

"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class    a__  (   UpperCamelCase_     ):
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase    )					->  Optional[Any]:
							'''simple docstring'''






							super().__init__()
							self.register_modules(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE    )


			@torch.no_grad()
			def __call__(      self , lowercase = 1 , lowercase = None , lowercase = 1000 , lowercase = "pil" , lowercase = True , )					->  Union[ImagePipelineOutput, Tuple]:
							'''simple docstring'''






							if isinstance(self.unet.config.sample_size , __SCREAMING_SNAKE_CASE    ):
											A__            =     (
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
							else:
											A__            =     (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
											# randn does not work reproducibly on mps
											A__            =     randn_tensor(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE    )
											A__            =     image.to(self.device    )
							else:
											A__            =     randn_tensor(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , device=self.device    )

							# set step values
							self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )

							for t in self.progress_bar(self.scheduler.timesteps    ):
											# 1. predict noise model_output
											A__            =     self.unet(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE    ).sample

											# 2. compute previous image: x_t -> x_t-1
											A__            =     self.scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE    ).prev_sample

							A__            =     (image / 2 + 0.5).clamp(0 , 1    )
							A__            =     image.cpu().permute(0 , 2 , 3 , 1    ).numpy()
							if output_type == "pil":
											A__            =     self.numpy_to_pil(__SCREAMING_SNAKE_CASE    )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE    )




from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> int:




				'''simple docstring'''





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



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> str:




				'''simple docstring'''





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


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

	testmod()
	lowerCAmelCase__						=	"""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))

