
"""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 	_lowercase       (    unittest.TestCase		):




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      str          =   tempfile.mkdtemp()
			os.makedirs(os.path.join(self.transformer_dir		,			'models/bert/' ) )
			lowerCamelCase					:      Any          =   self.transformer_dir
			shutil.copy(
			    os.path.join(UpperCAmelCase_		,			'src/transformers/models/bert/modeling_bert.py' )		,			os.path.join(self.transformer_dir		,			'models/bert/modeling_bert.py' )		,			)




	def       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase					:      Any          =   'src/transformers'
			shutil.rmtree(self.transformer_dir )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Optional[Any]:
			lowerCamelCase					:      Dict          =   comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
			if overwrite_result is not None:
					lowerCamelCase					:      List[Any]          =   comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
			lowerCamelCase					:      Optional[int]          =   black.Mode(target_versions={black.TargetVersion.PYaa}		,			line_length=119 )
			lowerCamelCase					:      Optional[Any]          =   black.format_str(UpperCAmelCase_		,			mode=UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   os.path.join(self.transformer_dir		,			'new_code.py' )
			with open(UpperCAmelCase_		,			'w'		,			newline='\n' ) as f:
					f.write(UpperCAmelCase_ )
			if overwrite_result is None:
					self.assertTrue(len(check_copies.is_copy_consistent(UpperCAmelCase_ ) ) == 0 )
			else:
					check_copies.is_copy_consistent(f.name		,			overwrite=UpperCAmelCase_ )
					with open(UpperCAmelCase_		,			'r' ) as f:
							self.assertTrue(f.read()		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			lowerCamelCase					:      int          =   check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
			self.assertEqual(UpperCAmelCase_		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Any:
			# 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'		,			UpperCAmelCase_		,			)

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

			# Copy consistency with a really long name
			lowerCamelCase					:      List[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'		,			UpperCAmelCase_		,			UpperCAmelCase_ )		,			)

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




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      str          =   check_copies.LOCALIZED_READMES['README_zh-hans.md']

			lowerCamelCase					:      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.\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.'
			)
			lowerCamelCase					:      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'
			)
			lowerCamelCase					:      Union[str, 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'
			)

			lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   check_copies.convert_to_localized_md(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			localized_readme['format_model_list'] )

			self.assertFalse(UpperCAmelCase_ )
			self.assertEqual(UpperCAmelCase_		,			UpperCAmelCase_ )

			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   check_copies.convert_to_localized_md(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			localized_readme['format_model_list'] )

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

			lowerCamelCase					:      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.'
			)
			lowerCamelCase					:      str          =   (
			    '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'
			)
			lowerCamelCase					:      List[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'
			)

			lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   check_copies.convert_to_localized_md(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			localized_readme['format_model_list'] )

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








"""simple docstring"""

def 		UpperCAmelCase (		a_ = 10      ):





		'''simple docstring'''


		if not isinstance(a_,						a_      ) or n < 0:
				raise ValueError('Invalid input'      )
		lowerCamelCase					:      Union[str, Any]          =   10**n
		lowerCamelCase					:      int          =   2_8433 * (pow(2,						783_0457,						a_      )) + 1
		return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"""{solution(1_0) = }""")






