
'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Union[str, Any]:
	_snake_case   						=		[]
	_snake_case   						=		[]
	_snake_case   						=		{
	    '^': 3,
	    '*': 2,
	    '/': 2,
	    '%': 2,
	    '+': 1,
	    '-': 1,
	}  # Priority of each operator
	_snake_case   						=		len(__A	) if (len(__A	) > 7) else 7

	# Print table header for output
	print(
	    'Symbol'.center(8	)			,				'Stack'.center(__A	)			,				'Postfix'.center(__A	)			,				sep=' | '			,				)
	print('-' * (print_width * 3 + 7)	)

	for x in infix:
		if x.isalpha() or x.isdigit():
			post_fix.append(__A	)  # if x is Alphabet / Digit, add it to Postfix
		elif x == "(":
			stack.append(__A	)  # if x is "(" push to Stack
		elif x == ")":  # if x is ")" pop stack until "(" is encountered
			while stack[-1] != "(":
				post_fix.append(stack.pop()	)  # Pop stack & add the content to Postfix
			stack.pop()
		else:
			if len(__A	) == 0:
				stack.append(__A	)  # If stack is empty, push x to stack
			else:  # while priority of x is not > priority of element in the stack
				while len(__A	) > 0 and priority[x] <= priority[stack[-1]]:
					post_fix.append(stack.pop()	)  # pop stack & add to Postfix
				stack.append(__A	)  # push x to stack

		print(
		    x.center(8	)			,				(''.join(__A	)).ljust(__A	)			,				(''.join(__A	)).ljust(__A	)			,				sep=' | '			,				)  # Output in tabular format

	while len(__A	) > 0:  # while stack is not empty
		post_fix.append(stack.pop()	)  # pop stack & add to Postfix
		print(
		    ' '.center(8	)			,				(''.join(__A	)).ljust(__A	)			,				(''.join(__A	)).ljust(__A	)			,				sep=' | '			,				)  # Output in tabular format

	return "".join(__A	)  # return Postfix as str







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	_snake_case   						=		list(infix[::-1]	)  # reverse the infix equation

	for i in range(len(__A	)	):
		if infix[i] == "(":
			_snake_case   						=		')'  # change "(" to ")"
		elif infix[i] == ")":
			_snake_case   						=		'('  # change ")" to "("

	return (infix_2_postfix(''.join(__A	)	))[
	    ::-1
	]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
					lowercase    :							Union[str, Any]      =					input("\nEnter an Infix Equation = ")  # Input an Infix equation
					lowercase    :							Union[str, Any]      =					"".join(Infix.split())  # Remove spaces from the input
					print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")



'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase    :							Optional[Any]      =					False


class 						__UpperCAmelCase      (					unittest.TestCase						):
							pass


@nightly
@require_torch_gpu
class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		'A painting of a squirrel eating a burger '
								_snake_case   						=		torch.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='numpy'  ).images

								with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(lowerCAmelCase_  )
									_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCAmelCase_  )
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		generator.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='numpy'  ).images

								assert np.abs(image - new_image  ).sum() < 1E-5, "Models don't have the same forward pass"




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
								    'shi-labs/versatile-diffusion'    ,   torch_dtype=torch.floataa  )
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		'A painting of a squirrel eating a burger '
								_snake_case   						=		torch.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type='numpy'  ).images

								_snake_case   						=		image[0, 2_53:2_56, 2_53:2_56, -1]

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case   						=		np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

