


'''simple docstring'''




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


_a   :   Tuple												=   logging.get_logger(__name__)

_a   :   List[Any]												=   {
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Union[str, Any]          =	"open-llama"
   def __init__(						self       :      Any       ,   SCREAMING_SNAKE_CASE_       :      Dict=10_0000       ,   SCREAMING_SNAKE_CASE_       :      int=4096       ,   SCREAMING_SNAKE_CASE_       :      str=1_1008       ,   SCREAMING_SNAKE_CASE_       :      List[str]=32       ,   SCREAMING_SNAKE_CASE_       :      Dict=32       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]="silu"       ,   SCREAMING_SNAKE_CASE_       :      Dict=2048       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=0.0_2       ,   SCREAMING_SNAKE_CASE_       :      int=1e-6       ,   SCREAMING_SNAKE_CASE_       :      int=True       ,   SCREAMING_SNAKE_CASE_       :      Tuple=0       ,   SCREAMING_SNAKE_CASE_       :      Any=1       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=2       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=False       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=True       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=0.1       ,   SCREAMING_SNAKE_CASE_       :      Dict=0.1       ,   SCREAMING_SNAKE_CASE_       :      Dict=True       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=True       ,   SCREAMING_SNAKE_CASE_       :      Tuple=None       ,   **SCREAMING_SNAKE_CASE_       :      Any       ,   ) ->							Optional[Any]:
        __snake_case           =						vocab_size
        __snake_case           =						max_position_embeddings
        __snake_case           =						hidden_size
        __snake_case           =						intermediate_size
        __snake_case           =						num_hidden_layers
        __snake_case           =						num_attention_heads
        __snake_case           =						hidden_act
        __snake_case           =						initializer_range
        __snake_case           =						rms_norm_eps
        __snake_case           =						use_cache
        __snake_case           =						kwargs.pop(
            'use_memorry_efficient_attention'       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						hidden_dropout_prob
        __snake_case           =						attention_dropout_prob
        __snake_case           =						use_stable_embedding
        __snake_case           =						shared_input_output_embedding
        __snake_case           =						rope_scaling
        self._rope_scaling_validation()

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





   def a  (						self       :      List[Any]      ) ->							List[Any]:
        if self.rope_scaling is None:
             return

        if not isinstance(self.rope_scaling       ,   SCREAMING_SNAKE_CASE_      ) 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}'      )
        __snake_case           =						self.rope_scaling.get('type'       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						self.rope_scaling.get('factor'       ,   SCREAMING_SNAKE_CASE_      )
        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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) or rope_scaling_factor <= 1.0:
             raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'      )









'''simple docstring'''

from __future__ import annotations

a							   =				list[tuple[int, int]]

a							   =				[
    [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],
]

a							   =				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 						a_		:


							def __init__(				self   :						Tuple      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						float      ,						a_   :						Node | None      ,						)			->					Optional[Any]:
										snake_case:    str      							=pos_x
										snake_case:    str      							=pos_y
										snake_case:    Optional[Any]      							=(pos_y, pos_x)
										snake_case:    Union[str, Any]      							=goal_x
										snake_case:    Tuple      							=goal_y
										snake_case:    Tuple      							=g_cost
										snake_case:    Tuple      							=parent
										snake_case:    Optional[Any]      							=self.calculate_heuristic()


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					float:
										snake_case:    Optional[Any]      							=abs(self.pos_x - self.goal_x			)
										snake_case:    Optional[int]      							=abs(self.pos_y - self.goal_y			)
										return dx + dy




							def __lt__(				self   :						Union[str, Any]      ,						a_   :						Tuple			)			->					bool:
										return self.f_cost < other.f_cost






class 						a_		:


							def __init__(				self   :						Any      ,						a_   :						tuple[int, int]      ,						a_   :						tuple[int, int]			)			->					Optional[Any]:
										snake_case:    Any      							=Node(start[1]      ,						start[0]      ,						goal[1]      ,						goal[0]      ,						0      ,						a_			)
										snake_case:    str      							=Node(goal[1]      ,						goal[0]      ,						goal[1]      ,						goal[0]      ,						9_9_9_9_9      ,						a_			)

										snake_case:    int      							=[self.start]
										snake_case:    list[Node]      							=[]

										snake_case:    Tuple      							=False


							def    UpperCamelCase     (				self   :						str			)			->					Path | None:
										while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													snake_case:    Tuple      							=self.open_nodes.pop(0			)

													if current_node.pos == self.target.pos:
																snake_case:    Tuple      							=True
																return self.retrace_path(a_			)

													self.closed_nodes.append(a_			)
													snake_case:    Optional[Any]      							=self.get_successors(a_			)

													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(a_			)
																else:
																			# retrieve the best current path
																			snake_case:    List[Any]      							=self.open_nodes.pop(self.open_nodes.index(a_			)			)

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

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


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Node			)			->					list[Node]:
										snake_case:    Any      							=[]
										for action in delta:
													snake_case:    Any      							=parent.pos_x + action[1]
													snake_case:    str      							=parent.pos_y + action[0]

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

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

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




							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						Node | None			)			->					Path:
										snake_case:    Dict      							=node
										snake_case:    Optional[int]      							=[]
										while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)			)
													snake_case:    List[str]      							=current_node.parent
										path.reverse()
										return path


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

	print('------')

	a							   =				GreedyBestFirst(init, goal)
	a							   =				greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			a							   =				2

		for elem in grid:
			print(elem)




