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'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class snake_case__ ( unittest.TestCase ):
@parameterized.expand([(None,), ('foo.json',)] )
def A_ ( self : str , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dict = GenerationConfig(
do_sample=__a , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__a , config_name=__a )
__snake_case : Optional[int] = GenerationConfig.from_pretrained(__a , config_name=__a )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , __a )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , __a )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : str = AutoConfig.from_pretrained('gpt2' )
__snake_case : Optional[int] = GenerationConfig.from_model_config(__a )
__snake_case : str = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(__a , __a )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
__snake_case : Optional[int] = GenerationConfig()
__snake_case : Any = {
'max_new_tokens': 1024,
'foo': 'bar',
}
__snake_case : Optional[Any] = copy.deepcopy(__a )
__snake_case : Tuple = generation_config.update(**__a )
# update_kwargs was not modified (no side effects)
self.assertEqual(__a , __a )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(__a , {'foo': 'bar'} )
def A_ ( self : Dict ) -> str:
'''simple docstring'''
__snake_case : int = GenerationConfig()
__snake_case : Tuple = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(__a )
__snake_case : List[str] = GenerationConfig.from_pretrained(__a )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
__snake_case : List[str] = GenerationConfig.from_model_config(__a )
assert not hasattr(__a , 'foo' ) # no new kwargs should be initialized if from config
def A_ ( self : Tuple ) -> str:
'''simple docstring'''
__snake_case : List[Any] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , __a )
self.assertEqual(default_config.num_beams , 1 )
__snake_case : List[Any] = GenerationConfig(
do_sample=__a , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , __a )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__a )
__snake_case : List[str] = GenerationConfig.from_pretrained(__a , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , __a )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class snake_case__ ( unittest.TestCase ):
@classmethod
def A_ ( cls : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[str] = TOKEN
HfFolder.save_token(__a )
@classmethod
def A_ ( cls : Dict ) -> List[Any]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Optional[Any] = GenerationConfig(
do_sample=__a , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
__snake_case : str = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__a , repo_id='test-generation-config' , push_to_hub=__a , use_auth_token=self._token )
__snake_case : List[str] = GenerationConfig.from_pretrained(f'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
def A_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[int] = GenerationConfig(
do_sample=__a , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
__snake_case : Dict = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__a , repo_id='valid_org/test-generation-config-org' , push_to_hub=__a , use_auth_token=self._token )
__snake_case : Dict = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__a , getattr(__a , __a ) )
| 354 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = SMALL_MODEL_IDENTIFIER
__snake_case : str = 'pt'
__snake_case : Union[str, Any] = 'tf'
def A_ ( self : Dict , __a : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(__a )
def A_ ( self : Any , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=__a )
model_tf.save_pretrained(__a )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
__snake_case : int = FeaturesManager.determine_framework(self.test_model , __a )
self.assertEqual(__a , __a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : List[Any] = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Union[str, Any] = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a ):
__snake_case : Optional[int] = FeaturesManager.determine_framework(__a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ):
__snake_case : int = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_tf )
# Both in environment -> use PyTorch
__snake_case : Optional[Any] = MagicMock(return_value=__a )
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# Both not in environment -> raise error
__snake_case : str = MagicMock(return_value=__a )
__snake_case : List[Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
with self.assertRaises(__a ):
__snake_case : Tuple = FeaturesManager.determine_framework(self.test_model )
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case__ :
def __init__( self : str , __a : Optional[int] , __a : str=3 , __a : Optional[int]=32 , __a : Optional[Any]=3 , __a : Optional[int]=10 , __a : List[Any]=[10, 20, 30, 40] , __a : Dict=[1, 1, 2, 1] , __a : Tuple=True , __a : Union[str, Any]=True , __a : Dict="relu" , __a : Any=3 , __a : str=None , ) -> int:
'''simple docstring'''
__snake_case : str = parent
__snake_case : Union[str, Any] = batch_size
__snake_case : List[str] = image_size
__snake_case : Tuple = num_channels
__snake_case : Any = embeddings_size
__snake_case : List[Any] = hidden_sizes
__snake_case : str = depths
__snake_case : int = is_training
__snake_case : List[str] = use_labels
__snake_case : Tuple = hidden_act
__snake_case : Any = num_labels
__snake_case : List[str] = scope
__snake_case : Dict = len(__a )
def A_ ( self : int ) -> Optional[int]:
'''simple docstring'''
__snake_case : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__snake_case : str = None
if self.use_labels:
__snake_case : Optional[int] = ids_tensor([self.batch_size] , self.num_labels )
__snake_case : Tuple = self.get_config()
return config, pixel_values, labels
def A_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def A_ ( self : Optional[Any] , __a : Any , __a : Union[str, Any] , __a : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Union[str, Any] = TFRegNetModel(config=__a )
__snake_case : Union[str, Any] = model(__a , training=__a )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A_ ( self : List[str] , __a : Dict , __a : Union[str, Any] , __a : Optional[Any] ) -> Tuple:
'''simple docstring'''
__snake_case : List[str] = self.num_labels
__snake_case : List[Any] = TFRegNetForImageClassification(__a )
__snake_case : Optional[int] = model(__a , labels=__a , training=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.prepare_config_and_inputs()
__snake_case : Any = config_and_inputs
__snake_case : Any = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
A__ = (
{'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
if is_tf_available()
else {}
)
A__ = False
A__ = False
A__ = False
A__ = False
A__ = False
def A_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
__snake_case : str = TFRegNetModelTester(self )
__snake_case : Optional[Any] = ConfigTester(self , config_class=__a , has_text_modality=__a )
def A_ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
return
@unittest.skip(reason='RegNet does not use inputs_embeds' )
def A_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0 , reason='TF does not support backprop for grouped convolutions on CPU.' , )
@slow
def A_ ( self : List[str] ) -> Any:
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason='RegNet does not support input and output embeddings' )
def A_ ( self : str ) -> str:
'''simple docstring'''
pass
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case : Tuple = model_class(__a )
__snake_case : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case : Optional[int] = [*signature.parameters.keys()]
__snake_case : Any = ['pixel_values']
self.assertListEqual(arg_names[:1] , __a )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def A_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
def check_hidden_states_output(__a : List[str] , __a : List[Any] , __a : Optional[Any] ):
__snake_case : List[Any] = model_class(__a )
__snake_case : int = model(**self._prepare_for_class(__a , __a ) , training=__a )
__snake_case : Optional[int] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__snake_case : List[Any] = self.model_tester.num_stages
self.assertEqual(len(__a ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
__snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case : Optional[int] = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__snake_case : List[str] = layer_type
__snake_case : Optional[Any] = True
check_hidden_states_output(__a , __a , __a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__snake_case : str = True
check_hidden_states_output(__a , __a , __a )
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(__a : Optional[int] , __a : Tuple , __a : Union[str, Any] , __a : List[Any]={} ):
__snake_case : Optional[Any] = model(__a , return_dict=__a , **__a )
__snake_case : Tuple = model(__a , return_dict=__a , **__a ).to_tuple()
def recursive_check(__a : Tuple , __a : Optional[int] ):
if isinstance(__a , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(__a , __a ):
recursive_check(__a , __a )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(__a , __a ) ) , msg=(
'Tuple and dict output are not equal. Difference:'
f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'''
) , )
recursive_check(__a , __a )
for model_class in self.all_model_classes:
__snake_case : List[str] = model_class(__a )
__snake_case : List[Any] = self._prepare_for_class(__a , __a )
__snake_case : str = self._prepare_for_class(__a , __a )
check_equivalence(__a , __a , __a )
__snake_case : List[str] = self._prepare_for_class(__a , __a , return_labels=__a )
__snake_case : Tuple = self._prepare_for_class(__a , __a , return_labels=__a )
check_equivalence(__a , __a , __a )
__snake_case : Union[str, Any] = self._prepare_for_class(__a , __a )
__snake_case : Union[str, Any] = self._prepare_for_class(__a , __a )
check_equivalence(__a , __a , __a , {'output_hidden_states': True} )
__snake_case : Optional[Any] = self._prepare_for_class(__a , __a , return_labels=__a )
__snake_case : Any = self._prepare_for_class(__a , __a , return_labels=__a )
check_equivalence(__a , __a , __a , {'output_hidden_states': True} )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a )
@slow
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : Tuple = TFRegNetModel.from_pretrained(__a )
self.assertIsNotNone(__a )
def a_ ( ) -> Optional[int]:
__snake_case : Any = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class snake_case__ ( unittest.TestCase ):
@cached_property
def A_ ( self : List[Any] ) -> str:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[str] = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__snake_case : str = self.default_image_processor
__snake_case : Union[str, Any] = prepare_img()
__snake_case : str = image_processor(images=__a , return_tensors='tf' )
# forward pass
__snake_case : Optional[int] = model(**__a , training=__a )
# verify the logits
__snake_case : List[str] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __a )
__snake_case : List[str] = tf.constant([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , __a , atol=1e-4 )
| 355 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ProphetNetTokenizer
A__ = False
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case : Dict = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__snake_case : Any = os.path.join(self.tmpdirname , 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] ) )
def A_ ( self : int , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = 'UNwant\u00E9d,running'
__snake_case : List[str] = 'unwanted, running'
return input_text, output_text
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = self.tokenizer_class(self.vocab_file )
__snake_case : List[str] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(__a , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[int] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def A_ ( self : int ) -> Any:
'''simple docstring'''
__snake_case : int = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case : str = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = BasicTokenizer(do_lower_case=__a , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__snake_case : List[Any] = {}
for i, token in enumerate(__a ):
__snake_case : List[str] = i
__snake_case : Any = WordpieceTokenizer(vocab=__a , 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'] )
@require_torch
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__snake_case : str = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__snake_case : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors='pt' )
self.assertIsInstance(__a , __a )
__snake_case : int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
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 A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
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 A_ ( self : List[Any] ) -> int:
'''simple docstring'''
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(' ' ) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__snake_case : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : List[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 0 |
'''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_ ( _UpperCAmelCase : str ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> Any:
# Initialise PyTorch model
__snake_case : List[str] = TaConfig.from_json_file(_UpperCAmelCase )
print(f'''Building PyTorch model from configuration: {config}''' )
__snake_case : Optional[Any] = TaForConditionalGeneration(_UpperCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_ta(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
A__ : 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 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.'''
)
A__ : str = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 356 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
A__ = 42
A__ = None
A__ = None
A__ : List[str] = namedtuple('''CoinsDistribResult''', '''moves excess''')
def a_ ( _UpperCAmelCase : TreeNode | None ) -> int:
if root is None:
return 0
# Validation
def count_nodes(_UpperCAmelCase : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(_UpperCAmelCase : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_UpperCAmelCase ) != count_coins(_UpperCAmelCase ):
raise ValueError('The nodes number should be same as the number of coins' )
# Main calculation
def get_distrib(_UpperCAmelCase : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 ,1 )
__snake_case : Tuple = get_distrib(node.left )
__snake_case : List[Any] = get_distrib(node.right )
__snake_case : List[Any] = 1 - left_distrib_excess
__snake_case : Any = 1 - right_distrib_excess
__snake_case : Union[str, Any] = (
left_distrib_moves
+ right_distrib_moves
+ abs(_UpperCAmelCase )
+ abs(_UpperCAmelCase )
)
__snake_case : Optional[Any] = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_UpperCAmelCase ,_UpperCAmelCase )
return get_distrib(_UpperCAmelCase )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 357 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__snake_case : Optional[Any] = gray_code_sequence_string(_UpperCAmelCase )
#
# convert them to integers
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Optional[Any] = int(sequence[i] ,2 )
return sequence
def a_ ( _UpperCAmelCase : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__snake_case : Dict = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__snake_case : Dict = gray_code_sequence_string(bit_count - 1 )
__snake_case : Any = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__snake_case : str = '0' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__snake_case : Any = '1' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
A__ : Union[str, Any] = logging.get_logger(__name__)
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Dict ,_UpperCAmelCase : Any ) -> int:
__snake_case : List[Any] = UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase ,config=_UpperCAmelCase )
__snake_case : List[str] = downstream_dict['projector.weight']
__snake_case : int = downstream_dict['projector.bias']
__snake_case : str = downstream_dict['model.post_net.linear.weight']
__snake_case : Tuple = downstream_dict['model.post_net.linear.bias']
return model
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : List[str] ) -> str:
__snake_case : List[Any] = UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase ,config=_UpperCAmelCase )
__snake_case : Optional[Any] = downstream_dict['model.linear.weight']
__snake_case : Union[str, Any] = downstream_dict['model.linear.bias']
return model
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Optional[int] ) -> Tuple:
__snake_case : Dict = UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase ,config=_UpperCAmelCase )
__snake_case : List[Any] = downstream_dict['connector.weight']
__snake_case : int = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
__snake_case : List[str] = downstream_dict[
f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
__snake_case : int = downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
__snake_case : Union[str, Any] = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
__snake_case : Any = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
__snake_case : Union[str, Any] = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
__snake_case : Tuple = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
__snake_case : int = downstream_dict['objective.W']
return model
@torch.no_grad()
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
__snake_case : int = torch.load(_UpperCAmelCase ,map_location='cpu' )
__snake_case : Union[str, Any] = checkpoint['Downstream']
__snake_case : List[Any] = UniSpeechSatConfig.from_pretrained(_UpperCAmelCase )
__snake_case : List[Any] = WavaVecaFeatureExtractor.from_pretrained(
_UpperCAmelCase ,return_attention_mask=_UpperCAmelCase ,do_normalize=_UpperCAmelCase )
__snake_case : Any = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
__snake_case : Optional[Any] = convert_classification(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
elif arch.endswith('ForAudioFrameClassification' ):
__snake_case : Optional[int] = convert_diarization(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
elif arch.endswith('ForXVector' ):
__snake_case : Tuple = convert_xvector(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
else:
raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
__snake_case : Optional[Any] = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(_UpperCAmelCase )
hf_model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
A__ : str = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 358 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case__ ( unittest.TestCase ):
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = tempfile.mkdtemp()
# fmt: off
__snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
__snake_case : Any = os.path.join(self.tmpdirname , 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] ) )
__snake_case : List[str] = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : Optional[int] , **__a : Dict ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : int , **__a : Dict ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case : List[str] = [Image.fromarray(np.moveaxis(__a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Dict = self.get_image_processor()
__snake_case : Any = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Any = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case : Optional[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : Tuple = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = self.prepare_image_inputs()
__snake_case : List[str] = image_processor(__a , return_tensors='np' )
__snake_case : List[str] = processor(images=__a , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = 'lower newer'
__snake_case : Dict = processor(text=__a )
__snake_case : List[Any] = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : List[Any] = 'lower newer'
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(__a ):
processor()
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_image_processor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : int = processor.batch_decode(__a )
__snake_case : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Dict = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Union[str, Any] = 'lower newer'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 0 | 0 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a_ ( *_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[Union[Dict, Any]] = None ,_UpperCAmelCase : str=True ,_UpperCAmelCase : str=2 ) -> Optional[int]:
from .. import __version__
__snake_case : Union[str, Any] = take_from
__snake_case : List[str] = ()
if not isinstance(args[0] ,_UpperCAmelCase ):
__snake_case : List[str] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(_UpperCAmelCase ).base_version ) >= version.parse(_UpperCAmelCase ):
raise ValueError(
f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
f''' version {__version__} is >= {version_name}''' )
__snake_case : str = None
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(_UpperCAmelCase ),)
__snake_case : str = f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
elif hasattr(_UpperCAmelCase ,_UpperCAmelCase ):
values += (getattr(_UpperCAmelCase ,_UpperCAmelCase ),)
__snake_case : Optional[int] = f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
elif deprecated_kwargs is None:
__snake_case : Optional[Any] = f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''
if warning is not None:
__snake_case : List[str] = warning + ' ' if standard_warn else ''
warnings.warn(warning + message ,_UpperCAmelCase ,stacklevel=_UpperCAmelCase )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and len(_UpperCAmelCase ) > 0:
__snake_case : Optional[int] = inspect.getouterframes(inspect.currentframe() )[1]
__snake_case : Union[str, Any] = call_frame.filename
__snake_case : Any = call_frame.lineno
__snake_case : Tuple = call_frame.function
__snake_case : Any = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`''' )
if len(_UpperCAmelCase ) == 0:
return
elif len(_UpperCAmelCase ) == 1:
return values[0]
return values
| 359 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Tuple = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
__snake_case : Union[str, Any] = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def a_ ( ) -> Optional[Any]:
__snake_case : Any = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Tuple:
__snake_case : List[str] = 'imagenet-1k-id2label.json'
__snake_case : Dict = 10_00
__snake_case : Union[str, Any] = 'huggingface/label-files'
__snake_case : str = num_labels
__snake_case : str = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__snake_case : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[Any] = idalabel
__snake_case : str = {v: k for k, v in idalabel.items()}
__snake_case : Dict = CvtConfig(num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' ,1 )[-1][4:6] == "13":
__snake_case : Tuple = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' ,1 )[-1][4:6] == "21":
__snake_case : str = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case : Dict = [2, 2, 20]
__snake_case : Any = [3, 12, 16]
__snake_case : Tuple = [1_92, 7_68, 10_24]
__snake_case : str = CvtForImageClassification(_UpperCAmelCase )
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__snake_case : int = image_size
__snake_case : int = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
__snake_case : List[Any] = OrderedDict()
__snake_case : Union[str, Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
__snake_case : Tuple = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case : Optional[int] = list_of_state_dict + attention(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : List[str] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_8_4,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 0 | 0 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
A__ : List[str] = get_logger(__name__)
A__ : int = Path(__file__).parent / '''model_card_template.md'''
A__ : str = uuida().hex
A__ : Optional[Any] = os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
A__ : Optional[int] = os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
A__ : Any = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''
def a_ ( _UpperCAmelCase : Union[Dict, str, None] = None ) -> str:
__snake_case : List[str] = f'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f'''; torch/{_torch_version}'''
if is_flax_available():
ua += f'''; jax/{_jax_version}'''
ua += f'''; flax/{_flax_version}'''
if is_onnx_available():
ua += f'''; onnxruntime/{_onnxruntime_version}'''
# CI will set this value to True
if os.environ.get('DIFFUSERS_IS_CI' ,'' ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items() )
elif isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
ua += "; " + user_agent
return ua
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Optional[str] = None ,_UpperCAmelCase : Optional[str] = None ) -> str:
if token is None:
__snake_case : Optional[int] = HfFolder.get_token()
if organization is None:
__snake_case : Union[str, Any] = whoami(_UpperCAmelCase )['name']
return f'''{username}/{model_id}'''
else:
return f'''{organization}/{model_id}'''
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : List[Any] ) -> int:
if not is_jinja_available():
raise ValueError(
'Modelcard rendering is based on Jinja templates.'
' Please make sure to have `jinja` installed before using `create_model_card`.'
' To install it, please run `pip install Jinja2`.' )
if hasattr(_UpperCAmelCase ,'local_rank' ) and args.local_rank not in [-1, 0]:
return
__snake_case : str = args.hub_token if hasattr(_UpperCAmelCase ,'hub_token' ) else None
__snake_case : Optional[int] = get_full_repo_name(_UpperCAmelCase ,token=_UpperCAmelCase )
__snake_case : int = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language='en' ,license='apache-2.0' ,library_name='diffusers' ,tags=[] ,datasets=args.dataset_name ,metrics=[] ,) ,template_path=_UpperCAmelCase ,model_name=_UpperCAmelCase ,repo_name=_UpperCAmelCase ,dataset_name=args.dataset_name if hasattr(_UpperCAmelCase ,'dataset_name' ) else None ,learning_rate=args.learning_rate ,train_batch_size=args.train_batch_size ,eval_batch_size=args.eval_batch_size ,gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(_UpperCAmelCase ,'gradient_accumulation_steps' ) else None
) ,adam_betaa=args.adam_betaa if hasattr(_UpperCAmelCase ,'adam_beta1' ) else None ,adam_betaa=args.adam_betaa if hasattr(_UpperCAmelCase ,'adam_beta2' ) else None ,adam_weight_decay=args.adam_weight_decay if hasattr(_UpperCAmelCase ,'adam_weight_decay' ) else None ,adam_epsilon=args.adam_epsilon if hasattr(_UpperCAmelCase ,'adam_epsilon' ) else None ,lr_scheduler=args.lr_scheduler if hasattr(_UpperCAmelCase ,'lr_scheduler' ) else None ,lr_warmup_steps=args.lr_warmup_steps if hasattr(_UpperCAmelCase ,'lr_warmup_steps' ) else None ,ema_inv_gamma=args.ema_inv_gamma if hasattr(_UpperCAmelCase ,'ema_inv_gamma' ) else None ,ema_power=args.ema_power if hasattr(_UpperCAmelCase ,'ema_power' ) else None ,ema_max_decay=args.ema_max_decay if hasattr(_UpperCAmelCase ,'ema_max_decay' ) else None ,mixed_precision=args.mixed_precision ,)
__snake_case : List[Any] = os.path.join(args.output_dir ,'README.md' )
model_card.save(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : Optional[str] ,_UpperCAmelCase : Optional[str] = None ) -> Dict:
if resolved_file is None or commit_hash is not None:
return commit_hash
__snake_case : List[str] = str(Path(_UpperCAmelCase ).as_posix() )
__snake_case : List[Any] = re.search(r'snapshots/([^/]+)/' ,_UpperCAmelCase )
if search is None:
return None
__snake_case : str = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(_UpperCAmelCase ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
A__ : Optional[Any] = os.path.expanduser(
os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
A__ : List[str] = os.path.join(hf_cache_home, '''diffusers''')
def a_ ( _UpperCAmelCase : Optional[str] = None ,_UpperCAmelCase : Optional[str] = None ) -> None:
if new_cache_dir is None:
__snake_case : Tuple = DIFFUSERS_CACHE
if old_cache_dir is None:
__snake_case : str = old_diffusers_cache
__snake_case : str = Path(_UpperCAmelCase ).expanduser()
__snake_case : int = Path(_UpperCAmelCase ).expanduser()
for old_blob_path in old_cache_dir.glob('**/blobs/*' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
__snake_case : Optional[Any] = new_cache_dir / old_blob_path.relative_to(_UpperCAmelCase )
new_blob_path.parent.mkdir(parents=_UpperCAmelCase ,exist_ok=_UpperCAmelCase )
os.replace(_UpperCAmelCase ,_UpperCAmelCase )
try:
os.symlink(_UpperCAmelCase ,_UpperCAmelCase )
except OSError:
logger.warning(
'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.' )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
A__ : str = os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
A__ : List[Any] = 0
else:
with open(cache_version_file) as f:
try:
A__ : Tuple = int(f.read())
except ValueError:
A__ : List[str] = 0
if cache_version < 1:
A__ : Any = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
'''The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '''
'''existing cached models. This is a one-time operation, you can interrupt it or run it '''
'''later by calling `diffusers.utils.hub_utils.move_cache()`.'''
)
try:
move_cache()
except Exception as e:
A__ : int = '''\n'''.join(traceback.format_tb(e.__traceback__))
logger.error(
F"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
'''file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '''
'''message and we will do our best to help.'''
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, '''w''') as f:
f.write('''1''')
except Exception:
logger.warning(
F"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
'''the directory exists and can be written to.'''
)
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Optional[str] = None ) -> str:
if variant is not None:
__snake_case : List[Any] = weights_name.split('.' )
__snake_case : Dict = splits[:-1] + [variant] + splits[-1:]
__snake_case : Optional[Any] = '.'.join(_UpperCAmelCase )
return weights_name
def a_ ( _UpperCAmelCase : Dict ,*,
_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : int ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : int ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : str ,_UpperCAmelCase : List[str]=None ,) -> Optional[Any]:
__snake_case : Optional[Any] = str(_UpperCAmelCase )
if os.path.isfile(_UpperCAmelCase ):
return pretrained_model_name_or_path
elif os.path.isdir(_UpperCAmelCase ):
if os.path.isfile(os.path.join(_UpperCAmelCase ,_UpperCAmelCase ) ):
# Load from a PyTorch checkpoint
__snake_case : Tuple = os.path.join(_UpperCAmelCase ,_UpperCAmelCase )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ) ):
__snake_case : List[Any] = os.path.join(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
return model_file
else:
raise EnvironmentError(
f'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.''' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(_UpperCAmelCase ).base_version ) >= version.parse('0.20.0' )
):
try:
__snake_case : Optional[Any] = hf_hub_download(
_UpperCAmelCase ,filename=_add_variant(_UpperCAmelCase ,_UpperCAmelCase ) ,cache_dir=_UpperCAmelCase ,force_download=_UpperCAmelCase ,proxies=_UpperCAmelCase ,resume_download=_UpperCAmelCase ,local_files_only=_UpperCAmelCase ,use_auth_token=_UpperCAmelCase ,user_agent=_UpperCAmelCase ,subfolder=_UpperCAmelCase ,revision=revision or commit_hash ,)
warnings.warn(
f'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.''' ,_UpperCAmelCase ,)
return model_file
except: # noqa: E722
warnings.warn(
f'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(_UpperCAmelCase ,_UpperCAmelCase )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(_UpperCAmelCase ,_UpperCAmelCase )}\' so that the correct variant file can be added.''' ,_UpperCAmelCase ,)
try:
# 2. Load model file as usual
__snake_case : List[Any] = hf_hub_download(
_UpperCAmelCase ,filename=_UpperCAmelCase ,cache_dir=_UpperCAmelCase ,force_download=_UpperCAmelCase ,proxies=_UpperCAmelCase ,resume_download=_UpperCAmelCase ,local_files_only=_UpperCAmelCase ,use_auth_token=_UpperCAmelCase ,user_agent=_UpperCAmelCase ,subfolder=_UpperCAmelCase ,revision=revision or commit_hash ,)
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
'login`.' )
except RevisionNotFoundError:
raise EnvironmentError(
f'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
'this model name. Check the model page at '
f'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.''' )
except EntryNotFoundError:
raise EnvironmentError(
f'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.''' )
except HTTPError as err:
raise EnvironmentError(
f'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}''' )
except ValueError:
raise EnvironmentError(
f'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
f''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
f''' directory containing a file named {weights_name} or'''
' \nCheckout your internet connection or see how to run the library in'
' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.' )
except EnvironmentError:
raise EnvironmentError(
f'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
'\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
f'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
f'''containing a file named {weights_name}''' )
| 360 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A__ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( _UpperCAmelCase : Matrix ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( _UpperCAmelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(_UpperCAmelCase ):
__snake_case , __snake_case : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10 ):
if is_safe(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
__snake_case : Optional[Any] = 0
return None
def a_ ( _UpperCAmelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(_UpperCAmelCase ,end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
A__ : List[str] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 0 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : List[str] , __a : Tuple="" , __a : Any="train" ) -> Optional[Any]:
'''simple docstring'''
assert os.path.isdir(__a )
__snake_case : str = []
__snake_case : List[str] = os.listdir(__a )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
__snake_case : str = os.path.join(__a , __a )
if not os.path.isfile(__a ):
continue
self.documents.append(__a )
def __len__( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return len(self.documents )
def __getitem__( self : Tuple , __a : Tuple ) -> int:
'''simple docstring'''
__snake_case : Optional[Any] = self.documents[idx]
__snake_case : Any = document_path.split('/' )[-1]
with open(__a , encoding='utf-8' ) as source:
__snake_case : List[str] = source.read()
__snake_case : Any = process_story(__a )
return document_name, story_lines, summary_lines
def a_ ( _UpperCAmelCase : Optional[int] ) -> Tuple:
__snake_case : Union[str, Any] = list(filter(lambda _UpperCAmelCase : len(_UpperCAmelCase ) != 0 ,[line.strip() for line in raw_story.split('\n' )] ) )
# for some unknown reason some lines miss a period, add it
__snake_case : Tuple = [_add_missing_period(_UpperCAmelCase ) for line in nonempty_lines]
# gather article lines
__snake_case : Optional[Any] = []
__snake_case : str = deque(_UpperCAmelCase )
while True:
try:
__snake_case : str = lines.popleft()
if element.startswith('@highlight' ):
break
story_lines.append(_UpperCAmelCase )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
__snake_case : Optional[Any] = list(filter(lambda _UpperCAmelCase : not t.startswith('@highlight' ) ,_UpperCAmelCase ) )
return story_lines, summary_lines
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> int:
__snake_case : List[Any] = ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
if line.startswith('@highlight' ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : str ) -> int:
if len(_UpperCAmelCase ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(_UpperCAmelCase )) )
return sequence
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : List[str] ) -> List[str]:
__snake_case : Optional[Any] = torch.ones_like(_UpperCAmelCase )
__snake_case : List[Any] = sequence == pad_token_id
__snake_case : Tuple = 0
return mask
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Optional[Any] ) -> Dict:
__snake_case : Optional[int] = [tokenizer.encode(_UpperCAmelCase ) for line in story_lines]
__snake_case : Union[str, Any] = [token for sentence in story_lines_token_ids for token in sentence]
__snake_case : Any = [tokenizer.encode(_UpperCAmelCase ) for line in summary_lines]
__snake_case : str = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : str ) -> Tuple:
__snake_case : int = []
for sequence in batch:
__snake_case : Union[str, Any] = -1
__snake_case : List[Any] = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(_UpperCAmelCase )
return torch.tensor(_UpperCAmelCase )
| 361 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = KandinskyVaaPriorPipeline
A__ = ['''prompt''']
A__ = ['''prompt''', '''negative_prompt''']
A__ = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Dict ) -> List[str]:
'''simple docstring'''
return 32
@property
def A_ ( self : Any ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return self.time_input_dim
@property
def A_ ( self : str ) -> int:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return 100
@property
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Union[str, Any] = 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(__a )
@property
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Any = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
__snake_case : List[Any] = PriorTransformer(**__a )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__snake_case : Any = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__snake_case : Optional[Any] = CLIPVisionModelWithProjection(__a )
return model
@property
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = CLIPImageProcessor(
crop_size=224 , do_center_crop=__a , do_normalize=__a , do_resize=__a , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : List[str] = self.dummy_image_encoder
__snake_case : str = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : List[str] = self.dummy_image_processor
__snake_case : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample=__a , clip_sample_range=1_0.0 , )
__snake_case : str = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def A_ ( self : List[Any] , __a : Optional[Any] , __a : Tuple=0 ) -> Any:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : List[str] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : List[Any] = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def A_ ( self : str ) -> Dict:
'''simple docstring'''
__snake_case : str = 'cpu'
__snake_case : List[str] = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Optional[Any] = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : List[str] = output.image_embeds
__snake_case : str = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
__snake_case : Union[str, Any] = image[0, -10:]
__snake_case : Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__snake_case : List[Any] = np.array(
[-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = torch_device == 'cpu'
__snake_case : Dict = True
__snake_case : Union[str, Any] = False
self._test_inference_batch_single_identical(
test_max_difference=__a , relax_max_difference=__a , test_mean_pixel_difference=__a , )
@skip_mps
def A_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = torch_device == 'cpu'
__snake_case : Optional[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=__a , test_mean_pixel_difference=__a , )
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class snake_case__ :
def __init__( self : int , __a : list[tuple[float, float]] ) -> Dict:
'''simple docstring'''
__snake_case : List[Any] = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__snake_case : Union[str, Any] = len(__a ) - 1
def A_ ( self : str , __a : float ) -> list[float]:
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__snake_case : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , __a ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__a ) , 5 ) == 1
return output_values
def A_ ( self : List[Any] , __a : float ) -> tuple[float, float]:
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__snake_case : List[Any] = self.basis_function(__a )
__snake_case : List[Any] = 0.0
__snake_case : List[Any] = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def A_ ( self : Any , __a : float = 0.0_1 ) -> str:
'''simple docstring'''
from matplotlib import pyplot as plt # type: ignore
__snake_case : list[float] = [] # x coordinates of points to plot
__snake_case : list[float] = [] # y coordinates of points to plot
__snake_case : Dict = 0.0
while t <= 1:
__snake_case : Optional[Any] = self.bezier_curve_function(__a )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__snake_case : List[Any] = [i[0] for i in self.list_of_points]
__snake_case : Optional[int] = [i[1] for i in self.list_of_points]
plt.plot(
__a , __a , color='blue' , label='Curve of Degree ' + str(self.degree ) , )
plt.scatter(__a , __a , color='red' , label='Control Points' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 362 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = 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(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : Optional[Any] = 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()}""")
| 0 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Union[str, Any] = logging.get_logger(__name__)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''timm_backbone'''
def __init__( self : Optional[Any] , __a : List[str]=None , __a : int=3 , __a : Tuple=True , __a : Tuple=True , __a : Tuple=None , **__a : Tuple , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**__a )
__snake_case : Any = backbone
__snake_case : Union[str, Any] = num_channels
__snake_case : Optional[int] = features_only
__snake_case : str = use_pretrained_backbone
__snake_case : Any = True
__snake_case : Union[str, Any] = out_indices if out_indices is not None else (-1,)
| 363 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
__snake_case : int = b * b - 4 * a * c
__snake_case : Optional[int] = (-b + sqrt(_UpperCAmelCase )) / (2 * a)
__snake_case : List[Any] = (-b - sqrt(_UpperCAmelCase )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def a_ ( ) -> Optional[int]:
__snake_case : Union[str, Any] = quadratic_roots(a=5 ,b=6 ,c=1 )
print(f'''The solutions are: {solutiona} and {solutiona}''' )
if __name__ == "__main__":
main()
| 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 0 |
'''simple docstring'''
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 ViTImageProcessor
class snake_case__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , __a : str , __a : int=13 , __a : List[str]=3 , __a : int=224 , __a : str=30 , __a : Tuple=400 , __a : int=True , __a : Any=None , __a : List[str]=True , __a : Optional[Any]=[0.5, 0.5, 0.5] , __a : List[Any]=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = size if size is not None else {'height': 18, 'width': 18}
__snake_case : List[Any] = parent
__snake_case : Optional[Any] = batch_size
__snake_case : str = num_channels
__snake_case : List[Any] = image_size
__snake_case : Optional[Any] = min_resolution
__snake_case : Optional[Any] = max_resolution
__snake_case : Dict = do_resize
__snake_case : Union[str, Any] = size
__snake_case : List[Any] = do_normalize
__snake_case : List[str] = image_mean
__snake_case : Any = image_std
def A_ ( self : Optional[int] ) -> str:
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ViTImageProcessor if is_vision_available() else None
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[Any] = EfficientFormerImageProcessorTester(self )
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
return self.image_proc_tester.prepare_image_processor_dict()
def A_ ( self : Tuple ) -> int:
'''simple docstring'''
__snake_case : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'image_mean' ) )
self.assertTrue(hasattr(__a , 'image_std' ) )
self.assertTrue(hasattr(__a , 'do_normalize' ) )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
def A_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
pass
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
__snake_case : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__snake_case : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
__snake_case : int = image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
__snake_case : int = image_processor(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__snake_case : List[str] = prepare_image_inputs(self.image_proc_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
__snake_case : Optional[Any] = image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
__snake_case : Union[str, Any] = image_processor(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
def A_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
__snake_case : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__snake_case : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
__snake_case : List[str] = image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
__snake_case : str = image_processor(__a , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
| 365 |
'''simple docstring'''
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 snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = 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(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'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,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'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,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
A__ : Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_ )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Dict , **__a : Dict ) -> List[str]:
'''simple docstring'''
super().__init__(**__a )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , 'vision' )
self.check_model_type(__a )
def __call__( self : Dict , __a : Union[str, "Image.Image", List[Dict[str, Any]]] , __a : Union[str, List[str]] = None , **__a : Any , ) -> str:
'''simple docstring'''
if "text_queries" in kwargs:
__snake_case : Tuple = kwargs.pop('text_queries' )
if isinstance(__a , (str, Image.Image) ):
__snake_case : List[str] = {'image': image, 'candidate_labels': candidate_labels}
else:
__snake_case : Optional[int] = image
__snake_case : Union[str, Any] = super().__call__(__a , **__a )
return results
def A_ ( self : List[str] , **__a : int ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = {}
if "threshold" in kwargs:
__snake_case : int = kwargs['threshold']
if "top_k" in kwargs:
__snake_case : int = kwargs['top_k']
return {}, {}, postprocess_params
def A_ ( self : List[str] , __a : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = load_image(inputs['image'] )
__snake_case : Optional[int] = inputs['candidate_labels']
if isinstance(__a , __a ):
__snake_case : Dict = candidate_labels.split(',' )
__snake_case : Tuple = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(__a ):
__snake_case : str = self.tokenizer(__a , return_tensors=self.framework )
__snake_case : Tuple = self.image_processor(__a , return_tensors=self.framework )
yield {
"is_last": i == len(__a ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def A_ ( self : Optional[Any] , __a : Optional[int] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Any = model_inputs.pop('target_size' )
__snake_case : Tuple = model_inputs.pop('candidate_label' )
__snake_case : List[Any] = model_inputs.pop('is_last' )
__snake_case : Any = self.model(**__a )
__snake_case : Optional[int] = {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
return model_outputs
def A_ ( self : Union[str, Any] , __a : Optional[Any] , __a : List[Any]=0.1 , __a : Dict=None ) -> Optional[Any]:
'''simple docstring'''
__snake_case : str = []
for model_output in model_outputs:
__snake_case : Optional[Any] = model_output['candidate_label']
__snake_case : List[str] = BaseModelOutput(__a )
__snake_case : Optional[int] = self.image_processor.post_process_object_detection(
outputs=__a , threshold=__a , target_sizes=model_output['target_size'] )[0]
for index in outputs["scores"].nonzero():
__snake_case : Dict = outputs['scores'][index].item()
__snake_case : List[Any] = self._get_bounding_box(outputs['boxes'][index][0] )
__snake_case : Any = {'score': score, 'label': label, 'box': box}
results.append(__a )
__snake_case : str = sorted(__a , key=lambda __a : x["score"] , reverse=__a )
if top_k:
__snake_case : Any = results[:top_k]
return results
def A_ ( self : Optional[int] , __a : "torch.Tensor" ) -> Dict[str, int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.' )
__snake_case : Dict = box.int().tolist()
__snake_case : str = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 366 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[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 : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''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 A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = 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:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, 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:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) 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 A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 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(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class snake_case__ ( unittest.TestCase ):
def A_ ( self : List[str] ) -> int:
'''simple docstring'''
__snake_case : Dict = get_activation('swish' )
self.assertIsInstance(__a , nn.SiLU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def A_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Tuple = get_activation('silu' )
self.assertIsInstance(__a , nn.SiLU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Optional[Any] = get_activation('mish' )
self.assertIsInstance(__a , nn.Mish )
self.assertEqual(act(torch.tensor(-200 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = get_activation('gelu' )
self.assertIsInstance(__a , nn.GELU )
self.assertEqual(act(torch.tensor(-100 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 367 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
A__ : Union[str, Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : List[Any] = {
'''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'''
),
},
}
A__ : List[Any] = {
'''google/electra-small-generator''': 5_1_2,
'''google/electra-base-generator''': 5_1_2,
'''google/electra-large-generator''': 5_1_2,
'''google/electra-small-discriminator''': 5_1_2,
'''google/electra-base-discriminator''': 5_1_2,
'''google/electra-large-discriminator''': 5_1_2,
}
A__ : Optional[Any] = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_INIT_CONFIGURATION
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ElectraTokenizer
def __init__( self : int , __a : List[Any]=None , __a : int=None , __a : List[str]=True , __a : Any="[UNK]" , __a : Any="[SEP]" , __a : Union[str, Any]="[PAD]" , __a : Dict="[CLS]" , __a : List[Any]="[MASK]" , __a : str=True , __a : Optional[int]=None , **__a : Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
__snake_case : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __a ) != do_lower_case
or normalizer_state.get('strip_accents' , __a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __a ) != tokenize_chinese_chars
):
__snake_case : List[Any] = getattr(__a , normalizer_state.pop('type' ) )
__snake_case : str = do_lower_case
__snake_case : Optional[int] = strip_accents
__snake_case : Any = tokenize_chinese_chars
__snake_case : Union[str, Any] = normalizer_class(**__a )
__snake_case : Any = do_lower_case
def A_ ( self : Any , __a : List[str] , __a : Optional[Any]=None ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = [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 A_ ( self : List[Any] , __a : List[int] , __a : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
__snake_case : int = [self.sep_token_id]
__snake_case : List[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 A_ ( self : Optional[int] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Tuple = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 0 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = (PNDMScheduler,)
A__ = (('''num_inference_steps''', 50),)
def A_ ( self : Any , **__a : Any ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = {
'num_train_timesteps': 1000,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
}
config.update(**__a )
return config
def A_ ( self : List[str] , __a : Dict=0 , **__a : str ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = dict(self.forward_default_kwargs )
__snake_case : List[Any] = kwargs.pop('num_inference_steps' , __a )
__snake_case : Dict = self.dummy_sample
__snake_case : Any = 0.1 * sample
__snake_case : str = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
__snake_case : str = self.get_scheduler_config(**__a )
__snake_case : List[str] = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# copy over dummy past residuals
__snake_case : Dict = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a )
__snake_case : Optional[int] = scheduler_class.from_pretrained(__a )
new_scheduler.set_timesteps(__a )
# copy over dummy past residuals
__snake_case : Optional[int] = dummy_past_residuals[:]
__snake_case : Optional[int] = scheduler.step_prk(__a , __a , __a , **__a ).prev_sample
__snake_case : Optional[Any] = new_scheduler.step_prk(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__snake_case : Dict = scheduler.step_plms(__a , __a , __a , **__a ).prev_sample
__snake_case : Dict = new_scheduler.step_plms(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
pass
def A_ ( self : Any , __a : int=0 , **__a : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = dict(self.forward_default_kwargs )
__snake_case : Any = kwargs.pop('num_inference_steps' , __a )
__snake_case : Union[str, Any] = self.dummy_sample
__snake_case : Union[str, Any] = 0.1 * sample
__snake_case : Any = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
__snake_case : Optional[Any] = self.get_scheduler_config()
__snake_case : int = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# copy over dummy past residuals (must be after setting timesteps)
__snake_case : Tuple = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a )
__snake_case : int = scheduler_class.from_pretrained(__a )
# copy over dummy past residuals
new_scheduler.set_timesteps(__a )
# copy over dummy past residual (must be after setting timesteps)
__snake_case : str = dummy_past_residuals[:]
__snake_case : Dict = scheduler.step_prk(__a , __a , __a , **__a ).prev_sample
__snake_case : List[str] = new_scheduler.step_prk(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__snake_case : List[Any] = scheduler.step_plms(__a , __a , __a , **__a ).prev_sample
__snake_case : List[str] = new_scheduler.step_plms(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def A_ ( self : Tuple , **__a : int ) -> Dict:
'''simple docstring'''
__snake_case : List[Any] = self.scheduler_classes[0]
__snake_case : Tuple = self.get_scheduler_config(**__a )
__snake_case : Any = scheduler_class(**__a )
__snake_case : int = 10
__snake_case : List[str] = self.dummy_model()
__snake_case : Any = self.dummy_sample_deter
scheduler.set_timesteps(__a )
for i, t in enumerate(scheduler.prk_timesteps ):
__snake_case : List[str] = model(__a , __a )
__snake_case : Optional[Any] = scheduler.step_prk(__a , __a , __a ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
__snake_case : Dict = model(__a , __a )
__snake_case : List[Any] = scheduler.step_plms(__a , __a , __a ).prev_sample
return sample
def A_ ( self : int ) -> str:
'''simple docstring'''
__snake_case : int = dict(self.forward_default_kwargs )
__snake_case : Any = kwargs.pop('num_inference_steps' , __a )
for scheduler_class in self.scheduler_classes:
__snake_case : List[Any] = self.get_scheduler_config()
__snake_case : Tuple = scheduler_class(**__a )
__snake_case : List[Any] = self.dummy_sample
__snake_case : Union[str, Any] = 0.1 * sample
if num_inference_steps is not None and hasattr(__a , 'set_timesteps' ):
scheduler.set_timesteps(__a )
elif num_inference_steps is not None and not hasattr(__a , 'set_timesteps' ):
__snake_case : Dict = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__snake_case : str = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
__snake_case : Optional[Any] = dummy_past_residuals[:]
__snake_case : Optional[int] = scheduler.step_prk(__a , 0 , __a , **__a ).prev_sample
__snake_case : Union[str, Any] = scheduler.step_prk(__a , 1 , __a , **__a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
__snake_case : List[Any] = scheduler.step_plms(__a , 0 , __a , **__a ).prev_sample
__snake_case : List[str] = scheduler.step_plms(__a , 1 , __a , **__a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A_ ( self : str ) -> Optional[Any]:
'''simple docstring'''
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=__a )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=__a )
__snake_case : str = self.scheduler_classes[0]
__snake_case : List[Any] = self.get_scheduler_config(steps_offset=1 )
__snake_case : int = scheduler_class(**__a )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def A_ ( self : int ) -> List[str]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1] , [0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=__a , beta_end=__a )
def A_ ( self : Dict ) -> Any:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a )
def A_ ( self : Optional[int] ) -> int:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def A_ ( self : Tuple ) -> int:
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=__a )
def A_ ( self : List[str] ) -> Any:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=__a )
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = 27
for scheduler_class in self.scheduler_classes:
__snake_case : int = self.dummy_sample
__snake_case : str = 0.1 * sample
__snake_case : Optional[Any] = self.get_scheduler_config()
__snake_case : Union[str, Any] = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# 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] ):
__snake_case : int = scheduler.step_prk(__a , __a , __a ).prev_sample
def A_ ( self : Tuple ) -> str:
