
'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class   a_  (_a    ):




       def __init__(     self   ,   snake_case_   ,   snake_case_  ):
         super().__init__()
         self.register_modules(unet=snake_case_   ,   scheduler=snake_case_  )







       @torch.no_grad()
       def __call__(     self   ,   snake_case_ = 1   ,   snake_case_ = None   ,   snake_case_ = 1_0_0_0   ,   snake_case_ = "pil"   ,   snake_case_ = True   ,   ):
         # Sample gaussian noise to begin loop
         if isinstance(self.unet.config.sample_size   ,   snake_case_  ):
           _lowerCAmelCase       :  Tuple   				=					(
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size,
               self.unet.config.sample_size,
           )
         else:
           _lowerCAmelCase       :  Union[str, 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
           _lowerCAmelCase       :  Dict   				=					randn_tensor(snake_case_   ,   generator=snake_case_  )
           _lowerCAmelCase       :  List[Any]   				=					image.to(self.device  )
         else:
           _lowerCAmelCase       :  Tuple   				=					randn_tensor(snake_case_   ,   generator=snake_case_   ,   device=self.device  )

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

         for t in self.progress_bar(self.scheduler.timesteps  ):
           # 1. predict noise model_output
           _lowerCAmelCase       :  List[Any]   				=					self.unet(snake_case_   ,   snake_case_  ).sample

           # 2. compute previous image: x_t -> x_t-1
           _lowerCAmelCase       :  Tuple   				=					self.scheduler.step(snake_case_   ,   snake_case_   ,   snake_case_   ,   generator=snake_case_  ).prev_sample

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

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=snake_case_  )







'''simple docstring'''

def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int		)    ->						float:
  if common_ratio == 1:
    # Formula for sum if common ratio is 1
    return num_of_terms * first_term

  # Formula for finding sum of n terms of a GeometricProgression
  return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)


