'''.join(['''''' for _ in range(11 )] ).strip(), [])\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif method == \"dense\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCamelCase ,\t\t\t\t\t\t\t_UpperCamelCase : Dict = query_qa_dense_index(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t _lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\t_lowercase )\r\n\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCamelCase ,\t\t\t\t\t\t\t_UpperCamelCase : List[str] = query_es_index(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t _lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\tindex_name='''english_wiki40b_snippets_100w'''\t,\t\t\t\t\t\tn_results=_lowercase\t,\t\t\t\t\t\t)\r\n\t\t\t\t\t\t_UpperCamelCase : Any = [\r\n\t\t\t\t\t\t (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst\r\n\t\t\t\t\t\t]\r\n\t\t\t\t\t\t_UpperCamelCase : List[Any] = '''question: {} context: {}'''.format(_lowercase\t,\t\t\t\t\t\t_lowercase )\r\n\t\t\t\t\t\treturn question_doc, support_list\r\n\r\n\r\n\r\n\r\n\r\n@st.cache(\r\n hash_funcs={\r\n torch.Tensor: (lambda _lowercase : None),\r\n transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowercase : None),\r\n } )\r\ndef \ta_ (\t\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\t_lowercase=64\t,\t\t\t\t\t\t_lowercase=256\t,\t\t\t\t\t\t_lowercase=False\t,\t\t\t\t\t\t_lowercase=2\t,\t\t\t\t\t\t_lowercase=0.95\t,\t\t\t\t\t\t_lowercase=0.8 ):\r\n\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t\t_UpperCamelCase : List[Any] = qa_sas_generate(\r\n\t\t\t\t\t\t\t\t\t\t\t\t _lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\t_lowercase\t,\t\t\t\t\t\tnum_answers=1\t,\t\t\t\t\t\tnum_beams=_lowercase\t,\t\t\t\t\t\tmin_len=_lowercase\t,\t\t\t\t\t\tmax_len=_lowercase\t,\t\t\t\t\t\tdo_sample=_lowercase\t,\t\t\t\t\t\ttemp=_lowercase\t,\t\t\t\t\t\ttop_p=_lowercase\t,\t\t\t\t\t\ttop_k=_lowercase\t,\t\t\t\t\t\tmax_input_length=1024\t,\t\t\t\t\t\tdevice='''cuda:0'''\t,\t\t\t\t\t\t)[0]\r\n\t\t\t\t\t\treturn (answer, support_list)\r\n\r\n\r\nst.title(\"\"\"Long Form Question Answering with ELI5\"\"\")\r\n\r\n# Start sidebar\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"\"\"\"\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"\n\n \n \n \n \n \n %s\n \n \n\n\"\"\" % (\r\n header_html,\r\n)\r\nst.sidebar.markdown(\r\n header_full,\r\n unsafe_allow_html=True,\r\n)\r\n\r\n# Long Form QA with ELI5 and Wikipedia\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n\"\"\"\r\nst.sidebar.markdown(description, unsafe_allow_html=True)\r\n\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=[\r\n \"\"\"Answer the question\"\"\",\r\n \"\"\"View the retrieved document only\"\"\",\r\n \"\"\"View the most similar ELI5 question and answer\"\"\",\r\n \"\"\"Show me everything, please!\"\"\",\r\n]\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.checkbox(\"\"\"Demo options\"\"\")\r\nif demo_options:\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.selectbox(\r\n\t\t\t\t\t\t \"\"\"\"\"\",\r\n\t\t\t\t\t\t action_list,\r\n\t\t\t\t\t\t index=3,\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=action_list.index(action_st)\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.selectbox(\r\n\t\t\t\t\t\t \"\"\"\"\"\",\r\n\t\t\t\t\t\t [\"\"\"Show full text of passages\"\"\", \"\"\"Show passage section titles\"\"\"],\r\n\t\t\t\t\t\t index=0,\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=show_type == \"\"\"Show full text of passages\"\"\"\r\nelse:\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=3\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=True\r\n\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.checkbox(\"\"\"Retrieval options\"\"\")\r\nif retrieval_options:\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n \"\"\"\r\n\t\t\t\t\t\tst.sidebar.markdown(retriever_info)\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.selectbox(\"\"\"Which Wikipedia format should the model use?\"\"\", [\"\"\"wiki40b\"\"\", \"\"\"none\"\"\"])\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.selectbox(\"\"\"Which Wikipedia indexer should the model use?\"\"\", [\"\"\"dense\"\"\", \"\"\"sparse\"\"\", \"\"\"mixed\"\"\"])\r\nelse:\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"wiki40b\"\"\"\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"dense\"\"\"\r\n\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"beam\"\"\"\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=2\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=64\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=256\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=None\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=None\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.checkbox(\"\"\"Generation options\"\"\")\r\nif generate_options:\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder's output probabilities.\n \"\"\"\r\n\t\t\t\t\t\tst.sidebar.markdown(generate_info)\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.selectbox(\"\"\"Would you like to use beam search or sample an answer?\"\"\", [\"\"\"beam\"\"\", \"\"\"sampled\"\"\"])\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.slider(\r\n\t\t\t\t\t\t \"\"\"Minimum generation length\"\"\", min_value=8, max_value=256, value=64, step=8, format=None, key=None\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.slider(\r\n\t\t\t\t\t\t \"\"\"Maximum generation length\"\"\", min_value=64, max_value=512, value=256, step=16, format=None, key=None\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t\tif sampled == \"beam\":\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.slider(\"\"\"Beam size\"\"\", min_value=1, max_value=8, value=2, step=None, format=None, key=None)\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.slider(\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"Nucleus sampling p\"\"\", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None\r\n\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.sidebar.slider(\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"Temperature\"\"\", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None\r\n\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=None\r\n\r\n# start main text\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=[\r\n \"\"\"\"\"\",\r\n \"\"\"How do people make chocolate?\"\"\",\r\n \"\"\"Why do we get a fever when we are sick?\"\"\",\r\n \"\"\"How can different animals perceive different colors?\"\"\",\r\n \"\"\"What is natural language processing?\"\"\",\r\n \"\"\"What's the best way to treat a sunburn?\"\"\",\r\n \"\"\"What exactly are vitamins ?\"\"\",\r\n \"\"\"How does nuclear energy provide electricity?\"\"\",\r\n \"\"\"What's the difference between viruses and bacteria?\"\"\",\r\n \"\"\"Why are flutes classified as woodwinds when most of them are made out of metal ?\"\"\",\r\n \"\"\"Why do people like drinking coffee even though it tastes so bad?\"\"\",\r\n \"\"\"What happens when wine ages? How does it make the wine taste better?\"\"\",\r\n \"\"\"If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?\"\"\",\r\n \"\"\"How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?\"\"\",\r\n \"\"\"How does New Zealand have so many large bird predators?\"\"\",\r\n]\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.selectbox(\r\n \"\"\"What would you like to ask? ---- select to enter a new query\"\"\",\r\n questions_list,\r\n index=1,\r\n)\r\nif question_s == \"\":\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=st.text_input(\"\"\"Enter your question here:\"\"\", \"\"\"\"\"\")\r\nelse:\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=question_s\r\n\r\nif st.button(\"\"\"Show me!\"\"\"):\r\n\t\t\t\t\t\tif action in [0, 1, 3]:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif index_type == \"mixed\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t, UpperCamelCase_\t\t\t\t\t\t\t\t\t=make_support(question, source=wiki_source, method=\"\"\"dense\"\"\", n_results=10)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t, UpperCamelCase_\t\t\t\t\t\t\t\t\t=make_support(question, source=wiki_source, method=\"\"\"sparse\"\"\", n_results=10)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=[]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor res_d, res_s in zip(support_list_dense, support_list_sparse):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif tuple(res_d) not in support_list:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsupport_list += [tuple(res_d)]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif tuple(res_s) not in support_list:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsupport_list += [tuple(res_s)]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=support_list[:10]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"

\"\"\" + \"\"\"

\"\"\".join([res[-1] for res in support_list])\r\n\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t, UpperCamelCase_\t\t\t\t\t\t\t\t\t=make_support(question, source=wiki_source, method=index_type, n_results=10)\r\n\t\t\t\t\t\tif action in [0, 3]:\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t, UpperCamelCase_\t\t\t\t\t\t\t\t\t=answer_question(\r\n\t\t\t\t\t\t\t\t\t\t\t\t question_doc,\r\n\t\t\t\t\t\t\t\t\t\t\t\t sas_model,\r\n\t\t\t\t\t\t\t\t\t\t\t\t sas_tokenizer,\r\n\t\t\t\t\t\t\t\t\t\t\t\t min_len=min_len,\r\n\t\t\t\t\t\t\t\t\t\t\t\t max_len=int(max_len),\r\n\t\t\t\t\t\t\t\t\t\t\t\t sampling=(sampled == \"\"\"sampled\"\"\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\t n_beams=n_beams,\r\n\t\t\t\t\t\t\t\t\t\t\t\t top_p=top_p,\r\n\t\t\t\t\t\t\t\t\t\t\t\t temp=temp,\r\n\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tst.markdown(\"\"\"### The model generated answer is:\"\"\")\r\n\t\t\t\t\t\t\t\t\t\t\t\tst.write(answer)\r\n\t\t\t\t\t\tif action in [0, 1, 3] and wiki_source != \"none\":\r\n\t\t\t\t\t\t\t\t\t\t\t\tst.markdown(\"\"\"--- \\n ### The model is drawing information from the following Wikipedia passages:\"\"\")\r\n\t\t\t\t\t\t\t\t\t\t\t\tfor i, res in enumerate(support_list):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"https://en.wikipedia.org/wiki/{}\"\"\".format(res[0].replace(\"\"\" \"\"\", \"\"\"_\"\"\"))\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=res[1].strip()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif sec_titles == \"\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"[{}]({})\"\"\".format(res[0], wiki_url)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=sec_titles.split(\"\"\" & \"\"\")\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\" & \"\"\".join(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t [\"\"\"[{}]({}#{})\"\"\".format(sec.strip(), wiki_url, sec.strip().replace(\"\"\" \"\"\", \"\"\"_\"\"\")) for sec in sec_list]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tst.markdown(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"{0:02d} - **Article**: {1:<18}
_Section_: {2}\"\"\".format(i + 1, res[0], sections),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t unsafe_allow_html=True,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif show_passages:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tst.write(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"> \"\"\" + res[-1] + \"\"\"\"\"\", unsafe_allow_html=True\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\tif action in [2, 3]:\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=find_nearest_training(question)\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=nn_train_list[0]\r\n\t\t\t\t\t\t\t\t\t\t\t\tst.markdown(\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"--- \\n ### The most similar question in the ELI5 training set was: \\n\\n {}\"\"\".format(train_exple[\"\"\"title\"\"\"])\r\n\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t\t\t\t\t=[\r\n\t\t\t\t\t\t\t\t\t\t\t\t \"\"\"{}. {}\"\"\".format(i + 1, \"\"\" \\n\"\"\".join([line.strip() for line in ans.split(\"\"\"\\n\"\"\") if line.strip() != \"\"\"\"\"\"]))\r\n\t\t\t\t\t\t\t\t\t\t\t\t for i, (ans, sc) in enumerate(zip(train_exple[\"\"\"answers\"\"\"][\"\"\"text\"\"\"], train_exple[\"\"\"answers\"\"\"][\"\"\"score\"\"\"]))\r\n\t\t\t\t\t\t\t\t\t\t\t\t if i == 0 or sc > 2\r\n\t\t\t\t\t\t\t\t\t\t\t\t]\r\n\t\t\t\t\t\t\t\t\t\t\t\tst.markdown(\"\"\"##### Its answers were: \\n\\n {}\"\"\".format(\"\"\"\\n\"\"\".join(answers_st)))\r\n\r\n\r\nUpperCamelCase_\t\t\t\t\t\t\t\t\t=\"\"\"\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\nst.sidebar.markdown(disclaimer, unsafe_allow_html=True)\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":128,"string":"128"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":260,"cells":{"code":{"kind":"string","value":"\r'''simple docstring'''\r\r\r\r\r\r\r\rimport argparse\r\rfrom transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta\rfrom transformers.utils import logging\r\r\rlogging.set_verbosity_info()\r\r\r\r\r\r\r\rdef \t\t\t\tSCREAMING_SNAKE_CASE__\t( __A\t\t\t,\t\t\t\t__A\t\t\t,\t\t\t\t__A\t) ->\tstr:\r\t# Initialise PyTorch model\r\t_snake_case \t\t\t\t\t\t=\t\tTaConfig.from_json_file(UpperCamelCase__\t)\r\tprint(F'Building PyTorch model from configuration: {config}'\t)\r\t_snake_case \t\t\t\t\t\t=\t\tTaForConditionalGeneration(UpperCamelCase__\t)\r\r\t# Load weights from tf checkpoint\r\tload_tf_weights_in_ta(UpperCamelCase__\t\t\t,\t\t\t\tUpperCamelCase__\t\t\t,\t\t\t\tUpperCamelCase__\t)\r\r\t# Save pytorch-model\r\tprint(F'Save PyTorch model to {pytorch_dump_path}'\t)\r\tmodel.save_pretrained(UpperCamelCase__\t)\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\tlowercase :\t\t\t\t\t\t\tAny =\t\t\t\t\targparse.ArgumentParser()\r\t\t\t\t\t# Required parameters\r\t\t\t\t\tparser.add_argument(\r\t\t\t\t\t \"--tf_checkpoint_path\", default=None, type=str, required=True, help=\"Path to the TensorFlow checkpoint path.\"\r\t\t\t\t\t)\r\t\t\t\t\tparser.add_argument(\r\t\t\t\t\t \"--config_file\",\r\t\t\t\t\t default=None,\r\t\t\t\t\t type=str,\r\t\t\t\t\t required=True,\r\t\t\t\t\t help=(\r\t\t\t\t\t \"The config json file corresponding to the pre-trained T5 model. \\nThis specifies the model architecture.\"\r\t\t\t\t\t ),\r\t\t\t\t\t)\r\t\t\t\t\tparser.add_argument(\r\t\t\t\t\t \"--pytorch_dump_path\", default=None, type=str, required=True, help=\"Path to the output PyTorch model.\"\r\t\t\t\t\t)\r\t\t\t\t\tlowercase :\t\t\t\t\t\t\tstr =\t\t\t\t\tparser.parse_args()\r\t\t\t\t\tconvert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)\r\r"},"code_codestyle":{"kind":"number","value":42,"string":"42"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n'''simple docstring'''\r\n\r\n\r\n\r\nfrom collections import namedtuple\r\n\r\nimport requests\r\nfrom lxml import html # type: ignore\r\n\r\n__A\t\t=namedtuple('covid_data', 'cases deaths recovered')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t_UpperCamelCase\t\t\t\t\t(\t\t\t\t\t\tUpperCamelCase__ = \"https://www.worldometers.info/coronavirus/\" ):\r\n\t\t\tUpperCAmelCase__ : Union[str, Any] = \"\"\"//div[@class = \\\"maincounter-number\\\"]/span/text()\"\"\"\r\n\t\t\treturn covid_data(*html.fromstring(requests.get(UpperCamelCase__ ).content ).xpath(UpperCamelCase__ ) )\r\n\r\n\r\n__A\t\t='Total COVID-19 cases in the world: {}\\nTotal deaths due to COVID-19 in the world: {}\\nTotal COVID-19 patients recovered in the world: {}'\r\n\r\n\r\nprint(fmt.format(*covid_stats()))"},"style_context_codestyle":{"kind":"number","value":163,"string":"163"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":261,"cells":{"code":{"kind":"string","value":"\r\r\"\"\"simple docstring\"\"\"\r\r\rimport timeit\r\rimport numpy as np\r\rimport datasets\rfrom datasets.arrow_writer import ArrowWriter\rfrom datasets.features.features import _ArrayXD\r\r\r\r\rdef \t_lowercase ( __lowerCAmelCase\t\t\t\t\t\t\t) ->\t\t\tstr:\r def wrapper(*__lowerCAmelCase\t\t\t\t\t, **__lowerCAmelCase\t\t\t\t\t\t\t):\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\ttimeit.default_timer()\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[Any] \t\t\t\t\t\t=\t\tfunc(*__lowerCAmelCase\t\t\t\t\t, **__lowerCAmelCase\t\t\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__\t\t\t\t: Any \t\t\t\t\t\t=\t\ttimeit.default_timer() - starttime\r return delta\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tfunc.__name__\r\r return wrapper\r\r\r\r\rdef \t_lowercase ( __lowerCAmelCase\t\t\t\t\t, __lowerCAmelCase=100\t\t\t\t\t, __lowerCAmelCase=None\t\t\t\t\t\t\t) ->\t\t\tint:\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\t[]\r SCREAMING_SNAKE_CASE__\t\t\t\t: Dict \t\t\t\t\t\t=\t\tseq_shapes or {}\r for i in range(__lowerCAmelCase\t\t\t\t\t\t\t):\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[Any] \t\t\t\t\t\t=\t\t{}\r for col_id, (k, v) in enumerate(features.items()\t\t\t\t\t\t\t):\r if isinstance(__lowerCAmelCase\t\t\t\t\t, _ArrayXD\t\t\t\t\t\t\t):\r SCREAMING_SNAKE_CASE__\t\t\t\t: Dict \t\t\t\t\t\t=\t\tnp.random.rand(*v.shape\t\t\t\t\t\t\t).astype(v.dtype\t\t\t\t\t\t\t)\r elif isinstance(__lowerCAmelCase\t\t\t\t\t, datasets.Value\t\t\t\t\t\t\t):\r if v.dtype == \"string\":\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\t\"\"\"The small grey turtle was surprisingly fast when challenged.\"\"\"\r else:\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tnp.random.randint(10\t\t\t\t\t, size=1\t\t\t\t\t\t\t).astype(v.dtype\t\t\t\t\t\t\t).item()\r elif isinstance(__lowerCAmelCase\t\t\t\t\t, datasets.Sequence\t\t\t\t\t\t\t):\r while isinstance(__lowerCAmelCase\t\t\t\t\t, datasets.Sequence\t\t\t\t\t\t\t):\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[Any] \t\t\t\t\t\t=\t\tv.feature\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tseq_shapes[k]\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tnp.random.rand(*__lowerCAmelCase\t\t\t\t\t\t\t).astype(v.dtype\t\t\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__\t\t\t\t: Any \t\t\t\t\t\t=\t\tdata\r\r dummy_data.append((i, example)\t\t\t\t\t\t\t)\r\r return dummy_data\r\r\r\r\rdef \t_lowercase ( __lowerCAmelCase\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t, __lowerCAmelCase=100\t\t\t\t\t, __lowerCAmelCase=None\t\t\t\t\t\t\t) ->\t\t\tstr:\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\tgenerate_examples(__lowerCAmelCase\t\t\t\t\t, num_examples=__lowerCAmelCase\t\t\t\t\t, seq_shapes=__lowerCAmelCase\t\t\t\t\t\t\t)\r\r with ArrowWriter(features=__lowerCAmelCase\t\t\t\t\t, path=__lowerCAmelCase\t\t\t\t\t\t\t) as writer:\r for key, record in dummy_data:\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tfeatures.encode_example(__lowerCAmelCase\t\t\t\t\t\t\t)\r writer.write(__lowerCAmelCase\t\t\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__\t\t\t\t\t\t,\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\twriter.finalize()\r\r if not num_final_examples == num_examples:\r raise ValueError(\r F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''\t\t\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tdatasets.Dataset.from_file(filename=__lowerCAmelCase\t\t\t\t\t, info=datasets.DatasetInfo(features=__lowerCAmelCase\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r return dataset\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":56,"string":"56"},"style_context":{"kind":"string","value":"\r\r\"\"\"simple docstring\"\"\"\r\r\rimport unittest\r\rfrom transformers import DebertaConfig, is_torch_available\rfrom transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device\r\rfrom ...test_configuration_common import ConfigTester\rfrom ...test_modeling_common import ModelTesterMixin, ids_tensor\rfrom ...test_pipeline_mixin import PipelineTesterMixin\r\r\rif is_torch_available():\r import torch\r\r from transformers import (\r DebertaForMaskedLM,\r DebertaForQuestionAnswering,\r DebertaForSequenceClassification,\r DebertaForTokenClassification,\r DebertaModel,\r )\r from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST\r\rclass __a\t\t\t\t\t\t\t(UpperCamelCase_):\r\r\r\r\r\r\r '''simple docstring'''\r\r def __init__( self\t\t, _a\t\t, _a=13\t\t, _a=7\t\t, _a=True\t\t, _a=True\t\t, _a=True\t\t, _a=True\t\t, _a=99\t\t, _a=32\t\t, _a=5\t\t, _a=4\t\t, _a=37\t\t, _a=\"gelu\"\t\t, _a=0.1\t\t, _a=0.1\t\t, _a=512\t\t, _a=16\t\t, _a=2\t\t, _a=0.02\t\t, _a=False\t\t, _a=True\t\t, _a=\"None\"\t\t, _a=3\t\t, _a=4\t\t, _a=None\t\t, )\t\t\t\t\t-> List[Any]:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tparent\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\tbatch_size\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tseq_length\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tis_training\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[Any] \t\t\t\t\t\t=\t\tuse_input_mask\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tuse_token_type_ids\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\tuse_labels\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tvocab_size\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\thidden_size\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\tnum_hidden_layers\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tnum_attention_heads\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tintermediate_size\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\thidden_act\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\thidden_dropout_prob\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[Any] \t\t\t\t\t\t=\t\tattention_probs_dropout_prob\r SCREAMING_SNAKE_CASE__\t\t\t\t: Any \t\t\t\t\t\t=\t\tmax_position_embeddings\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\ttype_vocab_size\r SCREAMING_SNAKE_CASE__\t\t\t\t: Dict \t\t\t\t\t\t=\t\ttype_sequence_label_size\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tinitializer_range\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tnum_labels\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tnum_choices\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\trelative_attention\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tposition_biased_input\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tpos_att_type\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\tscope\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Any:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\tids_tensor([self.batch_size, self.seq_length]\t\t, self.vocab_size\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\tNone\r if self.use_input_mask:\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\tids_tensor([self.batch_size, self.seq_length]\t\t, vocab_size=2\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tNone\r if self.use_token_type_ids:\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\tids_tensor([self.batch_size, self.seq_length]\t\t, self.type_vocab_size\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[Any] \t\t\t\t\t\t=\t\tNone\r SCREAMING_SNAKE_CASE__\t\t\t\t: int \t\t\t\t\t\t=\t\tNone\r SCREAMING_SNAKE_CASE__\t\t\t\t: Any \t\t\t\t\t\t=\t\tNone\r if self.use_labels:\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tids_tensor([self.batch_size]\t\t, self.type_sequence_label_size\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[Any] \t\t\t\t\t\t=\t\tids_tensor([self.batch_size, self.seq_length]\t\t, self.num_labels\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[Any] \t\t\t\t\t\t=\t\tids_tensor([self.batch_size]\t\t, self.num_choices\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\tself.get_config()\r\r return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Tuple:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r return DebertaConfig(\r vocab_size=self.vocab_size\t\t, hidden_size=self.hidden_size\t\t, num_hidden_layers=self.num_hidden_layers\t\t, num_attention_heads=self.num_attention_heads\t\t, intermediate_size=self.intermediate_size\t\t, hidden_act=self.hidden_act\t\t, hidden_dropout_prob=self.hidden_dropout_prob\t\t, attention_probs_dropout_prob=self.attention_probs_dropout_prob\t\t, max_position_embeddings=self.max_position_embeddings\t\t, type_vocab_size=self.type_vocab_size\t\t, initializer_range=self.initializer_range\t\t, relative_attention=self.relative_attention\t\t, position_biased_input=self.position_biased_input\t\t, pos_att_type=self.pos_att_type\t\t, )\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Any:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tself.get_config()\r SCREAMING_SNAKE_CASE__\t\t\t\t: Any \t\t\t\t\t\t=\t\t300\r return config\r\r def _a ( self\t\t, _a\t\t\t\t\t)\t\t\t\t\t-> List[str]:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r self.parent.assertListEqual(list(result.loss.size()\t\t\t\t\t)\t\t, []\t\t\t\t\t)\r\r def _a ( self\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t\t\t\t)\t\t\t\t\t-> Dict:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Dict \t\t\t\t\t\t=\t\tDebertaModel(config=_a\t\t\t\t\t)\r model.to(_a\t\t\t\t\t)\r model.eval()\r SCREAMING_SNAKE_CASE__\t\t\t\t: Any \t\t\t\t\t\t=\t\tmodel(_a\t\t, attention_mask=_a\t\t, token_type_ids=_a\t\t\t\t\t)[0]\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\tmodel(_a\t\t, token_type_ids=_a\t\t\t\t\t)[0]\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\tmodel(_a\t\t\t\t\t)[0]\r\r self.parent.assertListEqual(list(sequence_output.size()\t\t\t\t\t)\t\t, [self.batch_size, self.seq_length, self.hidden_size]\t\t\t\t\t)\r\r def _a ( self\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t\t\t\t)\t\t\t\t\t-> Tuple:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[Any] \t\t\t\t\t\t=\t\tDebertaForMaskedLM(config=_a\t\t\t\t\t)\r model.to(_a\t\t\t\t\t)\r model.eval()\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tmodel(_a\t\t, attention_mask=_a\t\t, token_type_ids=_a\t\t, labels=_a\t\t\t\t\t)\r\r self.parent.assertEqual(result.logits.shape\t\t, (self.batch_size, self.seq_length, self.vocab_size)\t\t\t\t\t)\r\r def _a ( self\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t\t\t\t)\t\t\t\t\t-> Dict:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[Any] \t\t\t\t\t\t=\t\tself.num_labels\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\tDebertaForSequenceClassification(_a\t\t\t\t\t)\r model.to(_a\t\t\t\t\t)\r model.eval()\r SCREAMING_SNAKE_CASE__\t\t\t\t: Any \t\t\t\t\t\t=\t\tmodel(_a\t\t, attention_mask=_a\t\t, token_type_ids=_a\t\t, labels=_a\t\t\t\t\t)\r self.parent.assertListEqual(list(result.logits.size()\t\t\t\t\t)\t\t, [self.batch_size, self.num_labels]\t\t\t\t\t)\r self.check_loss_output(_a\t\t\t\t\t)\r\r def _a ( self\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t\t\t\t)\t\t\t\t\t-> List[Any]:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[Any] \t\t\t\t\t\t=\t\tself.num_labels\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[Any] \t\t\t\t\t\t=\t\tDebertaForTokenClassification(config=_a\t\t\t\t\t)\r model.to(_a\t\t\t\t\t)\r model.eval()\r SCREAMING_SNAKE_CASE__\t\t\t\t: int \t\t\t\t\t\t=\t\tmodel(_a\t\t, attention_mask=_a\t\t, token_type_ids=_a\t\t, labels=_a\t\t\t\t\t)\r self.parent.assertEqual(result.logits.shape\t\t, (self.batch_size, self.seq_length, self.num_labels)\t\t\t\t\t)\r\r def _a ( self\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t, _a\t\t\t\t\t)\t\t\t\t\t-> Union[str, Any]:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[Any] \t\t\t\t\t\t=\t\tDebertaForQuestionAnswering(config=_a\t\t\t\t\t)\r model.to(_a\t\t\t\t\t)\r model.eval()\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tmodel(\r _a\t\t, attention_mask=_a\t\t, token_type_ids=_a\t\t, start_positions=_a\t\t, end_positions=_a\t\t, )\r self.parent.assertEqual(result.start_logits.shape\t\t, (self.batch_size, self.seq_length)\t\t\t\t\t)\r self.parent.assertEqual(result.end_logits.shape\t\t, (self.batch_size, self.seq_length)\t\t\t\t\t)\r\r\r\r\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Dict:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tself.prepare_config_and_inputs()\r (\r (\r SCREAMING_SNAKE_CASE__\r )\t\t\t\t\t\t,\t\t\t\t\t\t(\r SCREAMING_SNAKE_CASE__\r )\t\t\t\t\t\t,\t\t\t\t\t\t(\r SCREAMING_SNAKE_CASE__\r )\t\t\t\t\t\t,\t\t\t\t\t\t(\r SCREAMING_SNAKE_CASE__\r )\t\t\t\t\t\t,\t\t\t\t\t\t(\r SCREAMING_SNAKE_CASE__\r )\t\t\t\t\t\t,\t\t\t\t\t\t(\r SCREAMING_SNAKE_CASE__\r )\t\t\t\t\t\t,\t\t\t\t\t\t(\r SCREAMING_SNAKE_CASE__\r )\t\t\t\t\t\t,\t\t\t\t\t\t\r )\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tconfig_and_inputs\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[Any] \t\t\t\t\t\t=\t\t{\"\"\"input_ids\"\"\": input_ids, \"\"\"token_type_ids\"\"\": token_type_ids, \"\"\"attention_mask\"\"\": input_mask}\r return config, inputs_dict\r\r\r\r\r\r@require_torch\rclass __a\t\t\t\t\t\t\t(UpperCamelCase_\t\t\t\t,\t\tUpperCamelCase_\t\t\t\t,\t\tunittest.TestCase):\r\r\r\r\r\r\r '''simple docstring'''\r _SCREAMING_SNAKE_CASE :List[str] =\t(\r (\r DebertaModel,\r DebertaForMaskedLM,\r DebertaForSequenceClassification,\r DebertaForTokenClassification,\r DebertaForQuestionAnswering,\r )\r if is_torch_available()\r else ()\r )\r _SCREAMING_SNAKE_CASE :str =\t(\r {\r \"\"\"feature-extraction\"\"\": DebertaModel,\r \"\"\"fill-mask\"\"\": DebertaForMaskedLM,\r \"\"\"question-answering\"\"\": DebertaForQuestionAnswering,\r \"\"\"text-classification\"\"\": DebertaForSequenceClassification,\r \"\"\"token-classification\"\"\": DebertaForTokenClassification,\r \"\"\"zero-shot\"\"\": DebertaForSequenceClassification,\r }\r if is_torch_available()\r else {}\r )\r\r _SCREAMING_SNAKE_CASE :Union[str, Any] =\tTrue\r _SCREAMING_SNAKE_CASE :str =\tFalse\r _SCREAMING_SNAKE_CASE :Dict =\tFalse\r _SCREAMING_SNAKE_CASE :Dict =\tFalse\r _SCREAMING_SNAKE_CASE :Union[str, Any] =\tFalse\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Optional[Any]:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[Any] \t\t\t\t\t\t=\t\tDebertaModelTester(self\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tConfigTester(self\t\t, config_class=_a\t\t, hidden_size=37\t\t\t\t\t)\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Any:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r self.config_tester.run_common_tests()\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> int:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_deberta_model(*_a\t\t\t\t\t)\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Optional[Any]:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_deberta_for_sequence_classification(*_a\t\t\t\t\t)\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Tuple:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Any \t\t\t\t\t\t=\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_deberta_for_masked_lm(*_a\t\t\t\t\t)\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Dict:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: str \t\t\t\t\t\t=\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_deberta_for_question_answering(*_a\t\t\t\t\t)\r\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Union[str, Any]:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: int \t\t\t\t\t\t=\t\tself.model_tester.prepare_config_and_inputs()\r self.model_tester.create_and_check_deberta_for_token_classification(*_a\t\t\t\t\t)\r\r\r\r\r\r @slow\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Optional[int]:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:\r SCREAMING_SNAKE_CASE__\t\t\t\t: Dict \t\t\t\t\t\t=\t\tDebertaModel.from_pretrained(_a\t\t\t\t\t)\r self.assertIsNotNone(_a\t\t\t\t\t)\r\r\r\r\r\r@require_torch\r@require_sentencepiece\r@require_tokenizers\rclass __a\t\t\t\t\t\t\t(unittest.TestCase):\r\r\r\r\r\r\r '''simple docstring'''\r\r @unittest.skip(reason=\"\"\"Model not available yet\"\"\"\t\t\t\t\t)\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> Any:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r pass\r\r\r\r\r\r @slow\r def _a ( self\t\t\t\t\t)\t\t\t\t\t-> int:\r\r\r \"\"\"simple docstring\"\"\"\r\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: List[str] \t\t\t\t\t\t=\t\tDebertaModel.from_pretrained(\"\"\"microsoft/deberta-base\"\"\"\t\t\t\t\t)\r\r SCREAMING_SNAKE_CASE__\t\t\t\t: Tuple \t\t\t\t\t\t=\t\ttorch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\ttorch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]\t\t\t\t\t)\r with torch.no_grad():\r SCREAMING_SNAKE_CASE__\t\t\t\t: Optional[int] \t\t\t\t\t\t=\t\tmodel(_a\t\t, attention_mask=_a\t\t\t\t\t)[0]\r # compare the actual values for a slice.\r SCREAMING_SNAKE_CASE__\t\t\t\t: Union[str, Any] \t\t\t\t\t\t=\t\ttorch.tensor(\r [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]\t\t\t\t\t)\r self.assertTrue(torch.allclose(output[:, 1:4, 1:4]\t\t, _a\t\t, atol=1E-4\t\t\t\t\t)\t\t, f'''{output[:, 1:4, 1:4]}'''\t\t\t\t\t)\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":56,"string":"56"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":262,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\nimport os\r\nimport unicodedata\r\nfrom shutil import copyfile\r\nfrom typing import Any, Dict, List, Optional, Tuple\r\n\r\nimport sentencepiece as spm\r\n\r\nfrom ...tokenization_utils import AddedToken, PreTrainedTokenizer\r\nfrom ...utils import logging\r\n\r\n\r\n_lowerCamelCase :\tDict =\t\t\t\tlogging.get_logger(__name__)\r\n_lowerCamelCase :\tOptional[Any] =\t\t\t\t{\"\"\"vocab_file\"\"\": \"\"\"spiece.model\"\"\"}\r\n\r\n_lowerCamelCase :\tTuple =\t\t\t\t{\r\n \"\"\"vocab_file\"\"\": {\r\n \"\"\"albert-base-v1\"\"\": \"\"\"https://huggingface.co/albert-base-v1/resolve/main/spiece.model\"\"\",\r\n \"\"\"albert-large-v1\"\"\": \"\"\"https://huggingface.co/albert-large-v1/resolve/main/spiece.model\"\"\",\r\n \"\"\"albert-xlarge-v1\"\"\": \"\"\"https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model\"\"\",\r\n \"\"\"albert-xxlarge-v1\"\"\": \"\"\"https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model\"\"\",\r\n \"\"\"albert-base-v2\"\"\": \"\"\"https://huggingface.co/albert-base-v2/resolve/main/spiece.model\"\"\",\r\n \"\"\"albert-large-v2\"\"\": \"\"\"https://huggingface.co/albert-large-v2/resolve/main/spiece.model\"\"\",\r\n \"\"\"albert-xlarge-v2\"\"\": \"\"\"https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model\"\"\",\r\n \"\"\"albert-xxlarge-v2\"\"\": \"\"\"https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model\"\"\",\r\n }\r\n}\r\n\r\n_lowerCamelCase :\tint =\t\t\t\t{\r\n \"\"\"albert-base-v1\"\"\": 512,\r\n \"\"\"albert-large-v1\"\"\": 512,\r\n \"\"\"albert-xlarge-v1\"\"\": 512,\r\n \"\"\"albert-xxlarge-v1\"\"\": 512,\r\n \"\"\"albert-base-v2\"\"\": 512,\r\n \"\"\"albert-large-v2\"\"\": 512,\r\n \"\"\"albert-xlarge-v2\"\"\": 512,\r\n \"\"\"albert-xxlarge-v2\"\"\": 512,\r\n}\r\n\r\n_lowerCamelCase :\tList[Any] =\t\t\t\t\"\"\"▁\"\"\"\r\n\r\nclass \t\t\t\tlowercase\t\t\t\t\t(\t\t\tUpperCamelCase__):\r\n\t\t\t\t__lowerCAmelCase\t\t\t:\t\t\tList[Any] \t\t\t\t= VOCAB_FILES_NAMES\r\n\t\t\t\t__lowerCAmelCase\t\t\t:\t\t\tint \t\t\t\t= PRETRAINED_VOCAB_FILES_MAP\r\n\t\t\t\t__lowerCAmelCase\t\t\t:\t\t\tDict \t\t\t\t= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef __init__(\t\t\t\t\t\t\tself : Any\t\t\t\t\t, _lowerCamelCase : List[str]\t\t\t\t\t, _lowerCamelCase : List[Any]=True\t\t\t\t\t, _lowerCamelCase : int=True\t\t\t\t\t, _lowerCamelCase : List[Any]=False\t\t\t\t\t, _lowerCamelCase : Union[str, Any]=\"[CLS]\"\t\t\t\t\t, _lowerCamelCase : str=\"[SEP]\"\t\t\t\t\t, _lowerCamelCase : List[Any]=\"\"\t\t\t\t\t, _lowerCamelCase : Optional[Any]=\"[SEP]\"\t\t\t\t\t, _lowerCamelCase : List[str]=\"\"\t\t\t\t\t, _lowerCamelCase : Optional[Any]=\"[CLS]\"\t\t\t\t\t, _lowerCamelCase : List[Any]=\"[MASK]\"\t\t\t\t\t, _lowerCamelCase : Optional[int] = None\t\t\t\t\t, **_lowerCamelCase : Any\t\t\t\t\t, ):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tA_ : List[str]\t\t =\t\t\t\t\t\t(\r\n\t\t\t\t\t AddedToken(lowerCAmelCase__\t\t\t\t\t, lstrip=lowerCAmelCase__\t\t\t\t\t, rstrip=lowerCAmelCase__\t\t\t\t\t, normalized=lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\t if isinstance(lowerCAmelCase__\t\t\t\t\t, lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\t else mask_token\r\n\t\t\t\t\t)\r\n\r\n\t\t\t\t\tA_ : Dict\t\t =\t\t\t\t\t\t{} if sp_model_kwargs is None else sp_model_kwargs\r\n\r\n\t\t\t\t\tsuper().__init__(\r\n\t\t\t\t\t do_lower_case=lowerCAmelCase__\t\t\t\t\t, remove_space=lowerCAmelCase__\t\t\t\t\t, keep_accents=lowerCAmelCase__\t\t\t\t\t, bos_token=lowerCAmelCase__\t\t\t\t\t, eos_token=lowerCAmelCase__\t\t\t\t\t, unk_token=lowerCAmelCase__\t\t\t\t\t, sep_token=lowerCAmelCase__\t\t\t\t\t, pad_token=lowerCAmelCase__\t\t\t\t\t, cls_token=lowerCAmelCase__\t\t\t\t\t, mask_token=lowerCAmelCase__\t\t\t\t\t, sp_model_kwargs=self.sp_model_kwargs\t\t\t\t\t, **lowerCAmelCase__\t\t\t\t\t, )\r\n\r\n\t\t\t\t\tA_ : Tuple\t\t =\t\t\t\t\t\tdo_lower_case\r\n\t\t\t\t\tA_ : int\t\t =\t\t\t\t\t\tremove_space\r\n\t\t\t\t\tA_ : List[Any]\t\t =\t\t\t\t\t\tkeep_accents\r\n\t\t\t\t\tA_ : List[Any]\t\t =\t\t\t\t\t\tvocab_file\r\n\r\n\t\t\t\t\tA_ : List[str]\t\t =\t\t\t\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs\t\t\t\t)\r\n\t\t\t\t\tself.sp_model.Load(lowerCAmelCase__\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t@property\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Optional[int]\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\treturn len(self.sp_model\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Optional[int]\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tA_ : Tuple\t\t =\t\t\t\t\t\t{self.convert_ids_to_tokens(lowerCAmelCase__\t\t\t\t): i for i in range(self.vocab_size\t\t\t\t)}\r\n\t\t\t\t\tvocab.update(self.added_tokens_encoder\t\t\t\t)\r\n\t\t\t\t\treturn vocab\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef __getstate__(\t\t\t\t\t\t\tself : Any\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tA_ : Union[str, Any]\t\t =\t\t\t\t\t\tself.__dict__.copy()\r\n\t\t\t\t\tA_ : Optional[int]\t\t =\t\t\t\t\t\tNone\r\n\t\t\t\t\treturn state\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef __setstate__(\t\t\t\t\t\t\tself : Dict\t\t\t\t\t, _lowerCamelCase : Any\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tA_ : List[Any]\t\t =\t\t\t\t\t\td\r\n\r\n\t\t\t\t\t# for backward compatibility\r\n\t\t\t\t\tif not hasattr(self\t\t\t\t\t, '''sp_model_kwargs'''\t\t\t\t):\r\n\t\t\t\t\t\tA_ : Optional[int]\t\t =\t\t\t\t\t\t{}\r\n\r\n\t\t\t\t\tA_ : str\t\t =\t\t\t\t\t\tspm.SentencePieceProcessor(**self.sp_model_kwargs\t\t\t\t)\r\n\t\t\t\t\tself.sp_model.Load(self.vocab_file\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : List[Any]\t\t\t\t\t, _lowerCamelCase : List[str]\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tif self.remove_space:\r\n\t\t\t\t\t\tA_ : Dict\t\t =\t\t\t\t\t\t\" \".join(inputs.strip().split()\t\t\t\t)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tA_ : List[Any]\t\t =\t\t\t\t\t\tinputs\r\n\t\t\t\t\tA_ : str\t\t =\t\t\t\t\t\toutputs.replace('''``'''\t\t\t\t\t, '''\\\"'''\t\t\t\t).replace('''\\'\\''''\t\t\t\t\t, '''\\\"'''\t\t\t\t)\r\n\r\n\t\t\t\t\tif not self.keep_accents:\r\n\t\t\t\t\t\tA_ : List[Any]\t\t =\t\t\t\t\t\tunicodedata.normalize('''NFKD'''\t\t\t\t\t, lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\t\tA_ : Any\t\t =\t\t\t\t\t\t\"\".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase__\t\t\t\t)]\t\t\t\t)\r\n\t\t\t\t\tif self.do_lower_case:\r\n\t\t\t\t\t\tA_ : int\t\t =\t\t\t\t\t\toutputs.lower()\r\n\r\n\t\t\t\t\treturn outputs\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Optional[int]\t\t\t\t\t, _lowerCamelCase : List[Any]\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tA_ : Optional[int]\t\t =\t\t\t\t\t\tself.preprocess_text(lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\tA_ : Optional[Any]\t\t =\t\t\t\t\t\tself.sp_model.encode(lowerCAmelCase__\t\t\t\t\t, out_type=lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\tA_ : Optional[int]\t\t =\t\t\t\t\t\t[]\r\n\t\t\t\t\tfor piece in pieces:\r\n\t\t\t\t\t\tif len(lowerCAmelCase__\t\t\t\t) > 1 and piece[-1] == str(''','''\t\t\t\t) and piece[-2].isdigit():\r\n\t\t\t\t\t\t\tA_ : Optional[int]\t\t =\t\t\t\t\t\tself.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase__\t\t\t\t\t, ''''''\t\t\t\t)\t\t\t\t)\r\n\t\t\t\t\t\t\tif piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:\r\n\t\t\t\t\t\t\t\tif len(cur_pieces[0]\t\t\t\t) == 1:\r\n\t\t\t\t\t\t\t\t\tA_ : Optional[Any]\t\t =\t\t\t\t\t\tcur_pieces[1:]\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\tA_ : List[str]\t\t =\t\t\t\t\t\tcur_pieces[0][1:]\r\n\t\t\t\t\t\t\tcur_pieces.append(piece[-1]\t\t\t\t)\r\n\t\t\t\t\t\t\tnew_pieces.extend(lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tnew_pieces.append(lowerCAmelCase__\t\t\t\t)\r\n\r\n\t\t\t\t\treturn new_pieces\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : str\t\t\t\t\t, _lowerCamelCase : Optional[int]\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\treturn self.sp_model.PieceToId(lowerCAmelCase__\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Dict\t\t\t\t\t, _lowerCamelCase : Any\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\treturn self.sp_model.IdToPiece(lowerCAmelCase__\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Tuple\t\t\t\t\t, _lowerCamelCase : Tuple\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tA_ : Dict\t\t =\t\t\t\t\t\t[]\r\n\t\t\t\t\tA_ : Tuple\t\t =\t\t\t\t\t\t\"\"\r\n\t\t\t\t\tA_ : Tuple\t\t =\t\t\t\t\t\tFalse\r\n\t\t\t\t\tfor token in tokens:\r\n\t\t\t\t\t\t# make sure that special tokens are not decoded using sentencepiece model\r\n\t\t\t\t\t\tif token in self.all_special_tokens:\r\n\t\t\t\t\t\t\tif not prev_is_special:\r\n\t\t\t\t\t\t\t\tout_string += \" \"\r\n\t\t\t\t\t\t\tout_string += self.sp_model.decode(lowerCAmelCase__\t\t\t\t) + token\r\n\t\t\t\t\t\t\tA_ : str\t\t =\t\t\t\t\t\tTrue\r\n\t\t\t\t\t\t\tA_ : Tuple\t\t =\t\t\t\t\t\t[]\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tcurrent_sub_tokens.append(lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\t\t\tA_ : List[Any]\t\t =\t\t\t\t\t\tFalse\r\n\t\t\t\t\tout_string += self.sp_model.decode(lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\treturn out_string.strip()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Tuple\t\t\t\t\t, _lowerCamelCase : Union[str, Any]\t\t\t\t\t, _lowerCamelCase : Optional[Any] = None\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tA_ : Union[str, Any]\t\t =\t\t\t\t\t\t[self.sep_token_id]\r\n\t\t\t\t\tA_ : List[Any]\t\t =\t\t\t\t\t\t[self.cls_token_id]\r\n\t\t\t\t\tif token_ids_a is None:\r\n\t\t\t\t\t\treturn cls + token_ids_a + sep\r\n\t\t\t\t\treturn cls + token_ids_a + sep + token_ids_a + sep\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Dict\t\t\t\t\t, _lowerCamelCase : Optional[Any]\t\t\t\t\t, _lowerCamelCase : Union[str, Any] = None\t\t\t\t\t, _lowerCamelCase : int = False\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tif already_has_special_tokens:\r\n\t\t\t\t\t\treturn super().get_special_tokens_mask(\r\n\t\t\t\t\t\t token_ids_a=lowerCAmelCase__\t\t\t\t\t, token_ids_a=lowerCAmelCase__\t\t\t\t\t, already_has_special_tokens=lowerCAmelCase__\t\t\t\t)\r\n\r\n\t\t\t\t\tif token_ids_a is not None:\r\n\t\t\t\t\t\treturn [1] + ([0] * len(lowerCAmelCase__\t\t\t\t)) + [1] + ([0] * len(lowerCAmelCase__\t\t\t\t)) + [1]\r\n\t\t\t\t\treturn [1] + ([0] * len(lowerCAmelCase__\t\t\t\t)) + [1]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Optional[Any]\t\t\t\t\t, _lowerCamelCase : int\t\t\t\t\t, _lowerCamelCase : int = None\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tA_ : Tuple\t\t =\t\t\t\t\t\t[self.sep_token_id]\r\n\t\t\t\t\tA_ : Union[str, Any]\t\t =\t\t\t\t\t\t[self.cls_token_id]\r\n\r\n\t\t\t\t\tif token_ids_a is None:\r\n\t\t\t\t\t\treturn len(cls + token_ids_a + sep\t\t\t\t) * [0]\r\n\t\t\t\t\treturn len(cls + token_ids_a + sep\t\t\t\t) * [0] + len(token_ids_a + sep\t\t\t\t) * [1]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\tdef a_ (\t\t\t\t\t\t\tself : Optional[int]\t\t\t\t\t, _lowerCamelCase : List[Any]\t\t\t\t\t, _lowerCamelCase : int = None\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tif not os.path.isdir(lowerCAmelCase__\t\t\t\t):\r\n\t\t\t\t\t\tlogger.error(F\"\"\"Vocabulary path ({save_directory}) should be a directory\"\"\"\t\t\t\t)\r\n\t\t\t\t\t\treturn\r\n\t\t\t\t\tA_ : List[str]\t\t =\t\t\t\t\t\tos.path.join(\r\n\t\t\t\t\t lowerCAmelCase__\t\t\t\t\t, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']\t\t\t\t)\r\n\r\n\t\t\t\t\tif os.path.abspath(self.vocab_file\t\t\t\t) != os.path.abspath(lowerCAmelCase__\t\t\t\t) and os.path.isfile(self.vocab_file\t\t\t\t):\r\n\t\t\t\t\t\tcopyfile(self.vocab_file\t\t\t\t\t, lowerCAmelCase__\t\t\t\t)\r\n\t\t\t\t\telif not os.path.isfile(self.vocab_file\t\t\t\t):\r\n\t\t\t\t\t\twith open(lowerCAmelCase__\t\t\t\t\t, '''wb'''\t\t\t\t) as fi:\r\n\t\t\t\t\t\t\tA_ : List[str]\t\t =\t\t\t\t\t\tself.sp_model.serialized_model_proto()\r\n\t\t\t\t\t\t\tfi.write(lowerCAmelCase__\t\t\t\t)\r\n\r\n\t\t\t\t\treturn (out_vocab_file,)\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":167,"string":"167"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\nimport unittest\r\n\r\nimport numpy as np\r\n\r\nfrom transformers.testing_utils import require_torch, require_vision\r\nfrom transformers.utils import is_torch_available, is_vision_available\r\n\r\nfrom ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs\r\n\r\n\r\nif is_torch_available():\r\n import torch\r\n\r\nif is_vision_available():\r\n from PIL import Image\r\n\r\n from transformers import MobileNetVaImageProcessor\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass \t\t\t\t__lowerCAmelCase ( unittest.TestCase):\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __init__( self\t\t, lowerCAmelCase__\t\t, lowerCAmelCase__=7\t\t, lowerCAmelCase__=3\t\t, lowerCAmelCase__=1_8\t\t, lowerCAmelCase__=3_0\t\t, lowerCAmelCase__=4_0_0\t\t, lowerCAmelCase__=True\t\t, lowerCAmelCase__=None\t\t, lowerCAmelCase__=True\t\t, lowerCAmelCase__=None\t\t, ) ->\t\t\t\t\t\tOptional[int]:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n a__\t:\t\t\t\t\tstr\t\t\t\t\t\t\t\t\t\t=size if size is not None else {\"shortest_edge\": 2_0}\r\n a__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t\t=crop_size if crop_size is not None else {\"height\": 1_8, \"width\": 1_8}\r\n a__\t:\t\t\t\t\tTuple\t\t\t\t\t\t\t\t\t\t=parent\r\n a__\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t=batch_size\r\n a__\t:\t\t\t\t\tAny\t\t\t\t\t\t\t\t\t\t=num_channels\r\n a__\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t\t\t=image_size\r\n a__\t:\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t\t=min_resolution\r\n a__\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t=max_resolution\r\n a__\t:\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t\t=do_resize\r\n a__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t\t=size\r\n a__\t:\t\t\t\t\tstr\t\t\t\t\t\t\t\t\t\t=do_center_crop\r\n a__\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t\t\t=crop_size\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tstr:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n return {\r\n \"do_resize\": self.do_resize,\r\n \"size\": self.size,\r\n \"do_center_crop\": self.do_center_crop,\r\n \"crop_size\": self.crop_size,\r\n }\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_torch\r\n@require_vision\r\nclass \t\t\t\t__lowerCAmelCase ( UpperCamelCase__\t\t\t\t\t\t,\t\t\t\tunittest.TestCase):\r\n _lowercase\t\t\t\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t=\t\t\tMobileNetVaImageProcessor if is_vision_available() else None\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tTuple:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n a__\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t\t\t=MobileNetVaImageProcessingTester(self\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n @property\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tList[str]:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n return self.image_processor_tester.prepare_image_processor_dict()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tAny:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n a__\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t\t\t=self.image_processing_class(**self.image_processor_dict\t\t\t\t\t\t\t)\r\n self.assertTrue(hasattr(lowerCAmelCase__\t\t, \"do_resize\"\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n self.assertTrue(hasattr(lowerCAmelCase__\t\t, \"size\"\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n self.assertTrue(hasattr(lowerCAmelCase__\t\t, \"do_center_crop\"\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n self.assertTrue(hasattr(lowerCAmelCase__\t\t, \"crop_size\"\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tstr:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n a__\t:\t\t\t\t\tAny\t\t\t\t\t\t\t\t\t\t=self.image_processing_class.from_dict(self.image_processor_dict\t\t\t\t\t\t\t)\r\n self.assertEqual(image_processor.size\t\t, {\"shortest_edge\": 2_0}\t\t\t\t\t\t\t)\r\n self.assertEqual(image_processor.crop_size\t\t, {\"height\": 1_8, \"width\": 1_8}\t\t\t\t\t\t\t)\r\n\r\n a__\t:\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t\t=self.image_processing_class.from_dict(self.image_processor_dict\t\t, size=4_2\t\t, crop_size=8_4\t\t\t\t\t\t\t)\r\n self.assertEqual(image_processor.size\t\t, {\"shortest_edge\": 4_2}\t\t\t\t\t\t\t)\r\n self.assertEqual(image_processor.crop_size\t\t, {\"height\": 8_4, \"width\": 8_4}\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tAny:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n pass\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tOptional[int]:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n a__\t:\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t\t=self.image_processing_class(**self.image_processor_dict\t\t\t\t\t\t\t)\r\n # create random PIL images\r\n a__\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t=prepare_image_inputs(self.image_processor_tester\t\t, equal_resolution=lowerCAmelCase__\t\t\t\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(lowerCAmelCase__\t\t, Image.Image\t\t\t\t\t\t\t)\r\n\r\n # Test not batched input\r\n a__\t:\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t\t\t=image_processing(image_inputs[0]\t\t, return_tensors=\"pt\"\t\t\t\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t, (\r\n 1,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size[\"height\"],\r\n self.image_processor_tester.crop_size[\"width\"],\r\n )\t\t, )\r\n\r\n # Test batched\r\n a__\t:\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t\t=image_processing(lowerCAmelCase__\t\t, return_tensors=\"pt\"\t\t\t\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t, (\r\n self.image_processor_tester.batch_size,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size[\"height\"],\r\n self.image_processor_tester.crop_size[\"width\"],\r\n )\t\t, )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tint:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n a__\t:\t\t\t\t\tstr\t\t\t\t\t\t\t\t\t\t=self.image_processing_class(**self.image_processor_dict\t\t\t\t\t\t\t)\r\n # create random numpy tensors\r\n a__\t:\t\t\t\t\tstr\t\t\t\t\t\t\t\t\t\t=prepare_image_inputs(self.image_processor_tester\t\t, equal_resolution=lowerCAmelCase__\t\t, numpify=lowerCAmelCase__\t\t\t\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(lowerCAmelCase__\t\t, np.ndarray\t\t\t\t\t\t\t)\r\n\r\n # Test not batched input\r\n a__\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t\t\t=image_processing(image_inputs[0]\t\t, return_tensors=\"pt\"\t\t\t\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t, (\r\n 1,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size[\"height\"],\r\n self.image_processor_tester.crop_size[\"width\"],\r\n )\t\t, )\r\n\r\n # Test batched\r\n a__\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t\t=image_processing(lowerCAmelCase__\t\t, return_tensors=\"pt\"\t\t\t\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t, (\r\n self.image_processor_tester.batch_size,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size[\"height\"],\r\n self.image_processor_tester.crop_size[\"width\"],\r\n )\t\t, )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n def _lowercase ( self\t\t\t\t\t\t\t) ->\t\t\t\t\t\tOptional[int]:\r\n\r\n\r\n\r\n '''simple docstring'''\r\n\r\n\r\n\r\n\r\n a__\t:\t\t\t\t\tAny\t\t\t\t\t\t\t\t\t\t=self.image_processing_class(**self.image_processor_dict\t\t\t\t\t\t\t)\r\n # create random PyTorch tensors\r\n a__\t:\t\t\t\t\tint\t\t\t\t\t\t\t\t\t\t=prepare_image_inputs(self.image_processor_tester\t\t, equal_resolution=lowerCAmelCase__\t\t, torchify=lowerCAmelCase__\t\t\t\t\t\t\t)\r\n for image in image_inputs:\r\n self.assertIsInstance(lowerCAmelCase__\t\t, torch.Tensor\t\t\t\t\t\t\t)\r\n\r\n # Test not batched input\r\n a__\t:\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t=image_processing(image_inputs[0]\t\t, return_tensors=\"pt\"\t\t\t\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t, (\r\n 1,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size[\"height\"],\r\n self.image_processor_tester.crop_size[\"width\"],\r\n )\t\t, )\r\n\r\n # Test batched\r\n a__\t:\t\t\t\t\tstr\t\t\t\t\t\t\t\t\t\t=image_processing(lowerCAmelCase__\t\t, return_tensors=\"pt\"\t\t\t\t\t\t\t).pixel_values\r\n self.assertEqual(\r\n encoded_images.shape\t\t, (\r\n self.image_processor_tester.batch_size,\r\n self.image_processor_tester.num_channels,\r\n self.image_processor_tester.crop_size[\"height\"],\r\n self.image_processor_tester.crop_size[\"width\"],\r\n )\t\t, )\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":95,"string":"95"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":263,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\nimport itertools\nimport string\nfrom collections.abc import Generator, Iterable\n\n\n\n\n\n\n\ndef __lowerCamelCase (\t\t\t\t\t\t\t__a :List[str] ,\t\t\t\t\t__a :Optional[int] )\t\t-> List[Any]:\n\n\n\n\n\n\n\t\t\"\"\"simple docstring\"\"\"\n\n\t\tA__ \t\t\t\t= iter(A_ )\n\t\twhile True:\n\t\t\t\tA__ \t\t\t\t= tuple(itertools.islice(A_ ,\t\t\t\t\tA_ ) )\n\t\t\t\tif not chunk:\n\t\t\t\t\t\treturn\n\t\t\t\tyield chunk\n\n\n\n\n\n\n\ndef __lowerCamelCase (\t\t\t\t\t\t\t__a :Union[str, Any] )\t\t-> Optional[int]:\n\n\n\n\n\n\n\t\t\"\"\"simple docstring\"\"\"\n\n\t\tA__ \t\t\t\t= ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )\n\t\tA__ \t\t\t\t= ''''''\n\n\t\tif len(A_ ) < 2:\n\t\t\t\treturn dirty\n\n\t\tfor i in range(len(A_ ) - 1 ):\n\t\t\t\tclean += dirty[i]\n\n\t\t\t\tif dirty[i] == dirty[i + 1]:\n\t\t\t\t\t\tclean += \"X\"\n\n\t\tclean += dirty[-1]\n\n\t\tif len(A_ ) & 1:\n\t\t\t\tclean += \"X\"\n\n\t\treturn clean\n\n\n\n\n\n\n\ndef __lowerCamelCase (\t\t\t\t\t\t\t__a :Optional[Any] )\t\t-> List[Any]:\n\n\n\n\n\n\n\t\t\"\"\"simple docstring\"\"\"\n\n\t\tA__ \t\t\t\t= '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''\n\t\t# we're using a list instead of a '2d' array because it makes the math\n\t\t# for setting up the table and doing the actual encoding/decoding simpler\n\t\tA__ \t\t\t\t= []\n\n\t\t# copy key chars into the table if they are in `alphabet` ignoring duplicates\n\t\tfor char in key.upper():\n\t\t\t\tif char not in table and char in alphabet:\n\t\t\t\t\t\ttable.append(A_ )\n\n # fill the rest of the table in with the remaining alphabet chars\n\t\tfor char in alphabet:\n\t\t\t\tif char not in table:\n\t\t\t\t\t\ttable.append(A_ )\n\n\t\treturn table\n\n\n\n\n\n\n\ndef __lowerCamelCase (\t\t\t\t\t\t\t__a :int ,\t\t\t\t\t__a :Any )\t\t-> str:\n\n\n\n\n\n\n\t\t\"\"\"simple docstring\"\"\"\n\n\t\tA__ \t\t\t\t= generate_table(A_ )\n\t\tA__ \t\t\t\t= prepare_input(A_ )\n\t\tA__ \t\t\t\t= ''''''\n\n\t\t# https://en.wikipedia.org/wiki/Playfair_cipher#Description\n\t\tfor chara, chara in chunker(A_ ,\t\t\t\t\t2 ):\n\t\t\t\tA__ \t\t\t\t= divmod(table.index(A_ ) ,\t\t\t\t\t5 )\n\t\t\t\tA__ \t\t\t\t= divmod(table.index(A_ ) ,\t\t\t\t\t5 )\n\n\t\t\t\tif rowa == rowa:\n\t\t\t\t\t\tciphertext += table[rowa * 5 + (cola + 1) % 5]\n\t\t\t\t\t\tciphertext += table[rowa * 5 + (cola + 1) % 5]\n\t\t\t\telif cola == cola:\n\t\t\t\t\t\tciphertext += table[((rowa + 1) % 5) * 5 + cola]\n\t\t\t\t\t\tciphertext += table[((rowa + 1) % 5) * 5 + cola]\n\t\t\t\telse: # rectangle\n\t\t\t\t\t\tciphertext += table[rowa * 5 + cola]\n\t\t\t\t\t\tciphertext += table[rowa * 5 + cola]\n\n\t\treturn ciphertext\n\n\n\n\n\n\n\ndef __lowerCamelCase (\t\t\t\t\t\t\t__a :Dict ,\t\t\t\t\t__a :int )\t\t-> List[str]:\n\n\n\n\n\n\n\t\t\"\"\"simple docstring\"\"\"\n\n\t\tA__ \t\t\t\t= generate_table(A_ )\n\t\tA__ \t\t\t\t= ''''''\n\n\t\t# https://en.wikipedia.org/wiki/Playfair_cipher#Description\n\t\tfor chara, chara in chunker(A_ ,\t\t\t\t\t2 ):\n\t\t\t\tA__ \t\t\t\t= divmod(table.index(A_ ) ,\t\t\t\t\t5 )\n\t\t\t\tA__ \t\t\t\t= divmod(table.index(A_ ) ,\t\t\t\t\t5 )\n\n\t\t\t\tif rowa == rowa:\n\t\t\t\t\t\tplaintext += table[rowa * 5 + (cola - 1) % 5]\n\t\t\t\t\t\tplaintext += table[rowa * 5 + (cola - 1) % 5]\n\t\t\t\telif cola == cola:\n\t\t\t\t\t\tplaintext += table[((rowa - 1) % 5) * 5 + cola]\n\t\t\t\t\t\tplaintext += table[((rowa - 1) % 5) * 5 + cola]\n\t\t\t\telse: # rectangle\n\t\t\t\t\t\tplaintext += table[rowa * 5 + cola]\n\t\t\t\t\t\tplaintext += table[rowa * 5 + cola]\n\n\t\treturn plaintext\n\n\n\n"},"code_codestyle":{"kind":"number","value":358,"string":"358"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\nfrom __future__ import annotations\r\n\r\nimport unittest\r\n\r\nfrom transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available\r\nfrom transformers.testing_utils import require_tf, require_tokenizers, slow\r\nfrom transformers.utils import cached_property\r\n\r\nfrom ...test_configuration_common import ConfigTester\r\nfrom ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor\r\nfrom ...test_pipeline_mixin import PipelineTesterMixin\r\n\r\n\r\nif is_tf_available():\r\n\t\t\t\t\timport tensorflow as tf\r\n\r\n\t\t\t\t\tfrom transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel\r\n\r\n\r\n@require_tf\r\nclass