

'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np







def    a__       (  a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    ):




     """simple docstring"""


     for nxt, d in graph[v]:
          if nxt in visited_forward:
               continue
          __SCREAMING_SNAKE_CASE     =      cst_fwd.get(lowercase__      ,    np.inf						)
          __SCREAMING_SNAKE_CASE     =      cst_fwd[v] + d
          if new_cost_f < old_cost_f:
               queue.put((new_cost_f, nxt)						)
               __SCREAMING_SNAKE_CASE     =      new_cost_f
               __SCREAMING_SNAKE_CASE     =      v
          if nxt in visited_backward:
               if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                    __SCREAMING_SNAKE_CASE     =      cst_fwd[v] + d + cst_bwd[nxt]
     return shortest_distance







def    a__       (  a__      ,    a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      -1

     __SCREAMING_SNAKE_CASE     =      set()
     __SCREAMING_SNAKE_CASE     =      set()
     __SCREAMING_SNAKE_CASE     =      {source: 0}
     __SCREAMING_SNAKE_CASE     =      {destination: 0}
     __SCREAMING_SNAKE_CASE     =      {source: None}
     __SCREAMING_SNAKE_CASE     =      {destination: None}
     __SCREAMING_SNAKE_CASE     =      PriorityQueue()
     __SCREAMING_SNAKE_CASE     =      PriorityQueue()

     __SCREAMING_SNAKE_CASE     =      np.inf

     queue_forward.put((0, source)						)
     queue_backward.put((0, destination)						)

     if source == destination:
          return 0

     while not queue_forward.empty() and not queue_backward.empty():
          __SCREAMING_SNAKE_CASE     =      queue_forward.get()
          visited_forward.add(lowercase__						)

          __SCREAMING_SNAKE_CASE     =      queue_backward.get()
          visited_backward.add(lowercase__						)

          __SCREAMING_SNAKE_CASE     =      pass_and_relaxation(
              lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    )

          __SCREAMING_SNAKE_CASE     =      pass_and_relaxation(
              lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    )

          if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
               break

     if shortest_distance != np.inf:
          __SCREAMING_SNAKE_CASE     =      shortest_distance
     return shortest_path_distance


UpperCAmelCase :       int			   =     {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
UpperCAmelCase :       Tuple			   =     {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
  import doctest

  doctest.testmod()










from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:int			,			lowerCAmelCase__			:NestedDataStructureLike[PathLike]			,			lowerCAmelCase__			:Optional[NamedSplit] = None			,			lowerCAmelCase__			:Optional[Features] = None			,			lowerCAmelCase__			:str = None			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:Optional[int] = None			,			**lowerCAmelCase__			:Optional[int]			,			)		->			Tuple:
										super().__init__(
										    lowerCAmelCase__			,			split=lowerCAmelCase__			,			features=lowerCAmelCase__			,			cache_dir=lowerCAmelCase__			,			keep_in_memory=lowerCAmelCase__			,			streaming=lowerCAmelCase__			,			num_proc=lowerCAmelCase__			,			**lowerCAmelCase__			,			)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		path_or_paths if isinstance(lowerCAmelCase__			,			lowerCAmelCase__	) else {self.split: path_or_paths}
										__SCREAMING_SNAKE_CASE						:     int					=		Text(
										    cache_dir=lowerCAmelCase__			,			data_files=lowerCAmelCase__			,			features=lowerCAmelCase__			,			**lowerCAmelCase__			,			)



				def 		__magic_name__(       self			:Dict	)		->			Tuple:
										# Build iterable dataset
										if self.streaming:
																__SCREAMING_SNAKE_CASE						:     int					=		self.builder.as_streaming_dataset(split=self.split	)
										# Build regular (map-style) dataset
										else:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		None
																__SCREAMING_SNAKE_CASE						:     str					=		None
																__SCREAMING_SNAKE_CASE						:     Dict					=		None
																__SCREAMING_SNAKE_CASE						:     Tuple					=		None

																self.builder.download_and_prepare(
																    download_config=lowerCAmelCase__			,			download_mode=lowerCAmelCase__			,			verification_mode=lowerCAmelCase__			,			base_path=lowerCAmelCase__			,			num_proc=self.num_proc			,			)
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.builder.as_dataset(
																    split=self.split			,			verification_mode=lowerCAmelCase__			,			in_memory=self.keep_in_memory	)
										return dataset
