


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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				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(__lowerCAmelCase				).astype(np.floataa				) / 255.0
  snake_case__						= image[None].transpose(0							,   3							,   1							,   2				)
  snake_case__						= torch.from_numpy(__lowerCAmelCase				)
  return 2.0 * image - 1.0


class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     def __init__(					self:List[str]     ,		_a:VQModel     ,		_a:UNetaDModel     ,		_a:Union[
             DDIMScheduler,
             PNDMScheduler,
             LMSDiscreteScheduler,
             EulerDiscreteScheduler,
             EulerAncestralDiscreteScheduler,
             DPMSolverMultistepScheduler,
         ]     ,		):
       super().__init__()
       self.register_modules(vqvae=_a     ,		unet=_a     ,		scheduler=_a   )







     @torch.no_grad()
     def __call__(					self:int     ,		_a:Union[torch.Tensor, PIL.Image.Image] = None     ,		_a:Optional[int] = 1     ,		_a:Optional[int] = 1_00     ,		_a:Optional[float] = 0.0     ,		_a:Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		_a:Optional[str] = "pil"     ,		_a:bool = True     ,		):
       if isinstance(_a     ,		PIL.Image.Image   ):
         snake_case__						= 1
       elif isinstance(_a     ,		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(_a   )}"""   )

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

       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(_a     ,		generator=_a     ,		device=self.device     ,		dtype=_a   )

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

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(_a     ,		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(_a   ):
         # 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(_a     ,		_a   )
         # predict the noise residual
         snake_case__						= self.unet(_a     ,		_a   ).sample
         # compute the previous noisy sample x_t -> x_t-1
         snake_case__						= self.scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample

       # decode the image latents with the VQVAE
       snake_case__						= self.vqvae.decode(_a   ).sample
       snake_case__						= torch.clamp(_a     ,		-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(_a   )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=_a   )





lowerCamelCase__							:						List[str]							     =   """Alexander Joslin"""

import operator as op

from .stack import Stack







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				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(__lowerCAmelCase				)				)
    elif i in operators:
      # RULE 2
      operator_stack.push(__lowerCAmelCase				)
    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](__lowerCAmelCase							,   __lowerCAmelCase				)
      operand_stack.push(__lowerCAmelCase				)

    # RULE 5
  return operand_stack.peek()


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

