



from __future__ import annotations

lowercase_						=       list[tuple[int, int]]

lowercase_						=       [
    [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],
]

lowercase_						=       ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 			_snake_case     :




				def __init__(  self   :  List[str],   __lowercase   :  List[str],   __lowercase   :  int,   __lowercase   :  Optional[int],   __lowercase   :  str,   __lowercase   :  Union[str, Any],   __lowercase   :  List[Any],   ):
							lowercase__   					=			pos_x
							lowercase__   					=			pos_y
							lowercase__   					=			(pos_y, pos_x)
							lowercase__   					=			goal_x
							lowercase__   					=			goal_y
							lowercase__   					=			g_cost
							lowercase__   					=			parent
							lowercase__   					=			self.calculate_heuristic()




				def 							A__ (  self   :  int  ):
							lowercase__   					=			abs(self.pos_x - self.goal_x  )
							lowercase__   					=			abs(self.pos_y - self.goal_y  )
							return dx + dy




				def __lt__(  self   :  Optional[Any],   __lowercase   :  Optional[int]  ):
							return self.f_cost < other.f_cost
class 			_snake_case     :




				def __init__(  self   :  Union[str, Any],   __lowercase   :  List[str],   __lowercase   :  str  ):
							lowercase__   					=			Node(start[1],   start[0],   goal[1],   goal[0],   0,   _SCREAMING_SNAKE_CASE  )
							lowercase__   					=			Node(goal[1],   goal[0],   goal[1],   goal[0],   9_9999,   _SCREAMING_SNAKE_CASE  )

							lowercase__   					=			[self.start]
							lowercase__   					=			[]

							lowercase__   					=			False




				def 							A__ (  self   :  Union[str, Any]  ):
							while self.open_nodes:
										# Open Nodes are sorted using __lt__
										self.open_nodes.sort()
										lowercase__   					=			self.open_nodes.pop(0  )

										if current_node.pos == self.target.pos:
													lowercase__   					=			True
													return self.retrace_path(_SCREAMING_SNAKE_CASE  )

										self.closed_nodes.append(_SCREAMING_SNAKE_CASE  )
										lowercase__   					=			self.get_successors(_SCREAMING_SNAKE_CASE  )

										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(_SCREAMING_SNAKE_CASE  )
													else:
																# retrieve the best current path
																lowercase__   					=			self.open_nodes.pop(self.open_nodes.index(_SCREAMING_SNAKE_CASE  )  )

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

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




				def 							A__ (  self   :  Optional[int],   __lowercase   :  int  ):
							lowercase__   					=			[]
							for action in delta:
										lowercase__   					=			parent.pos_x + action[1]
										lowercase__   					=			parent.pos_y + action[0]

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

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

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




				def 							A__ (  self   :  List[str],   __lowercase   :  Optional[Any]  ):
							lowercase__   					=			node
							lowercase__   					=			[]
							while current_node is not None:
										path.append((current_node.pos_y, current_node.pos_x)  )
										lowercase__   					=			current_node.parent
							path.reverse()
							return path


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

							print("""------""")

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

														for elem in grid:
																					print(elem)








import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowercase_						=       logging.get_logger(__name__)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			r"\w+[.]\d+"
			lowercase__   					=			re.findall(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			for pat in pats:
						lowercase__   					=			key.replace(SCREAMING_SNAKE_CASE_  ,					"_".join(pat.split("."					)					)					)
			return key



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
			if (
			    any("norm" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						lowercase__   					=			pt_tuple_key[:-1] + ("embedding",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			lowercase__   					=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						lowercase__   					=			pt_tensor.transpose(2  ,					3  ,					1  ,					0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			lowercase__   					=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight":
						lowercase__   					=			pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			lowercase__   					=			pt_tuple_key[:-1] + ("weight",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			lowercase__   					=			pt_tuple_key[:-1] + ("bias",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor




def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=42					):
			# Step 1: Convert pytorch tensor to numpy
			lowercase__   					=			{k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			lowercase__   					=			flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE_					)					)

			lowercase__   					=			flatten_dict(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						lowercase__   					=			rename_key(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			tuple(renamed_pt_key.split("."					)					)

						# Correctly rename weight parameters
						lowercase__					,    lowercase__   					=			rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						lowercase__   					=			jnp.asarray(SCREAMING_SNAKE_CASE_					)

			return unflatten_dict(SCREAMING_SNAKE_CASE_					)




