



'''simple docstring'''

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

import black


_A				:    Optional[int]     						=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				:    Optional[Any]     						='''    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 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir     ,  'models/bert/'					)					)
						_lowercase					:       List[str]       =		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				:	int					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		'src/transformers'
						shutil.rmtree(self.transformer_dir					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Dict=None					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
						if overwrite_result is not None:
							_lowercase					:       Dict       =		comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
						_lowercase					:       Tuple       =		black.Mode(target_versions={black.TargetVersion.PYaa}     ,  line_length=119					)
						_lowercase					:       Any       =		black.format_str(UpperCamelCase_     ,  mode=UpperCamelCase_					)
						_lowercase					:       List[Any]       =		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				:	Tuple					)  ->       int:



						'''simple docstring'''





						_lowercase					:       str       =		check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'					)
						self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





						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
						_lowercase					:       Optional[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'     ,  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				:	Optional[Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		check_copies.LOCALIZED_READMES['README_zh-hans.md']

						_lowercase					:       Union[str, Any]       =		(
						    '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					:       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。\n'
						)
						_lowercase					:       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'
						)

						_lowercase					:       List[Any]       =		check_copies.convert_to_localized_md(
						    UpperCamelCase_     ,  UpperCamelCase_     ,  localized_readme['format_model_list']					)

						self.assertFalse(UpperCamelCase_					)
						self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)

						_lowercase					:       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_					)

						_lowercase					:       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.'
						)
						_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					:       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					:       Tuple       =		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				(							_lowercase					) ->			bool:
	return str(_lowercase					) == str(_lowercase					)[::-1]







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return int(_lowercase					) + int(str(_lowercase					)[::-1]					)







def 						__UpperCamelCase				(							_lowercase = 1_0000					) ->			int:
	_lowercase					:       List[str]       =		[]
	for num in range(1,				_lowercase					):
		_lowercase					:       Tuple       =		0
		_lowercase					:       Tuple       =		num
		while iterations < 50:
			_lowercase					:       Union[str, Any]       =		sum_reverse(_lowercase					)
			iterations += 1
			if is_palindrome(_lowercase					):
				break
		else:
			lychrel_nums.append(_lowercase					)
	return len(_lowercase					)


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