





from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowercase_          =		0

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

lowercase_          =		tuple[int, int]
class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Any      ,					a      :    int      ,					a      :    int      ,					a      :    int      ,					a      :    int      ,					a      :    int      ,					a      :    Node | None      ,					)->       None:





     """simple docstring"""







     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()
     lowercase__           =  self.g_cost + self.h_cost


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       float:





     """simple docstring"""







     lowercase__           =  self.pos_x - self.goal_x
     lowercase__           =  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      ,					a      :    Node	)->       bool:





     """simple docstring"""







     return self.f_cost < other.f_cost






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    str      ,					a      :    TPosition      ,					a      :    TPosition	)->       int:





     """simple docstring"""







     lowercase__           =  Node(start[1]      ,					start[0]      ,					goal[1]      ,					goal[0]      ,					0      ,					a	)
     lowercase__           =  Node(goal[1]      ,					goal[0]      ,					goal[1]      ,					goal[0]      ,					99_999      ,					a	)

     lowercase__           =  [self.start]
     lowercase__           =  []

     lowercase__           =  False


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       list[TPosition]:





     """simple docstring"""







     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:
         return self.retrace_path(a	)

       self.closed_nodes.append(a	)
       lowercase__           =  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
           lowercase__           =  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      ,					a      :    Node	)->       list[Node]:





     """simple docstring"""







     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(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      :    str      ,					a      :    Node | None	)->       list[TPosition]:





     """simple docstring"""







     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






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Union[str, Any]      ,					a      :    TPosition      ,					a      :    TPosition	)->       None:





     """simple docstring"""







     lowercase__           =  AStar(a      ,					a	)
     lowercase__           =  AStar(a      ,					a	)
     lowercase__           =  False


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       list[TPosition]:





     """simple docstring"""







     while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
       self.fwd_astar.open_nodes.sort()
       self.bwd_astar.open_nodes.sort()
       lowercase__           =  self.fwd_astar.open_nodes.pop(0	)
       lowercase__           =  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	)

       lowercase__           =  current_bwd_node
       lowercase__           =  current_fwd_node

       lowercase__           =  {
           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
             lowercase__           =  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      :    Optional[int]      ,					a      :    Node      ,					a      :    Node	)->       list[TPosition]:





     """simple docstring"""







     lowercase__           =  self.fwd_astar.retrace_path(a	)
     lowercase__           =  self.bwd_astar.retrace_path(a	)
     bwd_path.pop()
     bwd_path.reverse()
     lowercase__           =  fwd_path + bwd_path
     return path


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

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

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












import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  [
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      '_float_tensor',
      'decoder.output_projection.weight',
  ]
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__      ,   lowercase__           =  emb.weight.shape
  lowercase__           =  nn.Linear(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						bias=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  emb.weight.data
  return lin_layer




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE="facebook/mbart-large-en-ro"		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		)    ->       str:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)['model']
  remove_ignore_keys_(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  state_dict['encoder.embed_tokens.weight'].shape[0]

  lowercase__           =  MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE		,						vocab_size=_SCREAMING_SNAKE_CASE		)
  if mbart_aa and finetuned:
    lowercase__           =  'relu'

  lowercase__           =  state_dict['decoder.embed_tokens.weight']
  lowercase__           =  MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
  model.model.load_state_dict(_SCREAMING_SNAKE_CASE		)

  if finetuned:
    lowercase__           =  make_linear_from_emb(model.model.shared		)

  return model


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a 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.""")
      parser.add_argument(
          """--hf_config""",
          default="""facebook/mbart-large-cc25""",
          type=str,
          help="""Which huggingface architecture to use: mbart-large""",
      )
      parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
      parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
      lowercase_          =		parser.parse_args()
      lowercase_          =		convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)


