






from __future__ import annotations

__lowerCamelCase      :    List[str]   		=   list[tuple[int, int]]

__lowerCamelCase      :    Union[str, Any]   		=   [
    [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],
]

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



class 							SCREAMING_SNAKE_CASE__		:





     """simple docstring"""






     def __init__(      self  :						Tuple							,	__A  :						List[Any]							,	__A  :						List[Any]							,	__A  :						List[Any]							,	__A  :						Tuple							,	__A  :						Optional[int]							,	__A  :						Tuple							,	):
      snake_case__				:   Optional[Any]     =						pos_x
      snake_case__				:   List[str]     =						pos_y
      snake_case__				:   Any     =						(pos_y, pos_x)
      snake_case__				:   Any     =						goal_x
      snake_case__				:   str     =						goal_y
      snake_case__				:   int     =						g_cost
      snake_case__				:   Tuple     =						parent
      snake_case__				:   Optional[int]     =						self.calculate_heuristic()



     def  _lowercase  (      self  :						List[Any] ):
      snake_case__				:   Union[str, Any]     =						abs(self.pos_x - self.goal_x )
      snake_case__				:   Tuple     =						abs(self.pos_y - self.goal_y )
      return dx + dy






     def __lt__(      self  :						Any							,	__A  :						Union[str, Any] ):
      return self.f_cost < other.f_cost



class 							SCREAMING_SNAKE_CASE__		:





     """simple docstring"""






     def __init__(      self  :						str							,	__A  :						Any							,	__A  :						List[Any] ):
      snake_case__				:   Union[str, Any]     =						Node(start[1]							,	start[0]							,	goal[1]							,	goal[0]							,	0							,	_lowerCamelCase )
      snake_case__				:   int     =						Node(goal[1]							,	goal[0]							,	goal[1]							,	goal[0]							,	9_9_9_9_9							,	_lowerCamelCase )

      snake_case__				:   Tuple     =						[self.start]
      snake_case__				:   str     =						[]

      snake_case__				:   Optional[Any]     =						False



     def  _lowercase  (      self  :						List[Any] ):





      while self.open_nodes:
       # Open Nodes are sorted using __lt__
       self.open_nodes.sort()
       snake_case__				:   Union[str, Any]     =						self.open_nodes.pop(0 )

       if current_node.pos == self.target.pos:
        snake_case__				:   str     =						True
        return self.retrace_path(_lowerCamelCase )

       self.closed_nodes.append(_lowerCamelCase )
       snake_case__				:   Union[str, Any]     =						self.get_successors(_lowerCamelCase )

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

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

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



     def  _lowercase  (      self  :						Optional[Any]							,	__A  :						int ):
      snake_case__				:   Optional[int]     =						[]
      for action in delta:
       snake_case__				:   Optional[Any]     =						parent.pos_x + action[1]
       snake_case__				:   Tuple     =						parent.pos_y + action[0]

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

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

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






     def  _lowercase  (      self  :						Tuple							,	__A  :						List[str] ):
      snake_case__				:   Any     =						node
      snake_case__				:   Optional[Any]     =						[]
      while current_node is not None:
       path.append((current_node.pos_y, current_node.pos_x) )
       snake_case__				:   int     =						current_node.parent
      path.reverse()
      return path


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

 print("""------""")

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

  for elem in grid:
   print(elem)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :    List[str]   		=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   ["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   ["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   [
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   [
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

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



