
"""simple docstring"""





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


_a		       =							get_tests_dir('fixtures/test_sentencepiece.model')
_a		       =							get_tests_dir('fixtures/test_sentencepiece_bpe.model')

_a		       =							'pt' if is_torch_available() else 'tf'


@require_sentencepiece
@require_tokenizers
class A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Any       =		CamembertTokenizer
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		CamembertTokenizerFast
  SCREAMING_SNAKE_CASE__			:						Tuple       =		True
  SCREAMING_SNAKE_CASE__			:						str       =		True







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().setUp()

     # We have a SentencePiece fixture for testing
     UpperCAmelCase_					:					str			     =    CamembertTokenizer(lowercase_  )
     tokenizer.save_pretrained(self.tmpdirname  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    "<pad>"
     UpperCAmelCase_					:					str			     =    1

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    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(lowercase_  )				,   1004  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.assertEqual(self.get_tokenizer().vocab_size				,   1005  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    CamembertTokenizer(lowercase_  )
     tokenizer.save_pretrained(self.tmpdirname  )
     UpperCAmelCase_					:					List[str]			     =    CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

     UpperCAmelCase_					:					Tuple			     =    "I was born in 92000, and this is falsé."

     UpperCAmelCase_					:					List[str]			     =    tokenizer.encode(lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    rust_tokenizer.encode(lowercase_  )
     self.assertListEqual(lowercase_				,   lowercase_  )

     UpperCAmelCase_					:					Tuple			     =    tokenizer.encode(lowercase_				,   add_special_tokens=lowercase_  )
     UpperCAmelCase_					:					Any			     =    rust_tokenizer.encode(lowercase_				,   add_special_tokens=lowercase_  )
     self.assertListEqual(lowercase_				,   lowercase_  )

     # <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)
     UpperCAmelCase_					:					List[Any]			     =    tokenizer.convert_ids_to_tokens(lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    rust_tokenizer.tokenize(lowercase_  )
     self.assertListEqual(lowercase_				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if not self.test_rust_tokenizer:
        return

     UpperCAmelCase_					:					Optional[int]			     =    self.get_tokenizer()
     UpperCAmelCase_					:					List[str]			     =    self.get_rust_tokenizer()

     UpperCAmelCase_					:					str			     =    "I was born in 92000, and this is falsé."

     UpperCAmelCase_					:					List[str]			     =    tokenizer.tokenize(lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    rust_tokenizer.tokenize(lowercase_  )
     self.assertListEqual(lowercase_				,   lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    tokenizer.encode(lowercase_				,   add_special_tokens=lowercase_  )
     UpperCAmelCase_					:					Union[str, Any]			     =    rust_tokenizer.encode(lowercase_				,   add_special_tokens=lowercase_  )
     self.assertListEqual(lowercase_				,   lowercase_  )

     UpperCAmelCase_					:					Any			     =    self.get_rust_tokenizer()
     UpperCAmelCase_					:					Dict			     =    tokenizer.encode(lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    rust_tokenizer.encode(lowercase_  )
     self.assertListEqual(lowercase_				,   lowercase_  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # fmt: off
     UpperCAmelCase_					:					int			     =    {"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, 2_7575, 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, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 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.
     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=lowercase_				,   model_name="camembert-base"				,   revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"				,   sequences=lowercase_				,   )










"""simple docstring"""



def 						lowercase    (				__snake_case				: Optional[int]	):
		lowercase_ : int							       =   0
		lowercase_ : Optional[Any]							       =   len(__snake_case	)

		for i in range(n - 1	):
				for j in range(i + 1		,					__snake_case	):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions



def 						lowercase    (				__snake_case				: str	):
		if len(__snake_case	) <= 1:
				return arr, 0
		lowercase_ : Optional[Any]							       =   len(__snake_case	) // 2
		lowercase_ : List[Any]							       =   arr[0:mid]
		lowercase_ : Union[str, Any]							       =   arr[mid:]

		lowercase_       ,					lowercase_ : Tuple							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   _count_cross_inversions(__snake_case		,					__snake_case	)

		lowercase_ : List[Any]							       =   inversion_p + inversions_q + cross_inversions
		return c, num_inversions



def 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[int]	):
		lowercase_ : Optional[Any]							       =   []
		lowercase_ : Any							       =   0
		while i < len(__snake_case	) and j < len(__snake_case	):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(__snake_case	) - i
						r.append(q[j]	)
						j += 1
				else:
						r.append(p[i]	)
						i += 1

		if i < len(__snake_case	):
				r.extend(p[i:]	)
		else:
				r.extend(q[j:]	)

		return r, num_inversion



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   [1_0, 2, 1, 5, 5, 2, 1_1]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		lowercase_ : int							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = '''		,					__snake_case	)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		lowercase_ : Dict							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)

		# an empty list should also have zero inversions
		lowercase_ : List[Any]							       =   []
		lowercase_ : Any							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : List[str]							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)


if __name__ == "__main__":
						main()



