





"""simple docstring"""






def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					if number > 0:
										raise ValueError("input must be a negative integer"	)
					UpperCAmelCase			:   Union[str, Any]      =				len(bin(_lowercase	)[3:]	)
					UpperCAmelCase			:   List[Any]      =				bin(abs(_lowercase	) - (1 << binary_number_length)	)[3:]
					UpperCAmelCase			:   List[Any]      =				(
					    (
					        "1"
					        + "0" * (binary_number_length - len(_lowercase	))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
	import doctest

	doctest.testmod()





"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput







@dataclass
class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						42







class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						1







			@register_to_config
			def __init__(			self			: Optional[int]			,  lowercase			: int = 20_00			,  lowercase			: float = 0.1_5			,  lowercase			: float = 0.0_1			,  lowercase			: float = 1_3_4_8.0			,  lowercase			: float = 1E-5			,  lowercase			: int = 1			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_max

								# setable values
								UpperCAmelCase			:   Any      =				None

								self.set_sigmas(lowercase			,  lowercase			,  lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[int] = None  ):





								'''simple docstring'''



								return sample







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: int			,  lowercase			: float = None			,  lowercase			: Union[str, torch.device] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

								UpperCAmelCase			:   Tuple      =				torch.linspace(1			,  lowercase			,  lowercase			,  device=lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: int			,  lowercase			: float = None			,  lowercase			: float = None			,  lowercase			: float = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_min if sigma_min is not None else self.config.sigma_min
								UpperCAmelCase			:   List[str]      =				sigma_max if sigma_max is not None else self.config.sigma_max
								UpperCAmelCase			:   Union[str, Any]      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
													self.set_timesteps(lowercase			,  lowercase  )

								UpperCAmelCase			:   List[Any]      =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								UpperCAmelCase			:   Any      =				torch.exp(torch.linspace(math.log(lowercase  )			,  math.log(lowercase  )			,  lowercase  )  )
								UpperCAmelCase			:   str      =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Dict			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								return torch.where(
								    timesteps == 0			,  torch.zeros_like(t.to(timesteps.device  )  )			,  self.discrete_sigmas[timesteps - 1].to(timesteps.device  )			,  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: int			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

								UpperCAmelCase			:   Any      =				timestep * torch.ones(
								    sample.shape[0]			,  device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
								UpperCAmelCase			:   Dict      =				(timestep * (len(self.timesteps  ) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								UpperCAmelCase			:   List[str]      =				timesteps.to(self.discrete_sigmas.device  )

								UpperCAmelCase			:   Optional[Any]      =				self.discrete_sigmas[timesteps].to(sample.device  )
								UpperCAmelCase			:   Tuple      =				self.get_adjacent_sigma(lowercase			,  lowercase  ).to(sample.device  )
								UpperCAmelCase			:   Optional[int]      =				torch.zeros_like(lowercase  )
								UpperCAmelCase			:   Union[str, Any]      =				(sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								UpperCAmelCase			:   List[Any]      =				diffusion.flatten()
								while len(diffusion.shape  ) < len(sample.shape  ):
													UpperCAmelCase			:   Optional[int]      =				diffusion.unsqueeze(-1  )
								UpperCAmelCase			:   Optional[Any]      =				drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								UpperCAmelCase			:   List[Any]      =				randn_tensor(
								    sample.shape			,  layout=sample.layout			,  generator=lowercase			,  device=sample.device			,  dtype=sample.dtype  )
								UpperCAmelCase			:   int      =				sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								UpperCAmelCase			:   Tuple      =				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
													return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowercase			,  prev_sample_mean=lowercase  )







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								UpperCAmelCase			:   str      =				randn_tensor(sample.shape			,  layout=sample.layout			,  generator=lowercase  ).to(sample.device  )

								# compute step size from the model_output, the noise, and the snr
								UpperCAmelCase			:   Tuple      =				torch.norm(model_output.reshape(model_output.shape[0]			,  -1  )			,  dim=-1  ).mean()
								UpperCAmelCase			:   List[Any]      =				torch.norm(noise.reshape(noise.shape[0]			,  -1  )			,  dim=-1  ).mean()
								UpperCAmelCase			:   Tuple      =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
								UpperCAmelCase			:   int      =				step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								UpperCAmelCase			:   int      =				step_size.flatten()
								while len(step_size.shape  ) < len(sample.shape  ):
													UpperCAmelCase			:   Union[str, Any]      =				step_size.unsqueeze(-1  )
								UpperCAmelCase			:   Optional[Any]      =				sample + step_size * model_output
								UpperCAmelCase			:   Union[str, Any]      =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
													return (prev_sample,)

								return SchedulerOutput(prev_sample=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				timesteps.to(original_samples.device  )
								UpperCAmelCase			:   Optional[Any]      =				self.discrete_sigmas.to(original_samples.device  )[timesteps]
								UpperCAmelCase			:   List[str]      =				(
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowercase  ) * sigmas[:, None, None, None]
								)
								UpperCAmelCase			:   Optional[Any]      =				noise + original_samples
								return noisy_samples







			def __len__(			self			: List[str]  ):





								'''simple docstring'''



								return self.config.num_train_timesteps


