


"""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,
)


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   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__      				=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				):


  '''simple docstring'''







  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      lowerCAmelCase     :     Optional[int]								=							model_type_to_module_name(SCREAMING_SNAKE_CASE				)

      lowerCAmelCase     :     str								=							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.
  lowerCAmelCase     :     Optional[int]								=							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    :    Union[str, Any]				,      ):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							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       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self						):






         """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(snake_case__						)
       def        lowercase__							(						cls		,     snake_case__		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							kwargs.pop("config"		,     snake_case__						)
         lowerCAmelCase     :     List[str]								=							kwargs.pop("trust_remote_code"		,     snake_case__						)
         lowerCAmelCase     :     List[Any]								=							True

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

         if image_processor_class is not None:
           lowerCAmelCase     :     Union[str, Any]								=							image_processor_class_from_name(snake_case__						)

         lowerCAmelCase     :     List[Any]								=							image_processor_auto_map is not None
         lowerCAmelCase     :     int								=							image_processor_class is not None or type(snake_case__						) in IMAGE_PROCESSOR_MAPPING
         lowerCAmelCase     :     int								=							resolve_trust_remote_code(
             snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)

         if has_remote_code and trust_remote_code:
           lowerCAmelCase     :     List[Any]								=							get_class_from_dynamic_module(
               snake_case__		,     snake_case__		,     **snake_case__						)
           lowerCAmelCase     :     str								=							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:
           lowerCAmelCase     :     Optional[Any]								=							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        lowercase__							(						snake_case__		,     snake_case__						):






         """simple docstring"""





         IMAGE_PROCESSOR_MAPPING.register(snake_case__		,     snake_case__						)








"""simple docstring"""


from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url






@pytest.mark.parametrize("repo_id"					, ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path"					, ["filename.csv", "filename with blanks.csv"] )
@pytest.mark.parametrize("revision"					, [None, "v2"] )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	List[str]:
		__lowerCAmelCase:       Optional[Any]          =			hf_hub_url(repo_id=__SCREAMING_SNAKE_CASE					, path=__SCREAMING_SNAKE_CASE					, revision=__SCREAMING_SNAKE_CASE )
		assert url == F"https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(__SCREAMING_SNAKE_CASE )}"


