




"""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(  lowercase__				):







 '''simple docstring'''





 lowercase__				      =      42
 lowercase__				      =      42







class       lowercase(  lowercase__ ,    lowercase__				):







 '''simple docstring'''





 lowercase__				      =      1


 @register_to_config
 def __init__(       self:	Dict,   a_:	List[Any] = 2_000,   a_:	Any = 0.15,   a_:	Optional[int] = 0.01,   a_:	Tuple = 1_348.0,   a_:	Union[str, Any] = 1E-5,   a_:	Any = 1,   ):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						sigma_max

   # setable values
   _snake_case							:	str			=						None

   self.set_sigmas(a_,   a_,   a_,   a_	)


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	str,   a_:	List[Any] = None	):




   '''simple docstring'''



   return sample


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	Dict,   a_:	str = None,   a_:	Optional[Any] = None	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						sampling_eps if sampling_eps is not None else self.config.sampling_eps

   _snake_case							:	List[Any]			=						torch.linspace(1,   a_,   a_,   device=a_	)


 def 				UpperCamelCase_     (       self:	Dict,   a_:	Optional[Any],   a_:	Optional[int] = None,   a_:	List[str] = None,   a_:	int = None	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						sigma_min if sigma_min is not None else self.config.sigma_min
   _snake_case							:	Optional[int]			=						sigma_max if sigma_max is not None else self.config.sigma_max
   _snake_case							:	int			=						sampling_eps if sampling_eps is not None else self.config.sampling_eps
   if self.timesteps is None:
     self.set_timesteps(a_,   a_	)

   _snake_case							:	Optional[int]			=						sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
   _snake_case							:	str			=						torch.exp(torch.linspace(math.log(a_	),   math.log(a_	),   a_	)	)
   _snake_case							:	List[Any]			=						torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]	)


 def 				UpperCamelCase_     (       self:	int,   a_:	Dict,   a_:	str	):




   '''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:	Optional[Any],   a_:	Dict,   a_:	int,   a_:	List[Any],   a_:	str = None,   a_:	Optional[Any] = 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"""	)

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

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

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


 def 				UpperCamelCase_     (       self:	int,   a_:	str,   a_:	Optional[Any],   a_:	Any = None,   a_:	int = 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
   _snake_case							:	Dict			=						randn_tensor(sample.shape,   layout=sample.layout,   generator=a_	).to(sample.device	)

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

   if not return_dict:
     return (prev_sample,)

   return SchedulerOutput(prev_sample=a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	str,   a_:	Optional[Any],   a_:	Any,   ):




   '''simple docstring'''



   _snake_case							:	Any			=						timesteps.to(original_samples.device	)
   _snake_case							:	List[Any]			=						self.discrete_sigmas.to(original_samples.device	)[timesteps]
   _snake_case							:	Dict			=						(
       noise * sigmas[:, None, None, None]
       if noise is not None
       else torch.randn_like(a_	) * sigmas[:, None, None, None]
   )
   _snake_case							:	Optional[Any]			=						noise + original_samples
   return noisy_samples


 def __len__(       self:	int	):




   '''simple docstring'''



   return self.config.num_train_timesteps








"""simple docstring"""


from typing import Any


def       UpperCAmelCase__ (snake_case__							:      list					):
  """simple docstring"""

  if not input_list:
    return []
  _snake_case							:	List[Any]			=						[input_list.count(snake_case__					) for value in input_list]
  _snake_case							:	Optional[int]			=						max(snake_case__					)  # Gets the maximum count in the input list.
  # Gets values of modes
  return sorted({input_list[i] for i, value in enumerate(snake_case__					) if value == y}					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()


