


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,
)


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						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'''),
    ]
)

__snake_case   							=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
          if class_name in extractors:
               __UpperCamelCase =			model_type_to_module_name(_lowercase )

               __UpperCamelCase =			importlib.import_module(f'''.{module_name}'''					,  'transformers.models' )
               try:
                    return getattr(_lowercase					,  _lowercase )
               except AttributeError:
                    continue

     for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
          if getattr(_lowercase					,  '__name__'					,  _lowercase ) == 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 =			importlib.import_module('transformers' )
     if hasattr(_lowercase					,  _lowercase ):
          return getattr(_lowercase					,  _lowercase )

     return None






def 		_A		(		_lowercase					,  _lowercase = None					,  _lowercase = False					,  _lowercase = False					,  _lowercase = None					,  _lowercase = None					,  _lowercase = None					,  _lowercase = False					,  **_lowercase					,  )					-> Dict:


     """simple docstring"""


     __UpperCamelCase =			get_file_from_repo(
         _lowercase					,  _lowercase					,  cache_dir=_lowercase					,  force_download=_lowercase					,  resume_download=_lowercase					,  proxies=_lowercase					,  use_auth_token=_lowercase					,  revision=_lowercase					,  local_files_only=_lowercase					,  )
     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(_lowercase					,  encoding='utf-8' ) as reader:
          return json.load(_lowercase )



class 	__lowerCamelCase			:







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






            '''simple docstring'''




            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(A_					)
       def 		snake_case_	(			cls: List[Any],A_: Dict,**A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			kwargs.pop('config',A_					)
            __UpperCamelCase =			kwargs.pop('trust_remote_code',A_					)
            __UpperCamelCase =			True

            __UpperCamelCase,    __UpperCamelCase =			FeatureExtractionMixin.get_feature_extractor_dict(A_,**A_					)
            __UpperCamelCase =			config_dict.get('feature_extractor_type',A_					)
            __UpperCamelCase =			None
            if "AutoFeatureExtractor" in config_dict.get('auto_map',{}					):
                 __UpperCamelCase =			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(A_,A_					):
                      __UpperCamelCase =			AutoConfig.from_pretrained(A_,**A_					)
                 # It could be in `config.feature_extractor_type``
                 __UpperCamelCase =			getattr(A_,'feature_extractor_type',A_					)
                 if hasattr(A_,'auto_map'					) and "AutoFeatureExtractor" in config.auto_map:
                      __UpperCamelCase =			config.auto_map['AutoFeatureExtractor']

            if feature_extractor_class is not None:
                 __UpperCamelCase =			feature_extractor_class_from_name(A_					)

            __UpperCamelCase =			feature_extractor_auto_map is not None
            __UpperCamelCase =			feature_extractor_class is not None or type(A_					) in FEATURE_EXTRACTOR_MAPPING
            __UpperCamelCase =			resolve_trust_remote_code(
                A_,A_,A_,A_					)

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

            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_	(			A_: Any,A_: Tuple					):






            '''simple docstring'''




            FEATURE_EXTRACTOR_MAPPING.register(A_,A_					)







'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''CLIPImageProcessor'''
 A__				= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
 def __init__(   self						:							Any      ,     __a						:							str=None      ,     __a						:							List[Any]=None      ,     **__a						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   List[str]													=    kwargs.pop('feature_extractor'  )

       __snake_case :   List[Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
 def __call__(   self						:							List[Any]      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[int]=None      ,     __a						:							Union[str, Any]=None      ,     **__a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''

       if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.'  )

       if text is not None:
             __snake_case :   Any													=    self.tokenizer(__a      ,     return_tensors=__a      ,     **__a  )

       if images is not None:
             __snake_case :   str													=    self.image_processor(__a      ,     return_tensors=__a      ,     **__a  )

       if text is not None and images is not None:
             __snake_case :   Optional[Any]													=    image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**__a  )      ,     tensor_type=__a  )
 def A_   (   self						:							List[Any]      ,     *__a						:							Dict      ,     **__a						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							str      ,     *__a						:							Tuple      ,     **__a						:							List[str]  )      ->   int:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.tokenizer.model_input_names
       __snake_case :   Tuple													=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





 @property
 def A_   (   self						:							Optional[int]  )      ->   Optional[Any]:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



