
import torch
from torch import autograd







class 							UpperCAmelCase	(   autograd.Function					):

					@staticmethod
					def        _lowerCAmelCase	(			lowerCAmelCase      :					Dict ,      lowerCAmelCase      :					Union[str, Any] ,      lowerCAmelCase      :					Dict ,      lowerCAmelCase      :					Tuple					):
											lowercase       :    Optional[int]											=  inputs.numel()
											lowercase       :    List[Any]											=  int(0.0_05 * nb_elems					) + 1
											if sigmoid:
																	lowercase       :    Optional[Any]											=  (torch.sigmoid(lowerCAmelCase					) > threshold).type(inputs.type()					)
											else:
																	lowercase       :    Union[str, Any]											=  (inputs > threshold).type(inputs.type()					)
											if mask.sum() < nb_min:
																	# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																	lowercase       :    Dict											=  inputs.flatten().kthvalue(max(nb_elems - nb_min ,      1					)					).values
																	lowercase       :    Tuple											=  (inputs > k_threshold).type(inputs.type()					)
											return mask




					@staticmethod
					def        _lowerCAmelCase	(			lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					Optional[Any]					):
											return gradOutput, None, None







class 							UpperCAmelCase	(   autograd.Function					):

					@staticmethod
					def        _lowerCAmelCase	(			lowerCAmelCase      :					Any ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					Any					):
											lowercase       :    Tuple											=  inputs.clone()
											lowercase				,				lowercase       :    Optional[Any]											=  inputs.flatten().sort(descending=lowerCAmelCase					)
											lowercase       :    List[str]											=  int(threshold * inputs.numel()					)

											# flat_out and mask access the same memory.
											lowercase       :    List[str]											=  mask.flatten()
											lowercase       :    Union[str, Any]											=  0
											lowercase       :    List[Any]											=  1
											return mask




					@staticmethod
					def        _lowerCAmelCase	(			lowerCAmelCase      :					int ,      lowerCAmelCase      :					Dict					):
											return gradOutput, None







class 							UpperCAmelCase	(   A_					):

					@staticmethod
					def        _lowerCAmelCase	(			lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					List[Any]					):
											lowercase       :    List[Any]											=  inputs.clone()
											lowercase				,				lowercase       :    str											=  inputs.abs().flatten().sort(descending=lowerCAmelCase					)
											lowercase       :    int											=  int(threshold * inputs.numel()					)

											# flat_out and mask access the same memory.
											lowercase       :    Optional[int]											=  mask.flatten()
											lowercase       :    Dict											=  0
											lowercase       :    List[str]											=  1
											return mask




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 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

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




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

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

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


