
'''simple docstring'''






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


A			      =      get_tests_dir('fixtures/test_sentencepiece.model')
A			      =      get_tests_dir('fixtures/test_sentencepiece_bpe.model')

A			      =      'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class     __snake_case		(   __lowercase    ,   unittest.TestCase):
					_lowerCAmelCase			     =	CamembertTokenizer
					_lowerCAmelCase			     =	CamembertTokenizerFast
					_lowerCAmelCase			     =	True
					_lowerCAmelCase			     =	True
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().setUp()

									# We have a SentencePiece fixture for testing
									lowerCamelCase					:			List[Any]									= CamembertTokenizer(__a      )
									tokenizer.save_pretrained(self.tmpdirname      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= """<pad>"""
									lowerCamelCase					:			str									= 1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a      ),    __a      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a      ),    __a      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0],    '<s>NOTUSED'      )
									self.assertEqual(vocab_keys[1],    '<pad>'      )
									self.assertEqual(vocab_keys[-1],    '<mask>'      )
									self.assertEqual(len(__a      ),    1004      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.assertEqual(self.get_tokenizer().vocab_size,    1005      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= CamembertTokenizer(__a      )
									tokenizer.save_pretrained(self.tmpdirname      )
									lowerCamelCase					:			List[str]									= CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

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

									lowerCamelCase					:			List[str]									= tokenizer.encode(__a      )
									lowerCamelCase					:			int									= rust_tokenizer.encode(__a      )
									self.assertListEqual(__a,    __a      )

									lowerCamelCase					:			int									= tokenizer.encode(__a,    add_special_tokens=__a      )
									lowerCamelCase					:			str									= rust_tokenizer.encode(__a,    add_special_tokens=__a      )
									self.assertListEqual(__a,    __a      )

									# <unk> tokens are not the same for `rust` than for `slow`.
									# Because spm gives back raw token instead of `unk` in EncodeAsPieces
									# tokens = tokenizer.tokenize(sequence)
									lowerCamelCase					:			List[Any]									= tokenizer.convert_ids_to_tokens(__a      )
									lowerCamelCase					:			Tuple									= rust_tokenizer.tokenize(__a      )
									self.assertListEqual(__a,    __a      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if not self.test_rust_tokenizer:
													return

									lowerCamelCase					:			Any									= self.get_tokenizer()
									lowerCamelCase					:			Dict									= self.get_rust_tokenizer()

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

									lowerCamelCase					:			Optional[int]									= tokenizer.tokenize(__a      )
									lowerCamelCase					:			Optional[Any]									= rust_tokenizer.tokenize(__a      )
									self.assertListEqual(__a,    __a      )

									lowerCamelCase					:			Tuple									= tokenizer.encode(__a,    add_special_tokens=__a      )
									lowerCamelCase					:			str									= rust_tokenizer.encode(__a,    add_special_tokens=__a      )
									self.assertListEqual(__a,    __a      )

									lowerCamelCase					:			Dict									= self.get_rust_tokenizer()
									lowerCamelCase					:			Tuple									= tokenizer.encode(__a      )
									lowerCamelCase					:			List[str]									= rust_tokenizer.encode(__a      )
									self.assertListEqual(__a,    __a      )




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """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, 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]]}  # noqa: E501
									# fmt: on

									# camembert is a french model. So we also use french texts.
									lowerCamelCase					:			Optional[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=__a,    model_name='camembert-base',    revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf',    sequences=__a,    )

'''simple docstring'''






from typing import TYPE_CHECKING

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


A			      =      {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      ['CLIPFeatureExtractor']
					A			      =      ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					A			      =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
