

def 	a (     A__      :  bytes	)       -> str:





      """simple docstring"""



      return "".join([hex(A__	)[2:].zfill(2	).upper() for byte in list(A__	)]	)
def 	a (     A__      :  str	)       -> bytes:





      """simple docstring"""



      if (len(A__	) % 2) != 0:
            raise ValueError(
                'Base16 encoded data is invalid:\nData does not have an even number of hex digits.'	)
      # Check the character set - the standard base16 alphabet
      # is uppercase according to RFC3548 section 6
      if not set(A__	) <= set('0123456789ABCDEF'	):
            raise ValueError(
                'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.'	)
      # For every two hexadecimal digits (= a byte), turn it into an integer.
      # Then, string the result together into bytes, and return it.
      return bytes(int(data[i] + data[i + 1] ,  16	) for i in range(0 ,  len(A__	) ,  2	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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

import black


lowercase_   							=  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_   							=  '    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 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir				,  'models/bert/'   )   )
        _lowercase      			=self.transformer_dir
        shutil.copy(
            os.path.join(lowerCAmelCase				,  'src/transformers/models/bert/modeling_bert.py'   )				,  os.path.join(self.transformer_dir				,  'models/bert/modeling_bert.py'   )				,  )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			='src/transformers'
        shutil.rmtree(self.transformer_dir   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=None   )    ->					str:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'   )
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''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'				,  lowerCAmelCase				,  )

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

        # Copy consistency with a really long name
        _lowercase      			='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'				,  lowerCAmelCase				,  lowerCAmelCase   )				,  )

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




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=check_copies.LOCALIZED_READMES['README_zh-hans.md']

        _lowercase      			=(
            '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.'
        )
        _lowercase      			=(
            '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'
        )
        _lowercase      			=(
            '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'
        )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

        self.assertFalse(lowerCAmelCase   )
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

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

        _lowercase      			=(
            '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.'
        )
        _lowercase      			=(
            '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'
        )
        _lowercase      			=(
            '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'
        )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

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


