



# 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     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  42
			UpperCamelCase           =  42







class     __lowerCAmelCase					(    A       ,		A		):
			UpperCamelCase           =  1



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



						"""simple docstring"""






						_UpperCAmelCase	 =     sigma_max

						# setable values
						_UpperCAmelCase	 =     None

						self.set_sigmas(A				,	A				,	A				,	A)



			def 					_lowerCamelCase (		self     :						int				,	A     :						torch.FloatTensor				,	A     :						Optional[int] = None)     ->					torch.FloatTensor:



						"""simple docstring"""






						return sample



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						int				,	A     :						float = None				,	A     :						Union[str, torch.device] = None)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						_UpperCAmelCase	 =     torch.linspace(1				,	A				,	A				,	device=A)



			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						int				,	A     :						float = None				,	A     :						float = None				,	A     :						float = None)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     sigma_min if sigma_min is not None else self.config.sigma_min
						_UpperCAmelCase	 =     sigma_max if sigma_max is not None else self.config.sigma_max
						_UpperCAmelCase	 =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if self.timesteps is None:
									self.set_timesteps(A				,	A)

						_UpperCAmelCase	 =     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
						_UpperCAmelCase	 =     torch.exp(torch.linspace(math.log(A)				,	math.log(A)				,	A))
						_UpperCAmelCase	 =     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						Optional[Any]				,	A     :						Union[str, Any])     ->					str:



						"""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     :						int				,	A     :						torch.FloatTensor				,	A     :						int				,	A     :						torch.FloatTensor				,	A     :						Optional[torch.Generator] = None				,	A     :						bool = True				,	)     ->					Union[SdeVeOutput, Tuple]:



						"""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	 =     timestep * torch.ones(
						    sample.shape[0]				,	device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
						_UpperCAmelCase	 =     (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	 =     timesteps.to(self.discrete_sigmas.device)

						_UpperCAmelCase	 =     self.discrete_sigmas[timesteps].to(sample.device)
						_UpperCAmelCase	 =     self.get_adjacent_sigma(A				,	A).to(sample.device)
						_UpperCAmelCase	 =     torch.zeros_like(A)
						_UpperCAmelCase	 =     (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	 =     diffusion.flatten()
						while len(diffusion.shape) < len(sample.shape):
									_UpperCAmelCase	 =     diffusion.unsqueeze(-1)
						_UpperCAmelCase	 =     drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						_UpperCAmelCase	 =     randn_tensor(
						    sample.shape				,	layout=sample.layout				,	generator=A				,	device=sample.device				,	dtype=sample.dtype)
						_UpperCAmelCase	 =     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						_UpperCAmelCase	 =     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=A				,	prev_sample_mean=A)



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						torch.FloatTensor				,	A     :						torch.FloatTensor				,	A     :						Optional[torch.Generator] = None				,	A     :						bool = True				,	)     ->					Union[SchedulerOutput, Tuple]:



						"""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	 =     randn_tensor(sample.shape				,	layout=sample.layout				,	generator=A).to(sample.device)

						# compute step size from the model_output, the noise, and the snr
						_UpperCAmelCase	 =     torch.norm(model_output.reshape(model_output.shape[0]				,	-1)				,	dim=-1).mean()
						_UpperCAmelCase	 =     torch.norm(noise.reshape(noise.shape[0]				,	-1)				,	dim=-1).mean()
						_UpperCAmelCase	 =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						_UpperCAmelCase	 =     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	 =     step_size.flatten()
						while len(step_size.shape) < len(sample.shape):
									_UpperCAmelCase	 =     step_size.unsqueeze(-1)
						_UpperCAmelCase	 =     sample + step_size * model_output
						_UpperCAmelCase	 =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=A)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						torch.FloatTensor				,	A     :						torch.FloatTensor				,	A     :						torch.FloatTensor				,	)     ->					torch.FloatTensor:



						"""simple docstring"""






						_UpperCAmelCase	 =     timesteps.to(original_samples.device)
						_UpperCAmelCase	 =     self.discrete_sigmas.to(original_samples.device)[timesteps]
						_UpperCAmelCase	 =     (
						    noise * sigmas[:, None, None, None]
						    if noise is not None
						    else torch.randn_like(A) * sigmas[:, None, None, None]
						)
						_UpperCAmelCase	 =     noise + original_samples
						return noisy_samples



			def __len__(		self     :						List[Any])     ->					str:



						"""simple docstring"""






						return self.config.num_train_timesteps





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''
			if a < 0 or b < 0:
						raise ValueError('the value of both inputs must be positive'					)

			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"
			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"

			_UpperCAmelCase	 =     max(len(_UpperCAmelCase					)							,     len(_UpperCAmelCase					)					)

			return "0b" + "".join(
			    str(int(char_a != char_b					)					)
			    for char_a, char_b in zip(a_binary.zfill(_UpperCAmelCase					)							,     b_binary.zfill(_UpperCAmelCase					)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
