






from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      __SCREAMING_SNAKE_CASE       ( __lowercase):






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												super().__init__()
												self.register_modules(unet=_UpperCamelCase  ,    scheduler=_UpperCamelCase )







						@torch.no_grad()
						def __call__(						self  ,    _UpperCamelCase = 1  ,    _UpperCamelCase = None  ,    _UpperCamelCase = 10_00  ,    _UpperCamelCase = "pil"  ,    _UpperCamelCase = True  ,    ):






												"""simple docstring"""
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    _UpperCamelCase ):
																		lowerCAmelCase__         =    (
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		lowerCAmelCase__         =    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		lowerCAmelCase__         =    randn_tensor(_UpperCamelCase  ,    generator=_UpperCamelCase )
																		lowerCAmelCase__         =    image.to(self.device )
												else:
																		lowerCAmelCase__         =    randn_tensor(_UpperCamelCase  ,    generator=_UpperCamelCase  ,    device=self.device )

												# set step values
												self.scheduler.set_timesteps(_UpperCamelCase )

												for t in self.progress_bar(self.scheduler.timesteps ):
																		# 1. predict noise model_output
																		lowerCAmelCase__         =    self.unet(_UpperCamelCase  ,    _UpperCamelCase ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		lowerCAmelCase__         =    self.scheduler.step(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    generator=_UpperCamelCase ).prev_sample

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_UpperCamelCase )













from __future__ import annotations

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

__snake_case     :     str       =  TypeVar("""T""", bound=bool)


class      __SCREAMING_SNAKE_CASE       ( Generic[T]):






						def __init__(						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    value
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return self._value







						@staticmethod
						def    UpperCamelCase__   (						_UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if not roota:
																		return roota

												if not roota:
																		return roota

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

												lowerCAmelCase__         =    roota
												lowerCAmelCase__         =    roota.right
												lowerCAmelCase__         =    roota.left
												lowerCAmelCase__         =    SkewNode.merge(_UpperCamelCase  ,    _UpperCamelCase )

												return result




class      __SCREAMING_SNAKE_CASE       ( Generic[T]):






						def __init__(						self  ,    _UpperCamelCase = () ):






												"""simple docstring"""
												lowerCAmelCase__         =    None
												if data:
																		for item in data:
																								self.insert(_UpperCamelCase )






						def __bool__(						self ):






												"""simple docstring"""
												return self._root is not None






						def __iter__(						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    []
												while self:
																		result.append(self.pop() )

												# Pushing items back to the heap not to clear it.
												for item in result:
																		self.insert(_UpperCamelCase )

												return iter(_UpperCamelCase )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    SkewNode.merge(self._root  ,    SkewNode(_UpperCamelCase ) )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.top()
												lowerCAmelCase__         =    (
												    SkewNode.merge(self._root.left  ,    self._root.right ) if self._root else None
												)

												return result






						def    UpperCamelCase__   (						self ):






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







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    None


if __name__ == "__main__":
					import doctest

					doctest.testmod()





