





"""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


lowercase__				:Union[str, Any]				   =       get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__				:Union[str, Any]				   =       get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__				:Dict				   =       'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class 							snake_case							(			__UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       Union[str, Any]         =       CamembertTokenizer
      _A     :       Optional[int]         =       CamembertTokenizerFast
      _A     :       str         =       True
      _A     :       str         =       True

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        super().setUp()

        # We have a SentencePiece fixture for testing
        __UpperCAmelCase    :						List[str]       =						CamembertTokenizer(__lowercase )
        tokenizer.save_pretrained(self.tmpdirname )

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						'''<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       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						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 )						,				1_004 )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        self.assertEqual(self.get_tokenizer().vocab_size						,				1_005 )

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						CamembertTokenizer(__lowercase )
        tokenizer.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						Dict       =						CamembertTokenizerFast.from_pretrained(self.tmpdirname )

        __UpperCAmelCase    :						Optional[Any]       =						'''I was born in 92000, and this is falsé.'''

        __UpperCAmelCase    :						str       =						tokenizer.encode(__lowercase )
        __UpperCAmelCase    :						Dict       =						rust_tokenizer.encode(__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						List[Any]       =						tokenizer.encode(__lowercase						,				add_special_tokens=__lowercase )
        __UpperCAmelCase    :						List[str]       =						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    :						Union[str, Any]       =						tokenizer.convert_ids_to_tokens(__lowercase )
        __UpperCAmelCase    :						List[str]       =						rust_tokenizer.tokenize(__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        if not self.test_rust_tokenizer:
          return

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

        __UpperCAmelCase    :						Optional[int]       =						'''I was born in 92000, and this is falsé.'''

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

        __UpperCAmelCase    :						Optional[int]       =						tokenizer.encode(__lowercase						,				add_special_tokens=__lowercase )
        __UpperCAmelCase    :						int       =						rust_tokenizer.encode(__lowercase						,				add_special_tokens=__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						int       =						self.get_rust_tokenizer()
        __UpperCAmelCase    :						Tuple       =						tokenizer.encode(__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						rust_tokenizer.encode(__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )







      @slow
      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						{'''input_ids''': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->Dict:





  """simple docstring"""


  return sorted(UpperCAmelCase_  ,			key=lambda UpperCAmelCase_  : x[column]  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(points_counts - 1  ):
    for j in range(i + 1  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(min(6  ,			points_counts - 1  )  ,			UpperCAmelCase_  ):
    for j in range(max(0  ,			i - 6  )  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						Dict       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  if points_counts <= 3:
    return dis_between_closest_pair(UpperCAmelCase_  ,			UpperCAmelCase_  )

  # recursion
  __UpperCAmelCase    :						Any       =						points_counts // 2
  __UpperCAmelCase    :						Any       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[:mid]  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						Tuple       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[mid:]  ,			points_counts - mid  )
  __UpperCAmelCase    :						List[Any]       =						min(UpperCAmelCase_  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]  ) < closest_pair_dis:
      cross_strip.append(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						dis_between_closest_in_strip(
      UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ,			UpperCAmelCase_  )
  return min(UpperCAmelCase_  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						column_based_sort(UpperCAmelCase_  ,			column=0  )
  __UpperCAmelCase    :						Any       =						column_based_sort(UpperCAmelCase_  ,			column=1  )
  return (
      closest_pair_of_points_sqr(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  ) ** 0.5


if __name__ == "__main__":
  lowercase__				:Optional[Any]				   =       [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print('Distance:', closest_pair_of_points(points, len(points)))