

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 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def __init__(				self			:	Dict,lowercase_			:	Union[str, Any],lowercase_			:	List[str]		)->   str:





        '''simple docstring'''







        super().__init__()

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

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


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





        '''simple docstring'''







        if isinstance(self.unet.config.sample_size,lowercase_		):
            A__   				=       (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size,
                self.unet.config.sample_size,
            )
        else:
            A__   				=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

        A__   				=       randn_tensor(lowercase_,generator=lowercase_,device=self.device,dtype=self.unet.dtype		)

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

        for t in self.progress_bar(self.scheduler.timesteps		):
            # 1. predict noise model_output
            A__   				=       self.unet(lowercase_,lowercase_		).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
            A__   				=       self.scheduler.step(
                lowercase_,lowercase_,lowercase_,eta=lowercase_,use_clipped_model_output=lowercase_,generator=lowercase_		).prev_sample

        A__   				=       (image / 2 + 0.5).clamp(0,1		)
        A__   				=       image.cpu().permute(0,2,3,1		).numpy()
        if output_type == "pil":
            A__   				=       self.numpy_to_pil(lowercase_		)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=lowercase_		)




import random
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				tuple:





    '''simple docstring'''






    A__				,						A__				,						A__   				=       [], [], []
    for element in data:
        if element < pivot:
            less.append(SCREAMING_SNAKE_CASE__				)
        elif element > pivot:
            greater.append(SCREAMING_SNAKE_CASE__				)
        else:
            equal.append(SCREAMING_SNAKE_CASE__				)
    return less, equal, greater

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				str:





    '''simple docstring'''






    if index >= len(SCREAMING_SNAKE_CASE__				) or index < 0:
        return None

    A__   				=       items[random.randint(0   ,      len(SCREAMING_SNAKE_CASE__				) - 1				)]
    A__   				=       0
    A__				,						A__				,						A__   				=       _partition(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    A__   				=       len(SCREAMING_SNAKE_CASE__				)
    A__   				=       len(SCREAMING_SNAKE_CASE__				)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    # must be in larger
    else:
        return quick_select(SCREAMING_SNAKE_CASE__   ,      index - (m + count)				)

