





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							List[Any]:
										super().__init__()
										self.register_modules(vqvae=lowerCAmelCase_				,						unet=lowerCAmelCase_				,						scheduler=lowerCAmelCase_  )






					@torch.no_grad()
					def __call__(		self				,						lowerCAmelCase_ = 1				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 0.0				,						lowerCAmelCase_ = 50				,						lowerCAmelCase_ = "pil"				,						lowerCAmelCase_ = True				,						**lowerCAmelCase_				,						)		->							Union[Tuple, ImagePipelineOutput]:
										_snake_case									=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,						generator=lowerCAmelCase_				,						)
										_snake_case									=				latents.to(self.device  )

										# scale the initial noise by the standard deviation required by the scheduler
										_snake_case									=				latents * self.scheduler.init_noise_sigma

										self.scheduler.set_timesteps(lowerCAmelCase_  )

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										_snake_case									=				'eta' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )

										_snake_case									=				{}
										if accepts_eta:
															_snake_case									=				eta

										for t in self.progress_bar(self.scheduler.timesteps  ):
															_snake_case									=				self.scheduler.scale_model_input(lowerCAmelCase_				,						lowerCAmelCase_  )
															# predict the noise residual
															_snake_case									=				self.unet(lowerCAmelCase_				,						lowerCAmelCase_  ).sample
															# compute the previous noisy sample x_t -> x_t-1
															_snake_case									=				self.scheduler.step(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						**lowerCAmelCase_  ).prev_sample

										# decode the image latents with the VAE
										_snake_case									=				self.vqvae.decode(lowerCAmelCase_  ).sample

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

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase_  )










def 						lowerCamelCase__     (     UpperCamelCase__			:       list[int]       ,    UpperCamelCase__			:       int						)			-> bool:



					'''simple docstring'''




					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

					# for each arr value, a sum of zero(0) can be formed by not taking any element
					# hence True/1
					for i in range(arr_len + 1						):
										_snake_case									=				True

					# sum is not zero and set is empty then false
					for i in range(1       ,    required_sum + 1						):
										_snake_case									=				False

					for i in range(1       ,    arr_len + 1						):
										for j in range(1       ,    required_sum + 1						):
															if arr[i - 1] > j:
																				_snake_case									=				subset[i - 1][j]
															if arr[i - 1] <= j:
																				_snake_case									=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

					return subset[arr_len][required_sum]


if __name__ == "__main__":
					import doctest

					doctest.testmod()



