






'''simple docstring'''





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

import black


SCREAMING_SNAKE_CASE 							=						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.
SCREAMING_SNAKE_CASE 							=						'    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			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir							,  '''models/bert/'''))
     _UpperCAmelCase				:      str			  =				self.transformer_dir
     shutil.copy(
         os.path.join(lowercase__							,  '''src/transformers/models/bert/modeling_bert.py''')							,  os.path.join(self.transformer_dir							,  '''models/bert/modeling_bert.py''')							,  )


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				"""src/transformers"""
     shutil.rmtree(self.transformer_dir)


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A=None) ->		Union[str, Any]:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''')
     self.assertEqual(lowercase__							,  lowercase__)


 def  snake_case__				(	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'''							,  lowercase__							,  )

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

     # Copy consistency with a really long name
     _UpperCAmelCase				:      List[Any]			  =				"""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'''							,  lowercase__							,  lowercase__)							,  )

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


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

     _UpperCAmelCase				:      List[Any]			  =				(
         """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."""
     )
     _UpperCAmelCase				:      Optional[int]			  =				(
         """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"""
     )
     _UpperCAmelCase				:      Tuple			  =				(
         """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"""
     )

     _UpperCAmelCase				:      Any			  =				check_copies.convert_to_localized_md(
         lowercase__							,  lowercase__							,  localized_readme['''format_model_list'''])

     self.assertFalse(lowercase__)
     self.assertEqual(lowercase__							,  lowercase__)

     _UpperCAmelCase				:      Tuple			  =				check_copies.convert_to_localized_md(
         lowercase__							,  lowercase__							,  localized_readme['''format_model_list'''])

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

     _UpperCAmelCase				:      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."""
     )
     _UpperCAmelCase				:      Union[str, Any]			  =				(
         """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"""
     )
     _UpperCAmelCase				:      str			  =				(
         """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"""
     )

     _UpperCAmelCase				:      Dict			  =				check_copies.convert_to_localized_md(
         lowercase__							,  lowercase__							,  localized_readme['''format_model_list'''])

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












def      _lowerCamelCase	(						__A    :							int )      ->					bool:
    if not isinstance(__A		,		__A ):
        _UpperCAmelCase				:      Tuple			  =				f'''Input value of [number={number}] must be an integer'''
        raise TypeError(__A )
    if number < 0:
        return False
    _UpperCAmelCase				:      List[str]			  =				number * number
    while number > 0:
        if number % 10 != number_square % 10:
            return False
        number //= 10
        number_square //= 10
    return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()




