
"""simple docstring"""



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCamelCase__          =	logging.get_logger(__name__)

UpperCamelCase__          =	OrderedDict(
    [
        ('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
        ('beit', 'BeitFeatureExtractor'),
        ('chinese_clip', 'ChineseCLIPFeatureExtractor'),
        ('clap', 'ClapFeatureExtractor'),
        ('clip', 'CLIPFeatureExtractor'),
        ('clipseg', 'ViTFeatureExtractor'),
        ('conditional_detr', 'ConditionalDetrFeatureExtractor'),
        ('convnext', 'ConvNextFeatureExtractor'),
        ('cvt', 'ConvNextFeatureExtractor'),
        ('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
        ('data2vec-vision', 'BeitFeatureExtractor'),
        ('deformable_detr', 'DeformableDetrFeatureExtractor'),
        ('deit', 'DeiTFeatureExtractor'),
        ('detr', 'DetrFeatureExtractor'),
        ('dinat', 'ViTFeatureExtractor'),
        ('donut-swin', 'DonutFeatureExtractor'),
        ('dpt', 'DPTFeatureExtractor'),
        ('encodec', 'EncodecFeatureExtractor'),
        ('flava', 'FlavaFeatureExtractor'),
        ('glpn', 'GLPNFeatureExtractor'),
        ('groupvit', 'CLIPFeatureExtractor'),
        ('hubert', 'Wav2Vec2FeatureExtractor'),
        ('imagegpt', 'ImageGPTFeatureExtractor'),
        ('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
        ('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
        ('levit', 'LevitFeatureExtractor'),
        ('maskformer', 'MaskFormerFeatureExtractor'),
        ('mctct', 'MCTCTFeatureExtractor'),
        ('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
        ('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
        ('mobilevit', 'MobileViTFeatureExtractor'),
        ('nat', 'ViTFeatureExtractor'),
        ('owlvit', 'OwlViTFeatureExtractor'),
        ('perceiver', 'PerceiverFeatureExtractor'),
        ('poolformer', 'PoolFormerFeatureExtractor'),
        ('regnet', 'ConvNextFeatureExtractor'),
        ('resnet', 'ConvNextFeatureExtractor'),
        ('segformer', 'SegformerFeatureExtractor'),
        ('sew', 'Wav2Vec2FeatureExtractor'),
        ('sew-d', 'Wav2Vec2FeatureExtractor'),
        ('speech_to_text', 'Speech2TextFeatureExtractor'),
        ('speecht5', 'SpeechT5FeatureExtractor'),
        ('swiftformer', 'ViTFeatureExtractor'),
        ('swin', 'ViTFeatureExtractor'),
        ('swinv2', 'ViTFeatureExtractor'),
        ('table-transformer', 'DetrFeatureExtractor'),
        ('timesformer', 'VideoMAEFeatureExtractor'),
        ('tvlt', 'TvltFeatureExtractor'),
        ('unispeech', 'Wav2Vec2FeatureExtractor'),
        ('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
        ('van', 'ConvNextFeatureExtractor'),
        ('videomae', 'VideoMAEFeatureExtractor'),
        ('vilt', 'ViltFeatureExtractor'),
        ('vit', 'ViTFeatureExtractor'),
        ('vit_mae', 'ViTFeatureExtractor'),
        ('vit_msn', 'ViTFeatureExtractor'),
        ('wav2vec2', 'Wav2Vec2FeatureExtractor'),
        ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
        ('wavlm', 'Wav2Vec2FeatureExtractor'),
        ('whisper', 'WhisperFeatureExtractor'),
        ('xclip', 'CLIPFeatureExtractor'),
        ('yolos', 'YolosFeatureExtractor'),
    ]
)

UpperCamelCase__          =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def 					lowerCamelCase   (     _snake_case ):
    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            UpperCAmelCase__	:      str     							=					model_type_to_module_name(_snake_case )

            UpperCAmelCase__	:      int     							=					importlib.import_module(F'''.{module_name}'''    ,'transformers.models' )
            try:
                return getattr(_snake_case    ,_snake_case )
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(_snake_case    ,'__name__'    ,_snake_case ) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    UpperCAmelCase__	:      int     							=					importlib.import_module('transformers' )
    if hasattr(_snake_case    ,_snake_case ):
        return getattr(_snake_case    ,_snake_case )

    return None
def 					lowerCamelCase   (     _snake_case    ,_snake_case = None    ,_snake_case = False    ,_snake_case = False    ,_snake_case = None    ,_snake_case = None    ,_snake_case = None    ,_snake_case = False    ,**_snake_case    ,):
    UpperCAmelCase__	:      List[Any]     							=					get_file_from_repo(
        _snake_case    ,_snake_case    ,cache_dir=_snake_case    ,force_download=_snake_case    ,resume_download=_snake_case    ,proxies=_snake_case    ,use_auth_token=_snake_case    ,revision=_snake_case    ,local_files_only=_snake_case    ,)
    if resolved_config_file is None:
        logger.info(
            'Could not locate the feature extractor configuration file, will try to use the model config instead.' )
        return {}

    with open(_snake_case    ,encoding='utf-8' ) as reader:
        return json.load(_snake_case )





class 				a			:



      def __init__(   self      ):
          raise EnvironmentError(
              'AutoFeatureExtractor is designed to be instantiated '
              'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'      )



