



'''simple docstring'''






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

import torch

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

class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						int		,    __snake_case				:						VQModel		,    __snake_case				:						UNetaDModel		,    __snake_case				:						DDIMScheduler			):
      super().__init__()
      self.register_modules(vqvae=__snake_case		,    unet=__snake_case		,    scheduler=__snake_case			)







    @torch.no_grad()
    def __call__(   self				:						List[Any]		,    __snake_case				:						int = 1		,    __snake_case				:						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,    __snake_case				:						float = 0.0		,    __snake_case				:						int = 50		,    __snake_case				:						Optional[str] = "pil"		,    __snake_case				:						bool = True		,    **__snake_case				:						Optional[Any]		,    ):
      a		:      List[str]       =  randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,    generator=__snake_case		,    )
      a		:      int       =  latents.to(self.device			)

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

      self.scheduler.set_timesteps(__snake_case			)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      a		:      Dict       =  'eta' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

      a		:      Optional[Any]       =  {}
      if accepts_eta:
        a		:      Union[str, Any]       =  eta

      for t in self.progress_bar(self.scheduler.timesteps			):
        a		:      int       =  self.scheduler.scale_model_input(__snake_case		,    __snake_case			)
        # predict the noise residual
        a		:      Any       =  self.unet(__snake_case		,    __snake_case			).sample
        # compute the previous noisy sample x_t -> x_t-1
        a		:      Optional[Any]       =  self.scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    **__snake_case			).prev_sample

      # decode the image latents with the VAE
      a		:      int       =  self.vqvae.decode(__snake_case			).sample

      a		:      List[Any]       =  (image / 2 + 0.5).clamp(0		,    1			)
      a		:      Optional[Any]       =  image.cpu().permute(0		,    2		,    3		,    1			).numpy()
      if output_type == "pil":
        a		:      Optional[Any]       =  self.numpy_to_pil(__snake_case			)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__snake_case			)




'''simple docstring'''






class 							a__:







    def __init__(   self				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Any		,    __snake_case				:						Tuple			):
      a		:      List[str]       =  name
      a		:      Dict       =  value
      a		:      List[str]       =  weight







    def __repr__(   self				:						int			):
      return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""







    def     lowercase_ (   self				:						Optional[int]			):
      return self.value







    def     lowercase_ (   self				:						List[str]			):
      return self.name







    def     lowercase_ (   self				:						int			):
      return self.weight







    def     lowercase_ (   self				:						List[str]			):
      return self.value / self.weight
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[int]       =  []
  for i in range(len(_A       )       ):
    menu.append(Things(name[i]				,    value[i]				,    weight[i]       )       )
  return menu
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  sorted(_A				,    key=_A				,    reverse=_A       )
  a		:      Optional[int]       =  []
  a     ,			a		:      str       =  0.0, 0.0
  for i in range(len(_A       )       ):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]       )
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)
def    lowerCamelCase__ (		):
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()