
from typing import List, Optional, Tuple, Union

import torch

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




class        SCREAMING_SNAKE_CASE__					(     snake_case_ ):





	'''simple docstring'''



	_UpperCAmelCase	:		str          = 4_2
	_UpperCAmelCase	:		List[str]          = 4_2



	def __init__(  self     :   Optional[Any]      ,  lowercase     :   UNetaDModel      ,  lowercase     :   KarrasVeScheduler     ):


				'''simple docstring'''


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





	@torch.no_grad()
	def __call__(  self     :   Dict      ,  lowercase     :   int = 1      ,  lowercase     :   int = 50      ,  lowercase     :   Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,  lowercase     :   Optional[str] = "pil"      ,  lowercase     :   bool = True      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


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

				_snake_case          =      self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				_snake_case          =      randn_tensor(lowercase      ,  generator=lowercase      ,  device=self.device     ) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(lowercase     )

				for t in self.progress_bar(self.scheduler.timesteps     ):
							# here sigma_t == t_i from the paper
							_snake_case          =      self.scheduler.schedule[t]
							_snake_case          =      self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							_snake_case          =      self.scheduler.add_noise_to_input(lowercase      ,  lowercase      ,  generator=lowercase     )

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							_snake_case          =      (sigma_hat / 2) * model((sample_hat + 1) / 2      ,  sigma_hat / 2     ).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							_snake_case          =      self.scheduler.step(lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										_snake_case          =      (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2      ,  sigma_prev / 2     ).sample
										_snake_case          =      self.scheduler.step_correct(
										    lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  step_output.prev_sample      ,  step_output['derivative']      ,  )
							_snake_case          =      step_output.prev_sample

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

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=lowercase     )

from functools import lru_cache






@lru_cache
def 	a_		(	__lowercase       :     int			)			->  int:
			if num < 0:
						raise ValueError('Number should not be negative.'			)

			return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()