



'''simple docstring'''





# 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 				_snake_case			(					lowercase__    ,	lowercase__):
				UpperCamelCase__				:					str     		=1




				@register_to_config
				def __init__(						self					:						Optional[Any],					__lowercase					:						Optional[int]=2000,					__lowercase					:						Optional[Any]=0.1,					__lowercase					:						str=20,					__lowercase					:						Tuple=1e-3						):
							lowercase__					  =				None
							lowercase__					  =				None
							lowercase__					  =				None




				def 							A__      (						self					:						Dict,					__lowercase					:						Tuple,					__lowercase					:						Union[str, torch.device] = None						):
							lowercase__					  =				torch.linspace(1,					self.config.sampling_eps,					__lowercase,					device=__lowercase						)




				def 							A__      (						self					:						Any,					__lowercase					:						Tuple,					__lowercase					:						Union[str, Any],					__lowercase					:						int,					__lowercase					:						List[Any]=None						):
							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(__lowercase						)
							lowercase__					  =				drift - diffusion**2 * score
							lowercase__					  =				x + drift * dt

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

							return x, x_mean




				def __len__(						self					:						int						):
							return self.config.num_train_timesteps









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__  ,       lowercase__					  =				len(SCREAMING_SNAKE_CASE_  ), len(grid[0]  )
			if (
			    min(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)  )

			lowercase__					  =				0
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row + 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  row - 1						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col + 1						,  SCREAMING_SNAKE_CASE_  )
			count += depth_first_search(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  col - 1						,  SCREAMING_SNAKE_CASE_  )

			visit.remove((row, col)  )
			return count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




