


'''simple docstring'''

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       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										super().__init__()
										self.register_modules(vqvae=__lowercase       ,			unet=__lowercase       ,			scheduler=__lowercase		)




				@torch.no_grad()
				def __call__(self       ,			__lowercase = 1       ,			__lowercase = None       ,			__lowercase = 0.0       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,			generator=__lowercase       ,			)
										__lowerCAmelCase							       =				latents.to(self.device		)

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

										self.scheduler.set_timesteps(__lowercase		)

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

										__lowerCAmelCase							       =				{}
										if accepts_eta:
																__lowerCAmelCase							       =				eta

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

										# decode the image latents with the VAE
										__lowerCAmelCase							       =				self.vqvae.decode(__lowercase		).sample

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

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=__lowercase		)





'''simple docstring'''

from __future__ import annotations

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				sorted(numsa + numsa)
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				divmod(len(lowerCamelCase),				2)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_UpperCAmelCase				: Union[str, Any]							    =				[float(x) for x in input("""Enter the elements of first array: """).split()]
	_UpperCAmelCase				: int							    =				[float(x) for x in input("""Enter the elements of second array: """).split()]
	print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")