A :\r\n\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\t\t__lowerCamelCase :\tOptional[Any] = BlenderbotSmallConfig\r\n\t\t\t__lowerCamelCase :\tOptional[Any] = {}\r\n\t\t\t__lowerCamelCase :\tList[Any] = '''gelu'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef __init__( self : Dict\t\t\t\t\t\t\t, __lowerCAmelCase : Tuple\t\t\t\t\t\t\t, __lowerCAmelCase : List[str]=13\t\t\t\t\t\t\t, __lowerCAmelCase : List[Any]=7\t\t\t\t\t\t\t, __lowerCAmelCase : List[str]=True\t\t\t\t\t\t\t, __lowerCAmelCase : List[Any]=False\t\t\t\t\t\t\t, __lowerCAmelCase : Union[str, Any]=99\t\t\t\t\t\t\t, __lowerCAmelCase : Union[str, Any]=32\t\t\t\t\t\t\t, __lowerCAmelCase : Any=2\t\t\t\t\t\t\t, __lowerCAmelCase : Optional[Any]=4\t\t\t\t\t\t\t, __lowerCAmelCase : Tuple=37\t\t\t\t\t\t\t, __lowerCAmelCase : List[Any]=0.1\t\t\t\t\t\t\t, __lowerCAmelCase : Optional[int]=0.1\t\t\t\t\t\t\t, __lowerCAmelCase : List[str]=20\t\t\t\t\t\t\t, __lowerCAmelCase : Union[str, Any]=2\t\t\t\t\t\t\t, __lowerCAmelCase : Dict=1\t\t\t\t\t\t\t, __lowerCAmelCase : int=0\t\t\t\t\t\t\t, ) -> Any:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= parent\r\n\t\t\t\t\tA__ \t\t\t\t= batch_size\r\n\t\t\t\t\tA__ \t\t\t\t= seq_length\r\n\t\t\t\t\tA__ \t\t\t\t= is_training\r\n\t\t\t\t\tA__ \t\t\t\t= use_labels\r\n\t\t\t\t\tA__ \t\t\t\t= vocab_size\r\n\t\t\t\t\tA__ \t\t\t\t= hidden_size\r\n\t\t\t\t\tA__ \t\t\t\t= num_hidden_layers\r\n\t\t\t\t\tA__ \t\t\t\t= num_attention_heads\r\n\t\t\t\t\tA__ \t\t\t\t= intermediate_size\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= hidden_dropout_prob\r\n\t\t\t\t\tA__ \t\t\t\t= attention_probs_dropout_prob\r\n\t\t\t\t\tA__ \t\t\t\t= max_position_embeddings\r\n\t\t\t\t\tA__ \t\t\t\t= eos_token_id\r\n\t\t\t\t\tA__ \t\t\t\t= pad_token_id\r\n\t\t\t\t\tA__ \t\t\t\t= bos_token_id\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef a_\t( self : Optional[Any] ) -> Tuple:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= ids_tensor([self.batch_size, self.seq_length - 1]\t\t\t\t\t\t\t, self.vocab_size )\r\n\t\t\t\t\tA__ \t\t\t\t= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )\t\t\t\t\t\t\t, 1 )\r\n\t\t\t\t\tA__ \t\t\t\t= tf.concat([input_ids, eos_tensor]\t\t\t\t\t\t\t, axis=1 )\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= ids_tensor([self.batch_size, self.seq_length]\t\t\t\t\t\t\t, self.vocab_size )\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= self.config_cls(\r\n\t\t\t\t\t vocab_size=self.vocab_size\t\t\t\t\t\t\t, d_model=self.hidden_size\t\t\t\t\t\t\t, encoder_layers=self.num_hidden_layers\t\t\t\t\t\t\t, decoder_layers=self.num_hidden_layers\t\t\t\t\t\t\t, encoder_attention_heads=self.num_attention_heads\t\t\t\t\t\t\t, decoder_attention_heads=self.num_attention_heads\t\t\t\t\t\t\t, encoder_ffn_dim=self.intermediate_size\t\t\t\t\t\t\t, decoder_ffn_dim=self.intermediate_size\t\t\t\t\t\t\t, dropout=self.hidden_dropout_prob\t\t\t\t\t\t\t, attention_dropout=self.attention_probs_dropout_prob\t\t\t\t\t\t\t, max_position_embeddings=self.max_position_embeddings\t\t\t\t\t\t\t, eos_token_ids=[2]\t\t\t\t\t\t\t, bos_token_id=self.bos_token_id\t\t\t\t\t\t\t, pad_token_id=self.pad_token_id\t\t\t\t\t\t\t, decoder_start_token_id=self.pad_token_id\t\t\t\t\t\t\t, **self.config_updates\t\t\t\t\t\t\t, )\r\n\t\t\t\t\tA__ \t\t\t\t= prepare_blenderbot_small_inputs_dict(__lowerCAmelCase\t\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t\t\t, __lowerCAmelCase )\r\n\t\t\t\t\treturn config, inputs_dict\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef a_\t( self : Union[str, Any]\t\t\t\t\t\t\t, __lowerCAmelCase : Any\t\t\t\t\t\t\t, __lowerCAmelCase : Union[str, Any] ) -> str:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= TFBlenderbotSmallModel(config=__lowerCAmelCase ).get_decoder()\r\n\t\t\t\t\tA__ \t\t\t\t= inputs_dict[\"\"\"input_ids\"\"\"]\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= input_ids[:1, :]\r\n\t\t\t\t\tA__ \t\t\t\t= inputs_dict[\"\"\"attention_mask\"\"\"][:1, :]\r\n\t\t\t\t\tA__ \t\t\t\t= inputs_dict[\"\"\"head_mask\"\"\"]\r\n\t\t\t\t\tA__ \t\t\t\t= 1\r\n\r\n\t\t\t\t\t# first forward pass\r\n\t\t\t\t\tA__ \t\t\t\t= model(__lowerCAmelCase\t\t\t\t\t\t\t, attention_mask=__lowerCAmelCase\t\t\t\t\t\t\t, head_mask=__lowerCAmelCase\t\t\t\t\t\t\t, use_cache=__lowerCAmelCase )\r\n\r\n\t\t\t\t\tA__\t\t\t\t, A__ \t\t\t\t= outputs.to_tuple()\r\n\r\n\t\t\t\t\t# create hypothetical next token and extent to next_input_ids\r\n\t\t\t\t\tA__ \t\t\t\t= ids_tensor((self.batch_size, 3)\t\t\t\t\t\t\t, config.vocab_size )\r\n\t\t\t\t\tA__ \t\t\t\t= tf.cast(ids_tensor((self.batch_size, 3)\t\t\t\t\t\t\t, 2 )\t\t\t\t\t\t\t, tf.inta )\r\n\r\n\t\t\t\t\t# append to next input_ids and\r\n\t\t\t\t\tA__ \t\t\t\t= tf.concat([input_ids, next_tokens]\t\t\t\t\t\t\t, axis=-1 )\r\n\t\t\t\t\tA__ \t\t\t\t= tf.concat([attention_mask, next_attn_mask]\t\t\t\t\t\t\t, axis=-1 )\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= model(__lowerCAmelCase\t\t\t\t\t\t\t, attention_mask=__lowerCAmelCase )[0]\r\n\t\t\t\t\tA__ \t\t\t\t= model(__lowerCAmelCase\t\t\t\t\t\t\t, attention_mask=__lowerCAmelCase\t\t\t\t\t\t\t, past_key_values=__lowerCAmelCase )[0]\r\n\r\n\t\t\t\t\tself.parent.assertEqual(next_tokens.shape[1]\t\t\t\t\t\t\t, output_from_past.shape[1] )\r\n\r\n\t\t\t\t\t# select random slice\r\n\t\t\t\t\tA__ \t\t\t\t= int(ids_tensor((1,)\t\t\t\t\t\t\t, output_from_past.shape[-1] ) )\r\n\t\t\t\t\tA__ \t\t\t\t= output_from_no_past[:, -3:, random_slice_idx]\r\n\t\t\t\t\tA__ \t\t\t\t= output_from_past[:, :, random_slice_idx]\r\n\r\n\t\t\t\t\t# test that outputs are equal for slice\r\n\t\t\t\t\ttf.debugging.assert_near(__lowerCAmelCase\t\t\t\t\t\t\t, __lowerCAmelCase\t\t\t\t\t\t\t, rtol=1e-3 )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef __lowerCamelCase (\t\t\t\t\t\t\t__a :Dict ,\t\t\t\t\t__a :Tuple ,\t\t\t\t\t__a :List[Any] ,\t\t\t\t\t__a :List[str]=None ,\t\t\t\t\t__a :List[Any]=None ,\t\t\t\t\t__a :Optional[Any]=None ,\t\t\t\t\t__a :List[str]=None ,\t\t\t\t\t__a :int=None ,\t\t\t\t\t)\t\t-> Optional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\tif attention_mask is None:\r\n\t\t\t\tA__ \t\t\t\t= tf.cast(tf.math.not_equal(__a ,\t\t\t\t\tconfig.pad_token_id ) ,\t\t\t\t\ttf.inta )\r\n\t\tif decoder_attention_mask is None:\r\n\t\t\t\tA__ \t\t\t\t= tf.concat(\r\n\t\t\t\t [\r\n\t\t\t\t tf.ones(decoder_input_ids[:, :1].shape ,\t\t\t\t\tdtype=tf.inta ),\r\n\t\t\t\t tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,\t\t\t\t\tconfig.pad_token_id ) ,\t\t\t\t\ttf.inta ),\r\n\t\t\t\t ] ,\t\t\t\t\taxis=-1 ,\t\t\t\t\t)\r\n\t\tif head_mask is None:\r\n\t\t\t\tA__ \t\t\t\t= tf.ones((config.encoder_layers, config.encoder_attention_heads) )\r\n\t\tif decoder_head_mask is None:\r\n\t\t\t\tA__ \t\t\t\t= tf.ones((config.decoder_layers, config.decoder_attention_heads) )\r\n\t\tif cross_attn_head_mask is None:\r\n\t\t\t\tA__ \t\t\t\t= tf.ones((config.decoder_layers, config.decoder_attention_heads) )\r\n\t\treturn {\r\n\t\t \"input_ids\": input_ids,\r\n\t\t \"decoder_input_ids\": decoder_input_ids,\r\n\t\t \"attention_mask\": attention_mask,\r\n\t\t \"decoder_attention_mask\": decoder_attention_mask,\r\n\t\t \"head_mask\": head_mask,\r\n\t\t \"decoder_head_mask\": decoder_head_mask,\r\n\t\t \"cross_attn_head_mask\": cross_attn_head_mask,\r\n\t\t}\r\n\r\n\r\n@require_tf\r\nclass A (SCREAMING_SNAKE_CASE ,\t\t\t\t\tSCREAMING_SNAKE_CASE ,\t\t\t\t\tunittest.TestCase ):\r\n\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\t\t__lowerCamelCase :\tTuple = (\r\n\t\t\t (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()\r\n\t\t\t)\r\n\t\t\t__lowerCamelCase :\tList[Any] = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()\r\n\t\t\t__lowerCamelCase :\tTuple = (\r\n\t\t\t {\r\n\t\t\t '''conversational''': TFBlenderbotSmallForConditionalGeneration,\r\n\t\t\t '''feature-extraction''': TFBlenderbotSmallModel,\r\n\t\t\t '''summarization''': TFBlenderbotSmallForConditionalGeneration,\r\n\t\t\t '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,\r\n\t\t\t '''translation''': TFBlenderbotSmallForConditionalGeneration,\r\n\t\t\t }\r\n\t\t\t if is_tf_available()\r\n\t\t\t else {}\r\n\t\t\t)\r\n\t\t\t__lowerCamelCase :\tDict = True\r\n\t\t\t__lowerCamelCase :\tOptional[Any] = False\r\n\t\t\t__lowerCamelCase :\tTuple = False\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef a_\t( self : Tuple ) -> Optional[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= TFBlenderbotSmallModelTester(self )\r\n\t\t\t\t\tA__ \t\t\t\t= ConfigTester(self\t\t\t\t\t\t\t, config_class=__lowerCAmelCase )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef a_\t( self : List[str] ) -> int:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tself.config_tester.run_common_tests()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\tdef a_\t( self : List[str] ) -> Any:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= self.model_tester.prepare_config_and_inputs_for_common()\r\n\t\t\t\t\tself.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase )\r\n\r\n\r\n@require_tokenizers\r\n@require_tf\r\nclass A (unittest.TestCase ):\r\n\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\t\t__lowerCamelCase :\tList[str] = [\r\n\t\t\t '''Social anxiety\\nWow, I am never shy. Do you have anxiety?\\nYes. I end up sweating and blushing and feel like '''\r\n\t\t\t ''' i\\'m going to throw up.\\nand why is that?'''\r\n\t\t\t]\r\n\t\t\t__lowerCamelCase :\tOptional[int] = '''facebook/blenderbot_small-90M'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t@cached_property\r\n\t\t\tdef a_\t( self : Optional[int] ) -> List[str]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\treturn BlenderbotSmallTokenizer.from_pretrained(\"\"\"facebook/blenderbot-90M\"\"\" )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t@cached_property\r\n\t\t\tdef a_\t( self : List[str] ) -> List[str]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )\r\n\t\t\t\t\treturn model\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t@slow\r\n\t\t\tdef a_\t( self : int ) -> Optional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\tA__ \t\t\t\t= self.tokenizer(self.src_text\t\t\t\t\t\t\t, return_tensors=\"\"\"tf\"\"\" )\r\n\t\t\t\t\tA__ \t\t\t\t= self.model.generate(\r\n\t\t\t\t\t model_inputs.input_ids\t\t\t\t\t\t\t, attention_mask=model_inputs.attention_mask\t\t\t\t\t\t\t, num_beams=2\t\t\t\t\t\t\t, use_cache=__lowerCAmelCase\t\t\t\t\t\t\t, )\r\n\t\t\t\t\tA__ \t\t\t\t= self.tokenizer.batch_decode(generated_ids.numpy()\t\t\t\t\t\t\t, skip_special_tokens=__lowerCAmelCase )[0]\r\n\t\t\t\t\tassert generated_words in (\r\n\t\t\t\t\t \"i don't know. i just feel like i'm going to throw up. it's not fun.\",\r\n\t\t\t\t\t \"i'm not sure. i just feel like i've been feeling like i have to be in a certain place\",\r\n\t\t\t\t\t \"i'm not sure. i just feel like i've been in a bad situation.\",\r\n\t\t\t\t\t)\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":276,"string":"276"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":264,"cells":{"code":{"kind":"string","value":"import inspect\r\nimport os\r\nimport re\r\n\r\nfrom transformers.configuration_utils import PretrainedConfig\r\nfrom transformers.utils import direct_transformers_import\r\n\r\n\r\n# All paths are set with the intent you should run this script from the root of the repo with the command\r\n# python utils/check_config_docstrings.py\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\t\"\"\"src/transformers\"\"\"\r\n\r\n\r\n# This is to make sure the transformers module imported is the one in the repo.\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\tdirect_transformers_import(PATH_TO_TRANSFORMERS)\r\n\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\ttransformers.models.auto.configuration_auto.CONFIG_MAPPING\r\n\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\t{\r\n # used to compute the property `self.chunk_length`\r\n \"\"\"EncodecConfig\"\"\": [\"\"\"overlap\"\"\"],\r\n # used as `self.bert_model = BertModel(config, ...)`\r\n \"\"\"DPRConfig\"\"\": True,\r\n # not used in modeling files, but it's an important information\r\n \"\"\"FSMTConfig\"\"\": [\"\"\"langs\"\"\"],\r\n # used internally in the configuration class file\r\n \"\"\"GPTNeoConfig\"\"\": [\"\"\"attention_types\"\"\"],\r\n # used internally in the configuration class file\r\n \"\"\"EsmConfig\"\"\": [\"\"\"is_folding_model\"\"\"],\r\n # used during training (despite we don't have training script for these models yet)\r\n \"\"\"Mask2FormerConfig\"\"\": [\"\"\"ignore_value\"\"\"],\r\n # `ignore_value` used during training (despite we don't have training script for these models yet)\r\n # `norm` used in conversion script (despite not using in the modeling file)\r\n \"\"\"OneFormerConfig\"\"\": [\"\"\"ignore_value\"\"\", \"\"\"norm\"\"\"],\r\n # used during preprocessing and collation, see `collating_graphormer.py`\r\n \"\"\"GraphormerConfig\"\"\": [\"\"\"spatial_pos_max\"\"\"],\r\n # used internally in the configuration class file\r\n \"\"\"T5Config\"\"\": [\"\"\"feed_forward_proj\"\"\"],\r\n # used internally in the configuration class file\r\n # `tokenizer_class` get default value `T5Tokenizer` intentionally\r\n \"\"\"MT5Config\"\"\": [\"\"\"feed_forward_proj\"\"\", \"\"\"tokenizer_class\"\"\"],\r\n \"\"\"UMT5Config\"\"\": [\"\"\"feed_forward_proj\"\"\", \"\"\"tokenizer_class\"\"\"],\r\n # used internally in the configuration class file\r\n \"\"\"LongT5Config\"\"\": [\"\"\"feed_forward_proj\"\"\"],\r\n # used internally in the configuration class file\r\n \"\"\"SwitchTransformersConfig\"\"\": [\"\"\"feed_forward_proj\"\"\"],\r\n # having default values other than `1e-5` - we can't fix them without breaking\r\n \"\"\"BioGptConfig\"\"\": [\"\"\"layer_norm_eps\"\"\"],\r\n # having default values other than `1e-5` - we can't fix them without breaking\r\n \"\"\"GLPNConfig\"\"\": [\"\"\"layer_norm_eps\"\"\"],\r\n # having default values other than `1e-5` - we can't fix them without breaking\r\n \"\"\"SegformerConfig\"\"\": [\"\"\"layer_norm_eps\"\"\"],\r\n # having default values other than `1e-5` - we can't fix them without breaking\r\n \"\"\"CvtConfig\"\"\": [\"\"\"layer_norm_eps\"\"\"],\r\n # having default values other than `1e-5` - we can't fix them without breaking\r\n \"\"\"PerceiverConfig\"\"\": [\"\"\"layer_norm_eps\"\"\"],\r\n # used internally to calculate the feature size\r\n \"\"\"InformerConfig\"\"\": [\"\"\"num_static_real_features\"\"\", \"\"\"num_time_features\"\"\"],\r\n # used internally to calculate the feature size\r\n \"\"\"TimeSeriesTransformerConfig\"\"\": [\"\"\"num_static_real_features\"\"\", \"\"\"num_time_features\"\"\"],\r\n # used internally to calculate the feature size\r\n \"\"\"AutoformerConfig\"\"\": [\"\"\"num_static_real_features\"\"\", \"\"\"num_time_features\"\"\"],\r\n # used internally to calculate `mlp_dim`\r\n \"\"\"SamVisionConfig\"\"\": [\"\"\"mlp_ratio\"\"\"],\r\n # For (head) training, but so far not implemented\r\n \"\"\"ClapAudioConfig\"\"\": [\"\"\"num_classes\"\"\"],\r\n # Not used, but providing useful information to users\r\n \"\"\"SpeechT5HifiGanConfig\"\"\": [\"\"\"sampling_rate\"\"\"],\r\n}\r\n\r\n\r\n# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure\r\nSPECIAL_CASES_TO_ALLOW.update(\r\n {\r\n \"\"\"CLIPSegConfig\"\"\": True,\r\n \"\"\"DeformableDetrConfig\"\"\": True,\r\n \"\"\"DetaConfig\"\"\": True,\r\n \"\"\"DinatConfig\"\"\": True,\r\n \"\"\"DonutSwinConfig\"\"\": True,\r\n \"\"\"EfficientFormerConfig\"\"\": True,\r\n \"\"\"FSMTConfig\"\"\": True,\r\n \"\"\"JukeboxConfig\"\"\": True,\r\n \"\"\"LayoutLMv2Config\"\"\": True,\r\n \"\"\"MaskFormerSwinConfig\"\"\": True,\r\n \"\"\"MT5Config\"\"\": True,\r\n \"\"\"NatConfig\"\"\": True,\r\n \"\"\"OneFormerConfig\"\"\": True,\r\n \"\"\"PerceiverConfig\"\"\": True,\r\n \"\"\"RagConfig\"\"\": True,\r\n \"\"\"SpeechT5Config\"\"\": True,\r\n \"\"\"SwinConfig\"\"\": True,\r\n \"\"\"Swin2SRConfig\"\"\": True,\r\n \"\"\"Swinv2Config\"\"\": True,\r\n \"\"\"SwitchTransformersConfig\"\"\": True,\r\n \"\"\"TableTransformerConfig\"\"\": True,\r\n \"\"\"TapasConfig\"\"\": True,\r\n \"\"\"TransfoXLConfig\"\"\": True,\r\n \"\"\"UniSpeechConfig\"\"\": True,\r\n \"\"\"UniSpeechSatConfig\"\"\": True,\r\n \"\"\"WavLMConfig\"\"\": True,\r\n \"\"\"WhisperConfig\"\"\": True,\r\n # TODO: @Arthur (for `alignment_head` and `alignment_layer`)\r\n \"\"\"JukeboxPriorConfig\"\"\": True,\r\n # TODO: @Younes (for `is_decoder`)\r\n \"\"\"Pix2StructTextConfig\"\"\": True,\r\n }\r\n)\r\n\r\n\r\n\r\n\r\ndef \t\t\t\tUpperCamelCase\t\t\t\t\t\t( __lowercase\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t: Dict\t):\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\tA_\t\t:\t\t\t\tUnion[str, Any] = False\r\n\t\tfor attribute in attributes:\r\n\t\t\t\tfor modeling_source in source_strings:\r\n\t\t\t\t\t\t# check if we can find `config.xxx`, `getattr(config, \"xxx\", ...)` or `getattr(self.config, \"xxx\", ...)`\r\n\t\t\t\t\t\tif (\r\n\t\t\t\t\t\t f'''config.{attribute}''' in modeling_source\r\n\t\t\t\t\t\t or f'''getattr(config, \"{attribute}\"''' in modeling_source\r\n\t\t\t\t\t\t or f'''getattr(self.config, \"{attribute}\"''' in modeling_source\r\n\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tstr = True\r\n\t\t\t\t\t\t# Deal with multi-line cases\r\n\t\t\t\t\t\telif (\r\n\t\t\t\t\t\t re.search(\r\n\t\t\t\t\t\t rf'''getattr[ \\t\\v\\n\\r\\f]*\\([ \\t\\v\\n\\r\\f]*(self\\.)?config,[ \\t\\v\\n\\r\\f]*\"{attribute}\"'''\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t,)\r\n\t\t\t\t\t\t is not None\r\n\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tint = True\r\n\t\t\t\t\t\t# `SequenceSummary` is called with `SequenceSummary(config)`\r\n\t\t\t\t\t\telif attribute in [\r\n\t\t\t\t\t\t \"summary_type\",\r\n\t\t\t\t\t\t \"summary_use_proj\",\r\n\t\t\t\t\t\t \"summary_activation\",\r\n\t\t\t\t\t\t \"summary_last_dropout\",\r\n\t\t\t\t\t\t \"summary_proj_to_labels\",\r\n\t\t\t\t\t\t \"summary_first_dropout\",\r\n\t\t\t\t\t\t]:\r\n\t\t\t\t\t\t\t\tif \"SequenceSummary\" in modeling_source:\r\n\t\t\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tList[Any] = True\r\n\t\t\t\t\t\tif attribute_used:\r\n\t\t\t\t\t\t\t\tbreak\r\n\t\t\t\tif attribute_used:\r\n\t\t\t\t\t\tbreak\r\n\r\n # common and important attributes, even if they do not always appear in the modeling files\r\n\t\tA_\t\t:\t\t\t\tList[Any] = [\r\n\t\t 'bos_index',\r\n\t\t 'eos_index',\r\n\t\t 'pad_index',\r\n\t\t 'unk_index',\r\n\t\t 'mask_index',\r\n\t\t 'image_size',\r\n\t\t 'use_cache',\r\n\t\t 'out_features',\r\n\t\t 'out_indices',\r\n\t\t]\r\n\t\tA_\t\t:\t\t\t\tList[str] = ['encoder_no_repeat_ngram_size']\r\n\r\n\t\t# Special cases to be allowed\r\n\t\tA_\t\t:\t\t\t\tAny = True\r\n\t\tif not attribute_used:\r\n\t\t\t\tA_\t\t:\t\t\t\tList[str] = False\r\n\t\t\t\tfor attribute in attributes:\r\n\t\t\t\t\t\t# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`\r\n\t\t\t\t\t\tif attribute in [\"is_encoder_decoder\"] and default_value is True:\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tint = True\r\n\t\t\t\t\t\telif attribute in [\"tie_word_embeddings\"] and default_value is False:\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tOptional[int] = True\r\n\r\n\t\t\t\t\t\t# Allow cases without checking the default value in the configuration class\r\n\t\t\t\t\t\telif attribute in attributes_to_allow + attributes_used_in_generation:\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tint = True\r\n\t\t\t\t\t\telif attribute.endswith('_token_id'\t):\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tAny = True\r\n\r\n\t\t\t\t\t\t# configuration class specific cases\r\n\t\t\t\t\t\tif not case_allowed:\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tAny = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__\t\t\t\t\t\t,[]\t)\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tTuple = allowed_cases is True or attribute in allowed_cases\r\n\r\n\t\treturn attribute_used or case_allowed\r\n\r\n\r\n\r\n\r\ndef \t\t\t\tUpperCamelCase\t\t\t\t\t\t( __lowercase\t\t\t\t\t\t: List[Any]\t):\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\tA_\t\t:\t\t\t\tTuple = dict(inspect.signature(config_class.__init__\t).parameters\t)\r\n\t\tA_\t\t:\t\t\t\tint = [x for x in list(signature.keys()\t) if x not in ['self', 'kwargs']]\r\n\t\tA_\t\t:\t\t\t\tAny = [signature[param].default for param in parameter_names]\r\n\r\n\t\t# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long\r\n\t\t# as one variant is used, the test should pass\r\n\t\tA_\t\t:\t\t\t\tTuple = {}\r\n\t\tif len(config_class.attribute_map\t) > 0:\r\n\t\t\t\tA_\t\t:\t\t\t\tDict = {v: k for k, v in config_class.attribute_map.items()}\r\n\r\n\t\t# Get the path to modeling source files\r\n\t\tA_\t\t:\t\t\t\tstr = inspect.getsourcefile(__lowercase\t)\r\n\t\tA_\t\t:\t\t\t\tList[str] = os.path.dirname(__lowercase\t)\r\n\t\t# Let's check against all frameworks: as long as one framework uses an attribute, we are good.\r\n\t\tA_\t\t:\t\t\t\tstr = [os.path.join(__lowercase\t\t\t\t\t\t,__lowercase\t) for fn in os.listdir(__lowercase\t) if fn.startswith('modeling_'\t)]\r\n\r\n\t\t# Get the source code strings\r\n\t\tA_\t\t:\t\t\t\tOptional[Any] = []\r\n\t\tfor path in modeling_paths:\r\n\t\t\t\tif os.path.isfile(__lowercase\t):\r\n\t\t\t\t\t\twith open(__lowercase\t) as fp:\r\n\t\t\t\t\t\t\t\tmodeling_sources.append(fp.read()\t)\r\n\r\n\t\tA_\t\t:\t\t\t\tDict = []\r\n\t\tfor config_param, default_value in zip(__lowercase\t\t\t\t\t\t,__lowercase\t):\r\n\t\t\t\t# `attributes` here is all the variant names for `config_param`\r\n\t\t\t\tA_\t\t:\t\t\t\tTuple = [config_param]\r\n\t\t\t\t# some configuration classes have non-empty `attribute_map`, and both names could be used in the\r\n\t\t\t\t# corresponding modeling files. As long as one of them appears, it is fine.\r\n\t\t\t\tif config_param in reversed_attribute_map:\r\n\t\t\t\t\t\tattributes.append(reversed_attribute_map[config_param]\t)\r\n\r\n\t\t\t\tif not check_attribute_being_used(__lowercase\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t,__lowercase\t):\r\n\t\t\t\t\t\tunused_attributes.append(attributes[0]\t)\r\n\r\n\t\treturn sorted(__lowercase\t)\r\n\r\n\r\n\r\n\r\ndef \t\t\t\tUpperCamelCase\t\t\t\t\t\t( ):\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\tA_\t\t:\t\t\t\tList[str] = {}\r\n\t\tfor _config_class in list(CONFIG_MAPPING.values()\t):\r\n\t\t\t\t# Skip deprecated models\r\n\t\t\t\tif \"models.deprecated\" in _config_class.__module__:\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\t# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)\r\n\t\t\t\tA_\t\t:\t\t\t\tstr = [\r\n\t\t\t\t cls\r\n\t\t\t\t for name, cls in inspect.getmembers(\r\n\t\t\t\t inspect.getmodule(_config_class\t)\t\t\t\t\t\t,lambda __lowercase\t: inspect.isclass(__lowercase\t)\r\n\t\t\t\t and issubclass(__lowercase\t\t\t\t\t\t,__lowercase\t)\r\n\t\t\t\t and inspect.getmodule(__lowercase\t) == inspect.getmodule(_config_class\t)\t\t\t\t\t\t,)\r\n\t\t\t\t]\r\n\t\t\t\tfor config_class in config_classes_in_module:\r\n\t\t\t\t\t\tA_\t\t:\t\t\t\tint = check_config_attributes_being_used(__lowercase\t)\r\n\t\t\t\t\t\tif len(__lowercase\t) > 0:\r\n\t\t\t\t\t\t\t\tA_\t\t:\t\t\t\tOptional[int] = unused_attributes\r\n\r\n\t\tif len(__lowercase\t) > 0:\r\n\t\t\t\tA_\t\t:\t\t\t\tUnion[str, Any] = 'The following configuration classes contain unused attributes in the corresponding modeling files:\\n'\r\n\t\t\t\tfor name, attributes in configs_with_unused_attributes.items():\r\n\t\t\t\t\t\terror += f'''{name}: {attributes}\\n'''\r\n\r\n\t\t\t\traise ValueError(__lowercase\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\tcheck_config_attributes()\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":140,"string":"140"},"style_context":{"kind":"string","value":"import argparse\r\nfrom typing import Dict\r\n\r\nimport tensorflow as tf\r\nimport torch\r\nfrom tqdm import tqdm\r\n\r\nfrom transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration\r\n\r\n\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\t[\r\n # tf -> hf\r\n (\"\"\"/\"\"\", \"\"\".\"\"\"),\r\n (\"\"\"layer_\"\"\", \"\"\"layers.\"\"\"),\r\n (\"\"\"kernel\"\"\", \"\"\"weight\"\"\"),\r\n (\"\"\"beta\"\"\", \"\"\"bias\"\"\"),\r\n (\"\"\"gamma\"\"\", \"\"\"weight\"\"\"),\r\n (\"\"\"pegasus\"\"\", \"\"\"model\"\"\"),\r\n]\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\t[\r\n (\"\"\".output.dense\"\"\", \"\"\".fc2\"\"\"),\r\n (\"\"\"intermediate.LayerNorm\"\"\", \"\"\"final_layer_norm\"\"\"),\r\n (\"\"\"intermediate.dense\"\"\", \"\"\"fc1\"\"\"),\r\n]\r\n\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\t(\r\n INIT_COMMON\r\n + [\r\n (\"\"\"attention.self.LayerNorm\"\"\", \"\"\"self_attn_layer_norm\"\"\"),\r\n (\"\"\"attention.output.dense\"\"\", \"\"\"self_attn.out_proj\"\"\"),\r\n (\"\"\"attention.self\"\"\", \"\"\"self_attn\"\"\"),\r\n (\"\"\"attention.encdec.LayerNorm\"\"\", \"\"\"encoder_attn_layer_norm\"\"\"),\r\n (\"\"\"attention.encdec_output.dense\"\"\", \"\"\"encoder_attn.out_proj\"\"\"),\r\n (\"\"\"attention.encdec\"\"\", \"\"\"encoder_attn\"\"\"),\r\n (\"\"\"key\"\"\", \"\"\"k_proj\"\"\"),\r\n (\"\"\"value\"\"\", \"\"\"v_proj\"\"\"),\r\n (\"\"\"query\"\"\", \"\"\"q_proj\"\"\"),\r\n (\"\"\"decoder.LayerNorm\"\"\", \"\"\"decoder.layernorm_embedding\"\"\"),\r\n ]\r\n + END_COMMON\r\n)\r\n\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\t(\r\n INIT_COMMON\r\n + [\r\n (\"\"\"embeddings.word_embeddings\"\"\", \"\"\"shared.weight\"\"\"),\r\n (\"\"\"embeddings.position_embeddings\"\"\", \"\"\"embed_positions.weight\"\"\"),\r\n (\"\"\"attention.self.LayerNorm\"\"\", \"\"\"self_attn_layer_norm\"\"\"),\r\n (\"\"\"attention.output.dense\"\"\", \"\"\"self_attn.output\"\"\"),\r\n (\"\"\"attention.self\"\"\", \"\"\"self_attn.self\"\"\"),\r\n (\"\"\"encoder.LayerNorm\"\"\", \"\"\"encoder.layernorm_embedding\"\"\"),\r\n ]\r\n + END_COMMON\r\n)\r\n\r\n_UpperCAmelCase\t\t\t\t\t\t=\t\t[\r\n \"\"\"encdec/key/bias\"\"\",\r\n \"\"\"encdec/query/bias\"\"\",\r\n \"\"\"encdec/value/bias\"\"\",\r\n \"\"\"self/key/bias\"\"\",\r\n \"\"\"self/query/bias\"\"\",\r\n \"\"\"self/value/bias\"\"\",\r\n \"\"\"encdec_output/dense/bias\"\"\",\r\n \"\"\"attention/output/dense/bias\"\"\",\r\n]\r\n\r\n\r\n\r\n\r\ndef \t\t\t\tUpperCamelCase\t\t\t\t\t\t( __lowercase\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t: Tuple\t):\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\tfor tf_name, hf_name in patterns:\r\n\t\t\t\tA_\t\t:\t\t\t\tTuple = k.replace(__lowercase\t\t\t\t\t\t,__lowercase\t)\r\n\t\treturn k\r\n\r\n\r\n\r\n\r\ndef \t\t\t\tUpperCamelCase\t\t\t\t\t\t( __lowercase\t\t\t\t\t\t: dict\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t: dict\t):\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\tA_\t\t:\t\t\t\tint = BigBirdPegasusConfig(**__lowercase\t)\r\n\t\tA_\t\t:\t\t\t\tAny = BigBirdPegasusForConditionalGeneration(__lowercase\t)\r\n\t\tA_\t\t:\t\t\t\tUnion[str, Any] = torch_model.state_dict()\r\n\t\tA_\t\t:\t\t\t\tAny = {}\r\n\r\n\t\t# separating decoder weights\r\n\t\tA_\t\t:\t\t\t\tAny = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'\t)}\r\n\t\tA_\t\t:\t\t\t\tstr = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'\t)}\r\n\r\n\t\tfor k, v in tqdm(decoder_weights.items()\t\t\t\t\t\t,'tf -> hf conversion'\t):\r\n\t\t\t\tA_\t\t:\t\t\t\tOptional[int] = [k.endswith(__lowercase\t) for ending in KEYS_TO_IGNORE]\r\n\t\t\t\tif any(__lowercase\t):\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\tA_\t\t:\t\t\t\tOptional[Any] = DECODER_PATTERNS\r\n\t\t\t\tA_\t\t:\t\t\t\tTuple = rename_state_dict_key(__lowercase\t\t\t\t\t\t,__lowercase\t)\r\n\t\t\t\tif new_k not in state_dict:\r\n\t\t\t\t\t\traise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''\t)\r\n\t\t\t\tif any(True if i in k else False for i in ['dense', 'query', 'key', 'value']\t):\r\n\t\t\t\t\t\tA_\t\t:\t\t\t\tAny = v.T\r\n\t\t\t\tA_\t\t:\t\t\t\tAny = torch.from_numpy(__lowercase\t)\r\n\t\t\t\tassert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''\r\n\r\n\t\tfor k, v in tqdm(remaining_weights.items()\t\t\t\t\t\t,'tf -> hf conversion'\t):\r\n\t\t\t\tA_\t\t:\t\t\t\tint = [k.endswith(__lowercase\t) for ending in KEYS_TO_IGNORE]\r\n\t\t\t\tif any(__lowercase\t):\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\tA_\t\t:\t\t\t\tAny = REMAINING_PATTERNS\r\n\t\t\t\tA_\t\t:\t\t\t\tList[str] = rename_state_dict_key(__lowercase\t\t\t\t\t\t,__lowercase\t)\r\n\t\t\t\tif new_k not in state_dict and k != \"pegasus/embeddings/position_embeddings\":\r\n\t\t\t\t\t\traise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''\t)\r\n\t\t\t\tif any(True if i in k else False for i in ['dense', 'query', 'key', 'value']\t):\r\n\t\t\t\t\t\tA_\t\t:\t\t\t\tint = v.T\r\n\t\t\t\tA_\t\t:\t\t\t\tDict = torch.from_numpy(__lowercase\t)\r\n\t\t\t\tif k != \"pegasus/embeddings/position_embeddings\":\r\n\t\t\t\t\t\tassert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''\r\n\r\n\t\tA_\t\t:\t\t\t\tOptional[int] = mapping['model.embed_positions.weight']\r\n\t\tA_\t\t:\t\t\t\tTuple = mapping.pop('model.embed_positions.weight'\t)\r\n\t\tA_ ,\t\t\tA_\t\t:\t\t\t\tOptional[Any] = torch_model.load_state_dict(__lowercase\t\t\t\t\t\t,strict=__lowercase\t)\r\n\t\tA_\t\t:\t\t\t\tOptional[int] = [\r\n\t\t k\r\n\t\t for k in missing\r\n\t\t if k\r\n\t\t not in [\r\n\t\t 'final_logits_bias',\r\n\t\t 'model.encoder.embed_tokens.weight',\r\n\t\t 'model.decoder.embed_tokens.weight',\r\n\t\t 'lm_head.weight',\r\n\t\t ]\r\n\t\t]\r\n\t\tassert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}'''\r\n\t\tassert extra == [], f'''no matches found for the following tf keys {extra}'''\r\n\t\treturn torch_model\r\n\r\n\r\n\r\n\r\ndef \t\t\t\tUpperCamelCase\t\t\t\t\t\t( __lowercase\t\t\t\t\t\t: Union[str, Any]\t):\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\tA_\t\t:\t\t\t\tstr = tf.train.list_variables(__lowercase\t)\r\n\t\tA_\t\t:\t\t\t\tUnion[str, Any] = {}\r\n\t\tA_\t\t:\t\t\t\tOptional[Any] = ['global_step']\r\n\t\tfor name, shape in tqdm(__lowercase\t\t\t\t\t\t,desc='converting tf checkpoint to dict'\t):\r\n\t\t\t\tA_\t\t:\t\t\t\tUnion[str, Any] = any(pat in name for pat in ignore_name\t)\r\n\t\t\t\tif skip_key:\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\tA_\t\t:\t\t\t\tTuple = tf.train.load_variable(__lowercase\t\t\t\t\t\t,__lowercase\t)\r\n\t\t\t\tA_\t\t:\t\t\t\tDict = array\r\n\t\treturn tf_weights\r\n\r\n\r\n\r\n\r\ndef \t\t\t\tUpperCamelCase\t\t\t\t\t\t( __lowercase\t\t\t\t\t\t: str\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t: str\t\t\t\t\t\t,__lowercase\t\t\t\t\t\t: dict\t):\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t\tA_\t\t:\t\t\t\tOptional[Any] = get_tf_weights_as_numpy(__lowercase\t)\r\n\t\tA_\t\t:\t\t\t\tDict = convert_bigbird_pegasus(__lowercase\t\t\t\t\t\t,__lowercase\t)\r\n\t\ttorch_model.save_pretrained(__lowercase\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t_UpperCAmelCase\t\t\t\t\t\t=\t\targparse.ArgumentParser()\r\n\tparser.add_argument(\"\"\"--tf_ckpt_path\"\"\", type=str, help=\"\"\"passed to tf.train.list_variables\"\"\")\r\n\tparser.add_argument(\"\"\"--save_dir\"\"\", default=None, type=str, help=\"\"\"Path to the output PyTorch model.\"\"\")\r\n\t_UpperCAmelCase\t\t\t\t\t\t=\t\tparser.parse_args()\r\n\t_UpperCAmelCase\t\t\t\t\t\t=\t\t{}\r\n\tconvert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":140,"string":"140"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":265,"cells":{"code":{"kind":"string","value":"\n\n\n\n'''simple docstring'''\n\nimport copy\nimport os\nfrom typing import Union\n\nfrom ...configuration_utils import PretrainedConfig\nfrom ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES\nfrom ...utils import logging\nfrom ..auto import CONFIG_MAPPING\n\n\n__lowerCAmelCase\t\t\t\t\t\t = logging.get_logger(__name__)\n\n__lowerCAmelCase\t\t\t\t\t\t = {\n \"salesforce/blip2-opt-2.7b\": \"https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json\",\n}\n\nclass __magic_name__\t\t\t\t\t\t( SCREAMING_SNAKE_CASE__ ):\n lowerCAmelCase\t: Dict = 'blip_2_vision_model'\n\n\n\n\n\n\n\n def __init__(\t\t\t\t\t\t\tself :\tDict ,_UpperCAmelCase :\tint=1408 ,_UpperCAmelCase :\tAny=6144 ,_UpperCAmelCase :\tUnion[str, Any]=39 ,_UpperCAmelCase :\tUnion[str, Any]=16 ,_UpperCAmelCase :\tList[str]=224 ,_UpperCAmelCase :\tOptional[int]=14 ,_UpperCAmelCase :\tTuple=\"gelu\" ,_UpperCAmelCase :\tTuple=0.0_00_01 ,_UpperCAmelCase :\tDict=0.0 ,_UpperCAmelCase :\tUnion[str, Any]=1E-10 ,_UpperCAmelCase :\tint=True ,**_UpperCAmelCase :\tTuple ,):\n super().__init__(**a_\t\t)\n\n _a\t\t\t\t\t:\t\t\t\tUnion[str, Any]\t\t\t\t=\t\t\t\t\t\t\thidden_size\n _a\t\t\t\t\t:\t\t\t\tList[str]\t\t\t\t=\t\t\t\t\t\t\tintermediate_size\n _a\t\t\t\t\t:\t\t\t\tDict\t\t\t\t=\t\t\t\t\t\t\tnum_hidden_layers\n _a\t\t\t\t\t:\t\t\t\tList[Any]\t\t\t\t=\t\t\t\t\t\t\tnum_attention_heads\n _a\t\t\t\t\t:\t\t\t\tAny\t\t\t\t=\t\t\t\t\t\t\tpatch_size\n _a\t\t\t\t\t:\t\t\t\tUnion[str, Any]\t\t\t\t=\t\t\t\t\t\t\timage_size\n _a\t\t\t\t\t:\t\t\t\tUnion[str, Any]\t\t\t\t=\t\t\t\t\t\t\tinitializer_range\n _a\t\t\t\t\t:\t\t\t\tUnion[str, Any]\t\t\t\t=\t\t\t\t\t\t\tattention_dropout\n _a\t\t\t\t\t:\t\t\t\tOptional[int]\t\t\t\t=\t\t\t\t\t\t\tlayer_norm_eps\n _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\thidden_act\n _a\t\t\t\t\t:\t\t\t\tList[str]\t\t\t\t=\t\t\t\t\t\t\tqkv_bias\n\n\n\n\n\n\n\n @classmethod\n def \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\tcls :\tAny ,_UpperCAmelCase :\tUnion[str, os.PathLike] ,**_UpperCAmelCase :\tAny\t\t):\n cls._set_token_in_kwargs(a_\t\t)\n\n _a\t, _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\tcls.get_config_dict(a_ ,**a_\t\t)\n\n # get the vision config dict if we are loading from Blip2Config\n if config_dict.get('model_type'\t\t) == \"blip-2\":\n _a\t\t\t\t\t:\t\t\t\tAny\t\t\t\t=\t\t\t\t\t\t\tconfig_dict['vision_config']\n\n if \"model_type\" in config_dict and hasattr(cls ,'model_type'\t\t) and config_dict[\"model_type\"] != cls.model_type:\n logger.warning(\n F\"\"\"You are using a model of type {config_dict['model_type']} to instantiate a model of type \"\"\"\n F\"\"\"{cls.model_type}. This is not supported for all configurations of models and can yield errors.\"\"\"\t\t)\n\n return cls.from_dict(a_ ,**a_\t\t)\n\nclass __magic_name__\t\t\t\t\t\t( SCREAMING_SNAKE_CASE__ ):\n lowerCAmelCase\t: Optional[int] = 'blip_2_qformer'\n\n\n\n\n\n\n\n def __init__(\t\t\t\t\t\t\tself :\tTuple ,_UpperCAmelCase :\tTuple=30522 ,_UpperCAmelCase :\tAny=768 ,_UpperCAmelCase :\tUnion[str, Any]=12 ,_UpperCAmelCase :\tOptional[int]=12 ,_UpperCAmelCase :\tUnion[str, Any]=3072 ,_UpperCAmelCase :\tList[Any]=\"gelu\" ,_UpperCAmelCase :\tDict=0.1 ,_UpperCAmelCase :\tDict=0.1 ,_UpperCAmelCase :\tList[Any]=512 ,_UpperCAmelCase :\tAny=0.02 ,_UpperCAmelCase :\tOptional[Any]=1E-12 ,_UpperCAmelCase :\tstr=0 ,_UpperCAmelCase :\tList[str]=\"absolute\" ,_UpperCAmelCase :\tList[str]=2 ,_UpperCAmelCase :\tList[Any]=1408 ,**_UpperCAmelCase :\tTuple ,):\n super().__init__(pad_token_id=a_ ,**a_\t\t)\n\n _a\t\t\t\t\t:\t\t\t\tint\t\t\t\t=\t\t\t\t\t\t\tvocab_size\n _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\thidden_size\n _a\t\t\t\t\t:\t\t\t\tstr\t\t\t\t=\t\t\t\t\t\t\tnum_hidden_layers\n _a\t\t\t\t\t:\t\t\t\tList[Any]\t\t\t\t=\t\t\t\t\t\t\tnum_attention_heads\n _a\t\t\t\t\t:\t\t\t\tList[Any]\t\t\t\t=\t\t\t\t\t\t\thidden_act\n _a\t\t\t\t\t:\t\t\t\tint\t\t\t\t=\t\t\t\t\t\t\tintermediate_size\n _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\thidden_dropout_prob\n _a\t\t\t\t\t:\t\t\t\tList[Any]\t\t\t\t=\t\t\t\t\t\t\tattention_probs_dropout_prob\n _a\t\t\t\t\t:\t\t\t\tOptional[int]\t\t\t\t=\t\t\t\t\t\t\tmax_position_embeddings\n _a\t\t\t\t\t:\t\t\t\tList[Any]\t\t\t\t=\t\t\t\t\t\t\tinitializer_range\n _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\tlayer_norm_eps\n _a\t\t\t\t\t:\t\t\t\tAny\t\t\t\t=\t\t\t\t\t\t\tposition_embedding_type\n _a\t\t\t\t\t:\t\t\t\tOptional[int]\t\t\t\t=\t\t\t\t\t\t\tcross_attention_frequency\n _a\t\t\t\t\t:\t\t\t\tTuple\t\t\t\t=\t\t\t\t\t\t\tencoder_hidden_size\n\n\n\n\n\n\n\n @classmethod\n def \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\tcls :\tOptional[int] ,_UpperCAmelCase :\tUnion[str, os.PathLike] ,**_UpperCAmelCase :\tList[str]\t\t):\n cls._set_token_in_kwargs(a_\t\t)\n\n _a\t, _a\t\t\t\t\t:\t\t\t\tDict\t\t\t\t=\t\t\t\t\t\t\tcls.get_config_dict(a_ ,**a_\t\t)\n\n # get the qformer config dict if we are loading from Blip2Config\n if config_dict.get('model_type'\t\t) == \"blip-2\":\n _a\t\t\t\t\t:\t\t\t\tint\t\t\t\t=\t\t\t\t\t\t\tconfig_dict['qformer_config']\n\n if \"model_type\" in config_dict and hasattr(cls ,'model_type'\t\t) and config_dict[\"model_type\"] != cls.model_type:\n logger.warning(\n F\"\"\"You are using a model of type {config_dict['model_type']} to instantiate a model of type \"\"\"\n F\"\"\"{cls.model_type}. This is not supported for all configurations of models and can yield errors.\"\"\"\t\t)\n\n return cls.from_dict(a_ ,**a_\t\t)\n\nclass __magic_name__\t\t\t\t\t\t( SCREAMING_SNAKE_CASE__ ):\n lowerCAmelCase\t: Optional[int] = 'blip-2'\n lowerCAmelCase\t: Dict = True\n\n\n\n\n\n\n\n def __init__(\t\t\t\t\t\t\tself :\tDict ,_UpperCAmelCase :\tList[Any]=None ,_UpperCAmelCase :\tOptional[Any]=None ,_UpperCAmelCase :\tDict=None ,_UpperCAmelCase :\tTuple=32 ,**_UpperCAmelCase :\tOptional[int]\t\t):\n super().__init__(**a_\t\t)\n\n if vision_config is None:\n _a\t\t\t\t\t:\t\t\t\tList[Any]\t\t\t\t=\t\t\t\t\t\t\t{}\n logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.'\t\t)\n\n if qformer_config is None:\n _a\t\t\t\t\t:\t\t\t\tList[Any]\t\t\t\t=\t\t\t\t\t\t\t{}\n logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'\t\t)\n\n if text_config is None:\n _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\t{}\n logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'\t\t)\n\n _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\tBlipaVisionConfig(**a_\t\t)\n _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\tBlipaQFormerConfig(**a_\t\t)\n _a\t\t\t\t\t:\t\t\t\tOptional[Any]\t\t\t\t=\t\t\t\t\t\t\ttext_config['model_type'] if 'model_type' in text_config else 'opt'\n _a\t\t\t\t\t:\t\t\t\tDict\t\t\t\t=\t\t\t\t\t\t\tCONFIG_MAPPING[text_model_type](**a_\t\t)\n\n _a\t\t\t\t\t:\t\t\t\tDict\t\t\t\t=\t\t\t\t\t\t\tself.text_config.tie_word_embeddings\n _a\t\t\t\t\t:\t\t\t\tUnion[str, Any]\t\t\t\t=\t\t\t\t\t\t\tself.text_config.is_encoder_decoder\n\n _a\t\t\t\t\t:\t\t\t\tstr\t\t\t\t=\t\t\t\t\t\t\tnum_query_tokens\n _a\t\t\t\t\t:\t\t\t\tint\t\t\t\t=\t\t\t\t\t\t\tself.vision_config.hidden_size\n _a\t\t\t\t\t:\t\t\t\tOptional[int]\t\t\t\t=\t\t\t\t\t\t\tself.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES\n _a\t\t\t\t\t:\t\t\t\tint\t\t\t\t=\t\t\t\t\t\t\t1.0\n _a\t\t\t\t\t:\t\t\t\tAny\t\t\t\t=\t\t\t\t\t\t\t0.02\n\n\n\n\n\n\n\n @classmethod\n def \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\tcls :\tint ,_UpperCAmelCase :\tBlipaVisionConfig ,_UpperCAmelCase :\tBlipaQFormerConfig ,_UpperCAmelCase :\tPretrainedConfig ,**_UpperCAmelCase :\tint ,):\n return cls(\n vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**a_ ,)\n\n\n\n\n\n\n\n def \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\tself :\tTuple\t\t):\n _a\t\t\t\t\t:\t\t\t\tTuple\t\t\t\t=\t\t\t\t\t\t\tcopy.deepcopy(self.__dict__\t\t)\n _a\t\t\t\t\t:\t\t\t\tstr\t\t\t\t=\t\t\t\t\t\t\tself.vision_config.to_dict()\n _a\t\t\t\t\t:\t\t\t\tAny\t\t\t\t=\t\t\t\t\t\t\tself.qformer_config.to_dict()\n _a\t\t\t\t\t:\t\t\t\tOptional[int]\t\t\t\t=\t\t\t\t\t\t\tself.text_config.to_dict()\n _a\t\t\t\t\t:\t\t\t\tAny\t\t\t\t=\t\t\t\t\t\t\tself.__class__.model_type\n return output\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":359,"string":"359"},"style_context":{"kind":"string","value":"\n\n\n\n'''simple docstring'''\n\nfrom binascii import hexlify\nfrom hashlib import shaaaa\nfrom os import urandom\n\n# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for\n# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526\n\n__lowerCAmelCase\t\t\t\t\t\t = {\n # 1536-bit\n 5: {\n '''prime''': int(\n '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''\n + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''\n + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''\n + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''\n + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''\n + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''\n + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''\n + '''670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF''',\n base=16,\n ),\n '''generator''': 2,\n },\n # 2048-bit\n 14: {\n '''prime''': int(\n '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''\n + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''\n + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''\n + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''\n + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''\n + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''\n + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''\n + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''\n + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''\n + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''\n + '''15728E5A8AACAA68FFFFFFFFFFFFFFFF''',\n base=16,\n ),\n '''generator''': 2,\n },\n # 3072-bit\n 15: {\n '''prime''': int(\n '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''\n + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''\n + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''\n + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''\n + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''\n + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''\n + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''\n + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''\n + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''\n + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''\n + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''\n + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''\n + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''\n + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''\n + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''\n + '''43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF''',\n base=16,\n ),\n '''generator''': 2,\n },\n # 4096-bit\n 16: {\n '''prime''': int(\n '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''\n + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''\n + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''\n + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''\n + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''\n + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''\n + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''\n + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''\n + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''\n + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''\n + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''\n + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''\n + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''\n + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''\n + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''\n + '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''\n + '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''\n + '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''\n + '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''\n + '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''\n + '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'''\n + '''FFFFFFFFFFFFFFFF''',\n base=16,\n ),\n '''generator''': 2,\n },\n # 6144-bit\n 17: {\n '''prime''': int(\n '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'''\n + '''8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'''\n + '''302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'''\n + '''A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'''\n + '''49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'''\n + '''FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'''\n + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'''\n + '''180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'''\n + '''3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'''\n + '''04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'''\n + '''B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'''\n + '''1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''\n + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'''\n + '''E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'''\n + '''99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'''\n + '''04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'''\n + '''233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'''\n + '''D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''\n + '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'''\n + '''AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'''\n + '''DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'''\n + '''2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'''\n + '''F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'''\n + '''BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''\n + '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'''\n + '''B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'''\n + '''387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'''\n + '''6DCC4024FFFFFFFFFFFFFFFF''',\n base=16,\n ),\n '''generator''': 2,\n },\n # 8192-bit\n 18: {\n '''prime''': int(\n '''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''\n + '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''\n + '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''\n + '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''\n + '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''\n + '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''\n + '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''\n + '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''\n + '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''\n + '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''\n + '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''\n + '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''\n + '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''\n + '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''\n + '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''\n + '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''\n + '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''\n + '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''\n + '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''\n + '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''\n + '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''\n + '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'''\n + '''F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'''\n + '''179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'''\n + '''DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'''\n + '''5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'''\n + '''D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'''\n + '''23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''\n + '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'''\n + '''06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'''\n + '''DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'''\n + '''12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'''\n + '''38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'''\n + '''741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'''\n + '''3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'''\n + '''22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'''\n + '''4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'''\n + '''062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'''\n + '''4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'''\n + '''B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'''\n + '''4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'''\n + '''9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'''\n + '''60C980DD98EDD3DFFFFFFFFFFFFFFFFF''',\n base=16,\n ),\n '''generator''': 2,\n },\n}\n\nclass __magic_name__\t\t\t\t\t\t:\n\n\n\n\n\n\n\n\t\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself :\tUnion[str, Any] ,_UpperCAmelCase :\tint = 14\t\t):\n\t\t\t\t\t\t\t\t\tif group not in primes:\n\t\t\t\t\t\t\t\t\t\t\traise ValueError('Unsupported Group'\t\t)\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tstr\t\t\t\t=\t\t\t\t\t\t\tprimes[group]['prime']\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tOptional[int]\t\t\t\t=\t\t\t\t\t\t\tprimes[group]['generator']\n\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tTuple\t\t\t\t=\t\t\t\t\t\t\tint(hexlify(urandom(32\t\t)\t\t) ,base=16\t\t)\n\n\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\tself :\tDict\t\t):\n\t\t\t\t\t\t\t\t\treturn hex(self.__private_key\t\t)[2:]\n\n\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\tself :\tList[str]\t\t):\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tint\t\t\t\t=\t\t\t\t\t\t\tpow(self.generator ,self.__private_key ,self.prime\t\t)\n\t\t\t\t\t\t\t\t\treturn hex(_UpperCAmelCase\t\t)[2:]\n\n\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\tself :\tint ,_UpperCAmelCase :\tint\t\t):\n\t\t\t\t\t\t\t\t\t# check if the other public key is valid based on NIST SP800-56\n\t\t\t\t\t\t\t\t\treturn (\n\t\t\t\t\t\t\t\t\t 2 <= key <= self.prime - 2\n\t\t\t\t\t\t\t\t\t and pow(_UpperCAmelCase ,(self.prime - 1) // 2 ,self.prime\t\t) == 1\n\t\t\t\t\t\t\t\t\t)\n\n\n\n\n\n\n\n\t\t\t\t\t\t\tdef \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\tself :\tTuple ,_UpperCAmelCase :\tstr\t\t):\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tList[Any]\t\t\t\t=\t\t\t\t\t\t\tint(_UpperCAmelCase ,base=16\t\t)\n\t\t\t\t\t\t\t\t\tif not self.is_valid_public_key(_UpperCAmelCase\t\t):\n\t\t\t\t\t\t\t\t\t\t\traise ValueError('Invalid public key'\t\t)\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tAny\t\t\t\t=\t\t\t\t\t\t\tpow(_UpperCAmelCase ,self.__private_key ,self.prime\t\t)\n\t\t\t\t\t\t\t\t\treturn shaaaa(str(_UpperCAmelCase\t\t).encode()\t\t).hexdigest()\n\n\n\n\n\n\n\n\t\t\t\t\t\t\t@staticmethod\n\t\t\t\t\t\t\tdef \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\t_UpperCAmelCase :\tint ,_UpperCAmelCase :\tint\t\t):\n\t\t\t\t\t\t\t\t\t# check if the other public key is valid based on NIST SP800-56\n\t\t\t\t\t\t\t\t\treturn (\n\t\t\t\t\t\t\t\t\t 2 <= remote_public_key_str <= prime - 2\n\t\t\t\t\t\t\t\t\t and pow(_UpperCAmelCase ,(prime - 1) // 2 ,_UpperCAmelCase\t\t) == 1\n\t\t\t\t\t\t\t\t\t)\n\n\n\n\n\n\n\n\t\t\t\t\t\t\t@staticmethod\n\t\t\t\t\t\t\tdef \t\t\t\t\t\t\t__lowercase (\t\t\t\t\t\t\t_UpperCAmelCase :\tstr ,_UpperCAmelCase :\tstr ,_UpperCAmelCase :\tint = 14\t\t):\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tstr\t\t\t\t=\t\t\t\t\t\t\tint(_UpperCAmelCase ,base=16\t\t)\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tint\t\t\t\t=\t\t\t\t\t\t\tint(_UpperCAmelCase ,base=16\t\t)\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tAny\t\t\t\t=\t\t\t\t\t\t\tprimes[group]['prime']\n\t\t\t\t\t\t\t\t\tif not DiffieHellman.is_valid_public_key_static(_UpperCAmelCase ,_UpperCAmelCase\t\t):\n\t\t\t\t\t\t\t\t\t\t\traise ValueError('Invalid public key'\t\t)\n\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t:\t\t\t\tList[str]\t\t\t\t=\t\t\t\t\t\t\tpow(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase\t\t)\n\t\t\t\t\t\t\t\t\treturn shaaaa(str(_UpperCAmelCase\t\t).encode()\t\t).hexdigest()\n\n\nif __name__ == \"__main__\":\n\t\t\t\t\t\t\timport doctest\n\n\t\t\t\t\t\t\tdoctest.testmod()\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":107,"string":"107"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":266,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\r'''simple docstring'''\r\r\r\r\r\r\r\rimport warnings\rfrom typing import List, Optional, Union\r\rfrom ...processing_utils import ProcessorMixin\rfrom ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy\rfrom ...utils import TensorType\rclass \t\t\t\t__SCREAMING_SNAKE_CASE (lowerCamelCase_ ):\r\r\r\r\r\r\r\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\r\t\t\t\t__a =['image_processor', 'tokenizer']\r\t\t\t\t__a ='LayoutLMv3ImageProcessor'\r\t\t\t\t__a =('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')\r\r\r\r\r\t\t\t\tdef __init__( self :\t\t\t\t\tTuple\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tint=None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tUnion[str, Any]=None\t\t\t\t\t,\t\t\t\t**__a :\t\t\t\t\tOptional[Any]\t\t\t\t\t):\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= None\r\t\t\t\t\t\t\t\tif \"feature_extractor\" in kwargs:\r\t\t\t\t\t\t\t\t\t\t\t\twarnings.warn(\r\t\t\t\t\t\t\t\t\t\t\t\t \"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`\"\r\t\t\t\t\t\t\t\t\t\t\t\t \" instead.\"\t\t\t\t\t,\t\t\t\t__a\t\t\t\t\t,\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= kwargs.pop(\"feature_extractor\"\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= image_processor if image_processor is not None else feature_extractor\r\t\t\t\t\t\t\t\tif image_processor is None:\r\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\"You need to specify an `image_processor`.\"\t\t\t\t\t)\r\t\t\t\t\t\t\t\tif tokenizer is None:\r\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\"You need to specify a `tokenizer`.\"\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\tsuper().__init__(__a\t\t\t\t\t,\t\t\t\t__a\t\t\t\t\t)\r\r\r\r\r\t\t\t\tdef __call__( self :\t\t\t\t\tAny\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tList[str]\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tUnion[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tOptional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tUnion[List[List[int]], List[List[List[int]]]] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tOptional[Union[List[int], List[List[int]]]] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tbool = True\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tUnion[bool, str, PaddingStrategy] = False\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tUnion[bool, str, TruncationStrategy] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tOptional[int] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tint = 0\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tOptional[int] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tOptional[bool] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tOptional[bool] = None\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tbool = False\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tbool = False\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tbool = False\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tbool = False\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tbool = True\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tOptional[Union[str, TensorType]] = None\t\t\t\t\t,\t\t\t\t**__a :\t\t\t\t\tDict\t\t\t\t\t,\t\t\t\t):\r\t\t\t\t\t\t\t\t# verify input\r\t\t\t\t\t\t\t\tif self.image_processor.apply_ocr and (boxes is not None):\r\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t\t \"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.\"\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\tif self.image_processor.apply_ocr and (word_labels is not None):\r\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t\t \"You cannot provide word labels if you initialized the image processor with apply_ocr set to True.\"\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t# first, apply the image processor\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= self.image_processor(images=__a\t\t\t\t\t,\t\t\t\treturn_tensors=__a\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\t# second, apply the tokenizer\r\t\t\t\t\t\t\t\tif text is not None and self.image_processor.apply_ocr and text_pair is None:\r\t\t\t\t\t\t\t\t\t\t\t\tif isinstance(__a\t\t\t\t\t,\t\t\t\t__a\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= [text] # add batch dimension (as the image processor always adds a batch dimension)\r\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= features[\"words\"]\r\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= self.tokenizer(\r\t\t\t\t\t\t\t\t text=text if text is not None else features[\"words\"]\t\t\t\t\t,\t\t\t\ttext_pair=text_pair if text_pair is not None else None\t\t\t\t\t,\t\t\t\tboxes=boxes if boxes is not None else features[\"boxes\"]\t\t\t\t\t,\t\t\t\tword_labels=__a\t\t\t\t\t,\t\t\t\tadd_special_tokens=__a\t\t\t\t\t,\t\t\t\tpadding=__a\t\t\t\t\t,\t\t\t\ttruncation=__a\t\t\t\t\t,\t\t\t\tmax_length=__a\t\t\t\t\t,\t\t\t\tstride=__a\t\t\t\t\t,\t\t\t\tpad_to_multiple_of=__a\t\t\t\t\t,\t\t\t\treturn_token_type_ids=__a\t\t\t\t\t,\t\t\t\treturn_attention_mask=__a\t\t\t\t\t,\t\t\t\treturn_overflowing_tokens=__a\t\t\t\t\t,\t\t\t\treturn_special_tokens_mask=__a\t\t\t\t\t,\t\t\t\treturn_offsets_mapping=__a\t\t\t\t\t,\t\t\t\treturn_length=__a\t\t\t\t\t,\t\t\t\tverbose=__a\t\t\t\t\t,\t\t\t\treturn_tensors=__a\t\t\t\t\t,\t\t\t\t**__a\t\t\t\t\t,\t\t\t\t)\r\r\t\t\t\t\t\t\t\t# add pixel values\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= features.pop(\"pixel_values\"\t\t\t\t\t)\r\t\t\t\t\t\t\t\tif return_overflowing_tokens is True:\r\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= self.get_overflowing_images(__a\t\t\t\t\t,\t\t\t\tencoded_inputs[\"overflow_to_sample_mapping\"]\t\t\t\t\t)\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= images\r\r\t\t\t\t\t\t\t\treturn encoded_inputs\r\r\r\r\r\t\t\t\tdef UpperCamelCase__ ( self :\t\t\t\t\tOptional[int]\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tstr\t\t\t\t\t,\t\t\t\t__a :\t\t\t\t\tList[Any]\t\t\t\t\t):\r\t\t\t\t\t\t\t\t# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image\r\t\t\t\t\t\t\t\t_a\t\t\t\t\t\t\t\t= []\r\t\t\t\t\t\t\t\tfor sample_idx in overflow_to_sample_mapping:\r\t\t\t\t\t\t\t\t\t\t\t\timages_with_overflow.append(images[sample_idx]\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\tif len(__a\t\t\t\t\t) != len(__a\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t\t \"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got\"\r\t\t\t\t\t\t\t\t\t\t\t\t f' {len(__a\t\t\t\t\t)} and {len(__a\t\t\t\t\t)}'\t\t\t\t\t)\r\r\t\t\t\t\t\t\t\treturn images_with_overflow\r\r\r\r\r\t\t\t\tdef UpperCamelCase__ ( self :\t\t\t\t\tint\t\t\t\t\t,\t\t\t\t*__a :\t\t\t\t\tstr\t\t\t\t\t,\t\t\t\t**__a :\t\t\t\t\tTuple\t\t\t\t\t):\r\t\t\t\t\t\t\t\treturn self.tokenizer.batch_decode(*__a\t\t\t\t\t,\t\t\t\t**__a\t\t\t\t\t)\r\r\r\r\r\t\t\t\tdef UpperCamelCase__ ( self :\t\t\t\t\tstr\t\t\t\t\t,\t\t\t\t*__a :\t\t\t\t\tList[Any]\t\t\t\t\t,\t\t\t\t**__a :\t\t\t\t\tList[str]\t\t\t\t\t):\r\t\t\t\t\t\t\t\treturn self.tokenizer.decode(*__a\t\t\t\t\t,\t\t\t\t**__a\t\t\t\t\t)\r\r\r\r\r\t\t\t\t@property\r\t\t\t\tdef UpperCamelCase__ ( self :\t\t\t\t\tTuple\t\t\t\t\t):\r\t\t\t\t\t\t\t\treturn [\"input_ids\", \"bbox\", \"attention_mask\", \"pixel_values\"]\r\r\r\r\r\t\t\t\t@property\r\t\t\t\tdef UpperCamelCase__ ( self :\t\t\t\t\tint\t\t\t\t\t):\r\t\t\t\t\t\t\t\twarnings.warn(\r\t\t\t\t\t\t\t\t \"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.