost _a : Any = c return matrix, sol def UpperCAmelCase_ (__a : Tuple , __a : List[str] , __a : Dict ): """simple docstring""" if i == j: print('A' + str(__a ) , end=' ' ) else: print('(' , end=' ' ) print_optiomal_solution(__a , __a , optimal_solution[i][j] ) print_optiomal_solution(__a , optimal_solution[i][j] + 1 , __a ) print(')' , end=' ' ) def UpperCAmelCase_ (): """simple docstring""" _a : Any = [3_0, 3_5, 1_5, 5, 1_0, 2_0, 2_5] _a : Any = len(__a ) # Size of matrix created from above array will be # 30*35 35*15 15*5 5*10 10*20 20*25 _a, _a : Union[str, Any] = matrix_chain_order(__a ) print('No. of Operation required: ' + str(matrix[1][n - 1] ) ) print_optiomal_solution(__a , 1 , n - 1 ) if __name__ == "__main__": main()	5	1
'''simple docstring''' from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices A__ : Optional[Any] =logging.get_logger(__name__) A__ : str ={ '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''', } class UpperCAmelCase ( snake_case_ , snake_case_ ): _lowercase: str = '''resnet''' _lowercase: str = ['''basic''', '''bottleneck'''] def __init__( self : Optional[int] , __snake_case : Tuple=3 , __snake_case : List[str]=64 , __snake_case : Optional[Any]=[2_56, 5_12, 10_24, 20_48] , __snake_case : str=[3, 4, 6, 3] , __snake_case : int="bottleneck" , __snake_case : Optional[Any]="relu" , __snake_case : int=False , __snake_case : int=None , __snake_case : Union[str, Any]=None , **__snake_case : Any , ) -> int: super().__init__(**__snake_case ) if layer_type not in self.layer_types: raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types )}" ) _lowerCAmelCase = num_channels _lowerCAmelCase = embedding_size _lowerCAmelCase = hidden_sizes _lowerCAmelCase = depths _lowerCAmelCase = layer_type _lowerCAmelCase = hidden_act _lowerCAmelCase = downsample_in_first_stage _lowerCAmelCase = ["""stem"""] + [f"stage{idx}" for idx in range(1 , len(__snake_case ) + 1 )] _lowerCAmelCase , _lowerCAmelCase = get_aligned_output_features_output_indices( out_features=__snake_case , out_indices=__snake_case , stage_names=self.stage_names ) class UpperCAmelCase ( snake_case_ ): _lowercase: Optional[int] = version.parse('''1.11''' ) @property def lowercase__ ( self : Dict ) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}), ] ) @property def lowercase__ ( self : Optional[int] ) -> float: return 1E-3	70	import argparse import os import re import packaging.version A__ : Dict = '''examples/''' A__ : Any = { '''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''), '''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''), '''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''), '''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''), } A__ : Any = { '''init''': '''src/transformers/__init__.py''', '''setup''': '''setup.py''', } A__ : Any = '''README.md''' def UpperCamelCase( __UpperCamelCase : int ,__UpperCamelCase : List[Any] ,__UpperCamelCase : List[Any] ): with open(__UpperCamelCase ,'''r''' ,encoding='''utf-8''' ,newline='''\n''' ) as f: lowerCAmelCase_ : Tuple = f.read() lowerCAmelCase_ , lowerCAmelCase_ : Dict = REPLACE_PATTERNS[pattern] lowerCAmelCase_ : Tuple = replace.replace('''VERSION''' ,__UpperCamelCase ) lowerCAmelCase_ : Optional[int] = re_pattern.sub(__UpperCamelCase ,__UpperCamelCase ) with open(__UpperCamelCase ,'''w''' ,encoding='''utf-8''' ,newline='''\n''' ) as f: f.write(__UpperCamelCase ) def UpperCamelCase( __UpperCamelCase : Union[str, Any] ): for folder, directories, fnames in os.walk(__UpperCamelCase ): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove('''research_projects''' ) if "legacy" in directories: directories.remove('''legacy''' ) for fname in fnames: if fname.endswith('''.py''' ): update_version_in_file(os.path.join(__UpperCamelCase ,__UpperCamelCase ) ,__UpperCamelCase ,pattern='''examples''' ) def UpperCamelCase( __UpperCamelCase : int ,__UpperCamelCase : List[Any]=False ): for pattern, fname in REPLACE_FILES.items(): update_version_in_file(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) if not patch: update_version_in_examples(__UpperCamelCase ) def UpperCamelCase( ): lowerCAmelCase_ : List[str] = '''🤗 Transformers currently provides the following architectures''' lowerCAmelCase_ : List[Any] = '''1. Want to contribute a new model?''' with open(__UpperCamelCase ,'''r''' ,encoding='''utf-8''' ,newline='''\n''' ) as f: lowerCAmelCase_ : Union[str, Any] = f.readlines() # Find the start of the list. lowerCAmelCase_ : int = 0 while not lines[start_index].startswith(_start_prompt ): start_index += 1 start_index += 1 lowerCAmelCase_ : str = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt ): if lines[index].startswith('''1.''' ): lowerCAmelCase_ : int = lines[index].replace( '''https://huggingface.co/docs/transformers/main/model_doc''' ,'''https://huggingface.co/docs/transformers/model_doc''' ,) index += 1 with open(__UpperCamelCase ,'''w''' ,encoding='''utf-8''' ,newline='''\n''' ) as f: f.writelines(__UpperCamelCase ) def UpperCamelCase( ): with open(REPLACE_FILES['''init'''] ,'''r''' ) as f: lowerCAmelCase_ : Optional[Any] = f.read() lowerCAmelCase_ : Dict = REPLACE_PATTERNS['''init'''][0].search(__UpperCamelCase ).groups()[0] return packaging.version.parse(__UpperCamelCase ) def UpperCamelCase( __UpperCamelCase : Dict=False ): lowerCAmelCase_ : Union[str, Any] = get_version() if patch and default_version.is_devrelease: raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' ) if default_version.is_devrelease: lowerCAmelCase_ : List[str] = default_version.base_version elif patch: lowerCAmelCase_ : int = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}""" else: lowerCAmelCase_ : int = f"""{default_version.major}.{default_version.minor + 1}.0""" # Now let's ask nicely if that's the right one. lowerCAmelCase_ : Optional[Any] = input(f"""Which version are you releasing? [{default_version}]""" ) if len(__UpperCamelCase ) == 0: lowerCAmelCase_ : List[str] = default_version print(f"""Updating version to {version}.""" ) global_version_update(__UpperCamelCase ,patch=__UpperCamelCase ) if not patch: print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' ) clean_main_ref_in_model_list() def UpperCamelCase( ): lowerCAmelCase_ : Any = get_version() lowerCAmelCase_ : int = f"""{current_version.major}.{current_version.minor + 1}.0.dev0""" lowerCAmelCase_ : Optional[Any] = current_version.base_version # Check with the user we got that right. lowerCAmelCase_ : Optional[Any] = input(f"""Which version are we developing now? [{dev_version}]""" ) if len(__UpperCamelCase ) == 0: lowerCAmelCase_ : int = dev_version print(f"""Updating version to {version}.""" ) global_version_update(__UpperCamelCase ) print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' ) clean_main_ref_in_model_list() if __name__ == "__main__": A__ : Dict = argparse.ArgumentParser() parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''') parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''') A__ : Optional[int] = parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print('''Nothing to do after a patch :-)''') else: post_release_work()	103	0
'''simple docstring''' from typing import List, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging __a = logging.get_logger(__name__) __a = { 'huggingface/time-series-transformer-tourism-monthly': ( 'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json' ), # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer } class A__ ( UpperCamelCase ): """simple docstring""" UpperCamelCase_ : Tuple = '''time_series_transformer''' UpperCamelCase_ : Optional[Any] = { '''hidden_size''': '''d_model''', '''num_attention_heads''': '''encoder_attention_heads''', '''num_hidden_layers''': '''encoder_layers''', } def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "student_t" , lowerCAmelCase__ : str = "nll" , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase__ : Optional[Union[str, bool]] = "mean" , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : bool = True , lowerCAmelCase__ : str = "gelu" , lowerCAmelCase__ : int = 6_4 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : float = 0.1 , lowerCAmelCase__ : int = 1_0_0 , lowerCAmelCase__ : float = 0.02 , lowerCAmelCase__ : Dict=True , **lowerCAmelCase__ : Tuple , ) -> Tuple: """simple docstring""" _UpperCAmelCase : Optional[int] = prediction_length _UpperCAmelCase : Optional[Any] = context_length or prediction_length _UpperCAmelCase : Optional[Any] = distribution_output _UpperCAmelCase : Union[str, Any] = loss _UpperCAmelCase : Dict = input_size _UpperCAmelCase : int = num_time_features _UpperCAmelCase : Any = lags_sequence _UpperCAmelCase : Dict = scaling _UpperCAmelCase : Tuple = num_dynamic_real_features _UpperCAmelCase : Dict = num_static_real_features _UpperCAmelCase : Union[str, Any] = num_static_categorical_features if cardinality and num_static_categorical_features > 0: if len(lowerCAmelCase__ ) != num_static_categorical_features: raise ValueError( "The cardinality should be a list of the same length as `num_static_categorical_features`" ) _UpperCAmelCase : Optional[int] = cardinality else: _UpperCAmelCase : Optional[Any] = [0] if embedding_dimension and num_static_categorical_features > 0: if len(lowerCAmelCase__ ) != num_static_categorical_features: raise ValueError( "The embedding dimension should be a list of the same length as `num_static_categorical_features`" ) _UpperCAmelCase : List[Any] = embedding_dimension else: _UpperCAmelCase : Optional[Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality] _UpperCAmelCase : str = num_parallel_samples # Transformer architecture configuration _UpperCAmelCase : Union[str, Any] = input_size * len(lowerCAmelCase__ ) + self._number_of_features _UpperCAmelCase : str = d_model _UpperCAmelCase : Optional[Any] = encoder_attention_heads _UpperCAmelCase : Dict = decoder_attention_heads _UpperCAmelCase : List[Any] = encoder_ffn_dim _UpperCAmelCase : str = decoder_ffn_dim _UpperCAmelCase : Dict = encoder_layers _UpperCAmelCase : str = decoder_layers _UpperCAmelCase : Any = dropout _UpperCAmelCase : str = attention_dropout _UpperCAmelCase : List[Any] = activation_dropout _UpperCAmelCase : Dict = encoder_layerdrop _UpperCAmelCase : Any = decoder_layerdrop _UpperCAmelCase : Optional[Any] = activation_function _UpperCAmelCase : Tuple = init_std _UpperCAmelCase : List[str] = use_cache super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__ ) @property def _lowerCAmelCase ( self : str ) -> int: """simple docstring""" return ( sum(self.embedding_dimension ) + self.num_dynamic_real_features + self.num_time_features + self.num_static_real_features + self.input_size * 2 # the log1p(abs(loc)) and log(scale) features )	363	'''simple docstring''' import argparse from collections import OrderedDict from pathlib import Path import requests import torch from PIL import Image from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor from transformers.utils import logging logging.set_verbosity_info() __a = logging.get_logger(__name__) def __UpperCAmelCase ( a_: List[str] ): _UpperCAmelCase : Union[str, Any] = OrderedDict() for key, value in state_dict.items(): if key.startswith("module.encoder" ): _UpperCAmelCase : Optional[int] = key.replace("module.encoder", "glpn.encoder" ) if key.startswith("module.decoder" ): _UpperCAmelCase : List[Any] = key.replace("module.decoder", "decoder.stages" ) if "patch_embed" in key: # replace for example patch_embed1 by patch_embeddings.0 _UpperCAmelCase : int = key[key.find("patch_embed" ) + len("patch_embed" )] _UpperCAmelCase : Union[str, Any] = key.replace(f"""patch_embed{idx}""", f"""patch_embeddings.{int(a_ )-1}""" ) if "norm" in key: _UpperCAmelCase : Union[str, Any] = key.replace("norm", "layer_norm" ) if "glpn.encoder.layer_norm" in key: # replace for example layer_norm1 by layer_norm.0 _UpperCAmelCase : str = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )] _UpperCAmelCase : Optional[Any] = key.replace(f"""layer_norm{idx}""", f"""layer_norm.{int(a_ )-1}""" ) if "layer_norm1" in key: _UpperCAmelCase : Union[str, Any] = key.replace("layer_norm1", "layer_norm_1" ) if "layer_norm2" in key: _UpperCAmelCase : List[Any] = key.replace("layer_norm2", "layer_norm_2" ) if "block" in key: # replace for example block1 by block.0 _UpperCAmelCase : Optional[Any] = key[key.find("block" ) + len("block" )] _UpperCAmelCase : List[str] = key.replace(f"""block{idx}""", f"""block.{int(a_ )-1}""" ) if "attn.q" in key: _UpperCAmelCase : Optional[int] = key.replace("attn.q", "attention.self.query" ) if "attn.proj" in key: _UpperCAmelCase : List[str] = key.replace("attn.proj", "attention.output.dense" ) if "attn" in key: _UpperCAmelCase : Dict = key.replace("attn", "attention.self" ) if "fc1" in key: _UpperCAmelCase : List[Any] = key.replace("fc1", "dense1" ) if "fc2" in key: _UpperCAmelCase : List[Any] = key.replace("fc2", "dense2" ) if "linear_pred" in key: _UpperCAmelCase : Any = key.replace("linear_pred", "classifier" ) if "linear_fuse" in key: _UpperCAmelCase : Dict = key.replace("linear_fuse.conv", "linear_fuse" ) _UpperCAmelCase : List[str] = key.replace("linear_fuse.bn", "batch_norm" ) if "linear_c" in key: # replace for example linear_c4 by linear_c.3 _UpperCAmelCase : List[Any] = key[key.find("linear_c" ) + len("linear_c" )] _UpperCAmelCase : Tuple = key.replace(f"""linear_c{idx}""", f"""linear_c.{int(a_ )-1}""" ) if "bot_conv" in key: _UpperCAmelCase : Union[str, Any] = key.replace("bot_conv", "0.convolution" ) if "skip_conv1" in key: _UpperCAmelCase : Optional[int] = key.replace("skip_conv1", "1.convolution" ) if "skip_conv2" in key: _UpperCAmelCase : Optional[int] = key.replace("skip_conv2", "2.convolution" ) if "fusion1" in key: _UpperCAmelCase : List[str] = key.replace("fusion1", "1.fusion" ) if "fusion2" in key: _UpperCAmelCase : List[str] = key.replace("fusion2", "2.fusion" ) if "fusion3" in key: _UpperCAmelCase : Optional[Any] = key.replace("fusion3", "3.fusion" ) if "fusion" in key and "conv" in key: _UpperCAmelCase : List[Any] = key.replace("conv", "convolutional_layer" ) if key.startswith("module.last_layer_depth" ): _UpperCAmelCase : Optional[int] = key.replace("module.last_layer_depth", "head.head" ) _UpperCAmelCase : int = value return new_state_dict def __UpperCAmelCase ( a_: str, a_: List[Any] ): # for each of the encoder blocks: for i in range(config.num_encoder_blocks ): for j in range(config.depths[i] ): # read in weights + bias of keys and values (which is a single matrix in the original implementation) _UpperCAmelCase : Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" ) _UpperCAmelCase : Union[str, Any] = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" ) # next, add keys and values (in that order) to the state dict _UpperCAmelCase : Optional[int] = kv_weight[ : config.hidden_sizes[i], : ] _UpperCAmelCase : Dict = kv_bias[: config.hidden_sizes[i]] _UpperCAmelCase : Optional[int] = kv_weight[ config.hidden_sizes[i] :, : ] _UpperCAmelCase : Optional[Any] = kv_bias[config.hidden_sizes[i] :] def __UpperCAmelCase ( ): _UpperCAmelCase : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg" _UpperCAmelCase : List[Any] = Image.open(requests.get(a_, stream=a_ ).raw ) return image @torch.no_grad() def __UpperCAmelCase ( a_: Tuple, a_: Any, a_: Optional[Any]=False, a_: List[Any]=None ): _UpperCAmelCase : Optional[Any] = GLPNConfig(hidden_sizes=[64, 128, 320, 512], decoder_hidden_size=64, depths=[3, 8, 27, 3] ) # load image processor (only resize + rescale) _UpperCAmelCase : Dict = GLPNImageProcessor() # prepare image _UpperCAmelCase : List[Any] = prepare_img() _UpperCAmelCase : Optional[int] = image_processor(images=a_, return_tensors="pt" ).pixel_values logger.info("Converting model..." ) # load original state dict _UpperCAmelCase : Union[str, Any] = torch.load(a_, map_location=torch.device("cpu" ) ) # rename keys _UpperCAmelCase : List[str] = rename_keys(a_ ) # key and value matrices need special treatment read_in_k_v(a_, a_ ) # create HuggingFace model and load state dict _UpperCAmelCase : List[str] = GLPNForDepthEstimation(a_ ) model.load_state_dict(a_ ) model.eval() # forward pass _UpperCAmelCase : Dict = model(a_ ) _UpperCAmelCase : List[str] = outputs.predicted_depth # verify output if model_name is not None: if "nyu" in model_name: _UpperCAmelCase : Optional[Any] = torch.tensor( [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]] ) elif "kitti" in model_name: _UpperCAmelCase : Tuple = torch.tensor( [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]] ) else: raise ValueError(f"""Unknown model name: {model_name}""" ) _UpperCAmelCase : Dict = torch.Size([1, 480, 640] ) assert predicted_depth.shape == expected_shape assert torch.allclose(predicted_depth[0, :3, :3], a_, atol=1e-4 ) print("Looks ok!" ) # finally, push to hub if required if push_to_hub: logger.info("Pushing model and image processor to the hub..." ) model.push_to_hub( repo_path_or_name=Path(a_, a_ ), organization="nielsr", commit_message="Add model", use_temp_dir=a_, ) image_processor.push_to_hub( repo_path_or_name=Path(a_, a_ ), organization="nielsr", commit_message="Add image processor", use_temp_dir=a_, ) if __name__ == "__main__": __a = argparse.ArgumentParser() parser.add_argument( '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).', ) parser.add_argument( '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.' ) parser.add_argument( '--push_to_hub', action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true", help='Whether to upload the model to the HuggingFace hub.' ) parser.add_argument( '--model_name', default='glpn-kitti', type=str, help='Name of the model in case you\'re pushing to the hub.', ) __a = parser.parse_args() convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)	17	0
"""simple docstring""" def _A ( UpperCamelCase_ : float, UpperCamelCase_ : float) -> float: '''simple docstring''' if mass < 0: raise ValueError("The mass of a body cannot be negative") return 0.5 * mass * abs(UpperCamelCase_) * abs(UpperCamelCase_) if __name__ == "__main__": import doctest doctest.testmod(verbose=True)	17	'''simple docstring''' import argparse import shlex import runhouse as rh if __name__ == "__main__": # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access # setup instructions, if using on-demand hardware # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster # Throw an error if user passes both BYO and on-demand cluster args # Otherwise, use default values lowercase_ = argparse.ArgumentParser() parser.add_argument("""--user""", type=str, default="""ubuntu""") parser.add_argument("""--host""", type=str, default="""localhost""") parser.add_argument("""--key_path""", type=str, default=None) parser.add_argument("""--instance""", type=str, default="""V100:1""") parser.add_argument("""--provider""", type=str, default="""cheapest""") parser.add_argument("""--use_spot""", type=bool, default=False) parser.add_argument("""--example""", type=str, default="""pytorch/text-generation/run_generation.py""") lowercase_ , lowercase_ = parser.parse_known_args() if args.host != "localhost": if args.instance != "V100:1" or args.provider != "cheapest": raise ValueError("""Cannot specify both BYO and on-demand cluster args""") lowercase_ = rh.cluster( name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path} ) else: lowercase_ = rh.cluster( name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot ) lowercase_ = args.example.rsplit("""/""", 1)[0] # Set up remote environment cluster.install_packages(["""pip:./"""]) # Installs transformers from local source # Note transformers is copied into the home directory on the remote machine, so we can install from there cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""]) cluster.run(["""pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"""]) # Run example. You can bypass the CLI wrapper and paste your own code here. cluster.run([f"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""]) # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI): # from my_script... import train # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard'] # launch_train_gpu = rh.function(fn=train, # system=gpu, # reqs=reqs, # name='train_bert_glue') # # We can pass in arguments just like we would to a function: # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16 # stream_logs=True)	58	0
# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os from accelerate.utils import ComputeEnvironment from .cluster import get_cluster_input from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401 from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401 from .sagemaker import get_sagemaker_input lowerCAmelCase = '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine''' def _lowerCamelCase( ) -> str: '''simple docstring''' __lowercase= _ask_options( 'In which compute environment are you running?' , ['This machine', 'AWS (Amazon SageMaker)'] , _convert_compute_environment , ) if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER: __lowercase= get_sagemaker_input() else: __lowercase= get_cluster_input() return config def _lowerCamelCase( lowercase__=None ) -> List[str]: '''simple docstring''' if subparsers is not None: __lowercase= subparsers.add_parser('config' , description=lowercase__ ) else: __lowercase= argparse.ArgumentParser('Accelerate config command' , description=lowercase__ ) parser.add_argument( '--config_file' , default=lowercase__ , help=( 'The path to use to store the config file. Will default to a file named default_config.yaml in the cache ' 'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have ' 'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed ' 'with \'huggingface\'.' ) , ) if subparsers is not None: parser.set_defaults(func=lowercase__ ) return parser def _lowerCamelCase( lowercase__ ) -> Tuple: '''simple docstring''' __lowercase= get_user_input() if args.config_file is not None: __lowercase= args.config_file else: if not os.path.isdir(lowercase__ ): os.makedirs(lowercase__ ) __lowercase= default_yaml_config_file if config_file.endswith('.json' ): config.to_json_file(lowercase__ ) else: config.to_yaml_file(lowercase__ ) print(F'accelerate configuration saved at {config_file}' ) def _lowerCamelCase( ) -> Union[str, Any]: '''simple docstring''' __lowercase= config_command_parser() __lowercase= parser.parse_args() config_command(lowercase__ ) if __name__ == "__main__": main()	304	def _lowerCamelCase( lowercase__ = 1_0_0_0 ) -> int: '''simple docstring''' __lowercase= 2**power __lowercase= str(lowercase__ ) __lowercase= list(lowercase__ ) __lowercase= 0 for i in list_num: sum_of_num += int(lowercase__ ) return sum_of_num if __name__ == "__main__": lowerCAmelCase = int(input('''Enter the power of 2: ''').strip()) print('''2 ^ ''', power, ''' = ''', 2**power) lowerCAmelCase = solution(power) print('''Sum of the digits is: ''', result)	304	1
"""simple docstring""" from __future__ import annotations def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float , _lowercase : float , ) ->tuple[str, float]: '''simple docstring''' if (stress, tangential_force, area).count(0 ) != 1: raise ValueError("You cannot supply more or less than 2 values" ) elif stress < 0: raise ValueError("Stress cannot be negative" ) elif tangential_force < 0: raise ValueError("Tangential Force cannot be negative" ) elif area < 0: raise ValueError("Area cannot be negative" ) elif stress == 0: return ( "stress", tangential_force / area, ) elif tangential_force == 0: return ( "tangential_force", stress * area, ) else: return ( "area", tangential_force / stress, ) if __name__ == "__main__": import doctest doctest.testmod()	105	"""simple docstring""" import torch from diffusers import DDIMParallelScheduler from .test_schedulers import SchedulerCommonTest class A_ (lowercase__ ): '''simple docstring''' SCREAMING_SNAKE_CASE__ : Optional[int] = (DDIMParallelScheduler,) SCREAMING_SNAKE_CASE__ : Optional[Any] = (("""eta""", 0.0), ("""num_inference_steps""", 50)) def UpperCamelCase__ ( self , **lowercase_ ): """simple docstring""" UpperCAmelCase_ : int = { "num_train_timesteps": 1000, "beta_start": 0.00_01, "beta_end": 0.02, "beta_schedule": "linear", "clip_sample": True, } config.update(**lowercase_ ) return config def UpperCamelCase__ ( self , **lowercase_ ): """simple docstring""" UpperCAmelCase_ : Dict = self.scheduler_classes[0] UpperCAmelCase_ : Union[str, Any] = self.get_scheduler_config(**lowercase_ ) UpperCAmelCase_ : int = scheduler_class(**lowercase_ ) UpperCAmelCase_ , UpperCAmelCase_ : str = 10, 0.0 UpperCAmelCase_ : Optional[int] = self.dummy_model() UpperCAmelCase_ : str = self.dummy_sample_deter scheduler.set_timesteps(lowercase_ ) for t in scheduler.timesteps: UpperCAmelCase_ : Dict = model(lowercase_ , lowercase_ ) UpperCAmelCase_ : Dict = scheduler.step(lowercase_ , lowercase_ , lowercase_ , lowercase_ ).prev_sample return sample def UpperCamelCase__ ( self ): """simple docstring""" for timesteps in [100, 500, 1000]: self.check_over_configs(num_train_timesteps=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" for steps_offset in [0, 1]: self.check_over_configs(steps_offset=lowercase_ ) UpperCAmelCase_ : str = self.scheduler_classes[0] UpperCAmelCase_ : List[str] = self.get_scheduler_config(steps_offset=1 ) UpperCAmelCase_ : List[str] = scheduler_class(**lowercase_ ) scheduler.set_timesteps(5 ) assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) ) def UpperCamelCase__ ( self ): """simple docstring""" for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=lowercase_ , beta_end=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" for clip_sample in [True, False]: self.check_over_configs(clip_sample=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" for timestep_spacing in ["trailing", "leading"]: self.check_over_configs(timestep_spacing=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" for rescale_betas_zero_snr in [True, False]: self.check_over_configs(rescale_betas_zero_snr=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" self.check_over_configs(thresholding=lowercase_ ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs( thresholding=lowercase_ , prediction_type=lowercase_ , sample_max_value=lowercase_ , ) def UpperCamelCase__ ( self ): """simple docstring""" for t in [1, 10, 49]: self.check_over_forward(time_step=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ): self.check_over_forward(time_step=lowercase_ , num_inference_steps=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ): self.check_over_forward(time_step=lowercase_ , eta=lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Union[str, Any] = self.scheduler_classes[0] UpperCAmelCase_ : List[str] = self.get_scheduler_config() UpperCAmelCase_ : List[str] = scheduler_class(**lowercase_ ) assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.1_47_71 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.3_24_60 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.0_09_79 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.02 ) ) < 1E-5 def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Tuple = self.scheduler_classes[0] UpperCAmelCase_ : Optional[int] = self.get_scheduler_config() UpperCAmelCase_ : List[str] = scheduler_class(**lowercase_ ) UpperCAmelCase_ , UpperCAmelCase_ : Tuple = 10, 0.0 scheduler.set_timesteps(lowercase_ ) UpperCAmelCase_ : Union[str, Any] = self.dummy_model() UpperCAmelCase_ : List[str] = self.dummy_sample_deter UpperCAmelCase_ : Any = self.dummy_sample_deter + 0.1 UpperCAmelCase_ : int = self.dummy_sample_deter - 0.1 UpperCAmelCase_ : List[Any] = samplea.shape[0] UpperCAmelCase_ : int = torch.stack([samplea, samplea, samplea] , dim=0 ) UpperCAmelCase_ : int = torch.arange(lowercase_ )[0:3, None].repeat(1 , lowercase_ ) UpperCAmelCase_ : int = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) ) UpperCAmelCase_ : Optional[Any] = scheduler.batch_step_no_noise(lowercase_ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , lowercase_ ) UpperCAmelCase_ : List[Any] = torch.sum(torch.abs(lowercase_ ) ) UpperCAmelCase_ : str = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2 assert abs(result_mean.item() - 0.49_82 ) < 1E-3 def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Tuple = self.full_loop() UpperCAmelCase_ : int = torch.sum(torch.abs(lowercase_ ) ) UpperCAmelCase_ : List[str] = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2 assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3 def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : List[str] = self.full_loop(prediction_type="v_prediction" ) UpperCAmelCase_ : str = torch.sum(torch.abs(lowercase_ ) ) UpperCAmelCase_ : Dict = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 52.53_02 ) < 1E-2 assert abs(result_mean.item() - 0.06_84 ) < 1E-3 def UpperCamelCase__ ( self ): """simple docstring""" # We specify different beta, so that the first alpha is 0.99 UpperCAmelCase_ : List[str] = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 ) UpperCAmelCase_ : Dict = torch.sum(torch.abs(lowercase_ ) ) UpperCAmelCase_ : Tuple = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2 assert abs(result_mean.item() - 0.19_51 ) < 1E-3 def UpperCamelCase__ ( self ): """simple docstring""" # We specify different beta, so that the first alpha is 0.99 UpperCAmelCase_ : int = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 ) UpperCAmelCase_ : List[Any] = torch.sum(torch.abs(lowercase_ ) ) UpperCAmelCase_ : Dict = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2 assert abs(result_mean.item() - 0.19_41 ) < 1E-3	61	0
"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging __magic_name__ = logging.get_logger(__name__) __magic_name__ = { "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json", } class SCREAMING_SNAKE_CASE_ ( __a ): """simple docstring""" __lowercase : str = '''switch_transformers''' __lowercase : str = ['''past_key_values'''] __lowercase : Optional[int] = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''} def __init__( self , lowerCAmelCase__=3_2_1_2_8 , lowerCAmelCase__=7_6_8 , lowerCAmelCase__=6_4 , lowerCAmelCase__=2_0_4_8 , lowerCAmelCase__=6_4 , lowerCAmelCase__=1_2 , lowerCAmelCase__=3 , lowerCAmelCase__=1_2 , lowerCAmelCase__=3 , lowerCAmelCase__=1_2 , lowerCAmelCase__=8 , lowerCAmelCase__=False , lowerCAmelCase__=0.01 , lowerCAmelCase__="float32" , lowerCAmelCase__=False , lowerCAmelCase__=3_2 , lowerCAmelCase__=1_2_8 , lowerCAmelCase__=0.1 , lowerCAmelCase__=1E-6 , lowerCAmelCase__=0.0_01 , lowerCAmelCase__=0.0_01 , lowerCAmelCase__=1.0 , lowerCAmelCase__="relu" , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__=True , lowerCAmelCase__=0 , lowerCAmelCase__=1 , **lowerCAmelCase__ , ): __SCREAMING_SNAKE_CASE = vocab_size __SCREAMING_SNAKE_CASE = d_model __SCREAMING_SNAKE_CASE = d_kv __SCREAMING_SNAKE_CASE = d_ff __SCREAMING_SNAKE_CASE = num_sparse_encoder_layers __SCREAMING_SNAKE_CASE = num_layers __SCREAMING_SNAKE_CASE = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry __SCREAMING_SNAKE_CASE = num_sparse_decoder_layers # This tells us, each how many encoder layer we'll have to set a sparse layer. if self.num_sparse_encoder_layers > 0: __SCREAMING_SNAKE_CASE = self.num_layers // self.num_sparse_encoder_layers else: __SCREAMING_SNAKE_CASE = self.num_layers # HACK: this will create 0 sparse layers # This tells us, each how many encoder layer we'll have to set a sparse layer. if self.num_sparse_decoder_layers > 0: __SCREAMING_SNAKE_CASE = self.num_decoder_layers // self.num_sparse_decoder_layers else: __SCREAMING_SNAKE_CASE = self.num_decoder_layers # HACK: this will create 0 sparse layers __SCREAMING_SNAKE_CASE = num_heads __SCREAMING_SNAKE_CASE = num_experts __SCREAMING_SNAKE_CASE = expert_capacity __SCREAMING_SNAKE_CASE = router_bias __SCREAMING_SNAKE_CASE = router_jitter_noise if router_dtype not in ["float32", "float16", "bfloat16"]: raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}") __SCREAMING_SNAKE_CASE = router_dtype __SCREAMING_SNAKE_CASE = router_ignore_padding_tokens __SCREAMING_SNAKE_CASE = relative_attention_num_buckets __SCREAMING_SNAKE_CASE = relative_attention_max_distance __SCREAMING_SNAKE_CASE = dropout_rate __SCREAMING_SNAKE_CASE = layer_norm_epsilon __SCREAMING_SNAKE_CASE = initializer_factor __SCREAMING_SNAKE_CASE = feed_forward_proj __SCREAMING_SNAKE_CASE = use_cache __SCREAMING_SNAKE_CASE = add_router_probs __SCREAMING_SNAKE_CASE = router_z_loss_coef __SCREAMING_SNAKE_CASE = router_aux_loss_coef __SCREAMING_SNAKE_CASE = self.feed_forward_proj.split("""-""") __SCREAMING_SNAKE_CASE = act_info[-1] __SCREAMING_SNAKE_CASE = act_info[0] == """gated""" if len(lowerCAmelCase__) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__) > 2: raise ValueError( f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer." """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """ """'gated-gelu' or 'relu'""") # for backwards compatibility if feed_forward_proj == "gated-gelu": __SCREAMING_SNAKE_CASE = """gelu_new""" super().__init__( pad_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__ , )	255	"""simple docstring""" import unittest from typing import Dict, List, Optional, Union import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import BridgeTowerImageProcessor class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ): """simple docstring""" def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 3_2 , lowerCAmelCase__ = True , lowerCAmelCase__ = 1 / 2_5_5 , lowerCAmelCase__ = True , lowerCAmelCase__ = True , lowerCAmelCase__ = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , lowerCAmelCase__ = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , lowerCAmelCase__ = True , lowerCAmelCase__=7 , lowerCAmelCase__=3_0 , lowerCAmelCase__=4_0_0 , lowerCAmelCase__=3 , ): __SCREAMING_SNAKE_CASE = parent __SCREAMING_SNAKE_CASE = do_resize __SCREAMING_SNAKE_CASE = size if size is not None else {"""shortest_edge""": 2_8_8} __SCREAMING_SNAKE_CASE = size_divisor __SCREAMING_SNAKE_CASE = do_rescale __SCREAMING_SNAKE_CASE = rescale_factor __SCREAMING_SNAKE_CASE = do_normalize __SCREAMING_SNAKE_CASE = do_center_crop __SCREAMING_SNAKE_CASE = image_mean __SCREAMING_SNAKE_CASE = image_std __SCREAMING_SNAKE_CASE = do_pad __SCREAMING_SNAKE_CASE = batch_size __SCREAMING_SNAKE_CASE = num_channels __SCREAMING_SNAKE_CASE = min_resolution __SCREAMING_SNAKE_CASE = max_resolution def snake_case_ ( self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, "size_divisor": self.size_divisor, } def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__=False): if not batched: __SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""] __SCREAMING_SNAKE_CASE = image_inputs[0] if isinstance(lowerCAmelCase__ , Image.Image): __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = image.size else: __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2] __SCREAMING_SNAKE_CASE = size / min(lowerCAmelCase__ , lowerCAmelCase__) if h < w: __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = size, scale * w else: __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = scale * h, size __SCREAMING_SNAKE_CASE = int((1_3_3_3 / 8_0_0) * size) if max(lowerCAmelCase__ , lowerCAmelCase__) > max_size: __SCREAMING_SNAKE_CASE = max_size / max(lowerCAmelCase__ , lowerCAmelCase__) __SCREAMING_SNAKE_CASE = newh * scale __SCREAMING_SNAKE_CASE = neww * scale __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = int(newh + 0.5), int(neww + 0.5) __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = ( newh // self.size_divisor * self.size_divisor, neww // self.size_divisor * self.size_divisor, ) else: __SCREAMING_SNAKE_CASE = [] for image in image_inputs: __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.get_expected_values([image]) expected_values.append((expected_height, expected_width)) __SCREAMING_SNAKE_CASE = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__: item[0])[0] __SCREAMING_SNAKE_CASE = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__: item[1])[1] return expected_height, expected_width @require_torch @require_vision class SCREAMING_SNAKE_CASE_ ( __a , unittest.TestCase ): """simple docstring""" __lowercase : Tuple = BridgeTowerImageProcessor if is_vision_available() else None def snake_case_ ( self): __SCREAMING_SNAKE_CASE = BridgeTowerImageProcessingTester(self) @property def snake_case_ ( self): return self.image_processor_tester.prepare_image_processor_dict() def snake_case_ ( self): __SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(lowerCAmelCase__ , """image_mean""")) self.assertTrue(hasattr(lowerCAmelCase__ , """image_std""")) self.assertTrue(hasattr(lowerCAmelCase__ , """do_normalize""")) self.assertTrue(hasattr(lowerCAmelCase__ , """do_resize""")) self.assertTrue(hasattr(lowerCAmelCase__ , """size""")) self.assertTrue(hasattr(lowerCAmelCase__ , """size_divisor""")) def snake_case_ ( self): pass def snake_case_ ( self): # Initialize image processor __SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict) # create random PIL images __SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__) for image in image_inputs: self.assertIsInstance(lowerCAmelCase__ , Image.Image) # Test not batched input __SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__) self.assertEqual( encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , ) # Test batched __SCREAMING_SNAKE_CASE = image_processing(lowerCAmelCase__ , return_tensors="""pt""").pixel_values __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__) self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ) , ) def snake_case_ ( self): # Initialize image processor __SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict) # create random numpy tensors __SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__) for image in image_inputs: self.assertIsInstance(lowerCAmelCase__ , np.ndarray) # Test not batched input __SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__) self.assertEqual( encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , ) # Test batched __SCREAMING_SNAKE_CASE = image_processing(lowerCAmelCase__ , return_tensors="""pt""").pixel_values __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__) self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ) , ) def snake_case_ ( self): # Initialize image processor __SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict) # create random PyTorch tensors __SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__) for image in image_inputs: self.assertIsInstance(lowerCAmelCase__ , torch.Tensor) # Test not batched input __SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__) self.assertEqual( encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , ) # Test batched __SCREAMING_SNAKE_CASE = image_processing(lowerCAmelCase__ , return_tensors="""pt""").pixel_values __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__) self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ) , )	255	1
lowercase : Any = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/""" def A_ ( A__ ) -> bytes: # Make sure the supplied data is a bytes-like object if not isinstance(A__ , A__ ): a__ : List[Any] = F'a bytes-like object is required, not \'{data.__class__.__name__}\'' raise TypeError(A__ ) a__ : str = ''.join(bin(A__ )[2:].zfill(8 ) for byte in data ) a__ : List[Any] = len(A__ ) % 6 != 0 if padding_needed: # The padding that will be added later a__ : str = B'=' * ((6 - len(A__ ) % 6) // 2) # Append binary_stream with arbitrary binary digits (0's by default) to make its # length a multiple of 6. binary_stream += "0" * (6 - len(A__ ) % 6) else: a__ : Any = B'' # Encode every 6 binary digits to their corresponding Base64 character return ( "".join( B64_CHARSET[int(binary_stream[index : index + 6] , 2 )] for index in range(0 , len(A__ ) , 6 ) ).encode() + padding ) def A_ ( A__ ) -> bytes: # Make sure encoded_data is either a string or a bytes-like object if not isinstance(A__ , A__ ) and not isinstance(A__ , A__ ): a__ : int = ( 'argument should be a bytes-like object or ASCII string, ' F'not \'{encoded_data.__class__.__name__}\'' ) raise TypeError(A__ ) # In case encoded_data is a bytes-like object, make sure it contains only # ASCII characters so we convert it to a string object if isinstance(A__ , A__ ): try: a__ : Optional[int] = encoded_data.decode('utf-8' ) except UnicodeDecodeError: raise ValueError('base64 encoded data should only contain ASCII characters' ) a__ : List[Any] = encoded_data.count('=' ) # Check if the encoded string contains non base64 characters if padding: assert all( char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found." else: assert all( char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found." # Check the padding assert len(A__ ) % 4 == 0 and padding < 3, "Incorrect padding" if padding: # Remove padding if there is one a__ : Optional[int] = encoded_data[:-padding] a__ : Optional[int] = ''.join( bin(B64_CHARSET.index(A__ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2] else: a__ : Optional[int] = ''.join( bin(B64_CHARSET.index(A__ ) )[2:].zfill(6 ) for char in encoded_data ) a__ : Any = [ int(binary_stream[index : index + 8] , 2 ) for index in range(0 , len(A__ ) , 8 ) ] return bytes(A__ ) if __name__ == "__main__": import doctest doctest.testmod()	99	'''simple docstring''' # DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim from dataclasses import dataclass from typing import Optional, Tuple, Union import flax import jax import jax.numpy as jnp from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils_flax import ( CommonSchedulerState, FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, add_noise_common, get_velocity_common, ) @flax.struct.dataclass class lowerCAmelCase__ : lowerCAmelCase_ = 42 # setable values lowerCAmelCase_ = 42 lowerCAmelCase_ = 42 lowerCAmelCase_ = None @classmethod def _snake_case ( cls , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ): """simple docstring""" return cls(common=__SCREAMING_SNAKE_CASE , init_noise_sigma=__SCREAMING_SNAKE_CASE , timesteps=__SCREAMING_SNAKE_CASE ) @dataclass class lowerCAmelCase__ ( lowerCamelCase_ ): lowerCAmelCase_ = 42 class lowerCAmelCase__ ( lowerCamelCase_ , lowerCamelCase_ ): lowerCAmelCase_ = [e.name for e in FlaxKarrasDiffusionSchedulers] lowerCAmelCase_ = 42 @property def _snake_case ( self ): """simple docstring""" return True @register_to_config def __init__( self , __SCREAMING_SNAKE_CASE = 10_00 , __SCREAMING_SNAKE_CASE = 0.0_001 , __SCREAMING_SNAKE_CASE = 0.02 , __SCREAMING_SNAKE_CASE = "linear" , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = "fixed_small" , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE = "epsilon" , __SCREAMING_SNAKE_CASE = jnp.floataa , ): """simple docstring""" lowercase_ : Dict = dtype def _snake_case ( self , __SCREAMING_SNAKE_CASE = None ): """simple docstring""" if common is None: lowercase_ : Tuple = CommonSchedulerState.create(self ) # standard deviation of the initial noise distribution lowercase_ : Union[str, Any] = jnp.array(1.0 , dtype=self.dtype ) lowercase_ : List[Any] = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1] return DDPMSchedulerState.create( common=__SCREAMING_SNAKE_CASE , init_noise_sigma=__SCREAMING_SNAKE_CASE , timesteps=__SCREAMING_SNAKE_CASE , ) def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ): """simple docstring""" return sample def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = () ): """simple docstring""" lowercase_ : Optional[Any] = self.config.num_train_timesteps // num_inference_steps # creates integer timesteps by multiplying by ratio # rounding to avoid issues when num_inference_step is power of 3 lowercase_ : int = (jnp.arange(0 , __SCREAMING_SNAKE_CASE ) * step_ratio).round()[::-1] return state.replace( num_inference_steps=__SCREAMING_SNAKE_CASE , timesteps=__SCREAMING_SNAKE_CASE , ) def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ): """simple docstring""" lowercase_ : List[Any] = state.common.alphas_cumprod[t] lowercase_ : str = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) ) # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) # and sample from it to get previous sample # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample lowercase_ : int = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t] if variance_type is None: lowercase_ : str = self.config.variance_type # hacks - were probably added for training stability if variance_type == "fixed_small": lowercase_ : int = jnp.clip(__SCREAMING_SNAKE_CASE , a_min=1E-2_0 ) # for rl-diffuser https://arxiv.org/abs/2205.09991 elif variance_type == "fixed_small_log": lowercase_ : List[str] = jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE , a_min=1E-2_0 ) ) elif variance_type == "fixed_large": lowercase_ : List[Any] = state.common.betas[t] elif variance_type == "fixed_large_log": # Glide max_log lowercase_ : List[Any] = jnp.log(state.common.betas[t] ) elif variance_type == "learned": return predicted_variance elif variance_type == "learned_range": lowercase_ : Optional[Any] = variance lowercase_ : Union[str, Any] = state.common.betas[t] lowercase_ : Union[str, Any] = (predicted_variance + 1) / 2 lowercase_ : Any = frac * max_log + (1 - frac) * min_log return variance def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = True , ): """simple docstring""" lowercase_ : Optional[int] = timestep if key is None: lowercase_ : int = jax.random.PRNGKey(0 ) if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]: lowercase_ , lowercase_ : Optional[Any] = jnp.split(__SCREAMING_SNAKE_CASE , sample.shape[1] , axis=1 ) else: lowercase_ : int = None # 1. compute alphas, betas lowercase_ : Any = state.common.alphas_cumprod[t] lowercase_ : Optional[int] = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) ) lowercase_ : int = 1 - alpha_prod_t lowercase_ : str = 1 - alpha_prod_t_prev # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf if self.config.prediction_type == "epsilon": lowercase_ : Tuple = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 elif self.config.prediction_type == "sample": lowercase_ : Any = model_output elif self.config.prediction_type == "v_prediction": lowercase_ : List[Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output else: raise ValueError( F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` ''' ''' for the FlaxDDPMScheduler.''' ) # 3. Clip "predicted x_0" if self.config.clip_sample: lowercase_ : Optional[Any] = jnp.clip(__SCREAMING_SNAKE_CASE , -1 , 1 ) # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf lowercase_ : List[Any] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t lowercase_ : Optional[Any] = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t # 5. Compute predicted previous sample µ_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf lowercase_ : Optional[Any] = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample # 6. Add noise def random_variance(): lowercase_ : str = jax.random.split(__SCREAMING_SNAKE_CASE , num=1 ) lowercase_ : List[Any] = jax.random.normal(__SCREAMING_SNAKE_CASE , shape=model_output.shape , dtype=self.dtype ) return (self._get_variance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , predicted_variance=__SCREAMING_SNAKE_CASE ) ** 0.5) * noise lowercase_ : Optional[Any] = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) ) lowercase_ : Any = pred_prev_sample + variance if not return_dict: return (pred_prev_sample, state) return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE , state=__SCREAMING_SNAKE_CASE ) def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ): """simple docstring""" return add_noise_common(state.common , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) def _snake_case ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ): """simple docstring""" return get_velocity_common(state.common , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) def __len__( self ): """simple docstring""" return self.config.num_train_timesteps	93	0
'''simple docstring''' def _a ( _lowercase : str ): '''simple docstring''' return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') ) def _a ( _lowercase : str ): '''simple docstring''' __UpperCAmelCase : int = credit_card_number __UpperCAmelCase : Union[str, Any] = 0 __UpperCAmelCase : Optional[Any] = len(_lowercase ) - 2 for i in range(_lowercase , -1 , -2 ): # double the value of every second digit __UpperCAmelCase : Tuple = int(cc_number[i] ) digit *= 2 # If doubling of a number results in a two digit number # i.e greater than 9(e.g., 6 × 2 = 12), # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6), # to get a single digit number. if digit > 9: digit %= 10 digit += 1 __UpperCAmelCase : List[Any] = cc_number[:i] + str(_lowercase ) + cc_number[i + 1 :] total += digit # Sum up the remaining digits for i in range(len(_lowercase ) - 1 , -1 , -2 ): total += int(cc_number[i] ) return total % 10 == 0 def _a ( _lowercase : str ): '''simple docstring''' __UpperCAmelCase : Tuple = F'{credit_card_number} is an invalid credit card number because' if not credit_card_number.isdigit(): print(F'{error_message} it has nonnumerical characters.' ) return False if not 13 <= len(_lowercase ) <= 16: print(F'{error_message} of its length.' ) return False if not validate_initial_digits(_lowercase ): print(F'{error_message} of its first two digits.' ) return False if not luhn_validation(_lowercase ): print(F'{error_message} it fails the Luhn check.' ) return False print(F'{credit_card_number} is a valid credit card number.' ) return True if __name__ == "__main__": import doctest doctest.testmod() validate_credit_card_number("4111111111111111") validate_credit_card_number("32323")	240	'''simple docstring''' import argparse import torch from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration from transformers.utils import logging logging.set_verbosity_info() __UpperCAmelCase :Any = logging.get_logger(__name__) __UpperCAmelCase :Dict = [ ["attention", "attn"], ["encoder_attention", "encoder_attn"], ["q_lin", "q_proj"], ["k_lin", "k_proj"], ["v_lin", "v_proj"], ["out_lin", "out_proj"], ["norm_embeddings", "layernorm_embedding"], ["position_embeddings", "embed_positions"], ["embeddings", "embed_tokens"], ["ffn.lin", "fc"], ] def _a ( _lowercase : Tuple ): '''simple docstring''' if k == "embeddings.weight": return "shared.weight" for parlai_name, hf_name in PATTERNS: __UpperCAmelCase : Any = k.replace(_lowercase , _lowercase ) if k.startswith('''encoder''' ): __UpperCAmelCase : str = k.replace('''.attn''' , '''.self_attn''' ) __UpperCAmelCase : Any = k.replace('''norm1''' , '''self_attn_layer_norm''' ) __UpperCAmelCase : List[str] = k.replace('''norm2''' , '''final_layer_norm''' ) elif k.startswith('''decoder''' ): __UpperCAmelCase : int = k.replace('''norm1''' , '''self_attn_layer_norm''' ) __UpperCAmelCase : Union[str, Any] = k.replace('''norm2''' , '''encoder_attn_layer_norm''' ) __UpperCAmelCase : List[Any] = k.replace('''norm3''' , '''final_layer_norm''' ) return k def _a ( _lowercase : Union[str, Any] ): '''simple docstring''' __UpperCAmelCase : int = [ '''model.encoder.layernorm_embedding.weight''', '''model.encoder.layernorm_embedding.bias''', '''model.decoder.layernorm_embedding.weight''', '''model.decoder.layernorm_embedding.bias''', ] for k in keys: __UpperCAmelCase : Any = sd.pop(_lowercase ) __UpperCAmelCase : Optional[int] = k.replace('''layernorm_embedding''' , '''layer_norm''' ) assert new_k not in sd __UpperCAmelCase : List[str] = v __UpperCAmelCase :str = ["START"] @torch.no_grad() def _a ( _lowercase : Optional[int] , _lowercase : Optional[int] , _lowercase : str ): '''simple docstring''' __UpperCAmelCase : Any = torch.load(_lowercase , map_location='''cpu''' ) __UpperCAmelCase : List[str] = model['''model'''] __UpperCAmelCase : Optional[Any] = BlenderbotConfig.from_json_file(_lowercase ) __UpperCAmelCase : Optional[Any] = BlenderbotForConditionalGeneration(_lowercase ) __UpperCAmelCase : Optional[Any] = m.model.state_dict().keys() __UpperCAmelCase : int = [] __UpperCAmelCase : List[str] = {} for k, v in sd.items(): if k in IGNORE_KEYS: continue __UpperCAmelCase : int = rename_state_dict_key(_lowercase ) if new_k not in valid_keys: failures.append([k, new_k] ) else: __UpperCAmelCase : Union[str, Any] = v if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm rename_layernorm_keys(_lowercase ) m.model.load_state_dict(_lowercase , strict=_lowercase ) m.half() m.save_pretrained(_lowercase ) if __name__ == "__main__": __UpperCAmelCase :Optional[int] = argparse.ArgumentParser() # Required parameters parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin") parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.") parser.add_argument( "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use" ) __UpperCAmelCase :Tuple = parser.parse_args() convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)	240	1
