


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class       lowerCamelCase__   (       lowercase__   ,							lowercase__):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[Any]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   Dict:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     __lowerCamelCase		,     device=__lowerCamelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Tuple:



      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(__lowerCamelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=__lowerCamelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   Optional[Any]:
      return self.config.num_train_timesteps





class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Any					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  n
             lowerCamelCase__		:				Union[str, Any]    =  [None] * self.n
             lowerCamelCase__		:				List[str]    =  0  # index of the first element
             lowerCamelCase__		:				Any    =  0
             lowerCamelCase__		:				Any    =  0





       def __len__(   self							:			Tuple       ):


             '''simple docstring'''



             return self.size





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             return self.size == 0





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return False if self.is_empty() else self.array[self.front]





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             if self.size >= self.n:
                   raise Exception("QUEUE IS FULL"       )

             lowerCamelCase__		:				Optional[Any]    =  data
             lowerCamelCase__		:				Tuple    =  (self.rear + 1) % self.n
             self.size += 1
             return self





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             if self.size == 0:
                   raise Exception("UNDERFLOW"       )

             lowerCamelCase__		:				Any    =  self.array[self.front]
             lowerCamelCase__		:				List[Any]    =  None
             lowerCamelCase__		:				str    =  (self.front + 1) % self.n
             self.size -= 1
             return temp


