





'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class      SCREAMING_SNAKE_CASE__      (    lowercase__				):




    def __init__( self    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






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







    @torch.no_grad()
    def __call__( self    ,					lowercase__ = 1    ,					lowercase__ = None    ,					lowercase__ = 1000    ,					lowercase__ = "pil"    ,					lowercase__ = True    ,					):






          """simple docstring"""






          if isinstance(self.unet.config.sample_size    ,					lowerCAmelCase__      ):
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	(
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size,
                    self.unet.config.sample_size,
                )
          else:
                SCREAMING_SNAKE_CASE_				:      Any					=	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
                # randn does not work reproducibly on mps
                SCREAMING_SNAKE_CASE_				:      Tuple					=	randn_tensor(lowerCAmelCase__    ,					generator=lowerCAmelCase__      )
                SCREAMING_SNAKE_CASE_				:      int					=	image.to(self.device      )
          else:
                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	randn_tensor(lowerCAmelCase__    ,					generator=lowerCAmelCase__    ,					device=self.device      )

          # set step values
          self.scheduler.set_timesteps(lowerCAmelCase__      )

          for t in self.progress_bar(self.scheduler.timesteps      ):
                # 1. predict noise model_output
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.unet(lowerCAmelCase__    ,					lowerCAmelCase__      ).sample

                # 2. compute previous image: x_t -> x_t-1
                SCREAMING_SNAKE_CASE_				:      List[str]					=	self.scheduler.step(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					generator=lowerCAmelCase__      ).prev_sample

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

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=lowerCAmelCase__      )




'''simple docstring'''






def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[int]  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       List[str]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(boundary[1] - boundary[0]) / steps
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	boundary[0]
      SCREAMING_SNAKE_CASE_				:      int					=	boundary[1]
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	make_points(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	0.0
      y += (h / 3.0) * f(SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Tuple					=	2
      for i in x_i:
            y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE_   )
            cnt += 1
      y += (h / 3.0) * f(SCREAMING_SNAKE_CASE_   )
      return y


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[Any]  ,  SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     Optional[Any]   )							->       List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	a + h
      while x < (b - h):
            yield x
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	x + h


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str   )							->       List[Any]:  # enter your function here
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(x - 0) * (x - 0)
      return y


def  __lowerCamelCase      (				)							->       int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	0.0  # Lower bound of integration
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	1.0  # Upper bound of integration
      SCREAMING_SNAKE_CASE_				:      str					=	10.0  # define number of steps or resolution
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	[a, b]  # define boundary of integration
      SCREAMING_SNAKE_CASE_				:      List[str]					=	method_a(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )
      print(F"y = {y}"   )


if __name__ == "__main__":
      main()

