

from typing import List, Optional, Tuple, Union

import torch

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						UNetaDModel
							UpperCAmelCase__       :						KarrasVeScheduler


							def __init__(			self       ,							_a       ,							_a       )    ->    int:
												super().__init__()
												self.register_modules(unet=_a       ,							scheduler=_a       )




							@torch.no_grad()
							def __call__(			self       ,							_a = 1       ,							_a = 5_0       ,							_a = None       ,							_a = "pil"       ,							_a = True       ,							**_a       ,							)    ->    Union[Tuple, ImagePipelineOutput]:
												_a	:    str         =		self.unet.config.sample_size
												_a	:    Any         =		(batch_size, 3, img_size, img_size)

												_a	:    Dict         =		self.unet

												# sample x_0 ~ N(0, sigma_0^2 * I)
												_a	:    Union[str, Any]         =		randn_tensor(_a       ,							generator=_a       ,							device=self.device       ) * self.scheduler.init_noise_sigma

												self.scheduler.set_timesteps(_a       )

												for t in self.progress_bar(self.scheduler.timesteps       ):
																	# here sigma_t == t_i from the paper
																	_a	:    Any         =		self.scheduler.schedule[t]
																	_a	:    int         =		self.scheduler.schedule[t - 1] if t > 0 else 0

																	# 1. Select temporarily increased noise level sigma_hat
																	# 2. Add new noise to move from sample_i to sample_hat
																	_a      ,					_a	:    Optional[int]         =		self.scheduler.add_noise_to_input(_a       ,							_a       ,							generator=_a       )

																	# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																	# The model inputs and output are adjusted by following eq. (213) in [1].
																	_a	:    str         =		(sigma_hat / 2) * model((sample_hat + 1) / 2       ,							sigma_hat / 2       ).sample

																	# 4. Evaluate dx/dt at sigma_hat
																	# 5. Take Euler step from sigma to sigma_prev
																	_a	:    Any         =		self.scheduler.step(_a       ,							_a       ,							_a       ,							_a       )

																	if sigma_prev != 0:
																						# 6. Apply 2nd order correction
																						# The model inputs and output are adjusted by following eq. (213) in [1].
																						_a	:    List[Any]         =		(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,							sigma_prev / 2       ).sample
																						_a	:    List[str]         =		self.scheduler.step_correct(
																						    _a       ,							_a       ,							_a       ,							_a       ,							step_output.prev_sample       ,							step_output['''derivative''']       ,							)
																	_a	:    Tuple         =		step_output.prev_sample

												_a	:    Dict         =		(sample / 2 + 0.5).clamp(0       ,							1       )
												_a	:    Tuple         =		sample.cpu().permute(0       ,							2       ,							3       ,							1       ).numpy()
												if output_type == "pil":
																	_a	:    Tuple         =		self.numpy_to_pil(_a       )

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_a       )






from collections.abc import Callable

class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a = None       )    ->    None:
												# Stores actual heap items.
												_a	:    list         =		[]
												# Stores indexes of each item for supporting updates and deletion.
												_a	:    dict         =		{}
												# Stores current size of heap.
												_a	:    Tuple         =		0
												# Stores function used to evaluate the score of an item on which basis ordering
												# will be done.
												_a	:    Dict         =		key or (lambda _a       : x)


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												return int((i - 1) / 2       ) if i > 0 else None


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												_a	:    Optional[int]         =		int(2 * i + 1       )
												return left if 0 < left < self.size else None


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												_a	:    int         =		int(2 * i + 2       )
												return right if 0 < right < self.size else None


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												_a      ,					_a	:    Union[str, Any]         =		(
												    self.pos_map[self.arr[j][0]],
												    self.pos_map[self.arr[i][0]],
												)
												# Then swap the items in the list.
												_a      ,					_a	:    List[Any]         =		self.arr[j], self.arr[i]


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    bool:
												return self.arr[i][1] < self.arr[j][1]


							def 				__lowercase				(			self       ,							_a       )    ->    int:
												_a	:    Dict         =		self._left(_a       )
												_a	:    str         =		self._right(_a       )
												_a	:    str         =		i

												if left is not None and not self._cmp(_a       ,							_a       ):
																	_a	:    Optional[Any]         =		left
												if right is not None and not self._cmp(_a       ,							_a       ):
																	_a	:    Any         =		right

												return valid_parent


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[str]         =		self._parent(_a       )
												while parent is not None and not self._cmp(_a       ,							_a       ):
																	self._swap(_a       ,							_a       )
																	_a      ,					_a	:    Any         =		parent, self._parent(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[Any]         =		self._get_valid_parent(_a       )
												while valid_parent != index:
																	self._swap(_a       ,							_a       )
																	_a      ,					_a	:    int         =		valid_parent, self._get_valid_parent(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												if item not in self.pos_map:
																	return
												_a	:    str         =		self.pos_map[item]
												_a	:    List[Any]         =		[item, self.key(_a       )]
												# Make sure heap is right in both up and down direction.
												# Ideally only one of them will make any change.
												self._heapify_up(_a       )
												self._heapify_down(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												if item not in self.pos_map:
																	return
												_a	:    Tuple         =		self.pos_map[item]
												del self.pos_map[item]
												_a	:    Tuple         =		self.arr[self.size - 1]
												_a	:    str         =		index
												self.size -= 1
												# Make sure heap is right in both up and down direction. Ideally only one
												# of them will make any change- so no performance loss in calling both.
												if self.size > index:
																	self._heapify_up(_a       )
																	self._heapify_down(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												_a	:    Union[str, Any]         =		len(self.arr       )
												if arr_len == self.size:
																	self.arr.append([item, self.key(_a       )]       )
												else:
																	_a	:    Optional[int]         =		[item, self.key(_a       )]
												_a	:    Tuple         =		self.size
												self.size += 1
												self._heapify_up(self.size - 1       )


							def 				__lowercase				(			self       )    ->    tuple | None:
												return self.arr[0] if self.size else None




							def 				__lowercase				(			self       )    ->    tuple | None:
												_a	:    Tuple         =		self.get_top()
												if top_item_tuple:
																	self.delete_item(top_item_tuple[0]       )
												return top_item_tuple



def    __UpperCAmelCase (	)			->     None:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



