
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__    (							SCREAMING_SNAKE_CASE_    ):

       """simple docstring"""






       _snake_case     		=  42
       _snake_case     		=  42



       def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[Any]:





             '''simple docstring'''






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



       @torch.no_grad()
       def __call__(  self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Union[Tuple, ImagePipelineOutput]:





             '''simple docstring'''






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

             __UpperCamelCase       =			self.unet

             # sample x_0 ~ N(0, sigma_0^2 * I)
             __UpperCamelCase       =			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
                   __UpperCamelCase       =			self.scheduler.schedule[t]
                   __UpperCamelCase       =			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
                   __UpperCamelCase  ,							__UpperCamelCase       =			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].
                   __UpperCamelCase       =			(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
                   __UpperCamelCase       =			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].
                         __UpperCamelCase       =			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2     ,	sigma_prev / 2			).sample
                         __UpperCamelCase       =			self.scheduler.step_correct(
                             lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	step_output.prev_sample     ,	step_output['''derivative''']     ,	)
                   __UpperCamelCase       =			step_output.prev_sample

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

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=lowercase_			)






def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




