






'''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 _lowercase	(				_lowercase				,      _lowercase		):
							a				      =       1




							@register_to_config
							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Any=2_000			,							UpperCamelCase__:				Any=0.1			,							UpperCamelCase__:				Union[str, Any]=20			,							UpperCamelCase__:				int=1e-3     ):
														lowerCamelCase__					:	List[Any]         =       None
														lowerCamelCase__					:	Dict         =       None
														lowerCamelCase__					:	Dict         =       None




							def 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Union[str, torch.device] = None     ):
														lowerCamelCase__					:	List[Any]         =       torch.linspace(1			,							self.config.sampling_eps			,							UpperCamelCase__			,							device=UpperCamelCase__     )




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

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

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

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

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

														return x, x_mean




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









'''simple docstring'''







from __future__ import annotations

_A	:			Any         ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}

class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				dict[str, list[str]]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	str         =       graph
														# mapping node to its parent in resulting breadth first tree
														lowerCamelCase__					:	dict[str, str | None]         =       {}
														lowerCamelCase__					:	Any         =       source_vertex




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	List[str]         =       {self.source_vertex}
														lowerCamelCase__					:	List[str]         =       None
														lowerCamelCase__					:	Tuple         =       [self.source_vertex]  # first in first out queue

														while queue:
																					lowerCamelCase__					:	Tuple         =       queue.pop(0     )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(UpperCamelCase__     )
																																			lowerCamelCase__					:	List[str]         =       vertex
																																			queue.append(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														lowerCamelCase__					:	Tuple         =       self.parent.get(UpperCamelCase__     )
														if target_vertex_parent is None:
																					lowerCamelCase__					:	int         =       (
																					    F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																					)
																					raise ValueError(UpperCamelCase__     )

														return self.shortest_path(UpperCamelCase__     ) + F'''->{target_vertex}'''


if __name__ == "__main__":
			_A	:			int         =Graph(graph, '''G''')
			g.breath_first_search()
			print(g.shortest_path('''D'''))
			print(g.shortest_path('''G'''))
			print(g.shortest_path('''Foo'''))

