






# 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  (   A__	):
 _lowerCAmelCase	:	torch.FloatTensor
 _lowerCAmelCase	:	torch.FloatTensor







class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Union[str, Any]				=			1







 @register_to_config
 def __init__(		self :       List[str]							,       SCREAMING_SNAKE_CASE :       int = 20_00							,       SCREAMING_SNAKE_CASE :       float = 0.1_5							,       SCREAMING_SNAKE_CASE :       float = 0.0_1							,       SCREAMING_SNAKE_CASE :       float = 1_3_4_8.0							,       SCREAMING_SNAKE_CASE :       float = 1e-5							,       SCREAMING_SNAKE_CASE :       int = 1							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  sigma_max

      # setable values
      UpperCamelCase__      :       str	    =  None

      self.set_sigmas(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






      return sample







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


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCamelCase__      :       Any	    =  torch.linspace(1							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       device=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       float = None							,       SCREAMING_SNAKE_CASE :       float = None							,       SCREAMING_SNAKE_CASE :       float = None						):


      '''simple docstring'''






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

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







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       torch.FloatTensor							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       torch.FloatTensor							,       SCREAMING_SNAKE_CASE :       Optional[torch.Generator] = None							,       SCREAMING_SNAKE_CASE :       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"						)

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

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

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







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       torch.FloatTensor							,       SCREAMING_SNAKE_CASE :       torch.FloatTensor							,       SCREAMING_SNAKE_CASE :       Optional[torch.Generator] = None							,       SCREAMING_SNAKE_CASE :       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
      UpperCamelCase__      :       Optional[Any]	    =  randn_tensor(sample.shape							,       layout=sample.layout							,       generator=SCREAMING_SNAKE_CASE						).to(sample.device						)

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

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






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







 def __len__(		self :       Dict						):


      '''simple docstring'''






      return self.config.num_train_timesteps







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Any:
     # "extended trapezoidal rule"
     # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
     UpperCamelCase__      :       int	    =  (boundary[1] - boundary[0]) / steps
     UpperCamelCase__      :       Optional[Any]	    =  boundary[0]
     UpperCamelCase__      :       List[Any]	    =  boundary[1]
     UpperCamelCase__      :       List[Any]	    =  make_points(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )
     UpperCamelCase__      :       int	    =  0.0
     y += (h / 2.0) * f(__lowerCAmelCase      )
     for i in x_i:
          # print(i)
          y += h * f(__lowerCAmelCase      )
     y += (h / 2.0) * f(__lowerCAmelCase      )
     return y







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[str]:
     UpperCamelCase__      :       Optional[int]	    =  a + h
     while x < (b - h):
          yield x
          UpperCamelCase__      :       Union[str, Any]	    =  x + h







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Any:  # enter your function here
     UpperCamelCase__      :       Dict	    =  (x - 0) * (x - 0)
     return y







def 		SCREAMING_SNAKE_CASE							( )			->     Dict:
     UpperCamelCase__      :       List[Any]	    =  0.0  # Lower bound of integration
     UpperCamelCase__      :       Tuple	    =  1.0  # Upper bound of integration
     UpperCamelCase__      :       Any	    =  1_0.0  # define number of steps or resolution
     UpperCamelCase__      :       List[str]	    =  [a, b]  # define boundary of integration
     UpperCamelCase__      :       Any	    =  method_a(__lowerCAmelCase ,   __lowerCAmelCase      )
     print(f'y = {y}'      )


if __name__ == "__main__":
 main()