





'''simple docstring'''




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Optional[int]:
	snake_case__       ,  snake_case__	       =   image.size
	snake_case__       ,  snake_case__	       =   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	snake_case__	       =   image.resize((w, h)					,				resample=PIL_INTERPOLATION['lanczos']							)
	snake_case__	       =   np.array(UpperCAmelCase_							).astype(np.floataa							) / 255.0
	snake_case__	       =   image[None].transpose(0					,				3					,				1					,				2							)
	snake_case__	       =   torch.from_numpy(UpperCAmelCase_							)
	return 2.0 * image - 1.0







class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		Optional[Any]:
		super().__init__()
		self.register_modules(vqvae=UpperCamelCase_      ,       unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = 100      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[Tuple, ImagePipelineOutput]:
		if isinstance(UpperCamelCase_      ,       PIL.Image.Image		):
			snake_case__	       =   1
		elif isinstance(UpperCamelCase_      ,       torch.Tensor		):
			snake_case__	       =   image.shape[0]
		else:
			raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase_		)}'''		)

		if isinstance(UpperCamelCase_      ,       PIL.Image.Image		):
			snake_case__	       =   preprocess(UpperCamelCase_		)

		snake_case__       ,  snake_case__	       =   image.shape[-2:]

		# in_channels should be 6: 3 for latents, 3 for low resolution image
		snake_case__	       =   (batch_size, self.unet.config.in_channels // 2, height, width)
		snake_case__	       =   next(self.unet.parameters()		).dtype

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=UpperCamelCase_		)

		snake_case__	       =   image.to(device=self.device      ,       dtype=UpperCamelCase_		)

		# set timesteps and move to the correct device
		self.scheduler.set_timesteps(UpperCamelCase_      ,       device=self.device		)
		snake_case__	       =   self.scheduler.timesteps

		# scale the initial noise by the standard deviation required by the scheduler
		snake_case__	       =   latents * self.scheduler.init_noise_sigma

		# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
		# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
		# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
		# and should be between [0, 1]
		snake_case__	       =   'eta' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
		snake_case__	       =   {}
		if accepts_eta:
			snake_case__	       =   eta

		for t in self.progress_bar(UpperCamelCase_		):
			# concat latents and low resolution image in the channel dimension.
			snake_case__	       =   torch.cat([latents, image]      ,       dim=1		)
			snake_case__	       =   self.scheduler.scale_model_input(UpperCamelCase_      ,       UpperCamelCase_		)
			# predict the noise residual
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample
			# compute the previous noisy sample x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       **UpperCamelCase_		).prev_sample

		# decode the image latents with the VQVAE
		snake_case__	       =   self.vqvae.decode(UpperCamelCase_		).sample
		snake_case__	       =   torch.clamp(UpperCamelCase_      ,       -1.0      ,       1.0		)
		snake_case__	       =   image / 2 + 0.5
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		).numpy()

		if output_type == "pil":
			snake_case__	       =   self.numpy_to_pil(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)










'''simple docstring'''




a__					:			Optional[Any]          =     '''Alexander Joslin'''

import operator as op

from .stack import Stack




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	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(UpperCAmelCase_							)							)
		elif i in operators:
			# RULE 2
			operator_stack.push(UpperCAmelCase_							)
		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](UpperCAmelCase_					,				UpperCAmelCase_							)
			operand_stack.push(UpperCAmelCase_							)

    # RULE 5
	return operand_stack.peek()


if __name__ == "__main__":
			a__					:			Any          =     '''(5 + ((4 * 2) * (2 + 3)))'''
			# answer = 45
			print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



