

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	UpperCAmelCase__		(	A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= BarthezTokenizer
  a									= BarthezTokenizerFast
  a									= True
  a									= True






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

       SCREAMING_SNAKE_CASE__   =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
       tokenizer.save_pretrained(self.tmpdirname )
       tokenizer.save_pretrained(self.tmpdirname     ,       legacy_format=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   tokenizer






  def 				lowercase_		(							self						:		Optional[int] )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   '''<pad>'''
       SCREAMING_SNAKE_CASE__   =   1

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






  def 				lowercase_		(							self						:		Union[str, Any] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0]     ,       '''<s>''' )
       self.assertEqual(vocab_keys[1]     ,       '''<pad>''' )
       self.assertEqual(vocab_keys[-1]     ,       '''<mask>''' )
       self.assertEqual(len(__lowerCamelCase )     ,       10_1122 )






  def 				lowercase_		(							self						:		Optional[Any] )		->							Optional[int]:
       self.assertEqual(self.get_tokenizer().vocab_size     ,       10_1122 )






  @require_torch
  def 				lowercase_		(							self						:		Optional[int] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       SCREAMING_SNAKE_CASE__   =   [0, 57, 3018, 7_0307, 91, 2]

       SCREAMING_SNAKE_CASE__   =   self.tokenizer(
           __lowerCamelCase     ,       max_length=len(__lowerCamelCase )     ,       padding=__lowerCamelCase     ,       truncation=__lowerCamelCase     ,       return_tensors='''pt''' )
       self.assertIsInstance(__lowerCamelCase     ,       __lowerCamelCase )

       self.assertEqual((2, 6)     ,       batch.input_ids.shape )
       self.assertEqual((2, 6)     ,       batch.attention_mask.shape )
       SCREAMING_SNAKE_CASE__   =   batch.input_ids.tolist()[0]
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Any )		->							Dict:
       if not self.test_rust_tokenizer:
            return

       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()
       SCREAMING_SNAKE_CASE__   =   self.get_rust_tokenizer()

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

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

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

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







  @slow
  def 				lowercase_		(							self						:		Tuple )		->							Optional[int]:
       # fmt: off
       SCREAMING_SNAKE_CASE__   =   {'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # moussaKam/mbarthez is a french model. So we also use french texts.
       SCREAMING_SNAKE_CASE__   =   [
           '''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='''moussaKam/mbarthez'''     ,       revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''     ,       sequences=__lowerCamelCase     ,       )





from __future__ import annotations
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   len(_A    ) // 2

     # choose the middle 3 elements
     SCREAMING_SNAKE_CASE__   =   lst[m - 1 : m + 2]

     # if middle element is peak
     if three[1] > three[0] and three[1] > three[2]:
          return three[1]

     # if increasing, recurse on right
     elif three[0] < three[2]:
          if len(lst[:m]    ) == 2:
               m -= 1
          return peak(lst[m:]    )

     # decreasing
     else:
          if len(lst[:m]    ) == 2:
               m += 1
          return peak(lst[:m]    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


