




"""simple docstring"""

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

def 						lowercase_					(						_UpperCAmelCase     ,					_UpperCAmelCase     ,					_UpperCAmelCase		):




	"""simple docstring"""






	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
	A_     : Tuple		      =						(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
	A_     : Dict		      =						_calculate(days - 1     ,					lowerCAmelCase_     ,					late + 1		)

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

	# 3) if we are on time, this resets the "late" counter and keeps the
	# absent counter
	A_     : Optional[Any]		      =						_calculate(days - 1     ,					lowerCAmelCase_     ,					0		)

	A_     : List[Any]		      =						state_late + state_absent + state_ontime

	A_     : List[Any]		      =						prizestrings
	return prizestrings

def 						lowercase_					(						_UpperCAmelCase = 30		):




	"""simple docstring"""






	return _calculate(lowerCAmelCase_     ,					absent=0     ,					late=0		)


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













import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase       :       Any        =				get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCamelCase       :       Any        =				get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCamelCase       :       Optional[int]        =				'''pt''' if is_torch_available() else '''tf'''

@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	Dict     =					CamembertTokenizer
					_A  :	Union[str, Any]     =					CamembertTokenizerFast
					_A  :	Union[str, Any]     =					True
					_A  :	Tuple     =					True



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[int]:


												"""simple docstring"""

												super().setUp()

												# We have a SentencePiece fixture for testing
												__lowercase					:							Union[str, Any]			=   CamembertTokenizer(__a							)
												tokenizer.save_pretrained(self.tmpdirname							)



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Dict			=   """<pad>"""
												__lowercase					:							Dict			=   1

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



					def 					lowerCAmelCase (     self :		Optional[Any]							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							int			=   list(self.get_tokenizer().get_vocab().keys()							)

												self.assertEqual(vocab_keys[0]						,   """<s>NOTUSED"""							)
												self.assertEqual(vocab_keys[1]						,   """<pad>"""							)
												self.assertEqual(vocab_keys[-1]						,   """<mask>"""							)
												self.assertEqual(len(__a							)						,   1004							)



					def 					lowerCAmelCase (     self :		str							)	->     List[Any]:


												"""simple docstring"""

												self.assertEqual(self.get_tokenizer().vocab_size						,   1005							)



					def 					lowerCAmelCase (     self :		Any							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   CamembertTokenizer(__a							)
												tokenizer.save_pretrained(self.tmpdirname							)
												__lowercase					:							Tuple			=   CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

												__lowercase					:							List[str]			=   """I was born in 92000, and this is falsé."""

												__lowercase					:							Optional[Any]			=   tokenizer.encode(__a							)
												__lowercase					:							List[Any]			=   rust_tokenizer.encode(__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Tuple			=   tokenizer.encode(__a						,   add_special_tokens=__a							)
												__lowercase					:							Union[str, Any]			=   rust_tokenizer.encode(__a						,   add_special_tokens=__a							)
												self.assertListEqual(__a						,   __a							)

												# <unk> tokens are not the same for `rust` than for `slow`.
												# Because spm gives back raw token instead of `unk` in EncodeAsPieces
												# tokens = tokenizer.tokenize(sequence)
												__lowercase					:							Dict			=   tokenizer.convert_ids_to_tokens(__a							)
												__lowercase					:							Any			=   rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a						,   __a							)



					def 					lowerCAmelCase (     self :		Any							)	->     Dict:


												"""simple docstring"""

												if not self.test_rust_tokenizer:
																			return

												__lowercase					:							List[str]			=   self.get_tokenizer()
												__lowercase					:							Any			=   self.get_rust_tokenizer()

												__lowercase					:							Any			=   """I was born in 92000, and this is falsé."""

												__lowercase					:							Tuple			=   tokenizer.tokenize(__a							)
												__lowercase					:							Optional[Any]			=   rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Dict			=   tokenizer.encode(__a						,   add_special_tokens=__a							)
												__lowercase					:							Dict			=   rust_tokenizer.encode(__a						,   add_special_tokens=__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Any			=   self.get_rust_tokenizer()
												__lowercase					:							str			=   tokenizer.encode(__a							)
												__lowercase					:							Union[str, Any]			=   rust_tokenizer.encode(__a							)
												self.assertListEqual(__a						,   __a							)



					@slow
					def 					lowerCAmelCase (     self :		Optional[int]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							str			=   {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """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, 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]]}  # noqa: E501
												# fmt: on

												# camembert is a french model. So we also use french texts.
												__lowercase					:							List[str]			=   [
												    """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=__a						,   model_name="""camembert-base"""						,   revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""						,   sequences=__a						,   )