





"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCamelCase     :						str						       =  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.
__UpperCamelCase     :						List[str]						       =  '''    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       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






  lowerCAmelCase    				= comment + F'\nclass {class_name}(nn.Module):\n' + class_code
  if overwrite_result is not None:
   lowerCAmelCase    				= comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
  lowerCAmelCase    				= black.Mode(target_versions={black.TargetVersion.PYaa}				,       line_length=1_19     )
  lowerCAmelCase    				= black.format_str(_snake_case				,       mode=_snake_case     )
  lowerCAmelCase    				= os.path.join(self.transformer_dir				,       'new_code.py'     )
  with open(_snake_case				,       'w'				,       newline='\n'     ) as f:
   f.write(_snake_case     )
  if overwrite_result is None:
   self.assertTrue(len(check_copies.is_copy_consistent(_snake_case     )     ) == 0     )
  else:
   check_copies.is_copy_consistent(f.name				,       overwrite=_snake_case     )
   with open(_snake_case				,       'r'     ) as f:
    self.assertTrue(f.read()				,       _snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     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'				,       _snake_case				,       )

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

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

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

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

  self.assertFalse(_snake_case     )
  self.assertEqual(_snake_case				,       _snake_case     )

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

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

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

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








'''simple docstring'''



import operator


def       snake_case   (	snake_case      : list					,		snake_case      : bool = False					,		snake_case      : list | None = None			) ->  list:


    """simple docstring"""


    lowerCAmelCase 						=							operator.lt if reverse else operator.gt
    lowerCAmelCase 						=							solution or []

    if not arr:
        return solution

    lowerCAmelCase 						=							[arr.pop(0			)]
    for i, item in enumerate(snake_case			):
        if _operator(snake_case					,		sublist[-1]			):
            sublist.append(snake_case			)
            arr.pop(snake_case			)

    #  merging sublist into solution list
    if not solution:
        solution.extend(snake_case			)
    else:
        while sublist:
            lowerCAmelCase 						=							sublist.pop(0			)
            for i, xx in enumerate(snake_case			):
                if not _operator(snake_case					,		snake_case			):
                    solution.insert(snake_case					,		snake_case			)
                    break
            else:
                solution.append(snake_case			)

    strand_sort(snake_case					,		snake_case					,		snake_case			)
    return solution


if __name__ == "__main__":
     assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
     assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]





