




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image






def    snake_case   (				A		,  A=256     )				->				Dict:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[Any]				  =				min(img.size     )

	if s < target_image_size:
		raise ValueError(f"min dim for image {s} < {target_image_size}"     )

	lowerCamelCase_				:						Union[str, Any]				  =				target_image_size / s
	lowerCamelCase_				:						int				  =				(round(r * img.size[1]     ), round(r * img.size[0]     ))
	lowerCamelCase_				:						List[Any]				  =				TF.resize(__UpperCamelCase		,  __UpperCamelCase		,  interpolation=PIL.Image.LANCZOS     )
	lowerCamelCase_				:						Optional[int]				  =				TF.center_crop(__UpperCamelCase		,  output_size=2 * [target_image_size]     )
	lowerCamelCase_				:						Tuple				  =				torch.unsqueeze(T.ToTensor()(__UpperCamelCase     )		,  0     )
	return img






def    snake_case   (				A     )				->				Any:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				2.0 * x - 1.0
	return x






def    snake_case   (				A		,  A=True		,  A="RGB"     )				->				Tuple:




	'''simple docstring'''


	lowerCamelCase_				:						Union[str, Any]				  =				x.detach().cpu()
	if process:
		lowerCamelCase_				:						Optional[int]				  =				post_process_tensor(__UpperCamelCase     )
	lowerCamelCase_				:						Union[str, Any]				  =				x.numpy()
	if process:
		lowerCamelCase_				:						int				  =				(255 * x).astype(np.uinta     )
	lowerCamelCase_				:						str				  =				Image.fromarray(__UpperCamelCase     )
	if not x.mode == mode:
		lowerCamelCase_				:						List[str]				  =				x.convert(__UpperCamelCase     )
	return x






def    snake_case   (				A     )				->				Optional[int]:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[int]				  =				torch.clamp(__UpperCamelCase		,  -1.0		,  1.0     )
	lowerCamelCase_				:						Optional[int]				  =				(x + 1.0) / 2.0
	lowerCamelCase_				:						Tuple				  =				x.permute(1		,  2		,  0     )
	return x






def    snake_case   (				A     )				->				Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_				:						int				  =				post_process_tensor(x.squeeze()     )
	return x.permute(2		,  0		,  1     ).unsqueeze(0     )


_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652E26,
}

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660540199E-27,
}

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    float							)			->							float:

				"""simple docstring"""







				if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
								A__	:						int								=				(
								    F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
								    F"Supported values are: {', '.join(__UpperCamelCase							)}"
								)
								raise ValueError(__UpperCamelCase							)
				return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()