'''simple docstring'''
with self.assertRaises(__a ):
__snake_case : int = self.scheduler_classes[0]
__snake_case : int = self.get_scheduler_config()
__snake_case : List[Any] = scheduler_class(**__a )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def A_ ( self : Dict ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.full_loop()
__snake_case : int = torch.sum(torch.abs(__a ) )
__snake_case : List[str] = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 198.1318 ) < 1e-2
assert abs(result_mean.item() - 0.2_5_8_0 ) < 1e-3
def A_ ( self : List[str] ) -> int:
'''simple docstring'''
__snake_case : List[str] = self.full_loop(prediction_type='v_prediction' )
__snake_case : int = torch.sum(torch.abs(__a ) )
__snake_case : Union[str, Any] = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 67.3986 ) < 1e-2
assert abs(result_mean.item() - 0.0_8_7_8 ) < 1e-3
def A_ ( self : Dict ) -> str:
'''simple docstring'''
__snake_case : Any = self.full_loop(set_alpha_to_one=__a , beta_start=0.0_1 )
__snake_case : int = torch.sum(torch.abs(__a ) )
__snake_case : Optional[Any] = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 230.0399 ) < 1e-2
assert abs(result_mean.item() - 0.2_9_9_5 ) < 1e-3
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : str = self.full_loop(set_alpha_to_one=__a , beta_start=0.0_1 )
__snake_case : List[Any] = torch.sum(torch.abs(__a ) )
__snake_case : Any = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 186.9482 ) < 1e-2
assert abs(result_mean.item() - 0.2_4_3_4 ) < 1e-3
| 368 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
__snake_case : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class snake_case__ :
def __init__( self : List[str] ) -> None:
'''simple docstring'''
__snake_case : list[Any] = []
__snake_case : int = 0
__snake_case : int = 0
def A_ ( self : Tuple ) -> bool:
'''simple docstring'''
return self.head == self.tail
def A_ ( self : int , __a : Any ) -> None:
'''simple docstring'''
self.data.append(__a )
__snake_case : Optional[int] = self.tail + 1
def A_ ( self : Dict ) -> Any:
'''simple docstring'''
__snake_case : Any = self.data[self.head]
__snake_case : Dict = self.head + 1
return ret
def A_ ( self : List[str] ) -> int:
'''simple docstring'''
return self.tail - self.head
def A_ ( self : Union[str, Any] ) -> None:
'''simple docstring'''
print(self.data )
print('**************' )
print(self.data[self.head : self.tail] )
class snake_case__ :
def __init__( self : List[Any] , __a : Any ) -> None:
'''simple docstring'''
__snake_case : List[str] = data
__snake_case : MyNode | None = None
__snake_case : MyNode | None = None
__snake_case : int = 1
def A_ ( self : Dict ) -> Any:
'''simple docstring'''
return self.data
def A_ ( self : List[str] ) -> MyNode | None:
'''simple docstring'''
return self.left
def A_ ( self : Optional[Any] ) -> MyNode | None:
'''simple docstring'''
return self.right
def A_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.height
def A_ ( self : Tuple , __a : Any ) -> None:
'''simple docstring'''
__snake_case : Dict = data
def A_ ( self : List[str] , __a : MyNode | None ) -> None:
'''simple docstring'''
__snake_case : List[str] = node
def A_ ( self : str , __a : MyNode | None ) -> None:
'''simple docstring'''
__snake_case : int = node
def A_ ( self : Tuple , __a : int ) -> None:
'''simple docstring'''
__snake_case : Tuple = height
def a_ ( _UpperCAmelCase : MyNode | None ) -> int:
if node is None:
return 0
return node.get_height()
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> int:
if a > b:
return a
return b
def a_ ( _UpperCAmelCase : MyNode ) -> MyNode:
print('left rotation node:' ,node.get_data() )
__snake_case : Any = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(_UpperCAmelCase )
__snake_case : Optional[int] = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(_UpperCAmelCase )
__snake_case : Optional[Any] = my_max(get_height(ret.get_right() ) ,get_height(ret.get_left() ) ) + 1
ret.set_height(_UpperCAmelCase )
return ret
def a_ ( _UpperCAmelCase : MyNode ) -> MyNode:
print('right rotation node:' ,node.get_data() )
__snake_case : Dict = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(_UpperCAmelCase )
__snake_case : Union[str, Any] = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(_UpperCAmelCase )
__snake_case : List[str] = my_max(get_height(ret.get_right() ) ,get_height(ret.get_left() ) ) + 1
ret.set_height(_UpperCAmelCase )
return ret
def a_ ( _UpperCAmelCase : MyNode ) -> MyNode:
__snake_case : str = node.get_left()
assert left_child is not None
node.set_left(left_rotation(_UpperCAmelCase ) )
return right_rotation(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : MyNode ) -> MyNode:
__snake_case : int = node.get_right()
assert right_child is not None
node.set_right(right_rotation(_UpperCAmelCase ) )
return left_rotation(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : MyNode | None ,_UpperCAmelCase : Any ) -> MyNode | None:
if node is None:
return MyNode(_UpperCAmelCase )
if data < node.get_data():
node.set_left(insert_node(node.get_left() ,_UpperCAmelCase ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
__snake_case : List[str] = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
__snake_case : Any = right_rotation(_UpperCAmelCase )
else:
__snake_case : List[Any] = lr_rotation(_UpperCAmelCase )
else:
node.set_right(insert_node(node.get_right() ,_UpperCAmelCase ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
__snake_case : Dict = node.get_right()
assert right_child is not None
if data < right_child.get_data():
__snake_case : Tuple = rl_rotation(_UpperCAmelCase )
else:
__snake_case : int = left_rotation(_UpperCAmelCase )
__snake_case : Optional[Any] = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(_UpperCAmelCase )
return node
def a_ ( _UpperCAmelCase : MyNode ) -> Any:
while True:
__snake_case : int = root.get_right()
if right_child is None:
break
__snake_case : int = right_child
return root.get_data()
def a_ ( _UpperCAmelCase : MyNode ) -> Any:
while True:
__snake_case : List[Any] = root.get_left()
if left_child is None:
break
__snake_case : List[str] = left_child
return root.get_data()
def a_ ( _UpperCAmelCase : MyNode ,_UpperCAmelCase : Any ) -> MyNode | None:
__snake_case : Tuple = root.get_left()
__snake_case : int = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
__snake_case : Any = get_left_most(_UpperCAmelCase )
root.set_data(_UpperCAmelCase )
root.set_right(del_node(_UpperCAmelCase ,_UpperCAmelCase ) )
elif left_child is not None:
__snake_case : Tuple = left_child
elif right_child is not None:
__snake_case : str = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('No such data' )
return root
else:
root.set_left(del_node(_UpperCAmelCase ,_UpperCAmelCase ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(_UpperCAmelCase ,_UpperCAmelCase ) )
if get_height(_UpperCAmelCase ) - get_height(_UpperCAmelCase ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
__snake_case : List[Any] = left_rotation(_UpperCAmelCase )
else:
__snake_case : Tuple = rl_rotation(_UpperCAmelCase )
elif get_height(_UpperCAmelCase ) - get_height(_UpperCAmelCase ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
__snake_case : List[Any] = right_rotation(_UpperCAmelCase )
else:
__snake_case : Any = lr_rotation(_UpperCAmelCase )
__snake_case : List[str] = my_max(get_height(root.get_right() ) ,get_height(root.get_left() ) ) + 1
root.set_height(_UpperCAmelCase )
return root
class snake_case__ :
def __init__( self : Dict ) -> None:
'''simple docstring'''
__snake_case : MyNode | None = None
def A_ ( self : str ) -> int:
'''simple docstring'''
return get_height(self.root )
def A_ ( self : List[str] , __a : Any ) -> None:
'''simple docstring'''
print('insert:' + str(__a ) )
__snake_case : Optional[Any] = insert_node(self.root , __a )
def A_ ( self : Union[str, Any] , __a : Any ) -> None:
'''simple docstring'''
print('delete:' + str(__a ) )
if self.root is None:
print('Tree is empty!' )
return
__snake_case : Optional[Any] = del_node(self.root , __a )
def __str__( self : Optional[Any] , ) -> str: # a level traversale, gives a more intuitive look on the tree
'''simple docstring'''
__snake_case : Union[str, Any] = ''
__snake_case : List[Any] = MyQueue()
q.push(self.root )
__snake_case : Dict = self.get_height()
if layer == 0:
return output
__snake_case : Any = 0
while not q.is_empty():
__snake_case : List[str] = q.pop()
__snake_case : List[Any] = ' ' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(__a )
q.push(__a )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
__snake_case : Dict = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , __a ) - 1:
__snake_case : Union[str, Any] = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def a_ ( ) -> None:
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
A__ = AVLtree()
A__ = list(range(1_0))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 369 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A__ : Tuple = pytest.mark.integration
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__a ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
__snake_case : Dict = dset.map(
lambda __a , __a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__a , keep_in_memory=__a )
__snake_case : List[Any] = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__snake_case , __snake_case : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(__a , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
__snake_case : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : Any = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
__snake_case : Union[str, Any] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=__a )
__snake_case , __snake_case : str = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : str ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__snake_case : Dict = np.zeros(5 , dtype=np.floataa )
__snake_case : List[str] = 1
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertRaises(__a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__snake_case : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__snake_case , __snake_case : Dict = index.search_batch(__a )
self.assertRaises(__a , index.search_batch , queries[0] )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __a )
def A_ ( self : int ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__snake_case : List[str] = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__a ):
__snake_case : Dict = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Tuple = faiss.IndexFlat(5 )
__snake_case : List[Any] = FaissIndex(custom_index=__a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
import faiss
__snake_case : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
index.save(tmp_file.name )
__snake_case : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__snake_case : List[Any] = np.zeros(5 , dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : int = index.search(__a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
__snake_case : Dict = 'index.faiss'
__snake_case : Any = f'''mock://{index_name}'''
index.save(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = FaissIndex.load(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = np.zeros(5 ,dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : Tuple = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : int = Elasticsearch()
__snake_case : Dict = {'acknowledged': True}
__snake_case : List[Any] = ElasticSearchIndex(es_client=__a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__snake_case : Optional[Any] = 'foo'
__snake_case : int = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__snake_case : Dict = 'foo'
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : Optional[Any] = index.search(__a , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__snake_case : List[Any] = ['foo', 'bar', 'foobar']
__snake_case : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : Any = index.search_batch(__a )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
# batched queries with timeout
__snake_case : Tuple = ['foo', 'bar', 'foobar']
__snake_case : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : int = index.search_batch(__a , request_timeout=30 )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
| 0 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : int = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
),
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''swin2sr'''
A__ = {
'''hidden_size''': '''embed_dim''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Optional[Any] , __a : List[Any]=64 , __a : Dict=1 , __a : Dict=3 , __a : List[str]=180 , __a : Union[str, Any]=[6, 6, 6, 6, 6, 6] , __a : Any=[6, 6, 6, 6, 6, 6] , __a : int=8 , __a : int=2.0 , __a : List[str]=True , __a : str=0.0 , __a : Any=0.0 , __a : str=0.1 , __a : List[str]="gelu" , __a : str=False , __a : str=0.0_2 , __a : List[Any]=1e-5 , __a : Union[str, Any]=2 , __a : List[str]=1.0 , __a : Tuple="1conv" , __a : Dict="pixelshuffle" , **__a : Dict , ):
'''simple docstring'''
super().__init__(**__a )
__snake_case : Any = image_size
__snake_case : Union[str, Any] = patch_size
__snake_case : Tuple = num_channels
__snake_case : int = embed_dim
__snake_case : Dict = depths
__snake_case : Tuple = len(__a )
__snake_case : Union[str, Any] = num_heads
__snake_case : Optional[Any] = window_size
__snake_case : List[str] = mlp_ratio
__snake_case : int = qkv_bias
__snake_case : str = hidden_dropout_prob
__snake_case : Union[str, Any] = attention_probs_dropout_prob
__snake_case : Optional[Any] = drop_path_rate
__snake_case : str = hidden_act
__snake_case : str = use_absolute_embeddings
__snake_case : int = layer_norm_eps
__snake_case : List[Any] = initializer_range
__snake_case : int = upscale
__snake_case : int = img_range
__snake_case : Dict = resi_connection
__snake_case : Dict = upsampler
| 370 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''t5'''
A__ = ['''past_key_values''']
A__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __a : Dict=32128 , __a : Dict=512 , __a : Union[str, Any]=64 , __a : str=2048 , __a : Union[str, Any]=6 , __a : Any=None , __a : Any=8 , __a : List[Any]=32 , __a : Any=128 , __a : Tuple=0.1 , __a : str=1e-6 , __a : Dict=1.0 , __a : Tuple="relu" , __a : Dict=True , __a : Union[str, Any]=True , __a : Any=0 , __a : Dict=1 , **__a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : str = d_model
__snake_case : str = d_kv
__snake_case : List[Any] = d_ff
__snake_case : List[str] = num_layers
__snake_case : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__snake_case : Union[str, Any] = num_heads
__snake_case : Tuple = relative_attention_num_buckets
__snake_case : Optional[int] = relative_attention_max_distance
__snake_case : Optional[Any] = dropout_rate
__snake_case : str = layer_norm_epsilon
__snake_case : List[str] = initializer_factor
__snake_case : int = feed_forward_proj
__snake_case : Optional[Any] = use_cache
__snake_case : Optional[Any] = self.feed_forward_proj.split('-' )
__snake_case : Dict = act_info[-1]
__snake_case : List[str] = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 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":
__snake_case : Dict = 'gelu_new'
super().__init__(
pad_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__snake_case : Tuple = 'past_encoder_sequence + sequence'
__snake_case : Dict = {0: 'batch'}
__snake_case : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'decoder_sequence'}
__snake_case : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
return 13
| 0 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = (DEISMultistepScheduler,)
A__ = (('''num_inference_steps''', 25),)
def A_ ( self : Tuple , **__a : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : int = {
'num_train_timesteps': 1000,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
'solver_order': 2,
}
config.update(**__a )
return config
def A_ ( self : List[str] , __a : List[str]=0 , **__a : List[str] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = dict(self.forward_default_kwargs )
__snake_case : int = kwargs.pop('num_inference_steps' , __a )
__snake_case : List[Any] = self.dummy_sample
__snake_case : int = 0.1 * sample
__snake_case : Any = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
__snake_case : Optional[Any] = self.get_scheduler_config(**__a )
__snake_case : Optional[Any] = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# copy over dummy past residuals
__snake_case : int = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a )
__snake_case : Dict = scheduler_class.from_pretrained(__a )
new_scheduler.set_timesteps(__a )
# copy over dummy past residuals
__snake_case : str = dummy_past_residuals[: new_scheduler.config.solver_order]
__snake_case : Any = sample, sample
for t in range(__a , time_step + scheduler.config.solver_order + 1 ):
__snake_case : List[str] = scheduler.step(__a , __a , __a , **__a ).prev_sample
__snake_case : Optional[Any] = new_scheduler.step(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
pass
def A_ ( self : Tuple , __a : List[str]=0 , **__a : List[str] ) -> str:
'''simple docstring'''
__snake_case : List[str] = dict(self.forward_default_kwargs )
__snake_case : int = kwargs.pop('num_inference_steps' , __a )
__snake_case : Tuple = self.dummy_sample
__snake_case : List[Any] = 0.1 * sample
__snake_case : Dict = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
__snake_case : Optional[Any] = self.get_scheduler_config()
__snake_case : Optional[Any] = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# copy over dummy past residuals (must be after setting timesteps)
__snake_case : List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a )
__snake_case : Optional[int] = scheduler_class.from_pretrained(__a )
# copy over dummy past residuals
new_scheduler.set_timesteps(__a )
# copy over dummy past residual (must be after setting timesteps)
__snake_case : Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
__snake_case : Any = scheduler.step(__a , __a , __a , **__a ).prev_sample
__snake_case : str = new_scheduler.step(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def A_ ( self : List[str] , __a : List[Any]=None , **__a : List[str] ) -> Any:
'''simple docstring'''
if scheduler is None:
__snake_case : Any = self.scheduler_classes[0]
__snake_case : Optional[int] = self.get_scheduler_config(**__a )
__snake_case : List[Any] = scheduler_class(**__a )
__snake_case : int = self.scheduler_classes[0]
__snake_case : int = self.get_scheduler_config(**__a )
__snake_case : List[Any] = scheduler_class(**__a )
__snake_case : Any = 10
__snake_case : List[str] = self.dummy_model()
__snake_case : List[Any] = self.dummy_sample_deter
scheduler.set_timesteps(__a )
for i, t in enumerate(scheduler.timesteps ):
__snake_case : str = model(__a , __a )
__snake_case : Optional[Any] = scheduler.step(__a , __a , __a ).prev_sample
return sample
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Tuple = dict(self.forward_default_kwargs )
__snake_case : List[Any] = kwargs.pop('num_inference_steps' , __a )
for scheduler_class in self.scheduler_classes:
__snake_case : Any = self.get_scheduler_config()
__snake_case : Tuple = scheduler_class(**__a )
__snake_case : Tuple = self.dummy_sample
__snake_case : Tuple = 0.1 * sample
if num_inference_steps is not None and hasattr(__a , 'set_timesteps' ):
scheduler.set_timesteps(__a )
elif num_inference_steps is not None and not hasattr(__a , 'set_timesteps' ):
__snake_case : Dict = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__snake_case : int = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
__snake_case : Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
__snake_case : Union[str, Any] = scheduler.timesteps[5]
__snake_case : int = scheduler.timesteps[6]
__snake_case : List[Any] = scheduler.step(__a , __a , __a , **__a ).prev_sample
__snake_case : Tuple = scheduler.step(__a , __a , __a , **__a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : List[str] = DEISMultistepScheduler(**self.get_scheduler_config() )
__snake_case : List[str] = self.full_loop(scheduler=__a )
__snake_case : Optional[int] = torch.mean(torch.abs(__a ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
__snake_case : str = DPMSolverSinglestepScheduler.from_config(scheduler.config )
__snake_case : Union[str, Any] = DPMSolverMultistepScheduler.from_config(scheduler.config )
__snake_case : Union[str, Any] = UniPCMultistepScheduler.from_config(scheduler.config )
__snake_case : Optional[Any] = DEISMultistepScheduler.from_config(scheduler.config )
__snake_case : List[Any] = self.full_loop(scheduler=__a )
__snake_case : int = torch.mean(torch.abs(__a ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__a )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
self.check_over_configs(thresholding=__a )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , algorithm_type='deis' , solver_order=__a , solver_type=__a , )
def A_ ( self : List[str] ) -> Any:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__a , solver_type=__a , prediction_type=__a , algorithm_type=__a , )
__snake_case : Union[str, Any] = self.full_loop(
solver_order=__a , solver_type=__a , prediction_type=__a , algorithm_type=__a , )
assert not torch.isnan(__a ).any(), "Samples have nan numbers"
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
self.check_over_configs(lower_order_final=__a )
self.check_over_configs(lower_order_final=__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__a , time_step=0 )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Any = self.full_loop()
__snake_case : List[Any] = torch.mean(torch.abs(__a ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
def A_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__snake_case : int = self.full_loop(prediction_type='v_prediction' )
__snake_case : Any = torch.mean(torch.abs(__a ) )
assert abs(result_mean.item() - 0.0_9_1 ) < 1e-3
def A_ ( self : Tuple ) -> int:
'''simple docstring'''
__snake_case : int = self.scheduler_classes[0]
__snake_case : Dict = self.get_scheduler_config(thresholding=__a , dynamic_thresholding_ratio=0 )
__snake_case : List[str] = scheduler_class(**__a )
__snake_case : Dict = 10
__snake_case : Optional[int] = self.dummy_model()
__snake_case : Dict = self.dummy_sample_deter.half()
scheduler.set_timesteps(__a )
for i, t in enumerate(scheduler.timesteps ):
__snake_case : List[Any] = model(__a , __a )
__snake_case : List[str] = scheduler.step(__a , __a , __a ).prev_sample
assert sample.dtype == torch.floataa
| 371 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''past_key_values''']
def __init__( self : Any , __a : List[str]=32000 , __a : Union[str, Any]=4096 , __a : Optional[Any]=11008 , __a : Any=32 , __a : str=32 , __a : Optional[int]=None , __a : Dict="silu" , __a : Dict=2048 , __a : List[str]=0.0_2 , __a : Union[str, Any]=1e-6 , __a : Dict=True , __a : List[str]=0 , __a : Tuple=1 , __a : Tuple=2 , __a : Optional[Any]=1 , __a : Any=False , __a : Tuple=None , **__a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = vocab_size
__snake_case : List[str] = max_position_embeddings
__snake_case : List[Any] = hidden_size
__snake_case : Union[str, Any] = intermediate_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : List[Any] = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[Any] = num_key_value_heads
__snake_case : int = hidden_act
__snake_case : Any = initializer_range
__snake_case : Any = rms_norm_eps
__snake_case : Union[str, Any] = pretraining_tp
__snake_case : Optional[int] = use_cache
__snake_case : Any = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , tie_word_embeddings=__a , **__a , )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __a ) 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 : Optional[Any] = self.rope_scaling.get('type' , __a )
__snake_case : Tuple = self.rope_scaling.get('factor' , __a )
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(__a , __a ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 0 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
A__ : Tuple = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wikisql-supervised''': (
'''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-tabfact''': (
'''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
),
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
A__ = '''tapas'''
def __init__( self : Optional[int] , __a : List[Any]=30522 , __a : int=768 , __a : int=12 , __a : Tuple=12 , __a : List[Any]=3072 , __a : str="gelu" , __a : Union[str, Any]=0.1 , __a : Union[str, Any]=0.1 , __a : Union[str, Any]=1024 , __a : int=[3, 256, 256, 2, 256, 256, 10] , __a : Dict=0.0_2 , __a : int=1e-12 , __a : str=0 , __a : Dict=10.0 , __a : Tuple=0 , __a : Dict=1.0 , __a : str=None , __a : List[Any]=1.0 , __a : Union[str, Any]=False , __a : Any=None , __a : Optional[Any]=1.0 , __a : Dict=1.0 , __a : Dict=False , __a : List[str]=False , __a : List[Any]="ratio" , __a : Tuple=None , __a : str=None , __a : Dict=64 , __a : str=32 , __a : List[Any]=False , __a : int=True , __a : List[str]=False , __a : Any=False , __a : Dict=True , __a : Dict=False , __a : int=None , __a : Optional[int]=None , **__a : List[Any] , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=__a , **__a )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
__snake_case : Optional[int] = vocab_size
__snake_case : str = hidden_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : Union[str, Any] = num_attention_heads
__snake_case : Any = hidden_act
__snake_case : str = intermediate_size
__snake_case : Tuple = hidden_dropout_prob
__snake_case : List[str] = attention_probs_dropout_prob
__snake_case : Dict = max_position_embeddings
__snake_case : List[str] = type_vocab_sizes
__snake_case : str = initializer_range
__snake_case : Tuple = layer_norm_eps
# Fine-tuning task hyperparameters
__snake_case : Optional[Any] = positive_label_weight
__snake_case : Dict = num_aggregation_labels
__snake_case : Optional[int] = aggregation_loss_weight
__snake_case : List[str] = use_answer_as_supervision
__snake_case : int = answer_loss_importance
__snake_case : List[Any] = use_normalized_answer_loss
__snake_case : Tuple = huber_loss_delta
__snake_case : Union[str, Any] = temperature
__snake_case : Optional[Any] = aggregation_temperature
__snake_case : Tuple = use_gumbel_for_cells
__snake_case : List[str] = use_gumbel_for_aggregation
__snake_case : str = average_approximation_function
__snake_case : Optional[int] = cell_selection_preference
__snake_case : Union[str, Any] = answer_loss_cutoff
__snake_case : Dict = max_num_rows
__snake_case : int = max_num_columns
__snake_case : Optional[Any] = average_logits_per_cell
__snake_case : Any = select_one_column
__snake_case : Any = allow_empty_column_selection
__snake_case : str = init_cell_selection_weights_to_zero
__snake_case : Union[str, Any] = reset_position_index_per_cell
__snake_case : Optional[Any] = disable_per_token_loss
# Aggregation hyperparameters
__snake_case : Any = aggregation_labels
__snake_case : int = no_aggregation_label_index
if isinstance(self.aggregation_labels , __a ):
__snake_case : int = {int(__a ): v for k, v in aggregation_labels.items()}
| 350 |
'''simple docstring'''
from __future__ import annotations
A__ : str = '''Muhammad Umer Farooq'''
A__ : int = '''MIT'''
A__ : Optional[int] = '''1.0.0'''
A__ : List[Any] = '''Muhammad Umer Farooq'''
A__ : Optional[Any] = '''contact@muhammadumerfarooq.me'''
A__ : Optional[Any] = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , __a : str ) -> None:
'''simple docstring'''
super().__init__()
__snake_case : list[str] = []
__snake_case : Dict = domain
def A_ ( self : Dict , __a : str , __a : list[tuple[str, str | None]] ) -> None:
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case : Optional[Any] = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def a_ ( _UpperCAmelCase : str ) -> str:
return ".".join(get_sub_domain_name(_UpperCAmelCase ).split('.' )[-2:] )
def a_ ( _UpperCAmelCase : str ) -> str:
return parse.urlparse(_UpperCAmelCase ).netloc
def a_ ( _UpperCAmelCase : str = "https://github.com" ) -> list[str]:
__snake_case : List[Any] = get_domain_name(_UpperCAmelCase )
# Initialize the parser
__snake_case : Tuple = Parser(_UpperCAmelCase )
try:
# Open URL
__snake_case : Any = requests.get(_UpperCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case : Dict = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case : List[Any] = requests.get(_UpperCAmelCase )
# Get the valid email.
__snake_case : Optional[Any] = re.findall('[a-zA-Z0-9]+@' + domain ,read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_UpperCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Tuple = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 0 | 0 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def a_ ( ) -> str:
__snake_case : str = 10
__snake_case : Tuple = datasets.Features(
{
'tokens': datasets.Sequence(datasets.Value('string' ) ),
'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive'] ) ),
'answers': datasets.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
'id': datasets.Value('int64' ),
} )
__snake_case : str = datasets.Dataset.from_dict(
{
'tokens': [['foo'] * 5] * n,
'labels': [[1] * 5] * n,
'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
'id': list(range(_UpperCAmelCase ) ),
} ,features=_UpperCAmelCase ,)
return dataset
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Any = str(tmp_path_factory.mktemp('data' ) / 'file.arrow' )
dataset.map(cache_file_name=_UpperCAmelCase )
return filename
# FILE_CONTENT + files
A__ : Optional[int] = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ) -> Union[str, Any]:
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt'
__snake_case : Union[str, Any] = FILE_CONTENT
with open(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase )
return filename
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Dict ) -> Tuple:
import bza
__snake_case : Dict = tmp_path_factory.mktemp('data' ) / 'file.txt.bz2'
__snake_case : Any = bytes(_UpperCAmelCase ,'utf-8' )
with bza.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : int ) -> str:
import gzip
__snake_case : Optional[int] = str(tmp_path_factory.mktemp('data' ) / 'file.txt.gz' )
__snake_case : Tuple = bytes(_UpperCAmelCase ,'utf-8' )
with gzip.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> str:
if datasets.config.LZ4_AVAILABLE:
import lza.frame
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt.lz4'
__snake_case : int = bytes(_UpperCAmelCase ,'utf-8' )
with lza.frame.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : List[Any] ) -> Optional[Any]:
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
__snake_case : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'file.txt.7z'
with pyazr.SevenZipFile(_UpperCAmelCase ,'w' ) as archive:
archive.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Dict ) -> str:
import tarfile
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt.tar'
with tarfile.TarFile(_UpperCAmelCase ,'w' ) as f:
f.add(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ) -> str:
import lzma
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'file.txt.xz'
__snake_case : Tuple = bytes(_UpperCAmelCase ,'utf-8' )
with lzma.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Tuple ) -> Dict:
import zipfile
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> Any:
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
__snake_case : int = tmp_path_factory.mktemp('data' ) / 'file.txt.zst'
__snake_case : int = bytes(_UpperCAmelCase ,'utf-8' )
with zstd.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
__snake_case : Tuple = tmp_path_factory.mktemp('data' ) / 'file.xml'
__snake_case : str = textwrap.dedent(
'\\n <?xml version="1.0" encoding="UTF-8" ?>\n <tmx version="1.4">\n <header segtype="sentence" srclang="ca" />\n <body>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>' )
with open(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase )
return filename
A__ : List[Any] = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
A__ : Any = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
A__ : Union[str, Any] = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
A__ : int = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
A__ : Optional[Any] = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope='session' )
def a_ ( ) -> List[Any]:
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[str] ) -> Any:
__snake_case : Any = datasets.Dataset.from_dict(_UpperCAmelCase )
__snake_case : Union[str, Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset.arrow' )
dataset.map(cache_file_name=_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[Any] ) -> Optional[Any]:
__snake_case : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.sqlite' )
with contextlib.closing(sqlitea.connect(_UpperCAmelCase ) ) as con:
__snake_case : str = con.cursor()
cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)' )
for item in DATA:
cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)' ,tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> Optional[int]:
__snake_case : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.csv' )
with open(_UpperCAmelCase ,'w' ,newline='' ) as f:
__snake_case : Optional[int] = csv.DictWriter(_UpperCAmelCase ,fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ) -> Tuple:
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.csv' )
with open(_UpperCAmelCase ,'w' ,newline='' ) as f:
__snake_case : Optional[int] = csv.DictWriter(_UpperCAmelCase ,fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : str ) -> Optional[int]:
import bza
__snake_case : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.csv.bz2'
with open(_UpperCAmelCase ,'rb' ) as f:
__snake_case : List[Any] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : Dict ) -> Any:
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[Any] ) -> Optional[int]:
__snake_case : Dict = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(csv_path.replace('.csv' ,'.CSV' ) ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(csva_path.replace('.csv' ,'.CSV' ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : str ,_UpperCAmelCase : Tuple ) -> List[str]:
__snake_case : Optional[int] = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.csv.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : str ) -> Union[str, Any]:
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset.parquet' )
__snake_case : Union[str, Any] = pa.schema(
{
'col_1': pa.string(),
'col_2': pa.intaa(),
'col_3': pa.floataa(),
} )
with open(_UpperCAmelCase ,'wb' ) as f:
__snake_case : Union[str, Any] = pq.ParquetWriter(_UpperCAmelCase ,schema=_UpperCAmelCase )
__snake_case : Dict = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_UpperCAmelCase ) )] for k in DATA[0]} ,schema=_UpperCAmelCase )
writer.write_table(_UpperCAmelCase )
writer.close()
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> str:
__snake_case : int = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
__snake_case : Optional[int] = {'data': DATA}
with open(_UpperCAmelCase ,'w' ) as f:
json.dump(_UpperCAmelCase ,_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ) -> Optional[Any]:
__snake_case : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
__snake_case : str = {'data': DATA_DICT_OF_LISTS}
with open(_UpperCAmelCase ,'w' ) as f:
json.dump(_UpperCAmelCase ,_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ) -> Dict:
__snake_case : Tuple = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in DATA:
f.write(json.dumps(_UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ) -> Tuple:
__snake_case : Union[str, Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.jsonl' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in DATA:
f.write(json.dumps(_UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> Optional[int]:
__snake_case : int = str(tmp_path_factory.mktemp('data' ) / 'dataset_312.jsonl' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in DATA_312:
f.write(json.dumps(_UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ) -> List[str]:
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset-str.jsonl' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in DATA_STR:
f.write(json.dumps(_UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Tuple ) -> str:
import gzip
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt.gz' )
with open(_UpperCAmelCase ,'rb' ) as orig_file:
with gzip.open(_UpperCAmelCase ,'wb' ) as zipped_file:
zipped_file.writelines(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : List[Any] ) -> Optional[int]:
import gzip
__snake_case : str = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.gz' )
with open(_UpperCAmelCase ,'rb' ) as orig_file:
with gzip.open(_UpperCAmelCase ,'wb' ) as zipped_file:
zipped_file.writelines(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : str ,_UpperCAmelCase : int ) -> Union[str, Any]:
__snake_case : Any = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : int ) -> Tuple:
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.join('nested' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
__snake_case : Tuple = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.jsonl.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Any ) -> int:
__snake_case : Optional[Any] = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.tar'
with tarfile.TarFile(_UpperCAmelCase ,'w' ) as f:
f.add(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.add(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[Any] ) -> Dict:
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.tar'
with tarfile.TarFile(_UpperCAmelCase ,'w' ) as f:
f.add(_UpperCAmelCase ,arcname=os.path.join('nested' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> int:
__snake_case : int = ['0', '1', '2', '3']
__snake_case : Dict = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[str] ) -> Dict:
__snake_case : Optional[int] = ['0', '1', '2', '3']
__snake_case : List[Any] = str(tmp_path_factory.mktemp('data' ) / 'dataset2.txt' )
with open(_UpperCAmelCase ,'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
__snake_case : Dict = ['0', '1', '2', '3']
__snake_case : str = tmp_path_factory.mktemp('data' ) / 'dataset.abc'
with open(_UpperCAmelCase ,'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Optional[int] ) -> Tuple:
__snake_case : Optional[int] = tmp_path_factory.mktemp('data' ) / 'dataset.text.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : Any ) -> Optional[int]:
__snake_case : str = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.text.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
f.write(_UpperCAmelCase ,arcname=os.path.join('main_dir' ,os.path.basename(_UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : int ) -> List[str]:
__snake_case : str = tmp_path_factory.mktemp('data' ) / 'dataset.ext.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename('unsupported.ext' ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename('unsupported_2.ext' ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Optional[int] ) -> int:
__snake_case : List[Any] = '\n'.join(['First', 'Second\u2029with Unicode new line', 'Third'] )
__snake_case : List[str] = str(tmp_path_factory.mktemp('data' ) / 'dataset_with_unicode_new_lines.txt' )
with open(_UpperCAmelCase ,'w' ,encoding='utf-8' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def a_ ( ) -> List[str]:
return os.path.join('tests' ,'features' ,'data' ,'test_image_rgb.jpg' )
@pytest.fixture(scope='session' )
def a_ ( ) -> int:
return os.path.join('tests' ,'features' ,'data' ,'test_audio_44100.wav' )
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : Any ,_UpperCAmelCase : Optional[Any] ) -> Dict:
__snake_case : List[str] = tmp_path_factory.mktemp('data' ) / 'dataset.img.zip'
with zipfile.ZipFile(_UpperCAmelCase ,'w' ) as f:
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ) )
f.write(_UpperCAmelCase ,arcname=os.path.basename(_UpperCAmelCase ).replace('.jpg' ,'2.jpg' ) )
return path
@pytest.fixture(scope='session' )
def a_ ( _UpperCAmelCase : int ) -> Optional[Any]:
__snake_case : Tuple = tmp_path_factory.mktemp('data_dir' )
(data_dir / "subdir").mkdir()
with open(data_dir / 'subdir' / 'train.txt' ,'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / 'subdir' / 'test.txt' ,'w' ) as f:
f.write('bar\n' * 10 )
# hidden file
with open(data_dir / 'subdir' / '.test.txt' ,'w' ) as f:
f.write('bar\n' * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '.subdir' / 'train.txt' ,'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / '.subdir' / 'test.txt' ,'w' ) as f:
f.write('bar\n' * 10 )
return data_dir
| 351 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A__ : Dict = logging.getLogger()
def a_ ( ) -> Tuple:
__snake_case : List[Any] = argparse.ArgumentParser()
parser.add_argument('-f' )
__snake_case : Any = parser.parse_args()
return args.f
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
__snake_case : Tuple = {}
__snake_case : Union[str, Any] = os.path.join(_UpperCAmelCase ,'all_results.json' )
if os.path.exists(_UpperCAmelCase ):
with open(_UpperCAmelCase ,'r' ) as f:
__snake_case : List[str] = json.load(_UpperCAmelCase )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
def a_ ( ) -> Union[str, Any]:
__snake_case : Union[str, Any] = torch.cuda.is_available() and torch_device == 'cuda'
return is_using_cuda and is_apex_available()
A__ : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@classmethod
def A_ ( cls : Any ) -> List[str]:
'''simple docstring'''
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__snake_case : Optional[int] = tempfile.mkdtemp()
__snake_case : Dict = os.path.join(cls.tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
__snake_case : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def A_ ( cls : List[str] ) -> List[str]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : List[Any] = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'glue_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
'''.split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertLess(result['perplexity'] , 100 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'clm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : int = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertLess(result['perplexity'] , 42 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'mlm_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__snake_case : Any = 7 if get_gpu_count() > 1 else 2
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.7_5 )
self.assertLess(result['train_loss'] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'ner_no_trainer' ) ) )
@unittest.skip(reason='Fix me @muellerzr' )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.get_auto_remove_tmp_dir()
__snake_case : Tuple = f'''
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['eval_f1'] , 28 )
self.assertGreaterEqual(result['eval_exact'] , 28 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'qa_no_trainer' ) ) )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : str = self.get_auto_remove_tmp_dir()
__snake_case : Any = f'''
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : str = get_results(__a )
self.assertGreaterEqual(result['eval_accuracy'] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__a , 'swag_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : List[str] = f'''
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : int = get_results(__a )
self.assertGreaterEqual(result['eval_rouge1'] , 10 )
self.assertGreaterEqual(result['eval_rouge2'] , 2 )
self.assertGreaterEqual(result['eval_rougeL'] , 7 )
self.assertGreaterEqual(result['eval_rougeLsum'] , 7 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'summarization_no_trainer' ) ) )
@slow
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
__snake_case : Tuple = self.get_auto_remove_tmp_dir()
__snake_case : str = f'''
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
'''.split()
run_command(self._launch_args + testargs )
__snake_case : Dict = get_results(__a )
self.assertGreaterEqual(result['eval_bleu'] , 30 )
self.assertTrue(os.path.exists(os.path.join(__a , 'epoch_0' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'translation_no_trainer' ) ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = logging.StreamHandler(sys.stdout )
logger.addHandler(__a )
__snake_case : List[str] = self.get_auto_remove_tmp_dir()
__snake_case : int = f'''
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
'''.split()
run_command(self._launch_args + testargs )
__snake_case : List[str] = get_results(__a )
self.assertGreaterEqual(result['eval_overall_accuracy'] , 0.1_0 )
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Dict = self.get_auto_remove_tmp_dir()
__snake_case : Dict = f'''
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
'''.split()
if is_cuda_and_apex_available():
testargs.append('--fp16' )
run_command(self._launch_args + testargs )
__snake_case : Optional[int] = get_results(__a )
# The base model scores a 25%
self.assertGreaterEqual(result['eval_accuracy'] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__a , 'step_1' ) ) )
self.assertTrue(os.path.exists(os.path.join(__a , 'image_classification_no_trainer' ) ) )
| 0 | 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 : Union[str, Any] , __a : Any , __a : int=13 , __a : Dict=7 , __a : Union[str, Any]=True , __a : Optional[Any]=True , __a : List[Any]=True , __a : Tuple=True , __a : Union[str, Any]=99 , __a : Dict=32 , __a : Dict=5 , __a : str=4 , __a : Optional[int]=37 , __a : str="gelu" , __a : Any=0.1 , __a : Optional[int]=0.1 , __a : List[Any]=512 , __a : Any=16 , __a : Optional[int]=2 , __a : str=0.0_2 , __a : Union[str, Any]=4 , ) -> List[str]:
'''simple docstring'''
__snake_case : List[Any] = parent
__snake_case : Optional[int] = batch_size
__snake_case : List[Any] = seq_length
__snake_case : int = is_training
__snake_case : int = use_attention_mask
__snake_case : List[Any] = use_token_type_ids
__snake_case : Tuple = use_labels
__snake_case : Dict = vocab_size
__snake_case : Tuple = hidden_size
__snake_case : List[str] = num_hidden_layers
__snake_case : int = num_attention_heads
__snake_case : Union[str, Any] = intermediate_size
__snake_case : str = hidden_act
__snake_case : Optional[int] = hidden_dropout_prob
__snake_case : Optional[int] = attention_probs_dropout_prob
__snake_case : List[Any] = max_position_embeddings
__snake_case : Union[str, Any] = type_vocab_size
__snake_case : str = type_sequence_label_size
__snake_case : Tuple = initializer_range
__snake_case : Optional[Any] = num_choices
def A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
__snake_case : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case : Optional[int] = None
if self.use_attention_mask:
__snake_case : Dict = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case : Optional[int] = 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_=__a , )
return config, input_ids, attention_mask
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.prepare_config_and_inputs()
__snake_case : Optional[int] = config_and_inputs
__snake_case : List[str] = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Optional[int] = FlaxDistilBertModelTester(self )
@slow
def A_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__snake_case : Optional[int] = model_class_name.from_pretrained('distilbert-base-uncased' )
__snake_case : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__a )
@require_flax
class snake_case__ ( unittest.TestCase ):
@slow
def A_ ( self : Any ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )
__snake_case : Tuple = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__snake_case : Tuple = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__snake_case : str = model(__a , attention_mask=__a )[0]
__snake_case : List[str] = (1, 11, 768)
self.assertEqual(output.shape , __a )
__snake_case : Dict = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __a , atol=1e-4 ) )
| 352 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
for i in range(3 ,int(n**0.5 + 1 ) ,2 ):
__snake_case : Optional[int] = i * 2
while index < n:
__snake_case : Union[str, Any] = False
__snake_case : int = index + i
__snake_case : Dict = [2]
for i in range(3 ,_UpperCAmelCase ,2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def a_ ( _UpperCAmelCase : int = 99_99_66_66_33_33 ) -> int:
__snake_case : List[Any] = math.floor(math.sqrt(_UpperCAmelCase ) ) + 1_00
__snake_case : Tuple = prime_sieve(_UpperCAmelCase )
__snake_case : List[Any] = 0
__snake_case : List[Any] = 0
__snake_case : Optional[int] = primes[prime_index]
while (last_prime**2) <= limit:
__snake_case : Optional[int] = primes[prime_index + 1]
__snake_case : Union[str, Any] = last_prime**2
__snake_case : Dict = next_prime**2
# Get numbers divisible by lps(current)
__snake_case : Optional[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
__snake_case : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
__snake_case : List[str] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
__snake_case : Dict = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 0 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ : Tuple = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[Any] = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
A__ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 353 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : float ,_UpperCAmelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(1_0_0, 0.25) = }""")
print(F"""{price_plus_tax(1_25.50, 0.05) = }""")
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class snake_case__ :
A__ = XGLMConfig
A__ = {}
A__ = '''gelu'''
def __init__( self : Optional[Any] , __a : Optional[Any] , __a : int=14 , __a : Dict=7 , __a : Optional[Any]=True , __a : Optional[int]=True , __a : List[Any]=True , __a : Optional[int]=99 , __a : Union[str, Any]=32 , __a : Union[str, Any]=2 , __a : List[str]=4 , __a : Optional[int]=37 , __a : List[Any]="gelu" , __a : Tuple=0.1 , __a : str=0.1 , __a : int=512 , __a : Tuple=0.0_2 , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Any = parent
__snake_case : List[Any] = batch_size
__snake_case : List[Any] = seq_length
__snake_case : str = is_training
__snake_case : str = use_input_mask
__snake_case : Tuple = use_labels
__snake_case : Optional[int] = vocab_size
__snake_case : Dict = d_model
__snake_case : str = num_hidden_layers
__snake_case : Dict = num_attention_heads
__snake_case : Optional[int] = ffn_dim
__snake_case : Tuple = activation_function
__snake_case : List[Any] = activation_dropout
__snake_case : str = attention_dropout
__snake_case : Union[str, Any] = max_position_embeddings
__snake_case : Optional[int] = initializer_range
__snake_case : Any = None
__snake_case : Any = 0
__snake_case : Dict = 2
__snake_case : Union[str, Any] = 1
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
__snake_case : List[str] = None
if self.use_input_mask:
__snake_case : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case : int = self.get_config()
__snake_case : List[str] = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def A_ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=__a , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=__a , )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
__snake_case : List[str] = self.prepare_config_and_inputs()
(
__snake_case
) : List[Any] = config_and_inputs
__snake_case : Union[str, Any] = {
'input_ids': input_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
A__ = (TFXGLMForCausalLM,) if is_tf_available() else ()
A__ = (
{'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
)
A__ = False
A__ = False
A__ = False
def A_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = TFXGLMModelTester(self )
__snake_case : str = ConfigTester(self , config_class=__a , n_embd=37 )
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
@slow
def A_ ( self : Dict ) -> int:
'''simple docstring'''
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : str = TFXGLMModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def A_ ( self : Any ) -> str:
'''simple docstring'''
super().test_resize_token_embeddings()
@require_tf
class snake_case__ ( unittest.TestCase ):
@slow
def A_ ( self : Optional[Any] , __a : Optional[Any]=True ) -> Any:
'''simple docstring'''
__snake_case : Optional[Any] = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__snake_case : Union[str, Any] = tf.convert_to_tensor([[2, 268, 9865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
__snake_case : str = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
# fmt: on
__snake_case : int = model.generate(__a , do_sample=__a , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , __a )
@slow
def A_ ( self : Any ) -> int:
'''simple docstring'''
__snake_case : Union[str, Any] = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__snake_case : int = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
__snake_case : int = tokenizer('Today is a nice day and' , return_tensors='tf' )
__snake_case : Any = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
__snake_case : Dict = model.generate(__a , do_sample=__a , seed=[7, 0] )
__snake_case : Any = tokenizer.decode(output_ids[0] , skip_special_tokens=__a )
__snake_case : str = (
'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
)
self.assertEqual(__a , __a )
@slow
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
__snake_case : Optional[int] = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__snake_case : Dict = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__snake_case : int = 'left'
# use different length sentences to test batching
__snake_case : Optional[Any] = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When',
'Hello, my dog is a little',
]
__snake_case : int = tokenizer(__a , return_tensors='tf' , padding=__a )
__snake_case : List[Any] = inputs['input_ids']
__snake_case : Tuple = model.generate(input_ids=__a , attention_mask=inputs['attention_mask'] , max_new_tokens=12 )
__snake_case : List[str] = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
__snake_case : List[Any] = model.generate(input_ids=__a , max_new_tokens=12 )
__snake_case : List[str] = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
__snake_case : List[str] = model.generate(input_ids=__a , max_new_tokens=12 )
__snake_case : Optional[int] = tokenizer.batch_decode(__a , skip_special_tokens=__a )
__snake_case : Union[str, Any] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__a )
__snake_case : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=__a )
__snake_case : int = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
'a single',
'Hello, my dog is a little bit of a shy one, but he is very friendly',
]
self.assertListEqual(__a , __a )
self.assertListEqual(__a , [non_padded_sentence, padded_sentence] )
| 354 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
__snake_case : Optional[int] = SMALL_MODEL_IDENTIFIER
__snake_case : str = 'pt'
__snake_case : Union[str, Any] = 'tf'
def A_ ( self : Dict , __a : Tuple ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(__a )
def A_ ( self : Any , __a : Optional[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=__a )
model_tf.save_pretrained(__a )
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
__snake_case : int = FeaturesManager.determine_framework(self.test_model , __a )
self.assertEqual(__a , __a )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : List[Any] = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a , __a )
self.assertEqual(__a , __a )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(__a )
__snake_case : Tuple = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(__a )
__snake_case : Union[str, Any] = FeaturesManager.determine_framework(__a )
self.assertEqual(__a , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(__a ):
__snake_case : Optional[int] = FeaturesManager.determine_framework(__a )
def A_ ( self : Any ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ):
__snake_case : int = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_tf )
# Both in environment -> use PyTorch
__snake_case : Optional[Any] = MagicMock(return_value=__a )
__snake_case : Tuple = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
__snake_case : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(__a , self.framework_pt )
# Both not in environment -> raise error
__snake_case : str = MagicMock(return_value=__a )
__snake_case : List[Any] = MagicMock(return_value=__a )
with patch('transformers.onnx.features.is_tf_available' , __a ), patch(
'transformers.onnx.features.is_torch_available' , __a ):
with self.assertRaises(__a ):
__snake_case : Tuple = FeaturesManager.determine_framework(self.test_model )
| 0 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def a_ ( _UpperCAmelCase : Tuple ) -> Union[str, Any]:
__snake_case : Any = image.size
__snake_case : Optional[int] = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
__snake_case : int = image.resize((w, h) ,resample=PIL_INTERPOLATION['lanczos'] )
__snake_case : Dict = np.array(_UpperCAmelCase ).astype(np.floataa ) / 2_55.0
__snake_case : Tuple = image[None].transpose(0 ,3 ,1 ,2 )
__snake_case : int = torch.from_numpy(_UpperCAmelCase )
return 2.0 * image - 1.0
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : List[str] , __a : VQModel , __a : UNetaDModel , __a : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(vqvae=__a , unet=__a , scheduler=__a )
@torch.no_grad()
def __call__( self : Tuple , __a : Union[torch.Tensor, PIL.Image.Image] = None , __a : Optional[int] = 1 , __a : Optional[int] = 100 , __a : Optional[float] = 0.0 , __a : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __a : Optional[str] = "pil" , __a : bool = True , ) -> Union[Tuple, ImagePipelineOutput]:
'''simple docstring'''
if isinstance(__a , PIL.Image.Image ):
__snake_case : Union[str, Any] = 1
elif isinstance(__a , torch.Tensor ):
__snake_case : str = image.shape[0]
else:
raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__a )}''' )
if isinstance(__a , PIL.Image.Image ):
__snake_case : Tuple = preprocess(__a )
__snake_case : Any = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
__snake_case : Dict = (batch_size, self.unet.config.in_channels // 2, height, width)
__snake_case : Optional[int] = next(self.unet.parameters() ).dtype
__snake_case : int = randn_tensor(__a , generator=__a , device=self.device , dtype=__a )
__snake_case : Union[str, Any] = image.to(device=self.device , dtype=__a )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(__a , device=self.device )
__snake_case : List[Any] = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
__snake_case : Optional[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__snake_case : str = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__snake_case : str = {}
if accepts_eta:
__snake_case : Optional[Any] = eta
for t in self.progress_bar(__a ):
