




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			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Dict          =  BarthezTokenizer
   UpperCAmelCase__  :    Optional[Any]          =  BarthezTokenizerFast
   UpperCAmelCase__  :    Union[str, Any]          =  True
   UpperCAmelCase__  :    Optional[Any]          =  True







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().setUp()

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







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''<pad>'''
        UpperCAmelCase_          =							1

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







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							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(_snake_case)					,						101122)







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


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







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_          =							[0, 57, 3018, 70307, 91, 2]

        UpperCAmelCase_          =							self.tokenizer(
            _snake_case					,						max_length=len(_snake_case)					,						padding=_snake_case					,						truncation=_snake_case					,						return_tensors='''pt''')
        self.assertIsInstance(_snake_case					,						_snake_case)

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







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        if not self.test_rust_tokenizer:
             return

        UpperCAmelCase_          =							self.get_tokenizer()
        UpperCAmelCase_          =							self.get_rust_tokenizer()

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

        UpperCAmelCase_          =							tokenizer.tokenize(_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.tokenize(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							self.get_rust_tokenizer()
        UpperCAmelCase_          =							tokenizer.encode(_snake_case)
        UpperCAmelCase_          =							rust_tokenizer.encode(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)







   @slow
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							{'''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_          =							[
            '''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=_snake_case					,						model_name='''moussaKam/mbarthez'''					,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''					,						sequences=_snake_case					,						)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__   :			List[str]												=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			str												=    ["BlipImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BlipModel",
					    "BlipPreTrainedModel",
					    "BlipForConditionalGeneration",
					    "BlipForQuestionAnswering",
					    "BlipVisionModel",
					    "BlipTextModel",
					    "BlipForImageTextRetrieval",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Any												=    [
					    "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFBlipModel",
					    "TFBlipPreTrainedModel",
					    "TFBlipForConditionalGeneration",
					    "TFBlipForQuestionAnswering",
					    "TFBlipVisionModel",
					    "TFBlipTextModel",
					    "TFBlipForImageTextRetrieval",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Tuple												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



