
"""simple docstring"""





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 A_			(lowercase__   ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__    :		UNetaDModel
  SCREAMING_SNAKE_CASE__    :		PNDMScheduler







  def __init__(	self       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     super().__init__()

     UpperCAmelCase_      :      Tuple      =					PNDMScheduler.from_config(scheduler.config						)

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







  @torch.no_grad()
  def __call__(	self       ,     lowercase_ = 1       ,     lowercase_ = 50       ,     lowercase_ = None       ,     lowercase_ = "pil"       ,     lowercase_ = True       ,     **lowercase_       ,     ):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					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						):
        UpperCAmelCase_      :      Optional[int]      =					self.unet(lowercase_       ,     lowercase_						).sample

        UpperCAmelCase_      :      Union[str, Any]      =					self.scheduler.step(lowercase_       ,     lowercase_       ,     lowercase_						).prev_sample

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowercase_						)




"""simple docstring"""





from __future__ import annotations

from typing import Any


class A_			:




  '''simple docstring'''







  def __init__(	self       ,     lowercase_       ,     lowercase_						):  # noqa: A002

     """simple docstring"""
     UpperCAmelCase_      :      Optional[Any]      =					item
     UpperCAmelCase_      :      Optional[Any]      =					next




class A_			:




  '''simple docstring'''







  def __init__(	self						):

     """simple docstring"""
     UpperCAmelCase_      :      Node | None      =					None
     UpperCAmelCase_      :      Optional[int]      =					0







  def  UpperCamelCase__      (	self       ,     lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      int      =					Node(lowercase_       ,     self.head						)
     self.size += 1







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     # Switched 'self.is_empty()' to 'self.head is None'
     # because mypy was considering the possibility that 'self.head'
     # can be None in below else part and giving error
     if self.head is None:
        return None
     else:
        UpperCAmelCase_      :      int      =					self.head.item
        UpperCAmelCase_      :      str      =					self.head.next
        self.size -= 1
        return item







  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     return self.head is None







  def __str__(	self						):

     """simple docstring"""
     if self.is_empty():
        return ""
     else:
        UpperCAmelCase_      :      str      =					self.head
        UpperCAmelCase_      :      str      =					""
        UpperCAmelCase_      :      list[str]      =					[]
        while iterate:
           item_list.append(str(iterate.item						)						)
           UpperCAmelCase_      :      Tuple      =					iterate.next

        UpperCAmelCase_      :      Optional[Any]      =					" --> ".join(lowercase_						)

        return item_str







  def __len__(	self						):

     """simple docstring"""
     return self.size


