


"""simple docstring"""


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				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       UNetaDModel
   _a   :       KarrasVeScheduler


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Dict:
    super().__init__()
    self.register_modules(unet=lowerCamelCase__ ,     scheduler=lowerCamelCase__						)







   @torch.no_grad()
   def __call__(						self ,     lowerCamelCase__ = 1 ,     lowerCamelCase__ = 50 ,     lowerCamelCase__ = None ,     lowerCamelCase__ = "pil" ,     lowerCamelCase__ = True ,     **lowerCamelCase__ ,     )      ->							Union[Tuple, ImagePipelineOutput]:
    lowercase__       :       Union[str, Any]				=						self.unet.config.sample_size
    lowercase__       :       int				=						(batch_size, 3, img_size, img_size)

    lowercase__       :       Optional[Any]				=						self.unet

    # sample x_0 ~ N(0, sigma_0^2 * I)
    lowercase__       :       Tuple				=						randn_tensor(lowerCamelCase__ ,     generator=lowerCamelCase__ ,     device=self.device						) * self.scheduler.init_noise_sigma

    self.scheduler.set_timesteps(lowerCamelCase__						)

    for t in self.progress_bar(self.scheduler.timesteps						):
     # here sigma_t == t_i from the paper
     lowercase__       :       List[Any]				=						self.scheduler.schedule[t]
     lowercase__       :       Tuple				=						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
     lowercase__	,       lowercase__       :       str				=						self.scheduler.add_noise_to_input(lowerCamelCase__ ,     lowerCamelCase__ ,     generator=lowerCamelCase__						)

     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
     # The model inputs and output are adjusted by following eq. (213) in [1].
     lowercase__       :       List[Any]				=						(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
     lowercase__       :       Any				=						self.scheduler.step(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)

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

    lowercase__       :       int				=						(sample / 2 + 0.5).clamp(0 ,     1						)
    lowercase__       :       Optional[int]				=						sample.cpu().permute(0 ,     2 ,     3 ,     1						).numpy()
    if output_type == "pil":
     lowercase__       :       List[Any]				=						self.numpy_to_pil(lowerCamelCase__						)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=lowerCamelCase__						)



"""simple docstring"""


from ..utils import DummyObject, requires_backends

class 				_SCREAMING_SNAKE_CASE				(							metaclass=__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    ['''note_seq''']


   def __init__(						self ,     *lowerCamelCase__ ,     **lowerCamelCase__						)      ->							Tuple:
    requires_backends(self ,     ["""note_seq"""]						)


   @classmethod
   def 					UpperCAmelCase__(						cls ,     *lowerCamelCase__ ,     **lowerCamelCase__						)      ->							Tuple:
    requires_backends(cls ,     ["""note_seq"""]						)







   @classmethod
   def 					UpperCAmelCase__(						cls ,     *lowerCamelCase__ ,     **lowerCamelCase__						)      ->							Tuple:
    requires_backends(cls ,     ["""note_seq"""]						)