from collections.abc import Sequence def _UpperCAmelCase ( snake_case , snake_case = False ): """simple docstring""" if not arr: return 0 _lowerCAmelCase = 0 if allow_empty_subarrays else float("""-inf""" ) _lowerCAmelCase = 0.0 for num in arr: _lowerCAmelCase = max(0 if allow_empty_subarrays else num , curr_sum + num ) _lowerCAmelCase = max(snake_case , snake_case ) return max_sum if __name__ == "__main__": from doctest import testmod testmod() A__ = [-2, 1, -3, 4, -1, 2, 1, -5, 4] print(f"{max_subarray_sum(nums) = }")	82	from __future__ import annotations import numpy as np from numpy import floataa from numpy.typing import NDArray def __snake_case ( __UpperCamelCase : NDArray[floataa] ,__UpperCamelCase : NDArray[floataa] ,__UpperCamelCase : list[int] ,__UpperCamelCase : int ,): """simple docstring""" A_ , A_ = coefficient_matrix.shape A_ , A_ = constant_matrix.shape if rowsa != colsa: A_ = f'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}''' raise ValueError(__UpperCamelCase ) if colsa != 1: A_ = f'''Constant matrix must be nx1 but received {rowsa}x{colsa}''' raise ValueError(__UpperCamelCase ) if rowsa != rowsa: A_ = ( "Coefficient and constant matrices dimensions must be nxn and nx1 but " f'''received {rowsa}x{colsa} and {rowsa}x{colsa}''' ) raise ValueError(__UpperCamelCase ) if len(__UpperCamelCase ) != rowsa: A_ = ( "Number of initial values must be equal to number of rows in coefficient " f'''matrix but received {len(__UpperCamelCase )} and {rowsa}''' ) raise ValueError(__UpperCamelCase ) if iterations <= 0: raise ValueError("Iterations must be at least 1" ) A_ = np.concatenate( (coefficient_matrix, constant_matrix) ,axis=1 ) A_ , A_ = table.shape strictly_diagonally_dominant(__UpperCamelCase ) # Iterates the whole matrix for given number of times for _ in range(__UpperCamelCase ): A_ = [] for row in range(__UpperCamelCase ): A_ = 0 for col in range(__UpperCamelCase ): if col == row: A_ = table[row][col] elif col == cols - 1: A_ = table[row][col] else: temp += (-1) * table[row][col] * init_val[col] A_ = (temp + val) / denom new_val.append(__UpperCamelCase ) A_ = new_val return [float(__UpperCamelCase ) for i in new_val] def __snake_case ( __UpperCamelCase : NDArray[floataa] ): """simple docstring""" A_ , A_ = table.shape A_ = True for i in range(0 ,__UpperCamelCase ): A_ = 0 for j in range(0 ,cols - 1 ): if i == j: continue else: total += table[i][j] if table[i][i] <= total: raise ValueError("Coefficient matrix is not strictly diagonally dominant" ) return is_diagonally_dominant # Test Cases if __name__ == "__main__": import doctest doctest.testmod()	312	0
import inspect import os import unittest import torch import accelerate from accelerate import Accelerator from accelerate.test_utils import execute_subprocess_async, require_multi_gpu from accelerate.utils import patch_environment class __a ( unittest.TestCase ): def __lowercase ( self : List[Any] ): '''simple docstring''' UpperCamelCase__ : Dict = inspect.getfile(accelerate.test_utils ) UpperCamelCase__ : Dict = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] ) UpperCamelCase__ : List[str] = os.path.sep.join( mod_file.split(os.path.sep )[:-1] + ["scripts", "test_distributed_data_loop.py"] ) UpperCamelCase__ : int = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_ops.py"] ) @require_multi_gpu def __lowercase ( self : Tuple ): '''simple docstring''' print(F'Found {torch.cuda.device_count()} devices.' ) UpperCamelCase__ : Dict = ["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path] with patch_environment(omp_num_threads=1 ): execute_subprocess_async(SCREAMING_SNAKE_CASE , env=os.environ.copy() ) @require_multi_gpu def __lowercase ( self : int ): '''simple docstring''' print(F'Found {torch.cuda.device_count()} devices.' ) UpperCamelCase__ : int = ["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path] print(F'Command: {cmd}' ) with patch_environment(omp_num_threads=1 ): execute_subprocess_async(SCREAMING_SNAKE_CASE , env=os.environ.copy() ) @require_multi_gpu def __lowercase ( self : Any ): '''simple docstring''' UpperCamelCase__ : Optional[Any] = ["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )] with patch_environment(omp_num_threads=1 ): execute_subprocess_async(SCREAMING_SNAKE_CASE , env=os.environ.copy() ) @require_multi_gpu def __lowercase ( self : Dict ): '''simple docstring''' print(F'Found {torch.cuda.device_count()} devices, using 2 devices only' ) UpperCamelCase__ : Tuple = ["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path] with patch_environment(omp_num_threads=1 , cuda_visible_devices="0,1" ): execute_subprocess_async(SCREAMING_SNAKE_CASE , env=os.environ.copy() ) if __name__ == "__main__": lowerCamelCase : List[Any] =Accelerator() lowerCamelCase : Dict =(accelerator.state.process_index + 2, 10) lowerCamelCase : int =torch.randint(0, 10, shape).to(accelerator.device) lowerCamelCase : Tuple ='''''' lowerCamelCase : Optional[int] =accelerator.pad_across_processes(tensor) if tensora.shape[0] != accelerator.state.num_processes + 1: error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0." if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor): error_msg += "Tensors have different values." if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0): error_msg += "Padding was not done with the right value (0)." lowerCamelCase : str =accelerator.pad_across_processes(tensor, pad_first=True) if tensora.shape[0] != accelerator.state.num_processes + 1: error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0." lowerCamelCase : Optional[int] =accelerator.state.num_processes - accelerator.state.process_index - 1 if not torch.equal(tensora[index:], tensor): error_msg += "Tensors have different values." if not torch.all(tensora[:index] == 0): error_msg += "Padding was not done with the right value (0)." # Raise error at the end to make sure we don't stop at the first failure. if len(error_msg) > 0: raise ValueError(error_msg)	196	import argparse import os import re import packaging.version lowerCamelCase : Optional[Any] ='''examples/''' lowerCamelCase : List[Any] ={ '''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''), '''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''), '''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''), '''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''), } lowerCamelCase : List[str] ={ '''init''': '''src/transformers/__init__.py''', '''setup''': '''setup.py''', } lowerCamelCase : int ='''README.md''' def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]: with open(__lowerCAmelCase , "r" , encoding="utf-8" , newline="\n" ) as f: UpperCamelCase__ : List[Any] = f.read() UpperCamelCase__ , UpperCamelCase__ : List[str] = REPLACE_PATTERNS[pattern] UpperCamelCase__ : Union[str, Any] = replace.replace("VERSION" , __lowerCAmelCase ) UpperCamelCase__ : Tuple = re_pattern.sub(__lowerCAmelCase , __lowerCAmelCase ) with open(__lowerCAmelCase , "w" , encoding="utf-8" , newline="\n" ) as f: f.write(__lowerCAmelCase ) def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Union[str, Any]: for folder, directories, fnames in os.walk(__lowerCAmelCase ): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove("research_projects" ) if "legacy" in directories: directories.remove("legacy" ) for fname in fnames: if fname.endswith(".py" ): update_version_in_file(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , pattern="examples" ) def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase=False ) -> Optional[int]: for pattern, fname in REPLACE_FILES.items(): update_version_in_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) if not patch: update_version_in_examples(__lowerCAmelCase ) def SCREAMING_SNAKE_CASE ( ) -> Optional[Any]: UpperCamelCase__ : Tuple = "🤗 Transformers currently provides the following architectures" UpperCamelCase__ : Tuple = "1. Want to contribute a new model?" with open(__lowerCAmelCase , "r" , encoding="utf-8" , newline="\n" ) as f: UpperCamelCase__ : Optional[int] = f.readlines() # Find the start of the list. UpperCamelCase__ : List[Any] = 0 while not lines[start_index].startswith(_start_prompt ): start_index += 1 start_index += 1 UpperCamelCase__ : Dict = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt ): if lines[index].startswith("1." ): UpperCamelCase__ : str = lines[index].replace( "https://huggingface.co/docs/transformers/main/model_doc" , "https://huggingface.co/docs/transformers/model_doc" , ) index += 1 with open(__lowerCAmelCase , "w" , encoding="utf-8" , newline="\n" ) as f: f.writelines(__lowerCAmelCase ) def SCREAMING_SNAKE_CASE ( ) -> Tuple: with open(REPLACE_FILES["init"] , "r" ) as f: UpperCamelCase__ : str = f.read() UpperCamelCase__ : Dict = REPLACE_PATTERNS["init"][0].search(__lowerCAmelCase ).groups()[0] return packaging.version.parse(__lowerCAmelCase ) def SCREAMING_SNAKE_CASE ( __lowerCAmelCase=False ) -> Optional[int]: UpperCamelCase__ : Dict = get_version() if patch and default_version.is_devrelease: raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" ) if default_version.is_devrelease: UpperCamelCase__ : List[str] = default_version.base_version elif patch: UpperCamelCase__ : int = f'{default_version.major}.{default_version.minor}.{default_version.micro + 1}' else: UpperCamelCase__ : Tuple = f'{default_version.major}.{default_version.minor + 1}.0' # Now let's ask nicely if that's the right one. UpperCamelCase__ : Tuple = input(f'Which version are you releasing? [{default_version}]' ) if len(__lowerCAmelCase ) == 0: UpperCamelCase__ : Any = default_version print(f'Updating version to {version}.' ) global_version_update(__lowerCAmelCase , patch=__lowerCAmelCase ) if not patch: print("Cleaning main README, don't forget to run `make fix-copies`." ) clean_main_ref_in_model_list() def SCREAMING_SNAKE_CASE ( ) -> int: UpperCamelCase__ : str = get_version() UpperCamelCase__ : Dict = f'{current_version.major}.{current_version.minor + 1}.0.dev0' UpperCamelCase__ : int = current_version.base_version # Check with the user we got that right. UpperCamelCase__ : List[str] = input(f'Which version are we developing now? [{dev_version}]' ) if len(__lowerCAmelCase ) == 0: UpperCamelCase__ : Optional[Any] = dev_version print(f'Updating version to {version}.' ) global_version_update(__lowerCAmelCase ) print("Cleaning main README, don't forget to run `make fix-copies`." ) clean_main_ref_in_model_list() if __name__ == "__main__": lowerCamelCase : List[Any] =argparse.ArgumentParser() parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''') parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''') lowerCamelCase : Optional[Any] =parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print('''Nothing to do after a patch :-)''') else: post_release_work()	196	1
'''simple docstring''' import copy from typing import Any, Dict, List, Optional, Union import numpy as np from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import TensorType, logging a_ : Union[str, Any] = logging.get_logger(__name__) class snake_case ( __lowerCAmelCase ): """simple docstring""" _lowerCamelCase = ["input_features"] def __init__( self , UpperCamelCase=80 , UpperCamelCase=1_6000 , UpperCamelCase=160 , UpperCamelCase=30 , UpperCamelCase=400 , UpperCamelCase=0.0 , UpperCamelCase=False , **UpperCamelCase , ): """simple docstring""" super().__init__( feature_size=UpperCamelCase , sampling_rate=UpperCamelCase , padding_value=UpperCamelCase , return_attention_mask=UpperCamelCase , **UpperCamelCase , ) lowerCamelCase_ = n_fft lowerCamelCase_ = hop_length lowerCamelCase_ = chunk_length lowerCamelCase_ = chunk_length * sampling_rate lowerCamelCase_ = self.n_samples // hop_length lowerCamelCase_ = sampling_rate lowerCamelCase_ = mel_filter_bank( num_frequency_bins=1 + n_fft // 2 , num_mel_filters=UpperCamelCase , min_frequency=0.0 , max_frequency=8_000.0 , sampling_rate=UpperCamelCase , norm="slaney" , mel_scale="slaney" , ) def snake_case ( self , UpperCamelCase ): """simple docstring""" lowerCamelCase_ = spectrogram( UpperCamelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel="log10" , ) lowerCamelCase_ = log_spec[:, :-1] lowerCamelCase_ = np.maximum(UpperCamelCase , log_spec.max() - 8.0 ) lowerCamelCase_ = (log_spec + 4.0) / 4.0 return log_spec @staticmethod # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm def snake_case ( UpperCamelCase , UpperCamelCase , UpperCamelCase = 0.0 ): """simple docstring""" if attention_mask is not None: lowerCamelCase_ = np.array(UpperCamelCase , np.intaa ) lowerCamelCase_ = [] for vector, length in zip(UpperCamelCase , attention_mask.sum(-1 ) ): lowerCamelCase_ = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 ) if length < normed_slice.shape[0]: lowerCamelCase_ = padding_value normed_input_values.append(UpperCamelCase ) else: lowerCamelCase_ = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values] return normed_input_values def __call__( self , UpperCamelCase , UpperCamelCase = True , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = "max_length" , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = None , **UpperCamelCase , ): """simple docstring""" if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a''' f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input''' f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' ) else: logger.warning( "It is strongly recommended to pass the `sampling_rate` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) lowerCamelCase_ = isinstance(UpperCamelCase , np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' ) lowerCamelCase_ = is_batched_numpy or ( isinstance(UpperCamelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) )) ) if is_batched: lowerCamelCase_ = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech] elif not is_batched and not isinstance(UpperCamelCase , np.ndarray ): lowerCamelCase_ = np.asarray(UpperCamelCase , dtype=np.floataa ) elif isinstance(UpperCamelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): lowerCamelCase_ = raw_speech.astype(np.floataa ) # always return batch if not is_batched: lowerCamelCase_ = [np.asarray([raw_speech] ).T] lowerCamelCase_ = BatchFeature({"input_features": raw_speech} ) # convert into correct format for padding lowerCamelCase_ = self.pad( UpperCamelCase , padding=UpperCamelCase , max_length=max_length if max_length else self.n_samples , truncation=UpperCamelCase , pad_to_multiple_of=UpperCamelCase , return_attention_mask=return_attention_mask or do_normalize , ) # zero-mean and unit-variance normalization if do_normalize: lowerCamelCase_ = self.zero_mean_unit_var_norm( padded_inputs["input_features"] , attention_mask=padded_inputs["attention_mask"] , padding_value=self.padding_value , ) lowerCamelCase_ = np.stack(padded_inputs["input_features"] , axis=0 ) # make sure list is in array format lowerCamelCase_ = padded_inputs.get("input_features" ).transpose(2 , 0 , 1 ) lowerCamelCase_ = [self._np_extract_fbank_features(UpperCamelCase ) for waveform in input_features[0]] if isinstance(input_features[0] , UpperCamelCase ): lowerCamelCase_ = [np.asarray(UpperCamelCase , dtype=np.floataa ) for feature in input_features] else: lowerCamelCase_ = input_features if return_attention_mask: # rescale from sample (48000) to feature (3000) lowerCamelCase_ = padded_inputs['attention_mask'][:, :: self.hop_length] if return_tensors is not None: lowerCamelCase_ = padded_inputs.convert_to_tensors(UpperCamelCase ) return padded_inputs def snake_case ( self ): """simple docstring""" lowerCamelCase_ = copy.deepcopy(self.__dict__ ) lowerCamelCase_ = self.__class__.__name__ if "mel_filters" in output: del output["mel_filters"] return output	55	"""simple docstring""" import os from typing import Dict, List, Tuple, TypeVar, Union lowercase__ = TypeVar('T') lowercase__ = Union[List[T], Tuple[T, ...]] lowercase__ = Union[T, List[T], Dict[str, T]] lowercase__ = Union[str, bytes, os.PathLike]	290	0
import json import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from transformers import OneFormerImageProcessor from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput if is_vision_available(): from PIL import Image def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Any , __UpperCamelCase : Tuple="shi-labs/oneformer_demo" ) -> Tuple: """simple docstring""" with open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) , """r""" ) as f: SCREAMING_SNAKE_CASE__ = json.load(__UpperCamelCase ) SCREAMING_SNAKE_CASE__ = {} SCREAMING_SNAKE_CASE__ = [] SCREAMING_SNAKE_CASE__ = [] for key, info in class_info.items(): SCREAMING_SNAKE_CASE__ = info["""name"""] class_names.append(info["""name"""] ) if info["isthing"]: thing_ids.append(int(__UpperCamelCase ) ) SCREAMING_SNAKE_CASE__ = thing_ids SCREAMING_SNAKE_CASE__ = class_names return metadata class __snake_case ( unittest.TestCase ): def __init__( self : List[Any] , _lowercase : Optional[Any] , _lowercase : int=7 , _lowercase : Any=3 , _lowercase : int=30 , _lowercase : List[Any]=4_00 , _lowercase : Union[str, Any]=None , _lowercase : Dict=True , _lowercase : Tuple=True , _lowercase : int=[0.5, 0.5, 0.5] , _lowercase : List[str]=[0.5, 0.5, 0.5] , _lowercase : str=10 , _lowercase : Union[str, Any]=False , _lowercase : int=2_55 , _lowercase : List[str]="shi-labs/oneformer_demo" , _lowercase : Any="ade20k_panoptic.json" , _lowercase : Any=10 , ): """simple docstring""" SCREAMING_SNAKE_CASE__ = parent SCREAMING_SNAKE_CASE__ = batch_size SCREAMING_SNAKE_CASE__ = num_channels SCREAMING_SNAKE_CASE__ = min_resolution SCREAMING_SNAKE_CASE__ = max_resolution SCREAMING_SNAKE_CASE__ = do_resize SCREAMING_SNAKE_CASE__ = {"""shortest_edge""": 32, """longest_edge""": 13_33} if size is None else size SCREAMING_SNAKE_CASE__ = do_normalize SCREAMING_SNAKE_CASE__ = image_mean SCREAMING_SNAKE_CASE__ = image_std SCREAMING_SNAKE_CASE__ = class_info_file SCREAMING_SNAKE_CASE__ = prepare_metadata(_lowercase , _lowercase ) SCREAMING_SNAKE_CASE__ = num_text SCREAMING_SNAKE_CASE__ = repo_path # for the post_process_functions SCREAMING_SNAKE_CASE__ = 2 SCREAMING_SNAKE_CASE__ = 10 SCREAMING_SNAKE_CASE__ = 10 SCREAMING_SNAKE_CASE__ = 3 SCREAMING_SNAKE_CASE__ = 4 SCREAMING_SNAKE_CASE__ = num_labels SCREAMING_SNAKE_CASE__ = do_reduce_labels SCREAMING_SNAKE_CASE__ = ignore_index def __a ( self : Optional[int] ): """simple docstring""" return { "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "num_labels": self.num_labels, "do_reduce_labels": self.do_reduce_labels, "ignore_index": self.ignore_index, "class_info_file": self.class_info_file, "metadata": self.metadata, "num_text": self.num_text, } def __a ( self : List[Any] , _lowercase : Any , _lowercase : Optional[Any]=False ): """simple docstring""" if not batched: SCREAMING_SNAKE_CASE__ = image_inputs[0] if isinstance(_lowercase , Image.Image ): SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = image.size else: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = image.shape[1], image.shape[2] if w < h: SCREAMING_SNAKE_CASE__ = int(self.size["""shortest_edge"""] * h / w ) SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""] elif w > h: SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""] SCREAMING_SNAKE_CASE__ = int(self.size["""shortest_edge"""] * w / h ) else: SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""] SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""] else: SCREAMING_SNAKE_CASE__ = [] for image in image_inputs: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.get_expected_values([image] ) expected_values.append((expected_height, expected_width) ) SCREAMING_SNAKE_CASE__ = max(_lowercase , key=lambda _lowercase : item[0] )[0] SCREAMING_SNAKE_CASE__ = max(_lowercase , key=lambda _lowercase : item[1] )[1] return expected_height, expected_width def __a ( self : Dict ): """simple docstring""" return OneFormerForUniversalSegmentationOutput( # +1 for null class class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , ) @require_torch @require_vision class __snake_case ( lowerCamelCase_ , unittest.TestCase ): lowerCAmelCase_ = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None # only for test_image_processing_common.test_image_proc_to_json_string lowerCAmelCase_ = image_processing_class def __a ( self : str ): """simple docstring""" SCREAMING_SNAKE_CASE__ = OneFormerImageProcessorTester(self ) @property def __a ( self : str ): """simple docstring""" return self.image_processing_tester.prepare_image_processor_dict() def __a ( self : str ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(_lowercase , """image_mean""" ) ) self.assertTrue(hasattr(_lowercase , """image_std""" ) ) self.assertTrue(hasattr(_lowercase , """do_normalize""" ) ) self.assertTrue(hasattr(_lowercase , """do_resize""" ) ) self.assertTrue(hasattr(_lowercase , """size""" ) ) self.assertTrue(hasattr(_lowercase , """ignore_index""" ) ) self.assertTrue(hasattr(_lowercase , """class_info_file""" ) ) self.assertTrue(hasattr(_lowercase , """num_text""" ) ) self.assertTrue(hasattr(_lowercase , """repo_path""" ) ) self.assertTrue(hasattr(_lowercase , """metadata""" ) ) self.assertTrue(hasattr(_lowercase , """do_reduce_labels""" ) ) def __a ( self : Any ): """simple docstring""" pass def __a ( self : Optional[int] ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict ) # create random PIL images SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase ) for image in image_inputs: self.assertIsInstance(_lowercase , Image.Image ) # Test not batched input SCREAMING_SNAKE_CASE__ = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase ) self.assertEqual( encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , ) # Test batched SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase ) SCREAMING_SNAKE_CASE__ = image_processor( _lowercase , ["""semantic"""] * len(_lowercase ) , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processing_tester.batch_size, self.image_processing_tester.num_channels, expected_height, expected_width, ) , ) def __a ( self : int ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase , numpify=_lowercase ) for image in image_inputs: self.assertIsInstance(_lowercase , np.ndarray ) # Test not batched input SCREAMING_SNAKE_CASE__ = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase ) self.assertEqual( encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , ) # Test batched SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase ) SCREAMING_SNAKE_CASE__ = image_processor( _lowercase , ["""semantic"""] * len(_lowercase ) , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processing_tester.batch_size, self.image_processing_tester.num_channels, expected_height, expected_width, ) , ) def __a ( self : str ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase , torchify=_lowercase ) for image in image_inputs: self.assertIsInstance(_lowercase , torch.Tensor ) # Test not batched input SCREAMING_SNAKE_CASE__ = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase ) self.assertEqual( encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , ) # Test batched SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_expected_values(_lowercase , batched=_lowercase ) SCREAMING_SNAKE_CASE__ = image_processor( _lowercase , ["""semantic"""] * len(_lowercase ) , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processing_tester.batch_size, self.image_processing_tester.num_channels, expected_height, expected_width, ) , ) def __a ( self : Tuple , _lowercase : Optional[int]=False , _lowercase : Any=False , _lowercase : List[str]="np" ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict ) # prepare image and target SCREAMING_SNAKE_CASE__ = self.image_processing_tester.num_labels SCREAMING_SNAKE_CASE__ = None SCREAMING_SNAKE_CASE__ = None SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=_lowercase ) if with_segmentation_maps: SCREAMING_SNAKE_CASE__ = num_labels if is_instance_map: SCREAMING_SNAKE_CASE__ = list(range(_lowercase ) ) * 2 SCREAMING_SNAKE_CASE__ = dict(enumerate(_lowercase ) ) SCREAMING_SNAKE_CASE__ = [ np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs ] if segmentation_type == "pil": SCREAMING_SNAKE_CASE__ = [Image.fromarray(_lowercase ) for annotation in annotations] SCREAMING_SNAKE_CASE__ = image_processor( _lowercase , ["""semantic"""] * len(_lowercase ) , _lowercase , return_tensors="""pt""" , instance_id_to_semantic_id=_lowercase , pad_and_return_pixel_mask=_lowercase , ) return inputs def __a ( self : str ): """simple docstring""" pass def __a ( self : Tuple ): """simple docstring""" def common(_lowercase : Optional[int]=False , _lowercase : Union[str, Any]=None ): SCREAMING_SNAKE_CASE__ = self.comm_get_image_processor_inputs( with_segmentation_maps=_lowercase , is_instance_map=_lowercase , segmentation_type=_lowercase ) SCREAMING_SNAKE_CASE__ = inputs["""mask_labels"""] SCREAMING_SNAKE_CASE__ = inputs["""class_labels"""] SCREAMING_SNAKE_CASE__ = inputs["""pixel_values"""] SCREAMING_SNAKE_CASE__ = inputs["""text_inputs"""] # check the batch_size for mask_label, class_label, text_input in zip(_lowercase , _lowercase , _lowercase ): self.assertEqual(mask_label.shape[0] , class_label.shape[0] ) # this ensure padding has happened self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] ) self.assertEqual(len(_lowercase ) , self.image_processing_tester.num_text ) common() common(is_instance_map=_lowercase ) common(is_instance_map=_lowercase , segmentation_type="""pil""" ) common(is_instance_map=_lowercase , segmentation_type="""pil""" ) def __a ( self : Any ): """simple docstring""" SCREAMING_SNAKE_CASE__ = np.zeros((20, 50) ) SCREAMING_SNAKE_CASE__ = 1 SCREAMING_SNAKE_CASE__ = 1 SCREAMING_SNAKE_CASE__ = 1 SCREAMING_SNAKE_CASE__ = binary_mask_to_rle(_lowercase ) self.assertEqual(len(_lowercase ) , 4 ) self.assertEqual(rle[0] , 21 ) self.assertEqual(rle[1] , 45 ) def __a ( self : Dict ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.image_processing_class( num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , ) SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_fake_oneformer_outputs() SCREAMING_SNAKE_CASE__ = fature_extractor.post_process_semantic_segmentation(_lowercase ) self.assertEqual(len(_lowercase ) , self.image_processing_tester.batch_size ) self.assertEqual( segmentation[0].shape , ( self.image_processing_tester.height, self.image_processing_tester.width, ) , ) SCREAMING_SNAKE_CASE__ = [(1, 4) for i in range(self.image_processing_tester.batch_size )] SCREAMING_SNAKE_CASE__ = fature_extractor.post_process_semantic_segmentation(_lowercase , target_sizes=_lowercase ) self.assertEqual(segmentation[0].shape , target_sizes[0] ) def __a ( self : List[str] ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.image_processing_class( num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , ) SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_fake_oneformer_outputs() SCREAMING_SNAKE_CASE__ = image_processor.post_process_instance_segmentation(_lowercase , threshold=0 ) self.assertTrue(len(_lowercase ) == self.image_processing_tester.batch_size ) for el in segmentation: self.assertTrue("""segmentation""" in el ) self.assertTrue("""segments_info""" in el ) self.assertEqual(type(el["""segments_info"""] ) , _lowercase ) self.assertEqual( el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) ) def __a ( self : Optional[Any] ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.image_processing_class( num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , ) SCREAMING_SNAKE_CASE__ = self.image_processing_tester.get_fake_oneformer_outputs() SCREAMING_SNAKE_CASE__ = image_processor.post_process_panoptic_segmentation(_lowercase , threshold=0 ) self.assertTrue(len(_lowercase ) == self.image_processing_tester.batch_size ) for el in segmentation: self.assertTrue("""segmentation""" in el ) self.assertTrue("""segments_info""" in el ) self.assertEqual(type(el["""segments_info"""] ) , _lowercase ) self.assertEqual( el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )	204	import PIL.Image import PIL.ImageOps from packaging import version from PIL import Image if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''): __lowerCamelCase : Optional[Any] = { '''linear''': PIL.Image.Resampling.BILINEAR, '''bilinear''': PIL.Image.Resampling.BILINEAR, '''bicubic''': PIL.Image.Resampling.BICUBIC, '''lanczos''': PIL.Image.Resampling.LANCZOS, '''nearest''': PIL.Image.Resampling.NEAREST, } else: __lowerCamelCase : int = { '''linear''': PIL.Image.LINEAR, '''bilinear''': PIL.Image.BILINEAR, '''bicubic''': PIL.Image.BICUBIC, '''lanczos''': PIL.Image.LANCZOS, '''nearest''': PIL.Image.NEAREST, } def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ = (images / 2 + 0.5).clamp(0 , 1 ) SCREAMING_SNAKE_CASE__ = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy() SCREAMING_SNAKE_CASE__ = numpy_to_pil(__UpperCamelCase ) return images def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int: """simple docstring""" if images.ndim == 3: SCREAMING_SNAKE_CASE__ = images[None, ...] SCREAMING_SNAKE_CASE__ = (images * 2_55).round().astype("""uint8""" ) if images.shape[-1] == 1: # special case for grayscale (single channel) images SCREAMING_SNAKE_CASE__ = [Image.fromarray(image.squeeze() , mode="""L""" ) for image in images] else: SCREAMING_SNAKE_CASE__ = [Image.fromarray(__UpperCamelCase ) for image in images] return pil_images	204	1
import gc import importlib.metadata import tempfile import unittest from packaging import version from transformers import ( AutoModel, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig, pipeline, ) from transformers.testing_utils import ( is_torch_available, require_accelerate, require_bitsandbytes, require_torch, require_torch_gpu, require_torch_multi_gpu, slow, ) def a( A : List[str] ) -> List[str]: """simple docstring""" if model.config.model_type == "gpt2": return model.transformer.h[0].mlp.c_fc return model.transformer.h[0].mlp.dense_ah_to_h if is_torch_available(): import torch import torch.nn as nn class _lowercase ( nn.Module ): """simple docstring""" def __init__(self , lowerCamelCase_ , lowerCamelCase_ ): """simple docstring""" super().__init__() a = module a = nn.Sequential( nn.Linear(module.in_features , lowerCamelCase_ , bias=lowerCamelCase_ ) , nn.Linear(lowerCamelCase_ , module.out_features , bias=lowerCamelCase_ ) , ) a = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5 nn.init.normal_(self.adapter[0].weight , std=lowerCamelCase_ ) nn.init.zeros_(self.adapter[1].weight ) self.adapter.to(module.weight.device ) def UpperCamelCase_ (self , lowerCamelCase_ , *lowerCamelCase_ , **lowerCamelCase_ ): """simple docstring""" return self.module(lowerCamelCase_ , *lowerCamelCase_ , **lowerCamelCase_ ) + self.adapter(lowerCamelCase_ ) @require_bitsandbytes @require_accelerate @require_torch @require_torch_gpu @slow class _lowercase ( unittest.TestCase ): """simple docstring""" # We keep the constants inside the init function and model loading inside setUp function # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected) # Therefore here we use only bloom-1b3 to test our module __A = "bigscience/bloom-1b7" # Constant values __A = 2.109_659_552_692_574 __A = "Hello my name is" __A = set() EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I" ) EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n" ) EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University" ) __A = 10 def UpperCamelCase_ (self ): """simple docstring""" a = AutoTokenizer.from_pretrained(self.model_name ) class _lowercase ( lowerCAmelCase ): """simple docstring""" def UpperCamelCase_ (self ): """simple docstring""" super().setUp() # Models and tokenizer a = AutoModelForCausalLM.from_pretrained( self.model_name , torch_dtype=torch.floataa , device_map="auto" ) a = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) def UpperCamelCase_ (self ): """simple docstring""" del self.model_fpaa del self.model_abit gc.collect() torch.cuda.empty_cache() def UpperCamelCase_ (self ): """simple docstring""" a = self.model_abit.config self.assertTrue(hasattr(lowerCamelCase_ , "quantization_config" ) ) a = config.to_dict() a = config.to_diff_dict() a = config.to_json_string() def UpperCamelCase_ (self ): """simple docstring""" from bitsandbytes.nn import Paramsabit a = self.model_fpaa.get_memory_footprint() a = self.model_abit.get_memory_footprint() self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE ) a = get_some_linear_layer(self.model_abit ) self.assertTrue(linear.weight.__class__ == Paramsabit ) def UpperCamelCase_ (self ): """simple docstring""" from transformers import TaPreTrainedModel self.model_fpaa.get_memory_footprint() self.model_abit.get_memory_footprint() for name, module in self.model_abit.named_modules(): if isinstance(lowerCamelCase_ , torch.nn.Linear ): if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules: # 4-bit parameters are packed in uint8 variables self.assertTrue(module.weight.dtype == torch.uinta ) def UpperCamelCase_ (self ): """simple docstring""" a = self.tokenizer(self.input_text , return_tensors="pt" ) a = self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 ) self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowerCamelCase_ ) , self.EXPECTED_OUTPUTS ) def UpperCamelCase_ (self ): """simple docstring""" a = BitsAndBytesConfig() a = True a = AutoModelForCausalLM.from_pretrained( self.model_name , quantization_config=lowerCamelCase_ , device_map="auto" ) a = self.tokenizer(self.input_text , return_tensors="pt" ) a = model_abit_from_config.generate( input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 ) self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowerCamelCase_ ) , self.EXPECTED_OUTPUTS ) def UpperCamelCase_ (self ): """simple docstring""" with self.assertRaises(lowerCamelCase_ ), tempfile.TemporaryDirectory() as tmpdirname: self.model_abit.save_pretrained(lowerCamelCase_ ) def UpperCamelCase_ (self ): """simple docstring""" a = BitsAndBytesConfig() with self.assertRaises(lowerCamelCase_ ): a = AutoModelForCausalLM.from_pretrained( self.model_name , quantization_config=lowerCamelCase_ , load_in_abit=lowerCamelCase_ , device_map="auto" , bnb_abit_quant_type="nf4" , ) def UpperCamelCase_ (self ): """simple docstring""" with self.assertRaises(lowerCamelCase_ ): # Tries with `str` self.model_abit.to("cpu" ) with self.assertRaises(lowerCamelCase_ ): # Tries with a `dtype`` self.model_abit.to(torch.floataa ) with self.assertRaises(lowerCamelCase_ ): # Tries with a `device` self.model_abit.to(torch.device("cuda:0" ) ) with self.assertRaises(lowerCamelCase_ ): # Tries with a `device` self.model_abit.float() with self.assertRaises(lowerCamelCase_ ): # Tries with a `device` self.model_abit.half() # Test if we did not break anything a = self.tokenizer(self.input_text , return_tensors="pt" ) a = self.model_fpaa.to(torch.floataa ) a = self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 ) # Check this does not throw an error a = self.model_fpaa.to("cpu" ) # Check this does not throw an error a = self.model_fpaa.half() # Check this does not throw an error a = self.model_fpaa.float() def UpperCamelCase_ (self ): """simple docstring""" a = AutoModelForSeqaSeqLM.from_pretrained("t5-small" , load_in_abit=lowerCamelCase_ , device_map="auto" ) self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa ) @require_bitsandbytes @require_accelerate @require_torch @require_torch_gpu @slow class _lowercase ( unittest.TestCase ): """simple docstring""" @classmethod def UpperCamelCase_ (cls ): """simple docstring""" a = "t5-small" a = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense a = AutoTokenizer.from_pretrained(cls.model_name ) a = "Translate in German: Hello, my dog is cute" def UpperCamelCase_ (self ): """simple docstring""" gc.collect() torch.cuda.empty_cache() def UpperCamelCase_ (self ): """simple docstring""" from transformers import TaForConditionalGeneration a = TaForConditionalGeneration._keep_in_fpaa_modules a = None # test with `t5-small` a = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) a = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 ) a = model.generate(**lowerCamelCase_ ) # test with `flan-t5-small` a = TaForConditionalGeneration.from_pretrained( self.dense_act_model_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) a = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 ) a = model.generate(**lowerCamelCase_ ) a = modules def UpperCamelCase_ (self ): """simple docstring""" import bitsandbytes as bnb from transformers import TaForConditionalGeneration # test with `t5-small` a = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) # there was a bug with decoders - this test checks that it is fixed self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) ) a = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 ) a = model.generate(**lowerCamelCase_ ) # test with `flan-t5-small` a = TaForConditionalGeneration.from_pretrained( self.dense_act_model_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) a = self.tokenizer(self.input_text , return_tensors="pt" ).to(0 ) a = model.generate(**lowerCamelCase_ ) class _lowercase ( lowerCAmelCase ): """simple docstring""" def UpperCamelCase_ (self ): """simple docstring""" super().setUp() # model_name a = "bigscience/bloom-560m" a = "t5-small" # Different types of model a = AutoModel.from_pretrained(self.model_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) # Sequence classification model a = AutoModelForSequenceClassification.from_pretrained( self.model_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) # CausalLM model a = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) # Seq2seq model a = AutoModelForSeqaSeqLM.from_pretrained( self.seq_to_seq_name , load_in_abit=lowerCamelCase_ , device_map="auto" ) def UpperCamelCase_ (self ): """simple docstring""" del self.base_model del self.sequence_model del self.model_abit del self.seq_to_seq_model gc.collect() torch.cuda.empty_cache() def UpperCamelCase_ (self ): """simple docstring""" from bitsandbytes.nn import Paramsabit self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit ) # Other heads should be nn.Parameter self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter ) self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter ) self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter ) class _lowercase ( lowerCAmelCase ): """simple docstring""" def UpperCamelCase_ (self ): """simple docstring""" super().setUp() def UpperCamelCase_ (self ): """simple docstring""" del self.pipe gc.collect() torch.cuda.empty_cache() def UpperCamelCase_ (self ): """simple docstring""" a = pipeline( "text-generation" , model=self.model_name , model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , ) # Real second forward pass a = self.pipe(self.input_text ) self.assertIn(pipeline_output[0]["generated_text"] , self.EXPECTED_OUTPUTS ) @require_torch_multi_gpu class _lowercase ( lowerCAmelCase ): """simple docstring""" def UpperCamelCase_ (self ): """simple docstring""" super().setUp() def UpperCamelCase_ (self ): """simple docstring""" a = AutoModelForCausalLM.from_pretrained( self.model_name , load_in_abit=lowerCamelCase_ , device_map="balanced" ) # Check correct device map self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} ) # Check that inference pass works on the model a = self.tokenizer(self.input_text , return_tensors="pt" ) # Second real batch a = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0 ) , max_new_tokens=10 ) self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=lowerCamelCase_ ) , self.EXPECTED_OUTPUTS ) class _lowercase ( lowerCAmelCase ): """simple docstring""" def UpperCamelCase_ (self ): """simple docstring""" a = "facebook/opt-350m" super().setUp() def UpperCamelCase_ (self ): """simple docstring""" if version.parse(importlib.metadata.version("bitsandbytes" ) ) < version.parse("0.37.0" ): return # Step 1: freeze all parameters a = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCamelCase_ ) self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} ) for param in model.parameters(): a = False # freeze the model - train adapters later if param.ndim == 1: # cast the small parameters (e.g. layernorm) to fp32 for stability a = param.data.to(torch.floataa ) # Step 2: add adapters for _, module in model.named_modules(): if "OPTAttention" in repr(type(lowerCamelCase_ ) ): a = LoRALayer(module.q_proj , rank=16 ) a = LoRALayer(module.k_proj , rank=16 ) a = LoRALayer(module.v_proj , rank=16 ) # Step 3: dummy batch a = self.tokenizer("Test batch " , return_tensors="pt" ).to(0 ) # Step 4: Check if the gradient is not None with torch.cuda.amp.autocast(): a = model.forward(**lowerCamelCase_ ) out.logits.norm().backward() for module in model.modules(): if isinstance(lowerCamelCase_ , lowerCamelCase_ ): self.assertTrue(module.adapter[1].weight.grad is not None ) self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 ) elif isinstance(lowerCamelCase_ , nn.Embedding ): self.assertTrue(module.weight.grad is None ) class _lowercase ( lowerCAmelCase ): """simple docstring""" __A = "gpt2-xl" __A = 3.3_191_854_854_152_187	227	import unittest from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin _lowercase: Optional[int] = get_tests_dir("fixtures/test_sentencepiece.model") @require_sentencepiece class _lowercase ( lowerCAmelCase, unittest.TestCase ): """simple docstring""" __A = XLMProphetNetTokenizer __A = False __A = True def UpperCamelCase_ (self ): """simple docstring""" super().setUp() # We have a SentencePiece fixture for testing a = XLMProphetNetTokenizer(lowerCamelCase_ , keep_accents=lowerCamelCase_ ) tokenizer.save_pretrained(self.tmpdirname ) def UpperCamelCase_ (self ): """simple docstring""" a = "[PAD]" a = 0 self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_ ) , lowerCamelCase_ ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_ ) , lowerCamelCase_ ) def UpperCamelCase_ (self ): """simple docstring""" a = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] , "[PAD]" ) self.assertEqual(vocab_keys[1] , "[CLS]" ) self.assertEqual(vocab_keys[-1] , "j" ) self.assertEqual(len(lowerCamelCase_ ) , 1012 ) def UpperCamelCase_ (self ): """simple docstring""" self.assertEqual(self.get_tokenizer().vocab_size , 1012 ) def UpperCamelCase_ (self ): """simple docstring""" a = XLMProphetNetTokenizer(lowerCamelCase_ , keep_accents=lowerCamelCase_ ) a = tokenizer.tokenize("This is a test" ) self.assertListEqual(lowerCamelCase_ , ["▁This", "▁is", "▁a", "▁t", "est"] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(lowerCamelCase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , ) a = tokenizer.tokenize("I was born in 92000, and this is falsé." ) self.assertListEqual( lowerCamelCase_ , [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ] , ) a = tokenizer.convert_tokens_to_ids(lowerCamelCase_ ) self.assertListEqual( lowerCamelCase_ , [ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4] ] , ) a = tokenizer.convert_ids_to_tokens(lowerCamelCase_ ) self.assertListEqual( lowerCamelCase_ , [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "[UNK]", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "[UNK]", ".", ] , ) @cached_property def UpperCamelCase_ (self ): """simple docstring""" return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased" ) @slow def UpperCamelCase_ (self ): """simple docstring""" a = "Hello World!" a = [35389, 6672, 49, 2] self.assertListEqual(lowerCamelCase_ , self.big_tokenizer.encode(lowerCamelCase_ ) ) @slow def UpperCamelCase_ (self ): """simple docstring""" a = {"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=lowerCamelCase_ , model_name="microsoft/xprophetnet-large-wiki100-cased" , revision="1acad1643ddd54a44df6a1b797ada8373685d90e" , )	227	1
"""simple docstring""" from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available SCREAMING_SNAKE_CASE : Optional[Any] = { '''configuration_pix2struct''': [ '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Pix2StructConfig''', '''Pix2StructTextConfig''', '''Pix2StructVisionConfig''', ], '''processing_pix2struct''': ['''Pix2StructProcessor'''], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: SCREAMING_SNAKE_CASE : List[Any] = ['''Pix2StructImageProcessor'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: SCREAMING_SNAKE_CASE : Tuple = [ '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Pix2StructPreTrainedModel''', '''Pix2StructForConditionalGeneration''', '''Pix2StructVisionModel''', '''Pix2StructTextModel''', ] if TYPE_CHECKING: from .configuration_pixastruct import ( PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP, PixaStructConfig, PixaStructTextConfig, PixaStructVisionConfig, ) from .processing_pixastruct import PixaStructProcessor try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_pixastruct import PixaStructImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_pixastruct import ( PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST, PixaStructForConditionalGeneration, PixaStructPreTrainedModel, PixaStructTextModel, PixaStructVisionModel, ) else: import sys SCREAMING_SNAKE_CASE : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)	317	"""simple docstring""" from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available SCREAMING_SNAKE_CASE : List[Any] = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: SCREAMING_SNAKE_CASE : Union[str, Any] = [ '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''', '''FocalNetForImageClassification''', '''FocalNetForMaskedImageModeling''', '''FocalNetBackbone''', '''FocalNetModel''', '''FocalNetPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_focalnet import ( FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST, FocalNetBackbone, FocalNetForImageClassification, FocalNetForMaskedImageModeling, FocalNetModel, FocalNetPreTrainedModel, ) else: import sys SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)	317	1
"""simple docstring""" import copy import inspect import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers import TimesformerConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, TimesformerForVideoClassification, TimesformerModel, ) from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from transformers import VideoMAEImageProcessor class lowerCAmelCase__ : '''simple docstring''' def __init__( self , lowercase , lowercase=13 , lowercase=10 , lowercase=3 , lowercase=2 , lowercase=2 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.02 , lowercase="divided_space_time" , lowercase=None , ): _lowerCamelCase : Tuple = parent _lowerCamelCase : Any = batch_size _lowerCamelCase : Tuple = image_size _lowerCamelCase : str = num_channels _lowerCamelCase : Optional[Any] = patch_size _lowerCamelCase : Any = num_frames _lowerCamelCase : List[Any] = is_training _lowerCamelCase : List[Any] = use_labels _lowerCamelCase : List[Any] = hidden_size _lowerCamelCase : Optional[Any] = num_hidden_layers _lowerCamelCase : List[Any] = num_attention_heads _lowerCamelCase : List[str] = intermediate_size _lowerCamelCase : Any = hidden_act _lowerCamelCase : int = hidden_dropout_prob _lowerCamelCase : Any = attention_probs_dropout_prob _lowerCamelCase : List[str] = attention_type _lowerCamelCase : int = initializer_range _lowerCamelCase : Any = scope _lowerCamelCase : int = num_labels # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token _lowerCamelCase : Optional[Any] = (image_size // patch_size) ** 2 _lowerCamelCase : Dict = (num_frames) * self.num_patches_per_frame + 1 def A_ ( self ): _lowerCamelCase : str = floats_tensor( [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] ) _lowerCamelCase : Union[str, Any] = None if self.use_labels: _lowerCamelCase : Dict = ids_tensor([self.batch_size] , self.num_labels ) _lowerCamelCase : int = self.get_config() return config, pixel_values, labels def A_ ( self ): _lowerCamelCase : str = TimesformerConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , ) _lowerCamelCase : List[str] = self.num_labels return config def A_ ( self , lowercase , lowercase , lowercase ): _lowerCamelCase : Union[str, Any] = TimesformerModel(config=lowercase ) model.to(lowercase ) model.eval() _lowerCamelCase : Any = model(lowercase ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A_ ( self , lowercase , lowercase , lowercase ): _lowerCamelCase : Optional[Any] = TimesformerForVideoClassification(lowercase ) model.to(lowercase ) model.eval() _lowerCamelCase : List[Any] = model(lowercase ) # verify the logits shape _lowerCamelCase : List[Any] = torch.Size((self.batch_size, self.num_labels) ) self.parent.assertEqual(result.logits.shape , lowercase ) def A_ ( self ): _lowerCamelCase : Optional[int] = self.prepare_config_and_inputs() _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : List[str] = config_and_inputs _lowerCamelCase : Dict = {'pixel_values': pixel_values} return config, inputs_dict @require_torch class lowerCAmelCase__ ( lowercase, lowercase, unittest.TestCase ): '''simple docstring''' lowerCamelCase__ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else () lowerCamelCase__ = ( {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification} if is_torch_available() else {} ) lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False def A_ ( self ): _lowerCamelCase : Union[str, Any] = TimesformerModelTester(self ) _lowerCamelCase : List[Any] = ConfigTester( self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 ) def A_ ( self , lowercase , lowercase , lowercase=False ): _lowerCamelCase : Tuple = copy.deepcopy(lowercase ) if return_labels: if model_class in get_values(lowercase ): _lowerCamelCase : Optional[int] = torch.zeros( self.model_tester.batch_size , dtype=torch.long , device=lowercase ) return inputs_dict def A_ ( self ): self.config_tester.run_common_tests() @unittest.skip(reason='TimeSformer does not use inputs_embeds' ) def A_ ( self ): pass def A_ ( self ): _lowerCamelCase, _lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _lowerCamelCase : Union[str, Any] = model_class(lowercase ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) _lowerCamelCase : List[str] = model.get_output_embeddings() self.assertTrue(x is None or isinstance(lowercase , nn.Linear ) ) def A_ ( self ): _lowerCamelCase, _lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _lowerCamelCase : Optional[int] = model_class(lowercase ) _lowerCamelCase : int = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic _lowerCamelCase : Dict = [*signature.parameters.keys()] _lowerCamelCase : Optional[int] = ['pixel_values'] self.assertListEqual(arg_names[:1] , lowercase ) def A_ ( self ): _lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*lowercase ) def A_ ( self ): _lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_video_classification(*lowercase ) @slow def A_ ( self ): for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _lowerCamelCase : str = TimesformerModel.from_pretrained(lowercase ) self.assertIsNotNone(lowercase ) def A_ ( self ): if not self.has_attentions: pass else: _lowerCamelCase, _lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common() _lowerCamelCase : Union[str, Any] = True for model_class in self.all_model_classes: _lowerCamelCase : List[Any] = self.model_tester.seq_length _lowerCamelCase : List[str] = self.model_tester.num_frames _lowerCamelCase : Dict = True _lowerCamelCase : List[Any] = False _lowerCamelCase : Optional[Any] = True _lowerCamelCase : Optional[int] = model_class(lowercase ) model.to(lowercase ) model.eval() with torch.no_grad(): _lowerCamelCase : List[Any] = model(**self._prepare_for_class(lowercase , lowercase ) ) _lowerCamelCase : Optional[int] = outputs.attentions self.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers ) # check that output_attentions also work using config del inputs_dict["output_attentions"] _lowerCamelCase : int = True _lowerCamelCase : Optional[int] = model_class(lowercase ) model.to(lowercase ) model.eval() with torch.no_grad(): _lowerCamelCase : List[Any] = model(**self._prepare_for_class(lowercase , lowercase ) ) _lowerCamelCase : List[Any] = outputs.attentions self.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers ) # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1) self.assertListEqual( list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , ) _lowerCamelCase : Any = len(lowercase ) # Check attention is always last and order is fine _lowerCamelCase : Optional[Any] = True _lowerCamelCase : Union[str, Any] = True _lowerCamelCase : Union[str, Any] = model_class(lowercase ) model.to(lowercase ) model.eval() with torch.no_grad(): _lowerCamelCase : str = model(**self._prepare_for_class(lowercase , lowercase ) ) self.assertEqual(out_len + 1 , len(lowercase ) ) _lowerCamelCase : Optional[Any] = outputs.attentions self.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers ) # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1) self.assertListEqual( list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , ) def A_ ( self ): def check_hidden_states_output(lowercase , lowercase , lowercase ): _lowerCamelCase : Any = model_class(lowercase ) model.to(lowercase ) model.eval() with torch.no_grad(): _lowerCamelCase : List[Any] = model(**self._prepare_for_class(lowercase , lowercase ) ) _lowerCamelCase : int = outputs.hidden_states _lowerCamelCase : str = self.model_tester.num_hidden_layers + 1 self.assertEqual(len(lowercase ) , lowercase ) _lowerCamelCase : Optional[Any] = self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , ) _lowerCamelCase, _lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _lowerCamelCase : Tuple = True check_hidden_states_output(lowercase , lowercase , lowercase ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _lowerCamelCase : Dict = True check_hidden_states_output(lowercase , lowercase , lowercase ) def _snake_case ( ): _lowerCamelCase : str = hf_hub_download( repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' ) _lowerCamelCase : Optional[Any] = np.load(lowercase__ ) return list(lowercase__ ) @require_torch @require_vision class lowerCAmelCase__ ( unittest.TestCase ): '''simple docstring''' @cached_property def A_ ( self ): # logits were tested with a different mean and std, so we use the same here return ( VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] ) if is_vision_available() else None ) @slow def A_ ( self ): _lowerCamelCase : List[Any] = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400' ).to( lowercase ) _lowerCamelCase : Any = self.default_image_processor _lowerCamelCase : Union[str, Any] = prepare_video() _lowerCamelCase : Any = image_processor(video[:8] , return_tensors='pt' ).to(lowercase ) # forward pass with torch.no_grad(): _lowerCamelCase : Any = model(**lowercase ) # verify the logits _lowerCamelCase : Dict = torch.Size((1, 400) ) self.assertEqual(outputs.logits.shape , lowercase ) _lowerCamelCase : Optional[Any] = torch.tensor([-0.30_16, -0.77_13, -0.42_05] ).to(lowercase ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1E-4 ) )	96	class SCREAMING_SNAKE_CASE__ : def __init__( self,__lowerCamelCase ): A__ = set_counts A__ = max(__lowerCamelCase ) A__ = len(__lowerCamelCase ) A__ = [1] * num_sets A__ = list(range(__lowerCamelCase ) ) def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ): A__ = self.get_parent(__lowerCamelCase ) A__ = self.get_parent(__lowerCamelCase ) if src_parent == dst_parent: return False if self.ranks[dst_parent] >= self.ranks[src_parent]: self.set_counts[dst_parent] += self.set_counts[src_parent] A__ = 0 A__ = dst_parent if self.ranks[dst_parent] == self.ranks[src_parent]: self.ranks[dst_parent] += 1 A__ = self.set_counts[dst_parent] else: self.set_counts[src_parent] += self.set_counts[dst_parent] A__ = 0 A__ = src_parent A__ = self.set_counts[src_parent] A__ = max(self.max_set,__lowerCamelCase ) return True def UpperCamelCase ( self,__lowerCamelCase ): if self.parents[disj_set] == disj_set: return disj_set A__ = self.get_parent(self.parents[disj_set] ) return self.parents[disj_set]	193	0
