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

import black


_SCREAMING_SNAKE_CASE										=						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.
_SCREAMING_SNAKE_CASE										=						'    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       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir		,				"""models/bert/"""							)							)
        _A        =  self.transformer_dir
        shutil.copy(
            os.path.join(lowerCAmelCase_		,				"""src/transformers/models/bert/modeling_bert.py"""							)		,				os.path.join(self.transformer_dir		,				"""models/bert/modeling_bert.py"""							)		,				)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  """src/transformers"""
        shutil.rmtree(self.transformer_dir							)



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



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""							)
        self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        # 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"""		,				lowerCAmelCase_		,				)

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

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

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



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

        _A        =  (
            """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."""
        )
        _A        =  (
            """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"""
        )
        _A        =  (
            """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"""
        )

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

        self.assertFalse(lowerCAmelCase_							)
        self.assertEqual(lowerCAmelCase_		,				lowerCAmelCase_							)

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

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

        _A        =  (
            """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."""
        )
        _A        =  (
            """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"""
        )
        _A        =  (
            """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"""
        )

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

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





from math import isqrt, loga






def  snake_case  (       snake_case__			:int)						->   list[int]:
       _A        =  [True] * max_number
       for i in range(2   ,	isqrt(max_number - 1) + 1):
              if is_prime[i]:
                     for j in range(i**2   ,	snake_case__   ,	snake_case__):
                            _A        =  False

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






def  snake_case  (       snake_case__			:int = 800_800   ,	snake_case__			:int = 800_800)						->   int:
       _A        =  degree * loga(snake_case__)
       _A        =  int(snake_case__)
       _A        =  calculate_prime_numbers(snake_case__)

       _A        =  0
       _A        =  0
       _A        =  len(snake_case__) - 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() = }''')




