






"""simple docstring"""





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,
)


A__					: List[Any]=	logging.get_logger(__name__)

A__					: Tuple=	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"""),
    ]
)

A__					: Optional[Any]=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = False ,	SCREAMING_SNAKE_CASE = False ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = False ,	**SCREAMING_SNAKE_CASE ,	)					->	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    __lowerCamelCase    :

   def __init__(					self      )		->       Any:
       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(snake_case_      )
   def 						SCREAMING_SNAKE_CASE__ (					cls ,				snake_case_ ,				**snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    kwargs.pop('config' ,				snake_case_      )
       UpperCamelCase__         =    kwargs.pop('trust_remote_code' ,				snake_case_      )
       UpperCamelCase__         =    True

       UpperCamelCase__       ,						UpperCamelCase__         =    ImageProcessingMixin.get_image_processor_dict(snake_case_ ,				**snake_case_      )
       UpperCamelCase__         =    config_dict.get('image_processor_type' ,				snake_case_      )
       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' ,				snake_case_      )
           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(snake_case_ ,				snake_case_      ):
               UpperCamelCase__         =    AutoConfig.from_pretrained(snake_case_ ,				**snake_case_      )
           # It could be in `config.image_processor_type``
           UpperCamelCase__         =    getattr(snake_case_ ,				'image_processor_type' ,				snake_case_      )
           if hasattr(snake_case_ ,				'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(snake_case_      )

       UpperCamelCase__         =    image_processor_auto_map is not None
       UpperCamelCase__         =    image_processor_class is not None or type(snake_case_      ) in IMAGE_PROCESSOR_MAPPING
       UpperCamelCase__         =    resolve_trust_remote_code(
           snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

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

       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__ (					snake_case_ ,				snake_case_      )		->       Any:
       IMAGE_PROCESSOR_MAPPING.register(snake_case_ ,				snake_case_      )












"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  A__					: str=	{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  A__					: str=	{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    (images / 2 + 0.5).clamp(0 ,	1					)
    UpperCamelCase__         =    images.cpu().permute(0 ,	2 ,	3 ,	1					).float().numpy()
    UpperCamelCase__         =    numpy_to_pil(SCREAMING_SNAKE_CASE					)
    return images







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    if images.ndim == 3:
        UpperCamelCase__         =    images[None, ...]
    UpperCamelCase__         =    (images * 2_55).round().astype('uint8'					)
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCamelCase__         =    [Image.fromarray(image.squeeze() ,	mode='L'					) for image in images]
    else:
        UpperCamelCase__         =    [Image.fromarray(SCREAMING_SNAKE_CASE					) for image in images]

    return pil_images




