





"""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__     :Optional[Any]											=				tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir       ,					'models/bert/' ) )
         lowerCAmelCase__     :List[Any]											=				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__     :int											=				'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__     :List[Any]											=				comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
         lowerCAmelCase__     :Any											=				black.Mode(target_versions={black.TargetVersion.PYaa}       ,					line_length=1_1_9 )
         lowerCAmelCase__     :int											=				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__     :Optional[int]											=				'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__     :Tuple											=				check_copies.LOCALIZED_READMES['README_zh-hans.md']

         lowerCAmelCase__     :Optional[int]											=				(
             '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__     :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'
         )
         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。\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__     :Union[str, Any]											=				check_copies.convert_to_localized_md(
             __UpperCAmelCase       ,					__UpperCAmelCase       ,					localized_readme['format_model_list'] )

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

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				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__     :Dict											=				(
             '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__     :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。\n'
         )

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

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







from math import sqrt
def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5				,	int(sqrt(SCREAMING_SNAKE_CASE_						) + 1						)				,	6						):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 1_00_01						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   0
   _UpperCAmelCase           =   1
   while count != nth and number < 3:
      number += 1
      if is_prime(SCREAMING_SNAKE_CASE_						):
         count += 1
   while count != nth:
      number += 2
      if is_prime(SCREAMING_SNAKE_CASE_						):
         count += 1
   return number


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