# concat latents and low resolution image in the channel dimension.
__snake_case : int = torch.cat([latents, image] , dim=1 )
__snake_case : Optional[int] = self.scheduler.scale_model_input(__a , __a )
# predict the noise residual
__snake_case : List[str] = self.unet(__a , __a ).sample
# compute the previous noisy sample x_t -> x_t-1
__snake_case : List[Any] = self.scheduler.step(__a , __a , __a , **__a ).prev_sample
# decode the image latents with the VQVAE
__snake_case : Any = self.vqvae.decode(__a ).sample
__snake_case : List[Any] = torch.clamp(__a , -1.0 , 1.0 )
__snake_case : Union[str, Any] = image / 2 + 0.5
__snake_case : str = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__snake_case : str = self.numpy_to_pil(__a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__a )
| 355 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ProphetNetTokenizer
A__ = False
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
super().setUp()
__snake_case : Dict = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__snake_case : Any = os.path.join(self.tmpdirname , 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] ) )
def A_ ( self : int , __a : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[int] = 'UNwant\u00E9d,running'
__snake_case : List[str] = 'unwanted, running'
return input_text, output_text
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Dict = self.tokenizer_class(self.vocab_file )
__snake_case : List[str] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(__a , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
__snake_case : Optional[int] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def A_ ( self : int ) -> Any:
'''simple docstring'''
__snake_case : int = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case : str = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[Any] = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = BasicTokenizer(do_lower_case=__a , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def A_ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__snake_case : List[Any] = {}
for i, token in enumerate(__a ):
__snake_case : List[str] = i
__snake_case : Any = WordpieceTokenizer(vocab=__a , 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'] )
@require_torch
def A_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__snake_case : str = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
__snake_case : Union[str, Any] = tokenizer(__a , padding=__a , return_tensors='pt' )
self.assertIsInstance(__a , __a )
__snake_case : int = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
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 A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
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 A_ ( self : List[Any] ) -> int:
'''simple docstring'''
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(' ' ) )
@slow
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
__snake_case : Optional[int] = tokenizer.encode('sequence builders' , add_special_tokens=__a )
__snake_case : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=__a )
__snake_case : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__a )
__snake_case : List[Any] = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 0 | 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : str = "The quick brown fox jumps over the lazy dog" ,) -> bool:
__snake_case : Optional[Any] = set()
# Replace all the whitespace in our sentence
__snake_case : int = input_str.replace(' ' ,'' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(_UpperCAmelCase ) == 26
def a_ ( _UpperCAmelCase : str = "The quick brown fox jumps over the lazy dog" ,) -> bool:
__snake_case : int = [False] * 26
for char in input_str:
if char.islower():
__snake_case : Optional[int] = True
elif char.isupper():
__snake_case : Union[str, Any] = True
return all(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : str = "The quick brown fox jumps over the lazy dog" ,) -> bool:
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def a_ ( ) -> None:
from timeit import timeit
__snake_case : Union[str, Any] = 'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
print(timeit('is_pangram()' ,setup=_UpperCAmelCase ) )
print(timeit('is_pangram_faster()' ,setup=_UpperCAmelCase ) )
print(timeit('is_pangram_fastest()' ,setup=_UpperCAmelCase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 356 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
A__ : Optional[int] = logging.get_logger(__name__)
def a_ ( _UpperCAmelCase : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(_UpperCAmelCase ,np.ndarray ):
return list(tensor.shape )
__snake_case : Optional[Any] = tf.shape(_UpperCAmelCase )
if tensor.shape == tf.TensorShape(_UpperCAmelCase ):
return dynamic
__snake_case : int = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_UpperCAmelCase )]
def a_ ( _UpperCAmelCase : tf.Tensor ,_UpperCAmelCase : Optional[int] = None ,_UpperCAmelCase : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1E-9 ,axis=_UpperCAmelCase ,name=_UpperCAmelCase )
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : str ,_UpperCAmelCase : str=1E-5 ,_UpperCAmelCase : Union[str, Any]=-1 ) -> str:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.' )
# Get mean and variance on the axis to be normalized
__snake_case : Union[str, Any] = tf.nn.moments(_UpperCAmelCase ,axes=[axis] ,keepdims=_UpperCAmelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
__snake_case : Optional[Any] = [1] * inputs.shape.rank
__snake_case : str = shape_list(_UpperCAmelCase )[axis]
__snake_case : Any = tf.reshape(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : int = tf.reshape(_UpperCAmelCase ,_UpperCAmelCase )
# Compute layer normalization using the batch_normalization
# function.
__snake_case : int = tf.nn.batch_normalization(
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,offset=_UpperCAmelCase ,scale=_UpperCAmelCase ,variance_epsilon=_UpperCAmelCase ,)
return outputs
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Dict=0 ,_UpperCAmelCase : int=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
__snake_case : Any = tf.shape(_UpperCAmelCase )
__snake_case : int = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__snake_case : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,axis=0 )
return tf.reshape(_UpperCAmelCase ,_UpperCAmelCase )
def a_ ( _UpperCAmelCase : tf.Tensor ) -> tf.Tensor:
if not isinstance(_UpperCAmelCase ,tf.Tensor ):
__snake_case : List[str] = tf.convert_to_tensor(_UpperCAmelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__snake_case : Dict = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__snake_case : List[str] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
__snake_case : Any = (
tf.cast(1 ,encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def a_ ( _UpperCAmelCase : tf.Tensor ,_UpperCAmelCase : int ,_UpperCAmelCase : str = "input_ids" ) -> None:
tf.debugging.assert_less(
_UpperCAmelCase ,tf.cast(_UpperCAmelCase ,dtype=tensor.dtype ) ,message=(
f'''The maximum value of {tensor_name} ({tf.math.reduce_max(_UpperCAmelCase )}) must be smaller than the embedding '''
f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) ,)
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : int ,_UpperCAmelCase : Tuple ) -> List[str]:
__snake_case : Union[str, Any] = 6_45_12
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
__snake_case : Any = [x for x in data if len(_UpperCAmelCase ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'The following attributes cannot be saved to HDF5 file because '
f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
f'''bytes: {bad_attributes}''' )
__snake_case : Tuple = np.asarray(_UpperCAmelCase )
__snake_case : List[Any] = 1
__snake_case : List[str] = np.array_split(_UpperCAmelCase ,_UpperCAmelCase )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
__snake_case : List[str] = np.array_split(_UpperCAmelCase ,_UpperCAmelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_UpperCAmelCase ):
__snake_case : Dict = chunk_data
else:
__snake_case : Optional[int] = data
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
if name in group.attrs:
__snake_case : List[str] = [n.decode('utf8' ) if hasattr(_UpperCAmelCase ,'decode' ) else n for n in group.attrs[name]]
else:
__snake_case : int = []
__snake_case : Tuple = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('utf8' ) if hasattr(_UpperCAmelCase ,'decode' ) else n for n in group.attrs['%s%d' % (name, chunk_id)]] )
chunk_id += 1
return data
def a_ ( _UpperCAmelCase : Optional[int] ) -> List[str]:
def _expand_single_ad_tensor(_UpperCAmelCase : Tuple ):
if isinstance(_UpperCAmelCase ,tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_UpperCAmelCase ,axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor ,_UpperCAmelCase )
| 357 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
__snake_case : Optional[Any] = gray_code_sequence_string(_UpperCAmelCase )
#
# convert them to integers
for i in range(len(_UpperCAmelCase ) ):
__snake_case : Optional[Any] = int(sequence[i] ,2 )
return sequence
def a_ ( _UpperCAmelCase : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__snake_case : Dict = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__snake_case : Dict = gray_code_sequence_string(bit_count - 1 )
__snake_case : Any = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__snake_case : str = '0' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__snake_case : Any = '1' + smaller_sequence[i]
sequence.append(_UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class snake_case__ :
def __init__( self : List[Any] , __a : Any , __a : Dict , __a : bool = True , __a : bool = False ) -> str:
'''simple docstring'''
__snake_case : Union[str, Any] = scheduler
__snake_case : List[Any] = optimizers if isinstance(__a , (list, tuple) ) else [optimizers]
__snake_case : List[Any] = split_batches
__snake_case : str = step_with_optimizer
__snake_case : int = GradientState()
def A_ ( self : Optional[Any] , *__a : Tuple , **__a : Optional[Any] ) -> str:
'''simple docstring'''
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*__a , **__a )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*__a , **__a )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
__snake_case : Dict = AcceleratorState().num_processes
for _ in range(__a ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*__a , **__a )
else:
self.scheduler.step(*__a , **__a )
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return self.scheduler.get_last_lr()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
return self.scheduler.state_dict()
def A_ ( self : List[str] , __a : List[str] ) -> Optional[int]:
'''simple docstring'''
self.scheduler.load_state_dict(__a )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return self.scheduler.get_lr()
def A_ ( self : str , *__a : Union[str, Any] , **__a : int ) -> List[str]:
'''simple docstring'''
return self.scheduler.print_lr(*__a , **__a )
| 358 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case__ ( unittest.TestCase ):
def A_ ( self : int ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = tempfile.mkdtemp()
# fmt: off
__snake_case : List[str] = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
# fmt: on
__snake_case : Any = os.path.join(self.tmpdirname , 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] ) )
__snake_case : List[str] = {
'do_resize': True,
'size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.5, 0.5, 0.5],
'image_std': [0.5, 0.5, 0.5],
}
__snake_case : Optional[Any] = os.path.join(self.tmpdirname , __a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__a , __a )
def A_ ( self : Optional[int] , **__a : Dict ) -> int:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : int , **__a : Dict ) -> Tuple:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__a )
def A_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case : List[str] = [Image.fromarray(np.moveaxis(__a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : Dict = self.get_image_processor()
__snake_case : Any = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
processor.save_pretrained(self.tmpdirname )
__snake_case : Any = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case : Optional[Any] = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__snake_case : Tuple = self.get_image_processor(do_normalize=__a , padding_value=1.0 )
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __a )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Tuple = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : str = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = self.prepare_image_inputs()
__snake_case : List[str] = image_processor(__a , return_tensors='np' )
__snake_case : List[str] = processor(images=__a , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : int = self.get_tokenizer()
__snake_case : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Optional[int] = 'lower newer'
__snake_case : Dict = processor(text=__a )
__snake_case : List[Any] = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Dict = self.get_image_processor()
__snake_case : Union[str, Any] = self.get_tokenizer()
__snake_case : int = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : List[Any] = 'lower newer'
__snake_case : Optional[Any] = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(__a ):
processor()
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
__snake_case : Union[str, Any] = self.get_image_processor()
__snake_case : Any = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case : int = processor.batch_decode(__a )
__snake_case : Optional[Any] = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : List[str] = self.get_image_processor()
__snake_case : Dict = self.get_tokenizer()
__snake_case : Dict = VisionTextDualEncoderProcessor(tokenizer=__a , image_processor=__a )
__snake_case : Union[str, Any] = 'lower newer'
__snake_case : Tuple = self.prepare_image_inputs()
__snake_case : Union[str, Any] = processor(text=__a , images=__a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 0 | 0 |
'''simple docstring'''
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
A__ : str = logging.get_logger(__name__)
A__ : Optional[int] = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''gpt_neo'''
A__ = ['''past_key_values''']
A__ = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : Union[str, Any] , __a : Tuple=50257 , __a : str=2048 , __a : Optional[Any]=2048 , __a : Union[str, Any]=24 , __a : List[str]=[[["global", "local"], 12]] , __a : Optional[int]=16 , __a : List[str]=None , __a : List[Any]=256 , __a : Union[str, Any]="gelu_new" , __a : Optional[int]=0.0 , __a : int=0.0 , __a : Dict=0.0 , __a : List[Any]=0.1 , __a : Tuple=1e-5 , __a : Union[str, Any]=0.0_2 , __a : Union[str, Any]=True , __a : List[str]=50256 , __a : Dict=50256 , **__a : int , ) -> Optional[Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : List[str] = max_position_embeddings
__snake_case : Optional[Any] = hidden_size
__snake_case : List[str] = num_layers
__snake_case : List[Any] = num_heads
__snake_case : Optional[Any] = intermediate_size
__snake_case : Optional[Any] = window_size
__snake_case : int = activation_function
__snake_case : Optional[int] = resid_dropout
__snake_case : List[Any] = embed_dropout
__snake_case : int = attention_dropout
__snake_case : Any = classifier_dropout
__snake_case : int = layer_norm_epsilon
__snake_case : List[Any] = initializer_range
__snake_case : List[Any] = use_cache
__snake_case : Dict = bos_token_id
__snake_case : List[Any] = eos_token_id
__snake_case : Union[str, Any] = attention_types
__snake_case : Union[str, Any] = self.expand_attention_types_params(__a )
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=__a , eos_token_id=__a , **__a )
@staticmethod
def A_ ( __a : Tuple ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Tuple = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def a_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Dict ,_UpperCAmelCase : Union[str, Any] ) -> List[Any]:
import torch
__snake_case : Tuple = input.size()
__snake_case : Any = len(_UpperCAmelCase )
__snake_case : Any = shape[dimension]
__snake_case : Any = torch.arange(0 ,_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : int = torch.div(sizedim - size ,_UpperCAmelCase ,rounding_mode='floor' ) + 1
__snake_case : List[Any] = torch.arange(_UpperCAmelCase ) + low_indices[:min_length][:, None]
__snake_case : int = [slice(_UpperCAmelCase )] * rank
__snake_case : Optional[Any] = indices
__snake_case : Union[str, Any] = input[s]
__snake_case : List[Any] = list(range(0 ,rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ) -> Any:
import torch
__snake_case : Union[str, Any] = torch.arange(1 ,_UpperCAmelCase )
__snake_case : Dict = torch.remainder(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : Optional[int] = remainders == 0
__snake_case : Tuple = candidates[divisor_indices]
__snake_case : str = torch.max(_UpperCAmelCase )
return largest_divisor, torch.div(_UpperCAmelCase ,_UpperCAmelCase ,rounding_mode='floor' )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Optional[int] = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
__snake_case : Dict = {0: 'batch', 1: 'past_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self._config.num_heads
def A_ ( self : Dict , __a : PreTrainedTokenizer , __a : int = -1 , __a : int = -1 , __a : bool = False , __a : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
__snake_case : Tuple = super(__a , self ).generate_dummy_inputs(
__a , batch_size=__a , seq_length=__a , is_pair=__a , framework=__a )
# We need to order the input in the way they appears in the forward()
__snake_case : Union[str, Any] = 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 : int = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
__snake_case : List[Any] = seqlen + 2
__snake_case : Union[str, Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__snake_case : Optional[Any] = [
(torch.zeros(__a ), torch.zeros(__a )) for _ in range(self.num_layers )
]
__snake_case : Dict = common_inputs['attention_mask']
if self.use_past:
__snake_case : Dict = ordered_inputs['attention_mask'].dtype
__snake_case : Dict = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(__a , __a , dtype=__a )] , dim=1 )
return ordered_inputs
@property
def A_ ( self : int ) -> int:
'''simple docstring'''
return 13
| 359 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
__snake_case : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> List[str]:
__snake_case : Tuple = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def a_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
__snake_case : Union[str, Any] = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def a_ ( ) -> Optional[Any]:
__snake_case : Any = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def a_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Tuple:
__snake_case : List[str] = 'imagenet-1k-id2label.json'
__snake_case : Dict = 10_00
__snake_case : Union[str, Any] = 'huggingface/label-files'
__snake_case : str = num_labels
__snake_case : str = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__snake_case : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__snake_case : Optional[Any] = idalabel
__snake_case : str = {v: k for k, v in idalabel.items()}
__snake_case : Dict = CvtConfig(num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' ,1 )[-1][4:6] == "13":
__snake_case : Tuple = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' ,1 )[-1][4:6] == "21":
__snake_case : str = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case : Dict = [2, 2, 20]
__snake_case : Any = [3, 12, 16]
__snake_case : Tuple = [1_92, 7_68, 10_24]
__snake_case : str = CvtForImageClassification(_UpperCAmelCase )
__snake_case : List[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
__snake_case : int = image_size
__snake_case : int = torch.load(_UpperCAmelCase ,map_location=torch.device('cpu' ) )
__snake_case : List[Any] = OrderedDict()
__snake_case : Union[str, Any] = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
__snake_case : Tuple = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case : Optional[int] = list_of_state_dict + attention(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
__snake_case : List[str] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
A__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=3_8_4,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A__ : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 0 | 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 10_00 ) -> int:
__snake_case : Union[str, Any] = 1, 1
__snake_case : Dict = []
for i in range(1 ,n + 1 ):
__snake_case : Optional[Any] = prev_numerator + 2 * prev_denominator
__snake_case : int = prev_numerator + prev_denominator
if len(str(_UpperCAmelCase ) ) > len(str(_UpperCAmelCase ) ):
result.append(_UpperCAmelCase )
__snake_case : int = numerator
__snake_case : Union[str, Any] = denominator
return len(_UpperCAmelCase )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 360 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
A__ : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def a_ ( _UpperCAmelCase : Matrix ,_UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def a_ ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def a_ ( _UpperCAmelCase : Matrix ) -> Matrix | None:
if location := find_empty_location(_UpperCAmelCase ):
__snake_case , __snake_case : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 ,10 ):
if is_safe(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : Union[str, Any] = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
__snake_case : Optional[Any] = 0
return None
def a_ ( _UpperCAmelCase : Matrix ) -> None:
for row in grid:
for cell in row:
print(_UpperCAmelCase ,end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 2_0)
print_solution(example_grid)
print('''\nExample grid solution:''')
A__ : List[str] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 0 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 361 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = KandinskyVaaPriorPipeline
A__ = ['''prompt''']
A__ = ['''prompt''', '''negative_prompt''']
A__ = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Dict ) -> List[str]:
'''simple docstring'''
return 32
@property
def A_ ( self : Any ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return self.time_input_dim
@property
def A_ ( self : str ) -> int:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return 100
@property
def A_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Union[str, Any] = 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(__a )
@property
def A_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Any = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
__snake_case : List[Any] = PriorTransformer(**__a )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__snake_case : Any = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__snake_case : Optional[Any] = CLIPVisionModelWithProjection(__a )
return model
@property
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = CLIPImageProcessor(
crop_size=224 , do_center_crop=__a , do_normalize=__a , do_resize=__a , image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , resample=3 , size=224 , )
return image_processor
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : List[str] = self.dummy_image_encoder
__snake_case : str = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : List[str] = self.dummy_image_processor
__snake_case : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample=__a , clip_sample_range=1_0.0 , )
__snake_case : str = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def A_ ( self : List[Any] , __a : Optional[Any] , __a : Tuple=0 ) -> Any:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : List[str] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : List[Any] = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def A_ ( self : str ) -> Dict:
'''simple docstring'''
__snake_case : str = 'cpu'
__snake_case : List[str] = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Optional[Any] = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : List[str] = output.image_embeds
__snake_case : str = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
__snake_case : Union[str, Any] = image[0, -10:]
__snake_case : Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__snake_case : List[Any] = np.array(
[-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = torch_device == 'cpu'
__snake_case : Dict = True
__snake_case : Union[str, Any] = False
self._test_inference_batch_single_identical(
test_max_difference=__a , relax_max_difference=__a , test_mean_pixel_difference=__a , )
@skip_mps
def A_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Dict = torch_device == 'cpu'
__snake_case : Optional[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=__a , test_mean_pixel_difference=__a , )
| 0 | 0 |
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class snake_case__ :
A__ = None
def A_ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict )
__snake_case : str = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __a )
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Any = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Dict = os.path.join(__a , 'feat_extract.json' )
feat_extract_first.to_json_file(__a )
__snake_case : List[str] = self.feature_extraction_class.from_json_file(__a )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def A_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : List[str] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Optional[int] = feat_extract_first.save_pretrained(__a )[0]
check_json_file_has_correct_format(__a )
__snake_case : Tuple = self.feature_extraction_class.from_pretrained(__a )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def A_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Any = self.feature_extraction_class()
self.assertIsNotNone(__a )
| 362 |
'''simple docstring'''
from math import factorial
A__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def a_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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(_UpperCAmelCase ) )
def a_ ( _UpperCAmelCase : int = 60 ,_UpperCAmelCase : int = 1_00_00_00 ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
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
__snake_case : List[str] = 0
# the cached sizes of the previous chains
__snake_case : dict[int, int] = {}
for start_chain_element in range(1 ,_UpperCAmelCase ):
# The temporary set will contain the elements of the chain
__snake_case : Optional[int] = set()
__snake_case : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
__snake_case : str = 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(_UpperCAmelCase )
chain_set_length += 1
__snake_case : Tuple = digit_factorial_sum(_UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
__snake_case : Optional[Any] = 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()}""")
| 0 | 0 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A__ : Optional[Any] = '''.'''
if __name__ == "__main__":
A__ : Optional[Any] = os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
A__ : int = []
A__ : str = []
with open(doctest_file_path) as fp:
for line in fp:
A__ : Union[str, Any] = line.strip()
A__ : Optional[Any] = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
A__ : Union[str, Any] = '''\n'''.join(non_existent_paths)
raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
if all_paths != sorted(all_paths):
raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')
| 363 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int = 1_00 ) -> int:
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : Union[str, Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : float ) -> float:
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 a_ ( _UpperCAmelCase : float ) -> float:
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()
| 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 0 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
A__ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 365 |
'''simple docstring'''
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 snake_case__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
A__ = ShapEPipeline
A__ = ['''prompt''']
A__ = ['''prompt''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def A_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return 32
@property
def A_ ( self : str ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def A_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def A_ ( self : Tuple ) -> Dict:
'''simple docstring'''
return 8
@property
def A_ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def A_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = 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(__a )
@property
def A_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Dict = {
'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,
}
__snake_case : Optional[Any] = PriorTransformer(**__a )
return model
@property
def A_ ( self : Dict ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Tuple = {
'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,
),
}
__snake_case : Optional[int] = ShapERenderer(**__a )
return model
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = self.dummy_prior
__snake_case : Union[str, Any] = self.dummy_text_encoder
__snake_case : List[str] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_renderer
__snake_case : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__a , clip_sample=__a , clip_sample_range=1.0 , )
__snake_case : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def A_ ( self : Union[str, Any] , __a : Dict , __a : int=0 ) -> Optional[Any]:
'''simple docstring'''
if str(__a ).startswith('mps' ):
__snake_case : List[str] = torch.manual_seed(__a )
else:
__snake_case : Optional[Any] = torch.Generator(device=__a ).manual_seed(__a )
__snake_case : Optional[int] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def A_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
__snake_case : Dict = 'cpu'
__snake_case : Dict = self.get_dummy_components()
__snake_case : int = self.pipeline_class(**__a )
__snake_case : str = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[Any] = pipe(**self.get_dummy_inputs(__a ) )
__snake_case : Dict = output.images[0]
__snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__snake_case : str = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
__snake_case : int = torch_device == 'cpu'
__snake_case : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__a , relax_max_difference=__a , )
def A_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**__a )
__snake_case : Dict = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : int = 1
__snake_case : Tuple = 2
__snake_case : Tuple = self.get_dummy_inputs(__a )
for key in inputs.keys():
if key in self.batch_params:
__snake_case : Union[str, Any] = batch_size * [inputs[key]]
__snake_case : str = pipe(**__a , num_images_per_prompt=__a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
def A_ ( self : str ) -> Dict:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[int] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__snake_case : Union[str, Any] = ShapEPipeline.from_pretrained('openai/shap-e' )
__snake_case : Any = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__snake_case : Optional[int] = torch.Generator(device=__a ).manual_seed(0 )
__snake_case : Union[str, Any] = pipe(
'a shark' , generator=__a , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__a , __a )
| 0 | 0 |
'''simple docstring'''
A__ : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def a_ ( _UpperCAmelCase : int ) -> int:
__snake_case : Dict = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
A__ : list[bool | None] = [None] * 1_0_0_0_0_0_0_0
A__ : Optional[Any] = True
A__ : int = False
def a_ ( _UpperCAmelCase : int ) -> bool:
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__snake_case : List[str] = chain(next_number(_UpperCAmelCase ) )
__snake_case : Optional[Any] = number_chain
while number < 10_00_00_00:
__snake_case : Tuple = number_chain
number *= 10
return number_chain
def a_ ( _UpperCAmelCase : int = 10_00_00_00 ) -> int:
for i in range(1 ,_UpperCAmelCase ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{solution() = }""")
| 366 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
A__ : str = [8, 5, 9, 7]
A__ : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A__ : Dict = [
[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 : Union[str, Any] , __a : list[int] , __a : list[list[int]] , __a : list[list[int]] , ) -> None:
'''simple docstring'''
__snake_case : int = claim_vector
__snake_case : Optional[int] = allocated_resources_table
__snake_case : List[str] = maximum_claim_table
def A_ ( self : str ) -> list[int]:
'''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 A_ ( self : int ) -> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def A_ ( self : int ) -> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(__a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def A_ ( self : str ) -> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(__a ): i for i in self.__need()}
def A_ ( self : Union[str, Any] , **__a : int ) -> None:
'''simple docstring'''
__snake_case : str = self.__need()
__snake_case : List[Any] = self.__allocated_resources_table
__snake_case : Optional[int] = self.__available_resources()
__snake_case : Union[str, Any] = 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:
__snake_case : Tuple = False
for each_need in need_list:
__snake_case : Any = True
for index, need in enumerate(__a ):
if need > available_resources[index]:
__snake_case : List[str] = False
break
if execution:
__snake_case : Union[str, 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:
__snake_case : str = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(__a )
# update available/freed resources stack
__snake_case : Union[str, Any] = np.array(__a ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(__a ) 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 A_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(__a ) + 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(__a ) + 1}'''
+ ' '.join(f'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(__a ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(__a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
import numpy as np
def a_ ( _UpperCAmelCase : np.ndarray ,_UpperCAmelCase : np.ndarray ,_UpperCAmelCase : float = 1E-12 ,_UpperCAmelCase : int = 1_00 ,) -> tuple[float, np.ndarray]:
assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[1]
# Ensure proper dimensionality.
assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(_UpperCAmelCase ) == np.iscomplexobj(_UpperCAmelCase )
__snake_case : Dict = np.iscomplexobj(_UpperCAmelCase )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(_UpperCAmelCase ,input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__snake_case : Union[str, Any] = False
__snake_case : Dict = 0
__snake_case : Any = 0
__snake_case : List[str] = 1E12
while not convergence:
# Multiple matrix by the vector.
__snake_case : Union[str, Any] = np.dot(_UpperCAmelCase ,_UpperCAmelCase )
# Normalize the resulting output vector.
__snake_case : int = w / np.linalg.norm(_UpperCAmelCase )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__snake_case : Dict = vector.conj().T if is_complex else vector.T
__snake_case : Optional[Any] = np.dot(_UpperCAmelCase ,np.dot(_UpperCAmelCase ,_UpperCAmelCase ) )
# Check convergence.
__snake_case : Any = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__snake_case : str = True
__snake_case : List[Any] = lambda_
if is_complex:
__snake_case : List[Any] = np.real(lambda_ )
return lambda_, vector
def a_ ( ) -> None:
__snake_case : Optional[int] = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
__snake_case : List[Any] = np.array([41, 4, 20] )
__snake_case : str = real_input_matrix.astype(np.complexaaa )
__snake_case : Optional[int] = np.triu(1J * complex_input_matrix ,1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__snake_case : int = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__snake_case : List[Any] = real_input_matrix
__snake_case : Optional[int] = real_vector
elif problem_type == "complex":
__snake_case : Dict = complex_input_matrix
__snake_case : Dict = complex_vector
# Our implementation.
__snake_case : Any = power_iteration(_UpperCAmelCase ,_UpperCAmelCase )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__snake_case : List[Any] = np.linalg.eigh(_UpperCAmelCase )
# Last eigenvalue is the maximum one.
__snake_case : str = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__snake_case : str = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(_UpperCAmelCase ) - np.abs(_UpperCAmelCase ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 367 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
A__ : Union[str, Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : List[Any] = {
'''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'''
),
},
}
A__ : List[Any] = {
'''google/electra-small-generator''': 5_1_2,
'''google/electra-base-generator''': 5_1_2,
'''google/electra-large-generator''': 5_1_2,
'''google/electra-small-discriminator''': 5_1_2,
'''google/electra-base-discriminator''': 5_1_2,
'''google/electra-large-discriminator''': 5_1_2,
}
A__ : Optional[Any] = {
'''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 snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_INIT_CONFIGURATION
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ElectraTokenizer
def __init__( self : int , __a : List[Any]=None , __a : int=None , __a : List[str]=True , __a : Any="[UNK]" , __a : Any="[SEP]" , __a : Union[str, Any]="[PAD]" , __a : Dict="[CLS]" , __a : List[Any]="[MASK]" , __a : str=True , __a : Optional[int]=None , **__a : Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
__snake_case : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __a ) != do_lower_case
or normalizer_state.get('strip_accents' , __a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __a ) != tokenize_chinese_chars
):
__snake_case : List[Any] = getattr(__a , normalizer_state.pop('type' ) )
__snake_case : str = do_lower_case
__snake_case : Optional[int] = strip_accents
__snake_case : Any = tokenize_chinese_chars
__snake_case : Union[str, Any] = normalizer_class(**__a )
__snake_case : Any = do_lower_case
def A_ ( self : Any , __a : List[str] , __a : Optional[Any]=None ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = [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 A_ ( self : List[Any] , __a : List[int] , __a : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
__snake_case : int = [self.sep_token_id]
__snake_case : List[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 A_ ( self : Optional[int] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__snake_case : Tuple = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 0 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str] ) -> List[str]:
# Load configuration defined in the metadata file
with open(_UpperCAmelCase ) as metadata_file:
__snake_case : Optional[Any] = json.load(_UpperCAmelCase )
__snake_case : Optional[Any] = LukeConfig(use_entity_aware_attention=_UpperCAmelCase ,**metadata['model_config'] )
# Load in the weights from the checkpoint_path
__snake_case : int = torch.load(_UpperCAmelCase ,map_location='cpu' )
# Load the entity vocab file
__snake_case : Optional[int] = load_entity_vocab(_UpperCAmelCase )
__snake_case : Tuple = RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
__snake_case : int = AddedToken('<ent>' ,lstrip=_UpperCAmelCase ,rstrip=_UpperCAmelCase )
__snake_case : str = AddedToken('<ent2>' ,lstrip=_UpperCAmelCase ,rstrip=_UpperCAmelCase )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'''Saving tokenizer to {pytorch_dump_folder_path}''' )
tokenizer.save_pretrained(_UpperCAmelCase )
with open(os.path.join(_UpperCAmelCase ,LukeTokenizer.vocab_files_names['entity_vocab_file'] ) ,'w' ) as f:
json.dump(_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : Optional[int] = LukeTokenizer.from_pretrained(_UpperCAmelCase )
# Initialize the embeddings of the special tokens
__snake_case : Dict = state_dict['embeddings.word_embeddings.weight']
__snake_case : List[str] = word_emb[tokenizer.convert_tokens_to_ids(['@'] )[0]].unsqueeze(0 )
__snake_case : str = word_emb[tokenizer.convert_tokens_to_ids(['#'] )[0]].unsqueeze(0 )
__snake_case : int = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
__snake_case : Tuple = f'''encoder.layer.{layer_index}.attention.self.'''
__snake_case : Optional[Any] = state_dict[prefix + matrix_name]
__snake_case : List[Any] = state_dict[prefix + matrix_name]
__snake_case : int = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
__snake_case : Dict = state_dict['entity_embeddings.entity_embeddings.weight']
__snake_case : List[str] = entity_emb[entity_vocab['[MASK]']]
__snake_case : int = LukeModel(config=_UpperCAmelCase ).eval()
__snake_case : str = model.load_state_dict(_UpperCAmelCase ,strict=_UpperCAmelCase )
if not (len(_UpperCAmelCase ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(f'''Missing keys {", ".join(_UpperCAmelCase )}. Expected only missing embeddings.position_ids''' )
if not (all(key.startswith('entity_predictions' ) or key.startswith('lm_head' ) for key in unexpected_keys )):
raise ValueError(
'Unexpected keys'
f''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}''' )
# Check outputs
__snake_case : Optional[Any] = LukeTokenizer.from_pretrained(_UpperCAmelCase ,task='entity_classification' )
__snake_case : List[Any] = (
'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
' new world number one avoid a humiliating second- round exit at Wimbledon .'
)
__snake_case : str = (39, 42)
__snake_case : str = tokenizer(_UpperCAmelCase ,entity_spans=[span] ,add_prefix_space=_UpperCAmelCase ,return_tensors='pt' )
__snake_case : Tuple = model(**_UpperCAmelCase )
# Verify word hidden states
if model_size == "large":
__snake_case : List[str] = torch.Size((1, 42, 10_24) )
__snake_case : Tuple = torch.tensor(
[[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]] )
else: # base
__snake_case : List[str] = torch.Size((1, 42, 7_68) )
__snake_case : Union[str, Any] = torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,_UpperCAmelCase ,atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
__snake_case : Any = torch.Size((1, 1, 10_24) )
__snake_case : Optional[int] = torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]] )
else: # base
__snake_case : str = torch.Size((1, 1, 7_68) )
__snake_case : Tuple = torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
f''' {expected_shape}''' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,_UpperCAmelCase ,atol=1E-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(_UpperCAmelCase ) )
model.save_pretrained(_UpperCAmelCase )
def a_ ( _UpperCAmelCase : List[Any] ) -> Any:
__snake_case : List[str] = {}
with open(_UpperCAmelCase ,'r' ,encoding='utf-8' ) as f:
for index, line in enumerate(_UpperCAmelCase ):
__snake_case : List[Any] = line.rstrip().split('\t' )
__snake_case : str = index
return entity_vocab
if __name__ == "__main__":
A__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
A__ : str = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 368 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
__snake_case : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 0 | 0 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> bool:
return math.sqrt(_UpperCAmelCase ) * math.sqrt(_UpperCAmelCase ) == num
def a_ ( _UpperCAmelCase : int ) -> bool:
__snake_case : str = 0
__snake_case : str = n
while left <= right:
__snake_case : Optional[int] = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
__snake_case : int = mid - 1
else:
__snake_case : int = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
A__ : Tuple = pytest.mark.integration
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = Dataset.from_dict({'filename': ['my_name-train' + '_' + str(__a ) for x in np.arange(30 ).tolist()]} )
return dset
def A_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
__snake_case : Dict = dset.map(
lambda __a , __a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__a , keep_in_memory=__a )
__snake_case : List[Any] = dset.add_faiss_index('vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
dset.drop_index('vecs' )
def A_ ( self : Tuple ) -> Any:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__snake_case , __snake_case : Any = dset.get_nearest_examples('vecs' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
dset.save_faiss_index('vecs' , tmp_file.name )
dset.load_faiss_index('vecs2' , tmp_file.name )
os.unlink(tmp_file.name )
__snake_case , __snake_case : str = dset.get_nearest_examples('vecs2' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
def A_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
__snake_case : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='vecs' )
dset.drop_index('vecs' )
self.assertRaises(__a , partial(dset.get_nearest_examples , 'vecs2' , np.ones(5 , dtype=np.floataa ) ) )
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
__snake_case : Dataset = self._create_dummy_dataset()
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : Any = {'acknowledged': True}
mocked_bulk.return_value([(True, None)] * 30 )
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
__snake_case : Union[str, Any] = Elasticsearch()
dset.add_elasticsearch_index('filename' , es_client=__a )
__snake_case , __snake_case : str = dset.get_nearest_examples('filename' , 'my_name-train_29' )
self.assertEqual(examples['filename'][0] , 'my_name-train_29' )
@require_faiss
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : str ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__snake_case : Dict = np.zeros(5 , dtype=np.floataa )
__snake_case : List[str] = 1
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertRaises(__a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__snake_case : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__snake_case , __snake_case : Dict = index.search_batch(__a )
self.assertRaises(__a , index.search_batch , queries[0] )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __a )
def A_ ( self : int ) -> int:
'''simple docstring'''
import faiss
__snake_case : int = FaissIndex(string_factory='Flat' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__snake_case : List[str] = FaissIndex(string_factory='LSH' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__a ):
__snake_case : Dict = FaissIndex(string_factory='Flat' , custom_index=faiss.IndexFlat(5 ) )
def A_ ( self : str ) -> Dict:
'''simple docstring'''
import faiss
__snake_case : Tuple = faiss.IndexFlat(5 )
__snake_case : List[Any] = FaissIndex(custom_index=__a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
import faiss
__snake_case : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__a ) as tmp_file:
index.save(tmp_file.name )
__snake_case : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__snake_case : List[Any] = np.zeros(5 , dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : int = index.search(__a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a_ ( _UpperCAmelCase : str ) -> Optional[int]:
import faiss
__snake_case : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
__snake_case : Dict = 'index.faiss'
__snake_case : Any = f'''mock://{index_name}'''
index.save(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = FaissIndex.load(_UpperCAmelCase ,storage_options=mockfs.storage_options )
__snake_case : Any = np.zeros(5 ,dtype=np.floataa )
__snake_case : Any = 1
__snake_case , __snake_case : Tuple = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : List[str] ) -> List[str]:
'''simple docstring'''
from elasticsearch import Elasticsearch
with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
__snake_case : int = Elasticsearch()
__snake_case : Dict = {'acknowledged': True}
__snake_case : List[Any] = ElasticSearchIndex(es_client=__a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['foo', 'bar', 'foobar'] )
# single query
__snake_case : Optional[Any] = 'foo'
__snake_case : int = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : List[Any] = index.search(__a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__snake_case : Dict = 'foo'
__snake_case : Dict = {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
__snake_case , __snake_case : Optional[Any] = index.search(__a , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__snake_case : List[Any] = ['foo', 'bar', 'foobar']
__snake_case : str = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : Any = index.search_batch(__a )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
# batched queries with timeout
__snake_case : Tuple = ['foo', 'bar', 'foobar']
__snake_case : List[Any] = {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
__snake_case , __snake_case : int = index.search_batch(__a , request_timeout=30 )
__snake_case : Any = [scores[0] for scores in total_scores]
__snake_case : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__a ) , 0 )
self.assertListEqual([1, 1, 1] , __a )
| 0 | 0 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
A__ : Dict = logging.get_logger('''transformers.models.speecht5''')
def a_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Optional[int] ,_UpperCAmelCase : List[str] ) -> int:
hf_model.apply_weight_norm()
__snake_case : List[Any] = checkpoint['input_conv.weight_g']
__snake_case : Optional[int] = checkpoint['input_conv.weight_v']
__snake_case : int = checkpoint['input_conv.bias']
for i in range(len(config.upsample_rates ) ):
__snake_case : List[Any] = checkpoint[f'''upsamples.{i}.1.weight_g''']
__snake_case : List[str] = checkpoint[f'''upsamples.{i}.1.weight_v''']
__snake_case : Any = checkpoint[f'''upsamples.{i}.1.bias''']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
__snake_case : Any = checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
__snake_case : Dict = checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
__snake_case : Dict = checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']
__snake_case : Optional[int] = checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
__snake_case : Any = checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
__snake_case : Optional[int] = checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']
__snake_case : List[Any] = checkpoint['output_conv.1.weight_g']
__snake_case : Any = checkpoint['output_conv.1.weight_v']
__snake_case : List[Any] = checkpoint['output_conv.1.bias']
hf_model.remove_weight_norm()
@torch.no_grad()
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : Dict ,_UpperCAmelCase : Optional[int]=None ,_UpperCAmelCase : Dict=None ,) -> Dict:
if config_path is not None:
__snake_case : Tuple = SpeechTaHifiGanConfig.from_pretrained(_UpperCAmelCase )
else:
__snake_case : Union[str, Any] = SpeechTaHifiGanConfig()
__snake_case : Optional[Any] = SpeechTaHifiGan(_UpperCAmelCase )
__snake_case : Dict = torch.load(_UpperCAmelCase )
load_weights(orig_checkpoint['model']['generator'] ,_UpperCAmelCase ,_UpperCAmelCase )
__snake_case : List[Any] = np.load(_UpperCAmelCase )
__snake_case : Optional[int] = stats[0].reshape(-1 )
__snake_case : Dict = stats[1].reshape(-1 )
__snake_case : str = torch.from_numpy(_UpperCAmelCase ).float()
__snake_case : str = torch.from_numpy(_UpperCAmelCase ).float()
model.save_pretrained(_UpperCAmelCase )
if repo_id:
print('Pushing to the hub...' )
model.push_to_hub(_UpperCAmelCase )
if __name__ == "__main__":
A__ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
parser.add_argument('''--stats_path''', required=True, default=None, type=str, help='''Path to stats.npy file''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
A__ : Optional[int] = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 370 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''t5'''
A__ = ['''past_key_values''']
A__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __a : Dict=32128 , __a : Dict=512 , __a : Union[str, Any]=64 , __a : str=2048 , __a : Union[str, Any]=6 , __a : Any=None , __a : Any=8 , __a : List[Any]=32 , __a : Any=128 , __a : Tuple=0.1 , __a : str=1e-6 , __a : Dict=1.0 , __a : Tuple="relu" , __a : Dict=True , __a : Union[str, Any]=True , __a : Any=0 , __a : Dict=1 , **__a : Union[str, Any] , ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = vocab_size
__snake_case : str = d_model
__snake_case : str = d_kv
__snake_case : List[Any] = d_ff
__snake_case : List[str] = num_layers
__snake_case : Tuple = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__snake_case : Union[str, Any] = num_heads
__snake_case : Tuple = relative_attention_num_buckets
__snake_case : Optional[int] = relative_attention_max_distance
__snake_case : Optional[Any] = dropout_rate
__snake_case : str = layer_norm_epsilon
__snake_case : List[str] = initializer_factor
__snake_case : int = feed_forward_proj
__snake_case : Optional[Any] = use_cache
__snake_case : Optional[Any] = self.feed_forward_proj.split('-' )
__snake_case : Dict = act_info[-1]
__snake_case : List[str] = act_info[0] == 'gated'
if len(__a ) > 1 and act_info[0] != "gated" or len(__a ) > 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":
__snake_case : Dict = 'gelu_new'
super().__init__(
pad_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , **__a , )
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
@property
def A_ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
__snake_case : Union[str, Any] = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
__snake_case : Tuple = 'past_encoder_sequence + sequence'
__snake_case : Dict = {0: 'batch'}
__snake_case : Dict = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
__snake_case : Tuple = {0: 'batch', 1: 'decoder_sequence'}
__snake_case : int = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(__a , direction='inputs' )
return common_inputs
@property
def A_ ( self : List[Any] ) -> int:
'''simple docstring'''
return 13
| 0 | 0 |
'''simple docstring'''
import functools
from typing import Any
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : list[str] ) -> bool:
# Validation
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or len(_UpperCAmelCase ) == 0:
raise ValueError('the string should be not empty string' )
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not all(
isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and len(_UpperCAmelCase ) > 0 for item in words ):
raise ValueError('the words should be a list of non-empty strings' )
# Build trie
__snake_case : dict[str, Any] = {}
__snake_case : Any = 'WORD_KEEPER'
for word in words:
__snake_case : Optional[Any] = trie
for c in word:
if c not in trie_node:
__snake_case : int = {}
__snake_case : Any = trie_node[c]
__snake_case : str = True
__snake_case : Optional[int] = len(_UpperCAmelCase )
# Dynamic programming method
@functools.cache
def is_breakable(_UpperCAmelCase : int ) -> bool:
if index == len_string:
return True
__snake_case : List[str] = trie
for i in range(_UpperCAmelCase ,_UpperCAmelCase ):
__snake_case : List[str] = trie_node.get(string[i] ,_UpperCAmelCase )
if trie_node is None:
return False
if trie_node.get(_UpperCAmelCase ,_UpperCAmelCase ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 371 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''past_key_values''']
def __init__( self : Any , __a : List[str]=32000 , __a : Union[str, Any]=4096 , __a : Optional[Any]=11008 , __a : Any=32 , __a : str=32 , __a : Optional[int]=None , __a : Dict="silu" , __a : Dict=2048 , __a : List[str]=0.0_2 , __a : Union[str, Any]=1e-6 , __a : Dict=True , __a : List[str]=0 , __a : Tuple=1 , __a : Tuple=2 , __a : Optional[Any]=1 , __a : Any=False , __a : Tuple=None , **__a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
__snake_case : str = vocab_size
__snake_case : List[str] = max_position_embeddings
__snake_case : List[Any] = hidden_size
__snake_case : Union[str, Any] = intermediate_size
__snake_case : Optional[int] = num_hidden_layers
__snake_case : List[Any] = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[Any] = num_key_value_heads
__snake_case : int = hidden_act
__snake_case : Any = initializer_range
__snake_case : Any = rms_norm_eps
__snake_case : Union[str, Any] = pretraining_tp
__snake_case : Optional[int] = use_cache
__snake_case : Any = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , tie_word_embeddings=__a , **__a , )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __a ) 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 : Optional[Any] = self.rope_scaling.get('type' , __a )
__snake_case : Tuple = self.rope_scaling.get('factor' , __a )
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(__a , __a ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 0 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _SCREAMING_SNAKE_CASE ( datasets.BuilderConfig ):
UpperCAmelCase_ :Optional[datasets.Features] = None
class _SCREAMING_SNAKE_CASE ( datasets.ArrowBasedBuilder ):
UpperCAmelCase_ :int = PandasConfig
def __lowerCAmelCase ( self ) -> Any:
return datasets.DatasetInfo(features=self.config.features )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
lowerCAmelCase_ :Any = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__A , (str, list, tuple) ):
lowerCAmelCase_ :Any = data_files
if isinstance(__A , __A ):
lowerCAmelCase_ :List[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase_ :Optional[Any] = [dl_manager.iter_files(__A ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
lowerCAmelCase_ :int = []
for split_name, files in data_files.items():
if isinstance(__A , __A ):
lowerCAmelCase_ :str = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase_ :List[str] = [dl_manager.iter_files(__A ) for file in files]
splits.append(datasets.SplitGenerator(name=__A , gen_kwargs={"""files""": files} ) )
return splits
def __lowerCAmelCase ( self , __A ) -> pa.Table:
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
lowerCAmelCase_ :int = table_cast(__A , self.config.features.arrow_schema )
return pa_table
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
for i, file in enumerate(itertools.chain.from_iterable(__A ) ):
with open(__A , """rb""" ) as f:
lowerCAmelCase_ :List[Any] = pa.Table.from_pandas(pd.read_pickle(__A ) )
yield i, self._cast_table(__A )
| 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def _snake_case ( lowercase__ : Accelerator , lowercase__ : int = 1_6 , lowercase__ : str = "bert-base-cased" ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = AutoTokenizer.from_pretrained(lowercase__ )
lowerCAmelCase_ :Optional[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ : List[str] ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ :str = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase_ :str = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowercase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ :List[str] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowercase__ , padding="""max_length""" , max_length=1_2_8 , return_tensors="""pt""" )
return tokenizer.pad(lowercase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase_ :Optional[int] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowerCAmelCase_ :Any = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : Tuple , lowercase__ : int ) -> List[str]:
'''simple docstring'''
model.eval()
lowerCAmelCase_ :Dict = 0
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase__ ) - 1:
lowerCAmelCase_ :Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCAmelCase_ :Any = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowerCAmelCase_ :Tuple = metric.compute()
return eval_metric["accuracy"]
def _snake_case ( lowercase__ : str , lowercase__ : List[str] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ :int = config["""lr"""]
lowerCAmelCase_ :Union[str, Any] = int(config["""num_epochs"""] )
lowerCAmelCase_ :Optional[int] = int(config["""seed"""] )
lowerCAmelCase_ :Union[str, Any] = int(config["""batch_size"""] )
lowerCAmelCase_ :Optional[Any] = args.model_name_or_path
set_seed(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = get_dataloaders(lowercase__ , lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ :str = AutoModelForSequenceClassification.from_pretrained(lowercase__ , return_dict=lowercase__ )
# Instantiate optimizer
lowerCAmelCase_ :List[str] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCAmelCase_ :str = optimizer_cls(params=model.parameters() , lr=lowercase__ )
if accelerator.state.deepspeed_plugin is not None:
lowerCAmelCase_ :Union[str, Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
lowerCAmelCase_ :Any = 1
lowerCAmelCase_ :str = (len(lowercase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCAmelCase_ :List[str] = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=0 , num_training_steps=lowercase__ , )
else:
lowerCAmelCase_ :int = DummyScheduler(lowercase__ , total_num_steps=lowercase__ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# We need to keep track of how many total steps we have iterated over
lowerCAmelCase_ :List[str] = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCAmelCase_ :List[Any] = 0
lowerCAmelCase_ :str = evaluate.load("""glue""" , """mrpc""" )
lowerCAmelCase_ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
lowerCAmelCase_ :Dict = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
lowerCAmelCase_ :Optional[Any] = args.resume_from_checkpoint.split("""epoch_""" )[1]
lowerCAmelCase_ :int = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
lowerCAmelCase_ :Union[str, Any] = int(lowercase__ ) + 1
lowerCAmelCase_ :Optional[int] = evaluation_loop(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
accelerator.print("""resumed checkpoint performance:""" , lowercase__ )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , f"""state_{starting_epoch-1}.json""" ) , """r""" ) as f:
lowerCAmelCase_ :List[str] = json.load(lowercase__ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
lowerCAmelCase_ :List[Any] = {}
for epoch in range(lowercase__ , lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Dict = outputs.loss
lowerCAmelCase_ :int = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
lowerCAmelCase_ :List[str] = f"""epoch_{epoch}"""
lowerCAmelCase_ :Any = os.path.join(args.output_dir , lowercase__ )
accelerator.save_state(lowercase__ )
lowerCAmelCase_ :List[Any] = evaluation_loop(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = accuracy
lowerCAmelCase_ :Any = lr_scheduler.get_lr()[0]
lowerCAmelCase_ :str = optimizer.param_groups[0]["""lr"""]
lowerCAmelCase_ :List[Any] = epoch
lowerCAmelCase_ :Tuple = overall_step
accelerator.print(f"""epoch {epoch}:""" , lowercase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f"""state_{epoch}.json""" ) , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
def _snake_case ( ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowercase__ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowercase__ , )
parser.add_argument(
"""--output_dir""" , type=lowercase__ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=lowercase__ , default=lowercase__ , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=lowercase__ , default=lowercase__ , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowercase__ , default=2 , help="""Number of train epochs.""" , )
lowerCAmelCase_ :Optional[int] = parser.parse_args()
lowerCAmelCase_ :List[Any] = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 1 | 1 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case ( lowercase__ : List[str] , lowercase__ : Any , lowercase__ : int ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = AlbertConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase_ :str = AlbertForPreTraining(lowercase__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = 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(
'--albert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained ALBERT 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.'
