

import numpy as np

class   _a					:




      """simple docstring"""

      def __init__(   self							:				str	):
            A_						  =				(0, 0)
            A_						  =				None
            A_						  =				0
            A_						  =				0
            A_						  =				0





      def __eq__(   self							:				str						,			UpperCAmelCase							:				str	):
            return self.position == cell.position







      def 		__A	(   self							:				Union[str, Any]	):
            print(self.position	)




class   _a					:




      """simple docstring"""

      def __init__(   self							:				Tuple						,			UpperCAmelCase							:				Union[str, Any]=(5, 5)	):
            A_						  =				np.zeros(UpperCAmelCase	)
            A_						  =				world_size[0]
            A_						  =				world_size[1]





      def 		__A	(   self							:				Dict	):
            print(self.w	)







      def 		__A	(   self							:				List[Any]						,			UpperCAmelCase							:				List[str]	):
            A_						  =				[
                (-1, -1),
                (-1, 0),
                (-1, 1),
                (0, -1),
                (0, 1),
                (1, -1),
                (1, 0),
                (1, 1),
            ]
            A_						  =				cell.position[0]
            A_						  =				cell.position[1]
            A_						  =				[]
            for n in neughbour_cord:
                  A_						  =				current_x + n[0]
                  A_						  =				current_y + n[1]
                  if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                        A_						  =				Cell()
                        A_						  =				(x, y)
                        A_						  =				cell
                        neighbours.append(UpperCAmelCase	)
            return neighbours




def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: str				,__UpperCamelCase	: List[Any]    ):



      """simple docstring"""

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

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

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


if __name__ == "__main__":
      __a    :List[Any]      =			Gridworld()
      # Start position and goal
      __a    :str      =			Cell()
      __a    :Union[str, Any]      =			(0, 0)
      __a    :int      =			Cell()
      __a    :int      =			(4, 4)
      print(F"path from {start.position} to {goal.position}")
      __a    :List[str]      =			astar(world, start, goal)
      #   Just for visual reasons.
      for i in s:
            __a    :Tuple      =			1
      print(world.w)






'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Any								=       "falcon"
 __lowerCamelCase    :				List[str]								=       ["past_key_values"]




 def __init__(						self      ,  _lowerCAmelCase=65024      ,  _lowerCAmelCase=4544      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=71      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=11      ,  _lowerCAmelCase=11      ,  **_lowerCAmelCase      ,  )     ->				Union[str, Any]:
     _lowerCAmelCase						      =   vocab_size
     # Backward compatibility with n_embed kwarg
     _lowerCAmelCase						      =   kwargs.pop("n_embed"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   hidden_size if n_embed is None else n_embed
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   hidden_dropout
     _lowerCAmelCase						      =   attention_dropout

     _lowerCAmelCase						      =   bos_token_id
     _lowerCAmelCase						      =   eos_token_id
     _lowerCAmelCase						      =   num_attention_heads if num_kv_heads is None else num_kv_heads
     _lowerCAmelCase						      =   alibi
     _lowerCAmelCase						      =   new_decoder_architecture
     _lowerCAmelCase						      =   multi_query  # Ignored when new_decoder_architecture is True
     _lowerCAmelCase						      =   parallel_attn
     _lowerCAmelCase						      =   bias

     super().__init__(bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return self.hidden_size // self.num_attention_heads





 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return not self.alibi


