





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_			=      {
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""Mask2FormerImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """Mask2FormerForUniversalSegmentation""",
           """Mask2FormerModel""",
           """Mask2FormerPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_maskaformer import MaskaFormerImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_maskaformer import (
                  MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaskaFormerForUniversalSegmentation,
                  MaskaFormerModel,
                  MaskaFormerPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure)











"""simple docstring"""



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 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					BarthezTokenizer
 _lowerCamelCase				=					BarthezTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     __A   :						List[Any]									=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''  )
     tokenizer.save_pretrained(self.tmpdirname  )
     tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=__lowerCamelCase  )
     __A   :						Any									=					tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''<pad>'''
     __A   :						Union[str, Any]									=					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  ):



     '''simple docstring'''




     __A   :						Optional[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(__lowerCamelCase  ) ,						10_1122  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size ,						10_1122  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						Any									=					[0, 57, 3018, 7_0307, 91, 2]

     __A   :						List[Any]									=					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  )
     __A   :						Any									=					batch.input_ids.tolist()[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A   :						Tuple									=					self.get_tokenizer()
     __A   :						Dict									=					self.get_rust_tokenizer()

     __A   :						Union[str, Any]									=					'''I was born in 92000, and this is falsé.'''

     __A   :						List[Any]									=					tokenizer.tokenize(__lowerCamelCase  )
     __A   :						Optional[int]									=					rust_tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					self.get_rust_tokenizer()
     __A   :						Optional[int]									=					tokenizer.encode(__lowerCamelCase  )
     __A   :						Any									=					rust_tokenizer.encode(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					{'''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.
     __A   :						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=__lowerCamelCase ,						model_name='''moussaKam/mbarthez''' ,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,						sequences=__lowerCamelCase ,						)




