






"""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    (     __UpperCAmelCase      :    str  )    ->							Optional[int]:
						for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		__magic_name__:	List[Any]							=       model_type_to_module_name(__UpperCAmelCase  )

																		__magic_name__:	List[str]							=       importlib.import_module(f'.{module_name}'      ,   """transformers.models"""  )
																		try:
																								return getattr(__UpperCAmelCase      ,   __UpperCAmelCase  )
																		except AttributeError:
																								continue

						for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
												if getattr(__UpperCAmelCase      ,   """__name__"""      ,   __UpperCAmelCase  ) == 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.
						__magic_name__:	int							=       importlib.import_module("""transformers"""  )
						if hasattr(__UpperCAmelCase      ,   __UpperCAmelCase  ):
												return getattr(__UpperCAmelCase      ,   __UpperCAmelCase  )

						return None



def   a    (     __UpperCAmelCase      :    Union[str, os.PathLike]      ,   __UpperCAmelCase      :    Optional[Union[str, os.PathLike]] = None      ,   __UpperCAmelCase      :    bool = False      ,   __UpperCAmelCase      :    bool = False      ,   __UpperCAmelCase      :    Optional[Dict[str, str]] = None      ,   __UpperCAmelCase      :    Optional[Union[bool, str]] = None      ,   __UpperCAmelCase      :    Optional[str] = None      ,   __UpperCAmelCase      :    bool = False      ,   **__UpperCAmelCase      :    Any      ,   )    ->							Dict:
						__magic_name__:	Union[str, Any]							=       get_file_from_repo(
						    __UpperCAmelCase      ,   __UpperCAmelCase      ,   cache_dir=__UpperCAmelCase      ,   force_download=__UpperCAmelCase      ,   resume_download=__UpperCAmelCase      ,   proxies=__UpperCAmelCase      ,   use_auth_token=__UpperCAmelCase      ,   revision=__UpperCAmelCase      ,   local_files_only=__UpperCAmelCase      ,   )
						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(__UpperCAmelCase      ,   encoding="""utf-8"""  ) as reader:
												return json.load(__UpperCAmelCase  )



class       __A		:

					def __init__(			self  :      int					)			->							int:
											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   lowerCamelCase__   (			cls  :      int     ,		__snake_case  :      Optional[Any]     ,		**__snake_case  :      Dict					)			->							List[str]:
											__magic_name__:	Dict							=       kwargs.pop("""config"""     ,		__snake_case					)
											__magic_name__:	int							=       kwargs.pop("""trust_remote_code"""     ,		__snake_case					)
											__magic_name__:	Dict							=       True

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

											if image_processor_class is not None:
																	__magic_name__:	str							=       image_processor_class_from_name(__snake_case					)

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

											if has_remote_code and trust_remote_code:
																	__magic_name__:	Optional[Any]							=       get_class_from_dynamic_module(
																	    __snake_case     ,		__snake_case     ,		**__snake_case					)
																	__magic_name__:	int							=       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:
																	__magic_name__:	Tuple							=       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   lowerCamelCase__   (			__snake_case  :      Union[str, Any]     ,		__snake_case  :      str					)			->							Any:
											IMAGE_PROCESSOR_MAPPING.register(__snake_case     ,		__snake_case					)











"""simple docstring"""





from __future__ import annotations



def   a    (     __UpperCAmelCase      :    list  )    ->							list:
						if len(__UpperCAmelCase  ) == 0:
												return []
						__magic_name__,			__magic_name__:	List[str]							=       min(__UpperCAmelCase  ), max(__UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       int(max_value - min_value  ) + 1
						__magic_name__:	list[list]							=       [[] for _ in range(__UpperCAmelCase  )]

						for i in my_list:
												buckets[int(i - min_value  )].append(__UpperCAmelCase  )

						return [v for bucket in buckets for v in sorted(__UpperCAmelCase  )]


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]