)
__UpperCAmelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 1 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[str, Any]:
if isinstance(__A , __A ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
lowerCAmelCase_ :Tuple = deepcopy(__A )
elif os.path.exists(__A ):
with io.open(__A , """r""" , encoding="""utf-8""" ) as f:
lowerCAmelCase_ :str = json.load(__A )
else:
try:
lowerCAmelCase_ :Dict = baseaa.urlsafe_baadecode(__A ).decode("""utf-8""" )
lowerCAmelCase_ :int = json.loads(__A )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""" )
lowerCAmelCase_ :Optional[Any] = config
self.set_stage_and_offload()
def __lowerCAmelCase ( self ) -> Tuple:
# zero stage - this is done as early as possible, before model is created, to allow
# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
lowerCAmelCase_ :Tuple = self.get_value("""zero_optimization.stage""" , -1 )
# offload
lowerCAmelCase_ :Dict = False
if self.is_zeroa() or self.is_zeroa():
lowerCAmelCase_ :Optional[int] = set(["""cpu""", """nvme"""] )
lowerCAmelCase_ :Union[str, Any] = set(
[
self.get_value("""zero_optimization.offload_optimizer.device""" ),
self.get_value("""zero_optimization.offload_param.device""" ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
lowerCAmelCase_ :Optional[int] = True
def __lowerCAmelCase ( self , __A ) -> Optional[Any]:
lowerCAmelCase_ :str = self.config
# find the config node of interest if it exists
lowerCAmelCase_ :Tuple = ds_key_long.split(""".""" )
lowerCAmelCase_ :List[str] = nodes.pop()
for node in nodes:
lowerCAmelCase_ :Tuple = config.get(__A )
if config is None:
return None, ds_key
return config, ds_key
def __lowerCAmelCase ( self , __A , __A=None ) -> Optional[Any]:
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = self.find_config_node(__A )
if config is None:
return default
return config.get(__A , __A )
def __lowerCAmelCase ( self , __A , __A=False ) -> Optional[Any]:
lowerCAmelCase_ :Tuple = self.config
# find the config node of interest if it exists
lowerCAmelCase_ :Union[str, Any] = ds_key_long.split(""".""" )
for node in nodes:
lowerCAmelCase_ :int = config
lowerCAmelCase_ :Any = config.get(__A )
if config is None:
if must_exist:
raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}""" )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(__A )
def __lowerCAmelCase ( self , __A ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[int] = self.get_value(__A )
return False if value is None else bool(__A )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :List[str] = self.get_value(__A )
return False if value is None else not bool(__A )
def __lowerCAmelCase ( self ) -> str:
return self._stage == 2
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return self._stage == 3
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return self._offload
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Optional[int]:
lowerCAmelCase_ :Dict = engine
def __lowerCAmelCase ( self , __A , **__A ) -> str:
# runs backpropagation and handles mixed precision
self.engine.backward(__A , **__A )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A ) -> List[str]:
super().__init__(__A , device_placement=__A , scaler=__A )
lowerCAmelCase_ :List[str] = hasattr(self.optimizer , """overflow""" )
def __lowerCAmelCase ( self , __A=None ) -> Optional[Any]:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def __lowerCAmelCase ( self ) -> List[Any]:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def __lowerCAmelCase ( self ) -> int:
if self.__has_overflow__:
return self.optimizer.overflow
return False
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A , __A ) -> Optional[int]:
super().__init__(__A , __A )
def __lowerCAmelCase ( self ) -> Any:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=0.0_0_1 , __A=0 , **__A ) -> List[Any]:
lowerCAmelCase_ :str = params
lowerCAmelCase_ :Any = lr
lowerCAmelCase_ :List[Any] = weight_decay
lowerCAmelCase_ :Any = kwargs
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=None , __A=0 , **__A ) -> List[str]:
lowerCAmelCase_ :Optional[int] = optimizer
lowerCAmelCase_ :int = total_num_steps
lowerCAmelCase_ :List[Any] = warmup_num_steps
lowerCAmelCase_ :int = kwargs
| 1 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = set()
lowerCAmelCase_ :Union[str, Any] = int((limit - 2_4) ** (1 / 2) )
lowerCAmelCase_ :Any = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , lowercase__ ) ) )
for primea in primes:
lowerCAmelCase_ :int = primea * primea
for primea in primes:
lowerCAmelCase_ :Any = primea * primea * primea
if square + cube >= limit - 1_6:
break
for primea in primes:
lowerCAmelCase_ :Optional[Any] = primea * primea * primea * primea
lowerCAmelCase_ :Tuple = square + cube + tetr
if total >= limit:
break
ret.add(lowercase__ )
return len(lowercase__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Dict = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
UpperCAmelCase_ :List[str] = "CIDAS/clipseg-rd64-refined"
UpperCAmelCase_ :List[Any] = "image_segmenter"
UpperCAmelCase_ :Optional[int] = CLIPSegForImageSegmentation
UpperCAmelCase_ :Tuple = ["image", "text"]
UpperCAmelCase_ :Dict = ["image"]
def __init__( self , *__A , **__A ) -> Optional[Any]:
requires_backends(self , ["""vision"""] )
super().__init__(*__A , **__A )
def __lowerCAmelCase ( self , __A , __A ) -> Any:
return self.pre_processor(text=[label] , images=[image] , padding=__A , return_tensors="""pt""" )
def __lowerCAmelCase ( self , __A ) -> Tuple:
with torch.no_grad():
lowerCAmelCase_ :Dict = self.model(**__A ).logits
return logits
def __lowerCAmelCase ( self , __A ) -> Tuple:
lowerCAmelCase_ :Optional[int] = outputs.cpu().detach().numpy()
lowerCAmelCase_ :List[str] = 0
lowerCAmelCase_ :str = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 1 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Optional[Any] = "big_bird"
def __init__( self , __A=5_0358 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu_new" , __A=0.1 , __A=0.1 , __A=4096 , __A=2 , __A=0.0_2 , __A=1E-12 , __A=True , __A=0 , __A=1 , __A=2 , __A=66 , __A="block_sparse" , __A=True , __A=False , __A=64 , __A=3 , __A=None , **__A , ) -> Optional[Any]:
super().__init__(
pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , sep_token_id=__A , **__A , )
lowerCAmelCase_ :Tuple = vocab_size
lowerCAmelCase_ :List[Any] = max_position_embeddings
lowerCAmelCase_ :Union[str, Any] = hidden_size
lowerCAmelCase_ :List[Any] = num_hidden_layers
lowerCAmelCase_ :str = num_attention_heads
lowerCAmelCase_ :Tuple = intermediate_size
lowerCAmelCase_ :Tuple = hidden_act
lowerCAmelCase_ :Any = hidden_dropout_prob
lowerCAmelCase_ :Tuple = attention_probs_dropout_prob
lowerCAmelCase_ :List[str] = initializer_range
lowerCAmelCase_ :Dict = type_vocab_size
lowerCAmelCase_ :List[str] = layer_norm_eps
lowerCAmelCase_ :Optional[int] = use_cache
lowerCAmelCase_ :List[Any] = rescale_embeddings
lowerCAmelCase_ :Dict = attention_type
lowerCAmelCase_ :Tuple = use_bias
lowerCAmelCase_ :Optional[int] = block_size
lowerCAmelCase_ :Dict = num_random_blocks
lowerCAmelCase_ :Dict = classifier_dropout
class _SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase_ :Dict = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCAmelCase_ :Dict = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCAmelCase_ :Any = 0
lowerCAmelCase_ :str = 0
lowerCAmelCase_ :Dict = knapsack(lowercase__ , lowercase__ , lowercase__ , lowercase__ , index + 1 )
if weights[index] <= max_weight:
lowerCAmelCase_ :str = values[index] + knapsack(
lowercase__ , lowercase__ , lowercase__ , max_weight - weights[index] , index + 1 )
return max(lowercase__ , lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : str ) -> Optional[Any]:
'''simple docstring'''
if not is_tqdm_available():
raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.""" )
lowerCAmelCase_ :Tuple = False
if main_process_only:
lowerCAmelCase_ :Dict = PartialState().local_process_index == 0
return _tqdm(*lowercase__ , **lowercase__ , disable=lowercase__ )
| 1 | 1 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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 (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=14 , __A=7 , __A=True , __A=True , __A=True , __A=True , __A=True , __A=99 , __A=32 , __A=5 , __A=4 , __A=37 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=16 , __A=2 , __A=0.0_2 , __A=3 , __A=4 , __A=None , ) -> Optional[Any]:
lowerCAmelCase_ :Any = parent
lowerCAmelCase_ :List[Any] = batch_size
lowerCAmelCase_ :Union[str, Any] = seq_length
lowerCAmelCase_ :Optional[Any] = is_training
lowerCAmelCase_ :Optional[Any] = use_token_type_ids
lowerCAmelCase_ :List[Any] = use_input_mask
lowerCAmelCase_ :List[Any] = use_labels
lowerCAmelCase_ :Optional[Any] = use_mc_token_ids
lowerCAmelCase_ :Union[str, Any] = vocab_size
lowerCAmelCase_ :Any = hidden_size
lowerCAmelCase_ :List[Any] = num_hidden_layers
lowerCAmelCase_ :Optional[Any] = num_attention_heads
lowerCAmelCase_ :Optional[int] = intermediate_size
lowerCAmelCase_ :List[Any] = hidden_act
lowerCAmelCase_ :Any = hidden_dropout_prob
lowerCAmelCase_ :List[str] = attention_probs_dropout_prob
lowerCAmelCase_ :str = max_position_embeddings
lowerCAmelCase_ :List[Any] = type_vocab_size
lowerCAmelCase_ :Tuple = type_sequence_label_size
lowerCAmelCase_ :str = initializer_range
lowerCAmelCase_ :List[Any] = num_labels
lowerCAmelCase_ :Dict = num_choices
lowerCAmelCase_ :List[Any] = scope
lowerCAmelCase_ :Union[str, Any] = self.vocab_size - 1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ :int = None
if self.use_input_mask:
lowerCAmelCase_ :List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ :Union[str, Any] = None
if self.use_token_type_ids:
lowerCAmelCase_ :int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase_ :str = None
if self.use_mc_token_ids:
lowerCAmelCase_ :Dict = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
lowerCAmelCase_ :Optional[Any] = None
lowerCAmelCase_ :Dict = None
lowerCAmelCase_ :Optional[Any] = None
if self.use_labels:
lowerCAmelCase_ :str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ :Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ :int = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase_ :Union[str, Any] = self.get_config()
lowerCAmelCase_ :Dict = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , *__A ) -> List[Any]:
lowerCAmelCase_ :int = CTRLModel(config=__A )
model.to(__A )
model.eval()
model(__A , token_type_ids=__A , head_mask=__A )
model(__A , token_type_ids=__A )
lowerCAmelCase_ :Dict = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , *__A ) -> Optional[int]:
lowerCAmelCase_ :Dict = CTRLLMHeadModel(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :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 __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :str = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) :Optional[Any] = config_and_inputs
lowerCAmelCase_ :Any = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}
return config, inputs_dict
def __lowerCAmelCase ( self , __A , __A , __A , __A , *__A ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = self.num_labels
lowerCAmelCase_ :Optional[int] = CTRLForSequenceClassification(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ :str = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :Optional[int] = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
UpperCAmelCase_ :Optional[Any] = (CTRLLMHeadModel,) if is_torch_available() else ()
UpperCAmelCase_ :Tuple = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase_ :int = True
UpperCAmelCase_ :int = False
UpperCAmelCase_ :List[Any] = False
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A ) -> Optional[Any]:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :int = CTRLModelTester(self )
lowerCAmelCase_ :Union[str, Any] = ConfigTester(self , config_class=__A , n_embd=37 )
def __lowerCAmelCase ( self ) -> str:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*__A )
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __lowerCAmelCase ( self ) -> Optional[Any]:
pass
@slow
def __lowerCAmelCase ( self ) -> Any:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ :str = CTRLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip("""The model doesn't support left padding""" ) # and it's not used enough to be worth fixing :)
def __lowerCAmelCase ( self ) -> Any:
pass
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :List[str] = CTRLLMHeadModel.from_pretrained("""ctrl""" )
model.to(__A )
lowerCAmelCase_ :Union[str, Any] = torch.tensor(
[[1_1859, 0, 1611, 8]] , dtype=torch.long , device=__A ) # Legal the president is
lowerCAmelCase_ :Dict = [
1_1859,
0,
1611,
8,
5,
150,
2_6449,
2,
19,
348,
469,
3,
2595,
48,
2_0740,
24_6533,
24_6533,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
lowerCAmelCase_ :str = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 1 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
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
__UpperCAmelCase = get_tests_dir('fixtures/dummy-config.json')
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :int = 0
def __lowerCAmelCase ( self ) -> List[str]:
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Tuple = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :int = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :Any = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :int = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Tuple:
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
lowerCAmelCase_ :int = os.path.join(__A , """fake-roberta""" )
os.makedirs(__A , exist_ok=__A )
with open(os.path.join(__A , """config.json""" ) , """w""" ) as f:
f.write(json.dumps({} ) )
lowerCAmelCase_ :Any = AutoConfig.from_pretrained(__A )
self.assertEqual(type(__A ) , __A )
def __lowerCAmelCase ( self ) -> Optional[int]:
try:
AutoConfig.register("""custom""" , __A )
# Wrong model type will raise an error
with self.assertRaises(__A ):
AutoConfig.register("""model""" , __A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__A ):
AutoConfig.register("""bert""" , __A )
# Now that the config is registered, it can be used as any other config with the auto-API
lowerCAmelCase_ :Union[str, Any] = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A )
lowerCAmelCase_ :Optional[int] = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def __lowerCAmelCase ( self ) -> Tuple:
with self.assertRaisesRegex(
__A , """bert-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase_ :List[str] = AutoConfig.from_pretrained("""bert-base""" )
def __lowerCAmelCase ( self ) -> Any:
with self.assertRaisesRegex(
__A , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase_ :Dict = AutoConfig.from_pretrained(__A , revision="""aaaaaa""" )
def __lowerCAmelCase ( self ) -> int:
with self.assertRaisesRegex(
__A , """hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" , ):
lowerCAmelCase_ :Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def __lowerCAmelCase ( self ) -> Tuple:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__A ):
lowerCAmelCase_ :Tuple = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__A ):
lowerCAmelCase_ :List[str] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
lowerCAmelCase_ :str = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A )
lowerCAmelCase_ :Dict = AutoConfig.from_pretrained(__A , trust_remote_code=__A )
self.assertEqual(reloaded_config.__class__.__name__ , """NewModelConfig""" )
def __lowerCAmelCase ( self ) -> int:
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :int = "new-model"
try:
AutoConfig.register("""new-model""" , __A )
# If remote code is not set, the default is to use local
lowerCAmelCase_ :Any = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
lowerCAmelCase_ :Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
lowerCAmelCase_ :Optional[Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 1 | 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : str ) -> Optional[Any]:
'''simple docstring'''
if not is_tqdm_available():
raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.""" )
lowerCAmelCase_ :Tuple = False
if main_process_only:
lowerCAmelCase_ :Dict = PartialState().local_process_index == 0
return _tqdm(*lowercase__ , **lowercase__ , disable=lowercase__ )
| 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :str = GPTSanJapaneseTokenizer
UpperCAmelCase_ :Optional[int] = False
UpperCAmelCase_ :Optional[int] = {"do_clean_text": False, "add_prefix_space": False}
def __lowerCAmelCase ( self ) -> Tuple:
super().setUp()
# fmt: off
lowerCAmelCase_ :Dict = ["""こん""", """こんに""", """にちは""", """ばんは""", """世界,㔺界""", """、""", """。""", """<BR>""", """<SP>""", """<TAB>""", """<URL>""", """<EMAIL>""", """<TEL>""", """<DATE>""", """<PRICE>""", """<BLOCK>""", """<KIGOU>""", """<U2000U2BFF>""", """<|emoji1|>""", """<unk>""", """<|bagoftoken|>""", """<|endoftext|>"""]
# fmt: on
lowerCAmelCase_ :List[str] = {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}} # 😀
lowerCAmelCase_ :int = {"""unk_token""": """<unk>"""}
lowerCAmelCase_ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ :int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""emoji_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.emoji_file , """w""" ) as emoji_writer:
emoji_writer.write(json.dumps(__A ) )
def __lowerCAmelCase ( self , **__A ) -> int:
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **__A )
def __lowerCAmelCase ( self , __A ) -> Dict:
lowerCAmelCase_ :List[Any] = """こんにちは、世界。 \nこんばんは、㔺界。😀"""
lowerCAmelCase_ :Optional[int] = """こんにちは、世界。 \nこんばんは、世界。😀"""
return input_text, output_text
def __lowerCAmelCase ( self , __A ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = self.get_input_output_texts(__A )
lowerCAmelCase_ :List[str] = tokenizer.encode(__A , add_special_tokens=__A )
lowerCAmelCase_ :str = tokenizer.decode(__A , clean_up_tokenization_spaces=__A )
return text, ids
def __lowerCAmelCase ( self ) -> str:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Dict:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> int:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Union[str, Any] = self.get_tokenizer()
# Testing tokenization
lowerCAmelCase_ :Optional[int] = """こんにちは、世界。 こんばんは、㔺界。"""
lowerCAmelCase_ :Any = ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
lowerCAmelCase_ :Tuple = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
# Testing conversion to ids without special tokens
lowerCAmelCase_ :List[Any] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCAmelCase_ :List[str] = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(__A , __A )
# Testing conversion to ids with special tokens
lowerCAmelCase_ :Any = tokens + [tokenizer.unk_token]
lowerCAmelCase_ :Union[str, Any] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCAmelCase_ :Union[str, Any] = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(__A , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :int = self.get_tokenizer()
# Testing tokenization
lowerCAmelCase_ :Optional[int] = """こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
lowerCAmelCase_ :str = """こんにちは、、、、世界。こんばんは、、、、世界。"""
lowerCAmelCase_ :str = tokenizer.encode(__A )
lowerCAmelCase_ :Dict = tokenizer.decode(__A )
self.assertEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Tuple = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
# Testing tokenization
lowerCAmelCase_ :Optional[Any] = """こんにちは、世界。"""
lowerCAmelCase_ :Any = """こんばんは、㔺界。😀"""
lowerCAmelCase_ :Optional[Any] = """こんにちは、世界。こんばんは、世界。😀"""
lowerCAmelCase_ :List[Any] = tokenizer.encode(prefix_text + input_text )
lowerCAmelCase_ :List[str] = tokenizer.encode("""""" , prefix_text=prefix_text + input_text )
lowerCAmelCase_ :int = tokenizer.encode(__A , prefix_text=__A )
lowerCAmelCase_ :int = tokenizer.decode(__A )
lowerCAmelCase_ :Dict = tokenizer.decode(__A )
lowerCAmelCase_ :Tuple = tokenizer.decode(__A )
self.assertEqual(__A , __A )
self.assertEqual(__A , __A )
self.assertEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :int = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
# Testing tokenization
lowerCAmelCase_ :List[Any] = """こんにちは、世界。"""
lowerCAmelCase_ :Optional[int] = """こんばんは、㔺界。😀"""
lowerCAmelCase_ :List[str] = len(tokenizer.encode(__A ) ) - 2
lowerCAmelCase_ :Dict = len(tokenizer.encode(__A ) ) - 2
lowerCAmelCase_ :int = [1] + [0] * (len_prefix + len_text + 1)
lowerCAmelCase_ :List[Any] = [1] * (len_prefix + len_text + 1) + [0]
lowerCAmelCase_ :Dict = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCAmelCase_ :List[Any] = tokenizer(prefix_text + input_text ).token_type_ids
lowerCAmelCase_ :List[str] = tokenizer("""""" , prefix_text=prefix_text + input_text ).token_type_ids
lowerCAmelCase_ :List[Any] = tokenizer(__A , prefix_text=__A ).token_type_ids
self.assertListEqual(__A , __A )
self.assertListEqual(__A , __A )
self.assertListEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Dict = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
lowerCAmelCase_ :int = tokenizer.encode("""あンいワ""" )
lowerCAmelCase_ :Optional[Any] = tokenizer.encode("""""" , prefix_text="""あンいワ""" )
lowerCAmelCase_ :int = tokenizer.encode("""いワ""" , prefix_text="""あン""" )
self.assertEqual(tokenizer.decode(__A ) , tokenizer.decode(__A ) )
self.assertEqual(tokenizer.decode(__A ) , tokenizer.decode(__A ) )
self.assertNotEqual(__A , __A )
self.assertNotEqual(__A , __A )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
lowerCAmelCase_ :int = [["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
lowerCAmelCase_ :Dict = tokenizer(__A , padding=__A )
lowerCAmelCase_ :Any = tokenizer.batch_encode_plus(__A , padding=__A )
# fmt: off
lowerCAmelCase_ :int = [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
lowerCAmelCase_ :List[str] = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCAmelCase_ :int = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , __A )
self.assertListEqual(x_token.token_type_ids , __A )
self.assertListEqual(x_token.attention_mask , __A )
self.assertListEqual(x_token_a.input_ids , __A )
self.assertListEqual(x_token_a.token_type_ids , __A )
self.assertListEqual(x_token_a.attention_mask , __A )
def __lowerCAmelCase ( self ) -> Tuple:
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def __lowerCAmelCase ( self ) -> str:
# tokenizer has no padding token
pass
| 1 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :List[Any] = ConsistencyModelPipeline
UpperCAmelCase_ :Tuple = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
UpperCAmelCase_ :Union[str, Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
UpperCAmelCase_ :int = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
] )
@property
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :int = UNetaDModel.from_pretrained(
"""diffusers/consistency-models-test""" , subfolder="""test_unet""" , )
return unet
@property
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Optional[int] = UNetaDModel.from_pretrained(
"""diffusers/consistency-models-test""" , subfolder="""test_unet_class_cond""" , )
return unet
def __lowerCAmelCase ( self , __A=False ) -> Dict:
if class_cond:
lowerCAmelCase_ :int = self.dummy_cond_unet
else:
lowerCAmelCase_ :int = self.dummy_uncond_unet
# Default to CM multistep sampler
lowerCAmelCase_ :Dict = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
lowerCAmelCase_ :str = {
"""unet""": unet,
"""scheduler""": scheduler,
}
return components
def __lowerCAmelCase ( self , __A , __A=0 ) -> Tuple:
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :Union[str, Any] = torch.manual_seed(__A )
else:
lowerCAmelCase_ :Any = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :List[str] = {
"""batch_size""": 1,
"""num_inference_steps""": None,
"""timesteps""": [22, 0],
"""generator""": generator,
"""output_type""": """np""",
}
return inputs
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :str = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ :List[Any] = self.get_dummy_components()
lowerCAmelCase_ :int = ConsistencyModelPipeline(**__A )
lowerCAmelCase_ :Any = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = pipe(**__A ).images
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase_ :Optional[int] = image[0, -3:, -3:, -1]
lowerCAmelCase_ :str = np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ :Optional[int] = self.get_dummy_components(class_cond=__A )
lowerCAmelCase_ :Optional[Any] = ConsistencyModelPipeline(**__A )
lowerCAmelCase_ :Tuple = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Optional[Any] = pipe(**__A ).images
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase_ :Tuple = image[0, -3:, -3:, -1]
lowerCAmelCase_ :List[Any] = np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ :Any = self.get_dummy_components()
lowerCAmelCase_ :str = ConsistencyModelPipeline(**__A )
lowerCAmelCase_ :Optional[int] = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[Any] = 1
lowerCAmelCase_ :Any = None
lowerCAmelCase_ :str = pipe(**__A ).images
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase_ :Dict = image[0, -3:, -3:, -1]
lowerCAmelCase_ :Dict = np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Dict = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ :List[Any] = self.get_dummy_components(class_cond=__A )
lowerCAmelCase_ :Union[str, Any] = ConsistencyModelPipeline(**__A )
lowerCAmelCase_ :Union[str, Any] = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Tuple = 1
lowerCAmelCase_ :Dict = None
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Optional[Any] = pipe(**__A ).images
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase_ :List[str] = image[0, -3:, -3:, -1]
lowerCAmelCase_ :Any = np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Any:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self , __A=0 , __A=False , __A="cpu" , __A=torch.floataa , __A=(1, 3, 64, 64) ) -> Tuple:
lowerCAmelCase_ :Any = torch.manual_seed(__A )
lowerCAmelCase_ :Optional[int] = {
"""num_inference_steps""": None,
"""timesteps""": [22, 0],
"""class_labels""": 0,
"""generator""": generator,
"""output_type""": """np""",
}
if get_fixed_latents:
lowerCAmelCase_ :Dict = self.get_fixed_latents(seed=__A , device=__A , dtype=__A , shape=__A )
lowerCAmelCase_ :Tuple = latents
return inputs
def __lowerCAmelCase ( self , __A=0 , __A="cpu" , __A=torch.floataa , __A=(1, 3, 64, 64) ) -> List[str]:
if type(__A ) == str:
lowerCAmelCase_ :int = torch.device(__A )
lowerCAmelCase_ :str = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :Union[str, Any] = randn_tensor(__A , generator=__A , device=__A , dtype=__A )
return latents
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
lowerCAmelCase_ :int = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
lowerCAmelCase_ :Optional[int] = ConsistencyModelPipeline(unet=__A , scheduler=__A )
pipe.to(torch_device=__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[Any] = self.get_inputs()
lowerCAmelCase_ :Union[str, Any] = pipe(**__A ).images
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
lowerCAmelCase_ :Union[str, Any] = np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Dict = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
lowerCAmelCase_ :Tuple = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
lowerCAmelCase_ :List[str] = ConsistencyModelPipeline(unet=__A , scheduler=__A )
pipe.to(torch_device=__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :str = self.get_inputs()
lowerCAmelCase_ :List[str] = 1
lowerCAmelCase_ :Optional[int] = None
lowerCAmelCase_ :str = pipe(**__A ).images
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase_ :str = image[0, -3:, -3:, -1]
lowerCAmelCase_ :Union[str, Any] = np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
@require_torch_a
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :int = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
lowerCAmelCase_ :List[str] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
lowerCAmelCase_ :Dict = ConsistencyModelPipeline(unet=__A , scheduler=__A )
pipe.to(torch_device=__A , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_inputs(get_fixed_latents=__A , device=__A )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=__A , enable_math=__A , enable_mem_efficient=__A ):
lowerCAmelCase_ :Tuple = pipe(**__A ).images
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase_ :Union[str, Any] = image[0, -3:, -3:, -1]
lowerCAmelCase_ :Optional[int] = np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@require_torch_a
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[Any] = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
lowerCAmelCase_ :Union[str, Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_0_2 , sigma_max=8_0.0 , )
lowerCAmelCase_ :Dict = ConsistencyModelPipeline(unet=__A , scheduler=__A )
pipe.to(torch_device=__A , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Dict = self.get_inputs(get_fixed_latents=__A , device=__A )
lowerCAmelCase_ :Any = 1
lowerCAmelCase_ :List[Any] = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=__A , enable_math=__A , enable_mem_efficient=__A ):
lowerCAmelCase_ :Any = pipe(**__A ).images
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase_ :Union[str, Any] = image[0, -3:, -3:, -1]
lowerCAmelCase_ :List[str] = np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
__UpperCAmelCase = pd.read_csv(
'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
'position_salaries.csv'
)
__UpperCAmelCase = dataset.iloc[:, 1:2].values
__UpperCAmelCase = dataset.iloc[:, 2].values
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = train_test_split(X, y, test_size=0.2, random_state=0)
__UpperCAmelCase = PolynomialFeatures(degree=4)
__UpperCAmelCase = poly_reg.fit_transform(X)
__UpperCAmelCase = LinearRegression()
pol_reg.fit(X_poly, y)
def _snake_case ( ) -> str:
'''simple docstring'''
plt.scatter(lowercase__ , lowercase__ , color="""red""" )
plt.plot(lowercase__ , pol_reg.predict(poly_reg.fit_transform(lowercase__ ) ) , color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 1 | 1 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = 0
lowerCAmelCase_ :int = len(lowercase__ )
for i in range(n - 1 ):
for j in range(i + 1 , lowercase__ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _snake_case ( lowercase__ : List[str] ) -> Optional[Any]:
'''simple docstring'''
if len(lowercase__ ) <= 1:
return arr, 0
lowerCAmelCase_ :Dict = len(lowercase__ ) // 2
lowerCAmelCase_ :List[str] = arr[0:mid]
lowerCAmelCase_ :Dict = arr[mid:]
lowerCAmelCase_ , lowerCAmelCase_ :str = count_inversions_recursive(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = count_inversions_recursive(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Any = _count_cross_inversions(lowercase__ , lowercase__ )
lowerCAmelCase_ :str = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = []
lowerCAmelCase_ :str = 0
while i < len(lowercase__ ) and j < len(lowercase__ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(lowercase__ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(lowercase__ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _snake_case ( ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :int = [1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
lowerCAmelCase_ :List[Any] = count_inversions_bf(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = count_inversions_recursive(lowercase__ )
assert num_inversions_bf == num_inversions_recursive == 8
print("""number of inversions = """ , lowercase__ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
lowerCAmelCase_ :List[Any] = count_inversions_bf(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :str = count_inversions_recursive(lowercase__ )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , lowercase__ )
# an empty list should also have zero inversions
lowerCAmelCase_ :Union[str, Any] = []
lowerCAmelCase_ :str = count_inversions_bf(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = count_inversions_recursive(lowercase__ )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , lowercase__ )
if __name__ == "__main__":
main()
| 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , **__A ) -> None:
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , __A , )
super().__init__(*__A , **__A )
| 1 | 1 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A ) -> None:
lowerCAmelCase_ :Dict = graph
# mapping node to its parent in resulting breadth first tree
lowerCAmelCase_ :dict[str, str | None] = {}
lowerCAmelCase_ :Any = source_vertex
def __lowerCAmelCase ( self ) -> None:
lowerCAmelCase_ :Optional[int] = {self.source_vertex}
lowerCAmelCase_ :str = None
lowerCAmelCase_ :Tuple = [self.source_vertex] # first in first out queue
while queue:
lowerCAmelCase_ :Optional[int] = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(__A )
lowerCAmelCase_ :Any = vertex
queue.append(__A )
def __lowerCAmelCase ( self , __A ) -> str:
if target_vertex == self.source_vertex:
return self.source_vertex
lowerCAmelCase_ :Optional[Any] = self.parent.get(__A )
if target_vertex_parent is None:
lowerCAmelCase_ :str = (
f"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
)
raise ValueError(__A )
return self.shortest_path(__A ) + f"""->{target_vertex}"""
if __name__ == "__main__":
__UpperCAmelCase = Graph(graph, 'G')
g.breath_first_search()
print(g.shortest_path('D'))
print(g.shortest_path('G'))
print(g.shortest_path('Foo'))
| 1 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _snake_case ( lowercase__ : str = "laptop" ) -> DataFrame:
'''simple docstring'''
lowerCAmelCase_ :Dict = f"""https://www.amazon.in/laptop/s?k={product}"""
lowerCAmelCase_ :List[str] = {
"""User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
"""Accept-Language""": """en-US, en;q=0.5""",
}
lowerCAmelCase_ :List[Any] = BeautifulSoup(requests.get(lowercase__ , headers=lowercase__ ).text )
# Initialize a Pandas dataframe with the column titles
lowerCAmelCase_ :Union[str, Any] = DataFrame(
columns=[
"""Product Title""",
"""Product Link""",
"""Current Price of the product""",
"""Product Rating""",
"""MRP of the product""",
"""Discount""",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"""div""" , attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""} , ) , soup.find_all("""div""" , attrs={"""class""": """a-row a-size-base a-color-base"""} ) , ):
try:
lowerCAmelCase_ :str = item.ha.text
lowerCAmelCase_ :Dict = """https://www.amazon.in/""" + item.ha.a["""href"""]
lowerCAmelCase_ :int = item.find("""span""" , attrs={"""class""": """a-offscreen"""} ).text
try:
lowerCAmelCase_ :Dict = item.find("""span""" , attrs={"""class""": """a-icon-alt"""} ).text
except AttributeError:
lowerCAmelCase_ :int = """Not available"""
try:
lowerCAmelCase_ :str = (
"""₹"""
+ item.find(
"""span""" , attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
)
except AttributeError:
lowerCAmelCase_ :Optional[Any] = """"""
try:
lowerCAmelCase_ :str = float(
(
(
float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
- float(product_price.strip("""₹""" ).replace(""",""" , """""" ) )
)
/ float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
)
* 1_0_0 )
except ValueError:
lowerCAmelCase_ :Union[str, Any] = float("""nan""" )
except AttributeError:
pass
lowerCAmelCase_ :Any = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
lowerCAmelCase_ :List[Any] = """ """
lowerCAmelCase_ :Tuple = """ """
data_frame.index += 1
return data_frame
if __name__ == "__main__":
__UpperCAmelCase = 'headphones'
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 1 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
]
)
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Tuple , lowercase__ : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = state_dict.pop(lowercase__ )
lowerCAmelCase_ :List[str] = val
def _snake_case ( lowercase__ : Optional[Any] ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Any = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowerCAmelCase_ :Union[str, Any] = key.replace("""backbone.0.body""" , """backbone.conv_encoder.model""" )
lowerCAmelCase_ :Dict = value
else:
lowerCAmelCase_ :Union[str, Any] = value
return new_state_dict
def _snake_case ( lowercase__ : Any ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = """"""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowerCAmelCase_ :Optional[Any] = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
lowerCAmelCase_ :str = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ :List[str] = in_proj_weight[:2_5_6, :]
lowerCAmelCase_ :Tuple = in_proj_bias[:2_5_6]
lowerCAmelCase_ :Tuple = in_proj_weight[2_5_6:5_1_2, :]
lowerCAmelCase_ :List[Any] = in_proj_bias[2_5_6:5_1_2]
lowerCAmelCase_ :int = in_proj_weight[-2_5_6:, :]
lowerCAmelCase_ :Any = in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowerCAmelCase_ :Dict = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
lowerCAmelCase_ :List[str] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ :Union[str, Any] = in_proj_weight[:2_5_6, :]
lowerCAmelCase_ :Dict = in_proj_bias[:2_5_6]
lowerCAmelCase_ :Optional[Any] = in_proj_weight[2_5_6:5_1_2, :]
lowerCAmelCase_ :List[Any] = in_proj_bias[2_5_6:5_1_2]
lowerCAmelCase_ :Any = in_proj_weight[-2_5_6:, :]
lowerCAmelCase_ :Optional[int] = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
lowerCAmelCase_ :Tuple = state_dict.pop(
f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
lowerCAmelCase_ :Union[str, Any] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowerCAmelCase_ :Optional[int] = in_proj_weight_cross_attn[:2_5_6, :]
lowerCAmelCase_ :str = in_proj_bias_cross_attn[:2_5_6]
lowerCAmelCase_ :List[Any] = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
lowerCAmelCase_ :Optional[int] = in_proj_bias_cross_attn[2_5_6:5_1_2]
lowerCAmelCase_ :Any = in_proj_weight_cross_attn[-2_5_6:, :]
lowerCAmelCase_ :List[Any] = in_proj_bias_cross_attn[-2_5_6:]
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : Dict ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = image.size
lowerCAmelCase_ :Any = max(lowercase__ , lowercase__ )
lowerCAmelCase_ :Optional[Any] = 8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
lowerCAmelCase_ :int = target_max_size / current_max_size
lowerCAmelCase_ :Optional[Any] = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def _snake_case ( lowercase__ : Any ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = F.to_tensor(lowercase__ )
lowerCAmelCase_ :List[str] = F.normalize(lowercase__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def _snake_case ( lowercase__ : List[str] , lowercase__ : int , lowercase__ : Any ) -> Tuple:
'''simple docstring'''
logger.info("""Converting model...""" )
# load original state dict
lowerCAmelCase_ :int = torch.hub.load_state_dict_from_url(lowercase__ , map_location="""cpu""" )
# rename keys
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
lowerCAmelCase_ :Tuple = rename_backbone_keys(lowercase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowercase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowerCAmelCase_ :Optional[Any] = """model."""
for key in state_dict.copy().keys():
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
lowerCAmelCase_ :Union[str, Any] = state_dict.pop(lowercase__ )
lowerCAmelCase_ :str = val
# create HuggingFace model and load state dict
lowerCAmelCase_ :List[Any] = TableTransformerConfig(
backbone="""resnet18""" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowerCAmelCase_ :str = 1_5
lowerCAmelCase_ :Any = 2
lowerCAmelCase_ :Union[str, Any] = {0: """table""", 1: """table rotated"""}
lowerCAmelCase_ :List[Any] = idalabel
lowerCAmelCase_ :Dict = {v: k for k, v in idalabel.items()}
else:
lowerCAmelCase_ :int = 1_2_5
lowerCAmelCase_ :Optional[Any] = 6
lowerCAmelCase_ :Optional[int] = {
0: """table""",
1: """table column""",
2: """table row""",
3: """table column header""",
4: """table projected row header""",
5: """table spanning cell""",
}
lowerCAmelCase_ :Optional[int] = idalabel
lowerCAmelCase_ :List[Any] = {v: k for k, v in idalabel.items()}
lowerCAmelCase_ :int = DetrImageProcessor(
format="""coco_detection""" , max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0 )
lowerCAmelCase_ :Any = TableTransformerForObjectDetection(lowercase__ )
model.load_state_dict(lowercase__ )
model.eval()
# verify our conversion
lowerCAmelCase_ :Union[str, Any] = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
lowerCAmelCase_ :Union[str, Any] = hf_hub_download(repo_id="""nielsr/example-pdf""" , repo_type="""dataset""" , filename=lowercase__ )
lowerCAmelCase_ :Union[str, Any] = Image.open(lowercase__ ).convert("""RGB""" )
lowerCAmelCase_ :int = normalize(resize(lowercase__ , lowercase__ ) ).unsqueeze(0 )
lowerCAmelCase_ :List[str] = model(lowercase__ )
if "detection" in checkpoint_url:
lowerCAmelCase_ :List[str] = (1, 1_5, 3)
lowerCAmelCase_ :Optional[Any] = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
lowerCAmelCase_ :Union[str, Any] = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
lowerCAmelCase_ :List[Any] = (1, 1_2_5, 7)
lowerCAmelCase_ :Dict = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
lowerCAmelCase_ :Tuple = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , lowercase__ , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , lowercase__ , atol=1E-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
model.save_pretrained(lowercase__ )
image_processor.save_pretrained(lowercase__ )
if push_to_hub:
# Push model to HF hub
logger.info("""Pushing model to the hub...""" )
lowerCAmelCase_ :Dict = (
"""microsoft/table-transformer-detection"""
if """detection""" in checkpoint_url
else """microsoft/table-transformer-structure-recognition"""
)
model.push_to_hub(lowercase__ )
image_processor.push_to_hub(lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
type=str,
choices=[
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
],
help='URL of the Table Transformer checkpoint you\'d like to convert.',
)
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-single-500/viewer/default/store_true", help='Whether or not to push the converted model to the 🤗 hub.'
)
__UpperCAmelCase = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :Any = """laion/clap-htsat-unfused"""
lowerCAmelCase_ :Optional[Any] = tempfile.mkdtemp()
def __lowerCAmelCase ( self , **__A ) -> List[Any]:
return RobertaTokenizer.from_pretrained(self.checkpoint , **__A )
def __lowerCAmelCase ( self , **__A ) -> Tuple:
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__A )
def __lowerCAmelCase ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Optional[Any] = self.get_tokenizer()
lowerCAmelCase_ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ :Optional[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Dict = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ :str = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase_ :Dict = self.get_feature_extractor(do_normalize=__A , padding_value=1.0 )
lowerCAmelCase_ :Union[str, Any] = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Dict = self.get_feature_extractor()
lowerCAmelCase_ :str = self.get_tokenizer()
lowerCAmelCase_ :List[Any] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :Optional[Any] = floats_list((3, 1000) )
lowerCAmelCase_ :Optional[Any] = feature_extractor(__A , return_tensors="""np""" )
lowerCAmelCase_ :str = processor(audios=__A , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Any = self.get_tokenizer()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :List[Any] = """This is a test string"""
lowerCAmelCase_ :Dict = processor(text=__A )
lowerCAmelCase_ :List[str] = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :int = self.get_feature_extractor()
lowerCAmelCase_ :Tuple = self.get_tokenizer()
lowerCAmelCase_ :Optional[Any] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase_ :Tuple = processor.batch_decode(__A )
lowerCAmelCase_ :Optional[Any] = tokenizer.batch_decode(__A )
self.assertListEqual(__A , __A )
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Any = self.get_tokenizer()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 1 | 1 |
"""simple docstring"""
import os
def _snake_case ( ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Any = os.path.dirname(os.path.realpath(lowercase__ ) )
lowerCAmelCase_ :Optional[int] = os.path.join(lowercase__ , """triangle.txt""" )
with open(lowercase__ ) as f:
lowerCAmelCase_ :Optional[int] = f.readlines()
lowerCAmelCase_ :Optional[Any] = []
for line in triangle:
lowerCAmelCase_ :Union[str, Any] = []
for number in line.strip().split(""" """ ):
numbers_from_line.append(int(lowercase__ ) )
a.append(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
for j in range(len(a[i] ) ):
lowerCAmelCase_ :List[str] = a[i - 1][j] if j != len(a[i - 1] ) else 0
lowerCAmelCase_ :Dict = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowercase__ , lowercase__ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 1 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) ):
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = list(map(lowercase__ , line.split(""",""" ) ) )
if x * logaa(lowercase__ ) > largest:
lowerCAmelCase_ :Any = x * logaa(lowercase__ )
lowerCAmelCase_ :List[Any] = i + 1
return result
if __name__ == "__main__":
print(solution())
| 1 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[Any] = "gpt_neox_japanese"
def __init__( self , __A=3_2000 , __A=2560 , __A=32 , __A=32 , __A=4 , __A="gelu" , __A=1.0_0 , __A=1_0000 , __A=2048 , __A=0.0_2 , __A=1E-5 , __A=True , __A=3_1996 , __A=3_1999 , __A=0.1 , __A=0.0 , **__A , ) -> Tuple:
super().__init__(bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :int = vocab_size
lowerCAmelCase_ :Dict = max_position_embeddings
lowerCAmelCase_ :Dict = hidden_size
lowerCAmelCase_ :str = num_hidden_layers
lowerCAmelCase_ :Any = num_attention_heads
lowerCAmelCase_ :str = intermediate_multiple_size
lowerCAmelCase_ :Dict = hidden_act
lowerCAmelCase_ :List[Any] = rotary_pct
lowerCAmelCase_ :List[Any] = rotary_emb_base
lowerCAmelCase_ :Union[str, Any] = initializer_range
lowerCAmelCase_ :Tuple = layer_norm_eps
lowerCAmelCase_ :Optional[Any] = use_cache
lowerCAmelCase_ :int = attention_dropout
lowerCAmelCase_ :Dict = hidden_dropout
| 1 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
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(lowercase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _snake_case ( ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = 2
while True:
if is_prime(lowercase__ ):
yield num
num += 1
def _snake_case ( lowercase__ : int = 1_0_0_0_1 ) -> int:
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , lowercase__ ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 | 1 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( lowercase__ : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = MobileNetVaConfig(layer_norm_eps=0.001 )
if "_quant" in model_name:
raise ValueError("""Quantized models are not supported.""" )
lowerCAmelCase_ :List[Any] = re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$""" , lowercase__ )
if matches:
lowerCAmelCase_ :Optional[int] = float(matches[1] )
lowerCAmelCase_ :Dict = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
lowerCAmelCase_ :Union[str, Any] = 1_0_0_1
lowerCAmelCase_ :Dict = """imagenet-1k-id2label.json"""
lowerCAmelCase_ :List[str] = """huggingface/label-files"""
lowerCAmelCase_ :Dict = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type="""dataset""" ) , """r""" ) )
lowerCAmelCase_ :Tuple = {int(lowercase__ ) + 1: v for k, v in idalabel.items()}
lowerCAmelCase_ :str = """background"""
lowerCAmelCase_ :Union[str, Any] = idalabel
lowerCAmelCase_ :List[str] = {v: k for k, v in idalabel.items()}
return config
def _snake_case ( ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Dict = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ :Optional[int] = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return im
@torch.no_grad()
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Optional[int] , lowercase__ : Union[str, Any] , lowercase__ : str=False ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Dict = get_mobilenet_va_config(lowercase__ )
# Load 🤗 model
lowerCAmelCase_ :str = MobileNetVaForImageClassification(lowercase__ ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(lowercase__ , lowercase__ , lowercase__ )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
lowerCAmelCase_ :Optional[int] = MobileNetVaImageProcessor(
crop_size={"""width""": config.image_size, """height""": config.image_size} , size={"""shortest_edge""": config.image_size + 3_2} , )
lowerCAmelCase_ :List[str] = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowerCAmelCase_ :int = model(**lowercase__ )
lowerCAmelCase_ :List[Any] = outputs.logits
assert logits.shape == (1, 1_0_0_1)
if model_name == "mobilenet_v1_1.0_224":
lowerCAmelCase_ :str = torch.tensor([-4.1739, -1.1233, 3.1205] )
elif model_name == "mobilenet_v1_0.75_192":
lowerCAmelCase_ :Optional[int] = torch.tensor([-3.9440, -2.3141, -0.3333] )
else:
lowerCAmelCase_ :List[str] = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , lowercase__ , atol=1E-4 )
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase__ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase__ )
if push_to_hub:
print("""Pushing to the hub...""" )
lowerCAmelCase_ :Union[str, Any] = """google/""" + model_name
image_processor.push_to_hub(lowercase__ )
model.push_to_hub(lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='mobilenet_v1_1.0_224',
type=str,
help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action="https://huggingface.co/datasets/infinityofspace/python_codestyles-single-500/viewer/default/store_true", help='Whether or not to push the converted model to the 🤗 hub.'
