'''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 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   torch.FloatTensor
      UpperCAmelCase_   :   torch.FloatTensor




class 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   str   				= 1




      @register_to_config
      def __init__(					self       ,lowerCamelCase_ = 2000       ,lowerCamelCase_ = 0.15       ,lowerCamelCase_ = 0.01       ,lowerCamelCase_ = 1348.0       ,lowerCamelCase_ = 1e-5       ,lowerCamelCase_ = 1       ,)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	sigma_max

         # setable values
         UpperCAmelCase__				:					List[str] 					=	None

         self.set_sigmas(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      torch.FloatTensor:



         '''simple docstring'''



         return sample




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None				)      ->      List[Any]:



         '''simple docstring'''



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

         UpperCAmelCase__				:					Tuple 					=	torch.linspace(1       ,lowerCamelCase_       ,lowerCamelCase_       ,device=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None       ,lowerCamelCase_ = None				)      ->      Union[str, Any]:



         '''simple docstring'''



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

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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      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       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,lowerCamelCase_ = 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__				:					Tuple 					=	timestep * torch.ones(
             sample.shape[0]       ,device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
         UpperCAmelCase__				:					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
         UpperCAmelCase__				:					Union[str, Any] 					=	timesteps.to(self.discrete_sigmas.device				)

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

         #  equation 6: sample noise for the diffusion term of
         UpperCAmelCase__				:					Optional[int] 					=	randn_tensor(
             sample.shape       ,layout=sample.layout       ,generator=lowerCamelCase_       ,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__				:					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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,lowerCamelCase_ = 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__				:					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
         UpperCAmelCase__				:					int 					=	torch.norm(model_output.reshape(model_output.shape[0]       ,-1				)       ,dim=-1				).mean()
         UpperCAmelCase__				:					str 					=	torch.norm(noise.reshape(noise.shape[0]       ,-1				)       ,dim=-1				).mean()
         UpperCAmelCase__				:					Dict 					=	(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__				:					str 					=	step_size.flatten()
         while len(step_size.shape				) < len(sample.shape				):
            UpperCAmelCase__				:					Tuple 					=	step_size.unsqueeze(-1				)
         UpperCAmelCase__				:					str 					=	sample + step_size * model_output
         UpperCAmelCase__				:					Dict 					=	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

         if not return_dict:
            return (prev_sample,)

         return SchedulerOutput(prev_sample=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,)      ->      torch.FloatTensor:



         '''simple docstring'''



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




      def __len__(					self				)      ->      Tuple:



         '''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,
)




