


"""simple docstring"""
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        __A       (_lowerCAmelCase  ,						_lowerCAmelCase):





							'''simple docstring'''

							__lowercase:						Tuple							 =      1



							@register_to_config
							def __init__(			self							: str						,    UpperCAmelCase_							: Union[str, Any]=2_000						,    UpperCAmelCase_							: Union[str, Any]=0.1						,    UpperCAmelCase_							: Tuple=20						,    UpperCAmelCase_							: str=1E-3    )   ->int:



														"""simple docstring"""



														snake_case_          =						None
														snake_case_          =						None
														snake_case_          =						None



							def 						lowerCAmelCase    (			self							: Dict						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: Union[str, torch.device] = None    )   ->Any:



														"""simple docstring"""



														snake_case_          =						torch.linspace(1						,    self.config.sampling_eps						,    _lowercase						,    device=_lowercase    )



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: Tuple						,    UpperCAmelCase_							: Optional[int]						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: Any=None    )   ->Union[str, 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
														snake_case_          =						(
														    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
														)
														snake_case_          =						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
														snake_case_          =						std.flatten()
														while len(std.shape    ) < len(score.shape    ):
																					snake_case_          =						std.unsqueeze(-1    )
														snake_case_          =						-score / std

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

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

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

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

														return x, x_mean



							def __len__(			self							: int    )   ->Union[str, Any]:



														"""simple docstring"""



														return self.config.num_train_timesteps







"""simple docstring"""
import math





class        __A       :





							'''simple docstring'''

							def __init__(			self							: List[str]						,    UpperCAmelCase_							: Tuple=0    )   ->Optional[int]:  # a graph with Node 0,1,...,N-1



														"""simple docstring"""



														snake_case_          =						n
														snake_case_          =						[
														    [math.inf for j in range(0						,    UpperCAmelCase_    )] for i in range(0						,    UpperCAmelCase_    )
														]  # adjacency matrix for weight
														snake_case_          =						[
														    [math.inf for j in range(0						,    UpperCAmelCase_    )] for i in range(0						,    UpperCAmelCase_    )
														]  # dp[i][j] stores minimum distance from i to j



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: List[str]						,    UpperCAmelCase_							: Optional[Any]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						w



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Any:



														"""simple docstring"""



														for k in range(0						,    self.n    ):
																					for i in range(0						,    self.n    ):
																												for j in range(0						,    self.n    ):
																																			snake_case_          =						min(self.dp[i][j]						,    self.dp[i][k] + self.dp[k][j]    )



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: Dict						,    UpperCAmelCase_							: Optional[Any]    )   ->Union[str, Any]:



														"""simple docstring"""



														return self.dp[u][v]


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      Graph(5)
				graph.add_edge(0, 2, 9)
				graph.add_edge(0, 4, 10)
				graph.add_edge(1, 3, 5)
				graph.add_edge(2, 3, 7)
				graph.add_edge(3, 0, 10)
				graph.add_edge(3, 1, 2)
				graph.add_edge(3, 2, 1)
				graph.add_edge(3, 4, 6)
				graph.add_edge(4, 1, 3)
				graph.add_edge(4, 2, 4)
				graph.add_edge(4, 3, 9)
				graph.floyd_warshall()
				graph.show_min(1, 4)
				graph.show_min(0, 3)



