
'''simple docstring'''
from __future__ import annotations

import time

a_											=						list[tuple[int, int]]

a_											=						[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a_											=						[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    Tuple     			=						pos_x
     snake_case_ :    Optional[Any]     			=						pos_y
     snake_case_ :    Union[str, Any]     			=						(pos_y, pos_x)
     snake_case_ :    int     			=						goal_x
     snake_case_ :    Any     			=						goal_y
     snake_case_ :    Tuple     			=						parent






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    str     			=						Node(start[1] ,       start[0] ,       goal[1] ,       goal[0] ,       _snake_case)
     snake_case_ :    Optional[Any]     			=						Node(goal[1] ,       goal[0] ,       goal[1] ,       goal[0] ,       _snake_case)

     snake_case_ :    Optional[Any]     			=						[self.start]
     snake_case_ :    Any     			=						False





 def        snake_case__		(    self):


     while self.node_queue:
         snake_case_ :    int     			=						self.node_queue.pop(0)

         if current_node.pos == self.target.pos:
             snake_case_ :    Any     			=						True
             return self.retrace_path(_snake_case)

         snake_case_ :    List[Any]     			=						self.get_successors(_snake_case)

         for node in successors:
             self.node_queue.append(_snake_case)

     if not self.reached:
         return [self.start.pos]
     return None





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Optional[Any]     			=						[]
     for action in delta:
         snake_case_ :    Tuple     			=						parent.pos_x + action[1]
         snake_case_ :    Optional[int]     			=						parent.pos_y + action[0]
         if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(_snake_case) - 1):
             continue

         if grid[pos_y][pos_x] != 0:
             continue

         successors.append(
             Node(_snake_case ,       _snake_case ,       self.target.pos_y ,       self.target.pos_x ,       _snake_case))
     return successors





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    int     			=						node
     snake_case_ :    Optional[Any]     			=						[]
     while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x))
         snake_case_ :    Optional[Any]     			=						current_node.parent
     path.reverse()
     return path






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Tuple     			=						BreadthFirstSearch(_snake_case ,       _snake_case)
     snake_case_ :    int     			=						BreadthFirstSearch(_snake_case ,       _snake_case)
     snake_case_ :    Optional[int]     			=						False





 def        snake_case__		(    self):


     while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
         snake_case_ :    Optional[int]     			=						self.fwd_bfs.node_queue.pop(0)
         snake_case_ :    List[str]     			=						self.bwd_bfs.node_queue.pop(0)

         if current_bwd_node.pos == current_fwd_node.pos:
             snake_case_ :    List[Any]     			=						True
             return self.retrace_bidirectional_path(
                 _snake_case ,       _snake_case)

         snake_case_ :    int     			=						current_bwd_node
         snake_case_ :    Optional[Any]     			=						current_fwd_node

         snake_case_ :    Any     			=						{
             self.fwd_bfs: self.fwd_bfs.get_successors(_snake_case),
             self.bwd_bfs: self.bwd_bfs.get_successors(_snake_case),
         }

         for bfs in [self.fwd_bfs, self.bwd_bfs]:
             for node in successors[bfs]:
                 bfs.node_queue.append(_snake_case)

     if not self.reached:
         return [self.fwd_bfs.start.pos]
     return None





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    List[Any]     			=						self.fwd_bfs.retrace_path(_snake_case)
     snake_case_ :    Any     			=						self.bwd_bfs.retrace_path(_snake_case)
     bwd_path.pop()
     bwd_path.reverse()
     snake_case_ :    List[Any]     			=						fwd_path + bwd_path
     return path


if __name__ == "__main__":
  # all coordinates are given in format [y,x]
  import doctest

  doctest.testmod()
  a_											=						(0, 0)
  a_											=						(len(grid) - 1, len(grid[0]) - 1)
  for elem in grid:
    print(elem)

  a_											=						time.time()
  a_											=						BreadthFirstSearch(init, goal)
  a_											=						bfs.search()
  a_											=						time.time() - start_bfs_time

  print("Unidirectional BFS computation time : ", bfs_time)

  a_											=						time.time()
  a_											=						BidirectionalBreadthFirstSearch(init, goal)
  a_											=						bd_bfs.search()
  a_											=						time.time() - start_bd_bfs_time

  print("Bidirectional BFS computation time : ", bd_bfs_time)






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_											=						{"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WavLMForAudioFrameClassification",
      "WavLMForCTC",
      "WavLMForSequenceClassification",
      "WavLMForXVector",
      "WavLMModel",
      "WavLMPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




