
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 					_lowerCamelCase			(   UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	BarthezTokenizer
     SCREAMING_SNAKE_CASE_		 =	BarthezTokenizerFast
     SCREAMING_SNAKE_CASE_		 =	True
     SCREAMING_SNAKE_CASE_		 =	True






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           super().setUp()

           UpperCamelCase__     : int											=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
           tokenizer.save_pretrained(self.tmpdirname	)
           tokenizer.save_pretrained(self.tmpdirname    ,    legacy_format=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					tokenizer






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					'''<pad>'''
           UpperCamelCase__     : Optional[int]											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					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(__SCREAMING_SNAKE_CASE	)    ,    1_0_1_1_2_2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           self.assertEqual(self.get_tokenizer().vocab_size    ,    1_0_1_1_2_2	)






     @require_torch
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           UpperCamelCase__     : Any											=					[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

           UpperCamelCase__     : str											=					self.tokenizer(
               __SCREAMING_SNAKE_CASE    ,    max_length=len(__SCREAMING_SNAKE_CASE	)    ,    padding=__SCREAMING_SNAKE_CASE    ,    truncation=__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)
           self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

           self.assertEqual((2, 6)    ,    batch.input_ids.shape	)
           self.assertEqual((2, 6)    ,    batch.attention_mask.shape	)
           UpperCamelCase__     : Union[str, Any]											=					batch.input_ids.tolist()[0]
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           if not self.test_rust_tokenizer:
                 return

           UpperCamelCase__     : Tuple											=					self.get_tokenizer()
           UpperCamelCase__     : Any											=					self.get_rust_tokenizer()

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

           UpperCamelCase__     : Optional[Any]											=					tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

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

           UpperCamelCase__     : Optional[Any]											=					self.get_rust_tokenizer()
           UpperCamelCase__     : Union[str, Any]											=					tokenizer.encode(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					{'''input_ids''': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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.
           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=__SCREAMING_SNAKE_CASE    ,    model_name='''moussaKam/mbarthez'''    ,    revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''    ,    sequences=__SCREAMING_SNAKE_CASE    ,    )




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      if index == number_of_items:
            return 0
      UpperCamelCase__     : str											=					0
      UpperCamelCase__     : Optional[Any]											=					0
      UpperCamelCase__     : int											=					knapsack(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		index + 1		)
      if weights[index] <= max_weight:
            UpperCamelCase__     : List[Any]											=					values[index] + knapsack(
                UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		max_weight - weights[index]			,		index + 1		)
      return max(UpperCamelCase__			,		UpperCamelCase__		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