      @classmethod
      @replace_list_option_in_docstrings(UpperCamelCase_      )
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					kwargs.pop('config'     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					kwargs.pop('trust_remote_code'     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					True

          UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase_     ,	**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					config_dict.get('feature_extractor_type'     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					None
          if "AutoFeatureExtractor" in config_dict.get('auto_map'     ,	{}      ):
              UpperCAmelCase__	:      List[str]     							=					config_dict['auto_map']['AutoFeatureExtractor']

          # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
          if feature_extractor_class is None and feature_extractor_auto_map is None:
              if not isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
                  UpperCAmelCase__	:      List[Any]     							=					AutoConfig.from_pretrained(UpperCamelCase_     ,	**UpperCamelCase_      )
              # It could be in `config.feature_extractor_type``
              UpperCAmelCase__	:      List[Any]     							=					getattr(UpperCamelCase_     ,	'feature_extractor_type'     ,	UpperCamelCase_      )
              if hasattr(UpperCamelCase_     ,	'auto_map'      ) and "AutoFeatureExtractor" in config.auto_map:
                  UpperCAmelCase__	:      str     							=					config.auto_map['AutoFeatureExtractor']

          if feature_extractor_class is not None:
              UpperCAmelCase__	:      Tuple     							=					feature_extractor_class_from_name(UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					feature_extractor_auto_map is not None
          UpperCAmelCase__	:      Dict     							=					feature_extractor_class is not None or type(UpperCamelCase_      ) in FEATURE_EXTRACTOR_MAPPING
          UpperCAmelCase__	:      int     							=					resolve_trust_remote_code(
              UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )

          if has_remote_code and trust_remote_code:
              UpperCAmelCase__	:      Dict     							=					get_class_from_dynamic_module(
                  UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )
              UpperCAmelCase__	:      List[Any]     							=					kwargs.pop('code_revision'     ,	UpperCamelCase_      )
              if os.path.isdir(UpperCamelCase_      ):
                  feature_extractor_class.register_for_auto_class()
              return feature_extractor_class.from_dict(UpperCamelCase_     ,	**UpperCamelCase_      )
          elif feature_extractor_class is not None:
              return feature_extractor_class.from_dict(UpperCamelCase_     ,	**UpperCamelCase_      )
          # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
          elif type(UpperCamelCase_      ) in FEATURE_EXTRACTOR_MAPPING:
              UpperCAmelCase__	:      List[Any]     							=					FEATURE_EXTRACTOR_MAPPING[type(UpperCamelCase_      )]
              return feature_extractor_class.from_dict(UpperCamelCase_     ,	**UpperCamelCase_      )

          raise ValueError(
              F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
              F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
              F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()      )}'''      )



      @staticmethod
      def  __snake_case							(   UpperCamelCase_     ,	UpperCamelCase_      ):
          FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase_     ,	UpperCamelCase_      )



"""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


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

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





@require_sentencepiece
@require_tokenizers
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizer
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizerFast
      UpperCamelCase							:   str           =     True
      UpperCamelCase							:   Tuple           =     True



      def  __snake_case							(   self      ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__	:      int     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'<pad>'
          UpperCAmelCase__	:      Tuple     							=					1

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



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[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(UpperCamelCase_      )     ,	1_004      )



      def  __snake_case							(   self      ):
          self.assertEqual(self.get_tokenizer().vocab_size     ,	1_005      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )
          UpperCAmelCase__	:      str     							=					CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

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

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

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

          # <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)
          UpperCAmelCase__	:      List[Any]     							=					tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          if not self.test_rust_tokenizer:
              return

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

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

          UpperCAmelCase__	:      List[str]     							=					tokenizer.tokenize(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					rust_tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					self.get_rust_tokenizer()
          UpperCAmelCase__	:      Union[str, Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      @slow
      def  __snake_case							(   self      ):
          # fmt: off
          UpperCAmelCase__	:      Any     							=					{'input_ids': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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.
          UpperCAmelCase__	:      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=UpperCamelCase_     ,	model_name='camembert-base'     ,	revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'     ,	sequences=UpperCamelCase_     ,	)

