

"""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


UpperCAmelCase													=get_tests_dir("fixtures/test_sentencepiece.model")
UpperCAmelCase													=get_tests_dir("fixtures/test_sentencepiece_bpe.model")

UpperCAmelCase													="pt" if is_torch_available() else "tf"





@require_sentencepiece
@require_tokenizers
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   CamembertTokenizer
    _lowerCamelCase					=   CamembertTokenizerFast
    _lowerCamelCase					=   True
    _lowerCamelCase					=   True




    def UpperCamelCase__		( self    )    ->      List[str]:
          super().setUp()

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




    def UpperCamelCase__		( self    )    ->      List[str]:
          A				  =		"""<pad>"""
          A				  =		1

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




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		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(lowerCamelCase_    ) ,1_0_0_4    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertEqual(self.get_tokenizer().vocab_size ,1_0_0_5    )




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		CamembertTokenizer(lowerCamelCase_    )
          tokenizer.save_pretrained(self.tmpdirname    )
          A				  =		CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

          A				  =		"""I was born in 92000, and this is falsé."""

          A				  =		tokenizer.encode(lowerCamelCase_    )
          A				  =		rust_tokenizer.encode(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		tokenizer.encode(lowerCamelCase_ ,add_special_tokens=lowerCamelCase_    )
          A				  =		rust_tokenizer.encode(lowerCamelCase_ ,add_special_tokens=lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          # <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)
          A				  =		tokenizer.convert_ids_to_tokens(lowerCamelCase_    )
          A				  =		rust_tokenizer.tokenize(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      str:
          if not self.test_rust_tokenizer:
                return

          A				  =		self.get_tokenizer()
          A				  =		self.get_rust_tokenizer()

          A				  =		"""I was born in 92000, and this is falsé."""

          A				  =		tokenizer.tokenize(lowerCamelCase_    )
          A				  =		rust_tokenizer.tokenize(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		tokenizer.encode(lowerCamelCase_ ,add_special_tokens=lowerCamelCase_    )
          A				  =		rust_tokenizer.encode(lowerCamelCase_ ,add_special_tokens=lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		self.get_rust_tokenizer()
          A				  =		tokenizer.encode(lowerCamelCase_    )
          A				  =		rust_tokenizer.encode(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )




    @slow
    def UpperCamelCase__		( self    )    ->      int:
          # fmt: off
          A				  =		{"""input_ids""": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 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_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 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.
          A				  =		[
              """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=lowerCamelCase_ ,model_name="""camembert-base""" ,revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" ,sequences=lowerCamelCase_ ,)







"""simple docstring"""


from __future__ import annotations



def   _A					(	_a	:						list[float]     ,   _a	:						list[float]				):



      """simple docstring"""






      A				  =		sorted(numsa + numsa				)
      A  ,			A				  =		divmod(len(_a				)     ,   2				)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of first array: ").split()]
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of second array: ").split()]
  print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")