"""simple docstring""" import warnings from typing import List, Optional, Union from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from ...utils import TensorType class a ( _lowerCamelCase ): """simple docstring""" UpperCAmelCase = ["image_processor", "tokenizer"] UpperCAmelCase = "LayoutLMv3ImageProcessor" UpperCAmelCase = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast") def __init__( self: Tuple , UpperCamelCase: Union[str, Any]=None , UpperCamelCase: Tuple=None , **UpperCamelCase: Dict ): """simple docstring""" A__ = None if "feature_extractor" in kwargs: warnings.warn( """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`""" """ instead.""" , lowercase_ , ) A__ = kwargs.pop("""feature_extractor""" ) A__ = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("""You need to specify an `image_processor`.""" ) if tokenizer is None: raise ValueError("""You need to specify a `tokenizer`.""" ) super().__init__(lowercase_ , lowercase_ ) def __call__( self: Optional[Any] , UpperCamelCase: Any , UpperCamelCase: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCamelCase: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , UpperCamelCase: Union[List[List[int]], List[List[List[int]]]] = None , UpperCamelCase: Optional[Union[List[int], List[List[int]]]] = None , UpperCamelCase: bool = True , UpperCamelCase: Union[bool, str, PaddingStrategy] = False , UpperCamelCase: Union[bool, str, TruncationStrategy] = None , UpperCamelCase: Optional[int] = None , UpperCamelCase: int = 0 , UpperCamelCase: Optional[int] = None , UpperCamelCase: Optional[bool] = None , UpperCamelCase: Optional[bool] = None , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = False , UpperCamelCase: bool = True , UpperCamelCase: Optional[Union[str, TensorType]] = None , **UpperCamelCase: List[str] , ): """simple docstring""" if self.image_processor.apply_ocr and (boxes is not None): raise ValueError( """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.""" ) if self.image_processor.apply_ocr and (word_labels is not None): raise ValueError( """You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""" ) # first, apply the image processor A__ = self.image_processor(images=lowercase_ , return_tensors=lowercase_ ) # second, apply the tokenizer if text is not None and self.image_processor.apply_ocr and text_pair is None: if isinstance(lowercase_ , lowercase_ ): A__ = [text] # add batch dimension (as the image processor always adds a batch dimension) A__ = features["""words"""] A__ = self.tokenizer( text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_token_type_ids=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , ) # add pixel values A__ = features.pop("""pixel_values""" ) if return_overflowing_tokens is True: A__ = self.get_overflowing_images(lowercase_ , encoded_inputs["""overflow_to_sample_mapping"""] ) A__ = images return encoded_inputs def UpperCamelCase ( self: str , UpperCamelCase: List[str] , UpperCamelCase: List[str] ): """simple docstring""" A__ = [] for sample_idx in overflow_to_sample_mapping: images_with_overflow.append(images[sample_idx] ) if len(lowercase_ ) != len(lowercase_ ): raise ValueError( """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got""" f""" {len(lowercase_ )} and {len(lowercase_ )}""" ) return images_with_overflow def UpperCamelCase ( self: int , *UpperCamelCase: Tuple , **UpperCamelCase: Tuple ): """simple docstring""" return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ ) def UpperCamelCase ( self: Any , *UpperCamelCase: Union[str, Any] , **UpperCamelCase: Dict ): """simple docstring""" return self.tokenizer.decode(*lowercase_ , **lowercase_ ) @property def UpperCamelCase ( self: Optional[Any] ): """simple docstring""" return ["input_ids", "bbox", "attention_mask", "pixel_values"] @property def UpperCamelCase ( self: int ): """simple docstring""" warnings.warn( """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowercase_ , ) return self.image_processor_class @property def UpperCamelCase ( self: Any ): """simple docstring""" warnings.warn( """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , lowercase_ , ) return self.image_processor	369	"""simple docstring""" from typing import List, Union from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING from ..tf_utils import stable_softmax if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING SCREAMING_SNAKE_CASE_ : List[Any] = logging.get_logger(__name__) @add_end_docstrings(_lowerCamelCase ) class a ( _lowerCamelCase ): """simple docstring""" def __init__( self: Union[str, Any] , *UpperCamelCase: List[str] , **UpperCamelCase: Union[str, Any] ): """simple docstring""" super().__init__(*UpperCamelCase , **UpperCamelCase ) requires_backends(self , """vision""" ) self.check_model_type( TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING if self.framework == """tf""" else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING ) def UpperCamelCase ( self: List[str] , UpperCamelCase: Any=None ): """simple docstring""" A__ = {} if top_k is not None: A__ = top_k return {}, {}, postprocess_params def __call__( self: Union[str, Any] , UpperCamelCase: Union[str, List[str], "Image.Image", List["Image.Image"]] , **UpperCamelCase: Dict ): """simple docstring""" return super().__call__(UpperCamelCase , **UpperCamelCase ) def UpperCamelCase ( self: Any , UpperCamelCase: int ): """simple docstring""" A__ = load_image(UpperCamelCase ) A__ = self.image_processor(images=UpperCamelCase , return_tensors=self.framework ) return model_inputs def UpperCamelCase ( self: List[Any] , UpperCamelCase: Any ): """simple docstring""" A__ = self.model(**UpperCamelCase ) return model_outputs def UpperCamelCase ( self: Any , UpperCamelCase: Optional[Any] , UpperCamelCase: int=5 ): """simple docstring""" if top_k > self.model.config.num_labels: A__ = self.model.config.num_labels if self.framework == "pt": A__ = model_outputs.logits.softmax(-1 )[0] A__ , A__ = probs.topk(UpperCamelCase ) elif self.framework == "tf": A__ = stable_softmax(model_outputs.logits , axis=-1 )[0] A__ = tf.math.top_k(UpperCamelCase , k=UpperCamelCase ) A__ , A__ = topk.values.numpy(), topk.indices.numpy() else: raise ValueError(f"""Unsupported framework: {self.framework}""" ) A__ = scores.tolist() A__ = ids.tolist() return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase , UpperCamelCase )]	69	0
'''simple docstring''' import unittest import torch from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel from diffusers.training_utils import set_seed from diffusers.utils.testing_utils import slow _A : Union[str, Any] =False class _lowercase ( unittest.TestCase ): def lowerCamelCase_ ( self: int , UpperCamelCase__: Dict=32 ): set_seed(0 ) lowerCamelCase__ : str = UNetaDModel(sample_size=UpperCamelCase__ , in_channels=3 , out_channels=3 ) lowerCamelCase__ : Dict = torch.optim.SGD(model.parameters() , lr=0.0_001 ) return model, optimizer @slow def lowerCamelCase_ ( self: Dict ): lowerCamelCase__ : Any = """cpu""" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable lowerCamelCase__ : int = DDPMScheduler( num_train_timesteps=1_000 , beta_start=0.0_001 , beta_end=0.02 , beta_schedule="""linear""" , clip_sample=UpperCamelCase__ , ) lowerCamelCase__ : int = DDIMScheduler( num_train_timesteps=1_000 , beta_start=0.0_001 , beta_end=0.02 , beta_schedule="""linear""" , clip_sample=UpperCamelCase__ , ) assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps # shared batches for DDPM and DDIM set_seed(0 ) lowerCamelCase__ : Tuple = [torch.randn((4, 3, 32, 32) ).clip(-1 , 1 ).to(UpperCamelCase__ ) for _ in range(4 )] lowerCamelCase__ : str = [torch.randn((4, 3, 32, 32) ).to(UpperCamelCase__ ) for _ in range(4 )] lowerCamelCase__ : Tuple = [torch.randint(0 , 1_000 , (4,) ).long().to(UpperCamelCase__ ) for _ in range(4 )] # train with a DDPM scheduler lowerCamelCase__ , lowerCamelCase__ : int = self.get_model_optimizer(resolution=32 ) model.train().to(UpperCamelCase__ ) for i in range(4 ): optimizer.zero_grad() lowerCamelCase__ : Any = ddpm_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] ) lowerCamelCase__ : List[Any] = model(UpperCamelCase__ , timesteps[i] ).sample lowerCamelCase__ : Union[str, Any] = torch.nn.functional.mse_loss(UpperCamelCase__ , noise[i] ) loss.backward() optimizer.step() del model, optimizer # recreate the model and optimizer, and retry with DDIM lowerCamelCase__ , lowerCamelCase__ : Any = self.get_model_optimizer(resolution=32 ) model.train().to(UpperCamelCase__ ) for i in range(4 ): optimizer.zero_grad() lowerCamelCase__ : Union[str, Any] = ddim_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] ) lowerCamelCase__ : List[Any] = model(UpperCamelCase__ , timesteps[i] ).sample lowerCamelCase__ : int = torch.nn.functional.mse_loss(UpperCamelCase__ , noise[i] ) loss.backward() optimizer.step() del model, optimizer self.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-5 ) ) self.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-5 ) )	41	import torch from diffusers import KDPMaDiscreteScheduler from diffusers.utils import torch_device from .test_schedulers import SchedulerCommonTest class __snake_case ( lowerCamelCase__ ): __lowerCamelCase : Optional[int] = (KDPMaDiscreteScheduler,) __lowerCamelCase : List[str] = 10 def UpperCAmelCase__ ( self , **snake_case__ ) -> str: '''simple docstring''' UpperCAmelCase : int ={ '''num_train_timesteps''': 1100, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', } config.update(**snake_case__ ) return config def UpperCAmelCase__ ( self ) -> Tuple: '''simple docstring''' for timesteps in [10, 50, 100, 1000]: self.check_over_configs(num_train_timesteps=snake_case__ ) def UpperCAmelCase__ ( self ) -> Optional[int]: '''simple docstring''' for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ): self.check_over_configs(beta_start=snake_case__ , beta_end=snake_case__ ) def UpperCAmelCase__ ( self ) -> str: '''simple docstring''' for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=snake_case__ ) def UpperCAmelCase__ ( self ) -> Dict: '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=snake_case__ ) def UpperCAmelCase__ ( self ) -> str: '''simple docstring''' UpperCAmelCase : Optional[Any] =self.scheduler_classes[0] UpperCAmelCase : Optional[int] =self.get_scheduler_config(prediction_type='''v_prediction''' ) UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ ) scheduler.set_timesteps(self.num_inference_steps ) UpperCAmelCase : str =self.dummy_model() UpperCAmelCase : Optional[Any] =self.dummy_sample_deter * scheduler.init_noise_sigma UpperCAmelCase : Union[str, Any] =sample.to(snake_case__ ) for i, t in enumerate(scheduler.timesteps ): UpperCAmelCase : str =scheduler.scale_model_input(snake_case__ , snake_case__ ) UpperCAmelCase : Any =model(snake_case__ , snake_case__ ) UpperCAmelCase : Union[str, Any] =scheduler.step(snake_case__ , snake_case__ , snake_case__ ) UpperCAmelCase : int =output.prev_sample UpperCAmelCase : Dict =torch.sum(torch.abs(snake_case__ ) ) UpperCAmelCase : Optional[Any] =torch.mean(torch.abs(snake_case__ ) ) if torch_device in ["cpu", "mps"]: assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2 assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3 else: # CUDA assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2 assert abs(result_mean.item() - 0.0002 ) < 1e-3 def UpperCAmelCase__ ( self ) -> int: '''simple docstring''' if torch_device == "mps": return UpperCAmelCase : Any =self.scheduler_classes[0] UpperCAmelCase : Optional[int] =self.get_scheduler_config() UpperCAmelCase : Optional[Any] =scheduler_class(**snake_case__ ) scheduler.set_timesteps(self.num_inference_steps ) UpperCAmelCase : Optional[int] =self.dummy_model() UpperCAmelCase : Union[str, Any] =self.dummy_sample_deter * scheduler.init_noise_sigma UpperCAmelCase : str =sample.to(snake_case__ ) for i, t in enumerate(scheduler.timesteps ): UpperCAmelCase : Dict =scheduler.scale_model_input(snake_case__ , snake_case__ ) UpperCAmelCase : Union[str, Any] =model(snake_case__ , snake_case__ ) UpperCAmelCase : List[str] =scheduler.step(snake_case__ , snake_case__ , snake_case__ ) UpperCAmelCase : Optional[int] =output.prev_sample UpperCAmelCase : Any =torch.sum(torch.abs(snake_case__ ) ) UpperCAmelCase : Union[str, Any] =torch.mean(torch.abs(snake_case__ ) ) if torch_device in ["cpu", "mps"]: assert abs(result_sum.item() - 20.4125 ) < 1e-2 assert abs(result_mean.item() - 0.0266 ) < 1e-3 else: # CUDA assert abs(result_sum.item() - 20.4125 ) < 1e-2 assert abs(result_mean.item() - 0.0266 ) < 1e-3 def UpperCAmelCase__ ( self ) -> str: '''simple docstring''' if torch_device == "mps": return UpperCAmelCase : List[Any] =self.scheduler_classes[0] UpperCAmelCase : Dict =self.get_scheduler_config() UpperCAmelCase : List[str] =scheduler_class(**snake_case__ ) scheduler.set_timesteps(self.num_inference_steps , device=snake_case__ ) UpperCAmelCase : int =self.dummy_model() UpperCAmelCase : Tuple =self.dummy_sample_deter.to(snake_case__ ) * scheduler.init_noise_sigma for t in scheduler.timesteps: UpperCAmelCase : Optional[Any] =scheduler.scale_model_input(snake_case__ , snake_case__ ) UpperCAmelCase : int =model(snake_case__ , snake_case__ ) UpperCAmelCase : str =scheduler.step(snake_case__ , snake_case__ , snake_case__ ) UpperCAmelCase : List[str] =output.prev_sample UpperCAmelCase : List[str] =torch.sum(torch.abs(snake_case__ ) ) UpperCAmelCase : Dict =torch.mean(torch.abs(snake_case__ ) ) if str(snake_case__ ).startswith('''cpu''' ): # The following sum varies between 148 and 156 on mps. Why? assert abs(result_sum.item() - 20.4125 ) < 1e-2 assert abs(result_mean.item() - 0.0266 ) < 1e-3 else: # CUDA assert abs(result_sum.item() - 20.4125 ) < 1e-2 assert abs(result_mean.item() - 0.0266 ) < 1e-3	348	0
from multiprocessing import Lock, Pipe, Process # lock used to ensure that two processes do not access a pipe at the same time _A = Lock() def lowercase_ ( A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any: """simple docstring""" global process_lock # we perform n swaps since after n swaps we know we are sorted # we *could* stop early if we are sorted already, but it takes as long to # find out we are sorted as it does to sort the list with this algorithm for i in range(0 , 10 ): if (i + position) % 2 == 0 and r_send is not None: # send your value to your right neighbor process_lock.acquire() r_send[1].send(__a ) process_lock.release() # receive your right neighbor's value process_lock.acquire() snake_case = rr_cv[0].recv() process_lock.release() # take the lower value since you are on the left snake_case = min(__a , __a ) elif (i + position) % 2 != 0 and l_send is not None: # send your value to your left neighbor process_lock.acquire() l_send[1].send(__a ) process_lock.release() # receive your left neighbor's value process_lock.acquire() snake_case = lr_cv[0].recv() process_lock.release() # take the higher value since you are on the right snake_case = max(__a , __a ) # after all swaps are performed, send the values back to main result_pipe[1].send(__a ) def lowercase_ ( A__ ) -> Optional[Any]: """simple docstring""" snake_case = [] snake_case = [] # initialize the list of pipes where the values will be retrieved for _ in arr: result_pipe.append(Pipe() ) # creates the processes # the first and last process only have one neighbor so they are made outside # of the loop snake_case = Pipe() snake_case = Pipe() process_array_.append( Process( target=__a , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) ) snake_case = temp_rs snake_case = temp_rr for i in range(1 , len(__a ) - 1 ): snake_case = Pipe() snake_case = Pipe() process_array_.append( Process( target=__a , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) ) snake_case = temp_rs snake_case = temp_rr process_array_.append( Process( target=__a , args=( len(__a ) - 1, arr[len(__a ) - 1], temp_ls, None, temp_lr, None, result_pipe[len(__a ) - 1], ) , ) ) # start the processes for p in process_array_: p.start() # wait for the processes to end and write their values to the list for p in range(0 , len(__a ) ): snake_case = result_pipe[p][0].recv() process_array_[p].join() return arr def lowercase_ ( ) -> Union[str, Any]: """simple docstring""" snake_case = list(range(10 , 0 , -1 ) ) print("Initial List" ) print(*__a ) snake_case = odd_even_transposition(__a ) print("Sorted List\n" ) print(*__a ) if __name__ == "__main__": main()	355	from collections import defaultdict class lowerCamelCase : def __init__(self : Tuple , _A : Optional[int] , _A : List[str] ) -> Union[str, Any]: snake_case = total # total no of tasks (N) # DP table will have a dimension of (2^M)*N # initially all values are set to -1 snake_case = [ [-1 for i in range(total + 1 )] for j in range(2 ** len(_A ) ) ] snake_case = defaultdict(_A ) # stores the list of persons for each task # final_mask is used to check if all persons are included by setting all bits # to 1 snake_case = (1 << len(_A )) - 1 def UpperCAmelCase(self : str , _A : Optional[Any] , _A : List[Any] ) -> str: # if mask == self.finalmask all persons are distributed tasks, return 1 if mask == self.final_mask: return 1 # if not everyone gets the task and no more tasks are available, return 0 if task_no > self.total_tasks: return 0 # if case already considered if self.dp[mask][task_no] != -1: return self.dp[mask][task_no] # Number of ways when we don't this task in the arrangement snake_case = self.count_ways_until(_A , task_no + 1 ) # now assign the tasks one by one to all possible persons and recursively # assign for the remaining tasks. if task_no in self.task: for p in self.task[task_no]: # if p is already given a task if mask & (1 << p): continue # assign this task to p and change the mask value. And recursively # assign tasks with the new mask value. total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 ) # save the value. snake_case = total_ways_util return self.dp[mask][task_no] def UpperCAmelCase(self : Any , _A : Dict ) -> Optional[Any]: # Store the list of persons for each task for i in range(len(_A ) ): for j in task_performed[i]: self.task[j].append(_A ) # call the function to fill the DP table, final answer is stored in dp[0][1] return self.count_ways_until(0 , 1 ) if __name__ == "__main__": _A = 5 # total no of tasks (the value of N) # the list of tasks that can be done by M persons. _A = [[1, 3, 4], [1, 2, 5], [3, 4]] print( AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways( task_performed ) )	137	0
"""simple docstring""" def UpperCAmelCase ( UpperCAmelCase ) -> Dict: snake_case_ = len(UpperCAmelCase ) snake_case_ = sum(UpperCAmelCase ) snake_case_ = [[False for x in range(s + 1 )] for y in range(n + 1 )] for i in range(1 , n + 1 ): snake_case_ = True for i in range(1 , s + 1 ): snake_case_ = False for i in range(1 , n + 1 ): for j in range(1 , s + 1 ): snake_case_ = dp[i][j - 1] if arr[i - 1] <= j: snake_case_ = dp[i][j] or dp[i - 1][j - arr[i - 1]] for j in range(int(s / 2 ) , -1 , -1 ): if dp[n][j] is True: snake_case_ = s - 2 * j break return diff	69	"""simple docstring""" import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel from ...utils import logging __UpperCamelCase = logging.get_logger(__name__) def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int: snake_case_ = nn.functional.normalize(UpperCAmelCase ) snake_case_ = nn.functional.normalize(UpperCAmelCase ) return torch.mm(UpperCAmelCase , normalized_text_embeds.t() ) class UpperCamelCase ( lowerCAmelCase__ ): SCREAMING_SNAKE_CASE_ = CLIPConfig SCREAMING_SNAKE_CASE_ = ["CLIPEncoderLayer"] def __init__( self, lowerCAmelCase__) -> Optional[int]: super().__init__(lowerCAmelCase__) snake_case_ = CLIPVisionModel(config.vision_config) snake_case_ = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=lowerCAmelCase__) snake_case_ = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=lowerCAmelCase__) snake_case_ = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=lowerCAmelCase__) snake_case_ = nn.Parameter(torch.ones(17), requires_grad=lowerCAmelCase__) snake_case_ = nn.Parameter(torch.ones(3), requires_grad=lowerCAmelCase__) @torch.no_grad() def a_ ( self, lowerCAmelCase__, lowerCAmelCase__) -> Tuple: snake_case_ = self.vision_model(lowerCAmelCase__)[1] # pooled_output snake_case_ = self.visual_projection(lowerCAmelCase__) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 snake_case_ = cosine_distance(lowerCAmelCase__, self.special_care_embeds).cpu().float().numpy() snake_case_ = cosine_distance(lowerCAmelCase__, self.concept_embeds).cpu().float().numpy() snake_case_ = [] snake_case_ = image_embeds.shape[0] for i in range(lowerCAmelCase__): snake_case_ = {'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []} # increase this value to create a stronger `nfsw` filter # at the cost of increasing the possibility of filtering benign images snake_case_ = 0.0 for concept_idx in range(len(special_cos_dist[0])): snake_case_ = special_cos_dist[i][concept_idx] snake_case_ = self.special_care_embeds_weights[concept_idx].item() snake_case_ = round(concept_cos - concept_threshold + adjustment, 3) if result_img["special_scores"][concept_idx] > 0: result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]}) snake_case_ = 0.01 for concept_idx in range(len(cos_dist[0])): snake_case_ = cos_dist[i][concept_idx] snake_case_ = self.concept_embeds_weights[concept_idx].item() snake_case_ = round(concept_cos - concept_threshold + adjustment, 3) if result_img["concept_scores"][concept_idx] > 0: result_img["bad_concepts"].append(lowerCAmelCase__) result.append(lowerCAmelCase__) snake_case_ = [len(res['bad_concepts']) > 0 for res in result] return images, has_nsfw_concepts @torch.no_grad() def a_ ( self, lowerCAmelCase__, lowerCAmelCase__) -> Optional[int]: snake_case_ = self.vision_model(lowerCAmelCase__)[1] # pooled_output snake_case_ = self.visual_projection(lowerCAmelCase__) snake_case_ = cosine_distance(lowerCAmelCase__, self.special_care_embeds) snake_case_ = cosine_distance(lowerCAmelCase__, self.concept_embeds) # increase this value to create a stronger `nsfw` filter # at the cost of increasing the possibility of filtering benign images snake_case_ = 0.0 snake_case_ = special_cos_dist - self.special_care_embeds_weights + adjustment # special_scores = special_scores.round(decimals=3) snake_case_ = torch.any(special_scores > 0, dim=1) snake_case_ = special_care * 0.01 snake_case_ = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1]) snake_case_ = (cos_dist - self.concept_embeds_weights) + special_adjustment # concept_scores = concept_scores.round(decimals=3) snake_case_ = torch.any(concept_scores > 0, dim=1) return images, has_nsfw_concepts	69	1
"""simple docstring""" from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _a : Dict= {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _a : List[str]= [ "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "SwinForImageClassification", "SwinForMaskedImageModeling", "SwinModel", "SwinPreTrainedModel", "SwinBackbone", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _a : List[Any]= [ "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSwinForImageClassification", "TFSwinForMaskedImageModeling", "TFSwinModel", "TFSwinPreTrainedModel", ] if TYPE_CHECKING: from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_swin import ( SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel, SwinPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_swin import ( TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, TFSwinForImageClassification, TFSwinForMaskedImageModeling, TFSwinModel, TFSwinPreTrainedModel, ) else: import sys _a : Tuple= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)	363	"""simple docstring""" import argparse import json from dataclasses import dataclass, field from functools import partial from pathlib import Path from typing import List import timm import torch import torch.nn as nn from huggingface_hub import hf_hub_download from torch import Tensor from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification from transformers.utils import logging logging.set_verbosity_info() _a : Optional[int]= logging.get_logger() @dataclass class UpperCamelCase : UpperCAmelCase : nn.Module UpperCAmelCase : List[nn.Module] = field(default_factory=lowercase ) UpperCAmelCase : list = field(default_factory=lowercase ) def _lowercase (self : str , _A : Optional[Any] , _A : Tensor , _A : Tensor) -> Any: __snake_case : str = len(list(m.modules())) == 1 or isinstance(_A , nn.Convad) or isinstance(_A , nn.BatchNormad) if has_not_submodules: self.traced.append(_A) def __call__(self : Dict , _A : Tensor) -> Optional[Any]: for m in self.module.modules(): self.handles.append(m.register_forward_hook(self._forward_hook)) self.module(_A) [x.remove() for x in self.handles] return self @property def _lowercase (self : Union[str, Any]) -> List[str]: # check the len of the state_dict keys to see if we have learnable params return list(filter(lambda _A: len(list(x.state_dict().keys())) > 0 , self.traced)) @dataclass class UpperCamelCase : UpperCAmelCase : nn.Module UpperCAmelCase : nn.Module UpperCAmelCase : int = 0 UpperCAmelCase : List = field(default_factory=lowercase ) UpperCAmelCase : List = field(default_factory=lowercase ) def __call__(self : List[str] , _A : Tensor) -> List[Any]: __snake_case : Any = Tracker(self.dest)(_A).parametrized __snake_case : int = Tracker(self.src)(_A).parametrized __snake_case : List[Any] = list(filter(lambda _A: type(_A) not in self.src_skip , _A)) __snake_case : Any = list(filter(lambda _A: type(_A) not in self.dest_skip , _A)) if len(_A) != len(_A): raise Exception( f"Numbers of operations are different. Source module has {len(_A)} operations while" f" destination module has {len(_A)}.") for dest_m, src_m in zip(_A , _A): dest_m.load_state_dict(src_m.state_dict()) if self.verbose == 1: print(f"Transfered from={src_m} to={dest_m}") def __UpperCAmelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : ResNetConfig , UpperCAmelCase_ : Path , UpperCAmelCase_ : bool = True ) -> List[str]: '''simple docstring''' print(F"Converting {name}..." ) with torch.no_grad(): __snake_case : Dict = timm.create_model(UpperCAmelCase_ , pretrained=UpperCAmelCase_ ).eval() __snake_case : List[Any] = ResNetForImageClassification(UpperCAmelCase_ ).eval() __snake_case : int = ModuleTransfer(src=UpperCAmelCase_ , dest=UpperCAmelCase_ ) __snake_case : Optional[Any] = torch.randn((1, 3, 2_24, 2_24) ) module_transfer(UpperCAmelCase_ ) assert torch.allclose(from_model(UpperCAmelCase_ ) , our_model(UpperCAmelCase_ ).logits ), "The model logits don't match the original one." __snake_case : str = F"resnet{'-'.join(name.split('resnet' ) )}" print(UpperCAmelCase_ ) if push_to_hub: our_model.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='Add model' , use_temp_dir=UpperCAmelCase_ , ) # we can use the convnext one __snake_case : int = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' ) image_processor.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='Add image processor' , use_temp_dir=UpperCAmelCase_ , ) print(F"Pushed {checkpoint_name}" ) def __UpperCAmelCase ( UpperCAmelCase_ : Path , UpperCAmelCase_ : str = None , UpperCAmelCase_ : bool = True ) -> Union[str, Any]: '''simple docstring''' __snake_case : str = 'imagenet-1k-id2label.json' __snake_case : Optional[Any] = 10_00 __snake_case : Any = (1, num_labels) __snake_case : List[Any] = 'huggingface/label-files' __snake_case : Dict = num_labels __snake_case : Any = json.load(open(hf_hub_download(UpperCAmelCase_ , UpperCAmelCase_ , repo_type='dataset' ) , 'r' ) ) __snake_case : Any = {int(UpperCAmelCase_ ): v for k, v in idalabel.items()} __snake_case : Optional[Any] = idalabel __snake_case : Optional[Any] = {v: k for k, v in idalabel.items()} __snake_case : Optional[int] = partial(UpperCAmelCase_ , num_labels=UpperCAmelCase_ , idalabel=UpperCAmelCase_ , labelaid=UpperCAmelCase_ ) __snake_case : str = { 'resnet18': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[64, 1_28, 2_56, 5_12] , layer_type='basic' ), 'resnet26': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type='bottleneck' ), 'resnet34': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[64, 1_28, 2_56, 5_12] , layer_type='basic' ), 'resnet50': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type='bottleneck' ), 'resnet101': ImageNetPreTrainedConfig( depths=[3, 4, 23, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type='bottleneck' ), 'resnet152': ImageNetPreTrainedConfig( depths=[3, 8, 36, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type='bottleneck' ), } if model_name: convert_weight_and_push(UpperCAmelCase_ , names_to_config[model_name] , UpperCAmelCase_ , UpperCAmelCase_ ) else: for model_name, config in names_to_config.items(): convert_weight_and_push(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ) return config, expected_shape if __name__ == "__main__": _a : Optional[Any]= argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default=None, type=str, help=( "The name of the model you wish to convert, it must be one of the supported resnet* architecture," " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted." ), ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=Path, required=True, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", default=True, type=bool, required=False, help="If True, push model and image processor to the hub.", ) _a : Union[str, Any]= parser.parse_args() _a : Path= args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)	95	0
import copy import os from typing import Union from ...configuration_utils import PretrainedConfig from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES from ...utils import logging from ..auto import CONFIG_MAPPING _a = logging.get_logger(__name__) _a = { "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json", } class __A ( lowerCAmelCase ): '''simple docstring''' lowerCAmelCase_ = """blip_2_vision_model""" def __init__( self , __lowerCAmelCase=1_4_0_8 , __lowerCAmelCase=6_1_4_4 , __lowerCAmelCase=3_9 , __lowerCAmelCase=1_6 , __lowerCAmelCase=2_2_4 , __lowerCAmelCase=1_4 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.0_0001 , __lowerCAmelCase=0.0 , __lowerCAmelCase=1E-10 , __lowerCAmelCase=True , **__lowerCAmelCase , ): '''simple docstring''' super().__init__(**__lowerCAmelCase ) lowerCamelCase__ = hidden_size lowerCamelCase__ = intermediate_size lowerCamelCase__ = num_hidden_layers lowerCamelCase__ = num_attention_heads lowerCamelCase__ = patch_size lowerCamelCase__ = image_size lowerCamelCase__ = initializer_range lowerCamelCase__ = attention_dropout lowerCamelCase__ = layer_norm_eps lowerCamelCase__ = hidden_act lowerCamelCase__ = qkv_bias @classmethod def __lowerCamelCase ( cls , __lowerCAmelCase , **__lowerCAmelCase ): '''simple docstring''' cls._set_token_in_kwargs(__lowerCAmelCase ) lowerCamelCase__ , lowerCamelCase__ = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase ) # get the vision config dict if we are loading from Blip2Config if config_dict.get('''model_type''' ) == "blip-2": lowerCamelCase__ = config_dict['''vision_config'''] if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type: logger.warning( F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type ' F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' ) return cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase ) class __A ( lowerCAmelCase ): '''simple docstring''' lowerCAmelCase_ = """blip_2_qformer""" def __init__( self , __lowerCAmelCase=3_0_5_2_2 , __lowerCAmelCase=7_6_8 , __lowerCAmelCase=1_2 , __lowerCAmelCase=1_2 , __lowerCAmelCase=3_0_7_2 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=5_1_2 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-12 , __lowerCAmelCase=0 , __lowerCAmelCase="absolute" , __lowerCAmelCase=2 , __lowerCAmelCase=1_4_0_8 , **__lowerCAmelCase , ): '''simple docstring''' super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase ) lowerCamelCase__ = vocab_size lowerCamelCase__ = hidden_size lowerCamelCase__ = num_hidden_layers lowerCamelCase__ = num_attention_heads lowerCamelCase__ = hidden_act lowerCamelCase__ = intermediate_size lowerCamelCase__ = hidden_dropout_prob lowerCamelCase__ = attention_probs_dropout_prob lowerCamelCase__ = max_position_embeddings lowerCamelCase__ = initializer_range lowerCamelCase__ = layer_norm_eps lowerCamelCase__ = position_embedding_type lowerCamelCase__ = cross_attention_frequency lowerCamelCase__ = encoder_hidden_size @classmethod def __lowerCamelCase ( cls , __lowerCAmelCase , **__lowerCAmelCase ): '''simple docstring''' cls._set_token_in_kwargs(__lowerCAmelCase ) lowerCamelCase__ , lowerCamelCase__ = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase ) # get the qformer config dict if we are loading from Blip2Config if config_dict.get('''model_type''' ) == "blip-2": lowerCamelCase__ = config_dict['''qformer_config'''] if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type: logger.warning( F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type ' F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' ) return cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase ) class __A ( lowerCAmelCase ): '''simple docstring''' lowerCAmelCase_ = """blip-2""" lowerCAmelCase_ = True def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=3_2 , **__lowerCAmelCase ): '''simple docstring''' super().__init__(**__lowerCAmelCase ) if vision_config is None: lowerCamelCase__ = {} logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''' ) if qformer_config is None: lowerCamelCase__ = {} logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''' ) if text_config is None: lowerCamelCase__ = {} logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' ) lowerCamelCase__ = BlipaVisionConfig(**__lowerCAmelCase ) lowerCamelCase__ = BlipaQFormerConfig(**__lowerCAmelCase ) lowerCamelCase__ = text_config['''model_type'''] if '''model_type''' in text_config else '''opt''' lowerCamelCase__ = CONFIG_MAPPING[text_model_type](**__lowerCAmelCase ) lowerCamelCase__ = self.text_config.tie_word_embeddings lowerCamelCase__ = self.text_config.is_encoder_decoder lowerCamelCase__ = num_query_tokens lowerCamelCase__ = self.vision_config.hidden_size lowerCamelCase__ = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES lowerCamelCase__ = 1.0 lowerCamelCase__ = 0.02 @classmethod def __lowerCamelCase ( cls , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase , ): '''simple docstring''' return cls( vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__lowerCAmelCase , ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = copy.deepcopy(self.__dict__ ) lowerCamelCase__ = self.vision_config.to_dict() lowerCamelCase__ = self.qformer_config.to_dict() lowerCamelCase__ = self.text_config.to_dict() lowerCamelCase__ = self.__class__.model_type return output	209	import pytest from datasets.parallel import ParallelBackendConfig, parallel_backend from datasets.utils.py_utils import map_nested from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows def lowerCAmelCase__(__snake_case ) -> int: # picklable for multiprocessing '''simple docstring''' return i + 1 @require_dill_gt_0_3_2 @require_joblibspark @require_not_windows def lowerCAmelCase__() -> Any: '''simple docstring''' with parallel_backend('''spark''' ): assert ParallelBackendConfig.backend_name == "spark" lowerCamelCase__ = [1, 2, 3] with pytest.raises(__snake_case ): with parallel_backend('''unsupported backend''' ): map_nested(__snake_case ,__snake_case ,num_proc=2 ) with pytest.raises(__snake_case ): with parallel_backend('''unsupported backend''' ): map_nested(__snake_case ,__snake_case ,num_proc=-1 ) @require_dill_gt_0_3_2 @require_joblibspark @require_not_windows @pytest.mark.parametrize('''num_proc''' ,[2, -1] ) def lowerCAmelCase__(__snake_case ) -> Tuple: '''simple docstring''' lowerCamelCase__ = [1, 2] lowerCamelCase__ = {'''a''': 1, '''b''': 2} lowerCamelCase__ = {'''a''': [1, 2], '''b''': [3, 4]} lowerCamelCase__ = {'''a''': {'''1''': 1}, '''b''': 2} lowerCamelCase__ = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4} lowerCamelCase__ = [2, 3] lowerCamelCase__ = {'''a''': 2, '''b''': 3} lowerCamelCase__ = {'''a''': [2, 3], '''b''': [4, 5]} lowerCamelCase__ = {'''a''': {'''1''': 2}, '''b''': 3} lowerCamelCase__ = {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5} with parallel_backend('''spark''' ): assert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case ) == expected_map_nested_sa assert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case ) == expected_map_nested_sa assert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case ) == expected_map_nested_sa assert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case ) == expected_map_nested_sa assert map_nested(__snake_case ,__snake_case ,num_proc=__snake_case ) == expected_map_nested_sa	209	1
'''simple docstring''' def A (__lowerCamelCase :str , __lowerCamelCase :str ): _lowerCAmelCase = len(__lowerCamelCase ) + 1 _lowerCAmelCase = len(__lowerCamelCase ) + 1 # dp is a 2d matrix where dp[i][j] denotes whether prefix string of # length i of input_string matches with prefix string of length j of # given pattern. # "dp" stands for dynamic programming. _lowerCAmelCase = [[0 for i in range(__lowerCamelCase )] for j in range(__lowerCamelCase )] # since string of zero length match pattern of zero length _lowerCAmelCase = 1 # since pattern of zero length will never match with string of non-zero length for i in range(1 , __lowerCamelCase ): _lowerCAmelCase = 0 # since string of zero length will match with pattern where there # is at least one * alternatively for j in range(1 , __lowerCamelCase ): _lowerCAmelCase = dp[0][j - 2] if pattern[j - 1] == """*""" else 0 # now using bottom-up approach to find for all remaining lengths for i in range(1 , __lowerCamelCase ): for j in range(1 , __lowerCamelCase ): if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".": _lowerCAmelCase = dp[i - 1][j - 1] elif pattern[j - 1] == "*": if dp[i][j - 2] == 1: _lowerCAmelCase = 1 elif pattern[j - 2] in (input_string[i - 1], "."): _lowerCAmelCase = dp[i - 1][j] else: _lowerCAmelCase = 0 else: _lowerCAmelCase = 0 return bool(dp[-1][-1] ) if __name__ == "__main__": import doctest doctest.testmod() # inputing the strings # input_string = input("input a string :") # pattern = input("input a pattern :") _lowercase = """aab""" _lowercase = """c*a*b""" # using function to check whether given string matches the given pattern if match_pattern(input_string, pattern): print(F"""{input_string} matches the given pattern {pattern}""") else: print(F"""{input_string} does not match with the given pattern {pattern}""")	229	'''simple docstring''' import logging from transformers import PretrainedConfig _lowercase = logging.getLogger(__name__) _lowercase = { """bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""", } class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ): '''simple docstring''' _lowercase : Optional[Any] = '''bertabs''' def __init__( self , _lowercase=30_522 , _lowercase=512 , _lowercase=6 , _lowercase=512 , _lowercase=8 , _lowercase=512 , _lowercase=0.2 , _lowercase=6 , _lowercase=768 , _lowercase=8 , _lowercase=2_048 , _lowercase=0.2 , **_lowercase , ): """simple docstring""" super().__init__(**_lowercase ) _lowerCAmelCase = vocab_size _lowerCAmelCase = max_pos _lowerCAmelCase = enc_layers _lowerCAmelCase = enc_hidden_size _lowerCAmelCase = enc_heads _lowerCAmelCase = enc_ff_size _lowerCAmelCase = enc_dropout _lowerCAmelCase = dec_layers _lowerCAmelCase = dec_hidden_size _lowerCAmelCase = dec_heads _lowerCAmelCase = dec_ff_size _lowerCAmelCase = dec_dropout	229	1
from typing import List, Optional, Union import torch from transformers import ( XLMRobertaTokenizer, ) from ...models import UNetaDConditionModel, VQModel from ...pipelines import DiffusionPipeline from ...pipelines.pipeline_utils import ImagePipelineOutput from ...schedulers import DDIMScheduler, DDPMScheduler from ...utils import ( is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) from .text_encoder import MultilingualCLIP lowerCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name lowerCAmelCase = ''' Examples: ```py >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline >>> import torch >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior") >>> pipe_prior.to("cuda") >>> prompt = "red cat, 4k photo" >>> out = pipe_prior(prompt) >>> image_emb = out.image_embeds >>> negative_image_emb = out.negative_image_embeds >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1") >>> pipe.to("cuda") >>> image = pipe( ... prompt, ... image_embeds=image_emb, ... negative_image_embeds=negative_image_emb, ... height=768, ... width=768, ... num_inference_steps=100, ... ).images >>> image[0].save("cat.png") ``` ''' def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=8 ): """simple docstring""" lowercase__ = h // scale_factor**2 if h % scale_factor**2 != 0: new_h += 1 lowercase__ = w // scale_factor**2 if w % scale_factor**2 != 0: new_w += 1 return new_h * scale_factor, new_w * scale_factor class _a ( A__ ): def __init__( self: Tuple , UpperCamelCase_: MultilingualCLIP , UpperCamelCase_: XLMRobertaTokenizer , UpperCamelCase_: UNetaDConditionModel , UpperCamelCase_: Union[DDIMScheduler, DDPMScheduler] , UpperCamelCase_: VQModel , ) -> List[str]: """simple docstring""" super().__init__() self.register_modules( text_encoder=UpperCamelCase_ , tokenizer=UpperCamelCase_ , unet=UpperCamelCase_ , scheduler=UpperCamelCase_ , movq=UpperCamelCase_ , ) lowercase__ = 2 ** (len(self.movq.config.block_out_channels ) - 1) def lowerCamelCase_ ( self: int , UpperCamelCase_: Optional[Any] , UpperCamelCase_: int , UpperCamelCase_: Any , UpperCamelCase_: Dict , UpperCamelCase_: str , UpperCamelCase_: str ) -> Tuple: """simple docstring""" if latents is None: lowercase__ = randn_tensor(UpperCamelCase_ , generator=UpperCamelCase_ , device=UpperCamelCase_ , dtype=UpperCamelCase_ ) else: if latents.shape != shape: raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' ) lowercase__ = latents.to(UpperCamelCase_ ) lowercase__ = latents * scheduler.init_noise_sigma return latents def lowerCamelCase_ ( self: Dict , UpperCamelCase_: Optional[int] , UpperCamelCase_: Dict , UpperCamelCase_: List[Any] , UpperCamelCase_: str , UpperCamelCase_: Union[str, Any]=None , ) -> List[str]: """simple docstring""" lowercase__ = len(UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else 1 # get prompt text embeddings lowercase__ = self.tokenizer( UpperCamelCase_ , padding='''max_length''' , truncation=UpperCamelCase_ , max_length=77 , return_attention_mask=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors='''pt''' , ) lowercase__ = text_inputs.input_ids lowercase__ = self.tokenizer(UpperCamelCase_ , padding='''longest''' , return_tensors='''pt''' ).input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCamelCase_ , UpperCamelCase_ ): lowercase__ = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( '''The following part of your input was truncated because CLIP can only handle sequences up to''' f' {self.tokenizer.model_max_length} tokens: {removed_text}' ) lowercase__ = text_input_ids.to(UpperCamelCase_ ) lowercase__ = text_inputs.attention_mask.to(UpperCamelCase_ ) lowercase__ = self.text_encoder( input_ids=UpperCamelCase_ , attention_mask=UpperCamelCase_ ) lowercase__ = prompt_embeds.repeat_interleave(UpperCamelCase_ , dim=0 ) lowercase__ = text_encoder_hidden_states.repeat_interleave(UpperCamelCase_ , dim=0 ) lowercase__ = text_mask.repeat_interleave(UpperCamelCase_ , dim=0 ) if do_classifier_free_guidance: lowercase__ = 42 if negative_prompt is None: lowercase__ = [""] * batch_size elif type(UpperCamelCase_ ) is not type(UpperCamelCase_ ): raise TypeError( f'`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase_ )} !=' f' {type(UpperCamelCase_ )}.' ) elif isinstance(UpperCamelCase_ , UpperCamelCase_ ): lowercase__ = [negative_prompt] elif batch_size != len(UpperCamelCase_ ): raise ValueError( f'`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase_ )}, but `prompt`:' f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches' ''' the batch size of `prompt`.''' ) else: lowercase__ = negative_prompt lowercase__ = self.tokenizer( UpperCamelCase_ , padding='''max_length''' , max_length=77 , truncation=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors='''pt''' , ) lowercase__ = uncond_input.input_ids.to(UpperCamelCase_ ) lowercase__ = uncond_input.attention_mask.to(UpperCamelCase_ ) lowercase__ = self.text_encoder( input_ids=UpperCamelCase_ , attention_mask=UpperCamelCase_ ) # duplicate unconditional embeddings for each generation per prompt, using mps friendly method lowercase__ = negative_prompt_embeds.shape[1] lowercase__ = negative_prompt_embeds.repeat(1 , UpperCamelCase_ ) lowercase__ = negative_prompt_embeds.view(batch_size * num_images_per_prompt , UpperCamelCase_ ) lowercase__ = uncond_text_encoder_hidden_states.shape[1] lowercase__ = uncond_text_encoder_hidden_states.repeat(1 , UpperCamelCase_ , 1 ) lowercase__ = uncond_text_encoder_hidden_states.view( batch_size * num_images_per_prompt , UpperCamelCase_ , -1 ) lowercase__ = uncond_text_mask.repeat_interleave(UpperCamelCase_ , dim=0 ) # done duplicates # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes lowercase__ = torch.cat([negative_prompt_embeds, prompt_embeds] ) lowercase__ = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] ) lowercase__ = torch.cat([uncond_text_mask, text_mask] ) return prompt_embeds, text_encoder_hidden_states, text_mask def lowerCamelCase_ ( self: Any , UpperCamelCase_: List[str]=0 ) -> Dict: """simple docstring""" if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError('''Please install accelerate via `pip install accelerate`''' ) lowercase__ = torch.device(f'cuda:{gpu_id}' ) lowercase__ = [ self.unet, self.text_encoder, self.movq, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(UpperCamelCase_ , UpperCamelCase_ ) def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: Tuple=0 ) -> Tuple: """simple docstring""" if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ): from accelerate import cpu_offload_with_hook else: raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' ) lowercase__ = torch.device(f'cuda:{gpu_id}' ) if self.device.type != "cpu": self.to('''cpu''' , silence_dtype_warnings=UpperCamelCase_ ) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) lowercase__ = None for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]: lowercase__ = cpu_offload_with_hook(UpperCamelCase_ , UpperCamelCase_ , prev_module_hook=UpperCamelCase_ ) if self.safety_checker is not None: lowercase__ = cpu_offload_with_hook(self.safety_checker , UpperCamelCase_ , prev_module_hook=UpperCamelCase_ ) # We'll offload the last model manually. lowercase__ = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def lowerCamelCase_ ( self: List[Any] ) -> List[str]: """simple docstring""" if not hasattr(self.unet , '''_hf_hook''' ): return self.device for module in self.unet.modules(): if ( hasattr(UpperCamelCase_ , '''_hf_hook''' ) and hasattr(module._hf_hook , '''execution_device''' ) and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device ) return self.device @torch.no_grad() @replace_example_docstring(UpperCamelCase_ ) def __call__( self: str , UpperCamelCase_: Union[str, List[str]] , UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]] , UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]] , UpperCamelCase_: Optional[Union[str, List[str]]] = None , UpperCamelCase_: int = 512 , UpperCamelCase_: int = 512 , UpperCamelCase_: int = 100 , UpperCamelCase_: float = 4.0 , UpperCamelCase_: int = 1 , UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase_: Optional[torch.FloatTensor] = None , UpperCamelCase_: Optional[str] = "pil" , UpperCamelCase_: bool = True , ) -> Tuple: """simple docstring""" if isinstance(UpperCamelCase_ , UpperCamelCase_ ): lowercase__ = 1 elif isinstance(UpperCamelCase_ , UpperCamelCase_ ): lowercase__ = len(UpperCamelCase_ ) else: raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_ )}' ) lowercase__ = self._execution_device lowercase__ = batch_size * num_images_per_prompt lowercase__ = guidance_scale > 1.0 lowercase__ = self._encode_prompt( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ): lowercase__ = torch.cat(UpperCamelCase_ , dim=0 ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ): lowercase__ = torch.cat(UpperCamelCase_ , dim=0 ) if do_classifier_free_guidance: lowercase__ = image_embeds.repeat_interleave(UpperCamelCase_ , dim=0 ) lowercase__ = negative_image_embeds.repeat_interleave(UpperCamelCase_ , dim=0 ) lowercase__ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to( dtype=prompt_embeds.dtype , device=UpperCamelCase_ ) self.scheduler.set_timesteps(UpperCamelCase_ , device=UpperCamelCase_ ) lowercase__ = self.scheduler.timesteps lowercase__ = self.unet.config.in_channels lowercase__ = get_new_h_w(UpperCamelCase_ , UpperCamelCase_ , self.movq_scale_factor ) # create initial latent lowercase__ = self.prepare_latents( (batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , self.scheduler , ) for i, t in enumerate(self.progress_bar(UpperCamelCase_ ) ): # expand the latents if we are doing classifier free guidance lowercase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents lowercase__ = {"text_embeds": prompt_embeds, "image_embeds": image_embeds} lowercase__ = self.unet( sample=UpperCamelCase_ , timestep=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , added_cond_kwargs=UpperCamelCase_ , return_dict=UpperCamelCase_ , )[0] if do_classifier_free_guidance: lowercase__ = noise_pred.split(latents.shape[1] , dim=1 ) lowercase__ = noise_pred.chunk(2 ) lowercase__ = variance_pred.chunk(2 ) lowercase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) lowercase__ = torch.cat([noise_pred, variance_pred_text] , dim=1 ) if not ( hasattr(self.scheduler.config , '''variance_type''' ) and self.scheduler.config.variance_type in ["learned", "learned_range"] ): lowercase__ = noise_pred.split(latents.shape[1] , dim=1 ) # compute the previous noisy sample x_t -> x_t-1 lowercase__ = self.scheduler.step( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , generator=UpperCamelCase_ , ).prev_sample # post-processing lowercase__ = self.movq.decode(UpperCamelCase_ , force_not_quantize=UpperCamelCase_ )["sample"] if output_type not in ["pt", "np", "pil"]: raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' ) if output_type in ["np", "pil"]: lowercase__ = image * 0.5 + 0.5 lowercase__ = image.clamp(0 , 1 ) lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy() if output_type == "pil": lowercase__ = self.numpy_to_pil(UpperCamelCase_ ) if not return_dict: return (image,) return ImagePipelineOutput(images=UpperCamelCase_ )	110	from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available lowerCamelCase : int ={ '''configuration_audio_spectrogram_transformer''': [ '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ASTConfig''', ] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCamelCase : Union[str, Any] =[ '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''ASTForAudioClassification''', '''ASTModel''', '''ASTPreTrainedModel''', ] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCamelCase : Optional[int] =['''ASTFeatureExtractor'''] if TYPE_CHECKING: from .configuration_audio_spectrogram_transformer import ( AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ASTConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_audio_spectrogram_transformer import ( AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ASTForAudioClassification, ASTModel, ASTPreTrainedModel, ) try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor else: import sys lowerCamelCase : Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)	189	0
