


"""simple docstring"""






def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					)					->  float:
			_UpperCamelCase					:Tuple					     =	[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:
						_UpperCamelCase					:Dict					     =	1 - (matter_density + radiation_density + dark_energy)

						_UpperCamelCase					:List[Any]					     =	(
						    radiation_density * (redshift + 1) ** 4
						    + matter_density * (redshift + 1) ** 3
						    + curvature * (redshift + 1) ** 2
						    + dark_energy
						)

						_UpperCamelCase					:Dict					     =	hubble_constant * e_a ** (1 / 2)
						return hubble


if __name__ == "__main__":
					import doctest

					# run doctest
					doctest.testmod()

					# demo LCDM approximation
					UpperCamelCase__				:List[str]			   =	0.3

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





"""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      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()

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

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





		@torch.no_grad()
		def __call__( self					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = 0.0					,				SCREAMING_SNAKE_CASE__ = 50					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "pil"					,				SCREAMING_SNAKE_CASE__ = True					,				)      ->	Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""



					if isinstance(self.unet.config.sample_size					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	(
								    batch_size,
								    self.unet.config.in_channels,
								    self.unet.config.sample_size,
								    self.unet.config.sample_size,
								)
					else:
								_UpperCamelCase					:Tuple					     =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

					_UpperCamelCase					:Optional[int]					     =	randn_tensor(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device=self.device					,				dtype=self.unet.dtype   )

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

					for t in self.progress_bar(self.scheduler.timesteps   ):
								# 1. predict noise model_output
								_UpperCamelCase					:List[str]					     =	self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).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
								_UpperCamelCase					:Optional[Any]					     =	self.scheduler.step(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				eta=SCREAMING_SNAKE_CASE__					,				use_clipped_model_output=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

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

					if not return_dict:
								return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )

