




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       lowercase(  __snake_case ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      BarthezTokenizer
 lowercase__				      =      BarthezTokenizerFast
 lowercase__				      =      True
 lowercase__				      =      True


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   super().setUp()

   _snake_case							:	Tuple			=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""	)
   tokenizer.save_pretrained(self.tmpdirname	)
   tokenizer.save_pretrained(self.tmpdirname,   legacy_format=_lowercase	)
   _snake_case							:	List[str]			=						tokenizer


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						"""<pad>"""
   _snake_case							:	Any			=						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:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Any			=						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(_lowercase	),   101_122	)


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,   101_122	)


 @require_torch
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Dict			=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   _snake_case							:	str			=						[0, 57, 3_018, 70_307, 91, 2]

   _snake_case							:	Any			=						self.tokenizer(
       _lowercase,   max_length=len(_lowercase	),   padding=_lowercase,   truncation=_lowercase,   return_tensors="""pt"""	)
   self.assertIsInstance(_lowercase,   _lowercase	)

   self.assertEqual((2, 6),   batch.input_ids.shape	)
   self.assertEqual((2, 6),   batch.attention_mask.shape	)
   _snake_case							:	Optional[int]			=						batch.input_ids.tolist()[0]
   self.assertListEqual(_lowercase,   _lowercase	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:	Optional[Any]			=						self.get_tokenizer()
   _snake_case							:	List[str]			=						self.get_rust_tokenizer()

   _snake_case							:	List[str]			=						"""I was born in 92000, and this is falsé."""

   _snake_case							:	Optional[Any]			=						tokenizer.tokenize(_lowercase	)
   _snake_case							:	Tuple			=						rust_tokenizer.tokenize(_lowercase	)
   self.assertListEqual(_lowercase,   _lowercase	)

   _snake_case							:	Any			=						tokenizer.encode(_lowercase,   add_special_tokens=_lowercase	)
   _snake_case							:	List[Any]			=						rust_tokenizer.encode(_lowercase,   add_special_tokens=_lowercase	)
   self.assertListEqual(_lowercase,   _lowercase	)

   _snake_case							:	Dict			=						self.get_rust_tokenizer()
   _snake_case							:	Optional[Any]			=						tokenizer.encode(_lowercase	)
   _snake_case							:	Tuple			=						rust_tokenizer.encode(_lowercase	)
   self.assertListEqual(_lowercase,   _lowercase	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						{"""input_ids""": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 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.
   _snake_case							:	Union[str, Any]			=						[
       """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="""moussaKam/mbarthez""",   revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6""",   sequences=_lowercase,   )








"""simple docstring"""


from ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


