




"""simple docstring"""






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

import black


lowercase__  		=		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.
lowercase__  		=		"""    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 lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



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

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

							# Copy consistency with a really long name
							_lowerCamelCase  :    Dict					=					'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'						,   lowercase						,   lowercase						)						,   )

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






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

							_lowerCamelCase  :    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.\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[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  :    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  :    Union[str, Any]					=					check_copies.convert_to_localized_md(
							    lowercase						,   lowercase						,   localized_readme['format_model_list']						)

							self.assertFalse(lowercase						)
							self.assertEqual(lowercase						,   lowercase						)

							_lowerCamelCase  :    List[str]					=					check_copies.convert_to_localized_md(
							    lowercase						,   lowercase						,   localized_readme['format_model_list']						)

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

							_lowerCamelCase  :    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.'
							)
							_lowerCamelCase  :    List[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  :    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'
							)

							_lowerCamelCase  :    int					=					check_copies.convert_to_localized_md(
							    lowercase						,   lowercase						,   localized_readme['format_model_list']						)

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





"""simple docstring"""






def   _snake_case    (       lowercase__ = 1000	):
				_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					1, 1
				_lowerCamelCase  :    int					=					[]
				for i in range(1							,					n + 1	):
								_lowerCamelCase  :    Tuple					=					prev_numerator + 2 * prev_denominator
								_lowerCamelCase  :    str					=					prev_numerator + prev_denominator
								if len(str(lowercase__	)	) > len(str(lowercase__	)	):
												result.append(lowercase__	)
								_lowerCamelCase  :    Dict					=					numerator
								_lowerCamelCase  :    Optional[int]					=					denominator

				return len(lowercase__	)


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