)
__UpperCAmelCase = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 | 1 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
__UpperCAmelCase = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def _snake_case ( ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Any = Github(os.environ["""GITHUB_TOKEN"""] )
lowerCAmelCase_ :Dict = g.get_repo("""huggingface/transformers""" )
lowerCAmelCase_ :List[str] = repo.get_issues(state="""open""" )
for issue in open_issues:
lowerCAmelCase_ :Dict = sorted([comment for comment in issue.get_comments()] , key=lambda lowercase__ : i.created_at , reverse=lowercase__ )
lowerCAmelCase_ :Any = comments[0] if len(lowercase__ ) > 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 >= 3_0
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 > 2_3
and (dt.utcnow() - issue.created_at).days >= 3_0
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()
| 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :int = StableDiffusionControlNetImgaImgPipeline
UpperCAmelCase_ :str = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
UpperCAmelCase_ :Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ :List[str] = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"} )
UpperCAmelCase_ :Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :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 , )
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
lowerCAmelCase_ :List[str] = 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 )
lowerCAmelCase_ :Union[str, 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=1000 , )
lowerCAmelCase_ :List[Any] = CLIPTextModel(__A )
lowerCAmelCase_ :int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase_ :Union[str, Any] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def __lowerCAmelCase ( self , __A , __A=0 ) -> List[str]:
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :Tuple = torch.manual_seed(__A )
else:
lowerCAmelCase_ :Optional[int] = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :List[Any] = 2
lowerCAmelCase_ :int = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , )
lowerCAmelCase_ :Optional[int] = floats_tensor(control_image.shape , rng=random.Random(__A ) ).to(__A )
lowerCAmelCase_ :Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ :List[Any] = Image.fromarray(np.uinta(__A ) ).convert("""RGB""" ).resize((64, 64) )
lowerCAmelCase_ :Union[str, Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def __lowerCAmelCase ( self ) -> int:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> List[str]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class _SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :List[str] = StableDiffusionControlNetImgaImgPipeline
UpperCAmelCase_ :int = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
UpperCAmelCase_ :str = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ :int = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def __lowerCAmelCase ( self ) -> Optional[int]:
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = 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 , )
torch.manual_seed(0 )
def init_weights(__A ):
if isinstance(__A , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowerCAmelCase_ :List[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(__A )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(__A )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = 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 )
lowerCAmelCase_ :Optional[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=1000 , )
lowerCAmelCase_ :str = CLIPTextModel(__A )
lowerCAmelCase_ :str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase_ :Optional[Any] = MultiControlNetModel([controlneta, controlneta] )
lowerCAmelCase_ :List[Any] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def __lowerCAmelCase ( self , __A , __A=0 ) -> str:
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :Optional[Any] = torch.manual_seed(__A )
else:
lowerCAmelCase_ :List[Any] = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :Optional[Any] = 2
lowerCAmelCase_ :Optional[int] = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , ),
]
lowerCAmelCase_ :int = floats_tensor(control_image[0].shape , rng=random.Random(__A ) ).to(__A )
lowerCAmelCase_ :Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ :List[Any] = Image.fromarray(np.uinta(__A ) ).convert("""RGB""" ).resize((64, 64) )
lowerCAmelCase_ :List[str] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[str] = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
lowerCAmelCase_ :Union[str, Any] = 1_0.0
lowerCAmelCase_ :Union[str, Any] = 4
lowerCAmelCase_ :Tuple = self.get_dummy_inputs(__A )
lowerCAmelCase_ :List[str] = steps
lowerCAmelCase_ :int = scale
lowerCAmelCase_ :Union[str, Any] = pipe(**__A )[0]
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :str = steps
lowerCAmelCase_ :str = scale
lowerCAmelCase_ :Tuple = pipe(**__A , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Union[str, Any] = steps
lowerCAmelCase_ :Union[str, Any] = scale
lowerCAmelCase_ :str = pipe(**__A , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
lowerCAmelCase_ :List[str] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = steps
lowerCAmelCase_ :Tuple = scale
lowerCAmelCase_ :str = pipe(**__A , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def __lowerCAmelCase ( self ) -> Dict:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowerCAmelCase ( self ) -> Tuple:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> Optional[int]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :str = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(__A )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :Any = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
lowerCAmelCase_ :int = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , safety_checker=__A , controlnet=__A )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[str] = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase_ :List[Any] = """evil space-punk bird"""
lowerCAmelCase_ :List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((512, 512) )
lowerCAmelCase_ :int = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((512, 512) )
lowerCAmelCase_ :Union[str, Any] = pipe(
__A , __A , control_image=__A , generator=__A , output_type="""np""" , num_inference_steps=50 , strength=0.6 , )
lowerCAmelCase_ :Tuple = output.images[0]
assert image.shape == (512, 512, 3)
lowerCAmelCase_ :Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2
| 1 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :List[Any] = IFImgaImgSuperResolutionPipeline
UpperCAmelCase_ :Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
UpperCAmelCase_ :Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"} )
UpperCAmelCase_ :int = PipelineTesterMixin.required_optional_params - {"latents"}
def __lowerCAmelCase ( self ) -> List[str]:
return self._get_superresolution_dummy_components()
def __lowerCAmelCase ( self , __A , __A=0 ) -> Optional[int]:
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :Optional[int] = torch.manual_seed(__A )
else:
lowerCAmelCase_ :Any = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :int = floats_tensor((1, 3, 32, 32) , rng=random.Random(__A ) ).to(__A )
lowerCAmelCase_ :List[Any] = floats_tensor((1, 3, 16, 16) , rng=random.Random(__A ) ).to(__A )
lowerCAmelCase_ :Tuple = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""original_image""": original_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowerCAmelCase ( self ) -> Dict:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def __lowerCAmelCase ( self ) -> Tuple:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def __lowerCAmelCase ( self ) -> Any:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def __lowerCAmelCase ( self ) -> str:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def __lowerCAmelCase ( self ) -> Dict:
self._test_save_load_local()
def __lowerCAmelCase ( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 1 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ ):
UpperCAmelCase_ :List[str] = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]
@register_to_config
def __init__( self , __A , __A , __A = None , __A = 5_0257 , __A = 1024 , __A = 768 , __A = 12 , __A = 12 , __A = None , __A = "gelu_new" , __A = 0.1 , __A = 0.1 , __A = 0.1 , __A = 1E-5 , __A = 0.0_2 , __A = True , __A = True , __A = False , __A = False , ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ :List[str] = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
f""" `n_embd`: {n_embd} are not equal.""" )
lowerCAmelCase_ :Union[str, Any] = prefix_inner_dim
lowerCAmelCase_ :str = prefix_hidden_dim
lowerCAmelCase_ :str = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
lowerCAmelCase_ :List[Any] = (
nn.Linear(self.prefix_hidden_dim , __A ) if self.prefix_hidden_dim is not None else nn.Identity()
)
lowerCAmelCase_ :Any = GPTaConfig(
vocab_size=__A , n_positions=__A , n_embd=__A , n_layer=__A , n_head=__A , n_inner=__A , activation_function=__A , resid_pdrop=__A , embd_pdrop=__A , attn_pdrop=__A , layer_norm_epsilon=__A , initializer_range=__A , scale_attn_weights=__A , use_cache=__A , scale_attn_by_inverse_layer_idx=__A , reorder_and_upcast_attn=__A , )
lowerCAmelCase_ :Any = GPTaLMHeadModel(__A )
def __lowerCAmelCase ( self , __A , __A , __A = None , __A = None , ) -> List[str]:
lowerCAmelCase_ :str = self.transformer.transformer.wte(__A )
lowerCAmelCase_ :Any = self.encode_prefix(__A )
lowerCAmelCase_ :Optional[Any] = self.decode_prefix(__A )
lowerCAmelCase_ :Optional[int] = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
lowerCAmelCase_ :int = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
lowerCAmelCase_ :Optional[Any] = torch.cat((dummy_token, input_ids) , dim=1 )
lowerCAmelCase_ :Tuple = self.transformer(inputs_embeds=__A , labels=__A , attention_mask=__A )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def __lowerCAmelCase ( self , __A , __A ) -> torch.Tensor:
return torch.zeros(__A , self.prefix_length , dtype=torch.intaa , device=__A )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
return self.encode_prefix(__A )
@torch.no_grad()
def __lowerCAmelCase ( self , __A , __A , __A ) -> Optional[int]:
lowerCAmelCase_ :Tuple = torch.split(__A , 1 , dim=0 )
lowerCAmelCase_ :Optional[int] = []
lowerCAmelCase_ :List[str] = []
for feature in features:
lowerCAmelCase_ :Tuple = self.decode_prefix(feature.to(__A ) ) # back to the clip feature
# Only support beam search for now
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = self.generate_beam(
input_embeds=__A , device=__A , eos_token_id=__A )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
lowerCAmelCase_ :Tuple = torch.stack(__A )
lowerCAmelCase_ :int = torch.stack(__A )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def __lowerCAmelCase ( self , __A=None , __A=None , __A=None , __A = 5 , __A = 67 , __A = 1.0 , __A = None , ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[int] = eos_token_id
lowerCAmelCase_ :Optional[int] = None
lowerCAmelCase_ :Any = None
lowerCAmelCase_ :int = torch.ones(__A , device=__A , dtype=torch.int )
lowerCAmelCase_ :Optional[int] = torch.zeros(__A , device=__A , dtype=torch.bool )
if input_embeds is not None:
lowerCAmelCase_ :List[str] = input_embeds
else:
lowerCAmelCase_ :Union[str, Any] = self.transformer.transformer.wte(__A )
for i in range(__A ):
lowerCAmelCase_ :Optional[int] = self.transformer(inputs_embeds=__A )
lowerCAmelCase_ :str = outputs.logits
lowerCAmelCase_ :str = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
lowerCAmelCase_ :Dict = logits.softmax(-1 ).log()
if scores is None:
lowerCAmelCase_ , lowerCAmelCase_ :Any = logits.topk(__A , -1 )
lowerCAmelCase_ :Union[str, Any] = generated.expand(__A , *generated.shape[1:] )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
lowerCAmelCase_ :List[str] = next_tokens
else:
lowerCAmelCase_ :List[Any] = tokens.expand(__A , *tokens.shape[1:] )
lowerCAmelCase_ :Any = torch.cat((tokens, next_tokens) , dim=1 )
else:
lowerCAmelCase_ :List[Any] = -float(np.inf )
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Optional[int] = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
lowerCAmelCase_ :List[Any] = scores_sum / seq_lengths[:, None]
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = scores_sum_average.view(-1 ).topk(__A , -1 )
lowerCAmelCase_ :Optional[Any] = next_tokens // scores_sum.shape[1]
lowerCAmelCase_ :Dict = seq_lengths[next_tokens_source]
lowerCAmelCase_ :Tuple = next_tokens % scores_sum.shape[1]
lowerCAmelCase_ :Optional[Any] = next_tokens.unsqueeze(1 )
lowerCAmelCase_ :str = tokens[next_tokens_source]
lowerCAmelCase_ :List[Any] = torch.cat((tokens, next_tokens) , dim=1 )
lowerCAmelCase_ :Dict = generated[next_tokens_source]
lowerCAmelCase_ :Dict = scores_sum_average * seq_lengths
lowerCAmelCase_ :Tuple = is_stopped[next_tokens_source]
lowerCAmelCase_ :str = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
lowerCAmelCase_ :List[Any] = torch.cat((generated, next_token_embed) , dim=1 )
lowerCAmelCase_ :Optional[int] = is_stopped + next_tokens.eq(__A ).squeeze()
if is_stopped.all():
break
lowerCAmelCase_ :str = scores / seq_lengths
lowerCAmelCase_ :Optional[int] = scores.argsort(descending=__A )
# tokens tensors are already padded to max_seq_length
lowerCAmelCase_ :Optional[Any] = [tokens[i] for i in order]
lowerCAmelCase_ :Dict = torch.stack(__A , dim=0 )
lowerCAmelCase_ :Tuple = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 1 | 1 |
"""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 _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[str] = ["image_processor", "tokenizer"]
UpperCAmelCase_ :str = "ViltImageProcessor"
UpperCAmelCase_ :str = ("BertTokenizer", "BertTokenizerFast")
def __init__( self , __A=None , __A=None , **__A ) -> Any:
lowerCAmelCase_ :Any = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __A , )
lowerCAmelCase_ :Optional[Any] = kwargs.pop("""feature_extractor""" )
lowerCAmelCase_ :Dict = 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__(__A , __A )
lowerCAmelCase_ :Optional[int] = self.image_processor
def __call__( self , __A , __A = None , __A = True , __A = False , __A = None , __A = None , __A = 0 , __A = None , __A = None , __A = None , __A = False , __A = False , __A = False , __A = False , __A = True , __A = None , **__A , ) -> BatchEncoding:
lowerCAmelCase_ :int = self.tokenizer(
text=__A , add_special_tokens=__A , padding=__A , truncation=__A , max_length=__A , stride=__A , pad_to_multiple_of=__A , return_token_type_ids=__A , return_attention_mask=__A , return_overflowing_tokens=__A , return_special_tokens_mask=__A , return_offsets_mapping=__A , return_length=__A , verbose=__A , return_tensors=__A , **__A , )
# add pixel_values + pixel_mask
lowerCAmelCase_ :int = self.image_processor(__A , return_tensors=__A )
encoding.update(__A )
return encoding
def __lowerCAmelCase ( self , *__A , **__A ) -> Any:
return self.tokenizer.batch_decode(*__A , **__A )
def __lowerCAmelCase ( self , *__A , **__A ) -> Union[str, Any]:
return self.tokenizer.decode(*__A , **__A )
@property
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Dict = self.tokenizer.model_input_names
lowerCAmelCase_ :Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __lowerCAmelCase ( self ) -> Any:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , __A , )
return self.image_processor_class
@property
def __lowerCAmelCase ( self ) -> Dict:
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , __A , )
return self.image_processor
| 1 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "detr"
UpperCAmelCase_ :str = ["past_key_values"]
UpperCAmelCase_ :Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self , __A=True , __A=None , __A=3 , __A=100 , __A=6 , __A=2048 , __A=8 , __A=6 , __A=2048 , __A=8 , __A=0.0 , __A=0.0 , __A=True , __A="relu" , __A=256 , __A=0.1 , __A=0.0 , __A=0.0 , __A=0.0_2 , __A=1.0 , __A=False , __A="sine" , __A="resnet50" , __A=True , __A=False , __A=1 , __A=5 , __A=2 , __A=1 , __A=1 , __A=5 , __A=2 , __A=0.1 , **__A , ) -> List[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
lowerCAmelCase_ :int = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(__A , __A ):
lowerCAmelCase_ :str = backbone_config.get("""model_type""" )
lowerCAmelCase_ :List[Any] = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ :Optional[Any] = config_class.from_dict(__A )
# set timm attributes to None
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = None, None, None
lowerCAmelCase_ :Tuple = use_timm_backbone
lowerCAmelCase_ :Optional[int] = backbone_config
lowerCAmelCase_ :Optional[int] = num_channels
lowerCAmelCase_ :int = num_queries
lowerCAmelCase_ :List[Any] = d_model
lowerCAmelCase_ :Optional[int] = encoder_ffn_dim
lowerCAmelCase_ :Tuple = encoder_layers
lowerCAmelCase_ :int = encoder_attention_heads
lowerCAmelCase_ :Optional[Any] = decoder_ffn_dim
lowerCAmelCase_ :List[str] = decoder_layers
lowerCAmelCase_ :Dict = decoder_attention_heads
lowerCAmelCase_ :Dict = dropout
lowerCAmelCase_ :Tuple = attention_dropout
lowerCAmelCase_ :Union[str, Any] = activation_dropout
lowerCAmelCase_ :Any = activation_function
lowerCAmelCase_ :List[str] = init_std
lowerCAmelCase_ :Optional[int] = init_xavier_std
lowerCAmelCase_ :int = encoder_layerdrop
lowerCAmelCase_ :Union[str, Any] = decoder_layerdrop
lowerCAmelCase_ :List[str] = encoder_layers
lowerCAmelCase_ :Union[str, Any] = auxiliary_loss
lowerCAmelCase_ :str = position_embedding_type
lowerCAmelCase_ :List[Any] = backbone
lowerCAmelCase_ :str = use_pretrained_backbone
lowerCAmelCase_ :str = dilation
# Hungarian matcher
lowerCAmelCase_ :List[Any] = class_cost
lowerCAmelCase_ :Union[str, Any] = bbox_cost
lowerCAmelCase_ :Tuple = giou_cost
# Loss coefficients
lowerCAmelCase_ :Optional[int] = mask_loss_coefficient
lowerCAmelCase_ :Union[str, Any] = dice_loss_coefficient
lowerCAmelCase_ :Tuple = bbox_loss_coefficient
lowerCAmelCase_ :Tuple = giou_loss_coefficient
lowerCAmelCase_ :Dict = eos_coefficient
super().__init__(is_encoder_decoder=__A , **__A )
@property
def __lowerCAmelCase ( self ) -> int:
return self.encoder_attention_heads
@property
def __lowerCAmelCase ( self ) -> int:
return self.d_model
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> Any:
return cls(backbone_config=__A , **__A )
def __lowerCAmelCase ( self ) -> Dict[str, any]:
lowerCAmelCase_ :List[str] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
lowerCAmelCase_ :Dict = self.backbone_config.to_dict()
lowerCAmelCase_ :str = self.__class__.model_type
return output
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[Any] = version.parse("1.11" )
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
return 1E-5
@property
def __lowerCAmelCase ( self ) -> int:
return 12
| 1 | 1 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _snake_case ( lowercase__ : Any ) -> int:
'''simple docstring'''
monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings""" , set() )
@pytest.fixture
def _snake_case ( lowercase__ : List[str] ) -> Any:
'''simple docstring'''
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> int:
lowerCAmelCase_ :Union[str, Any] = metric_id
class _SCREAMING_SNAKE_CASE :
UpperCAmelCase_ :Any = [MetricMock(A__ ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
def __lowerCAmelCase ( self ) -> List[str]:
return self._metrics
monkeypatch.setattr("""datasets.inspect.huggingface_hub""" , HfhMock() )
@pytest.mark.parametrize(
"""func, args""" , [(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))] )
def _snake_case ( lowercase__ : int , lowercase__ : Dict , lowercase__ : Tuple , lowercase__ : Dict , lowercase__ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
if "tmp_path" in args:
lowerCAmelCase_ :Union[str, Any] = tuple(arg if arg != """tmp_path""" else tmp_path for arg in args )
with pytest.warns(lowercase__ , match="""https://huggingface.co/docs/evaluate""" ):
func(*lowercase__ )
| 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['DeiTFeatureExtractor']
__UpperCAmelCase = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 1 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__UpperCAmelCase = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
__UpperCAmelCase = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__UpperCAmelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _SCREAMING_SNAKE_CASE :
UpperCAmelCase_ :Optional[str] = field(
default="cifar10" , metadata={"help": "Name of a dataset from the datasets package"} )
UpperCAmelCase_ :Optional[str] = field(
default=A__ , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
UpperCAmelCase_ :Optional[str] = field(
default=A__ , metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."} , )
UpperCAmelCase_ :Optional[str] = field(default=A__ , metadata={"help": "A folder containing the training data."} )
UpperCAmelCase_ :Optional[str] = field(default=A__ , metadata={"help": "A folder containing the validation data."} )
UpperCAmelCase_ :Optional[float] = field(
default=0.1_5 , metadata={"help": "Percent to split off of train for validation."} )
UpperCAmelCase_ :int = field(default=32 , metadata={"help": "The size of the square patches to use for masking."} )
UpperCAmelCase_ :float = field(
default=0.6 , metadata={"help": "Percentage of patches to mask."} , )
UpperCAmelCase_ :Optional[int] = field(
default=A__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
UpperCAmelCase_ :Optional[int] = field(
default=A__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Tuple = {}
if self.train_dir is not None:
lowerCAmelCase_ :str = self.train_dir
if self.validation_dir is not None:
lowerCAmelCase_ :str = self.validation_dir
lowerCAmelCase_ :Any = data_files if data_files else None
@dataclass
class _SCREAMING_SNAKE_CASE :
UpperCAmelCase_ :str = field(
default=A__ , metadata={
"help": (
"The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
"checkpoint identifier on the hub. "
"Don't set if you want to train a model from scratch."
)
} , )
UpperCAmelCase_ :Optional[str] = field(
default=A__ , metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A__ )} , )
UpperCAmelCase_ :Optional[str] = field(
default=A__ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase_ :Optional[str] = field(
default=A__ , metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
} , )
UpperCAmelCase_ :Optional[str] = field(
default=A__ , metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"} , )
UpperCAmelCase_ :str = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
UpperCAmelCase_ :str = field(default=A__ , metadata={"help": "Name or path of preprocessor config."} )
UpperCAmelCase_ :bool = field(
default=A__ , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
UpperCAmelCase_ :Optional[int] = field(
default=A__ , metadata={
"help": (
"The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
)
} , )
UpperCAmelCase_ :Optional[int] = field(
default=A__ , metadata={
"help": (
"The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
)
} , )
UpperCAmelCase_ :Optional[int] = field(
default=A__ , metadata={"help": "Stride to use for the encoder."} , )
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A=192 , __A=32 , __A=4 , __A=0.6 ) -> List[str]:
lowerCAmelCase_ :List[str] = input_size
lowerCAmelCase_ :Optional[Any] = mask_patch_size
lowerCAmelCase_ :Tuple = model_patch_size
lowerCAmelCase_ :Optional[int] = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError("""Input size must be divisible by mask patch size""" )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError("""Mask patch size must be divisible by model patch size""" )
lowerCAmelCase_ :Any = self.input_size // self.mask_patch_size
lowerCAmelCase_ :Tuple = self.mask_patch_size // self.model_patch_size
lowerCAmelCase_ :Union[str, Any] = self.rand_size**2
lowerCAmelCase_ :str = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self ) -> str:
lowerCAmelCase_ :Optional[Any] = np.random.permutation(self.token_count )[: self.mask_count]
lowerCAmelCase_ :Optional[Any] = np.zeros(self.token_count , dtype=__A )
lowerCAmelCase_ :Optional[Any] = 1
lowerCAmelCase_ :Any = mask.reshape((self.rand_size, self.rand_size) )
lowerCAmelCase_ :Union[str, Any] = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def _snake_case ( lowercase__ : Dict ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :int = torch.stack([example["""pixel_values"""] for example in examples] )
lowerCAmelCase_ :str = torch.stack([example["""mask"""] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def _snake_case ( ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Tuple = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mim""" , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCAmelCase_ :str = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowerCAmelCase_ :Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCAmelCase_ :int = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
lowerCAmelCase_ :List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowerCAmelCase_ :str = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , lowercase__ ) and data_args.train_val_split > 0.0:
lowerCAmelCase_ :Optional[int] = ds["""train"""].train_test_split(data_args.train_val_split )
lowerCAmelCase_ :Union[str, Any] = split["""train"""]
lowerCAmelCase_ :Any = split["""test"""]
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase_ :Any = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
lowerCAmelCase_ :Dict = AutoConfig.from_pretrained(model_args.config_name_or_path , **lowercase__ )
elif model_args.model_name_or_path:
lowerCAmelCase_ :List[Any] = AutoConfig.from_pretrained(model_args.model_name_or_path , **lowercase__ )
else:
lowerCAmelCase_ :Union[str, Any] = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(f"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(f"""New config: {config}""" )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(lowercase__ , """decoder_type""" ):
lowerCAmelCase_ :Optional[Any] = """simmim"""
# adapt config
lowerCAmelCase_ :Optional[int] = model_args.image_size if model_args.image_size is not None else config.image_size
lowerCAmelCase_ :List[Any] = model_args.patch_size if model_args.patch_size is not None else config.patch_size
lowerCAmelCase_ :str = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
"""image_size""": model_args.image_size,
"""patch_size""": model_args.patch_size,
"""encoder_stride""": model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
lowerCAmelCase_ :Optional[int] = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **lowercase__ )
elif model_args.model_name_or_path:
lowerCAmelCase_ :int = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **lowercase__ )
else:
lowerCAmelCase_ :Union[str, Any] = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
lowerCAmelCase_ :Dict = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
lowerCAmelCase_ :Dict = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
lowerCAmelCase_ :List[str] = AutoModelForMaskedImageModeling.from_config(lowercase__ )
if training_args.do_train:
lowerCAmelCase_ :Dict = ds["""train"""].column_names
else:
lowerCAmelCase_ :str = ds["""validation"""].column_names
if data_args.image_column_name is not None:
lowerCAmelCase_ :Optional[int] = data_args.image_column_name
elif "image" in column_names:
lowerCAmelCase_ :str = """image"""
elif "img" in column_names:
lowerCAmelCase_ :Dict = """img"""
else:
lowerCAmelCase_ :List[Any] = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
lowerCAmelCase_ :Union[str, Any] = Compose(
[
Lambda(lambda lowercase__ : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
lowerCAmelCase_ :Union[str, Any] = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(lowercase__ : Any ):
lowerCAmelCase_ :Any = [transforms(lowercase__ ) for image in examples[image_column_name]]
lowerCAmelCase_ :Optional[Any] = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
lowerCAmelCase_ :str = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(lowercase__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
lowerCAmelCase_ :Dict = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(lowercase__ )
# Initialize our trainer
lowerCAmelCase_ :List[str] = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , )
# Training
if training_args.do_train:
lowerCAmelCase_ :Tuple = None
if training_args.resume_from_checkpoint is not None:
lowerCAmelCase_ :List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCAmelCase_ :Optional[int] = last_checkpoint
lowerCAmelCase_ :Dict = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowerCAmelCase_ :List[Any] = trainer.evaluate()
trainer.log_metrics("""eval""" , lowercase__ )
trainer.save_metrics("""eval""" , lowercase__ )
# Write model card and (optionally) push to hub
lowerCAmelCase_ :Dict = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """masked-image-modeling""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-image-modeling"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
if __name__ == "__main__":
main()
| 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
'SqueezeBertOnnxConfig',
],
'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['SqueezeBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'SqueezeBertForMaskedLM',
'SqueezeBertForMultipleChoice',
'SqueezeBertForQuestionAnswering',
'SqueezeBertForSequenceClassification',
'SqueezeBertForTokenClassification',
'SqueezeBertModel',
'SqueezeBertModule',
'SqueezeBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 1 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=13 , __A=7 , __A=True , __A=True , __A=True , __A=True , __A=99 , __A=32 , __A=5 , __A=4 , __A=37 , __A="gelu" , __A=0.1 , __A=0.1 , __A=128 , __A=32 , __A=16 , __A=2 , __A=0.0_2 , __A=3 , __A=4 , __A=None , ) -> str:
lowerCAmelCase_ :Union[str, Any] = parent
lowerCAmelCase_ :str = batch_size
lowerCAmelCase_ :Optional[Any] = seq_length
lowerCAmelCase_ :Union[str, Any] = is_training
lowerCAmelCase_ :Any = use_input_mask
lowerCAmelCase_ :Optional[int] = use_token_type_ids
lowerCAmelCase_ :List[str] = use_labels
lowerCAmelCase_ :Any = vocab_size
lowerCAmelCase_ :Union[str, Any] = hidden_size
lowerCAmelCase_ :Tuple = num_hidden_layers
lowerCAmelCase_ :Optional[int] = num_attention_heads
lowerCAmelCase_ :Tuple = intermediate_size
lowerCAmelCase_ :Tuple = hidden_act
lowerCAmelCase_ :List[str] = hidden_dropout_prob
lowerCAmelCase_ :Optional[int] = attention_probs_dropout_prob
lowerCAmelCase_ :Optional[int] = max_position_embeddings
lowerCAmelCase_ :Tuple = type_vocab_size
lowerCAmelCase_ :Union[str, Any] = type_sequence_label_size
lowerCAmelCase_ :int = initializer_range
lowerCAmelCase_ :str = num_labels
lowerCAmelCase_ :List[str] = num_choices
lowerCAmelCase_ :int = scope
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ :Dict = None
if self.use_input_mask:
lowerCAmelCase_ :Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ :Union[str, Any] = None
if self.use_token_type_ids:
lowerCAmelCase_ :Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase_ :List[Any] = None
lowerCAmelCase_ :List[Any] = None
lowerCAmelCase_ :str = None
if self.use_labels:
lowerCAmelCase_ :Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ :List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ :Any = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase_ :List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ) -> Optional[int]:
return NezhaConfig(
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=__A , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self ) -> str:
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) :Optional[Any] = self.prepare_config_and_inputs()
lowerCAmelCase_ :List[Any] = True
lowerCAmelCase_ :Optional[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowerCAmelCase_ :Dict = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> List[str]:
lowerCAmelCase_ :Tuple = NezhaModel(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :str = model(__A , attention_mask=__A , token_type_ids=__A )
lowerCAmelCase_ :int = model(__A , token_type_ids=__A )
lowerCAmelCase_ :Dict = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A , __A , __A , ) -> Tuple:
lowerCAmelCase_ :Optional[Any] = True
lowerCAmelCase_ :int = NezhaModel(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :str = model(
__A , attention_mask=__A , token_type_ids=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , )
lowerCAmelCase_ :Tuple = model(
__A , attention_mask=__A , token_type_ids=__A , encoder_hidden_states=__A , )
lowerCAmelCase_ :str = model(__A , attention_mask=__A , token_type_ids=__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> int:
lowerCAmelCase_ :List[Any] = NezhaForMaskedLM(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :List[str] = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> Optional[int]:
lowerCAmelCase_ :Any = NezhaForNextSentencePrediction(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :str = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> Any:
lowerCAmelCase_ :Dict = NezhaForPreTraining(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :str = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , next_sentence_label=__A , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> List[Any]:
lowerCAmelCase_ :Tuple = NezhaForQuestionAnswering(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :Tuple = model(
__A , attention_mask=__A , token_type_ids=__A , start_positions=__A , end_positions=__A , )
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 __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> Any:
lowerCAmelCase_ :Union[str, Any] = self.num_labels
lowerCAmelCase_ :Tuple = NezhaForSequenceClassification(__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :List[Any] = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> List[Any]:
lowerCAmelCase_ :Tuple = self.num_labels
lowerCAmelCase_ :List[str] = NezhaForTokenClassification(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :int = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A , __A ) -> Dict:
lowerCAmelCase_ :Dict = self.num_choices
lowerCAmelCase_ :Dict = NezhaForMultipleChoice(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase_ :Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ :List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ :Any = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ :Dict = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :List[Any] = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) :int = config_and_inputs
lowerCAmelCase_ :List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :Union[str, Any] = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCAmelCase_ :Optional[int] = (
{
"feature-extraction": NezhaModel,
"fill-mask": NezhaForMaskedLM,
"question-answering": NezhaForQuestionAnswering,
"text-classification": NezhaForSequenceClassification,
"token-classification": NezhaForTokenClassification,
"zero-shot": NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase_ :Optional[Any] = True
def __lowerCAmelCase ( self , __A , __A , __A=False ) -> Optional[Any]:
lowerCAmelCase_ :str = super()._prepare_for_class(__A , __A , return_labels=__A )
if return_labels:
if model_class in get_values(__A ):
lowerCAmelCase_ :Dict = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__A )
lowerCAmelCase_ :Optional[int] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
return inputs_dict
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :List[Any] = NezhaModelTester(self )
lowerCAmelCase_ :int = ConfigTester(self , config_class=__A , hidden_size=37 )
def __lowerCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__A )
def __lowerCAmelCase ( self ) -> str:
# This regression test was failing with PyTorch < 1.3
(
(
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) , (
lowerCAmelCase_
) ,
) :Tuple = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCAmelCase_ :Any = None
self.model_tester.create_and_check_model_as_decoder(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__A )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__A )
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*__A )
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__A )
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__A )
@slow
def __lowerCAmelCase ( self ) -> str:
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ :Tuple = NezhaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@slow
@require_torch_gpu
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ , lowerCAmelCase_ :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
lowerCAmelCase_ :int = True
lowerCAmelCase_ :Optional[int] = model_class(config=__A )
lowerCAmelCase_ :Any = self._prepare_for_class(__A , __A )
lowerCAmelCase_ :Optional[int] = torch.jit.trace(
__A , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(__A , os.path.join(__A , """bert.pt""" ) )
lowerCAmelCase_ :Union[str, Any] = torch.jit.load(os.path.join(__A , """bert.pt""" ) , map_location=__A )
loaded(inputs_dict["""input_ids"""].to(__A ) , inputs_dict["""attention_mask"""].to(__A ) )
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Optional[int] = NezhaModel.from_pretrained("""sijunhe/nezha-cn-base""" )
lowerCAmelCase_ :List[str] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase_ :Union[str, Any] = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowerCAmelCase_ :Dict = model(__A , attention_mask=__A )[0]
lowerCAmelCase_ :int = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , __A )
lowerCAmelCase_ :Optional[Any] = torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __A , atol=1E-4 ) )
@slow
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Dict = NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base""" )
lowerCAmelCase_ :Tuple = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase_ :Optional[int] = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowerCAmelCase_ :Tuple = model(__A , attention_mask=__A )[0]
lowerCAmelCase_ :Dict = torch.Size((1, 6, 2_1128) )
self.assertEqual(output.shape , __A )
lowerCAmelCase_ :str = torch.tensor(
[[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __A , atol=1E-4 ) )
| 1 |
"""simple docstring"""
__UpperCAmelCase = 2_56
# Modulus to hash a string
__UpperCAmelCase = 1_00_00_03
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple = len(lowercase__ )
lowerCAmelCase_ :List[str] = len(lowercase__ )
if p_len > t_len:
return False
lowerCAmelCase_ :List[str] = 0
lowerCAmelCase_ :Optional[int] = 0
lowerCAmelCase_ :Any = 1
# Calculating the hash of pattern and substring of text
for i in range(lowercase__ ):
lowerCAmelCase_ :int = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
lowerCAmelCase_ :Any = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
lowerCAmelCase_ :Optional[Any] = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
lowerCAmelCase_ :Any = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _snake_case ( ) -> None:
'''simple docstring'''
lowerCAmelCase_ :int = """abc1abc12"""
lowerCAmelCase_ :Dict = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
lowerCAmelCase_ :int = """alskfjaldsk23adsfabcabc"""
assert rabin_karp(lowercase__ , lowercase__ ) and not rabin_karp(lowercase__ , lowercase__ )
# Test 2)
lowerCAmelCase_ :Dict = """ABABX"""
lowerCAmelCase_ :int = """ABABZABABYABABX"""
assert rabin_karp(lowercase__ , lowercase__ )
# Test 3)
lowerCAmelCase_ :Union[str, Any] = """AAAB"""
lowerCAmelCase_ :List[str] = """ABAAAAAB"""
assert rabin_karp(lowercase__ , lowercase__ )
# Test 4)
lowerCAmelCase_ :Dict = """abcdabcy"""
lowerCAmelCase_ :Union[str, Any] = """abcxabcdabxabcdabcdabcy"""
assert rabin_karp(lowercase__ , lowercase__ )
# Test 5)
lowerCAmelCase_ :Optional[int] = """Lü"""
lowerCAmelCase_ :Optional[int] = """Lüsai"""
assert rabin_karp(lowercase__ , lowercase__ )
lowerCAmelCase_ :Optional[int] = """Lue"""
assert not rabin_karp(lowercase__ , lowercase__ )
print("""Success.""" )
if __name__ == "__main__":
test_rabin_karp()
| 1 | 1 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__UpperCAmelCase = 'scheduler_config.json'
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Tuple = 1
UpperCAmelCase_ :str = 2
UpperCAmelCase_ :Optional[int] = 3
UpperCAmelCase_ :Optional[Any] = 4
UpperCAmelCase_ :Dict = 5
UpperCAmelCase_ :Optional[Any] = 6
UpperCAmelCase_ :Optional[Any] = 7
UpperCAmelCase_ :Optional[int] = 8
UpperCAmelCase_ :Optional[Any] = 9
UpperCAmelCase_ :List[Any] = 10
UpperCAmelCase_ :List[Any] = 11
UpperCAmelCase_ :Dict = 12
UpperCAmelCase_ :Tuple = 13
UpperCAmelCase_ :int = 14
@dataclass
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :torch.FloatTensor
class _SCREAMING_SNAKE_CASE :
UpperCAmelCase_ :Any = SCHEDULER_CONFIG_NAME
UpperCAmelCase_ :Optional[int] = []
UpperCAmelCase_ :Optional[Any] = True
@classmethod
def __lowerCAmelCase ( cls , __A = None , __A = None , __A=False , **__A , ) -> int:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = cls.load_config(
pretrained_model_name_or_path=__A , subfolder=__A , return_unused_kwargs=__A , return_commit_hash=__A , **__A , )
return cls.from_config(__A , return_unused_kwargs=__A , **__A )
def __lowerCAmelCase ( self , __A , __A = False , **__A ) -> int:
self.save_config(save_directory=__A , push_to_hub=__A , **__A )
@property
def __lowerCAmelCase ( self ) -> Dict:
return self._get_compatibles()
@classmethod
def __lowerCAmelCase ( cls ) -> Tuple:
lowerCAmelCase_ :str = list(set([cls.__name__] + cls._compatibles ) )
lowerCAmelCase_ :Dict = importlib.import_module(__name__.split(""".""" )[0] )
lowerCAmelCase_ :int = [
getattr(__A , __A ) for c in compatible_classes_str if hasattr(__A , __A )
]
return compatible_classes
| 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def _snake_case ( lowercase__ : Accelerator , lowercase__ : int = 1_6 ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[str] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase_ :Optional[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ : int ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ :Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowerCAmelCase_ :Optional[Any] = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ :str = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase_ :int = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase_ :List[Any] = 1_6
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ :List[str] = 8
else:
lowerCAmelCase_ :Optional[int] = None
return tokenizer.pad(
lowercase__ , padding="""longest""" , max_length=lowercase__ , pad_to_multiple_of=lowercase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowerCAmelCase_ :Optional[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowerCAmelCase_ :List[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__UpperCAmelCase = mocked_dataloaders # noqa: F811
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowercase__ ) == "1":
lowerCAmelCase_ :Optional[Any] = 2
# New Code #
lowerCAmelCase_ :List[str] = int(args.gradient_accumulation_steps )
lowerCAmelCase_ :int = int(args.local_sgd_steps )
# Initialize accelerator
lowerCAmelCase_ :str = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase__ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ :int = config["""lr"""]
lowerCAmelCase_ :Union[str, Any] = int(config["""num_epochs"""] )
lowerCAmelCase_ :int = int(config["""seed"""] )
lowerCAmelCase_ :Union[str, Any] = int(config["""batch_size"""] )
lowerCAmelCase_ :Union[str, Any] = evaluate.load("""glue""" , """mrpc""" )
set_seed(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = get_dataloaders(lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ :Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowerCAmelCase_ :Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ :Optional[Any] = AdamW(params=model.parameters() , lr=lowercase__ )
# Instantiate scheduler
lowerCAmelCase_ :Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(lowercase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Now we train the model
for epoch in range(lowercase__ ):
model.train()
with LocalSGD(
accelerator=lowercase__ , model=lowercase__ , local_sgd_steps=lowercase__ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase__ ):
lowerCAmelCase_ :str = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = output.loss
accelerator.backward(lowercase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowerCAmelCase_ :Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , lowercase__ )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase__ , default=lowercase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=lowercase__ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=lowercase__ , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowerCAmelCase_ :Optional[Any] = parser.parse_args()
lowerCAmelCase_ :Tuple = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 1 | 1 |
"""simple docstring"""
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
__UpperCAmelCase = get_tests_dir('fixtures')
__UpperCAmelCase = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
__UpperCAmelCase = get_tests_dir('fixtures/dummy-config.json')
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[str] = 0
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :List[str] = AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :List[Any] = AutoFeatureExtractor.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> int:
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase_ :Optional[int] = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
lowerCAmelCase_ :Tuple = AutoFeatureExtractor.from_pretrained(__A ).to_dict()
config_dict.pop("""feature_extractor_type""" )
lowerCAmelCase_ :Optional[Any] = WavaVecaFeatureExtractor(**__A )
# save in new folder
model_config.save_pretrained(__A )
config.save_pretrained(__A )
lowerCAmelCase_ :Dict = AutoFeatureExtractor.from_pretrained(__A )
# make sure private variable is not incorrectly saved
lowerCAmelCase_ :Union[str, Any] = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :List[Any] = AutoFeatureExtractor.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Tuple:
with self.assertRaisesRegex(
__A , """bert-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase_ :Optional[int] = AutoFeatureExtractor.from_pretrained("""bert-base""" )
def __lowerCAmelCase ( self ) -> Optional[int]:
with self.assertRaisesRegex(
__A , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase_ :int = AutoFeatureExtractor.from_pretrained(__A , revision="""aaaaaa""" )
def __lowerCAmelCase ( self ) -> Any:
with self.assertRaisesRegex(
__A , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCAmelCase_ :Tuple = AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model""" )
def __lowerCAmelCase ( self ) -> Tuple:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__A ):
lowerCAmelCase_ :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 ):
lowerCAmelCase_ :Any = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
lowerCAmelCase_ :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 )
lowerCAmelCase_ :Tuple = AutoFeatureExtractor.from_pretrained(__A , trust_remote_code=__A )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
def __lowerCAmelCase ( self ) -> Optional[int]:
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
lowerCAmelCase_ :List[str] = CustomFeatureExtractor.from_pretrained(__A )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(__A )
lowerCAmelCase_ :Tuple = 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 __lowerCAmelCase ( self ) -> Union[str, Any]:
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[str] = True
try:
AutoConfig.register("""custom""" , __A )
AutoFeatureExtractor.register(__A , __A )
# If remote code is not set, the default is to use local
lowerCAmelCase_ :Union[str, 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.
