
from typing import List, Optional, Tuple, Union

import torch

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


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      def __init__(      self		:       List[str]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       Union[str, Any]				):
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          __lowercase		:			Union[str, Any]	    =       DDIMScheduler.from_config(scheduler.config				)

          self.register_modules(unet=_snake_case				,       scheduler=_snake_case				)




      @torch.no_grad()
      def __call__(      self		:       Dict				,       _snake_case		:       int = 1				,       _snake_case		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None				,       _snake_case		:       float = 0.0				,       _snake_case		:       int = 50				,       _snake_case		:       Optional[bool] = None				,       _snake_case		:       Optional[str] = "pil"				,       _snake_case		:       bool = True				,       ):

          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,       _snake_case				):
              __lowercase		:			Tuple	    =       (
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size,
                  self.unet.config.sample_size,
              )
          else:
              __lowercase		:			Union[str, Any]	    =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if isinstance(_snake_case				,       _snake_case				) and len(_snake_case				) != batch_size:
              raise ValueError(
                  F'You have passed a list of generators of length {len(_snake_case				)}, but requested an effective batch'
                  F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

          __lowercase		:			Dict	    =       randn_tensor(_snake_case				,       generator=_snake_case				,       device=self.device				,       dtype=self.unet.dtype				)

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

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

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              __lowercase		:			List[str]	    =       self.scheduler.step(
                  _snake_case				,       _snake_case				,       _snake_case				,       eta=_snake_case				,       use_clipped_model_output=_snake_case				,       generator=_snake_case				).prev_sample

          __lowercase		:			Any	    =       (image / 2 + 0.5).clamp(0				,       1				)
          __lowercase		:			int	    =       image.cpu().permute(0				,       2				,       3				,       1				).numpy()
          if output_type == "pil":
              __lowercase		:			Dict	    =       self.numpy_to_pil(_snake_case				)

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=_snake_case				)




def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:

    if not isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
        raise ValueError('''multiplicative_persistence() only accepts integral values'''	)
    if num < 0:
        raise ValueError('''multiplicative_persistence() does not accept negative values'''	)

    __lowercase		:			Dict	    =       0
    __lowercase		:			Optional[int]	    =       str(__lowerCAmelCase	)

    while len(__lowerCAmelCase	) != 1:
        __lowercase		:			str	    =       [int(__lowerCAmelCase	) for i in num_string]

        __lowercase		:			Union[str, Any]	    =       1
        for i in range(0	,					len(__lowerCAmelCase	)	):
            total *= numbers[i]

        __lowercase		:			Any	    =       str(__lowerCAmelCase	)

        steps += 1
    return steps


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:

    if not isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
        raise ValueError('''additive_persistence() only accepts integral values'''	)
    if num < 0:
        raise ValueError('''additive_persistence() does not accept negative values'''	)

    __lowercase		:			Any	    =       0
    __lowercase		:			Union[str, Any]	    =       str(__lowerCAmelCase	)

    while len(__lowerCAmelCase	) != 1:
        __lowercase		:			List[str]	    =       [int(__lowerCAmelCase	) for i in num_string]

        __lowercase		:			Tuple	    =       0
        for i in range(0	,					len(__lowerCAmelCase	)	):
            total += numbers[i]

        __lowercase		:			List[str]	    =       str(__lowerCAmelCase	)

        steps += 1
    return steps


if __name__ == "__main__":
 import doctest

 doctest.testmod()


