






import numpy as np



class 				_A			:
    """simple docstring"""

    def __init__(				self      :     Any      )      ->							Tuple:
           __UpperCAmelCase     				=(0, 0)
           __UpperCAmelCase     				=None
           __UpperCAmelCase     				=0
           __UpperCAmelCase     				=0
           __UpperCAmelCase     				=0



    def __eq__(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     List[Any]      )      ->							List[Any]:
           return self.position == cell.position



    def _a							(				self      :     str      )      ->							Any:
           print(self.position      )




class 				_A			:
    """simple docstring"""

    def __init__(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Tuple=(5, 5)      )      ->							int:
           __UpperCAmelCase     				=np.zeros(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=world_size[0]
           __UpperCAmelCase     				=world_size[1]



    def _a							(				self      :     Optional[Any]      )      ->							Tuple:
           print(self.w      )



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Tuple      )      ->							int:
           __UpperCAmelCase     				=[
               (-1, -1),
               (-1, 0),
               (-1, 1),
               (0, -1),
               (0, 1),
               (1, -1),
               (1, 0),
               (1, 1),
           ]
           __UpperCAmelCase     				=cell.position[0]
           __UpperCAmelCase     				=cell.position[1]
           __UpperCAmelCase     				=[]
           for n in neughbour_cord:
                  __UpperCAmelCase     				=current_x + n[0]
                  __UpperCAmelCase     				=current_y + n[1]
                  if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                         __UpperCAmelCase     				=Cell()
                         __UpperCAmelCase     				=(x, y)
                         __UpperCAmelCase     				=cell
                         neighbours.append(__SCREAMING_SNAKE_CASE      )
           return neighbours







def       lowercase__	(					A_:						Dict  ,      A_:						Tuple  ,      A_:						Tuple		)	->				str:







       """simple docstring"""






       __UpperCAmelCase     				=[]
       __UpperCAmelCase     				=[]
       _open.append(A_		)

       while _open:
              __UpperCAmelCase     				=np.argmin([n.f for n in _open]		)
              __UpperCAmelCase     				=_open[min_f]
              _closed.append(_open.pop(A_		)		)
              if current == goal:
                     break
              for n in world.get_neigbours(A_		):
                     for c in _closed:
                            if c == n:
                                   continue
                     __UpperCAmelCase     				=current.g + 1
                     __UpperCAmelCase				, __UpperCAmelCase     				=n.position
                     __UpperCAmelCase				, __UpperCAmelCase     				=goal.position
                     __UpperCAmelCase     				=(ya - ya) ** 2 + (xa - xa) ** 2
                     __UpperCAmelCase     				=n.h + n.g

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


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










import math

import tensorflow as tf
from packaging import version




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						str										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						List[str]										=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 )						,  x.dtype ) ))

				return x * cdf




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						str										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Optional[Any]										=tf.cast(math.pi						,  x.dtype )
				__magic_name__						:						int										=tf.cast(0.0_4_4_7_1_5						,  x.dtype )
				__magic_name__						:						Tuple										=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase						,  3 )) ))

				return x * cdf




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Any										=tf.convert_to_tensor(lowerCamelCase )

				return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Optional[Any]										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Union[str, Any]										=tf.cast(0.0_4_4_7_1_5						,  x.dtype )
				__magic_name__						:						Tuple										=tf.cast(0.7_9_7_8_8_4_5_6_0_8						,  x.dtype )

				return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						List[str]										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Dict										=tf.cast(1.7_0_2						,  x.dtype )
				return x * tf.math.sigmoid(coeff * x )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				return tf.clip_by_value(_gelu(lowerCamelCase )						,  -10						,  10 )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=-1 ):
				__magic_name__					,			__magic_name__						:						List[Any]										=tf.split(lowerCamelCase						,  2						,  axis=lowerCamelCase )
				return a * tf.math.sigmoid(lowerCamelCase )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):




							def 		lowerCAmelCase_ (						lowerCamelCase ):
											return tf.keras.activations.gelu(lowerCamelCase						,  approximate=lowerCamelCase )

							UpperCAmelCase_		:					List[str]          =  tf.keras.activations.gelu



							UpperCAmelCase_		:					Dict          =  approximate_gelu_wrap
else:
							UpperCAmelCase_		:					Dict          =  _gelu
							UpperCAmelCase_		:					str          =  _gelu_new


UpperCAmelCase_		:					Any          =  {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )




