



"""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 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					42
      _SCREAMING_SNAKE_CASE      =					42



      def __init__(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				UNetaDModel   ,      SCREAMING_SNAKE_CASE_    :				KarrasVeScheduler	):
        super().__init__()
        self.register_modules(unet=SCREAMING_SNAKE_CASE_   ,      scheduler=SCREAMING_SNAKE_CASE_	)





      @torch.no_grad()
      def __call__(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				int = 1   ,      SCREAMING_SNAKE_CASE_    :				int = 5_0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[str] = "pil"   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      **SCREAMING_SNAKE_CASE_    :				List[Any]   ,      ):
        lowerCAmelCase_       :    List[Any]								=			self.unet.config.sample_size
        lowerCAmelCase_       :    str								=			(batch_size, 3, img_size, img_size)

        lowerCAmelCase_       :    Optional[int]								=			self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        lowerCAmelCase_       :    Optional[Any]								=			randn_tensor(SCREAMING_SNAKE_CASE_   ,      generator=SCREAMING_SNAKE_CASE_   ,      device=self.device	) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

        for t in self.progress_bar(self.scheduler.timesteps	):
          # here sigma_t == t_i from the paper
          lowerCAmelCase_       :    Dict								=			self.scheduler.schedule[t]
          lowerCAmelCase_       :    Optional[int]								=			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
          lowerCAmelCase_						,lowerCAmelCase_       :    int								=			self.scheduler.add_noise_to_input(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      generator=SCREAMING_SNAKE_CASE_	)

          # 3. Predict the noise residual given the noise magnitude `sigma_hat`
          # The model inputs and output are adjusted by following eq. (213) in [1].
          lowerCAmelCase_       :    Union[str, 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
          lowerCAmelCase_       :    int								=			self.scheduler.step(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

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

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

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)










"""simple docstring"""







from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase__		:    Any				   =     TypeVar("""_T""")







class 				UpperCamelCase__       (     Generic[_T]  ):




      """simple docstring"""




      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Iterable[_T] | None = None	):
        lowerCAmelCase_       :    list[_T]								=			list(iterable or []	)
        lowerCAmelCase_       :    list[_T]								=			[]



      def __len__(				self    :				List[str]	):
        return len(self._stacka	) + len(self._stacka	)



      def __repr__(				self    :				int	):
        return F"Queue({tuple(self._stacka[::-1] + self._stacka	)})"



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				_T	):
        self._stacka.append(SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    List[str]								=			self._stacka.pop
        lowerCAmelCase_       :    int								=			self._stacka.append

        if not self._stacka:
          while self._stacka:
            stacka_append(stacka_pop()	)

        if not self._stacka:
          raise IndexError('Queue is empty'	)
        return self._stacka.pop()


if __name__ == "__main__":
     from doctest import testmod

     testmod()





