






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,
)


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

lowerCamelCase	:							List[Any]				=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'''),
    ]
)

lowerCamelCase	:							int				=_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Any:
     for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
          if class_name in extractors:
               UpperCamelCase__			:   List[str]       				=  model_type_to_module_name(__lowerCAmelCase			)

               UpperCamelCase__			:   Any       				=  importlib.import_module(f'.{module_name}'					,    "transformers.models"			)
               try:
                    return getattr(__lowerCAmelCase					,    __lowerCAmelCase			)
               except AttributeError:
                    continue

     for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
          if getattr(__lowerCAmelCase					,    "__name__"					,    __lowerCAmelCase			) == 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(__lowerCAmelCase					,    __lowerCAmelCase			):
          return getattr(__lowerCAmelCase					,    __lowerCAmelCase			)

     return None







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = None					,    __lowerCAmelCase = False					,    __lowerCAmelCase = False					,    __lowerCAmelCase = None					,    __lowerCAmelCase = None					,    __lowerCAmelCase = None					,    __lowerCAmelCase = False					,    **__lowerCAmelCase					,    ) ->	Tuple:
     UpperCamelCase__			:   List[str]       				=  get_file_from_repo(
         __lowerCAmelCase					,    __lowerCAmelCase					,    cache_dir=__lowerCAmelCase					,    force_download=__lowerCAmelCase					,    resume_download=__lowerCAmelCase					,    proxies=__lowerCAmelCase					,    use_auth_token=__lowerCAmelCase					,    revision=__lowerCAmelCase					,    local_files_only=__lowerCAmelCase					,    )
     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(__lowerCAmelCase					,    encoding="utf-8"			) as reader:
          return json.load(__lowerCAmelCase			)





class 			__a  :







 def __init__(		self					:   List[Any]						):


      '''simple docstring'''






      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(SCREAMING_SNAKE_CASE						)
 def 					__lowercase					(		cls					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Dict							,   **SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  kwargs.pop("config"							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   int       				=  kwargs.pop("trust_remote_code"							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  True

      UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  config_dict.get("image_processor_type"							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  None
      if "AutoImageProcessor" in config_dict.get("auto_map"							,   {}						):
           UpperCamelCase__			:   str       				=  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__			:   Dict       				=  config_dict.pop("feature_extractor_type"							,   SCREAMING_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__			:   List[Any]       				=  feature_extractor_class.replace("FeatureExtractor"							,   "ImageProcessor"						)
           if "AutoFeatureExtractor" in config_dict.get("auto_map"							,   {}						):
                UpperCamelCase__			:   Any       				=  config_dict["auto_map"]["AutoFeatureExtractor"]
                UpperCamelCase__			:   Tuple       				=  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(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   int       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
           # It could be in `config.image_processor_type``
           UpperCamelCase__			:   List[Any]       				=  getattr(SCREAMING_SNAKE_CASE							,   "image_processor_type"							,   SCREAMING_SNAKE_CASE						)
           if hasattr(SCREAMING_SNAKE_CASE							,   "auto_map"						) and "AutoImageProcessor" in config.auto_map:
                UpperCamelCase__			:   str       				=  config.auto_map["AutoImageProcessor"]

      if image_processor_class is not None:
           UpperCamelCase__			:   int       				=  image_processor_class_from_name(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   str       				=  image_processor_auto_map is not None
      UpperCamelCase__			:   int       				=  image_processor_class is not None or type(SCREAMING_SNAKE_CASE						) in IMAGE_PROCESSOR_MAPPING
      UpperCamelCase__			:   Optional[Any]       				=  resolve_trust_remote_code(
          SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      if has_remote_code and trust_remote_code:
           UpperCamelCase__			:   str       				=  get_class_from_dynamic_module(
               SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   str       				=  kwargs.pop("code_revision"							,   SCREAMING_SNAKE_CASE						)
           if os.path.isdir(SCREAMING_SNAKE_CASE						):
                image_processor_class.register_for_auto_class()
           return image_processor_class.from_dict(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
      elif image_processor_class is not None:
           return image_processor_class.from_dict(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
      # Last try: we use the IMAGE_PROCESSOR_MAPPING.
      elif type(SCREAMING_SNAKE_CASE						) in IMAGE_PROCESSOR_MAPPING:
           UpperCamelCase__			:   List[str]       				=  IMAGE_PROCESSOR_MAPPING[type(SCREAMING_SNAKE_CASE						)]
           return image_processor_class.from_dict(SCREAMING_SNAKE_CASE							,   **SCREAMING_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 					__lowercase					(		SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      IMAGE_PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







lowerCamelCase	:							List[Any]				=[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Optional[int]       				=  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
     UpperCamelCase__			:   Any       				=  0
     UpperCamelCase__			:   Any       				=  0
     while place < len(__lowerCAmelCase			):
          if (place + 1 < len(__lowerCAmelCase			)) and (vals[roman[place]] < vals[roman[place + 1]]):
               total += vals[roman[place + 1]] - vals[roman[place]]
               place += 2
          else:
               total += vals[roman[place]]
               place += 1
     return total







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Optional[int]       				=  []
     for arabic, roman in ROMAN:
          ((UpperCamelCase__)				,     (UpperCamelCase__))			:   Optional[int]       				=  divmod(__lowerCAmelCase					,    __lowerCAmelCase			)
          result.append(roman * factor			)
          if number == 0:
               break
     return "".join(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()