





# 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      (       _UpperCamelCase		,_UpperCamelCase	):
   _UpperCamelCase			:    Tuple  		=					1





   @register_to_config
   def __init__(			self       ,      snake_case=2_000       ,      snake_case=0.1       ,      snake_case=20       ,      snake_case=1E-3   )					->	str:
      """simple docstring"""





      a__						:    Dict   		=	None
      a__						:    Any   		=	None
      a__						:    Dict   		=	None





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Any:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	torch.linspace(1       ,      self.config.sampling_eps       ,      snake_case       ,      device=snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=None   )					->	Optional[Any]:
      """simple docstring"""





      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
      a__						:    int   		=	(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      a__						:    Optional[Any]   		=	torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
      a__						:    List[str]   		=	std.flatten()
      while len(std.shape   ) < len(score.shape   ):
         a__						:    int   		=	std.unsqueeze(-1   )
      a__						:    Tuple   		=	-score / std

      # compute
      a__						:    str   		=	-1.0 / len(self.timesteps   )

      a__						:    Tuple   		=	self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      a__						:    List[str]   		=	beta_t.flatten()
      while len(beta_t.shape   ) < len(x.shape   ):
         a__						:    int   		=	beta_t.unsqueeze(-1   )
      a__						:    Optional[Any]   		=	-0.5 * beta_t * x

      a__						:    int   		=	torch.sqrt(snake_case   )
      a__						:    Optional[int]   		=	drift - diffusion**2 * score
      a__						:    Optional[Any]   		=	x + drift * dt

      # add noise
      a__						:    Dict   		=	randn_tensor(x.shape       ,      layout=x.layout       ,      generator=snake_case       ,      device=x.device       ,      dtype=x.dtype   )
      a__						:    int   		=	x_mean + diffusion * math.sqrt(-dt   ) * noise

      return x, x_mean





   def __len__(			self   )					->	List[str]:
      """simple docstring"""





      return self.config.num_train_timesteps








def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	[]
   a__						:    str   		=	set({"(", "[", "{"} )
   a__						:    List[str]   		=	set({")", "]", "}"} )
   a__						:    int   		=	{"{": "}", "[": "]", "(": ")"}

   for i in range(len(lowerCamelCase ) ):
      if s[i] in open_brackets:
         stack.append(s[i] )

      elif s[i] in closed_brackets and (
          len(lowerCamelCase ) == 0 or (len(lowerCamelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
      ):
         return False

   return len(lowerCamelCase ) == 0






def    _A	(   ):
   a__						:    Optional[int]   		=	input("Enter sequence of brackets: " )
   if is_balanced(lowerCamelCase ):
      print(lowerCamelCase				,			"is balanced" )
   else:
      print(lowerCamelCase				,			"is not balanced" )


if __name__ == "__main__":
  main()