import warnings from ...utils import logging from .image_processing_imagegpt import ImageGPTImageProcessor lowerCAmelCase = logging.get_logger(__name__) class A ( A_ ): def __init__(self , *lowerCAmelCase , **lowerCAmelCase ): warnings.warn( 'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.' ' Please use ImageGPTImageProcessor instead.' , lowerCAmelCase , ) super().__init__(*lowerCAmelCase , **lowerCAmelCase )	304	import math from datetime import datetime, timedelta def _lowerCamelCase( lowercase__ ) -> datetime: '''simple docstring''' __lowercase= year % 1_9 __lowercase= year % 4 __lowercase= year % 7 __lowercase= math.floor(year / 1_0_0 ) __lowercase= math.floor((1_3 + 8 * leap_day_inhibits) / 2_5 ) __lowercase= leap_day_inhibits / 4 __lowercase= ( 1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number ) % 3_0 __lowercase= (4 + leap_day_inhibits - leap_day_reinstall_number) % 7 # days to be added to March 21 __lowercase= (1_9 * metonic_cycle + secular_moon_shift) % 3_0 # PHM -> Paschal Full Moon __lowercase= ( 2 * julian_leap_year + 4 * non_leap_year + 6 * days_to_add + century_starting_point ) % 7 if days_to_add == 2_9 and days_from_phm_to_sunday == 6: return datetime(lowercase__ , 4 , 1_9 ) elif days_to_add == 2_8 and days_from_phm_to_sunday == 6: return datetime(lowercase__ , 4 , 1_8 ) else: return datetime(lowercase__ , 3 , 2_2 ) + timedelta( days=int(days_to_add + days_from_phm_to_sunday ) ) if __name__ == "__main__": for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3): lowerCAmelCase = '''will be''' if year > datetime.now().year else '''was''' print(F'Easter in {year} {tense} {gauss_easter(year)}')	304	1
"""simple docstring""" import gc import unittest from parameterized import parameterized from diffusers import FlaxUNetaDConditionModel from diffusers.utils import is_flax_available from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow if is_flax_available(): import jax import jax.numpy as jnp @slow @require_flax class lowercase__ ( unittest.TestCase ): def UpperCAmelCase__ ( self : int , snake_case__ : List[str] , snake_case__ : List[str] ): return F"""gaussian_noise_s={seed}_shape={'_'.join([str(__SCREAMING_SNAKE_CASE ) for s in shape] )}.npy""" def UpperCAmelCase__ ( self : List[Any] ): super().tearDown() gc.collect() def UpperCAmelCase__ ( self : Optional[int] , snake_case__ : Tuple=0 , snake_case__ : List[Any]=(4, 4, 64, 64) , snake_case__ : List[Any]=False ): lowerCamelCase_ : Any =jnp.bfloataa if fpaa else jnp.floataa lowerCamelCase_ : int =jnp.array(load_hf_numpy(self.get_file_format(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) , dtype=__SCREAMING_SNAKE_CASE ) return image def UpperCAmelCase__ ( self : List[Any] , snake_case__ : Tuple=False , snake_case__ : Union[str, Any]="CompVis/stable-diffusion-v1-4" ): lowerCamelCase_ : Union[str, Any] =jnp.bfloataa if fpaa else jnp.floataa lowerCamelCase_ : Union[str, Any] ='''bf16''' if fpaa else None lowerCamelCase_ : Dict =FlaxUNetaDConditionModel.from_pretrained( __SCREAMING_SNAKE_CASE , subfolder="unet" , dtype=__SCREAMING_SNAKE_CASE , revision=__SCREAMING_SNAKE_CASE ) return model, params def UpperCAmelCase__ ( self : Dict , snake_case__ : str=0 , snake_case__ : List[Any]=(4, 77, 768) , snake_case__ : Any=False ): lowerCamelCase_ : Optional[Any] =jnp.bfloataa if fpaa else jnp.floataa lowerCamelCase_ : List[str] =jnp.array(load_hf_numpy(self.get_file_format(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) , dtype=__SCREAMING_SNAKE_CASE ) return hidden_states @parameterized.expand( [ # fmt: off [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]], [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]], [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]], [3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]], # fmt: on ] ) def UpperCAmelCase__ ( self : Dict , snake_case__ : List[str] , snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] ): lowerCamelCase_ : Tuple =self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4" , fpaa=__SCREAMING_SNAKE_CASE ) lowerCamelCase_ : Any =self.get_latents(__SCREAMING_SNAKE_CASE , fpaa=__SCREAMING_SNAKE_CASE ) lowerCamelCase_ : Optional[Any] =self.get_encoder_hidden_states(__SCREAMING_SNAKE_CASE , fpaa=__SCREAMING_SNAKE_CASE ) lowerCamelCase_ : Optional[Any] =model.apply( {"params": params} , __SCREAMING_SNAKE_CASE , jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.intaa ) , encoder_hidden_states=__SCREAMING_SNAKE_CASE , ).sample assert sample.shape == latents.shape lowerCamelCase_ : Dict =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa ) lowerCamelCase_ : Tuple =jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.floataa ) # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware assert jnp.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1E-2 ) @parameterized.expand( [ # fmt: off [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]], [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]], [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]], [3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]], # fmt: on ] ) def UpperCAmelCase__ ( self : Tuple , snake_case__ : int , snake_case__ : List[str] , snake_case__ : Any ): lowerCamelCase_ : Union[str, Any] =self.get_unet_model(model_id="stabilityai/stable-diffusion-2" , fpaa=__SCREAMING_SNAKE_CASE ) lowerCamelCase_ : List[Any] =self.get_latents(__SCREAMING_SNAKE_CASE , shape=(4, 4, 96, 96) , fpaa=__SCREAMING_SNAKE_CASE ) lowerCamelCase_ : Optional[Any] =self.get_encoder_hidden_states(__SCREAMING_SNAKE_CASE , shape=(4, 77, 1024) , fpaa=__SCREAMING_SNAKE_CASE ) lowerCamelCase_ : Optional[Any] =model.apply( {"params": params} , __SCREAMING_SNAKE_CASE , jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.intaa ) , encoder_hidden_states=__SCREAMING_SNAKE_CASE , ).sample assert sample.shape == latents.shape lowerCamelCase_ : Optional[int] =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa ) lowerCamelCase_ : List[Any] =jnp.array(__SCREAMING_SNAKE_CASE , dtype=jnp.floataa ) # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware assert jnp.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1E-2 )	144	'''simple docstring''' import unittest from knapsack import greedy_knapsack as kp class lowerCAmelCase__ ( unittest.TestCase ): def _snake_case ( self ): """simple docstring""" lowercase_ : List[str] = [10, 20, 30, 40, 50, 60] lowercase_ : Optional[Any] = [2, 4, 6, 8, 10, 12] lowercase_ : Union[str, Any] = 1_00 self.assertEqual(kp.calc_profit(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , 2_10 ) def _snake_case ( self ): """simple docstring""" self.assertRaisesRegex(__SCREAMING_SNAKE_CASE , '''max_weight must greater than zero.''' ) def _snake_case ( self ): """simple docstring""" self.assertRaisesRegex(__SCREAMING_SNAKE_CASE , '''Weight can not be negative.''' ) def _snake_case ( self ): """simple docstring""" self.assertRaisesRegex(__SCREAMING_SNAKE_CASE , '''Profit can not be negative.''' ) def _snake_case ( self ): """simple docstring""" self.assertRaisesRegex(__SCREAMING_SNAKE_CASE , '''max_weight must greater than zero.''' ) def _snake_case ( self ): """simple docstring""" self.assertRaisesRegex( __SCREAMING_SNAKE_CASE , '''The length of profit and weight must be same.''' ) if __name__ == "__main__": unittest.main()	93	0
"""simple docstring""" import unittest import numpy as np from transformers import DistilBertConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax.numpy as jnp from transformers.models.distilbert.modeling_flax_distilbert import ( FlaxDistilBertForMaskedLM, FlaxDistilBertForMultipleChoice, FlaxDistilBertForQuestionAnswering, FlaxDistilBertForSequenceClassification, FlaxDistilBertForTokenClassification, FlaxDistilBertModel, ) class snake_case ( unittest.TestCase ): def __init__( self : List[Any] , A : Dict , A : Optional[int]=1_3 , A : Dict=7 , A : Union[str, Any]=True , A : Union[str, Any]=True , A : Optional[Any]=True , A : str=True , A : Any=9_9 , A : Dict=3_2 , A : Union[str, Any]=5 , A : Tuple=4 , A : List[Any]=3_7 , A : Tuple="gelu" , A : str=0.1 , A : Union[str, Any]=0.1 , A : Optional[int]=5_1_2 , A : int=1_6 , A : Any=2 , A : List[str]=0.02 , A : int=4 , ): '''simple docstring''' a : Dict = parent a : List[Any] = batch_size a : Optional[Any] = seq_length a : Tuple = is_training a : int = use_attention_mask a : Optional[int] = use_token_type_ids a : Any = use_labels a : List[Any] = vocab_size a : Optional[int] = hidden_size a : List[Any] = num_hidden_layers a : Union[str, Any] = num_attention_heads a : Any = intermediate_size a : Tuple = hidden_act a : Optional[int] = hidden_dropout_prob a : Dict = attention_probs_dropout_prob a : Any = max_position_embeddings a : Tuple = type_vocab_size a : Any = type_sequence_label_size a : str = initializer_range a : Dict = num_choices def lowerCamelCase__ ( self : Tuple ): '''simple docstring''' a : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) a : List[str] = None if self.use_attention_mask: a : List[Any] = random_attention_mask([self.batch_size, self.seq_length] ) a : Optional[Any] = DistilBertConfig( vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=_UpperCAmelCase , ) return config, input_ids, attention_mask def lowerCamelCase__ ( self : List[Any] ): '''simple docstring''' a : Union[str, Any] = self.prepare_config_and_inputs() a, a, a : Optional[int] = config_and_inputs a : int = {'input_ids': input_ids, 'attention_mask': attention_mask} return config, inputs_dict @require_flax class snake_case ( lowerCamelCase_ , unittest.TestCase ): __magic_name__ = ( ( FlaxDistilBertModel, FlaxDistilBertForMaskedLM, FlaxDistilBertForMultipleChoice, FlaxDistilBertForQuestionAnswering, FlaxDistilBertForSequenceClassification, FlaxDistilBertForTokenClassification, FlaxDistilBertForQuestionAnswering, ) if is_flax_available() else () ) def lowerCamelCase__ ( self : Any ): '''simple docstring''' a : Any = FlaxDistilBertModelTester(self ) @slow def lowerCamelCase__ ( self : int ): '''simple docstring''' for model_class_name in self.all_model_classes: a : List[Any] = model_class_name.from_pretrained('distilbert-base-uncased' ) a : Optional[Any] = model(np.ones((1, 1) ) ) self.assertIsNotNone(_UpperCAmelCase ) @require_flax class snake_case ( unittest.TestCase ): @slow def lowerCamelCase__ ( self : Optional[int] ): '''simple docstring''' a : Union[str, Any] = FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' ) a : List[Any] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] ) a : Tuple = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) a : List[str] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0] a : List[str] = (1, 1_1, 7_6_8) self.assertEqual(output.shape , _UpperCAmelCase ) a : Optional[int] = np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]] ) self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )	367	"""simple docstring""" import argparse from collections import defaultdict import yaml _UpperCamelCase : int = 'docs/source/en/_toctree.yml' def snake_case (A_ :Optional[Any] ): '''simple docstring''' a : List[Any] = defaultdict(A_ ) for doc in model_doc: counts[doc["local"]] += 1 a : Optional[Any] = [key for key, value in counts.items() if value > 1] a : List[str] = [] for duplicate_key in duplicates: a : int = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key} ) if len(A_ ) > 1: raise ValueError( f'''{duplicate_key} is present several times in the documentation table of content at ''' '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the ' 'others.' ) # Only add this once new_doc.append({'local': duplicate_key, 'title': titles[0]} ) # Add none duplicate-keys new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1] ) # Sort return sorted(A_ , key=lambda A_ : s["title"].lower() ) def snake_case (A_ :List[str]=False ): '''simple docstring''' with open(A_ , encoding='utf-8' ) as f: a : Dict = yaml.safe_load(f.read() ) # Get to the API doc a : Optional[Any] = 0 while content[api_idx]["title"] != "API": api_idx += 1 a : List[str] = content[api_idx]['sections'] # Then to the model doc a : Optional[int] = 0 while api_doc[model_idx]["title"] != "Models": model_idx += 1 a : Optional[Any] = api_doc[model_idx]['sections'] a : Dict = [(idx, section) for idx, section in enumerate(A_ ) if 'sections' in section] a : List[str] = False for idx, modality_doc in modalities_docs: a : str = modality_doc['sections'] a : str = clean_model_doc_toc(A_ ) if old_modality_doc != new_modality_doc: a : str = True if overwrite: a : Any = new_modality_doc if diff: if overwrite: a : Any = model_doc a : str = api_doc with open(A_ , 'w' , encoding='utf-8' ) as f: f.write(yaml.dump(A_ , allow_unicode=A_ ) ) else: raise ValueError( 'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' ) if __name__ == "__main__": _UpperCamelCase : Optional[int] = argparse.ArgumentParser() parser.add_argument('--fix_and_overwrite', action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true", help='Whether to fix inconsistencies.') _UpperCamelCase : Any = parser.parse_args() check_model_doc(args.fix_and_overwrite)	186	0
"""simple docstring""" def __UpperCAmelCase ( UpperCAmelCase_ : dict ) -> bool: '''simple docstring''' __snake_case : set[int] = set() # To detect a back edge, keep track of vertices currently in the recursion stack __snake_case : set[int] = set() return any( node not in visited and depth_first_search(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ) for node in graph ) def __UpperCAmelCase ( UpperCAmelCase_ : dict , UpperCAmelCase_ : int , UpperCAmelCase_ : set , UpperCAmelCase_ : set ) -> bool: '''simple docstring''' visited.add(UpperCAmelCase_ ) rec_stk.add(UpperCAmelCase_ ) for node in graph[vertex]: if node not in visited: if depth_first_search(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ): return True elif node in rec_stk: return True # The node needs to be removed from recursion stack before function ends rec_stk.remove(UpperCAmelCase_ ) return False if __name__ == "__main__": from doctest import testmod testmod()	172	"""simple docstring""" import os from argparse import ArgumentParser, Namespace from ..data import SingleSentenceClassificationProcessor as Processor from ..pipelines import TextClassificationPipeline from ..utils import is_tf_available, is_torch_available, logging from . import BaseTransformersCLICommand if not is_tf_available() and not is_torch_available(): raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training") # TF training parameters _a : Optional[int]= False _a : int= False def __UpperCAmelCase ( UpperCAmelCase_ : Namespace ) -> Optional[Any]: '''simple docstring''' return TrainCommand(UpperCAmelCase_ ) class UpperCamelCase ( lowercase ): @staticmethod def _lowercase (_A : ArgumentParser) -> Any: __snake_case : Any = parser.add_parser('train' , help='CLI tool to train a model on a task.') train_parser.add_argument( '--train_data' , type=_A , required=_A , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , ) train_parser.add_argument( '--column_label' , type=_A , default=0 , help='Column of the dataset csv file with example labels.') train_parser.add_argument( '--column_text' , type=_A , default=1 , help='Column of the dataset csv file with example texts.') train_parser.add_argument( '--column_id' , type=_A , default=2 , help='Column of the dataset csv file with example ids.') train_parser.add_argument( '--skip_first_row' , action="https://huggingface.co/datasets/infinityofspace/python_codestyles-mixed1-500/viewer/default/store_true" , help='Skip the first row of the csv file (headers).') train_parser.add_argument('--validation_data' , type=_A , default='' , help='path to validation dataset.') train_parser.add_argument( '--validation_split' , type=_A , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , ) train_parser.add_argument('--output' , type=_A , default='./' , help='path to saved the trained model.') train_parser.add_argument( '--task' , type=_A , default='text_classification' , help='Task to train the model on.') train_parser.add_argument( '--model' , type=_A , default='bert-base-uncased' , help='Model\'s name or path to stored model.') train_parser.add_argument('--train_batch_size' , type=_A , default=32 , help='Batch size for training.') train_parser.add_argument('--valid_batch_size' , type=_A , default=64 , help='Batch size for validation.') train_parser.add_argument('--learning_rate' , type=_A , default=3E-5 , help='Learning rate.') train_parser.add_argument('--adam_epsilon' , type=_A , default=1E-08 , help='Epsilon for Adam optimizer.') train_parser.set_defaults(func=_A) def __init__(self : int , _A : Namespace) -> Tuple: __snake_case : Optional[int] = logging.get_logger('transformers-cli/training') __snake_case : Optional[int] = 'tf' if is_tf_available() else 'torch' os.makedirs(args.output , exist_ok=_A) __snake_case : List[Any] = args.output __snake_case : Any = args.column_label __snake_case : str = args.column_text __snake_case : Any = args.column_id self.logger.info(f"Loading {args.task} pipeline for {args.model}") if args.task == "text_classification": __snake_case : List[str] = TextClassificationPipeline.from_pretrained(args.model) elif args.task == "token_classification": raise NotImplementedError elif args.task == "question_answering": raise NotImplementedError self.logger.info(f"Loading dataset from {args.train_data}") __snake_case : List[Any] = Processor.create_from_csv( args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , ) __snake_case : List[str] = None if args.validation_data: self.logger.info(f"Loading validation dataset from {args.validation_data}") __snake_case : Dict = Processor.create_from_csv( args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , ) __snake_case : List[str] = args.validation_split __snake_case : str = args.train_batch_size __snake_case : Any = args.valid_batch_size __snake_case : Union[str, Any] = args.learning_rate __snake_case : str = args.adam_epsilon def _lowercase (self : List[str]) -> str: if self.framework == "tf": return self.run_tf() return self.run_torch() def _lowercase (self : str) -> int: raise NotImplementedError def _lowercase (self : Union[str, Any]) -> Optional[Any]: self.pipeline.fit( self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , ) # Save trained pipeline self.pipeline.save_pretrained(self.output)	172	1
"""simple docstring""" from dataclasses import dataclass from typing import Tuple import numpy as np import torch @dataclass class __A : '''simple docstring''' lowerCAmelCase : torch.Tensor # [batch_size x 3] lowerCAmelCase : torch.Tensor # [batch_size x 3] lowerCAmelCase : torch.Tensor # [batch_size x 3] lowerCAmelCase : torch.Tensor # [batch_size x 3] lowerCAmelCase : int lowerCAmelCase : int lowerCAmelCase : float lowerCAmelCase : float lowerCAmelCase : Tuple[int] def UpperCAmelCase ( self : Optional[int] ) -> Tuple: """simple docstring""" assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0] assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3 assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2 def UpperCAmelCase ( self : Union[str, Any] ) -> int: """simple docstring""" return torch.from_numpy(np.array([self.width, self.height] ,dtype=np.floataa ) ) def UpperCAmelCase ( self : List[str] ) -> List[str]: """simple docstring""" return torch.from_numpy(np.array([self.x_fov, self.y_fov] ,dtype=np.floataa ) ) def UpperCAmelCase ( self : Dict ) -> torch.Tensor: """simple docstring""" lowercase__ : Optional[Any] = torch.arange(self.height * self.width ) lowercase__ : Optional[int] = torch.stack( [ pixel_indices % self.width, torch.div(_snake_case ,self.width ,rounding_mode='''trunc''' ), ] ,axis=1 ,) return coords @property def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]: """simple docstring""" lowercase__ , *lowercase__ : Dict = self.shape lowercase__ : Union[str, Any] = int(np.prod(_snake_case ) ) lowercase__ : str = self.get_image_coords() lowercase__ : Tuple = torch.broadcast_to(coords.unsqueeze(0 ) ,[batch_size * inner_batch_size, *coords.shape] ) lowercase__ : Union[str, Any] = self.get_camera_rays(_snake_case ) lowercase__ : str = rays.view(_snake_case ,inner_batch_size * self.height * self.width ,2 ,3 ) return rays def UpperCAmelCase ( self : Any ,_snake_case : torch.Tensor ) -> torch.Tensor: """simple docstring""" lowercase__ , *lowercase__ , lowercase__ : Any = coords.shape assert n_coords == 2 assert batch_size == self.origin.shape[0] lowercase__ : int = coords.view(_snake_case ,-1 ,2 ) lowercase__ : Optional[Any] = self.resolution() lowercase__ : List[str] = self.fov() lowercase__ : Dict = (flat.float() / (res - 1)) * 2 - 1 lowercase__ : List[str] = fracs * torch.tan(fov / 2 ) lowercase__ : Dict = fracs.view(_snake_case ,-1 ,2 ) lowercase__ : Tuple = ( self.z.view(_snake_case ,1 ,3 ) + self.x.view(_snake_case ,1 ,3 ) * fracs[:, :, :1] + self.y.view(_snake_case ,1 ,3 ) * fracs[:, :, 1:] ) lowercase__ : Tuple = directions / directions.norm(dim=-1 ,keepdim=_snake_case ) lowercase__ : Tuple = torch.stack( [ torch.broadcast_to(self.origin.view(_snake_case ,1 ,3 ) ,[batch_size, directions.shape[1], 3] ), directions, ] ,dim=2 ,) return rays.view(_snake_case ,*_snake_case ,2 ,3 ) def UpperCAmelCase ( self : int ,_snake_case : int ,_snake_case : int ) -> "DifferentiableProjectiveCamera": """simple docstring""" assert width * self.height == height * self.width, "The aspect ratio should not change." return DifferentiableProjectiveCamera( origin=self.origin ,x=self.x ,y=self.y ,z=self.z ,width=_snake_case ,height=_snake_case ,x_fov=self.x_fov ,y_fov=self.y_fov ,) def __UpperCAmelCase ( __lowerCamelCase ) -> DifferentiableProjectiveCamera: lowercase__ : List[str] = [] lowercase__ : Dict = [] lowercase__ : Dict = [] lowercase__ : Dict = [] for theta in np.linspace(0 , 2 * np.pi , num=20 ): lowercase__ : Any = np.array([np.sin(__lowerCamelCase ), np.cos(__lowerCamelCase ), -0.5] ) z /= np.sqrt(np.sum(z**2 ) ) lowercase__ : int = -z * 4 lowercase__ : int = np.array([np.cos(__lowerCamelCase ), -np.sin(__lowerCamelCase ), 0.0] ) lowercase__ : Any = np.cross(__lowerCamelCase , __lowerCamelCase ) origins.append(__lowerCamelCase ) xs.append(__lowerCamelCase ) ys.append(__lowerCamelCase ) zs.append(__lowerCamelCase ) return DifferentiableProjectiveCamera( origin=torch.from_numpy(np.stack(__lowerCamelCase , axis=0 ) ).float() , x=torch.from_numpy(np.stack(__lowerCamelCase , axis=0 ) ).float() , y=torch.from_numpy(np.stack(__lowerCamelCase , axis=0 ) ).float() , z=torch.from_numpy(np.stack(__lowerCamelCase , axis=0 ) ).float() , width=__lowerCamelCase , height=__lowerCamelCase , x_fov=0.7 , y_fov=0.7 , shape=(1, len(__lowerCamelCase )) , )	302	"""simple docstring""" import unittest from transformers import AutoTokenizer, is_flax_available from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow if is_flax_available(): import jax.numpy as jnp from transformers import FlaxXLMRobertaModel @require_sentencepiece @require_tokenizers @require_flax class __A ( unittest.TestCase ): '''simple docstring''' @slow def UpperCAmelCase ( self : List[str] ) -> Any: """simple docstring""" lowercase__ : List[str] = FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base''' ) lowercase__ : List[str] = AutoTokenizer.from_pretrained('''xlm-roberta-base''' ) lowercase__ : List[str] = '''The dog is cute and lives in the garden house''' lowercase__ : int = jnp.array([tokenizer.encode(_snake_case )] ) lowercase__ : Any = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim lowercase__ : Tuple = jnp.array( [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] ) lowercase__ : Optional[Any] = model(_snake_case )['''last_hidden_state'''] self.assertEqual(output.shape ,_snake_case ) # compare the actual values for a slice of last dim self.assertTrue(jnp.allclose(output[:, :, -1] ,_snake_case ,atol=1e-3 ) )	302	1
"""simple docstring""" import os import unittest from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer from ...test_tokenization_common import TokenizerTesterMixin class SCREAMING_SNAKE_CASE_ ( __a , unittest.TestCase ): """simple docstring""" __lowercase : Union[str, Any] = PhobertTokenizer __lowercase : Any = False def snake_case_ ( self): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt __SCREAMING_SNAKE_CASE = ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""] __SCREAMING_SNAKE_CASE = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__)))) __SCREAMING_SNAKE_CASE = ["""#version: 0.2""", """l à</w>"""] __SCREAMING_SNAKE_CASE = {"""unk_token""": """<unk>"""} __SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""]) __SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""]) with open(self.vocab_file , """w""" , encoding="""utf-8""") as fp: for token in vocab_tokens: fp.write(f"{token} {vocab_tokens[token]}\n") with open(self.merges_file , """w""" , encoding="""utf-8""") as fp: fp.write("""\n""".join(lowerCAmelCase__)) def snake_case_ ( self , **lowerCAmelCase__): kwargs.update(self.special_tokens_map) return PhobertTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__) def snake_case_ ( self , lowerCAmelCase__): __SCREAMING_SNAKE_CASE = """Tôi là VinAI Research""" __SCREAMING_SNAKE_CASE = """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>""" return input_text, output_text def snake_case_ ( self): __SCREAMING_SNAKE_CASE = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map) __SCREAMING_SNAKE_CASE = """Tôi là VinAI Research""" __SCREAMING_SNAKE_CASE = """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split() __SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCAmelCase__) print(lowerCAmelCase__) self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__) __SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token] __SCREAMING_SNAKE_CASE = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__) , lowerCAmelCase__)	100	"""simple docstring""" __magic_name__ = "Tobias Carryer" from time import time class SCREAMING_SNAKE_CASE_ : """simple docstring""" def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=int(time())): # noqa: B008 __SCREAMING_SNAKE_CASE = multiplier __SCREAMING_SNAKE_CASE = increment __SCREAMING_SNAKE_CASE = modulo __SCREAMING_SNAKE_CASE = seed def snake_case_ ( self): __SCREAMING_SNAKE_CASE = (self.multiplier * self.seed + self.increment) % self.modulo return self.seed if __name__ == "__main__": # Show the LCG in action. __magic_name__ = LinearCongruentialGenerator(1664525, 1013904223, 2 << 31) while True: print(lcg.next_number())	100	1
'''simple docstring''' lowerCamelCase_ = {str(digit): digit**5 for digit in range(10)} def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int: return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__A ) ) def SCREAMING_SNAKE_CASE_ ( ) -> int: return sum( number for number in range(10_00 , 1_00_00_00 ) if number == digits_fifth_powers_sum(__A ) ) if __name__ == "__main__": print(solution())	111	'''simple docstring''' from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging if TYPE_CHECKING: from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType lowerCamelCase_ = logging.get_logger(__name__) lowerCamelCase_ = { 'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json', 'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json', 'microsoft/deberta-v2-xlarge-mnli': ( 'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json' ), 'microsoft/deberta-v2-xxlarge-mnli': ( 'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json' ), } class lowercase_ ( A ): """simple docstring""" lowerCamelCase_ = '''deberta-v2''' def __init__( self : str , __lowerCamelCase : Union[str, Any]=1_2_8_1_0_0 , __lowerCamelCase : Optional[int]=1_5_3_6 , __lowerCamelCase : Optional[int]=2_4 , __lowerCamelCase : Optional[int]=2_4 , __lowerCamelCase : Tuple=6_1_4_4 , __lowerCamelCase : List[str]="gelu" , __lowerCamelCase : int=0.1 , __lowerCamelCase : Optional[Any]=0.1 , __lowerCamelCase : Union[str, Any]=5_1_2 , __lowerCamelCase : Optional[Any]=0 , __lowerCamelCase : str=0.0_2 , __lowerCamelCase : int=1e-7 , __lowerCamelCase : Any=False , __lowerCamelCase : Any=-1 , __lowerCamelCase : Tuple=0 , __lowerCamelCase : str=True , __lowerCamelCase : List[Any]=None , __lowerCamelCase : Optional[int]=0 , __lowerCamelCase : Any="gelu" , **__lowerCamelCase : Union[str, Any] , ): """simple docstring""" super().__init__(**__lowerCamelCase ) _SCREAMING_SNAKE_CASE = hidden_size _SCREAMING_SNAKE_CASE = num_hidden_layers _SCREAMING_SNAKE_CASE = num_attention_heads _SCREAMING_SNAKE_CASE = intermediate_size _SCREAMING_SNAKE_CASE = hidden_act _SCREAMING_SNAKE_CASE = hidden_dropout_prob _SCREAMING_SNAKE_CASE = attention_probs_dropout_prob _SCREAMING_SNAKE_CASE = max_position_embeddings _SCREAMING_SNAKE_CASE = type_vocab_size _SCREAMING_SNAKE_CASE = initializer_range _SCREAMING_SNAKE_CASE = relative_attention _SCREAMING_SNAKE_CASE = max_relative_positions _SCREAMING_SNAKE_CASE = pad_token_id _SCREAMING_SNAKE_CASE = position_biased_input # Backwards compatibility if type(__lowerCamelCase ) == str: _SCREAMING_SNAKE_CASE = [x.strip() for x in pos_att_type.lower().split("|" )] _SCREAMING_SNAKE_CASE = pos_att_type _SCREAMING_SNAKE_CASE = vocab_size _SCREAMING_SNAKE_CASE = layer_norm_eps _SCREAMING_SNAKE_CASE = kwargs.get("pooler_hidden_size" , __lowerCamelCase ) _SCREAMING_SNAKE_CASE = pooler_dropout _SCREAMING_SNAKE_CASE = pooler_hidden_act class lowercase_ ( A ): """simple docstring""" @property def lowerCAmelCase_ ( self : List[Any] ): """simple docstring""" if self.task == "multiple-choice": _SCREAMING_SNAKE_CASE = {0: "batch", 1: "choice", 2: "sequence"} else: _SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"} if self._config.type_vocab_size > 0: return OrderedDict( [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] ) else: return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] ) @property def lowerCAmelCase_ ( self : List[str] ): """simple docstring""" return 1_2 def lowerCAmelCase_ ( self : List[str] , __lowerCamelCase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : int = -1 , __lowerCamelCase : bool = False , __lowerCamelCase : Optional["TensorType"] = None , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 4_0 , __lowerCamelCase : int = 4_0 , __lowerCamelCase : "PreTrainedTokenizerBase" = None , ): """simple docstring""" _SCREAMING_SNAKE_CASE = super().generate_dummy_inputs(preprocessor=__lowerCamelCase , framework=__lowerCamelCase ) if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs: del dummy_inputs["token_type_ids"] return dummy_inputs	111	1
import itertools import random import unittest import numpy as np from transformers import ASTFeatureExtractor from transformers.testing_utils import require_torch, require_torchaudio from transformers.utils.import_utils import is_torch_available from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin SCREAMING_SNAKE_CASE__ = random.Random() if is_torch_available(): import torch def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : str=1.0 , SCREAMING_SNAKE_CASE : Optional[Any]=None , SCREAMING_SNAKE_CASE : Union[str, Any]=None ) -> Union[str, Any]: if rng is None: __lowercase = global_rng __lowercase = [] for batch_idx in range(shape[0] ): values.append([] ) for _ in range(shape[1] ): values[-1].append(rng.random() * scale ) return values class A__ ( unittest.TestCase ): def __init__( self : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=7 , _UpperCAmelCase : Dict=4_00 , _UpperCAmelCase : Optional[int]=20_00 , _UpperCAmelCase : Any=1 , _UpperCAmelCase : List[str]=0.0 , _UpperCAmelCase : Union[str, Any]=1_60_00 , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Union[str, Any]=True , ) -> List[Any]: """simple docstring""" __lowercase = parent __lowercase = batch_size __lowercase = min_seq_length __lowercase = max_seq_length __lowercase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) __lowercase = feature_size __lowercase = padding_value __lowercase = sampling_rate __lowercase = return_attention_mask __lowercase = do_normalize def a__ ( self : Optional[int] ) -> Dict: """simple docstring""" return { "feature_size": self.feature_size, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } def a__ ( self : str , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : int=False ) -> Any: """simple docstring""" def _flatten(_UpperCAmelCase : Union[str, Any] ): return list(itertools.chain(*_SCREAMING_SNAKE_CASE ) ) if equal_length: __lowercase = floats_list((self.batch_size, self.max_seq_length) ) else: # make sure that inputs increase in size __lowercase = [ _flatten(floats_list((x, self.feature_size) ) ) for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff ) ] if numpify: __lowercase = [np.asarray(_SCREAMING_SNAKE_CASE ) for x in speech_inputs] return speech_inputs @require_torch @require_torchaudio class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ): lowerCAmelCase__ : int = ASTFeatureExtractor def a__ ( self : int ) -> List[str]: """simple docstring""" __lowercase = ASTFeatureExtractionTester(self ) def a__ ( self : Optional[int] ) -> Optional[int]: """simple docstring""" __lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) # create three inputs of length 800, 1000, and 1200 __lowercase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] __lowercase = [np.asarray(_SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs] # Test not batched input __lowercase = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values __lowercase = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 ) ) # Test batched __lowercase = feat_extract(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='np' ).input_values __lowercase = feat_extract(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='np' ).input_values for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 ) ) # Test 2-D numpy arrays are batched. __lowercase = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)] __lowercase = np.asarray(_SCREAMING_SNAKE_CASE ) __lowercase = feat_extract(_SCREAMING_SNAKE_CASE , return_tensors='np' ).input_values __lowercase = feat_extract(_SCREAMING_SNAKE_CASE , return_tensors='np' ).input_values for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 ) ) @require_torch def a__ ( self : Dict ) -> Tuple: """simple docstring""" import torch __lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) __lowercase = np.random.rand(1_00 ).astype(np.floataa ) __lowercase = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: __lowercase = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' ) self.assertTrue(np_processed.input_values.dtype == np.floataa ) __lowercase = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' ) self.assertTrue(pt_processed.input_values.dtype == torch.floataa ) def a__ ( self : Dict , _UpperCAmelCase : Optional[int] ) -> int: """simple docstring""" from datasets import load_dataset __lowercase = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' ) # automatic decoding with librispeech __lowercase = ds.sort('id' ).select(range(_SCREAMING_SNAKE_CASE ) )[:num_samples]["audio"] return [x["array"] for x in speech_samples] @require_torch def a__ ( self : Dict ) -> Dict: """simple docstring""" __lowercase = torch.tensor( [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776, -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133, -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936, -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869] ) # fmt: on __lowercase = self._load_datasamples(1 ) __lowercase = ASTFeatureExtractor() __lowercase = feature_extractor(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).input_values self.assertEquals(input_values.shape , (1, 10_24, 1_28) ) self.assertTrue(torch.allclose(input_values[0, 0, :30] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )	325	"""simple docstring""" import numpy as np from scipy.spatial.distance import cdist from sklearn.metrics import fa_score import datasets __snake_case : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n year={2020},\n booktitle={Findings of EMNLP},\n}\n' __snake_case : str = '\\n IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n' __snake_case : str = '\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n predictions: list of predictions to score (as int64),\n except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n references: list of ground truth labels corresponding to the predictions (as int64),\n except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n "accuracy": Accuracy\n "f1": F1 score\n "precision": Precision@10\nExamples:\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\') # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'precision@10\': 1.0}\n\n' def _lowercase ( __snake_case ,__snake_case ) -> Union[str, Any]: return float((preds == labels).mean() ) def _lowercase ( __snake_case ,__snake_case ) -> str: __lowerCAmelCase : str = simple_accuracy(__snake_case ,__snake_case ) __lowerCAmelCase : Any = float(fa_score(y_true=__snake_case ,y_pred=__snake_case ) ) return { "accuracy": acc, "f1": fa, } def _lowercase ( __snake_case ,__snake_case ) -> int: __lowerCAmelCase : Union[str, Any] = np.array(__snake_case ) __lowerCAmelCase : Tuple = np.array(__snake_case ) __lowerCAmelCase : List[Any] = en_sentvecs.shape[0] # mean centering __lowerCAmelCase : Union[str, Any] = en_sentvecs - np.mean(__snake_case ,axis=0 ) __lowerCAmelCase : int = in_sentvecs - np.mean(__snake_case ,axis=0 ) __lowerCAmelCase : Optional[Any] = cdist(__snake_case ,__snake_case ,"cosine" ) __lowerCAmelCase : int = np.array(range(__snake_case ) ) __lowerCAmelCase : int = sim.argsort(axis=1 )[:, :10] __lowerCAmelCase : Optional[Any] = np.any(preds == actual[:, None] ,axis=1 ) return float(matches.mean() ) @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class A__ ( datasets.Metric ): '''simple docstring''' def _SCREAMING_SNAKE_CASE ( self: int) -> str: """simple docstring""" if self.config_name not in [ "wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", "cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", "wiki-ner", ]: raise KeyError( "You should supply a configuration name selected in " "[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", " "\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", " "\"wiki-ner\"]") return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { "predictions": datasets.Value("int64") if self.config_name != "cvit-mkb-clsr" else datasets.Sequence(datasets.Value("float32")), "references": datasets.Value("int64") if self.config_name != "cvit-mkb-clsr" else datasets.Sequence(datasets.Value("float32")), }) , codebase_urls=[] , reference_urls=[] , format="numpy" if self.config_name != "cvit-mkb-clsr" else None , ) def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Optional[Any]) -> int: """simple docstring""" if self.config_name == "cvit-mkb-clsr": return {"precision@10": precision_at_aa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)} elif self.config_name in ["wiki-ner"]: return acc_and_fa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE) elif self.config_name in [ "wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md", ]: return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)} else: raise KeyError( "You should supply a configuration name selected in " "[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", " "\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", " "\"wiki-ner\"]")	269	0
def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int )-> int: while a != 0: _snake_case , _snake_case : Optional[Any] = b % a, a return b def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int )-> int: if gcd(lowerCAmelCase , lowerCAmelCase ) != 1: _snake_case : Any = F"""mod inverse of {a!r} and {m!r} does not exist""" raise ValueError(lowerCAmelCase ) _snake_case , _snake_case , _snake_case : Optional[Any] = 1, 0, a _snake_case , _snake_case , _snake_case : Optional[int] = 0, 1, m while va != 0: _snake_case : Dict = ua // va _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case : List[Any] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va return ua % m	260	import argparse import torch from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert from transformers.utils import logging logging.set_verbosity_info() def lowerCamelCase_ ( lowerCAmelCase: List[str] , lowerCAmelCase: Dict , lowerCAmelCase: str )-> List[str]: # Initialise PyTorch model _snake_case : Optional[Any] = MobileBertConfig.from_json_file(lowerCAmelCase ) print(F"""Building PyTorch model from configuration: {config}""" ) _snake_case : Optional[int] = MobileBertForPreTraining(lowerCAmelCase ) # Load weights from tf checkpoint _snake_case : Optional[int] = load_tf_weights_in_mobilebert(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) # Save pytorch-model print(F"""Save PyTorch model to {pytorch_dump_path}""" ) torch.save(model.state_dict() , lowerCAmelCase ) if __name__ == "__main__": lowerCAmelCase_ = argparse.ArgumentParser() # Required parameters parser.add_argument( """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path.""" ) parser.add_argument( """--mobilebert_config_file""", default=None, type=str, required=True, help=( """The config json file corresponding to the pre-trained MobileBERT model. \n""" """This specifies the model architecture.""" ), ) parser.add_argument( """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model.""" ) lowerCAmelCase_ = parser.parse_args() convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)	260	1
from collections import deque from math import floor from random import random from time import time class A : """simple docstring""" def __init__( self : List[Any] )-> Optional[int]: '''simple docstring''' A__ = {} def snake_case__ ( self : Dict,lowercase_ : Union[str, Any],lowercase_ : str,lowercase_ : List[str]=1 )-> Optional[Any]: '''simple docstring''' if self.graph.get(lowercase_ ): if self.graph[u].count([w, v] ) == 0: self.graph[u].append([w, v] ) else: A__ = [[w, v]] if not self.graph.get(lowercase_ ): A__ = [] def snake_case__ ( self : List[Any] )-> str: '''simple docstring''' return list(self.graph ) def snake_case__ ( self : Dict,lowercase_ : Optional[int],lowercase_ : List[Any] )-> List[Any]: '''simple docstring''' if self.graph.get(lowercase_ ): for _ in self.graph[u]: if _[1] == v: self.graph[u].remove(lowercase_ ) def snake_case__ ( self : List[Any],lowercase_ : Optional[int]=-2,lowercase_ : str=-1 )-> List[Any]: '''simple docstring''' if s == d: return [] A__ = [] A__ = [] if s == -2: A__ = list(self.graph )[0] stack.append(lowercase_ ) visited.append(lowercase_ ) A__ = s while True: # check if there is any non isolated nodes if len(self.graph[s] ) != 0: A__ = s for node in self.graph[s]: if visited.count(node[1] ) < 1: if node[1] == d: visited.append(lowercase_ ) return visited else: stack.append(node[1] ) visited.append(node[1] ) A__ = node[1] break # check if all the children are visited if s == ss: stack.pop() if len(lowercase_ ) != 0: A__ = stack[len(lowercase_ ) - 1] else: A__ = ss # check if se have reached the starting point if len(lowercase_ ) == 0: return visited def snake_case__ ( self : List[Any],lowercase_ : str=-1 )-> str: '''simple docstring''' if c == -1: A__ = floor(random() * 1_0_0_0_0 ) + 1_0 for i in range(lowercase_ ): # every vertex has max 100 edges for _ in range(floor(random() * 1_0_2 ) + 1 ): A__ = floor(random() * c ) + 1 if n != i: self.add_pair(lowercase_,lowercase_,1 ) def snake_case__ ( self : Dict,lowercase_ : Dict=-2 )-> int: '''simple docstring''' A__ = deque() A__ = [] if s == -2: A__ = list(self.graph )[0] d.append(lowercase_ ) visited.append(lowercase_ ) while d: A__ = d.popleft() if len(self.graph[s] ) != 0: for node in self.graph[s]: if visited.count(node[1] ) < 1: d.append(node[1] ) visited.append(node[1] ) return visited def snake_case__ ( self : Optional[Any],lowercase_ : Optional[int] )-> Optional[Any]: '''simple docstring''' A__ = 0 for x in self.graph: for y in self.graph[x]: if y[1] == u: count += 1 return count def snake_case__ ( self : Tuple,lowercase_ : Optional[Any] )-> Any: '''simple docstring''' return len(self.graph[u] ) def snake_case__ ( self : Union[str, Any],lowercase_ : int=-2 )-> int: '''simple docstring''' A__ = [] A__ = [] if s == -2: A__ = list(self.graph )[0] stack.append(lowercase_ ) visited.append(lowercase_ ) A__ = s A__ = [] while True: # check if there is any non isolated nodes if len(self.graph[s] ) != 0: A__ = s for node in self.graph[s]: if visited.count(node[1] ) < 1: stack.append(node[1] ) visited.append(node[1] ) A__ = node[1] break # check if all the children are visited if s == ss: sorted_nodes.append(stack.pop() ) if len(lowercase_ ) != 0: A__ = stack[len(lowercase_ ) - 1] else: A__ = ss # check if se have reached the starting point if len(lowercase_ ) == 0: return sorted_nodes def snake_case__ ( self : int )-> Optional[int]: '''simple docstring''' A__ = [] A__ = [] A__ = list(self.graph )[0] stack.append(lowercase_ ) visited.append(lowercase_ ) A__ = -2 A__ = [] A__ = s A__ = False A__ = set() while True: # check if there is any non isolated nodes if len(self.graph[s] ) != 0: A__ = s for node in self.graph[s]: if ( visited.count(node[1] ) > 0 and node[1] != parent and indirect_parents.count(node[1] ) > 0 and not on_the_way_back ): A__ = len(lowercase_ ) - 1 while len_stack >= 0: if stack[len_stack] == node[1]: anticipating_nodes.add(node[1] ) break else: anticipating_nodes.add(stack[len_stack] ) len_stack -= 1 if visited.count(node[1] ) < 1: stack.append(node[1] ) visited.append(node[1] ) A__ = node[1] break # check if all the children are visited if s == ss: stack.pop() A__ = True if len(lowercase_ ) != 0: A__ = stack[len(lowercase_ ) - 1] else: A__ = False indirect_parents.append(lowercase_ ) A__ = s A__ = ss # check if se have reached the starting point if len(lowercase_ ) == 0: return list(lowercase_ ) def snake_case__ ( self : List[Any] )-> Union[str, Any]: '''simple docstring''' A__ = [] A__ = [] A__ = list(self.graph )[0] stack.append(lowercase_ ) visited.append(lowercase_ ) A__ = -2 A__ = [] A__ = s A__ = False A__ = set() while True: # check if there is any non isolated nodes if len(self.graph[s] ) != 0: A__ = s for node in self.graph[s]: if ( visited.count(node[1] ) > 0 and node[1] != parent and indirect_parents.count(node[1] ) > 0 and not on_the_way_back ): A__ = len(lowercase_ ) - 1 while len_stack_minus_one >= 0: if stack[len_stack_minus_one] == node[1]: anticipating_nodes.add(node[1] ) break else: return True if visited.count(node[1] ) < 1: stack.append(node[1] ) visited.append(node[1] ) A__ = node[1] break # check if all the children are visited if s == ss: stack.pop() A__ = True if len(lowercase_ ) != 0: A__ = stack[len(lowercase_ ) - 1] else: A__ = False indirect_parents.append(lowercase_ ) A__ = s A__ = ss # check if se have reached the starting point if len(lowercase_ ) == 0: return False def snake_case__ ( self : Tuple,lowercase_ : List[Any]=-2,lowercase_ : Optional[int]=-1 )-> int: '''simple docstring''' A__ = time() self.dfs(lowercase_,lowercase_ ) A__ = time() return end - begin def snake_case__ ( self : int,lowercase_ : List[str]=-2 )-> Union[str, Any]: '''simple docstring''' A__ = time() self.bfs(lowercase_ ) A__ = time() return end - begin class A : """simple docstring""" def __init__( self : Tuple )-> Optional[Any]: '''simple docstring''' A__ = {} def snake_case__ ( self : str,lowercase_ : Optional[Any],lowercase_ : str,lowercase_ : Any=1 )-> Union[str, Any]: '''simple docstring''' if self.graph.get(lowercase_ ): # if there already is a edge if self.graph[u].count([w, v] ) == 0: self.graph[u].append([w, v] ) else: # if u does not exist A__ = [[w, v]] # add the other way if self.graph.get(lowercase_ ): # if there already is a edge if self.graph[v].count([w, u] ) == 0: self.graph[v].append([w, u] ) else: # if u does not exist A__ = [[w, u]] def snake_case__ ( self : List[str],lowercase_ : Optional[int],lowercase_ : Optional[int] )-> List[Any]: '''simple docstring''' if self.graph.get(lowercase_ ): for _ in self.graph[u]: if _[1] == v: self.graph[u].remove(lowercase_ ) # the other way round if self.graph.get(lowercase_ ): for _ in self.graph[v]: if _[1] == u: self.graph[v].remove(lowercase_ ) def snake_case__ ( self : Dict,lowercase_ : Any=-2,lowercase_ : List[str]=-1 )-> Any: '''simple docstring''' if s == d: return [] A__ = [] A__ = [] if s == -2: A__ = list(self.graph )[0] stack.append(lowercase_ ) visited.append(lowercase_ ) A__ = s while True: # check if there is any non isolated nodes if len(self.graph[s] ) != 0: A__ = s for node in self.graph[s]: if visited.count(node[1] ) < 1: if node[1] == d: visited.append(lowercase_ ) return visited else: stack.append(node[1] ) visited.append(node[1] ) A__ = node[1] break # check if all the children are visited if s == ss: stack.pop() if len(lowercase_ ) != 0: A__ = stack[len(lowercase_ ) - 1] else: A__ = ss # check if se have reached the starting point if len(lowercase_ ) == 0: return visited def snake_case__ ( self : Tuple,lowercase_ : Any=-1 )-> Optional[int]: '''simple docstring''' if c == -1: A__ = floor(random() * 1_0_0_0_0 ) + 1_0 for i in range(lowercase_ ): # every vertex has max 100 edges for _ in range(floor(random() * 1_0_2 ) + 1 ): A__ = floor(random() * c ) + 1 if n != i: self.add_pair(lowercase_,lowercase_,1 ) def snake_case__ ( self : Union[str, Any],lowercase_ : List[Any]=-2 )-> Union[str, Any]: '''simple docstring''' A__ = deque() A__ = [] if s == -2: A__ = list(self.graph )[0] d.append(lowercase_ ) visited.append(lowercase_ ) while d: A__ = d.popleft() if len(self.graph[s] ) != 0: for node in self.graph[s]: if visited.count(node[1] ) < 1: d.append(node[1] ) visited.append(node[1] ) return visited def snake_case__ ( self : Optional[Any],lowercase_ : int )-> Optional[Any]: '''simple docstring''' return len(self.graph[u] ) def snake_case__ ( self : Union[str, Any] )-> str: '''simple docstring''' A__ = [] A__ = [] A__ = list(self.graph )[0] stack.append(lowercase_ ) visited.append(lowercase_ ) A__ = -2 A__ = [] A__ = s A__ = False A__ = set() while True: # check if there is any non isolated nodes if len(self.graph[s] ) != 0: A__ = s for node in self.graph[s]: if ( visited.count(node[1] ) > 0 and node[1] != parent and indirect_parents.count(node[1] ) > 0 and not on_the_way_back ): A__ = len(lowercase_ ) - 1 while len_stack >= 0: if stack[len_stack] == node[1]: anticipating_nodes.add(node[1] ) break else: anticipating_nodes.add(stack[len_stack] ) len_stack -= 1 if visited.count(node[1] ) < 1: stack.append(node[1] ) visited.append(node[1] ) A__ = node[1] break # check if all the children are visited if s == ss: stack.pop() A__ = True if len(lowercase_ ) != 0: A__ = stack[len(lowercase_ ) - 1] else: A__ = False indirect_parents.append(lowercase_ ) A__ = s A__ = ss # check if se have reached the starting point if len(lowercase_ ) == 0: return list(lowercase_ ) def snake_case__ ( self : Optional[Any] )-> Tuple: '''simple docstring''' A__ = [] A__ = [] A__ = list(self.graph )[0] stack.append(lowercase_ ) visited.append(lowercase_ ) A__ = -2 A__ = [] A__ = s A__ = False A__ = set() while True: # check if there is any non isolated nodes if len(self.graph[s] ) != 0: A__ = s for node in self.graph[s]: if ( visited.count(node[1] ) > 0 and node[1] != parent and indirect_parents.count(node[1] ) > 0 and not on_the_way_back ): A__ = len(lowercase_ ) - 1 while len_stack_minus_one >= 0: if stack[len_stack_minus_one] == node[1]: anticipating_nodes.add(node[1] ) break else: return True if visited.count(node[1] ) < 1: stack.append(node[1] ) visited.append(node[1] ) A__ = node[1] break # check if all the children are visited if s == ss: stack.pop() A__ = True if len(lowercase_ ) != 0: A__ = stack[len(lowercase_ ) - 1] else: A__ = False indirect_parents.append(lowercase_ ) A__ = s A__ = ss # check if se have reached the starting point if len(lowercase_ ) == 0: return False def snake_case__ ( self : Dict )-> List[str]: '''simple docstring''' return list(self.graph ) def snake_case__ ( self : Dict,lowercase_ : Union[str, Any]=-2,lowercase_ : str=-1 )-> str: '''simple docstring''' A__ = time() self.dfs(lowercase_,lowercase_ ) A__ = time() return end - begin def snake_case__ ( self : Optional[int],lowercase_ : List[Any]=-2 )-> Dict: '''simple docstring''' A__ = time() self.bfs(lowercase_ ) A__ = time() return end - begin	7	import os # Precomputes a list of the 100 first triangular numbers lowercase_ = [int(0.5 * n * (n + 1)) for n in range(1, 101)] def _snake_case( ) -> int: '''simple docstring''' A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__ ) ) A__ = os.path.join(SCREAMING_SNAKE_CASE__ , 'words.txt' ) A__ = '' with open(SCREAMING_SNAKE_CASE__ ) as f: A__ = f.readline() A__ = [word.strip('"' ) for word in words.strip('\r\n' ).split(',' )] A__ = [ word for word in [sum(ord(SCREAMING_SNAKE_CASE__ ) - 64 for x in word ) for word in words] if word in TRIANGULAR_NUMBERS ] return len(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": print(solution())	7	1
