


# 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 			_lowercase (							lowercase__							,  lowercase__):


       """simple docstring"""




       A__											=			1





       @register_to_config
       def __init__(   self							:			int					,      __lowerCamelCase							:			Union[str, Any]=2000					,      __lowerCamelCase							:			Any=0.1					,      __lowerCamelCase							:			Optional[Any]=20					,      __lowerCamelCase							:			str=1E-3       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  None
             lowerCamelCase__		:				Optional[Any]    =  None
             lowerCamelCase__		:				Any    =  None





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Union[str, torch.device] = None       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  torch.linspace(1					,      self.config.sampling_eps					,      __lowerCamelCase					,      device=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			Optional[int]=None       ):


             '''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
             lowerCamelCase__		:				Union[str, Any]    =  (
                 -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             lowerCamelCase__		:				List[str]    =  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
             lowerCamelCase__		:				Optional[Any]    =  std.flatten()
             while len(std.shape       ) < len(score.shape       ):
                   lowerCamelCase__		:				Tuple    =  std.unsqueeze(-1       )
             lowerCamelCase__		:				int    =  -score / std

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

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

             lowerCamelCase__		:				str    =  torch.sqrt(__lowerCamelCase       )
             lowerCamelCase__		:				Optional[Any]    =  drift - diffusion**2 * score
             lowerCamelCase__		:				Any    =  x + drift * dt

             # add noise
             lowerCamelCase__		:				Dict    =  randn_tensor(x.shape					,      layout=x.layout					,      generator=__lowerCamelCase					,      device=x.device					,      dtype=x.dtype       )
             lowerCamelCase__		:				Union[str, Any]    =  x_mean + diffusion * math.sqrt(-dt       ) * noise

             return x, x_mean





       def __len__(   self							:			int       ):


             '''simple docstring'''



             return self.config.num_train_timesteps






class 			_lowercase :  # Public class to implement a graph


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  row
             lowerCamelCase__		:				Optional[Any]    =  col
             lowerCamelCase__		:				Union[str, Any]    =  graph





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowerCamelCase__		:				Tuple    =  [-1, 0, 1, -1, 1, -1, 0, 1]
             lowerCamelCase__		:				List[Any]    =  True  # Make those cells visited
             for k in range(8       ):
                   if self.is_safe(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       ):
                         self.diffs(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):  # And finally, count all islands.


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  [[False for j in range(self.COL       )] for i in range(self.ROW       )]
             lowerCamelCase__		:				Optional[Any]    =  0
             for i in range(self.ROW       ):
                   for j in range(self.COL       ):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                               self.diffs(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                               count += 1
             return count


