

from __future__ import annotations

_lowerCamelCase				:   int					   =							list[tuple[int, int]]

_lowerCamelCase				:   Optional[Any]					   =							[
    [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],
]

_lowerCamelCase				:   Union[str, Any]					   =							([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class 					__UpperCAmelCase     :



   def __init__(							self							:     List[str],		__A							:     int,		__A							:     int,		__A							:     int,		__A							:     int,		__A							:     float,		__A							:     Node | None,		):
     UpperCAmelCase       :							int  = pos_x
     UpperCAmelCase       :							List[Any]  = pos_y
     UpperCAmelCase       :							Dict  = (pos_y, pos_x)
     UpperCAmelCase       :							Union[str, Any]  = goal_x
     UpperCAmelCase       :							Any  = goal_y
     UpperCAmelCase       :							Optional[Any]  = g_cost
     UpperCAmelCase       :							List[str]  = parent
     UpperCAmelCase       :							List[Any]  = self.calculate_heuristic()



   def        __magic_name__    (							self							:     Any						):
     UpperCAmelCase       :							str  = abs(self.pos_x - self.goal_x						)
     UpperCAmelCase       :							Any  = abs(self.pos_y - self.goal_y						)
     return dx + dy





   def __lt__(							self							:     List[str],		__A							:     Optional[Any]						):
     return self.f_cost < other.f_cost






class 					__UpperCAmelCase     :



   def __init__(							self							:     List[Any],		__A							:     tuple[int, int],		__A							:     tuple[int, int]						):
     UpperCAmelCase       :							Optional[int]  = Node(start[1],		start[0],		goal[1],		goal[0],		0,		_a						)
     UpperCAmelCase       :							Dict  = Node(goal[1],		goal[0],		goal[1],		goal[0],		9_9_9_9_9,		_a						)

     UpperCAmelCase       :							str  = [self.start]
     UpperCAmelCase       :							list[Node]  = []

     UpperCAmelCase       :							int  = False



   def        __magic_name__    (							self							:     List[Any]						):
     while self.open_nodes:
       # Open Nodes are sorted using __lt__
       self.open_nodes.sort()
       UpperCAmelCase       :							Tuple  = self.open_nodes.pop(0						)

       if current_node.pos == self.target.pos:
         UpperCAmelCase       :							List[str]  = True
         return self.retrace_path(_a						)

       self.closed_nodes.append(_a						)
       UpperCAmelCase       :							List[str]  = 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       :							Tuple  = 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        __magic_name__    (							self							:     Union[str, Any],		__A							:     Node						):
     UpperCAmelCase       :							Any  = []
     for action in delta:
       UpperCAmelCase       :							Optional[int]  = parent.pos_x + action[1]
       UpperCAmelCase       :							Tuple  = 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        __magic_name__    (							self							:     Optional[int],		__A							:     Node | None						):
     UpperCAmelCase       :							Any  = node
     UpperCAmelCase       :							List[Any]  = []
     while current_node is not None:
       path.append((current_node.pos_y, current_node.pos_x)						)
       UpperCAmelCase       :							str  = current_node.parent
     path.reverse()
     return path


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

     print("------")

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

          for elem in grid:
               print(elem)










'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       str           = model_class.from_pretrained_question_encoder_generator(
      _lowerCAmelCase       ,_lowerCAmelCase       ,config=_lowerCAmelCase       )
  rag_model.save_pretrained(_lowerCAmelCase       )

  # Sanity check.
  model_class.from_pretrained(_lowerCAmelCase       )

  # Save tokenizers.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






