

'''simple docstring'''



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

import black


UpperCAmelCase :       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.
UpperCAmelCase :       Optional[Any]			   =     '    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					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       str			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir					,							"""models/bert/"""			)			)
      __SCREAMING_SNAKE_CASE     =      self.transformer_dir
      shutil.copy(
          os.path.join(__SCREAMING_SNAKE_CASE					,							"""src/transformers/models/bert/modeling_bert.py"""			)					,							os.path.join(self.transformer_dir					,							"""models/bert/modeling_bert.py"""			)					,							)
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """src/transformers"""
      shutil.rmtree(self.transformer_dir			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=None			)  -> Dict:


      """simple docstring"""




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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Dict:


      """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"""					,							__SCREAMING_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"""					,							__SCREAMING_SNAKE_CASE			)					,							)

      # Copy consistency with a really long name
      __SCREAMING_SNAKE_CASE     =      """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"""					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)					,							)

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


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> List[Any]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      (
          """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."""
      )
      __SCREAMING_SNAKE_CASE     =      (
          """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"""
      )
      __SCREAMING_SNAKE_CASE     =      (
          """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"""
      )

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

      self.assertFalse(__SCREAMING_SNAKE_CASE			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

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

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

      __SCREAMING_SNAKE_CASE     =      (
          """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."""
      )
      __SCREAMING_SNAKE_CASE     =      (
          """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"""
      )
      __SCREAMING_SNAKE_CASE     =      (
          """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"""
      )

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

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









'''simple docstring'''



from itertools import count







def    a__       (  a__ = 50						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      [1] * min_block_length

     for n in count(a__						):
          fill_count_functions.append(1						)

          for block_length in range(a__      ,    n + 1						):
               for block_start in range(n - block_length						):
                    fill_count_functions[n] += fill_count_functions[
                        n - block_start - block_length - 1
                    ]

               fill_count_functions[n] += 1

          if fill_count_functions[n] > 1_00_00_00:
               break

     return n


if __name__ == "__main__":
  print(f"""{solution() = }""")






