





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = 42


    def __init__(		self ,		A ,		A       )		->   Optional[Any]:
        super().__init__()
        self.register_modules(unet=A ,		scheduler=A       )





    @torch.no_grad()
    def __call__(		self ,		A = 1 ,		A = 2000 ,		A = None ,		A = "pil" ,		A = True ,		**A ,		)		->   Union[ImagePipelineOutput, Tuple]:
        _SCREAMING_SNAKE_CASE									=			self.unet.config.sample_size
        _SCREAMING_SNAKE_CASE									=			(batch_size, 3, img_size, img_size)

        _SCREAMING_SNAKE_CASE									=			self.unet

        _SCREAMING_SNAKE_CASE									=			randn_tensor(A ,		generator=A       ) * self.scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE									=			sample.to(self.device       )

        self.scheduler.set_timesteps(A       )
        self.scheduler.set_sigmas(A       )

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
            _SCREAMING_SNAKE_CASE									=			self.scheduler.sigmas[i] * torch.ones(shape[0] ,		device=self.device       )

            # correction step
            for _ in range(self.scheduler.config.correct_steps       ):
                _SCREAMING_SNAKE_CASE									=			self.unet(A ,		A       ).sample
                _SCREAMING_SNAKE_CASE									=			self.scheduler.step_correct(A ,		A ,		generator=A       ).prev_sample

            # prediction step
            _SCREAMING_SNAKE_CASE									=			model(A ,		A       ).sample
            _SCREAMING_SNAKE_CASE									=			self.scheduler.step_pred(A ,		A ,		A ,		generator=A       )

            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			output.prev_sample, output.prev_sample_mean

        _SCREAMING_SNAKE_CASE									=			sample_mean.clamp(0 ,		1       )
        _SCREAMING_SNAKE_CASE									=			sample.cpu().permute(0 ,		2 ,		3 ,		1       ).numpy()
        if output_type == "pil":
            _SCREAMING_SNAKE_CASE									=			self.numpy_to_pil(A       )

        if not return_dict:
            return (sample,)

        return ImagePipelineOutput(images=A       )





import os
from distutils.util import strtobool
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			for e in env_keys:
						lowerCAmelCase__      :      Union[str, Any]         =		int(os.environ.get(SCREAMING_SNAKE_CASE_       ,							-1     )     )
						if val >= 0:
									return val
			return default
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=False     )				->		List[str]:
			lowerCAmelCase__      :      Optional[int]         =		os.environ.get(SCREAMING_SNAKE_CASE_       ,							str(SCREAMING_SNAKE_CASE_     )     )
			return strtobool(SCREAMING_SNAKE_CASE_     ) == 1  # As its name indicates `strtobool` actually returns an int...







def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_="no"     )				->		List[str]:
			lowerCAmelCase__      :      Optional[int]         =		os.environ.get(SCREAMING_SNAKE_CASE_       ,							str(SCREAMING_SNAKE_CASE_     )     )
			return value