





import operator as op





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				[]
			UpperCAmelCase   		=				lambda UpperCamelCase__			,			UpperCamelCase__ : int(x / y )  # noqa: E731 integer division operation
			UpperCAmelCase   		=				{
			    '''^''': op.pow,
			    '''*''': op.mul,
			    '''/''': div,
			    '''+''': op.add,
			    '''-''': op.sub,
			}  # operators & their respective operation

			# print table header
			print('''Symbol'''.center(8 )			,			'''Action'''.center(12 )			,			'''Stack'''			,			sep=''' | ''' )
			print('''-''' * (30 + len(UpperCamelCase__ )) )

			for x in post_fix:
						if x.isdigit():  # if x in digit
									stack.append(UpperCamelCase__ )  # append x to stack
									# output in tabular format
									print(x.rjust(8 )			,			('''push(''' + x + ''')''').ljust(12 )			,			''','''.join(UpperCamelCase__ )			,			sep=''' | ''' )
						else:
									UpperCAmelCase   		=				stack.pop()  # pop stack
									# output in tabular format
									print(''''''.rjust(8 )			,			('''pop(''' + b + ''')''').ljust(12 )			,			''','''.join(UpperCamelCase__ )			,			sep=''' | ''' )

									UpperCAmelCase   		=				stack.pop()  # pop stack
									# output in tabular format
									print(''''''.rjust(8 )			,			('''pop(''' + a + ''')''').ljust(12 )			,			''','''.join(UpperCamelCase__ )			,			sep=''' | ''' )

									stack.append(
									    str(opr[x](int(UpperCamelCase__ )			,			int(UpperCamelCase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
									# output in tabular format
									print(
									    x.rjust(8 )			,			('''push(''' + a + x + b + ''')''').ljust(12 )			,			''','''.join(UpperCamelCase__ )			,			sep=''' | '''			,			)

			return int(stack[0] )


if __name__ == "__main__":
				__A				:    int       =   input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
				print("\n\tResult = ", solve(Postfix))










from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 	A_       (a_			):
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







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







	@torch.no_grad()
	def __call__(    self							,					_A = 1							,					_A = 5_0							,					_A = None							,					_A = "pil"							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.unet.config.sample_size
				UpperCAmelCase   		=				(batch_size, 3, img_size, img_size)

				UpperCAmelCase   		=				self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				UpperCAmelCase   		=				randn_tensor(_A							,					generator=_A							,					device=self.device		) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_A		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							UpperCAmelCase   		=				self.scheduler.schedule[t]
							UpperCAmelCase   		=				self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							UpperCAmelCase		,			UpperCAmelCase   		=				self.scheduler.add_noise_to_input(_A							,					_A							,					generator=_A		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							UpperCAmelCase   		=				(sigma_hat / 2) * model((sample_hat + 1) / 2							,					sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							UpperCAmelCase   		=				self.scheduler.step(_A							,					_A							,					_A							,					_A		)

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										UpperCAmelCase   		=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,					sigma_prev / 2		).sample
										UpperCAmelCase   		=				self.scheduler.step_correct(
										    _A							,					_A							,					_A							,					_A							,					step_output.prev_sample							,					step_output['''derivative''']							,					)
							UpperCAmelCase   		=				step_output.prev_sample

				UpperCAmelCase   		=				(sample / 2 + 0.5).clamp(0							,					1		)
				UpperCAmelCase   		=				sample.cpu().permute(0							,					2							,					3							,					1		).numpy()
				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)