lowerCAmelCase_ :Tuple = 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
lowerCAmelCase_ :Union[str, 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]
| 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def _snake_case ( lowercase__ : Accelerator , lowercase__ : int = 1_6 , lowercase__ : str = "bert-base-cased" ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = AutoTokenizer.from_pretrained(lowercase__ )
lowerCAmelCase_ :Optional[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ : List[str] ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ :str = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase_ :str = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowercase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ :List[str] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowercase__ , padding="""max_length""" , max_length=1_2_8 , return_tensors="""pt""" )
return tokenizer.pad(lowercase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase_ :Optional[int] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowerCAmelCase_ :Any = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : Tuple , lowercase__ : int ) -> List[str]:
'''simple docstring'''
model.eval()
lowerCAmelCase_ :Dict = 0
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase__ ) - 1:
lowerCAmelCase_ :Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCAmelCase_ :Any = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowerCAmelCase_ :Tuple = metric.compute()
return eval_metric["accuracy"]
def _snake_case ( lowercase__ : str , lowercase__ : List[str] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ :int = config["""lr"""]
lowerCAmelCase_ :Union[str, Any] = int(config["""num_epochs"""] )
lowerCAmelCase_ :Optional[int] = int(config["""seed"""] )
lowerCAmelCase_ :Union[str, Any] = int(config["""batch_size"""] )
lowerCAmelCase_ :Optional[Any] = args.model_name_or_path
set_seed(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = get_dataloaders(lowercase__ , lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ :str = AutoModelForSequenceClassification.from_pretrained(lowercase__ , return_dict=lowercase__ )
# Instantiate optimizer
lowerCAmelCase_ :List[str] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCAmelCase_ :str = optimizer_cls(params=model.parameters() , lr=lowercase__ )
if accelerator.state.deepspeed_plugin is not None:
lowerCAmelCase_ :Union[str, Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
lowerCAmelCase_ :Any = 1
lowerCAmelCase_ :str = (len(lowercase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCAmelCase_ :List[str] = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=0 , num_training_steps=lowercase__ , )
else:
lowerCAmelCase_ :int = DummyScheduler(lowercase__ , total_num_steps=lowercase__ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# We need to keep track of how many total steps we have iterated over
lowerCAmelCase_ :List[str] = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCAmelCase_ :List[Any] = 0
lowerCAmelCase_ :str = evaluate.load("""glue""" , """mrpc""" )
lowerCAmelCase_ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
lowerCAmelCase_ :Dict = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
lowerCAmelCase_ :Optional[Any] = args.resume_from_checkpoint.split("""epoch_""" )[1]
lowerCAmelCase_ :int = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
lowerCAmelCase_ :Union[str, Any] = int(lowercase__ ) + 1
lowerCAmelCase_ :Optional[int] = evaluation_loop(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
accelerator.print("""resumed checkpoint performance:""" , lowercase__ )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , f"""state_{starting_epoch-1}.json""" ) , """r""" ) as f:
lowerCAmelCase_ :List[str] = json.load(lowercase__ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
lowerCAmelCase_ :List[Any] = {}
for epoch in range(lowercase__ , lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Dict = outputs.loss
lowerCAmelCase_ :int = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
lowerCAmelCase_ :List[str] = f"""epoch_{epoch}"""
lowerCAmelCase_ :Any = os.path.join(args.output_dir , lowercase__ )
accelerator.save_state(lowercase__ )
lowerCAmelCase_ :List[Any] = evaluation_loop(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = accuracy
lowerCAmelCase_ :Any = lr_scheduler.get_lr()[0]
lowerCAmelCase_ :str = optimizer.param_groups[0]["""lr"""]
lowerCAmelCase_ :List[Any] = epoch
lowerCAmelCase_ :Tuple = overall_step
accelerator.print(f"""epoch {epoch}:""" , lowercase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f"""state_{epoch}.json""" ) , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
def _snake_case ( ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowercase__ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowercase__ , )
parser.add_argument(
"""--output_dir""" , type=lowercase__ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=lowercase__ , default=lowercase__ , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=lowercase__ , default=lowercase__ , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowercase__ , default=2 , help="""Number of train epochs.""" , )
lowerCAmelCase_ :Optional[int] = parser.parse_args()
lowerCAmelCase_ :List[Any] = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 1 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( A__ , A__ ):
UpperCAmelCase_ :Union[str, Any] = "convnextv2"
def __init__( self , __A=3 , __A=4 , __A=4 , __A=None , __A=None , __A="gelu" , __A=0.0_2 , __A=1E-12 , __A=0.0 , __A=224 , __A=None , __A=None , **__A , ) -> Any:
super().__init__(**__A )
lowerCAmelCase_ :str = num_channels
lowerCAmelCase_ :Tuple = patch_size
lowerCAmelCase_ :str = num_stages
lowerCAmelCase_ :Tuple = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
lowerCAmelCase_ :Optional[Any] = [3, 3, 9, 3] if depths is None else depths
lowerCAmelCase_ :Optional[int] = hidden_act
lowerCAmelCase_ :int = initializer_range
lowerCAmelCase_ :List[Any] = layer_norm_eps
lowerCAmelCase_ :List[Any] = drop_path_rate
lowerCAmelCase_ :Any = image_size
lowerCAmelCase_ :Union[str, Any] = ["""stem"""] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = get_aligned_output_features_output_indices(
out_features=__A , out_indices=__A , stage_names=self.stage_names )
| 1 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[str, Any]:
if isinstance(__A , __A ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
lowerCAmelCase_ :Tuple = deepcopy(__A )
elif os.path.exists(__A ):
with io.open(__A , """r""" , encoding="""utf-8""" ) as f:
lowerCAmelCase_ :str = json.load(__A )
else:
try:
lowerCAmelCase_ :Dict = baseaa.urlsafe_baadecode(__A ).decode("""utf-8""" )
lowerCAmelCase_ :int = json.loads(__A )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""" )
lowerCAmelCase_ :Optional[Any] = config
self.set_stage_and_offload()
def __lowerCAmelCase ( self ) -> Tuple:
# zero stage - this is done as early as possible, before model is created, to allow
# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
lowerCAmelCase_ :Tuple = self.get_value("""zero_optimization.stage""" , -1 )
# offload
lowerCAmelCase_ :Dict = False
if self.is_zeroa() or self.is_zeroa():
lowerCAmelCase_ :Optional[int] = set(["""cpu""", """nvme"""] )
lowerCAmelCase_ :Union[str, Any] = set(
[
self.get_value("""zero_optimization.offload_optimizer.device""" ),
self.get_value("""zero_optimization.offload_param.device""" ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
lowerCAmelCase_ :Optional[int] = True
def __lowerCAmelCase ( self , __A ) -> Optional[Any]:
lowerCAmelCase_ :str = self.config
# find the config node of interest if it exists
lowerCAmelCase_ :Tuple = ds_key_long.split(""".""" )
lowerCAmelCase_ :List[str] = nodes.pop()
for node in nodes:
lowerCAmelCase_ :Tuple = config.get(__A )
if config is None:
return None, ds_key
return config, ds_key
def __lowerCAmelCase ( self , __A , __A=None ) -> Optional[Any]:
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = self.find_config_node(__A )
if config is None:
return default
return config.get(__A , __A )
def __lowerCAmelCase ( self , __A , __A=False ) -> Optional[Any]:
lowerCAmelCase_ :Tuple = self.config
# find the config node of interest if it exists
lowerCAmelCase_ :Union[str, Any] = ds_key_long.split(""".""" )
for node in nodes:
lowerCAmelCase_ :int = config
lowerCAmelCase_ :Any = config.get(__A )
if config is None:
if must_exist:
raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}""" )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(__A )
def __lowerCAmelCase ( self , __A ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[int] = self.get_value(__A )
return False if value is None else bool(__A )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :List[str] = self.get_value(__A )
return False if value is None else not bool(__A )
def __lowerCAmelCase ( self ) -> str:
return self._stage == 2
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return self._stage == 3
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return self._offload
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Optional[int]:
lowerCAmelCase_ :Dict = engine
def __lowerCAmelCase ( self , __A , **__A ) -> str:
# runs backpropagation and handles mixed precision
self.engine.backward(__A , **__A )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A ) -> List[str]:
super().__init__(__A , device_placement=__A , scaler=__A )
lowerCAmelCase_ :List[str] = hasattr(self.optimizer , """overflow""" )
def __lowerCAmelCase ( self , __A=None ) -> Optional[Any]:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def __lowerCAmelCase ( self ) -> List[Any]:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def __lowerCAmelCase ( self ) -> int:
if self.__has_overflow__:
return self.optimizer.overflow
return False
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A , __A ) -> Optional[int]:
super().__init__(__A , __A )
def __lowerCAmelCase ( self ) -> Any:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=0.0_0_1 , __A=0 , **__A ) -> List[Any]:
lowerCAmelCase_ :str = params
lowerCAmelCase_ :Any = lr
lowerCAmelCase_ :List[Any] = weight_decay
lowerCAmelCase_ :Any = kwargs
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=None , __A=0 , **__A ) -> List[str]:
lowerCAmelCase_ :Optional[int] = optimizer
lowerCAmelCase_ :int = total_num_steps
lowerCAmelCase_ :List[Any] = warmup_num_steps
lowerCAmelCase_ :int = kwargs
| 1 | 1 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) ):
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = list(map(lowercase__ , line.split(""",""" ) ) )
if x * logaa(lowercase__ ) > largest:
lowerCAmelCase_ :Any = x * logaa(lowercase__ )
lowerCAmelCase_ :List[Any] = i + 1
return result
if __name__ == "__main__":
print(solution())
| 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Dict = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
UpperCAmelCase_ :List[str] = "CIDAS/clipseg-rd64-refined"
UpperCAmelCase_ :List[Any] = "image_segmenter"
UpperCAmelCase_ :Optional[int] = CLIPSegForImageSegmentation
UpperCAmelCase_ :Tuple = ["image", "text"]
UpperCAmelCase_ :Dict = ["image"]
def __init__( self , *__A , **__A ) -> Optional[Any]:
requires_backends(self , ["""vision"""] )
super().__init__(*__A , **__A )
def __lowerCAmelCase ( self , __A , __A ) -> Any:
return self.pre_processor(text=[label] , images=[image] , padding=__A , return_tensors="""pt""" )
def __lowerCAmelCase ( self , __A ) -> Tuple:
with torch.no_grad():
lowerCAmelCase_ :Dict = self.model(**__A ).logits
return logits
def __lowerCAmelCase ( self , __A ) -> Tuple:
lowerCAmelCase_ :Optional[int] = outputs.cpu().detach().numpy()
lowerCAmelCase_ :List[str] = 0
lowerCAmelCase_ :str = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 1 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self , __A , __A=13 , __A=7 , __A=True , __A=True , __A=True , __A=True , __A=99 , __A=32 , __A=5 , __A=4 , __A=37 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=16 , __A=2 , __A=0.0_2 , __A=4 , ) -> List[str]:
lowerCAmelCase_ :Union[str, Any] = parent
lowerCAmelCase_ :List[str] = batch_size
lowerCAmelCase_ :List[Any] = seq_length
lowerCAmelCase_ :int = is_training
lowerCAmelCase_ :Optional[int] = use_attention_mask
lowerCAmelCase_ :Tuple = use_token_type_ids
lowerCAmelCase_ :List[Any] = use_labels
lowerCAmelCase_ :Tuple = vocab_size
lowerCAmelCase_ :Dict = hidden_size
lowerCAmelCase_ :int = num_hidden_layers
lowerCAmelCase_ :Optional[Any] = num_attention_heads
lowerCAmelCase_ :Optional[int] = intermediate_size
lowerCAmelCase_ :Optional[int] = hidden_act
lowerCAmelCase_ :List[Any] = hidden_dropout_prob
lowerCAmelCase_ :Any = attention_probs_dropout_prob
lowerCAmelCase_ :int = max_position_embeddings
lowerCAmelCase_ :Dict = type_vocab_size
lowerCAmelCase_ :Any = type_sequence_label_size
lowerCAmelCase_ :Optional[int] = initializer_range
lowerCAmelCase_ :Tuple = num_choices
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ :Any = None
if self.use_attention_mask:
lowerCAmelCase_ :Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ :Any = None
if self.use_token_type_ids:
lowerCAmelCase_ :List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase_ :Dict = RoFormerConfig(
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=__A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Any = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Tuple = config_and_inputs
lowerCAmelCase_ :str = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :int = True
UpperCAmelCase_ :int = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Tuple = FlaxRoFormerModelTester(self )
@slow
def __lowerCAmelCase ( self ) -> str:
for model_class_name in self.all_model_classes:
lowerCAmelCase_ :Optional[int] = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=__A )
lowerCAmelCase_ :Union[str, Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A )
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :Any = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
lowerCAmelCase_ :List[str] = jnp.array([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase_ :Union[str, Any] = model(__A )[0]
lowerCAmelCase_ :int = 5_0000
lowerCAmelCase_ :Optional[int] = (1, 6, vocab_size)
self.assertEqual(output.shape , __A )
lowerCAmelCase_ :List[str] = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , __A , atol=1E-4 ) )
| 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCAmelCase_ :Any = 0
lowerCAmelCase_ :str = 0
lowerCAmelCase_ :Dict = knapsack(lowercase__ , lowercase__ , lowercase__ , lowercase__ , index + 1 )
if weights[index] <= max_weight:
lowerCAmelCase_ :str = values[index] + knapsack(
lowercase__ , lowercase__ , lowercase__ , max_weight - weights[index] , index + 1 )
return max(lowercase__ , lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : Any ) -> int:
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
lowerCAmelCase_ :List[str] = len(lowercase__ )
lowerCAmelCase_ :List[str] = max(lowercase__ )
lowerCAmelCase_ :int = min(lowercase__ )
# create the counting array
lowerCAmelCase_ :Dict = coll_max + 1 - coll_min
lowerCAmelCase_ :Optional[Any] = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , lowercase__ ):
lowerCAmelCase_ :Tuple = counting_arr[i] + counting_arr[i - 1]
# create the output collection
lowerCAmelCase_ :Tuple = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , lowercase__ ) ):
lowerCAmelCase_ :List[Any] = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def _snake_case ( lowercase__ : Any ) -> int:
'''simple docstring'''
return "".join([chr(lowercase__ ) for i in counting_sort([ord(lowercase__ ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"
__UpperCAmelCase = input('Enter numbers separated by a comma:\n').strip()
__UpperCAmelCase = [int(item) for item in user_input.split(',')]
print(counting_sort(unsorted))
| 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : str ) -> Optional[Any]:
'''simple docstring'''
if not is_tqdm_available():
raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.""" )
lowerCAmelCase_ :Tuple = False
if main_process_only:
lowerCAmelCase_ :Dict = PartialState().local_process_index == 0
return _tqdm(*lowercase__ , **lowercase__ , disable=lowercase__ )
| 1 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__UpperCAmelCase = ' \"""\n Output class for the scheduler\'s step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"""\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n'
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :int = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , """schedulers/""" ) )
lowerCAmelCase_ :Any = self.diffusers_dir
shutil.copy(
os.path.join(__A , """src/diffusers/schedulers/scheduling_ddpm.py""" ) , os.path.join(self.diffusers_dir , """schedulers/scheduling_ddpm.py""" ) , )
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = """src/diffusers"""
shutil.rmtree(self.diffusers_dir )
def __lowerCAmelCase ( self , __A , __A , __A , __A=None ) -> int:
lowerCAmelCase_ :Optional[int] = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
lowerCAmelCase_ :Optional[int] = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
lowerCAmelCase_ :Optional[int] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowerCAmelCase_ :List[str] = black.format_str(__A , mode=__A )
lowerCAmelCase_ :Any = os.path.join(self.diffusers_dir , """new_code.py""" )
with open(__A , """w""" , newline="""\n""" ) as f:
f.write(__A )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__A ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__A )
with open(__A , """r""" ) as f:
self.assertTrue(f.read() , __A )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Dict = check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" )
self.assertEqual(__A , __A )
def __lowerCAmelCase ( self ) -> List[Any]:
# Base copy consistency
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , REFERENCE_CODE + """\n""" , )
# With no empty line at the end
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , __A , )
# Copy consistency with rename
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , re.sub("""DDPM""" , """Test""" , __A ) , )
# Copy consistency with a really long name
lowerCAmelCase_ :List[Any] = """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub("""Bert""" , __A , __A ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , __A , overwrite_result=re.sub("""DDPM""" , """Test""" , __A ) , )
| 1 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
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
__UpperCAmelCase = get_tests_dir('fixtures/dummy-config.json')
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :int = 0
def __lowerCAmelCase ( self ) -> List[str]:
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Tuple = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :int = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :Any = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :int = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Tuple:
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
lowerCAmelCase_ :int = os.path.join(__A , """fake-roberta""" )
os.makedirs(__A , exist_ok=__A )
with open(os.path.join(__A , """config.json""" ) , """w""" ) as f:
f.write(json.dumps({} ) )
lowerCAmelCase_ :Any = AutoConfig.from_pretrained(__A )
self.assertEqual(type(__A ) , __A )
def __lowerCAmelCase ( self ) -> Optional[int]:
try:
AutoConfig.register("""custom""" , __A )
# Wrong model type will raise an error
with self.assertRaises(__A ):
AutoConfig.register("""model""" , __A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__A ):
AutoConfig.register("""bert""" , __A )
# Now that the config is registered, it can be used as any other config with the auto-API
lowerCAmelCase_ :Union[str, Any] = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A )
lowerCAmelCase_ :Optional[int] = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def __lowerCAmelCase ( self ) -> Tuple:
with self.assertRaisesRegex(
__A , """bert-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase_ :List[str] = AutoConfig.from_pretrained("""bert-base""" )
def __lowerCAmelCase ( self ) -> Any:
with self.assertRaisesRegex(
__A , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase_ :Dict = AutoConfig.from_pretrained(__A , revision="""aaaaaa""" )
def __lowerCAmelCase ( self ) -> int:
with self.assertRaisesRegex(
__A , """hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" , ):
lowerCAmelCase_ :Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def __lowerCAmelCase ( self ) -> Tuple:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__A ):
lowerCAmelCase_ :Tuple = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__A ):
lowerCAmelCase_ :List[str] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
lowerCAmelCase_ :str = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A )
lowerCAmelCase_ :Dict = AutoConfig.from_pretrained(__A , trust_remote_code=__A )
self.assertEqual(reloaded_config.__class__.__name__ , """NewModelConfig""" )
def __lowerCAmelCase ( self ) -> int:
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :int = "new-model"
try:
AutoConfig.register("""new-model""" , __A )
# If remote code is not set, the default is to use local
lowerCAmelCase_ :Any = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
lowerCAmelCase_ :Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
lowerCAmelCase_ :Optional[Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 1 | 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
__UpperCAmelCase = pd.read_csv(
'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
'position_salaries.csv'
)
__UpperCAmelCase = dataset.iloc[:, 1:2].values
__UpperCAmelCase = dataset.iloc[:, 2].values
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = train_test_split(X, y, test_size=0.2, random_state=0)
__UpperCAmelCase = PolynomialFeatures(degree=4)
__UpperCAmelCase = poly_reg.fit_transform(X)
__UpperCAmelCase = LinearRegression()
pol_reg.fit(X_poly, y)
def _snake_case ( ) -> str:
'''simple docstring'''
plt.scatter(lowercase__ , lowercase__ , color="""red""" )
plt.plot(lowercase__ , pol_reg.predict(poly_reg.fit_transform(lowercase__ ) ) , color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :str = GPTSanJapaneseTokenizer
UpperCAmelCase_ :Optional[int] = False
UpperCAmelCase_ :Optional[int] = {"do_clean_text": False, "add_prefix_space": False}
def __lowerCAmelCase ( self ) -> Tuple:
super().setUp()
# fmt: off
lowerCAmelCase_ :Dict = ["""こん""", """こんに""", """にちは""", """ばんは""", """世界,㔺界""", """、""", """。""", """<BR>""", """<SP>""", """<TAB>""", """<URL>""", """<EMAIL>""", """<TEL>""", """<DATE>""", """<PRICE>""", """<BLOCK>""", """<KIGOU>""", """<U2000U2BFF>""", """<|emoji1|>""", """<unk>""", """<|bagoftoken|>""", """<|endoftext|>"""]
# fmt: on
lowerCAmelCase_ :List[str] = {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}} # 😀
lowerCAmelCase_ :int = {"""unk_token""": """<unk>"""}
lowerCAmelCase_ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ :int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""emoji_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.emoji_file , """w""" ) as emoji_writer:
emoji_writer.write(json.dumps(__A ) )
def __lowerCAmelCase ( self , **__A ) -> int:
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **__A )
def __lowerCAmelCase ( self , __A ) -> Dict:
lowerCAmelCase_ :List[Any] = """こんにちは、世界。 \nこんばんは、㔺界。😀"""
lowerCAmelCase_ :Optional[int] = """こんにちは、世界。 \nこんばんは、世界。😀"""
return input_text, output_text
def __lowerCAmelCase ( self , __A ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = self.get_input_output_texts(__A )
lowerCAmelCase_ :List[str] = tokenizer.encode(__A , add_special_tokens=__A )
lowerCAmelCase_ :str = tokenizer.decode(__A , clean_up_tokenization_spaces=__A )
return text, ids
def __lowerCAmelCase ( self ) -> str:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Dict:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> int:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Union[str, Any] = self.get_tokenizer()
# Testing tokenization
lowerCAmelCase_ :Optional[int] = """こんにちは、世界。 こんばんは、㔺界。"""
lowerCAmelCase_ :Any = ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
lowerCAmelCase_ :Tuple = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
# Testing conversion to ids without special tokens
lowerCAmelCase_ :List[Any] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCAmelCase_ :List[str] = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(__A , __A )
# Testing conversion to ids with special tokens
lowerCAmelCase_ :Any = tokens + [tokenizer.unk_token]
lowerCAmelCase_ :Union[str, Any] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCAmelCase_ :Union[str, Any] = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(__A , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :int = self.get_tokenizer()
# Testing tokenization
lowerCAmelCase_ :Optional[int] = """こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
lowerCAmelCase_ :str = """こんにちは、、、、世界。こんばんは、、、、世界。"""
lowerCAmelCase_ :str = tokenizer.encode(__A )
lowerCAmelCase_ :Dict = tokenizer.decode(__A )
self.assertEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Tuple = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
# Testing tokenization
lowerCAmelCase_ :Optional[Any] = """こんにちは、世界。"""
lowerCAmelCase_ :Any = """こんばんは、㔺界。😀"""
lowerCAmelCase_ :Optional[Any] = """こんにちは、世界。こんばんは、世界。😀"""
lowerCAmelCase_ :List[Any] = tokenizer.encode(prefix_text + input_text )
lowerCAmelCase_ :List[str] = tokenizer.encode("""""" , prefix_text=prefix_text + input_text )
lowerCAmelCase_ :int = tokenizer.encode(__A , prefix_text=__A )
lowerCAmelCase_ :int = tokenizer.decode(__A )
lowerCAmelCase_ :Dict = tokenizer.decode(__A )
lowerCAmelCase_ :Tuple = tokenizer.decode(__A )
self.assertEqual(__A , __A )
self.assertEqual(__A , __A )
self.assertEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :int = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
# Testing tokenization
lowerCAmelCase_ :List[Any] = """こんにちは、世界。"""
lowerCAmelCase_ :Optional[int] = """こんばんは、㔺界。😀"""
lowerCAmelCase_ :List[str] = len(tokenizer.encode(__A ) ) - 2
lowerCAmelCase_ :Dict = len(tokenizer.encode(__A ) ) - 2
lowerCAmelCase_ :int = [1] + [0] * (len_prefix + len_text + 1)
lowerCAmelCase_ :List[Any] = [1] * (len_prefix + len_text + 1) + [0]
lowerCAmelCase_ :Dict = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCAmelCase_ :List[Any] = tokenizer(prefix_text + input_text ).token_type_ids
lowerCAmelCase_ :List[str] = tokenizer("""""" , prefix_text=prefix_text + input_text ).token_type_ids
lowerCAmelCase_ :List[Any] = tokenizer(__A , prefix_text=__A ).token_type_ids
self.assertListEqual(__A , __A )
self.assertListEqual(__A , __A )
self.assertListEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Dict = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
lowerCAmelCase_ :int = tokenizer.encode("""あンいワ""" )
lowerCAmelCase_ :Optional[Any] = tokenizer.encode("""""" , prefix_text="""あンいワ""" )
lowerCAmelCase_ :int = tokenizer.encode("""いワ""" , prefix_text="""あン""" )
self.assertEqual(tokenizer.decode(__A ) , tokenizer.decode(__A ) )
self.assertEqual(tokenizer.decode(__A ) , tokenizer.decode(__A ) )
self.assertNotEqual(__A , __A )
self.assertNotEqual(__A , __A )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
lowerCAmelCase_ :int = [["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
lowerCAmelCase_ :Dict = tokenizer(__A , padding=__A )
lowerCAmelCase_ :Any = tokenizer.batch_encode_plus(__A , padding=__A )
# fmt: off
lowerCAmelCase_ :int = [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
lowerCAmelCase_ :List[str] = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCAmelCase_ :int = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , __A )
self.assertListEqual(x_token.token_type_ids , __A )
self.assertListEqual(x_token.attention_mask , __A )
self.assertListEqual(x_token_a.input_ids , __A )
self.assertListEqual(x_token_a.token_type_ids , __A )
self.assertListEqual(x_token_a.attention_mask , __A )
def __lowerCAmelCase ( self ) -> Tuple:
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def __lowerCAmelCase ( self ) -> str:
# tokenizer has no padding token
pass
| 1 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
__UpperCAmelCase = None
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
__UpperCAmelCase = {
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
},
}
__UpperCAmelCase = {
'facebook/mbart-large-en-ro': 10_24,
'facebook/mbart-large-cc25': 10_24,
}
# fmt: off
__UpperCAmelCase = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[str] = VOCAB_FILES_NAMES
UpperCAmelCase_ :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ :Any = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ :str = ["input_ids", "attention_mask"]
UpperCAmelCase_ :str = MBartTokenizer
UpperCAmelCase_ :List[int] = []
UpperCAmelCase_ :List[int] = []
def __init__( self , __A=None , __A=None , __A="<s>" , __A="</s>" , __A="</s>" , __A="<s>" , __A="<unk>" , __A="<pad>" , __A="<mask>" , __A=None , __A=None , __A=None , **__A , ) -> int:
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase_ :List[str] = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else mask_token
super().__init__(
vocab_file=__A , tokenizer_file=__A , bos_token=__A , eos_token=__A , sep_token=__A , cls_token=__A , unk_token=__A , pad_token=__A , mask_token=__A , src_lang=__A , tgt_lang=__A , additional_special_tokens=__A , **__A , )
lowerCAmelCase_ :Any = vocab_file
lowerCAmelCase_ :Any = False if not self.vocab_file else True
lowerCAmelCase_ :List[Any] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
lowerCAmelCase_ :Union[str, Any] = {
lang_code: self.convert_tokens_to_ids(__A ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowerCAmelCase_ :List[str] = src_lang if src_lang is not None else """en_XX"""
lowerCAmelCase_ :Tuple = self.convert_tokens_to_ids(self._src_lang )
lowerCAmelCase_ :List[Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __lowerCAmelCase ( self ) -> str:
return self._src_lang
@src_lang.setter
def __lowerCAmelCase ( self , __A ) -> None:
lowerCAmelCase_ :int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __lowerCAmelCase ( self , __A , __A = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __lowerCAmelCase ( self , __A , __A = None ) -> List[int]:
lowerCAmelCase_ :Any = [self.sep_token_id]
lowerCAmelCase_ :Tuple = [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 __lowerCAmelCase ( self , __A , __A , __A , __A , **__A ) -> Tuple:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
lowerCAmelCase_ :Any = src_lang
lowerCAmelCase_ :Dict = self(__A , add_special_tokens=__A , return_tensors=__A , **__A )
lowerCAmelCase_ :List[Any] = self.convert_tokens_to_ids(__A )
lowerCAmelCase_ :str = tgt_lang_id
return inputs
def __lowerCAmelCase ( self , __A , __A = "en_XX" , __A = None , __A = "ro_RO" , **__A , ) -> BatchEncoding:
lowerCAmelCase_ :str = src_lang
lowerCAmelCase_ :str = tgt_lang
return super().prepare_seqaseq_batch(__A , __A , **__A )
def __lowerCAmelCase ( self ) -> Any:
return self.set_src_lang_special_tokens(self.src_lang )
def __lowerCAmelCase ( self ) -> List[str]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __lowerCAmelCase ( self , __A ) -> None:
lowerCAmelCase_ :Optional[Any] = self.convert_tokens_to_ids(__A )
lowerCAmelCase_ :Any = []
lowerCAmelCase_ :Union[str, Any] = [self.eos_token_id, self.cur_lang_code]
lowerCAmelCase_ :Dict = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ :Optional[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ :int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __lowerCAmelCase ( self , __A ) -> None:
lowerCAmelCase_ :Dict = self.convert_tokens_to_ids(__A )
lowerCAmelCase_ :Dict = []
lowerCAmelCase_ :str = [self.eos_token_id, self.cur_lang_code]
lowerCAmelCase_ :str = self.convert_ids_to_tokens(self.prefix_tokens )
lowerCAmelCase_ :int = self.convert_ids_to_tokens(self.suffix_tokens )
lowerCAmelCase_ :Optional[int] = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __lowerCAmelCase ( self , __A , __A = 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(__A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
return
lowerCAmelCase_ :int = os.path.join(
__A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ):
copyfile(self.vocab_file , __A )
return (out_vocab_file,)
| 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
__UpperCAmelCase = pd.read_csv(
'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
'position_salaries.csv'
)
__UpperCAmelCase = dataset.iloc[:, 1:2].values
__UpperCAmelCase = dataset.iloc[:, 2].values
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = train_test_split(X, y, test_size=0.2, random_state=0)
__UpperCAmelCase = PolynomialFeatures(degree=4)
__UpperCAmelCase = poly_reg.fit_transform(X)
__UpperCAmelCase = LinearRegression()
pol_reg.fit(X_poly, y)
def _snake_case ( ) -> str:
'''simple docstring'''
plt.scatter(lowercase__ , lowercase__ , color="""red""" )
plt.plot(lowercase__ , pol_reg.predict(poly_reg.fit_transform(lowercase__ ) ) , color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 1 | 1 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__UpperCAmelCase = get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
__UpperCAmelCase = json.load(f)
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self , __A ) -> List[Any]:
return FSMTTokenizer.from_pretrained(__A )
def __lowerCAmelCase ( self , __A ) -> Any:
lowerCAmelCase_ :Any = FSMTForConditionalGeneration.from_pretrained(__A ).to(__A )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 2_6.0],
["""ru-en""", 2_2.0],
["""en-de""", 2_2.0],
["""de-en""", 2_9.0],
] )
@slow
def __lowerCAmelCase ( self , __A , __A ) -> Dict:
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
lowerCAmelCase_ :int = f"""facebook/wmt19-{pair}"""
lowerCAmelCase_ :List[str] = self.get_tokenizer(__A )
lowerCAmelCase_ :Union[str, Any] = self.get_model(__A )
lowerCAmelCase_ :Optional[Any] = bleu_data[pair]["""src"""]
lowerCAmelCase_ :List[Any] = bleu_data[pair]["""tgt"""]
lowerCAmelCase_ :int = tokenizer(__A , return_tensors="""pt""" , truncation=__A , padding="""longest""" ).to(__A )
lowerCAmelCase_ :Dict = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
lowerCAmelCase_ :Tuple = tokenizer.batch_decode(
__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )
lowerCAmelCase_ :List[str] = calculate_bleu(__A , __A )
print(__A )
self.assertGreaterEqual(scores["""bleu"""] , __A )
| 1 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ : list[int] , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> None:
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = array[indexa], array[indexa]
def _snake_case ( lowercase__ : list[int] , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> None:
'''simple docstring'''
if length > 1:
lowerCAmelCase_ :Optional[int] = int(length / 2 )
for i in range(lowercase__ , low + middle ):
comp_and_swap(lowercase__ , lowercase__ , i + middle , lowercase__ )
bitonic_merge(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
bitonic_merge(lowercase__ , low + middle , lowercase__ , lowercase__ )
def _snake_case ( lowercase__ : list[int] , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> None:
'''simple docstring'''
if length > 1:
lowerCAmelCase_ :int = int(length / 2 )
bitonic_sort(lowercase__ , lowercase__ , lowercase__ , 1 )
bitonic_sort(lowercase__ , low + middle , lowercase__ , 0 )
bitonic_merge(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
if __name__ == "__main__":
__UpperCAmelCase = input('Enter numbers separated by a comma:\n').strip()
__UpperCAmelCase = [int(item.strip()) for item in user_input.split(',')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('\nSorted array in ascending order is: ', end='')
print(*unsorted, sep=', ')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('Sorted array in descending order is: ', end='')
print(*unsorted, sep=', ')
| 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , **__A ) -> None:
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , __A , )
super().__init__(*__A , **__A )
| 1 | 1 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ ):
UpperCAmelCase_ :List[str] = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]
@register_to_config
def __init__( self , __A , __A , __A = None , __A = 5_0257 , __A = 1024 , __A = 768 , __A = 12 , __A = 12 , __A = None , __A = "gelu_new" , __A = 0.1 , __A = 0.1 , __A = 0.1 , __A = 1E-5 , __A = 0.0_2 , __A = True , __A = True , __A = False , __A = False , ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ :List[str] = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
f""" `n_embd`: {n_embd} are not equal.""" )
lowerCAmelCase_ :Union[str, Any] = prefix_inner_dim
lowerCAmelCase_ :str = prefix_hidden_dim
lowerCAmelCase_ :str = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
lowerCAmelCase_ :List[Any] = (
nn.Linear(self.prefix_hidden_dim , __A ) if self.prefix_hidden_dim is not None else nn.Identity()
)
lowerCAmelCase_ :Any = GPTaConfig(
vocab_size=__A , n_positions=__A , n_embd=__A , n_layer=__A , n_head=__A , n_inner=__A , activation_function=__A , resid_pdrop=__A , embd_pdrop=__A , attn_pdrop=__A , layer_norm_epsilon=__A , initializer_range=__A , scale_attn_weights=__A , use_cache=__A , scale_attn_by_inverse_layer_idx=__A , reorder_and_upcast_attn=__A , )
lowerCAmelCase_ :Any = GPTaLMHeadModel(__A )
def __lowerCAmelCase ( self , __A , __A , __A = None , __A = None , ) -> List[str]:
lowerCAmelCase_ :str = self.transformer.transformer.wte(__A )
lowerCAmelCase_ :Any = self.encode_prefix(__A )
lowerCAmelCase_ :Optional[Any] = self.decode_prefix(__A )
lowerCAmelCase_ :Optional[int] = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
lowerCAmelCase_ :int = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
lowerCAmelCase_ :Optional[Any] = torch.cat((dummy_token, input_ids) , dim=1 )
lowerCAmelCase_ :Tuple = self.transformer(inputs_embeds=__A , labels=__A , attention_mask=__A )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def __lowerCAmelCase ( self , __A , __A ) -> torch.Tensor:
return torch.zeros(__A , self.prefix_length , dtype=torch.intaa , device=__A )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
return self.encode_prefix(__A )
@torch.no_grad()
def __lowerCAmelCase ( self , __A , __A , __A ) -> Optional[int]:
lowerCAmelCase_ :Tuple = torch.split(__A , 1 , dim=0 )
lowerCAmelCase_ :Optional[int] = []
lowerCAmelCase_ :List[str] = []
for feature in features:
lowerCAmelCase_ :Tuple = self.decode_prefix(feature.to(__A ) ) # back to the clip feature
# Only support beam search for now
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = self.generate_beam(
input_embeds=__A , device=__A , eos_token_id=__A )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
lowerCAmelCase_ :Tuple = torch.stack(__A )
lowerCAmelCase_ :int = torch.stack(__A )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def __lowerCAmelCase ( self , __A=None , __A=None , __A=None , __A = 5 , __A = 67 , __A = 1.0 , __A = None , ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[int] = eos_token_id
lowerCAmelCase_ :Optional[int] = None
lowerCAmelCase_ :Any = None
lowerCAmelCase_ :int = torch.ones(__A , device=__A , dtype=torch.int )
lowerCAmelCase_ :Optional[int] = torch.zeros(__A , device=__A , dtype=torch.bool )
if input_embeds is not None:
lowerCAmelCase_ :List[str] = input_embeds
else:
lowerCAmelCase_ :Union[str, Any] = self.transformer.transformer.wte(__A )
for i in range(__A ):
lowerCAmelCase_ :Optional[int] = self.transformer(inputs_embeds=__A )
lowerCAmelCase_ :str = outputs.logits
lowerCAmelCase_ :str = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
lowerCAmelCase_ :Dict = logits.softmax(-1 ).log()
if scores is None:
lowerCAmelCase_ , lowerCAmelCase_ :Any = logits.topk(__A , -1 )
lowerCAmelCase_ :Union[str, Any] = generated.expand(__A , *generated.shape[1:] )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
lowerCAmelCase_ :List[str] = next_tokens
else:
lowerCAmelCase_ :List[Any] = tokens.expand(__A , *tokens.shape[1:] )
lowerCAmelCase_ :Any = torch.cat((tokens, next_tokens) , dim=1 )
else:
lowerCAmelCase_ :List[Any] = -float(np.inf )
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Optional[int] = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
lowerCAmelCase_ :List[Any] = scores_sum / seq_lengths[:, None]
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = scores_sum_average.view(-1 ).topk(__A , -1 )
lowerCAmelCase_ :Optional[Any] = next_tokens // scores_sum.shape[1]
lowerCAmelCase_ :Dict = seq_lengths[next_tokens_source]
lowerCAmelCase_ :Tuple = next_tokens % scores_sum.shape[1]
lowerCAmelCase_ :Optional[Any] = next_tokens.unsqueeze(1 )
lowerCAmelCase_ :str = tokens[next_tokens_source]
lowerCAmelCase_ :List[Any] = torch.cat((tokens, next_tokens) , dim=1 )
lowerCAmelCase_ :Dict = generated[next_tokens_source]
lowerCAmelCase_ :Dict = scores_sum_average * seq_lengths
lowerCAmelCase_ :Tuple = is_stopped[next_tokens_source]
lowerCAmelCase_ :str = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
lowerCAmelCase_ :List[Any] = torch.cat((generated, next_token_embed) , dim=1 )
lowerCAmelCase_ :Optional[int] = is_stopped + next_tokens.eq(__A ).squeeze()
if is_stopped.all():
break
lowerCAmelCase_ :str = scores / seq_lengths
lowerCAmelCase_ :Optional[int] = scores.argsort(descending=__A )
# tokens tensors are already padded to max_seq_length
lowerCAmelCase_ :Optional[Any] = [tokens[i] for i in order]
lowerCAmelCase_ :Dict = torch.stack(__A , dim=0 )
lowerCAmelCase_ :Tuple = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 1 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _snake_case ( lowercase__ : str = "laptop" ) -> DataFrame:
'''simple docstring'''
lowerCAmelCase_ :Dict = f"""https://www.amazon.in/laptop/s?k={product}"""
lowerCAmelCase_ :List[str] = {
"""User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
"""Accept-Language""": """en-US, en;q=0.5""",
}
lowerCAmelCase_ :List[Any] = BeautifulSoup(requests.get(lowercase__ , headers=lowercase__ ).text )
# Initialize a Pandas dataframe with the column titles
lowerCAmelCase_ :Union[str, Any] = DataFrame(
columns=[
"""Product Title""",
"""Product Link""",
"""Current Price of the product""",
"""Product Rating""",
"""MRP of the product""",
"""Discount""",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"""div""" , attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""} , ) , soup.find_all("""div""" , attrs={"""class""": """a-row a-size-base a-color-base"""} ) , ):
try:
lowerCAmelCase_ :str = item.ha.text
lowerCAmelCase_ :Dict = """https://www.amazon.in/""" + item.ha.a["""href"""]
lowerCAmelCase_ :int = item.find("""span""" , attrs={"""class""": """a-offscreen"""} ).text
try:
lowerCAmelCase_ :Dict = item.find("""span""" , attrs={"""class""": """a-icon-alt"""} ).text
except AttributeError:
lowerCAmelCase_ :int = """Not available"""
try:
lowerCAmelCase_ :str = (
"""₹"""
+ item.find(
"""span""" , attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
)
except AttributeError:
lowerCAmelCase_ :Optional[Any] = """"""
try:
lowerCAmelCase_ :str = float(
(
(
float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
- float(product_price.strip("""₹""" ).replace(""",""" , """""" ) )
)
/ float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
)
* 1_0_0 )
except ValueError:
lowerCAmelCase_ :Union[str, Any] = float("""nan""" )
except AttributeError:
pass
lowerCAmelCase_ :Any = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
lowerCAmelCase_ :List[Any] = """ """
lowerCAmelCase_ :Tuple = """ """
data_frame.index += 1
return data_frame
if __name__ == "__main__":
__UpperCAmelCase = 'headphones'
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 1 | 1 |
"""simple docstring"""
import math
import sys
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
if number != int(lowercase__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of input must not be a negative number""" )
if number == 0:
return 1
lowerCAmelCase_ :Any = [-1] * (number + 1)
lowerCAmelCase_ :Dict = 0
for i in range(1 , number + 1 ):
lowerCAmelCase_ :Union[str, Any] = sys.maxsize
lowerCAmelCase_ :List[Any] = int(math.sqrt(lowercase__ ) )
for j in range(1 , root + 1 ):
lowerCAmelCase_ :List[str] = 1 + answers[i - (j**2)]
lowerCAmelCase_ :Any = min(lowercase__ , lowercase__ )
lowerCAmelCase_ :Tuple = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :Any = """laion/clap-htsat-unfused"""
lowerCAmelCase_ :Optional[Any] = tempfile.mkdtemp()
def __lowerCAmelCase ( self , **__A ) -> List[Any]:
return RobertaTokenizer.from_pretrained(self.checkpoint , **__A )
def __lowerCAmelCase ( self , **__A ) -> Tuple:
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__A )
def __lowerCAmelCase ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Optional[Any] = self.get_tokenizer()
lowerCAmelCase_ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ :Optional[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Dict = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ :str = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase_ :Dict = self.get_feature_extractor(do_normalize=__A , padding_value=1.0 )
lowerCAmelCase_ :Union[str, Any] = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Dict = self.get_feature_extractor()
lowerCAmelCase_ :str = self.get_tokenizer()
lowerCAmelCase_ :List[Any] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :Optional[Any] = floats_list((3, 1000) )
lowerCAmelCase_ :Optional[Any] = feature_extractor(__A , return_tensors="""np""" )
lowerCAmelCase_ :str = processor(audios=__A , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Any = self.get_tokenizer()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :List[Any] = """This is a test string"""
lowerCAmelCase_ :Dict = processor(text=__A )
lowerCAmelCase_ :List[str] = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :int = self.get_feature_extractor()
lowerCAmelCase_ :Tuple = self.get_tokenizer()
lowerCAmelCase_ :Optional[Any] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase_ :Tuple = processor.batch_decode(__A )
lowerCAmelCase_ :Optional[Any] = tokenizer.batch_decode(__A )
self.assertListEqual(__A , __A )
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Any = self.get_tokenizer()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 1 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Any = "philschmid/bart-large-cnn-samsum"
UpperCAmelCase_ :Tuple = (
"This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
"and returns a summary of the text."
