



import numpy as np



class 				lowerCAmelCase_		:
       def __init__(			self       )     ->		int:
          UpperCamelCase    :  Union[str, Any]							=	(0, 0)
          UpperCamelCase    :  str							=	None
          UpperCamelCase    :  str							=	0
          UpperCamelCase    :  Dict							=	0
          UpperCamelCase    :  Optional[int]							=	0
       def __eq__(			self,			SCREAMING_SNAKE_CASE_       )     ->		str:
          return self.position == cell.position






       def 					snake_case_       (			self       )     ->		str:
          print(self.position       )






class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_=(5, 5)       )     ->		Union[str, Any]:
          UpperCamelCase    :  Any							=	np.zeros(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[int]							=	world_size[0]
          UpperCamelCase    :  Union[str, Any]							=	world_size[1]
       def 					snake_case_       (			self       )     ->		Dict:
          print(self.w       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Dict:
          UpperCamelCase    :  Optional[Any]							=	[
              (-1, -1),
              (-1, 0),
              (-1, 1),
              (0, -1),
              (0, 1),
              (1, -1),
              (1, 0),
              (1, 1),
          ]
          UpperCamelCase    :  List[str]							=	cell.position[0]
          UpperCamelCase    :  Tuple							=	cell.position[1]
          UpperCamelCase    :  Optional[Any]							=	[]
          for n in neughbour_cord:
             UpperCamelCase    :  Any							=	current_x + n[0]
             UpperCamelCase    :  Optional[int]							=	current_y + n[1]
             if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                UpperCamelCase    :  List[Any]							=	Cell()
                UpperCamelCase    :  Optional[int]							=	(x, y)
                UpperCamelCase    :  List[Any]							=	cell
                neighbours.append(SCREAMING_SNAKE_CASE_       )
          return neighbours






def 		UpperCamelCase	(   snake_case__       :    Tuple       ,     snake_case__       :    Optional[Any]       ,     snake_case__       :    Tuple	)	->     Dict:
   UpperCamelCase    :  Union[str, Any]							=	[]
   UpperCamelCase    :  Optional[int]							=	[]
   _open.append(snake_case__	)

   while _open:
      UpperCamelCase    :  List[str]							=	np.argmin([n.f for n in _open]	)
      UpperCamelCase    :  Optional[int]							=	_open[min_f]
      _closed.append(_open.pop(snake_case__	)	)
      if current == goal:
         break
      for n in world.get_neigbours(snake_case__	):
         for c in _closed:
            if c == n:
               continue
         UpperCamelCase    :  Tuple							=	current.g + 1
         UpperCamelCase	,     UpperCamelCase    :  Any							=	n.position
         UpperCamelCase	,     UpperCamelCase    :  str							=	goal.position
         UpperCamelCase    :  Tuple							=	(ya - ya) ** 2 + (xa - xa) ** 2
         UpperCamelCase    :  List[str]							=	n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(snake_case__	)
   UpperCamelCase    :  Any							=	[]
   while current.parent is not None:
      path.append(current.position	)
      UpperCamelCase    :  Optional[int]							=	current.parent
   path.append(current.position	)
   return path[::-1]


if __name__ == "__main__":
    __UpperCAmelCase          =       Gridworld()
    # Start position and goal
    __UpperCAmelCase          =       Cell()
    __UpperCAmelCase          =       (0, 0)
    __UpperCAmelCase          =       Cell()
    __UpperCAmelCase          =       (4, 4)
    print(F"""path from {start.position} to {goal.position}""")
    __UpperCAmelCase          =       astar(world, start, goal)
    #   Just for visual reasons.
    for i in s:
        __UpperCAmelCase          =       1
    print(world.w)








from typing import TYPE_CHECKING

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


__UpperCAmelCase          =       {
    '''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
    '''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       [
        '''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AdaptiveEmbedding''',
        '''TransfoXLForSequenceClassification''',
        '''TransfoXLLMHeadModel''',
        '''TransfoXLModel''',
        '''TransfoXLPreTrainedModel''',
        '''load_tf_weights_in_transfo_xl''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase          =       [
        '''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFAdaptiveEmbedding''',
        '''TFTransfoXLForSequenceClassification''',
        '''TFTransfoXLLMHeadModel''',
        '''TFTransfoXLMainLayer''',
        '''TFTransfoXLModel''',
        '''TFTransfoXLPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    __UpperCAmelCase          =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



