

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     =			logging.get_logger(__name__)

__a     =			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     =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:






   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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						**lowerCAmelCase_  ,						)						->List[str]:
   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   __lowercase				:





      def __init__(		self						:					str       )    ->   List[str]:






         """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(lowercase_       )
      def        _lowercase				(		cls						:					str					,       __lowerCamelCase						:					str					,       **__lowerCamelCase						:					Any       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     kwargs.pop("""config"""					,       lowercase_       )
         UpperCAmelCase							=     kwargs.pop("""trust_remote_code"""					,       lowercase_       )
         UpperCAmelCase							=     True

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

         UpperCAmelCase							=     image_processor_auto_map is not None
         UpperCAmelCase							=     image_processor_class is not None or type(lowercase_       ) in IMAGE_PROCESSOR_MAPPING
         UpperCAmelCase							=     resolve_trust_remote_code(
             lowercase_					,       lowercase_					,       lowercase_					,       lowercase_       )

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

         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				(		__lowerCamelCase						:					str					,       __lowerCamelCase						:					List[Any]       )    ->   Tuple:






         """simple docstring"""







         IMAGE_PROCESSOR_MAPPING.register(lowercase_					,       lowercase_       )







import argparse


__a     =			"""docs/source/_static/js/custom.js"""
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   with open(lowerCAmelCase_  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      UpperCAmelCase							=     f.readlines()
   UpperCAmelCase							=     0

   # First let's put the right version
   while not lines[index].startswith("""const stableVersion ="""   ):
      index += 1
   UpperCAmelCase							=     F"""const stableVersion = \"v{version}\"\n"""

   # Then update the dictionary
   while not lines[index].startswith("""const versionMapping = {"""   ):
      index += 1

   # We go until the end
   while not lines[index].startswith("""}"""   ):
      index += 1
   # We add the new version at the end
   lines[index - 1] += F"""    \"v{version}\": \"v{version}\",\n"""

   with open(lowerCAmelCase_  ,						"""w"""  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      f.writelines(lowerCAmelCase_   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()
 parser.add_argument("""--version""", help="""Release version.""")
 __a     =			parser.parse_args()
 update_custom_js(args.version)



