





"""simple docstring"""




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 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					42
				UpperCamelCase			=					42






				def __init__(				self	:					Tuple,				lowerCAmelCase__	:					UNetaDModel,				lowerCAmelCase__	:					PNDMScheduler							)			->   List[str]:




										'''simple docstring'''



										super().__init__()

										_UpperCamelCase    :      Tuple						     =					PNDMScheduler.from_config(scheduler.config							)

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






				@torch.no_grad()
				def __call__(				self	:					int,				lowerCAmelCase__	:					int = 1,				lowerCAmelCase__	:					int = 5_0,				lowerCAmelCase__	:					Optional[Union[torch.Generator, List[torch.Generator]]] = None,				lowerCAmelCase__	:					Optional[str] = "pil",				lowerCAmelCase__	:					bool = True,				**lowerCAmelCase__	:					int,				)			->   Union[ImagePipelineOutput, Tuple]:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					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							):
																_UpperCamelCase    :      Union[str, Any]						     =					self.unet(lowerCAmelCase__,				lowerCAmelCase__							).sample

																_UpperCamelCase    :      List[Any]						     =					self.scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							).prev_sample

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

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__							)










"""simple docstring"""




def  a_       (							_lowercase     ):
						if len(_lowercase     ) > 1:
												_UpperCamelCase    :      List[str]						     =					len(_lowercase     ) // 2  # Finds the middle of the array
												_UpperCamelCase    :      Dict						     =					arr[
												    :middle_length
												]  # Creates an array of the elements in the first half.
												_UpperCamelCase    :      Union[str, Any]						     =					arr[
												    middle_length:
												]  # Creates an array of the elements in the second half.
												_UpperCamelCase    :      str						     =					len(_lowercase     )
												_UpperCamelCase    :      List[str]						     =					len(_lowercase     )
												merge(_lowercase     )  # Starts sorting the left.
												merge(_lowercase     )  # Starts sorting the right
												_UpperCamelCase    :      Dict						     =					0  # Left Counter
												_UpperCamelCase    :      Union[str, Any]						     =					0  # Right Counter
												_UpperCamelCase    :      Optional[int]						     =					0  # Position Counter
												while (
												    left_index < left_size and right_index < right_size
												):  # Runs until the lowers size of the left and right are sorted.
																		if left_array[left_index] < right_array[right_index]:
																								_UpperCamelCase    :      Dict						     =					left_array[left_index]
																								left_index += 1
																		else:
																								_UpperCamelCase    :      Tuple						     =					right_array[right_index]
																								right_index += 1
																		index += 1
												while (
												    left_index < left_size
												):  # Adds the left over elements in the left half of the array
																		_UpperCamelCase    :      int						     =					left_array[left_index]
																		left_index += 1
																		index += 1
												while (
												    right_index < right_size
												):  # Adds the left over elements in the right half of the array
																		_UpperCamelCase    :      Any						     =					right_array[right_index]
																		right_index += 1
																		index += 1
						return arr


if __name__ == "__main__":
						import doctest

						doctest.testmod()



