

import inspect
from typing import List, Optional, Tuple, Union

import torch

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


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def __init__(      self :       Optional[Any],lowercase_ :       VQModel,lowercase_ :       UNetaDModel,lowercase_ :       DDIMScheduler		)->    Optional[Any]:





        '''simple docstring'''







        super().__init__()
        self.register_modules(vqvae=lowercase_,unet=lowercase_,scheduler=lowercase_		)


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





        '''simple docstring'''







        A__          =   randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),generator=lowercase_,)
        A__          =   latents.to(self.device		)

        # scale the initial noise by the standard deviation required by the scheduler
        A__          =   latents * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowercase_		)

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        A__          =   'eta' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)

        A__          =   {}
        if accepts_eta:
            A__          =   eta

        for t in self.progress_bar(self.scheduler.timesteps		):
            A__          =   self.scheduler.scale_model_input(lowercase_,lowercase_		)
            # predict the noise residual
            A__          =   self.unet(lowercase_,lowercase_		).sample
            # compute the previous noisy sample x_t -> x_t-1
            A__          =   self.scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

        # decode the image latents with the VAE
        A__          =   self.vqvae.decode(lowercase_		).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_		)




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