)
UpperCAmelCase_ :Union[str, Any] = "summarizer"
UpperCAmelCase_ :str = AutoTokenizer
UpperCAmelCase_ :str = AutoModelForSeqaSeqLM
UpperCAmelCase_ :Tuple = ["text"]
UpperCAmelCase_ :Tuple = ["text"]
def __lowerCAmelCase ( self , __A ) -> Optional[Any]:
return self.pre_processor(__A , return_tensors="""pt""" , truncation=__A )
def __lowerCAmelCase ( self , __A ) -> Optional[Any]:
return self.model.generate(**__A )[0]
def __lowerCAmelCase ( self , __A ) -> Tuple:
return self.pre_processor.decode(__A , skip_special_tokens=__A , clean_up_tokenization_spaces=__A )
| 1 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) ):
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = list(map(lowercase__ , line.split(""",""" ) ) )
if x * logaa(lowercase__ ) > largest:
lowerCAmelCase_ :Any = x * logaa(lowercase__ )
lowerCAmelCase_ :List[Any] = i + 1
return result
if __name__ == "__main__":
print(solution())
| 1 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
__UpperCAmelCase = logging.getLogger()
__UpperCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __lowerCAmelCase ( self , __A ) -> Optional[Any]:
os.makedirs(__A , exist_ok=__A )
lowerCAmelCase_ :Union[str, Any] = {"""source""": """What is love ?""", """target""": """life"""}
lowerCAmelCase_ :Optional[Any] = {"""train""": 12, """val""": 2, """test""": 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
lowerCAmelCase_ :Optional[Any] = """\n""".join([contents[field]] * n_lines[split] )
with open(os.path.join(__A , f"""{split}.{field}""" ) , """w""" ) as f:
f.write(__A )
def __lowerCAmelCase ( self , __A , __A = "pytorch" ) -> Optional[Any]:
lowerCAmelCase_ :Dict = self.get_auto_remove_tmp_dir()
lowerCAmelCase_ :Optional[Any] = os.path.join(__A , """output""" )
lowerCAmelCase_ :str = os.path.join(__A , """data""" )
self._create_dummy_data(data_dir=__A )
lowerCAmelCase_ :Optional[Any] = f"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(f"""--gpus={gpus}""" )
if is_apex_available():
testargs.append("""--fp16""" )
else:
testargs.append("""--gpus=0""" )
testargs.append("""--distributed_backend=ddp_cpu""" )
testargs.append("""--num_processes=2""" )
lowerCAmelCase_ :List[Any] = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(__A , env=self.get_env() )
lowerCAmelCase_ :str = os.path.join(__A , """metrics.json""" )
with open(__A ) as f:
lowerCAmelCase_ :List[Any] = json.load(__A )
return result
@require_torch_gpu
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :Tuple = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] , 0.2 )
@require_torch_multi_gpu
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :str = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] , 0.2 )
@require_torch_gpu
@require_ray
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :Optional[Any] = self._run_finetune(gpus=1 , distributed_retriever="""ray""" )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] , 0.2 )
@require_torch_multi_gpu
@require_ray
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :int = self._run_finetune(gpus=1 , distributed_retriever="""ray""" )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] , 0.2 )
| 1 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
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(lowercase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _snake_case ( ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = 2
while True:
if is_prime(lowercase__ ):
yield num
num += 1
def _snake_case ( lowercase__ : int = 1_0_0_0_1 ) -> int:
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , lowercase__ ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 | 1 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _SCREAMING_SNAKE_CASE ( TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
def __init__( self , __A=None , **__A ) -> List[Any]:
super().__init__(features=__A )
lowerCAmelCase_ :List[Any] = torch_tensor_kwargs
import torch # noqa import torch at initialization
def __lowerCAmelCase ( self , __A ) -> str:
import torch
if isinstance(__A , __A ) and column:
if all(
isinstance(__A , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(__A )
return column
def __lowerCAmelCase ( self , __A ) -> List[str]:
import torch
if isinstance(__A , (str, bytes, type(__A )) ):
return value
elif isinstance(__A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowerCAmelCase_ :str = {}
if isinstance(__A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
lowerCAmelCase_ :str = {"""dtype""": torch.intaa}
elif isinstance(__A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowerCAmelCase_ :Tuple = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__A , PIL.Image.Image ):
lowerCAmelCase_ :Optional[Any] = np.asarray(__A )
return torch.tensor(__A , **{**default_dtype, **self.torch_tensor_kwargs} )
def __lowerCAmelCase ( self , __A ) -> Union[str, Any]:
import torch
# support for torch, tf, jax etc.
if hasattr(__A , """__array__""" ) and not isinstance(__A , torch.Tensor ):
lowerCAmelCase_ :Tuple = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__A , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
elif isinstance(__A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
return self._tensorize(__A )
def __lowerCAmelCase ( self , __A ) -> Optional[Any]:
return map_nested(self._recursive_tensorize , __A , map_list=__A )
def __lowerCAmelCase ( self , __A ) -> Mapping:
lowerCAmelCase_ :Optional[Any] = self.numpy_arrow_extractor().extract_row(__A )
lowerCAmelCase_ :List[str] = self.python_features_decoder.decode_row(__A )
return self.recursive_tensorize(__A )
def __lowerCAmelCase ( self , __A ) -> "torch.Tensor":
lowerCAmelCase_ :Union[str, Any] = self.numpy_arrow_extractor().extract_column(__A )
lowerCAmelCase_ :Union[str, Any] = self.python_features_decoder.decode_column(__A , pa_table.column_names[0] )
lowerCAmelCase_ :Optional[int] = self.recursive_tensorize(__A )
lowerCAmelCase_ :List[Any] = self._consolidate(__A )
return column
def __lowerCAmelCase ( self , __A ) -> Mapping:
lowerCAmelCase_ :int = self.numpy_arrow_extractor().extract_batch(__A )
lowerCAmelCase_ :str = self.python_features_decoder.decode_batch(__A )
lowerCAmelCase_ :List[str] = self.recursive_tensorize(__A )
for column_name in batch:
lowerCAmelCase_ :int = self._consolidate(batch[column_name] )
return batch
| 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 | 1 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE :
UpperCAmelCase_ :str
UpperCAmelCase_ :str = None
@staticmethod
def __lowerCAmelCase ( ) -> Optional[int]:
raise NotImplementedError
def __lowerCAmelCase ( self , __A , __A , __A , **__A ) -> Dict:
raise NotImplementedError
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
raise NotImplementedError
def __lowerCAmelCase ( self ) -> Optional[Any]:
if not self.is_available():
raise RuntimeError(
f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )
@classmethod
def __lowerCAmelCase ( cls ) -> Dict:
return f"""`pip install {cls.pip_package or cls.name}`"""
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[Any] = "optuna"
@staticmethod
def __lowerCAmelCase ( ) -> Any:
return is_optuna_available()
def __lowerCAmelCase ( self , __A , __A , __A , **__A ) -> List[Any]:
return run_hp_search_optuna(__A , __A , __A , **__A )
def __lowerCAmelCase ( self , __A ) -> Tuple:
return default_hp_space_optuna(__A )
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Optional[int] = "ray"
UpperCAmelCase_ :Any = "'ray[tune]'"
@staticmethod
def __lowerCAmelCase ( ) -> Optional[Any]:
return is_ray_available()
def __lowerCAmelCase ( self , __A , __A , __A , **__A ) -> Optional[Any]:
return run_hp_search_ray(__A , __A , __A , **__A )
def __lowerCAmelCase ( self , __A ) -> str:
return default_hp_space_ray(__A )
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[str] = "sigopt"
@staticmethod
def __lowerCAmelCase ( ) -> Optional[int]:
return is_sigopt_available()
def __lowerCAmelCase ( self , __A , __A , __A , **__A ) -> List[Any]:
return run_hp_search_sigopt(__A , __A , __A , **__A )
def __lowerCAmelCase ( self , __A ) -> Any:
return default_hp_space_sigopt(__A )
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Optional[Any] = "wandb"
@staticmethod
def __lowerCAmelCase ( ) -> List[Any]:
return is_wandb_available()
def __lowerCAmelCase ( self , __A , __A , __A , **__A ) -> Dict:
return run_hp_search_wandb(__A , __A , __A , **__A )
def __lowerCAmelCase ( self , __A ) -> Any:
return default_hp_space_wandb(__A )
__UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def _snake_case ( ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowercase__ ) > 0:
lowerCAmelCase_ :Any = available_backends[0].name
if len(lowercase__ ) > 1:
logger.info(
f"""{len(lowercase__ )} hyperparameter search backends available. Using {name} as the default.""" )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f""" - To install {backend.name} run {backend.pip_install()}"""
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :int = StableDiffusionControlNetImgaImgPipeline
UpperCAmelCase_ :str = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
UpperCAmelCase_ :Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ :List[str] = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"} )
UpperCAmelCase_ :Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :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 , )
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
lowerCAmelCase_ :List[str] = 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 )
lowerCAmelCase_ :Union[str, 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=1000 , )
lowerCAmelCase_ :List[Any] = CLIPTextModel(__A )
lowerCAmelCase_ :int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase_ :Union[str, Any] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def __lowerCAmelCase ( self , __A , __A=0 ) -> List[str]:
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :Tuple = torch.manual_seed(__A )
else:
lowerCAmelCase_ :Optional[int] = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :List[Any] = 2
lowerCAmelCase_ :int = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , )
lowerCAmelCase_ :Optional[int] = floats_tensor(control_image.shape , rng=random.Random(__A ) ).to(__A )
lowerCAmelCase_ :Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ :List[Any] = Image.fromarray(np.uinta(__A ) ).convert("""RGB""" ).resize((64, 64) )
lowerCAmelCase_ :Union[str, Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def __lowerCAmelCase ( self ) -> int:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> List[str]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class _SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :List[str] = StableDiffusionControlNetImgaImgPipeline
UpperCAmelCase_ :int = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
UpperCAmelCase_ :str = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ :int = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def __lowerCAmelCase ( self ) -> Optional[int]:
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = 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 , )
torch.manual_seed(0 )
def init_weights(__A ):
if isinstance(__A , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowerCAmelCase_ :List[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(__A )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(__A )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = 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 )
lowerCAmelCase_ :Optional[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=1000 , )
lowerCAmelCase_ :str = CLIPTextModel(__A )
lowerCAmelCase_ :str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase_ :Optional[Any] = MultiControlNetModel([controlneta, controlneta] )
lowerCAmelCase_ :List[Any] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def __lowerCAmelCase ( self , __A , __A=0 ) -> str:
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :Optional[Any] = torch.manual_seed(__A )
else:
lowerCAmelCase_ :List[Any] = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :Optional[Any] = 2
lowerCAmelCase_ :Optional[int] = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , ),
]
lowerCAmelCase_ :int = floats_tensor(control_image[0].shape , rng=random.Random(__A ) ).to(__A )
lowerCAmelCase_ :Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ :List[Any] = Image.fromarray(np.uinta(__A ) ).convert("""RGB""" ).resize((64, 64) )
lowerCAmelCase_ :List[str] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[str] = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
lowerCAmelCase_ :Union[str, Any] = 1_0.0
lowerCAmelCase_ :Union[str, Any] = 4
lowerCAmelCase_ :Tuple = self.get_dummy_inputs(__A )
lowerCAmelCase_ :List[str] = steps
lowerCAmelCase_ :int = scale
lowerCAmelCase_ :Union[str, Any] = pipe(**__A )[0]
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :str = steps
lowerCAmelCase_ :str = scale
lowerCAmelCase_ :Tuple = pipe(**__A , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Union[str, Any] = steps
lowerCAmelCase_ :Union[str, Any] = scale
lowerCAmelCase_ :str = pipe(**__A , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
lowerCAmelCase_ :List[str] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = steps
lowerCAmelCase_ :Tuple = scale
lowerCAmelCase_ :str = pipe(**__A , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def __lowerCAmelCase ( self ) -> Dict:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowerCAmelCase ( self ) -> Tuple:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> Optional[int]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :str = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(__A )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :Any = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
lowerCAmelCase_ :int = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , safety_checker=__A , controlnet=__A )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[str] = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase_ :List[Any] = """evil space-punk bird"""
lowerCAmelCase_ :List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((512, 512) )
lowerCAmelCase_ :int = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((512, 512) )
lowerCAmelCase_ :Union[str, Any] = pipe(
__A , __A , control_image=__A , generator=__A , output_type="""np""" , num_inference_steps=50 , strength=0.6 , )
lowerCAmelCase_ :Tuple = output.images[0]
assert image.shape == (512, 512, 3)
lowerCAmelCase_ :Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2
| 1 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( lowercase__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Any = SwinConfig(
embed_dim=1_9_2 , depths=(2, 2, 1_8, 2) , num_heads=(6, 1_2, 2_4, 4_8) , window_size=1_2 , out_features=["""stage2""", """stage3""", """stage4"""] , )
lowerCAmelCase_ :str = DetaConfig(
backbone_config=lowercase__ , num_queries=9_0_0 , encoder_ffn_dim=2_0_4_8 , decoder_ffn_dim=2_0_4_8 , num_feature_levels=5 , assign_first_stage=lowercase__ , with_box_refine=lowercase__ , two_stage=lowercase__ , )
# set labels
lowerCAmelCase_ :List[Any] = """huggingface/label-files"""
if "o365" in model_name:
lowerCAmelCase_ :Union[str, Any] = 3_6_6
lowerCAmelCase_ :Union[str, Any] = """object365-id2label.json"""
else:
lowerCAmelCase_ :Any = 9_1
lowerCAmelCase_ :Optional[Any] = """coco-detection-id2label.json"""
lowerCAmelCase_ :str = num_labels
lowerCAmelCase_ :List[str] = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type="""dataset""" ) ) , """r""" ) )
lowerCAmelCase_ :Union[str, Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
lowerCAmelCase_ :str = idalabel
lowerCAmelCase_ :Optional[Any] = {v: k for k, v in idalabel.items()}
return config
def _snake_case ( lowercase__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Dict = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""") )
rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""") )
rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""") )
rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""") )
rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""") )
rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""") )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""") )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""") )
# fmt: on
return rename_keys
def _snake_case ( lowercase__ : int , lowercase__ : Union[str, Any] , lowercase__ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :str = dct.pop(lowercase__ )
lowerCAmelCase_ :int = val
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :int = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
lowerCAmelCase_ :Optional[int] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
lowerCAmelCase_ :Tuple = state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight""" )
lowerCAmelCase_ :Any = state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ :Optional[Any] = in_proj_weight[:dim, :]
lowerCAmelCase_ :Dict = in_proj_bias[: dim]
lowerCAmelCase_ :Optional[Any] = in_proj_weight[
dim : dim * 2, :
]
lowerCAmelCase_ :List[Any] = in_proj_bias[
dim : dim * 2
]
lowerCAmelCase_ :List[str] = in_proj_weight[
-dim :, :
]
lowerCAmelCase_ :List[Any] = in_proj_bias[-dim :]
# fmt: on
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Any = config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
lowerCAmelCase_ :List[Any] = state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
lowerCAmelCase_ :Union[str, Any] = state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase_ :List[str] = in_proj_weight[:hidden_size, :]
lowerCAmelCase_ :Optional[int] = in_proj_bias[:hidden_size]
lowerCAmelCase_ :Optional[int] = in_proj_weight[
hidden_size : hidden_size * 2, :
]
lowerCAmelCase_ :int = in_proj_bias[hidden_size : hidden_size * 2]
lowerCAmelCase_ :Dict = in_proj_weight[-hidden_size:, :]
lowerCAmelCase_ :List[Any] = in_proj_bias[-hidden_size:]
def _snake_case ( ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Any = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ :Tuple = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return im
@torch.no_grad()
def _snake_case ( lowercase__ : Dict , lowercase__ : Optional[Any] , lowercase__ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :str = get_deta_config(lowercase__ )
# load original state dict
if model_name == "deta-swin-large":
lowerCAmelCase_ :str = hf_hub_download(repo_id="""nielsr/deta-checkpoints""" , filename="""adet_swin_ft.pth""" )
elif model_name == "deta-swin-large-o365":
lowerCAmelCase_ :Union[str, Any] = hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""" , filename="""deta_swin_pt_o365.pth""" )
else:
raise ValueError(f"""Model name {model_name} not supported""" )
lowerCAmelCase_ :int = torch.load(lowercase__ , map_location="""cpu""" )["""model"""]
# original state dict
for name, param in state_dict.items():
print(lowercase__ , param.shape )
# rename keys
lowerCAmelCase_ :Any = create_rename_keys(lowercase__ )
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
read_in_swin_q_k_v(lowercase__ , config.backbone_config )
read_in_decoder_q_k_v(lowercase__ , lowercase__ )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
lowerCAmelCase_ :List[str] = state_dict.pop(lowercase__ )
lowerCAmelCase_ :Optional[int] = val
if "input_proj" in key:
lowerCAmelCase_ :str = state_dict.pop(lowercase__ )
lowerCAmelCase_ :List[str] = val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
lowerCAmelCase_ :List[str] = state_dict.pop(lowercase__ )
lowerCAmelCase_ :Tuple = val
# finally, create HuggingFace model and load state dict
lowerCAmelCase_ :Any = DetaForObjectDetection(lowercase__ )
model.load_state_dict(lowercase__ )
model.eval()
lowerCAmelCase_ :Optional[int] = """cuda""" if torch.cuda.is_available() else """cpu"""
model.to(lowercase__ )
# load image processor
lowerCAmelCase_ :Any = DetaImageProcessor(format="""coco_detection""" )
# verify our conversion on image
lowerCAmelCase_ :str = prepare_img()
lowerCAmelCase_ :int = processor(images=lowercase__ , return_tensors="""pt""" )
lowerCAmelCase_ :int = encoding["""pixel_values"""]
lowerCAmelCase_ :Tuple = model(pixel_values.to(lowercase__ ) )
# verify logits
print("""Logits:""" , outputs.logits[0, :3, :3] )
print("""Boxes:""" , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
lowerCAmelCase_ :Optional[int] = torch.tensor(
[[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
lowerCAmelCase_ :Union[str, Any] = torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] )
elif model_name == "deta-swin-large-o365":
lowerCAmelCase_ :Union[str, Any] = torch.tensor(
[[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
lowerCAmelCase_ :Optional[int] = torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(lowercase__ ) , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(lowercase__ ) , atol=1E-4 )
print("""Everything ok!""" )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}...""" )
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
# Push to hub
if push_to_hub:
print("""Pushing model and processor to hub...""" )
model.push_to_hub(f"""jozhang97/{model_name}""" )
processor.push_to_hub(f"""jozhang97/{model_name}""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
type=str,
default='deta-swin-large',
choices=['deta-swin-large', 'deta-swin-large-o365'],
help='Name of the model you\'d like to convert.',
)
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-single-500/viewer/default/store_true", help='Whether or not to push the converted model to the 🤗 hub.'
)
__UpperCAmelCase = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 1 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ ):
UpperCAmelCase_ :List[str] = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]
@register_to_config
def __init__( self , __A , __A , __A = None , __A = 5_0257 , __A = 1024 , __A = 768 , __A = 12 , __A = 12 , __A = None , __A = "gelu_new" , __A = 0.1 , __A = 0.1 , __A = 0.1 , __A = 1E-5 , __A = 0.0_2 , __A = True , __A = True , __A = False , __A = False , ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ :List[str] = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
f""" `n_embd`: {n_embd} are not equal.""" )
lowerCAmelCase_ :Union[str, Any] = prefix_inner_dim
lowerCAmelCase_ :str = prefix_hidden_dim
lowerCAmelCase_ :str = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
lowerCAmelCase_ :List[Any] = (
nn.Linear(self.prefix_hidden_dim , __A ) if self.prefix_hidden_dim is not None else nn.Identity()
)
lowerCAmelCase_ :Any = GPTaConfig(
vocab_size=__A , n_positions=__A , n_embd=__A , n_layer=__A , n_head=__A , n_inner=__A , activation_function=__A , resid_pdrop=__A , embd_pdrop=__A , attn_pdrop=__A , layer_norm_epsilon=__A , initializer_range=__A , scale_attn_weights=__A , use_cache=__A , scale_attn_by_inverse_layer_idx=__A , reorder_and_upcast_attn=__A , )
lowerCAmelCase_ :Any = GPTaLMHeadModel(__A )
def __lowerCAmelCase ( self , __A , __A , __A = None , __A = None , ) -> List[str]:
lowerCAmelCase_ :str = self.transformer.transformer.wte(__A )
lowerCAmelCase_ :Any = self.encode_prefix(__A )
lowerCAmelCase_ :Optional[Any] = self.decode_prefix(__A )
lowerCAmelCase_ :Optional[int] = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
lowerCAmelCase_ :int = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
lowerCAmelCase_ :Optional[Any] = torch.cat((dummy_token, input_ids) , dim=1 )
lowerCAmelCase_ :Tuple = self.transformer(inputs_embeds=__A , labels=__A , attention_mask=__A )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def __lowerCAmelCase ( self , __A , __A ) -> torch.Tensor:
return torch.zeros(__A , self.prefix_length , dtype=torch.intaa , device=__A )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
return self.encode_prefix(__A )
@torch.no_grad()
def __lowerCAmelCase ( self , __A , __A , __A ) -> Optional[int]:
lowerCAmelCase_ :Tuple = torch.split(__A , 1 , dim=0 )
lowerCAmelCase_ :Optional[int] = []
lowerCAmelCase_ :List[str] = []
for feature in features:
lowerCAmelCase_ :Tuple = self.decode_prefix(feature.to(__A ) ) # back to the clip feature
# Only support beam search for now
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = self.generate_beam(
input_embeds=__A , device=__A , eos_token_id=__A )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
lowerCAmelCase_ :Tuple = torch.stack(__A )
lowerCAmelCase_ :int = torch.stack(__A )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def __lowerCAmelCase ( self , __A=None , __A=None , __A=None , __A = 5 , __A = 67 , __A = 1.0 , __A = None , ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[int] = eos_token_id
lowerCAmelCase_ :Optional[int] = None
lowerCAmelCase_ :Any = None
lowerCAmelCase_ :int = torch.ones(__A , device=__A , dtype=torch.int )
lowerCAmelCase_ :Optional[int] = torch.zeros(__A , device=__A , dtype=torch.bool )
if input_embeds is not None:
lowerCAmelCase_ :List[str] = input_embeds
else:
lowerCAmelCase_ :Union[str, Any] = self.transformer.transformer.wte(__A )
for i in range(__A ):
lowerCAmelCase_ :Optional[int] = self.transformer(inputs_embeds=__A )
lowerCAmelCase_ :str = outputs.logits
lowerCAmelCase_ :str = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
lowerCAmelCase_ :Dict = logits.softmax(-1 ).log()
if scores is None:
lowerCAmelCase_ , lowerCAmelCase_ :Any = logits.topk(__A , -1 )
lowerCAmelCase_ :Union[str, Any] = generated.expand(__A , *generated.shape[1:] )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
lowerCAmelCase_ :List[str] = next_tokens
else:
lowerCAmelCase_ :List[Any] = tokens.expand(__A , *tokens.shape[1:] )
lowerCAmelCase_ :Any = torch.cat((tokens, next_tokens) , dim=1 )
else:
lowerCAmelCase_ :List[Any] = -float(np.inf )
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Optional[int] = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
lowerCAmelCase_ :List[Any] = scores_sum / seq_lengths[:, None]
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = scores_sum_average.view(-1 ).topk(__A , -1 )
lowerCAmelCase_ :Optional[Any] = next_tokens // scores_sum.shape[1]
lowerCAmelCase_ :Dict = seq_lengths[next_tokens_source]
lowerCAmelCase_ :Tuple = next_tokens % scores_sum.shape[1]
lowerCAmelCase_ :Optional[Any] = next_tokens.unsqueeze(1 )
lowerCAmelCase_ :str = tokens[next_tokens_source]
lowerCAmelCase_ :List[Any] = torch.cat((tokens, next_tokens) , dim=1 )
lowerCAmelCase_ :Dict = generated[next_tokens_source]
lowerCAmelCase_ :Dict = scores_sum_average * seq_lengths
lowerCAmelCase_ :Tuple = is_stopped[next_tokens_source]
lowerCAmelCase_ :str = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
lowerCAmelCase_ :List[Any] = torch.cat((generated, next_token_embed) , dim=1 )
lowerCAmelCase_ :Optional[int] = is_stopped + next_tokens.eq(__A ).squeeze()
if is_stopped.all():
break
lowerCAmelCase_ :str = scores / seq_lengths
lowerCAmelCase_ :Optional[int] = scores.argsort(descending=__A )
# tokens tensors are already padded to max_seq_length
lowerCAmelCase_ :Optional[Any] = [tokens[i] for i in order]
lowerCAmelCase_ :Dict = torch.stack(__A , dim=0 )
lowerCAmelCase_ :Tuple = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 1 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "umt5"
UpperCAmelCase_ :Union[str, Any] = ["past_key_values"]
def __init__( self , __A=25_0112 , __A=512 , __A=64 , __A=1024 , __A=8 , __A=None , __A=6 , __A=32 , __A=128 , __A=0.1 , __A=1E-6 , __A=1.0 , __A="gated-gelu" , __A=True , __A=True , __A="T5Tokenizer" , __A=True , __A=0 , __A=1 , __A=0 , **__A , ) -> Tuple:
super().__init__(
is_encoder_decoder=__A , tokenizer_class=__A , tie_word_embeddings=__A , pad_token_id=__A , eos_token_id=__A , decoder_start_token_id=__A , **__A , )
lowerCAmelCase_ :Optional[Any] = vocab_size
lowerCAmelCase_ :List[Any] = d_model
lowerCAmelCase_ :Dict = d_kv
lowerCAmelCase_ :List[Any] = d_ff
lowerCAmelCase_ :str = num_layers
lowerCAmelCase_ :List[str] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowerCAmelCase_ :Tuple = num_heads
lowerCAmelCase_ :List[Any] = relative_attention_num_buckets
lowerCAmelCase_ :Tuple = relative_attention_max_distance
lowerCAmelCase_ :Optional[Any] = dropout_rate
lowerCAmelCase_ :Optional[Any] = layer_norm_epsilon
lowerCAmelCase_ :Tuple = initializer_factor
lowerCAmelCase_ :Optional[Any] = feed_forward_proj
lowerCAmelCase_ :List[str] = use_cache
lowerCAmelCase_ :List[Any] = self.feed_forward_proj.split("""-""" )
lowerCAmelCase_ :Optional[Any] = act_info[-1]
lowerCAmelCase_ :Any = act_info[0] == """gated"""
if len(__A ) > 1 and act_info[0] != "gated" or len(__A ) > 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'""" )
if feed_forward_proj == "gated-gelu":
lowerCAmelCase_ :List[Any] = """gelu_new"""
@property
def __lowerCAmelCase ( self ) -> int:
return self.d_model
@property
def __lowerCAmelCase ( self ) -> List[str]:
return self.num_heads
@property
def __lowerCAmelCase ( self ) -> str:
return self.num_layers
class _SCREAMING_SNAKE_CASE ( A__ ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
lowerCAmelCase_ :List[Any] = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
lowerCAmelCase_ :Optional[int] = """past_encoder_sequence + sequence"""
lowerCAmelCase_ :List[Any] = {0: """batch"""}
lowerCAmelCase_ :List[str] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
lowerCAmelCase_ :List[str] = {0: """batch""", 1: """decoder_sequence"""}
lowerCAmelCase_ :Optional[int] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(__A , direction="""inputs""" )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def __lowerCAmelCase ( self ) -> int:
return 13
@property
def __lowerCAmelCase ( self ) -> float:
return 5E-4
| 1 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "detr"
UpperCAmelCase_ :str = ["past_key_values"]
UpperCAmelCase_ :Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self , __A=True , __A=None , __A=3 , __A=100 , __A=6 , __A=2048 , __A=8 , __A=6 , __A=2048 , __A=8 , __A=0.0 , __A=0.0 , __A=True , __A="relu" , __A=256 , __A=0.1 , __A=0.0 , __A=0.0 , __A=0.0_2 , __A=1.0 , __A=False , __A="sine" , __A="resnet50" , __A=True , __A=False , __A=1 , __A=5 , __A=2 , __A=1 , __A=1 , __A=5 , __A=2 , __A=0.1 , **__A , ) -> List[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
lowerCAmelCase_ :int = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(__A , __A ):
lowerCAmelCase_ :str = backbone_config.get("""model_type""" )
lowerCAmelCase_ :List[Any] = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ :Optional[Any] = config_class.from_dict(__A )
# set timm attributes to None
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = None, None, None
lowerCAmelCase_ :Tuple = use_timm_backbone
lowerCAmelCase_ :Optional[int] = backbone_config
lowerCAmelCase_ :Optional[int] = num_channels
lowerCAmelCase_ :int = num_queries
lowerCAmelCase_ :List[Any] = d_model
lowerCAmelCase_ :Optional[int] = encoder_ffn_dim
lowerCAmelCase_ :Tuple = encoder_layers
lowerCAmelCase_ :int = encoder_attention_heads
lowerCAmelCase_ :Optional[Any] = decoder_ffn_dim
lowerCAmelCase_ :List[str] = decoder_layers
lowerCAmelCase_ :Dict = decoder_attention_heads
lowerCAmelCase_ :Dict = dropout
lowerCAmelCase_ :Tuple = attention_dropout
lowerCAmelCase_ :Union[str, Any] = activation_dropout
lowerCAmelCase_ :Any = activation_function
lowerCAmelCase_ :List[str] = init_std
lowerCAmelCase_ :Optional[int] = init_xavier_std
lowerCAmelCase_ :int = encoder_layerdrop
lowerCAmelCase_ :Union[str, Any] = decoder_layerdrop
lowerCAmelCase_ :List[str] = encoder_layers
lowerCAmelCase_ :Union[str, Any] = auxiliary_loss
lowerCAmelCase_ :str = position_embedding_type
lowerCAmelCase_ :List[Any] = backbone
lowerCAmelCase_ :str = use_pretrained_backbone
lowerCAmelCase_ :str = dilation
# Hungarian matcher
lowerCAmelCase_ :List[Any] = class_cost
lowerCAmelCase_ :Union[str, Any] = bbox_cost
lowerCAmelCase_ :Tuple = giou_cost
# Loss coefficients
lowerCAmelCase_ :Optional[int] = mask_loss_coefficient
lowerCAmelCase_ :Union[str, Any] = dice_loss_coefficient
lowerCAmelCase_ :Tuple = bbox_loss_coefficient
lowerCAmelCase_ :Tuple = giou_loss_coefficient
lowerCAmelCase_ :Dict = eos_coefficient
super().__init__(is_encoder_decoder=__A , **__A )
@property
def __lowerCAmelCase ( self ) -> int:
return self.encoder_attention_heads
@property
def __lowerCAmelCase ( self ) -> int:
return self.d_model
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> Any:
return cls(backbone_config=__A , **__A )
def __lowerCAmelCase ( self ) -> Dict[str, any]:
lowerCAmelCase_ :List[str] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
lowerCAmelCase_ :Dict = self.backbone_config.to_dict()
lowerCAmelCase_ :str = self.__class__.model_type
return output
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[Any] = version.parse("1.11" )
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
return 1E-5
@property
def __lowerCAmelCase ( self ) -> int:
return 12
| 1 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(lowercase__ , 2 ) - pow(lowercase__ , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(lowercase__ , 2 ) - pow(lowercase__ , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(lowercase__ , 2 ) + pow(lowercase__ , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['DeiTFeatureExtractor']
__UpperCAmelCase = ['DeiTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DeiTForImageClassification',
'DeiTForImageClassificationWithTeacher',
'DeiTForMaskedImageModeling',
'DeiTModel',
'DeiTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDeiTForImageClassification',
'TFDeiTForImageClassificationWithTeacher',
'TFDeiTForMaskedImageModeling',
'TFDeiTModel',
'TFDeiTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 1 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , **__A ) -> None:
warnings.warn(
"""The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use OwlViTImageProcessor instead.""" , __A , )
super().__init__(*__A , **__A )
| 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
'SqueezeBertOnnxConfig',
],
'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['SqueezeBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'SqueezeBertForMaskedLM',
'SqueezeBertForMultipleChoice',
'SqueezeBertForQuestionAnswering',
'SqueezeBertForSequenceClassification',
'SqueezeBertForTokenClassification',
'SqueezeBertModel',
'SqueezeBertModule',
'SqueezeBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 1 | 1 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
__UpperCAmelCase = {
'vocab_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
},
'merges_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
},
'tokenizer_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
},
}
__UpperCAmelCase = {
'allenai/led-base-16384': 1_63_84,
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Any = VOCAB_FILES_NAMES
UpperCAmelCase_ :Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ :Tuple = LEDTokenizer
UpperCAmelCase_ :Union[str, Any] = ["input_ids", "attention_mask"]
def __init__( self , __A=None , __A=None , __A=None , __A="replace" , __A="<s>" , __A="</s>" , __A="</s>" , __A="<s>" , __A="<unk>" , __A="<pad>" , __A="<mask>" , __A=False , __A=True , **__A , ) -> int:
super().__init__(
__A , __A , tokenizer_file=__A , errors=__A , bos_token=__A , eos_token=__A , sep_token=__A , cls_token=__A , unk_token=__A , pad_token=__A , mask_token=__A , add_prefix_space=__A , trim_offsets=__A , **__A , )
lowerCAmelCase_ :str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , __A ) != add_prefix_space:
lowerCAmelCase_ :Union[str, Any] = getattr(__A , pre_tok_state.pop("""type""" ) )
lowerCAmelCase_ :Any = add_prefix_space
lowerCAmelCase_ :Dict = pre_tok_class(**__A )
lowerCAmelCase_ :Optional[Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowerCAmelCase_ :int = """post_processor"""
lowerCAmelCase_ :Dict = getattr(self.backend_tokenizer , __A , __A )
if tokenizer_component_instance:
lowerCAmelCase_ :Tuple = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowerCAmelCase_ :Tuple = tuple(state["""sep"""] )
if "cls" in state:
lowerCAmelCase_ :Dict = tuple(state["""cls"""] )
lowerCAmelCase_ :Dict = False
if state.get("""add_prefix_space""" , __A ) != add_prefix_space:
lowerCAmelCase_ :str = add_prefix_space
lowerCAmelCase_ :str = True
if state.get("""trim_offsets""" , __A ) != trim_offsets:
lowerCAmelCase_ :Tuple = trim_offsets
lowerCAmelCase_ :Tuple = True
if changes_to_apply:
lowerCAmelCase_ :Tuple = getattr(__A , state.pop("""type""" ) )
lowerCAmelCase_ :List[str] = component_class(**__A )
setattr(self.backend_tokenizer , __A , __A )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def __lowerCAmelCase ( self ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def __lowerCAmelCase ( self , __A ) -> Any:
lowerCAmelCase_ :Dict = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else value
lowerCAmelCase_ :int = value
def __lowerCAmelCase ( self , *__A , **__A ) -> BatchEncoding:
lowerCAmelCase_ :Optional[Any] = kwargs.get("""is_split_into_words""" , __A )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*__A , **__A )
def __lowerCAmelCase ( self , *__A , **__A ) -> BatchEncoding:
lowerCAmelCase_ :str = kwargs.get("""is_split_into_words""" , __A )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*__A , **__A )
def __lowerCAmelCase ( self , __A , __A = None ) -> Tuple[str]:
lowerCAmelCase_ :Tuple = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
def __lowerCAmelCase ( self , __A , __A=None ) -> Tuple:
lowerCAmelCase_ :List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __lowerCAmelCase ( self , __A , __A = None ) -> List[int]:
lowerCAmelCase_ :Any = [self.sep_token_id]
lowerCAmelCase_ :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 __lowerCAmelCase ( self , __A , __A = None , __A = PaddingStrategy.DO_NOT_PAD , __A = None , __A = None , ) -> dict:
lowerCAmelCase_ :Tuple = super()._pad(
encoded_inputs=__A , max_length=__A , padding_strategy=__A , pad_to_multiple_of=__A , return_attention_mask=__A , )
# Load from model defaults
if return_attention_mask is None:
lowerCAmelCase_ :List[str] = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowerCAmelCase_ :List[str] = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowerCAmelCase_ :Dict = len(encoded_inputs["""global_attention_mask"""] ) != len(__A )
if needs_to_be_padded:
lowerCAmelCase_ :Any = len(__A ) - len(encoded_inputs["""global_attention_mask"""] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowerCAmelCase_ :Tuple = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
lowerCAmelCase_ :Optional[int] = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return encoded_inputs
| 1 |
"""simple docstring"""
__UpperCAmelCase = 2_56
# Modulus to hash a string
__UpperCAmelCase = 1_00_00_03
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple = len(lowercase__ )
lowerCAmelCase_ :List[str] = len(lowercase__ )
if p_len > t_len:
return False
lowerCAmelCase_ :List[str] = 0
lowerCAmelCase_ :Optional[int] = 0
lowerCAmelCase_ :Any = 1
# Calculating the hash of pattern and substring of text
for i in range(lowercase__ ):
lowerCAmelCase_ :int = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
lowerCAmelCase_ :Any = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
lowerCAmelCase_ :Optional[Any] = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
lowerCAmelCase_ :Any = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _snake_case ( ) -> None:
'''simple docstring'''
lowerCAmelCase_ :int = """abc1abc12"""
lowerCAmelCase_ :Dict = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
lowerCAmelCase_ :int = """alskfjaldsk23adsfabcabc"""
assert rabin_karp(lowercase__ , lowercase__ ) and not rabin_karp(lowercase__ , lowercase__ )
# Test 2)
lowerCAmelCase_ :Dict = """ABABX"""
lowerCAmelCase_ :int = """ABABZABABYABABX"""
assert rabin_karp(lowercase__ , lowercase__ )
# Test 3)
lowerCAmelCase_ :Union[str, Any] = """AAAB"""
lowerCAmelCase_ :List[str] = """ABAAAAAB"""
assert rabin_karp(lowercase__ , lowercase__ )
# Test 4)
lowerCAmelCase_ :Dict = """abcdabcy"""
lowerCAmelCase_ :Union[str, Any] = """abcxabcdabxabcdabcdabcy"""
assert rabin_karp(lowercase__ , lowercase__ )
# Test 5)
lowerCAmelCase_ :Optional[int] = """Lü"""
lowerCAmelCase_ :Optional[int] = """Lüsai"""
assert rabin_karp(lowercase__ , lowercase__ )
lowerCAmelCase_ :Optional[int] = """Lue"""
assert not rabin_karp(lowercase__ , lowercase__ )
print("""Success.""" )
if __name__ == "__main__":
test_rabin_karp()
| 1 | 1 |
"""simple docstring"""
# 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
__UpperCAmelCase = 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)
__UpperCAmelCase = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
__UpperCAmelCase = 'zero2'
__UpperCAmelCase = 'zero3'
__UpperCAmelCase = [ZEROa, ZEROa]
def _snake_case ( lowercase__ : str , lowercase__ : Optional[int] , lowercase__ : Tuple ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = parameterized.to_safe_name("""_""".join(str(lowercase__ ) for x in param.args ) )
return f"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
__UpperCAmelCase = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( A__ ):
@parameterized.expand(__A , name_func=__A )
def __lowerCAmelCase ( self , __A , __A ) -> str:
self.run_and_check(
stage=__A , model=__A , distributed=__A , fpaa=__A , )
@require_torch_multi_gpu
@parameterized.expand(__A , name_func=__A )
def __lowerCAmelCase ( self , __A , __A ) -> List[Any]:
self.run_and_check(
stage=__A , model=__A , distributed=__A , fpaa=__A , )
@parameterized.expand(__A , name_func=__A )
def __lowerCAmelCase ( self , __A , __A ) -> str:
self.run_and_check(
stage=__A , model=__A , distributed=__A , fpaa=__A , )
@require_torch_multi_gpu
@parameterized.expand(__A , name_func=__A )
def __lowerCAmelCase ( self , __A , __A ) -> Tuple:
self.run_and_check(
stage=__A , model=__A , distributed=__A , fpaa=__A , )
def __lowerCAmelCase ( self , __A ) -> List[str]:
# XXX: run_asr is premature and doesn't save any results
# so all we check for now is that the process didn't fail
pass
def __lowerCAmelCase ( self , __A , __A , __A = 10 , __A = True , __A = True , __A = True , ) -> Tuple:
lowerCAmelCase_ :Optional[Any] = models[model]
lowerCAmelCase_ :Tuple = self.run_trainer(
stage=__A , model_name=__A , eval_steps=__A , num_train_epochs=1 , distributed=__A , fpaa=__A , )
self.do_checks(__A )
return output_dir
def __lowerCAmelCase ( self , __A , __A , __A = 10 , __A = 1 , __A = True , __A = True , ) -> List[str]:
lowerCAmelCase_ :Optional[int] = self.get_auto_remove_tmp_dir("""./xxx""" , after=__A )
lowerCAmelCase_ :Tuple = 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(__A )}
--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_ :Tuple = f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
lowerCAmelCase_ :List[str] = [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
lowerCAmelCase_ :List[Any] = self.get_launcher(__A )
lowerCAmelCase_ :Union[str, Any] = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(__A , env=self.get_env() )
return output_dir
def __lowerCAmelCase ( self , __A=False ) -> str:
# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
# - it won't be able to handle that
# 2. for now testing with just 2 gpus max (since some quality tests may give different
# results with mode gpus because we use very little data)
lowerCAmelCase_ :Any = min(2 , get_gpu_count() ) if distributed else 1
return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()
| 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def _snake_case ( lowercase__ : Accelerator , lowercase__ : int = 1_6 ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[str] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase_ :Optional[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ : int ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ :Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowerCAmelCase_ :Optional[Any] = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ :str = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase_ :int = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase_ :List[Any] = 1_6
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ :List[str] = 8
else:
lowerCAmelCase_ :Optional[int] = None
return tokenizer.pad(
lowercase__ , padding="""longest""" , max_length=lowercase__ , pad_to_multiple_of=lowercase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowerCAmelCase_ :Optional[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowerCAmelCase_ :List[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__UpperCAmelCase = mocked_dataloaders # noqa: F811
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowercase__ ) == "1":
lowerCAmelCase_ :Optional[Any] = 2
# New Code #
lowerCAmelCase_ :List[str] = int(args.gradient_accumulation_steps )
lowerCAmelCase_ :int = int(args.local_sgd_steps )
# Initialize accelerator
lowerCAmelCase_ :str = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase__ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ :int = config["""lr"""]
lowerCAmelCase_ :Union[str, Any] = int(config["""num_epochs"""] )
lowerCAmelCase_ :int = int(config["""seed"""] )
lowerCAmelCase_ :Union[str, Any] = int(config["""batch_size"""] )
lowerCAmelCase_ :Union[str, Any] = evaluate.load("""glue""" , """mrpc""" )
set_seed(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = get_dataloaders(lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ :Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowerCAmelCase_ :Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ :Optional[Any] = AdamW(params=model.parameters() , lr=lowercase__ )
# Instantiate scheduler
lowerCAmelCase_ :Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(lowercase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Now we train the model
for epoch in range(lowercase__ ):
model.train()
with LocalSGD(
accelerator=lowercase__ , model=lowercase__ , local_sgd_steps=lowercase__ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase__ ):
lowerCAmelCase_ :str = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = output.loss
accelerator.backward(lowercase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowerCAmelCase_ :Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , lowercase__ )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase__ , default=lowercase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=lowercase__ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=lowercase__ , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowerCAmelCase_ :Optional[Any] = parser.parse_args()
lowerCAmelCase_ :Tuple = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 1 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCAmelCase = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'ResNetForImageClassification',
'ResNetModel',
'ResNetPreTrainedModel',
'ResNetBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFResNetForImageClassification',
'TFResNetModel',
'TFResNetPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'FlaxResNetForImageClassification',
'FlaxResNetModel',
'FlaxResNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def _snake_case ( lowercase__ : Accelerator , lowercase__ : int = 1_6 , lowercase__ : str = "bert-base-cased" ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = AutoTokenizer.from_pretrained(lowercase__ )
lowerCAmelCase_ :Optional[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ : List[str] ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ :str = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase_ :str = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowercase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ :List[str] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowercase__ , padding="""max_length""" , max_length=1_2_8 , return_tensors="""pt""" )
return tokenizer.pad(lowercase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase_ :Optional[int] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowerCAmelCase_ :Any = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : Tuple , lowercase__ : int ) -> List[str]:
'''simple docstring'''
model.eval()
lowerCAmelCase_ :Dict = 0
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase__ ) - 1:
lowerCAmelCase_ :Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCAmelCase_ :Any = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowerCAmelCase_ :Tuple = metric.compute()
return eval_metric["accuracy"]
def _snake_case ( lowercase__ : str , lowercase__ : List[str] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ :int = config["""lr"""]
lowerCAmelCase_ :Union[str, Any] = int(config["""num_epochs"""] )
lowerCAmelCase_ :Optional[int] = int(config["""seed"""] )
lowerCAmelCase_ :Union[str, Any] = int(config["""batch_size"""] )
lowerCAmelCase_ :Optional[Any] = args.model_name_or_path
set_seed(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = get_dataloaders(lowercase__ , lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ :str = AutoModelForSequenceClassification.from_pretrained(lowercase__ , return_dict=lowercase__ )
# Instantiate optimizer
lowerCAmelCase_ :List[str] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowerCAmelCase_ :str = optimizer_cls(params=model.parameters() , lr=lowercase__ )
if accelerator.state.deepspeed_plugin is not None:
lowerCAmelCase_ :Union[str, Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
lowerCAmelCase_ :Any = 1
lowerCAmelCase_ :str = (len(lowercase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowerCAmelCase_ :List[str] = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=0 , num_training_steps=lowercase__ , )
else:
lowerCAmelCase_ :int = DummyScheduler(lowercase__ , total_num_steps=lowercase__ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# We need to keep track of how many total steps we have iterated over
lowerCAmelCase_ :List[str] = 0
# We also need to keep track of the stating epoch so files are named properly
lowerCAmelCase_ :List[Any] = 0
lowerCAmelCase_ :str = evaluate.load("""glue""" , """mrpc""" )
lowerCAmelCase_ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
lowerCAmelCase_ :Dict = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
lowerCAmelCase_ :Optional[Any] = args.resume_from_checkpoint.split("""epoch_""" )[1]
lowerCAmelCase_ :int = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
lowerCAmelCase_ :Union[str, Any] = int(lowercase__ ) + 1
lowerCAmelCase_ :Optional[int] = evaluation_loop(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
accelerator.print("""resumed checkpoint performance:""" , lowercase__ )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , f"""state_{starting_epoch-1}.json""" ) , """r""" ) as f:
lowerCAmelCase_ :List[str] = json.load(lowercase__ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
lowerCAmelCase_ :List[Any] = {}
for epoch in range(lowercase__ , lowercase__ ):
model.train()
for step, batch in enumerate(lowercase__ ):
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Dict = outputs.loss
lowerCAmelCase_ :int = loss / gradient_accumulation_steps
accelerator.backward(lowercase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
lowerCAmelCase_ :List[str] = f"""epoch_{epoch}"""
lowerCAmelCase_ :Any = os.path.join(args.output_dir , lowercase__ )
accelerator.save_state(lowercase__ )
lowerCAmelCase_ :List[Any] = evaluation_loop(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = accuracy
lowerCAmelCase_ :Any = lr_scheduler.get_lr()[0]
lowerCAmelCase_ :str = optimizer.param_groups[0]["""lr"""]
lowerCAmelCase_ :List[Any] = epoch
lowerCAmelCase_ :Tuple = overall_step
accelerator.print(f"""epoch {epoch}:""" , lowercase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f"""state_{epoch}.json""" ) , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
def _snake_case ( ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowercase__ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowercase__ , )
parser.add_argument(
"""--output_dir""" , type=lowercase__ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=lowercase__ , default=lowercase__ , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=lowercase__ , default=lowercase__ , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowercase__ , default=2 , help="""Number of train epochs.""" , )
lowerCAmelCase_ :Optional[int] = parser.parse_args()
lowerCAmelCase_ :List[Any] = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 1 | 1 |
"""simple docstring"""
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
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :List[str] = XLMProphetNetTokenizer
UpperCAmelCase_ :Dict = False
UpperCAmelCase_ :List[str] = True
def __lowerCAmelCase ( self ) -> Optional[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase_ :Optional[int] = XLMProphetNetTokenizer(__A , keep_accents=__A )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[Any] = """[PAD]"""
lowerCAmelCase_ :Any = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A ) , __A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A ) , __A )
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[str] = 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(__A ) , 1012 )
def __lowerCAmelCase ( self ) -> str:
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = XLMProphetNetTokenizer(__A , keep_accents=__A )
lowerCAmelCase_ :Dict = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__A , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCAmelCase_ :List[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__A , [
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""",
""".""",
] , )
lowerCAmelCase_ :Optional[int] = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(
__A , [
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]
] , )
lowerCAmelCase_ :Any = tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(
__A , [
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 __lowerCAmelCase ( self ) -> Any:
return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
@slow
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[Any] = """Hello World!"""
