





from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_snake_case				= TypeVar("""T""", bound=bool)





class 					lowerCAmelCase							(   Generic[T]					):







  def __init__(			self  :Optional[int]    ,							_lowercase  :T					):






     '''simple docstring'''





     lowercase__    =  value
     lowercase__    =  None
     lowercase__    =  None







  @property
  def   UpperCAmelCase     (			self  :str					):






     '''simple docstring'''





     return self._value







  @staticmethod
  def   UpperCAmelCase     (			_lowercase  :RandomizedHeapNode[T] | None    ,							_lowercase  :RandomizedHeapNode[T] | None					):






     '''simple docstring'''





     if not roota:
        return roota

     if not roota:
        return roota

     if roota.value > roota.value:
        lowercase__   ,							lowercase__    =  roota, roota

     if random.choice([True, False]					):
        lowercase__   ,							lowercase__    =  roota.right, roota.left

     lowercase__    =  RandomizedHeapNode.merge(roota.left    ,							_lowercase					)

     return roota





class 					lowerCAmelCase							(   Generic[T]					):







  def __init__(			self  :Union[str, Any]    ,							_lowercase  :Iterable[T] | None = ()					):






     '''simple docstring'''





     lowercase__    =  None

     if data:
        for item in data:
           self.insert(_lowercase					)







  def   UpperCAmelCase     (			self  :Union[str, Any]    ,							_lowercase  :T					):






     '''simple docstring'''





     lowercase__    =  RandomizedHeapNode.merge(self._root    ,							RandomizedHeapNode(_lowercase					)					)







  def   UpperCAmelCase     (			self  :Optional[int]					):






     '''simple docstring'''





     lowercase__    =  self.top()

     if self._root is None:
        return None

     lowercase__    =  RandomizedHeapNode.merge(self._root.left    ,							self._root.right					)

     return result







  def   UpperCAmelCase     (			self  :int					):






     '''simple docstring'''





     if not self._root:
        raise IndexError("Can't get top element for the empty heap."					)
     return self._root.value







  def   UpperCAmelCase     (			self  :Optional[Any]					):






     '''simple docstring'''





     lowercase__    =  None







  def   UpperCAmelCase     (			self  :Dict					):






     '''simple docstring'''





     lowercase__    =  []
     while self:
        result.append(self.pop()					)

     return result







  def __bool__(			self  :Optional[Any]					):






     '''simple docstring'''





     return self._root is not None


if __name__ == "__main__":
  import doctest

  doctest.testmod()













from typing import List, Optional, Tuple, Union

import torch

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





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







  @torch.no_grad()
  def __call__(			self  :Union[str, Any]    ,							_lowercase  :int = 1    ,							_lowercase  :int = 50    ,							_lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							_lowercase  :Optional[str] = "pil"    ,							_lowercase  :bool = True    ,							**_lowercase  :List[Any]    ,							):






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






