



'''simple docstring'''





from __future__ import annotations

_UpperCamelCase											=						list[tuple[int, int]]

_UpperCamelCase											=						[
    [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],
]

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






class 					lowerCamelCase_    :
  """simple docstring"""




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




  def 						_lowercase     ( self      :	Any   )					->					float:
    __lowerCamelCase       :       str           = abs(self.pos_x - self.goal_x   )
    __lowerCamelCase       :       Any           = abs(self.pos_y - self.goal_y   )
    return dx + dy




  def __lt__( self      :	List[str]			,	_a      :	Optional[Any]   )					->					bool:
    return self.f_cost < other.f_cost






class 					lowerCamelCase_    :
  """simple docstring"""




  def __init__( self      :	List[Any]			,	_a      :	tuple[int, int]			,	_a      :	tuple[int, int]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[int]           = Node(start[1]			,	start[0]			,	goal[1]			,	goal[0]			,	0			,	_a   )
    __lowerCamelCase       :       Dict           = Node(goal[1]			,	goal[0]			,	goal[1]			,	goal[0]			,	9_9999			,	_a   )

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

    __lowerCamelCase       :       int           = False




  def 						_lowercase     ( self      :	List[Any]   )					->					Path | None:
    while self.open_nodes:
      # Open Nodes are sorted using __lt__
      self.open_nodes.sort()
      __lowerCamelCase       :       Tuple           = self.open_nodes.pop(0   )

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

      self.closed_nodes.append(_a   )
      __lowerCamelCase       :       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
          __lowerCamelCase       :       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 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	Node   )					->					list[Node]:
    __lowerCamelCase       :       Any           = []
    for action in delta:
      __lowerCamelCase       :       Optional[int]           = parent.pos_x + action[1]
      __lowerCamelCase       :       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 						_lowercase     ( self      :	Optional[int]			,	_a      :	Node | None   )					->					Path:
    __lowerCamelCase       :       Any           = node
    __lowerCamelCase       :       List[Any]           = []
    while current_node is not None:
      path.append((current_node.pos_y, current_node.pos_x)   )
      __lowerCamelCase       :       str           = current_node.parent
    path.reverse()
    return path


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

   print('------')

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






