





'''simple docstring'''




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

import black


__UpperCAmelCase			    =    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.
__UpperCAmelCase			    =    """    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 			UpperCamelCase__ ( unittest.TestCase       ):




					"""simple docstring"""


					def lowerCamelCase_       (		self							:       List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							List[str]						=       tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir	,		"""models/bert/"""		)		)
							SCREAMING_SNAKE_CASE      :							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 lowerCamelCase_       (		self							:       List[str]		):

							'''simple docstring'''



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


					def lowerCamelCase_       (		self							:       Tuple	,		lowerCamelCase_							:       int	,		lowerCamelCase_							:       List[Any]	,		lowerCamelCase_							:       Optional[int]	,		lowerCamelCase_							:       Optional[int]=None		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							Optional[int]						=       comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
							if overwrite_result is not None:
									SCREAMING_SNAKE_CASE      :							List[Any]						=       comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
							SCREAMING_SNAKE_CASE      :							List[str]						=       black.Mode(target_versions={black.TargetVersion.PYaa}	,		line_length=1_19		)
							SCREAMING_SNAKE_CASE      :							Tuple						=       black.format_str(lowerCamelCase_	,		mode=lowerCamelCase_		)
							SCREAMING_SNAKE_CASE      :							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 lowerCamelCase_       (		self							:       Union[str, Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							int						=       check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""		)
							self.assertEqual(lowerCamelCase_	,		lowerCamelCase_		)


					def lowerCamelCase_       (		self							:       Union[str, 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"""	,		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
							SCREAMING_SNAKE_CASE      :							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"""	,		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 lowerCamelCase_       (		self							:       Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							int						=       check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

							SCREAMING_SNAKE_CASE      :							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.'''
							)
							SCREAMING_SNAKE_CASE      :							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'''
							)
							SCREAMING_SNAKE_CASE      :							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。\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'''
							)

							SCREAMING_SNAKE_CASE      :							str						=       check_copies.convert_to_localized_md(
							    lowerCamelCase_	,		lowerCamelCase_	,		localized_readme["""format_model_list"""]		)

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

							SCREAMING_SNAKE_CASE      :							str						=       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_		)

							SCREAMING_SNAKE_CASE      :							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.'''
							)
							SCREAMING_SNAKE_CASE      :							Optional[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'''
							)
							SCREAMING_SNAKE_CASE      :							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'''
							)

							SCREAMING_SNAKE_CASE      :							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_		)










from math import factorial

lowerCAmelCase_    =				{str(digit): factorial(digit) for digit in range(1_0)}






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase ,				_UpperCamelCase ):
								raise TypeError('''Parameter number must be int''' )

				if number < 0:
								raise ValueError('''Parameter number must be greater than or equal to 0''' )

				# Converts number in string to iterate on its digits and adds its factorial.
				return sum(DIGIT_FACTORIAL[digit] for digit in str(_UpperCamelCase ) )






def      lowerCamelCase_						(				_UpperCamelCase = 60 ,				_UpperCamelCase = 1_000_000 )		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase ,				_UpperCamelCase ) or not isinstance(_UpperCamelCase ,				_UpperCamelCase ):
								raise TypeError('''Parameters chain_length and number_limit must be int''' )

				if chain_length <= 0 or number_limit <= 0:
								raise ValueError(
								    '''Parameters chain_length and number_limit must be greater than 0''' )

				# the counter for the chains with the exact desired length
				snake_case_						:    Optional[Any]				=  0
				# the cached sizes of the previous chains
				snake_case_						:    dict[int, int]				=  {}

				for start_chain_element in range(1 ,				_UpperCamelCase ):
								# The temporary set will contain the elements of the chain
								snake_case_						:    List[str]				=  set()
								snake_case_						:    List[Any]				=  0

								# Stop computing the chain when you find a cached size, a repeating item or the
								# length is greater then the desired one.
								snake_case_						:    Any				=  start_chain_element
								while (
								    chain_element not in chain_sets_lengths
								    and chain_element not in chain_set
								    and chain_set_length <= chain_length
								):
												chain_set.add(_UpperCamelCase )
												chain_set_length += 1
												snake_case_						:    List[Any]				=  digit_factorial_sum(_UpperCamelCase )

								if chain_element in chain_sets_lengths:
												chain_set_length += chain_sets_lengths[chain_element]

								snake_case_						:    List[str]				=  chain_set_length

								# If chain contains the exact amount of elements increase the counter
								if chain_set_length == chain_length:
												chains_counter += 1

				return chains_counter


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution()}''')