\"\t\t\t\t\t,\t\t\t\t__a\t\t\t\t\t,\t\t\t\t)\r\t\t\t\t\t\t\t\treturn self.image_processor_class\r\r\r\r\r\t\t\t\t@property\r\t\t\t\tdef UpperCamelCase__ ( self :\t\t\t\t\tList[str]\t\t\t\t\t):\r\t\t\t\t\t\t\t\twarnings.warn(\r\t\t\t\t\t\t\t\t \"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.\"\t\t\t\t\t,\t\t\t\t__a\t\t\t\t\t,\t\t\t\t)\r\t\t\t\t\t\t\t\treturn self.image_processor\r"},"code_codestyle":{"kind":"number","value":63,"string":"63"},"style_context":{"kind":"string","value":"\rimport numpy as np\r\r\r\r\r\r\rdef a_\t\t( __lowercase :\tnp.array ) -> np.array:\r\t\t\treturn 1 / (1 + np.exp(-vector ))\r\r\rif __name__ == \"__main__\":\r\t\t\timport doctest\r\r\t\t\tdoctest.testmod()"},"style_context_codestyle":{"kind":"number","value":282,"string":"282"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":267,"cells":{"code":{"kind":"string","value":"\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available\r\r\rSCREAMING_SNAKE_CASE__ \t=\t\t\t\t\t\t\t{\r \"\"\"configuration_ernie\"\"\": [\"\"\"ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP\"\"\", \"\"\"ErnieConfig\"\"\", \"\"\"ErnieOnnxConfig\"\"\"],\r}\r\rtry:\r\t\t\t\t\t\t\tif not is_torch_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\tpass\relse:\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t=\t\t\t\t\t\t\t[\r\t\t\t\t\t\t\t \"\"\"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieForCausalLM\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieForMaskedLM\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieForMultipleChoice\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieForNextSentencePrediction\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieForPreTraining\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieForQuestionAnswering\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieForSequenceClassification\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieForTokenClassification\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErnieModel\"\"\",\r\t\t\t\t\t\t\t \"\"\"ErniePreTrainedModel\"\"\",\r\t\t\t\t\t\t\t]\r\rif TYPE_CHECKING:\r\t\t\t\t\t\t\tfrom .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig\r\r\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not is_torch_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\t\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_ernie import (\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieForCausalLM,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieForMaskedLM,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieForMultipleChoice,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieForNextSentencePrediction,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieForPreTraining,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieForQuestionAnswering,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieForSequenceClassification,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieForTokenClassification,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErnieModel,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t ErniePreTrainedModel,\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\relse:\r\t\t\t\t\t\t\timport sys\r\r\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__ \t=\t\t\t\t\t\t\t_LazyModule(__name__, globals()[\"\"\"__file__\"\"\"], _import_structure, module_spec=__spec__)\r\r"},"code_codestyle":{"kind":"number","value":297,"string":"297"},"style_context":{"kind":"string","value":"\r\r\rfrom scipy.stats import pearsonr\r\rimport datasets\r\r\rSCREAMING_SNAKE_CASE__ \t=\t\t\t\t\t\t\t\"\"\"\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n\"\"\"\r\r\rSCREAMING_SNAKE_CASE__ \t=\t\t\t\t\t\t\t\"\"\"\nArgs:\n predictions (`list` of `int`): Predicted class labels, as returned by a model.\n references (`list` of `int`): Ground truth labels.\n return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n Example 1-A simple example using only predictions and references.\n >>> pearsonr_metric = datasets.load_metric(\\\"pearsonr\\\")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n >>> print(round(results['pearsonr'], 2))\n -0.74\n\n Example 2-The same as Example 1, but that also returns the `p-value`.\n >>> pearsonr_metric = datasets.load_metric(\\\"pearsonr\\\")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n >>> print(sorted(list(results.keys())))\n ['p-value', 'pearsonr']\n >>> print(round(results['pearsonr'], 2))\n -0.74\n >>> print(round(results['p-value'], 2))\n 0.15\n\"\"\"\r\r\rSCREAMING_SNAKE_CASE__ \t=\t\t\t\t\t\t\t\"\"\"\n@article{2020SciPy-NMeth,\nauthor = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Antonio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\njournal = {Nature Methods},\nyear = {2020},\nvolume = {17},\npages = {261--272},\nadsurl = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n\"\"\"\r\r\r\r\r\r\r\r@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )\rclass __lowerCamelCase ( datasets.Metric ):\r\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self ) -> int:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\treturn datasets.MetricInfo(\r\t\t\t\t\t\t\t\t description=_DESCRIPTION\t\t, citation=_CITATION\t\t, inputs_description=_KWARGS_DESCRIPTION\t\t, features=datasets.Features(\r\t\t\t\t\t\t\t\t {\r\t\t\t\t\t\t\t\t \"predictions\": datasets.Value(\"float\" ),\r\t\t\t\t\t\t\t\t \"references\": datasets.Value(\"float\" ),\r\t\t\t\t\t\t\t\t } )\t\t, reference_urls=[\"https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html\"]\t\t, )\r\r\r\r\r\t\t\t\t\t\t\tdef \t\t\t\t\t\t\tA__ ( self\t\t, UpperCAmelCase\t\t, UpperCAmelCase\t\t, UpperCAmelCase=False ) -> int:\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\tif return_pvalue:\r\t\t\t\t\t\t\t\t\tlowercase_\t\t\t\t\t=\t\t\t\tpearsonr(UpperCAmelCase\t\t, UpperCAmelCase )\r\t\t\t\t\t\t\t\t\treturn {\"pearsonr\": results[0], \"p-value\": results[1]}\r\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\treturn {\"pearsonr\": float(pearsonr(UpperCAmelCase\t\t, UpperCAmelCase )[0] )}\r\r"},"style_context_codestyle":{"kind":"number","value":297,"string":"297"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":268,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport argparse\r\nimport os\r\nimport re\r\n\r\n\r\nlowercase__ \t\t\t\t\t\t=\t\t\t\t\t\t'''src/transformers'''\r\n\r\n# Pattern that looks at the indentation in a line.\r\nlowercase__ \t\t\t\t\t\t=\t\t\t\t\t\tre.compile(R\"\"\"^(\\s*)\\S\"\"\")\r\n# Pattern that matches `\"key\":\" and puts `key` in group 0.\r\nlowercase__ \t\t\t\t\t\t=\t\t\t\t\t\tre.compile(R\"\"\"^\\s*\\\"([^\\\"]+)\\\":\"\"\")\r\n# Pattern that matches `_import_structure[\"key\"]` and puts `key` in group 0.\r\nlowercase__ \t\t\t\t\t\t=\t\t\t\t\t\tre.compile(R\"\"\"^\\s*_import_structure\\[\\\"([^\\\"]+)\\\"\\]\"\"\")\r\n# Pattern that matches `\"key\",` and puts `key` in group 0.\r\nlowercase__ \t\t\t\t\t\t=\t\t\t\t\t\tre.compile(R\"\"\"^\\s*\\\"([^\\\"]+)\\\",\\s*$\"\"\")\r\n# Pattern that matches any `[stuff]` and puts `stuff` in group 0.\r\nlowercase__ \t\t\t\t\t\t=\t\t\t\t\t\tre.compile(R\"\"\"\\[([^\\]]+)\\]\"\"\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\tlowercase__\t\t):\r\n\t\t\t\t_lowerCamelCase :\t\t\tTuple = _re_indent.search(a__\t\t)\r\n\t\t\t\treturn \"\" if search is None else search.groups()[0]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\tlowercase__\t\t\t\t\t\t\t, lowercase__=\"\"\t\t\t\t\t\t\t, lowercase__=None\t\t\t\t\t\t\t, lowercase__=None\t\t):\r\n\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = 0\r\n\t\t\t\t_lowerCamelCase :\t\t\tint = code.split('\\n'\t\t)\r\n\t\t\t\tif start_prompt is not None:\r\n\t\t\t\t\t\t\t\twhile not lines[index].startswith(a__\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\tindex += 1\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = ['''\\n'''.join(lines[:index]\t\t)]\r\n\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = []\r\n\r\n\t\t\t\t# We split into blocks until we get to the `end_prompt` (or the end of the block).\r\n\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = [lines[index]]\r\n\t\t\t\tindex += 1\r\n\t\t\t\twhile index < len(a__\t\t) and (end_prompt is None or not lines[index].startswith(a__\t\t)):\r\n\t\t\t\t\t\t\t\tif len(lines[index]\t\t) > 0 and get_indent(lines[index]\t\t) == indent_level:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif len(a__\t\t) > 0 and get_indent(current_block[-1]\t\t).startswith(indent_level + ' '\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcurrent_block.append(lines[index]\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tblocks.append('\\n'.join(a__\t\t)\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif index < len(a__\t\t) - 1:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = [lines[index + 1]]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tindex += 1\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = []\r\n\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tblocks.append('\\n'.join(a__\t\t)\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = [lines[index]]\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\tcurrent_block.append(lines[index]\t\t)\r\n\t\t\t\t\t\t\t\tindex += 1\r\n\r\n\t\t\t\t# Adds current block if it's nonempty.\r\n\t\t\t\tif len(a__\t\t) > 0:\r\n\t\t\t\t\t\t\t\tblocks.append('\\n'.join(a__\t\t)\t\t)\r\n\r\n\t\t\t\t# Add final block after end_prompt if provided.\r\n\t\t\t\tif end_prompt is not None and index < len(a__\t\t):\r\n\t\t\t\t\t\t\t\tblocks.append('\\n'.join(lines[index:]\t\t)\t\t)\r\n\r\n\t\t\t\treturn blocks\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\tlowercase__\t\t):\r\n\r\n\r\n\r\n\t\t\t\tdef _inner(lowercase__\t\t):\r\n\t\t\t\t\t\t\t\treturn key(a__\t\t).lower().replace('_'\t\t\t\t\t\t\t, ''\t\t)\r\n\r\n\t\t\t\treturn _inner\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\tlowercase__\t\t\t\t\t\t\t, lowercase__=None\t\t):\r\n\r\n\r\n\r\n\t\t\t\tdef noop(lowercase__\t\t):\r\n\t\t\t\t\t\t\t\treturn x\r\n\r\n\t\t\t\tif key is None:\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = noop\r\n\t\t\t\t# Constants are all uppercase, they go first.\r\n\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = [obj for obj in objects if key(a__\t\t).isupper()]\r\n\t\t\t\t# Classes are not all uppercase but start with a capital, they go second.\r\n\t\t\t\t_lowerCamelCase :\t\t\tint = [obj for obj in objects if key(a__\t\t)[0].isupper() and not key(a__\t\t).isupper()]\r\n\t\t\t\t# Functions begin with a lowercase, they go last.\r\n\t\t\t\t_lowerCamelCase :\t\t\tDict = [obj for obj in objects if not key(a__\t\t)[0].isupper()]\r\n\r\n\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = ignore_underscore(a__\t\t)\r\n\t\t\t\treturn sorted(a__\t\t\t\t\t\t\t, key=a__\t\t) + sorted(a__\t\t\t\t\t\t\t, key=a__\t\t) + sorted(a__\t\t\t\t\t\t\t, key=a__\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\tlowercase__\t\t):\r\n\r\n\r\n\r\n\t\t\t\tdef _replace(lowercase__\t\t):\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = match.groups()[0]\r\n\t\t\t\t\t\t\t\tif \",\" not in imports:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn f'''[{imports}]'''\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[int] = [part.strip().replace('\"'\t\t\t\t\t\t\t, ''\t\t) for part in imports.split(','\t\t)]\r\n\t\t\t\t\t\t\t\t# We will have a final empty element if the line finished with a comma.\r\n\t\t\t\t\t\t\t\tif len(keys[-1]\t\t) == 0:\r\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = keys[:-1]\r\n\t\t\t\t\t\t\t\treturn \"[\" + \", \".join([f'''\\\"{k}\\\"''' for k in sort_objects(a__\t\t)]\t\t) + \"]\"\r\n\r\n\t\t\t\t_lowerCamelCase :\t\t\tDict = import_statement.split('\\n'\t\t)\r\n\t\t\t\tif len(a__\t\t) > 3:\r\n\t\t\t\t\t\t\t\t# Here we have to sort internal imports that are on several lines (one per name):\r\n\t\t\t\t\t\t\t\t# key: [\r\n\t\t\t\t\t\t\t\t# \"object1\",\r\n\t\t\t\t\t\t\t\t# \"object2\",\r\n\t\t\t\t\t\t\t\t# ...\r\n\t\t\t\t\t\t\t\t# ]\r\n\r\n\t\t\t\t\t\t\t\t# We may have to ignore one or two lines on each side.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = 2 if lines[1].strip() == '''[''' else 1\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = [(i, _re_strip_line.search(a__\t\t).groups()[0]) for i, line in enumerate(lines[idx:-idx]\t\t)]\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = sort_objects(a__\t\t\t\t\t\t\t, key=lambda lowercase__\t\t: x[1]\t\t)\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = [lines[x[0] + idx] for x in sorted_indices]\r\n\t\t\t\t\t\t\t\treturn \"\\n\".join(lines[:idx] + sorted_lines + lines[-idx:]\t\t)\r\n\t\t\t\telif len(a__\t\t) == 3:\r\n\t\t\t\t\t\t\t\t# Here we have to sort internal imports that are on one separate line:\r\n\t\t\t\t\t\t\t\t# key: [\r\n\t\t\t\t\t\t\t\t# \"object1\", \"object2\", ...\r\n\t\t\t\t\t\t\t\t# ]\r\n\t\t\t\t\t\t\t\tif _re_bracket_content.search(lines[1]\t\t) is not None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = _re_bracket_content.sub(_replace\t\t\t\t\t\t\t, lines[1]\t\t)\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = [part.strip().replace('\"'\t\t\t\t\t\t\t, ''\t\t) for part in lines[1].split(','\t\t)]\r\n\t\t\t\t\t\t\t\t\t\t\t\t# We will have a final empty element if the line finished with a comma.\r\n\t\t\t\t\t\t\t\t\t\t\t\tif len(keys[-1]\t\t) == 0:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = keys[:-1]\r\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[str] = get_indent(lines[1]\t\t) + ''', '''.join([f'''\\\"{k}\\\"''' for k in sort_objects(a__\t\t)]\t\t)\r\n\t\t\t\t\t\t\t\treturn \"\\n\".join(a__\t\t)\r\n\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t# Finally we have to deal with imports fitting on one line\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = _re_bracket_content.sub(_replace\t\t\t\t\t\t\t, a__\t\t)\r\n\t\t\t\t\t\t\t\treturn import_statement\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\tlowercase__\t\t\t\t\t\t\t, lowercase__=True\t\t):\r\n\r\n\r\n\r\n\t\t\t\twith open(a__\t\t\t\t\t\t\t, encoding='utf-8'\t\t) as f:\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tList[Any] = f.read()\r\n\r\n\t\t\t\tif \"_import_structure\" not in code:\r\n\t\t\t\t\t\t\t\treturn\r\n\r\n\t\t\t\t# Blocks of indent level 0\r\n\t\t\t\t_lowerCamelCase :\t\t\tTuple = split_code_in_indented_blocks(\r\n\t\t\t\t a__\t\t\t\t\t\t\t, start_prompt='_import_structure = {'\t\t\t\t\t\t\t, end_prompt='if TYPE_CHECKING:'\t\t)\r\n\r\n\t\t\t\t# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).\r\n\t\t\t\tfor block_idx in range(1\t\t\t\t\t\t\t, len(a__\t\t) - 1\t\t):\r\n\t\t\t\t\t\t\t\t# Check if the block contains some `_import_structure`s thingy to sort.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = main_blocks[block_idx]\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = block.split('\\n'\t\t)\r\n\r\n\t\t\t\t\t\t\t\t# Get to the start of the imports.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = 0\r\n\t\t\t\t\t\t\t\twhile line_idx < len(a__\t\t) and \"_import_structure\" not in block_lines[line_idx]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t# Skip dummy import blocks\r\n\t\t\t\t\t\t\t\t\t\t\t\tif \"import dummy\" in block_lines[line_idx]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tTuple = len(a__\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tline_idx += 1\r\n\t\t\t\t\t\t\t\tif line_idx >= len(a__\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\tcontinue\r\n\r\n\t\t\t\t\t\t\t\t# Ignore beginning and last line: they don't contain anything.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = '''\\n'''.join(block_lines[line_idx:-1]\t\t)\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = get_indent(block_lines[1]\t\t)\r\n\t\t\t\t\t\t\t\t# Slit the internal block into blocks of indent level 1.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = split_code_in_indented_blocks(a__\t\t\t\t\t\t\t, indent_level=a__\t\t)\r\n\t\t\t\t\t\t\t\t# We have two categories of import key: list or _import_structure[key].append/extend\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = _re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key\r\n\t\t\t\t\t\t\t\t# Grab the keys, but there is a trap: some lines are empty or just comments.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tDict = [(pattern.search(a__\t\t).groups()[0] if pattern.search(a__\t\t) is not None else None) for b in internal_blocks]\r\n\t\t\t\t\t\t\t\t# We only sort the lines with a key.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tAny = [(i, key) for i, key in enumerate(a__\t\t) if key is not None]\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = [x[0] for x in sorted(a__\t\t\t\t\t\t\t, key=lambda lowercase__\t\t: x[1]\t\t)]\r\n\r\n\t\t\t\t\t\t\t\t# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tOptional[Any] = 0\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = []\r\n\t\t\t\t\t\t\t\tfor i in range(len(a__\t\t)\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\tif keys[i] is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treorderded_blocks.append(internal_blocks[i]\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tint = sort_objects_in_import(internal_blocks[sorted_indices[count]]\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treorderded_blocks.append(a__\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcount += 1\r\n\r\n # And we put our main block back together with its first and last line.\r\n\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tUnion[str, Any] = '''\\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]\t\t)\r\n\r\n\t\t\t\tif code != \"\\n\".join(a__\t\t):\r\n\t\t\t\t\t\t\t\tif check_only:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn True\r\n\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\tprint(f'''Overwriting {file}.'''\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\twith open(a__\t\t\t\t\t\t\t, 'w'\t\t\t\t\t\t\t, encoding='utf-8'\t\t) as f:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tf.write('\\n'.join(a__\t\t)\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef _snake_case\t\t\t(\t\t\t\tlowercase__=True\t\t):\r\n\t\t\t\t_lowerCamelCase :\t\t\tTuple = []\r\n\t\t\t\tfor root, _, files in os.walk(a__\t\t):\r\n\t\t\t\t\t\t\t\tif \"__init__.py\" in files:\r\n\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = sort_imports(os.path.join(a__\t\t\t\t\t\t\t, '__init__.py'\t\t)\t\t\t\t\t\t\t, check_only=a__\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tif result:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_lowerCamelCase :\t\t\tstr = [os.path.join(a__\t\t\t\t\t\t\t, '__init__.py'\t\t)]\r\n\t\t\t\tif len(a__\t\t) > 0:\r\n\t\t\t\t\t\t\t\traise ValueError(f'''Would overwrite {len(a__\t\t)} files, run `make style`.'''\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\tlowercase__ \t\t\t\t\t\t=\t\t\t\t\t\targparse.ArgumentParser()\r\n\t\t\tparser.add_argument(\"\"\"--check_only\"\"\", action=\"\"\"store_true\"\"\", help=\"\"\"Whether to only check or fix style.\"\"\")\r\n\t\t\tlowercase__ \t\t\t\t\t\t=\t\t\t\t\t\tparser.parse_args()\r\n\r\n\t\t\tsort_imports_in_all_inits(check_only=args.check_only)"},"code_codestyle":{"kind":"number","value":96,"string":"96"},"style_context":{"kind":"string","value":"\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import XLMRobertaTokenizerFast\n\nfrom diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel\nfrom diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP\nfrom diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu\n\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\n\n\nenable_full_determinism()\n\n\n\n\n\n\nclass a_ ( a__\t\t\t\t\t\t\t, unittest.TestCase\t\t\t\t):\n\n\n\n \"\"\"simple docstring\"\"\"\n __SCREAMING_SNAKE_CASE : str\t\t= KandinskyImgaImgPipeline\n __SCREAMING_SNAKE_CASE : str\t\t= ['prompt', 'image_embeds', 'negative_image_embeds', 'image']\n __SCREAMING_SNAKE_CASE : int\t\t= [\n 'prompt',\n 'negative_prompt',\n 'image_embeds',\n 'negative_image_embeds',\n 'image',\n ]\n __SCREAMING_SNAKE_CASE : int\t\t= [\n 'generator',\n 'height',\n 'width',\n 'strength',\n 'guidance_scale',\n 'negative_prompt',\n 'num_inference_steps',\n 'return_dict',\n 'guidance_scale',\n 'num_images_per_prompt',\n 'output_type',\n 'return_dict',\n ]\n __SCREAMING_SNAKE_CASE : List[Any]\t\t= False\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->int:\n return 32\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->List[str]:\n return 32\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Optional[int]:\n return self.time_input_dim\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Tuple:\n return self.time_input_dim * 4\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Optional[int]:\n return 100\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Dict:\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''\t\t\t\t\t)\n return tokenizer\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Tuple:\n torch.manual_seed(0\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t= MCLIPConfig(\n numDims=self.cross_attention_dim\t\t\t\t\t\t\t, transformerDimensions=self.text_embedder_hidden_size\t\t\t\t\t\t\t, hidden_size=self.text_embedder_hidden_size\t\t\t\t\t\t\t, intermediate_size=37\t\t\t\t\t\t\t, num_attention_heads=4\t\t\t\t\t\t\t, num_hidden_layers=5\t\t\t\t\t\t\t, vocab_size=1005\t\t\t\t\t\t\t, )\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t= MultilingualCLIP(_lowerCamelCase\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= text_encoder.eval()\n\n return text_encoder\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Union[str, Any]:\n torch.manual_seed(0\t\t\t\t\t)\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t= {\n '''in_channels''': 4,\n # Out channels is double in channels because predicts mean and variance\n '''out_channels''': 8,\n '''addition_embed_type''': '''text_image''',\n '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),\n '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),\n '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',\n '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),\n '''layers_per_block''': 1,\n '''encoder_hid_dim''': self.text_embedder_hidden_size,\n '''encoder_hid_dim_type''': '''text_image_proj''',\n '''cross_attention_dim''': self.cross_attention_dim,\n '''attention_head_dim''': 4,\n '''resnet_time_scale_shift''': '''scale_shift''',\n '''class_embed_type''': None,\n }\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tTuple\t\t\t\t\t\t\t\t= UNetaDConditionModel(**_lowerCamelCase\t\t\t\t\t)\n return model\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->List[str]:\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n\n\n\n\n\n @property\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Optional[Any]:\n torch.manual_seed(0\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= VQModel(**self.dummy_movq_kwargs\t\t\t\t\t)\n return model\n\n\n\n\n\n\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Dict:\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t= self.dummy_text_encoder\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t= self.dummy_tokenizer\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t= self.dummy_unet\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t= self.dummy_movq\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t= {\n '''num_train_timesteps''': 1000,\n '''beta_schedule''': '''linear''',\n '''beta_start''': 0.0_0_0_8_5,\n '''beta_end''': 0.0_1_2,\n '''clip_sample''': False,\n '''set_alpha_to_one''': False,\n '''steps_offset''': 0,\n '''prediction_type''': '''epsilon''',\n '''thresholding''': False,\n }\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t= DDIMScheduler(**_lowerCamelCase\t\t\t\t\t)\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t= {\n '''text_encoder''': text_encoder,\n '''tokenizer''': tokenizer,\n '''unet''': unet,\n '''scheduler''': scheduler,\n '''movq''': movq,\n }\n\n return components\n\n\n\n\n\n\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t\t\t, _lowerCamelCase\t\t\t\t\t\t\t, _lowerCamelCase=0\t\t\t\t\t) ->str:\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= floats_tensor((1, self.cross_attention_dim)\t\t\t\t\t\t\t, rng=random.Random(_lowerCamelCase\t\t\t\t\t)\t\t\t\t\t).to(_lowerCamelCase\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t= floats_tensor((1, self.cross_attention_dim)\t\t\t\t\t\t\t, rng=random.Random(seed + 1\t\t\t\t\t)\t\t\t\t\t).to(_lowerCamelCase\t\t\t\t\t)\n # create init_image\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= floats_tensor((1, 3, 64, 64)\t\t\t\t\t\t\t, rng=random.Random(_lowerCamelCase\t\t\t\t\t)\t\t\t\t\t).to(_lowerCamelCase\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t= image.cpu().permute(0\t\t\t\t\t\t\t, 2\t\t\t\t\t\t\t, 3\t\t\t\t\t\t\t, 1\t\t\t\t\t)[0]\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= Image.fromarray(np.uinta(_lowerCamelCase\t\t\t\t\t)\t\t\t\t\t).convert('''RGB'''\t\t\t\t\t).resize((256, 256)\t\t\t\t\t)\n\n if str(_lowerCamelCase\t\t\t\t\t).startswith('''mps'''\t\t\t\t\t):\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t= torch.manual_seed(_lowerCamelCase\t\t\t\t\t)\n else:\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= torch.Generator(device=_lowerCamelCase\t\t\t\t\t).manual_seed(_lowerCamelCase\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t= {\n '''prompt''': '''horse''',\n '''image''': init_image,\n '''image_embeds''': image_embeds,\n '''negative_image_embeds''': negative_image_embeds,\n '''generator''': generator,\n '''height''': 64,\n '''width''': 64,\n '''num_inference_steps''': 10,\n '''guidance_scale''': 7.0,\n '''strength''': 0.2,\n '''output_type''': '''np''',\n }\n return inputs\n\n\n\n\n\n\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Dict:\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= '''cpu'''\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t= self.get_dummy_components()\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= self.pipeline_class(**_lowerCamelCase\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t= pipe.to(_lowerCamelCase\t\t\t\t\t)\n\n pipe.set_progress_bar_config(disable=_lowerCamelCase\t\t\t\t\t)\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t= pipe(**self.get_dummy_inputs(_lowerCamelCase\t\t\t\t\t)\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t= output.images\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t= pipe(\n **self.get_dummy_inputs(_lowerCamelCase\t\t\t\t\t)\t\t\t\t\t\t\t, return_dict=_lowerCamelCase\t\t\t\t\t\t\t, )[0]\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t= image[0, -3:, -3:, -1]\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[Any]\t\t\t\t\t\t\t\t= image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t= np.array(\n [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]\t\t\t\t\t)\n assert (\n np.abs(image_slice.flatten() - expected_slice\t\t\t\t\t).max() < 1e-2\n ), F\"\"\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\"\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice\t\t\t\t\t).max() < 1e-2\n ), F\"\"\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\"\"\n\n\n\n\n\n\n@slow\n@require_torch_gpu\nclass a_ ( unittest.TestCase\t\t\t\t):\n\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n\n\n\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->List[Any]:\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n\n\n\n\n\n def __lowerCAmelCase\t\t\t(\t\t\t\t\t\tself\t\t\t\t\t) ->Optional[int]:\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t= load_numpy(\n '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''\n '''/kandinsky/kandinsky_img2img_frog.npy'''\t\t\t\t\t)\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t= load_image(\n '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= '''A red cartoon frog, 4k'''\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tAny\t\t\t\t\t\t\t\t= KandinskyPriorPipeline.from_pretrained(\n '''kandinsky-community/kandinsky-2-1-prior'''\t\t\t\t\t\t\t, torch_dtype=torch.floataa\t\t\t\t\t)\n pipe_prior.to(_lowerCamelCase\t\t\t\t\t)\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= KandinskyImgaImgPipeline.from_pretrained(\n '''kandinsky-community/kandinsky-2-1'''\t\t\t\t\t\t\t, torch_dtype=torch.floataa\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tstr\t\t\t\t\t\t\t\t= pipeline.to(_lowerCamelCase\t\t\t\t\t)\n\n pipeline.set_progress_bar_config(disable=_lowerCamelCase\t\t\t\t\t)\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t= torch.Generator(device='''cpu'''\t\t\t\t\t).manual_seed(0\t\t\t\t\t)\n SCREAMING_SNAKE_CASE\t,\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t= pipe_prior(\n _lowerCamelCase\t\t\t\t\t\t\t, generator=_lowerCamelCase\t\t\t\t\t\t\t, num_inference_steps=5\t\t\t\t\t\t\t, negative_prompt=''''''\t\t\t\t\t\t\t, ).to_tuple()\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t= pipeline(\n _lowerCamelCase\t\t\t\t\t\t\t, image=_lowerCamelCase\t\t\t\t\t\t\t, image_embeds=_lowerCamelCase\t\t\t\t\t\t\t, negative_image_embeds=_lowerCamelCase\t\t\t\t\t\t\t, generator=_lowerCamelCase\t\t\t\t\t\t\t, num_inference_steps=100\t\t\t\t\t\t\t, height=768\t\t\t\t\t\t\t, width=768\t\t\t\t\t\t\t, strength=0.2\t\t\t\t\t\t\t, output_type='''np'''\t\t\t\t\t\t\t, )\n\n SCREAMING_SNAKE_CASE\t\t\t\t\t\t\t:\t\t\t\t\t\t\tTuple\t\t\t\t\t\t\t\t= output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n assert_mean_pixel_difference(_lowerCamelCase\t\t\t\t\t\t\t, _lowerCamelCase\t\t\t\t\t)\n"},"style_context_codestyle":{"kind":"number","value":313,"string":"313"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":269,"cells":{"code":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\nfrom __future__ import annotations\r\n\r\nimport os\r\nimport tempfile\r\nimport unittest\r\n\r\nimport numpy as np\r\nfrom huggingface_hub import hf_hub_download\r\n\r\nfrom transformers import is_tensorflow_text_available, is_tf_available\r\nfrom transformers.testing_utils import require_tensorflow_text, require_tf, slow\r\n\r\nfrom ..test_modeling_tf_common import floats_tensor\r\nfrom .test_framework_agnostic import GenerationIntegrationTestsMixin\r\n\r\n\r\nif is_tf_available():\r\n\t\t\t\timport tensorflow as tf\r\n\r\n\t\t\t\tfrom transformers import (\r\n\t\t\t\t AutoTokenizer,\r\n\t\t\t\t TFAutoModelForCausalLM,\r\n\t\t\t\t TFAutoModelForSeqaSeqLM,\r\n\t\t\t\t TFAutoModelForSpeechSeqaSeq,\r\n\t\t\t\t TFAutoModelForVisionaSeq,\r\n\t\t\t\t TFBartForConditionalGeneration,\r\n\t\t\t\t TFLogitsProcessorList,\r\n\t\t\t\t TFMinLengthLogitsProcessor,\r\n\t\t\t\t tf_top_k_top_p_filtering,\r\n\t\t\t\t)\r\n\r\nif is_tensorflow_text_available():\r\n\t\t\t\timport tensorflow_text as text\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_tf\r\nclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\tunittest.TestCase ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Any\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tTuple:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf.convert_to_tensor(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 8.2_22_09_91, # 3rd highest value; idx. 0\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -0.5_62_00_44,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 5.23_22_97_52,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 4.0_38_63_93,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -6.8_79_83_78,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -0.54_78_58_02,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -3.2_01_21_53,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 2.92_77_71_76,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 1.88_17_19_53,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 7.35_34_12_76, # 5th highest value; idx. 9\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 8.43_20_78_33, # 2nd highest value; idx. 10\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -9.85_71_18_36,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -5.96_20_92_36,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -1.13_03_91_61,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -7.1_11_52_94,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -0.8_36_96_33,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -5.3_18_64_08,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 7.06_42_74_07,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.81_36_93_44,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -0.82_02_38_17,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -5.9_17_97_96,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.58_81_34_43,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -6.99_77_84_38,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 4.71_55_11_89,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -0.18_77_16_37,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 7.44_02_07_59, # 4th highest value; idx. 25\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 9.38_45_09_87, # 1st highest value; idx. 26\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 2.12_66_29_41,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -9.32_56_20_38,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 2.35_65_25_22,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ], # cummulative prob of 5 highest values <= 0.6\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.58_42_55_18,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 4.53_13_92_38,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -5.57_51_04_64,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -6.28_03_06_99,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -7.19_52_95_03,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -4.02_12_25_51,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 1.39_33_70_37,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -6.06_70_70_57,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 1.59_48_05_17,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -9.64_31_19,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.03_90_77_99,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 0.67_23_17_62,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -8.88_20_67_26,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 6.27_11_59_22, # 4th highest value; idx. 13\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 2.28_52_07_23,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 4.82_76_75_06,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 4.30_42_13_68,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 8.8_27_53_13, # 2nd highest value; idx. 17\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 5.44_02_99_58, # 5th highest value; idx. 18\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -4.4_73_57_94,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 7.38_57_95_36, # 3rd highest value; idx. 20\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -2.91_05_16_63,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 2.61_94_60_77,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -2.5_67_47_62,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -9.48_95_93_02,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -4.02_92_26_45,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -1.35_41_69_18,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 9.67_70_23_23, # 1st highest value; idx. 27\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t -5.89_47_85_53,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t 1.85_37_04_67,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ], # cummulative prob of 5 highest values <= 0.6\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ] ,\t\t\t\tdtype=tf.floataa ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf.convert_to_tensor(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] ,\t\t\t\tdtype=tf.intaa ,\t\t\t\t) # expected non filtered idx as noted above\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf.convert_to_tensor(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [8.22_20_99, 7.3_53_41_26, 8.43_20_78, 7.4_40_20_75, 9.3_84_51, 6.27_11_59, 8.82_75_31, 5.4_40_29_95, 7.3_85_79_56, 9.67_70_23] ,\t\t\t\tdtype=tf.floataa ,\t\t\t\t) # expected non filtered values as noted above\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf_top_k_top_p_filtering(UpperCamelCase__ ,\t\t\t\ttop_k=10 ,\t\t\t\ttop_p=0.6 ,\t\t\t\tmin_tokens_to_keep=4\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=output[output != -float('''inf'''\t\t\t\t\t\t\t)]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf.cast(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t tf.where(tf.not_equal(UpperCamelCase__ ,\t\t\t\ttf.constant(-float('''inf'''\t\t\t\t\t\t\t) ,\t\t\t\tdtype=tf.floataa\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\t\t\t\t\t\t\t) ,\t\t\t\tdtype=tf.intaa ,\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\ttf.debugging.assert_near(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__ ,\t\t\t\trtol=1E-12\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\ttf.debugging.assert_equal(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n@require_tf\r\nclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\tunittest.TestCase\t\t\t,\t\t\t__a ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tif is_tf_available():\r\n\t\t\t\t\t\t\t\t\t\t\t\tlowercase__ = {\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''AutoModelForCausalLM''': TFAutoModelForCausalLM,\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''LogitsProcessorList''': TFLogitsProcessorList,\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''create_tensor_fn''': tf.convert_to_tensor,\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''floats_tensor''': floats_tensor,\r\n\t\t\t\t\t\t\t\t\t\t\t\t '''return_tensors''': '''tf''',\r\n\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@slow\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Optional[int]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tint:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=2\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=2\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\ttf.Module ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself : Optional[Any] ,\t\t\t\tUpperCamelCase__ : List[str]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tOptional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsuper(UpperCamelCase__ ,\t\t\t\tself\t\t\t\t\t\t\t).__init__()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=model\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t@tf.function(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t input_signature=(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t tf.TensorSpec((None, input_length) ,\t\t\t\ttf.intaa ,\t\t\t\tname='''input_ids'''\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t tf.TensorSpec((None, input_length) ,\t\t\t\ttf.intaa ,\t\t\t\tname='''attention_mask'''\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ) ,\t\t\t\tjit_compile=UpperCamelCase__ ,\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Dict ,\t\t\t\tUpperCamelCase__ : str ,\t\t\t\tUpperCamelCase__ : Optional[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tstr:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.model.generate(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t input_ids=UpperCamelCase__ ,\t\t\t\tattention_mask=UpperCamelCase__ ,\t\t\t\tmax_new_tokens=UpperCamelCase__ ,\t\t\t\treturn_dict_in_generate=UpperCamelCase__ ,\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn {\"sequences\": outputs[\"sequences\"]}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[[2, 0], [102, 103]]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[[1, 0], [1, 1]]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=DummyModel(model=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttf.saved_model.save(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__ ,\t\t\t\tsignatures={'''serving_default''': dummy_model.serving}\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf.saved_model.load(UpperCamelCase__\t\t\t\t\t\t\t).signatures['''serving_default''']\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor batch_size in range(1 ,\t\t\t\tlen(UpperCamelCase__\t\t\t\t\t\t\t) + 1\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t={\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''input_ids''': tf.constant(dummy_input_ids[:batch_size]\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=serving_func(**UpperCamelCase__\t\t\t\t\t\t\t)['''sequences''']\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=test_model.generate(**UpperCamelCase__ ,\t\t\t\tmax_new_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttf.debugging.assert_equal(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@slow\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : List[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tAny:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=1\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=2\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\ttf.Module ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself : List[str] ,\t\t\t\tUpperCamelCase__ : Tuple\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tstr:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsuper(UpperCamelCase__ ,\t\t\t\tself\t\t\t\t\t\t\t).__init__()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=model\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t@tf.function(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t input_signature=(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t tf.TensorSpec((batch_size, None) ,\t\t\t\ttf.intaa ,\t\t\t\tname='''input_ids'''\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t tf.TensorSpec((batch_size, None) ,\t\t\t\ttf.intaa ,\t\t\t\tname='''attention_mask'''\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ) ,\t\t\t\tjit_compile=UpperCamelCase__ ,\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : List[str] ,\t\t\t\tUpperCamelCase__ : Optional[Any] ,\t\t\t\tUpperCamelCase__ : int\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tAny:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.model.generate(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t input_ids=UpperCamelCase__ ,\t\t\t\tattention_mask=UpperCamelCase__ ,\t\t\t\tmax_new_tokens=UpperCamelCase__ ,\t\t\t\treturn_dict_in_generate=UpperCamelCase__ ,\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn {\"sequences\": outputs[\"sequences\"]}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[[2], [102, 103]]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[[1], [1, 1]]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=DummyModel(model=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttf.saved_model.save(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__ ,\t\t\t\tsignatures={'''serving_default''': dummy_model.serving}\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf.saved_model.load(UpperCamelCase__\t\t\t\t\t\t\t).signatures['''serving_default''']\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor input_row in range(len(UpperCamelCase__\t\t\t\t\t\t\t)\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t={\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''input_ids''': tf.constant([dummy_input_ids[input_row]]\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''attention_mask''': tf.constant([dummy_attention_masks[input_row]]\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=serving_func(**UpperCamelCase__\t\t\t\t\t\t\t)['''sequences''']\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=test_model.generate(**UpperCamelCase__ ,\t\t\t\tmax_new_tokens=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttf.debugging.assert_equal(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@slow\r\n\t\t\t\t\t\t@require_tensorflow_text\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : List[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith tempfile.TemporaryDirectory() as tmp_dir:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# file needed to load the TF tokenizer\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\thf_hub_download(repo_id='''google/flan-t5-small''' ,\t\t\t\tfilename='''spiece.model''' ,\t\t\t\tlocal_dir=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\ttf.keras.layers.Layer ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself : Optional[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tOptional[int]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=text.SentencepieceTokenizer(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t model=tf.io.gfile.GFile(os.path.join(UpperCamelCase__ ,\t\t\t\t'''spiece.model'''\t\t\t\t\t\t\t) ,\t\t\t\t'''rb'''\t\t\t\t\t\t\t).read()\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : str ,\t\t\t\tUpperCamelCase__ : Any ,\t\t\t\t*UpperCamelCase__ : Any ,\t\t\t\t**UpperCamelCase__ : Dict\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tint:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.tokenizer.tokenize(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t, __UpperCamelCase\t\t\t\t\t\t\t=text.pad_model_inputs(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCamelCase__ ,\t\t\t\tmax_seq_length=64 ,\t\t\t\tpad_value=self.model.config.pad_token_id\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=self.model.generate(input_ids=UpperCamelCase__ ,\t\t\t\tattention_mask=UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn self.tokenizer.detokenize(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=CompleteSentenceTransformer()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf.keras.layers.Input(shape=(1,) ,\t\t\t\tdtype=tf.string ,\t\t\t\tname='''inputs'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=complete_model(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tf.keras.Model(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tkeras_model.save(UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : List[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tAny:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t={\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''do_sample''': True,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''num_beams''': 1,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''top_p''': 0.7,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''top_k''': 10,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t '''temperature''': 0.7,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=14\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''Hello, my dog is cute and'''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=tokenizer(UpperCamelCase__ ,\t\t\t\treturn_tensors='''tf'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=638\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# forces the generation to happen on CPU, to avoid GPU-related quirks\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith tf.device(''':/CPU:0'''\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttf.random.set_seed(0\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=model.generate(**UpperCamelCase__ ,\t\t\t\teos_token_id=UpperCamelCase__ ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(expectation == len(generated_tokens[0]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[638, 198]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith tf.device(''':/CPU:0'''\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttf.random.set_seed(0\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=model.generate(**UpperCamelCase__ ,\t\t\t\teos_token_id=UpperCamelCase__ ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(expectation == len(generated_tokens[0]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Union[str, Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t='''Hugging Face is a technology company based in New York and Paris.'''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=bart_tokenizer(UpperCamelCase__ ,\t\t\t\treturn_tensors='''tf'''\t\t\t\t\t\t\t).input_ids\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=bart_model.generate(UpperCamelCase__\t\t\t\t\t\t\t).numpy()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\t__a ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Union[str, Any] ,\t\t\t\tUpperCamelCase__ : List[Any] ,\t\t\t\tUpperCamelCase__ : Tuple=None ,\t\t\t\t**UpperCamelCase__ : Optional[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tList[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn super().call(UpperCamelCase__ ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart'''\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=bart_model.generate(UpperCamelCase__ ,\t\t\t\tfoo='''bar'''\t\t\t\t\t\t\t).numpy()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(np.array_equal(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\tbart_model.model.encoder.__class__ ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Union[str, Any] ,\t\t\t\tUpperCamelCase__ : List[str] ,\t\t\t\t**UpperCamelCase__ : Union[str, Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn super().call(UpperCamelCase__ ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=FakeEncoder(bart_model.config ,\t\t\t\tbart_model.model.shared\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=fake_encoder\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Normal generation still works (the output will be different because the encoder weights are different)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=bart_model.generate(UpperCamelCase__\t\t\t\t\t\t\t).numpy()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twith self.assertRaises(UpperCamelCase__\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input \"foo\"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbart_model.generate(UpperCamelCase__ ,\t\t\t\tfoo='''bar'''\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":85,"string":"85"},"style_context":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\nfrom collections import OrderedDict\r\nfrom typing import Mapping\r\n\r\nfrom ...configuration_utils import PretrainedConfig\r\nfrom ...onnx import OnnxConfig\r\n\r\n\r\n__lowercase\t\t\t\t =\t\t\t\t\t\t\t{\r\n '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',\r\n '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',\r\n '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',\r\n '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',\r\n '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',\r\n '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',\r\n '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',\r\n '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',\r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\t__a ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tlowercase__ = '''albert'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself : List[Any] ,\t\t\t\tUpperCamelCase__ : List[Any]=30000 ,\t\t\t\tUpperCamelCase__ : int=128 ,\t\t\t\tUpperCamelCase__ : str=4096 ,\t\t\t\tUpperCamelCase__ : Optional[Any]=12 ,\t\t\t\tUpperCamelCase__ : Dict=1 ,\t\t\t\tUpperCamelCase__ : Union[str, Any]=64 ,\t\t\t\tUpperCamelCase__ : Any=16384 ,\t\t\t\tUpperCamelCase__ : Any=1 ,\t\t\t\tUpperCamelCase__ : Optional[int]=\"gelu_new\" ,\t\t\t\tUpperCamelCase__ : int=0 ,\t\t\t\tUpperCamelCase__ : List[Any]=0 ,\t\t\t\tUpperCamelCase__ : Dict=512 ,\t\t\t\tUpperCamelCase__ : Optional[Any]=2 ,\t\t\t\tUpperCamelCase__ : str=0.02 ,\t\t\t\tUpperCamelCase__ : Tuple=1E-12 ,\t\t\t\tUpperCamelCase__ : Tuple=0.1 ,\t\t\t\tUpperCamelCase__ : Dict=\"absolute\" ,\t\t\t\tUpperCamelCase__ : List[Any]=0 ,\t\t\t\tUpperCamelCase__ : int=2 ,\t\t\t\tUpperCamelCase__ : Optional[Any]=3 ,\t\t\t\t**UpperCamelCase__ : List[str] ,\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(pad_token_id=UpperCamelCase__ ,\t\t\t\tbos_token_id=UpperCamelCase__ ,\t\t\t\teos_token_id=UpperCamelCase__ ,\t\t\t\t**UpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=vocab_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=embedding_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=hidden_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=num_hidden_layers\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=num_hidden_groups\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=num_attention_heads\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=inner_group_num\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=hidden_act\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=intermediate_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=hidden_dropout_prob\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=attention_probs_dropout_prob\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=max_position_embeddings\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=type_vocab_size\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=initializer_range\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=layer_norm_eps\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=classifier_dropout_prob\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=position_embedding_type\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass \t\t\t\t\t\t_lowercase\t\t\t\t(\t\t__a ):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@property\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : Union[str, Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tMapping[str, Mapping[int, str]]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.task == \"multiple-choice\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t={0: '''batch''', 1: '''choice''', 2: '''sequence'''}\r\n\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t={0: '''batch''', 1: '''sequence'''}\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn OrderedDict(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t [\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ('''input_ids''', dynamic_axis),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ('''attention_mask''', dynamic_axis),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ('''token_type_ids''', dynamic_axis),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t ]\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":85,"string":"85"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":270,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n'''simple docstring'''\r\n\r\n\r\nimport argparse\r\n\r\nimport gdown\r\nimport numpy as np\r\nimport torch\r\nfrom huggingface_hub import hf_hub_download\r\n\r\nfrom transformers import (\r\n CLIPTokenizer,\r\n CLIPTokenizerFast,\r\n VideoMAEImageProcessor,\r\n XCLIPConfig,\r\n XCLIPModel,\r\n XCLIPProcessor,\r\n XCLIPTextConfig,\r\n XCLIPVisionConfig,\r\n)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t__lowerCamelCase\t\t\t\t( A__ ,\t\t\t\t\t\tA__ ) ->\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n UpperCamelCase\t\t\t\t\t= XCLIPTextConfig()\r\n\r\n # derive patch size from model name\r\n UpperCamelCase\t\t\t\t\t= model_name.find('patch' )\r\n UpperCamelCase\t\t\t\t\t= int(model_name[start_idx + len('patch' ) : start_idx + len('patch' ) + 2] )\r\n UpperCamelCase\t\t\t\t\t= XCLIPVisionConfig(patch_size=A__ ,\t\t\t\t\t\tnum_frames=A__ )\r\n\r\n if \"large\" in model_name:\r\n UpperCamelCase\t\t\t\t\t= 768\r\n UpperCamelCase\t\t\t\t\t= 3_072\r\n UpperCamelCase\t\t\t\t\t= 12\r\n\r\n UpperCamelCase\t\t\t\t\t= 1_024\r\n UpperCamelCase\t\t\t\t\t= 4_096\r\n UpperCamelCase\t\t\t\t\t= 16\r\n UpperCamelCase\t\t\t\t\t= 24\r\n UpperCamelCase\t\t\t\t\t= 768\r\n UpperCamelCase\t\t\t\t\t= 3_072\r\n\r\n if model_name == \"xclip-large-patch14-16-frames\":\r\n UpperCamelCase\t\t\t\t\t= 336\r\n\r\n UpperCamelCase\t\t\t\t\t= XCLIPConfig.from_text_vision_configs(A__ ,\t\t\t\t\t\tA__ )\r\n\r\n if \"large\" in model_name:\r\n UpperCamelCase\t\t\t\t\t= 768\r\n\r\n return config\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t__lowerCamelCase\t\t\t\t( A__ ) ->\tTuple:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n # text encoder\r\n if name == \"token_embedding.weight\":\r\n UpperCamelCase\t\t\t\t\t= name.replace('token_embedding.weight' ,\t\t\t\t\t\t'text_model.embeddings.token_embedding.weight' )\r\n if name == \"positional_embedding\":\r\n UpperCamelCase\t\t\t\t\t= name.replace('positional_embedding' ,\t\t\t\t\t\t'text_model.embeddings.position_embedding.weight' )\r\n if \"ln_1\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('ln_1' ,\t\t\t\t\t\t'layer_norm1' )\r\n if \"ln_2\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('ln_2' ,\t\t\t\t\t\t'layer_norm2' )\r\n if \"c_fc\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('c_fc' ,\t\t\t\t\t\t'fc1' )\r\n if \"c_proj\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('c_proj' ,\t\t\t\t\t\t'fc2' )\r\n if name.startswith('transformer.resblocks' ):\r\n UpperCamelCase\t\t\t\t\t= name.replace('transformer.resblocks' ,\t\t\t\t\t\t'text_model.encoder.layers' )\r\n if \"attn.out_proj\" in name and \"message\" not in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('attn.out_proj' ,\t\t\t\t\t\t'self_attn.out_proj' )\r\n if \"ln_final\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('ln_final' ,\t\t\t\t\t\t'text_model.final_layer_norm' )\r\n # visual encoder\r\n if name == \"visual.class_embedding\":\r\n UpperCamelCase\t\t\t\t\t= name.replace('visual.class_embedding' ,\t\t\t\t\t\t'vision_model.embeddings.class_embedding' )\r\n if name == \"visual.positional_embedding\":\r\n UpperCamelCase\t\t\t\t\t= name.replace('visual.positional_embedding' ,\t\t\t\t\t\t'vision_model.embeddings.position_embedding.weight' )\r\n if name.startswith('visual.transformer.resblocks' ):\r\n UpperCamelCase\t\t\t\t\t= name.replace('visual.transformer.resblocks' ,\t\t\t\t\t\t'vision_model.encoder.layers' )\r\n if \"visual.conv1\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('visual.conv1' ,\t\t\t\t\t\t'vision_model.embeddings.patch_embedding' )\r\n if \"visual.ln_pre\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('visual.ln_pre' ,\t\t\t\t\t\t'vision_model.pre_layernorm' )\r\n if \"visual.ln_post\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('visual.ln_post' ,\t\t\t\t\t\t'vision_model.post_layernorm' )\r\n if \"visual.proj\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('visual.proj' ,\t\t\t\t\t\t'visual_projection.weight' )\r\n if \"text_projection\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('text_projection' ,\t\t\t\t\t\t'text_projection.weight' )\r\n # things on top\r\n if \"prompts_visual_proj\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('prompts_visual_proj' ,\t\t\t\t\t\t'prompts_visual_projection' )\r\n if \"prompts_visual_ln\" in name:\r\n UpperCamelCase\t\t\t\t\t= name.replace('prompts_visual_ln' ,\t\t\t\t\t\t'prompts_visual_layernorm' )\r\n # mit\r\n if name == \"mit.positional_embedding\":\r\n UpperCamelCase\t\t\t\t\t= name.replace('positional' ,\t\t\t\t\t\t'position' )\r\n if name.startswith('mit.resblocks' ):\r\n UpperCamelCase\t\t\t\t\t= name.replace('mit.resblocks' ,\t\t\t\t\t\t'mit.encoder.layers' )\r\n # prompts generator\r\n if name.startswith('prompts_generator.norm' ):\r\n UpperCamelCase\t\t\t\t\t= name.replace('prompts_generator.norm' ,\t\t\t\t\t\t'prompts_generator.layernorm' )\r\n\r\n return name\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t__lowerCamelCase\t\t\t\t( A__ ,\t\t\t\t\t\tA__ ) ->\tOptional[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n for key in orig_state_dict.copy().keys():\r\n UpperCamelCase\t\t\t\t\t= orig_state_dict.pop(A__ )\r\n\r\n if \"attn.in_proj\" in key:\r\n UpperCamelCase\t\t\t\t\t= key.split('.' )\r\n if key.startswith('visual' ):\r\n UpperCamelCase\t\t\t\t\t= key_split[3]\r\n UpperCamelCase\t\t\t\t\t= config.vision_config.hidden_size\r\n if \"message_attn\" in key:\r\n if \"weight\" in key:\r\n UpperCamelCase\t\t\t\t\t= val[\r\n :dim, :\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n dim : dim * 2, :\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n -dim:, :\r\n ]\r\n else:\r\n UpperCamelCase\t\t\t\t\t= val[\r\n :dim\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n dim : dim * 2\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n -dim:\r\n ]\r\n else:\r\n if \"weight\" in key:\r\n UpperCamelCase\t\t\t\t\t= val[\r\n :dim, :\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n dim : dim * 2, :\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n -dim:, :\r\n ]\r\n else:\r\n UpperCamelCase\t\t\t\t\t= val[:dim]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n dim : dim * 2\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[-dim:]\r\n elif key.startswith('mit' ):\r\n UpperCamelCase\t\t\t\t\t= key_split[2]\r\n UpperCamelCase\t\t\t\t\t= config.vision_config.mit_hidden_size\r\n if \"weight\" in key:\r\n UpperCamelCase\t\t\t\t\t= val[:dim, :]\r\n UpperCamelCase\t\t\t\t\t= val[dim : dim * 2, :]\r\n UpperCamelCase\t\t\t\t\t= val[-dim:, :]\r\n else:\r\n UpperCamelCase\t\t\t\t\t= val[:dim]\r\n UpperCamelCase\t\t\t\t\t= val[dim : dim * 2]\r\n UpperCamelCase\t\t\t\t\t= val[-dim:]\r\n else:\r\n UpperCamelCase\t\t\t\t\t= key_split[2]\r\n UpperCamelCase\t\t\t\t\t= config.text_config.hidden_size\r\n if \"weight\" in key:\r\n UpperCamelCase\t\t\t\t\t= val[:dim, :]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n dim : dim * 2, :\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[-dim:, :]\r\n else:\r\n UpperCamelCase\t\t\t\t\t= val[:dim]\r\n UpperCamelCase\t\t\t\t\t= val[\r\n dim : dim * 2\r\n ]\r\n UpperCamelCase\t\t\t\t\t= val[-dim:]\r\n else:\r\n UpperCamelCase\t\t\t\t\t= rename_key(A__ )\r\n if new_key_name in [\"visual_projection.weight\", \"text_projection.weight\"]:\r\n UpperCamelCase\t\t\t\t\t= val.T\r\n UpperCamelCase\t\t\t\t\t= val\r\n\r\n return orig_state_dict\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t__lowerCamelCase\t\t\t\t( A__ ) ->\tOptional[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n if num_frames == 8:\r\n UpperCamelCase\t\t\t\t\t= 'eating_spaghetti_8_frames.npy'\r\n elif num_frames == 16:\r\n UpperCamelCase\t\t\t\t\t= 'eating_spaghetti.npy'\r\n elif num_frames == 32:\r\n UpperCamelCase\t\t\t\t\t= 'eating_spaghetti_32_frames.npy'\r\n UpperCamelCase\t\t\t\t\t= hf_hub_download(\r\n repo_id='hf-internal-testing/spaghetti-video' ,\t\t\t\t\t\tfilename=A__ ,\t\t\t\t\t\trepo_type='dataset' ,\t\t\t\t\t\t)\r\n UpperCamelCase\t\t\t\t\t= np.load(A__ )\r\n return list(A__ )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t__lowerCamelCase\t\t\t\t( A__ ,\t\t\t\t\t\tA__=None ,\t\t\t\t\t\tA__=False ) ->\tList[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n UpperCamelCase\t\t\t\t\t= {\r\n # fully supervised kinetics-400 checkpoints\r\n 'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',\r\n 'xclip-base-patch32-16-frames': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'\r\n ),\r\n 'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',\r\n 'xclip-base-patch16-16-frames': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'\r\n ),\r\n 'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',\r\n 'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',\r\n # fully supervised kinetics-600 checkpoints\r\n 'xclip-base-patch16-kinetics-600': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'\r\n ),\r\n 'xclip-base-patch16-kinetics-600-16-frames': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'\r\n ),\r\n 'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',\r\n # few shot\r\n 'xclip-base-patch16-hmdb-2-shot': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'\r\n ),\r\n 'xclip-base-patch16-hmdb-4-shot': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'\r\n ),\r\n 'xclip-base-patch16-hmdb-8-shot': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'\r\n ),\r\n 'xclip-base-patch16-hmdb-16-shot': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'\r\n ),\r\n 'xclip-base-patch16-ucf-2-shot': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'\r\n ),\r\n 'xclip-base-patch16-ucf-4-shot': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'\r\n ),\r\n 'xclip-base-patch16-ucf-8-shot': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'\r\n ),\r\n 'xclip-base-patch16-ucf-16-shot': (\r\n 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'\r\n ),\r\n # zero shot\r\n 'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',\r\n }\r\n\r\n UpperCamelCase\t\t\t\t\t= model_to_url[model_name]\r\n UpperCamelCase\t\t\t\t\t= 8\r\n if \"16-frames\" in model_name:\r\n UpperCamelCase\t\t\t\t\t= 16\r\n elif \"shot\" in model_name:\r\n UpperCamelCase\t\t\t\t\t= 32\r\n\r\n UpperCamelCase\t\t\t\t\t= get_xclip_config(A__ ,\t\t\t\t\t\tA__ )\r\n UpperCamelCase\t\t\t\t\t= XCLIPModel(A__ )\r\n model.eval()\r\n\r\n if \"drive\" in checkpoint_url:\r\n UpperCamelCase\t\t\t\t\t= 'pytorch_model.bin'\r\n gdown.cached_download(A__ ,\t\t\t\t\t\tA__ ,\t\t\t\t\t\tquiet=A__ )\r\n UpperCamelCase\t\t\t\t\t= torch.load(A__ ,\t\t\t\t\t\tmap_location='cpu' )['model']\r\n else:\r\n UpperCamelCase\t\t\t\t\t= torch.hub.load_state_dict_from_url(A__ )['model']\r\n\r\n UpperCamelCase\t\t\t\t\t= convert_state_dict(A__ ,\t\t\t\t\t\tA__ )\r\n\r\n UpperCamelCase\t\t\t\t\t= XCLIPModel(A__ )\r\n UpperCamelCase\t\t\t\t, UpperCamelCase\t\t\t\t\t= model.load_state_dict(A__ ,\t\t\t\t\t\tstrict=A__ )\r\n assert missing_keys == [\"text_model.embeddings.position_ids\", \"vision_model.embeddings.position_ids\"]\r\n model.eval()\r\n\r\n UpperCamelCase\t\t\t\t\t= 336 if model_name == 'xclip-large-patch14-16-frames' else 224\r\n UpperCamelCase\t\t\t\t\t= VideoMAEImageProcessor(size=A__ )\r\n UpperCamelCase\t\t\t\t\t= CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32' )\r\n UpperCamelCase\t\t\t\t\t= CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32' )\r\n UpperCamelCase\t\t\t\t\t= XCLIPProcessor(image_processor=A__ ,\t\t\t\t\t\ttokenizer=A__ )\r\n\r\n UpperCamelCase\t\t\t\t\t= prepare_video(A__ )\r\n UpperCamelCase\t\t\t\t\t= processor(\r\n text=['playing sports', 'eating spaghetti', 'go shopping'] ,\t\t\t\t\t\tvideos=A__ ,\t\t\t\t\t\treturn_tensors='pt' ,\t\t\t\t\t\tpadding=A__ )\r\n\r\n print('Shape of pixel values:' ,\t\t\t\t\t\tinputs.pixel_values.shape )\r\n\r\n with torch.no_grad():\r\n UpperCamelCase\t\t\t\t\t= model(**A__ )\r\n\r\n # Verify outputs\r\n UpperCamelCase\t\t\t\t\t= outputs.logits_per_video\r\n UpperCamelCase\t\t\t\t\t= logits_per_video.softmax(dim=1 )\r\n print('Probs:' ,\t\t\t\t\t\tA__ )\r\n # kinetics-400\r\n if model_name == \"xclip-base-patch32\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )\r\n elif model_name == \"xclip-base-patch32-16-frames\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[7.0_9_9_9e-0_4, 9.9_8_8_3e-0_1, 4.5_5_8_0e-0_4]] )\r\n elif model_name == \"xclip-base-patch16\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )\r\n elif model_name == \"xclip-base-patch16-16-frames\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[7.6_9_3_7e-0_4, 9.9_7_2_8e-0_1, 1.9_4_7_3e-0_3]] )\r\n elif model_name == \"xclip-large-patch14\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )\r\n elif model_name == \"xclip-large-patch14-16-frames\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[3.3_8_7_7e-0_4, 9.9_9_3_7e-0_1, 2.8_8_8_8e-0_4]] )\r\n # kinetics-600\r\n elif model_name == \"xclip-base-patch16-kinetics-600\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )\r\n elif model_name == \"xclip-base-patch16-kinetics-600-16-frames\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[3.8_5_5_4e-0_4, 9.9_9_2_9e-0_1, 3.2_7_5_4e-0_4]] )\r\n elif model_name == \"xclip-large-patch14-kinetics-600\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )\r\n # few shot\r\n elif model_name == \"xclip-base-patch16-hmdb-2-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[7.1_8_9_0e-0_6, 9.9_9_9_4e-0_1, 5.6_5_5_9e-0_5]] )\r\n elif model_name == \"xclip-base-patch16-hmdb-4-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[1.0_3_2_0e-0_5, 9.9_9_9_3e-0_1, 6.2_4_3_5e-0_5]] )\r\n elif model_name == \"xclip-base-patch16-hmdb-8-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[4.1_3_7_7e-0_6, 9.9_9_9_0e-0_1, 9.8_3_8_6e-0_5]] )\r\n elif model_name == \"xclip-base-patch16-hmdb-16-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[4.1_3_4_7e-0_5, 9.9_9_6_2e-0_1, 3.3_4_1_1e-0_4]] )\r\n elif model_name == \"xclip-base-patch16-ucf-2-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]] )\r\n elif model_name == \"xclip-base-patch16-ucf-4-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]] )\r\n elif model_name == \"xclip-base-patch16-ucf-8-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )\r\n elif model_name == \"xclip-base-patch16-ucf-16-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[9.8_2_1_9e-0_4, 9.9_5_9_3e-0_1, 3.0_8_6_3e-0_3]] )\r\n # zero shot\r\n elif model_name == \"xclip-base-patch16-zero-shot\":\r\n UpperCamelCase\t\t\t\t\t= torch.tensor([[3.5_0_8_2e-0_4, 9.9_7_8_5e-0_1, 1.7_9_6_6e-0_3]] )\r\n else:\r\n raise ValueError(F\"\"\"Model name {model_name} not supported\"\"\" )\r\n assert torch.allclose(A__ ,\t\t\t\t\t\tA__ ,\t\t\t\t\t\tatol=1e-3 )\r\n print('Looks ok!' )\r\n\r\n if pytorch_dump_folder_path is not None:\r\n print(F\"\"\"Saving model {model_name} to {pytorch_dump_folder_path}\"\"\" )\r\n model.save_pretrained(A__ )\r\n\r\n if push_to_hub:\r\n print('Pushing model, processor and slow tokenizer files to the hub...' )\r\n model.push_to_hub(A__ ,\t\t\t\t\t\torganization='nielsr' )\r\n processor.push_to_hub(A__ ,\t\t\t\t\t\torganization='nielsr' )\r\n slow_tokenizer.push_to_hub(A__ ,\t\t\t\t\t\torganization='nielsr' )\r\n\r\n\r\nif __name__ == \"__main__\":\r\n _lowerCamelCase\t: Union[str, Any] \t=\targparse.ArgumentParser()\r\n # Required parameters\r\n parser.add_argument(\r\n \"--model_name\",\r\n default=\"xclip-base-patch32\",\r\n type=str,\r\n help=\"Name of the model.\",\r\n )\r\n parser.add_argument(\r\n \"--pytorch_dump_folder_path\", default=None, type=str, help=\"Path to the output PyTorch model directory.\"\r\n )\r\n parser.add_argument(\r\n \"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the converted model to the 🤗 hub.\"\r\n )\r\n\r\n _lowerCamelCase\t: str \t=\tparser.parse_args()\r\n convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)\r\n\r\n"},"code_codestyle":{"kind":"number","value":28,"string":"28"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n'''simple docstring'''\r\n\r\n\r\nfrom typing import Optional, Tuple\r\n\r\nimport jax\r\nimport jax.numpy as jnp\r\nfrom flax import linen as nn\r\nfrom flax.core.frozen_dict import FrozenDict\r\nfrom transformers import CLIPConfig, FlaxPreTrainedModel\r\nfrom transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef \t\t\t\t__lowerCamelCase\t\t\t\t( A__ ,\t\t\t\t\t\tA__ ,\t\t\t\t\t\tA__=1e-1_2 ) ->\tDict:\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n UpperCamelCase\t\t\t\t\t= jnp.divide(emb_a.T ,\t\t\t\t\t\tjnp.clip(jnp.linalg.norm(A__ ,\t\t\t\t\t\taxis=1 ) ,\t\t\t\t\t\ta_min=A__ ) ).T\r\n UpperCamelCase\t\t\t\t\t= jnp.divide(emb_a.T ,\t\t\t\t\t\tjnp.clip(jnp.linalg.norm(A__ ,\t\t\t\t\t\taxis=1 ) ,\t\t\t\t\t\ta_min=A__ ) ).T\r\n return jnp.matmul(A__ ,\t\t\t\t\t\tnorm_emb_a.T )\r\n\r\nclass SCREAMING_SNAKE_CASE ( nn.Module\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n _SCREAMING_SNAKE_CASE\t = 42\r\n _SCREAMING_SNAKE_CASE\t = jnp.floataa\r\n\r\n\r\n\r\n\r\n\r\n\r\n def A ( self :\t\t\t\t\tList[Any] ):\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n UpperCamelCase\t\t\t\t\t= FlaxCLIPVisionModule(self.config.vision_config )\r\n UpperCamelCase\t\t\t\t\t= nn.Dense(self.config.projection_dim ,\t\t\t\t\t\tuse_bias=UpperCamelCase__ ,\t\t\t\t\t\tdtype=self.dtype )\r\n\r\n UpperCamelCase\t\t\t\t\t= self.param('concept_embeds' ,\t\t\t\t\t\tjax.nn.initializers.ones ,\t\t\t\t\t\t(1_7, self.config.projection_dim) )\r\n UpperCamelCase\t\t\t\t\t= self.param(\r\n 'special_care_embeds' ,\t\t\t\t\t\tjax.nn.initializers.ones ,\t\t\t\t\t\t(3, self.config.projection_dim) )\r\n\r\n UpperCamelCase\t\t\t\t\t= self.param('concept_embeds_weights' ,\t\t\t\t\t\tjax.nn.initializers.ones ,\t\t\t\t\t\t(1_7,) )\r\n UpperCamelCase\t\t\t\t\t= self.param('special_care_embeds_weights' ,\t\t\t\t\t\tjax.nn.initializers.ones ,\t\t\t\t\t\t(3,) )\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __call__( self :\t\t\t\t\tstr ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tList[str] ):\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n UpperCamelCase\t\t\t\t\t= self.vision_model(UpperCamelCase__ )[1]\r\n UpperCamelCase\t\t\t\t\t= self.visual_projection(UpperCamelCase__ )\r\n\r\n UpperCamelCase\t\t\t\t\t= jax_cosine_distance(UpperCamelCase__ ,\t\t\t\t\t\tself.special_care_embeds )\r\n UpperCamelCase\t\t\t\t\t= jax_cosine_distance(UpperCamelCase__ ,\t\t\t\t\t\tself.concept_embeds )\r\n\r\n # increase this value to create a stronger `nfsw` filter\r\n # at the cost of increasing the possibility of filtering benign image inputs\r\n UpperCamelCase\t\t\t\t\t= 0.0\r\n\r\n UpperCamelCase\t\t\t\t\t= special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment\r\n UpperCamelCase\t\t\t\t\t= jnp.round(UpperCamelCase__ ,\t\t\t\t\t\t3 )\r\n UpperCamelCase\t\t\t\t\t= jnp.any(special_scores > 0 ,\t\t\t\t\t\taxis=1 ,\t\t\t\t\t\tkeepdims=UpperCamelCase__ )\r\n # Use a lower threshold if an image has any special care concept\r\n UpperCamelCase\t\t\t\t\t= is_special_care * 0.0_1\r\n\r\n UpperCamelCase\t\t\t\t\t= cos_dist - self.concept_embeds_weights[None, :] + special_adjustment\r\n UpperCamelCase\t\t\t\t\t= jnp.round(UpperCamelCase__ ,\t\t\t\t\t\t3 )\r\n UpperCamelCase\t\t\t\t\t= jnp.any(concept_scores > 0 ,\t\t\t\t\t\taxis=1 )\r\n\r\n return has_nsfw_concepts\r\n\r\nclass SCREAMING_SNAKE_CASE ( _a\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n _SCREAMING_SNAKE_CASE\t = CLIPConfig\r\n _SCREAMING_SNAKE_CASE\t = \"\"\"clip_input\"\"\"\r\n _SCREAMING_SNAKE_CASE\t = FlaxStableDiffusionSafetyCheckerModule\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __init__( self :\t\t\t\t\tUnion[str, Any] ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tCLIPConfig ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tOptional[Tuple] = None ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tint = 0 ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tjnp.dtype = jnp.floataa ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tbool = True ,\t\t\t\t\t\t**UpperCamelCase__ :\t\t\t\t\tList[str] ,\t\t\t\t\t\t):\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n if input_shape is None:\r\n UpperCamelCase\t\t\t\t\t= (1, 2_2_4, 2_2_4, 3)\r\n UpperCamelCase\t\t\t\t\t= self.module_class(config=UpperCamelCase__ ,\t\t\t\t\t\tdtype=UpperCamelCase__ ,\t\t\t\t\t\t**UpperCamelCase__ )\r\n super().__init__(UpperCamelCase__ ,\t\t\t\t\t\tUpperCamelCase__ ,\t\t\t\t\t\tinput_shape=UpperCamelCase__ ,\t\t\t\t\t\tseed=UpperCamelCase__ ,\t\t\t\t\t\tdtype=UpperCamelCase__ ,\t\t\t\t\t\t_do_init=_do_init )\r\n\r\n\r\n\r\n\r\n\r\n\r\n def A ( self :\t\t\t\t\tint ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tjax.random.KeyArray ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tTuple ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tFrozenDict = None ):\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n UpperCamelCase\t\t\t\t\t= jax.random.normal(UpperCamelCase__ ,\t\t\t\t\t\tUpperCamelCase__ )\r\n\r\n UpperCamelCase\t\t\t\t, UpperCamelCase\t\t\t\t\t= jax.random.split(UpperCamelCase__ )\r\n UpperCamelCase\t\t\t\t\t= {'params': params_rng, 'dropout': dropout_rng}\r\n\r\n UpperCamelCase\t\t\t\t\t= self.module.init(UpperCamelCase__ ,\t\t\t\t\t\tUpperCamelCase__ )['params']\r\n\r\n return random_params\r\n\r\n\r\n\r\n\r\n\r\n\r\n def __call__( self :\t\t\t\t\tList[Any] ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tDict ,\t\t\t\t\t\tUpperCamelCase__ :\t\t\t\t\tdict = None ,\t\t\t\t\t\t):\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n UpperCamelCase\t\t\t\t\t= jnp.transpose(UpperCamelCase__ ,\t\t\t\t\t\t(0, 2, 3, 1) )\r\n\r\n return self.module.apply(\r\n {'params': params or self.params} ,\t\t\t\t\t\tjnp.array(UpperCamelCase__ ,\t\t\t\t\t\tdtype=jnp.floataa ) ,\t\t\t\t\t\trngs={} ,\t\t\t\t\t\t)\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":28,"string":"28"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":271,"cells":{"code":{"kind":"string","value":"\r\r\r\r\r\rfrom __future__ import annotations\r\rfrom math import pi\r\r\r\r\r\r\rdef \t_SCREAMING_SNAKE_CASE\t\t( SCREAMING_SNAKE_CASE\t\t:float , SCREAMING_SNAKE_CASE\t\t:float , SCREAMING_SNAKE_CASE\t\t:float ) -> dict[str, float]:\r\r if (inductance, frequency, reactance).count(0 ) != 1:\r raise ValueError(\"\"\"One and only one argument must be 0\"\"\" )\r if inductance < 0:\r raise ValueError(\"\"\"Inductance cannot be negative\"\"\" )\r if frequency < 0:\r raise ValueError(\"\"\"Frequency cannot be negative\"\"\" )\r if reactance < 0:\r raise ValueError(\"\"\"Inductive reactance cannot be negative\"\"\" )\r if inductance == 0:\r return {\"inductance\": reactance / (2 * pi * frequency)}\r elif frequency == 0:\r return {\"frequency\": reactance / (2 * pi * inductance)}\r elif reactance == 0:\r return {\"reactance\": 2 * pi * frequency * inductance}\r else:\r raise ValueError(\"\"\"Exactly one argument must be 0\"\"\" )\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()"},"code_codestyle":{"kind":"number","value":232,"string":"232"},"style_context":{"kind":"string","value":"\r\r\r\r\r\rfrom math import isqrt\r\r\r\r\r\r\rdef \t_SCREAMING_SNAKE_CASE\t\t( SCREAMING_SNAKE_CASE\t\t:int ) -> list[int]:\r __lowerCAmelCase\t\t\t\t\t:\tTuple = [True] * max_number\r for i in range(2 , isqrt(max_number - 1 ) + 1 ):\r if is_prime[i]:\r for j in range(i**2 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):\r __lowerCAmelCase\t\t\t\t\t:\tTuple = False\r\r return [i for i in range(2 , SCREAMING_SNAKE_CASE ) if is_prime[i]]\r\r\r\r\r\r\rdef \t_SCREAMING_SNAKE_CASE\t\t( SCREAMING_SNAKE_CASE\t\t:int = 10**8 ) -> int:\r __lowerCAmelCase\t\t\t\t\t:\tint = calculate_prime_numbers(max_number // 2 )\r\r __lowerCAmelCase\t\t\t\t\t:\tList[Any] = 0\r __lowerCAmelCase\t\t\t\t\t:\tList[str] = 0\r __lowerCAmelCase\t\t\t\t\t:\tList[Any] = len(SCREAMING_SNAKE_CASE ) - 1\r while left <= right:\r while prime_numbers[left] * prime_numbers[right] >= max_number:\r right -= 1\r semiprimes_count += right - left + 1\r left += 1\r\r return semiprimes_count\r\r\rif __name__ == \"__main__\":\r print(f'''{solution() = }''')"},"style_context_codestyle":{"kind":"number","value":232,"string":"232"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":272,"cells":{"code":{"kind":"string","value":"\r\r\r'''simple docstring'''\r\r\r\r\r\r\r\rimport copy\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\rfrom ..auto import CONFIG_MAPPING\r\r\ra__ :\t\t\t\t\tList[str] = logging.get_logger(__name__)\r\ra__ :\t\t\t\t\tOptional[int] = {\r 'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',\r}\rclass \t\t\t\t\tlowercase_ ( a__ ):\r __UpperCAmelCase \t\t\t=\t'deta'\r __UpperCAmelCase \t\t\t=\t{\r 'hidden_size': 'd_model',\r 'num_attention_heads': 'encoder_attention_heads',\r }\r\r\r def __init__( self ,\t\t\t\t\ta=None ,\t\t\t\t\ta=9_00 ,\t\t\t\t\ta=20_48 ,\t\t\t\t\ta=6 ,\t\t\t\t\ta=20_48 ,\t\t\t\t\ta=8 ,\t\t\t\t\ta=6 ,\t\t\t\t\ta=10_24 ,\t\t\t\t\ta=8 ,\t\t\t\t\ta=0.0 ,\t\t\t\t\ta=True ,\t\t\t\t\ta=\"relu\" ,\t\t\t\t\ta=2_56 ,\t\t\t\t\ta=0.1 ,\t\t\t\t\ta=0.0 ,\t\t\t\t\ta=0.0 ,\t\t\t\t\ta=0.02 ,\t\t\t\t\ta=1.0 ,\t\t\t\t\ta=True ,\t\t\t\t\ta=False ,\t\t\t\t\ta=\"sine\" ,\t\t\t\t\ta=5 ,\t\t\t\t\ta=4 ,\t\t\t\t\ta=4 ,\t\t\t\t\ta=True ,\t\t\t\t\ta=3_00 ,\t\t\t\t\ta=True ,\t\t\t\t\ta=True ,\t\t\t\t\ta=1 ,\t\t\t\t\ta=5 ,\t\t\t\t\ta=2 ,\t\t\t\t\ta=1 ,\t\t\t\t\ta=1 ,\t\t\t\t\ta=5 ,\t\t\t\t\ta=2 ,\t\t\t\t\ta=0.1 ,\t\t\t\t\ta=0.25 ,\t\t\t\t\t**a ,\t\t\t\t\t):\r if backbone_config is None:\r logger.info(\"`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.\" )\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tCONFIG_MAPPING[\"resnet\"](out_features=[\"stage2\", \"stage3\", \"stage4\"] )\r else:\r if isinstance(a ,\t\t\t\t\ta ):\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tbackbone_config.pop(\"model_type\" )\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tCONFIG_MAPPING[backbone_model_type]\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tconfig_class.from_dict(a )\r\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tbackbone_config\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tnum_queries\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tmax_position_embeddings\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\td_model\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tencoder_ffn_dim\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tencoder_layers\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tencoder_attention_heads\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tdecoder_ffn_dim\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tdecoder_layers\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tdecoder_attention_heads\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tdropout\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tattention_dropout\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tactivation_dropout\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tactivation_function\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tinit_std\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tinit_xavier_std\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tencoder_layerdrop\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tauxiliary_loss\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tposition_embedding_type\r # deformable attributes\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tnum_feature_levels\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tencoder_n_points\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tdecoder_n_points\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\ttwo_stage\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\ttwo_stage_num_proposals\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\twith_box_refine\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tassign_first_stage\r if two_stage is True and with_box_refine is False:\r raise ValueError(\"If two_stage is True, with_box_refine must be True.\" )\r # Hungarian matcher\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tclass_cost\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tbbox_cost\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tgiou_cost\r # Loss coefficients\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tmask_loss_coefficient\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tdice_loss_coefficient\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tbbox_loss_coefficient\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tgiou_loss_coefficient\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\teos_coefficient\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tfocal_alpha\r super().__init__(is_encoder_decoder=a ,\t\t\t\t\t**a )\r\r\r @property\r def __a\t\t\t\t\t\t\t( self ):\r return self.encoder_attention_heads\r\r\r @property\r def __a\t\t\t\t\t\t\t( self ):\r return self.d_model\r\r\r def __a\t\t\t\t\t\t\t( self ):\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tcopy.deepcopy(self.__dict__ )\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.backbone_config.to_dict()\r UpperCamelCase__\t\t\t\t\t\t\t\t\t\t=\t\t\t\tself.__class__.model_type\r return output\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":80,"string":"80"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\nimport json\r\nimport multiprocessing as mp\r\nimport re\r\nfrom collections import defaultdict\r\nfrom functools import partial\r\nfrom typing import Dict, List, Optional, Set, Tuple, Type\r\n\r\nfrom datasets import Dataset\r\nfrom datasketch import MinHash, MinHashLSH\r\nfrom dpu_utils.utils.iterators import ThreadedIterator\r\nfrom tqdm import tqdm\r\n\r\n\r\nlowercase_\t\t\t = re.compile(\"[^A-Za-z_0-9]\")\r\n# parameters used in DuplicationIndex\r\nlowercase_\t\t\t = 1_0\r\nlowercase_\t\t\t = 2_5_6\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : List[str]\t\t\t) -> Optional[MinHash]:\r\n\t\tif len(lowerCAmelCase__\t\t\t) < MIN_NUM_TOKENS:\r\n\t\t\t\treturn None\r\n\t\t__a =\t\t\tMinHash(num_perm=lowerCAmelCase__\t\t\t)\r\n\t\tfor token in set(lowerCAmelCase__\t\t\t):\r\n\t\t\t\tmin_hash.update(token.encode()\t\t\t)\r\n\t\treturn min_hash\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : str\t\t\t) -> Set[str]:\r\n\t\treturn {t for t in NON_ALPHA.split(lowerCAmelCase__\t\t\t) if len(t.strip()\t\t\t) > 0}\r\nclass \t\t\t\t\t__lowerCAmelCase\t\t\t\t\t:\r\n\r\n\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\tdef __init__( self\t\t\t\t\t\t\t, *,\r\n\t\t _a = 0.85\t\t\t\t\t\t\t, ):\r\n\t\t\t\t__a =\t\t\tduplication_jaccard_threshold\r\n\t\t\t\t__a =\t\t\tNUM_PERM\r\n\t\t\t\t__a =\t\t\tMinHashLSH(threshold=self._duplication_jaccard_threshold\t\t\t\t\t\t\t, num_perm=self._num_perm )\r\n\r\n\t\t\t\t__a =\t\t\tdefaultdict(_a )\r\n\r\n\t\tdef \t\t\t__UpperCAmelCase ( self\t\t\t\t\t\t\t, _a\t\t\t\t\t\t\t, _a ):\r\n\t\t\t\t__a =\t\t\tself._index.query(_a )\r\n\t\t\t\tif code_key in self._index.keys:\r\n\t\t\t\t\t\tprint(f'''Duplicate key {code_key}''' )\r\n\t\t\t\t\t\treturn\r\n\r\n\t\t\t\tself._index.insert(_a\t\t\t\t\t\t\t, _a )\r\n\t\t\t\tif len(_a ) > 0:\r\n\t\t\t\t\t\tfor base_duplicate in close_duplicates:\r\n\t\t\t\t\t\t\t\tif base_duplicate in self._duplicate_clusters:\r\n\t\t\t\t\t\t\t\t\t\tself._duplicate_clusters[base_duplicate].add(_a )\r\n\t\t\t\t\t\t\t\t\t\tbreak\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\tself._duplicate_clusters[close_duplicates[0]].add(_a )\r\n\r\n\t\tdef \t\t\t__UpperCAmelCase ( self ):\r\n\t\t\t\t__a =\t\t\t[]\r\n\t\t\t\tfor base, duplicates in self._duplicate_clusters.items():\r\n\t\t\t\t\t\t__a =\t\t\t[base] + list(_a )\r\n\t\t\t\t\t\t# reformat the cluster to be a list of dict\r\n\t\t\t\t\t\t__a =\t\t\t[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]\r\n\t\t\t\t\t\tduplicate_clusters.append(_a )\r\n\t\t\t\treturn duplicate_clusters\r\n\r\n\r\n\r\n\r\n\r\n\t\tdef \t\t\t__UpperCAmelCase ( self\t\t\t\t\t\t\t, _a ):\r\n\t\t\t\t__a =\t\t\tself.get_duplicate_clusters()\r\n\t\t\t\twith open(_a\t\t\t\t\t\t\t, '''w''' ) as f:\r\n\t\t\t\t\t\tjson.dump(_a\t\t\t\t\t\t\t, _a )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : List[str]\t\t\t) -> int:\r\n\t\t__a , __a =\t\t\telement\r\n\t\t__a =\t\t\tget_min_hash([t for t in NON_ALPHA.split(data['''content''']\t\t\t) if len(t.strip()\t\t\t) > 0]\t\t\t)\r\n\t\tif min_hash is not None:\r\n\t\t\t\treturn (index, data[\"repo_name\"], data[\"path\"]), min_hash\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : Type[Dataset]\t\t\t) -> str:\r\n\t\twith mp.Pool() as pool:\r\n\t\t\t\tfor data in pool.imap_unordered(\r\n\t\t\t\t _compute_min_hash ,\t\t\t\t\tThreadedIterator(lowerCAmelCase__ ,\t\t\t\t\tmax_queue_size=10000\t\t\t) ,\t\t\t\t\tchunksize=100 ,\t\t\t\t\t):\r\n\t\t\t\t\t\tif data is not None:\r\n\t\t\t\t\t\t\t\tyield data\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : Type[Dataset] ,\t\t\t\t\tlowerCAmelCase__ : float\t\t\t) -> Dict:\r\n\t\t__a =\t\t\tDuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase__\t\t\t)\r\n\r\n\t\tfor filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase__\t\t\t)\t\t\t) ,\t\t\t\t\tmax_queue_size=100\t\t\t)\t\t\t):\r\n\t\t\t\tdi.add(lowerCAmelCase__ ,\t\t\t\t\tlowerCAmelCase__\t\t\t)\r\n\r\n\t\t# Returns a List[Cluster] where Cluster is List[str] with the filenames.\r\n\t\treturn di.get_duplicate_clusters()\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : str ,\t\t\t\t\tlowerCAmelCase__ : str\t\t\t) -> float:\r\n\t\t__a =\t\t\tget_tokens(lowerCAmelCase__\t\t\t)\r\n\t\t__a =\t\t\tget_tokens(lowerCAmelCase__\t\t\t)\r\n\t\treturn len(tokensa & tokensa\t\t\t) / len(tokensa | tokensa\t\t\t)\r\n\r\n\r\nlowercase_\t\t\t = None\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : Optional[Any] ,\t\t\t\t\tlowerCAmelCase__ : Union[str, Any]\t\t\t) -> Any:\r\n\t\t__a =\t\t\t[]\r\n\t\tfor elementa in cluster:\r\n\t\t\t\t__a =\t\t\t_shared_dataset[elementa['''base_index''']]['''content''']\r\n\t\t\t\tfor elementa in extremes:\r\n\t\t\t\t\t\t__a =\t\t\t_shared_dataset[elementa['''base_index''']]['''content''']\r\n\t\t\t\t\t\tif jaccard_similarity(lowerCAmelCase__ ,\t\t\t\t\tlowerCAmelCase__\t\t\t) >= jaccard_threshold:\r\n\t\t\t\t\t\t\t\telementa[\"copies\"] += 1\r\n\t\t\t\t\t\t\t\tbreak\r\n\t\t\t\telse:\r\n\t\t\t\t\t\t__a =\t\t\t1\r\n\t\t\t\t\t\textremes.append(lowerCAmelCase__\t\t\t)\r\n\t\treturn extremes\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : Union[str, Any] ,\t\t\t\t\tlowerCAmelCase__ : Optional[Any] ,\t\t\t\t\tlowerCAmelCase__ : Optional[int]\t\t\t) -> Optional[int]:\r\n\t\tglobal _shared_dataset\r\n\t\t__a =\t\t\tdataset\r\n\t\t__a =\t\t\t[]\r\n\t\t__a =\t\t\tpartial(_find_cluster_extremes_shared ,\t\t\t\t\tjaccard_threshold=lowerCAmelCase__\t\t\t)\r\n\t\twith mp.Pool() as pool:\r\n\t\t\t\tfor extremes in tqdm(\r\n\t\t\t\t pool.imap_unordered(\r\n\t\t\t\t lowerCAmelCase__ ,\t\t\t\t\tlowerCAmelCase__ ,\t\t\t\t\t) ,\t\t\t\t\ttotal=len(lowerCAmelCase__\t\t\t) ,\t\t\t\t\t):\r\n\t\t\t\t\t\textremes_list.append(lowerCAmelCase__\t\t\t)\r\n\t\treturn extremes_list\r\n\r\n\r\n\r\n\r\ndef lowercase\t\t\t\t\t\t\t(\t\tlowerCAmelCase__ : Type[Dataset] ,\t\t\t\t\tlowerCAmelCase__ : float = 0.85\t\t\t) -> Tuple[Type[Dataset], List[List[Dict]]]:\r\n\t\t__a =\t\t\tmake_duplicate_clusters(lowerCAmelCase__ ,\t\t\t\t\tlowerCAmelCase__\t\t\t)\r\n\t\t__a =\t\t\t{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}\r\n\t\t__a =\t\t\t{}\r\n\t\t__a =\t\t\tfind_extremes(lowerCAmelCase__ ,\t\t\t\t\tlowerCAmelCase__ ,\t\t\t\t\tlowerCAmelCase__\t\t\t)\r\n\t\tfor extremes in extremes_clusters:\r\n\t\t\t\tfor element in extremes:\r\n\t\t\t\t\t\t__a =\t\t\telement\r\n\t\t__a =\t\t\tduplicate_indices - set(extreme_dict.keys()\t\t\t)\r\n\t\t__a =\t\t\tdataset.filter(lambda lowerCAmelCase__ ,\t\t\t\t\tlowerCAmelCase__\t\t\t: idx not in remove_indices ,\t\t\t\t\twith_indices=lowerCAmelCase__\t\t\t)\r\n\r\n\t\t# update duplicate_clusters\r\n\t\tfor cluster in duplicate_clusters:\r\n\t\t\t\tfor element in cluster:\r\n\t\t\t\t\t\t__a =\t\t\telement['''base_index'''] in extreme_dict\r\n\t\t\t\t\t\tif element[\"is_extreme\"]:\r\n\t\t\t\t\t\t\t\t__a =\t\t\textreme_dict[element['''base_index''']]['''copies''']\r\n\r\n\t\tprint(f'''Original dataset size: {len(lowerCAmelCase__\t\t\t)}'''\t\t\t)\r\n\t\tprint(f'''Number of duplicate clusters: {len(lowerCAmelCase__\t\t\t)}'''\t\t\t)\r\n\t\tprint(f'''Files in duplicate cluster: {len(lowerCAmelCase__\t\t\t)}'''\t\t\t)\r\n\t\tprint(f'''Unique files in duplicate cluster: {len(lowerCAmelCase__\t\t\t)}'''\t\t\t)\r\n\t\tprint(f'''Filtered dataset size: {len(lowerCAmelCase__\t\t\t)}'''\t\t\t)\r\n\r\n\t\treturn ds_filter, duplicate_clusters\r\n\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":45,"string":"45"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":273,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\nimport argparse\nimport json\nimport os\n\nimport fairseq\nimport torch\nfrom fairseq.data import Dictionary\n\nfrom transformers import (\n WavaVecaConformerConfig,\n WavaVecaConformerForCTC,\n WavaVecaConformerForPreTraining,\n WavaVecaCTCTokenizer,\n WavaVecaFeatureExtractor,\n WavaVecaProcessor,\n logging,\n)\n\n\nlogging.set_verbosity_info()\nUpperCamelCase__ =\tlogging.get_logger(__name__)\n\nUpperCamelCase__ =\t{\n \"post_extract_proj\": \"feature_projection.projection\",\n \"encoder.pos_conv.0\": \"encoder.pos_conv_embed.conv\",\n \"self_attn.linear_k\": \"encoder.layers.*.self_attn.linear_k\",\n \"self_attn.linear_v\": \"encoder.layers.*.self_attn.linear_v\",\n \"self_attn.linear_q\": \"encoder.layers.*.self_attn.linear_q\",\n \"self_attn.pos_bias_u\": \"encoder.layers.*.self_attn.pos_bias_u\",\n \"self_attn.pos_bias_v\": \"encoder.layers.*.self_attn.pos_bias_v\",\n \"self_attn.linear_out\": \"encoder.layers.*.self_attn.linear_out\",\n \"self_attn.linear_pos\": \"encoder.layers.*.self_attn.linear_pos\",\n \"self_attn.rotary_emb\": \"encoder.embed_positions\",\n \"self_attn_layer_norm\": \"encoder.layers.*.self_attn_layer_norm\",\n \"conv_module.pointwise_conv1\": \"encoder.layers.*.conv_module.pointwise_conv1\",\n \"conv_module.pointwise_conv2\": \"encoder.layers.*.conv_module.pointwise_conv2\",\n \"conv_module.depthwise_conv\": \"encoder.layers.*.conv_module.depthwise_conv\",\n \"conv_module.batch_norm\": \"encoder.layers.*.conv_module.batch_norm\",\n \"conv_module.layer_norm\": \"encoder.layers.*.conv_module.layer_norm\",\n \"ffn1.w_1\": \"encoder.layers.*.ffn1.intermediate_dense\",\n \"ffn1.w_2\": \"encoder.layers.*.ffn1.output_dense\",\n \"ffn1.layer_norm\": \"encoder.layers.*.ffn1_layer_norm\",\n \"ffn2.w_1\": \"encoder.layers.*.ffn2.intermediate_dense\",\n \"ffn2.w_2\": \"encoder.layers.*.ffn2.output_dense\",\n \"ffn2.layer_norm\": \"encoder.layers.*.ffn2_layer_norm\",\n \"final_layer_norm\": \"encoder.layers.*.final_layer_norm\",\n \"encoder.layer_norm\": \"encoder.layer_norm\",\n \"w2v_model.layer_norm\": \"feature_projection.layer_norm\",\n \"quantizer.weight_proj\": \"quantizer.weight_proj\",\n \"quantizer.vars\": \"quantizer.codevectors\",\n \"project_q\": \"project_q\",\n \"final_proj\": \"project_hid\",\n \"w2v_encoder.proj\": \"lm_head\",\n \"mask_emb\": \"masked_spec_embed\",\n}\nUpperCamelCase__ =\t[\n \"lm_head\",\n \"quantizer.weight_proj\",\n \"quantizer.codevectors\",\n \"project_q\",\n \"project_hid\",\n]\n\n\n\n\n\ndef _UpperCamelCase (a__\t\t\t\t:Optional[Any]\t\t\t\t, a__\t\t\t\t:Dict\t\t\t\t, a__\t\t\t\t:str\t\t\t\t, a__\t\t\t\t:Optional[int]\t\t\t\t, a__\t\t\t\t:str ):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\t\t\t\t\t\t\tfor attribute in key.split(\"\"\".\"\"\" ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = getattr(a__\t\t\t\t, a__ )\n\n\t\t\t\t\t\t\tif weight_type is not None:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = getattr(a__\t\t\t\t, a__ ).shape\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = hf_pointer.shape\n\n\t\t\t\t\t\t\tif hf_shape != value.shape:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"Shape of hf {key + \".\" + weight_type if weight_type is not None else \"\"} is {hf_shape}, but should be\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\" {value.shape} for {full_name}\"\"\" )\n\n\t\t\t\t\t\t\tif weight_type == \"weight\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\telif weight_type == \"weight_g\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\telif weight_type == \"weight_v\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\telif weight_type == \"bias\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\telif weight_type == \"running_mean\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\telif weight_type == \"running_var\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\telif weight_type == \"num_batches_tracked\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\telif weight_type == \"inv_freq\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\n\t\t\t\t\t\t\tlogger.info(f\"\"\"{key + \".\" + weight_type if weight_type is not None else \"\"} was initialized from {full_name}.\"\"\" )\n\n\n\n\n\ndef _UpperCamelCase (a__\t\t\t\t:Tuple\t\t\t\t, a__\t\t\t\t:Union[str, Any]\t\t\t\t, a__\t\t\t\t:Any ):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = []\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = fairseq_model.state_dict()\n\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = hf_model.wavaveca_conformer.feature_extractor\n\n\t\t\t\t\t\t\tfor name, value in fairseq_dict.items():\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = False\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif \"conv_layers\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tload_conv_layer(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t a__\t\t\t\t, a__\t\t\t\t, a__\t\t\t\t, a__\t\t\t\t, hf_model.config.feat_extract_norm == \"\"\"group\"\"\"\t\t\t\t, )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = True\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor key, mapped_key in MAPPING.items():\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"wav2vec2_conformer.\"\"\" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif key in name or key.split(\"\"\"w2v_model.\"\"\" )[-1] == name.split(\"\"\".\"\"\" )[0]:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = True\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif \"*\" in mapped_key:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = name.split(a__ )[0].split(\"\"\".\"\"\" )[-2]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = mapped_key.replace(\"\"\"*\"\"\"\t\t\t\t, a__ )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif \"pos_bias_u\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = None\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"pos_bias_v\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = None\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"weight_g\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"weight_g\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"weight_v\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"weight_v\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"bias\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"bias\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"weight\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# TODO: don't match quantizer.weight_proj\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"weight\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"running_mean\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"running_mean\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"inv_freq\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"inv_freq\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"running_var\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"running_var\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"num_batches_tracked\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"num_batches_tracked\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = None\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tset_recursively(a__\t\t\t\t, a__\t\t\t\t, a__\t\t\t\t, a__\t\t\t\t, a__ )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not is_used:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tunused_weights.append(a__ )\n\n\t\t\t\t\t\t\tlogger.warning(f\"\"\"Unused weights: {unused_weights}\"\"\" )\n\n\n\n\n\ndef _UpperCamelCase (a__\t\t\t\t:Tuple\t\t\t\t, a__\t\t\t\t:Optional[int]\t\t\t\t, a__\t\t\t\t:Optional[Any]\t\t\t\t, a__\t\t\t\t:List[str]\t\t\t\t, a__\t\t\t\t:List[Any] ):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = full_name.split(\"\"\"conv_layers.\"\"\" )[-1]\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = name.split(\"\"\".\"\"\" )\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = int(items[0] )\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = int(items[1] )\n\n\t\t\t\t\t\t\tif type_id == 0:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif \"bias\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"{full_name} has size {value.shape}, but\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.info(f\"\"\"Feat extract conv layer {layer_id} was initialized from {full_name}.\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"weight\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"{full_name} has size {value.shape}, but\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.info(f\"\"\"Feat extract conv layer {layer_id} was initialized from {full_name}.\"\"\" )\n\t\t\t\t\t\t\telif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif \"bias\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"{full_name} has size {value.shape}, but\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.info(f\"\"\"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telif \"weight\" in name:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"{full_name} has size {value.shape}, but\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = value\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.info(f\"\"\"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.\"\"\" )\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tunused_weights.append(a__ )\n\n\n\n\n\n@torch.no_grad()\ndef _UpperCamelCase (a__\t\t\t\t:List[str]\t\t\t\t, a__\t\t\t\t:Optional[int]\t\t\t\t, a__\t\t\t\t:Optional[Any]=None\t\t\t\t, a__\t\t\t\t:List[Any]=None\t\t\t\t, a__\t\t\t\t:Optional[Any]=True ):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\t\t\t\t\t\t\tif config_path is not None:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = WavaVecaConformerConfig.from_pretrained(a__\t\t\t\t, hidden_act=\"\"\"swish\"\"\" )\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = WavaVecaConformerConfig()\n\n\t\t\t\t\t\t\tif \"rope\" in checkpoint_path:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = \"\"\"rotary\"\"\"\n\n\t\t\t\t\t\t\tif is_finetuned:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif dict_path:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = Dictionary.load(a__ )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# important change bos & pad token id since CTC symbol is and\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# not as in fairseq\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = target_dict.pad_index\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = target_dict.bos_index\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = target_dict.eos_index\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = len(target_dict.symbols )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = os.path.join(a__\t\t\t\t, \"\"\"vocab.json\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not os.path.isdir(a__ ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.error(\"\"\"--pytorch_dump_folder_path ({}) should be a directory\"\"\".format(a__ ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tos.makedirs(a__\t\t\t\t, exist_ok=a__ )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = target_dict.indices\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# fairseq has the and switched\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = 0\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = 1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith open(a__\t\t\t\t, \"\"\"w\"\"\"\t\t\t\t, encoding=\"\"\"utf-8\"\"\" ) as vocab_handle:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tjson.dump(a__\t\t\t\t, a__ )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = WavaVecaCTCTokenizer(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t a__\t\t\t\t, unk_token=target_dict.unk_word\t\t\t\t, pad_token=target_dict.pad_word\t\t\t\t, bos_token=target_dict.bos_word\t\t\t\t, eos_token=target_dict.eos_word\t\t\t\t, word_delimiter_token=\"\"\"|\"\"\"\t\t\t\t, do_lower_case=a__\t\t\t\t, )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = True if config.feat_extract_norm == \"\"\"layer\"\"\" else False\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = WavaVecaFeatureExtractor(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t feature_size=1\t\t\t\t, sampling_rate=1_6000\t\t\t\t, padding_value=0\t\t\t\t, do_normalize=a__\t\t\t\t, return_attention_mask=a__\t\t\t\t, )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = WavaVecaProcessor(feature_extractor=a__\t\t\t\t, tokenizer=a__ )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tprocessor.save_pretrained(a__ )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = WavaVecaConformerForCTC(a__ )\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = WavaVecaConformerForPreTraining(a__ )\n\n\t\t\t\t\t\t\tif is_finetuned:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__\t\t\t = fairseq.checkpoint_utils.load_model_ensemble_and_task(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t [checkpoint_path]\t\t\t\t, arg_overrides={\"\"\"data\"\"\": \"\"\"/\"\"\".join(dict_path.split(\"\"\"/\"\"\" )[:-1] )} )\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = argparse.Namespace(task=\"\"\"audio_pretraining\"\"\" )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = fairseq.tasks.setup_task(a__ )\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__\t\t\t = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]\t\t\t\t, task=a__ )\n\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = model[0].eval()\n\n\t\t\t\t\t\t\trecursively_load_weights(a__\t\t\t\t, a__\t\t\t\t, not is_finetuned )\n\n\t\t\t\t\t\t\thf_wavavec.save_pretrained(a__ )\n\n\nif __name__ == \"__main__\":\n\t\t\t\t\tUpperCamelCase__ =\targparse.ArgumentParser()\n\t\t\t\t\tparser.add_argument(\"--pytorch_dump_folder_path\", default=None, type=str, help=\"Path to the output PyTorch model.\")\n\t\t\t\t\tparser.add_argument(\"--checkpoint_path\", default=None, type=str, help=\"Path to fairseq checkpoint\")\n\t\t\t\t\tparser.add_argument(\"--dict_path\", default=None, type=str, help=\"Path to dict of fine-tuned model\")\n\t\t\t\t\tparser.add_argument(\"--config_path\", default=None, type=str, help=\"Path to hf config.json of model to convert\")\n\t\t\t\t\tparser.add_argument(\n\t\t\t\t\t \"--not_finetuned\", action=\"store_true\", help=\"Whether the model to convert is a fine-tuned model or not\"\n\t\t\t\t\t)\n\t\t\t\t\tUpperCamelCase__ =\tparser.parse_args()\n\t\t\t\t\tconvert_wavaveca_conformer_checkpoint(\n\t\t\t\t\t args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned\n\t\t\t\t\t)\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":87,"string":"87"},"style_context":{"kind":"string","value":"\n\n\n\n\n\nUpperCamelCase__ =\t{\n \"meter\": \"m\",\n \"kilometer\": \"km\",\n \"megametre\": \"Mm\",\n \"gigametre\": \"Gm\",\n \"terametre\": \"Tm\",\n \"petametre\": \"Pm\",\n \"exametre\": \"Em\",\n \"zettametre\": \"Zm\",\n \"yottametre\": \"Ym\",\n}\n# Exponent of the factor(meter)\nUpperCamelCase__ =\t{\n \"m\": 0,\n \"km\": 3,\n \"Mm\": 6,\n \"Gm\": 9,\n \"Tm\": 12,\n \"Pm\": 15,\n \"Em\": 18,\n \"Zm\": 21,\n \"Ym\": 24,\n}\n\n\n\n\n\ndef _UpperCamelCase (a__\t\t\t\t:float\t\t\t\t, a__\t\t\t\t:str\t\t\t\t, a__\t\t\t\t:str ):\n\n\n\n\n\n\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = from_type.lower().strip(\"\"\"s\"\"\" )\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = to_type.lower().strip(\"\"\"s\"\"\" )\n\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = UNIT_SYMBOL.get(a__\t\t\t\t, a__ )\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = UNIT_SYMBOL.get(a__\t\t\t\t, a__ )\n\n\t\t\t\t\t\t\tif from_sanitized not in METRIC_CONVERSION:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = (\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"Invalid 'from_type' value: {from_type!r}.\\n\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"Conversion abbreviations are: {\", \".join(a__ )}\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(a__ )\n\t\t\t\t\t\t\tif to_sanitized not in METRIC_CONVERSION:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = (\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"Invalid 'to_type' value: {to_type!r}.\\n\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\"Conversion abbreviations are: {\", \".join(a__ )}\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(a__ )\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = METRIC_CONVERSION[from_sanitized]\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = METRIC_CONVERSION[to_sanitized]\n\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = 1\n\n\t\t\t\t\t\t\tif from_exponent > to_exponent:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = from_exponent - to_exponent\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCamelCase__\t\t\t = -(to_exponent - from_exponent)\n\n\t\t\t\t\t\t\treturn value * pow(10\t\t\t\t, a__ )\n\n\nif __name__ == \"__main__\":\n\t\t\t\t\tfrom doctest import testmod\n\n\t\t\t\t\ttestmod()\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":87,"string":"87"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":274,"cells":{"code":{"kind":"string","value":"\n\nimport re\n\nfrom filelock import FileLock\n\n\ntry:\n import nltk\n\n snake_case_\t\t\t = True\nexcept (ImportError, ModuleNotFoundError):\n snake_case_\t\t\t = False\n\nif NLTK_AVAILABLE:\n with FileLock('''.lock''') as lock:\n nltk.download('''punkt''', quiet=True)\n\ndef \tsnake_case__\t\t\t( SCREAMING_SNAKE_CASE_\t\t: str\t\t\t):\n\n\n\n '''simple docstring'''\n\n\n\n\n\n re.sub(''\t, ''\t, SCREAMING_SNAKE_CASE_\t\t\t) # remove pegasus newline char\n assert NLTK_AVAILABLE, \"nltk must be installed to separate newlines between sentences. (pip install nltk)\"\n return \"\\n\".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE_\t\t\t)\t\t\t)\n\n"},"code_codestyle":{"kind":"number","value":214,"string":"214"},"style_context":{"kind":"string","value":"\n\nimport itertools\nfrom dataclasses import dataclass\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport pandas as pd\nimport pyarrow as pa\n\nimport datasets\nimport datasets.config\nfrom datasets.features.features import require_storage_cast\nfrom datasets.table import table_cast\nfrom datasets.utils.py_utils import Literal\n\n\nsnake_case_\t\t\t = datasets.utils.logging.get_logger(__name__)\n\nsnake_case_\t\t\t = ['''names''', '''prefix''']\nsnake_case_\t\t\t = ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']\nsnake_case_\t\t\t = ['''encoding_errors''', '''on_bad_lines''']\nsnake_case_\t\t\t = ['''date_format''']\n\n\n@dataclass\nclass SCREAMING_SNAKE_CASE__\t\t\t\t\t(datasets.BuilderConfig ):\n __lowerCamelCase\t\t\t\t\t\t: str\t\t\t\t\t\t\t\t\t\t\t\t\t\t= \",\"\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[Union[int, List[int], str]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= \"infer\"\n __lowerCamelCase\t\t\t\t\t\t: Optional[List[str]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[List[str]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[Union[int, str, List[int], List[str]]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[Union[List[int], List[str]]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= True\n __lowerCamelCase\t\t\t\t\t\t: Optional[Literal[\"c\", \"python\", \"pyarrow\"]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Dict[Union[int, str], Callable[[Any], Any]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[list]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[list]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= False\n __lowerCamelCase\t\t\t\t\t\t: Optional[Union[int, List[int]]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[Union[str, List[str]]]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= True\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= True\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= False\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= True\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: str\t\t\t\t\t\t\t\t\t\t\t\t\t\t= \".