


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		:      Any     ,					UpperCamelCase		:      Union[str, Any]=256	)     ->	Dict:



	"""simple docstring"""






	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		:      int	)     ->	Union[str, Any]:



	"""simple docstring"""






	a_     			=   2.0 * x - 1.0
	return x

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict     ,					UpperCamelCase		:      str=True     ,					UpperCamelCase		:      Dict="RGB"	)     ->	List[Any]:



	"""simple docstring"""






	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		:      int	)     ->	Tuple:



	"""simple docstring"""






	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		:      Tuple	)     ->	Any:



	"""simple docstring"""






	a_     			=   post_process_tensor(x.squeeze()	)
	return x.permute(2     ,					0     ,					1	).unsqueeze(0	)



from .balanced_parentheses import balanced_parentheses
from .stack import Stack

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(UpperCamelCase     ,					-1	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	str:



	"""simple docstring"""






	if not balanced_parentheses(UpperCamelCase	):
		raise ValueError("""Mismatched parentheses"""	)
	a_     			=   Stack()
	a_     			=   []
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(UpperCamelCase	)
		elif char == "(":
			stack.push(UpperCamelCase	)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()	)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(UpperCamelCase	) <= precedence(stack.peek()	):
				postfix.append(stack.pop()	)
			stack.push(UpperCamelCase	)
	while not stack.is_empty():
		postfix.append(stack.pop()	)
	return " ".join(UpperCamelCase	)


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			_A							     =       'a+b*(c^d-e)^(f+g*h)-i'

			print('Infix to Postfix Notation demonstration:\n')
			print('Infix notation: ' + expression)
			print('Postfix notation: ' + infix_to_postfix(expression))