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,
)


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		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"""),
    ]
)

_UpperCAmelCase            =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def     UpperCamelCase						(	__lowercase						:			str     ) ->    int:
		'''simple docstring'''




		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						A_		:    Any         =				model_type_to_module_name(__SCREAMING_SNAKE_CASE     )

						A_		:    Optional[int]         =				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.
		A_		:    str         =				importlib.import_module('transformers'     )
		if hasattr(__SCREAMING_SNAKE_CASE						,__SCREAMING_SNAKE_CASE     ):
				return getattr(__SCREAMING_SNAKE_CASE						,__SCREAMING_SNAKE_CASE     )

		return None




def     UpperCamelCase						(	__lowercase						:			Union[str, os.PathLike]						,__lowercase						:			Optional[Union[str, os.PathLike]] = None						,__lowercase						:			bool = False						,__lowercase						:			bool = False						,__lowercase						:			Optional[Dict[str, str]] = None						,__lowercase						:			Optional[Union[bool, str]] = None						,__lowercase						:			Optional[str] = None						,__lowercase						:			bool = False						,**__lowercase						:			Optional[Any]						,) ->    List[str]:
		'''simple docstring'''




		A_		:    Optional[int]         =				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 UpperCAmelCase						:


				'''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(_lowerCAmelCase )
				def   lowerCAmelCase_       (						cls		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						A_		:    int         =				kwargs.pop('config'		,     _lowerCAmelCase )
						A_		:    str         =				kwargs.pop('trust_remote_code'		,     _lowerCAmelCase )
						A_		:    Any         =				True

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

						if image_processor_class is not None:
								A_		:    Optional[int]         =				image_processor_class_from_name(_lowerCAmelCase )

						A_		:    Optional[Any]         =				image_processor_auto_map is not None
						A_		:    Optional[Any]         =				image_processor_class is not None or type(_lowerCAmelCase ) in IMAGE_PROCESSOR_MAPPING
						A_		:    List[str]         =				resolve_trust_remote_code(
						    _lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase )

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

						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_       (						lowercase		,     lowercase ):



						"""simple docstring"""





						IMAGE_PROCESSOR_MAPPING.register(_lowerCAmelCase		,     _lowerCAmelCase )







import math




def     UpperCamelCase						(	__lowercase						:			int = 1_00     ):
		'''simple docstring'''




		A_		:    List[Any]         =				sum(i * i for i in range(1						,n + 1     )     )
		A_		:    int         =				int(math.pow(sum(range(1						,n + 1     )     )						,2     )     )
		return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")






