



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

import black


a	:   Optional[int]       			=       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	:   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 							_a							(					unittest.TestCase ):

			def 			__snake_case    (self )    ->					Union[str, Any]:
						UpperCAmelCase_:						Tuple					=			tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir,					"""models/bert/""" ) )
						UpperCAmelCase_:						Dict					=			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 			__snake_case    (self )    ->					Optional[Any]:
						UpperCAmelCase_:						Union[str, Any]					=			"""src/transformers"""
						shutil.rmtree(self.transformer_dir )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=None )    ->					Dict:
						UpperCAmelCase_:						str					=			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(_SCREAMING_SNAKE_CASE,					mode=_SCREAMING_SNAKE_CASE )
						UpperCAmelCase_:						Tuple					=			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 			__snake_case    (self )    ->					List[str]:
						UpperCAmelCase_:						Optional[int]					=			check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead""" )
						self.assertEqual(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE )

			def 			__snake_case    (self )    ->					int:
						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
						UpperCAmelCase_:						Dict					=			"""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 			__snake_case    (self )    ->					str:
						UpperCAmelCase_:						Tuple					=			check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

						UpperCAmelCase_:						Dict					=			(
						    """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_:						Dict					=			(
						    """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_:						Tuple					=			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 )

						UpperCAmelCase_:						Any					=			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 )

						UpperCAmelCase_:						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."""
						)
						UpperCAmelCase_:						Tuple					=			(
						    """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_:						List[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_:						List[Any]					=			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 math import isqrt, loga

def       _snake_case						(						UpperCamelCase						:		int						):
 UpperCAmelCase	:					Any	=	[True] * max_number
 for i in range(2			,		isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2			,		UpperCamelCase			,		UpperCamelCase						):
    UpperCAmelCase	:					str	=	False

 return [i for i in range(2			,		UpperCamelCase						) if is_prime[i]]

def       _snake_case						(						UpperCamelCase						:		int = 800800			,		UpperCamelCase						:		int = 800800						):
 UpperCAmelCase	:					Union[str, Any]	=	degree * loga(UpperCamelCase						)
 UpperCAmelCase	:					int	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Union[str, Any]	=	calculate_prime_numbers(UpperCamelCase						)

 UpperCAmelCase	:					Dict	=	0
 UpperCAmelCase	:					Optional[int]	=	0
 UpperCAmelCase	:					Dict	=	len(UpperCamelCase						) - 1
 while left < right:
  while (
      prime_numbers[right] * loga(prime_numbers[left]						)
      + prime_numbers[left] * loga(prime_numbers[right]						)
      > upper_bound
  ):
   right -= 1
  hybrid_integers_count += right - left
  left += 1

 return hybrid_integers_count


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