


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
       from ..ta.tokenization_ta import TaTokenizer
else:
       from ...utils.dummy_sentencepiece_objects import TaTokenizer

__UpperCamelCase       :       int						       =				TaTokenizer

if is_tokenizers_available():
       from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
       from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__UpperCamelCase       :       List[Any]						       =				TaTokenizerFast

__UpperCamelCase       :       Dict						       =				{"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Any						       =				[
           "MT5EncoderModel",
           "MT5ForConditionalGeneration",
           "MT5ForQuestionAnswering",
           "MT5Model",
           "MT5PreTrainedModel",
           "MT5Stack",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       str						       =				["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]


if TYPE_CHECKING:
       from .configuration_mta import MTaConfig, MTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mta import (
                  MTaEncoderModel,
                  MTaForConditionalGeneration,
                  MTaForQuestionAnswering,
                  MTaModel,
                  MTaPreTrainedModel,
                  MTaStack,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
       import sys

       __UpperCamelCase       :       Tuple						       =				_LazyModule(
           __name__,
           globals()["__file__"],
           _import_structure,
           extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
           module_spec=__spec__,
       )






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       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      str:





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
      tokenizer.save_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname							,	legacy_format=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   tokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   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			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    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(lowerCamelCase__			)							,	101122			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

      UpperCamelCase__					:    Optional[int]          =   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			)
      UpperCamelCase__					:    List[Any]          =   batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

      UpperCamelCase__					:    List[str]          =   tokenizer.tokenize(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   rust_tokenizer.tokenize(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   self.get_rust_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   tokenizer.encode(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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__					:    int          =   [
          '''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__							,	)


