


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

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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				:    Any       				=     logging.get_logger(__name__)

snake_case				:    Optional[int]       				=     OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

snake_case				:    Optional[int]       				=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  a						:Tuple 							=       model_type_to_module_name(UpperCAmelCase_						)

                  a						:Tuple 							=       importlib.import_module(F'''.{module_name}''' ,  '''transformers.models'''						)
                  try:
                        return getattr(UpperCAmelCase_ ,  UpperCAmelCase_						)
                  except AttributeError:
                        continue

      for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
            if getattr(UpperCAmelCase_ ,  '''__name__''' ,  UpperCAmelCase_						) == 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.
      a						:Optional[int] 							=       importlib.import_module('''transformers'''						)
      if hasattr(UpperCAmelCase_ ,  UpperCAmelCase_						):
            return getattr(UpperCAmelCase_ ,  UpperCAmelCase_						)

      return None
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, os.PathLike] ,  UpperCAmelCase_						:		Optional[Union[str, os.PathLike]] = None ,  UpperCAmelCase_						:		bool = False ,  UpperCAmelCase_						:		bool = False ,  UpperCAmelCase_						:		Optional[Dict[str, str]] = None ,  UpperCAmelCase_						:		Optional[Union[bool, str]] = None ,  UpperCAmelCase_						:		Optional[str] = None ,  UpperCAmelCase_						:		bool = False ,  **UpperCAmelCase_						:		Optional[Any] ,  ):




      """simple docstring"""





      a						:Any 							=       get_file_from_repo(
          UpperCAmelCase_ ,  UpperCAmelCase_ ,  cache_dir=UpperCAmelCase_ ,  force_download=UpperCAmelCase_ ,  resume_download=UpperCAmelCase_ ,  proxies=UpperCAmelCase_ ,  use_auth_token=UpperCAmelCase_ ,  revision=UpperCAmelCase_ ,  local_files_only=UpperCAmelCase_ ,  )
      if resolved_config_file is None:
            logger.info(
                '''Could not locate the image processor configuration file, will try to use the model config instead.'''						)
            return {}

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







class 		_snake_case  :




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




 @classmethod
 @replace_list_option_in_docstrings(_lowerCamelCase    )
 def        SCREAMING_SNAKE_CASE__				(		cls					,  _lowerCamelCase					,  **_lowerCamelCase    ):
       a						:str 							=       kwargs.pop('''config'''					,  _lowerCamelCase    )
       a						:Optional[Any] 							=       kwargs.pop('''trust_remote_code'''					,  _lowerCamelCase    )
       a						:Any 							=       True

       a							,				a						:Optional[int] 							=       ImageProcessingMixin.get_image_processor_dict(_lowerCamelCase					,  **_lowerCamelCase    )
       a						:List[Any] 							=       config_dict.get('''image_processor_type'''					,  _lowerCamelCase    )
       a						:Dict 							=       None
       if "AutoImageProcessor" in config_dict.get('''auto_map'''					,  {}    ):
             a						:Union[str, Any] 							=       config_dict['''auto_map''']['''AutoImageProcessor''']

       # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
       # and if so, infer the image processor class from there.
       if image_processor_class is None and image_processor_auto_map is None:
             a						:Union[str, Any] 							=       config_dict.pop('''feature_extractor_type'''					,  _lowerCamelCase    )
             if feature_extractor_class is not None:
                   logger.warning(
                       '''Could not find image processor class in the image processor config or the model config. Loading'''
                       ''' based on pattern matching with the model\'s feature extractor configuration.'''    )
                   a						:Union[str, Any] 							=       feature_extractor_class.replace('''FeatureExtractor'''					,  '''ImageProcessor'''    )
             if "AutoFeatureExtractor" in config_dict.get('''auto_map'''					,  {}    ):
                   a						:Optional[Any] 							=       config_dict['''auto_map''']['''AutoFeatureExtractor''']
                   a						:str 							=       feature_extractor_auto_map.replace('''FeatureExtractor'''					,  '''ImageProcessor'''    )
                   logger.warning(
                       '''Could not find image processor auto map in the image processor config or the model config.'''
                       ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''    )

        # If we don't find the image processor class in the image processor config, let's try the model config.
       if image_processor_class is None and image_processor_auto_map is None:
             if not isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
                   a						:Optional[int] 							=       AutoConfig.from_pretrained(_lowerCamelCase					,  **_lowerCamelCase    )
             # It could be in `config.image_processor_type``
             a						:List[str] 							=       getattr(_lowerCamelCase					,  '''image_processor_type'''					,  _lowerCamelCase    )
             if hasattr(_lowerCamelCase					,  '''auto_map'''    ) and "AutoImageProcessor" in config.auto_map:
                   a						:List[Any] 							=       config.auto_map['''AutoImageProcessor''']

       if image_processor_class is not None:
             a						:Optional[int] 							=       image_processor_class_from_name(_lowerCamelCase    )

       a						:str 							=       image_processor_auto_map is not None
       a						:Dict 							=       image_processor_class is not None or type(_lowerCamelCase    ) in IMAGE_PROCESSOR_MAPPING
       a						:Optional[Any] 							=       resolve_trust_remote_code(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

       if has_remote_code and trust_remote_code:
             a						:int 							=       get_class_from_dynamic_module(
                 _lowerCamelCase					,  _lowerCamelCase					,  **_lowerCamelCase    )
             a						:Any 							=       kwargs.pop('''code_revision'''					,  _lowerCamelCase    )
             if os.path.isdir(_lowerCamelCase    ):
                   image_processor_class.register_for_auto_class()
             return image_processor_class.from_dict(_lowerCamelCase					,  **_lowerCamelCase    )
       elif image_processor_class is not None:
             return image_processor_class.from_dict(_lowerCamelCase					,  **_lowerCamelCase    )
       # Last try: we use the IMAGE_PROCESSOR_MAPPING.
       elif type(_lowerCamelCase    ) in IMAGE_PROCESSOR_MAPPING:
             a						:Tuple 							=       IMAGE_PROCESSOR_MAPPING[type(_lowerCamelCase    )]
             return image_processor_class.from_dict(_lowerCamelCase					,  **_lowerCamelCase    )

       raise ValueError(
           F'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
           F'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
           F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()    )}'''    )




 @staticmethod
 def        SCREAMING_SNAKE_CASE__				(		_lowerCamelCase					,  _lowerCamelCase    ):
       IMAGE_PROCESSOR_MAPPING.register(_lowerCamelCase					,  _lowerCamelCase    )









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







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       kwargs.pop('''sampling_rate'''					,  _lowerCamelCase    )

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

       if text is not None:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )





