


'''simple docstring'''





from collections import namedtuple

lowercase_								=    namedtuple('from_to', 'from_ to')

lowercase_								=    {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

lowercase_								=    {
    'mm': from_to(0.001, 10_00),
    'cm': from_to(0.01, 1_00),
    'm': from_to(1, 1),
    'km': from_to(10_00, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.28084),
    'yd': from_to(0.9144, 1.09361),
    'mi': from_to(1609.34, 0.000621371),
}


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  float:
						_a     =			from_type.lower().rstrip('s'				)
						_a     =			TYPE_CONVERSION.get(_UpperCAmelCase							,     _UpperCAmelCase				)
						_a     =			to_type.lower().rstrip('s'				)
						_a     =			TYPE_CONVERSION.get(_UpperCAmelCase							,     _UpperCAmelCase				)
						if new_from not in METRIC_CONVERSION:
												_a     =			(
												    f"""Invalid 'from_type' value: {from_type!r}.\n"""
												    f"""Conversion abbreviations are: {', '.join(_UpperCAmelCase				)}"""
												)
												raise ValueError(_UpperCAmelCase				)
						if new_to not in METRIC_CONVERSION:
												_a     =			(
												    f"""Invalid 'to_type' value: {to_type!r}.\n"""
												    f"""Conversion abbreviations are: {', '.join(_UpperCAmelCase				)}"""
												)
												raise ValueError(_UpperCAmelCase				)
						return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
		import doctest

		doctest.testmod()






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


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
						_a     =			min(img.size				)

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

						_a     =			target_image_size / s
						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
						return img


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
						_a     =			2.0 * x - 1.0
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
						_a     =			x.detach().cpu()
						if process:
												_a     =			post_process_tensor(_UpperCAmelCase				)
						_a     =			x.numpy()
						if process:
												_a     =			(255 * x).astype(np.uinta				)
						_a     =			Image.fromarray(_UpperCAmelCase				)
						if not x.mode == mode:
												_a     =			x.convert(_UpperCAmelCase				)
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
						_a     =			(x + 1.0) / 2.0
						_a     =			x.permute(1							,     2							,     0				)
						return x






def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
						_a     =			post_process_tensor(x.squeeze()				)
						return x.permute(2							,     0							,     1				).unsqueeze(0				)


