





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,
)


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	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"),
    ]
)

UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  Optional[int]:






   """simple docstring"""







   for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
      if class_name in extractors:
         _UpperCAmelCase           =   model_type_to_module_name(SCREAMING_SNAKE_CASE_						)

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

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

   return None
def      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, os.PathLike]				,	SCREAMING_SNAKE_CASE_   :      Optional[Union[str, os.PathLike]] = None				,	SCREAMING_SNAKE_CASE_   :      bool = False				,	SCREAMING_SNAKE_CASE_   :      bool = False				,	SCREAMING_SNAKE_CASE_   :      Optional[Dict[str, str]] = None				,	SCREAMING_SNAKE_CASE_   :      Optional[Union[bool, str]] = None				,	SCREAMING_SNAKE_CASE_   :      Optional[str] = None				,	SCREAMING_SNAKE_CASE_   :      bool = False				,	**SCREAMING_SNAKE_CASE_   :      List[Any]				,	)  ->  Tuple:






   """simple docstring"""







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




class 			__UpperCamelCase    :





     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(_UpperCamelCase					)
     def 		UpperCamelCase(     cls				,							_UpperCamelCase				,							**_UpperCamelCase					):
        _UpperCAmelCase           =   kwargs.pop('''config'''				,							_UpperCamelCase					)
        _UpperCAmelCase           =   kwargs.pop('''trust_remote_code'''				,							_UpperCamelCase					)
        _UpperCAmelCase           =   True

        _UpperCAmelCase       ,      _UpperCAmelCase           =   ImageProcessingMixin.get_image_processor_dict(_UpperCamelCase				,							**_UpperCamelCase					)
        _UpperCAmelCase           =   config_dict.get('''image_processor_type'''				,							_UpperCamelCase					)
        _UpperCAmelCase           =   None
        if "AutoImageProcessor" in config_dict.get('''auto_map'''				,							{}					):
           _UpperCAmelCase           =   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:
           _UpperCAmelCase           =   config_dict.pop('''feature_extractor_type'''				,							_UpperCamelCase					)
           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.'''					)
              _UpperCAmelCase           =   feature_extractor_class.replace('''FeatureExtractor'''				,							'''ImageProcessor'''					)
           if "AutoFeatureExtractor" in config_dict.get('''auto_map'''				,							{}					):
              _UpperCAmelCase           =   config_dict['''auto_map''']['''AutoFeatureExtractor''']
              _UpperCAmelCase           =   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(_UpperCamelCase				,							_UpperCamelCase					):
              _UpperCAmelCase           =   AutoConfig.from_pretrained(_UpperCamelCase				,							**_UpperCamelCase					)
           # It could be in `config.image_processor_type``
           _UpperCAmelCase           =   getattr(_UpperCamelCase				,							'''image_processor_type'''				,							_UpperCamelCase					)
           if hasattr(_UpperCamelCase				,							'''auto_map'''					) and "AutoImageProcessor" in config.auto_map:
              _UpperCAmelCase           =   config.auto_map['''AutoImageProcessor''']

        if image_processor_class is not None:
           _UpperCAmelCase           =   image_processor_class_from_name(_UpperCamelCase					)

        _UpperCAmelCase           =   image_processor_auto_map is not None
        _UpperCAmelCase           =   image_processor_class is not None or type(_UpperCamelCase					) in IMAGE_PROCESSOR_MAPPING
        _UpperCAmelCase           =   resolve_trust_remote_code(
            _UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)

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

        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 		UpperCamelCase(     _UpperCamelCase				,							_UpperCamelCase					):
        IMAGE_PROCESSOR_MAPPING.register(_UpperCamelCase				,							_UpperCamelCase					)






import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()