\"\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: str\t\t\t\t\t\t\t\t\t\t\t\t\t\t= '\"'\n __lowerCamelCase\t\t\t\t\t\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t\t= 0\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= True\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= True\n __lowerCamelCase\t\t\t\t\t\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t\t= 0\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= True\n __lowerCamelCase\t\t\t\t\t\t: bool\t\t\t\t\t\t\t\t\t\t\t\t\t\t= False\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: int\t\t\t\t\t\t\t\t\t\t\t\t\t\t= 1_0000\n __lowerCamelCase\t\t\t\t\t\t: Optional[datasets.Features]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= \"strict\"\n __lowerCamelCase\t\t\t\t\t\t: Literal[\"error\", \"warn\", \"skip\"]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= \"error\"\n __lowerCamelCase\t\t\t\t\t\t: Optional[str]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= None\n\n\n\n\n\n def snake_case_\t\t\t\t( self):\n if self.delimiter is not None:\n lowercase__ : List[Any] = self.delimiter\n if self.column_names is not None:\n lowercase__ : Optional[int] = self.column_names\n\n\n\n\n\n @property\n def snake_case_\t\t\t\t( self):\n lowercase__ : Dict = {\n 'sep': self.sep,\n 'header': self.header,\n 'names': self.names,\n 'index_col': self.index_col,\n 'usecols': self.usecols,\n 'prefix': self.prefix,\n 'mangle_dupe_cols': self.mangle_dupe_cols,\n 'engine': self.engine,\n 'converters': self.converters,\n 'true_values': self.true_values,\n 'false_values': self.false_values,\n 'skipinitialspace': self.skipinitialspace,\n 'skiprows': self.skiprows,\n 'nrows': self.nrows,\n 'na_values': self.na_values,\n 'keep_default_na': self.keep_default_na,\n 'na_filter': self.na_filter,\n 'verbose': self.verbose,\n 'skip_blank_lines': self.skip_blank_lines,\n 'thousands': self.thousands,\n 'decimal': self.decimal,\n 'lineterminator': self.lineterminator,\n 'quotechar': self.quotechar,\n 'quoting': self.quoting,\n 'escapechar': self.escapechar,\n 'comment': self.comment,\n 'encoding': self.encoding,\n 'dialect': self.dialect,\n 'error_bad_lines': self.error_bad_lines,\n 'warn_bad_lines': self.warn_bad_lines,\n 'skipfooter': self.skipfooter,\n 'doublequote': self.doublequote,\n 'memory_map': self.memory_map,\n 'float_precision': self.float_precision,\n 'chunksize': self.chunksize,\n 'encoding_errors': self.encoding_errors,\n 'on_bad_lines': self.on_bad_lines,\n 'date_format': self.date_format,\n }\n\n # some kwargs must not be passed if they don't have a default value\n # some others are deprecated and we can also not pass them if they are the default value\n for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:\n if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()\t\t\t\t\t\t, a):\n del pd_read_csv_kwargs[pd_read_csv_parameter]\n\n # Remove 2.0 new arguments\n if not (datasets.config.PANDAS_VERSION.major >= 2):\n for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:\n del pd_read_csv_kwargs[pd_read_csv_parameter]\n\n # Remove 1.3 new arguments\n if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):\n for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:\n del pd_read_csv_kwargs[pd_read_csv_parameter]\n\n return pd_read_csv_kwargs\n\n\n\nclass SCREAMING_SNAKE_CASE__\t\t\t\t\t(datasets.ArrowBasedBuilder ):\n __lowerCamelCase\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t\t= CsvConfig\n\n\n\n\n\n def snake_case_\t\t\t\t( self):\n return datasets.DatasetInfo(features=self.config.features)\n\n\n\n\n\n def snake_case_\t\t\t\t( self\t\t\t\t\t\t, a):\n if not self.config.data_files:\n raise ValueError(f\"\"\"At least one data file must be specified, but got data_files={self.config.data_files}\"\"\")\n lowercase__ : Any = dl_manager.download_and_extract(self.config.data_files)\n if isinstance(a\t\t\t\t\t\t, (str, list, tuple)):\n lowercase__ : List[str] = data_files\n if isinstance(a\t\t\t\t\t\t, a):\n lowercase__ : Optional[Any] = [files]\n lowercase__ : Optional[int] = [dl_manager.iter_files(a) for file in files]\n return [datasets.SplitGenerator(name=datasets.Split.TRAIN\t\t\t\t\t\t, gen_kwargs={'files': files})]\n lowercase__ : int = []\n for split_name, files in data_files.items():\n if isinstance(a\t\t\t\t\t\t, a):\n lowercase__ : Optional[int] = [files]\n lowercase__ : Tuple = [dl_manager.iter_files(a) for file in files]\n splits.append(datasets.SplitGenerator(name=a\t\t\t\t\t\t, gen_kwargs={'files': files}))\n return splits\n\n\n\n\n\n def snake_case_\t\t\t\t( self\t\t\t\t\t\t, a):\n if self.config.features is not None:\n lowercase__ : Optional[int] = self.config.features.arrow_schema\n if all(not require_storage_cast(a) for feature in self.config.features.values()):\n # cheaper cast\n lowercase__ : Dict = pa.Table.from_arrays([pa_table[field.name] for field in schema]\t\t\t\t\t\t, schema=a)\n else:\n # more expensive cast; allows str <-> int/float or str to Audio for example\n lowercase__ : Optional[Any] = table_cast(a\t\t\t\t\t\t, a)\n return pa_table\n\n\n\n\n\n def snake_case_\t\t\t\t( self\t\t\t\t\t\t, a):\n lowercase__ : List[Any] = self.config.features.arrow_schema if self.config.features else None\n # dtype allows reading an int column as str\n lowercase__ : Optional[int] = (\n {\n name: dtype.to_pandas_dtype() if not require_storage_cast(a) else object\n for name, dtype, feature in zip(schema.names\t\t\t\t\t\t, schema.types\t\t\t\t\t\t, self.config.features.values())\n }\n if schema is not None\n else None\n )\n for file_idx, file in enumerate(itertools.chain.from_iterable(a)):\n lowercase__ : int = pd.read_csv(a\t\t\t\t\t\t, iterator=a\t\t\t\t\t\t, dtype=a\t\t\t\t\t\t, **self.config.pd_read_csv_kwargs)\n try:\n for batch_idx, df in enumerate(a):\n lowercase__ : List[str] = pa.Table.from_pandas(a)\n # Uncomment for debugging (will print the Arrow table size and elements)\n # logger.warning(f\"pa_table: {pa_table} num rows: {pa_table.num_rows}\")\n # logger.warning('\\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))\n yield (file_idx, batch_idx), self._cast_table(a)\n except ValueError as e:\n logger.error(f\"\"\"Failed to read file '{file}' with error {type(a)}: {e}\"\"\")\n raise\n\n"},"style_context_codestyle":{"kind":"number","value":214,"string":"214"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":275,"cells":{"code":{"kind":"string","value":"\rimport argparse\rimport json\rimport pickle\rfrom pathlib import Path\r\rimport requests\rimport torch\rfrom huggingface_hub import hf_hub_download\rfrom PIL import Image\r\rfrom transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig\rfrom transformers.utils import logging\r\r\rlogging.set_verbosity_info()\r__UpperCAmelCase\t\t\t\t\t\t= logging.get_logger(__name__)\r\r\rdef \t\t\t\tA__\t\t\t\t( __lowerCamelCase\t\t):\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= SwinConfig.from_pretrained(\r '''microsoft/swin-tiny-patch4-window7-224''', out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']\t\t)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= MaskFormerConfig(backbone_config=__lowerCamelCase\t\t)\r\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= '''huggingface/label-files'''\r if \"ade20k-full\" in model_name:\r # this should be ok\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 8_47\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= '''maskformer-ade20k-full-id2label.json'''\r elif \"ade\" in model_name:\r # this should be ok\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 1_50\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= '''ade20k-id2label.json'''\r elif \"coco-stuff\" in model_name:\r # this should be ok\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 1_71\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= '''maskformer-coco-stuff-id2label.json'''\r elif \"coco\" in model_name:\r # TODO\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 1_33\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= '''coco-panoptic-id2label.json'''\r elif \"cityscapes\" in model_name:\r # this should be ok\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 19\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= '''cityscapes-id2label.json'''\r elif \"vistas\" in model_name:\r # this should be ok\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 65\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= '''mapillary-vistas-id2label.json'''\r\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= json.load(open(hf_hub_download(__lowerCamelCase, __lowerCamelCase, repo_type='''dataset'''\t\t), '''r'''\t\t)\t\t)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= {int(__lowerCamelCase\t\t): v for k, v in idalabel.items()}\r\r return config\r\r\rdef \t\t\t\tA__\t\t\t\t( __lowerCamelCase\t\t):\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= []\r # stem\r # fmt: off\r rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')\t\t)\r rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')\t\t)\r rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')\t\t)\r rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')\t\t)\r # stages\r for i in range(len(config.backbone_config.depths\t\t)\t\t):\r for j in range(config.backbone_config.depths[i]\t\t):\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')\t\t)\r\r if i < 3:\r rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')\t\t)\r rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')\t\t)\r rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')\t\t)\r rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')\t\t)\r\r # FPN\r rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')\t\t)\r rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')\t\t)\r rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')\t\t)\r for source_index, target_index in zip(range(3, 0, -1\t\t), range(0, 3\t\t)\t\t):\r rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')\t\t)\r rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')\t\t)\r rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')\t\t)\r rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')\t\t)\r\r # Transformer decoder\r for idx in range(config.decoder_config.decoder_layers\t\t):\r # self-attention out projection\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')\t\t)\r # cross-attention out projection\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')\t\t)\r # MLP 1\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')\t\t)\r # MLP 2\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')\t\t)\r # layernorm 1 (self-attention layernorm)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')\t\t)\r # layernorm 2 (cross-attention layernorm)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')\t\t)\r # layernorm 3 (final layernorm)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')\t\t)\r\r rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')\t\t)\r rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')\t\t)\r\r # heads on top\r rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')\t\t)\r\r rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')\t\t)\r rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')\t\t)\r\r rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')\t\t)\r rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')\t\t)\r\r for i in range(3\t\t):\r rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')\t\t)\r rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')\t\t)\r # fmt: on\r\r return rename_keys\r\r\rdef \t\t\t\tA__\t\t\t\t( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase\t\t):\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= dct.pop(__lowerCamelCase\t\t)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= val\r\r\rdef \t\t\t\tA__\t\t\t\t( __lowerCamelCase, __lowerCamelCase\t\t):\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= [int(backbone_config.embed_dim * 2**i\t\t) for i in range(len(backbone_config.depths\t\t)\t\t)]\r for i in range(len(backbone_config.depths\t\t)\t\t):\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= num_features[i]\r for j in range(backbone_config.depths[i]\t\t):\r # fmt: off\r # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''\t\t)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''\t\t)\r # next, add query, keys and values (in that order) to the state dict\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[:dim, :]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[: dim]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[\r dim : dim * 2, :\r ]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[\r dim : dim * 2\r ]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[\r -dim :, :\r ]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[-dim :]\r # fmt: on\r\r\rdef \t\t\t\tA__\t\t\t\t( __lowerCamelCase, __lowerCamelCase\t\t):\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= config.decoder_config.hidden_size\r for idx in range(config.decoder_config.decoder_layers\t\t):\r # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''\t\t)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''\t\t)\r # next, add query, keys and values (in that order) to the state dict\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[: hidden_size, :]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[:config.hidden_size]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[hidden_size : hidden_size * 2, :]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[hidden_size : hidden_size * 2]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[-hidden_size :, :]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[-hidden_size :]\r # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''\t\t)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''\t\t)\r # next, add query, keys and values (in that order) to the state dict\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[: hidden_size, :]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[:config.hidden_size]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[hidden_size : hidden_size * 2, :]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[hidden_size : hidden_size * 2]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_weight[-hidden_size :, :]\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= in_proj_bias[-hidden_size :]\r # fmt: on\r\r\rdef \t\t\t\tA__\t\t\t\t( ):\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= '''http://images.cocodataset.org/val2017/000000039769.jpg'''\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= Image.open(requests.get(__lowerCamelCase, stream=__lowerCamelCase\t\t).raw\t\t)\r return im\r\r\r@torch.no_grad()\rdef \t\t\t\tA__\t\t\t\t( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = False\t\t):\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= get_maskformer_config(__lowerCamelCase\t\t)\r\r # load original state_dict\r with open(__lowerCamelCase, '''rb'''\t\t) as f:\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= pickle.load(__lowerCamelCase\t\t)\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= data['''model''']\r\r # for name, param in state_dict.items():\r # print(name, param.shape)\r\r # rename keys\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= create_rename_keys(__lowerCamelCase\t\t)\r for src, dest in rename_keys:\r rename_key(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase\t\t)\r read_in_swin_q_k_v(__lowerCamelCase, config.backbone_config\t\t)\r read_in_decoder_q_k_v(__lowerCamelCase, __lowerCamelCase\t\t)\r\r # update to torch tensors\r for key, value in state_dict.items():\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= torch.from_numpy(__lowerCamelCase\t\t)\r\r # load 🤗 model\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= MaskFormerForInstanceSegmentation(__lowerCamelCase\t\t)\r model.eval()\r\r for name, param in model.named_parameters():\r print(__lowerCamelCase, param.shape\t\t)\r\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= model.load_state_dict(__lowerCamelCase, strict=__lowerCamelCase\t\t)\r assert missing_keys == [\r \"model.pixel_level_module.encoder.model.layernorm.weight\",\r \"model.pixel_level_module.encoder.model.layernorm.bias\",\r ]\r assert len(__lowerCamelCase\t\t) == 0, F'''Unexpected keys: {unexpected_keys}'''\r\r # verify results\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= prepare_img()\r if \"vistas\" in model_name:\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 65\r elif \"cityscapes\" in model_name:\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 6_55_35\r else:\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= 2_55\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= True if '''ade''' in model_name else False\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= MaskFormerImageProcessor(ignore_index=__lowerCamelCase, reduce_labels=__lowerCamelCase\t\t)\r\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= image_processor(__lowerCamelCase, return_tensors='''pt'''\t\t)\r\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= model(**__lowerCamelCase\t\t)\r\r print('''Logits:''', outputs.class_queries_logits[0, :3, :3]\t\t)\r\r if model_name == \"maskformer-swin-tiny-ade\":\r SCREAMING_SNAKE_CASE_\t\t\t\t\t= torch.tensor(\r [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]\t\t)\r assert torch.allclose(outputs.class_queries_logits[0, :3, :3], __lowerCamelCase, atol=1E-4\t\t)\r print('''Looks ok!'''\t\t)\r\r if pytorch_dump_folder_path is not None:\r print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''\t\t)\r Path(__lowerCamelCase\t\t).mkdir(exist_ok=__lowerCamelCase\t\t)\r model.save_pretrained(__lowerCamelCase\t\t)\r image_processor.save_pretrained(__lowerCamelCase\t\t)\r\r if push_to_hub:\r print('''Pushing model and image processor to the hub...'''\t\t)\r model.push_to_hub(F'''nielsr/{model_name}'''\t\t)\r image_processor.push_to_hub(F'''nielsr/{model_name}'''\t\t)\r\r\rif __name__ == \"__main__\":\r __UpperCAmelCase\t\t\t\t\t\t= argparse.ArgumentParser()\r # Required parameters\r parser.add_argument(\r \"--model_name\",\r default=\"maskformer-swin-tiny-ade\",\r type=str,\r help=(\"Name of the MaskFormer model you\\'d like to convert\",),\r )\r parser.add_argument(\r \"--checkpoint_path\",\r default=\"/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl\",\r type=str,\r help=\"Path to the original state dict (.pth file).\",\r )\r parser.add_argument(\r \"--pytorch_dump_folder_path\", default=None, type=str, help=\"Path to the output PyTorch model directory.\"\r )\r parser.add_argument(\r \"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the converted model to the 🤗 hub.\"\r )\r\r __UpperCAmelCase\t\t\t\t\t\t= parser.parse_args()\r convert_maskformer_checkpoint(\r args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub\r )\r\r\r"},"code_codestyle":{"kind":"number","value":367,"string":"367"},"style_context":{"kind":"string","value":"\rfrom .data_collator import (\r DataCollatorForLanguageModeling,\r DataCollatorForPermutationLanguageModeling,\r DataCollatorForSeqaSeq,\r DataCollatorForSOP,\r DataCollatorForTokenClassification,\r DataCollatorForWholeWordMask,\r DataCollatorWithPadding,\r DefaultDataCollator,\r default_data_collator,\r)\rfrom .metrics import glue_compute_metrics, xnli_compute_metrics\rfrom .processors import (\r DataProcessor,\r InputExample,\r InputFeatures,\r SingleSentenceClassificationProcessor,\r SquadExample,\r SquadFeatures,\r SquadVaProcessor,\r SquadVaProcessor,\r glue_convert_examples_to_features,\r glue_output_modes,\r glue_processors,\r glue_tasks_num_labels,\r squad_convert_examples_to_features,\r xnli_output_modes,\r xnli_processors,\r xnli_tasks_num_labels,\r)\r\r\r"},"style_context_codestyle":{"kind":"number","value":257,"string":"257"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":276,"cells":{"code":{"kind":"string","value":"\r\rfrom typing import Mapping\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...onnx import OnnxSeqaSeqConfigWithPast\rfrom ...utils import logging\r\r\r_SCREAMING_SNAKE_CASE : Tuple \t\t\t=\t\t\tlogging.get_logger(__name__)\r\r_SCREAMING_SNAKE_CASE : Optional[Any] \t\t\t=\t\t\t{\r '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',\r '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',\r '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',\r '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',\r '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',\r}\r\r\r\r\r\r\rclass UpperCAmelCase__\t\t( A__ ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r a =\t\t\t\t\t\"t5\"\r a =\t\t\t\t\t[\"past_key_values\"]\r a =\t\t\t\t\t{\"hidden_size\": \"d_model\", \"num_attention_heads\": \"num_heads\", \"num_hidden_layers\": \"num_layers\"}\r\r\r\r\r\r\r def __init__(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tUnion[str, Any]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tstr=3_2128\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]=512\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]=64\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]=2048\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[Any]=6\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[str]=None\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[str]=8\t, __lowerCamelCase\t\t\t\t\t\t:\t\tint=32\t, __lowerCamelCase\t\t\t\t\t\t:\t\tDict=128\t, __lowerCamelCase\t\t\t\t\t\t:\t\tstr=0.1\t, __lowerCamelCase\t\t\t\t\t\t:\t\tDict=1e-6\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]=1.0\t, __lowerCamelCase\t\t\t\t\t\t:\t\tstr=\"relu\"\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[str]=True\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]=True\t, __lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]=0\t, __lowerCamelCase\t\t\t\t\t\t:\t\tUnion[str, Any]=1\t, **__lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]\t, )\t\t->\t\t\t\t\t\t\tDict:\r SCREAMING_SNAKE_CASE__ = vocab_size\r SCREAMING_SNAKE_CASE__ = d_model\r SCREAMING_SNAKE_CASE__ = d_kv\r SCREAMING_SNAKE_CASE__ = d_ff\r SCREAMING_SNAKE_CASE__ = num_layers\r SCREAMING_SNAKE_CASE__ = (\r num_decoder_layers if num_decoder_layers is not None else self.num_layers\r ) # default = symmetry\r SCREAMING_SNAKE_CASE__ = num_heads\r SCREAMING_SNAKE_CASE__ = relative_attention_num_buckets\r SCREAMING_SNAKE_CASE__ = relative_attention_max_distance\r SCREAMING_SNAKE_CASE__ = dropout_rate\r SCREAMING_SNAKE_CASE__ = layer_norm_epsilon\r SCREAMING_SNAKE_CASE__ = initializer_factor\r SCREAMING_SNAKE_CASE__ = feed_forward_proj\r SCREAMING_SNAKE_CASE__ = use_cache\r\r SCREAMING_SNAKE_CASE__ = self.feed_forward_proj.split('''-'''\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = act_info[-1]\r SCREAMING_SNAKE_CASE__ = act_info[0] == '''gated'''\r\r if len(__lowerCamelCase\t\t\t\t\t) > 1 and act_info[0] != \"gated\" or len(__lowerCamelCase\t\t\t\t\t) > 2:\r raise ValueError(\r f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''\r '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''\r '''\\'gated-gelu\\' or \\'relu\\''''\t\t\t\t\t)\r\r # for backwards compatibility\r if feed_forward_proj == \"gated-gelu\":\r SCREAMING_SNAKE_CASE__ = '''gelu_new'''\r\r super().__init__(\r pad_token_id=__lowerCamelCase\t, eos_token_id=__lowerCamelCase\t, is_encoder_decoder=__lowerCamelCase\t, **__lowerCamelCase\t, )\r\r\r\r\r\r\rclass UpperCAmelCase__\t\t( A__ ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r @property\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tAny\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tMapping[str, Mapping[int, str]]:\r SCREAMING_SNAKE_CASE__ = {\r '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},\r '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},\r }\r if self.use_past:\r SCREAMING_SNAKE_CASE__ = '''past_encoder_sequence + sequence'''\r SCREAMING_SNAKE_CASE__ = {0: '''batch'''}\r SCREAMING_SNAKE_CASE__ = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}\r else:\r SCREAMING_SNAKE_CASE__ = {0: '''batch''', 1: '''decoder_sequence'''}\r SCREAMING_SNAKE_CASE__ = {0: '''batch''', 1: '''decoder_sequence'''}\r\r if self.use_past:\r self.fill_with_past_key_values_(__lowerCamelCase\t, direction='''inputs'''\t\t\t\t\t)\r\r return common_inputs\r\r\r\r\r\r\r\r @property\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tTuple\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tint:\r return 13\r\r\r"},"code_codestyle":{"kind":"number","value":314,"string":"314"},"style_context":{"kind":"string","value":"\r\rimport unittest\r\rimport torch\rfrom torch import nn\r\rfrom accelerate.test_utils import require_cuda\rfrom accelerate.utils.memory import find_executable_batch_size, release_memory\rdef \t\t\t\tUpperCAmelCase_ (\t\t\t\t\t\t):\r\r\r\r\r\r '''simple docstring'''\r\r\r\r\r raise RuntimeError('''CUDA out of memory.'''\t\t\t\t)\r\r\r\r\r\r\rclass UpperCAmelCase__\t\t( nn.Module ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r def __init__(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tAny\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tint:\r super().__init__()\r SCREAMING_SNAKE_CASE__ = nn.Linear(3\t, 4\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = nn.BatchNormad(4\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = nn.Linear(4\t, 5\t\t\t\t\t)\r\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tint\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tTuple:\r return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase\t\t\t\t\t)\t\t\t\t\t)\t\t\t\t\t)\r\r\r\r\r\r\rclass UpperCAmelCase__\t\t( unittest.TestCase ):\r\r\r\r\r\r\r \"\"\"simple docstring\"\"\"\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tList[Any]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tDict:\r SCREAMING_SNAKE_CASE__ = []\r\r @find_executable_batch_size(starting_batch_size=128\t\t\t\t\t)\r def mock_training_loop_function(__lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]\t\t\t\t\t):\r nonlocal batch_sizes\r batch_sizes.append(__lowerCamelCase\t\t\t\t\t)\r if batch_size != 8:\r raise_fake_out_of_memory()\r\r mock_training_loop_function()\r self.assertListEqual(__lowerCamelCase\t, [128, 64, 32, 16, 8]\t\t\t\t\t)\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tOptional[Any]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tList[Any]:\r SCREAMING_SNAKE_CASE__ = []\r\r @find_executable_batch_size(starting_batch_size=128\t\t\t\t\t)\r def mock_training_loop_function(__lowerCamelCase\t\t\t\t\t\t:\t\tList[Any]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tUnion[str, Any]\t\t\t\t\t):\r nonlocal batch_sizes\r batch_sizes.append(__lowerCamelCase\t\t\t\t\t)\r if batch_size != 8:\r raise_fake_out_of_memory()\r return batch_size, arga\r\r SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = mock_training_loop_function('''hello'''\t\t\t\t\t)\r self.assertListEqual(__lowerCamelCase\t, [128, 64, 32, 16, 8]\t\t\t\t\t)\r self.assertListEqual([bs, arga]\t, [8, '''hello''']\t\t\t\t\t)\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tstr\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tList[Any]:\r @find_executable_batch_size(starting_batch_size=0\t\t\t\t\t)\r def mock_training_loop_function(__lowerCamelCase\t\t\t\t\t\t:\t\tOptional[Any]\t\t\t\t\t):\r pass\r\r with self.assertRaises(__lowerCamelCase\t\t\t\t\t) as cm:\r mock_training_loop_function()\r self.assertIn('''No executable batch size found, reached zero.'''\t, cm.exception.args[0]\t\t\t\t\t)\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tUnion[str, Any]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tList[str]:\r @find_executable_batch_size(starting_batch_size=16\t\t\t\t\t)\r def mock_training_loop_function(__lowerCamelCase\t\t\t\t\t\t:\t\tDict\t\t\t\t\t):\r if batch_size > 0:\r raise_fake_out_of_memory()\r pass\r\r with self.assertRaises(__lowerCamelCase\t\t\t\t\t) as cm:\r mock_training_loop_function()\r self.assertIn('''No executable batch size found, reached zero.'''\t, cm.exception.args[0]\t\t\t\t\t)\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tList[Any]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tList[str]:\r @find_executable_batch_size(starting_batch_size=128\t\t\t\t\t)\r def mock_training_loop_function(__lowerCamelCase\t\t\t\t\t\t:\t\tint\t, __lowerCamelCase\t\t\t\t\t\t:\t\tOptional[int]\t, __lowerCamelCase\t\t\t\t\t\t:\t\tAny\t\t\t\t\t):\r if batch_size != 8:\r raise raise_fake_out_of_memory()\r\r with self.assertRaises(__lowerCamelCase\t\t\t\t\t) as cm:\r mock_training_loop_function(128\t, '''hello'''\t, '''world'''\t\t\t\t\t)\r self.assertIn('''Batch size was passed into `f`'''\t, cm.exception.args[0]\t\t\t\t\t)\r self.assertIn('''`f(arg1=\\'hello\\', arg2=\\'world\\')'''\t, cm.exception.args[0]\t\t\t\t\t)\r\r\r\r\r\r\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tUnion[str, Any]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tint:\r @find_executable_batch_size(starting_batch_size=16\t\t\t\t\t)\r def mock_training_loop_function(__lowerCamelCase\t\t\t\t\t\t:\t\tTuple\t\t\t\t\t):\r raise ValueError('''Oops, we had an error!'''\t\t\t\t\t)\r\r with self.assertRaises(__lowerCamelCase\t\t\t\t\t) as cm:\r mock_training_loop_function()\r self.assertIn('''Oops, we had an error!'''\t, cm.exception.args[0]\t\t\t\t\t)\r\r\r\r\r\r\r\r @require_cuda\r def lowercase_\t\t(\t\t\t\t\t\t\tself\t\t\t\t\t\t:\t\tOptional[int]\t\t\t\t\t)\t\t->\t\t\t\t\t\t\tstr:\r SCREAMING_SNAKE_CASE__ = torch.cuda.memory_allocated()\r SCREAMING_SNAKE_CASE__ = ModelForTest()\r model.cuda()\r self.assertGreater(torch.cuda.memory_allocated()\t, __lowerCamelCase\t\t\t\t\t)\r SCREAMING_SNAKE_CASE__ = release_memory(__lowerCamelCase\t\t\t\t\t)\r self.assertEqual(torch.cuda.memory_allocated()\t, __lowerCamelCase\t\t\t\t\t)\r\r\r"},"style_context_codestyle":{"kind":"number","value":314,"string":"314"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":277,"cells":{"code":{"kind":"string","value":"\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\nimport pytest\r\n\r\n\r\nUpperCAmelCase\t\t =\"__dummy_dataset1__\"\r\n\r\nUpperCAmelCase\t\t =\"\\nimport json\\nimport os\\n\\nimport datasets\\n\\n\\nREPO_URL = \\\"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\\\"\\nURLS = {\\\"train\\\": REPO_URL + \\\"wikiann-bn-train.jsonl\\\", \\\"validation\\\": REPO_URL + \\\"wikiann-bn-validation.jsonl\\\"}\\n\\n\\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\\n\\n def _info(self):\\n features = datasets.Features(\\n {\\n \\\"tokens\\\": datasets.Sequence(datasets.Value(\\\"string\\\")),\\n \\\"ner_tags\\\": datasets.Sequence(\\n datasets.features.ClassLabel(\\n names=[\\n \\\"O\\\",\\n \\\"B-PER\\\",\\n \\\"I-PER\\\",\\n \\\"B-ORG\\\",\\n \\\"I-ORG\\\",\\n \\\"B-LOC\\\",\\n \\\"I-LOC\\\",\\n ]\\n )\\n ),\\n \\\"langs\\\": datasets.Sequence(datasets.Value(\\\"string\\\")),\\n \\\"spans\\\": datasets.Sequence(datasets.Value(\\\"string\\\")),\\n }\\n )\\n return datasets.DatasetInfo(features=features)\\n\\n def _split_generators(self, dl_manager):\\n dl_path = dl_manager.download(URLS)\\n return [\\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\\\"filepath\\\": dl_path[\\\"train\\\"]}),\\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\\\"filepath\\\": dl_path[\\\"validation\\\"]}),\\n ]\\n\\n def _generate_examples(self, filepath):\\n with open(filepath, \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n for i, line in enumerate(f):\\n yield i, json.loads(line)\\n\"\r\n\r\n\r\n\r\n@pytest.fixture\r\ndef _A\t\t\t(\t\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n return DATASET_LOADING_SCRIPT_NAME\r\n\r\n\r\n\r\n@pytest.fixture\r\ndef _A\t\t\t(\t\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n return DATASET_LOADING_SCRIPT_CODE\r\n\r\n\r\n\r\n\r\n\r\n@pytest.fixture\r\ndef _A\t\t\t(\t\t\t\t\t_a\t\t\t\t\t:\t\tstr , _a\t\t\t\t\t:\t\tList[Any] , _a\t\t\t\t\t:\t\tList[Any]\t\t\t\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n A\t\t\t\t\t\t\t =\t\t\t\t\t\tdataset_loading_script_name\r\n A\t\t\t\t\t\t\t =\t\t\t\t\t\ttmp_path / \"\"\"datasets\"\"\" / script_name\r\n script_dir.mkdir(parents=_a\t\t\t\t\t\t\t)\r\n A\t\t\t\t\t\t\t =\t\t\t\t\t\tscript_dir / f'{script_name}.py'\r\n with open(_a , \"\"\"w\"\"\"\t\t\t\t\t\t\t) as f:\r\n f.write(_a\t\t\t\t\t\t\t)\r\n return str(_a\t\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":77,"string":"77"},"style_context":{"kind":"string","value":"\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\nfrom math import factorial\r\n\r\n\r\n\r\ndef _A\t\t\t(\t\t\t\t\t_a\t\t\t\t\t:\t\tint = 1_0_0\t\t\t\t\t\t\t):\r\n\r\n\r\n\r\n \"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n return sum(map(_a , str(factorial(_a\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n print(solution(int(input(\"Enter the Number: \").strip())))\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":77,"string":"77"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":278,"cells":{"code":{"kind":"string","value":"\rimport json\rimport os\rfrom typing import Dict, List, Optional, Tuple\r\rfrom ...tokenization_utils import PreTrainedTokenizer\rfrom ...utils import logging\r\r\rlowercase__\t\t\t\t\t\t\t:\t\t\t\t\t\t\tTuple\t\t\t\t\t\t\t\t\t\t\t\t\t=\tlogging.get_logger(__name__)\r\r\rlowercase__\t\t\t\t\t\t\t:\t\t\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=\t{\r \"vocab_file\": \"vocab.json\",\r \"tokenizer_config_file\": \"tokenizer_config.json\",\r \"merges_file\": \"merges.txt\",\r}\r\rlowercase__\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=\t{\r \"vocab_file\": {\r \"facebook/s2t-wav2vec2-large-en-de\": (\r \"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json\"\r ),\r },\r \"tokenizer_config_file\": {\r \"facebook/s2t-wav2vec2-large-en-de\": (\r \"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json\"\r ),\r },\r \"merges_file\": {\r \"facebook/s2t-wav2vec2-large-en-de\": (\r \"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt\"\r ),\r },\r}\r\rlowercase__\t\t\t\t\t\t\t:\t\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\"\"\rlowercase__\t\t\t\t\t\t\t:\t\t\t\t\t\t\tint\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\"@@ \"\r\r\r\r\rdef \t\tA_ (\t\t\t\t\t\t\tsnake_case\t\t\t\t\t\t\t: Dict )\t\t\t\t\t->\t\t\tint:\r\r\r\r\t\t\t\t\t\t'''simple docstring'''\r\r\t\t\t\t\t\t__UpperCamelCase =\tset()\r\t\t\t\t\t\t__UpperCamelCase =\tword[0]\r\t\t\t\t\t\tfor char in word[1:]:\r\t\t\t\t\t\t\t\t\t\t\t\tpairs.add((prev_char, char) )\r\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tchar\r\t\t\t\t\t\treturn pairs\r\r\r# Speech2Text2 has no max input length\rlowercase__\t\t\t\t\t\t\t:\t\t\t\t\t\t\tDict\t\t\t\t\t\t\t\t\t\t\t\t\t=\t{\"facebook/s2t-wav2vec2-large-en-de\": 1_0_2_4}\r\r\r\r\r\r\rclass \t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t(\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t):\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\t\t\t\t\t\t\t_snake_case \t\t\t\t\t\t= VOCAB_FILES_NAMES\r\t\t\t\t\t\t\t_snake_case \t\t\t\t\t\t= PRETRAINED_VOCAB_FILES_MAP\r\t\t\t\t\t\t\t_snake_case \t\t\t\t\t\t= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r\t\t\t\t\t\t\t_snake_case \t\t\t\t\t\t= ['input_ids', 'attention_mask']\r\r\r\r\t\t\t\t\t\t\tdef __init__( self ,\t\tSCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_=\"\" ,\t\tSCREAMING_SNAKE_CASE_=\"\" ,\t\tSCREAMING_SNAKE_CASE_=\"\" ,\t\tSCREAMING_SNAKE_CASE_=\"\" ,\t\tSCREAMING_SNAKE_CASE_=False ,\t\tSCREAMING_SNAKE_CASE_=None ,\t\t**SCREAMING_SNAKE_CASE_ ,\t\t)-> Optional[Any]:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(\r\t\t\t\t\t\t\t\t\t\t\t\t\t unk_token=SCREAMING_SNAKE_CASE_ ,\t\tbos_token=SCREAMING_SNAKE_CASE_ ,\t\teos_token=SCREAMING_SNAKE_CASE_ ,\t\tpad_token=SCREAMING_SNAKE_CASE_ ,\t\tdo_lower_case=SCREAMING_SNAKE_CASE_ ,\t\t**SCREAMING_SNAKE_CASE_ ,\t\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tdo_lower_case\r\r\t\t\t\t\t\t\t\t\t\t\t\t\twith open(SCREAMING_SNAKE_CASE_ ,\t\tencoding='''utf-8'''\t) as vocab_handle:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tjson.load(SCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t{v: k for k, v in self.encoder.items()}\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tif merges_file is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.info(F\"No merges files provided. {self.__class__.__name__} can only be used for decoding.\"\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tNone\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tNone\r\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith open(SCREAMING_SNAKE_CASE_ ,\t\tencoding='''utf-8'''\t) as merges_handle:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tmerges_handle.read().split('''\\n'''\t)[:-1]\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t[tuple(merge.split()[:2]\t) for merge in merges]\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tdict(zip(SCREAMING_SNAKE_CASE_ ,\t\trange(len(SCREAMING_SNAKE_CASE_\t)\t)\t)\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t{}\r\r\r\r\t\t\t\t\t\t\t@property\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self\t)-> int:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn len(self.decoder\t)\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self\t)-> Dict:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn dict(self.encoder ,\t\t**self.added_tokens_encoder\t)\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self ,\t\tSCREAMING_SNAKE_CASE_\t)-> int:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\ttuple(token[:-1]\t) + (token[-1] + BPE_TOKEN_MERGES,)\r\t\t\t\t\t\t\t\t\t\t\t\t\tif token in self.cache:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn self.cache[token]\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tget_pairs(SCREAMING_SNAKE_CASE_\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tif not pairs:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn token\r\r\t\t\t\t\t\t\t\t\t\t\t\t\twhile True:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tmin(SCREAMING_SNAKE_CASE_ ,\t\tkey=lambda SCREAMING_SNAKE_CASE_\t: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ ,\t\tfloat('''inf'''\t)\t)\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif bigram not in self.bpe_ranks:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbreak\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t\t__UpperCamelCase =\tbigram\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t[]\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t0\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twhile i < len(SCREAMING_SNAKE_CASE_\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tword.index(SCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texcept ValueError:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnew_word.extend(word[i:]\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbreak\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnew_word.extend(word[i:j]\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tj\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif word[i] == first and i < len(SCREAMING_SNAKE_CASE_\t) - 1 and word[i + 1] == second:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnew_word.append(first + second\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti += 2\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnew_word.append(word[i]\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti += 1\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\ttuple(SCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tnew_word\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif len(SCREAMING_SNAKE_CASE_\t) == 1:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbreak\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tget_pairs(SCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t''' '''.join(SCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\tif word == \"\\n \" + BPE_TOKEN_MERGES:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t'''\\n''' + BPE_TOKEN_MERGES\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tif word.endswith(SCREAMING_SNAKE_CASE_\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tword.replace(SCREAMING_SNAKE_CASE_ ,\t\t''''''\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tword.replace(''' ''' ,\t\tSCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tword\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn word\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self ,\t\tSCREAMING_SNAKE_CASE_\t)-> Tuple:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tif self.bpe_ranks is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''This tokenizer was instantiated without a `merges.txt` file, so'''\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ''' that it can only be used for decoding, not for encoding.'''\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''Make sure to provide `merges.txt` file at instantiation to enable '''\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''encoding.'''\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tif self.do_lower_case:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\ttext.lower()\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\ttext.split()\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t[]\r\t\t\t\t\t\t\t\t\t\t\t\t\tfor token in text:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif token:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsplit_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_\t).split(''' '''\t)\t)\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn split_tokens\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self ,\t\tSCREAMING_SNAKE_CASE_\t)-> int:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn self.encoder.get(SCREAMING_SNAKE_CASE_ ,\t\tself.encoder.get(self.unk_token\t)\t)\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self ,\t\tSCREAMING_SNAKE_CASE_\t)-> str:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tself.decoder.get(SCREAMING_SNAKE_CASE_ ,\t\tself.unk_token\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn result\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self ,\t\tSCREAMING_SNAKE_CASE_\t)-> str:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t''' '''.join(SCREAMING_SNAKE_CASE_\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t# make sure @@ tokens are concatenated\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t''''''.join(string.split(SCREAMING_SNAKE_CASE_\t)\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn string\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self ,\t\tSCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_ = None\t)-> Tuple[str]:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tif not os.path.isdir(SCREAMING_SNAKE_CASE_\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.error(F\"Vocabulary path ({save_directory}) should be a directory\"\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tos.path.join(\r\t\t\t\t\t\t\t\t\t\t\t\t\t SCREAMING_SNAKE_CASE_ ,\t\t(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tos.path.join(\r\t\t\t\t\t\t\t\t\t\t\t\t\t SCREAMING_SNAKE_CASE_ ,\t\t(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\twith open(SCREAMING_SNAKE_CASE_ ,\t\t'''w''' ,\t\tencoding='''utf-8'''\t) as f:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tf.write(json.dumps(self.encoder ,\t\tindent=2 ,\t\tsort_keys=SCREAMING_SNAKE_CASE_ ,\t\tensure_ascii=SCREAMING_SNAKE_CASE_\t) + '''\\n'''\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t0\r\t\t\t\t\t\t\t\t\t\t\t\t\tif self.bpe_ranks is None:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn (vocab_file,)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\twith open(SCREAMING_SNAKE_CASE_ ,\t\t'''w''' ,\t\tencoding='''utf-8'''\t) as writer:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,\t\tkey=lambda SCREAMING_SNAKE_CASE_\t: kv[1]\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif index != token_index:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogger.warning(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t F\"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.\"\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ''' Please check that the tokenizer is not corrupted!'''\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\ttoken_index\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twriter.write(''' '''.join(SCREAMING_SNAKE_CASE_\t) + '''\\n'''\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tindex += 1\r\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn (vocab_file, merges_file)\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":328,"string":"328"},"style_context":{"kind":"string","value":"\rimport multiprocessing\rfrom typing import TYPE_CHECKING, Optional, Union\r\rfrom .. import Dataset, Features, config\rfrom ..formatting import query_table\rfrom ..packaged_modules.sql.sql import Sql\rfrom ..utils import logging\rfrom .abc import AbstractDatasetInputStream\r\r\rif TYPE_CHECKING:\r\t\t\t\timport sqlitea\r\r\t\t\t\timport sqlalchemy\r\r\r\r\r\r\rclass \t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t(\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE_\t\t\t):\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\t\t\t\t\t\t\tdef __init__( self ,\t\tSCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_ = None ,\t\tSCREAMING_SNAKE_CASE_ = None ,\t\tSCREAMING_SNAKE_CASE_ = False ,\t\t**SCREAMING_SNAKE_CASE_ ,\t\t)-> Optional[int]:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(features=SCREAMING_SNAKE_CASE_ ,\t\tcache_dir=SCREAMING_SNAKE_CASE_ ,\t\tkeep_in_memory=SCREAMING_SNAKE_CASE_ ,\t\t**SCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tSql(\r\t\t\t\t\t\t\t\t\t\t\t\t\t cache_dir=SCREAMING_SNAKE_CASE_ ,\t\tfeatures=SCREAMING_SNAKE_CASE_ ,\t\tsql=SCREAMING_SNAKE_CASE_ ,\t\tcon=SCREAMING_SNAKE_CASE_ ,\t\t**SCREAMING_SNAKE_CASE_ ,\t\t)\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self\t)-> Any:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tNone\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tNone\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tNone\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tNone\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tself.builder.download_and_prepare(\r\t\t\t\t\t\t\t\t\t\t\t\t\t download_config=SCREAMING_SNAKE_CASE_ ,\t\tdownload_mode=SCREAMING_SNAKE_CASE_ ,\t\tverification_mode=SCREAMING_SNAKE_CASE_ ,\t\tbase_path=SCREAMING_SNAKE_CASE_ ,\t\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t# Build dataset for splits\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tself.builder.as_dataset(\r\t\t\t\t\t\t\t\t\t\t\t\t\t split='''train''' ,\t\tverification_mode=SCREAMING_SNAKE_CASE_ ,\t\tin_memory=self.keep_in_memory\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn dataset\r\r\r\r\r\r\rclass \t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t:\r\r\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\t\t\t\t\t\t\tdef __init__( self ,\t\tSCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_ = None ,\t\tSCREAMING_SNAKE_CASE_ = None ,\t\t**SCREAMING_SNAKE_CASE_ ,\t\t)-> List[str]:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tif num_proc is not None and num_proc <= 0:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(F\"num_proc {num_proc} must be an integer > 0.\"\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tdataset\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tname\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tcon\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tbatch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tnum_proc\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tto_sql_kwargs\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self\t)-> int:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tself.to_sql_kwargs.pop('''sql''' ,\t\tSCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tself.to_sql_kwargs.pop('''con''' ,\t\tSCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tself.to_sql_kwargs.pop('''index''' ,\t\tSCREAMING_SNAKE_CASE_\t)\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tself._write(index=SCREAMING_SNAKE_CASE_ ,\t\t**self.to_sql_kwargs\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn written\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self ,\t\tSCREAMING_SNAKE_CASE_\t)-> Dict:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t\t__UpperCamelCase =\targs\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tquery_table(\r\t\t\t\t\t\t\t\t\t\t\t\t\t table=self.dataset.data ,\t\tkey=slice(SCREAMING_SNAKE_CASE_ ,\t\toffset + self.batch_size\t) ,\t\tindices=self.dataset._indices ,\t\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tbatch.to_pandas()\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\tdf.to_sql(self.name ,\t\tself.con ,\t\tindex=SCREAMING_SNAKE_CASE_ ,\t\t**SCREAMING_SNAKE_CASE_\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn num_rows or len(SCREAMING_SNAKE_CASE_\t)\r\r\r\r\t\t\t\t\t\t\tdef \t\t\tA__\t\t\t\t\t\t( self ,\t\tSCREAMING_SNAKE_CASE_ ,\t\t**SCREAMING_SNAKE_CASE_\t)-> int:\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase =\t0\r\r\t\t\t\t\t\t\t\t\t\t\t\t\tif self.num_proc is None or self.num_proc == 1:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor offset in logging.tqdm(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t range(0 ,\t\tlen(self.dataset\t) ,\t\tself.batch_size\t) ,\t\tunit='''ba''' ,\t\tdisable=not logging.is_progress_bar_enabled() ,\t\tdesc='''Creating SQL from Arrow format''' ,\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twritten += self._batch_sql((offset, index, to_sql_kwargs)\t)\r\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t\t__UpperCamelCase =\tlen(self.dataset\t), self.batch_size\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith multiprocessing.Pool(self.num_proc\t) as pool:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor num_rows in logging.tqdm(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t pool.imap(\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self._batch_sql ,\t\t[(offset, index, to_sql_kwargs) for offset in range(0 ,\t\tSCREAMING_SNAKE_CASE_ ,\t\tSCREAMING_SNAKE_CASE_\t)] ,\t\t) ,\t\ttotal=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,\t\tunit='''ba''' ,\t\tdisable=not logging.is_progress_bar_enabled() ,\t\tdesc='''Creating SQL from Arrow format''' ,\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twritten += num_rows\r\r\t\t\t\t\t\t\t\t\t\t\t\t\treturn written\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":328,"string":"328"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":279,"cells":{"code":{"kind":"string","value":"\r\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available\r\r\rsnake_case\t\t\t\t\t\t\t:\tUnion[str, Any]\t\t\t\t\t =\t\t\t\t{\r '''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],\r '''tokenization_tapas''': ['''TapasTokenizer'''],\r}\r\rtry:\r\t\t\t\t\t\tif not is_torch_available():\r\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\tpass\relse:\r\t\t\t\t\t\tsnake_case\t\t\t\t\t\t\t:\tTuple\t\t\t\t\t =\t\t\t\t[\r\t\t\t\t\t\t '''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',\r\t\t\t\t\t\t '''TapasForMaskedLM''',\r\t\t\t\t\t\t '''TapasForQuestionAnswering''',\r\t\t\t\t\t\t '''TapasForSequenceClassification''',\r\t\t\t\t\t\t '''TapasModel''',\r\t\t\t\t\t\t '''TapasPreTrainedModel''',\r\t\t\t\t\t\t '''load_tf_weights_in_tapas''',\r\t\t\t\t\t\t]\rtry:\r\t\t\t\t\t\tif not is_tf_available():\r\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\tpass\relse:\r\t\t\t\t\t\tsnake_case\t\t\t\t\t\t\t:\tList[Any]\t\t\t\t\t =\t\t\t\t[\r\t\t\t\t\t\t '''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',\r\t\t\t\t\t\t '''TFTapasForMaskedLM''',\r\t\t\t\t\t\t '''TFTapasForQuestionAnswering''',\r\t\t\t\t\t\t '''TFTapasForSequenceClassification''',\r\t\t\t\t\t\t '''TFTapasModel''',\r\t\t\t\t\t\t '''TFTapasPreTrainedModel''',\r\t\t\t\t\t\t]\r\r\rif TYPE_CHECKING:\r\t\t\t\t\t\tfrom .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig\r\t\t\t\t\t\tfrom .tokenization_tapas import TapasTokenizer\r\r\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\tif not is_torch_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\t\t\t\t\t\tpass\r\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_tapas import (\r\t\t\t\t\t\t\t\t\t\t\t\t TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,\r\t\t\t\t\t\t\t\t\t\t\t\t TapasForMaskedLM,\r\t\t\t\t\t\t\t\t\t\t\t\t TapasForQuestionAnswering,\r\t\t\t\t\t\t\t\t\t\t\t\t TapasForSequenceClassification,\r\t\t\t\t\t\t\t\t\t\t\t\t TapasModel,\r\t\t\t\t\t\t\t\t\t\t\t\t TapasPreTrainedModel,\r\t\t\t\t\t\t\t\t\t\t\t\t load_tf_weights_in_tapas,\r\t\t\t\t\t\t\t\t\t\t\t\t)\r\r\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\tif not is_tf_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\t\t\t\t\t\tpass\r\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_tf_tapas import (\r\t\t\t\t\t\t\t\t\t\t\t\t TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,\r\t\t\t\t\t\t\t\t\t\t\t\t TFTapasForMaskedLM,\r\t\t\t\t\t\t\t\t\t\t\t\t TFTapasForQuestionAnswering,\r\t\t\t\t\t\t\t\t\t\t\t\t TFTapasForSequenceClassification,\r\t\t\t\t\t\t\t\t\t\t\t\t TFTapasModel,\r\t\t\t\t\t\t\t\t\t\t\t\t TFTapasPreTrainedModel,\r\t\t\t\t\t\t\t\t\t\t\t\t)\r\r\relse:\r\t\t\t\t\t\timport sys\r\r\t\t\t\t\t\tsnake_case\t\t\t\t\t\t\t:\tUnion[str, Any]\t\t\t\t\t =\t\t\t\t_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":109,"string":"109"},"style_context":{"kind":"string","value":"\r\r\rfrom __future__ import annotations\r\r\r\r\r\r\rdef \t\t\t\t\t\t\t__lowercase (\t\t\t__lowerCAmelCase\t\t\t\t\t\t\t: list[int] , __lowerCAmelCase\t\t\t\t\t\t\t: int , __lowerCAmelCase\t\t\t\t\t\t\t: int , __lowerCAmelCase\t\t\t\t\t\t\t: int ):\r\t\t\t\t\t\t\tif (direction == 1 and array[indexa] > array[indexa]) or (\r\t\t\t\t\t\t\t direction == 0 and array[indexa] < array[indexa]\r\t\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\ta__\t\t\t\t, a__\t\t\t\t\t=\t\t\t\t\tarray[indexa], array[indexa]\r\r\r\r\r\r\rdef \t\t\t\t\t\t\t__lowercase (\t\t\t__lowerCAmelCase\t\t\t\t\t\t\t: list[int] , __lowerCAmelCase\t\t\t\t\t\t\t: int , __lowerCAmelCase\t\t\t\t\t\t\t: int , __lowerCAmelCase\t\t\t\t\t\t\t: int ):\r\t\t\t\t\t\t\tif length > 1:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\ta__\t\t\t\t\t=\t\t\t\t\tint(length / 2 )\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor i in range(__lowerCAmelCase , low + middle ):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcomp_and_swap(__lowerCAmelCase , __lowerCAmelCase , i + middle , __lowerCAmelCase )\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tbitonic_merge(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tbitonic_merge(__lowerCAmelCase , low + middle , __lowerCAmelCase , __lowerCAmelCase )\r\r\r\r\r\r\rdef \t\t\t\t\t\t\t__lowercase (\t\t\t__lowerCAmelCase\t\t\t\t\t\t\t: list[int] , __lowerCAmelCase\t\t\t\t\t\t\t: int , __lowerCAmelCase\t\t\t\t\t\t\t: int , __lowerCAmelCase\t\t\t\t\t\t\t: int ):\r\t\t\t\t\t\t\tif length > 1:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\ta__\t\t\t\t\t=\t\t\t\t\tint(length / 2 )\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tbitonic_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , 1 )\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tbitonic_sort(__lowerCAmelCase , low + middle , __lowerCAmelCase , 0 )\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tbitonic_merge(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )\r\r\rif __name__ == \"__main__\":\r\t\t\t\t\t\tsnake_case\t\t\t\t\t\t\t:\tint\t\t\t\t\t =\t\t\t\tinput('''Enter numbers separated by a comma:\\n''').strip()\r\t\t\t\t\t\tsnake_case\t\t\t\t\t\t\t:\tOptional[int]\t\t\t\t\t =\t\t\t\t[int(item.strip()) for item in user_input.split(''',''')]\r\r\t\t\t\t\t\tbitonic_sort(unsorted, 0, len(unsorted), 1)\r\t\t\t\t\t\tprint('''\\nSorted array in ascending order is: ''', end='''''')\r\t\t\t\t\t\tprint(*unsorted, sep=''', ''')\r\r\t\t\t\t\t\tbitonic_merge(unsorted, 0, len(unsorted), 0)\r\t\t\t\t\t\tprint('''Sorted array in descending order is: ''', end='''''')\r\t\t\t\t\t\tprint(*unsorted, sep=''', ''')\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":109,"string":"109"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":280,"cells":{"code":{"kind":"string","value":"\n\n'''simple docstring'''\nimport os\nfrom pathlib import Path\nfrom unittest.mock import patch\n\nimport pytest\nimport zstandard as zstd\n\nfrom datasets.download.download_config import DownloadConfig\nfrom datasets.utils.file_utils import (\n OfflineModeIsEnabled,\n cached_path,\n fsspec_get,\n fsspec_head,\n ftp_get,\n ftp_head,\n get_from_cache,\n http_get,\n http_head,\n)\n\n\n_snake_case = '\\\\n Text data.\\n Second line of data.'\n\n_snake_case = 'file'\n\n\n\n\n\n\n\n@pytest.fixture(scope=\"session\" )\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tList[Any]:\n _lowercase\t\t\t: List[Any]\t\t\t = tmp_path_factory.mktemp(\"data\" ) / (FILE_PATH + \".zstd\")\n _lowercase\t\t\t: str\t\t\t = bytes(A__\t\t,\t\t\t\t\t\t\t\"utf-8\" )\n with zstd.open(A__\t\t,\t\t\t\t\t\t\t\"wb\" ) as f:\n f.write(A__ )\n return path\n\n\n\n\n\n\n\n@pytest.fixture\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tOptional[Any]:\n with open(os.path.join(tmpfs.local_root_dir\t\t,\t\t\t\t\t\t\tA__ )\t\t,\t\t\t\t\t\t\t\"w\" ) as f:\n f.write(A__ )\n return FILE_PATH\n\n\n\n\n\n\n\n@pytest.mark.parametrize(\"compression_format\"\t\t,\t\t\t\t\t\t\t[\"gzip\", \"xz\", \"zstd\"] )\ndef \t\t\t\t\t_A\t\t(\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case )\t\t\t->\tList[str]:\n _lowercase\t\t\t: List[str]\t\t\t = {\"gzip\": gz_file, \"xz\": xz_file, \"zstd\": zstd_path}\n _lowercase\t\t\t: List[Any]\t\t\t = input_paths[compression_format]\n _lowercase\t\t\t: int\t\t\t = tmp_path / \"cache\"\n _lowercase\t\t\t: Optional[int]\t\t\t = DownloadConfig(cache_dir=A__\t\t,\t\t\t\t\t\t\textract_compressed_file=A__ )\n _lowercase\t\t\t: str\t\t\t = cached_path(A__\t\t,\t\t\t\t\t\t\tdownload_config=A__ )\n with open(A__ ) as f:\n _lowercase\t\t\t: Any\t\t\t = f.read()\n with open(A__ ) as f:\n _lowercase\t\t\t: List[Any]\t\t\t = f.read()\n assert extracted_file_content == expected_file_content\n\n\n\n\n\n\n\n@pytest.mark.parametrize(\"default_extracted\"\t\t,\t\t\t\t\t\t\t[True, False] )\n@pytest.mark.parametrize(\"default_cache_dir\"\t\t,\t\t\t\t\t\t\t[True, False] )\ndef \t\t\t\t\t_A\t\t(\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case\t\t,\t\t\t\t\t\t\tsnake_case )\t\t\t->\tList[str]:\n _lowercase\t\t\t: Optional[Any]\t\t\t = \"custom_cache\"\n _lowercase\t\t\t: str\t\t\t = \"custom_extracted_dir\"\n _lowercase\t\t\t: Dict\t\t\t = tmp_path / \"custom_extracted_path\"\n if default_extracted:\n _lowercase\t\t\t: Dict\t\t\t = (\"downloads\" if default_cache_dir else custom_cache_dir, \"extracted\")\n else:\n monkeypatch.setattr(\"datasets.config.EXTRACTED_DATASETS_DIR\"\t\t,\t\t\t\t\t\t\tA__ )\n monkeypatch.setattr(\"datasets.config.EXTRACTED_DATASETS_PATH\"\t\t,\t\t\t\t\t\t\tstr(A__ ) )\n _lowercase\t\t\t: List[Any]\t\t\t = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)\n\n _lowercase\t\t\t: str\t\t\t = xz_file\n _lowercase\t\t\t: Optional[Any]\t\t\t = (\n DownloadConfig(extract_compressed_file=A__ )\n if default_cache_dir\n else DownloadConfig(cache_dir=tmp_path / custom_cache_dir\t\t,\t\t\t\t\t\t\textract_compressed_file=A__ )\n )\n _lowercase\t\t\t: Tuple\t\t\t = cached_path(A__\t\t,\t\t\t\t\t\t\tdownload_config=A__ )\n assert Path(A__ ).parent.parts[-2:] == expected\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tAny:\n # absolute path\n _lowercase\t\t\t: Union[str, Any]\t\t\t = str(Path(A__ ).resolve() )\n assert cached_path(A__ ) == text_file\n # relative path\n _lowercase\t\t\t: Dict\t\t\t = str(Path(A__ ).resolve().relative_to(Path(os.getcwd() ) ) )\n assert cached_path(A__ ) == text_file\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tint:\n # absolute path\n _lowercase\t\t\t: Dict\t\t\t = str(tmp_path.resolve() / \"__missing_file__.txt\" )\n with pytest.raises(A__ ):\n cached_path(A__ )\n # relative path\n _lowercase\t\t\t: Any\t\t\t = \"./__missing_file__.txt\"\n with pytest.raises(A__ ):\n cached_path(A__ )\n\n\n\n\n\n\n\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tAny:\n _lowercase\t\t\t: Union[str, Any]\t\t\t = get_from_cache(F'''tmp://{tmpfs_file}''' )\n with open(A__ ) as f:\n _lowercase\t\t\t: Optional[Any]\t\t\t = f.read()\n assert output_file_content == FILE_CONTENT\n\n\n\n\n\n\n\n@patch(\"datasets.config.HF_DATASETS_OFFLINE\"\t\t,\t\t\t\t\t\t\tA__ )\ndef \t\t\t\t\t_A\t\t(\t)\t\t\t->\tOptional[Any]:\n with pytest.raises(A__ ):\n cached_path(\"https://huggingface.co\" )\n\n\n\n\n\n\n\n@patch(\"datasets.config.HF_DATASETS_OFFLINE\"\t\t,\t\t\t\t\t\t\tA__ )\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tOptional[Any]:\n _lowercase\t\t\t: List[Any]\t\t\t = tmp_path_factory.mktemp(\"data\" ) / \"file.html\"\n with pytest.raises(A__ ):\n http_get(\"https://huggingface.co\"\t\t,\t\t\t\t\t\t\ttemp_file=A__ )\n with pytest.raises(A__ ):\n http_head(\"https://huggingface.co\" )\n\n\n\n\n\n\n\n@patch(\"datasets.config.HF_DATASETS_OFFLINE\"\t\t,\t\t\t\t\t\t\tA__ )\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tOptional[int]:\n _lowercase\t\t\t: str\t\t\t = tmp_path_factory.mktemp(\"data\" ) / \"file.html\"\n with pytest.raises(A__ ):\n ftp_get(\"ftp://huggingface.co\"\t\t,\t\t\t\t\t\t\ttemp_file=A__ )\n with pytest.raises(A__ ):\n ftp_head(\"ftp://huggingface.co\" )\n\n\n\n\n\n\n\n@patch(\"datasets.config.HF_DATASETS_OFFLINE\"\t\t,\t\t\t\t\t\t\tA__ )\ndef \t\t\t\t\t_A\t\t(\tsnake_case )\t\t\t->\tOptional[Any]:\n _lowercase\t\t\t: List[str]\t\t\t = tmp_path_factory.mktemp(\"data\" ) / \"file.html\"\n with pytest.raises(A__ ):\n fsspec_get(\"s3://huggingface.co\"\t\t,\t\t\t\t\t\t\ttemp_file=A__ )\n with pytest.raises(A__ ):\n fsspec_head(\"s3://huggingface.co\" )\n\n\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":250,"string":"250"},"style_context":{"kind":"string","value":"\n\n\n\n\n\n\n\"\"\"simple docstring\"\"\"\nfrom typing import List, Union\n\nfrom ..utils import (\n add_end_docstrings,\n is_tf_available,\n is_torch_available,\n is_vision_available,\n logging,\n requires_backends,\n)\nfrom .base import PIPELINE_INIT_ARGS, Pipeline\n\n\nif is_vision_available():\n\t\t\tfrom PIL import Image\n\n\t\t\tfrom ..image_utils import load_image\n\nif is_tf_available():\n\t\t\timport tensorflow as tf\n\n\t\t\tfrom ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\n\t\t\tfrom ..tf_utils import stable_softmax\n\nif is_torch_available():\n\t\t\tfrom ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\n\nlowerCamelCase_\t\t\t\t\t= logging.get_logger(__name__)\n\n\n\n\n\n\n\n@add_end_docstrings(__A )\nclass \t\t\tUpperCamelCase_ (__A ):\n\n\n\n\n\n\tdef __init__( self\t\t\t\t\t: int\t,\t*lowerCAmelCase_\t\t\t\t\t: Tuple\t,\t**lowerCAmelCase_\t\t\t\t\t: List[str]\t\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tOptional[Any]:\n\t\t\t\tsuper().