




import numpy as np


class    lowerCamelCase    :






			'''simple docstring'''





			def __init__( self			:   Union[str, Any]  )						->   Optional[Any]:
								__magic_name__							:	List[str]    							=							(0, 0)
								__magic_name__							:	Any    							=							None
								__magic_name__							:	Optional[int]    							=							0
								__magic_name__							:	Optional[int]    							=							0
								__magic_name__							:	Optional[Any]    							=							0

			def __eq__( self			:   int       ,						lowerCamelCase_			:   Optional[Any]  )						->   Dict:
								return self.position == cell.position


			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
								print(self.position  )






class    lowerCamelCase    :






			'''simple docstring'''





			def __init__( self			:   List[str]       ,						lowerCamelCase_			:   Optional[int]=(5, 5)  )						->   Optional[Any]:
								__magic_name__							:	List[Any]    							=							np.zeros(lowerCamelCase_  )
								__magic_name__							:	Dict    							=							world_size[0]
								__magic_name__							:	Union[str, Any]    							=							world_size[1]

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Optional[Any]:
								print(self.w  )


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







def 		lowercase__   (		__A:	Tuple							,__A:	Optional[int]							,__A:	Optional[Any]				):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							[]
					__magic_name__							:	Any    							=							[]
					_open.append(__A				)

					while _open:
										__magic_name__							:	str    							=							np.argmin([n.f for n in _open]				)
										__magic_name__							:	Optional[int]    							=							_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
															__magic_name__							:	Tuple    							=							current.g + 1
															__magic_name__							,    __magic_name__							:	Optional[int]    							=							n.position
															__magic_name__							,    __magic_name__							:	Optional[int]    							=							goal.position
															__magic_name__							:	List[str]    							=							(ya - ya) ** 2 + (xa - xa) ** 2
															__magic_name__							:	List[Any]    							=							n.h + n.g

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


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









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


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict				=			{}


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''llama'''
			UpperCamelCase__								=['''past_key_values''']

			def __init__( self			:   Dict       ,						lowerCamelCase_			:   List[str]=32000       ,						lowerCamelCase_			:   List[Any]=4096       ,						lowerCamelCase_			:   Any=11008       ,						lowerCamelCase_			:   Optional[Any]=32       ,						lowerCamelCase_			:   Dict=32       ,						lowerCamelCase_			:   Optional[Any]=None       ,						lowerCamelCase_			:   Dict="silu"       ,						lowerCamelCase_			:   Optional[int]=2048       ,						lowerCamelCase_			:   int=0.0_2       ,						lowerCamelCase_			:   Tuple=1E-6       ,						lowerCamelCase_			:   Tuple=True       ,						lowerCamelCase_			:   List[str]=0       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Optional[int]=2       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Tuple       ,						)						->   Tuple:
								__magic_name__							:	Any    							=							vocab_size
								__magic_name__							:	Optional[int]    							=							max_position_embeddings
								__magic_name__							:	Union[str, Any]    							=							hidden_size
								__magic_name__							:	Dict    							=							intermediate_size
								__magic_name__							:	Union[str, Any]    							=							num_hidden_layers
								__magic_name__							:	Tuple    							=							num_attention_heads

								# for backward compatibility
								if num_key_value_heads is None:
													__magic_name__							:	Optional[int]    							=							num_attention_heads

								__magic_name__							:	Optional[int]    							=							num_key_value_heads
								__magic_name__							:	List[str]    							=							hidden_act
								__magic_name__							:	List[str]    							=							initializer_range
								__magic_name__							:	Tuple    							=							rms_norm_eps
								__magic_name__							:	Optional[int]    							=							pretraining_tp
								__magic_name__							:	Any    							=							use_cache
								__magic_name__							:	int    							=							rope_scaling
								self._rope_scaling_validation()

								super().__init__(
								    pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						tie_word_embeddings=lowerCamelCase_       ,						**lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   str  )						->   Tuple:
								if self.rope_scaling is None:
													return

								if not isinstance(self.rope_scaling       ,						lowerCamelCase_  ) or len(self.rope_scaling  ) != 2:
													raise ValueError(
													    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
													    F'''got {self.rope_scaling}'''  )
								__magic_name__							:	List[str]    							=							self.rope_scaling.get('''type'''       ,						lowerCamelCase_  )
								__magic_name__							:	str    							=							self.rope_scaling.get('''factor'''       ,						lowerCamelCase_  )
								if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''  )
								if rope_scaling_factor is None or not isinstance(lowerCamelCase_       ,						lowerCamelCase_  ) or rope_scaling_factor <= 1.0:
													raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''  )



