
'''simple docstring'''






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		( a__):
					_lowerCAmelCase        =						42
					_lowerCAmelCase        =						42





class      __snake_case		( a__     , a__):
					_lowerCAmelCase        =						1
					@register_to_config
					def __init__(  self,     A = 2000,     A = 0.15,     A = 0.01,     A = 1348.0,     A = 1e-5,     A = 1,     ):

									"""simple docstring"""


									lowerCamelCase       :      int          =			sigma_max

									# setable values
									lowerCamelCase       :      List[str]          =			None

									self.set_sigmas(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_   )
					def      UpperCAmelCase_				(  self,     A,     A = None   ):

									"""simple docstring"""


									return sample
					def      UpperCAmelCase_				(  self,     A,     A = None,     A = None   ):

									"""simple docstring"""


									lowerCamelCase       :      str          =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

									lowerCamelCase       :      Tuple          =			torch.linspace(1,     lowerCamelCase_,     lowerCamelCase_,     device=lowerCamelCase_   )
					def      UpperCAmelCase_				(  self,     A,     A = None,     A = None,     A = None   ):

									"""simple docstring"""


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

									lowerCamelCase       :      Dict          =			sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
									lowerCamelCase       :      List[Any]          =			torch.exp(torch.linspace(math.log(lowerCamelCase_   ),     math.log(lowerCamelCase_   ),     lowerCamelCase_   )   )
									lowerCamelCase       :      Tuple          =			torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]   )
					def      UpperCAmelCase_				(  self,     A,     A   ):

									"""simple docstring"""


									return torch.where(
									    timesteps == 0,     torch.zeros_like(t.to(timesteps.device   )   ),     self.discrete_sigmas[timesteps - 1].to(timesteps.device   ),     )
					def      UpperCAmelCase_				(  self,     A,     A,     A,     A = None,     A = 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'   )

									lowerCamelCase       :      Tuple          =			timestep * torch.ones(
									    sample.shape[0],     device=sample.device   )  # torch.repeat_interleave(timestep, sample.shape[0])
									lowerCamelCase       :      Union[str, Any]          =			(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
									lowerCamelCase       :      Union[str, Any]          =			timesteps.to(self.discrete_sigmas.device   )

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

									#  equation 6: sample noise for the diffusion term of
									lowerCamelCase       :      Optional[int]          =			randn_tensor(
									    sample.shape,     layout=sample.layout,     generator=lowerCamelCase_,     device=sample.device,     dtype=sample.dtype   )
									lowerCamelCase       :      int          =			sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									lowerCamelCase       :      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      UpperCAmelCase_				(  self,     A,     A,     A = None,     A = 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
									lowerCamelCase       :      Optional[Any]          =			randn_tensor(sample.shape,     layout=sample.layout,     generator=lowerCamelCase_   ).to(sample.device   )

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

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=lowerCamelCase_   )
					def      UpperCAmelCase_				(  self,     A,     A,     A,     ):

									"""simple docstring"""


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




					def __len__(  self   ):

									"""simple docstring"""


									return self.config.num_train_timesteps

'''simple docstring'''

import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)




