





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







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	int=256  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       min(img.size  )

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

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







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       2.0 * x - 1.0
						return x







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	int=True      , _lowerCamelCase   :	Optional[Any]="RGB"  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       x.detach().cpu()
						if process:
												SCREAMING_SNAKE_CASE__  :					Dict =       post_process_tensor(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       x.numpy()
						if process:
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       (255 * x).astype(np.uinta  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       Image.fromarray(_lowerCamelCase  )
						if not x.mode == mode:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       x.convert(_lowerCamelCase  )
						return x







def    UpperCAmelCase				(  _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       torch.clamp(_lowerCamelCase      , -1.0      , 1.0  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       (x + 1.0) / 2.0
						SCREAMING_SNAKE_CASE__  :					int =       x.permute(1      , 2      , 0  )
						return x







def    UpperCAmelCase				(  _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       post_process_tensor(x.squeeze()  )
						return x.permute(2      , 0      , 1  ).unsqueeze(0  )






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__lowercase							:List[Any]							=	TypeVar("T", bound="Enum")



@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    80
							snake_case_							=    70
							snake_case_							=    60
							snake_case_							=    50
							snake_case_							=    40
							snake_case_							=    30
							snake_case_							=    20
							snake_case_							=    10







@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    24
							snake_case_							=    21
							snake_case_							=    18
							snake_case_							=    15
							snake_case_							=    12
							snake_case_							=    9
							snake_case_							=    6
							snake_case_							=    3
							snake_case_							=    2
							snake_case_							=    1
							snake_case_							=    -1
							snake_case_							=    -2
							snake_case_							=    -3
							snake_case_							=    -6
							snake_case_							=    -9
							snake_case_							=    -12
							snake_case_							=    -15
							snake_case_							=    -18
							snake_case_							=    -21
							snake_case_							=    -24

							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value > 0}






							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value < 0}







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												SCREAMING_SNAKE_CASE__  :					str =       value / (10**value_prefix)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {name_prefix}"""
						return str(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						for prefix in BinaryUnit:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       value / (2**prefix.value)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {prefix.name}"""
						return str(_lowerCamelCase  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()