from ...configuration_utils import PretrainedConfig from ...utils import logging __magic_name__ = logging.get_logger(__name__) __magic_name__ = { "facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json", } class lowercase ( A__ ): '''simple docstring''' __SCREAMING_SNAKE_CASE = """nllb-moe""" __SCREAMING_SNAKE_CASE = ["""past_key_values"""] __SCREAMING_SNAKE_CASE = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""} def __init__( self , _snake_case=12_8112 , _snake_case=1024 , _snake_case=12 , _snake_case=4096 , _snake_case=16 , _snake_case=12 , _snake_case=4096 , _snake_case=16 , _snake_case=0.05 , _snake_case=0.05 , _snake_case=True , _snake_case=True , _snake_case="relu" , _snake_case=1024 , _snake_case=0.1 , _snake_case=0.1 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=2 , _snake_case=True , _snake_case=False , _snake_case="float32" , _snake_case=False , _snake_case=128 , _snake_case=64 , _snake_case=4 , _snake_case=4 , _snake_case=0.001 , _snake_case=0.001 , _snake_case="all" , _snake_case=False , _snake_case=False , _snake_case=1.0 , _snake_case=0.2 , _snake_case=1 , _snake_case=0 , _snake_case=2 , _snake_case=False , **_snake_case , ) -> List[str]: """simple docstring""" UpperCAmelCase = vocab_size UpperCAmelCase = max_position_embeddings UpperCAmelCase = d_model UpperCAmelCase = encoder_ffn_dim UpperCAmelCase = encoder_layers UpperCAmelCase = encoder_attention_heads UpperCAmelCase = decoder_ffn_dim UpperCAmelCase = decoder_layers UpperCAmelCase = decoder_attention_heads UpperCAmelCase = dropout UpperCAmelCase = attention_dropout UpperCAmelCase = activation_dropout UpperCAmelCase = activation_function UpperCAmelCase = init_std UpperCAmelCase = encoder_layerdrop UpperCAmelCase = decoder_layerdrop UpperCAmelCase = use_cache UpperCAmelCase = encoder_layers UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True UpperCAmelCase = router_z_loss_coef UpperCAmelCase = router_aux_loss_coef UpperCAmelCase = decoder_sparse_step UpperCAmelCase = encoder_sparse_step UpperCAmelCase = num_experts UpperCAmelCase = expert_capacity UpperCAmelCase = router_bias if router_dtype not in ["float32", "float16", "bfloat16"]: raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""" ) UpperCAmelCase = router_dtype UpperCAmelCase = router_ignore_padding_tokens UpperCAmelCase = batch_prioritized_routing UpperCAmelCase = second_expert_policy UpperCAmelCase = normalize_router_prob_before_dropping UpperCAmelCase = moe_eval_capacity_token_fraction UpperCAmelCase = moe_token_dropout UpperCAmelCase = output_router_logits super().__init__( pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , is_encoder_decoder=_snake_case , decoder_start_token_id=_snake_case , **_snake_case , )	152	from ...utils import ( OptionalDependencyNotAvailable, is_torch_available, is_transformers_available, is_transformers_version, ) try: if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline else: from .pipeline_unclip import UnCLIPPipeline from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline from .text_proj import UnCLIPTextProjModel	152	1
from __future__ import annotations lowercase = tuple[int, int, int] lowercase = tuple[str, str, str] # used alphabet -------------------------- # from string.ascii_uppercase lowercase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" # -------------------------- default selection -------------------------- # rotors -------------------------- lowercase = "EGZWVONAHDCLFQMSIPJBYUKXTR" lowercase = "FOBHMDKEXQNRAULPGSJVTYICZW" lowercase = "ZJXESIUQLHAVRMDOYGTNFWPBKC" # reflector -------------------------- lowercase = { "A": "N", "N": "A", "B": "O", "O": "B", "C": "P", "P": "C", "D": "Q", "Q": "D", "E": "R", "R": "E", "F": "S", "S": "F", "G": "T", "T": "G", "H": "U", "U": "H", "I": "V", "V": "I", "J": "W", "W": "J", "K": "X", "X": "K", "L": "Y", "Y": "L", "M": "Z", "Z": "M", } # -------------------------- extra rotors -------------------------- lowercase = "RMDJXFUWGISLHVTCQNKYPBEZOA" lowercase = "SGLCPQWZHKXAREONTFBVIYJUDM" lowercase = "HVSICLTYKQUBXDWAJZOMFGPREN" lowercase = "RZWQHFMVDBKICJLNTUXAGYPSOE" lowercase = "LFKIJODBEGAMQPXVUHYSTCZRWN" lowercase = "KOAEGVDHXPQZMLFTYWJNBRCIUS" def __UpperCAmelCase ( a_ , a_ , a_): # Checks if there are 3 unique rotors if (unique_rotsel := len(set(a_))) < 3: snake_case_ = f'''Please use 3 unique rotors (not {unique_rotsel})''' raise Exception(a_) # Checks if rotor positions are valid snake_case_ , snake_case_ , snake_case_ = rotpos if not 0 < rotorposa <= len(a_): snake_case_ = f'''First rotor position is not within range of 1..26 ({rotorposa}''' raise ValueError(a_) if not 0 < rotorposa <= len(a_): snake_case_ = f'''Second rotor position is not within range of 1..26 ({rotorposa})''' raise ValueError(a_) if not 0 < rotorposa <= len(a_): snake_case_ = f'''Third rotor position is not within range of 1..26 ({rotorposa})''' raise ValueError(a_) # Validates string and returns dict snake_case_ = _plugboard(a_) return rotpos, rotsel, pbdict def __UpperCAmelCase ( a_): # tests the input string if it # a) is type string # b) has even length (so pairs can be made) if not isinstance(a_ , a_): snake_case_ = f'''Plugboard setting isn\'t type string ({type(a_)})''' raise TypeError(a_) elif len(a_) % 2 != 0: snake_case_ = f'''Odd number of symbols ({len(a_)})''' raise Exception(a_) elif pbstring == "": return {} pbstring.replace(' ' , '') # Checks if all characters are unique snake_case_ = set() for i in pbstring: if i not in abc: snake_case_ = f'''\'{i}\' not in list of symbols''' raise Exception(a_) elif i in tmppbl: snake_case_ = f'''Duplicate symbol ({i})''' raise Exception(a_) else: tmppbl.add(a_) del tmppbl # Created the dictionary snake_case_ = {} for j in range(0 , len(a_) - 1 , 2): snake_case_ = pbstring[j + 1] snake_case_ = pbstring[j] return pb def __UpperCAmelCase ( a_ , a_ , a_ = (rotora, rotora, rotora) , a_ = "" , ): snake_case_ = text.upper() snake_case_ , snake_case_ , snake_case_ = _validator( a_ , a_ , plugb.upper()) snake_case_ , snake_case_ , snake_case_ = rotor_position snake_case_ , snake_case_ , snake_case_ = rotor_selection rotorposa -= 1 rotorposa -= 1 rotorposa -= 1 snake_case_ = [] # encryption/decryption process -------------------------- for symbol in text: if symbol in abc: # 1st plugboard -------------------------- if symbol in plugboard: snake_case_ = plugboard[symbol] # rotor ra -------------------------- snake_case_ = abc.index(a_) + rotorposa snake_case_ = rotora[index % len(a_)] # rotor rb -------------------------- snake_case_ = abc.index(a_) + rotorposa snake_case_ = rotora[index % len(a_)] # rotor rc -------------------------- snake_case_ = abc.index(a_) + rotorposa snake_case_ = rotora[index % len(a_)] # reflector -------------------------- # this is the reason you don't need another machine to decipher snake_case_ = reflector[symbol] # 2nd rotors snake_case_ = abc[rotora.index(a_) - rotorposa] snake_case_ = abc[rotora.index(a_) - rotorposa] snake_case_ = abc[rotora.index(a_) - rotorposa] # 2nd plugboard if symbol in plugboard: snake_case_ = plugboard[symbol] # moves/resets rotor positions rotorposa += 1 if rotorposa >= len(a_): snake_case_ = 0 rotorposa += 1 if rotorposa >= len(a_): snake_case_ = 0 rotorposa += 1 if rotorposa >= len(a_): snake_case_ = 0 # else: # pass # Error could be also raised # raise ValueError( # 'Invalid symbol('+repr(symbol)+')') result.append(a_) return "".join(a_) if __name__ == "__main__": lowercase = "This is my Python script that emulates the Enigma machine from WWII." lowercase = (1, 1, 1) lowercase = "pictures" lowercase = (rotora, rotora, rotora) lowercase = enigma(message, rotor_pos, rotor_sel, pb) print("Encrypted message:", en) print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))	178	from typing import List, Optional, Union from ...image_utils import ImageInput from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from ...utils import TensorType class UpperCamelCase_ ( snake_case_ ): '''simple docstring''' lowerCAmelCase = ['''image_processor''', '''tokenizer'''] lowerCAmelCase = '''BlipImageProcessor''' lowerCAmelCase = ('''BertTokenizer''', '''BertTokenizerFast''') def __init__( self , a , a ) -> Tuple: snake_case_ = False super().__init__(a , a ) snake_case_ = self.image_processor def __call__( self , a = None , a = None , a = True , a = False , a = None , a = None , a = 0 , a = None , a = None , a = False , a = False , a = False , a = False , a = False , a = True , a = None , **a , ) -> BatchEncoding: if images is None and text is None: raise ValueError('You have to specify either images or text.' ) # Get only text if images is None: snake_case_ = self.tokenizer snake_case_ = self.tokenizer( text=a , add_special_tokens=a , padding=a , truncation=a , max_length=a , stride=a , pad_to_multiple_of=a , return_attention_mask=a , return_overflowing_tokens=a , return_special_tokens_mask=a , return_offsets_mapping=a , return_token_type_ids=a , return_length=a , verbose=a , return_tensors=a , **a , ) return text_encoding # add pixel_values snake_case_ = self.image_processor(a , return_tensors=a ) if text is not None: snake_case_ = self.tokenizer( text=a , add_special_tokens=a , padding=a , truncation=a , max_length=a , stride=a , pad_to_multiple_of=a , return_attention_mask=a , return_overflowing_tokens=a , return_special_tokens_mask=a , return_offsets_mapping=a , return_token_type_ids=a , return_length=a , verbose=a , return_tensors=a , **a , ) else: snake_case_ = None if text_encoding is not None: encoding_image_processor.update(a ) return encoding_image_processor def _UpperCamelCase ( self , *a , **a ) -> int: return self.tokenizer.batch_decode(*a , **a ) def _UpperCamelCase ( self , *a , **a ) -> Any: return self.tokenizer.decode(*a , **a ) @property def _UpperCamelCase ( self ) -> List[str]: snake_case_ = self.tokenizer.model_input_names snake_case_ = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )	178	1
import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class __lowerCAmelCase ( unittest.TestCase ): def lowerCamelCase__ ( self :List[Any] ): '''simple docstring''' debug_launcher(test_script.main ) def lowerCamelCase__ ( self :Tuple ): '''simple docstring''' debug_launcher(test_ops.main )	347	import warnings from typing import Dict, List, Optional, Tuple from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging __UpperCamelCase : Dict = logging.get_logger(__name__) class __lowerCAmelCase ( __magic_name__ ): UpperCamelCase__ = ['''input_ids''', '''attention_mask'''] def __init__( self :List[str] , __magic_name__ :int="</s>" , __magic_name__ :List[Any]="<unk>" , __magic_name__ :Optional[Any]="<pad>" , __magic_name__ :Optional[int]=125 , __magic_name__ :List[str]=None , **__magic_name__ :List[str] , ): '''simple docstring''' if extra_ids > 0 and additional_special_tokens is None: a = [F'<extra_id_{i}>' for i in range(__magic_name__ )] elif extra_ids > 0 and additional_special_tokens is not None: # Check that we have the right number of extra_id special tokens a = len(set(filter(lambda __magic_name__ : bool("""extra_id""" in str(__magic_name__ ) ) , __magic_name__ ) ) ) if extra_tokens != extra_ids: raise ValueError( F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are' """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the""" """ extra_ids tokens""" ) a = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else pad_token a = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else eos_token a = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else unk_token super().__init__( eos_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , extra_ids=__magic_name__ , additional_special_tokens=__magic_name__ , **__magic_name__ , ) a = extra_ids a = 2**8 # utf is 8 bits # define special tokens dict a = { self.pad_token: 0, self.eos_token: 1, self.unk_token: 2, } a = len(self.special_tokens_encoder ) a = len(__magic_name__ ) for i, token in enumerate(__magic_name__ ): a = self.vocab_size + i - n a = {v: k for k, v in self.special_tokens_encoder.items()} @property def lowerCamelCase__ ( self :List[Any] ): '''simple docstring''' return self._utf_vocab_size + self._num_special_tokens + self._extra_ids def lowerCamelCase__ ( self :Any , __magic_name__ :List[int] , __magic_name__ :Optional[List[int]] = None , __magic_name__ :bool = False ): '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__magic_name__ , token_ids_a=__magic_name__ , already_has_special_tokens=__magic_name__ ) # normal case: some special tokens if token_ids_a is None: return ([0] * len(__magic_name__ )) + [1] return ([0] * len(__magic_name__ )) + [1] + ([0] * len(__magic_name__ )) + [1] def lowerCamelCase__ ( self :str , __magic_name__ :List[int] ): '''simple docstring''' if len(__magic_name__ ) > 0 and token_ids[-1] == self.eos_token_id: warnings.warn( F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated' """ eos tokens being added.""" ) return token_ids else: return token_ids + [self.eos_token_id] def lowerCamelCase__ ( self :Union[str, Any] , __magic_name__ :List[int] , __magic_name__ :Optional[List[int]] = None ): '''simple docstring''' a = [self.eos_token_id] if token_ids_a is None: return len(token_ids_a + eos ) * [0] return len(token_ids_a + eos + token_ids_a + eos ) * [0] def lowerCamelCase__ ( self :Union[str, Any] , __magic_name__ :List[int] , __magic_name__ :Optional[List[int]] = None ): '''simple docstring''' a = self._add_eos_if_not_present(__magic_name__ ) if token_ids_a is None: return token_ids_a else: a = self._add_eos_if_not_present(__magic_name__ ) return token_ids_a + token_ids_a def lowerCamelCase__ ( self :List[str] , __magic_name__ :str ): '''simple docstring''' a = [chr(__magic_name__ ) for i in text.encode("""utf-8""" )] return tokens def lowerCamelCase__ ( self :Tuple , __magic_name__ :str ): '''simple docstring''' if token in self.special_tokens_encoder: a = self.special_tokens_encoder[token] elif token in self.added_tokens_encoder: a = self.added_tokens_encoder[token] elif len(__magic_name__ ) != 1: a = self.unk_token_id else: a = ord(__magic_name__ ) + self._num_special_tokens return token_id def lowerCamelCase__ ( self :List[str] , __magic_name__ :Dict ): '''simple docstring''' if index in self.special_tokens_decoder: a = self.special_tokens_decoder[index] else: a = chr(index - self._num_special_tokens ) return token def lowerCamelCase__ ( self :Tuple , __magic_name__ :Optional[int] ): '''simple docstring''' a = b"""""" for token in tokens: if token in self.special_tokens_decoder: a = self.special_tokens_decoder[token].encode("""utf-8""" ) elif token in self.added_tokens_decoder: a = self.special_tokens_decoder[token].encode("""utf-8""" ) elif token in self.special_tokens_encoder: a = token.encode("""utf-8""" ) elif token in self.added_tokens_encoder: a = token.encode("""utf-8""" ) else: a = bytes([ord(__magic_name__ )] ) bstring += tok_string a = bstring.decode("""utf-8""" , errors="""ignore""" ) return string def lowerCamelCase__ ( self :Optional[Any] , __magic_name__ :str , __magic_name__ :Optional[str] = None ): '''simple docstring''' return ()	347	1
'''simple docstring''' import logging import os from dataclasses import dataclass, field from typing import Dict, Optional import numpy as np from utils_multiple_choice import MultipleChoiceDataset, Split, processors import transformers from transformers import ( AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import is_main_process A =logging.getLogger(__name__) def snake_case_ (_a : Dict , _a : Union[str, Any] ): return (preds == labels).mean() @dataclass class _a : __a : str = field( metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} ) __a : Optional[str] = field( default=__a , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} ) __a : Optional[str] = field( default=__a , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} ) __a : Optional[str] = field( default=__a , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) @dataclass class _a : __a : str = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} ) __a : str = field(metadata={"""help""": """Should contain the data files for the task."""} ) __a : int = field( default=128 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) __a : bool = field( default=__a , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} ) def snake_case_ (): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir ) and os.listdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( F"Output directory ({training_args.output_dir}) already exists and is not empty. Use" ''' --overwrite_output_dir to overcome.''' ) # Setup logging logging.basicConfig( format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , ) logger.warning( '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank ): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info('''Training/evaluation parameters %s''' , _a ) # Set seed set_seed(training_args.seed ) try: UpperCAmelCase = processors[data_args.task_name]() UpperCAmelCase = processor.get_labels() UpperCAmelCase = len(_a ) except KeyError: raise ValueError('''Task not found: %s''' % (data_args.task_name) ) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. UpperCAmelCase = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_a , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , ) UpperCAmelCase = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , ) UpperCAmelCase = AutoModelForMultipleChoice.from_pretrained( model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=_a , cache_dir=model_args.cache_dir , ) # Get datasets UpperCAmelCase = ( MultipleChoiceDataset( data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , ) if training_args.do_train else None ) UpperCAmelCase = ( MultipleChoiceDataset( data_dir=data_args.data_dir , tokenizer=_a , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , ) if training_args.do_eval else None ) def compute_metrics(_a : EvalPrediction ) -> Dict: UpperCAmelCase = np.argmax(p.predictions , axis=1 ) return {"acc": simple_accuracy(_a , p.label_ids )} # Data collator UpperCAmelCase = DataCollatorWithPadding(_a , pad_to_multiple_of=8 ) if training_args.fpaa else None # Initialize our Trainer UpperCAmelCase = Trainer( model=_a , args=_a , train_dataset=_a , eval_dataset=_a , compute_metrics=_a , data_collator=_a , ) # Training if training_args.do_train: trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None ) trainer.save_model() # For convenience, we also re-save the tokenizer to the same directory, # so that you can share your model easily on huggingface.co/models =) if trainer.is_world_master(): tokenizer.save_pretrained(training_args.output_dir ) # Evaluation UpperCAmelCase = {} if training_args.do_eval: logger.info('''*** Evaluate ***''' ) UpperCAmelCase = trainer.evaluate() UpperCAmelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' ) if trainer.is_world_master(): with open(_a , '''w''' ) as writer: logger.info('''***** Eval results *****''' ) for key, value in result.items(): logger.info(''' %s = %s''' , _a , _a ) writer.write('''%s = %s\n''' % (key, value) ) results.update(_a ) return results def snake_case_ (_a : Optional[int] ): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()	34	'''simple docstring''' def UpperCAmelCase_ ( __lowercase : int ) -> int: '''simple docstring''' if not isinstance(__lowercase , __lowercase ) or number < 0: raise ValueError("Input must be a non-negative integer" ) _UpperCAmelCase = 0 while number: # This way we arrive at next set bit (next 1) instead of looping # through each bit and checking for 1s hence the # loop won't run 32 times it will only run the number of `1` times number &= number - 1 count += 1 return count if __name__ == "__main__": import doctest doctest.testmod()	22	0
from math import loga def __A ( _lowercase ): '''simple docstring''' if a < 0: raise ValueError('''Input value must be a positive integer''' ) elif isinstance(_lowercase , _lowercase ): raise TypeError('''Input value must be a \'int\' type''' ) return 0 if (a == 0) else int(loga(a & -a ) ) if __name__ == "__main__": import doctest doctest.testmod()	75	import warnings from ...utils import logging from .image_processing_dpt import DPTImageProcessor __A = logging.get_logger(__name__) class SCREAMING_SNAKE_CASE ( snake_case ): """simple docstring""" def __init__( self: List[Any] , *__A: Union[str, Any] , **__A: Optional[Any] ) -> None: warnings.warn( '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please''' ''' use DPTImageProcessor instead.''' , __A , ) super().__init__(*__A , **__A )	75	1
import sys import tempfile import unittest import unittest.mock as mock from pathlib import Path from huggingface_hub import HfFolder, delete_repo from requests.exceptions import HTTPError from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test sys.path.append(str(Path(__file__).parent.parent / '''utils''')) from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402 lowerCAmelCase = get_tests_dir('''fixtures''') class A ( unittest.TestCase ): def _A (self ): # A mock response for an HTTP head request to emulate server down __lowercase= mock.Mock() __lowercase= 5_0_0 __lowercase= {} __lowercase= HTTPError __lowercase= {} # Download this model to make sure it's in the cache. __lowercase= WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' ) # Under the mock environment we get a 500 error when trying to reach the model. with mock.patch('requests.Session.request' , return_value=_lowerCAmelCase ) as mock_head: __lowercase= WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' ) # This check we did call the fake head request mock_head.assert_called() def _A (self ): # This test is for deprecated behavior and can be removed in v5 __lowercase= WavaVecaFeatureExtractor.from_pretrained( 'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json' ) @is_staging_test class A ( unittest.TestCase ): @classmethod def _A (cls ): __lowercase= TOKEN HfFolder.save_token(_lowerCAmelCase ) @classmethod def _A (cls ): try: delete_repo(token=cls._token , repo_id='test-feature-extractor' ) except HTTPError: pass try: delete_repo(token=cls._token , repo_id='valid_org/test-feature-extractor-org' ) except HTTPError: pass try: delete_repo(token=cls._token , repo_id='test-dynamic-feature-extractor' ) except HTTPError: pass def _A (self ): __lowercase= WavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase ) feature_extractor.push_to_hub('test-feature-extractor' , use_auth_token=self._token ) __lowercase= WavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor' ) for k, v in feature_extractor.__dict__.items(): self.assertEqual(_lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) ) # Reset repo delete_repo(token=self._token , repo_id='test-feature-extractor' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained( _lowerCAmelCase , repo_id='test-feature-extractor' , push_to_hub=_lowerCAmelCase , use_auth_token=self._token ) __lowercase= WavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor' ) for k, v in feature_extractor.__dict__.items(): self.assertEqual(_lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) ) def _A (self ): __lowercase= WavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase ) feature_extractor.push_to_hub('valid_org/test-feature-extractor' , use_auth_token=self._token ) __lowercase= WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor' ) for k, v in feature_extractor.__dict__.items(): self.assertEqual(_lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) ) # Reset repo delete_repo(token=self._token , repo_id='valid_org/test-feature-extractor' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained( _lowerCAmelCase , repo_id='valid_org/test-feature-extractor-org' , push_to_hub=_lowerCAmelCase , use_auth_token=self._token ) __lowercase= WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org' ) for k, v in feature_extractor.__dict__.items(): self.assertEqual(_lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) ) def _A (self ): CustomFeatureExtractor.register_for_auto_class() __lowercase= CustomFeatureExtractor.from_pretrained(_lowerCAmelCase ) feature_extractor.push_to_hub('test-dynamic-feature-extractor' , use_auth_token=self._token ) # This has added the proper auto_map field to the config self.assertDictEqual( feature_extractor.auto_map , {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'} , ) __lowercase= AutoFeatureExtractor.from_pretrained( f'{USER}/test-dynamic-feature-extractor' , trust_remote_code=_lowerCAmelCase ) # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module self.assertEqual(new_feature_extractor.__class__.__name__ , 'CustomFeatureExtractor' )	295	'''simple docstring''' import time from dataclasses import dataclass from multiprocessing import Pool from unittest import TestCase from unittest.mock import patch import multiprocess import numpy as np import pytest from datasets.utils.py_utils import ( NestedDataStructure, asdict, iflatmap_unordered, map_nested, temp_seed, temporary_assignment, zip_dict, ) from .utils import require_tf, require_torch def __a ( UpperCAmelCase ) ->Tuple: # picklable for multiprocessing """simple docstring""" return x.sum() def __a ( UpperCAmelCase ) ->int: # picklable for multiprocessing """simple docstring""" return i + 1 @dataclass class __UpperCAmelCase : '''simple docstring''' __lowerCAmelCase = 42 __lowerCAmelCase = 42 class __UpperCAmelCase ( A__ ): '''simple docstring''' def A (self : Tuple ): A = {} A = [] A = 1 A = [1, 2] A = {"""a""": 1, """b""": 2} A = {"""a""": [1, 2], """b""": [3, 4]} A = {"""a""": {"""1""": 1}, """b""": 2} A = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4} A = {} A = [] A = 2 A = [2, 3] A = {"""a""": 2, """b""": 3} A = {"""a""": [2, 3], """b""": [4, 5]} A = {"""a""": {"""1""": 2}, """b""": 3} A = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5} self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase ) A = 2 self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) A = {"""a""": np.eye(2 ), """b""": np.zeros(3 ), """c""": np.ones(2 )} A = {"""a""": 2, """b""": 0, """c""": 2} A = { """a""": np.eye(2 ).astype(_lowerCAmelCase ), """b""": np.zeros(3 ).astype(_lowerCAmelCase ), """c""": np.ones(2 ).astype(_lowerCAmelCase ), } self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , map_numpy=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual( {k: v.tolist() for k, v in map_nested(_lowerCAmelCase , _lowerCAmelCase , map_numpy=_lowerCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , ) self.assertEqual(map_nested(_lowerCAmelCase , _lowerCAmelCase , map_numpy=_lowerCAmelCase , num_proc=_lowerCAmelCase ) , _lowerCAmelCase ) self.assertEqual( {k: v.tolist() for k, v in map_nested(_lowerCAmelCase , _lowerCAmelCase , map_numpy=_lowerCAmelCase , num_proc=_lowerCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , ) with self.assertRaises(_lowerCAmelCase ): # can't pickle a local lambda map_nested(lambda _lowerCAmelCase : x + 1 , _lowerCAmelCase , num_proc=_lowerCAmelCase ) def A (self : List[Any] ): A = {"""a""": 1, """b""": 2} A = {"""a""": 3, """b""": 4} A = {"""a""": 5, """b""": 6} A = sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))] ) self.assertEqual(sorted(zip_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) ) , _lowerCAmelCase ) def A (self : Union[str, Any] ): class __UpperCAmelCase : '''simple docstring''' __lowerCAmelCase = '''bar''' A = Foo() self.assertEqual(foo.my_attr , """bar""" ) with temporary_assignment(_lowerCAmelCase , """my_attr""" , """BAR""" ): self.assertEqual(foo.my_attr , """BAR""" ) self.assertEqual(foo.my_attr , """bar""" ) @pytest.mark.parametrize( """iterable_length, num_proc, expected_num_proc""" , [ (1, None, 1), (1, 1, 1), (2, None, 1), (2, 1, 1), (2, 2, 1), (2, 3, 1), (3, 2, 1), (16, 16, 16), (16, 17, 16), (17, 16, 16), ] , ) def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Any: """simple docstring""" with patch("""datasets.utils.py_utils._single_map_nested""" ) as mock_single_map_nested, patch( """datasets.parallel.parallel.Pool""" ) as mock_multiprocessing_pool: A = {f"""{i}""": i for i in range(UpperCAmelCase )} A = map_nested(lambda UpperCAmelCase : x + 10 , UpperCAmelCase , num_proc=UpperCAmelCase , parallel_min_length=16 ) if expected_num_proc == 1: assert mock_single_map_nested.called assert not mock_multiprocessing_pool.called else: assert not mock_single_map_nested.called assert mock_multiprocessing_pool.called assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc class __UpperCAmelCase ( A__ ): '''simple docstring''' @require_tf def A (self : Dict ): import tensorflow as tf from tensorflow.keras import layers A = layers.Dense(2 ) def gen_random_output(): A = tf.random.uniform((1, 3) ) return model(_lowerCAmelCase ).numpy() with temp_seed(42 , set_tensorflow=_lowerCAmelCase ): A = gen_random_output() with temp_seed(42 , set_tensorflow=_lowerCAmelCase ): A = gen_random_output() A = gen_random_output() np.testing.assert_equal(_lowerCAmelCase , _lowerCAmelCase ) self.assertGreater(np.abs(outa - outa ).sum() , 0 ) @require_torch def A (self : Tuple ): import torch def gen_random_output(): A = torch.nn.Linear(3 , 2 ) A = torch.rand(1 , 3 ) return model(_lowerCAmelCase ).detach().numpy() with temp_seed(42 , set_pytorch=_lowerCAmelCase ): A = gen_random_output() with temp_seed(42 , set_pytorch=_lowerCAmelCase ): A = gen_random_output() A = gen_random_output() np.testing.assert_equal(_lowerCAmelCase , _lowerCAmelCase ) self.assertGreater(np.abs(outa - outa ).sum() , 0 ) def A (self : str ): def gen_random_output(): return np.random.rand(1 , 3 ) with temp_seed(42 ): A = gen_random_output() with temp_seed(42 ): A = gen_random_output() A = gen_random_output() np.testing.assert_equal(_lowerCAmelCase , _lowerCAmelCase ) self.assertGreater(np.abs(outa - outa ).sum() , 0 ) @pytest.mark.parametrize("""input_data""" , [{}] ) def __a ( UpperCAmelCase ) ->List[str]: """simple docstring""" A = NestedDataStructure(UpperCAmelCase ).data assert output_data == input_data @pytest.mark.parametrize( """data, expected_output""" , [ ({}, []), ([], []), ("""foo""", ["""foo"""]), (["""foo""", """bar"""], ["""foo""", """bar"""]), ([["""foo""", """bar"""]], ["""foo""", """bar"""]), ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]), ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]), ({"""a""": 1, """b""": 2}, [1, 2]), ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]), ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]), ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]), ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]), ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]), ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]), ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]), ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]), ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]), ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]), ] , ) def __a ( UpperCAmelCase , UpperCAmelCase ) ->List[Any]: """simple docstring""" A = NestedDataStructure(UpperCAmelCase ).flatten() assert output == expected_output def __a ( ) ->Optional[Any]: """simple docstring""" A = A(x=1 , y="""foobar""" ) A = {"""x""": 1, """y""": """foobar"""} assert asdict(UpperCAmelCase ) == expected_output A = {"""a""": {"""b""": A(x=10 , y="""foo""" )}, """c""": [A(x=20 , y="""bar""" )]} A = {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]} assert asdict(UpperCAmelCase ) == expected_output with pytest.raises(UpperCAmelCase ): asdict([1, A(x=10 , y="""foo""" )] ) def __a ( UpperCAmelCase ) ->Tuple: """simple docstring""" return text.split() def __a ( UpperCAmelCase ) ->List[str]: """simple docstring""" yield (time.time(), content) time.sleep(2 ) yield (time.time(), content) def __a ( ) ->Optional[int]: """simple docstring""" with Pool(2 ) as pool: A = list(iflatmap_unordered(UpperCAmelCase , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) ) assert out.count("""hello""" ) == 10 assert out.count("""there""" ) == 10 assert len(UpperCAmelCase ) == 20 # check multiprocess from pathos (uses dill for pickling) with multiprocess.Pool(2 ) as pool: A = list(iflatmap_unordered(UpperCAmelCase , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) ) assert out.count("""hello""" ) == 10 assert out.count("""there""" ) == 10 assert len(UpperCAmelCase ) == 20 # check that we get items as fast as possible with Pool(2 ) as pool: A = [] for yield_time, content in iflatmap_unordered( UpperCAmelCase , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}] ): assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded" out.append(UpperCAmelCase ) assert out.count("""a""" ) == 2 assert out.count("""b""" ) == 2 assert len(UpperCAmelCase ) == 4	258	0
"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_rembert import RemBertTokenizer else: A = None A = logging.get_logger(__name__) A = {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"} A = { "vocab_file": { "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model", }, "tokenizer_file": { "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json", }, } A = { "google/rembert": 2_5_6, } A = "▁" class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ): __lowerCAmelCase : str = VOCAB_FILES_NAMES __lowerCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP __lowerCAmelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES __lowerCAmelCase : Union[str, Any] = RemBertTokenizer def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="[CLS]" , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="[CLS]" , _SCREAMING_SNAKE_CASE="[MASK]" , **_SCREAMING_SNAKE_CASE , ) -> List[Any]: '''simple docstring''' UpperCAmelCase : List[str] = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else mask_token super().__init__( _SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , do_lower_case=_SCREAMING_SNAKE_CASE , remove_space=_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ) UpperCAmelCase : Dict = do_lower_case UpperCAmelCase : Dict = remove_space UpperCAmelCase : Any = keep_accents UpperCAmelCase : Optional[Any] = vocab_file UpperCAmelCase : List[str] = False if not self.vocab_file else True def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]: '''simple docstring''' UpperCAmelCase : List[str] = [self.sep_token_id] UpperCAmelCase : str = [self.cls_token_id] if token_ids_a is None: return cls + token_ids_a + sep return cls + token_ids_a + sep + token_ids_a + sep def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: if token_ids_a is not None: raise ValueError( """You should not supply a second sequence if the provided sequence of """ """ids is already formatted with special tokens for the model.""" ) return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a] if token_ids_a is not None: return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]: '''simple docstring''' UpperCAmelCase : str = [self.sep_token_id] UpperCAmelCase : Any = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(_SCREAMING_SNAKE_CASE ): logger.error("""Vocabulary path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE ) ) return UpperCAmelCase : Dict = os.path.join( _SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ): copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE ) return (out_vocab_file,)	371	"""simple docstring""" from __future__ import annotations from typing import Any class SCREAMING_SNAKE_CASE__ : def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None: '''simple docstring''' UpperCAmelCase : Node | None = None UpperCAmelCase : Node | None = None self.create_linked_list(_SCREAMING_SNAKE_CASE ) def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> None: '''simple docstring''' UpperCAmelCase : Union[str, Any] = Node() UpperCAmelCase : Dict = current_node UpperCAmelCase : Any = current_node UpperCAmelCase : Optional[int] = current_node for _ in range(1 , _SCREAMING_SNAKE_CASE ): UpperCAmelCase : Optional[Any] = Node() UpperCAmelCase : Tuple = current_node UpperCAmelCase : Any = previous_node UpperCAmelCase : List[Any] = current_node UpperCAmelCase : List[str] = self.front UpperCAmelCase : Tuple = previous_node def SCREAMING_SNAKE_CASE ( self ) -> bool: '''simple docstring''' return ( self.front == self.rear and self.front is not None and self.front.data is None ) def SCREAMING_SNAKE_CASE ( self ) -> Any | None: '''simple docstring''' self.check_can_perform_operation() return self.front.data if self.front else None def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> None: '''simple docstring''' if self.rear is None: return self.check_is_full() if not self.is_empty(): UpperCAmelCase : Optional[Any] = self.rear.next if self.rear: UpperCAmelCase : Optional[int] = data def SCREAMING_SNAKE_CASE ( self ) -> Any: '''simple docstring''' self.check_can_perform_operation() if self.rear is None or self.front is None: return None if self.front == self.rear: UpperCAmelCase : Tuple = self.front.data UpperCAmelCase : int = None return data UpperCAmelCase : Dict = self.front UpperCAmelCase : Tuple = old_front.next UpperCAmelCase : str = old_front.data UpperCAmelCase : int = None return data def SCREAMING_SNAKE_CASE ( self ) -> None: '''simple docstring''' if self.is_empty(): raise Exception("""Empty Queue""" ) def SCREAMING_SNAKE_CASE ( self ) -> None: '''simple docstring''' if self.rear and self.rear.next == self.front: raise Exception("""Full Queue""" ) class SCREAMING_SNAKE_CASE__ : def __init__( self ) -> None: '''simple docstring''' UpperCAmelCase : Any | None = None UpperCAmelCase : Node | None = None UpperCAmelCase : Node | None = None if __name__ == "__main__": import doctest doctest.testmod()	76	0
import argparse import os from pathlib import Path from typing import Dict import tensorflow as tf import torch from tqdm import tqdm from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params a__ : Optional[Any] = [ # replace left string with right string to get the relevant state_dict key (identical state dict to bart) ['''memory_attention''', '''encoder_attn'''], ['''attention''', '''attn'''], ['''/''', '''.'''], ['''.LayerNorm.gamma''', '''_layer_norm.weight'''], ['''.LayerNorm.beta''', '''_layer_norm.bias'''], ['''r.layer_''', '''r.layers.'''], ['''output_proj''', '''out_proj'''], ['''ffn.dense_1.''', '''fc2.'''], ['''ffn.dense.''', '''fc1.'''], ['''ffn_layer_norm''', '''final_layer_norm'''], ['''kernel''', '''weight'''], ['''encoder_layer_norm.''', '''encoder.layer_norm.'''], ['''decoder_layer_norm.''', '''decoder.layer_norm.'''], ['''embeddings.weights''', '''shared.weight'''], ] def UpperCAmelCase_( a__ ): """simple docstring""" for pegasus_name, hf_name in PATTERNS: SCREAMING_SNAKE_CASE : Union[str, Any] = k.replace(a__ , a__ ) return k def UpperCAmelCase_( a__ , a__ ): """simple docstring""" SCREAMING_SNAKE_CASE : Tuple = DEFAULTS.copy() cfg_kwargs.update(a__ ) SCREAMING_SNAKE_CASE : Union[str, Any] = PegasusConfig(**a__ ) SCREAMING_SNAKE_CASE : Optional[int] = PegasusForConditionalGeneration(a__ ) SCREAMING_SNAKE_CASE : Dict = torch_model.model.state_dict() SCREAMING_SNAKE_CASE : List[str] = {} for k, v in tf_weights.items(): SCREAMING_SNAKE_CASE : int = rename_state_dict_key(a__ ) if new_k not in sd: raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" ) if "dense" in k or "proj" in new_k: SCREAMING_SNAKE_CASE : Dict = v.T SCREAMING_SNAKE_CASE : Tuple = torch.tensor(a__ , dtype=sd[new_k].dtype ) assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}""" # make sure embedding.padding_idx is respected SCREAMING_SNAKE_CASE : Tuple = torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] ) SCREAMING_SNAKE_CASE : int = mapping['''shared.weight'''] SCREAMING_SNAKE_CASE : Union[str, Any] = mapping['''shared.weight'''] SCREAMING_SNAKE_CASE : Optional[Any] = {k: torch.zeros_like(a__ ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping} mapping.update(**a__ ) SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = torch_model.model.load_state_dict(a__ , strict=a__ ) SCREAMING_SNAKE_CASE : Optional[Any] = [ k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight'''] ] assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}""" assert extra == [], F"""no matches found for the following tf keys {extra}""" return torch_model def UpperCAmelCase_( a__="./ckpt/aeslc/model.ckpt-32000" ): """simple docstring""" SCREAMING_SNAKE_CASE : str = tf.train.list_variables(a__ ) SCREAMING_SNAKE_CASE : str = {} SCREAMING_SNAKE_CASE : List[Any] = ['''Adafactor''', '''global_step'''] for name, shape in tqdm(a__ , desc='''converting tf checkpoint to dict''' ): SCREAMING_SNAKE_CASE : Union[str, Any] = any(pat in name for pat in ignore_name ) if skip_key: continue SCREAMING_SNAKE_CASE : Dict = tf.train.load_variable(a__ , a__ ) SCREAMING_SNAKE_CASE : Any = array return tf_weights def UpperCAmelCase_( a__ , a__ ): """simple docstring""" SCREAMING_SNAKE_CASE : List[str] = Path(a__ ).parent.name SCREAMING_SNAKE_CASE : Union[str, Any] = task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings'''] SCREAMING_SNAKE_CASE : Dict = PegasusTokenizer.from_pretrained('''sshleifer/pegasus''' , model_max_length=a__ ) assert tok.model_max_length == desired_max_model_length tok.save_pretrained(a__ ) # convert model SCREAMING_SNAKE_CASE : Any = get_tf_weights_as_numpy(a__ ) SCREAMING_SNAKE_CASE : List[str] = task_specific_params[F"""summarization_{dataset}"""] if dataset == "large": SCREAMING_SNAKE_CASE : int = task_specific_params SCREAMING_SNAKE_CASE : List[str] = convert_pegasus(a__ , a__ ) torch_model.save_pretrained(a__ ) SCREAMING_SNAKE_CASE : Union[str, Any] = torch_model.state_dict() sd.pop('''model.decoder.embed_positions.weight''' ) sd.pop('''model.encoder.embed_positions.weight''' ) torch.save(a__ , Path(a__ ) / '''pytorch_model.bin''' ) if __name__ == "__main__": a__ : Union[str, Any] = argparse.ArgumentParser() # Required parameters parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''') parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''') a__ : List[str] = parser.parse_args() if args.save_dir is None: a__ : Any = Path(args.tf_ckpt_path).parent.name a__ : int = os.path.join('''pegasus''', dataset) convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)	313	from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a__ : Dict = [ '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''', '''WavLMForAudioFrameClassification''', '''WavLMForCTC''', '''WavLMForSequenceClassification''', '''WavLMForXVector''', '''WavLMModel''', '''WavLMPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_wavlm import ( WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST, WavLMForAudioFrameClassification, WavLMForCTC, WavLMForSequenceClassification, WavLMForXVector, WavLMModel, WavLMPreTrainedModel, ) else: import sys a__ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)	313	1
from sklearn.metrics import fa_score, matthews_corrcoef import datasets from .record_evaluation import evaluate as evaluate_record _lowerCamelCase : Optional[int] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n" _lowerCamelCase : List[str] = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n" _lowerCamelCase : Optional[int] = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for 'record': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'prediction_text': the predicted answer text\n - for 'multirc': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question-answer pair as specified by the dataset\n - 'prediction': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for 'record': list of question-answers dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'answers': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for 'record':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1': F1 score\n - for 'multirc':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1_m': Per-question macro-F1 score\n - 'f1_a': Average F1 score over all answers\n - for 'axb':\n 'matthews_correlation': Matthew Correlation\n - for 'cb':\n - 'accuracy': Accuracy\n - 'f1': F1 score\n - for all others:\n - 'accuracy': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n" def _UpperCAmelCase (UpperCamelCase_ : Dict , UpperCamelCase_ : int ): '''simple docstring''' return float((preds == labels).mean() ) def _UpperCAmelCase (UpperCamelCase_ : List[str] , UpperCamelCase_ : Dict , UpperCamelCase_ : Dict="binary" ): '''simple docstring''' _lowerCAmelCase : str = simple_accuracy(UpperCamelCase_ , UpperCamelCase_ ) _lowerCAmelCase : Tuple = float(fa_score(y_true=UpperCamelCase_ , y_pred=UpperCamelCase_ , average=UpperCamelCase_ ) ) return { "accuracy": acc, "f1": fa, } def _UpperCAmelCase (UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple ): '''simple docstring''' _lowerCAmelCase : Optional[Any] = {} for id_pred, label in zip(UpperCamelCase_ , UpperCamelCase_ ): _lowerCAmelCase : Optional[int] = F"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}" _lowerCAmelCase : Optional[Any] = id_pred["""prediction"""] if question_id in question_map: question_map[question_id].append((pred, label) ) else: _lowerCAmelCase : Tuple = [(pred, label)] _lowerCAmelCase : List[str] = [], [] for question, preds_labels in question_map.items(): _lowerCAmelCase : List[Any] = zip(*UpperCamelCase_ ) _lowerCAmelCase : Dict = fa_score(y_true=UpperCamelCase_ , y_pred=UpperCamelCase_ , average="""macro""" ) fas.append(UpperCamelCase_ ) _lowerCAmelCase : Optional[Any] = int(sum(pred == label for pred, label in preds_labels ) == len(UpperCamelCase_ ) ) ems.append(UpperCamelCase_ ) _lowerCAmelCase : List[Any] = float(sum(UpperCamelCase_ ) / len(UpperCamelCase_ ) ) _lowerCAmelCase : Union[str, Any] = sum(UpperCamelCase_ ) / len(UpperCamelCase_ ) _lowerCAmelCase : Any = float(fa_score(y_true=UpperCamelCase_ , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) ) return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a} @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class __snake_case (datasets.Metric ): def SCREAMING_SNAKE_CASE ( self : int ) -> Union[str, Any]: '''simple docstring''' if self.config_name not in [ "boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg", ]: raise KeyError( """You should supply a configuration name selected in """ """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" ) return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , ) def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[Any]: '''simple docstring''' if self.config_name == "record": return { "predictions": { "idx": { "passage": datasets.Value("""int64""" ), "query": datasets.Value("""int64""" ), }, "prediction_text": datasets.Value("""string""" ), }, "references": { "idx": { "passage": datasets.Value("""int64""" ), "query": datasets.Value("""int64""" ), }, "answers": datasets.Sequence(datasets.Value("""string""" ) ), }, } elif self.config_name == "multirc": return { "predictions": { "idx": { "answer": datasets.Value("""int64""" ), "paragraph": datasets.Value("""int64""" ), "question": datasets.Value("""int64""" ), }, "prediction": datasets.Value("""int64""" ), }, "references": datasets.Value("""int64""" ), } else: return { "predictions": datasets.Value("""int64""" ), "references": datasets.Value("""int64""" ), } def SCREAMING_SNAKE_CASE ( self : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str] ) -> Optional[int]: '''simple docstring''' if self.config_name == "axb": return {"matthews_correlation": matthews_corrcoef(_UpperCAmelCase , _UpperCAmelCase )} elif self.config_name == "cb": return acc_and_fa(_UpperCAmelCase , _UpperCAmelCase , fa_avg="""macro""" ) elif self.config_name == "record": _lowerCAmelCase : Any = [ { """qas""": [ {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]} for ref in references ] } ] _lowerCAmelCase : Dict = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions} return evaluate_record(_UpperCAmelCase , _UpperCAmelCase )[0] elif self.config_name == "multirc": return evaluate_multirc(_UpperCAmelCase , _UpperCAmelCase ) elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]: return {"accuracy": simple_accuracy(_UpperCAmelCase , _UpperCAmelCase )} else: raise KeyError( """You should supply a configuration name selected in """ """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )	359	import json import os import sys import tempfile import unittest from pathlib import Path from shutil import copyfile from huggingface_hub import HfFolder, Repository, create_repo, delete_repo from requests.exceptions import HTTPError import transformers from transformers import ( CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoConfig, AutoFeatureExtractor, AutoProcessor, AutoTokenizer, BertTokenizer, ProcessorMixin, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaProcessor, ) from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils")) from test_module.custom_configuration import CustomConfig # noqa E402 from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402 from test_module.custom_processing import CustomProcessor # noqa E402 from test_module.custom_tokenization import CustomTokenizer # noqa E402 _lowerCamelCase : Tuple = get_tests_dir("fixtures/dummy_feature_extractor_config.json") _lowerCamelCase : List[str] = get_tests_dir("fixtures/vocab.json") _lowerCamelCase : str = get_tests_dir("fixtures") class __snake_case (unittest.TestCase ): lowerCAmelCase__ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"] def SCREAMING_SNAKE_CASE ( self : Dict ) -> Any: '''simple docstring''' _lowerCAmelCase : Any = 0 def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Any: '''simple docstring''' _lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]: '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: _lowerCAmelCase : List[Any] = WavaVecaConfig() _lowerCAmelCase : str = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" ) # save in new folder model_config.save_pretrained(_UpperCAmelCase ) processor.save_pretrained(_UpperCAmelCase ) _lowerCAmelCase : Any = AutoProcessor.from_pretrained(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : int ) -> Union[str, Any]: '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: # copy relevant files copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , _UpperCAmelCase ) ) copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , """vocab.json""" ) ) _lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[Any]: '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: _lowerCAmelCase : Any = WavaVecaFeatureExtractor() _lowerCAmelCase : Optional[int] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" ) _lowerCAmelCase : List[str] = WavaVecaProcessor(_UpperCAmelCase , _UpperCAmelCase ) # save in new folder processor.save_pretrained(_UpperCAmelCase ) # drop `processor_class` in tokenizer with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """r""" ) as f: _lowerCAmelCase : Union[str, Any] = json.load(_UpperCAmelCase ) config_dict.pop("""processor_class""" ) with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """w""" ) as f: f.write(json.dumps(_UpperCAmelCase ) ) _lowerCAmelCase : List[Any] = AutoProcessor.from_pretrained(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]: '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: _lowerCAmelCase : Dict = WavaVecaFeatureExtractor() _lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" ) _lowerCAmelCase : str = WavaVecaProcessor(_UpperCAmelCase , _UpperCAmelCase ) # save in new folder processor.save_pretrained(_UpperCAmelCase ) # drop `processor_class` in feature extractor with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """r""" ) as f: _lowerCAmelCase : str = json.load(_UpperCAmelCase ) config_dict.pop("""processor_class""" ) with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """w""" ) as f: f.write(json.dumps(_UpperCAmelCase ) ) _lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str: '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: _lowerCAmelCase : Tuple = WavaVecaConfig(processor_class="""Wav2Vec2Processor""" ) model_config.save_pretrained(_UpperCAmelCase ) # copy relevant files copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , """vocab.json""" ) ) # create emtpy sample processor with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """w""" ) as f: f.write("""{}""" ) _lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]: '''simple docstring''' with self.assertRaises(_UpperCAmelCase ): _lowerCAmelCase : Any = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" ) # If remote code is disabled, we can't load this config. with self.assertRaises(_UpperCAmelCase ): _lowerCAmelCase : List[str] = AutoProcessor.from_pretrained( """hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase ) _lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase ) self.assertTrue(processor.special_attribute_present ) self.assertEqual(processor.__class__.__name__ , """NewProcessor""" ) _lowerCAmelCase : Optional[int] = processor.feature_extractor self.assertTrue(feature_extractor.special_attribute_present ) self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" ) _lowerCAmelCase : Union[str, Any] = processor.tokenizer self.assertTrue(tokenizer.special_attribute_present ) if is_tokenizers_available(): self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" ) # Test we can also load the slow version _lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained( """hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase , use_fast=_UpperCAmelCase ) _lowerCAmelCase : List[str] = new_processor.tokenizer self.assertTrue(new_tokenizer.special_attribute_present ) self.assertEqual(new_tokenizer.