



'''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 						lowerCamelCase_				(       __A      ):
						_lowerCAmelCase  : Union[str, Any]						  =					4_2
						_lowerCAmelCase  : Any						  =					4_2


class 						lowerCamelCase_				(       __A     ,					__A      ):
						_lowerCAmelCase  : int						  =					1







						@register_to_config
						def __init__(	self :   Dict					,     lowerCAmelCase__ :   int = 20_00					,     lowerCAmelCase__ :   float = 0.15					,     lowerCAmelCase__ :   float = 0.01					,     lowerCAmelCase__ :   float = 1348.0					,     lowerCAmelCase__ :   float = 1e-5					,     lowerCAmelCase__ :   int = 1					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      sigma_max

													# setable values
													SCREAMING_SNAKE_CASE       :   str         =      None

													self.set_sigmas(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   torch.FloatTensor					,     lowerCAmelCase__ :   Optional[int] = None					):

													"""simple docstring"""
													return sample







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   float = None					,     lowerCAmelCase__ :   Union[str, torch.device] = None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      sampling_eps if sampling_eps is not None else self.config.sampling_eps

													SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.linspace(1					,     lowerCAmelCase__					,     lowerCAmelCase__					,     device=lowerCAmelCase__					)







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   float = None					,     lowerCAmelCase__ :   float = None					,     lowerCAmelCase__ :   float = None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      sigma_min if sigma_min is not None else self.config.sigma_min
													SCREAMING_SNAKE_CASE       :   List[str]         =      sigma_max if sigma_max is not None else self.config.sigma_max
													SCREAMING_SNAKE_CASE       :   Tuple         =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
													if self.timesteps is None:
																				self.set_timesteps(lowerCAmelCase__					,     lowerCAmelCase__					)

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







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   Dict					):

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







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   torch.FloatTensor					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   torch.FloatTensor					,     lowerCAmelCase__ :   Optional[torch.Generator] = None					,     lowerCAmelCase__ :   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'''					)

													SCREAMING_SNAKE_CASE       :   int         =      timestep * torch.ones(
													    sample.shape[0]					,     device=sample.device					)  # torch.repeat_interleave(timestep, sample.shape[0])
													SCREAMING_SNAKE_CASE       :   Tuple         =      (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
													SCREAMING_SNAKE_CASE       :   int         =      timesteps.to(self.discrete_sigmas.device					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.discrete_sigmas[timesteps].to(sample.device					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_adjacent_sigma(lowerCAmelCase__					,     lowerCAmelCase__					).to(sample.device					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      torch.zeros_like(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[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
													SCREAMING_SNAKE_CASE       :   int         =      diffusion.flatten()
													while len(diffusion.shape					) < len(sample.shape					):
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      diffusion.unsqueeze(-1					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      drift - diffusion**2 * model_output

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







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   torch.FloatTensor					,     lowerCAmelCase__ :   torch.FloatTensor					,     lowerCAmelCase__ :   Optional[torch.Generator] = None					,     lowerCAmelCase__ :   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
													SCREAMING_SNAKE_CASE       :   List[str]         =      randn_tensor(sample.shape					,     layout=sample.layout					,     generator=lowerCAmelCase__					).to(sample.device					)

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

													if not return_dict:
																				return (prev_sample,)

													return SchedulerOutput(prev_sample=lowerCAmelCase__					)







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   torch.FloatTensor					,     lowerCAmelCase__ :   torch.FloatTensor					,     lowerCAmelCase__ :   torch.FloatTensor					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      timesteps.to(original_samples.device					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.discrete_sigmas.to(original_samples.device					)[timesteps]
													SCREAMING_SNAKE_CASE       :   str         =      (
													    noise * sigmas[:, None, None, None]
													    if noise is not None
													    else torch.randn_like(lowerCAmelCase__					) * sigmas[:, None, None, None]
													)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      noise + original_samples
													return noisy_samples







						def __len__(	self :   Optional[Any]					):

													"""simple docstring"""
													return self.config.num_train_timesteps







def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							0
	while b > 0:
		if b & 1:
			res += a

		a += a
		b >>= 1

	return res

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							0
	while b > 0:
		if b & 1:
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							((res % c) + (a % c)) % c

		a += a
		b >>= 1

	return res