

'''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 					A__      (      _snake_case			):
    lowercase						=		42
    lowercase						=		42


class 					A__      (      _snake_case						,						_snake_case			):
    lowercase						=		1



    @register_to_config
    def __init__(					self		,				UpperCamelCase__ = 2000		,				UpperCamelCase__ = 0.15		,				UpperCamelCase__ = 0.01		,				UpperCamelCase__ = 1348.0		,				UpperCamelCase__ = 1e-5		,				UpperCamelCase__ = 1		,				)						->      List[Any]:





           '''simple docstring'''
           A_						=      sigma_max

           # setable values
           A_						=      None

           self.set_sigmas(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )



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





           '''simple docstring'''
           return sample



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





           '''simple docstring'''
           A_						=      sampling_eps if sampling_eps is not None else self.config.sampling_eps

           A_						=      torch.linspace(1		,				UpperCamelCase__		,				UpperCamelCase__		,				device=UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      sigma_min if sigma_min is not None else self.config.sigma_min
           A_						=      sigma_max if sigma_max is not None else self.config.sigma_max
           A_						=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
           if self.timesteps is None:
                  self.set_timesteps(UpperCamelCase__		,				UpperCamelCase__       )

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



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           return torch.where(
               timesteps == 0		,				torch.zeros_like(t.to(timesteps.device       )       )		,				self.discrete_sigmas[timesteps - 1].to(timesteps.device       )		,				)



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = 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"""       )

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

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

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



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

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

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=UpperCamelCase__       )



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





           '''simple docstring'''
           A_						=      timesteps.to(original_samples.device       )
           A_						=      self.discrete_sigmas.to(original_samples.device       )[timesteps]
           A_						=      (
               noise * sigmas[:, None, None, None]
               if noise is not None
               else torch.randn_like(UpperCamelCase__       ) * sigmas[:, None, None, None]
           )
           A_						=      noise + original_samples
           return noisy_samples







    def __len__(					self       )						->      int:





           '''simple docstring'''
           return self.config.num_train_timesteps



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:
       if num < 0:
              return False

       A_						=      num
       A_						=      0
       while num > 0:
              A_						=      rev_num * 10 + (num % 10)
              num //= 10

       return num_copy == rev_num


if __name__ == "__main__":
      import doctest

      doctest.testmod()