lowerCAmelCase_ :Optional[Any] = [3_5389, 6672, 49, 2]
self.assertListEqual(__A , self.big_tokenizer.encode(__A ) )
@slow
def __lowerCAmelCase ( self ) -> Any:
# fmt: off
lowerCAmelCase_ :Optional[int] = {"""input_ids""": [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 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, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 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=__A , model_name="""microsoft/xprophetnet-large-wiki100-cased""" , revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" , )
| 1 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[str, Any]:
if isinstance(__A , __A ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
lowerCAmelCase_ :Tuple = deepcopy(__A )
elif os.path.exists(__A ):
with io.open(__A , """r""" , encoding="""utf-8""" ) as f:
lowerCAmelCase_ :str = json.load(__A )
else:
try:
lowerCAmelCase_ :Dict = baseaa.urlsafe_baadecode(__A ).decode("""utf-8""" )
lowerCAmelCase_ :int = json.loads(__A )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""" )
lowerCAmelCase_ :Optional[Any] = config
self.set_stage_and_offload()
def __lowerCAmelCase ( self ) -> Tuple:
# zero stage - this is done as early as possible, before model is created, to allow
# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
lowerCAmelCase_ :Tuple = self.get_value("""zero_optimization.stage""" , -1 )
# offload
lowerCAmelCase_ :Dict = False
if self.is_zeroa() or self.is_zeroa():
lowerCAmelCase_ :Optional[int] = set(["""cpu""", """nvme"""] )
lowerCAmelCase_ :Union[str, Any] = set(
[
self.get_value("""zero_optimization.offload_optimizer.device""" ),
self.get_value("""zero_optimization.offload_param.device""" ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
lowerCAmelCase_ :Optional[int] = True
def __lowerCAmelCase ( self , __A ) -> Optional[Any]:
lowerCAmelCase_ :str = self.config
# find the config node of interest if it exists
lowerCAmelCase_ :Tuple = ds_key_long.split(""".""" )
lowerCAmelCase_ :List[str] = nodes.pop()
for node in nodes:
lowerCAmelCase_ :Tuple = config.get(__A )
if config is None:
return None, ds_key
return config, ds_key
def __lowerCAmelCase ( self , __A , __A=None ) -> Optional[Any]:
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = self.find_config_node(__A )
if config is None:
return default
return config.get(__A , __A )
def __lowerCAmelCase ( self , __A , __A=False ) -> Optional[Any]:
lowerCAmelCase_ :Tuple = self.config
# find the config node of interest if it exists
lowerCAmelCase_ :Union[str, Any] = ds_key_long.split(""".""" )
for node in nodes:
lowerCAmelCase_ :int = config
lowerCAmelCase_ :Any = config.get(__A )
if config is None:
if must_exist:
raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}""" )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(__A )
def __lowerCAmelCase ( self , __A ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[int] = self.get_value(__A )
return False if value is None else bool(__A )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :List[str] = self.get_value(__A )
return False if value is None else not bool(__A )
def __lowerCAmelCase ( self ) -> str:
return self._stage == 2
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return self._stage == 3
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return self._offload
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Optional[int]:
lowerCAmelCase_ :Dict = engine
def __lowerCAmelCase ( self , __A , **__A ) -> str:
# runs backpropagation and handles mixed precision
self.engine.backward(__A , **__A )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A ) -> List[str]:
super().__init__(__A , device_placement=__A , scaler=__A )
lowerCAmelCase_ :List[str] = hasattr(self.optimizer , """overflow""" )
def __lowerCAmelCase ( self , __A=None ) -> Optional[Any]:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def __lowerCAmelCase ( self ) -> List[Any]:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def __lowerCAmelCase ( self ) -> int:
if self.__has_overflow__:
return self.optimizer.overflow
return False
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A , __A ) -> Optional[int]:
super().__init__(__A , __A )
def __lowerCAmelCase ( self ) -> Any:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=0.0_0_1 , __A=0 , **__A ) -> List[Any]:
lowerCAmelCase_ :str = params
lowerCAmelCase_ :Any = lr
lowerCAmelCase_ :List[Any] = weight_decay
lowerCAmelCase_ :Any = kwargs
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A=None , __A=0 , **__A ) -> List[str]:
lowerCAmelCase_ :Optional[int] = optimizer
lowerCAmelCase_ :int = total_num_steps
lowerCAmelCase_ :List[Any] = warmup_num_steps
lowerCAmelCase_ :int = kwargs
| 1 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : list[int] , lowercase__ : list[int] , lowercase__ : int ) -> bool:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(lowercase__ ) )
def _snake_case ( lowercase__ : list[list[int]] , lowercase__ : int , lowercase__ : list[int] , lowercase__ : int ) -> bool:
'''simple docstring'''
if index == len(lowercase__ ):
return True
# Recursive Step
for i in range(lowercase__ ):
if valid_coloring(graph[index] , lowercase__ , lowercase__ ):
# Color current vertex
lowerCAmelCase_ :List[str] = i
# Validate coloring
if util_color(lowercase__ , lowercase__ , lowercase__ , index + 1 ):
return True
# Backtrack
lowerCAmelCase_ :Tuple = -1
return False
def _snake_case ( lowercase__ : list[list[int]] , lowercase__ : int ) -> list[int]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = [-1] * len(lowercase__ )
if util_color(lowercase__ , lowercase__ , lowercase__ , 0 ):
return colored_vertices
return []
| 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Dict = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
UpperCAmelCase_ :List[str] = "CIDAS/clipseg-rd64-refined"
UpperCAmelCase_ :List[Any] = "image_segmenter"
UpperCAmelCase_ :Optional[int] = CLIPSegForImageSegmentation
UpperCAmelCase_ :Tuple = ["image", "text"]
UpperCAmelCase_ :Dict = ["image"]
def __init__( self , *__A , **__A ) -> Optional[Any]:
requires_backends(self , ["""vision"""] )
super().__init__(*__A , **__A )
def __lowerCAmelCase ( self , __A , __A ) -> Any:
return self.pre_processor(text=[label] , images=[image] , padding=__A , return_tensors="""pt""" )
def __lowerCAmelCase ( self , __A ) -> Tuple:
with torch.no_grad():
lowerCAmelCase_ :Dict = self.model(**__A ).logits
return logits
def __lowerCAmelCase ( self , __A ) -> Tuple:
lowerCAmelCase_ :Optional[int] = outputs.cpu().detach().numpy()
lowerCAmelCase_ :List[str] = 0
lowerCAmelCase_ :str = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 1 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(lowercase__ , x % y )
def _snake_case ( lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
return (x * y) // greatest_common_divisor(lowercase__ , lowercase__ )
def _snake_case ( lowercase__ : int = 2_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Tuple = 1
for i in range(1 , n + 1 ):
lowerCAmelCase_ :Dict = lcm(lowercase__ , lowercase__ )
return g
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCAmelCase_ :Any = 0
lowerCAmelCase_ :str = 0
lowerCAmelCase_ :Dict = knapsack(lowercase__ , lowercase__ , lowercase__ , lowercase__ , index + 1 )
if weights[index] <= max_weight:
lowerCAmelCase_ :str = values[index] + knapsack(
lowercase__ , lowercase__ , lowercase__ , max_weight - weights[index] , index + 1 )
return max(lowercase__ , lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def _snake_case ( lowercase__ : Accelerator , lowercase__ : int = 1_6 ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[str] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase_ :Optional[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ : int ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ :Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowerCAmelCase_ :Optional[Any] = datasets.map(
lowercase__ , batched=lowercase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ :str = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase_ :int = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase_ :List[Any] = 1_6
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ :List[str] = 8
else:
lowerCAmelCase_ :Optional[int] = None
return tokenizer.pad(
lowercase__ , padding="""longest""" , max_length=lowercase__ , pad_to_multiple_of=lowercase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowerCAmelCase_ :Optional[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowerCAmelCase_ :List[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__UpperCAmelCase = mocked_dataloaders # noqa: F811
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowercase__ ) == "1":
lowerCAmelCase_ :Optional[Any] = 2
# New Code #
lowerCAmelCase_ :List[str] = int(args.gradient_accumulation_steps )
lowerCAmelCase_ :int = int(args.local_sgd_steps )
# Initialize accelerator
lowerCAmelCase_ :str = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase__ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ :int = config["""lr"""]
lowerCAmelCase_ :Union[str, Any] = int(config["""num_epochs"""] )
lowerCAmelCase_ :int = int(config["""seed"""] )
lowerCAmelCase_ :Union[str, Any] = int(config["""batch_size"""] )
lowerCAmelCase_ :Union[str, Any] = evaluate.load("""glue""" , """mrpc""" )
set_seed(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = get_dataloaders(lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ :Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowerCAmelCase_ :Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ :Optional[Any] = AdamW(params=model.parameters() , lr=lowercase__ )
# Instantiate scheduler
lowerCAmelCase_ :Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(lowercase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Now we train the model
for epoch in range(lowercase__ ):
model.train()
with LocalSGD(
accelerator=lowercase__ , model=lowercase__ , local_sgd_steps=lowercase__ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowercase__ ):
lowerCAmelCase_ :str = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = output.loss
accelerator.backward(lowercase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowerCAmelCase_ :Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , lowercase__ )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase__ , default=lowercase__ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=lowercase__ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=lowercase__ , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowerCAmelCase_ :Optional[Any] = parser.parse_args()
lowerCAmelCase_ :Tuple = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : str ) -> Optional[Any]:
'''simple docstring'''
if not is_tqdm_available():
raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.""" )
lowerCAmelCase_ :Tuple = False
if main_process_only:
lowerCAmelCase_ :Dict = PartialState().local_process_index == 0
return _tqdm(*lowercase__ , **lowercase__ , disable=lowercase__ )
| 1 | 1 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : List[Any] , lowercase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def _snake_case ( lowercase__ : int , lowercase__ : Tuple , lowercase__ : Optional[Any] , lowercase__ : Tuple , lowercase__ : Tuple=True ) -> str:
'''simple docstring'''
model.train()
lowerCAmelCase_ :str = model(lowercase__ )
lowerCAmelCase_ :str = F.mse_loss(lowercase__ , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(lowercase__ )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : List[Any]=False ) -> List[Any]:
'''simple docstring'''
set_seed(4_2 )
lowerCAmelCase_ :Dict = RegressionModel()
lowerCAmelCase_ :Optional[Any] = deepcopy(lowercase__ )
lowerCAmelCase_ :Optional[int] = RegressionDataset(length=8_0 )
lowerCAmelCase_ :Tuple = DataLoader(lowercase__ , batch_size=1_6 )
model.to(accelerator.device )
if sched:
lowerCAmelCase_ :Optional[Any] = AdamW(params=model.parameters() , lr=1E-3 )
lowerCAmelCase_ :Dict = AdamW(params=ddp_model.parameters() , lr=1E-3 )
lowerCAmelCase_ :Union[str, Any] = LambdaLR(lowercase__ , lr_lambda=lambda lowercase__ : epoch**0.65 )
lowerCAmelCase_ :Optional[Any] = LambdaLR(lowercase__ , lr_lambda=lambda lowercase__ : epoch**0.65 )
# Make a copy of `model`
if sched:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = accelerator.prepare(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
else:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = accelerator.prepare(lowercase__ , lowercase__ )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def _snake_case ( lowercase__ : int ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[str] = get_training_setup(lowercase__ )
# Use a single batch
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = next(iter(lowercase__ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = accelerator.gather((ddp_input, ddp_target) )
lowerCAmelCase_ , lowerCAmelCase_ :str = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(lowercase__ ):
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
else:
# Sync grads
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
lowerCAmelCase_ :int = ddp_input[torch.randperm(len(lowercase__ ) )]
def _snake_case ( lowercase__ : Dict ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = get_training_setup(lowercase__ )
# Use a single batch
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = next(iter(lowercase__ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = accelerator.gather((ddp_input, ddp_target) )
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(lowercase__ ):
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
else:
# Sync grads
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
lowerCAmelCase_ :int = ddp_input[torch.randperm(len(lowercase__ ) )]
def _snake_case ( lowercase__ : Optional[int]=False , lowercase__ : Optional[Any]=False ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = Accelerator(
split_batches=lowercase__ , dispatch_batches=lowercase__ , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :str = get_training_setup(lowercase__ )
for iteration, batch in enumerate(lowercase__ ):
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = batch.values()
# Gather the distributed inputs and targs for the base model
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = accelerator.gather((ddp_input, ddp_target) )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(lowercase__ ):
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(lowercase__ ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
lowerCAmelCase_ :List[str] = ddp_input[torch.randperm(len(lowercase__ ) )]
GradientState._reset_state()
def _snake_case ( lowercase__ : Dict=False , lowercase__ : Any=False ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Dict = Accelerator(
split_batches=lowercase__ , dispatch_batches=lowercase__ , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :int = get_training_setup(lowercase__ , lowercase__ )
for iteration, batch in enumerate(lowercase__ ):
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = batch.values()
# Gather the distributed inputs and targs for the base model
lowerCAmelCase_ , lowerCAmelCase_ :Dict = accelerator.gather((ddp_input, ddp_target) )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase__ )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(lowercase__ ):
step_model(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
lowerCAmelCase_ :Any = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase__ ))
if accelerator.num_processes > 1:
check_model_parameters(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
GradientState._reset_state()
def _snake_case ( ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :Tuple = Accelerator()
lowerCAmelCase_ :List[Any] = RegressionDataset(length=8_0 )
lowerCAmelCase_ :Tuple = DataLoader(lowercase__ , batch_size=1_6 )
lowerCAmelCase_ :List[Any] = RegressionDataset(length=9_6 )
lowerCAmelCase_ :Dict = DataLoader(lowercase__ , batch_size=1_6 )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = accelerator.prepare(lowercase__ , lowercase__ )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(lowercase__ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(lowercase__ )
if iteration < len(lowercase__ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(lowercase__ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(lowercase__ )
if batch_num < len(lowercase__ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def _snake_case ( ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = Accelerator()
lowerCAmelCase_ :Dict = accelerator.state
if state.local_process_index == 0:
print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("""**Test NOOP `no_sync` context manager**""" )
test_noop_sync(lowercase__ )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("""**Test Distributed `no_sync` context manager**""" )
test_distributed_sync(lowercase__ )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation, """ , f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(lowercase__ , lowercase__ )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("""<""" , """2.0""" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , """`split_batches=False`, `dispatch_batches=False`**""" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"""**Test `accumulate` gradient accumulation with optimizer and scheduler, """ , f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(lowercase__ , lowercase__ )
def _snake_case ( lowercase__ : Any ) -> List[str]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 1 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
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
__UpperCAmelCase = get_tests_dir('fixtures/dummy-config.json')
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :int = 0
def __lowerCAmelCase ( self ) -> List[str]:
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Tuple = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :int = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :Any = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :int = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(__A , __A )
def __lowerCAmelCase ( self ) -> Tuple:
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
lowerCAmelCase_ :int = os.path.join(__A , """fake-roberta""" )
os.makedirs(__A , exist_ok=__A )
with open(os.path.join(__A , """config.json""" ) , """w""" ) as f:
f.write(json.dumps({} ) )
lowerCAmelCase_ :Any = AutoConfig.from_pretrained(__A )
self.assertEqual(type(__A ) , __A )
def __lowerCAmelCase ( self ) -> Optional[int]:
try:
AutoConfig.register("""custom""" , __A )
# Wrong model type will raise an error
with self.assertRaises(__A ):
AutoConfig.register("""model""" , __A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__A ):
AutoConfig.register("""bert""" , __A )
# Now that the config is registered, it can be used as any other config with the auto-API
lowerCAmelCase_ :Union[str, Any] = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A )
lowerCAmelCase_ :Optional[int] = AutoConfig.from_pretrained(__A )
self.assertIsInstance(__A , __A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def __lowerCAmelCase ( self ) -> Tuple:
with self.assertRaisesRegex(
__A , """bert-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase_ :List[str] = AutoConfig.from_pretrained("""bert-base""" )
def __lowerCAmelCase ( self ) -> Any:
with self.assertRaisesRegex(
__A , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase_ :Dict = AutoConfig.from_pretrained(__A , revision="""aaaaaa""" )
def __lowerCAmelCase ( self ) -> int:
with self.assertRaisesRegex(
__A , """hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" , ):
lowerCAmelCase_ :Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def __lowerCAmelCase ( self ) -> Tuple:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__A ):
lowerCAmelCase_ :Tuple = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__A ):
lowerCAmelCase_ :List[str] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
lowerCAmelCase_ :str = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A )
lowerCAmelCase_ :Dict = AutoConfig.from_pretrained(__A , trust_remote_code=__A )
self.assertEqual(reloaded_config.__class__.__name__ , """NewModelConfig""" )
def __lowerCAmelCase ( self ) -> int:
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :int = "new-model"
try:
AutoConfig.register("""new-model""" , __A )
# If remote code is not set, the default is to use local
lowerCAmelCase_ :Any = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
lowerCAmelCase_ :Union[str, Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
lowerCAmelCase_ :Optional[Any] = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" , trust_remote_code=__A )
self.assertEqual(config.__class__.__name__ , """NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 1 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=A__ ):
UpperCAmelCase_ :Union[str, Any] = ["onnx"]
def __init__( self , *__A , **__A ) -> Dict:
requires_backends(self , ["""onnx"""] )
@classmethod
def __lowerCAmelCase ( cls , *__A , **__A ) -> str:
requires_backends(cls , ["""onnx"""] )
@classmethod
def __lowerCAmelCase ( cls , *__A , **__A ) -> str:
requires_backends(cls , ["""onnx"""] )
| 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :str = GPTSanJapaneseTokenizer
UpperCAmelCase_ :Optional[int] = False
UpperCAmelCase_ :Optional[int] = {"do_clean_text": False, "add_prefix_space": False}
def __lowerCAmelCase ( self ) -> Tuple:
super().setUp()
# fmt: off
lowerCAmelCase_ :Dict = ["""こん""", """こんに""", """にちは""", """ばんは""", """世界,㔺界""", """、""", """。""", """<BR>""", """<SP>""", """<TAB>""", """<URL>""", """<EMAIL>""", """<TEL>""", """<DATE>""", """<PRICE>""", """<BLOCK>""", """<KIGOU>""", """<U2000U2BFF>""", """<|emoji1|>""", """<unk>""", """<|bagoftoken|>""", """<|endoftext|>"""]
# fmt: on
lowerCAmelCase_ :List[str] = {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}} # 😀
lowerCAmelCase_ :int = {"""unk_token""": """<unk>"""}
lowerCAmelCase_ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ :int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""emoji_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.emoji_file , """w""" ) as emoji_writer:
emoji_writer.write(json.dumps(__A ) )
def __lowerCAmelCase ( self , **__A ) -> int:
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **__A )
def __lowerCAmelCase ( self , __A ) -> Dict:
lowerCAmelCase_ :List[Any] = """こんにちは、世界。 \nこんばんは、㔺界。😀"""
lowerCAmelCase_ :Optional[int] = """こんにちは、世界。 \nこんばんは、世界。😀"""
return input_text, output_text
def __lowerCAmelCase ( self , __A ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = self.get_input_output_texts(__A )
lowerCAmelCase_ :List[str] = tokenizer.encode(__A , add_special_tokens=__A )
lowerCAmelCase_ :str = tokenizer.decode(__A , clean_up_tokenization_spaces=__A )
return text, ids
def __lowerCAmelCase ( self ) -> str:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Dict:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> int:
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Union[str, Any] = self.get_tokenizer()
# Testing tokenization
lowerCAmelCase_ :Optional[int] = """こんにちは、世界。 こんばんは、㔺界。"""
lowerCAmelCase_ :Any = ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
lowerCAmelCase_ :Tuple = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
# Testing conversion to ids without special tokens
lowerCAmelCase_ :List[Any] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCAmelCase_ :List[str] = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(__A , __A )
# Testing conversion to ids with special tokens
lowerCAmelCase_ :Any = tokens + [tokenizer.unk_token]
lowerCAmelCase_ :Union[str, Any] = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCAmelCase_ :Union[str, Any] = tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(__A , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :int = self.get_tokenizer()
# Testing tokenization
lowerCAmelCase_ :Optional[int] = """こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
lowerCAmelCase_ :str = """こんにちは、、、、世界。こんばんは、、、、世界。"""
lowerCAmelCase_ :str = tokenizer.encode(__A )
lowerCAmelCase_ :Dict = tokenizer.decode(__A )
self.assertEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Tuple = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
# Testing tokenization
lowerCAmelCase_ :Optional[Any] = """こんにちは、世界。"""
lowerCAmelCase_ :Any = """こんばんは、㔺界。😀"""
lowerCAmelCase_ :Optional[Any] = """こんにちは、世界。こんばんは、世界。😀"""
lowerCAmelCase_ :List[Any] = tokenizer.encode(prefix_text + input_text )
lowerCAmelCase_ :List[str] = tokenizer.encode("""""" , prefix_text=prefix_text + input_text )
lowerCAmelCase_ :int = tokenizer.encode(__A , prefix_text=__A )
lowerCAmelCase_ :int = tokenizer.decode(__A )
lowerCAmelCase_ :Dict = tokenizer.decode(__A )
lowerCAmelCase_ :Tuple = tokenizer.decode(__A )
self.assertEqual(__A , __A )
self.assertEqual(__A , __A )
self.assertEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> Any:
lowerCAmelCase_ :int = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
# Testing tokenization
lowerCAmelCase_ :List[Any] = """こんにちは、世界。"""
lowerCAmelCase_ :Optional[int] = """こんばんは、㔺界。😀"""
lowerCAmelCase_ :List[str] = len(tokenizer.encode(__A ) ) - 2
lowerCAmelCase_ :Dict = len(tokenizer.encode(__A ) ) - 2
lowerCAmelCase_ :int = [1] + [0] * (len_prefix + len_text + 1)
lowerCAmelCase_ :List[Any] = [1] * (len_prefix + len_text + 1) + [0]
lowerCAmelCase_ :Dict = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCAmelCase_ :List[Any] = tokenizer(prefix_text + input_text ).token_type_ids
lowerCAmelCase_ :List[str] = tokenizer("""""" , prefix_text=prefix_text + input_text ).token_type_ids
lowerCAmelCase_ :List[Any] = tokenizer(__A , prefix_text=__A ).token_type_ids
self.assertListEqual(__A , __A )
self.assertListEqual(__A , __A )
self.assertListEqual(__A , __A )
@slow
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :Dict = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
lowerCAmelCase_ :int = tokenizer.encode("""あンいワ""" )
lowerCAmelCase_ :Optional[Any] = tokenizer.encode("""""" , prefix_text="""あンいワ""" )
lowerCAmelCase_ :int = tokenizer.encode("""いワ""" , prefix_text="""あン""" )
self.assertEqual(tokenizer.decode(__A ) , tokenizer.decode(__A ) )
self.assertEqual(tokenizer.decode(__A ) , tokenizer.decode(__A ) )
self.assertNotEqual(__A , __A )
self.assertNotEqual(__A , __A )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" )
lowerCAmelCase_ :int = [["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
lowerCAmelCase_ :Dict = tokenizer(__A , padding=__A )
lowerCAmelCase_ :Any = tokenizer.batch_encode_plus(__A , padding=__A )
# fmt: off
lowerCAmelCase_ :int = [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
lowerCAmelCase_ :List[str] = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCAmelCase_ :int = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , __A )
self.assertListEqual(x_token.token_type_ids , __A )
self.assertListEqual(x_token.attention_mask , __A )
self.assertListEqual(x_token_a.input_ids , __A )
self.assertListEqual(x_token_a.token_type_ids , __A )
self.assertListEqual(x_token_a.attention_mask , __A )
def __lowerCAmelCase ( self ) -> Tuple:
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def __lowerCAmelCase ( self ) -> str:
# tokenizer has no padding token
pass
| 1 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :Any = UnCLIPImageVariationPipeline
UpperCAmelCase_ :Union[str, Any] = IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
UpperCAmelCase_ :Dict = IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ :Optional[Any] = [
"generator",
"return_dict",
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
UpperCAmelCase_ :Optional[int] = False
@property
def __lowerCAmelCase ( self ) -> int:
return 32
@property
def __lowerCAmelCase ( self ) -> Tuple:
return 32
@property
def __lowerCAmelCase ( self ) -> Dict:
return self.time_input_dim
@property
def __lowerCAmelCase ( self ) -> List[Any]:
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self ) -> int:
return 100
@property
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def __lowerCAmelCase ( self ) -> Optional[Any]:
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = 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(__A )
@property
def __lowerCAmelCase ( self ) -> Optional[Any]:
torch.manual_seed(0 )
lowerCAmelCase_ :str = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(__A )
@property
def __lowerCAmelCase ( self ) -> Optional[Any]:
torch.manual_seed(0 )
lowerCAmelCase_ :int = {
"""clip_embeddings_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""cross_attention_dim""": self.cross_attention_dim,
}
lowerCAmelCase_ :Any = UnCLIPTextProjModel(**__A )
return model
@property
def __lowerCAmelCase ( self ) -> Optional[int]:
torch.manual_seed(0 )
lowerCAmelCase_ :Union[str, Any] = {
"""sample_size""": 32,
# RGB in channels
"""in_channels""": 3,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 6,
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": """identity""",
}
lowerCAmelCase_ :Any = UNetaDConditionModel(**__A )
return model
@property
def __lowerCAmelCase ( self ) -> Any:
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def __lowerCAmelCase ( self ) -> Union[str, Any]:
# seeded differently to get different unet than `self.dummy_super_res_first`
torch.manual_seed(1 )
lowerCAmelCase_ :Union[str, Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Tuple = self.dummy_decoder
lowerCAmelCase_ :str = self.dummy_text_proj
lowerCAmelCase_ :Any = self.dummy_text_encoder
lowerCAmelCase_ :Optional[Any] = self.dummy_tokenizer
lowerCAmelCase_ :Dict = self.dummy_super_res_first
lowerCAmelCase_ :Optional[Any] = self.dummy_super_res_last
lowerCAmelCase_ :int = UnCLIPScheduler(
variance_type="""learned_range""" , prediction_type="""epsilon""" , num_train_timesteps=1000 , )
lowerCAmelCase_ :List[str] = UnCLIPScheduler(
variance_type="""fixed_small_log""" , prediction_type="""epsilon""" , num_train_timesteps=1000 , )
lowerCAmelCase_ :Optional[Any] = CLIPImageProcessor(crop_size=32 , size=32 )
lowerCAmelCase_ :Dict = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def __lowerCAmelCase ( self , __A , __A=0 , __A=True ) -> Dict:
lowerCAmelCase_ :Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(__A ) ).to(__A )
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :List[Any] = torch.manual_seed(__A )
else:
lowerCAmelCase_ :str = torch.Generator(device=__A ).manual_seed(__A )
if pil_image:
lowerCAmelCase_ :int = input_image * 0.5 + 0.5
lowerCAmelCase_ :Any = input_image.clamp(0 , 1 )
lowerCAmelCase_ :Optional[Any] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowerCAmelCase_ :Tuple = DiffusionPipeline.numpy_to_pil(__A )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Optional[Any] = """cpu"""
lowerCAmelCase_ :Optional[int] = self.get_dummy_components()
lowerCAmelCase_ :Optional[int] = self.pipeline_class(**__A )
lowerCAmelCase_ :int = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = self.get_dummy_inputs(__A , pil_image=__A )
lowerCAmelCase_ :Any = pipe(**__A )
lowerCAmelCase_ :Optional[int] = output.images
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A , pil_image=__A )
lowerCAmelCase_ :str = pipe(
**__A , return_dict=__A , )[0]
lowerCAmelCase_ :List[str] = image[0, -3:, -3:, -1]
lowerCAmelCase_ :Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase_ :Any = np.array(
[
0.9_9_9_7,
0.0_0_0_2,
0.9_9_9_7,
0.9_9_9_7,
0.9_9_6_9,
0.0_0_2_3,
0.9_9_9_7,
0.9_9_6_9,
0.9_9_7_0,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Optional[Any] = """cpu"""
lowerCAmelCase_ :List[str] = self.get_dummy_components()
lowerCAmelCase_ :Optional[Any] = self.pipeline_class(**__A )
lowerCAmelCase_ :Optional[Any] = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A , pil_image=__A )
lowerCAmelCase_ :str = pipe(**__A )
lowerCAmelCase_ :Dict = output.images
lowerCAmelCase_ :Union[str, Any] = self.get_dummy_inputs(__A , pil_image=__A )
lowerCAmelCase_ :List[str] = pipe(
**__A , return_dict=__A , )[0]
lowerCAmelCase_ :str = image[0, -3:, -3:, -1]
lowerCAmelCase_ :Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase_ :str = np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :Union[str, Any] = """cpu"""
lowerCAmelCase_ :Dict = self.get_dummy_components()
lowerCAmelCase_ :Union[str, Any] = self.pipeline_class(**__A )
lowerCAmelCase_ :Dict = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = self.get_dummy_inputs(__A , pil_image=__A )
lowerCAmelCase_ :int = [
pipeline_inputs["""image"""],
pipeline_inputs["""image"""],
]
lowerCAmelCase_ :List[str] = pipe(**__A )
lowerCAmelCase_ :Optional[int] = output.images
lowerCAmelCase_ :Dict = self.get_dummy_inputs(__A , pil_image=__A )
lowerCAmelCase_ :List[Any] = [
tuple_pipeline_inputs["""image"""],
tuple_pipeline_inputs["""image"""],
]
lowerCAmelCase_ :Union[str, Any] = pipe(
**__A , return_dict=__A , )[0]
lowerCAmelCase_ :str = image[0, -3:, -3:, -1]
lowerCAmelCase_ :List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
lowerCAmelCase_ :Optional[Any] = np.array(
[
0.9_9_9_7,
0.9_9_8_9,
0.0_0_0_8,
0.0_0_2_1,
0.9_9_6_0,
0.0_0_1_8,
0.0_0_1_4,
0.0_0_0_2,
0.9_9_3_3,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[Any] = torch.device("""cpu""" )
class _SCREAMING_SNAKE_CASE :
UpperCAmelCase_ :str = 1
lowerCAmelCase_ :Dict = self.get_dummy_components()
lowerCAmelCase_ :List[Any] = self.pipeline_class(**__A )
lowerCAmelCase_ :Optional[int] = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Dict = torch.Generator(device=__A ).manual_seed(0 )
lowerCAmelCase_ :Any = pipe.decoder.dtype
lowerCAmelCase_ :Dict = 1
lowerCAmelCase_ :Optional[Any] = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
lowerCAmelCase_ :Optional[int] = pipe.prepare_latents(
__A , dtype=__A , device=__A , generator=__A , latents=__A , scheduler=DummyScheduler() )
lowerCAmelCase_ :Tuple = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
lowerCAmelCase_ :Optional[Any] = pipe.prepare_latents(
__A , dtype=__A , device=__A , generator=__A , latents=__A , scheduler=DummyScheduler() )
lowerCAmelCase_ :List[str] = self.get_dummy_inputs(__A , pil_image=__A )
lowerCAmelCase_ :Dict = pipe(
**__A , decoder_latents=__A , super_res_latents=__A ).images
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A , pil_image=__A )
# Don't pass image, instead pass embedding
lowerCAmelCase_ :Optional[Any] = pipeline_inputs.pop("""image""" )
lowerCAmelCase_ :Optional[int] = pipe.image_encoder(__A ).image_embeds
lowerCAmelCase_ :Tuple = pipe(
**__A , decoder_latents=__A , super_res_latents=__A , image_embeddings=__A , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Union[str, Any] = torch_device == """cpu"""
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
lowerCAmelCase_ :Dict = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=__A , expected_max_diff=__A )
@skip_mps
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :List[Any] = torch_device == """cpu"""
lowerCAmelCase_ :int = True
lowerCAmelCase_ :Union[str, Any] = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
self._test_inference_batch_single_identical(
test_max_difference=__A , relax_max_difference=__A , additional_params_copy_to_batched_inputs=__A , )
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :str = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
lowerCAmelCase_ :List[str] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=__A , additional_params_copy_to_batched_inputs=__A , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=__A )
@skip_mps
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def __lowerCAmelCase ( self ) -> Optional[int]:
return super().test_save_load_local()
@skip_mps
def __lowerCAmelCase ( self ) -> Any:
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png""" )
lowerCAmelCase_ :str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/unclip/karlo_v1_alpha_cat_variation_fp16.npy""" )
lowerCAmelCase_ :List[Any] = UnCLIPImageVariationPipeline.from_pretrained(
"""kakaobrain/karlo-v1-alpha-image-variations""" , torch_dtype=torch.floataa )
lowerCAmelCase_ :Optional[int] = pipeline.to(__A )
pipeline.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :str = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase_ :str = pipeline(
__A , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :List[str] = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(__A , __A , 15 )
| 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
__UpperCAmelCase = pd.read_csv(
'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
'position_salaries.csv'
)
__UpperCAmelCase = dataset.iloc[:, 1:2].values
__UpperCAmelCase = dataset.iloc[:, 2].values
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = train_test_split(X, y, test_size=0.2, random_state=0)
__UpperCAmelCase = PolynomialFeatures(degree=4)
__UpperCAmelCase = poly_reg.fit_transform(X)
__UpperCAmelCase = LinearRegression()
pol_reg.fit(X_poly, y)
def _snake_case ( ) -> str:
'''simple docstring'''
plt.scatter(lowercase__ , lowercase__ , color="""red""" )
plt.plot(lowercase__ , pol_reg.predict(poly_reg.fit_transform(lowercase__ ) ) , color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 1 | 1 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _snake_case ( lowercase__ : Any , lowercase__ : bool = True , lowercase__ : float = math.inf , lowercase__ : float = -math.inf , lowercase__ : float = math.inf , lowercase__ : float = -math.inf , lowercase__ : bool = False , lowercase__ : float = 1_0_0 , lowercase__ : float = 0.01 , lowercase__ : float = 1 , ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Any = False
lowerCAmelCase_ :Optional[Any] = search_prob
lowerCAmelCase_ :str = start_temperate
lowerCAmelCase_ :Tuple = []
lowerCAmelCase_ :Tuple = 0
lowerCAmelCase_ :List[str] = None
while not search_end:
lowerCAmelCase_ :Tuple = current_state.score()
if best_state is None or current_score > best_state.score():
lowerCAmelCase_ :Optional[Any] = current_state
scores.append(lowercase__ )
iterations += 1
lowerCAmelCase_ :Union[str, Any] = None
lowerCAmelCase_ :List[str] = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
lowerCAmelCase_ :List[Any] = random.randint(0 , len(lowercase__ ) - 1 ) # picking a random neighbor
lowerCAmelCase_ :Optional[int] = neighbors.pop(lowercase__ )
lowerCAmelCase_ :Any = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
lowerCAmelCase_ :Optional[Any] = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
lowerCAmelCase_ :Optional[int] = picked_neighbor
else:
lowerCAmelCase_ :List[Any] = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
lowerCAmelCase_ :Union[str, Any] = picked_neighbor
lowerCAmelCase_ :List[Any] = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
lowerCAmelCase_ :Any = True
else:
lowerCAmelCase_ :str = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(lowercase__ ) , lowercase__ )
plt.xlabel("""Iterations""" )
plt.ylabel("""Function values""" )
plt.show()
return best_state
if __name__ == "__main__":
def _snake_case ( lowercase__ : Tuple , lowercase__ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
__UpperCAmelCase = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
__UpperCAmelCase = simulated_annealing(
prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
__UpperCAmelCase = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
__UpperCAmelCase = simulated_annealing(
prob, find_max=True, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def _snake_case ( lowercase__ : List[str] , lowercase__ : str ) -> List[str]:
'''simple docstring'''
return (3 * x**2) - (6 * y)
__UpperCAmelCase = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
__UpperCAmelCase = simulated_annealing(prob, find_max=False, visualization=True)
print(
'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
F"""{local_min.score()}"""
)
__UpperCAmelCase = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
__UpperCAmelCase = simulated_annealing(prob, find_max=True, visualization=True)
print(
'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
F"""{local_min.score()}"""
)
| 1 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 1 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__UpperCAmelCase = data_utils.TransfoXLTokenizer
__UpperCAmelCase = data_utils.TransfoXLCorpus
__UpperCAmelCase = data_utils
__UpperCAmelCase = data_utils
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : int , lowercase__ : str , lowercase__ : Any ) -> Optional[int]:
'''simple docstring'''
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(lowercase__ , """rb""" ) as fp:
lowerCAmelCase_ :Tuple = pickle.load(lowercase__ , encoding="""latin1""" )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
lowerCAmelCase_ :Optional[int] = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
print(f"""Save vocabulary to {pytorch_vocab_dump_path}""" )
lowerCAmelCase_ :Union[str, Any] = corpus.vocab.__dict__
torch.save(lowercase__ , lowercase__ )
lowerCAmelCase_ :str = corpus.__dict__
corpus_dict_no_vocab.pop("""vocab""" , lowercase__ )
lowerCAmelCase_ :str = pytorch_dump_folder_path + """/""" + CORPUS_NAME
print(f"""Save dataset to {pytorch_dataset_dump_path}""" )
torch.save(lowercase__ , lowercase__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
lowerCAmelCase_ :int = os.path.abspath(lowercase__ )
lowerCAmelCase_ :Any = os.path.abspath(lowercase__ )
print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
# Initialise PyTorch model
if transfo_xl_config_file == "":
lowerCAmelCase_ :Optional[int] = TransfoXLConfig()
else:
lowerCAmelCase_ :Optional[Any] = TransfoXLConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase_ :List[str] = TransfoXLLMHeadModel(lowercase__ )
lowerCAmelCase_ :Any = load_tf_weights_in_transfo_xl(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
lowerCAmelCase_ :Any = os.path.join(lowercase__ , lowercase__ )
lowerCAmelCase_ :Optional[int] = os.path.join(lowercase__ , lowercase__ )
print(f"""Save PyTorch model to {os.path.abspath(lowercase__ )}""" )
torch.save(model.state_dict() , lowercase__ )
print(f"""Save configuration file to {os.path.abspath(lowercase__ )}""" )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the folder to store the PyTorch model or dataset/vocab.',
)
parser.add_argument(
'--tf_checkpoint_path',
default='',
type=str,
help='An optional path to a TensorFlow checkpoint path to be converted.',
)
parser.add_argument(
'--transfo_xl_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--transfo_xl_dataset_file',
default='',
type=str,
help='An optional dataset file to be converted in a vocabulary.',
)
__UpperCAmelCase = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , **__A ) -> None:
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , __A , )
super().__init__(*__A , **__A )
| 1 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _snake_case ( lowercase__ : str = "laptop" ) -> DataFrame:
'''simple docstring'''
lowerCAmelCase_ :Dict = f"""https://www.amazon.in/laptop/s?k={product}"""
lowerCAmelCase_ :List[str] = {
"""User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
"""Accept-Language""": """en-US, en;q=0.5""",
}
lowerCAmelCase_ :List[Any] = BeautifulSoup(requests.get(lowercase__ , headers=lowercase__ ).text )
# Initialize a Pandas dataframe with the column titles
lowerCAmelCase_ :Union[str, Any] = DataFrame(
columns=[
"""Product Title""",
"""Product Link""",
"""Current Price of the product""",
"""Product Rating""",
"""MRP of the product""",
"""Discount""",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"""div""" , attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""} , ) , soup.find_all("""div""" , attrs={"""class""": """a-row a-size-base a-color-base"""} ) , ):
try:
lowerCAmelCase_ :str = item.ha.text
lowerCAmelCase_ :Dict = """https://www.amazon.in/""" + item.ha.a["""href"""]
lowerCAmelCase_ :int = item.find("""span""" , attrs={"""class""": """a-offscreen"""} ).text
try:
lowerCAmelCase_ :Dict = item.find("""span""" , attrs={"""class""": """a-icon-alt"""} ).text
except AttributeError:
lowerCAmelCase_ :int = """Not available"""
try:
lowerCAmelCase_ :str = (
"""₹"""
+ item.find(
"""span""" , attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
)
except AttributeError:
lowerCAmelCase_ :Optional[Any] = """"""
try:
lowerCAmelCase_ :str = float(
(
(
float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
- float(product_price.strip("""₹""" ).replace(""",""" , """""" ) )
)
/ float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
)
* 1_0_0 )
except ValueError:
lowerCAmelCase_ :Union[str, Any] = float("""nan""" )
except AttributeError:
pass
lowerCAmelCase_ :Any = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
lowerCAmelCase_ :List[Any] = """ """
lowerCAmelCase_ :Tuple = """ """
data_frame.index += 1
return data_frame
if __name__ == "__main__":
__UpperCAmelCase = 'headphones'
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 1 | 1 |
"""simple docstring"""
# 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
__UpperCAmelCase = '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 _snake_case ( ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Any = _ask_options(
"""In which compute environment are you running?""" , ["""This machine""", """AWS (Amazon SageMaker)"""] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
lowerCAmelCase_ :Tuple = get_sagemaker_input()
else:
lowerCAmelCase_ :Any = get_cluster_input()
return config
def _snake_case ( lowercase__ : Any=None ) -> Dict:
'''simple docstring'''
if subparsers is not None:
lowerCAmelCase_ :Tuple = subparsers.add_parser("""config""" , description=lowercase__ )
else:
lowerCAmelCase_ :Optional[Any] = 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 _snake_case ( lowercase__ : Any ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = get_user_input()
if args.config_file is not None:
lowerCAmelCase_ :Optional[int] = args.config_file
else:
if not os.path.isdir(lowercase__ ):
os.makedirs(lowercase__ )
lowerCAmelCase_ :str = 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 _snake_case ( ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :Tuple = config_command_parser()
lowerCAmelCase_ :Dict = parser.parse_args()
config_command(lowercase__ )
if __name__ == "__main__":
main()
| 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :Any = """laion/clap-htsat-unfused"""
lowerCAmelCase_ :Optional[Any] = tempfile.mkdtemp()
def __lowerCAmelCase ( self , **__A ) -> List[Any]:
return RobertaTokenizer.from_pretrained(self.checkpoint , **__A )
def __lowerCAmelCase ( self , **__A ) -> Tuple:
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__A )
def __lowerCAmelCase ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Optional[Any] = self.get_tokenizer()
lowerCAmelCase_ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ :Optional[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Dict = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ :str = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase_ :Dict = self.get_feature_extractor(do_normalize=__A , padding_value=1.0 )
lowerCAmelCase_ :Union[str, Any] = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Dict = self.get_feature_extractor()
lowerCAmelCase_ :str = self.get_tokenizer()
lowerCAmelCase_ :List[Any] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :Optional[Any] = floats_list((3, 1000) )
lowerCAmelCase_ :Optional[Any] = feature_extractor(__A , return_tensors="""np""" )
lowerCAmelCase_ :str = processor(audios=__A , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Any = self.get_tokenizer()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :List[Any] = """This is a test string"""
lowerCAmelCase_ :Dict = processor(text=__A )
lowerCAmelCase_ :List[str] = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :int = self.get_feature_extractor()
lowerCAmelCase_ :Tuple = self.get_tokenizer()
lowerCAmelCase_ :Optional[Any] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase_ :Tuple = processor.batch_decode(__A )
lowerCAmelCase_ :Optional[Any] = tokenizer.batch_decode(__A )
self.assertListEqual(__A , __A )
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Any = self.get_tokenizer()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 1 | 1 |
"""simple docstring"""
__UpperCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _snake_case ( lowercase__ : dict[int, list[int]] , lowercase__ : int , lowercase__ : list[bool] ) -> list[int]:
'''simple docstring'''
lowerCAmelCase_ :Dict = True
lowerCAmelCase_ :Tuple = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(lowercase__ , lowercase__ , lowercase__ )
order.append(lowercase__ )
return order
def _snake_case ( lowercase__ : dict[int, list[int]] , lowercase__ : int , lowercase__ : list[bool] ) -> list[int]:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = True
lowerCAmelCase_ :List[str] = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(lowercase__ , lowercase__ , lowercase__ )
return component
def _snake_case ( lowercase__ : dict[int, list[int]] ) -> list[list[int]]:
'''simple docstring'''
lowerCAmelCase_ :int = len(lowercase__ ) * [False]
lowerCAmelCase_ :dict[int, list[int]] = {vert: [] for vert in range(len(lowercase__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(lowercase__ )
lowerCAmelCase_ :str = []
for i, was_visited in enumerate(lowercase__ ):
if not was_visited:
order += topology_sort(lowercase__ , lowercase__ , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = []
lowerCAmelCase_ :Union[str, Any] = len(lowercase__ ) * [False]
for i in range(len(lowercase__ ) ):
lowerCAmelCase_ :Optional[Any] = order[len(lowercase__ ) - i - 1]
if not visited[vert]:
lowerCAmelCase_ :Optional[Any] = find_components(lowercase__ , lowercase__ , lowercase__ )
components_list.append(lowercase__ )
return components_list
| 1 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) ):
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = list(map(lowercase__ , line.split(""",""" ) ) )
if x * logaa(lowercase__ ) > largest:
lowerCAmelCase_ :Any = x * logaa(lowercase__ )
lowerCAmelCase_ :List[Any] = i + 1
return result
if __name__ == "__main__":
print(solution())
| 1 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Dict = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def _snake_case ( lowercase__ : int = 1_0_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Dict = 1
lowerCAmelCase_ :Union[str, Any] = 2
for i in range(2 , max_n + 1 ):
lowerCAmelCase_ :List[Any] = pre_numerator
lowerCAmelCase_ :Dict = 2 * i // 3 if i % 3 == 0 else 1
lowerCAmelCase_ :Any = cur_numerator
lowerCAmelCase_ :str = e_cont * pre_numerator + temp
return sum_digits(lowercase__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
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(lowercase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _snake_case ( ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = 2
while True:
if is_prime(lowercase__ ):
yield num
num += 1
def _snake_case ( lowercase__ : int = 1_0_0_0_1 ) -> int:
'''simple docstring'''
return next(itertools.islice(prime_generator() , nth - 1 , lowercase__ ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int , lowercase__ : int ) -> str:
'''simple docstring'''
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 1 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[Any] = "llama"
UpperCAmelCase_ :str = ["past_key_values"]
def __init__( self , __A=3_2000 , __A=4096 , __A=1_1008 , __A=32 , __A=32 , __A=None , __A="silu" , __A=2048 , __A=0.0_2 , __A=1E-6 , __A=True , __A=0 , __A=1 , __A=2 , __A=1 , __A=False , __A=None , **__A , ) -> List[str]:
lowerCAmelCase_ :str = vocab_size
lowerCAmelCase_ :str = max_position_embeddings
lowerCAmelCase_ :int = hidden_size
lowerCAmelCase_ :List[str] = intermediate_size
lowerCAmelCase_ :Tuple = num_hidden_layers
lowerCAmelCase_ :Dict = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
lowerCAmelCase_ :List[str] = num_attention_heads
lowerCAmelCase_ :Any = num_key_value_heads
lowerCAmelCase_ :Optional[int] = hidden_act
lowerCAmelCase_ :Any = initializer_range
lowerCAmelCase_ :str = rms_norm_eps
lowerCAmelCase_ :List[Any] = pretraining_tp
lowerCAmelCase_ :int = use_cache
lowerCAmelCase_ :List[Any] = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , tie_word_embeddings=__A , **__A , )
def __lowerCAmelCase ( self ) -> List[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __A ) 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}""" )
lowerCAmelCase_ :List[str] = self.rope_scaling.get("""type""" , __A )
lowerCAmelCase_ :List[Any] = self.rope_scaling.get("""factor""" , __A )
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(__A , __A ) or rope_scaling_factor <= 1.0:
raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( A__ , A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :int = StableDiffusionControlNetImgaImgPipeline
UpperCAmelCase_ :str = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
UpperCAmelCase_ :Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ :List[str] = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"} )
UpperCAmelCase_ :Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :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 , )
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
lowerCAmelCase_ :List[str] = 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 )
lowerCAmelCase_ :Union[str, 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=1000 , )
lowerCAmelCase_ :List[Any] = CLIPTextModel(__A )
lowerCAmelCase_ :int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase_ :Union[str, Any] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def __lowerCAmelCase ( self , __A , __A=0 ) -> List[str]:
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :Tuple = torch.manual_seed(__A )
else:
lowerCAmelCase_ :Optional[int] = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :List[Any] = 2
lowerCAmelCase_ :int = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , )
lowerCAmelCase_ :Optional[int] = floats_tensor(control_image.shape , rng=random.Random(__A ) ).to(__A )
lowerCAmelCase_ :Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ :List[Any] = Image.fromarray(np.uinta(__A ) ).convert("""RGB""" ).resize((64, 64) )
lowerCAmelCase_ :Union[str, Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def __lowerCAmelCase ( self ) -> int:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> List[str]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class _SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
UpperCAmelCase_ :List[str] = StableDiffusionControlNetImgaImgPipeline
UpperCAmelCase_ :int = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
UpperCAmelCase_ :str = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ :int = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def __lowerCAmelCase ( self ) -> Optional[int]:
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = 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 , )
torch.manual_seed(0 )
def init_weights(__A ):
if isinstance(__A , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowerCAmelCase_ :List[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(__A )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(__A )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=__A , set_alpha_to_one=__A , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = 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 )
lowerCAmelCase_ :Optional[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=1000 , )
lowerCAmelCase_ :str = CLIPTextModel(__A )
lowerCAmelCase_ :str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase_ :Optional[Any] = MultiControlNetModel([controlneta, controlneta] )
lowerCAmelCase_ :List[Any] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def __lowerCAmelCase ( self , __A , __A=0 ) -> str:
if str(__A ).startswith("""mps""" ):
lowerCAmelCase_ :Optional[Any] = torch.manual_seed(__A )
else:
lowerCAmelCase_ :List[Any] = torch.Generator(device=__A ).manual_seed(__A )
lowerCAmelCase_ :Optional[Any] = 2
lowerCAmelCase_ :Optional[int] = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__A , device=torch.device(__A ) , ),
]
lowerCAmelCase_ :int = floats_tensor(control_image[0].shape , rng=random.Random(__A ) ).to(__A )
lowerCAmelCase_ :Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ :List[Any] = Image.fromarray(np.uinta(__A ) ).convert("""RGB""" ).resize((64, 64) )
lowerCAmelCase_ :List[str] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :List[str] = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
lowerCAmelCase_ :Union[str, Any] = 1_0.0
lowerCAmelCase_ :Union[str, Any] = 4
lowerCAmelCase_ :Tuple = self.get_dummy_inputs(__A )
lowerCAmelCase_ :List[str] = steps
lowerCAmelCase_ :int = scale
lowerCAmelCase_ :Union[str, Any] = pipe(**__A )[0]
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :str = steps
lowerCAmelCase_ :str = scale
lowerCAmelCase_ :Tuple = pipe(**__A , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Union[str, Any] = steps
lowerCAmelCase_ :Union[str, Any] = scale
lowerCAmelCase_ :str = pipe(**__A , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
lowerCAmelCase_ :List[str] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = steps
lowerCAmelCase_ :Tuple = scale
lowerCAmelCase_ :str = pipe(**__A , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def __lowerCAmelCase ( self ) -> Dict:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowerCAmelCase ( self ) -> Tuple:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> Optional[int]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :str = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(__A )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :Any = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
lowerCAmelCase_ :int = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , safety_checker=__A , controlnet=__A )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[str] = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase_ :List[Any] = """evil space-punk bird"""
lowerCAmelCase_ :List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((512, 512) )
lowerCAmelCase_ :int = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((512, 512) )
lowerCAmelCase_ :Union[str, Any] = pipe(
__A , __A , control_image=__A , generator=__A , output_type="""np""" , num_inference_steps=50 , strength=0.6 , )
lowerCAmelCase_ :Tuple = output.images[0]
assert image.shape == (512, 512, 3)
lowerCAmelCase_ :Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2
| 1 | 1 |
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