



from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__A       =						0

__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

__A       =						tuple[int, int]






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Node | None						,  )      ->			str:
							UpperCAmelCase_=       pos_x
							UpperCAmelCase_=       pos_y
							UpperCAmelCase_=       (pos_y, pos_x)
							UpperCAmelCase_=       goal_x
							UpperCAmelCase_=       goal_y
							UpperCAmelCase_=       g_cost
							UpperCAmelCase_=       parent
							UpperCAmelCase_=       self.calculate_heuristic()
							UpperCAmelCase_=       self.g_cost + self.h_cost




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.pos_x - self.goal_x
							UpperCAmelCase_=       self.pos_y - self.goal_y
							if HEURISTIC == 1:
													return abs(_a					) + abs(_a					)
							else:
													return sqrt(dy**2 + dx**2					)




	def __lt__(     self     :						Any						,  __UpperCAmelCase     :						Node					)      ->			Optional[Any]:
							return self.f_cost < other.f_cost






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Any						,  __UpperCAmelCase     :						TPosition						,  __UpperCAmelCase     :						TPosition					)      ->			int:
							UpperCAmelCase_=       Node(start[1]						,  start[0]						,  goal[1]						,  goal[0]						,  0						,  _a					)
							UpperCAmelCase_=       Node(goal[1]						,  goal[0]						,  goal[1]						,  goal[0]						,  99_999						,  _a					)

							UpperCAmelCase_=       [self.start]
							UpperCAmelCase_=       []

							UpperCAmelCase_=       False




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Optional[Any]:
							while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													UpperCAmelCase_=       self.open_nodes.pop(0					)

													if current_node.pos == self.target.pos:
																			return self.retrace_path(_a					)

													self.closed_nodes.append(_a					)
													UpperCAmelCase_=       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
																									UpperCAmelCase_=       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					)

							return [self.start.pos]




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Node					)      ->			Any:
							UpperCAmelCase_=       []
							for action in delta:
													UpperCAmelCase_=       parent.pos_x + action[1]
													UpperCAmelCase_=       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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						Node | None					)      ->			Optional[Any]:
							UpperCAmelCase_=       node
							UpperCAmelCase_=       []
							while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)					)
													UpperCAmelCase_=       current_node.parent
							path.reverse()
							return path






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						TPosition						,  __UpperCAmelCase     :						TPosition					)      ->			List[Any]:
							UpperCAmelCase_=       AStar(_a						,  _a					)
							UpperCAmelCase_=       AStar(_a						,  _a					)
							UpperCAmelCase_=       False




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
													self.fwd_astar.open_nodes.sort()
													self.bwd_astar.open_nodes.sort()
													UpperCAmelCase_=       self.fwd_astar.open_nodes.pop(0					)
													UpperCAmelCase_=       self.bwd_astar.open_nodes.pop(0					)

													if current_bwd_node.pos == current_fwd_node.pos:
																			return self.retrace_bidirectional_path(
																			    _a						,  _a					)

													self.fwd_astar.closed_nodes.append(_a					)
													self.bwd_astar.closed_nodes.append(_a					)

													UpperCAmelCase_=       current_bwd_node
													UpperCAmelCase_=       current_fwd_node

													UpperCAmelCase_=       {
													    self.fwd_astar: self.fwd_astar.get_successors(_a					),
													    self.bwd_astar: self.bwd_astar.get_successors(_a					),
													}

													for astar in [self.fwd_astar, self.bwd_astar]:
																			for child_node in successors[astar]:
																									if child_node in astar.closed_nodes:
																															continue

																									if child_node not in astar.open_nodes:
																															astar.open_nodes.append(_a					)
																									else:
																															# retrieve the best current path
																															UpperCAmelCase_=       astar.open_nodes.pop(
																															    astar.open_nodes.index(_a					)					)

																															if child_node.g_cost < better_node.g_cost:
																																					astar.open_nodes.append(_a					)
																															else:
																																					astar.open_nodes.append(_a					)

							return [self.fwd_astar.start.pos]




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Node						,  __UpperCAmelCase     :						Node					)      ->			List[str]:
							UpperCAmelCase_=       self.fwd_astar.retrace_path(_a					)
							UpperCAmelCase_=       self.bwd_astar.retrace_path(_a					)
							bwd_path.pop()
							bwd_path.reverse()
							UpperCAmelCase_=       fwd_path + bwd_path
							return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	__A       =						(0, 0)
	__A       =						(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	__A       =						time.time()
	__A       =						AStar(init, goal)
	__A       =						a_star.search()
	__A       =						time.time() - start_time
	print(f'AStar execution time = {end_time:f} seconds')

	__A       =						time.time()
	__A       =						BidirectionalAStar(init, goal)
	__A       =						time.time() - bd_start_time
	print(f'BidirectionalAStar execution time = {bd_end_time:f} seconds')









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       [
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						    """_float_tensor""",
						    """decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   lowerCAmelCase_       : Any    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : List[str]    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       Namespace(**checkpoint["""cfg"""]["""model"""]    )
						UpperCAmelCase_=       checkpoint["""model"""]
						remove_ignore_keys_(lowerCAmelCase_    )
						UpperCAmelCase_=       state_dict["""decoder.embed_tokens.weight"""].shape[0]

						UpperCAmelCase_=       {key.replace("""decoder"""      ,"""model"""    ): val for key, val in state_dict.items()}

						UpperCAmelCase_=       XGLMConfig(
						    vocab_size=lowerCAmelCase_      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="""gelu"""      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

						UpperCAmelCase_=       XGLMForCausalLM(lowerCAmelCase_    )
						UpperCAmelCase_=       model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						print(lowerCAmelCase_    )
						UpperCAmelCase_=       make_linear_from_emb(model.model.embed_tokens    )

						return model


if __name__ == "__main__":
	__A       =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
	parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	__A       =						parser.parse_args()
	__A       =						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)




