




'''simple docstring'''





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 			_snake_case		(     lowercase_							):




			def __init__( self    ,							a__    ,							a__							)    ->    str:

										'''simple docstring'''




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




			@torch.no_grad()
			def __call__( self    ,							a__ = 1    ,							a__ = None    ,							a__ = 50    ,							a__ = "pil"    ,							a__ = True    ,							**a__    ,							)    ->    Union[ImagePipelineOutput, Tuple]:

										'''simple docstring'''




										snake_case_      =				torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=a__    ,							)
										snake_case_      =				image.to(self.device							)

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

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

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	snake_case_      =				self.scheduler.step(a__    ,							a__    ,							a__							).prev_sample

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

										if not return_dict:
																	return (image,), "This is a local test"

										return ImagePipelineOutput(images=a__							), "This is a local test"










'''simple docstring'''





_SCREAMING_SNAKE_CASE							:       Optional[int]						=			"Alexander Joslin"

import operator as op

from .stack import Stack


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				{"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

							snake_case_      =				Stack()
							snake_case_      =				Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(snake_case					)					)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(snake_case					)
														elif i == ")":
																					# RULE 4
																					snake_case_      =				operator_stack.peek()
																					operator_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()

																					snake_case_      =				operators[opr](snake_case							,    snake_case					)
																					operand_stack.push(snake_case					)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[Any]						=			"(5 + ((4 * 2) * (2 + 3)))"
		# answer = 45
		print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")




