


"""simple docstring"""





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 						snake_case_(				a__	):






     def __init__(		self     :    str    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    Tuple ):
           super().__init__()

           # make sure scheduler can always be converted to DDIM
           lowerCAmelCase    :		Tuple				=      DDIMScheduler.from_config(scheduler.config )

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






     @torch.no_grad()
     def __call__(		self     :    List[str]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[bool] = None    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , ):

           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size    , UpperCamelCase_ ):
                 lowerCAmelCase    :		Optional[int]				=      (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCAmelCase    :		List[str]				=      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

           lowerCAmelCase    :		Any				=      randn_tensor(UpperCamelCase_    , generator=UpperCamelCase_    , device=self.device    , dtype=self.unet.dtype )

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

           for t in self.progress_bar(self.scheduler.timesteps ):
                 # 1. predict noise model_output
                 lowerCAmelCase    :		Optional[int]				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).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
                 lowerCAmelCase    :		Optional[int]				=      self.scheduler.step(
                     UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , eta=UpperCamelCase_    , use_clipped_model_output=UpperCamelCase_    , generator=UpperCamelCase_ ).prev_sample

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )



def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		while b:
				SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    b, a % b
		return a




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		return a if b == 0 else euclidean_gcd_recursive(lowerCAmelCase   , a % b	)




def       _snake_case	(   ):
		"""simple docstring"""







		print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3   , 5	)}'	)
		print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5   , 3	)}'	)
		print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1   , 3	)}'	)
		print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3   , 6	)}'	)
		print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6   , 3	)}'	)

		print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3   , 5	)}'	)
		print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5   , 3	)}'	)
		print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1   , 3	)}'	)
		print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3   , 6	)}'	)
		print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6   , 3	)}'	)


if __name__ == "__main__":
		main()