__class__.__name__ , """NewTokenizer""" ) else: self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" ) def SCREAMING_SNAKE_CASE ( self : str ) -> Union[str, Any]: '''simple docstring''' try: AutoConfig.register("""custom""" , _UpperCAmelCase ) AutoFeatureExtractor.register(_UpperCAmelCase , _UpperCAmelCase ) AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase ) AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase ) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(_UpperCAmelCase ): AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase ) # Now that the config is registered, it can be used as any other config with the auto-API _lowerCAmelCase : List[str] = CustomFeatureExtractor.from_pretrained(_UpperCAmelCase ) with tempfile.TemporaryDirectory() as tmp_dir: _lowerCAmelCase : Tuple = os.path.join(_UpperCAmelCase , """vocab.txt""" ) with open(_UpperCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) ) _lowerCAmelCase : str = CustomTokenizer(_UpperCAmelCase ) _lowerCAmelCase : List[str] = CustomProcessor(_UpperCAmelCase , _UpperCAmelCase ) with tempfile.TemporaryDirectory() as tmp_dir: processor.save_pretrained(_UpperCAmelCase ) _lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content: del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig] if CustomConfig in TOKENIZER_MAPPING._extra_content: del TOKENIZER_MAPPING._extra_content[CustomConfig] if CustomConfig in PROCESSOR_MAPPING._extra_content: del PROCESSOR_MAPPING._extra_content[CustomConfig] def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]: '''simple docstring''' class __snake_case (_a ): lowerCAmelCase__ = False class __snake_case (_a ): lowerCAmelCase__ = False class __snake_case (_a ): lowerCAmelCase__ = "AutoFeatureExtractor" lowerCAmelCase__ = "AutoTokenizer" lowerCAmelCase__ = False try: AutoConfig.register("""custom""" , _UpperCAmelCase ) AutoFeatureExtractor.register(_UpperCAmelCase , _UpperCAmelCase ) AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase ) AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase ) # If remote code is not set, the default is to use local classes. _lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" ) self.assertEqual(processor.__class__.__name__ , """NewProcessor""" ) self.assertFalse(processor.special_attribute_present ) self.assertFalse(processor.feature_extractor.special_attribute_present ) self.assertFalse(processor.tokenizer.special_attribute_present ) # If remote code is disabled, we load the local ones. _lowerCAmelCase : str = AutoProcessor.from_pretrained( """hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase ) self.assertEqual(processor.__class__.__name__ , """NewProcessor""" ) self.assertFalse(processor.special_attribute_present ) self.assertFalse(processor.feature_extractor.special_attribute_present ) self.assertFalse(processor.tokenizer.special_attribute_present ) # If remote is enabled, we load from the Hub. _lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained( """hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase ) self.assertEqual(processor.__class__.__name__ , """NewProcessor""" ) self.assertTrue(processor.special_attribute_present ) self.assertTrue(processor.feature_extractor.special_attribute_present ) self.assertTrue(processor.tokenizer.special_attribute_present ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content: del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig] if CustomConfig in TOKENIZER_MAPPING._extra_content: del TOKENIZER_MAPPING._extra_content[CustomConfig] if CustomConfig in PROCESSOR_MAPPING._extra_content: del PROCESSOR_MAPPING._extra_content[CustomConfig] def SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]: '''simple docstring''' _lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert""" ) self.assertEqual(processor.__class__.__name__ , """BertTokenizerFast""" ) def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict: '''simple docstring''' _lowerCAmelCase : List[str] = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext""" ) self.assertEqual(processor.__class__.__name__ , """ConvNextImageProcessor""" ) @is_staging_test class __snake_case (unittest.TestCase ): lowerCAmelCase__ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"] @classmethod def SCREAMING_SNAKE_CASE ( cls : int ) -> Any: '''simple docstring''' _lowerCAmelCase : List[str] = TOKEN HfFolder.save_token(_UpperCAmelCase ) @classmethod def SCREAMING_SNAKE_CASE ( cls : Tuple ) -> Optional[int]: '''simple docstring''' try: delete_repo(token=cls._token , repo_id="""test-processor""" ) except HTTPError: pass try: delete_repo(token=cls._token , repo_id="""valid_org/test-processor-org""" ) except HTTPError: pass try: delete_repo(token=cls._token , repo_id="""test-dynamic-processor""" ) except HTTPError: pass def SCREAMING_SNAKE_CASE ( self : Dict ) -> str: '''simple docstring''' _lowerCAmelCase : Optional[int] = WavaVecaProcessor.from_pretrained(_UpperCAmelCase ) with tempfile.TemporaryDirectory() as tmp_dir: processor.save_pretrained( os.path.join(_UpperCAmelCase , """test-processor""" ) , push_to_hub=_UpperCAmelCase , use_auth_token=self._token ) _lowerCAmelCase : str = WavaVecaProcessor.from_pretrained(f"{USER}/test-processor" ) for k, v in processor.feature_extractor.__dict__.items(): self.assertEqual(_UpperCAmelCase , getattr(new_processor.feature_extractor , _UpperCAmelCase ) ) self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() ) def SCREAMING_SNAKE_CASE ( self : Dict ) -> int: '''simple docstring''' _lowerCAmelCase : int = WavaVecaProcessor.from_pretrained(_UpperCAmelCase ) with tempfile.TemporaryDirectory() as tmp_dir: processor.save_pretrained( os.path.join(_UpperCAmelCase , """test-processor-org""" ) , push_to_hub=_UpperCAmelCase , use_auth_token=self._token , organization="""valid_org""" , ) _lowerCAmelCase : str = WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org""" ) for k, v in processor.feature_extractor.__dict__.items(): self.assertEqual(_UpperCAmelCase , getattr(new_processor.feature_extractor , _UpperCAmelCase ) ) self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() ) def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]: '''simple docstring''' CustomFeatureExtractor.register_for_auto_class() CustomTokenizer.register_for_auto_class() CustomProcessor.register_for_auto_class() _lowerCAmelCase : Any = CustomFeatureExtractor.from_pretrained(_UpperCAmelCase ) with tempfile.TemporaryDirectory() as tmp_dir: _lowerCAmelCase : int = os.path.join(_UpperCAmelCase , """vocab.txt""" ) with open(_UpperCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) ) _lowerCAmelCase : List[str] = CustomTokenizer(_UpperCAmelCase ) _lowerCAmelCase : List[str] = CustomProcessor(_UpperCAmelCase , _UpperCAmelCase ) with tempfile.TemporaryDirectory() as tmp_dir: create_repo(f"{USER}/test-dynamic-processor" , token=self._token ) _lowerCAmelCase : Union[str, Any] = Repository(_UpperCAmelCase , clone_from=f"{USER}/test-dynamic-processor" , token=self._token ) processor.save_pretrained(_UpperCAmelCase ) # This has added the proper auto_map field to the feature extractor config self.assertDictEqual( processor.feature_extractor.auto_map , { """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""", """AutoProcessor""": """custom_processing.CustomProcessor""", } , ) # This has added the proper auto_map field to the tokenizer config with open(os.path.join(_UpperCAmelCase , """tokenizer_config.json""" ) ) as f: _lowerCAmelCase : str = json.load(_UpperCAmelCase ) self.assertDictEqual( tokenizer_config["""auto_map"""] , { """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None], """AutoProcessor""": """custom_processing.CustomProcessor""", } , ) # The code has been copied from fixtures self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , """custom_feature_extraction.py""" ) ) ) self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , """custom_tokenization.py""" ) ) ) self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , """custom_processing.py""" ) ) ) repo.push_to_hub() _lowerCAmelCase : Tuple = AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor" , trust_remote_code=_UpperCAmelCase ) # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module self.assertEqual(new_processor.__class__.__name__ , """CustomProcessor""" )	159	0
'''simple docstring''' def _UpperCAmelCase ( _lowerCamelCase : Tuple , _lowerCamelCase : List[str] ) -> Union[str, Any]: if not isinstance(_lowerCAmelCase , _lowerCAmelCase ): raise ValueError("""iterations must be defined as integers""" ) if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not number >= 1: raise ValueError( """starting number must be and integer and be more than 0""" ) if not iterations >= 1: raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz""" ) _lowerCAmelCase : Optional[int] = """""" while number <= iterations: if number % 3 == 0: out += "Fizz" if number % 5 == 0: out += "Buzz" if 0 not in (number % 3, number % 5): out += str(_lowerCAmelCase ) # print(out) number += 1 out += " " return out if __name__ == "__main__": import doctest doctest.testmod()	309	"""simple docstring""" # This is the module that test_patching.py uses to test patch_submodule() import os # noqa: this is just for tests import os as renamed_os # noqa: this is just for tests from os import path # noqa: this is just for tests from os import path as renamed_path # noqa: this is just for tests from os.path import join # noqa: this is just for tests from os.path import join as renamed_join # noqa: this is just for tests SCREAMING_SNAKE_CASE_ = open # noqa: we just need to have a builtin inside this module to test it properly	301	0
from __future__ import annotations import time import numpy as np lowercase : Optional[Any] = [8, 5, 9, 7] lowercase : Union[str, Any] = [ [2, 0, 1, 1], [0, 1, 2, 1], [4, 0, 0, 3], [0, 2, 1, 0], [1, 0, 3, 0], ] lowercase : Optional[Any] = [ [3, 2, 1, 4], [0, 2, 5, 2], [5, 1, 0, 5], [1, 5, 3, 0], [3, 0, 3, 3], ] class __snake_case : def __init__( self ,snake_case ,snake_case ,snake_case ,): '''simple docstring''' lowercase : Any = claim_vector lowercase : Union[str, Any] = allocated_resources_table lowercase : Dict = maximum_claim_table def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' return [ sum(p_item[i] for p_item in self.__allocated_resources_table ) for i in range(len(self.__allocated_resources_table[0] ) ) ] def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' return np.array(self.__claim_vector ) - np.array( self.__processes_resource_summation() ) def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' return [ list(np.array(self.__maximum_claim_table[i] ) - np.array(snake_case ) ) for i, allocated_resource in enumerate(self.__allocated_resources_table ) ] def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' return {self.__need().index(snake_case ): i for i in self.__need()} def _SCREAMING_SNAKE_CASE ( self ,**snake_case ): '''simple docstring''' lowercase : Any = self.__need() lowercase : List[str] = self.__allocated_resources_table lowercase : int = self.__available_resources() lowercase : int = self.__need_index_manager() for kw, val in kwargs.items(): if kw and val is True: self.__pretty_data() print("""_""" * 50 + """\n""" ) while need_list: lowercase : List[str] = False for each_need in need_list: lowercase : Optional[Any] = True for index, need in enumerate(snake_case ): if need > available_resources[index]: lowercase : Optional[Any] = False break if execution: lowercase : Optional[Any] = True # get the original index of the process from ind_ctrl db for original_need_index, need_clone in need_index_manager.items(): if each_need == need_clone: lowercase : Union[str, Any] = original_need_index print(f"Process {process_number + 1} is executing." ) # remove the process run from stack need_list.remove(snake_case ) # update available/freed resources stack lowercase : Dict = np.array(snake_case ) + np.array( alloc_resources_table[process_number] ) print( """Updated available resource stack for processes: """ + """ """.join([str(snake_case ) for x in available_resources] ) ) break if safe: print("""The process is in a safe state.\n""" ) else: print("""System in unsafe state. Aborting...\n""" ) break def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' print(""" """ * 9 + """Allocated Resource Table""" ) for item in self.__allocated_resources_table: print( f"P{self.__allocated_resources_table.index(snake_case ) + 1}" + """ """.join(f"{it:>8}" for it in item ) + """\n""" ) print(""" """ * 9 + """System Resource Table""" ) for item in self.__maximum_claim_table: print( f"P{self.__maximum_claim_table.index(snake_case ) + 1}" + """ """.join(f"{it:>8}" for it in item ) + """\n""" ) print( """Current Usage by Active Processes: """ + """ """.join(str(snake_case ) for x in self.__claim_vector ) ) print( """Initial Available Resources: """ + """ """.join(str(snake_case ) for x in self.__available_resources() ) ) time.sleep(1 ) if __name__ == "__main__": import doctest doctest.testmod()	285	from __future__ import annotations from itertools import permutations from random import randint from timeit import repeat def _snake_case( ) -> tuple[list[int], int]: lowercase : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )] lowercase : Tuple = randint(-5_000 , 5_000 ) return (arr, r) lowercase : List[Any] = make_dataset() def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> tuple[int, ...]: for triplet in permutations(SCREAMING_SNAKE_CASE__ , 3 ): if sum(SCREAMING_SNAKE_CASE__ ) == target: return tuple(sorted(SCREAMING_SNAKE_CASE__ ) ) return (0, 0, 0) def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> tuple[int, int, int]: arr.sort() lowercase : Optional[int] = len(SCREAMING_SNAKE_CASE__ ) for i in range(n - 1 ): lowercase , lowercase : Optional[Any] = i + 1, n - 1 while left < right: if arr[i] + arr[left] + arr[right] == target: return (arr[i], arr[left], arr[right]) elif arr[i] + arr[left] + arr[right] < target: left += 1 elif arr[i] + arr[left] + arr[right] > target: right -= 1 return (0, 0, 0) def _snake_case( ) -> tuple[float, float]: lowercase : Dict = """ from __main__ import dataset, triplet_sum1, triplet_sum2 """ lowercase : Tuple = """ triplet_sum1(*dataset) """ lowercase : int = """ triplet_sum2(*dataset) """ lowercase : str = repeat(setup=SCREAMING_SNAKE_CASE__ , stmt=SCREAMING_SNAKE_CASE__ , repeat=5 , number=10_000 ) lowercase : Dict = repeat(setup=SCREAMING_SNAKE_CASE__ , stmt=SCREAMING_SNAKE_CASE__ , repeat=5 , number=10_000 ) return (min(SCREAMING_SNAKE_CASE__ ), min(SCREAMING_SNAKE_CASE__ )) if __name__ == "__main__": from doctest import testmod testmod() lowercase : Union[str, Any] = solution_times() print(F'''The time for naive implementation is {times[0]}.''') print(F'''The time for optimized implementation is {times[1]}.''')	285	1
import itertools import os import random import tempfile import unittest import numpy as np from transformers import TvltFeatureExtractor, is_datasets_available from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio from transformers.utils.import_utils import is_torch_available from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_torch_available(): import torch if is_datasets_available(): from datasets import load_dataset SCREAMING_SNAKE_CASE_:int = random.Random() def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase=1.0 , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> Union[str, Any]: """simple docstring""" if rng is None: A : str = global_rng A : str = [] for batch_idx in range(shape[0] ): values.append([] ) for _ in range(shape[1] ): values[-1].append(rng.random() * scale ) return values class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ): '''simple docstring''' def __init__( self, lowerCamelCase__, lowerCamelCase__=7, lowerCamelCase__=400, lowerCamelCase__=2000, lowerCamelCase__=2048, lowerCamelCase__=128, lowerCamelCase__=1, lowerCamelCase__=512, lowerCamelCase__=30, lowerCamelCase__=4_4100, ): A : str = parent A : Optional[int] = batch_size A : str = min_seq_length A : int = max_seq_length A : Any = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) A : Tuple = spectrogram_length A : Optional[Any] = feature_size A : Any = num_audio_channels A : str = hop_length A : int = chunk_length A : Optional[Any] = sampling_rate def _lowerCAmelCase ( self ): return { "spectrogram_length": self.spectrogram_length, "feature_size": self.feature_size, "num_audio_channels": self.num_audio_channels, "hop_length": self.hop_length, "chunk_length": self.chunk_length, "sampling_rate": self.sampling_rate, } def _lowerCAmelCase ( self, lowerCamelCase__=False, lowerCamelCase__=False ): def _flatten(lowerCamelCase__ ): return list(itertools.chain(*UpperCAmelCase_ ) ) if equal_length: A : List[str] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )] else: # make sure that inputs increase in size A : int = [ floats_list((x, self.feature_size) ) for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff ) ] if numpify: A : Dict = [np.asarray(UpperCAmelCase_ ) for x in speech_inputs] return speech_inputs @require_torch @require_torchaudio class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ): '''simple docstring''' __lowerCamelCase : Optional[int] = TvltFeatureExtractor def _lowerCAmelCase ( self ): A : Dict = TvltFeatureExtractionTester(self ) def _lowerCAmelCase ( self ): A : List[str] = self.feature_extraction_class(**self.feat_extract_dict ) self.assertTrue(hasattr(UpperCAmelCase_, """spectrogram_length""" ) ) self.assertTrue(hasattr(UpperCAmelCase_, """feature_size""" ) ) self.assertTrue(hasattr(UpperCAmelCase_, """num_audio_channels""" ) ) self.assertTrue(hasattr(UpperCAmelCase_, """hop_length""" ) ) self.assertTrue(hasattr(UpperCAmelCase_, """chunk_length""" ) ) self.assertTrue(hasattr(UpperCAmelCase_, """sampling_rate""" ) ) def _lowerCAmelCase ( self ): A : List[Any] = self.feature_extraction_class(**self.feat_extract_dict ) with tempfile.TemporaryDirectory() as tmpdirname: A : Optional[int] = feat_extract_first.save_pretrained(UpperCAmelCase_ )[0] check_json_file_has_correct_format(UpperCAmelCase_ ) A : Any = self.feature_extraction_class.from_pretrained(UpperCAmelCase_ ) A : Union[str, Any] = feat_extract_first.to_dict() A : List[Any] = feat_extract_second.to_dict() A : Any = dict_first.pop("""mel_filters""" ) A : str = dict_second.pop("""mel_filters""" ) self.assertTrue(np.allclose(UpperCAmelCase_, UpperCAmelCase_ ) ) self.assertEqual(UpperCAmelCase_, UpperCAmelCase_ ) def _lowerCAmelCase ( self ): A : Any = self.feature_extraction_class(**self.feat_extract_dict ) with tempfile.TemporaryDirectory() as tmpdirname: A : List[str] = os.path.join(UpperCAmelCase_, """feat_extract.json""" ) feat_extract_first.to_json_file(UpperCAmelCase_ ) A : Union[str, Any] = self.feature_extraction_class.from_json_file(UpperCAmelCase_ ) A : List[Any] = feat_extract_first.to_dict() A : Dict = feat_extract_second.to_dict() A : Optional[Any] = dict_first.pop("""mel_filters""" ) A : Optional[Any] = dict_second.pop("""mel_filters""" ) self.assertTrue(np.allclose(UpperCAmelCase_, UpperCAmelCase_ ) ) self.assertEqual(UpperCAmelCase_, UpperCAmelCase_ ) def _lowerCAmelCase ( self ): # Initialize feature_extractor A : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict ) # create three inputs of length 800, 1000, and 1200 A : Optional[Any] = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )] A : Dict = [np.asarray(UpperCAmelCase_ ) for speech_input in speech_inputs] # Test not batched input A : List[Any] = feature_extractor(np_speech_inputs[0], return_tensors="""np""", sampling_rate=4_4100 ).audio_values self.assertTrue(encoded_audios.ndim == 4 ) self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size ) self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length ) self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels ) # Test batched A : List[str] = feature_extractor(UpperCAmelCase_, return_tensors="""np""", sampling_rate=4_4100 ).audio_values self.assertTrue(encoded_audios.ndim == 4 ) self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size ) self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length ) self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels ) # Test audio masking A : Tuple = feature_extractor( UpperCAmelCase_, return_tensors="""np""", sampling_rate=4_4100, mask_audio=UpperCAmelCase_ ).audio_values self.assertTrue(encoded_audios.ndim == 4 ) self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size ) self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length ) self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels ) # Test 2-D numpy arrays are batched. A : Tuple = [floats_list((1, x) )[0] for x in (800, 800, 800)] A : Optional[int] = np.asarray(UpperCAmelCase_ ) A : List[str] = feature_extractor(UpperCAmelCase_, return_tensors="""np""", sampling_rate=4_4100 ).audio_values self.assertTrue(encoded_audios.ndim == 4 ) self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size ) self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length ) self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels ) def _lowerCAmelCase ( self, lowerCamelCase__ ): A : Optional[int] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""", """clean""", split="""validation""" ) # automatic decoding with librispeech A : List[str] = ds.sort("""id""" ).select(range(UpperCAmelCase_ ) )[:num_samples]['audio'] return [x["array"] for x in speech_samples] def _lowerCAmelCase ( self ): A : List[Any] = self._load_datasamples(1 ) A : Union[str, Any] = TvltFeatureExtractor() A : List[Any] = feature_extractor(UpperCAmelCase_, return_tensors="""pt""" ).audio_values self.assertEquals(audio_values.shape, (1, 1, 192, 128) ) A : List[Any] = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] ) self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2], UpperCAmelCase_, atol=1e-4 ) )	116	from datetime import datetime as dt import os from github import Github __A : Dict = [ '''good first issue''', '''good second issue''', '''good difficult issue''', '''feature request''', '''new model''', '''wip''', ] def SCREAMING_SNAKE_CASE__ ( ) -> int: '''simple docstring''' lowerCAmelCase : Optional[Any] = Github(os.environ['GITHUB_TOKEN'] ) lowerCAmelCase : List[str] = g.get_repo('huggingface/transformers' ) lowerCAmelCase : Tuple = repo.get_issues(state='open' ) for issue in open_issues: lowerCAmelCase : Dict = sorted([comment for comment in issue.get_comments()], key=lambda _UpperCAmelCase : i.created_at, reverse=_UpperCAmelCase ) lowerCAmelCase : Optional[Any] = comments[0] if len(_UpperCAmelCase ) > 0 else None if ( last_comment is not None and last_comment.user.login == "github-actions[bot]" and (dt.utcnow() - issue.updated_at).days > 7 and (dt.utcnow() - issue.created_at).days >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() ) ): # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.") issue.edit(state='closed' ) elif ( (dt.utcnow() - issue.updated_at).days > 23 and (dt.utcnow() - issue.created_at).days >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() ) ): # print(f"Would add stale comment to {issue.number}") issue.create_comment( 'This issue has been automatically marked as stale because it has not had ' 'recent activity. If you think this still needs to be addressed ' 'please comment on this thread.\n\nPlease note that issues that do not follow the ' '[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) ' 'are likely to be ignored.' ) if __name__ == "__main__": main()	138	0
import json import os from collections import Counter import torch import torchvision import torchvision.transforms as transforms from PIL import Image from torch import nn from torch.utils.data import Dataset __snake_case : Optional[int] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)} class __SCREAMING_SNAKE_CASE ( nn.Module): def __init__( self , _UpperCamelCase ): """simple docstring""" super().__init__() lowerCAmelCase__ = torchvision.models.resnetaaa(pretrained=_UpperCamelCase ) lowerCAmelCase__ = list(model.children() )[:-2] lowerCAmelCase__ = nn.Sequential(*_UpperCamelCase ) lowerCAmelCase__ = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] ) def UpperCamelCase__ ( self , _UpperCamelCase ): """simple docstring""" # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048 lowerCAmelCase__ = self.pool(self.model(_UpperCamelCase ) ) lowerCAmelCase__ = torch.flatten(_UpperCamelCase , start_dim=2 ) lowerCAmelCase__ = out.transpose(1 , 2 ).contiguous() return out # BxNx2048 class __SCREAMING_SNAKE_CASE ( __lowercase): def __init__( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ): """simple docstring""" lowerCAmelCase__ = [json.loads(_UpperCamelCase ) for l in open(_UpperCamelCase )] lowerCAmelCase__ = os.path.dirname(_UpperCamelCase ) lowerCAmelCase__ = tokenizer lowerCAmelCase__ = labels lowerCAmelCase__ = len(_UpperCamelCase ) lowerCAmelCase__ = max_seq_length lowerCAmelCase__ = transforms def __len__( self ): """simple docstring""" return len(self.data ) def __getitem__( self , _UpperCamelCase ): """simple docstring""" lowerCAmelCase__ = torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] , add_special_tokens=_UpperCamelCase ) ) lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = sentence[0], sentence[1:-1], sentence[-1] lowerCAmelCase__ = sentence[: self.max_seq_length] lowerCAmelCase__ = torch.zeros(self.n_classes ) lowerCAmelCase__ = 1 lowerCAmelCase__ = Image.open(os.path.join(self.data_dir , self.data[index]['img'] ) ).convert('RGB' ) lowerCAmelCase__ = self.transforms(_UpperCamelCase ) return { "image_start_token": start_token, "image_end_token": end_token, "sentence": sentence, "image": image, "label": label, } def UpperCamelCase__ ( self ): """simple docstring""" lowerCAmelCase__ = Counter() for row in self.data: label_freqs.update(row['label'] ) return label_freqs def _UpperCamelCase ( UpperCamelCase_ : List[Any] ) -> Tuple: """simple docstring""" lowerCAmelCase__ = [len(row['sentence'] ) for row in batch] lowerCAmelCase__ , lowerCAmelCase__ = len(UpperCamelCase_ ), max(UpperCamelCase_ ) lowerCAmelCase__ = torch.zeros(UpperCamelCase_ , UpperCamelCase_ , dtype=torch.long ) lowerCAmelCase__ = torch.zeros(UpperCamelCase_ , UpperCamelCase_ , dtype=torch.long ) for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase_ , UpperCamelCase_ ) ): lowerCAmelCase__ = input_row['sentence'] lowerCAmelCase__ = 1 lowerCAmelCase__ = torch.stack([row['image'] for row in batch] ) lowerCAmelCase__ = torch.stack([row['label'] for row in batch] ) lowerCAmelCase__ = torch.stack([row['image_start_token'] for row in batch] ) lowerCAmelCase__ = torch.stack([row['image_end_token'] for row in batch] ) return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor def _UpperCamelCase ( ) -> Optional[int]: """simple docstring""" return [ "Crime", "Drama", "Thriller", "Action", "Comedy", "Romance", "Documentary", "Short", "Mystery", "History", "Family", "Adventure", "Fantasy", "Sci-Fi", "Western", "Horror", "Sport", "War", "Music", "Musical", "Animation", "Biography", "Film-Noir", ] def _UpperCamelCase ( ) -> Optional[int]: """simple docstring""" return transforms.Compose( [ transforms.Resize(256 ), transforms.CenterCrop(224 ), transforms.ToTensor(), transforms.Normalize( mean=[0.4677_7044, 0.4453_1429, 0.4066_1017] , std=[0.1222_1994, 0.1214_5835, 0.1438_0469] , ), ] )	122	from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available __snake_case : Dict = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __snake_case : str = [ """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""", """FocalNetForImageClassification""", """FocalNetForMaskedImageModeling""", """FocalNetBackbone""", """FocalNetModel""", """FocalNetPreTrainedModel""", ] if TYPE_CHECKING: from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_focalnet import ( FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST, FocalNetBackbone, FocalNetForImageClassification, FocalNetForMaskedImageModeling, FocalNetModel, FocalNetPreTrainedModel, ) else: import sys __snake_case : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)	122	1
"""simple docstring""" lowerCAmelCase__ = range(2, 20 + 1) lowerCAmelCase__ = [10**k for k in range(ks[-1] + 1)] lowerCAmelCase__ = {} def a__ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str] ): '''simple docstring''' lowerCAmelCase : Union[str, Any] = sum(a_i[j] for j in range(SCREAMING_SNAKE_CASE , len(SCREAMING_SNAKE_CASE ) ) ) lowerCAmelCase : int = sum(a_i[j] * base[j] for j in range(min(len(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ) ) ) lowerCAmelCase , lowerCAmelCase : List[Any] = 0, 0 lowerCAmelCase : int = n - i lowerCAmelCase : Optional[Any] = memo.get(SCREAMING_SNAKE_CASE ) if sub_memo is not None: lowerCAmelCase : Dict = sub_memo.get(SCREAMING_SNAKE_CASE ) if jumps is not None and len(SCREAMING_SNAKE_CASE ) > 0: # find and make the largest jump without going over lowerCAmelCase : int = -1 for _k in range(len(SCREAMING_SNAKE_CASE ) - 1 , -1 , -1 ): if jumps[_k][2] <= k and jumps[_k][1] <= max_dn: lowerCAmelCase : str = _k break if max_jump >= 0: lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Optional[int] = jumps[max_jump] # since the difference between jumps is cached, add c lowerCAmelCase : Optional[int] = diff + c for j in range(min(SCREAMING_SNAKE_CASE , len(SCREAMING_SNAKE_CASE ) ) ): lowerCAmelCase , lowerCAmelCase : Any = divmod(SCREAMING_SNAKE_CASE , 1_0 ) if new_c > 0: add(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else: lowerCAmelCase : Dict = [] else: lowerCAmelCase : Union[str, Any] = {c: []} lowerCAmelCase : List[Any] = sub_memo if dn >= max_dn or c + diff >= base[k]: return diff, dn if k > ks[0]: while True: # keep doing smaller jumps lowerCAmelCase , lowerCAmelCase : Any = next_term(SCREAMING_SNAKE_CASE , k - 1 , i + dn , SCREAMING_SNAKE_CASE ) diff += _diff dn += terms_jumped if dn >= max_dn or c + diff >= base[k]: break else: # would be too small a jump, just compute sequential terms instead lowerCAmelCase , lowerCAmelCase : Dict = compute(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , i + dn , SCREAMING_SNAKE_CASE ) diff += _diff dn += terms_jumped lowerCAmelCase : Any = sub_memo[c] # keep jumps sorted by # of terms skipped lowerCAmelCase : Optional[Any] = 0 while j < len(SCREAMING_SNAKE_CASE ): if jumps[j][1] > dn: break j += 1 # cache the jump for this value digitsum(b) and c sub_memo[c].insert(SCREAMING_SNAKE_CASE , (diff, dn, k) ) return (diff, dn) def a__ ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[Any] ): '''simple docstring''' if i >= n: return 0, i if k > len(SCREAMING_SNAKE_CASE ): a_i.extend([0 for _ in range(k - len(SCREAMING_SNAKE_CASE ) )] ) # note: a_i -> b * 10^k + c # ds_b -> digitsum(b) # ds_c -> digitsum(c) lowerCAmelCase : int = i lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : List[Any] = 0, 0, 0 for j in range(len(SCREAMING_SNAKE_CASE ) ): if j >= k: ds_b += a_i[j] else: ds_c += a_i[j] while i < n: i += 1 lowerCAmelCase : Dict = ds_c + ds_b diff += addend lowerCAmelCase : str = 0 for j in range(SCREAMING_SNAKE_CASE ): lowerCAmelCase : Any = a_i[j] + addend lowerCAmelCase , lowerCAmelCase : Any = divmod(SCREAMING_SNAKE_CASE , 1_0 ) ds_c += a_i[j] if addend > 0: break if addend > 0: add(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) return diff, i - start_i def a__ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple ): '''simple docstring''' for j in range(SCREAMING_SNAKE_CASE , len(SCREAMING_SNAKE_CASE ) ): lowerCAmelCase : Optional[int] = digits[j] + addend if s >= 1_0: lowerCAmelCase , lowerCAmelCase : Dict = divmod(SCREAMING_SNAKE_CASE , 1_0 ) lowerCAmelCase : str = addend // 1_0 + quotient else: lowerCAmelCase : List[Any] = s lowerCAmelCase : Dict = addend // 1_0 if addend == 0: break while addend > 0: lowerCAmelCase , lowerCAmelCase : List[str] = divmod(SCREAMING_SNAKE_CASE , 1_0 ) digits.append(SCREAMING_SNAKE_CASE ) def a__ ( SCREAMING_SNAKE_CASE : int = 1_0**1_5 ): '''simple docstring''' lowerCAmelCase : Any = [1] lowerCAmelCase : Optional[int] = 1 lowerCAmelCase : Optional[Any] = 0 while True: lowerCAmelCase , lowerCAmelCase : Any = next_term(SCREAMING_SNAKE_CASE , 2_0 , i + dn , SCREAMING_SNAKE_CASE ) dn += terms_jumped if dn == n - i: break lowerCAmelCase : Any = 0 for j in range(len(SCREAMING_SNAKE_CASE ) ): a_n += digits[j] * 1_0**j return a_n if __name__ == "__main__": print(F"{solution() = }")	108	"""simple docstring""" import json import os import shutil import tempfile from unittest import TestCase from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast from transformers.models.bart.configuration_bart import BartConfig from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES from transformers.models.dpr.configuration_dpr import DPRConfig from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available if is_torch_available() and is_datasets_available() and is_faiss_available(): from transformers.models.rag.configuration_rag import RagConfig from transformers.models.rag.tokenization_rag import RagTokenizer @require_faiss @require_torch class SCREAMING_SNAKE_CASE__ ( lowercase ): """simple docstring""" def lowercase__ ( self ): """simple docstring""" lowerCAmelCase : Optional[int] = tempfile.mkdtemp() lowerCAmelCase : Optional[int] = 8 # DPR tok lowerCAmelCase : Dict = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] lowerCAmelCase : List[str] = os.path.join(self.tmpdirname , "dpr_tokenizer" ) os.makedirs(snake_case__ , exist_ok=snake_case__ ) lowerCAmelCase : Dict = os.path.join(snake_case__ , DPR_VOCAB_FILES_NAMES["vocab_file"] ) with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) ) # BART tok lowerCAmelCase : Optional[int] = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", ] lowerCAmelCase : Optional[int] = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) ) lowerCAmelCase : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] lowerCAmelCase : str = {"unk_token": "<unk>"} lowerCAmelCase : int = os.path.join(self.tmpdirname , "bart_tokenizer" ) os.makedirs(snake_case__ , exist_ok=snake_case__ ) lowerCAmelCase : int = os.path.join(snake_case__ , BART_VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase : Dict = os.path.join(snake_case__ , BART_VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file , "w" , encoding="utf-8" ) as fp: fp.write(json.dumps(snake_case__ ) + "\n" ) with open(self.merges_file , "w" , encoding="utf-8" ) as fp: fp.write("\n".join(snake_case__ ) ) def lowercase__ ( self ): """simple docstring""" return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) ) def lowercase__ ( self ): """simple docstring""" return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , "bart_tokenizer" ) ) def lowercase__ ( self ): """simple docstring""" shutil.rmtree(self.tmpdirname ) @require_tokenizers def lowercase__ ( self ): """simple docstring""" lowerCAmelCase : str = os.path.join(self.tmpdirname , "rag_tokenizer" ) lowerCAmelCase : List[Any] = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() ) lowerCAmelCase : Optional[Any] = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() ) rag_config.save_pretrained(snake_case__ ) rag_tokenizer.save_pretrained(snake_case__ ) lowerCAmelCase : List[str] = RagTokenizer.from_pretrained(snake_case__ , config=snake_case__ ) self.assertIsInstance(new_rag_tokenizer.question_encoder , snake_case__ ) self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() ) self.assertIsInstance(new_rag_tokenizer.generator , snake_case__ ) self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() ) @slow def lowercase__ ( self ): """simple docstring""" lowerCAmelCase : Optional[int] = RagTokenizer.from_pretrained("facebook/rag-token-nq" ) lowerCAmelCase : Dict = [ "who got the first nobel prize in physics", "when is the next deadpool movie being released", "which mode is used for short wave broadcast service", "who is the owner of reading football club", "when is the next scandal episode coming out", "when is the last time the philadelphia won the superbowl", "what is the most current adobe flash player version", "how many episodes are there in dragon ball z", "what is the first step in the evolution of the eye", "where is gall bladder situated in human body", "what is the main mineral in lithium batteries", "who is the president of usa right now", "where do the greasers live in the outsiders", "panda is a national animal of which country", "what is the name of manchester united stadium", ] lowerCAmelCase : Union[str, Any] = tokenizer(snake_case__ ) self.assertIsNotNone(snake_case__ ) @slow def lowercase__ ( self ): """simple docstring""" lowerCAmelCase : List[str] = RagTokenizer.from_pretrained("facebook/rag-sequence-nq" ) lowerCAmelCase : List[str] = [ "who got the first nobel prize in physics", "when is the next deadpool movie being released", "which mode is used for short wave broadcast service", "who is the owner of reading football club", "when is the next scandal episode coming out", "when is the last time the philadelphia won the superbowl", "what is the most current adobe flash player version", "how many episodes are there in dragon ball z", "what is the first step in the evolution of the eye", "where is gall bladder situated in human body", "what is the main mineral in lithium batteries", "who is the president of usa right now", "where do the greasers live in the outsiders", "panda is a national animal of which country", "what is the name of manchester united stadium", ] lowerCAmelCase : str = tokenizer(snake_case__ ) self.assertIsNotNone(snake_case__ )	108	1
"""simple docstring""" import math from datetime import datetime, timedelta def lowerCAmelCase (__UpperCamelCase : int ): """simple docstring""" __UpperCamelCase =year % 1_9 __UpperCamelCase =year % 4 __UpperCamelCase =year % 7 __UpperCamelCase =math.floor(year / 1_0_0 ) __UpperCamelCase =math.floor((1_3 + 8 * leap_day_inhibits) / 2_5 ) __UpperCamelCase =leap_day_inhibits / 4 __UpperCamelCase =( 1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number ) % 3_0 __UpperCamelCase =(4 + leap_day_inhibits - leap_day_reinstall_number) % 7 # days to be added to March 21 __UpperCamelCase =(1_9 * metonic_cycle + secular_moon_shift) % 3_0 # PHM -> Paschal Full Moon __UpperCamelCase =( 2 * julian_leap_year + 4 * non_leap_year + 6 * days_to_add + century_starting_point ) % 7 if days_to_add == 2_9 and days_from_phm_to_sunday == 6: return datetime(__UpperCamelCase , 4 , 1_9 ) elif days_to_add == 2_8 and days_from_phm_to_sunday == 6: return datetime(__UpperCamelCase , 4 , 1_8 ) else: return datetime(__UpperCamelCase , 3 , 2_2 ) + timedelta( days=int(days_to_add + days_from_phm_to_sunday ) ) if __name__ == "__main__": for year in (1_994, 2_000, 2_010, 2_021, 2_023): __lowercase = '''will be''' if year > datetime.now().year else '''was''' print(f'''Easter in {year} {tense} {gauss_easter(year)}''')	85	"""simple docstring""" import itertools import json import os import unittest from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class _lowercase ( __a , unittest.TestCase ): """simple docstring""" lowercase__ = LongformerTokenizer lowercase__ = True lowercase__ = LongformerTokenizerFast lowercase__ = True def UpperCAmelCase_ ( self : Optional[Any] ) -> Any: '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt __UpperCamelCase =[ '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''\u0120''', '''\u0120l''', '''\u0120n''', '''\u0120lo''', '''\u0120low''', '''er''', '''\u0120lowest''', '''\u0120newer''', '''\u0120wider''', '''<unk>''', ] __UpperCamelCase =dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) ) __UpperCamelCase =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', ''''''] __UpperCamelCase ={'''unk_token''': '''<unk>'''} __UpperCamelCase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] ) __UpperCamelCase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] ) with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write(json.dumps(UpperCamelCase__ ) + '''\n''' ) with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write('''\n'''.join(UpperCamelCase__ ) ) def UpperCAmelCase_ ( self : Optional[int] , **UpperCamelCase__ : str ) -> Dict: '''simple docstring''' kwargs.update(self.special_tokens_map ) return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase__ ) def UpperCAmelCase_ ( self : List[str] , **UpperCamelCase__ : Optional[int] ) -> Union[str, Any]: '''simple docstring''' kwargs.update(self.special_tokens_map ) return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase__ ) def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : List[str] ) -> Optional[Any]: '''simple docstring''' __UpperCamelCase ='''lower newer''' __UpperCamelCase ='''lower newer''' return input_text, output_text def UpperCAmelCase_ ( self : int ) -> List[Any]: '''simple docstring''' __UpperCamelCase =self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map ) __UpperCamelCase ='''lower newer''' __UpperCamelCase =['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er'''] __UpperCamelCase =tokenizer.tokenize(UpperCamelCase__ ) # , add_prefix_space=True) self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ ) __UpperCamelCase =tokens + [tokenizer.unk_token] __UpperCamelCase =[0, 1, 2, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , UpperCamelCase__ ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> int: '''simple docstring''' __UpperCamelCase =self.get_tokenizer() self.assertListEqual(tokenizer.encode('''Hello world!''' , add_special_tokens=UpperCamelCase__ ) , [0, 31414, 232, 328, 2] ) self.assertListEqual( tokenizer.encode('''Hello world! cécé herlolip 418''' , add_special_tokens=UpperCamelCase__ ) , [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2] , ) @slow def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]: '''simple docstring''' __UpperCamelCase =self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096''' ) __UpperCamelCase =tokenizer.encode('''sequence builders''' , add_special_tokens=UpperCamelCase__ ) __UpperCamelCase =tokenizer.encode('''multi-sequence build''' , add_special_tokens=UpperCamelCase__ ) __UpperCamelCase =tokenizer.encode( '''sequence builders''' , add_special_tokens=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ ) __UpperCamelCase =tokenizer.encode( '''sequence builders''' , '''multi-sequence build''' , add_special_tokens=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ ) __UpperCamelCase =tokenizer.build_inputs_with_special_tokens(UpperCamelCase__ ) __UpperCamelCase =tokenizer.build_inputs_with_special_tokens(UpperCamelCase__ , UpperCamelCase__ ) assert encoded_sentence == encoded_text_from_decode assert encoded_pair == encoded_pair_from_decode def UpperCAmelCase_ ( self : int ) -> Dict: '''simple docstring''' __UpperCamelCase =self.get_tokenizer() __UpperCamelCase ='''Encode this sequence.''' __UpperCamelCase =tokenizer.byte_encoder[''' '''.encode('''utf-8''' )[0]] # Testing encoder arguments __UpperCamelCase =tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ ) __UpperCamelCase =tokenizer.convert_ids_to_tokens(encoded[0] )[0] self.assertNotEqual(UpperCamelCase__ , UpperCamelCase__ ) __UpperCamelCase =tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ ) __UpperCamelCase =tokenizer.convert_ids_to_tokens(encoded[0] )[0] self.assertEqual(UpperCamelCase__ , UpperCamelCase__ ) tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} ) __UpperCamelCase =tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) __UpperCamelCase =tokenizer.convert_ids_to_tokens(encoded[1] )[0] self.assertNotEqual(UpperCamelCase__ , UpperCamelCase__ ) # Testing spaces after special tokens __UpperCamelCase ='''<mask>''' tokenizer.add_special_tokens( {'''mask_token''': AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ )} ) # mask token has a left space __UpperCamelCase =tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) __UpperCamelCase ='''Encode <mask> sequence''' __UpperCamelCase ='''Encode <mask>sequence''' __UpperCamelCase =tokenizer.encode(UpperCamelCase__ ) __UpperCamelCase =encoded.index(UpperCamelCase__ ) __UpperCamelCase =tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0] self.assertEqual(UpperCamelCase__ , UpperCamelCase__ ) __UpperCamelCase =tokenizer.encode(UpperCamelCase__ ) __UpperCamelCase =encoded.index(UpperCamelCase__ ) __UpperCamelCase =tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0] self.assertNotEqual(UpperCamelCase__ , UpperCamelCase__ ) def UpperCAmelCase_ ( self : int ) -> Dict: '''simple docstring''' pass def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]: '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): __UpperCamelCase =self.rust_tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ ) __UpperCamelCase =self.tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ ) __UpperCamelCase ='''A, <mask> AllenNLP sentence.''' __UpperCamelCase =tokenizer_r.encode_plus(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ ) __UpperCamelCase =tokenizer_p.encode_plus(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ ) # token_type_ids should put 0 everywhere self.assertEqual(sum(tokens_r['''token_type_ids'''] ) , sum(tokens_p['''token_type_ids'''] ) ) # attention_mask should put 1 everywhere, so sum over length should be 1 self.assertEqual( sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ) , sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ) , ) __UpperCamelCase =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] ) __UpperCamelCase =tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] ) # Rust correctly handles the space before the mask while python doesnt self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] ) self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] ) self.assertSequenceEqual( UpperCamelCase__ , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] ) self.assertSequenceEqual( UpperCamelCase__ , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] ) def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]: '''simple docstring''' for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ): __UpperCamelCase =self.rust_tokenizer_class.from_pretrained( self.tmpdirname , use_fast=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ ) __UpperCamelCase =json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() ) __UpperCamelCase =json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() ) self.assertEqual(pre_tokenizer_state['''add_prefix_space'''] , UpperCamelCase__ ) self.assertEqual(post_processor_state['''add_prefix_space'''] , UpperCamelCase__ ) self.assertEqual(post_processor_state['''trim_offsets'''] , UpperCamelCase__ ) def UpperCAmelCase_ ( self : List[Any] ) -> int: '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): __UpperCamelCase ='''hello''' # `hello` is a token in the vocabulary of `pretrained_name` __UpperCamelCase =f"""{text_of_1_token} {text_of_1_token}""" __UpperCamelCase =self.rust_tokenizer_class.from_pretrained( UpperCamelCase__ , use_fast=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ ) __UpperCamelCase =tokenizer_r(UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase__ )) ) self.assertEqual( encoding.offset_mapping[1] , (len(UpperCamelCase__ ) + 1, len(UpperCamelCase__ ) + 1 + len(UpperCamelCase__ )) , ) __UpperCamelCase =self.rust_tokenizer_class.from_pretrained( UpperCamelCase__ , use_fast=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ ) __UpperCamelCase =tokenizer_r(UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase__ )) ) self.assertEqual( encoding.offset_mapping[1] , (len(UpperCamelCase__ ) + 1, len(UpperCamelCase__ ) + 1 + len(UpperCamelCase__ )) , ) __UpperCamelCase =self.rust_tokenizer_class.from_pretrained( UpperCamelCase__ , use_fast=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ ) __UpperCamelCase =tokenizer_r(UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase__ )) ) self.assertEqual( encoding.offset_mapping[1] , (len(UpperCamelCase__ ), len(UpperCamelCase__ ) + 1 + len(UpperCamelCase__ )) , ) __UpperCamelCase =self.rust_tokenizer_class.from_pretrained( UpperCamelCase__ , use_fast=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ ) __UpperCamelCase =tokenizer_r(UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase__ )) ) self.assertEqual( encoding.offset_mapping[1] , (len(UpperCamelCase__ ), len(UpperCamelCase__ ) + 1 + len(UpperCamelCase__ )) , ) __UpperCamelCase =f""" {text}""" # tokenizer_r = self.rust_tokenizer_class.from_pretrained( # pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True # ) # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token))) # self.assertEqual( # encoding.offset_mapping[1], # (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)), # ) __UpperCamelCase =self.rust_tokenizer_class.from_pretrained( UpperCamelCase__ , use_fast=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ ) __UpperCamelCase =tokenizer_r(UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(UpperCamelCase__ )) ) self.assertEqual( encoding.offset_mapping[1] , (1 + len(UpperCamelCase__ ) + 1, 1 + len(UpperCamelCase__ ) + 1 + len(UpperCamelCase__ )) , ) __UpperCamelCase =self.rust_tokenizer_class.from_pretrained( UpperCamelCase__ , use_fast=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ ) __UpperCamelCase =tokenizer_r(UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(UpperCamelCase__ )) ) self.assertEqual( encoding.offset_mapping[1] , (1 + len(UpperCamelCase__ ), 1 + len(UpperCamelCase__ ) + 1 + len(UpperCamelCase__ )) , ) __UpperCamelCase =self.rust_tokenizer_class.from_pretrained( UpperCamelCase__ , use_fast=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ ) __UpperCamelCase =tokenizer_r(UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(UpperCamelCase__ )) ) self.assertEqual( encoding.offset_mapping[1] , (1 + len(UpperCamelCase__ ), 1 + len(UpperCamelCase__ ) + 1 + len(UpperCamelCase__ )) , )	85	1
