





"""simple docstring"""





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

import black


__A											=		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.
__A											=		"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""






class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir    , 'models/bert/'      )      )
         lowerCAmelCase__			:Tuple											=							self.transformer_dir
         shutil.copy(
             os.path.join(__UpperCAmelCase    , 'src/transformers/models/bert/modeling_bert.py'      )    , os.path.join(self.transformer_dir    , 'models/bert/modeling_bert.py'      )    , )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							'src/transformers'
         shutil.rmtree(self.transformer_dir      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase=None      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							comment + F"\nclass {class_name}(nn.Module):\n" + class_code
         if overwrite_result is not None:
           lowerCAmelCase__			:int											=							comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
         lowerCAmelCase__			:int											=							black.Mode(target_versions={black.TargetVersion.PYaa}    , line_length=1_1_9      )
         lowerCAmelCase__			:Union[str, Any]											=							black.format_str(__UpperCAmelCase    , mode=__UpperCAmelCase      )
         lowerCAmelCase__			:Any											=							os.path.join(self.transformer_dir    , 'new_code.py'      )
         with open(__UpperCAmelCase    , 'w'    , newline='\n'      ) as f:
           f.write(__UpperCAmelCase      )
         if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(__UpperCAmelCase      )      ) == 0      )
         else:
           check_copies.is_copy_consistent(f.name    , overwrite=__UpperCAmelCase      )
           with open(__UpperCAmelCase    , 'r'      ) as f:
             self.assertTrue(f.read()    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'      )
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''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'    , __UpperCAmelCase    , )

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

         # Copy consistency with a really long name
         lowerCAmelCase__			:Dict											=							'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'    , __UpperCAmelCase    , __UpperCAmelCase      )    , )

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							check_copies.LOCALIZED_READMES['README_zh-hans.md']

         lowerCAmelCase__			:Dict											=							(
             '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.'
         )
         lowerCAmelCase__			:Any											=							(
             '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'
         )
         lowerCAmelCase__			:Optional[Any]											=							(
             '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'
         )

         lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							check_copies.convert_to_localized_md(
             __UpperCAmelCase    , __UpperCAmelCase    , localized_readme['format_model_list']      )

         self.assertFalse(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							check_copies.convert_to_localized_md(
             __UpperCAmelCase    , __UpperCAmelCase    , localized_readme['format_model_list']      )

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

         lowerCAmelCase__			:List[str]											=							(
             '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.'
         )
         lowerCAmelCase__			:str											=							(
             '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'
         )
         lowerCAmelCase__			:Dict											=							(
             '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'
         )

         lowerCAmelCase__							,				lowerCAmelCase__			:Optional[Any]											=							check_copies.convert_to_localized_md(
             __UpperCAmelCase    , __UpperCAmelCase    , localized_readme['format_model_list']      )

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







"""simple docstring"""





def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->bool:





  """simple docstring"""


  return numa ^ numa < 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()
