


import math
from collections.abc import Iterator
from itertools import takewhile



def  _a			(						__UpperCamelCase ):

     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(math.sqrt(_lowercase ) + 1 )  ,  6 ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True



def  _a			(						):
     a_    :     List[Any]										=    2
     while True:
          if is_prime(_lowercase ):
               yield num
          num += 1



def  _a			(						__UpperCamelCase = 2_0_0_0_0_0_0 ):
     return sum(takewhile(lambda __UpperCamelCase : x < n  ,  prime_generator() ) )


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







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

import black


__lowerCamelCase        =     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.
__lowerCamelCase        =     '''    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   a__	(						unittest.TestCase    ):

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Optional[Any]										=    tempfile.mkdtemp()
            os.makedirs(os.path.join(self.transformer_dir      ,							"""models/bert/"""    )    )
            a_    :     Optional[Any]										=    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      :     Dict    ):
            a_    :     Any										=    """src/transformers"""
            shutil.rmtree(self.transformer_dir    )

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Tuple=None    ):
            a_    :     Any										=    comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
            if overwrite_result is not None:
                 a_    :     Any										=    comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
            a_    :     Dict										=    black.Mode(target_versions={black.TargetVersion.PYaa}      ,							line_length=1_1_9    )
            a_    :     Optional[Any]										=    black.format_str(lowerCamelCase_      ,							mode=lowerCamelCase_    )
            a_    :     Optional[Any]										=    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_    :     Tuple										=    check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""    )
            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            # 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_    :     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"""      ,							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      :     int    ):
            a_    :     Optional[Any]										=    check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

            a_    :     Tuple										=    (
                """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_    :     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"""
            )
            a_    :     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"""
            )

            a_			,			a_    :     Optional[int]										=    check_copies.convert_to_localized_md(
                lowerCamelCase_      ,							lowerCamelCase_      ,							localized_readme["""format_model_list"""]    )

            self.assertFalse(lowerCamelCase_    )
            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

            a_			,			a_    :     Tuple										=    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_    :     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."""
            )
            a_    :     Union[str, 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"""
            )
            a_    :     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"""
            )

            a_			,			a_    :     Tuple										=    check_copies.convert_to_localized_md(
                lowerCamelCase_      ,							lowerCamelCase_      ,							localized_readme["""format_model_list"""]    )

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