__init__(*lowerCAmelCase_\t,\t**lowerCAmelCase_\t\t\t\t\t\t)\n\t\t\t\trequires_backends(self\t,\t\"vision\"\t\t\t\t\t\t)\n\t\t\t\tself.check_model_type(\n\t\t\t\t TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\n\t\t\t\t if self.framework == \"tf\"\n\t\t\t\t else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING\t\t\t\t\t\t)\n\n\n\n\n\n\tdef _SCREAMING_SNAKE_CASE ( self\t\t\t\t\t: str\t,\tlowerCAmelCase_\t\t\t\t\t: Optional[int]=None\t\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tList[Any]:\n\t\t\t\tUpperCAmelCase_ : str\t\t\t =\t\t\t\t\t{}\n\t\t\t\tif top_k is not None:\n\t\t\t\t\t\t\tUpperCAmelCase_ : List[str]\t\t\t =\t\t\t\t\ttop_k\n\t\t\t\treturn {}, {}, postprocess_params\n\n\n\n\n\n\tdef __call__( self\t\t\t\t\t: str\t,\tlowerCAmelCase_\t\t\t\t\t: Union[str, List[str], \"Image.Image\", List[\"Image.Image\"]]\t,\t**lowerCAmelCase_\t\t\t\t\t: Any\t\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tTuple:\n\t\t\t\treturn super().__call__(lowerCAmelCase_\t,\t**lowerCAmelCase_\t\t\t\t\t\t)\n\n\n\n\n\n\tdef _SCREAMING_SNAKE_CASE ( self\t\t\t\t\t: str\t,\tlowerCAmelCase_\t\t\t\t\t: str\t\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tAny:\n\t\t\t\tUpperCAmelCase_ : Tuple\t\t\t =\t\t\t\t\tload_image(lowerCAmelCase_\t\t\t\t\t\t)\n\t\t\t\tUpperCAmelCase_ : Dict\t\t\t =\t\t\t\t\tself.image_processor(images=lowerCAmelCase_\t,\treturn_tensors=self.framework\t\t\t\t\t\t)\n\t\t\t\treturn model_inputs\n\n\n\n\n\n\tdef _SCREAMING_SNAKE_CASE ( self\t\t\t\t\t: List[Any]\t,\tlowerCAmelCase_\t\t\t\t\t: Dict\t\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tstr:\n\t\t\t\tUpperCAmelCase_ : Any\t\t\t =\t\t\t\t\tself.model(**lowerCAmelCase_\t\t\t\t\t\t)\n\t\t\t\treturn model_outputs\n\n\n\n\n\n\tdef _SCREAMING_SNAKE_CASE ( self\t\t\t\t\t: Tuple\t,\tlowerCAmelCase_\t\t\t\t\t: Union[str, Any]\t,\tlowerCAmelCase_\t\t\t\t\t: Optional[int]=5\t\t\t\t\t\t)\t\t\t\t\t\t->\t\t\tAny:\n\t\t\t\tif top_k > self.model.config.num_labels:\n\t\t\t\t\t\t\tUpperCAmelCase_ : int\t\t\t =\t\t\t\t\tself.model.config.num_labels\n\n\t\t\t\tif self.framework == \"pt\":\n\t\t\t\t\t\t\tUpperCAmelCase_ : str\t\t\t =\t\t\t\t\tmodel_outputs.logits.softmax(-1\t\t\t\t\t\t)[0]\n\t\t\t\t\t\t\tUpperCAmelCase_ ,\tUpperCAmelCase_ : Tuple\t\t\t =\t\t\t\t\tprobs.topk(lowerCAmelCase_\t\t\t\t\t\t)\n\t\t\t\telif self.framework == \"tf\":\n\t\t\t\t\t\t\tUpperCAmelCase_ : str\t\t\t =\t\t\t\t\tstable_softmax(model_outputs.logits\t,\taxis=-1\t\t\t\t\t\t)[0]\n\t\t\t\t\t\t\tUpperCAmelCase_ : Union[str, Any]\t\t\t =\t\t\t\t\ttf.math.top_k(lowerCAmelCase_\t,\tk=lowerCAmelCase_\t\t\t\t\t\t)\n\t\t\t\t\t\t\tUpperCAmelCase_ ,\tUpperCAmelCase_ : List[Any]\t\t\t =\t\t\t\t\ttopk.values.numpy(), topk.indices.numpy()\n\t\t\t\telse:\n\t\t\t\t\t\t\traise ValueError(f\"\"\"Unsupported framework: {self.framework}\"\"\"\t\t\t\t\t\t)\n\n\t\t\t\tUpperCAmelCase_ : int\t\t\t =\t\t\t\t\tscores.tolist()\n\t\t\t\tUpperCAmelCase_ : Optional[Any]\t\t\t =\t\t\t\t\tids.tolist()\n\t\t\t\treturn [{\"score\": score, \"label\": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_\t,\tlowerCAmelCase_\t\t\t\t\t\t)]\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":268,"string":"268"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":281,"cells":{"code":{"kind":"string","value":"\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\rfrom collections.abc import Callable\rfrom math import pi, sqrt\rfrom random import uniform\rfrom statistics import mean\rdef a__ ( SCREAMING_SNAKE_CASE : int ):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def is_in_circle(SCREAMING_SNAKE_CASE : float ,\t\t\t\tSCREAMING_SNAKE_CASE : float ) -> bool:\r lowerCAmelCase\t\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tsqrt((x**2) + (y**2) )\r # Our circle has a radius of 1, so a distance\r # greater than 1 would land outside the circle.\r return distance_from_centre <= 1\r\r # The proportion of guesses that landed in the circle\r lowerCAmelCase\t\t\t\t\t:\t\t\t\t\tAny\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tmean(\r int(is_in_circle(uniform(-1.0 ,\t\t\t\t1.0 ) ,\t\t\t\tuniform(-1.0 ,\t\t\t\t1.0 ) ) )\r for _ in range(SCREAMING_SNAKE_CASE ) )\r # The ratio of the area for circle to square is pi/4.\r lowerCAmelCase\t\t\t\t\t:\t\t\t\t\tList[str]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tproportion * 4\r print(f\"\"\"The estimated value of pi is {pi_estimate}\"\"\" )\r print(f\"\"\"The numpy value of pi is {pi}\"\"\" )\r print(f\"\"\"The total error is {abs(pi - pi_estimate )}\"\"\" )\rdef a__ ( SCREAMING_SNAKE_CASE : int ,\t\t\t\tSCREAMING_SNAKE_CASE : Callable[[float], float] ,\t\t\t\tSCREAMING_SNAKE_CASE : float = 0.0 ,\t\t\t\tSCREAMING_SNAKE_CASE : float = 1.0 ,\t\t\t\t):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return mean(\r function_to_integrate(uniform(SCREAMING_SNAKE_CASE ,\t\t\t\tSCREAMING_SNAKE_CASE ) ) for _ in range(SCREAMING_SNAKE_CASE ) ) * (max_value - min_value)\rdef a__ ( SCREAMING_SNAKE_CASE : int ,\t\t\t\tSCREAMING_SNAKE_CASE : float = 0.0 ,\t\t\t\tSCREAMING_SNAKE_CASE : float = 1.0 ):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def identity_function(SCREAMING_SNAKE_CASE : float ) -> float:\r return x\r\r lowerCAmelCase\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tarea_under_curve_estimator(\r SCREAMING_SNAKE_CASE ,\t\t\t\tSCREAMING_SNAKE_CASE ,\t\t\t\tSCREAMING_SNAKE_CASE ,\t\t\t\tSCREAMING_SNAKE_CASE )\r lowerCAmelCase\t\t\t\t\t:\t\t\t\t\tOptional[int]\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\t(max_value * max_value - min_value * min_value) / 2\r\r print(\"******************\" )\r print(f\"\"\"Estimating area under y=x where x varies from {min_value} to {max_value}\"\"\" )\r print(f\"\"\"Estimated value is {estimated_value}\"\"\" )\r print(f\"\"\"Expected value is {expected_value}\"\"\" )\r print(f\"\"\"Total error is {abs(estimated_value - expected_value )}\"\"\" )\r print(\"******************\" )\rdef a__ ( SCREAMING_SNAKE_CASE : int ):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r def function_to_integrate(SCREAMING_SNAKE_CASE : float ) -> float:\r return sqrt(4.0 - x * x )\r\r lowerCAmelCase\t\t\t\t\t:\t\t\t\t\tDict\t\t\t\t\t\t\t\t=\t\t\t\t\t\t\tarea_under_curve_estimator(\r SCREAMING_SNAKE_CASE ,\t\t\t\tSCREAMING_SNAKE_CASE ,\t\t\t\t0.0 ,\t\t\t\t2.0 )\r\r print(\"******************\" )\r print(\"Estimating pi using area_under_curve_estimator\" )\r print(f\"\"\"Estimated value is {estimated_value}\"\"\" )\r print(f\"\"\"Expected value is {pi}\"\"\" )\r print(f\"\"\"Total error is {abs(estimated_value - pi )}\"\"\" )\r print(\"******************\" )\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":133,"string":"133"},"style_context":{"kind":"string","value":"\r\r\r\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\rimport re\r\rfrom filelock import FileLock\r\r\rtry:\r import nltk\r\r lowerCAmelCase__\t\t\t\t = True\rexcept (ImportError, ModuleNotFoundError):\r lowerCAmelCase__\t\t\t\t = False\r\rif NLTK_AVAILABLE:\r with FileLock('''.lock''') as lock:\r nltk.download('''punkt''', quiet=True)\rdef a__ ( SCREAMING_SNAKE_CASE : str ):\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r re.sub(\"\" ,\t\t\t\t\"\" ,\t\t\t\tSCREAMING_SNAKE_CASE ) # remove pegasus newline char\r assert NLTK_AVAILABLE, \"nltk must be installed to separate newlines between sentences. (pip install nltk)\"\r return \"\\n\".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE ) )\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":133,"string":"133"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":282,"cells":{"code":{"kind":"string","value":"\r\r\r\r'''simple docstring'''\r\r\r\r\r\r\rimport torch\r\rfrom diffusers import DDPMScheduler\r\rfrom .test_schedulers import SchedulerCommonTest\r\r\r\rclass \t\t\t\t\t\t\tlowercase ( _lowerCamelCase\t\t\t\t):\r\r\r \"\"\"simple docstring\"\"\"\r\r\r UpperCAmelCase\t\t\t\t\t\t\t= (DDPMScheduler,)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself ,**a_\t\t\t\t\t\t\t)\t\t-> List[str]:\r _UpperCAmelCase :\t\t\t\t\t\tList[str]\t\t\t = {\r \"\"\"num_train_timesteps\"\"\": 1_000,\r \"\"\"beta_start\"\"\": 0.0001,\r \"\"\"beta_end\"\"\": 0.02,\r \"\"\"beta_schedule\"\"\": \"\"\"linear\"\"\",\r \"\"\"variance_type\"\"\": \"\"\"fixed_small\"\"\",\r \"\"\"clip_sample\"\"\": True,\r }\r\r config.update(**a_\t\t\t\t\t\t\t)\r return config\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Any:\r for timesteps in [1, 5, 100, 1_000]:\r self.check_over_configs(num_train_timesteps=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Optional[Any]:\r for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,[0.002, 0.02, 0.2, 2]\t\t\t\t\t\t\t):\r self.check_over_configs(beta_start=a_ ,beta_end=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Tuple:\r for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\r self.check_over_configs(beta_schedule=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Optional[Any]:\r for variance in [\"fixed_small\", \"fixed_large\", \"other\"]:\r self.check_over_configs(variance_type=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> str:\r for clip_sample in [True, False]:\r self.check_over_configs(clip_sample=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> int:\r self.check_over_configs(thresholding=a_\t\t\t\t\t\t\t)\r for threshold in [0.5, 1.0, 2.0]:\r for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\r self.check_over_configs(\r thresholding=a_ ,prediction_type=a_ ,sample_max_value=a_ ,)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Optional[Any]:\r for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\r self.check_over_configs(prediction_type=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Optional[Any]:\r for t in [0, 500, 999]:\r self.check_over_forward(time_step=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Any:\r _UpperCAmelCase :\t\t\t\t\t\tAny\t\t\t = self.scheduler_classes[0]\r _UpperCAmelCase :\t\t\t\t\t\tint\t\t\t = self.get_scheduler_config()\r _UpperCAmelCase :\t\t\t\t\t\tOptional[Any]\t\t\t = scheduler_class(**a_\t\t\t\t\t\t\t)\r\r assert torch.sum(torch.abs(scheduler._get_variance(0\t\t\t\t\t\t\t) - 0.0\t\t\t\t\t\t\t)\t\t\t\t\t\t\t) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(487\t\t\t\t\t\t\t) - 0.0_0979\t\t\t\t\t\t\t)\t\t\t\t\t\t\t) < 1E-5\r assert torch.sum(torch.abs(scheduler._get_variance(999\t\t\t\t\t\t\t) - 0.02\t\t\t\t\t\t\t)\t\t\t\t\t\t\t) < 1E-5\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Optional[int]:\r _UpperCAmelCase :\t\t\t\t\t\tDict\t\t\t = self.scheduler_classes[0]\r _UpperCAmelCase :\t\t\t\t\t\tList[Any]\t\t\t = self.get_scheduler_config()\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = scheduler_class(**a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tList[Any]\t\t\t = len(a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tOptional[Any]\t\t\t = self.dummy_model()\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = self.dummy_sample_deter\r _UpperCAmelCase :\t\t\t\t\t\tOptional[int]\t\t\t = torch.manual_seed(0\t\t\t\t\t\t\t)\r\r for t in reversed(range(a_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t):\r # 1. predict noise residual\r _UpperCAmelCase :\t\t\t\t\t\tList[Any]\t\t\t = model(a_ ,a_\t\t\t\t\t\t\t)\r\r # 2. predict previous mean of sample x_t-1\r _UpperCAmelCase :\t\t\t\t\t\tUnion[str, Any]\t\t\t = scheduler.step(a_ ,a_ ,a_ ,generator=a_\t\t\t\t\t\t\t).prev_sample\r\r # if t > 0:\r # noise = self.dummy_sample_deter\r # variance = scheduler.get_variance(t) ** (0.5) * noise\r #\r # sample = pred_prev_sample + variance\r _UpperCAmelCase :\t\t\t\t\t\tstr\t\t\t = pred_prev_sample\r\r _UpperCAmelCase :\t\t\t\t\t\tList[str]\t\t\t = torch.sum(torch.abs(a_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = torch.mean(torch.abs(a_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r assert abs(result_sum.item() - 258.9606\t\t\t\t\t\t\t) < 1E-2\r assert abs(result_mean.item() - 0.3372\t\t\t\t\t\t\t) < 1E-3\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Dict:\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = self.scheduler_classes[0]\r _UpperCAmelCase :\t\t\t\t\t\tOptional[int]\t\t\t = self.get_scheduler_config(prediction_type=\"\"\"v_prediction\"\"\"\t\t\t\t\t\t\t)\r _UpperCAmelCase :\t\t\t\t\t\tstr\t\t\t = scheduler_class(**a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = len(a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tAny\t\t\t = self.dummy_model()\r _UpperCAmelCase :\t\t\t\t\t\tDict\t\t\t = self.dummy_sample_deter\r _UpperCAmelCase :\t\t\t\t\t\tOptional[Any]\t\t\t = torch.manual_seed(0\t\t\t\t\t\t\t)\r\r for t in reversed(range(a_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t):\r # 1. predict noise residual\r _UpperCAmelCase :\t\t\t\t\t\tDict\t\t\t = model(a_ ,a_\t\t\t\t\t\t\t)\r\r # 2. predict previous mean of sample x_t-1\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = scheduler.step(a_ ,a_ ,a_ ,generator=a_\t\t\t\t\t\t\t).prev_sample\r\r # if t > 0:\r # noise = self.dummy_sample_deter\r # variance = scheduler.get_variance(t) ** (0.5) * noise\r #\r # sample = pred_prev_sample + variance\r _UpperCAmelCase :\t\t\t\t\t\tAny\t\t\t = pred_prev_sample\r\r _UpperCAmelCase :\t\t\t\t\t\tOptional[Any]\t\t\t = torch.sum(torch.abs(a_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r _UpperCAmelCase :\t\t\t\t\t\tList[str]\t\t\t = torch.mean(torch.abs(a_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\r\r assert abs(result_sum.item() - 202.0296\t\t\t\t\t\t\t) < 1E-2\r assert abs(result_mean.item() - 0.2631\t\t\t\t\t\t\t) < 1E-3\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> List[Any]:\r _UpperCAmelCase :\t\t\t\t\t\tint\t\t\t = self.scheduler_classes[0]\r _UpperCAmelCase :\t\t\t\t\t\tAny\t\t\t = self.get_scheduler_config()\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = scheduler_class(**a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = [100, 87, 50, 1, 0]\r\r scheduler.set_timesteps(timesteps=a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tstr\t\t\t = scheduler.timesteps\r\r for i, timestep in enumerate(a_\t\t\t\t\t\t\t):\r if i == len(a_\t\t\t\t\t\t\t) - 1:\r _UpperCAmelCase :\t\t\t\t\t\tAny\t\t\t = -1\r else:\r _UpperCAmelCase :\t\t\t\t\t\tUnion[str, Any]\t\t\t = timesteps[i + 1]\r\r _UpperCAmelCase :\t\t\t\t\t\tstr\t\t\t = scheduler.previous_timestep(a_\t\t\t\t\t\t\t)\r _UpperCAmelCase :\t\t\t\t\t\tList[str]\t\t\t = prev_t.item()\r\r self.assertEqual(a_ ,a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Dict:\r _UpperCAmelCase :\t\t\t\t\t\tAny\t\t\t = self.scheduler_classes[0]\r _UpperCAmelCase :\t\t\t\t\t\tList[Any]\t\t\t = self.get_scheduler_config()\r _UpperCAmelCase :\t\t\t\t\t\tOptional[int]\t\t\t = scheduler_class(**a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tint\t\t\t = [100, 87, 50, 51, 0]\r\r with self.assertRaises(a_ ,msg=\"\"\"`custom_timesteps` must be in descending order.\"\"\"\t\t\t\t\t\t\t):\r scheduler.set_timesteps(timesteps=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Optional[int]:\r _UpperCAmelCase :\t\t\t\t\t\tList[Any]\t\t\t = self.scheduler_classes[0]\r _UpperCAmelCase :\t\t\t\t\t\tList[str]\t\t\t = self.get_scheduler_config()\r _UpperCAmelCase :\t\t\t\t\t\tOptional[Any]\t\t\t = scheduler_class(**a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tAny\t\t\t = [100, 87, 50, 1, 0]\r _UpperCAmelCase :\t\t\t\t\t\tAny\t\t\t = len(a_\t\t\t\t\t\t\t)\r\r with self.assertRaises(a_ ,msg=\"\"\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\"\"\"\t\t\t\t\t\t\t):\r scheduler.set_timesteps(num_inference_steps=a_ ,timesteps=a_\t\t\t\t\t\t\t)\r\r\r\r\r\r\r\r def _snake_case (\t\t\t\t\t\t\tself\t\t\t\t\t\t\t)\t\t-> Any:\r _UpperCAmelCase :\t\t\t\t\t\tDict\t\t\t = self.scheduler_classes[0]\r _UpperCAmelCase :\t\t\t\t\t\tTuple\t\t\t = self.get_scheduler_config()\r _UpperCAmelCase :\t\t\t\t\t\tOptional[Any]\t\t\t = scheduler_class(**a_\t\t\t\t\t\t\t)\r\r _UpperCAmelCase :\t\t\t\t\t\tUnion[str, Any]\t\t\t = [scheduler.config.num_train_timesteps]\r\r with self.assertRaises(\r a_ ,msg=\"\"\"`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}\"\"\" ,):\r scheduler.set_timesteps(timesteps=a_\t\t\t\t\t\t\t)\r\r\r\r"},"code_codestyle":{"kind":"number","value":215,"string":"215"},"style_context":{"kind":"string","value":"\r\r\r\r'''simple docstring'''\r\r\r\r\r\r\rfrom __future__ import annotations\r\rimport typing\rfrom collections import Counter\r\r\r\r\rdef snake_case_\t\t\t\t\t( lowerCAmelCase_\t\t)->\t\ttyping.Counter[int]:\r\r\r\r\r\r '''simple docstring'''\r\r\r _UpperCAmelCase :\t\t\t\t\t\ttyping.Counter[int]\t\t\t = Counter()\r for base in range(1 , max_perimeter + 1\t\t):\r for perpendicular in range(lowerCAmelCase_ , max_perimeter + 1\t\t):\r _UpperCAmelCase :\t\t\t\t\t\tList[str]\t\t\t = (base * base + perpendicular * perpendicular) ** 0.5\r if hypotenuse == int(lowerCAmelCase_\t\t):\r _UpperCAmelCase :\t\t\t\t\t\tOptional[Any]\t\t\t = int(base + perpendicular + hypotenuse\t\t)\r if perimeter > max_perimeter:\r continue\r triplets[perimeter] += 1\r return triplets\r\r\r\r\rdef snake_case_\t\t\t\t\t( lowerCAmelCase_ = 1000\t\t)->\t\tint:\r\r\r\r\r\r '''simple docstring'''\r\r\r _UpperCAmelCase :\t\t\t\t\t\tint\t\t\t = pythagorean_triple(lowerCAmelCase_\t\t)\r return triplets.most_common(1\t\t)[0][0]\r\r\rif __name__ == \"__main__\":\r print(f\"\"\"Perimeter {solution()} has maximum solutions\"\"\")\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":215,"string":"215"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":283,"cells":{"code":{"kind":"string","value":"\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\nfrom operator import delitem, getitem, setitem\r\n\r\nimport pytest\r\n\r\nfrom data_structures.hashing.hash_map import HashMap\r\n\r\n\r\n\r\ndef __UpperCAmelCase\t\t\t( __a\t\t\t\t: List[str] ) ->\t\t\t\t\tstr:\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\treturn getitem, k\r\n\r\n\r\n\r\ndef __UpperCAmelCase\t\t\t( __a\t\t\t\t: int\t\t,__a\t\t\t\t: List[Any] ) ->\t\t\t\t\tOptional[int]:\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\treturn setitem, k, v\r\n\r\n\r\n\r\ndef __UpperCAmelCase\t\t\t( __a\t\t\t\t: Any ) ->\t\t\t\t\tOptional[Any]:\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\treturn delitem, k\r\n\r\n\r\n\r\ndef __UpperCAmelCase\t\t\t( __a\t\t\t\t: Any\t\t,__a\t\t\t\t: List[str]\t\t,*__a\t\t\t\t: Any ) ->\t\t\t\t\tDict:\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\treturn fun(__a\t\t,*__a ), None\r\n\t\t\t\t\texcept Exception as e:\r\n\t\t\t\t\t\t\t\t\t\treturn None, e\r\n\r\n\r\na__\t\t\t\t=\t\t(\r\n _set('''key_a''', '''val_a'''),\r\n _set('''key_b''', '''val_b'''),\r\n)\r\n\r\na__\t\t\t\t=\t\t[\r\n _set('''key_a''', '''val_a'''),\r\n _set('''key_a''', '''val_b'''),\r\n]\r\n\r\na__\t\t\t\t=\t\t[\r\n _set('''key_a''', '''val_a'''),\r\n _set('''key_b''', '''val_b'''),\r\n _del('''key_a'''),\r\n _del('''key_b'''),\r\n _set('''key_a''', '''val_a'''),\r\n _del('''key_a'''),\r\n]\r\n\r\na__\t\t\t\t=\t\t[\r\n _get('''key_a'''),\r\n _del('''key_a'''),\r\n _set('''key_a''', '''val_a'''),\r\n _del('''key_a'''),\r\n _del('''key_a'''),\r\n _get('''key_a'''),\r\n]\r\n\r\na__\t\t\t\t=\t\t[\r\n *[_set(x, x) for x in range(5)], # guaranteed upsize\r\n]\r\n\r\na__\t\t\t\t=\t\t[\r\n *[_set(x, x) for x in range(5)], # guaranteed upsize\r\n *[_del(x) for x in range(5)],\r\n _set('''key_a''', '''val_b'''),\r\n]\r\n\r\n\r\n\r\n@pytest.mark.parametrize(\r\n '''operations'''\t\t,(\r\n pytest.param(_add_items\t\t,id='''add items''' ),\r\n pytest.param(_overwrite_items\t\t,id='''overwrite items''' ),\r\n pytest.param(_delete_items\t\t,id='''delete items''' ),\r\n pytest.param(_access_absent_items\t\t,id='''access absent items''' ),\r\n pytest.param(_add_with_resize_up\t\t,id='''add with resize up''' ),\r\n pytest.param(_add_with_resize_down\t\t,id='''add with resize down''' ),\r\n )\t\t,)\r\ndef __UpperCAmelCase\t\t\t( __a\t\t\t\t: int ) ->\t\t\t\t\tint:\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\t_a : str \t\t\t\t=\t\tHashMap(initial_block_size=4 )\r\n\t\t\t\t\t_a : Union[str, Any] \t\t\t\t=\t\t{}\r\n\t\t\t\t\tfor _, (fun, *args) in enumerate(__a ):\r\n\t\t\t\t\t\t\t\t\t\t_a : List[Any] \t\t\t\t=\t\t_run_operation(__a\t\t,__a\t\t,*__a )\r\n\t\t\t\t\t\t\t\t\t\t_a : List[Any] \t\t\t\t=\t\t_run_operation(__a\t\t,__a\t\t,*__a )\r\n\t\t\t\t\t\t\t\t\t\tassert my_res == py_res\r\n\t\t\t\t\t\t\t\t\t\tassert str(__a ) == str(__a )\r\n\t\t\t\t\t\t\t\t\t\tassert set(__a ) == set(__a )\r\n\t\t\t\t\t\t\t\t\t\tassert len(__a ) == len(__a )\r\n\t\t\t\t\t\t\t\t\t\tassert set(my.items() ) == set(py.items() )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef __UpperCAmelCase\t\t\t( ) ->\t\t\t\t\tOptional[Any]:\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\tdef is_public(__a\t\t\t\t: str ) -> bool:\r\n\t\t\t\t\t\t\t\t\t\treturn not name.startswith('''_''' )\r\n\r\n\t\t\t\t\t_a : List[Any] \t\t\t\t=\t\t{name for name in dir({} ) if is_public(__a )}\r\n\t\t\t\t\t_a : Tuple \t\t\t\t=\t\t{name for name in dir(HashMap() ) if is_public(__a )}\r\n\r\n\t\t\t\t\tassert dict_public_names > hash_public_names\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":352,"string":"352"},"style_context":{"kind":"string","value":"\r\n\r\nfrom typing import Dict, Iterable, Optional, Union\r\n\r\nimport numpy as np\r\n\r\nfrom ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict\r\nfrom ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image\r\nfrom ...image_utils import (\r\n IMAGENET_STANDARD_MEAN,\r\n IMAGENET_STANDARD_STD,\r\n ChannelDimension,\r\n ImageInput,\r\n PILImageResampling,\r\n make_list_of_images,\r\n to_numpy_array,\r\n valid_images,\r\n)\r\nfrom ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends\r\n\r\n\r\nif is_vision_available():\r\n\t\t\timport PIL\r\n\r\n# soft dependency\r\nif is_pytesseract_available():\r\n\t\t\timport pytesseract\r\n\r\na__\t\t\t\t=\t\tlogging.get_logger(__name__)\r\n\r\n\r\n\r\ndef __UpperCAmelCase\t\t\t( __a\t\t\t\t: Union[str, Any]\t\t,__a\t\t\t\t: str\t\t,__a\t\t\t\t: Union[str, Any] ) ->\t\t\t\t\tList[str]:\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\treturn [\r\n\t\t\t\t\t int(1_000 * (box[0] / width) ),\r\n\t\t\t\t\t int(1_000 * (box[1] / height) ),\r\n\t\t\t\t\t int(1_000 * (box[2] / width) ),\r\n\t\t\t\t\t int(1_000 * (box[3] / height) ),\r\n\t\t\t\t\t]\r\n\r\n\r\n\r\ndef __UpperCAmelCase\t\t\t( __a\t\t\t\t: np.ndarray\t\t,__a\t\t\t\t: Optional[str]\t\t,__a\t\t\t\t: Optional[str] ) ->\t\t\t\t\tList[Any]:\r\n\r\n\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\t\t\t\t\t_a : str \t\t\t\t=\t\tto_pil_image(__a )\r\n\t\t\t\t\t_a\t\t\t\t, _a : Optional[Any] \t\t\t\t=\t\tpil_image.size\r\n\t\t\t\t\t_a : Tuple \t\t\t\t=\t\tpytesseract.image_to_data(__a\t\t,lang=__a\t\t,output_type='''dict'''\t\t,config=__a )\r\n\t\t\t\t\t_a\t\t\t\t, _a\t\t\t\t, _a\t\t\t\t, _a\t\t\t\t, _a : List[str] \t\t\t\t=\t\tdata['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']\r\n\r\n\t\t\t\t\t# filter empty words and corresponding coordinates\r\n\t\t\t\t\t_a : Dict \t\t\t\t=\t\t[idx for idx, word in enumerate(__a ) if not word.strip()]\r\n\t\t\t\t\t_a : str \t\t\t\t=\t\t[word for idx, word in enumerate(__a ) if idx not in irrelevant_indices]\r\n\t\t\t\t\t_a : List[str] \t\t\t\t=\t\t[coord for idx, coord in enumerate(__a ) if idx not in irrelevant_indices]\r\n\t\t\t\t\t_a : Union[str, Any] \t\t\t\t=\t\t[coord for idx, coord in enumerate(__a ) if idx not in irrelevant_indices]\r\n\t\t\t\t\t_a : str \t\t\t\t=\t\t[coord for idx, coord in enumerate(__a ) if idx not in irrelevant_indices]\r\n\t\t\t\t\t_a : Union[str, Any] \t\t\t\t=\t\t[coord for idx, coord in enumerate(__a ) if idx not in irrelevant_indices]\r\n\r\n\t\t\t\t\t# turn coordinates into (left, top, left+width, top+height) format\r\n\t\t\t\t\t_a : int \t\t\t\t=\t\t[]\r\n\t\t\t\t\tfor x, y, w, h in zip(__a\t\t,__a\t\t,__a\t\t,__a ):\r\n\t\t\t\t\t\t\t\t\t\t_a : List[str] \t\t\t\t=\t\t[x, y, x + w, y + h]\r\n\t\t\t\t\t\t\t\t\t\tactual_boxes.append(__a )\r\n\r\n\t\t\t\t\t# finally, normalize the bounding boxes\r\n\t\t\t\t\t_a : Dict \t\t\t\t=\t\t[]\r\n\t\t\t\t\tfor box in actual_boxes:\r\n\t\t\t\t\t\t\t\t\t\tnormalized_boxes.append(normalize_box(__a\t\t,__a\t\t,__a ) )\r\n\r\n\t\t\t\t\tassert len(__a ) == len(__a ), \"Not as many words as there are bounding boxes\"\r\n\r\n\t\t\t\t\treturn words, normalized_boxes\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass UpperCAmelCase_\t\t( __lowercase ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tUpperCAmelCase__\t:\t\t\t\t\t\tOptional[int] = [\"pixel_values\"]\r\n\r\n\r\n\t\t\t\t\t\t\tdef __init__( self\t,\t\t\t\t\t\t\t_a = True\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = PILImageResampling.BILINEAR\t,\t\t\t\t\t\t\t_a = True\t,\t\t\t\t\t\t\t_a = 1 / 2_5_5\t,\t\t\t\t\t\t\t_a = True\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = True\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = \"\"\t,\t\t\t\t\t\t\t**_a\t,\t\t\t\t\t\t\t) -> None:\r\n\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(**_a\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : List[str] \t\t\t\t=\t\tsize if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Union[str, Any] \t\t\t\t=\t\tget_size_dict(_a\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : int \t\t\t\t=\t\tdo_resize\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Optional[int] \t\t\t\t=\t\tsize\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : str \t\t\t\t=\t\tresample\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : str \t\t\t\t=\t\tdo_rescale\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Any \t\t\t\t=\t\trescale_value\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Optional[Any] \t\t\t\t=\t\tdo_normalize\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : int \t\t\t\t=\t\timage_mean if image_mean is not None else IMAGENET_STANDARD_MEAN\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : List[str] \t\t\t\t=\t\timage_std if image_std is not None else IMAGENET_STANDARD_STD\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : List[Any] \t\t\t\t=\t\tapply_ocr\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Optional[int] \t\t\t\t=\t\tocr_lang\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Tuple \t\t\t\t=\t\ttesseract_config\r\n\r\n\r\n\t\t\t\t\t\t\tdef __lowercase ( self\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a = PILImageResampling.BILINEAR\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t**_a\t,\t\t\t\t\t\t\t) -> np.ndarray:\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Any \t\t\t\t=\t\tget_size_dict(_a\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tif \"height\" not in size or \"width\" not in size:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(F\"\"\"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}\"\"\"\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Optional[int] \t\t\t\t=\t\t(size['''height'''], size['''width'''])\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn resize(_a\t,\t\t\t\t\t\t\tsize=_a\t,\t\t\t\t\t\t\tresample=_a\t,\t\t\t\t\t\t\tdata_format=_a\t,\t\t\t\t\t\t\t**_a\t)\r\n\r\n\r\n\t\t\t\t\t\t\tdef __lowercase ( self\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t**_a\t,\t\t\t\t\t\t\t) -> np.ndarray:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn rescale(_a\t,\t\t\t\t\t\t\tscale=_a\t,\t\t\t\t\t\t\tdata_format=_a\t,\t\t\t\t\t\t\t**_a\t)\r\n\r\n\r\n\t\t\t\t\t\t\tdef __lowercase ( self\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t**_a\t,\t\t\t\t\t\t\t) -> np.ndarray:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn normalize(_a\t,\t\t\t\t\t\t\tmean=_a\t,\t\t\t\t\t\t\tstd=_a\t,\t\t\t\t\t\t\tdata_format=_a\t,\t\t\t\t\t\t\t**_a\t)\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tdef __lowercase ( self\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a=None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = None\t,\t\t\t\t\t\t\t_a = ChannelDimension.FIRST\t,\t\t\t\t\t\t\t**_a\t,\t\t\t\t\t\t\t) -> PIL.Image.Image:\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Optional[int] \t\t\t\t=\t\tdo_resize if do_resize is not None else self.do_resize\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Union[str, Any] \t\t\t\t=\t\tsize if size is not None else self.size\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Any \t\t\t\t=\t\tget_size_dict(_a\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : List[str] \t\t\t\t=\t\tresample if resample is not None else self.resample\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : int \t\t\t\t=\t\tdo_rescale if do_rescale is not None else self.do_rescale\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Union[str, Any] \t\t\t\t=\t\trescale_factor if rescale_factor is not None else self.rescale_factor\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : int \t\t\t\t=\t\tdo_normalize if do_normalize is not None else self.do_normalize\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : str \t\t\t\t=\t\timage_mean if image_mean is not None else self.image_mean\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Tuple \t\t\t\t=\t\timage_std if image_std is not None else self.image_std\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Any \t\t\t\t=\t\tapply_ocr if apply_ocr is not None else self.apply_ocr\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : int \t\t\t\t=\t\tocr_lang if ocr_lang is not None else self.ocr_lang\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Optional[int] \t\t\t\t=\t\ttesseract_config if tesseract_config is not None else self.tesseract_config\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : List[Any] \t\t\t\t=\t\tmake_list_of_images(_a\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif not valid_images(_a\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''torch.Tensor, tf.Tensor or jax.ndarray.'''\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif do_resize and size is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('''Size must be specified if do_resize is True.'''\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif do_rescale and rescale_factor is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('''Rescale factor must be specified if do_rescale is True.'''\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif do_normalize and (image_mean is None or image_std is None):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t# All transformations expect numpy arrays.\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : Any \t\t\t\t=\t\t[to_numpy_array(_a\t) for image in images]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t# Tesseract OCR to get words + normalized bounding boxes\r\n\t\t\t\t\t\t\t\t\t\t\t\tif apply_ocr:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trequires_backends(self\t,\t\t\t\t\t\t\t'''pytesseract'''\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a : str \t\t\t\t=\t\t[]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a : str \t\t\t\t=\t\t[]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor image in images:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a\t\t\t\t, _a : Union[str, Any] \t\t\t\t=\t\tapply_tesseract(_a\t,\t\t\t\t\t\t\t_a\t,\t\t\t\t\t\t\t_a\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twords_batch.append(_a\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tboxes_batch.append(_a\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif do_resize:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a : List[str] \t\t\t\t=\t\t[self.resize(image=_a\t,\t\t\t\t\t\t\tsize=_a\t,\t\t\t\t\t\t\tresample=_a\t) for image in images]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif do_rescale:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a : Optional[Any] \t\t\t\t=\t\t[self.rescale(image=_a\t,\t\t\t\t\t\t\tscale=_a\t) for image in images]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif do_normalize:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a : List[Any] \t\t\t\t=\t\t[self.normalize(image=_a\t,\t\t\t\t\t\t\tmean=_a\t,\t\t\t\t\t\t\tstd=_a\t) for image in images]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : List[str] \t\t\t\t=\t\t[to_channel_dimension_format(_a\t,\t\t\t\t\t\t\t_a\t) for image in images]\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t_a : List[str] \t\t\t\t=\t\tBatchFeature(data={'''pixel_values''': images}\t,\t\t\t\t\t\t\ttensor_type=_a\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif apply_ocr:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a : Optional[int] \t\t\t\t=\t\twords_batch\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t_a : List[Any] \t\t\t\t=\t\tboxes_batch\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn data\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":15,"string":"15"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":284,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n\n\n\"\"\"simple docstring\"\"\"\n\n\n\n\n\n\nfrom ...configuration_utils import PretrainedConfig\nfrom ...utils import logging\n\n\nUpperCAmelCase_ : Any \t\t\t= logging.get_logger(__name__)\n\nUpperCAmelCase_ : Any \t\t\t= {\n '''facebook/dpr-ctx_encoder-single-nq-base''': (\n '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''\n ),\n '''facebook/dpr-question_encoder-single-nq-base''': (\n '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''\n ),\n '''facebook/dpr-reader-single-nq-base''': (\n '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''\n ),\n '''facebook/dpr-ctx_encoder-multiset-base''': (\n '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''\n ),\n '''facebook/dpr-question_encoder-multiset-base''': (\n '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''\n ),\n '''facebook/dpr-reader-multiset-base''': (\n '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''\n ),\n}\n\n\n\n\nclass \t\t\t\t\t\t\tlowerCAmelCase__\t\t\t\t(\t\t\tlowercase__ ):\n\n\n\n\n\n\n '''simple docstring'''\n\n __UpperCamelCase\t = \"\"\"dpr\"\"\"\n def __init__(\t\t\t\t\t\tself : Dict\t\t\t\t\t\t,\tlowercase_ : Tuple=30522\t\t\t\t\t\t,\tlowercase_ : Optional[int]=768\t\t\t\t\t\t,\tlowercase_ : Any=12\t\t\t\t\t\t,\tlowercase_ : Optional[Any]=12\t\t\t\t\t\t,\tlowercase_ : Dict=3072\t\t\t\t\t\t,\tlowercase_ : Optional[int]=\"gelu\"\t\t\t\t\t\t,\tlowercase_ : Optional[Any]=0.1\t\t\t\t\t\t,\tlowercase_ : Union[str, Any]=0.1\t\t\t\t\t\t,\tlowercase_ : Optional[int]=512\t\t\t\t\t\t,\tlowercase_ : Optional[int]=2\t\t\t\t\t\t,\tlowercase_ : Optional[int]=0.02\t\t\t\t\t\t,\tlowercase_ : Tuple=1e-12\t\t\t\t\t\t,\tlowercase_ : List[str]=0\t\t\t\t\t\t,\tlowercase_ : Optional[int]=\"absolute\"\t\t\t\t\t\t,\tlowercase_ : int = 0\t\t\t\t\t\t,\t**lowercase_ : Union[str, Any]\t\t\t\t\t\t,\t):\n\n\n\n\n '''simple docstring'''\n\n\n super().__init__(pad_token_id=_A\t\t\t\t\t\t,\t**_A)\n\n SCREAMING_SNAKE_CASE_ : Any \t\t\t\t\t\t=\t\tvocab_size\n SCREAMING_SNAKE_CASE_ : Union[str, Any] \t\t\t\t\t\t=\t\thidden_size\n SCREAMING_SNAKE_CASE_ : Optional[int] \t\t\t\t\t\t=\t\tnum_hidden_layers\n SCREAMING_SNAKE_CASE_ : int \t\t\t\t\t\t=\t\tnum_attention_heads\n SCREAMING_SNAKE_CASE_ : Optional[Any] \t\t\t\t\t\t=\t\thidden_act\n SCREAMING_SNAKE_CASE_ : Union[str, Any] \t\t\t\t\t\t=\t\tintermediate_size\n SCREAMING_SNAKE_CASE_ : Union[str, Any] \t\t\t\t\t\t=\t\thidden_dropout_prob\n SCREAMING_SNAKE_CASE_ : List[Any] \t\t\t\t\t\t=\t\tattention_probs_dropout_prob\n SCREAMING_SNAKE_CASE_ : Any \t\t\t\t\t\t=\t\tmax_position_embeddings\n SCREAMING_SNAKE_CASE_ : Optional[int] \t\t\t\t\t\t=\t\ttype_vocab_size\n SCREAMING_SNAKE_CASE_ : List[str] \t\t\t\t\t\t=\t\tinitializer_range\n SCREAMING_SNAKE_CASE_ : Optional[Any] \t\t\t\t\t\t=\t\tlayer_norm_eps\n SCREAMING_SNAKE_CASE_ : Union[str, Any] \t\t\t\t\t\t=\t\tprojection_dim\n SCREAMING_SNAKE_CASE_ : int \t\t\t\t\t\t=\t\tposition_embedding_type\n\n\n"},"code_codestyle":{"kind":"number","value":91,"string":"91"},"style_context":{"kind":"string","value":"\r\r\r'''simple docstring'''\r\rfrom typing import TYPE_CHECKING\r\rfrom ...utils import (\r OptionalDependencyNotAvailable,\r _LazyModule,\r is_flax_available,\r is_tf_available,\r is_torch_available,\r)\r\r\rlowerCAmelCase :Union[str, Any] \t=\t\t\t{\r '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']\r}\r\rtry:\r\t\t\t\t\t\t\tif not is_torch_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\tpass\relse:\r\t\t\t\t\t\t\tlowerCAmelCase :str \t=\t\t\t['''VisionEncoderDecoderModel''']\r\rtry:\r\t\t\t\t\t\t\tif not is_tf_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\tpass\relse:\r\t\t\t\t\t\t\tlowerCAmelCase :Optional[int] \t=\t\t\t['''TFVisionEncoderDecoderModel''']\r\rtry:\r\t\t\t\t\t\t\tif not is_flax_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\rexcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\tpass\relse:\r\t\t\t\t\t\t\tlowerCAmelCase :Union[str, Any] \t=\t\t\t['''FlaxVisionEncoderDecoderModel''']\r\rif TYPE_CHECKING:\r\t\t\t\t\t\t\tfrom .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig\r\r\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not is_torch_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\t\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_vision_encoder_decoder import VisionEncoderDecoderModel\r\r\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not is_tf_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\t\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel\r\r\t\t\t\t\t\t\ttry:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not is_flax_available():\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\t\t\t\t\t\t\texcept OptionalDependencyNotAvailable:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tpass\r\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t\tfrom .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel\r\relse:\r\t\t\t\t\t\t\timport sys\r\r\t\t\t\t\t\t\tlowerCAmelCase :int \t=\t\t\t_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)"},"style_context_codestyle":{"kind":"number","value":331,"string":"331"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":285,"cells":{"code":{"kind":"string","value":"\r\r\r\r'''simple docstring'''\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\rsnake_case_\t\t\t: Tuple \t= logging.get_logger(__name__)\r\rsnake_case_\t\t\t: str \t= {\r 'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',\r # See all GLPN models at https://huggingface.co/models?filter=glpn\r}\r\r\r\r\r\r\rclass \t\t\t\t\t\t\tlowercase__ (\t\t\t\t\t\t\t__SCREAMING_SNAKE_CASE ):\r\tlowercase__ = \"\"\"glpn\"\"\"\r\r\r\r\r\r\tdef __init__(\t\t\tself : Any\t\t\t,lowerCamelCase__ : Optional[int]=3\t\t\t,lowerCamelCase__ : Tuple=4\t\t\t,lowerCamelCase__ : Optional[Any]=[2, 2, 2, 2]\t\t\t,lowerCamelCase__ : int=[8, 4, 2, 1]\t\t\t,lowerCamelCase__ : Union[str, Any]=[32, 64, 160, 256]\t\t\t,lowerCamelCase__ : Dict=[7, 3, 3, 3]\t\t\t,lowerCamelCase__ : Union[str, Any]=[4, 2, 2, 2]\t\t\t,lowerCamelCase__ : List[Any]=[1, 2, 5, 8]\t\t\t,lowerCamelCase__ : Tuple=[4, 4, 4, 4]\t\t\t,lowerCamelCase__ : Dict=\"gelu\"\t\t\t,lowerCamelCase__ : str=0.0\t\t\t,lowerCamelCase__ : int=0.0\t\t\t,lowerCamelCase__ : Optional[int]=0.0_2\t\t\t,lowerCamelCase__ : Union[str, Any]=0.1\t\t\t,lowerCamelCase__ : str=1E-6\t\t\t,lowerCamelCase__ : Optional[Any]=64\t\t\t,lowerCamelCase__ : List[str]=10\t\t\t,lowerCamelCase__ : Dict=-1\t\t\t,**lowerCamelCase__ : Union[str, Any]\t\t\t,):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\t\t\t\t\t\t\tsuper().__init__(**UpperCamelCase__\t\t\t)\r\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tDict\t\t\t = num_channels\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tList[str]\t\t\t = num_encoder_blocks\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[Any]\t\t\t = depths\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tList[Any]\t\t\t = sr_ratios\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tTuple\t\t\t = hidden_sizes\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tDict\t\t\t = patch_sizes\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tstr\t\t\t = strides\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tDict\t\t\t = mlp_ratios\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[Any]\t\t\t = num_attention_heads\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tList[Any]\t\t\t = hidden_act\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tUnion[str, Any]\t\t\t = hidden_dropout_prob\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tint\t\t\t = attention_probs_dropout_prob\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tTuple\t\t\t = initializer_range\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[Any]\t\t\t = drop_path_rate\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tList[Any]\t\t\t = layer_norm_eps\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tList[str]\t\t\t = decoder_hidden_size\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tDict\t\t\t = max_depth\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tAny\t\t\t = head_in_index\r\r\r\r"},"code_codestyle":{"kind":"number","value":367,"string":"367"},"style_context":{"kind":"string","value":"\r\r\r\r'''simple docstring'''\rfrom collections import OrderedDict\rfrom typing import Mapping\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...onnx import OnnxConfig\rfrom ...utils import logging\r\r\rsnake_case_\t\t\t: Optional[int] \t= logging.get_logger(__name__)\r\rsnake_case_\t\t\t: List[Any] \t= {\r 'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',\r 'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',\r # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl\r}\r\r\r\r\r\r\rclass \t\t\t\t\t\t\tlowercase__ (\t\t\t\t\t\t\tlowercase ):\r\tlowercase__ = \"\"\"xlm-roberta-xl\"\"\"\r\r\r\r\r\r\tdef __init__(\t\t\tself : Optional[int]\t\t\t,lowerCamelCase__ : Optional[Any]=250880\t\t\t,lowerCamelCase__ : Tuple=2560\t\t\t,lowerCamelCase__ : Union[str, Any]=36\t\t\t,lowerCamelCase__ : List[str]=32\t\t\t,lowerCamelCase__ : Optional[Any]=10240\t\t\t,lowerCamelCase__ : Tuple=\"gelu\"\t\t\t,lowerCamelCase__ : int=0.1\t\t\t,lowerCamelCase__ : int=0.1\t\t\t,lowerCamelCase__ : Optional[int]=514\t\t\t,lowerCamelCase__ : List[str]=1\t\t\t,lowerCamelCase__ : Dict=0.0_2\t\t\t,lowerCamelCase__ : Any=1E-05\t\t\t,lowerCamelCase__ : Union[str, Any]=1\t\t\t,lowerCamelCase__ : str=0\t\t\t,lowerCamelCase__ : Tuple=2\t\t\t,lowerCamelCase__ : Union[str, Any]=\"absolute\"\t\t\t,lowerCamelCase__ : Optional[Any]=True\t\t\t,lowerCamelCase__ : List[str]=None\t\t\t,**lowerCamelCase__ : Dict\t\t\t,):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\t\t\t\t\t\t\tsuper().__init__(pad_token_id=lowerCamelCase__\t\t\t,bos_token_id=lowerCamelCase__\t\t\t,eos_token_id=lowerCamelCase__\t\t\t,**lowerCamelCase__\t\t\t)\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[int]\t\t\t = vocab_size\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[Any]\t\t\t = hidden_size\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tstr\t\t\t = num_hidden_layers\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tstr\t\t\t = num_attention_heads\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tAny\t\t\t = hidden_act\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tDict\t\t\t = intermediate_size\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[int]\t\t\t = hidden_dropout_prob\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tAny\t\t\t = attention_probs_dropout_prob\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tList[Any]\t\t\t = max_position_embeddings\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tstr\t\t\t = type_vocab_size\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[Any]\t\t\t = initializer_range\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[int]\t\t\t = layer_norm_eps\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[int]\t\t\t = position_embedding_type\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[Any]\t\t\t = use_cache\r\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[Any]\t\t\t = classifier_dropout\r\r\r\r\r\r\rclass \t\t\t\t\t\t\tlowercase__ (\t\t\t\t\t\t\tlowercase ):\r\r\r\r\r\r\t@property\r\tdef UpperCamelCase_ (\t\t\tself : List[Any]\t\t\t):\r\r\r\r\r\r\r\r\t\t\t\t\t\t\t'''simple docstring'''\r\t\t\t\t\t\t\tif self.task == \"multiple-choice\":\r\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCamelCase :\t\tUnion[str, Any]\t\t\t = {0: 'batch', 1: 'choice', 2: 'sequence'}\r\t\t\t\t\t\t\telse:\r\t\t\t\t\t\t\t\t\t\t\t\t\t_UpperCamelCase :\t\tOptional[Any]\t\t\t = {0: 'batch', 1: 'sequence'}\r\t\t\t\t\t\t\treturn OrderedDict(\r\t\t\t\t\t\t\t [\r\t\t\t\t\t\t\t ('input_ids', dynamic_axis),\r\t\t\t\t\t\t\t ('attention_mask', dynamic_axis),\r\t\t\t\t\t\t\t ]\t\t\t)\r\r\r"},"style_context_codestyle":{"kind":"number","value":236,"string":"236"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":286,"cells":{"code":{"kind":"string","value":"\r\r'''simple docstring'''\r\r# Function to print upper half of diamond (pyramid)\r\r\r\r\r\rdef lowercase\t\t\t\t\t( __magic_name__\t\t\t\t\t\t\t):\r\r\r\r\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t\t\t\tfor i in range(0 , __magic_name__\t\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\tfor _ in range(0 , n - i - 1\t\t\t\t\t\t\t): # printing spaces\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tprint(\" \" , end=\"\"\t\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tfor _ in range(0 , i + 1\t\t\t\t\t\t\t): # printing stars\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tprint(\"* \" , end=\"\"\t\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tprint()\r\r\r\r\r\rdef lowercase\t\t\t\t\t( __magic_name__\t\t\t\t\t\t\t):\r\r\r\r\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t\t\t\tfor i in range(__magic_name__ , 0 , -1\t\t\t\t\t\t\t):\r\t\t\t\t\t\t\t\t\t\tfor _ in range(__magic_name__ , 0 , -1\t\t\t\t\t\t\t): # printing stars\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tprint(\"* \" , end=\"\"\t\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\tprint()\r\t\t\t\t\t\t\t\t\t\tfor _ in range(n - i + 1 , 0 , -1\t\t\t\t\t\t\t): # printing spaces\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tprint(\" \" , end=\"\"\t\t\t\t\t\t\t)\r\r\r\r\r\rdef lowercase\t\t\t\t\t( __magic_name__\t\t\t\t\t\t\t):\r\r\r\r\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t\t\t\tif n <= 0:\r\t\t\t\t\t\t\t\t\t\tprint(\" ... .... nothing printing :(\"\t\t\t\t\t\t\t)\r\t\t\t\t\t\t\t\t\t\treturn\r\t\t\t\t\tfloyd(__magic_name__\t\t\t\t\t\t\t) # upper half\r\t\t\t\t\treverse_floyd(__magic_name__\t\t\t\t\t\t\t) # lower half\r\r\rif __name__ == \"__main__\":\r\t\t\t\tprint(R\"| /\\ | |- | |- |--| |\\ /| |-\")\r\t\t\t\tprint(R\"|/ \\| |- |_ |_ |__| | \\/ | |_\")\r\t\t\t\ta :\t\t\tint = 1\r\t\t\t\twhile K:\r\t\t\t\t\t\t\t\ta :\t\t\tTuple = int(input(\"enter the number and , and see the magic : \"))\r\t\t\t\t\t\t\t\tprint()\r\t\t\t\t\t\t\t\tpretty_print(user_number)\r\t\t\t\t\t\t\t\ta :\t\t\tstr = int(input(\"press 0 to exit... and 1 to continue...\"))\r\r\t\t\t\tprint(\"Good Bye...\")\r"},"code_codestyle":{"kind":"number","value":311,"string":"311"},"style_context":{"kind":"string","value":"\r\r'''simple docstring'''\r\rimport jax.numpy as jnp\r\rfrom ...utils import logging\rfrom ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel\rfrom .configuration_mta import MTaConfig\r\r\ra :\t\t\tOptional[Any] = logging.get_logger(__name__)\r\ra :\t\t\tTuple = \"T5Config\"\r\r\r\r\r\rdef lowercase\t\t\t\t\t( __magic_name__ , __magic_name__ , __magic_name__\t\t\t\t\t\t\t):\r\r\r\r\t\t\t\t\t'''simple docstring'''\r\r\r\r\r\r\r\r\t\t\t\t\tUpperCAmelCase\t\t:\t\t\t\t\t\t\tAny \t\t\t\t\t= jnp.zeros_like(__magic_name__\t\t\t\t\t\t\t)\r\t\t\t\t\tUpperCAmelCase\t\t:\t\t\t\t\t\t\tOptional[int] \t\t\t\t\t= shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]\t\t\t\t\t\t\t)\r\t\t\t\t\tUpperCAmelCase\t\t:\t\t\t\t\t\t\tstr \t\t\t\t\t= shifted_input_ids.at[:, 0].set(__magic_name__\t\t\t\t\t\t\t)\r\r\t\t\t\t\tUpperCAmelCase\t\t:\t\t\t\t\t\t\tAny \t\t\t\t\t= jnp.where(shifted_input_ids == -100 , __magic_name__ , __magic_name__\t\t\t\t\t\t\t)\r\t\t\t\t\treturn shifted_input_ids\r\r\r\r\r\r\r\rclass UpperCamelCase__ ( lowercase__ ):\r\r\r\t\t\"\"\"simple docstring\"\"\"\r\r\t\tSCREAMING_SNAKE_CASE__ :\tint =\t\t\t\t\t\t\t\"mt5\"\r\t\tSCREAMING_SNAKE_CASE__ :\tDict =\t\t\t\t\t\t\tMTaConfig\r\r\r\r\r\r\r\rclass UpperCamelCase__ ( lowercase__ ):\r\r\r\t\t\"\"\"simple docstring\"\"\"\r\r\t\tSCREAMING_SNAKE_CASE__ :\tint =\t\t\t\t\t\t\t\"mt5\"\r\t\tSCREAMING_SNAKE_CASE__ :\tstr =\t\t\t\t\t\t\tMTaConfig\r\r\r\r\r\r\r\rclass UpperCamelCase__ ( lowercase__ ):\r\r\r\t\t\"\"\"simple docstring\"\"\"\r\r\t\tSCREAMING_SNAKE_CASE__ :\tList[Any] =\t\t\t\t\t\t\t\"mt5\"\r\t\tSCREAMING_SNAKE_CASE__ :\tstr =\t\t\t\t\t\t\tMTaConfig\r"},"style_context_codestyle":{"kind":"number","value":311,"string":"311"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":287,"cells":{"code":{"kind":"string","value":"\r\nimport os\r\nimport tempfile\r\nfrom functools import partial\r\nfrom unittest import TestCase\r\nfrom unittest.mock import patch\r\n\r\nimport datasets\r\nimport datasets.config\r\n\r\nfrom .utils import require_beam\r\n\r\n\r\n\r\n\r\nclass \t\t\tlowerCamelCase__\t\t\t\t\t\t\t( datasets.BeamBasedBuilder):\r\n\r\n\r\n\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\tdef _lowerCamelCase ( self\t\t:int\t\t\t\t\t\t)\t\t->\t\t\tUnion[str, Any]:\r\n\t\t\treturn datasets.DatasetInfo(\r\n\t\t\t features=datasets.Features({\"content\": datasets.Value(\"string\"\t\t\t\t\t\t)}\t\t\t\t\t\t)\t\t\t\t\t, supervised_keys=_a\t\t\t\t\t, )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tdef _lowerCamelCase ( self\t\t:Tuple\t\t\t\t\t, a\t\t:Optional[Any]\t\t\t\t\t, a\t\t:int\t\t\t\t\t\t)\t\t->\t\t\tUnion[str, Any]:\r\n\t\t\treturn [datasets.SplitGenerator(name=datasets.Split.TRAIN\t\t\t\t\t, gen_kwargs={\"examples\": get_test_dummy_examples()}\t\t\t\t\t\t)]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tdef _lowerCamelCase ( self\t\t:Dict\t\t\t\t\t, a\t\t:Any\t\t\t\t\t, a\t\t:str\t\t\t\t\t\t)\t\t->\t\t\tTuple:\r\n\t\t\timport apache_beam as beam\r\n\r\n\t\t\treturn pipeline | \"Load Examples\" >> beam.Create(_a\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\nclass \t\t\tlowerCamelCase__\t\t\t\t\t\t\t( datasets.BeamBasedBuilder):\r\n\r\n\r\n\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\tdef _lowerCamelCase ( self\t\t:Union[str, Any]\t\t\t\t\t\t)\t\t->\t\t\tDict:\r\n\t\t\treturn datasets.DatasetInfo(\r\n\t\t\t features=datasets.Features({\"a\": datasets.Sequence({\"b\": datasets.Value(\"string\"\t\t\t\t\t\t)}\t\t\t\t\t\t)}\t\t\t\t\t\t)\t\t\t\t\t, supervised_keys=_a\t\t\t\t\t, )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tdef _lowerCamelCase ( self\t\t:Any\t\t\t\t\t, a\t\t:Optional[int]\t\t\t\t\t, a\t\t:Any\t\t\t\t\t\t)\t\t->\t\t\tList[str]:\r\n\t\t\treturn [\r\n\t\t\t datasets.SplitGenerator(name=datasets.Split.TRAIN\t\t\t\t\t, gen_kwargs={\"examples\": get_test_nested_examples()}\t\t\t\t\t\t)\r\n\t\t\t]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tdef _lowerCamelCase ( self\t\t:Optional[Any]\t\t\t\t\t, a\t\t:int\t\t\t\t\t, a\t\t:List[str]\t\t\t\t\t\t)\t\t->\t\t\tOptional[Any]:\r\n\t\t\timport apache_beam as beam\r\n\r\n\t\t\treturn pipeline | \"Load Examples\" >> beam.Create(_a\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\ndef _SCREAMING_SNAKE_CASE (\t\t) -> List[Any]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\treturn [(i, {\"content\": content}) for i, content in enumerate([\"foo\", \"bar\", \"foobar\"])]\r\n\r\n\r\n\r\n\r\ndef _SCREAMING_SNAKE_CASE (\t\t) -> str:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\treturn [(i, {\"a\": {\"b\": [content]}}) for i, content in enumerate([\"foo\", \"bar\", \"foobar\"])]\r\n\r\n\r\n\r\n\r\nclass \t\t\tlowerCamelCase__\t\t\t\t\t\t\t( _a):\r\n\r\n\r\n\r\n\r\n\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\t@require_beam\r\n\tdef _lowerCamelCase ( self\t\t:Any\t\t\t\t\t\t)\t\t->\t\t\tstr:\r\n\t\t\t__UpperCamelCase : int = len(get_test_dummy_examples()\t\t\t\t\t\t)\r\n\t\t\twith tempfile.TemporaryDirectory() as tmp_cache_dir:\r\n\t\t\t\t\t__UpperCamelCase : Optional[int] = DummyBeamDataset(cache_dir=_a\t\t\t\t\t, beam_runner=\"DirectRunner\"\t\t\t\t\t\t)\r\n\t\t\t\t\tbuilder.download_and_prepare()\r\n\t\t\t\t\tself.assertTrue(\r\n\t\t\t\t\t os.path.exists(\r\n\t\t\t\t\t os.path.join(_a\t\t\t\t\t, builder.name\t\t\t\t\t, \"default\"\t\t\t\t\t, \"0.0.0\"\t\t\t\t\t, f'{builder.name}-train.arrow'\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertDictEqual(builder.info.features\t\t\t\t\t, datasets.Features({\"content\": datasets.Value(\"string\"\t\t\t\t\t\t)}\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\t__UpperCamelCase : Any = builder.as_dataset()\r\n\t\t\t\t\tself.assertEqual(dset[\"train\"].num_rows\t\t\t\t\t, _a\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertEqual(dset[\"train\"].info.splits[\"train\"].num_examples\t\t\t\t\t, _a\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertDictEqual(dset[\"train\"][0]\t\t\t\t\t, get_test_dummy_examples()[0][1]\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertDictEqual(\r\n\t\t\t\t\t dset[\"train\"][expected_num_examples - 1]\t\t\t\t\t, get_test_dummy_examples()[expected_num_examples - 1][1]\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertTrue(\r\n\t\t\t\t\t os.path.exists(os.path.join(_a\t\t\t\t\t, builder.name\t\t\t\t\t, \"default\"\t\t\t\t\t, \"0.0.0\"\t\t\t\t\t, \"dataset_info.json\"\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\tdel dset\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t@require_beam\r\n\tdef _lowerCamelCase ( self\t\t:int\t\t\t\t\t\t)\t\t->\t\t\tstr:\r\n\t\t\timport apache_beam as beam\r\n\r\n\t\t\t__UpperCamelCase : List[Any] = beam.io.parquetio.WriteToParquet\r\n\r\n\t\t\t__UpperCamelCase : List[Any] = len(get_test_dummy_examples()\t\t\t\t\t\t)\r\n\t\t\twith tempfile.TemporaryDirectory() as tmp_cache_dir:\r\n\t\t\t\t\t__UpperCamelCase : Optional[int] = DummyBeamDataset(cache_dir=_a\t\t\t\t\t, beam_runner=\"DirectRunner\"\t\t\t\t\t\t)\r\n\t\t\t\t\twith patch(\"apache_beam.io.parquetio.WriteToParquet\"\t\t\t\t\t\t) as write_parquet_mock:\r\n\t\t\t\t\t\t\t__UpperCamelCase : int = partial(_a\t\t\t\t\t, num_shards=2\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\tbuilder.download_and_prepare()\r\n\t\t\t\t\tself.assertTrue(\r\n\t\t\t\t\t os.path.exists(\r\n\t\t\t\t\t os.path.join(\r\n\t\t\t\t\t _a\t\t\t\t\t, builder.name\t\t\t\t\t, \"default\"\t\t\t\t\t, \"0.0.0\"\t\t\t\t\t, f'{builder.name}-train-00000-of-00002.arrow'\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertTrue(\r\n\t\t\t\t\t os.path.exists(\r\n\t\t\t\t\t os.path.join(\r\n\t\t\t\t\t _a\t\t\t\t\t, builder.name\t\t\t\t\t, \"default\"\t\t\t\t\t, \"0.0.0\"\t\t\t\t\t, f'{builder.name}-train-00000-of-00002.arrow'\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertDictEqual(builder.info.features\t\t\t\t\t, datasets.Features({\"content\": datasets.Value(\"string\"\t\t\t\t\t\t)}\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\t__UpperCamelCase : Union[str, Any] = builder.as_dataset()\r\n\t\t\t\t\tself.assertEqual(dset[\"train\"].num_rows\t\t\t\t\t, _a\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertEqual(dset[\"train\"].info.splits[\"train\"].num_examples\t\t\t\t\t, _a\t\t\t\t\t\t)\r\n\t\t\t\t\t# Order is not preserved when sharding, so we just check that all the elements are there\r\n\t\t\t\t\tself.assertListEqual(sorted(dset[\"train\"][\"content\"]\t\t\t\t\t\t)\t\t\t\t\t, sorted([\"foo\", \"bar\", \"foobar\"]\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertTrue(\r\n\t\t\t\t\t os.path.exists(os.path.join(_a\t\t\t\t\t, builder.name\t\t\t\t\t, \"default\"\t\t\t\t\t, \"0.0.0\"\t\t\t\t\t, \"dataset_info.json\"\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\tdel dset\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t@require_beam\r\n\tdef _lowerCamelCase ( self\t\t:Union[str, Any]\t\t\t\t\t\t)\t\t->\t\t\tUnion[str, Any]:\r\n\t\t\twith tempfile.TemporaryDirectory() as tmp_cache_dir:\r\n\t\t\t\t\t__UpperCamelCase : Union[str, Any] = DummyBeamDataset(cache_dir=_a\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertRaises(datasets.builder.MissingBeamOptions\t\t\t\t\t, builder.download_and_prepare\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t@require_beam\r\n\tdef _lowerCamelCase ( self\t\t:Optional[Any]\t\t\t\t\t\t)\t\t->\t\t\tList[Any]:\r\n\t\t\t__UpperCamelCase : Dict = len(get_test_nested_examples()\t\t\t\t\t\t)\r\n\t\t\twith tempfile.TemporaryDirectory() as tmp_cache_dir:\r\n\t\t\t\t\t__UpperCamelCase : Optional[Any] = NestedBeamDataset(cache_dir=_a\t\t\t\t\t, beam_runner=\"DirectRunner\"\t\t\t\t\t\t)\r\n\t\t\t\t\tbuilder.download_and_prepare()\r\n\t\t\t\t\tself.assertTrue(\r\n\t\t\t\t\t os.path.exists(\r\n\t\t\t\t\t os.path.join(_a\t\t\t\t\t, builder.name\t\t\t\t\t, \"default\"\t\t\t\t\t, \"0.0.0\"\t\t\t\t\t, f'{builder.name}-train.arrow'\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertDictEqual(\r\n\t\t\t\t\t builder.info.features\t\t\t\t\t, datasets.Features({\"a\": datasets.Sequence({\"b\": datasets.Value(\"string\"\t\t\t\t\t\t)}\t\t\t\t\t\t)}\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\t__UpperCamelCase : int = builder.as_dataset()\r\n\t\t\t\t\tself.assertEqual(dset[\"train\"].num_rows\t\t\t\t\t, _a\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertEqual(dset[\"train\"].info.splits[\"train\"].num_examples\t\t\t\t\t, _a\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertDictEqual(dset[\"train\"][0]\t\t\t\t\t, get_test_nested_examples()[0][1]\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertDictEqual(\r\n\t\t\t\t\t dset[\"train\"][expected_num_examples - 1]\t\t\t\t\t, get_test_nested_examples()[expected_num_examples - 1][1]\t\t\t\t\t\t)\r\n\t\t\t\t\tself.assertTrue(\r\n\t\t\t\t\t os.path.exists(os.path.join(_a\t\t\t\t\t, builder.name\t\t\t\t\t, \"default\"\t\t\t\t\t, \"0.0.0\"\t\t\t\t\t, \"dataset_info.json\"\t\t\t\t\t\t)\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\tdel dset"},"code_codestyle":{"kind":"number","value":370,"string":"370"},"style_context":{"kind":"string","value":"\r\nimport argparse\r\nimport os\r\n\r\nimport evaluate\r\nimport torch\r\nfrom datasets import load_dataset\r\nfrom torch.optim import AdamW\r\nfrom torch.utils.data import DataLoader\r\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\r\n\r\nfrom accelerate import Accelerator, DistributedType\r\n\r\n\r\n########################################################################\r\n# This is a fully working simple example to use Accelerate,\r\n# specifically showcasing the experiment tracking capability,\r\n# and builds off the `nlp_example.py` script.