




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowercase__				:      Dict	   = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowercase__				:      str	   = "    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"




class       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir  ,		"models/bert/"			)			)
          a     =				self.transformer_dir
          shutil.copy(
              os.path.join(A  ,		"src/transformers/models/bert/modeling_bert.py"			)  ,		os.path.join(self.transformer_dir  ,		"models/bert/modeling_bert.py"			)  ,		)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				"src/transformers"
          shutil.rmtree(self.transformer_dir			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A=None			)  ->   Tuple:



          '''simple docstring'''



          a     =				comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
          if overwrite_result is not None:
               a     =				comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
          a     =				black.Mode(target_versions={black.TargetVersion.PYaa}  ,		line_length=119			)
          a     =				black.format_str(A  ,		mode=A			)
          a     =				os.path.join(self.transformer_dir  ,		"new_code.py"			)
          with open(A  ,		"w"  ,		newline="\n"			) as f:
               f.write(A			)
          if overwrite_result is None:
               self.assertTrue(len(check_copies.is_copy_consistent(A			)			) == 0			)
          else:
               check_copies.is_copy_consistent(f.name  ,		overwrite=A			)
               with open(A  ,		"r"			) as f:
                    self.assertTrue(f.read()  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"			)
          self.assertEqual(A  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"  ,		"BertLMPredictionHead"  ,		REFERENCE_CODE + "\n"  ,		)

          # With no empty line at the end
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"  ,		"BertLMPredictionHead"  ,		A  ,		)

          # Copy consistency with rename
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"  ,		"TestModelLMPredictionHead"  ,		re.sub("Bert"  ,		"TestModel"  ,		A			)  ,		)

          # Copy consistency with a really long name
          a     =				"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
          self.check_copy_consistency(
              F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''  ,		F'''{long_class_name}LMPredictionHead'''  ,		re.sub("Bert"  ,		A  ,		A			)  ,		)

          # Copy consistency with overwrite
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"  ,		"TestModelLMPredictionHead"  ,		A  ,		overwrite_result=re.sub("Bert"  ,		"TestModel"  ,		A			)  ,		)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				check_copies.LOCALIZED_READMES["README_zh-hans.md"]

          a     =				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
              " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
              " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
              " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
              " Luong, Quoc V. Le, Christopher D. Manning."
          )
          a     =				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          a     =				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
              " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
              " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
              " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
              " Christopher D. Manning 发布。\n"
          )

          a  ,				a     =				check_copies.convert_to_localized_md(
              A  ,		A  ,		localized_readme["format_model_list"]			)

          self.assertFalse(A			)
          self.assertEqual(A  ,		A			)

          a  ,				a     =				check_copies.convert_to_localized_md(
              A  ,		A  ,		localized_readme["format_model_list"]			)

          # Check whether the number of models is equal to README.md after conversion.
          self.assertTrue(A			)

          a     =				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
          )
          a     =				(
              "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
              " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          a     =				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )

          a  ,				a     =				check_copies.convert_to_localized_md(
              A  ,		A  ,		localized_readme["format_model_list"]			)

          # Check if the model link is synchronized.
          self.assertEqual(A  ,		A			)







from __future__ import annotations

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					list[int]:
     a     =				2
     a     =				[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__UpperCamelCase)
     if n > 1:
          factors.append(__UpperCamelCase)
     return factors


if __name__ == "__main__":
   import doctest

   doctest.testmod()