import itertools import random import unittest import numpy as np from transformers import BatchFeature, SpeechTaFeatureExtractor from transformers.testing_utils import require_torch from transformers.utils.import_utils import is_torch_available from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_torch_available(): import torch _A = random.Random() def lowerCamelCase__ ( a__ : List[str] , a__ : int=1.0 , a__ : Any=None , a__ : Dict=None ) -> List[Any]: if rng is None: UpperCamelCase_ = global_rng UpperCamelCase_ = [] for batch_idx in range(shape[0] ): values.append([] ) for _ in range(shape[1] ): values[-1].append(rng.random() * scale ) return values @require_torch class lowercase_ ( unittest.TestCase ): def __init__( self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=4_0_0 , __UpperCamelCase=2_0_0_0 , __UpperCamelCase=1 , __UpperCamelCase=0.0 , __UpperCamelCase=1_6_0_0_0 , __UpperCamelCase=True , __UpperCamelCase=8_0 , __UpperCamelCase=1_6 , __UpperCamelCase=6_4 , __UpperCamelCase="hann_window" , __UpperCamelCase=8_0 , __UpperCamelCase=7_6_0_0 , __UpperCamelCase=1e-10 , __UpperCamelCase=True , ): """simple docstring""" UpperCamelCase_ = parent UpperCamelCase_ = batch_size UpperCamelCase_ = min_seq_length UpperCamelCase_ = max_seq_length UpperCamelCase_ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) UpperCamelCase_ = feature_size UpperCamelCase_ = padding_value UpperCamelCase_ = sampling_rate UpperCamelCase_ = do_normalize UpperCamelCase_ = num_mel_bins UpperCamelCase_ = hop_length UpperCamelCase_ = win_length UpperCamelCase_ = win_function UpperCamelCase_ = fmin UpperCamelCase_ = fmax UpperCamelCase_ = mel_floor UpperCamelCase_ = return_attention_mask def lowerCamelCase_ ( self ): """simple docstring""" return { "feature_size": self.feature_size, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "do_normalize": self.do_normalize, "num_mel_bins": self.num_mel_bins, "hop_length": self.hop_length, "win_length": self.win_length, "win_function": self.win_function, "fmin": self.fmin, "fmax": self.fmax, "mel_floor": self.mel_floor, "return_attention_mask": self.return_attention_mask, } def lowerCamelCase_ ( self , __UpperCamelCase=False , __UpperCamelCase=False ): """simple docstring""" def _flatten(__UpperCamelCase ): return list(itertools.chain(*__UpperCamelCase ) ) if equal_length: UpperCamelCase_ = floats_list((self.batch_size, self.max_seq_length) ) else: # make sure that inputs increase in size UpperCamelCase_ = [ _flatten(floats_list((x, self.feature_size) ) ) for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff ) ] if numpify: UpperCamelCase_ = [np.asarray(__UpperCamelCase ) for x in speech_inputs] return speech_inputs def lowerCamelCase_ ( self , __UpperCamelCase=False , __UpperCamelCase=False ): """simple docstring""" if equal_length: UpperCamelCase_ = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )] else: # make sure that inputs increase in size UpperCamelCase_ = [ floats_list((x, self.num_mel_bins) ) for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff ) ] if numpify: UpperCamelCase_ = [np.asarray(__UpperCamelCase ) for x in speech_inputs] return speech_inputs @require_torch class lowercase_ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ): A__ : Union[str, Any] = SpeechTaFeatureExtractor def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = SpeechTaFeatureExtractionTester(self ) def lowerCamelCase_ ( self , __UpperCamelCase ): """simple docstring""" self.assertTrue(np.all(np.mean(__UpperCamelCase , axis=0 ) < 1e-3 ) ) self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase , axis=0 ) - 1 ) < 1e-3 ) ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) # create three inputs of length 800, 1000, and 1200 UpperCamelCase_ = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )] UpperCamelCase_ = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs] # Test not batched input UpperCamelCase_ = feat_extract(speech_inputs[0] , return_tensors="""np""" ).input_values UpperCamelCase_ = feat_extract(np_speech_inputs[0] , return_tensors="""np""" ).input_values self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) ) # Test batched UpperCamelCase_ = feat_extract(__UpperCamelCase , return_tensors="""np""" ).input_values UpperCamelCase_ = feat_extract(__UpperCamelCase , return_tensors="""np""" ).input_values for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ): self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) UpperCamelCase_ = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )] UpperCamelCase_ = ["""longest""", """max_length""", """do_not_pad"""] UpperCamelCase_ = [None, 1_6_0_0, None] for max_length, padding in zip(__UpperCamelCase , __UpperCamelCase ): UpperCamelCase_ = feat_extract(__UpperCamelCase , padding=__UpperCamelCase , max_length=__UpperCamelCase , return_tensors="""np""" ) UpperCamelCase_ = processed.input_values self._check_zero_mean_unit_variance(input_values[0][:8_0_0] ) self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6 ) self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] ) self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6 ) self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) UpperCamelCase_ = range(8_0_0 , 1_4_0_0 , 2_0_0 ) UpperCamelCase_ = [floats_list((1, x) )[0] for x in lengths] UpperCamelCase_ = ["""longest""", """max_length""", """do_not_pad"""] UpperCamelCase_ = [None, 1_6_0_0, None] for max_length, padding in zip(__UpperCamelCase , __UpperCamelCase ): UpperCamelCase_ = feat_extract(__UpperCamelCase , max_length=__UpperCamelCase , padding=__UpperCamelCase ) UpperCamelCase_ = processed.input_values self._check_zero_mean_unit_variance(input_values[0][:8_0_0] ) self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] ) self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) UpperCamelCase_ = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )] UpperCamelCase_ = feat_extract( __UpperCamelCase , truncation=__UpperCamelCase , max_length=1_0_0_0 , padding="""max_length""" , return_tensors="""np""" ) UpperCamelCase_ = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_0_0] ) self._check_zero_mean_unit_variance(input_values[1] ) self._check_zero_mean_unit_variance(input_values[2] ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) UpperCamelCase_ = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )] UpperCamelCase_ = feat_extract( __UpperCamelCase , truncation=__UpperCamelCase , max_length=1_0_0_0 , padding="""longest""" , return_tensors="""np""" ) UpperCamelCase_ = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_0_0] ) self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] ) self._check_zero_mean_unit_variance(input_values[2] ) # make sure that if max_length < longest -> then pad to max_length self.assertTrue(input_values.shape == (3, 1_0_0_0) ) UpperCamelCase_ = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )] UpperCamelCase_ = feat_extract( __UpperCamelCase , truncation=__UpperCamelCase , max_length=2_0_0_0 , padding="""longest""" , return_tensors="""np""" ) UpperCamelCase_ = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_0_0] ) self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] ) self._check_zero_mean_unit_variance(input_values[2] ) # make sure that if max_length > longest -> then pad to longest self.assertTrue(input_values.shape == (3, 1_2_0_0) ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) UpperCamelCase_ = np.random.rand(1_0_0 ).astype(np.floataa ) UpperCamelCase_ = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: UpperCamelCase_ = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""np""" ) self.assertTrue(np_processed.input_values.dtype == np.floataa ) UpperCamelCase_ = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""pt""" ) self.assertTrue(pt_processed.input_values.dtype == torch.floataa ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) # create three inputs of length 800, 1000, and 1200 UpperCamelCase_ = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )] UpperCamelCase_ = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs] # Test feature size UpperCamelCase_ = feature_extractor(audio_target=__UpperCamelCase , padding=__UpperCamelCase , return_tensors="""np""" ).input_values self.assertTrue(input_values.ndim == 3 ) self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins ) # Test not batched input UpperCamelCase_ = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_values UpperCamelCase_ = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_values self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) ) # Test batched UpperCamelCase_ = feature_extractor(__UpperCamelCase , return_tensors="""np""" ).input_values UpperCamelCase_ = feature_extractor(__UpperCamelCase , return_tensors="""np""" ).input_values for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ): self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) ) # Test 2-D numpy arrays are batched. UpperCamelCase_ = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)] UpperCamelCase_ = np.asarray(__UpperCamelCase ) UpperCamelCase_ = feature_extractor(__UpperCamelCase , return_tensors="""np""" ).input_values UpperCamelCase_ = feature_extractor(__UpperCamelCase , return_tensors="""np""" ).input_values for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ): self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feat_extract_tester.prepare_inputs_for_target() UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_dict ) UpperCamelCase_ = feat_extract.model_input_names[0] UpperCamelCase_ = BatchFeature({input_name: speech_inputs} ) self.assertTrue(all(len(__UpperCamelCase ) == len(__UpperCamelCase ) for x, y in zip(__UpperCamelCase , processed_features[input_name] ) ) ) UpperCamelCase_ = self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCamelCase ) UpperCamelCase_ = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" ) UpperCamelCase_ = processed_features[input_name] if len(batch_features_input.shape ) < 3: UpperCamelCase_ = batch_features_input[:, :, None] self.assertTrue( batch_features_input.shape == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) ) @require_torch def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCamelCase ) UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_dict ) UpperCamelCase_ = feat_extract.model_input_names[0] UpperCamelCase_ = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" ) UpperCamelCase_ = processed_features[input_name] if len(batch_features_input.shape ) < 3: UpperCamelCase_ = batch_features_input[:, :, None] self.assertTrue( batch_features_input.shape == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) ) @require_torch def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feature_extraction_class(**self.feat_extract_dict ) UpperCamelCase_ = self.feat_extract_tester.prepare_inputs_for_target() UpperCamelCase_ = feat_extract.model_input_names[0] UpperCamelCase_ = BatchFeature({input_name: speech_inputs} ) UpperCamelCase_ = feat_extract.num_mel_bins # hack! UpperCamelCase_ = feat_extract.pad(__UpperCamelCase , padding="""longest""" , return_tensors="""np""" )[input_name] UpperCamelCase_ = feat_extract.pad(__UpperCamelCase , padding="""longest""" , return_tensors="""pt""" )[input_name] self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feat_extract_dict UpperCamelCase_ = True UpperCamelCase_ = self.feature_extraction_class(**__UpperCamelCase ) UpperCamelCase_ = self.feat_extract_tester.prepare_inputs_for_target() UpperCamelCase_ = [len(__UpperCamelCase ) for x in speech_inputs] UpperCamelCase_ = feat_extract.model_input_names[0] UpperCamelCase_ = BatchFeature({input_name: speech_inputs} ) UpperCamelCase_ = feat_extract.num_mel_bins # hack! UpperCamelCase_ = feat_extract.pad(__UpperCamelCase , padding="""longest""" , return_tensors="""np""" ) self.assertIn("""attention_mask""" , __UpperCamelCase ) self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) ) self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , __UpperCamelCase ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.feat_extract_dict UpperCamelCase_ = True UpperCamelCase_ = self.feature_extraction_class(**__UpperCamelCase ) UpperCamelCase_ = self.feat_extract_tester.prepare_inputs_for_target() UpperCamelCase_ = [len(__UpperCamelCase ) for x in speech_inputs] UpperCamelCase_ = feat_extract.model_input_names[0] UpperCamelCase_ = BatchFeature({input_name: speech_inputs} ) UpperCamelCase_ = min(__UpperCamelCase ) UpperCamelCase_ = feat_extract.num_mel_bins # hack! UpperCamelCase_ = feat_extract.pad( __UpperCamelCase , padding="""max_length""" , max_length=__UpperCamelCase , truncation=__UpperCamelCase , return_tensors="""np""" ) self.assertIn("""attention_mask""" , __UpperCamelCase ) self.assertListEqual( list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] ) self.assertListEqual( processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] ) def lowerCamelCase_ ( self , __UpperCamelCase ): """simple docstring""" from datasets import load_dataset UpperCamelCase_ = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" ) # automatic decoding with librispeech UpperCamelCase_ = ds.sort("""id""" ).select(range(__UpperCamelCase ) )[:num_samples]["""audio"""] return [x["array"] for x in speech_samples] def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = torch.tensor( [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03, 3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03, 2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04, 4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03, 7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04, 4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03] ) # fmt: on UpperCamelCase_ = self._load_datasamples(1 ) UpperCamelCase_ = SpeechTaFeatureExtractor() UpperCamelCase_ = feature_extractor(__UpperCamelCase , return_tensors="""pt""" ).input_values self.assertEquals(input_values.shape , (1, 9_3_6_8_0) ) self.assertTrue(torch.allclose(input_values[0, :3_0] , __UpperCamelCase , atol=1e-6 ) ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = torch.tensor( [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777, -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386, -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571, -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998] ) # fmt: on UpperCamelCase_ = self._load_datasamples(1 ) UpperCamelCase_ = SpeechTaFeatureExtractor() UpperCamelCase_ = feature_extractor(audio_target=__UpperCamelCase , return_tensors="""pt""" ).input_values self.assertEquals(input_values.shape , (1, 3_6_6, 8_0) ) self.assertTrue(torch.allclose(input_values[0, 0, :3_0] , __UpperCamelCase , atol=1e-4 ) )	122	import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _A = logging.get_logger(__name__) _A = '''▁''' _A = {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''} _A = { '''vocab_file''': { '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''', }, '''monolingual_vocab_file''': { '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''', }, } _A = {'''vinai/bartpho-syllable''': 1_024} class lowercase_ ( __SCREAMING_SNAKE_CASE ): A__ : List[Any] = VOCAB_FILES_NAMES A__ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP A__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES A__ : Union[str, Any] = ["""input_ids""", """attention_mask"""] def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="<s>" , __UpperCamelCase="</s>" , __UpperCamelCase="</s>" , __UpperCamelCase="<s>" , __UpperCamelCase="<unk>" , __UpperCamelCase="<pad>" , __UpperCamelCase="<mask>" , __UpperCamelCase = None , **__UpperCamelCase , ): """simple docstring""" UpperCamelCase_ = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else mask_token UpperCamelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCamelCase , ) UpperCamelCase_ = vocab_file UpperCamelCase_ = monolingual_vocab_file UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__UpperCamelCase ) ) # Load the reduced vocab # Keep order of special tokens for backward compatibility UpperCamelCase_ = {} UpperCamelCase_ = 0 for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]: if str(__UpperCamelCase ) not in self.fairseq_tokens_to_ids: UpperCamelCase_ = cnt cnt += 1 with open(__UpperCamelCase , """r""" , encoding="""utf-8""" ) as f: for line in f.readlines(): UpperCamelCase_ = line.strip().split()[0] UpperCamelCase_ = len(self.fairseq_tokens_to_ids ) if str(__UpperCamelCase ) not in self.fairseq_tokens_to_ids: UpperCamelCase_ = len(self.fairseq_tokens_to_ids ) UpperCamelCase_ = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def __getstate__( self ): """simple docstring""" UpperCamelCase_ = self.__dict__.copy() UpperCamelCase_ = None UpperCamelCase_ = self.sp_model.serialized_model_proto() return state def __setstate__( self , __UpperCamelCase ): """simple docstring""" UpperCamelCase_ = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): UpperCamelCase_ = {} UpperCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.LoadFromSerializedProto(self.sp_model_proto ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None ): """simple docstring""" if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] UpperCamelCase_ = [self.cls_token_id] UpperCamelCase_ = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__UpperCamelCase , token_ids_a=__UpperCamelCase , already_has_special_tokens=__UpperCamelCase ) if token_ids_a is None: return [1] + ([0] * len(__UpperCamelCase )) + [1] return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1] def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None ): """simple docstring""" UpperCamelCase_ = [self.sep_token_id] UpperCamelCase_ = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def lowerCamelCase_ ( self ): """simple docstring""" return len(self.fairseq_ids_to_tokens ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = {self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def lowerCamelCase_ ( self , __UpperCamelCase ): """simple docstring""" return self.sp_model.encode(__UpperCamelCase , out_type=__UpperCamelCase ) def lowerCamelCase_ ( self , __UpperCamelCase ): """simple docstring""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] else: return self.unk_token_id def lowerCamelCase_ ( self , __UpperCamelCase ): """simple docstring""" return self.fairseq_ids_to_tokens[index] def lowerCamelCase_ ( self , __UpperCamelCase ): """simple docstring""" UpperCamelCase_ = """""".join(__UpperCamelCase ).replace(__UpperCamelCase , """ """ ).strip() return out_string def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None ): """simple docstring""" if not os.path.isdir(__UpperCamelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCamelCase_ = os.path.join( __UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) UpperCamelCase_ = os.path.join( __UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] , ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , __UpperCamelCase ) elif not os.path.isfile(self.vocab_file ): with open(__UpperCamelCase , """wb""" ) as fi: UpperCamelCase_ = self.sp_model.serialized_model_proto() fi.write(__UpperCamelCase ) if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath( __UpperCamelCase ) and os.path.isfile(self.monolingual_vocab_file ): copyfile(self.monolingual_vocab_file , __UpperCamelCase ) elif not os.path.isfile(self.monolingual_vocab_file ): with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as fp: for token in self.fairseq_tokens_to_ids: if token not in self.all_special_tokens: fp.write(f'''{str(__UpperCamelCase )} \n''' ) return out_vocab_file, out_monolingual_vocab_file	122	1
import json import sys import tempfile import unittest from pathlib import Path import transformers from transformers import ( CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, AutoConfig, AutoFeatureExtractor, WavaVecaConfig, WavaVecaFeatureExtractor, ) from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils")) from test_module.custom_configuration import CustomConfig # noqa E402 from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402 _snake_case = get_tests_dir("fixtures") _snake_case = get_tests_dir("fixtures/dummy_feature_extractor_config.json") _snake_case = get_tests_dir("fixtures/dummy-config.json") class lowercase ( unittest.TestCase ): def a__ ( self ) -> List[str]: _A : Optional[int] = 0 def a__ ( self ) -> List[str]: _A : int = AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h""" ) self.assertIsInstance(_a , _a ) def a__ ( self ) -> Tuple: _A : Dict = AutoFeatureExtractor.from_pretrained(_a ) self.assertIsInstance(_a , _a ) def a__ ( self ) -> Dict: with tempfile.TemporaryDirectory() as tmpdirname: _A : Tuple = WavaVecaConfig() # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally _A : str = AutoFeatureExtractor.from_pretrained(_a ).to_dict() config_dict.pop("""feature_extractor_type""" ) _A : Optional[int] = WavaVecaFeatureExtractor(**_a ) # save in new folder model_config.save_pretrained(_a ) config.save_pretrained(_a ) _A : Optional[Any] = AutoFeatureExtractor.from_pretrained(_a ) # make sure private variable is not incorrectly saved _A : List[str] = json.loads(config.to_json_string() ) self.assertTrue("""_processor_class""" not in dict_as_saved ) self.assertIsInstance(_a , _a ) def a__ ( self ) -> Optional[Any]: _A : int = AutoFeatureExtractor.from_pretrained(_a ) self.assertIsInstance(_a , _a ) def a__ ( self ) -> int: with self.assertRaisesRegex( _a , """bert-base is not a local folder and is not a valid model identifier""" ): _A : Optional[Any] = AutoFeatureExtractor.from_pretrained("""bert-base""" ) def a__ ( self ) -> str: with self.assertRaisesRegex( _a , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ): _A : Tuple = AutoFeatureExtractor.from_pretrained(_a , revision="""aaaaaa""" ) def a__ ( self ) -> Any: with self.assertRaisesRegex( _a , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ): _A : int = AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model""" ) def a__ ( self ) -> Any: # If remote code is not set, we will time out when asking whether to load the model. with self.assertRaises(_a ): _A : Tuple = AutoFeatureExtractor.from_pretrained( """hf-internal-testing/test_dynamic_feature_extractor""" ) # If remote code is disabled, we can't load this config. with self.assertRaises(_a ): _A : Dict = AutoFeatureExtractor.from_pretrained( """hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=_a ) _A : Dict = AutoFeatureExtractor.from_pretrained( """hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=_a ) self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" ) # Test feature extractor can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained(_a ) _A : List[str] = AutoFeatureExtractor.from_pretrained(_a , trust_remote_code=_a ) self.assertEqual(reloaded_feature_extractor.__class__.__name__ , """NewFeatureExtractor""" ) def a__ ( self ) -> List[Any]: try: AutoConfig.register("""custom""" , _a ) AutoFeatureExtractor.register(_a , _a ) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(_a ): AutoFeatureExtractor.register(_a , _a ) # Now that the config is registered, it can be used as any other config with the auto-API _A : List[str] = CustomFeatureExtractor.from_pretrained(_a ) with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained(_a ) _A : List[Any] = AutoFeatureExtractor.from_pretrained(_a ) self.assertIsInstance(_a , _a ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content: del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig] def a__ ( self ) -> List[Any]: class lowercase ( UpperCamelCase__ ): _a = True try: AutoConfig.register("""custom""" , _a ) AutoFeatureExtractor.register(_a , _a ) # If remote code is not set, the default is to use local _A : List[Any] = AutoFeatureExtractor.from_pretrained( """hf-internal-testing/test_dynamic_feature_extractor""" ) self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" ) self.assertTrue(feature_extractor.is_local ) # If remote code is disabled, we load the local one. _A : List[str] = AutoFeatureExtractor.from_pretrained( """hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=_a ) self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" ) self.assertTrue(feature_extractor.is_local ) # If remote is enabled, we load from the Hub _A : List[Any] = AutoFeatureExtractor.from_pretrained( """hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=_a ) self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" ) self.assertTrue(not hasattr(_a , """is_local""" ) ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content: del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]	366	from __future__ import annotations from collections.abc import Callable _snake_case = list[list[float | int]] def lowerCAmelCase_ ( snake_case_,snake_case_ ): _A : int = len(snake_case_ ) _A : Matrix = [[0 for _ in range(size + 1 )] for _ in range(snake_case_ )] _A : int _A : int _A : int _A : int _A : int _A : float for row in range(snake_case_ ): for col in range(snake_case_ ): _A : Dict = matrix[row][col] _A : List[Any] = vector[row][0] _A : List[Any] = 0 _A : Optional[Any] = 0 while row < size and col < size: # pivoting _A : Any = max((abs(augmented[rowa][col] ), rowa) for rowa in range(snake_case_,snake_case_ ) )[ 1 ] if augmented[pivot_row][col] == 0: col += 1 continue else: _A , _A : Optional[Any] = augmented[pivot_row], augmented[row] for rowa in range(row + 1,snake_case_ ): _A : str = augmented[rowa][col] / augmented[row][col] _A : List[Any] = 0 for cola in range(col + 1,size + 1 ): augmented[rowa][cola] -= augmented[row][cola] * ratio row += 1 col += 1 # back substitution for col in range(1,snake_case_ ): for row in range(snake_case_ ): _A : int = augmented[row][col] / augmented[col][col] for cola in range(snake_case_,size + 1 ): augmented[row][cola] -= augmented[col][cola] * ratio # round to get rid of numbers like 2.000000000000004 return [ [round(augmented[row][size] / augmented[row][row],10 )] for row in range(snake_case_ ) ] def lowerCAmelCase_ ( snake_case_ ): _A : int = len(snake_case_ ) _A : Matrix = [[0 for _ in range(snake_case_ )] for _ in range(snake_case_ )] _A : Matrix = [[0] for _ in range(snake_case_ )] _A : Matrix _A : int _A : int _A : int for x_val, y_val in enumerate(snake_case_ ): for col in range(snake_case_ ): _A : str = (x_val + 1) ** (size - col - 1) _A : List[str] = y_val _A : Any = solve(snake_case_,snake_case_ ) def interpolated_func(snake_case_ ) -> int: return sum( round(coeffs[x_val][0] ) * (var ** (size - x_val - 1)) for x_val in range(snake_case_ ) ) return interpolated_func def lowerCAmelCase_ ( snake_case_ ): return ( 1 - variable + variable**2 - variable**3 + variable**4 - variable**5 + variable**6 - variable**7 + variable**8 - variable**9 + variable**10 ) def lowerCAmelCase_ ( snake_case_ = question_function,snake_case_ = 10 ): _A : list[int] = [func(snake_case_ ) for x_val in range(1,order + 1 )] _A : list[Callable[[int], int]] = [ interpolate(data_points[:max_coeff] ) for max_coeff in range(1,order + 1 ) ] _A : int = 0 _A : Callable[[int], int] _A : int for poly in polynomials: _A : Optional[int] = 1 while func(snake_case_ ) == poly(snake_case_ ): x_val += 1 ret += poly(snake_case_ ) return ret if __name__ == "__main__": print(f"""{solution() = }""")	343	0
import collections import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging _lowerCamelCase : int = logging.get_logger(__name__) _lowerCamelCase : List[str] = "▁" _lowerCamelCase : Optional[int] = {"vocab_file": "prophetnet.tokenizer"} _lowerCamelCase : Optional[Any] = { "vocab_file": { "microsoft/xprophetnet-large-wiki100-cased": ( "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer" ), } } _lowerCamelCase : Optional[Any] = { "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False}, } _lowerCamelCase : Optional[Any] = { "microsoft/xprophetnet-large-wiki100-cased": 5_1_2, } def a__ ( UpperCAmelCase : int ) -> List[str]: UpperCAmelCase : int = collections.OrderedDict() with open(lowercase__ , '''r''' , encoding='''utf-8''' ) as reader: UpperCAmelCase : Optional[Any] = reader.readlines() for index, token in enumerate(lowercase__ ): UpperCAmelCase : Tuple = token.rstrip('''\n''' ) UpperCAmelCase : Optional[int] = index return vocab class __UpperCAmelCase ( __UpperCAmelCase ): UpperCamelCase = VOCAB_FILES_NAMES UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase = ["""input_ids""", """attention_mask"""] def __init__( self : Optional[Any], __A : List[str], __A : Tuple="[SEP]", __A : Union[str, Any]="[SEP]", __A : Tuple="[SEP]", __A : Union[str, Any]="[UNK]", __A : Tuple="[PAD]", __A : List[Any]="[CLS]", __A : List[str]="[MASK]", __A : Tuple = None, **__A : Optional[Any], ): UpperCAmelCase : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=lowerCamelCase__, eos_token=lowerCamelCase__, sep_token=lowerCamelCase__, unk_token=lowerCamelCase__, pad_token=lowerCamelCase__, cls_token=lowerCamelCase__, mask_token=lowerCamelCase__, sp_model_kwargs=self.sp_model_kwargs, **lowerCamelCase__, ) try: import sentencepiece as spm except ImportError: logger.warning( '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece''' ''' pip install sentencepiece''' ) raise UpperCAmelCase : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(lowerCamelCase__ ) ) UpperCAmelCase : Dict = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # put special tokens and [unused] tokens into the vocab UpperCAmelCase : Dict = {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4} for i in range(1_0 ): UpperCAmelCase : Dict = F'''[unused{i}]''' UpperCAmelCase : Optional[Any] = 5 + i # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab UpperCAmelCase : int = 1_2 UpperCAmelCase : List[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()} for k in self.fairseq_tokens_to_ids.keys(): self.unique_no_split_tokens.append(lowerCamelCase__ ) def __getstate__( self : Dict ): UpperCAmelCase : Optional[int] = self.__dict__.copy() UpperCAmelCase : Dict = None return state def __setstate__( self : List[Any], __A : Optional[Any] ): UpperCAmelCase : Union[str, Any] = d try: import sentencepiece as spm except ImportError: logger.warning( '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece''' ''' pip install sentencepiece''' ) raise # for backward compatibility if not hasattr(self, '''sp_model_kwargs''' ): UpperCAmelCase : Union[str, Any] = {} UpperCAmelCase : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def __magic_name__ ( self : List[Any], __A : Tuple, __A : str = None, __A : int = False ): if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=lowerCamelCase__, token_ids_a=lowerCamelCase__, already_has_special_tokens=lowerCamelCase__ ) if token_ids_a is None: return ([0] * len(lowerCamelCase__ )) + [1] return ([0] * len(lowerCamelCase__ )) + [1] + ([0] * len(lowerCamelCase__ )) + [1] def __magic_name__ ( self : Dict, __A : Dict, __A : Any = None ): UpperCAmelCase : Optional[int] = [self.sep_token_id] if token_ids_a is None: return len(token_ids_a + sep ) * [0] return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def __magic_name__ ( self : Optional[Any] ): return len(self.sp_model ) + self.fairseq_offset def __magic_name__ ( self : Optional[int] ): UpperCAmelCase : str = {self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __magic_name__ ( self : List[Any], __A : Optional[int] ): return self.sp_model.encode(lowerCamelCase__, out_type=lowerCamelCase__ ) def __magic_name__ ( self : Tuple, __A : List[Any] ): if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] UpperCAmelCase : List[Any] = self.sp_model.PieceToId(lowerCamelCase__ ) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def __magic_name__ ( self : Optional[Any], __A : str ): if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset ) def __magic_name__ ( self : Union[str, Any], __A : Optional[Any] ): UpperCAmelCase : Optional[Any] = ''''''.join(lowerCamelCase__ ).replace(lowerCamelCase__, ''' ''' ).strip() return out_string def __magic_name__ ( self : Any, __A : Any, __A : Any = None ): if not os.path.isdir(lowerCamelCase__ ): logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase : Union[str, Any] = os.path.join( lowerCamelCase__, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase__ ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file, lowerCamelCase__ ) elif not os.path.isfile(self.vocab_file ): with open(lowerCamelCase__, '''wb''' ) as fi: UpperCAmelCase : int = self.sp_model.serialized_model_proto() fi.write(lowerCamelCase__ ) return (out_vocab_file,) def __magic_name__ ( self : str, __A : int, __A : Optional[int] = None ): if token_ids_a is None: return token_ids_a + [self.sep_token_id] UpperCAmelCase : str = [self.sep_token_id] return token_ids_a + sep + token_ids_a + sep	336	'''simple docstring''' import heapq as hq import math from collections.abc import Iterator class A : def __init__( self , lowerCamelCase__ ) -> Optional[Any]: '''simple docstring''' lowercase__ = str(id_ ) lowercase__ = None lowercase__ = None lowercase__ = [] lowercase__ = {} # {vertex:distance} def __lt__( self , lowerCamelCase__ ) -> Union[str, Any]: '''simple docstring''' return self.key < other.key def __repr__( self ) -> Optional[Any]: '''simple docstring''' return self.id def A__ ( self , lowerCamelCase__ ) -> Dict: '''simple docstring''' self.neighbors.append(lowerCamelCase__ ) def A__ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> List[Any]: '''simple docstring''' lowercase__ = weight def _A ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ): # add the neighbors: graph[a - 1].add_neighbor(graph[b - 1] ) graph[b - 1].add_neighbor(graph[a - 1] ) # add the edges: graph[a - 1].add_edge(graph[b - 1] , lowercase__ ) graph[b - 1].add_edge(graph[a - 1] , lowercase__ ) def _A ( lowercase__ , lowercase__ ): lowercase__ = [] for u in graph: lowercase__ = math.inf lowercase__ = None lowercase__ = 0 lowercase__ = graph[:] while q: lowercase__ = min(lowercase__ ) q.remove(lowercase__ ) for v in u.neighbors: if (v in q) and (u.edges[v.id] < v.key): lowercase__ = u lowercase__ = u.edges[v.id] for i in range(1 , len(lowercase__ ) ): a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) ) return a def _A ( lowercase__ , lowercase__ ): for u in graph: lowercase__ = math.inf lowercase__ = None lowercase__ = 0 lowercase__ = list(lowercase__ ) hq.heapify(lowercase__ ) while h: lowercase__ = hq.heappop(lowercase__ ) for v in u.neighbors: if (v in h) and (u.edges[v.id] < v.key): lowercase__ = u lowercase__ = u.edges[v.id] hq.heapify(lowercase__ ) for i in range(1 , len(lowercase__ ) ): yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) def _A ( ): pass if __name__ == "__main__": import doctest doctest.testmod()	164	0
"""simple docstring""" print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))	359	import argparse import io import requests import torch from omegaconf import OmegaConf from diffusers import AutoencoderKL from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( assign_to_checkpoint, conv_attn_to_linear, create_vae_diffusers_config, renew_vae_attention_paths, renew_vae_resnet_paths, ) def _a ( a :Union[str, Any] , a :List[Any] ) -> List[Any]: a = checkpoint a = {} a = vae_state_dict['''encoder.conv_in.weight'''] a = vae_state_dict['''encoder.conv_in.bias'''] a = vae_state_dict['''encoder.conv_out.weight'''] a = vae_state_dict['''encoder.conv_out.bias'''] a = vae_state_dict['''encoder.norm_out.weight'''] a = vae_state_dict['''encoder.norm_out.bias'''] a = vae_state_dict['''decoder.conv_in.weight'''] a = vae_state_dict['''decoder.conv_in.bias'''] a = vae_state_dict['''decoder.conv_out.weight'''] a = vae_state_dict['''decoder.conv_out.bias'''] a = vae_state_dict['''decoder.norm_out.weight'''] a = vae_state_dict['''decoder.norm_out.bias'''] a = vae_state_dict['''quant_conv.weight'''] a = vae_state_dict['''quant_conv.bias'''] a = vae_state_dict['''post_quant_conv.weight'''] a = vae_state_dict['''post_quant_conv.bias'''] # Retrieves the keys for the encoder down blocks only a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} ) a = { layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(a ) } # Retrieves the keys for the decoder up blocks only a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} ) a = { layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(a ) } for i in range(a ): a = [key for key in down_blocks[i] if F"""down.{i}""" in key and F"""down.{i}.downsample""" not in key] if F"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict: a = vae_state_dict.pop( F"""encoder.down.{i}.downsample.conv.weight""" ) a = vae_state_dict.pop( F"""encoder.down.{i}.downsample.conv.bias""" ) a = renew_vae_resnet_paths(a ) a = {'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""} assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a ) a = [key for key in vae_state_dict if '''encoder.mid.block''' in key] a = 2 for i in range(1 , num_mid_res_blocks + 1 ): a = [key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key] a = renew_vae_resnet_paths(a ) a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""} assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a ) a = [key for key in vae_state_dict if '''encoder.mid.attn''' in key] a = renew_vae_attention_paths(a ) a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''} assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a ) conv_attn_to_linear(a ) for i in range(a ): a = num_up_blocks - 1 - i a = [ key for key in up_blocks[block_id] if F"""up.{block_id}""" in key and F"""up.{block_id}.upsample""" not in key ] if F"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict: a = vae_state_dict[ F"""decoder.up.{block_id}.upsample.conv.weight""" ] a = vae_state_dict[ F"""decoder.up.{block_id}.upsample.conv.bias""" ] a = renew_vae_resnet_paths(a ) a = {'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""} assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a ) a = [key for key in vae_state_dict if '''decoder.mid.block''' in key] a = 2 for i in range(1 , num_mid_res_blocks + 1 ): a = [key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key] a = renew_vae_resnet_paths(a ) a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""} assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a ) a = [key for key in vae_state_dict if '''decoder.mid.attn''' in key] a = renew_vae_attention_paths(a ) a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''} assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a ) conv_attn_to_linear(a ) return new_checkpoint def _a ( a :str , a :str , ) -> List[str]: # Only support V1 a = requests.get( ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' ) a = io.BytesIO(r.content ) a = OmegaConf.load(a ) a = 512 a = '''cuda''' if torch.cuda.is_available() else '''cpu''' if checkpoint_path.endswith('''safetensors''' ): from safetensors import safe_open a = {} with safe_open(a , framework='''pt''' , device='''cpu''' ) as f: for key in f.keys(): a = f.get_tensor(a ) else: a = torch.load(a , map_location=a )['''state_dict'''] # Convert the VAE model. a = create_vae_diffusers_config(a , image_size=a ) a = custom_convert_ldm_vae_checkpoint(a , a ) a = AutoencoderKL(**a ) vae.load_state_dict(a ) vae.save_pretrained(a ) if __name__ == "__main__": UpperCAmelCase__ = argparse.ArgumentParser() parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.") parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.") UpperCAmelCase__ = parser.parse_args() vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)	26	0
from ...configuration_utils import PretrainedConfig from ...utils import logging _UpperCAmelCase : str = logging.get_logger(__name__) _UpperCAmelCase : str = {} class lowercase ( lowercase_ ): __SCREAMING_SNAKE_CASE : Optional[int] = '''llama''' __SCREAMING_SNAKE_CASE : str = ['''past_key_values'''] def __init__( self , snake_case=3_2000 , snake_case=4096 , snake_case=1_1008 , snake_case=32 , snake_case=32 , snake_case=None , snake_case="silu" , snake_case=2048 , snake_case=0.02 , snake_case=1e-6 , snake_case=True , snake_case=0 , snake_case=1 , snake_case=2 , snake_case=1 , snake_case=False , snake_case=None , **snake_case , ): snake_case_ = vocab_size snake_case_ = max_position_embeddings snake_case_ = hidden_size snake_case_ = intermediate_size snake_case_ = num_hidden_layers snake_case_ = num_attention_heads # for backward compatibility if num_key_value_heads is None: snake_case_ = num_attention_heads snake_case_ = num_key_value_heads snake_case_ = hidden_act snake_case_ = initializer_range snake_case_ = rms_norm_eps snake_case_ = pretraining_tp snake_case_ = use_cache snake_case_ = rope_scaling self._rope_scaling_validation() super().__init__( pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , tie_word_embeddings=snake_case , **snake_case , ) def a ( self ): if self.rope_scaling is None: return if not isinstance(self.rope_scaling , snake_case ) or len(self.rope_scaling ) != 2: raise ValueError( '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, ' F'''got {self.rope_scaling}''' ) snake_case_ = self.rope_scaling.get('type' , snake_case ) snake_case_ = self.rope_scaling.get('factor' , snake_case ) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: raise ValueError( F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' ) if rope_scaling_factor is None or not isinstance(snake_case , snake_case ) or rope_scaling_factor <= 1.0: raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )	285	def __lowerCamelCase ( UpperCamelCase__ ): '''simple docstring''' if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ): raise ValueError('Length must be a positive.' ) return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2) def __lowerCamelCase ( UpperCamelCase__ ): '''simple docstring''' if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ): raise ValueError('Length must be a positive.' ) return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3) if __name__ == "__main__": import doctest doctest.testmod()	285	1
import fire from utils import calculate_rouge, save_json def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=None , **lowerCamelCase ) -> Any: UpperCamelCase_: Any = [x.strip() for x in open(lowerCamelCase ).readlines()] UpperCamelCase_: Tuple = [x.strip() for x in open(lowerCamelCase ).readlines()][: len(lowerCamelCase )] UpperCamelCase_: Optional[Any] = calculate_rouge(lowerCamelCase , lowerCamelCase , **lowerCamelCase ) if save_path is not None: save_json(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase ) return metrics # these print nicely if __name__ == "__main__": fire.Fire(calculate_rouge_path)	223	from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCamelCase_ : Optional[int] = logging.get_logger(__name__) lowerCamelCase_ : Optional[int] = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""} class _UpperCamelCase ( _A ): '''simple docstring''' __UpperCamelCase : int = """ctrl""" __UpperCamelCase : Dict = ["""past_key_values"""] __UpperCamelCase : List[str] = { """max_position_embeddings""": """n_positions""", """hidden_size""": """n_embd""", """num_attention_heads""": """n_head""", """num_hidden_layers""": """n_layer""", } def __init__( self : Dict , snake_case_ : Any=24_6534 , snake_case_ : Dict=256 , snake_case_ : str=1280 , snake_case_ : Optional[int]=8192 , snake_case_ : Union[str, Any]=48 , snake_case_ : Any=16 , snake_case_ : Optional[int]=0.1 , snake_case_ : Any=0.1 , snake_case_ : Any=1e-6 , snake_case_ : Optional[Any]=0.02 , snake_case_ : Optional[int]=True , **snake_case_ : Union[str, Any] , ): UpperCamelCase_: Union[str, Any] = vocab_size UpperCamelCase_: Union[str, Any] = n_positions UpperCamelCase_: Optional[int] = n_embd UpperCamelCase_: int = n_layer UpperCamelCase_: str = n_head UpperCamelCase_: Optional[int] = dff UpperCamelCase_: Optional[Any] = resid_pdrop UpperCamelCase_: Union[str, Any] = embd_pdrop UpperCamelCase_: List[str] = layer_norm_epsilon UpperCamelCase_: Optional[Any] = initializer_range UpperCamelCase_: Optional[Any] = use_cache super().__init__(**snake_case_ )	223	1
import json import os import unittest from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class _snake_case ( _lowercase , unittest.TestCase ): lowerCamelCase__: List[str] = OpenAIGPTTokenizer lowerCamelCase__: str = OpenAIGPTTokenizerFast lowerCamelCase__: Dict = True lowerCamelCase__: List[Any] = False def _lowerCamelCase ( self: Any ) -> Dict: super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt __UpperCAmelCase : Dict = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "w</w>", "r</w>", "t</w>", "lo", "low", "er</w>", "low</w>", "lowest</w>", "newer</w>", "wider</w>", "<unk>", ] __UpperCAmelCase : List[str] = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) ) __UpperCAmelCase : Optional[Any] = ["#version: 0.2", "l o", "lo w", "e r</w>", ""] __UpperCAmelCase : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] ) __UpperCAmelCase : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file , "w" ) as fp: fp.write(json.dumps(__lowerCamelCase ) ) with open(self.merges_file , "w" ) as fp: fp.write("\n".join(__lowerCamelCase ) ) def _lowerCamelCase ( self: Optional[int] , __lowerCamelCase: List[Any] ) -> Tuple: return "lower newer", "lower newer" def _lowerCamelCase ( self: int ) -> List[str]: __UpperCAmelCase : Optional[Any] = OpenAIGPTTokenizer(self.vocab_file , self.merges_file ) __UpperCAmelCase : Any = "lower" __UpperCAmelCase : List[Any] = ["low", "er</w>"] __UpperCAmelCase : int = tokenizer.tokenize(__lowerCamelCase ) self.assertListEqual(__lowerCamelCase , __lowerCamelCase ) __UpperCAmelCase : Tuple = tokens + ["<unk>"] __UpperCAmelCase : List[str] = [14, 15, 20] self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase ) def _lowerCamelCase ( self: Optional[Any] , __lowerCamelCase: Optional[Any]=15 ) -> str: for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): __UpperCAmelCase : Optional[int] = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase ) # Simple input __UpperCAmelCase : int = "This is a simple input" __UpperCAmelCase : str = ["This is a simple input 1", "This is a simple input 2"] __UpperCAmelCase : int = ("This is a simple input", "This is a pair") __UpperCAmelCase : int = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" ) # Simple input self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" ) # Simple input self.assertRaises( __lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , ) # Pair input self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" ) # Pair input self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" ) # Pair input self.assertRaises( __lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , ) def _lowerCamelCase ( self: Union[str, Any] ) -> Optional[Any]: pass @require_ftfy @require_spacy @require_tokenizers class _snake_case ( _lowercase ): pass	157	import os import shutil from pathlib import Path from typing import Optional, Union import numpy as np from huggingface_hub import hf_hub_download from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging if is_onnx_available(): import onnxruntime as ort _snake_case = logging.get_logger(__name__) _snake_case = { '''tensor(bool)''': np.bool_, '''tensor(int8)''': np.inta, '''tensor(uint8)''': np.uinta, '''tensor(int16)''': np.intaa, '''tensor(uint16)''': np.uintaa, '''tensor(int32)''': np.intaa, '''tensor(uint32)''': np.uintaa, '''tensor(int64)''': np.intaa, '''tensor(uint64)''': np.uintaa, '''tensor(float16)''': np.floataa, '''tensor(float)''': np.floataa, '''tensor(double)''': np.floataa, } class _snake_case : def __init__( self: Tuple , __lowerCamelCase: Tuple=None , **__lowerCamelCase: Union[str, Any] ) -> Dict: logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." ) __UpperCAmelCase : Union[str, Any] = model __UpperCAmelCase : Optional[Any] = kwargs.get("model_save_dir" , __lowerCamelCase ) __UpperCAmelCase : str = kwargs.get("latest_model_name" , __lowerCamelCase ) def __call__( self: int , **__lowerCamelCase: Optional[Any] ) -> int: __UpperCAmelCase : Optional[Any] = {k: np.array(__lowerCamelCase ) for k, v in kwargs.items()} return self.model.run(__lowerCamelCase , __lowerCamelCase ) @staticmethod def _lowerCamelCase ( __lowerCamelCase: Union[str, Path] , __lowerCamelCase: Union[str, Any]=None , __lowerCamelCase: Tuple=None ) -> List[str]: if provider is None: logger.info("No onnxruntime provider specified, using CPUExecutionProvider" ) __UpperCAmelCase : Any = "CPUExecutionProvider" return ort.InferenceSession(__lowerCamelCase , providers=[provider] , sess_options=__lowerCamelCase ) def _lowerCamelCase ( self: Dict , __lowerCamelCase: Union[str, Path] , __lowerCamelCase: Optional[str] = None , **__lowerCamelCase: Union[str, Any] ) -> Optional[Any]: __UpperCAmelCase : Tuple = file_name if file_name is not None else ONNX_WEIGHTS_NAME __UpperCAmelCase : str = self.model_save_dir.joinpath(self.latest_model_name ) __UpperCAmelCase : Any = Path(__lowerCamelCase ).joinpath(__lowerCamelCase ) try: shutil.copyfile(__lowerCamelCase , __lowerCamelCase ) except shutil.SameFileError: pass # copy external weights (for models >2GB) __UpperCAmelCase : str = self.model_save_dir.joinpath(__lowerCamelCase ) if src_path.exists(): __UpperCAmelCase : List[str] = Path(__lowerCamelCase ).joinpath(__lowerCamelCase ) try: shutil.copyfile(__lowerCamelCase , __lowerCamelCase ) except shutil.SameFileError: pass def _lowerCamelCase ( self: Any , __lowerCamelCase: Union[str, os.PathLike] , **__lowerCamelCase: Any , ) -> List[Any]: if os.path.isfile(__lowerCamelCase ): logger.error(f'''Provided path ({save_directory}) should be a directory, not a file''' ) return os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase ) # saving model weights/files self._save_pretrained(__lowerCamelCase , **__lowerCamelCase ) @classmethod def _lowerCamelCase ( cls: Optional[Any] , __lowerCamelCase: Union[str, Path] , __lowerCamelCase: Optional[Union[bool, str, None]] = None , __lowerCamelCase: Optional[Union[str, None]] = None , __lowerCamelCase: bool = False , __lowerCamelCase: Optional[str] = None , __lowerCamelCase: Optional[str] = None , __lowerCamelCase: Optional[str] = None , __lowerCamelCase: Optional["ort.SessionOptions"] = None , **__lowerCamelCase: Union[str, Any] , ) -> Optional[Any]: __UpperCAmelCase : Tuple = file_name if file_name is not None else ONNX_WEIGHTS_NAME # load model from local directory if os.path.isdir(__lowerCamelCase ): __UpperCAmelCase : Optional[int] = OnnxRuntimeModel.load_model( os.path.join(__lowerCamelCase , __lowerCamelCase ) , provider=__lowerCamelCase , sess_options=__lowerCamelCase ) __UpperCAmelCase : Union[str, Any] = Path(__lowerCamelCase ) # load model from hub else: # download model __UpperCAmelCase : Optional[Any] = hf_hub_download( repo_id=__lowerCamelCase , filename=__lowerCamelCase , use_auth_token=__lowerCamelCase , revision=__lowerCamelCase , cache_dir=__lowerCamelCase , force_download=__lowerCamelCase , ) __UpperCAmelCase : Any = Path(__lowerCamelCase ).parent __UpperCAmelCase : List[Any] = Path(__lowerCamelCase ).name __UpperCAmelCase : Dict = OnnxRuntimeModel.load_model(__lowerCamelCase , provider=__lowerCamelCase , sess_options=__lowerCamelCase ) return cls(model=__lowerCamelCase , **__lowerCamelCase ) @classmethod def _lowerCamelCase ( cls: Optional[int] , __lowerCamelCase: Union[str, Path] , __lowerCamelCase: bool = True , __lowerCamelCase: Optional[str] = None , __lowerCamelCase: Optional[str] = None , **__lowerCamelCase: Tuple , ) -> Optional[Any]: __UpperCAmelCase : int = None if len(str(__lowerCamelCase ).split("@" ) ) == 2: __UpperCAmelCase , __UpperCAmelCase : Any = model_id.split("@" ) return cls._from_pretrained( model_id=__lowerCamelCase , revision=__lowerCamelCase , cache_dir=__lowerCamelCase , force_download=__lowerCamelCase , use_auth_token=__lowerCamelCase , **__lowerCamelCase , )	157	1
"""simple docstring""" from __future__ import annotations from collections.abc import Iterable, Iterator from dataclasses import dataclass _lowercase : str = (3, 9, -1_1, 0, 7, 5, 1, -1) _lowercase : Union[str, Any] = (4, 6, 2, 0, 8, 1_0, 3, -2) @dataclass class __SCREAMING_SNAKE_CASE : '''simple docstring''' _a = 42 _a = 42 class __SCREAMING_SNAKE_CASE : '''simple docstring''' def __init__( self : str, lowerCamelCase : Iterable[int] )-> None: lowerCamelCase__ : Node | None =None for i in sorted(lowerCamelCase, reverse=lowerCamelCase ): lowerCamelCase__ : Dict =Node(lowerCamelCase, self.head ) def __iter__( self : int )-> Iterator[int]: lowerCamelCase__ : int =self.head while node: yield node.data lowerCamelCase__ : Optional[int] =node.next_node def __len__( self : Optional[int] )-> int: return sum(1 for _ in self ) def __str__( self : List[Any] )-> str: return " -> ".join([str(lowerCamelCase ) for node in self] ) def snake_case__ ( __lowerCamelCase : SortedLinkedList , __lowerCamelCase : SortedLinkedList ): """simple docstring""" return SortedLinkedList(list(__lowerCamelCase ) + list(__lowerCamelCase ) ) if __name__ == "__main__": import doctest doctest.testmod() _lowercase : Optional[int] = SortedLinkedList print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))	272	"""simple docstring""" from ...processing_utils import ProcessorMixin class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ): '''simple docstring''' _a = 'SpeechT5FeatureExtractor' _a = 'SpeechT5Tokenizer' def __init__( self : Dict, lowerCamelCase : Optional[int], lowerCamelCase : str )-> Any: super().__init__(lowerCamelCase, lowerCamelCase ) def __call__( self : Tuple, *lowerCamelCase : List[str], **lowerCamelCase : Optional[int] )-> List[str]: lowerCamelCase__ : List[Any] =kwargs.pop('''audio''', lowerCamelCase ) lowerCamelCase__ : List[str] =kwargs.pop('''text''', lowerCamelCase ) lowerCamelCase__ : int =kwargs.pop('''text_target''', lowerCamelCase ) lowerCamelCase__ : Dict =kwargs.pop('''audio_target''', lowerCamelCase ) lowerCamelCase__ : Any =kwargs.pop('''sampling_rate''', lowerCamelCase ) if audio is not None and text is not None: raise ValueError( '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?''' ) if audio_target is not None and text_target is not None: raise ValueError( '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?''' ) if audio is None and audio_target is None and text is None and text_target is None: raise ValueError( '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.''' ) if audio is not None: lowerCamelCase__ : Union[str, Any] =self.feature_extractor(lowerCamelCase, *lowerCamelCase, sampling_rate=lowerCamelCase, **lowerCamelCase ) elif text is not None: lowerCamelCase__ : List[Any] =self.tokenizer(lowerCamelCase, **lowerCamelCase ) else: lowerCamelCase__ : Any =None if audio_target is not None: lowerCamelCase__ : List[str] =self.feature_extractor(audio_target=lowerCamelCase, *lowerCamelCase, sampling_rate=lowerCamelCase, **lowerCamelCase ) lowerCamelCase__ : Tuple =targets['''input_values'''] elif text_target is not None: lowerCamelCase__ : Dict =self.tokenizer(lowerCamelCase, **lowerCamelCase ) lowerCamelCase__ : int =targets['''input_ids'''] else: lowerCamelCase__ : List[str] =None if inputs is None: return targets if targets is not None: lowerCamelCase__ : Dict =labels lowerCamelCase__ : Any =targets.get('''attention_mask''' ) if decoder_attention_mask is not None: lowerCamelCase__ : Dict =decoder_attention_mask return inputs def snake_case ( self : int, *lowerCamelCase : Optional[Any], **lowerCamelCase : Optional[int] )-> Optional[Any]: lowerCamelCase__ : List[Any] =kwargs.pop('''input_values''', lowerCamelCase ) lowerCamelCase__ : Union[str, Any] =kwargs.pop('''input_ids''', lowerCamelCase ) lowerCamelCase__ : Optional[Any] =kwargs.pop('''labels''', lowerCamelCase ) if input_values is not None and input_ids is not None: raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.''' ) if input_values is None and input_ids is None and labels is None: raise ValueError( '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.''' ) if input_values is not None: lowerCamelCase__ : List[str] =self.feature_extractor.pad(lowerCamelCase, *lowerCamelCase, **lowerCamelCase ) elif input_ids is not None: lowerCamelCase__ : Tuple =self.tokenizer.pad(lowerCamelCase, **lowerCamelCase ) else: lowerCamelCase__ : Any =None if labels is not None: if "input_ids" in labels or (isinstance(lowerCamelCase, lowerCamelCase ) and "input_ids" in labels[0]): lowerCamelCase__ : str =self.tokenizer.pad(lowerCamelCase, **lowerCamelCase ) lowerCamelCase__ : List[Any] =targets['''input_ids'''] else: lowerCamelCase__ : Any =self.feature_extractor.feature_size lowerCamelCase__ : Optional[Any] =self.feature_extractor.num_mel_bins lowerCamelCase__ : Optional[int] =self.feature_extractor.pad(lowerCamelCase, *lowerCamelCase, **lowerCamelCase ) lowerCamelCase__ : List[Any] =feature_size_hack lowerCamelCase__ : Tuple =targets['''input_values'''] else: lowerCamelCase__ : Optional[Any] =None if inputs is None: return targets if targets is not None: lowerCamelCase__ : Tuple =labels lowerCamelCase__ : Optional[int] =targets.get('''attention_mask''' ) if decoder_attention_mask is not None: lowerCamelCase__ : Optional[Any] =decoder_attention_mask return inputs def snake_case ( self : List[str], *lowerCamelCase : Union[str, Any], **lowerCamelCase : List[Any] )-> List[Any]: return self.tokenizer.batch_decode(*lowerCamelCase, **lowerCamelCase ) def snake_case ( self : List[str], *lowerCamelCase : List[Any], **lowerCamelCase : Tuple )-> int: return self.tokenizer.decode(*lowerCamelCase, **lowerCamelCase )	272	1
