
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


SCREAMING_SNAKE_CASE__  :     Dict   				=       get_tests_dir("fixtures/test_sentencepiece.model")
SCREAMING_SNAKE_CASE__  :     List[Any]   				=       get_tests_dir("fixtures/test_sentencepiece_bpe.model")

SCREAMING_SNAKE_CASE__  :     int   				=       "pt" if is_torch_available() else "tf"

@require_sentencepiece
@require_tokenizers
class   lowerCAmelCase__	(      UpperCamelCase__    ,      unittest.TestCase     ):
		a__			:		Tuple     =						CamembertTokenizer
		a__			:		Optional[int]     =						CamembertTokenizerFast
		a__			:		Union[str, Any]     =						True
		a__			:		Any     =						True






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

						# We have a SentencePiece fixture for testing
						__lowerCamelCase          =      CamembertTokenizer(SCREAMING_SNAKE_CASE__    )
						tokenizer.save_pretrained(self.tmpdirname    )






		def 					__A			(						self						:				Optional[int]    )	->				str:
						__lowerCamelCase          =      '''<pad>'''
						__lowerCamelCase          =      1

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






		def 					__A			(						self						:				int    )	->				int:
						__lowerCamelCase          =      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(SCREAMING_SNAKE_CASE__    )				,     10_04    )






		def 					__A			(						self						:				Optional[int]    )	->				Any:
						self.assertEqual(self.get_tokenizer().vocab_size				,     10_05    )






		def 					__A			(						self						:				Union[str, Any]    )	->				Dict:
						__lowerCamelCase          =      CamembertTokenizer(SCREAMING_SNAKE_CASE__    )
						tokenizer.save_pretrained(self.tmpdirname    )
						__lowerCamelCase          =      CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

						__lowerCamelCase          =      '''I was born in 92000, and this is falsé.'''

						__lowerCamelCase          =      tokenizer.encode(SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      rust_tokenizer.encode(SCREAMING_SNAKE_CASE__    )
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,     SCREAMING_SNAKE_CASE__    )

						__lowerCamelCase          =      tokenizer.encode(SCREAMING_SNAKE_CASE__				,     add_special_tokens=SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      rust_tokenizer.encode(SCREAMING_SNAKE_CASE__				,     add_special_tokens=SCREAMING_SNAKE_CASE__    )
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,     SCREAMING_SNAKE_CASE__    )

						# <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)
						__lowerCamelCase          =      tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__    )
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,     SCREAMING_SNAKE_CASE__    )






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

						__lowerCamelCase          =      self.get_tokenizer()
						__lowerCamelCase          =      self.get_rust_tokenizer()

						__lowerCamelCase          =      '''I was born in 92000, and this is falsé.'''

						__lowerCamelCase          =      tokenizer.tokenize(SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__    )
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,     SCREAMING_SNAKE_CASE__    )

						__lowerCamelCase          =      tokenizer.encode(SCREAMING_SNAKE_CASE__				,     add_special_tokens=SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      rust_tokenizer.encode(SCREAMING_SNAKE_CASE__				,     add_special_tokens=SCREAMING_SNAKE_CASE__    )
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,     SCREAMING_SNAKE_CASE__    )

						__lowerCamelCase          =      self.get_rust_tokenizer()
						__lowerCamelCase          =      tokenizer.encode(SCREAMING_SNAKE_CASE__    )
						__lowerCamelCase          =      rust_tokenizer.encode(SCREAMING_SNAKE_CASE__    )
						self.assertListEqual(SCREAMING_SNAKE_CASE__				,     SCREAMING_SNAKE_CASE__    )







		@slow
		def 					__A			(						self						:				Union[str, Any]    )	->				Union[str, Any]:
						# fmt: off
						__lowerCamelCase          =      {'''input_ids''': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 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.
						__lowerCamelCase          =      [
						    '''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=SCREAMING_SNAKE_CASE__				,     model_name='''camembert-base'''				,     revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''				,     sequences=SCREAMING_SNAKE_CASE__				,     )





'''simple docstring'''
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):

	def count_of_possible_combinations(A__						) -> int:
		if target < 0:
			return 0
		if target == 0:
			return 1
		return sum(count_of_possible_combinations(target - item						) for item in array						)

	return count_of_possible_combinations(A__						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):

	def count_of_possible_combinations_with_dp_array(
	    A__  ,      A__						) -> int:
		if target < 0:
			return 0
		if target == 0:
			return 1
		if dp_array[target] != -1:
			return dp_array[target]
		a_      				=						sum(
		    count_of_possible_combinations_with_dp_array(target - item  ,      A__						)
		    for item in array						)
		a_      				=						answer
		return answer

	a_      				=						[-1] * (target + 1)
	return count_of_possible_combinations_with_dp_array(A__  ,      A__						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						[0] * (target + 1)
	a_      				=						1

	for i in range(1  ,      target + 1						):
		for j in range(A__						):
			if i - array[j] >= 0:
				dp_array[i] += dp_array[i - array[j]]

	return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		lowercase__											=3
		lowercase__											=5
		lowercase__											=[1, 2, 5]
		print(combination_sum_iv(n, array, target))
