



'''simple docstring'''






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        SCREAMING_SNAKE_CASE(							A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= BarthezTokenizer
	lowerCamelCase__    				= BarthezTokenizerFast
	lowerCamelCase__    				= True
	lowerCamelCase__    				= True







	def   A			(		self    :  List[str]	)    ->	Union[str, Any]:
								super().setUp()

								UpperCAmelCase				: Tuple											=	BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
								tokenizer.save_pretrained(self.tmpdirname	)
								tokenizer.save_pretrained(self.tmpdirname	,    legacy_format=__snake_case	)
								UpperCAmelCase				: Any											=	tokenizer







	def   A			(		self    :  str	)    ->	List[str]:
								UpperCAmelCase				: Any											=	'''<pad>'''
								UpperCAmelCase				: List[str]											=	1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case	)	,    __snake_case	)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case	)	,    __snake_case	)







	def   A			(		self    :  List[Any]	)    ->	Any:
								UpperCAmelCase				: Dict											=	list(self.get_tokenizer().get_vocab().keys()	)

								self.assertEqual(vocab_keys[0]	,    '''<s>'''	)
								self.assertEqual(vocab_keys[1]	,    '''<pad>'''	)
								self.assertEqual(vocab_keys[-1]	,    '''<mask>'''	)
								self.assertEqual(len(__snake_case	)	,    101122	)







	def   A			(		self    :  int	)    ->	Dict:
								self.assertEqual(self.get_tokenizer().vocab_size	,    101122	)







	@require_torch
	def   A			(		self    :  List[str]	)    ->	Union[str, Any]:
								UpperCAmelCase				: Optional[int]											=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								UpperCAmelCase				: List[Any]											=	[0, 57, 3018, 70307, 91, 2]

								UpperCAmelCase				: List[Any]											=	self.tokenizer(
								    __snake_case	,    max_length=len(__snake_case	)	,    padding=__snake_case	,    truncation=__snake_case	,    return_tensors='''pt'''	)
								self.assertIsInstance(__snake_case	,    __snake_case	)

								self.assertEqual((2, 6)	,    batch.input_ids.shape	)
								self.assertEqual((2, 6)	,    batch.attention_mask.shape	)
								UpperCAmelCase				: Tuple											=	batch.input_ids.tolist()[0]
								self.assertListEqual(__snake_case	,    __snake_case	)







	def   A			(		self    :  List[Any]	)    ->	List[Any]:
								if not self.test_rust_tokenizer:
															return

								UpperCAmelCase				: int											=	self.get_tokenizer()
								UpperCAmelCase				: Optional[int]											=	self.get_rust_tokenizer()

								UpperCAmelCase				: Tuple											=	'''I was born in 92000, and this is falsé.'''

								UpperCAmelCase				: Tuple											=	tokenizer.tokenize(__snake_case	)
								UpperCAmelCase				: int											=	rust_tokenizer.tokenize(__snake_case	)
								self.assertListEqual(__snake_case	,    __snake_case	)

								UpperCAmelCase				: List[Any]											=	tokenizer.encode(__snake_case	,    add_special_tokens=__snake_case	)
								UpperCAmelCase				: str											=	rust_tokenizer.encode(__snake_case	,    add_special_tokens=__snake_case	)
								self.assertListEqual(__snake_case	,    __snake_case	)

								UpperCAmelCase				: Optional[int]											=	self.get_rust_tokenizer()
								UpperCAmelCase				: Dict											=	tokenizer.encode(__snake_case	)
								UpperCAmelCase				: str											=	rust_tokenizer.encode(__snake_case	)
								self.assertListEqual(__snake_case	,    __snake_case	)







	@slow
	def   A			(		self    :  str	)    ->	Optional[Any]:
								# fmt: off
								UpperCAmelCase				: Tuple											=	{'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# moussaKam/mbarthez is a french model. So we also use french texts.
								UpperCAmelCase				: Tuple											=	[
								    '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
								    '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
								    '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
								    '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
								    '''telles que la traduction et la synthèse de texte.''',
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=__snake_case	,    model_name='''moussaKam/mbarthez'''	,    revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''	,    sequences=__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())