import os import re import warnings from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_ta import TaTokenizer else: lowerCAmelCase : List[Any] = None lowerCAmelCase : str = logging.get_logger(__name__) lowerCAmelCase : Union[str, Any] = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""} lowerCAmelCase : Optional[Any] = { """vocab_file""": { """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""", """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""", """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""", """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""", """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""", }, """tokenizer_file""": { """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""", """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""", """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""", """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""", """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""", }, } # TODO(PVP) - this should be removed in Transformers v5 lowerCAmelCase : str = { """t5-small""": 512, """t5-base""": 512, """t5-large""": 512, """t5-3b""": 512, """t5-11b""": 512, } class __lowercase ( UpperCAmelCase_ ): """simple docstring""" _UpperCAmelCase : Any = VOCAB_FILES_NAMES _UpperCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP _UpperCAmelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _UpperCAmelCase : str = ['''input_ids''', '''attention_mask'''] _UpperCAmelCase : Optional[int] = TaTokenizer _UpperCAmelCase : List[int] = [] def __init__( self : Any , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : Dict=None , lowerCAmelCase__ : int="</s>" , lowerCAmelCase__ : List[Any]="<unk>" , lowerCAmelCase__ : str="<pad>" , lowerCAmelCase__ : List[Any]=100 , lowerCAmelCase__ : List[Any]=None , **lowerCAmelCase__ : Tuple , ): # Add extra_ids to the special token list if extra_ids > 0 and additional_special_tokens is None: SCREAMING_SNAKE_CASE_: Optional[int] = [F"<extra_id_{i}>" for i in range(lowerCAmelCase__)] elif extra_ids > 0 and additional_special_tokens is not None: # Check that we have the right number of extra special tokens SCREAMING_SNAKE_CASE_: List[str] = len(set(filter(lambda lowerCAmelCase__: bool("extra_id_" in str(lowerCAmelCase__)) , lowerCAmelCase__))) if extra_tokens != extra_ids: raise ValueError( F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are" " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids" " tokens") super().__init__( lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , extra_ids=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , ) SCREAMING_SNAKE_CASE_: Dict = vocab_file SCREAMING_SNAKE_CASE_: Dict = False if not self.vocab_file else True SCREAMING_SNAKE_CASE_: List[str] = extra_ids @staticmethod def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any): if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes: SCREAMING_SNAKE_CASE_: List[str] = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path] if init_max_model_length is not None and init_max_model_length != max_model_length: return init_max_model_length elif init_max_model_length is None: warnings.warn( "This tokenizer was incorrectly instantiated with a model max length of" F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this" " behavior is kept to avoid breaking backwards compatibility when padding/encoding with" " `truncation is True`.\n- Be aware that you SHOULD NOT rely on" F" {pretrained_model_name_or_path} automatically truncating your input to" F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences" F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with" " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please" " instantiate this tokenizer with `model_max_length` set to your preferred value." , lowerCAmelCase__ , ) return max_model_length def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None): if not self.can_save_slow_tokenizer: raise ValueError( "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " "tokenizer.") if not os.path.isdir(lowerCAmelCase__): logger.error(F"Vocabulary path ({save_directory}) should be a directory") return SCREAMING_SNAKE_CASE_: Tuple = os.path.join( lowerCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]) if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__): copyfile(self.vocab_file , lowerCAmelCase__) logger.info(F"Copy vocab file to {out_vocab_file}") return (out_vocab_file,) def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None): SCREAMING_SNAKE_CASE_: Optional[int] = token_ids_a + [self.eos_token_id] if token_ids_a is None: return self.prefix_tokens + token_ids_a else: SCREAMING_SNAKE_CASE_: Optional[int] = token_ids_a + [self.eos_token_id] return self.prefix_tokens + token_ids_a + token_ids_a def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None): SCREAMING_SNAKE_CASE_: List[Any] = [self.eos_token_id] if token_ids_a is None: return len(token_ids_a + eos) * [0] return len(token_ids_a + eos + token_ids_a + eos) * [0] def _SCREAMING_SNAKE_CASE ( self : List[str]): return list( set(filter(lambda lowerCAmelCase__: bool(re.search(R"<extra_id_\d+>" , lowerCAmelCase__)) is not None , self.additional_special_tokens))) def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]): return [self.convert_tokens_to_ids(lowerCAmelCase__) for token in self.get_sentinel_tokens()]	13	'''simple docstring''' import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''} UpperCamelCase = { '''vocab_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json''' ), }, '''merges_file''': { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''', '''allenai/longformer-large-4096''': ( '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt''' ), }, } UpperCamelCase = { '''allenai/longformer-base-4096''': 4096, '''allenai/longformer-large-4096''': 4096, '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096, '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096, '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096, } @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def SCREAMING_SNAKE_CASE( ) -> Dict: A: Dict = ( list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) ) ) A: Union[str, Any] = bs[:] A: List[str] = 0 for b in range(2**8 ): if b not in bs: bs.append(__lowercase ) cs.append(2**8 + n ) n += 1 A: List[Any] = [chr(__lowercase ) for n in cs] return dict(zip(__lowercase , __lowercase ) ) def SCREAMING_SNAKE_CASE( __lowercase ) -> Optional[int]: A: Optional[Any] = set() A: Tuple = word[0] for char in word[1:]: pairs.add((prev_char, char) ) A: List[Any] = char return pairs class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : int = VOCAB_FILES_NAMES UpperCamelCase_ : int = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : int = ["""input_ids""", """attention_mask"""] def __init__( self : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str="replace" , SCREAMING_SNAKE_CASE_ : str="<s>" , SCREAMING_SNAKE_CASE_ : Any="</s>" , SCREAMING_SNAKE_CASE_ : int="</s>" , SCREAMING_SNAKE_CASE_ : List[Any]="<s>" , SCREAMING_SNAKE_CASE_ : str="<unk>" , SCREAMING_SNAKE_CASE_ : Dict="<pad>" , SCREAMING_SNAKE_CASE_ : Dict="<mask>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False , **SCREAMING_SNAKE_CASE_ : Tuple , ) -> List[str]: '''simple docstring''' A: int = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token A: Dict = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token A: int = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token A: Dict = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token A: Any = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token A: str = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token # Mask token behave like a normal word, i.e. include the space before it A: Dict = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token super().__init__( errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as vocab_handle: A: str = json.load(SCREAMING_SNAKE_CASE_ ) A: str = {v: k for k, v in self.encoder.items()} A: Union[str, Any] = errors # how to handle errors in decoding A: Optional[int] = bytes_to_unicode() A: Union[str, Any] = {v: k for k, v in self.byte_encoder.items()} with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as merges_handle: A: int = merges_handle.read().split('''\n''' )[1:-1] A: str = [tuple(merge.split() ) for merge in bpe_merges] A: Any = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) ) A: Union[str, Any] = {} A: Tuple = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions A: Dict = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' ) @property def _snake_case ( self : int ) -> List[Any]: '''simple docstring''' return len(self.encoder ) def _snake_case ( self : Optional[Any] ) -> int: '''simple docstring''' return dict(self.encoder , **self.added_tokens_encoder ) def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Optional[Any]: '''simple docstring''' if token in self.cache: return self.cache[token] A: str = tuple(SCREAMING_SNAKE_CASE_ ) A: str = get_pairs(SCREAMING_SNAKE_CASE_ ) if not pairs: return token while True: A: Dict = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('''inf''' ) ) ) if bigram not in self.bpe_ranks: break A , A: Optional[Any] = bigram A: Tuple = [] A: List[Any] = 0 while i < len(SCREAMING_SNAKE_CASE_ ): try: A: Union[str, Any] = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) A: int = j if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 A: Optional[Any] = tuple(SCREAMING_SNAKE_CASE_ ) A: Any = new_word if len(SCREAMING_SNAKE_CASE_ ) == 1: break else: A: Union[str, Any] = get_pairs(SCREAMING_SNAKE_CASE_ ) A: str = ''' '''.join(SCREAMING_SNAKE_CASE_ ) A: str = word return word def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Optional[int]: '''simple docstring''' A: Dict = [] for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ): A: Tuple = ''''''.join( self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(''' ''' ) ) return bpe_tokens def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Optional[Any]: '''simple docstring''' return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) ) def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> str: '''simple docstring''' return self.decoder.get(SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Tuple: '''simple docstring''' A: Optional[int] = ''''''.join(SCREAMING_SNAKE_CASE_ ) A: Tuple = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors ) return text def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(SCREAMING_SNAKE_CASE_ ): logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" ) return A: Union[str, Any] = os.path.join( SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) A: int = os.path.join( SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] ) with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as f: f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '''\n''' ) A: Any = 0 with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as writer: writer.write('''#version: 0.2\n''' ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ): if index != token_index: logger.warning( f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.""" ''' Please check that the tokenizer is not corrupted!''' ) A: Union[str, Any] = token_index writer.write(''' '''.join(SCREAMING_SNAKE_CASE_ ) + '''\n''' ) index += 1 return vocab_file, merge_file def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] A: int = [self.cls_token_id] A: str = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None , SCREAMING_SNAKE_CASE_ : bool = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ ) if token_ids_a is None: return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' A: Dict = [self.sep_token_id] A: Optional[Any] = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict=False , **SCREAMING_SNAKE_CASE_ : Optional[int] ) -> int: '''simple docstring''' A: Tuple = kwargs.pop('''add_prefix_space''' , self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()): A: List[Any] = ''' ''' + text return (text, kwargs)	319	0
import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging UpperCAmelCase_ : str = '▁' UpperCAmelCase_ : Dict = {'vocab_file': 'spiece.model'} UpperCAmelCase_ : str = { 'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'} } UpperCAmelCase_ : List[str] = { 'google/pegasus-xsum': 512, } UpperCAmelCase_ : str = logging.get_logger(__name__) class SCREAMING_SNAKE_CASE__ ( lowercase__ ): snake_case__ : List[Any] = VOCAB_FILES_NAMES snake_case__ : Tuple = VOCAB_FILES_NAMES snake_case__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP snake_case__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES snake_case__ : Tuple = ['''input_ids''', '''attention_mask'''] def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any]="<pad>" , SCREAMING_SNAKE_CASE__ : Tuple="</s>" , SCREAMING_SNAKE_CASE__ : str="<unk>" , SCREAMING_SNAKE_CASE__ : Optional[int]="<mask_2>" , SCREAMING_SNAKE_CASE__ : Optional[int]="<mask_1>" , SCREAMING_SNAKE_CASE__ : str=None , SCREAMING_SNAKE_CASE__ : int=1_0_3 , SCREAMING_SNAKE_CASE__ : Optional[Dict[str, Any]] = None , **SCREAMING_SNAKE_CASE__ : List[Any] , ) -> None: a_ : List[str] = offset if additional_special_tokens is not None: if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): raise TypeError( F"""additional_special_tokens should be of type {type(SCREAMING_SNAKE_CASE__ )}, but is""" F""" {type(SCREAMING_SNAKE_CASE__ )}""" ) a_ : int = ( ([mask_token_sent] + additional_special_tokens) if mask_token_sent not in additional_special_tokens and mask_token_sent is not None else additional_special_tokens ) # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken additional_special_tokens_extended += [ F"""<unk_{i}>""" for i in range(len(SCREAMING_SNAKE_CASE__ ) , self.offset - 1 ) ] if len(set(SCREAMING_SNAKE_CASE__ ) ) != len(SCREAMING_SNAKE_CASE__ ): raise ValueError( 'Please make sure that the provided additional_special_tokens do not contain an incorrectly' F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" ) a_ : Dict = additional_special_tokens_extended else: a_ : Union[str, Any] = [mask_token_sent] if mask_token_sent is not None else [] additional_special_tokens += [F"""<unk_{i}>""" for i in range(2 , self.offset )] a_ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token_sent=SCREAMING_SNAKE_CASE__ , offset=SCREAMING_SNAKE_CASE__ , additional_special_tokens=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , ) a_ : Optional[Any] = mask_token_sent a_ : Optional[int] = vocab_file a_ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(SCREAMING_SNAKE_CASE__ ) # add special tokens to encoder dict a_ : Dict[int, str] = { 0: self.pad_token, 1: self.eos_token, } if self.mask_token_sent is not None: self.encoder.update( { 2: self.mask_token_sent, 3: self.mask_token, } ) if self.offset > 0: # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102 # mask_token_sent is already added to list -> so start at 1 self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} ) a_ : Dict[str, int] = {v: k for k, v in self.encoder.items()} @property def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int: return len(self.sp_model ) + self.offset def SCREAMING_SNAKE_CASE ( self : Any ) -> Dict[str, int]: a_ : Tuple = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Tuple ) -> Tuple: a_ : int = self.__dict__.copy() a_ : Optional[Any] = None return state def __setstate__( self : int , SCREAMING_SNAKE_CASE__ : Any ) -> List[str]: a_ : Optional[int] = d # for backward compatibility if not hasattr(self , 'sp_model_kwargs' ): a_ : List[Any] = {} a_ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : str ) -> List[str]: return self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ ) def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : str ) -> int: if token in self.decoder: return self.decoder[token] elif token in self.added_tokens_decoder: return self.added_tokens_decoder[token] a_ : str = self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__ ) return sp_id + self.offset def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : int ) -> str: if index in self.encoder: return self.encoder[index] elif index in self.added_tokens_encoder: return self.added_tokens_encoder[index] else: a_ : Dict = self.sp_model.IdToPiece(index - self.offset ) return token def SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[Any]: a_ : str = [] a_ : Tuple = '' for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__ ) + token a_ : int = [] else: current_sub_tokens.append(SCREAMING_SNAKE_CASE__ ) out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__ ) return out_string.strip() def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : int=False ) -> Any: return 1 def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Dict ) -> Union[str, Any]: a_ : Union[str, Any] = set(self.all_special_ids ) # call it once instead of inside list comp all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special return [1 if x in all_special_ids else 0 for x in seq] def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : List , SCREAMING_SNAKE_CASE__ : Optional[List] = None , SCREAMING_SNAKE_CASE__ : bool = False ) -> List[int]: if already_has_special_tokens: return self._special_token_mask(SCREAMING_SNAKE_CASE__ ) elif token_ids_a is None: return self._special_token_mask(SCREAMING_SNAKE_CASE__ ) + [1] else: return self._special_token_mask(token_ids_a + token_ids_a ) + [1] def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int]=None ) -> List[int]: if token_ids_a is None: return token_ids_a + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return token_ids_a + token_ids_a + [self.eos_token_id] def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[str] = None ) -> Tuple[str]: if not os.path.isdir(SCREAMING_SNAKE_CASE__ ): logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" ) return a_ : Optional[int] = os.path.join( SCREAMING_SNAKE_CASE__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ ) elif not os.path.isfile(self.vocab_file ): with open(SCREAMING_SNAKE_CASE__ , 'wb' ) as fi: a_ : Union[str, Any] = self.sp_model.serialized_model_proto() fi.write(SCREAMING_SNAKE_CASE__ ) return (out_vocab_file,)	371	def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int: """simple docstring""" if not isinstance(__A , __A ): raise ValueError('Input must be an integer' ) if input_num <= 0: raise ValueError('Input must be positive' ) return sum( divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 ) if __name__ == "__main__": import doctest doctest.testmod()	120	0
'''simple docstring''' from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import torch from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available @dataclass class __UpperCamelCase ( lowerCAmelCase_ ): A_ = 42 try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_text_to_video_synth import TextToVideoSDPipeline from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline	27	import re from filelock import FileLock try: import nltk _snake_case : Any = True except (ImportError, ModuleNotFoundError): _snake_case : Union[str, Any] = False if NLTK_AVAILABLE: with FileLock('.lock') as lock: nltk.download('punkt', quiet=True) def a_ ( lowerCAmelCase_ : str ): re.sub('<n>', '', lowerCAmelCase_ ) # remove pegasus newline char assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)" return "\n".join(nltk.sent_tokenize(lowerCAmelCase_ ) )	284	0
import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import MobileViTImageProcessor class _A ( unittest.TestCase ): def __init__( self : Dict , _A : Union[str, Any] , _A : str=7 , _A : Any=3 , _A : Optional[int]=18 , _A : Optional[int]=30 , _A : Optional[Any]=400 , _A : Optional[Any]=True , _A : List[str]=None , _A : List[Any]=True , _A : Dict=None , _A : Optional[Any]=True , ) -> Any: """simple docstring""" lowercase : Union[str, Any] = size if size is not None else {'''shortest_edge''': 20} lowercase : Dict = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18} lowercase : List[Any] = parent lowercase : Union[str, Any] = batch_size lowercase : Union[str, Any] = num_channels lowercase : Optional[Any] = image_size lowercase : Dict = min_resolution lowercase : Tuple = max_resolution lowercase : Optional[int] = do_resize lowercase : int = size lowercase : int = do_center_crop lowercase : str = crop_size lowercase : Tuple = do_flip_channel_order def __a ( self : Any ) -> int: """simple docstring""" return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_flip_channel_order": self.do_flip_channel_order, } @require_torch @require_vision class _A ( _lowerCamelCase , unittest.TestCase ): _UpperCamelCase : Dict = MobileViTImageProcessor if is_vision_available() else None def __a ( self : Dict ) -> Tuple: """simple docstring""" lowercase : List[Any] = MobileViTImageProcessingTester(self ) @property def __a ( self : List[Any] ) -> str: """simple docstring""" return self.image_processor_tester.prepare_image_processor_dict() def __a ( self : Dict ) -> Dict: """simple docstring""" lowercase : Tuple = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(_A , '''do_resize''' ) ) self.assertTrue(hasattr(_A , '''size''' ) ) self.assertTrue(hasattr(_A , '''do_center_crop''' ) ) self.assertTrue(hasattr(_A , '''center_crop''' ) ) self.assertTrue(hasattr(_A , '''do_flip_channel_order''' ) ) def __a ( self : Union[str, Any] ) -> Dict: """simple docstring""" lowercase : List[str] = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size , {'''shortest_edge''': 20} ) self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} ) lowercase : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 ) self.assertEqual(image_processor.size , {'''shortest_edge''': 42} ) self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} ) def __a ( self : Union[str, Any] ) -> List[Any]: """simple docstring""" pass def __a ( self : int ) -> str: """simple docstring""" lowercase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict ) # create random PIL images lowercase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A ) for image in image_inputs: self.assertIsInstance(_A , Image.Image ) # Test not batched input lowercase : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size['''height'''], self.image_processor_tester.crop_size['''width'''], ) , ) # Test batched lowercase : Any = image_processing(_A , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size['''height'''], self.image_processor_tester.crop_size['''width'''], ) , ) def __a ( self : List[str] ) -> Optional[int]: """simple docstring""" lowercase : Dict = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors lowercase : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , numpify=_A ) for image in image_inputs: self.assertIsInstance(_A , np.ndarray ) # Test not batched input lowercase : int = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size['''height'''], self.image_processor_tester.crop_size['''width'''], ) , ) # Test batched lowercase : Tuple = image_processing(_A , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size['''height'''], self.image_processor_tester.crop_size['''width'''], ) , ) def __a ( self : Optional[int] ) -> Union[str, Any]: """simple docstring""" lowercase : Any = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors lowercase : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , torchify=_A ) for image in image_inputs: self.assertIsInstance(_A , torch.Tensor ) # Test not batched input lowercase : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size['''height'''], self.image_processor_tester.crop_size['''width'''], ) , ) # Test batched lowercase : Optional[Any] = image_processing(_A , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size['''height'''], self.image_processor_tester.crop_size['''width'''], ) , )	116	from string import ascii_uppercase lowerCAmelCase_ = {char: i for i, char in enumerate(ascii_uppercase)} lowerCAmelCase_ = dict(enumerate(ascii_uppercase)) def snake_case( __magic_name__ , __magic_name__ ) -> str: '''simple docstring''' lowercase : Optional[Any] = len(__magic_name__ ) lowercase : Any = 0 while True: if x == i: lowercase : Any = 0 if len(__magic_name__ ) == len(__magic_name__ ): break key += key[i] i += 1 return key def snake_case( __magic_name__ , __magic_name__ ) -> str: '''simple docstring''' lowercase : str = '''''' lowercase : Dict = 0 for letter in message: if letter == " ": cipher_text += " " else: lowercase : Dict = (dicta[letter] - dicta[key_new[i]]) % 26 i += 1 cipher_text += dicta[x] return cipher_text def snake_case( __magic_name__ , __magic_name__ ) -> str: '''simple docstring''' lowercase : Any = '''''' lowercase : str = 0 for letter in cipher_text: if letter == " ": or_txt += " " else: lowercase : Any = (dicta[letter] + dicta[key_new[i]] + 26) % 26 i += 1 or_txt += dicta[x] return or_txt def snake_case( ) -> None: '''simple docstring''' lowercase : Dict = '''THE GERMAN ATTACK''' lowercase : Dict = '''SECRET''' lowercase : Union[str, Any] = generate_key(__magic_name__ , __magic_name__ ) lowercase : List[str] = cipher_text(__magic_name__ , __magic_name__ ) print(F"""Encrypted Text = {s}""" ) print(F"""Original Text = {original_text(__magic_name__ , __magic_name__ )}""" ) if __name__ == "__main__": import doctest doctest.testmod() main()	116	1
from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) lowerCamelCase__ = { """configuration_mobilebert""": [ """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileBertConfig""", """MobileBertOnnxConfig""", ], """tokenization_mobilebert""": ["""MobileBertTokenizer"""], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCamelCase__ = ["""MobileBertTokenizerFast"""] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCamelCase__ = [ """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""", """MobileBertForMaskedLM""", """MobileBertForMultipleChoice""", """MobileBertForNextSentencePrediction""", """MobileBertForPreTraining""", """MobileBertForQuestionAnswering""", """MobileBertForSequenceClassification""", """MobileBertForTokenClassification""", """MobileBertLayer""", """MobileBertModel""", """MobileBertPreTrainedModel""", """load_tf_weights_in_mobilebert""", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCamelCase__ = [ """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""", """TFMobileBertForMaskedLM""", """TFMobileBertForMultipleChoice""", """TFMobileBertForNextSentencePrediction""", """TFMobileBertForPreTraining""", """TFMobileBertForQuestionAnswering""", """TFMobileBertForSequenceClassification""", """TFMobileBertForTokenClassification""", """TFMobileBertMainLayer""", """TFMobileBertModel""", """TFMobileBertPreTrainedModel""", ] if TYPE_CHECKING: from .configuration_mobilebert import ( MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertOnnxConfig, ) from .tokenization_mobilebert import MobileBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_mobilebert_fast import MobileBertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mobilebert import ( MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileBertForMaskedLM, MobileBertForMultipleChoice, MobileBertForNextSentencePrediction, MobileBertForPreTraining, MobileBertForQuestionAnswering, MobileBertForSequenceClassification, MobileBertForTokenClassification, MobileBertLayer, MobileBertModel, MobileBertPreTrainedModel, load_tf_weights_in_mobilebert, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_mobilebert import ( TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFMobileBertForMaskedLM, TFMobileBertForMultipleChoice, TFMobileBertForNextSentencePrediction, TFMobileBertForPreTraining, TFMobileBertForQuestionAnswering, TFMobileBertForSequenceClassification, TFMobileBertForTokenClassification, TFMobileBertMainLayer, TFMobileBertModel, TFMobileBertPreTrainedModel, ) else: import sys lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)	212	def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> list: if len(SCREAMING_SNAKE_CASE_ ) <= 1: return [tuple(SCREAMING_SNAKE_CASE_ )] lowerCAmelCase__ : Optional[Any] = [] def generate(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): if k == 1: res.append(tuple(arr[:] ) ) return generate(k - 1 , SCREAMING_SNAKE_CASE_ ) for i in range(k - 1 ): if k % 2 == 0: # k is even lowerCAmelCase__ , lowerCAmelCase__ : str = arr[k - 1], arr[i] else: # k is odd lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = arr[k - 1], arr[0] generate(k - 1 , SCREAMING_SNAKE_CASE_ ) generate(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) return res if __name__ == "__main__": lowerCamelCase__ = input("""Enter numbers separated by a comma:\n""").strip() lowerCamelCase__ = [int(item) for item in user_input.split(""",""")] print(heaps(arr))	212	1
'''simple docstring''' from random import randint from tempfile import TemporaryFile import numpy as np def _a( UpperCamelCase__ : str, UpperCamelCase__ : List[str], UpperCamelCase__ : List[Any] ): '''simple docstring''' SCREAMING_SNAKE_CASE__ : Dict =0 if start < end: SCREAMING_SNAKE_CASE__ : List[Any] =randint(UpperCamelCase__, UpperCamelCase__ ) SCREAMING_SNAKE_CASE__ : List[Any] =a[end] SCREAMING_SNAKE_CASE__ : Dict =a[pivot] SCREAMING_SNAKE_CASE__ : Union[str, Any] =temp SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =_in_place_partition(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ) count += _in_place_quick_sort(UpperCamelCase__, UpperCamelCase__, p - 1 ) count += _in_place_quick_sort(UpperCamelCase__, p + 1, UpperCamelCase__ ) return count def _a( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[int], UpperCamelCase__ : Union[str, Any] ): '''simple docstring''' SCREAMING_SNAKE_CASE__ : List[Any] =0 SCREAMING_SNAKE_CASE__ : Optional[int] =randint(UpperCamelCase__, UpperCamelCase__ ) SCREAMING_SNAKE_CASE__ : Any =a[end] SCREAMING_SNAKE_CASE__ : Tuple =a[pivot] SCREAMING_SNAKE_CASE__ : Any =temp SCREAMING_SNAKE_CASE__ : List[str] =start - 1 for index in range(UpperCamelCase__, UpperCamelCase__ ): count += 1 if a[index] < a[end]: # check if current val is less than pivot value SCREAMING_SNAKE_CASE__ : Dict =new_pivot_index + 1 SCREAMING_SNAKE_CASE__ : List[Any] =a[new_pivot_index] SCREAMING_SNAKE_CASE__ : Dict =a[index] SCREAMING_SNAKE_CASE__ : Optional[int] =temp SCREAMING_SNAKE_CASE__ : List[str] =a[new_pivot_index + 1] SCREAMING_SNAKE_CASE__ : List[Any] =a[end] SCREAMING_SNAKE_CASE__ : Optional[int] =temp return new_pivot_index + 1, count a_ = TemporaryFile() a_ = 1_0_0 # 1000 elements are to be sorted a_ , a_ = 0, 1 # mean and standard deviation a_ = np.random.normal(mu, sigma, p) np.save(outfile, X) print('The array is') print(X) outfile.seek(0) # using the same array a_ = np.load(outfile) a_ = len(M) - 1 a_ = _in_place_quick_sort(M, 0, r) print( 'No of Comparisons for 100 elements selected from a standard normal distribution' 'is :' ) print(z)	222	'''simple docstring''' import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, StableDiffusionSAGPipeline, UNetaDConditionModel, ) from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ): snake_case_ = StableDiffusionSAGPipeline snake_case_ = TEXT_TO_IMAGE_PARAMS snake_case_ = TEXT_TO_IMAGE_BATCH_PARAMS snake_case_ = TEXT_TO_IMAGE_IMAGE_PARAMS snake_case_ = TEXT_TO_IMAGE_IMAGE_PARAMS snake_case_ = False def __magic_name__ ( self : str ) -> Union[str, Any]: torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Tuple =UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , ) SCREAMING_SNAKE_CASE__ : Optional[Any] =DDIMScheduler( beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__lowercase , set_alpha_to_one=__lowercase , ) torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : List[Any] =AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : List[Any] =CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) SCREAMING_SNAKE_CASE__ : Optional[Any] =CLIPTextModel(__lowercase ) SCREAMING_SNAKE_CASE__ : List[str] =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ) SCREAMING_SNAKE_CASE__ : List[Any] ={ '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def __magic_name__ ( self : int , __lowercase : Union[str, Any] , __lowercase : Any=0 ) -> Optional[Any]: if str(__lowercase ).startswith('''mps''' ): SCREAMING_SNAKE_CASE__ : Optional[int] =torch.manual_seed(__lowercase ) else: SCREAMING_SNAKE_CASE__ : Optional[int] =torch.Generator(device=__lowercase ).manual_seed(__lowercase ) SCREAMING_SNAKE_CASE__ : Dict ={ '''prompt''': '''.''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 1.0, '''sag_scale''': 1.0, '''output_type''': '''numpy''', } return inputs def __magic_name__ ( self : int ) -> str: super().test_inference_batch_single_identical(expected_max_diff=3e-3 ) @slow @require_torch_gpu class __SCREAMING_SNAKE_CASE ( unittest.TestCase ): def __magic_name__ ( self : int ) -> Optional[int]: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def __magic_name__ ( self : int ) -> Optional[Any]: SCREAMING_SNAKE_CASE__ : Any =StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' ) SCREAMING_SNAKE_CASE__ : Optional[Any] =sag_pipe.to(__lowercase ) sag_pipe.set_progress_bar_config(disable=__lowercase ) SCREAMING_SNAKE_CASE__ : List[Any] ='''.''' SCREAMING_SNAKE_CASE__ : Tuple =torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : List[Any] =sag_pipe( [prompt] , generator=__lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' ) SCREAMING_SNAKE_CASE__ : int =output.images SCREAMING_SNAKE_CASE__ : int =image[0, -3:, -3:, -1] assert image.shape == (1, 5_12, 5_12, 3) SCREAMING_SNAKE_CASE__ : str =np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2 def __magic_name__ ( self : List[Any] ) -> Any: SCREAMING_SNAKE_CASE__ : Tuple =StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' ) SCREAMING_SNAKE_CASE__ : List[Any] =sag_pipe.to(__lowercase ) sag_pipe.set_progress_bar_config(disable=__lowercase ) SCREAMING_SNAKE_CASE__ : List[str] ='''.''' SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] =sag_pipe( [prompt] , generator=__lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' ) SCREAMING_SNAKE_CASE__ : Tuple =output.images SCREAMING_SNAKE_CASE__ : Tuple =image[0, -3:, -3:, -1] assert image.shape == (1, 5_12, 5_12, 3) SCREAMING_SNAKE_CASE__ : List[Any] =np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2 def __magic_name__ ( self : str ) -> Any: SCREAMING_SNAKE_CASE__ : Union[str, Any] =StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' ) SCREAMING_SNAKE_CASE__ : Optional[int] =sag_pipe.to(__lowercase ) sag_pipe.set_progress_bar_config(disable=__lowercase ) SCREAMING_SNAKE_CASE__ : List[Any] ='''.''' SCREAMING_SNAKE_CASE__ : Dict =torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Tuple =sag_pipe( [prompt] , width=7_68 , height=5_12 , generator=__lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' , ) SCREAMING_SNAKE_CASE__ : Any =output.images assert image.shape == (1, 5_12, 7_68, 3)	222	1
"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow if is_tf_available(): import tensorflow as tf from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM @require_tf @require_sentencepiece @require_tokenizers class _UpperCAmelCase ( unittest.TestCase): @slow def __snake_case ( self ) -> Dict: '''simple docstring''' _UpperCAmelCase : List[Any] = TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""" ) _UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained("""google/mt5-small""" ) _UpperCAmelCase : List[Any] = tokenizer("""Hello there""" , return_tensors="""tf""" ).input_ids _UpperCAmelCase : str = tokenizer("""Hi I am""" , return_tensors="""tf""" ).input_ids _UpperCAmelCase : Dict = model(_A , labels=_A ).loss _UpperCAmelCase : Tuple = -tf.math.reduce_mean(_A ).numpy() _UpperCAmelCase : str = -21.228168 self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2e-4 )	246	"""simple docstring""" import collections import gzip import os import urllib import numpy from tensorflow.python.framework import dtypes, random_seed from tensorflow.python.platform import gfile from tensorflow.python.util.deprecation import deprecated lowerCamelCase__ : str = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test''']) # CVDF mirror of http://yann.lecun.com/exdb/mnist/ lowerCamelCase__ : Union[str, Any] = '''https://storage.googleapis.com/cvdf-datasets/mnist/''' def UpperCamelCase ( _lowerCAmelCase : List[str] ) -> Optional[Any]: _UpperCAmelCase : str = numpy.dtype(numpy.uintaa ).newbyteorder(""">""" ) return numpy.frombuffer(bytestream.read(4 ), dtype=_lowerCAmelCase )[0] @deprecated(_lowerCAmelCase, """Please use tf.data to implement this functionality.""" ) def UpperCamelCase ( _lowerCAmelCase : int ) -> Optional[Any]: print("""Extracting""", f.name ) with gzip.GzipFile(fileobj=_lowerCAmelCase ) as bytestream: _UpperCAmelCase : Tuple = _readaa(_lowerCAmelCase ) if magic != 2051: raise ValueError( """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name) ) _UpperCAmelCase : Optional[int] = _readaa(_lowerCAmelCase ) _UpperCAmelCase : Union[str, Any] = _readaa(_lowerCAmelCase ) _UpperCAmelCase : List[str] = _readaa(_lowerCAmelCase ) _UpperCAmelCase : List[str] = bytestream.read(rows * cols * num_images ) _UpperCAmelCase : Optional[Any] = numpy.frombuffer(_lowerCAmelCase, dtype=numpy.uinta ) _UpperCAmelCase : Any = data.reshape(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, 1 ) return data @deprecated(_lowerCAmelCase, """Please use tf.one_hot on tensors.""" ) def UpperCamelCase ( _lowerCAmelCase : Tuple, _lowerCAmelCase : Tuple ) -> Union[str, Any]: _UpperCAmelCase : int = labels_dense.shape[0] _UpperCAmelCase : Any = numpy.arange(_lowerCAmelCase ) * num_classes _UpperCAmelCase : Tuple = numpy.zeros((num_labels, num_classes) ) _UpperCAmelCase : Dict = 1 return labels_one_hot @deprecated(_lowerCAmelCase, """Please use tf.data to implement this functionality.""" ) def UpperCamelCase ( _lowerCAmelCase : Optional[int], _lowerCAmelCase : Optional[int]=False, _lowerCAmelCase : Optional[Any]=10 ) -> Union[str, Any]: print("""Extracting""", f.name ) with gzip.GzipFile(fileobj=_lowerCAmelCase ) as bytestream: _UpperCAmelCase : Tuple = _readaa(_lowerCAmelCase ) if magic != 2049: raise ValueError( """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name) ) _UpperCAmelCase : str = _readaa(_lowerCAmelCase ) _UpperCAmelCase : Dict = bytestream.read(_lowerCAmelCase ) _UpperCAmelCase : Optional[Any] = numpy.frombuffer(_lowerCAmelCase, dtype=numpy.uinta ) if one_hot: return _dense_to_one_hot(_lowerCAmelCase, _lowerCAmelCase ) return labels class _UpperCAmelCase : @deprecated( _A , """Please use alternatives such as official/mnist/_DataSet.py""" """ from tensorflow/models.""" , ) def __init__( self , _A , _A , _A=False , _A=False , _A=dtypes.floataa , _A=True , _A=None , ) -> str: '''simple docstring''' _UpperCAmelCase , _UpperCAmelCase : int = random_seed.get_seed(_A ) # If op level seed is not set, use whatever graph level seed is returned numpy.random.seed(seeda if seed is None else seeda ) _UpperCAmelCase : Tuple = dtypes.as_dtype(_A ).base_dtype if dtype not in (dtypes.uinta, dtypes.floataa): raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype ) if fake_data: _UpperCAmelCase : Union[str, Any] = 1_00_00 _UpperCAmelCase : Union[str, Any] = one_hot else: assert ( images.shape[0] == labels.shape[0] ), f'''images.shape: {images.shape} labels.shape: {labels.shape}''' _UpperCAmelCase : Any = images.shape[0] # Convert shape from [num examples, rows, columns, depth] # to [num examples, rows*columns] (assuming depth == 1) if reshape: assert images.shape[3] == 1 _UpperCAmelCase : int = images.reshape( images.shape[0] , images.shape[1] * images.shape[2] ) if dtype == dtypes.floataa: # Convert from [0, 255] -> [0.0, 1.0]. _UpperCAmelCase : Dict = images.astype(numpy.floataa ) _UpperCAmelCase : Any = numpy.multiply(_A , 1.0 / 255.0 ) _UpperCAmelCase : Union[str, Any] = images _UpperCAmelCase : List[Any] = labels _UpperCAmelCase : Optional[Any] = 0 _UpperCAmelCase : Optional[Any] = 0 @property def __snake_case ( self ) -> Optional[int]: '''simple docstring''' return self._images @property def __snake_case ( self ) -> Any: '''simple docstring''' return self._labels @property def __snake_case ( self ) -> Union[str, Any]: '''simple docstring''' return self._num_examples @property def __snake_case ( self ) -> Optional[int]: '''simple docstring''' return self._epochs_completed def __snake_case ( self , _A , _A=False , _A=True ) -> Tuple: '''simple docstring''' if fake_data: _UpperCAmelCase : int = [1] * 7_84 _UpperCAmelCase : str = [1] + [0] * 9 if self.one_hot else 0 return ( [fake_image for _ in range(_A )], [fake_label for _ in range(_A )], ) _UpperCAmelCase : Tuple = self._index_in_epoch # Shuffle for the first epoch if self._epochs_completed == 0 and start == 0 and shuffle: _UpperCAmelCase : str = numpy.arange(self._num_examples ) numpy.random.shuffle(_A ) _UpperCAmelCase : List[Any] = self.images[perma] _UpperCAmelCase : Union[str, Any] = self.labels[perma] # Go to the next epoch if start + batch_size > self._num_examples: # Finished epoch self._epochs_completed += 1 # Get the rest examples in this epoch _UpperCAmelCase : List[Any] = self._num_examples - start _UpperCAmelCase : str = self._images[start : self._num_examples] _UpperCAmelCase : List[str] = self._labels[start : self._num_examples] # Shuffle the data if shuffle: _UpperCAmelCase : str = numpy.arange(self._num_examples ) numpy.random.shuffle(_A ) _UpperCAmelCase : Optional[int] = self.images[perm] _UpperCAmelCase : str = self.labels[perm] # Start next epoch _UpperCAmelCase : List[Any] = 0 _UpperCAmelCase : Tuple = batch_size - rest_num_examples _UpperCAmelCase : Union[str, Any] = self._index_in_epoch _UpperCAmelCase : Optional[int] = self._images[start:end] _UpperCAmelCase : str = self._labels[start:end] return ( numpy.concatenate((images_rest_part, images_new_part) , axis=0 ), numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ), ) else: self._index_in_epoch += batch_size _UpperCAmelCase : List[Any] = self._index_in_epoch return self._images[start:end], self._labels[start:end] @deprecated(_lowerCAmelCase, """Please write your own downloading logic.""" ) def UpperCamelCase ( _lowerCAmelCase : int, _lowerCAmelCase : List[Any], _lowerCAmelCase : Optional[Any] ) -> Union[str, Any]: if not gfile.Exists(_lowerCAmelCase ): gfile.MakeDirs(_lowerCAmelCase ) _UpperCAmelCase : Optional[int] = os.path.join(_lowerCAmelCase, _lowerCAmelCase ) if not gfile.Exists(_lowerCAmelCase ): urllib.request.urlretrieve(_lowerCAmelCase, _lowerCAmelCase ) # noqa: S310 with gfile.GFile(_lowerCAmelCase ) as f: _UpperCAmelCase : Optional[int] = f.size() print("""Successfully downloaded""", _lowerCAmelCase, _lowerCAmelCase, """bytes.""" ) return filepath @deprecated( _lowerCAmelCase, """Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')""" ) def UpperCamelCase ( _lowerCAmelCase : Tuple, _lowerCAmelCase : str=False, _lowerCAmelCase : List[str]=False, _lowerCAmelCase : Tuple=dtypes.floataa, _lowerCAmelCase : List[str]=True, _lowerCAmelCase : Union[str, Any]=5000, _lowerCAmelCase : Optional[Any]=None, _lowerCAmelCase : int=DEFAULT_SOURCE_URL, ) -> Optional[Any]: if fake_data: def fake(): return _DataSet( [], [], fake_data=_lowerCAmelCase, one_hot=_lowerCAmelCase, dtype=_lowerCAmelCase, seed=_lowerCAmelCase ) _UpperCAmelCase : List[Any] = fake() _UpperCAmelCase : int = fake() _UpperCAmelCase : Any = fake() return _Datasets(train=_lowerCAmelCase, validation=_lowerCAmelCase, test=_lowerCAmelCase ) if not source_url: # empty string check _UpperCAmelCase : Optional[Any] = DEFAULT_SOURCE_URL _UpperCAmelCase : Optional[int] = """train-images-idx3-ubyte.gz""" _UpperCAmelCase : int = """train-labels-idx1-ubyte.gz""" _UpperCAmelCase : Optional[Any] = """t10k-images-idx3-ubyte.gz""" _UpperCAmelCase : Tuple = """t10k-labels-idx1-ubyte.gz""" _UpperCAmelCase : Tuple = _maybe_download( _lowerCAmelCase, _lowerCAmelCase, source_url + train_images_file ) with gfile.Open(_lowerCAmelCase, """rb""" ) as f: _UpperCAmelCase : Optional[int] = _extract_images(_lowerCAmelCase ) _UpperCAmelCase : Any = _maybe_download( _lowerCAmelCase, _lowerCAmelCase, source_url + train_labels_file ) with gfile.Open(_lowerCAmelCase, """rb""" ) as f: _UpperCAmelCase : Optional[int] = _extract_labels(_lowerCAmelCase, one_hot=_lowerCAmelCase ) _UpperCAmelCase : Optional[int] = _maybe_download( _lowerCAmelCase, _lowerCAmelCase, source_url + test_images_file ) with gfile.Open(_lowerCAmelCase, """rb""" ) as f: _UpperCAmelCase : Union[str, Any] = _extract_images(_lowerCAmelCase ) _UpperCAmelCase : Optional[int] = _maybe_download( _lowerCAmelCase, _lowerCAmelCase, source_url + test_labels_file ) with gfile.Open(_lowerCAmelCase, """rb""" ) as f: _UpperCAmelCase : List[Any] = _extract_labels(_lowerCAmelCase, one_hot=_lowerCAmelCase ) if not 0 <= validation_size <= len(_lowerCAmelCase ): _UpperCAmelCase : int = ( """Validation size should be between 0 and """ f'''{len(_lowerCAmelCase )}. Received: {validation_size}.''' ) raise ValueError(_lowerCAmelCase ) _UpperCAmelCase : str = train_images[:validation_size] _UpperCAmelCase : Union[str, Any] = train_labels[:validation_size] _UpperCAmelCase : Optional[Any] = train_images[validation_size:] _UpperCAmelCase : Optional[int] = train_labels[validation_size:] _UpperCAmelCase : Optional[int] = {"""dtype""": dtype, """reshape""": reshape, """seed""": seed} _UpperCAmelCase : Tuple = _DataSet(_lowerCAmelCase, _lowerCAmelCase, **_lowerCAmelCase ) _UpperCAmelCase : Dict = _DataSet(_lowerCAmelCase, _lowerCAmelCase, **_lowerCAmelCase ) _UpperCAmelCase : List[Any] = _DataSet(_lowerCAmelCase, _lowerCAmelCase, **_lowerCAmelCase ) return _Datasets(train=_lowerCAmelCase, validation=_lowerCAmelCase, test=_lowerCAmelCase )	246	1
import pytest from datasets.parallel import ParallelBackendConfig, parallel_backend from datasets.utils.py_utils import map_nested from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows def lowerCamelCase_ ( _lowerCamelCase ): # picklable for multiprocessing return i + 1 @require_dill_gt_0_3_2 @require_joblibspark @require_not_windows def lowerCamelCase_ ( ): with parallel_backend('spark' ): assert ParallelBackendConfig.backend_name == "spark" lowerCamelCase__ : Dict = [1, 2, 3] with pytest.raises(_lowerCamelCase ): with parallel_backend('unsupported backend' ): map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=2 ) with pytest.raises(_lowerCamelCase ): with parallel_backend('unsupported backend' ): map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=-1 ) @require_dill_gt_0_3_2 @require_joblibspark @require_not_windows @pytest.mark.parametrize('num_proc' , [2, -1] ) def lowerCamelCase_ ( _lowerCamelCase ): lowerCamelCase__ : str = [1, 2] lowerCamelCase__ : Tuple = {'a': 1, 'b': 2} lowerCamelCase__ : List[Any] = {'a': [1, 2], 'b': [3, 4]} lowerCamelCase__ : Optional[int] = {'a': {'1': 1}, 'b': 2} lowerCamelCase__ : List[Any] = {'a': 1, 'b': 2, 'c': 3, 'd': 4} lowerCamelCase__ : List[str] = [2, 3] lowerCamelCase__ : List[Any] = {'a': 2, 'b': 3} lowerCamelCase__ : Optional[Any] = {'a': [2, 3], 'b': [4, 5]} lowerCamelCase__ : Any = {'a': {'1': 2}, 'b': 3} lowerCamelCase__ : List[Any] = {'a': 2, 'b': 3, 'c': 4, 'd': 5} with parallel_backend('spark' ): assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa assert map_nested(_lowerCamelCase , _lowerCamelCase , num_proc=_lowerCamelCase ) == expected_map_nested_sa	363	"""simple docstring""" from __future__ import annotations import queue class a_ : '''simple docstring''' def __init__(self, lowerCamelCase_ ): '''simple docstring''' lowerCamelCase__ : Union[str, Any] = data lowerCamelCase__ : Optional[int] = None lowerCamelCase__ : List[Any] = None def lowerCamelCase_ ( ): print('\n********Press N to stop entering at any point of time********\n' ) lowerCamelCase__ : str = input('Enter the value of the root node: ' ).strip().lower() lowerCamelCase__ : queue.Queue = queue.Queue() lowerCamelCase__ : Optional[Any] = TreeNode(int(_lowerCamelCase ) ) q.put(_lowerCamelCase ) while not q.empty(): lowerCamelCase__ : List[Any] = q.get() lowerCamelCase__ : str = f'''Enter the left node of {node_found.data}: ''' lowerCamelCase__ : Dict = input(_lowerCamelCase ).strip().lower() or 'n' if check == "n": return tree_node lowerCamelCase__ : str = TreeNode(int(_lowerCamelCase ) ) lowerCamelCase__ : Dict = left_node q.put(_lowerCamelCase ) lowerCamelCase__ : List[str] = f'''Enter the right node of {node_found.data}: ''' lowerCamelCase__ : List[str] = input(_lowerCamelCase ).strip().lower() or 'n' if check == "n": return tree_node lowerCamelCase__ : Optional[int] = TreeNode(int(_lowerCamelCase ) ) lowerCamelCase__ : Any = right_node q.put(_lowerCamelCase ) raise def lowerCamelCase_ ( _lowerCamelCase ): if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node: return print(node.data , end=',' ) pre_order(node.left ) pre_order(node.right ) def lowerCamelCase_ ( _lowerCamelCase ): if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node: return in_order(node.left ) print(node.data , end=',' ) in_order(node.right ) def lowerCamelCase_ ( _lowerCamelCase ): if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node: return post_order(node.left ) post_order(node.right ) print(node.data , end=',' ) def lowerCamelCase_ ( _lowerCamelCase ): if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node: return lowerCamelCase__ : queue.Queue = queue.Queue() q.put(_lowerCamelCase ) while not q.empty(): lowerCamelCase__ : Any = q.get() print(node_dequeued.data , end=',' ) if node_dequeued.left: q.put(node_dequeued.left ) if node_dequeued.right: q.put(node_dequeued.right ) def lowerCamelCase_ ( _lowerCamelCase ): if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node: return lowerCamelCase__ : queue.Queue = queue.Queue() q.put(_lowerCamelCase ) while not q.empty(): lowerCamelCase__ : List[Any] = [] while not q.empty(): lowerCamelCase__ : str = q.get() print(node_dequeued.data , end=',' ) if node_dequeued.left: list_.append(node_dequeued.left ) if node_dequeued.right: list_.append(node_dequeued.right ) print() for node in list_: q.put(_lowerCamelCase ) def lowerCamelCase_ ( _lowerCamelCase ): if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node: return lowerCamelCase__ : list[TreeNode] = [] lowerCamelCase__ : int = node while n or stack: while n: # start from root node, find its left child print(n.data , end=',' ) stack.append(_lowerCamelCase ) lowerCamelCase__ : Union[str, Any] = n.left # end of while means current node doesn't have left child lowerCamelCase__ : List[Any] = stack.pop() # start to traverse its right child lowerCamelCase__ : Optional[Any] = n.right def lowerCamelCase_ ( _lowerCamelCase ): if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node: return lowerCamelCase__ : list[TreeNode] = [] lowerCamelCase__ : int = node while n or stack: while n: stack.append(_lowerCamelCase ) lowerCamelCase__ : List[str] = n.left lowerCamelCase__ : Tuple = stack.pop() print(n.data , end=',' ) lowerCamelCase__ : Union[str, Any] = n.right def lowerCamelCase_ ( _lowerCamelCase ): if not isinstance(_lowerCamelCase , _lowerCamelCase ) or not node: return lowerCamelCase__ , lowerCamelCase__ : Any = [], [] lowerCamelCase__ : int = node stacka.append(_lowerCamelCase ) while stacka: # to find the reversed order of post order, store it in stack2 lowerCamelCase__ : List[str] = stacka.pop() if n.left: stacka.append(n.left ) if n.right: stacka.append(n.right ) stacka.append(_lowerCamelCase ) while stacka: # pop up from stack2 will be the post order print(stacka.pop().data , end=',' ) def lowerCamelCase_ ( _lowerCamelCase = "" , _lowerCamelCase=50 , _lowerCamelCase="*" ): if not s: return "\n" + width * char lowerCamelCase__ , lowerCamelCase__ : Dict = divmod(width - len(_lowerCamelCase ) - 2 , 2 ) return f'''{left * char} {s} {(left + extra) * char}''' if __name__ == "__main__": import doctest doctest.testmod() print(prompt("Binary Tree Traversals")) A_ : TreeNode = build_tree() print(prompt("Pre Order Traversal")) pre_order(node) print(prompt() + "\n") print(prompt("In Order Traversal")) in_order(node) print(prompt() + "\n") print(prompt("Post Order Traversal")) post_order(node) print(prompt() + "\n") print(prompt("Level Order Traversal")) level_order(node) print(prompt() + "\n") print(prompt("Actual Level Order Traversal")) level_order_actual(node) print("*" * 50 + "\n") print(prompt("Pre Order Traversal - Iteration Version")) pre_order_iter(node) print(prompt() + "\n") print(prompt("In Order Traversal - Iteration Version")) in_order_iter(node) print(prompt() + "\n") print(prompt("Post Order Traversal - Iteration Version")) post_order_iter(node) print(prompt())	316	0

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