

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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image.size
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						SCREAMING_SNAKE_CASE_: Tuple  		=       image.resize((w, h)				,	resample=PIL_INTERPOLATION["lanczos"]    )
						SCREAMING_SNAKE_CASE_: str  		=       np.array(_UpperCAmelCase    ).astype(np.floataa    ) / 2_5_5.0
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image[None].transpose(0				,	3				,	1				,	2    )
						SCREAMING_SNAKE_CASE_: str  		=       torch.from_numpy(_UpperCAmelCase    )
						return 2.0 * image - 1.0



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     VQModel   ,						lowerCAmelCase__  :     UNetaDModel   ,						lowerCAmelCase__  :     Union[
			        DDIMScheduler,
			        PNDMScheduler,
			        LMSDiscreteScheduler,
			        EulerDiscreteScheduler,
			        EulerAncestralDiscreteScheduler,
			        DPMSolverMultistepScheduler,
			    ]   ,						):
									super().__init__()
									self.register_modules(vqvae=lowerCAmelCase__   ,						unet=lowerCAmelCase__   ,						scheduler=lowerCAmelCase__)




			@torch.no_grad()
			def __call__( self  :     str   ,						lowerCAmelCase__  :     Union[torch.Tensor, PIL.Image.Image] = None   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     Optional[int] = 100   ,						lowerCAmelCase__  :     Optional[float] = 0.0   ,						lowerCAmelCase__  :     Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,						lowerCAmelCase__  :     Optional[str] = "pil"   ,						lowerCAmelCase__  :     bool = True   ,						):
									if isinstance(lowerCAmelCase__   ,						PIL.Image.Image):
															SCREAMING_SNAKE_CASE_: List[str]  		=       1
									elif isinstance(lowerCAmelCase__   ,						torch.Tensor):
															SCREAMING_SNAKE_CASE_: str  		=       image.shape[0]
									else:
															raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__)}")

									if isinstance(lowerCAmelCase__   ,						PIL.Image.Image):
															SCREAMING_SNAKE_CASE_: Dict  		=       preprocess(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       image.shape[-2:]

									# in_channels should be 6: 3 for latents, 3 for low resolution image
									SCREAMING_SNAKE_CASE_: List[str]  		=       (batch_size, self.unet.config.in_channels // 2, height, width)
									SCREAMING_SNAKE_CASE_: List[str]  		=       next(self.unet.parameters()).dtype

									SCREAMING_SNAKE_CASE_: Dict  		=       randn_tensor(lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						device=self.device   ,						dtype=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: str  		=       image.to(device=self.device   ,						dtype=lowerCAmelCase__)

									# set timesteps and move to the correct device
									self.scheduler.set_timesteps(lowerCAmelCase__   ,						device=self.device)
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.scheduler.timesteps

									# scale the initial noise by the standard deviation required by the scheduler
									SCREAMING_SNAKE_CASE_: List[str]  		=       latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									SCREAMING_SNAKE_CASE_: List[str]  		=       "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       {}
									if accepts_eta:
															SCREAMING_SNAKE_CASE_: int  		=       eta

									for t in self.progress_bar(lowerCAmelCase__):
															# concat latents and low resolution image in the channel dimension.
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.cat([latents, image]   ,						dim=1)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       self.scheduler.scale_model_input(lowerCAmelCase__   ,						lowerCAmelCase__)
															# predict the noise residual
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.unet(lowerCAmelCase__   ,						lowerCAmelCase__).sample
															# compute the previous noisy sample x_t -> x_t-1
															SCREAMING_SNAKE_CASE_: int  		=       self.scheduler.step(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						**lowerCAmelCase__).prev_sample

									# decode the image latents with the VQVAE
									SCREAMING_SNAKE_CASE_: int  		=       self.vqvae.decode(lowerCAmelCase__).sample
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.clamp(lowerCAmelCase__   ,						-1.0   ,						1.0)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       image / 2 + 0.5
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image.cpu().permute(0   ,						2   ,						3   ,						1).numpy()

									if output_type == "pil":
															SCREAMING_SNAKE_CASE_: Dict  		=       self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)









def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       [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:
												SCREAMING_SNAKE_CASE_: int  		=       1 - (matter_density + radiation_density + dark_energy)

												SCREAMING_SNAKE_CASE_: Dict  		=       (
												    radiation_density * (redshift + 1) ** 4
												    + matter_density * (redshift + 1) ** 3
												    + curvature * (redshift + 1) ** 2
												    + dark_energy
												)

												SCREAMING_SNAKE_CASE_: Any  		=       hubble_constant * e_a ** (1 / 2)
												return hubble


if __name__ == "__main__":
						import doctest

						# run doctest
						doctest.testmod()

						# demo LCDM approximation
						lowerCAmelCase							:  List[Any]   	=						0.3

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