\r\n#\r\n# This example trains a Bert base model on GLUE MRPC\r\n# in any of the following settings (with the same script):\r\n# - single CPU or single GPU\r\n# - multi GPUS (using PyTorch distributed mode)\r\n# - (multi) TPUs\r\n# - fp16 (mixed-precision) or fp32 (normal precision)\r\n#\r\n# To help focus on the differences in the code, building `DataLoaders`\r\n# was refactored into its own function.\r\n# New additions from the base script can be found quickly by\r\n# looking for the # New Code # tags\r\n#\r\n# To run it in each of these various modes, follow the instructions\r\n# in the readme for examples:\r\n# https://github.com/huggingface/accelerate/tree/main/examples\r\n#\r\n########################################################################\r\n\r\nlowercase : Any\t\t\t\t\t\t\t\t\t\t\t\t\t=\t16\r\nlowercase : Optional[int]\t\t\t\t\t\t\t\t\t\t\t\t\t=\t32\r\n\r\n\r\n\r\n\r\ndef _SCREAMING_SNAKE_CASE (\t\t_lowerCamelCase\t\t\t: Accelerator\t\t\t\t\t\t, _lowerCamelCase\t\t\t: int = 16) -> int:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase : Any = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n\t\t\t\t\t\t\t__UpperCamelCase : Optional[Any] = load_dataset(\"glue\"\t\t\t\t\t\t, \"mrpc\")\r\n\r\n\t\t\t\t\t\t\tdef tokenize_function(_lowerCamelCase\t\t\t: Dict):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# max_length=None => use the model max length (it's actually the default)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : List[str] = tokenizer(examples[\"sentence1\"]\t\t\t\t\t\t, examples[\"sentence2\"]\t\t\t\t\t\t, truncation=_lowerCamelCase\t\t\t\t\t\t, max_length=_lowerCamelCase)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn outputs\r\n\r\n\t\t\t\t\t\t\t# Apply the method we just defined to all the examples in all the splits of the dataset\r\n\t\t\t\t\t\t\t# starting with the main process first:\r\n\t\t\t\t\t\t\twith accelerator.main_process_first():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Optional[int] = datasets.map(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t _lowerCamelCase\t\t\t\t\t\t, batched=_lowerCamelCase\t\t\t\t\t\t, remove_columns=[\"idx\", \"sentence1\", \"sentence2\"]\t\t\t\t\t\t, )\r\n\r\n\t\t\t\t\t\t\t# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\r\n\t\t\t\t\t\t\t# transformers library\r\n\t\t\t\t\t\t\t__UpperCamelCase : List[str] = tokenized_datasets.rename_column(\"label\"\t\t\t\t\t\t, \"labels\")\r\n\r\n\t\t\t\t\t\t\tdef collate_fn(_lowerCamelCase\t\t\t: Union[str, Any]):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# On TPU it's best to pad everything to the same length or training will be very slow.\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : str = 128 if accelerator.distributed_type == DistributedType.TPU else None\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# When using mixed precision we want round multiples of 8/16\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif accelerator.mixed_precision == \"fp8\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Optional[Any] = 16\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telif accelerator.mixed_precision != \"no\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Dict = 8\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Optional[Any] = None\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn tokenizer.pad(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t _lowerCamelCase\t\t\t\t\t\t, padding=\"longest\"\t\t\t\t\t\t, max_length=_lowerCamelCase\t\t\t\t\t\t, pad_to_multiple_of=_lowerCamelCase\t\t\t\t\t\t, return_tensors=\"pt\"\t\t\t\t\t\t, )\r\n\r\n\t\t\t\t\t\t\t# Instantiate dataloaders.\r\n\t\t\t\t\t\t\t__UpperCamelCase : Optional[Any] = DataLoader(\r\n\t\t\t\t\t\t\t tokenized_datasets[\"train\"]\t\t\t\t\t\t, shuffle=_lowerCamelCase\t\t\t\t\t\t, collate_fn=_lowerCamelCase\t\t\t\t\t\t, batch_size=_lowerCamelCase)\r\n\t\t\t\t\t\t\t__UpperCamelCase : int = DataLoader(\r\n\t\t\t\t\t\t\t tokenized_datasets[\"validation\"]\t\t\t\t\t\t, shuffle=_lowerCamelCase\t\t\t\t\t\t, collate_fn=_lowerCamelCase\t\t\t\t\t\t, batch_size=_lowerCamelCase)\r\n\r\n\t\t\t\t\t\t\treturn train_dataloader, eval_dataloader\r\n\r\n\r\n# For testing only\r\nif os.environ.get('TESTING_MOCKED_DATALOADERS', None) == \"1\":\r\n\t\tfrom accelerate.test_utils.training import mocked_dataloaders\r\n\r\n\t\tlowercase : Union[str, Any]\t\t\t\t\t\t\t\t\t\t\t\t\t=\tmocked_dataloaders # noqa: F811\r\n\r\n\r\n\r\n\r\ndef _SCREAMING_SNAKE_CASE (\t\t_lowerCamelCase\t\t\t: Optional[Any]\t\t\t\t\t\t, _lowerCamelCase\t\t\t: Union[str, Any]) -> str:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\tif os.environ.get(\"TESTING_MOCKED_DATALOADERS\"\t\t\t\t\t\t, _lowerCamelCase) == \"1\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : List[str] = 2\r\n\t\t\t\t\t\t\t# Initialize Accelerator\r\n\r\n\t\t\t\t\t\t\t# New Code #\r\n\t\t\t\t\t\t\t# We pass in \"all\" to `log_with` to grab all available trackers in the environment\r\n\t\t\t\t\t\t\t# Note: If using a custom `Tracker` class, should be passed in here such as:\r\n\t\t\t\t\t\t\t# >>> log_with = [\"all\", MyCustomTrackerClassInstance()]\r\n\t\t\t\t\t\t\tif args.with_tracking:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Union[str, Any] = Accelerator(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t cpu=args.cpu\t\t\t\t\t\t, mixed_precision=args.mixed_precision\t\t\t\t\t\t, log_with=\"all\"\t\t\t\t\t\t, project_dir=args.project_dir)\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Optional[Any] = Accelerator(cpu=args.cpu\t\t\t\t\t\t, mixed_precision=args.mixed_precision)\r\n\t\t\t\t\t\t\t# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs\r\n\t\t\t\t\t\t\t__UpperCamelCase : List[str] = config[\"lr\"]\r\n\t\t\t\t\t\t\t__UpperCamelCase : Optional[Any] = int(config[\"num_epochs\"])\r\n\t\t\t\t\t\t\t__UpperCamelCase : List[Any] = int(config[\"seed\"])\r\n\t\t\t\t\t\t\t__UpperCamelCase : Any = int(config[\"batch_size\"])\r\n\t\t\t\t\t\t\tset_seed(_lowerCamelCase)\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t__UpperCamelCase : List[Any] = get_dataloaders(_lowerCamelCase\t\t\t\t\t\t, _lowerCamelCase)\r\n\t\t\t\t\t\t\t__UpperCamelCase : List[str] = evaluate.load(\"glue\"\t\t\t\t\t\t, \"mrpc\")\r\n\r\n\t\t\t\t\t\t\t# If the batch size is too big we use gradient accumulation\r\n\t\t\t\t\t\t\t__UpperCamelCase : Union[str, Any] = 1\r\n\t\t\t\t\t\t\tif batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : List[Any] = batch_size // MAX_GPU_BATCH_SIZE\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Union[str, Any] = MAX_GPU_BATCH_SIZE\r\n\r\n\t\t\t\t\t\t\t# Instantiate the model (we build the model here so that the seed also control new weights initialization)\r\n\t\t\t\t\t\t\t__UpperCamelCase : str = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\"\t\t\t\t\t\t, return_dict=_lowerCamelCase)\r\n\r\n\t\t\t\t\t\t\t# We could avoid this line since the accelerator is set with `device_placement=True` (default value).\r\n\t\t\t\t\t\t\t# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer\r\n\t\t\t\t\t\t\t# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).\r\n\t\t\t\t\t\t\t__UpperCamelCase : Optional[int] = model.to(accelerator.device)\r\n\r\n\t\t\t\t\t\t\t# Instantiate optimizer\r\n\t\t\t\t\t\t\t__UpperCamelCase : List[str] = AdamW(params=model.parameters()\t\t\t\t\t\t, lr=_lowerCamelCase)\r\n\r\n\t\t\t\t\t\t\t# Instantiate scheduler\r\n\t\t\t\t\t\t\t__UpperCamelCase : Union[str, Any] = get_linear_schedule_with_warmup(\r\n\t\t\t\t\t\t\t optimizer=_lowerCamelCase\t\t\t\t\t\t, num_warmup_steps=100\t\t\t\t\t\t, num_training_steps=(len(_lowerCamelCase) * num_epochs) // gradient_accumulation_steps\t\t\t\t\t\t, )\r\n\r\n\t\t\t\t\t\t\t# Prepare everything\r\n\t\t\t\t\t\t\t# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the\r\n\t\t\t\t\t\t\t# prepare method.\r\n\t\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t__UpperCamelCase : Union[str, Any] = accelerator.prepare(\r\n\t\t\t\t\t\t\t _lowerCamelCase\t\t\t\t\t\t, _lowerCamelCase\t\t\t\t\t\t, _lowerCamelCase\t\t\t\t\t\t, _lowerCamelCase\t\t\t\t\t\t, _lowerCamelCase)\r\n\r\n\t\t\t\t\t\t\t# New Code #\r\n\t\t\t\t\t\t\t# We need to initialize the trackers we use. Overall configurations can also be stored\r\n\t\t\t\t\t\t\tif args.with_tracking:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Dict = os.path.split(_lowerCamelCase)[-1].split(\".\")[0]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\taccelerator.init_trackers(_lowerCamelCase\t\t\t\t\t\t, _lowerCamelCase)\r\n\r\n\t\t\t\t\t\t\t# Now we train the model\r\n\t\t\t\t\t\t\tfor epoch in range(_lowerCamelCase):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.train()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# New Code #\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# For our tracking example, we will log the total loss of each epoch\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif args.with_tracking:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Tuple = 0\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor step, batch in enumerate(_lowerCamelCase):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# We could avoid this line since we set the accelerator with `device_placement=True`.\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbatch.to(accelerator.device)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Dict = model(**_lowerCamelCase)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Any = outputs.loss\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# New Code #\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif args.with_tracking:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttotal_loss += loss.detach().float()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : List[Any] = loss / gradient_accumulation_steps\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taccelerator.backward(_lowerCamelCase)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif step % gradient_accumulation_steps == 0:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\toptimizer.step()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlr_scheduler.step()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\toptimizer.zero_grad()\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmodel.eval()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor step, batch in enumerate(_lowerCamelCase):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# We could avoid this line since we set the accelerator with `device_placement=True` (the default).\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbatch.to(accelerator.device)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twith torch.no_grad():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Union[str, Any] = model(**_lowerCamelCase)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : str = outputs.logits.argmax(dim=-1)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase ,\t\t\t\t\t\t__UpperCamelCase : Dict = accelerator.gather_for_metrics((predictions, batch[\"labels\"]))\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmetric.add_batch(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t predictions=_lowerCamelCase\t\t\t\t\t\t, references=_lowerCamelCase\t\t\t\t\t\t, )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase : Optional[Any] = metric.compute()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Use accelerator.print to print only on the main process.\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\taccelerator.print(F'epoch {epoch}:'\t\t\t\t\t\t, _lowerCamelCase)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# New Code #\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# To actually log, we call `Accelerator.log`\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif args.with_tracking:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taccelerator.log(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t {\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"accuracy\": eval_metric[\"accuracy\"],\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"f1\": eval_metric[\"f1\"],\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"train_loss\": total_loss.item() / len(_lowerCamelCase),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"epoch\": epoch,\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t }\t\t\t\t\t\t, step=_lowerCamelCase\t\t\t\t\t\t, )\r\n\r\n # New Code #\r\n # When a run is finished, you should call `accelerator.end_training()`\r\n # to close all of the open trackers\r\n\t\t\t\t\t\t\tif args.with_tracking:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\taccelerator.end_training()\r\n\r\n\r\n\r\n\r\ndef _SCREAMING_SNAKE_CASE (\t\t) -> Optional[int]:\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase : str = argparse.ArgumentParser(description=\"Simple example of training script.\")\r\n\t\t\t\t\t\t\tparser.add_argument(\r\n\t\t\t\t\t\t\t \"--mixed_precision\"\t\t\t\t\t\t, type=_lowerCamelCase\t\t\t\t\t\t, default=_lowerCamelCase\t\t\t\t\t\t, choices=[\"no\", \"fp16\", \"bf16\", \"fp8\"]\t\t\t\t\t\t, help=\"Whether to use mixed precision. Choose\"\r\n\t\t\t\t\t\t\t \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\r\n\t\t\t\t\t\t\t \"and an Nvidia Ampere GPU.\"\t\t\t\t\t\t, )\r\n\t\t\t\t\t\t\tparser.add_argument(\"--cpu\"\t\t\t\t\t\t, action=\"store_true\"\t\t\t\t\t\t, help=\"If passed, will train on the CPU.\")\r\n\t\t\t\t\t\t\tparser.add_argument(\r\n\t\t\t\t\t\t\t \"--with_tracking\"\t\t\t\t\t\t, action=\"store_true\"\t\t\t\t\t\t, help=\"Whether to load in all available experiment trackers from the environment and use them for logging.\"\t\t\t\t\t\t, )\r\n\t\t\t\t\t\t\tparser.add_argument(\r\n\t\t\t\t\t\t\t \"--project_dir\"\t\t\t\t\t\t, type=_lowerCamelCase\t\t\t\t\t\t, default=\"logs\"\t\t\t\t\t\t, help=\"Location on where to store experiment tracking logs` and relevent project information\"\t\t\t\t\t\t, )\r\n\t\t\t\t\t\t\t__UpperCamelCase : Union[str, Any] = parser.parse_args()\r\n\t\t\t\t\t\t\t__UpperCamelCase : str = {\"lr\": 2e-5, \"num_epochs\": 3, \"seed\": 42, \"batch_size\": 16}\r\n\t\t\t\t\t\t\ttraining_function(_lowerCamelCase\t\t\t\t\t\t, _lowerCamelCase)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\tmain()"},"style_context_codestyle":{"kind":"number","value":151,"string":"151"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":288,"cells":{"code":{"kind":"string","value":"\n\n\n\n\n'''simple docstring'''\n\n\n\n\n\n\n\nfrom math import sqrt\n\n\n\n\n\ndef \t\t\t\t\t\tsnake_case_\t\t\t\t\t\t\t(_a\t:\t\t\t\t\t\t\tOptional[Any] = 1_0_0_0_0_0_0 ):\n UpperCAmelCase\t\t\t =\t\t0\n UpperCAmelCase\t\t\t =\t\t0\n UpperCAmelCase\t\t\t =\t\t4_2\n\n while num_cuboids <= limit:\n max_cuboid_size += 1\n for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):\n if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():\n num_cuboids += (\n min(snake_case__ , sum_shortest_sides // 2 )\n - max(1 , sum_shortest_sides - max_cuboid_size )\n + 1\n )\n\n return max_cuboid_size\n\n\nif __name__ == \"__main__\":\n print(f\"\"\"{solution() = }\"\"\")\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":34,"string":"34"},"style_context":{"kind":"string","value":"import unittest\r\n\r\nimport numpy as np\r\n\r\ndef SCREAMING_SNAKE_CASE_ (\t\t\t\t\t\tsnake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ) ->\tnp.ndarray:\r\n\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.shape(snake_case__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.shape(snake_case__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.shape(snake_case__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\tif shape_a[0] != shape_b[0]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\t(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''Expected the same number of rows for A and B. '''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"Instead found A of size {shape_a} and B of size {shape_b}\"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(snake_case__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\tif shape_b[1] != shape_c[1]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\t(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''Expected the same number of columns for B and C. '''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"Instead found B of size {shape_b} and C of size {shape_c}\"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(snake_case__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tpseudo_inv\r\n\t\t\t\t\t\t\tif a_inv is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.linalg.inv(snake_case__\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\texcept np.linalg.LinAlgError:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''Input matrix A is not invertible. Cannot compute Schur complement.'''\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\treturn mat_c - mat_b.T @ a_inv @ mat_b\r\n\r\n\r\n\r\n\r\nclass lowercase_ ( unittest.TestCase\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\tdef \t\t\tSCREAMING_SNAKE_CASE_\t\t\t\t(\t\t\t\t\t\t\tself\t\t\t\t)\t\t\t\t\t\t->None:\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]\t\t\t\t)\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[0, 3], [3, 0], [2, 3]]\t\t\t\t)\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[2, 1], [6, 3]]\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tschur_complement(__SCREAMING_SNAKE_CASE\t\t,\t\t\t__SCREAMING_SNAKE_CASE\t\t,\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.block([[a, b], [b.T, c]]\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.linalg.det(__SCREAMING_SNAKE_CASE\t\t\t\t)\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.linalg.det(__SCREAMING_SNAKE_CASE\t\t\t\t)\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.linalg.det(__SCREAMING_SNAKE_CASE\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\tself.assertAlmostEqual(__SCREAMING_SNAKE_CASE\t\t,\t\t\tdet_a * det_s\t\t\t\t)\r\n\r\n\r\n\tdef \t\t\tSCREAMING_SNAKE_CASE_\t\t\t\t(\t\t\t\t\t\t\tself\t\t\t\t)\t\t\t\t\t\t->None:\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]\t\t\t\t)\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[0, 3], [3, 0], [2, 3]]\t\t\t\t)\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[2, 1], [6, 3]]\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\twith self.assertRaises(__SCREAMING_SNAKE_CASE\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tschur_complement(__SCREAMING_SNAKE_CASE\t\t,\t\t\t__SCREAMING_SNAKE_CASE\t\t,\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\tdef \t\t\tSCREAMING_SNAKE_CASE_\t\t\t\t(\t\t\t\t\t\t\tself\t\t\t\t)\t\t\t\t\t\t->None:\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]\t\t\t\t)\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[0, 3], [3, 0], [2, 3]]\t\t\t\t)\r\n\t\t\t\t\t\t\t\tlowerCAmelCase\t\t\t=\t\tnp.array([[2, 1, 3], [6, 3, 5]]\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\twith self.assertRaises(__SCREAMING_SNAKE_CASE\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tschur_complement(__SCREAMING_SNAKE_CASE\t\t,\t\t\t__SCREAMING_SNAKE_CASE\t\t,\t\t\t__SCREAMING_SNAKE_CASE\t\t\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\timport doctest\r\n\r\n\tdoctest.testmod()\r\n\tunittest.main()\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":338,"string":"338"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":289,"cells":{"code":{"kind":"string","value":"\rfrom __future__ import annotations\r\rimport time\r\rimport numpy as np\r\rlowercase : Optional[Any]\t\t\t\t\t\t =\t\t\t\t\t\t\t[8, 5, 9, 7]\rlowercase : Union[str, Any]\t\t\t\t\t\t =\t\t\t\t\t\t\t[\r [2, 0, 1, 1],\r [0, 1, 2, 1],\r [4, 0, 0, 3],\r [0, 2, 1, 0],\r [1, 0, 3, 0],\r]\rlowercase : Optional[Any]\t\t\t\t\t\t =\t\t\t\t\t\t\t[\r [3, 2, 1, 4],\r [0, 2, 5, 2],\r [5, 1, 0, 5],\r [1, 5, 3, 0],\r [3, 0, 3, 3],\r]\r\rclass __snake_case\t\t:\r def __init__( self\t\t,snake_case\t\t,snake_case\t\t,snake_case\t\t,):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tAny\t =\t\t\t\tclaim_vector\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t =\t\t\t\tallocated_resources_table\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\tmaximum_claim_table\r def _SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return [\r sum(p_item[i] for p_item in self.__allocated_resources_table\t\t\t\t)\r for i in range(len(self.__allocated_resources_table[0]\t\t\t\t)\t\t\t\t)\r ]\r def _SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return np.array(self.__claim_vector\t\t\t\t) - np.array(\r self.__processes_resource_summation()\t\t\t\t)\r def _SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return [\r list(np.array(self.__maximum_claim_table[i]\t\t\t\t) - np.array(snake_case\t\t\t\t)\t\t\t\t)\r for i, allocated_resource in enumerate(self.__allocated_resources_table\t\t\t\t)\r ]\r def _SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r return {self.__need().index(snake_case\t\t\t\t): i for i in self.__need()}\r def _SCREAMING_SNAKE_CASE\t( self\t\t,**snake_case\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tAny\t =\t\t\t\tself.__need()\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[str]\t =\t\t\t\tself.__allocated_resources_table\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tint\t =\t\t\t\tself.__available_resources()\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tint\t =\t\t\t\tself.__need_index_manager()\r for kw, val in kwargs.items():\r if kw and val is True:\r self.__pretty_data()\r print(\"\"\"_\"\"\" * 50 + \"\"\"\\n\"\"\"\t\t\t\t)\r while need_list:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[str]\t =\t\t\t\tFalse\r for each_need in need_list:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tTrue\r for index, need in enumerate(snake_case\t\t\t\t):\r if need > available_resources[index]:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tFalse\r break\r if execution:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tTrue\r # get the original index of the process from ind_ctrl db\r for original_need_index, need_clone in need_index_manager.items():\r if each_need == need_clone:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t =\t\t\t\toriginal_need_index\r print(f\"Process {process_number + 1} is executing.\"\t\t\t\t)\r # remove the process run from stack\r need_list.remove(snake_case\t\t\t\t)\r # update available/freed resources stack\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\tnp.array(snake_case\t\t\t\t) + np.array(\r alloc_resources_table[process_number]\t\t\t\t)\r print(\r \"\"\"Updated available resource stack for processes: \"\"\"\r + \"\"\" \"\"\".join([str(snake_case\t\t\t\t) for x in available_resources]\t\t\t\t)\t\t\t\t)\r break\r if safe:\r print(\"\"\"The process is in a safe state.\\n\"\"\"\t\t\t\t)\r else:\r print(\"\"\"System in unsafe state. Aborting...\\n\"\"\"\t\t\t\t)\r break\r def _SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r print(\"\"\" \"\"\" * 9 + \"\"\"Allocated Resource Table\"\"\"\t\t\t\t)\r for item in self.__allocated_resources_table:\r print(\r f\"P{self.__allocated_resources_table.index(snake_case\t\t\t\t) + 1}\"\r + \"\"\" \"\"\".join(f\"{it:>8}\" for it in item\t\t\t\t)\r + \"\"\"\\n\"\"\"\t\t\t\t)\r print(\"\"\" \"\"\" * 9 + \"\"\"System Resource Table\"\"\"\t\t\t\t)\r for item in self.__maximum_claim_table:\r print(\r f\"P{self.__maximum_claim_table.index(snake_case\t\t\t\t) + 1}\"\r + \"\"\" \"\"\".join(f\"{it:>8}\" for it in item\t\t\t\t)\r + \"\"\"\\n\"\"\"\t\t\t\t)\r print(\r \"\"\"Current Usage by Active Processes: \"\"\"\r + \"\"\" \"\"\".join(str(snake_case\t\t\t\t) for x in self.__claim_vector\t\t\t\t)\t\t\t\t)\r print(\r \"\"\"Initial Available Resources: \"\"\"\r + \"\"\" \"\"\".join(str(snake_case\t\t\t\t) for x in self.__available_resources()\t\t\t\t)\t\t\t\t)\r time.sleep(1\t\t\t\t)\r\r\rif __name__ == \"__main__\":\r import doctest\r\r doctest.testmod()\r"},"code_codestyle":{"kind":"number","value":359,"string":"359"},"style_context":{"kind":"string","value":"\rimport os\rimport re\rimport shutil\rfrom argparse import ArgumentParser, Namespace\r\rfrom datasets.commands import BaseDatasetsCLICommand\rfrom datasets.utils.logging import get_logger\r\r\rlowercase : Tuple\t\t\t\t\t\t =\t\t\t\t\t\t\t\"\"\"<<<<<<< This should probably be modified because it mentions: \"\"\"\r\rlowercase : Any\t\t\t\t\t\t =\t\t\t\t\t\t\t\"\"\"=======\n>>>>>>>\n\"\"\"\r\rlowercase : List[str]\t\t\t\t\t\t =\t\t\t\t\t\t\t[\r \"\"\"TextEncoderConfig\"\"\",\r \"\"\"ByteTextEncoder\"\"\",\r \"\"\"SubwordTextEncoder\"\"\",\r \"\"\"encoder_config\"\"\",\r \"\"\"maybe_build_from_corpus\"\"\",\r \"\"\"manual_dir\"\"\",\r]\r\rlowercase : Any\t\t\t\t\t\t =\t\t\t\t\t\t\t[\r # (pattern, replacement)\r # Order is important here for some replacements\r (R\"\"\"tfds\\.core\"\"\", R\"\"\"datasets\"\"\"),\r (R\"\"\"tf\\.io\\.gfile\\.GFile\"\"\", R\"\"\"open\"\"\"),\r (R\"\"\"tf\\.([\\w\\d]+)\"\"\", R\"\"\"datasets.Value('\\1')\"\"\"),\r (R\"\"\"tfds\\.features\\.Text\\(\\)\"\"\", R\"\"\"datasets.Value('string')\"\"\"),\r (R\"\"\"tfds\\.features\\.Text\\(\"\"\", R\"\"\"datasets.Value('string'),\"\"\"),\r (R\"\"\"features\\s*=\\s*tfds.features.FeaturesDict\\(\"\"\", R\"\"\"features=datasets.Features(\"\"\"),\r (R\"\"\"tfds\\.features\\.FeaturesDict\\(\"\"\", R\"\"\"dict(\"\"\"),\r (R\"\"\"The TensorFlow Datasets Authors\"\"\", R\"\"\"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors\"\"\"),\r (R\"\"\"tfds\\.\"\"\", R\"\"\"datasets.\"\"\"),\r (R\"\"\"dl_manager\\.manual_dir\"\"\", R\"\"\"self.config.data_dir\"\"\"),\r (R\"\"\"self\\.builder_config\"\"\", R\"\"\"self.config\"\"\"),\r]\r\r\r\r\r\r\r\rdef \t\t\t\t\t\t_snake_case( SCREAMING_SNAKE_CASE__ )\t\t\t-> List[Any]:\r return ConvertCommand(args.tfds_path , args.datasets_directory )\r\r\r\r\rclass __snake_case\t\t(\t\t\t\tlowerCAmelCase\t\t\t\t\t):\r @staticmethod\r def \t\t\t\t_SCREAMING_SNAKE_CASE\t( snake_case\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tstr\t =\t\t\t\tparser.add_parser(\r \"\"\"convert\"\"\"\t\t,help=\"\"\"Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.\"\"\"\t\t,)\r train_parser.add_argument(\r \"\"\"--tfds_path\"\"\"\t\t,type=snake_case\t\t,required=snake_case\t\t,help=\"\"\"Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.\"\"\"\t\t,)\r train_parser.add_argument(\r \"\"\"--datasets_directory\"\"\"\t\t,type=snake_case\t\t,required=snake_case\t\t,help=\"\"\"Path to the HuggingFace Datasets folder.\"\"\"\t\t\t\t)\r train_parser.set_defaults(func=snake_case\t\t\t\t)\r def __init__( self\t\t,snake_case\t\t,snake_case\t\t,*snake_case\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tget_logger(\"\"\"datasets-cli/converting\"\"\"\t\t\t\t)\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int]\t =\t\t\t\ttfds_path\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\tdatasets_directory\r def \t\t\t\t_SCREAMING_SNAKE_CASE\t( self\t\t\t\t):\r\r\r\r '''simple docstring'''\r\r\r\r\r\r\r\r if os.path.isdir(self._tfds_path\t\t\t\t):\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[str]\t =\t\t\t\tos.path.abspath(self._tfds_path\t\t\t\t)\r elif os.path.isfile(self._tfds_path\t\t\t\t):\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tTuple\t =\t\t\t\tos.path.dirname(self._tfds_path\t\t\t\t)\r else:\r raise ValueError(\"\"\"--tfds_path is neither a directory nor a file. Please check path.\"\"\"\t\t\t\t)\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int]\t =\t\t\t\tos.path.abspath(self._datasets_directory\t\t\t\t)\r\r self._logger.info(f\"Converting datasets from {abs_tfds_path} to {abs_datasets_path}\"\t\t\t\t)\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[Any]\t =\t\t\t\t[]\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int]\t =\t\t\t\t[]\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\t{}\r\r if os.path.isdir(self._tfds_path\t\t\t\t):\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tint\t =\t\t\t\tos.listdir(snake_case\t\t\t\t)\r else:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[Any]\t =\t\t\t\t[os.path.basename(self._tfds_path\t\t\t\t)]\r\r for f_name in file_names:\r self._logger.info(f\"Looking at file {f_name}\"\t\t\t\t)\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[Any]\t =\t\t\t\tos.path.join(snake_case\t\t,snake_case\t\t\t\t)\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[str]\t =\t\t\t\tos.path.join(snake_case\t\t,snake_case\t\t\t\t)\r\r if not os.path.isfile(snake_case\t\t\t\t) or \"__init__\" in f_name or \"_test\" in f_name or \".py\" not in f_name:\r self._logger.info(\"\"\"Skipping file\"\"\"\t\t\t\t)\r continue\r\r with open(snake_case\t\t,encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as f:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tstr\t =\t\t\t\tf.readlines()\r\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t =\t\t\t\t[]\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tFalse\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tFalse\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int]\t =\t\t\t\t[]\r for line in lines:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tint\t =\t\t\t\tline\r\r # Convert imports\r if \"import tensorflow.compat.v2 as tf\" in out_line:\r continue\r elif \"@tfds.core\" in out_line:\r continue\r elif \"builder=self\" in out_line:\r continue\r elif \"import tensorflow_datasets.public_api as tfds\" in out_line:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t =\t\t\t\t\"\"\"import datasets\\n\"\"\"\r elif \"import tensorflow\" in out_line:\r # order is important here\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[Any]\t =\t\t\t\t\"\"\"\"\"\"\r continue\r elif \"from absl import logging\" in out_line:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int]\t =\t\t\t\t\"\"\"from datasets import logging\\n\"\"\"\r elif \"getLogger\" in out_line:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tAny\t =\t\t\t\tout_line.replace(\"\"\"getLogger\"\"\"\t\t,\"\"\"get_logger\"\"\"\t\t\t\t)\r elif any(expression in out_line for expression in TO_HIGHLIGHT\t\t\t\t):\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tTrue\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tlist(filter(lambda snake_case\t\t\t\t: e in out_line\t\t,snake_case\t\t\t\t)\t\t\t\t)\r out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(snake_case\t\t\t\t) + \"\"\"\\n\"\"\"\t\t\t\t)\r out_lines.append(snake_case\t\t\t\t)\r out_lines.append(snake_case\t\t\t\t)\r continue\r else:\r for pattern, replacement in TO_CONVERT:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t =\t\t\t\tre.sub(snake_case\t\t,snake_case\t\t,snake_case\t\t\t\t)\r\r # Take care of saving utilities (to later move them together with main script)\r if \"tensorflow_datasets\" in out_line:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tDict\t =\t\t\t\tre.match(r\"\"\"from\\stensorflow_datasets.*import\\s([^\\.\\r\\n]+)\"\"\"\t\t,snake_case\t\t\t\t)\r tfds_imports.extend(imp.strip() for imp in match.group(1\t\t\t\t).split(\"\"\",\"\"\"\t\t\t\t)\t\t\t\t)\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int]\t =\t\t\t\t\"\"\"from . import \"\"\" + match.group(1\t\t\t\t)\r\r # Check we have not forget anything\r if \"tf.\" in out_line or \"tfds.\" in out_line or \"tensorflow_datasets\" in out_line:\r raise ValueError(f\"Error converting {out_line.strip()}\"\t\t\t\t)\r\r if \"GeneratorBasedBuilder\" in out_line or \"BeamBasedBuilder\" in out_line:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tAny\t =\t\t\t\tTrue\r out_lines.append(snake_case\t\t\t\t)\r\r if is_builder or \"wmt\" in f_name:\r # We create a new directory for each dataset\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tUnion[str, Any]\t =\t\t\t\tf_name.replace(\"\"\".py\"\"\"\t\t,\"\"\"\"\"\"\t\t\t\t)\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[Any]\t =\t\t\t\tos.path.join(snake_case\t\t,snake_case\t\t\t\t)\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tList[str]\t =\t\t\t\tos.path.join(snake_case\t\t,snake_case\t\t\t\t)\r os.makedirs(snake_case\t\t,exist_ok=snake_case\t\t\t\t)\r self._logger.info(f\"Adding directory {output_dir}\"\t\t\t\t)\r imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}\t\t\t\t)\r else:\r # Utilities will be moved at the end\r utils_files.append(snake_case\t\t\t\t)\r\r if needs_manual_update:\r with_manual_update.append(snake_case\t\t\t\t)\r\r with open(snake_case\t\t,\"\"\"w\"\"\"\t\t,encoding=\"\"\"utf-8\"\"\"\t\t\t\t) as f:\r f.writelines(snake_case\t\t\t\t)\r self._logger.info(f\"Converted in {output_file}\"\t\t\t\t)\r\r for utils_file in utils_files:\r try:\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tOptional[int]\t =\t\t\t\tos.path.basename(snake_case\t\t\t\t)\r lowercase\t\t\t\t\t\t\t:\t\t\t\t\tint\t =\t\t\t\timports_to_builder_map[f_name.replace(\"\"\".py\"\"\"\t\t,\"\"\"\"\"\"\t\t\t\t)]\r self._logger.info(f\"Moving {dest_folder} to {utils_file}\"\t\t\t\t)\r shutil.copy(snake_case\t\t,snake_case\t\t\t\t)\r except KeyError:\r self._logger.error(f\"Cannot find destination folder for {utils_file}. Please copy manually.\"\t\t\t\t)\r\r if with_manual_update:\r for file_path in with_manual_update:\r self._logger.warning(\r f\"You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.\"\t\t\t\t)\r"},"style_context_codestyle":{"kind":"number","value":285,"string":"285"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":290,"cells":{"code":{"kind":"string","value":"\n\n\n\n'''simple docstring'''\n\n\n\n\n\nlowercase_\t\t = \"\\n# Transformers installation\\n! pip install transformers datasets\\n# To install from source instead of the last release, comment the command above and uncomment the following one.\\n# ! pip install git+https://github.com/huggingface/transformers.git\\n\"\n\nlowercase_\t\t = [{\"type\": \"code\", \"content\": INSTALL_CONTENT}]\nlowercase_\t\t = {\n \"{processor_class}\": \"FakeProcessorClass\",\n \"{model_class}\": \"FakeModelClass\",\n \"{object_class}\": \"FakeObjectClass\",\n}\n\n\n\n"},"code_codestyle":{"kind":"number","value":211,"string":"211"},"style_context":{"kind":"string","value":"\n\n\n\n'''simple docstring'''\n\n\n\n\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax\nimport jax.numpy as jnp\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils_flax import (\n CommonSchedulerState,\n FlaxKarrasDiffusionSchedulers,\n FlaxSchedulerMixin,\n FlaxSchedulerOutput,\n add_noise_common,\n get_velocity_common,\n)\n\n\n\n\n\n\n@flax.struct.dataclass\nclass __A\t\t\t:\n\n\n\n '''simple docstring'''\n\n\n\n\n\n\n\n __lowerCamelCase : CommonSchedulerState\n\n # setable values\n __lowerCamelCase : jnp.ndarray\n __lowerCamelCase : jnp.ndarray\n __lowerCamelCase : Optional[int] = None\n\n\n\n @classmethod\n def \t\t\t\t\ta__ (cls , A , A , A ) -> str:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n return cls(common=A , init_noise_sigma=A , timesteps=A )\n\n\n\n\n\n\n@dataclass\nclass __A\t\t\t( A\t\t\t\t\t\t):\n\n\n\n '''simple docstring'''\n\n\n\n\n\n\n\n __lowerCamelCase : DDPMSchedulerState\n\n\n\n\n\n\nclass __A\t\t\t( A , A\t\t\t\t\t\t):\n\n\n\n '''simple docstring'''\n\n\n\n\n\n\n\n __lowerCamelCase : Dict = [e.name for e in FlaxKarrasDiffusionSchedulers]\n\n __lowerCamelCase : jnp.dtype\n\n\n\n @property\n def \t\t\t\t\ta__ (self ) -> List[str]:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n return True\n\n\n\n @register_to_config\n def __init__(self , A = 1_000 , A = 0.0001 , A = 0.02 , A = \"linear\" , A = None , A = \"fixed_small\" , A = True , A = \"epsilon\" , A = jnp.floataa , ) -> Union[str, Any]:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n _a = dtype\n\n\n\n def \t\t\t\t\ta__ (self , A = None ) -> DDPMSchedulerState:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n if common is None:\n _a = CommonSchedulerState.create(self )\n\n # standard deviation of the initial noise distribution\n _a = jnp.array(1.0 , dtype=self.dtype )\n\n _a = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]\n\n return DDPMSchedulerState.create(\n common=A , init_noise_sigma=A , timesteps=A , )\n\n\n\n def \t\t\t\t\ta__ (self , A , A , A = None ) -> jnp.ndarray:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n return sample\n\n\n\n def \t\t\t\t\ta__ (self , A , A , A = () ) -> DDPMSchedulerState:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n _a = self.config.num_train_timesteps // num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # rounding to avoid issues when num_inference_step is power of 3\n _a = (jnp.arange(0 , A ) * step_ratio).round()[::-1]\n\n return state.replace(\n num_inference_steps=A , timesteps=A , )\n\n\n\n def \t\t\t\t\ta__ (self , A , A , A=None , A=None ) -> int:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n _a = state.common.alphas_cumprod[t]\n _a = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )\n\n # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)\n # and sample from it to get previous sample\n # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample\n _a = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]\n\n if variance_type is None:\n _a = self.config.variance_type\n\n # hacks - were probably added for training stability\n if variance_type == \"fixed_small\":\n _a = jnp.clip(A , a_min=1E-20 )\n # for rl-diffuser https://arxiv.org/abs/2205.09991\n elif variance_type == \"fixed_small_log\":\n _a = jnp.log(jnp.clip(A , a_min=1E-20 ) )\n elif variance_type == \"fixed_large\":\n _a = state.common.betas[t]\n elif variance_type == \"fixed_large_log\":\n # Glide max_log\n _a = jnp.log(state.common.betas[t] )\n elif variance_type == \"learned\":\n return predicted_variance\n elif variance_type == \"learned_range\":\n _a = variance\n _a = state.common.betas[t]\n _a = (predicted_variance + 1) / 2\n _a = frac * max_log + (1 - frac) * min_log\n\n return variance\n\n\n\n def \t\t\t\t\ta__ (self , A , A , A , A , A = None , A = True , ) -> Union[FlaxDDPMSchedulerOutput, Tuple]:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n _a = timestep\n\n if key is None:\n _a = jax.random.PRNGKey(0 )\n\n if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in [\"learned\", \"learned_range\"]:\n _a , _a = jnp.split(A , sample.shape[1] , axis=1 )\n else:\n _a = None\n\n # 1. compute alphas, betas\n _a = state.common.alphas_cumprod[t]\n _a = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )\n _a = 1 - alpha_prod_t\n _a = 1 - alpha_prod_t_prev\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n if self.config.prediction_type == \"epsilon\":\n _a = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5\n elif self.config.prediction_type == \"sample\":\n _a = model_output\n elif self.config.prediction_type == \"v_prediction\":\n _a = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n else:\n raise ValueError(\n f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''\n ''' for the FlaxDDPMScheduler.''' )\n\n # 3. Clip \"predicted x_0\"\n if self.config.clip_sample:\n _a = jnp.clip(A , -1 , 1 )\n\n # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n _a = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t\n _a = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t\n\n # 5. Compute predicted previous sample µ_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n _a = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\n\n # 6. Add noise\n def random_variance():\n _a = jax.random.split(A , num=1 )\n _a = jax.random.normal(A , shape=model_output.shape , dtype=self.dtype )\n return (self._get_variance(A , A , predicted_variance=A ) ** 0.5) * noise\n\n _a = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )\n\n _a = pred_prev_sample + variance\n\n if not return_dict:\n return (pred_prev_sample, state)\n\n return FlaxDDPMSchedulerOutput(prev_sample=A , state=A )\n\n\n\n def \t\t\t\t\ta__ (self , A , A , A , A , ) -> jnp.ndarray:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n return add_noise_common(state.common , A , A , A )\n\n\n\n def \t\t\t\t\ta__ (self , A , A , A , A , ) -> jnp.ndarray:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n return get_velocity_common(state.common , A , A , A )\n\n\n\n def __len__(self ) -> Tuple:\n\n\n \"\"\"simple docstring\"\"\"\n\n\n\n return self.config.num_train_timesteps\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":211,"string":"211"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":291,"cells":{"code":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\nfrom __future__ import annotations\r\n\r\nimport os\r\nfrom collections.abc import Mapping\r\n\r\n__lowercase\t\t\t\t =\t\t\t\t\t\t\ttuple[int, int]\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass \t\t\t\t\t\t_lowercase\t\t\t\t:\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef __init__(\t\t\t\t\t\t\tself : Optional[int] ,\t\t\t\tUpperCamelCase__ : set[int] ,\t\t\t\tUpperCamelCase__ : Mapping[EdgeT, int]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tNone:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=vertices\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t={\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t (min(UpperCamelCase__\t\t\t\t\t\t\t), max(UpperCamelCase__\t\t\t\t\t\t\t)): weight for edge, weight in edges.items()\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : str ,\t\t\t\tUpperCamelCase__ : EdgeT ,\t\t\t\tUpperCamelCase__ : int\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tNone:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.vertices.add(edge[0]\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.vertices.add(edge[1]\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=weight\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef UpperCAmelCase_\t\t\t\t(\t\t\t\t\t\t\tself : List[Any]\t\t\t\t\t\t\t)\t\t\t\t\t\t\t->\t\t\t\t\t\t\tGraph:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=Graph({min(self.vertices\t\t\t\t\t\t\t)} ,\t\t\t\t{}\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=42\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=42\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=42\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=42\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\twhile len(subgraph.vertices\t\t\t\t\t\t\t) < len(self.vertices\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=max(self.edges.values()\t\t\t\t\t\t\t) + 1\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor edge, weight in self.edges.items():\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif weight < min_weight:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=edge\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=weight\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsubgraph.add_edge(UpperCamelCase__ ,\t\t\t\tUpperCamelCase__\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\treturn subgraph\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef lowerCAmelCase\t(__UpperCamelCase : str = \"p107_network.txt\"\t):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=os.path.abspath(os.path.dirname(__UpperCamelCase\t)\t)\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=os.path.join(__UpperCamelCase ,\t\t\t__UpperCamelCase\t)\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t={}\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=42\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=42\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=42\r\n\r\n\t\t\t\t\t\t\twith open(__UpperCamelCase\t) as f:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=f.read().strip().split('''\\n'''\t)\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[line.split(''','''\t) for line in data]\r\n\r\n\t\t\t\t\t\t\tfor edgea in range(1 ,\t\t\tlen(__UpperCamelCase\t)\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor edgea in range(__UpperCamelCase\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif adjaceny_matrix[edgea][edgea] != \"-\":\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=int(adjaceny_matrix[edgea][edgea]\t)\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=Graph(set(range(len(__UpperCamelCase\t)\t)\t) ,\t\t\t__UpperCamelCase\t)\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=graph.prims_algorithm()\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=sum(graph.edges.values()\t)\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=sum(subgraph.edges.values()\t)\r\n\r\n\t\t\t\t\t\t\treturn initial_total - optimal_total\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\t\tprint(f'''{solution() = }''')\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":85,"string":"85"},"style_context":{"kind":"string","value":"\"\"\"simple docstring\"\"\"\r\n\r\ndef lowerCAmelCase\t(__UpperCamelCase : Dict ,\t\t\t__UpperCamelCase : Optional[Any]\t):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=[0 for i in range(r + 1\t)]\r\n\t\t\t\t\t\t\t# nc0 = 1\r\n\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=1\r\n\t\t\t\t\t\t\tfor i in range(1 ,\t\t\tn + 1\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t# to compute current row from previous row.\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t\t=min(__UpperCamelCase ,\t\t\t__UpperCamelCase\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\twhile j > 0:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tc[j] += c[j - 1]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tj -= 1\r\n\t\t\t\t\t\t\treturn c[r]\r\n\r\n\r\n\r\n\r\n\r\n\r\nprint(binomial_coefficient(n=10, r=5))\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":85,"string":"85"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":292,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\nfrom typing import TYPE_CHECKING\r\n\r\nfrom ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available\r\nfrom ...utils import OptionalDependencyNotAvailable\r\n\r\n\r\nlowerCAmelCase =\t\t\t\t\t\t\t{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}\r\n\r\ntry:\r\n\t\t\tif not is_vision_available():\r\n\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\t\t\tpass\r\nelse:\r\n\t\t\tlowerCAmelCase =\t\t\t\t\t\t\t['DPTFeatureExtractor']\r\n\t\t\tlowerCAmelCase =\t\t\t\t\t\t\t['DPTImageProcessor']\r\n\r\ntry:\r\n\t\t\tif not is_torch_available():\r\n\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n\t\t\tpass\r\nelse:\r\n\t\t\tlowerCAmelCase =\t\t\t\t\t\t\t[\r\n\t\t\t 'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',\r\n\t\t\t 'DPTForDepthEstimation',\r\n\t\t\t 'DPTForSemanticSegmentation',\r\n\t\t\t 'DPTModel',\r\n\t\t\t 'DPTPreTrainedModel',\r\n\t\t\t]\r\n\r\n\r\nif TYPE_CHECKING:\r\n\t\t\tfrom .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig\r\n\r\n\t\t\ttry:\r\n\t\t\t\t\t\tif not is_vision_available():\r\n\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\n\t\t\texcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\tpass\r\n\t\t\telse:\r\n\t\t\t\t\t\tfrom .feature_extraction_dpt import DPTFeatureExtractor\r\n\t\t\t\t\t\tfrom .image_processing_dpt import DPTImageProcessor\r\n\r\n\t\t\ttry:\r\n\t\t\t\t\t\tif not is_torch_available():\r\n\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\r\n\t\t\texcept OptionalDependencyNotAvailable:\r\n\t\t\t\t\t\tpass\r\n\t\t\telse:\r\n\t\t\t\t\t\tfrom .modeling_dpt import (\r\n\t\t\t\t\t\t DPT_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\t\t\t\t\t\t DPTForDepthEstimation,\r\n\t\t\t\t\t\t DPTForSemanticSegmentation,\r\n\t\t\t\t\t\t DPTModel,\r\n\t\t\t\t\t\t DPTPreTrainedModel,\r\n\t\t\t\t\t\t)\r\n\r\n\r\nelse:\r\n\t\t\timport sys\r\n\r\n\t\t\tlowerCAmelCase =\t\t\t\t\t\t\t_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":110,"string":"110"},"style_context":{"kind":"string","value":"\r\n\r\n\r\ndef \t\t\t_a ( SCREAMING_SNAKE_CASE ):\r\n\r\n\r\n\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\treturn credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )\r\ndef \t\t\t_a ( SCREAMING_SNAKE_CASE ):\r\n\r\n\r\n\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\tlowercase__ = credit_card_number\r\n\t\tlowercase__ = 0\r\n\t\tlowercase__ = len(SCREAMING_SNAKE_CASE ) - 2\r\n\t\tfor i in range(SCREAMING_SNAKE_CASE\t\t\t\t\t\t,\t\t\t\t-1\t\t\t\t\t\t,\t\t\t\t-2 ):\r\n\t\t\t\t# double the value of every second digit\r\n\t\t\t\tlowercase__ = int(cc_number[i] )\r\n\t\t\t\tdigit *= 2\r\n\t\t\t\t# If doubling of a number results in a two digit number\r\n\t\t\t\t# i.e greater than 9(e.g., 6 × 2 = 12),\r\n\t\t\t\t# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),\r\n\t\t\t\t# to get a single digit number.\r\n\t\t\t\tif digit > 9:\r\n\t\t\t\t\t\tdigit %= 10\r\n\t\t\t\t\t\tdigit += 1\r\n\t\t\t\tlowercase__ = cc_number[:i] + str(SCREAMING_SNAKE_CASE ) + cc_number[i + 1 :]\r\n\t\t\t\ttotal += digit\r\n\r\n\t\t# Sum up the remaining digits\r\n\t\tfor i in range(len(SCREAMING_SNAKE_CASE ) - 1\t\t\t\t\t\t,\t\t\t\t-1\t\t\t\t\t\t,\t\t\t\t-2 ):\r\n\t\t\t\ttotal += int(cc_number[i] )\r\n\r\n\t\treturn total % 10 == 0\r\ndef \t\t\t_a ( SCREAMING_SNAKE_CASE ):\r\n\r\n\r\n\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\t\tlowercase__ = f'{credit_card_number} is an invalid credit card number because'\r\n\t\tif not credit_card_number.isdigit():\r\n\t\t\t\tprint(f'{error_message} it has nonnumerical characters.' )\r\n\t\t\t\treturn False\r\n\r\n\t\tif not 13 <= len(SCREAMING_SNAKE_CASE ) <= 16:\r\n\t\t\t\tprint(f'{error_message} of its length.' )\r\n\t\t\t\treturn False\r\n\r\n\t\tif not validate_initial_digits(SCREAMING_SNAKE_CASE ):\r\n\t\t\t\tprint(f'{error_message} of its first two digits.' )\r\n\t\t\t\treturn False\r\n\r\n\t\tif not luhn_validation(SCREAMING_SNAKE_CASE ):\r\n\t\t\t\tprint(f'{error_message} it fails the Luhn check.' )\r\n\t\t\t\treturn False\r\n\r\n\t\tprint(f'{credit_card_number} is a valid credit card number.' )\r\n\t\treturn True\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\timport doctest\r\n\r\n\t\t\tdoctest.testmod()\r\n\t\t\tvalidate_credit_card_number('4111111111111111')\r\n\t\t\tvalidate_credit_card_number('32323')\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":110,"string":"110"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":293,"cells":{"code":{"kind":"string","value":"from timeit import timeit\r\n\r\n__UpperCamelCase\t\t:\tDict \t\t\t\t\t\t=\t\t\t\t\t{\r\n 'MALAYALAM': True,\r\n 'String': False,\r\n 'rotor': True,\r\n 'level': True,\r\n 'A': True,\r\n 'BB': True,\r\n 'ABC': False,\r\n 'amanaplanacanalpanama': True, # \"a man a plan a canal panama\"\r\n}\r\n# Ensure our test data is valid\r\nassert all((key == key[::-1]) is value for key, value in test_data.items())\r\ndef \t\t\t\tA ( _lowercase\t\t\t\t\t\t):\r\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: Any =\t\t\t\t\t\t\t0\r\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: str =\t\t\t\t\t\t\tlen(_lowercase\t\t\t\t\t\t) - 1\r\n\t\t\t\t\t\twhile start_i < end_i:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif s[start_i] == s[end_i]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tstart_i += 1\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tend_i -= 1\r\n\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn False\r\n\t\t\t\t\t\treturn True\r\ndef \t\t\t\tA ( _lowercase\t\t\t\t\t\t):\r\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: Tuple =\t\t\t\t\t\t\tlen(_lowercase\t\t\t\t\t\t) // 2\r\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[str] =\t\t\t\t\t\t\tlen(_lowercase\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t# We need to traverse till half of the length of string\r\n\t\t\t\t\t\t# as we can get access of the i'th last element from\r\n\t\t\t\t\t\t# i'th index.