
'''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,
)


lowercase					:							Optional[int]   =      logging.get_logger(__name__)

lowercase					:							List[str]   =      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"),
    ]
)

lowercase					:							Optional[int]   =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
		if class_name in extractors:
			_snake_case     		=	model_type_to_module_name(__A			)

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

	for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
		if getattr(__A    ,       '__name__'    ,       __A			) == 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(__A    ,       __A			):
		return getattr(__A    ,       __A			)

	return None







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A = None    ,       __A = False    ,       __A = False    ,       __A = None    ,       __A = None    ,       __A = None    ,       __A = False    ,       **__A    ,       ) ->			List[str]:
	_snake_case     		=	get_file_from_repo(
	    __A    ,       __A    ,       cache_dir=__A    ,       force_download=__A    ,       resume_download=__A    ,       proxies=__A    ,       use_auth_token=__A    ,       revision=__A    ,       local_files_only=__A    ,       )
	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(__A    ,       encoding='utf-8'			) as reader:
		return json.load(__A			)





class 		_UpperCAmelCase   :


							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					,     lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






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

								_snake_case		,				_snake_case     		=	ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase_					,     **lowerCAmelCase_						)
								_snake_case     		=	config_dict.get('image_processor_type'					,     lowerCAmelCase_						)
								_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'					,     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.'						)
										_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(lowerCAmelCase_					,     lowerCAmelCase_						):
										_snake_case     		=	AutoConfig.from_pretrained(lowerCAmelCase_					,     **lowerCAmelCase_						)
									# It could be in `config.image_processor_type``
									_snake_case     		=	getattr(lowerCAmelCase_					,     'image_processor_type'					,     lowerCAmelCase_						)
									if hasattr(lowerCAmelCase_					,     '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(lowerCAmelCase_						)

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

								if has_remote_code and trust_remote_code:
									_snake_case     		=	get_class_from_dynamic_module(
									    lowerCAmelCase_					,     lowerCAmelCase_					,     **lowerCAmelCase_						)
									_snake_case     		=	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:
									_snake_case     		=	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       ( lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase_					,     lowerCAmelCase_						)



'''simple docstring'''







import math







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * power_factor







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * math.sqrt(1 - power_factor**2			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

