



"""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):\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    lowerCAmelCase_				(       self:							List[Any]						)   ->	Union[str, Any]:
											snake_case_ :Optional[int]						=			tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir						,     """models/bert/"""						)						)
											snake_case_ :Any						=			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    lowerCAmelCase_				(       self:							Optional[int]						)   ->	str:
											snake_case_ :Union[str, Any]						=			"""src/transformers"""
											shutil.rmtree(self.transformer_dir						)






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Tuple						,     snake_case:							int						,     snake_case:							List[Any]						,     snake_case:							Dict=None						)   ->	str:
											snake_case_ :Union[str, Any]						=			comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
											if overwrite_result is not None:
																	snake_case_ :Tuple						=			comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
											snake_case_ :List[str]						=			black.Mode(target_versions={black.TargetVersion.PYaa}						,     line_length=119						)
											snake_case_ :List[Any]						=			black.format_str(snake_case						,     mode=snake_case						)
											snake_case_ :List[Any]						=			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    lowerCAmelCase_				(       self:							int						)   ->	int:
											snake_case_ :int						=			check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""						)
											self.assertEqual(snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							int						)   ->	str:
											# Base copy consistency
											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
											snake_case_ :str						=			"""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    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											snake_case_ :int						=			check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

											snake_case_ :Union[str, 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."""
											)
											snake_case_ :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"""
											)
											snake_case_ :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。\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"""
											)

											snake_case_,       snake_case_ :Tuple						=			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						)

											snake_case_,       snake_case_ :Optional[Any]						=			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						)

											snake_case_ :Optional[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."""
											)
											snake_case_ :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"""
											)
											snake_case_ :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"""
											)

											snake_case_,       snake_case_ :Optional[Any]						=			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"""





from __future__ import annotations







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						[True] * limit
  lowerCamelCase_         =						False
  lowerCamelCase_         =						False
  lowerCamelCase_         =						True

  for i in range(3		,				int(limit**0.5 + 1 )		,				2 ):
    lowerCamelCase_         =						i * 2
    while index < limit:
      lowerCamelCase_         =						False
      lowerCamelCase_         =						index + i

  lowerCamelCase_         =						[2]

  for i in range(3		,				_lowerCamelCase		,				2 ):
    if is_prime[i]:
      primes.append(_lowerCamelCase )

  return primes







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 1000000 )      ->				int:
  lowerCamelCase_         =						prime_sieve(_lowerCamelCase )
  lowerCamelCase_         =						0
  lowerCamelCase_         =						0

  for i in range(len(_lowerCamelCase ) ):
    for j in range(i + length		,				len(_lowerCamelCase ) ):
      lowerCamelCase_         =						sum(primes[i:j] )
      if sol >= ceiling:
        break

      if sol in primes:
        lowerCamelCase_         =						j - i
        lowerCamelCase_         =						sol

  return largest


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




