


from __future__ import annotations

A      :    Optional[Any]		=					list[tuple[int, int]]

A      :    Tuple		=					[
    [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      :    Optional[Any]		=					([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Optional[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			float					,      __lowerCamelCase							:			Node | None					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  pos_x
             lowerCamelCase__		:				Tuple    =  pos_y
             lowerCamelCase__		:				Any    =  (pos_y, pos_x)
             lowerCamelCase__		:				List[Any]    =  goal_x
             lowerCamelCase__		:				str    =  goal_y
             lowerCamelCase__		:				List[Any]    =  g_cost
             lowerCamelCase__		:				Any    =  parent
             lowerCamelCase__		:				List[Any]    =  self.calculate_heuristic()





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  abs(self.pos_x - self.goal_x       )
             lowerCamelCase__		:				Any    =  abs(self.pos_y - self.goal_y       )
             return dx + dy





       def __lt__(   self							:			Dict					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             return self.f_cost < other.f_cost







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			int					,      __lowerCamelCase							:			tuple[int, int]					,      __lowerCamelCase							:			tuple[int, int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  Node(start[1]					,      start[0]					,      goal[1]					,      goal[0]					,      0					,      SCREAMING_SNAKE_CASE__       )
             lowerCamelCase__		:				List[str]    =  Node(goal[1]					,      goal[0]					,      goal[1]					,      goal[0]					,      99999					,      SCREAMING_SNAKE_CASE__       )

             lowerCamelCase__		:				Optional[Any]    =  [self.start]
             lowerCamelCase__		:				int    =  []

             lowerCamelCase__		:				int    =  False





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             while self.open_nodes:
                   # Open Nodes are sorted using __lt__
                   self.open_nodes.sort()
                   lowerCamelCase__		:				int    =  self.open_nodes.pop(0       )

                   if current_node.pos == self.target.pos:
                         lowerCamelCase__		:				List[Any]    =  True
                         return self.retrace_path(SCREAMING_SNAKE_CASE__       )

                   self.closed_nodes.append(SCREAMING_SNAKE_CASE__       )
                   lowerCamelCase__		:				List[str]    =  self.get_successors(SCREAMING_SNAKE_CASE__       )

                   for child_node in successors:
                         if child_node in self.closed_nodes:
                               continue

                         if child_node not in self.open_nodes:
                               self.open_nodes.append(SCREAMING_SNAKE_CASE__       )
                         else:
                               # retrieve the best current path
                               lowerCamelCase__		:				str    =  self.open_nodes.pop(self.open_nodes.index(SCREAMING_SNAKE_CASE__       )       )

                               if child_node.g_cost < better_node.g_cost:
                                     self.open_nodes.append(SCREAMING_SNAKE_CASE__       )
                               else:
                                     self.open_nodes.append(SCREAMING_SNAKE_CASE__       )

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





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			Node       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  []
             for action in delta:
                   lowerCamelCase__		:				Tuple    =  parent.pos_x + action[1]
                   lowerCamelCase__		:				List[Any]    =  parent.pos_y + action[0]

                   if not (0 <= pos_x <= len(grid[0]       ) - 1 and 0 <= pos_y <= len(SCREAMING_SNAKE_CASE__       ) - 1):
                         continue

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

                   successors.append(
                       Node(
                           SCREAMING_SNAKE_CASE__					,      SCREAMING_SNAKE_CASE__					,      self.target.pos_y					,      self.target.pos_x					,      parent.g_cost + 1					,      SCREAMING_SNAKE_CASE__					,      )       )
             return successors





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Node | None       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  node
             lowerCamelCase__		:				str    =  []
             while current_node is not None:
                   path.append((current_node.pos_y, current_node.pos_x)       )
                   lowerCamelCase__		:				Union[str, Any]    =  current_node.parent
             path.reverse()
             return path


if __name__ == "__main__":
    A      :    Optional[int]		=					(0, 0)
    A      :    Optional[Any]		=					(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print("------")

    A      :    str		=					GreedyBestFirst(init, goal)
    A      :    Dict		=					greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            A      :    Tuple		=					2

        for elem in grid:
            print(elem)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "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

					SCREAMING_SNAKE_CASE__   :			int												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



