


# 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_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					42
					__UpperCAmelCase							  =					42




class 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					1






					@register_to_config
					def __init__(					self			,							snake_case_ = 2_0_0_0			,							snake_case_ = 0.1_5			,							snake_case_ = 0.0_1			,							snake_case_ = 1_3_4_8.0			,							snake_case_ = 1e-5			,							snake_case_ = 1			,							) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = sigma_max

								# setable values
								__lowercase			       = None

								self.set_sigmas(snake_case_			,							snake_case_			,							snake_case_			,							snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      torch.FloatTensor:







								'''simple docstring'''

								return sample






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None    ) ->      Optional[Any]:







								'''simple docstring'''

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

								__lowercase			       = torch.linspace(1			,							snake_case_			,							snake_case_			,							device=snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None    ) ->      Union[str, Any]:







								'''simple docstring'''

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

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






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      Dict:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							snake_case_ = 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'''    )

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

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

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							snake_case_ = 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
								__lowercase			       = randn_tensor(sample.shape			,							layout=sample.layout			,							generator=snake_case_    ).to(sample.device    )

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

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							) ->      torch.FloatTensor:







								'''simple docstring'''

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






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







								'''simple docstring'''

								return self.config.num_train_timesteps






a			:			Any												=					[
    '''Audio''',
    '''Array2D''',
    '''Array3D''',
    '''Array4D''',
    '''Array5D''',
    '''ClassLabel''',
    '''Features''',
    '''Sequence''',
    '''Value''',
    '''Image''',
    '''Translation''',
    '''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages


