



"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

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






class      UpperCAmelCase__		(      SCREAMING_SNAKE_CASE__     ):
		lowerCAmelCase_       :	Tuple 							=     42
		lowerCAmelCase_       :	Union[str, Any] 							=     42



		def __init__( self  :    Dict      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Any				)			->						Optional[int]:






									'''simple docstring'''

									super().__init__()
									self.register_modules(unet=snake_case__      ,							scheduler=snake_case__				)



		@torch.no_grad()
		def __call__( self  :    Dict      ,							snake_case  :    Any = 1      ,							snake_case  :    Union[str, Any] = 2_000      ,							snake_case  :    Optional[Any] = None      ,							snake_case  :    int = "pil"      ,							snake_case  :    Tuple = True      ,							**snake_case  :    Union[str, Any]      ,							)			->						Tuple:






									'''simple docstring'''

									A         =					self.unet.config.sample_size
									A         =					(batch_size, 3, img_size, img_size)

									A         =					self.unet

									A         =					randn_tensor(snake_case__      ,							generator=snake_case__				) * self.scheduler.init_noise_sigma
									A         =					sample.to(self.device				)

									self.scheduler.set_timesteps(snake_case__				)
									self.scheduler.set_sigmas(snake_case__				)

									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
																A         =					self.scheduler.sigmas[i] * torch.ones(shape[0]      ,							device=self.device				)

																# correction step
																for _ in range(self.scheduler.config.correct_steps				):
																							A         =					self.unet(snake_case__      ,							snake_case__				).sample
																							A         =					self.scheduler.step_correct(snake_case__      ,							snake_case__      ,							generator=snake_case__				).prev_sample

																# prediction step
																A         =					model(snake_case__      ,							snake_case__				).sample
																A         =					self.scheduler.step_pred(snake_case__      ,							snake_case__      ,							snake_case__      ,							generator=snake_case__				)

																A         =					output.prev_sample, output.prev_sample_mean

									A         =					sample_mean.clamp(0      ,							1				)
									A         =					sample.cpu().permute(0      ,							2      ,							3      ,							1				).numpy()
									if output_type == "pil":
																A         =					self.numpy_to_pil(snake_case__				)

									if not return_dict:
																return (sample,)

									return ImagePipelineOutput(images=snake_case__				)








"""simple docstring"""







from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A    =			TypeVar('T')






class      UpperCAmelCase__		(      Generic[T]     ):
		lowerCAmelCase_       :	deque[T]  # Cache store of keys
		lowerCAmelCase_       :	set[T]  # References of the keys in cache
		lowerCAmelCase_       :	int 							=     10  # Maximum capacity of cache



		def __init__( self  :    int      ,							snake_case  :    int				)			->						None:






									'''simple docstring'''

									A         =					deque()
									A         =					set()
									if not n:
																A         =					sys.maxsize
									elif n < 0:
																raise ValueError('n should be an integer greater than 0.'				)
									else:
																A         =					n



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    T				)			->						None:






									'''simple docstring'''

									if x not in self.key_reference:
																if len(self.dq_store				) == LRUCache._MAX_CAPACITY:
																							A         =					self.dq_store.pop()
																							self.key_reference.remove(snake_case				)
									else:
																self.dq_store.remove(snake_case				)

									self.dq_store.appendleft(snake_case				)
									self.key_reference.add(snake_case				)



		def 					A_		( self  :    Dict				)			->						None:






									'''simple docstring'''

									for k in self.dq_store:
																print(snake_case				)



		def __repr__( self  :    int				)			->						str:






									'''simple docstring'''

									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store				)}"""


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							A    =			LRUCache(4)
							lru_cache.refer('A')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('A')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



