'''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       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir       ,				'models/bert/'				)				)
        lowercase_					:       Tuple     				=					self.transformer_dir
        shutil.copy(
            os.path.join(_a       ,				'src/transformers/models/bert/modeling_bert.py'				)       ,				os.path.join(self.transformer_dir       ,				'models/bert/modeling_bert.py'				)       ,				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Dict     				=					'src/transformers'
        shutil.rmtree(self.transformer_dir				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a=None				)    ->     Tuple:
        lowercase_					:       int     				=					comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
             lowercase_					:       Tuple     				=					comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        lowercase_					:       str     				=					black.Mode(target_versions={black.TargetVersion.PYaa}       ,				line_length=119				)
        lowercase_					:       Dict     				=					black.format_str(_a       ,				mode=_a				)
        lowercase_					:       List[str]     				=					os.path.join(self.transformer_dir       ,				'new_code.py'				)
        with open(_a       ,				'w'       ,				newline='\n'				) as f:
             f.write(_a				)
        if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(_a				)				) == 0				)
        else:
             check_copies.is_copy_consistent(f.name       ,				overwrite=_a				)
             with open(_a       ,				'r'				) as f:
                  self.assertTrue(f.read()       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Union[str, Any]     				=					check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'				)
        self.assertEqual(_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     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'       ,				_a       ,				)

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

        # Copy consistency with a really long name
        lowercase_					:       Union[str, Any]     				=					'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'       ,				_a       ,				_a				)       ,				)

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





   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Optional[Any]     				=					check_copies.LOCALIZED_READMES['README_zh-hans.md']

        lowercase_					:       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.'
        )
        lowercase_					:       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'
        )
        lowercase_					:       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。\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'
        )

        lowercase_					:       List[str]     				=					check_copies.convert_to_localized_md(
            _a       ,				_a       ,				localized_readme['format_model_list']				)

        self.assertFalse(_a				)
        self.assertEqual(_a       ,				_a				)

        lowercase_					:       Union[str, Any]     				=					check_copies.convert_to_localized_md(
            _a       ,				_a       ,				localized_readme['format_model_list']				)

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

        lowercase_					:       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.'
        )
        lowercase_					:       int     				=					(
            '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'
        )
        lowercase_					:       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'
        )

        lowercase_					:       Tuple     				=					check_copies.convert_to_localized_md(
            _a       ,				_a       ,				localized_readme['format_model_list']				)

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

'''simple docstring'''




import math

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     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(SCREAMING_SNAKE_CASE_							) + 1							)  ,       6							):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_ = 10_001							):

     try:
          lowercase_					:       List[Any]     				=					int(SCREAMING_SNAKE_CASE_							)
     except (TypeError, ValueError):
          raise TypeError('Parameter nth must be int or castable to int.'							) from None
     if nth <= 0:
          raise ValueError('Parameter nth must be greater than or equal to one.'							)
     lowercase_					:       list[int]     				=					[]
     lowercase_					:       Optional[int]     				=					2
     while len(SCREAMING_SNAKE_CASE_							) < nth:
          if is_prime(SCREAMING_SNAKE_CASE_							):
               primes.append(SCREAMING_SNAKE_CASE_							)
               num += 1
          else:
               num += 1
     return primes[len(SCREAMING_SNAKE_CASE_							) - 1]


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




