
'''simple docstring'''

from __future__ import annotations

__snake_case:	Dict										=      list[tuple[int, int]]

__snake_case:	str										=      [
    [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],
]

__snake_case:	Dict										=      ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class 			_UpperCAmelCase			:

     """simple docstring"""

     def __init__(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_				,		):

       '''simple docstring'''

       a_   :						Tuple   =				pos_x
       a_   :						Dict   =				pos_y
       a_   :						Any   =				(pos_y, pos_x)
       a_   :						Tuple   =				goal_x
       a_   :						Optional[Any]   =				goal_y
       a_   :						Any   =				g_cost
       a_   :						Dict   =				parent
       a_   :						Optional[int]   =				self.calculate_heuristic()


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						Union[str, Any]   =				abs(self.pos_x - self.goal_x			)
       a_   :						Union[str, Any]   =				abs(self.pos_y - self.goal_y			)
       return dx + dy






     def __lt__(   self				,		lowerCAmelCase_			):

       '''simple docstring'''

       return self.f_cost < other.f_cost







class 			_UpperCAmelCase			:

     """simple docstring"""

     def __init__(   self				,		lowerCAmelCase_				,		lowerCAmelCase_			):

       '''simple docstring'''

       a_   :						Dict   =				Node(start[1]				,		start[0]				,		goal[1]				,		goal[0]				,		0				,		lowercase__			)
       a_   :						int   =				Node(goal[1]				,		goal[0]				,		goal[1]				,		goal[0]				,		9_99_99				,		lowercase__			)

       a_   :						Dict   =				[self.start]
       a_   :						List[str]   =				[]

       a_   :						Union[str, Any]   =				False


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       while self.open_nodes:
         # Open Nodes are sorted using __lt__
         self.open_nodes.sort()
         a_   :						Optional[int]   =				self.open_nodes.pop(0			)

         if current_node.pos == self.target.pos:
           a_   :						str   =				True
           return self.retrace_path(lowercase__			)

         self.closed_nodes.append(lowercase__			)
         a_   :						int   =				self.get_successors(lowercase__			)

         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(lowercase__			)
           else:
             # retrieve the best current path
             a_   :						str   =				self.open_nodes.pop(self.open_nodes.index(lowercase__			)			)

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

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


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_			):

       '''simple docstring'''

       a_   :						int   =				[]
       for action in delta:
         a_   :						List[str]   =				parent.pos_x + action[1]
         a_   :						Optional[int]   =				parent.pos_y + action[0]

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

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

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






     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_			):

       '''simple docstring'''

       a_   :						List[Any]   =				node
       a_   :						List[Any]   =				[]
       while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)			)
         a_   :						int   =				current_node.parent
       path.reverse()
       return path


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

    print("------")

    __snake_case:	Optional[Any]										=      GreedyBestFirst(init, goal)
    __snake_case:	Dict										=      greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            __snake_case:	Tuple										=      2

        for elem in grid:
            print(elem)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


