





'''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   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : torch.FloatTensor
 _lowerCamelCase  : torch.FloatTensor







class   A_	(  lowerCAmelCase_							,						lowerCAmelCase_			):
 _lowerCamelCase  : Tuple           =							1




 @register_to_config
 def __init__(					self       :    Dict	,			snake_case_       :    int = 2_0_0_0	,			snake_case_       :    float = 0.1_5	,			snake_case_       :    float = 0.0_1	,			snake_case_       :    float = 1_3_4_8.0	,			snake_case_       :    float = 1e-5	,			snake_case_       :    int = 1	,			):
      # standard deviation of the initial noise distribution
      _UpperCAmelCase     							=   sigma_max

      # setable values
      _UpperCAmelCase     							=   None

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




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    torch.FloatTensor	,			snake_case_       :    Optional[int] = None    ):
      return sample




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    int	,			snake_case_       :    float = None	,			snake_case_       :    Union[str, torch.device] = None    ):
      _UpperCAmelCase     							=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

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




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    float = None	,			snake_case_       :    float = None	,			snake_case_       :    float = None    ):
      _UpperCAmelCase     							=   sigma_min if sigma_min is not None else self.config.sigma_min
      _UpperCAmelCase     							=   sigma_max if sigma_max is not None else self.config.sigma_max
      _UpperCAmelCase     							=   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_    )

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




 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    int    ):
      return torch.where(
          timesteps == 0	,			torch.zeros_like(t.to(timesteps.device    )    )	,			self.discrete_sigmas[timesteps - 1].to(timesteps.device    )	,			)




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    torch.FloatTensor	,			snake_case_       :    int	,			snake_case_       :    torch.FloatTensor	,			snake_case_       :    Optional[torch.Generator] = None	,			snake_case_       :    bool = True	,			):
      if self.timesteps is None:
           raise ValueError(
               "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

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

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

      #  equation 6: sample noise for the diffusion term of
      _UpperCAmelCase     							=   randn_tensor(
          sample.shape	,			layout=sample.layout	,			generator=snake_case_	,			device=sample.device	,			dtype=sample.dtype    )
      _UpperCAmelCase     							=   sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      _UpperCAmelCase     							=   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      lowercase	(					self       :    str	,			snake_case_       :    torch.FloatTensor	,			snake_case_       :    torch.FloatTensor	,			snake_case_       :    Optional[torch.Generator] = None	,			snake_case_       :    bool = True	,			):
      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     							=   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
      _UpperCAmelCase     							=   torch.norm(model_output.reshape(model_output.shape[0]	,			-1    )	,			dim=-1    ).mean()
      _UpperCAmelCase     							=   torch.norm(noise.reshape(noise.shape[0]	,			-1    )	,			dim=-1    ).mean()
      _UpperCAmelCase     							=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
      _UpperCAmelCase     							=   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     							=   step_size.flatten()
      while len(step_size.shape    ) < len(sample.shape    ):
           _UpperCAmelCase     							=   step_size.unsqueeze(-1    )
      _UpperCAmelCase     							=   sample + step_size * model_output
      _UpperCAmelCase     							=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    torch.FloatTensor	,			snake_case_       :    torch.FloatTensor	,			snake_case_       :    torch.FloatTensor	,			):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      _UpperCAmelCase     							=   timesteps.to(original_samples.device    )
      _UpperCAmelCase     							=   self.discrete_sigmas.to(original_samples.device    )[timesteps]
      _UpperCAmelCase     							=   (
          noise * sigmas[:, None, None, None]
          if noise is not None
          else torch.randn_like(snake_case_    ) * sigmas[:, None, None, None]
      )
      _UpperCAmelCase     							=   noise + original_samples
      return noisy_samples




 def __len__(					self       :    Dict    ):
      return self.config.num_train_timesteps









'''simple docstring'''




UpperCamelCase__       :	Optional[Any]    = [
    (10_00, '''M'''),
    (9_00, '''CM'''),
    (5_00, '''D'''),
    (4_00, '''CD'''),
    (1_00, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
		__SCREAMING_SNAKE_CASE       :		Tuple           =		0
		__SCREAMING_SNAKE_CASE       :		str           =		0
		while place < len(_lowerCamelCase					):
				if (place + 1 < len(_lowerCamelCase					)) and (vals[roman[place]] < vals[roman[place + 1]]):
						total += vals[roman[place + 1]] - vals[roman[place]]
						place += 2
				else:
						total += vals[roman[place]]
						place += 1
		return total


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		Any           =		[]
		for arabic, roman in ROMAN:
				((__SCREAMING_SNAKE_CASE)       ,						(__SCREAMING_SNAKE_CASE))       :		str           =		divmod(_lowerCamelCase       ,      _lowerCamelCase					)
				result.append(roman * factor					)
				if number == 0:
						break
		return "".join(_lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()