
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[str]       =			logging.get_logger(__name__)

_lowerCamelCase       :				Optional[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       :				str       =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def        a_		(       __lowercase   :	str ) ->  List[str]:
			for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
						if class_name in extractors:
									_snake_case		=			model_type_to_module_name(__lowercase )

									_snake_case		=			importlib.import_module(f'''.{module_name}'''       ,					'transformers.models' )
									try:
												return getattr(__lowercase       ,					__lowercase )
									except AttributeError:
												continue

			for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
						if getattr(__lowercase       ,					'__name__'       ,					__lowercase ) == 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.
			_snake_case		=			importlib.import_module('transformers' )
			if hasattr(__lowercase       ,					__lowercase ):
						return getattr(__lowercase       ,					__lowercase )

			return None






def        a_		(       __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]       ,					) ->  Any:
			_snake_case		=			get_file_from_repo(
			    __lowercase       ,					__lowercase       ,					cache_dir=__lowercase       ,					force_download=__lowercase       ,					resume_download=__lowercase       ,					proxies=__lowercase       ,					use_auth_token=__lowercase       ,					revision=__lowercase       ,					local_files_only=__lowercase       ,					)
			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(__lowercase       ,					encoding='utf-8' ) as reader:
						return json.load(__lowercase )




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Tuple	):


				'''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  A							(  cls	:    Any			,  lowercase	:    Any			,  **lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			kwargs.pop('config'			,  lowercase	)
				_snake_case		=			kwargs.pop('trust_remote_code'			,  lowercase	)
				_snake_case		=			True

				_snake_case	,					_snake_case		=			ImageProcessingMixin.get_image_processor_dict(lowercase			,  **lowercase	)
				_snake_case		=			config_dict.get('image_processor_type'			,  lowercase	)
				_snake_case		=			None
				if "AutoImageProcessor" in config_dict.get('auto_map'			,  {}	):
							_snake_case		=			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:
							_snake_case		=			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.'	)
										_snake_case		=			feature_extractor_class.replace('FeatureExtractor'			,  'ImageProcessor'	)
							if "AutoFeatureExtractor" in config_dict.get('auto_map'			,  {}	):
										_snake_case		=			config_dict['auto_map']['AutoFeatureExtractor']
										_snake_case		=			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	):
										_snake_case		=			AutoConfig.from_pretrained(lowercase			,  **lowercase	)
							# It could be in `config.image_processor_type``
							_snake_case		=			getattr(lowercase			,  'image_processor_type'			,  lowercase	)
							if hasattr(lowercase			,  'auto_map'	) and "AutoImageProcessor" in config.auto_map:
										_snake_case		=			config.auto_map['AutoImageProcessor']

				if image_processor_class is not None:
							_snake_case		=			image_processor_class_from_name(lowercase	)

				_snake_case		=			image_processor_auto_map is not None
				_snake_case		=			image_processor_class is not None or type(lowercase	) in IMAGE_PROCESSOR_MAPPING
				_snake_case		=			resolve_trust_remote_code(
				    lowercase			,  lowercase			,  lowercase			,  lowercase	)

				if has_remote_code and trust_remote_code:
							_snake_case		=			get_class_from_dynamic_module(
							    lowercase			,  lowercase			,  **lowercase	)
							_snake_case		=			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:
							_snake_case		=			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  A							(  lowercase	:    str			,  lowercase	:    Tuple	):


				'''simple docstring'''


				IMAGE_PROCESSOR_MAPPING.register(lowercase			,  lowercase	)

class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    List[str]			,  lowercase	:    list[int]	):


				'''simple docstring'''


				_snake_case		=			len(lowercase	)
				_snake_case		=			[0] * len_array

				if len_array > 0:
							_snake_case		=			array[0]

				for i in range(1			,  lowercase	):
							_snake_case		=			self.prefix_sum[i - 1] + array[i]



	def  A							(  self	:    Optional[Any]			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				if start == 0:
							return self.prefix_sum[end]

				return self.prefix_sum[end] - self.prefix_sum[start - 1]





	def  A							(  self	:    Union[str, Any]			,  lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			{0}
				for sum_item in self.prefix_sum:
							if sum_item - target_sum in sums:
										return True

							sums.add(lowercase	)

				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()