



"""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 			__snake_case			(      _lowercase):
  snake_case__							:  torch.FloatTensor
  snake_case__							:  torch.FloatTensor






class 			__snake_case			(      _lowercase    ,	_lowercase):
  snake_case__							:  int         =		1




  @register_to_config
  def __init__(					self	:		str		,   __lowerCAmelCase	:		int = 2_0_0_0		,   __lowerCAmelCase	:		float = 0.15		,   __lowerCAmelCase	:		float = 0.01		,   __lowerCAmelCase	:		float = 13_48.0		,   __lowerCAmelCase	:		float = 1E-5		,   __lowerCAmelCase	:		int = 1		,   ):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					sigma_max

         # setable values
         _lowerCamelCase		:							Dict            =					None

         self.set_sigmas(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any		,   __lowerCAmelCase	:		torch.FloatTensor		,   __lowerCAmelCase	:		Optional[int] = None						):






         """simple docstring"""






         return sample




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		float = None		,   __lowerCAmelCase	:		Union[str, torch.device] = None						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

         _lowerCamelCase		:							Optional[int]            =					torch.linspace(1		,   __lowerCAmelCase		,   __lowerCAmelCase		,   device=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		float = None		,   __lowerCAmelCase	:		float = None		,   __lowerCAmelCase	:		float = None						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					sigma_min if sigma_min is not None else self.config.sigma_min
         _lowerCamelCase		:							int            =					sigma_max if sigma_max is not None else self.config.sigma_max
         _lowerCamelCase		:							Any            =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
                self.set_timesteps(__lowerCAmelCase		,   __lowerCAmelCase						)

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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		Tuple		,   __lowerCAmelCase	:		Optional[Any]						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]		,   __lowerCAmelCase	:		torch.FloatTensor		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		torch.FloatTensor		,   __lowerCAmelCase	:		Optional[torch.Generator] = None		,   __lowerCAmelCase	:		bool = 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'''						)

         _lowerCamelCase		:							Tuple            =					timestep * torch.ones(
             sample.shape[0]		,   device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
         _lowerCamelCase		:							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
         _lowerCamelCase		:							Optional[int]            =					timesteps.to(self.discrete_sigmas.device						)

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

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

         if not return_dict:
                return (prev_sample,)

         return SchedulerOutput(prev_sample=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any		,   __lowerCAmelCase	:		torch.FloatTensor		,   __lowerCAmelCase	:		torch.FloatTensor		,   __lowerCAmelCase	:		torch.FloatTensor		,   ):






         """simple docstring"""






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




  def __len__(					self	:		Optional[int]						):






         """simple docstring"""






         return self.config.num_train_timesteps







"""simple docstring"""






import operator




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None	)						->				list:
  _snake_case   	=      operator.lt if reverse else operator.gt
  _snake_case   	=      solution or []

  if not arr:
    return solution

  _snake_case   	=      [arr.pop(0	)]
  for i, item in enumerate(lowerCAmelCase_	):
    if _operator(lowerCAmelCase_  ,						sublist[-1]	):
      sublist.append(lowerCAmelCase_	)
      arr.pop(lowerCAmelCase_	)

    #  merging sublist into solution list
  if not solution:
    solution.extend(lowerCAmelCase_	)
  else:
    while sublist:
      _snake_case   	=      sublist.pop(0	)
      for i, xx in enumerate(lowerCAmelCase_	):
        if not _operator(lowerCAmelCase_  ,						lowerCAmelCase_	):
          solution.insert(lowerCAmelCase_  ,						lowerCAmelCase_	)
          break
      else:
        solution.append(lowerCAmelCase_	)

  strand_sort(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
  return solution


if __name__ == "__main__":
     assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
     assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]