\r\n\t\t\t\t\t\t# eg: [0,1,2,3,4,5] => 4th index can be accessed\r\n\t\t\t\t\t\t# with the help of 1st index (i==n-i-1)\r\n\t\t\t\t\t\t# where n is length of string\r\n\t\t\t\t\t\treturn all(s[i] == s[n - i - 1] for i in range(_lowercase\t\t\t\t\t\t)\t\t\t\t\t\t)\r\ndef \t\t\t\tA ( _lowercase\t\t\t\t\t\t):\r\n\t\t\t\t\t\tif len(_lowercase\t\t\t\t\t\t) <= 2:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn True\r\n\t\t\t\t\t\tif s[0] == s[len(_lowercase\t\t\t\t\t\t) - 1]:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn is_palindrome_recursive(s[1:-1]\t\t\t\t\t\t)\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn False\r\ndef \t\t\t\tA ( _lowercase\t\t\t\t\t\t):\r\n\t\t\t\t\t\treturn s == s[::-1]\r\ndef \t\t\t\tA ( _lowercase\t\t\t\t\t\t):\r\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: Any =\t\t\t\t\t\t\tf\"\"\"all({name}(key) is value for key, value in test_data.items())\"\"\"\r\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: str =\t\t\t\t\t\t\tf\"\"\"from __main__ import test_data, {name}\"\"\"\r\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[Any] =\t\t\t\t\t\t\t500_000\r\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[Any] =\t\t\t\t\t\t\ttimeit(stmt=_lowercase\t\t, setup=_lowercase\t\t, number=_lowercase\t\t\t\t\t\t)\r\n\t\t\t\t\t\tprint(f\"\"\"{name:<35} finished {number:,} runs in {result:.5f} seconds\"\"\"\t\t\t\t\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\t\t\t\t\tfor key, value in test_data.items():\r\n\t\t\t\t\t\t\t\t\t\tassert is_palindrome(key) is is_palindrome_recursive(key)\r\n\t\t\t\t\t\t\t\t\t\tassert is_palindrome(key) is is_palindrome_slice(key)\r\n\t\t\t\t\t\t\t\t\t\tprint(f\"\"\"{key:21} {value}\"\"\")\r\n\t\t\t\t\tprint('a man a plan a canal panama')\r\n\r\n\t\t\t\t\t# finished 500,000 runs in 0.46793 seconds\r\n\t\t\t\t\tbenchmark_function('is_palindrome_slice')\r\n\t\t\t\t\t# finished 500,000 runs in 0.85234 seconds\r\n\t\t\t\t\tbenchmark_function('is_palindrome')\r\n\t\t\t\t\t# finished 500,000 runs in 1.32028 seconds\r\n\t\t\t\t\tbenchmark_function('is_palindrome_recursive')\r\n\t\t\t\t\t# finished 500,000 runs in 2.08679 seconds\r\n\t\t\t\t\tbenchmark_function('is_palindrome_traversal')\r\n\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":258,"string":"258"},"style_context":{"kind":"string","value":"import os\r\nfrom typing import List, Optional, Union\r\n\r\nfrom ...tokenization_utils import PreTrainedTokenizer\r\nfrom ...tokenization_utils_base import AddedToken\r\nfrom ...utils import logging\r\n\r\n\r\n__UpperCamelCase\t\t:\tAny \t\t\t\t\t\t=\t\t\t\t\tlogging.get_logger(__name__)\r\n\r\n__UpperCamelCase\t\t:\tTuple \t\t\t\t\t\t=\t\t\t\t\t{'vocab_file': 'vocab.txt'}\r\n\r\n__UpperCamelCase\t\t:\tTuple \t\t\t\t\t\t=\t\t\t\t\t{\r\n 'vocab_file': {\r\n 'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',\r\n 'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',\r\n },\r\n}\r\n\r\n__UpperCamelCase\t\t:\tUnion[str, Any] \t\t\t\t\t\t=\t\t\t\t\t{\r\n 'facebook/esm2_t6_8M_UR50D': 1024,\r\n 'facebook/esm2_t12_35M_UR50D': 1024,\r\n}\r\ndef \t\t\t\tA ( _lowercase\t\t\t\t\t\t):\r\n\t\t\t\t\t\twith open(_lowercase\t\t, '''r'''\t\t\t\t\t\t) as f:\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: Optional[int] =\t\t\t\t\t\t\tf.read().splitlines()\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn [l.strip() for l in lines]\r\n\r\n\r\n\r\n\r\n\r\nclass lowercase__\t\t\t\t\t\t( UpperCamelCase_):\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t=\t\tVOCAB_FILES_NAMES\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t=\t\tPRETRAINED_VOCAB_FILES_MAP\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t=\t\tPRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\r\n\t\t\t\t\t\tUpperCamelCase_\t\t\t\t\t=\t\t[\"\"\"input_ids\"\"\", \"\"\"attention_mask\"\"\"]\r\n\r\n\r\n\t\t\t\t\t\tdef __init__(\tself : str ,\t\tUpperCamelCase__ : List[str] ,\t\tUpperCamelCase__ : Tuple=\"\" ,\t\tUpperCamelCase__ : Union[str, Any]=\"\" ,\t\tUpperCamelCase__ : Dict=\"\" ,\t\tUpperCamelCase__ : str=\"\" ,\t\tUpperCamelCase__ : Any=\"\" ,\t\t**UpperCamelCase__ : int ,\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(**UpperCamelCase__\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: Union[str, Any] =\t\t\t\t\t\t\tload_vocab_file(UpperCamelCase__\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: int =\t\t\t\t\t\t\tdict(enumerate(self.all_tokens\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[Any] =\t\t\t\t\t\t\t{tok: ind for ind, tok in enumerate(self.all_tokens\t\t\t\t\t\t)}\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: Union[str, Any] =\t\t\t\t\t\t\tunk_token\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: Any =\t\t\t\t\t\t\tcls_token\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[str] =\t\t\t\t\t\t\tpad_token\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[str] =\t\t\t\t\t\t\tmask_token\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: Any =\t\t\t\t\t\t\teos_token\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[str] =\t\t\t\t\t\t\tself.all_tokens\r\n\t\t\t\t\t\t\t\t\t\t\t\tself._create_trie(self.unique_no_split_tokens\t\t\t\t\t\t)\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : Union[str, Any] ,\t\tUpperCamelCase__ : int\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self._id_to_token.get(UpperCamelCase__ ,\t\tself.unk_token\t\t\t\t\t\t)\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : Dict ,\t\tUpperCamelCase__ : str\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self._token_to_id.get(UpperCamelCase__ ,\t\tself._token_to_id.get(self.unk_token\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : List[Any] ,\t\tUpperCamelCase__ : Union[str, Any] ,\t\t**UpperCamelCase__ : List[Any]\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn text.split()\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : List[str] ,\t\tUpperCamelCase__ : Dict=False\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn len(self._id_to_token\t\t\t\t\t\t)\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : Optional[Any]\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn {token: i for i, token in enumerate(self.all_tokens\t\t\t\t\t\t)}\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : Union[str, Any] ,\t\tUpperCamelCase__ : str\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self._token_to_id.get(UpperCamelCase__ ,\t\tself._token_to_id.get(self.unk_token\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : List[str] ,\t\tUpperCamelCase__ : int\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self._id_to_token.get(UpperCamelCase__ ,\t\tself.unk_token\t\t\t\t\t\t)\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : str ,\t\tUpperCamelCase__ : List[int] ,\t\tUpperCamelCase__ : Optional[List[int]] = None\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: str =\t\t\t\t\t\t\t[self.cls_token_id]\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[str] =\t\t\t\t\t\t\t[self.eos_token_id] # No sep token in ESM vocabulary\r\n\t\t\t\t\t\t\t\t\t\t\t\tif token_ids_a is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif self.eos_token_id is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn cls + token_ids_a\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn cls + token_ids_a + sep\r\n\t\t\t\t\t\t\t\t\t\t\t\telif self.eos_token_id is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : Union[str, Any] ,\t\tUpperCamelCase__ : List ,\t\tUpperCamelCase__ : Optional[List] = None ,\t\tUpperCamelCase__ : bool = False\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif already_has_special_tokens:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif token_ids_a is not None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''You should not supply a second sequence if the provided sequence of '''\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t '''ids is already formatted with special tokens for the model.'''\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn [1 if token in self.all_special_ids else 0 for token in token_ids_a]\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: List[str] =\t\t\t\t\t\t\t[1] + ([0] * len(UpperCamelCase__\t\t\t\t\t\t)) + [1]\r\n\t\t\t\t\t\t\t\t\t\t\t\tif token_ids_a is not None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmask += [0] * len(UpperCamelCase__\t\t\t\t\t\t) + [1]\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn mask\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : int ,\t\tUpperCamelCase__ : List[Any] ,\t\tUpperCamelCase__ : List[str]\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE\t\t\t\t\t\t\t: str =\t\t\t\t\t\t\tos.path.join(UpperCamelCase__ ,\t\t(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\twith open(UpperCamelCase__ ,\t\t'''w'''\t\t\t\t\t\t) as f:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tf.write('''\\n'''.join(self.all_tokens\t\t\t\t\t\t)\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn (vocab_file,)\r\n\r\n\r\n\t\t\t\t\t\t@property\r\n\t\t\t\t\t\tdef \t__A\t(\tself : Dict\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self.get_vocab_size(with_added_tokens=UpperCamelCase__\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef \t__A\t(\tself : str ,\t\tUpperCamelCase__ : Union[List[str], List[AddedToken]] ,\t\tUpperCamelCase__ : bool = False\t\t\t\t\t\t):\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t'''simple docstring'''\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn super()._add_tokens(UpperCamelCase__ ,\t\tspecial_tokens=UpperCamelCase__\t\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":258,"string":"258"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":294,"cells":{"code":{"kind":"string","value":"from __future__ import annotations\r\n\r\nimport string\r\nfrom itertools import cycle, product\r\nfrom pathlib import Path\r\n\r\n_snake_case\t\t\t\t\t\t\t\t= (\r\n string.ascii_letters + string.digits + string.punctuation + string.whitespace\r\n)\r\n_snake_case\t\t\t\t\t\t\t\t= [ord(letter) for letter in string.ascii_lowercase]\r\n_snake_case\t\t\t\t\t\t\t\t= {ord(char) for char in VALID_CHARS}\r\n\r\n_snake_case\t\t\t\t\t\t\t\t= ['''the''', '''be''', '''to''', '''of''', '''and''', '''in''', '''that''', '''have''']\r\n\r\n\r\n\r\n\r\ndef _UpperCamelCase (\tsnake_case__,\t\t\tsnake_case__\t\t\t\t\t\t\t) -> str | None:\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tUnion[str, Any] \t\t\t= \"\"\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tstr \t\t\t= 42\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tList[Any] \t\t\t= 42\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[Any] \t\t\t= 42\r\n\r\n\t\t\t\t\t\t\tfor keychar, cipherchar in zip(cycle(UpperCamelCase_\t\t\t\t\t\t\t),\t\t\tUpperCamelCase_\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tAny \t\t\t= cipherchar ^ keychar\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif decodedchar not in VALID_INTS:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn None\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdecoded += chr(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\treturn decoded\r\n\r\n\r\n\r\n\r\ndef _UpperCamelCase (\tsnake_case__\t\t\t\t\t\t\t) -> list[str]:\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tOptional[Any] \t\t\t= []\r\n\t\t\t\t\t\t\tfor key in product(UpperCamelCase_,\t\t\trepeat=3\t\t\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tint \t\t\t= try_key(UpperCamelCase_,\t\t\tUpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif encoded is not None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tpossibles.append(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\treturn possibles\r\n\r\n\r\n\r\n\r\ndef _UpperCamelCase (\tsnake_case__,\t\t\tsnake_case__\t\t\t\t\t\t\t) -> list[str]:\r\n\t\t\t\t\t\t\treturn [possible for possible in possibles if common_word in possible.lower()]\r\n\r\n\r\n\r\n\r\ndef _UpperCamelCase (\tsnake_case__ = \"p059_cipher.txt\"\t\t\t\t\t\t\t) -> int:\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tDict \t\t\t= 42\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tList[Any] \t\t\t= 42\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tTuple \t\t\t= 42\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tint \t\t\t= 42\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tList[str] \t\t\t= Path(UpperCamelCase_\t\t\t\t\t\t\t).parent.joinpath(UpperCamelCase_\t\t\t\t\t\t\t).read_text(encoding=\"utf-8\"\t\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tAny \t\t\t= [int(UpperCamelCase_\t\t\t\t\t\t\t) for number in data.strip().split(\",\"\t\t\t\t\t\t\t)]\r\n\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tList[Any] \t\t\t= filter_valid_chars(UpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\tfor common_word in COMMON_WORDS:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t__UpperCAmelCase :\tUnion[str, Any] \t\t\t= filter_common_word(UpperCamelCase_,\t\t\tUpperCamelCase_\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif len(UpperCamelCase_\t\t\t\t\t\t\t) == 1:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tbreak\r\n\r\n\t\t\t\t\t\t\t__UpperCAmelCase :\tDict \t\t\t= possibles[0]\r\n\t\t\t\t\t\t\treturn sum(ord(UpperCamelCase_\t\t\t\t\t\t\t) for char in decoded_text\t\t\t\t\t\t\t)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\tprint(F'{solution() = }')\r\n\r\n\r\n\r\n\r\n"},"code_codestyle":{"kind":"number","value":157,"string":"157"},"style_context":{"kind":"string","value":"\r\r\r\r\r\r\rimport math\r\rimport numpy as np\rimport qiskit\rfrom qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute\r\r\rdef \t\t\t\t_a ( UpperCamelCase_ : int = 3 ) -> qiskit.result.counts.Counts:\r\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\r\r\r\r\r\r\r\t\t\t\t\t\tif isinstance(UpperCamelCase_\t\t\t, UpperCamelCase_ ):\r\t\t\t\t\t\t\t\t\t\t\t\traise TypeError(\"number of qubits must be a integer.\" )\r\t\t\t\t\t\tif number_of_qubits <= 0:\r\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\"number of qubits must be > 0.\" )\r\t\t\t\t\t\tif math.floor(UpperCamelCase_ ) != number_of_qubits:\r\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\"number of qubits must be exact integer.\" )\r\t\t\t\t\t\tif number_of_qubits > 10:\r\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\"number of qubits too large to simulate(>10).\" )\r\r\t\t\t\t\t\tlowerCAmelCase__ \t\t\t\t\t= QuantumRegister(UpperCamelCase_\t\t\t, \"qr\" )\r\t\t\t\t\t\tlowerCAmelCase__ \t\t\t\t\t= ClassicalRegister(UpperCamelCase_\t\t\t, \"cr\" )\r\r\t\t\t\t\t\tlowerCAmelCase__ \t\t\t\t\t= QuantumCircuit(UpperCamelCase_\t\t\t, UpperCamelCase_ )\r\r\t\t\t\t\t\tlowerCAmelCase__ \t\t\t\t\t= number_of_qubits\r\r\t\t\t\t\t\tfor i in range(UpperCamelCase_ ):\r\t\t\t\t\t\t\t\t\t\t\t\tquantum_circuit.h(number_of_qubits - i - 1 )\r\t\t\t\t\t\t\t\t\t\t\t\tcounter -= 1\r\t\t\t\t\t\t\t\t\t\t\t\tfor j in range(UpperCamelCase_ ):\r\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tquantum_circuit.cp(np.pi / 2 ** (counter - j)\t\t\t, UpperCamelCase_\t\t\t, UpperCamelCase_ )\r\r\t\t\t\t\t\tfor k in range(number_of_qubits // 2 ):\r\t\t\t\t\t\t\t\t\t\t\t\tquantum_circuit.swap(UpperCamelCase_\t\t\t, number_of_qubits - k - 1 )\r\r\t\t\t\t\t\t# measure all the qubits\r\t\t\t\t\t\tquantum_circuit.measure(UpperCamelCase_\t\t\t, UpperCamelCase_ )\r\t\t\t\t\t\t# simulate with 10000 shots\r\t\t\t\t\t\tlowerCAmelCase__ \t\t\t\t\t= Aer.get_backend(\"qasm_simulator\" )\r\t\t\t\t\t\tlowerCAmelCase__ \t\t\t\t\t= execute(UpperCamelCase_\t\t\t, UpperCamelCase_\t\t\t, shots=10_000 )\r\r\t\t\t\t\t\treturn job.result().get_counts(UpperCamelCase_ )\r\r\rif __name__ == \"__main__\":\r\tprint(\r\t F\"Total count for quantum fourier transform state is: \\\n {quantum_fourier_transform(3)}\"\r\t)\r\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":340,"string":"340"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":295,"cells":{"code":{"kind":"string","value":"\ndef \t\t\t\t\t\t\t_lowercase\t\t\t\t\t\t\t(\t\t\t\t\tUpperCamelCase_\t\t)\t\t->\t\tbool:\n\t\t\t\t\t'''simple docstring'''\n\n\n\t\t\t\t\tif num < 0:\n\t\t\t\t\t\t\t\t\t\treturn False\n\n\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tnum\n\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t0\n\t\t\t\t\twhile num > 0:\n\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\trev_num * 10 + (num % 10)\n\t\t\t\t\t\t\t\t\t\tnum //= 10\n\n\t\t\t\t\treturn num_copy == rev_num\n\n\nif __name__ == \"__main__\":\n\t\timport doctest\n\n\t\tdoctest.testmod()\n\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":355,"string":"355"},"style_context":{"kind":"string","value":"\nimport shutil\nimport tempfile\nimport unittest\n\nfrom transformers import (\n SPIECE_UNDERLINE,\n AddedToken,\n BatchEncoding,\n NllbTokenizer,\n NllbTokenizerFast,\n is_torch_available,\n)\nfrom transformers.testing_utils import (\n get_tests_dir,\n nested_simplify,\n require_sentencepiece,\n require_tokenizers,\n require_torch,\n)\n\nfrom ...test_tokenization_common import TokenizerTesterMixin\n\n\n__snake_case\t\t\t\t\t\t\t = get_tests_dir(\"\"\"fixtures/test_sentencepiece.model\"\"\")\n\n\nif is_torch_available():\n\t\t\t\tfrom transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right\n\n__snake_case\t\t\t\t\t\t\t = 25_60_47\n__snake_case\t\t\t\t\t\t\t = 25_61_45\n\n\n\n\n@require_sentencepiece\n@require_tokenizers\nclass \t\t\t\t\t\tlowercase__ (\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t,\tunittest.TestCase ):\n\tA__ : int \t=NllbTokenizer\n\tA__ : Optional[int] \t=NllbTokenizerFast\n\tA__ : Union[str, Any] \t=True\n\tA__ : Dict \t=True\n\tA__ : Tuple \t={}\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tsuper().setUp()\n\n\t\t\t\t\t\t# We have a SentencePiece fixture for testing\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tNllbTokenizer(UpperCAmelCase_\t, keep_accents=UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\ttokenizer.save_pretrained(self.tmpdirname\t\t\t\t\t\t\t)\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Union[str, Any]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tNllbTokenizer(UpperCAmelCase_\t, keep_accents=UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize('This is a test'\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertListEqual(UpperCAmelCase_\t, ['▁This', '▁is', '▁a', '▁t', 'est']\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\tself.assertListEqual(\n\t\t\t\t\t\t tokenizer.convert_tokens_to_ids(UpperCAmelCase_\t\t\t\t\t\t\t)\t, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]\t, )\n\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.tokenize('I was born in 92000, and this is falsé.'\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertListEqual(\n\t\t\t\t\t\t UpperCAmelCase_\t, [\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'I',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'was',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'b',\n\t\t\t\t\t\t 'or',\n\t\t\t\t\t\t 'n',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'in',\n\t\t\t\t\t\t SPIECE_UNDERLINE + '',\n\t\t\t\t\t\t '9',\n\t\t\t\t\t\t '2',\n\t\t\t\t\t\t '0',\n\t\t\t\t\t\t '0',\n\t\t\t\t\t\t '0',\n\t\t\t\t\t\t ',',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'and',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'this',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'is',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'f',\n\t\t\t\t\t\t 'al',\n\t\t\t\t\t\t 's',\n\t\t\t\t\t\t 'é',\n\t\t\t\t\t\t '.',\n\t\t\t\t\t\t ]\t, )\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_tokens_to_ids(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertListEqual(\n\t\t\t\t\t\t UpperCAmelCase_\t, [\n\t\t\t\t\t\t value + tokenizer.fairseq_offset\n\t\t\t\t\t\t for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]\n\t\t\t\t\t\t ]\t, )\n\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.convert_ids_to_tokens(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertListEqual(\n\t\t\t\t\t\t UpperCAmelCase_\t, [\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'I',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'was',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'b',\n\t\t\t\t\t\t 'or',\n\t\t\t\t\t\t 'n',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'in',\n\t\t\t\t\t\t SPIECE_UNDERLINE + '',\n\t\t\t\t\t\t '',\n\t\t\t\t\t\t '2',\n\t\t\t\t\t\t '0',\n\t\t\t\t\t\t '0',\n\t\t\t\t\t\t '0',\n\t\t\t\t\t\t ',',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'and',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'this',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'is',\n\t\t\t\t\t\t SPIECE_UNDERLINE + 'f',\n\t\t\t\t\t\t 'al',\n\t\t\t\t\t\t 's',\n\t\t\t\t\t\t '',\n\t\t\t\t\t\t '.',\n\t\t\t\t\t\t ]\t, )\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})\n\t\t\t\t\t\tfor tokenizer, pretrained_name, kwargs in self.tokenizers_list:\n\t\t\t\t\t\t\t\t\t\t\twith self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'\t\t\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.rust_tokenizer_class.from_pretrained(UpperCAmelCase_\t, **UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer_class.from_pretrained(UpperCAmelCase_\t, **UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttempfile.mkdtemp()\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.save_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.save_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Checks it save with the same files + the tokenizer.json file for the fast one\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertSequenceEqual(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Checks everything loads correctly in the same way\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.from_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.from_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Check special tokens are set accordingly on Rust and Python\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor key in tokenizer_pp.special_tokens_map:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(hasattr(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tshutil.rmtree(UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Save tokenizer rust, legacy_format=True\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttempfile.mkdtemp()\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.save_pretrained(UpperCAmelCase_\t, legacy_format=UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.save_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Checks it save with the same files\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertSequenceEqual(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Checks everything loads correctly in the same way\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.from_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.from_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Check special tokens are set accordingly on Rust and Python\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor key in tokenizer_pp.special_tokens_map:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(hasattr(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tshutil.rmtree(UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Save tokenizer rust, legacy_format=False\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttempfile.mkdtemp()\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.save_pretrained(UpperCAmelCase_\t, legacy_format=UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.save_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Checks it saved the tokenizer.json file\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Checks everything loads correctly in the same way\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.from_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.from_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Check special tokens are set accordingly on Rust and Python\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor key in tokenizer_pp.special_tokens_map:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(hasattr(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tshutil.rmtree(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t@require_torch\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Tuple\t\t\t\t\t\t\t):\n\t\t\t\t\t\tif not self.test_seqaseq:\n\t\t\t\t\t\t\t\t\t\t\treturn\n\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.get_tokenizers()\n\t\t\t\t\t\tfor tokenizer in tokenizers:\n\t\t\t\t\t\t\t\t\t\t\twith self.subTest(F'{tokenizer.__class__.__name__}'\t\t\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# Longer text that will definitely require truncation.\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t[\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ' UN Chief Says There Is No Military Solution in Syria',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ' Secretary-General Ban Ki-moon says his response to Russia\\'s stepped up military support for'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ' Syria is that \\'there is no military solution\\' to the nearly five-year conflict and more weapons'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ' will only worsen the violence and misery for millions of people.',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t[\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'Şeful ONU declară că nu există o soluţie militară în Siria',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ' Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.prepare_seqaseq_batch(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t src_texts=UpperCAmelCase_\t, tgt_texts=UpperCAmelCase_\t, max_length=3\t, max_target_length=10\t, return_tensors='pt'\t, src_lang='eng_Latn'\t, tgt_lang='ron_Latn'\t, )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texcept NotImplementedError:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(batch.input_ids.shape[1]\t, 3\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(batch.labels.shape[1]\t, 10\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# max_target_length will default to max_length if not specified\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.prepare_seqaseq_batch(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCAmelCase_\t, tgt_texts=UpperCAmelCase_\t, max_length=3\t, return_tensors='pt'\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(batch.input_ids.shape[1]\t, 3\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(batch.labels.shape[1]\t, 3\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.prepare_seqaseq_batch(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t src_texts=UpperCAmelCase_\t, max_length=3\t, max_target_length=10\t, return_tensors='pt'\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(batch_encoder_only.input_ids.shape[1]\t, 3\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(batch_encoder_only.attention_mask.shape[1]\t, 3\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertNotIn('decoder_input_ids'\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\t@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'\t\t\t\t\t\t\t)\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: List[Any]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tpass\n\n\n\n\n\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tfor tokenizer, pretrained_name, kwargs in self.tokenizers_list:\n\t\t\t\t\t\t\t\t\t\t\twith self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'\t\t\t\t\t\t\t):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t[AddedToken(''\t, lstrip=UpperCAmelCase_\t\t\t\t\t\t\t)]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.rust_tokenizer_class.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCAmelCase_\t, additional_special_tokens=UpperCAmelCase_\t, **UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.encode('Hey this is a token'\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_r.encode(''\t, add_special_tokens=UpperCAmelCase_\t\t\t\t\t\t\t)[0]\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(special_token_id in r_output\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif self.test_slow_tokenizer:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.rust_tokenizer_class.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCAmelCase_\t, additional_special_tokens=UpperCAmelCase_\t, **UpperCAmelCase_\t, )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer_class.from_pretrained(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t UpperCAmelCase_\t, additional_special_tokens=UpperCAmelCase_\t, **UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_p.encode('Hey this is a token'\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer_cr.encode('Hey this is a token'\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertEqual(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(special_token_id in p_output\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.assertTrue(special_token_id in cr_output\t\t\t\t\t\t\t)\n\n\n\n\n\n@require_torch\n@require_sentencepiece\n@require_tokenizers\nclass \t\t\t\t\t\tlowercase__ (\t\t\t\t\t\tunittest.TestCase ):\n\tA__ : List[Any] \t=\"\"\"facebook/nllb-200-distilled-600M\"\"\"\n\tA__ : Tuple \t=[\n\t \"\"\" UN Chief Says There Is No Military Solution in Syria\"\"\",\n\t \"\"\" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \\\"there is no military solution\\\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.\"\"\",\n\t]\n\tA__ : Optional[Any] \t=[\n\t \"\"\"Şeful ONU declară că nu există o soluţie militară în Siria\"\"\",\n\t \"\"\"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei\"\"\"\n\t \"\"\" pentru Siria este că \\\"nu există o soluţie militară\\\" la conflictul de aproape cinci ani şi că noi arme nu vor\"\"\"\n\t \"\"\" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.\"\"\",\n\t]\n\tA__ : Optional[int] \t=[\n\t 2_5_6_0_4_7,\n\t 1_6_2_9_7,\n\t 1_3_4_4_0_8,\n\t 8_1_6_5,\n\t 2_4_8_0_6_6,\n\t 1_4_7_3_4,\n\t 9_5_0,\n\t 1_1_3_5,\n\t 1_0_5_7_2_1,\n\t 3_5_7_3,\n\t 8_3,\n\t 2_7_3_5_2,\n\t 1_0_8,\n\t 4_9_4_8_6,\n\t 2,\n\t]\n\t@classmethod\n\tdef \t\t\t\t\tA_ ( cls\t\t\t\t\t\t\t: Tuple\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tNllbTokenizer.from_pretrained(\n\t\t\t\t\t\t cls.checkpoint_name\t, src_lang='eng_Latn'\t, tgt_lang='ron_Latn'\t\t\t\t\t\t\t)\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t1\n\t\t\t\t\t\treturn cls\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: int\t\t\t\t\t\t\t):\n\t\t\t\t\t\tself.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']\t, 256001\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']\t, 256002\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']\t, 256057\t\t\t\t\t\t\t)\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer.batch_encode_plus(self.src_text\t\t\t\t\t\t\t).input_ids[0]\n\t\t\t\t\t\tself.assertListEqual(self.expected_src_tokens\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t):\n\t\t\t\t\t\tself.assertIn(UpperCAmelCase_\t, self.tokenizer.all_special_ids\t\t\t\t\t\t\t)\n\t\t\t\t\t\t# fmt: off\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t[RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]\n\t\t\t\t\t\t# fmt: on\n\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer.decode(UpperCAmelCase_\t, skip_special_tokens=UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer.decode(generated_ids[1:]\t, skip_special_tokens=UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertNotIn(self.tokenizer.eos_token\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: str\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t['this is gunna be a long sentence ' * 20]\n\t\t\t\t\t\tassert isinstance(src_text[0]\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\t10\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(UpperCAmelCase_\t, max_length=UpperCAmelCase_\t, truncation=UpperCAmelCase_\t\t\t\t\t\t\t).input_ids[0]\n\t\t\t\t\t\tself.assertEqual(ids[-1]\t, 2\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(ids[0]\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(len(UpperCAmelCase_\t\t\t\t\t\t\t)\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Optional[Any]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tself.assertListEqual(self.tokenizer.convert_tokens_to_ids(['', 'ar_AR']\t\t\t\t\t\t\t)\t, [256203, 3]\t\t\t\t\t\t\t)\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Dict\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttempfile.mkdtemp()\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer.fairseq_tokens_to_ids\n\t\t\t\t\t\tself.tokenizer.save_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tNllbTokenizer.from_pretrained(UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertDictEqual(new_tok.fairseq_tokens_to_ids\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\t@require_torch\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(\n\t\t\t\t\t\t self.src_text\t, text_target=self.tgt_text\t, padding=UpperCAmelCase_\t, truncation=UpperCAmelCase_\t, max_length=len(self.expected_src_tokens\t\t\t\t\t\t\t)\t, return_tensors='pt'\t, )\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tshift_tokens_right(\n\t\t\t\t\t\t batch['labels']\t, self.tokenizer.pad_token_id\t, self.tokenizer.lang_code_to_id['ron_Latn']\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\tself.assertIsInstance(UpperCAmelCase_\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\tself.assertEqual((2, 15)\t, batch.input_ids.shape\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual((2, 15)\t, batch.attention_mask.shape\t\t\t\t\t\t\t)\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tbatch.input_ids.tolist()[0]\n\t\t\t\t\t\tself.assertListEqual(self.expected_src_tokens\t, UpperCAmelCase_\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(UpperCAmelCase_\t, batch.decoder_input_ids[0, 0]\t\t\t\t\t\t\t) # EOS\n\t\t\t\t\t\t# Test that special tokens are reset\n\t\t\t\t\t\tself.assertEqual(self.tokenizer.prefix_tokens\t, [EN_CODE]\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(self.tokenizer.suffix_tokens\t, [self.tokenizer.eos_token_id]\t\t\t\t\t\t\t)\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: str\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(self.src_text\t, padding=UpperCAmelCase_\t, truncation=UpperCAmelCase_\t, max_length=3\t, return_tensors='pt'\t\t\t\t\t\t\t)\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(\n\t\t\t\t\t\t text_target=self.tgt_text\t, padding=UpperCAmelCase_\t, truncation=UpperCAmelCase_\t, max_length=10\t, return_tensors='pt'\t\t\t\t\t\t\t)\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\ttargets['input_ids']\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tshift_tokens_right(\n\t\t\t\t\t\t UpperCAmelCase_\t, self.tokenizer.pad_token_id\t, decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]\t, )\n\n\t\t\t\t\t\tself.assertEqual(batch.input_ids.shape[1]\t, 3\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(batch.decoder_input_ids.shape[1]\t, 10\t\t\t\t\t\t\t)\n\t@require_torch\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: List[str]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer._build_translation_inputs(\n\t\t\t\t\t\t 'A test'\t, return_tensors='pt'\t, src_lang='eng_Latn'\t, tgt_lang='fra_Latn'\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\tself.assertEqual(\n\t\t\t\t\t\t nested_simplify(UpperCAmelCase_\t\t\t\t\t\t\t)\t, {\n\t\t\t\t\t\t # A, test, EOS, en_XX\n\t\t\t\t\t\t 'input_ids': [[256047, 70, 7356, 2]],\n\t\t\t\t\t\t 'attention_mask': [[1, 1, 1, 1]],\n\t\t\t\t\t\t # ar_AR\n\t\t\t\t\t\t 'forced_bos_token_id': 256057,\n\t\t\t\t\t\t }\t, )\n\n\n\n\n\n\t@require_torch\n\tdef \t\t\t\t\tA_ ( self\t\t\t\t\t\t\t: Optional[int]\t\t\t\t\t\t\t):\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tTrue\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(\n\t\t\t\t\t\t 'UN Chief says there is no military solution in Syria'\t, src_lang='eng_Latn'\t, tgt_lang='fra_Latn'\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(\n\t\t\t\t\t\t inputs.input_ids\t, [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tFalse\n\t\t\t\t\t\tSCREAMING_SNAKE_CASE__\t\t\t\t\t\t\t\t\t\t\t\t\t=\t\t\tself.tokenizer(\n\t\t\t\t\t\t 'UN Chief says there is no military solution in Syria'\t, src_lang='eng_Latn'\t, tgt_lang='fra_Latn'\t\t\t\t\t\t\t)\n\t\t\t\t\t\tself.assertEqual(\n\t\t\t\t\t\t inputs.input_ids\t, [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]\t\t\t\t\t\t\t)\n\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":169,"string":"169"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":296,"cells":{"code":{"kind":"string","value":"\r\n\r\n\r\n\r\n\r\n\r\n\r\nfrom transformers import DistilBertTokenizer, DistilBertTokenizerFast\r\nfrom transformers.testing_utils import require_tokenizers, slow\r\n\r\nfrom ..bert.test_tokenization_bert import BertTokenizationTest\r\n\r\n\r\n\r\n\r\n\r\n@require_tokenizers\r\nclass __lowerCamelCase ( snake_case__):\r\n\r\n\r\n\r\n\r\n\r\n\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= DistilBertTokenizer\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= DistilBertTokenizerFast\r\n\tUpperCamelCase__\t\t\t\t\t\t\t\t= True\r\n\r\n\t@slow\r\n\tdef UpperCamelCase (\t\t\t\t\t\t\tself ):\r\n\t\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\tDistilBertTokenizer.from_pretrained('distilbert-base-uncased' )\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode('sequence builders' , add_special_tokens=UpperCAmelCase )\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.encode('multi-sequence build' , add_special_tokens=UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.build_inputs_with_special_tokens(UpperCAmelCase )\r\n\t\t\t\t\t\t\t_UpperCAmelCase\t\t\t\t\t\t\t\t\t=\t\t\ttokenizer.build_inputs_with_special_tokens(UpperCAmelCase , UpperCAmelCase )\r\n\r\n\t\t\t\t\t\t\tassert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]\r\n\t\t\t\t\t\t\tassert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [\r\n\t\t\t\t\t\t\t tokenizer.sep_token_id\r\n\t\t\t\t\t\t\t]\r\n"},"code_codestyle":{"kind":"number","value":39,"string":"39"},"style_context":{"kind":"string","value":"\r\n\r\n\r\n\r\n\"\"\"simple docstring\"\"\"\r\n\r\n\r\nimport warnings\r\nfrom typing import List, Optional, Union\r\n\r\nfrom ...processing_utils import ProcessorMixin\r\nfrom ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy\r\nfrom ...utils import TensorType\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass A_\t\t\t( SCREAMING_SNAKE_CASE_\t\t\t\t):\r\n\r\n\r\n\r\n\t\t\t\t\t\t\"\"\"simple docstring\"\"\"\r\n\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t=\t\t\t\t\t\t\t[\"\"\"image_processor\"\"\", \"\"\"tokenizer\"\"\"]\r\n\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t=\t\t\t\t\t\t\t\"\"\"LayoutLMv2ImageProcessor\"\"\"\r\n\t\t\t\t\t\t__UpperCamelCase\t\t\t\t\t\t=\t\t\t\t\t\t\t(\"\"\"LayoutXLMTokenizer\"\"\", \"\"\"LayoutXLMTokenizerFast\"\"\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef __init__(\t\t\t\t\tself :Any , lowercase_ :int=None , lowercase_ :Union[str, Any]=None , **lowercase_ :Optional[Any]\t\t\t\t\t)\t\t\t\t->\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\tif \"feature_extractor\" in kwargs:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\twarnings.warn(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ' instead.' , lowercase_ , )\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = kwargs.pop('feature_extractor'\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = image_processor if image_processor is not None else feature_extractor\r\n\t\t\t\t\t\t\t\t\t\t\t\tif image_processor is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('You need to specify an `image_processor`.'\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tif tokenizer is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('You need to specify a `tokenizer`.'\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tsuper().__init__(lowercase_ , lowercase_\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef __call__(\t\t\t\t\tself :str , lowercase_ :Optional[int] , lowercase_ :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowercase_ :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , lowercase_ :Union[List[List[int]], List[List[List[int]]]] = None , lowercase_ :Optional[Union[List[int], List[List[int]]]] = None , lowercase_ :bool = True , lowercase_ :Union[bool, str, PaddingStrategy] = False , lowercase_ :Union[bool, str, TruncationStrategy] = None , lowercase_ :Optional[int] = None , lowercase_ :int = 0 , lowercase_ :Optional[int] = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[bool] = None , lowercase_ :bool = False , lowercase_ :bool = False , lowercase_ :bool = False , lowercase_ :bool = False , lowercase_ :bool = True , lowercase_ :Optional[Union[str, TensorType]] = None , **lowercase_ :Any , )\t\t\t\t->\t\t\tBatchEncoding:\r\n\t\t\t\t\t\t\t\t\t\t\t\t# verify input\r\n\t\t\t\t\t\t\t\t\t\t\t\tif self.image_processor.apply_ocr and (boxes is not None):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'You cannot provide bounding boxes '\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'if you initialized the image processor with apply_ocr set to True.'\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif self.image_processor.apply_ocr and (word_labels is not None):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif return_overflowing_tokens is True and return_offsets_mapping is False:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t# first, apply the image processor\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = self.image_processor(images=lowercase_ , return_tensors=lowercase_\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t# second, apply the tokenizer\r\n\t\t\t\t\t\t\t\t\t\t\t\tif text is not None and self.image_processor.apply_ocr and text_pair is None:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif isinstance(lowercase_ , lowercase_\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = [text] # add batch dimension (as the image processor always adds a batch dimension)\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = features['words']\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = self.tokenizer(\r\n\t\t\t\t\t\t\t\t\t\t\t\t text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_token_type_ids=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\t# add pixel values\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = features.pop('pixel_values'\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tif return_overflowing_tokens is True:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = self.get_overflowing_images(lowercase_ , encoded_inputs['overflow_to_sample_mapping']\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = images\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn encoded_inputs\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :Dict , lowercase_ :List[Any] , lowercase_ :Any\t\t\t\t\t)\t\t\t\t->\t\t\tOptional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\t# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image\r\n\t\t\t\t\t\t\t\t\t\t\t\tUpperCAmelCase = []\r\n\t\t\t\t\t\t\t\t\t\t\t\tfor sample_idx in overflow_to_sample_mapping:\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\timages_with_overflow.append(images[sample_idx]\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\tif len(lowercase_\t\t\t\t\t) != len(lowercase_\t\t\t\t\t):\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\traise ValueError(\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t f\"\"\" {len(lowercase_\t\t\t\t\t)} and {len(lowercase_\t\t\t\t\t)}\"\"\"\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn images_with_overflow\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :Any , *lowercase_ :int , **lowercase_ :Tuple\t\t\t\t\t)\t\t\t\t->\t\t\tTuple:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self.tokenizer.batch_decode(*lowercase_ , **lowercase_\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :Any , *lowercase_ :List[Any] , **lowercase_ :Optional[int]\t\t\t\t\t)\t\t\t\t->\t\t\tOptional[Any]:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self.tokenizer.decode(*lowercase_ , **lowercase_\t\t\t\t\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@property\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :int\t\t\t\t\t)\t\t\t\t->\t\t\tOptional[int]:\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn [\"input_ids\", \"bbox\", \"attention_mask\", \"image\"]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@property\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :int\t\t\t\t\t)\t\t\t\t->\t\t\tDict:\r\n\t\t\t\t\t\t\t\t\t\t\t\twarnings.warn(\r\n\t\t\t\t\t\t\t\t\t\t\t\t '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , lowercase_ , )\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self.image_processor_class\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\t\t\t\t@property\r\n\t\t\t\t\t\tdef \tUpperCAmelCase__ (\t\t\t\t\tself :Union[str, Any]\t\t\t\t\t)\t\t\t\t->\t\t\tOptional[int]:\r\n\t\t\t\t\t\t\t\t\t\t\t\twarnings.warn(\r\n\t\t\t\t\t\t\t\t\t\t\t\t '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , lowercase_ , )\r\n\t\t\t\t\t\t\t\t\t\t\t\treturn self.image_processor\r\n\r\n\r\n\r\n\r\n\r\n"},"style_context_codestyle":{"kind":"number","value":78,"string":"78"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":297,"cells":{"code":{"kind":"string","value":"\n\n\nfrom typing import TYPE_CHECKING\n\nfrom ...utils import (\n OptionalDependencyNotAvailable,\n _LazyModule,\n is_tf_available,\n is_torch_available,\n is_vision_available,\n)\n\n\n__a\t\t\t\t\t\t\t= {\n '''configuration_blip''': [\n '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',\n '''BlipConfig''',\n '''BlipTextConfig''',\n '''BlipVisionConfig''',\n ],\n '''processing_blip''': ['''BlipProcessor'''],\n}\n\ntry:\n if not is_vision_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n pass\nelse:\n __a\t\t\t\t\t\t\t= ['''BlipImageProcessor''']\n\n\ntry:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n pass\nelse:\n __a\t\t\t\t\t\t\t= [\n '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',\n '''BlipModel''',\n '''BlipPreTrainedModel''',\n '''BlipForConditionalGeneration''',\n '''BlipForQuestionAnswering''',\n '''BlipVisionModel''',\n '''BlipTextModel''',\n '''BlipForImageTextRetrieval''',\n ]\n\ntry:\n if not is_tf_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n pass\nelse:\n __a\t\t\t\t\t\t\t= [\n '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',\n '''TFBlipModel''',\n '''TFBlipPreTrainedModel''',\n '''TFBlipForConditionalGeneration''',\n '''TFBlipForQuestionAnswering''',\n '''TFBlipVisionModel''',\n '''TFBlipTextModel''',\n '''TFBlipForImageTextRetrieval''',\n ]\n\nif TYPE_CHECKING:\n from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig\n from .processing_blip import BlipProcessor\n\n try:\n if not is_vision_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n pass\n else:\n from .image_processing_blip import BlipImageProcessor\n\n try:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n pass\n else:\n from .modeling_blip import (\n BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,\n BlipForConditionalGeneration,\n BlipForImageTextRetrieval,\n BlipForQuestionAnswering,\n BlipModel,\n BlipPreTrainedModel,\n BlipTextModel,\n BlipVisionModel,\n )\n\n try:\n if not is_tf_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n pass\n else:\n from .modeling_tf_blip import (\n TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,\n TFBlipForConditionalGeneration,\n TFBlipForImageTextRetrieval,\n TFBlipForQuestionAnswering,\n TFBlipModel,\n TFBlipPreTrainedModel,\n TFBlipTextModel,\n TFBlipVisionModel,\n )\n\nelse:\n import sys\n\n __a\t\t\t\t\t\t\t= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)\n\n"},"code_codestyle":{"kind":"number","value":364,"string":"364"},"style_context":{"kind":"string","value":"\n\n\nfrom maths.prime_factors import prime_factors\n\n\n\n\ndef __lowercase\t\t( _UpperCamelCase ) ->int:\n\n\n\n\n\n\n\n \"\"\"simple docstring\"\"\"\n\n if not isinstance(_UpperCamelCase, _UpperCamelCase ):\n lowercase :\t\t\t\tList[str] \t\t\t\t=\t\t\t\t\t\tf\"\"\"Input value of [number={number}] must be an integer\"\"\"\n raise TypeError(_UpperCamelCase )\n if number < 1:\n raise ValueError('''Input must be a positive integer''' )\n return -1 if len(prime_factors(_UpperCamelCase ) ) % 2 else 1\n\n\nif __name__ == \"__main__\":\n import doctest\n\n doctest.testmod()\n\n"},"style_context_codestyle":{"kind":"number","value":173,"string":"173"},"label":{"kind":"number","value":0,"string":"0"}}},{"rowIdx":298,"cells":{"code":{"kind":"string","value":"\r\rfrom __future__ import annotations\r\rimport time\rfrom math import sqrt\r\r# 1 for manhattan, 0 for euclidean\r__A : List[str] =\t0\r\r__A : List[str] =\t[\r [0, 0, 0, 0, 0, 0, 0],\r [0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles\r [0, 0, 0, 0, 0, 0, 0],\r [0, 0, 1, 0, 0, 0, 0],\r [1, 0, 1, 0, 0, 0, 0],\r [0, 0, 0, 0, 0, 0, 0],\r [0, 0, 0, 0, 1, 0, 0],\r]\r\r__A : Union[str, Any] =\t[[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right\r\r__A : Tuple =\ttuple[int, int]\r\r\r\r\r\r\rclass __A :\r\r\r\r\r\r\r def __init__(\t\tself\t:\t\t\tList[Any]\t\t\t, UpperCAmelCase_\t:\t\t\tint\t\t\t, UpperCAmelCase_\t:\t\t\tint\t\t\t, UpperCAmelCase_\t:\t\t\tint\t\t\t, UpperCAmelCase_\t:\t\t\tint\t\t\t, UpperCAmelCase_\t:\t\t\tint\t\t\t, UpperCAmelCase_\t:\t\t\tNode | None\t\t\t, ):\r lowerCAmelCase :\tUnion[str, Any]\t\t\t\t=\t\t\t\t\tpos_x\r lowerCAmelCase :\tDict\t\t\t\t=\t\t\t\t\tpos_y\r lowerCAmelCase :\tDict\t\t\t\t=\t\t\t\t\t(pos_y, pos_x)\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tgoal_x\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tgoal_y\r lowerCAmelCase :\tint\t\t\t\t=\t\t\t\t\tg_cost\r lowerCAmelCase :\tstr\t\t\t\t=\t\t\t\t\tparent\r lowerCAmelCase :\tstr\t\t\t\t=\t\t\t\t\tself.calculate_heuristic()\r lowerCAmelCase :\tint\t\t\t\t=\t\t\t\t\tself.g_cost + self.h_cost\r\r\r\r\r\r\r def \t\tlowercase__ (\t\tself\t:\t\t\tList[Any]\t\t\t):\r lowerCAmelCase :\tint\t\t\t\t=\t\t\t\t\tself.pos_x - self.goal_x\r lowerCAmelCase :\tint\t\t\t\t=\t\t\t\t\tself.pos_y - self.goal_y\r if HEURISTIC == 1:\r return abs(UpperCAmelCase_\t\t\t) + abs(UpperCAmelCase_\t\t\t)\r else:\r return sqrt(dy**2 + dx**2\t\t\t)\r\r\r\r\r\r\r def __lt__(\t\tself\t:\t\t\tTuple\t\t\t, UpperCAmelCase_\t:\t\t\tNode\t\t\t):\r return self.f_cost < other.f_cost\r\r\r\r\r\r\rclass __A :\r\r\r\r\r\r\r def __init__(\t\tself\t:\t\t\tTuple\t\t\t, UpperCAmelCase_\t:\t\t\tTPosition\t\t\t, UpperCAmelCase_\t:\t\t\tTPosition\t\t\t):\r lowerCAmelCase :\tOptional[Any]\t\t\t\t=\t\t\t\t\tNode(start[1]\t\t\t, start[0]\t\t\t, goal[1]\t\t\t, goal[0]\t\t\t, 0\t\t\t, UpperCAmelCase_\t\t\t)\r lowerCAmelCase :\tDict\t\t\t\t=\t\t\t\t\tNode(goal[1]\t\t\t, goal[0]\t\t\t, goal[1]\t\t\t, goal[0]\t\t\t, 99999\t\t\t, UpperCAmelCase_\t\t\t)\r\r lowerCAmelCase :\tAny\t\t\t\t=\t\t\t\t\t[self.start]\r lowerCAmelCase :\tlist[Node]\t\t\t\t=\t\t\t\t\t[]\r\r lowerCAmelCase :\tTuple\t\t\t\t=\t\t\t\t\tFalse\r\r\r\r\r\r\r def \t\tlowercase__ (\t\tself\t:\t\t\tAny\t\t\t):\r while self.open_nodes:\r # Open Nodes are sorted using __lt__\r self.open_nodes.sort()\r lowerCAmelCase :\tUnion[str, Any]\t\t\t\t=\t\t\t\t\tself.open_nodes.pop(0\t\t\t)\r\r if current_node.pos == self.target.pos:\r return self.retrace_path(UpperCAmelCase_\t\t\t)\r\r self.closed_nodes.append(UpperCAmelCase_\t\t\t)\r lowerCAmelCase :\tDict\t\t\t\t=\t\t\t\t\tself.get_successors(UpperCAmelCase_\t\t\t)\r\r for child_node in successors:\r if child_node in self.closed_nodes:\r continue\r\r if child_node not in self.open_nodes:\r self.open_nodes.append(UpperCAmelCase_\t\t\t)\r else:\r # retrieve the best current path\r lowerCAmelCase :\tstr\t\t\t\t=\t\t\t\t\tself.open_nodes.pop(self.open_nodes.index(UpperCAmelCase_\t\t\t)\t\t\t)\r\r if child_node.g_cost < better_node.g_cost:\r self.open_nodes.append(UpperCAmelCase_\t\t\t)\r else:\r self.open_nodes.append(UpperCAmelCase_\t\t\t)\r\r return [self.start.pos]\r\r\r\r\r\r\r def \t\tlowercase__ (\t\tself\t:\t\t\tOptional[int]\t\t\t, UpperCAmelCase_\t:\t\t\tNode\t\t\t):\r lowerCAmelCase :\tOptional[Any]\t\t\t\t=\t\t\t\t\t[]\r for action in delta:\r lowerCAmelCase :\tUnion[str, Any]\t\t\t\t=\t\t\t\t\tparent.pos_x + action[1]\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tparent.pos_y + action[0]\r if not (0 <= pos_x <= len(grid[0]\t\t\t) - 1 and 0 <= pos_y <= len(UpperCAmelCase_\t\t\t) - 1):\r continue\r\r if grid[pos_y][pos_x] != 0:\r continue\r\r successors.append(\r Node(\r UpperCAmelCase_\t\t\t, UpperCAmelCase_\t\t\t, self.target.pos_y\t\t\t, self.target.pos_x\t\t\t, parent.g_cost + 1\t\t\t, UpperCAmelCase_\t\t\t, )\t\t\t)\r return successors\r\r\r\r\r\r\r def \t\tlowercase__ (\t\tself\t:\t\t\tDict\t\t\t, UpperCAmelCase_\t:\t\t\tNode | None\t\t\t):\r lowerCAmelCase :\tint\t\t\t\t=\t\t\t\t\tnode\r lowerCAmelCase :\tstr\t\t\t\t=\t\t\t\t\t[]\r while current_node is not None:\r path.append((current_node.pos_y, current_node.pos_x)\t\t\t)\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tcurrent_node.parent\r path.reverse()\r return path\r\r\r\r\r\r\rclass __A :\r\r\r\r\r\r\r def __init__(\t\tself\t:\t\t\tint\t\t\t, UpperCAmelCase_\t:\t\t\tTPosition\t\t\t, UpperCAmelCase_\t:\t\t\tTPosition\t\t\t):\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tAStar(UpperCAmelCase_\t\t\t, UpperCAmelCase_\t\t\t)\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tAStar(UpperCAmelCase_\t\t\t, UpperCAmelCase_\t\t\t)\r lowerCAmelCase :\tTuple\t\t\t\t=\t\t\t\t\tFalse\r\r\r\r\r\r\r def \t\tlowercase__ (\t\tself\t:\t\t\tUnion[str, Any]\t\t\t):\r while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:\r self.fwd_astar.open_nodes.sort()\r self.bwd_astar.open_nodes.sort()\r lowerCAmelCase :\tOptional[int]\t\t\t\t=\t\t\t\t\tself.fwd_astar.open_nodes.pop(0\t\t\t)\r lowerCAmelCase :\tAny\t\t\t\t=\t\t\t\t\tself.bwd_astar.open_nodes.pop(0\t\t\t)\r\r if current_bwd_node.pos == current_fwd_node.pos:\r return self.retrace_bidirectional_path(\r UpperCAmelCase_\t\t\t, UpperCAmelCase_\t\t\t)\r\r self.fwd_astar.closed_nodes.append(UpperCAmelCase_\t\t\t)\r self.bwd_astar.closed_nodes.append(UpperCAmelCase_\t\t\t)\r\r lowerCAmelCase :\tstr\t\t\t\t=\t\t\t\t\tcurrent_bwd_node\r lowerCAmelCase :\tList[Any]\t\t\t\t=\t\t\t\t\tcurrent_fwd_node\r\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\t{\r self.fwd_astar: self.fwd_astar.get_successors(UpperCAmelCase_\t\t\t),\r self.bwd_astar: self.bwd_astar.get_successors(UpperCAmelCase_\t\t\t),\r }\r\r for astar in [self.fwd_astar, self.bwd_astar]:\r for child_node in successors[astar]:\r if child_node in astar.closed_nodes:\r continue\r\r if child_node not in astar.open_nodes:\r astar.open_nodes.append(UpperCAmelCase_\t\t\t)\r else:\r # retrieve the best current path\r lowerCAmelCase :\tint\t\t\t\t=\t\t\t\t\tastar.open_nodes.pop(\r astar.open_nodes.index(UpperCAmelCase_\t\t\t)\t\t\t)\r\r if child_node.g_cost < better_node.g_cost:\r astar.open_nodes.append(UpperCAmelCase_\t\t\t)\r else:\r astar.open_nodes.append(UpperCAmelCase_\t\t\t)\r\r return [self.fwd_astar.start.pos]\r\r\r\r\r\r\r def \t\tlowercase__ (\t\tself\t:\t\t\tUnion[str, Any]\t\t\t, UpperCAmelCase_\t:\t\t\tNode\t\t\t, UpperCAmelCase_\t:\t\t\tNode\t\t\t):\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tself.fwd_astar.retrace_path(UpperCAmelCase_\t\t\t)\r lowerCAmelCase :\tOptional[Any]\t\t\t\t=\t\t\t\t\tself.bwd_astar.retrace_path(UpperCAmelCase_\t\t\t)\r bwd_path.pop()\r bwd_path.reverse()\r lowerCAmelCase :\tAny\t\t\t\t=\t\t\t\t\tfwd_path + bwd_path\r return path\r\r\rif __name__ == \"__main__\":\r # all coordinates are given in format [y,x]\r __A : Optional[int] =\t(0, 0)\r __A : int =\t(len(grid) - 1, len(grid[0]) - 1)\r for elem in grid:\r print(elem)\r\r __A : Optional[Any] =\ttime.time()\r __A : List[Any] =\tAStar(init, goal)\r __A : List[str] =\ta_star.search()\r __A : Union[str, Any] =\ttime.time() - start_time\r print(F'AStar execution time = {end_time:f} seconds')\r\r __A : Union[str, Any] =\ttime.time()\r __A : Dict =\tBidirectionalAStar(init, goal)\r __A : List[Any] =\ttime.time() - bd_start_time\r print(F'BidirectionalAStar execution time = {bd_end_time:f} seconds')\r\r\r\r\r\r"},"code_codestyle":{"kind":"number","value":138,"string":"138"},"style_context":{"kind":"string","value":"\r\rfrom ...configuration_utils import PretrainedConfig\rfrom ...utils import logging\r\r\r__A : Dict =\tlogging.get_logger(__name__)\r\r__A : List[Any] =\t{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}\r\r\r\r\r\r\rclass __A (\t\t\t\t\t\t\tlowerCAmelCase ):\r lowerCAmelCase_\t\t\t\t\t\t\t:\t\t\t\t\t\tstr\t\t\t\t\t\t =\t\t\t\t\t\t\t\"ctrl\"\r lowerCAmelCase_\t\t\t\t\t\t\t:\t\t\t\t\t\tOptional[Any]\t\t\t\t\t\t =\t\t\t\t\t\t\t[\"past_key_values\"]\r lowerCAmelCase_\t\t\t\t\t\t\t:\t\t\t\t\t\tDict\t\t\t\t\t\t =\t\t\t\t\t\t\t{\r \"max_position_embeddings\": \"n_positions\",\r \"hidden_size\": \"n_embd\",\r \"num_attention_heads\": \"n_head\",\r \"num_hidden_layers\": \"n_layer\",\r }\r\r\r\r\r\r\r def __init__(\t\tself\t:\t\t\tAny\t\t\t, UpperCAmelCase_\t:\t\t\tint=246534\t\t\t, UpperCAmelCase_\t:\t\t\tOptional[Any]=256\t\t\t, UpperCAmelCase_\t:\t\t\tAny=1280\t\t\t, UpperCAmelCase_\t:\t\t\tint=8192\t\t\t, UpperCAmelCase_\t:\t\t\tint=48\t\t\t, UpperCAmelCase_\t:\t\t\tOptional[Any]=16\t\t\t, UpperCAmelCase_\t:\t\t\tDict=0.1\t\t\t, UpperCAmelCase_\t:\t\t\tAny=0.1\t\t\t, UpperCAmelCase_\t:\t\t\tList[str]=1E-6\t\t\t, UpperCAmelCase_\t:\t\t\tstr=0.02\t\t\t, UpperCAmelCase_\t:\t\t\tOptional[Any]=True\t\t\t, **UpperCAmelCase_\t:\t\t\tint\t\t\t, ):\r lowerCAmelCase :\tint\t\t\t\t=\t\t\t\t\tvocab_size\r lowerCAmelCase :\tint\t\t\t\t=\t\t\t\t\tn_positions\r lowerCAmelCase :\tOptional[Any]\t\t\t\t=\t\t\t\t\tn_embd\r lowerCAmelCase :\tOptional[Any]\t\t\t\t=\t\t\t\t\tn_layer\r lowerCAmelCase :\tList[str]\t\t\t\t=\t\t\t\t\tn_head\r lowerCAmelCase :\tUnion[str, Any]\t\t\t\t=\t\t\t\t\tdff\r lowerCAmelCase :\tDict\t\t\t\t=\t\t\t\t\tresid_pdrop\r lowerCAmelCase :\tList[Any]\t\t\t\t=\t\t\t\t\tembd_pdrop\r lowerCAmelCase :\tList[Any]\t\t\t\t=\t\t\t\t\tlayer_norm_epsilon\r lowerCAmelCase :\tDict\t\t\t\t=\t\t\t\t\tinitializer_range\r\r lowerCAmelCase :\tUnion[str, Any]\t\t\t\t=\t\t\t\t\tuse_cache\r\r super().__init__(**UpperCAmelCase_\t\t\t)\r\r\r\r\r\r"},"style_context_codestyle":{"kind":"number","value":138,"string":"138"},"label":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":299,"cells":{"code":{"kind":"string","value":"\n\n\"\"\"simple docstring\"\"\"\n\n\n\n\n\nfrom __future__ import annotations\n\nimport requests\n\ndef __SCREAMING_SNAKE_CASE (\t\t\t\tA_\t\t\t):\n\tlowerCAmelCase__ :\t\t\t\t\t\tDict = f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'\n\treturn requests.get(A_\t\t\t).json()\n\ndef __SCREAMING_SNAKE_CASE (\t\t\t\tA_ = 10\t\t\t):\n\tlowerCAmelCase__ :\t\t\t\t\t\tint = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''\n\tlowerCAmelCase__ :\t\t\t\t\t\tOptional[int] = requests.get(A_\t\t\t).json()[:max_stories]\n\treturn [get_hackernews_story(A_\t\t\t) for story_id in story_ids]\n\ndef __SCREAMING_SNAKE_CASE (\t\t\t\tA_ = 10\t\t\t):\n\tlowerCAmelCase__ :\t\t\t\t\t\tTuple = hackernews_top_stories(A_\t\t\t)\n\treturn \"\\n\".join('''* [{title}]({url})'''.format(**A_\t\t\t) for story in stories\t\t\t)\n\n\nif __name__ == \"__main__\":\n\t\t\t\t\t\t\tprint(hackernews_top_stories_as_markdown())\n\n\n\n\n"},"code_codestyle":{"kind":"number","value":74,"string":"74"},"style_context":{"kind":"string","value":"\n\n\"\"\"simple docstring\"\"\"\n\n\n\n\n\nfrom ...utils import (\n OptionalDependencyNotAvailable,\n is_torch_available,\n is_transformers_available,\n is_transformers_version,\n)\n\n\ntry:\n\t\t\t\t\t\t\tif not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\traise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n\t\t\t\t\t\t\tfrom ...utils.dummy_torch_and_transformers_objects import (\n\t\t\t\t\t\t\t VersatileDiffusionDualGuidedPipeline,\n\t\t\t\t\t\t\t VersatileDiffusionImageVariationPipeline,\n\t\t\t\t\t\t\t VersatileDiffusionPipeline,\n\t\t\t\t\t\t\t VersatileDiffusionTextToImagePipeline,\n\t\t\t\t\t\t\t)\nelse:\n\t\t\t\t\t\t\tfrom .modeling_text_unet import UNetFlatConditionModel\n\t\t\t\t\t\t\tfrom .pipeline_versatile_diffusion import VersatileDiffusionPipeline\n\t\t\t\t\t\t\tfrom .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline\n\t\t\t\t\t\t\tfrom .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline\n\t\t\t\t\t\t\tfrom .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline\n\n\n\n\n"},"style_context_codestyle":{"kind":"number","value":74,"string":"74"},"label":{"kind":"number","value":1,"string":"1"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":2,"numItemsPerPage":100,"numTotalItems":153992,"offset":200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc1NTQ1MDk2Nywic3ViIjoiL2RhdGFzZXRzL2luZmluaXR5b2ZzcGFjZS9weXRob25fY29kZXN0eWxlcy1taXhlZDEtNTAwIiwiZXhwIjoxNzU1NDU0NTY3LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.p0TF_3TI0unMLybyO_eKSIxfJhMHl3b9PYAH3wrob31PMnQzJjXiuh09HvaSdRhWUqbUFLr3m4_ryYrLyqdSCA","displayUrls":true},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">