



'''simple docstring'''






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

import black


UpperCamelCase__:     List[Any]										=    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__:     Dict										=    "    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        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  str	)    ->	Dict:
								UpperCAmelCase				: str											=	tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir	,    '''models/bert/'''	)	)
								UpperCAmelCase				: Tuple											=	self.transformer_dir
								shutil.copy(
								    os.path.join(__snake_case	,    '''src/transformers/models/bert/modeling_bert.py'''	)	,    os.path.join(self.transformer_dir	,    '''models/bert/modeling_bert.py'''	)	,    )







	def   A			(		self    :  Tuple	)    ->	List[Any]:
								UpperCAmelCase				: Any											=	'''src/transformers'''
								shutil.rmtree(self.transformer_dir	)







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







	def   A			(		self    :  List[Any]	)    ->	Optional[Any]:
								UpperCAmelCase				: List[str]											=	check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''	)
								self.assertEqual(__snake_case	,    __snake_case	)







	def   A			(		self    :  Union[str, Any]	)    ->	Union[str, 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'''	,    __snake_case	,    )

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

								# Copy consistency with a really long name
								UpperCAmelCase				: 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'''	,    __snake_case	,    __snake_case	)	,    )

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







	def   A			(		self    :  Optional[int]	)    ->	Dict:
								UpperCAmelCase				: Union[str, Any]											=	check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

								UpperCAmelCase				: 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.'''
								)
								UpperCAmelCase				: List[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'''
								)
								UpperCAmelCase				: 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'''
								)

								UpperCAmelCase							,	UpperCAmelCase				: List[str]											=	check_copies.convert_to_localized_md(
								    __snake_case	,    __snake_case	,    localized_readme['''format_model_list''']	)

								self.assertFalse(__snake_case	)
								self.assertEqual(__snake_case	,    __snake_case	)

								UpperCAmelCase							,	UpperCAmelCase				: Dict											=	check_copies.convert_to_localized_md(
								    __snake_case	,    __snake_case	,    localized_readme['''format_model_list''']	)

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

								UpperCAmelCase				: 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.'''
								)
								UpperCAmelCase				: Optional[Any]											=	(
								    '''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'''
								)
								UpperCAmelCase				: 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'''
								)

								UpperCAmelCase							,	UpperCAmelCase				: List[str]											=	check_copies.convert_to_localized_md(
								    __snake_case	,    __snake_case	,    localized_readme['''format_model_list''']	)

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










'''simple docstring'''






UpperCamelCase__:     dict[tuple[int, int, int], int]										=    {}


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		int						)   ->     int:

							# if we are absent twice, or late 3 consecutive days,
							# no further prize strings are possible
							if late == 3 or absent == 2:
														return 0

							# if we have no days left, and have not failed any other rules,
							# we have a prize string
							if days == 0:
														return 1

							# No easy solution, so now we need to do the recursive calculation

							# First, check if the combination is already in the cache, and
							# if yes, return the stored value from there since we already
							# know the number of possible prize strings from this point on
							UpperCAmelCase				: List[Any]											=	(days, absent, late)
							if key in cache:
														return cache[key]

							# now we calculate the three possible ways that can unfold from
							# this point on, depending on our attendance today

							# 1) if we are late (but not absent), the "absent" counter stays as
							# it is, but the "late" counter increases by one
							UpperCAmelCase				: int											=	_calculate(days - 1     ,   _lowerCAmelCase     ,   late + 1						)

							# 2) if we are absent, the "absent" counter increases by 1, and the
							# "late" counter resets to 0
							UpperCAmelCase				: Optional[Any]											=	_calculate(days - 1     ,   absent + 1     ,   0						)

							# 3) if we are on time, this resets the "late" counter and keeps the
							# absent counter
							UpperCAmelCase				: Tuple											=	_calculate(days - 1     ,   _lowerCAmelCase     ,   0						)

							UpperCAmelCase				: str											=	state_late + state_absent + state_ontime

							UpperCAmelCase				: List[Any]											=	prizestrings
							return prizestrings


def 				snake_case_  (      _lowerCAmelCase						:		int = 30						)   ->     int:
							return _calculate(_lowerCAmelCase     ,   absent=0     ,   late=0						)


if __name__ == "__main__":
						print(solution())





