


"""simple docstring"""


import argparse
from pathlib import Path

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



def   snake_case						(				_a:     List[str]		,   _a:     str		,   _a:     Any		,   _a:     str		,   _a:     Tuple = None		,   _a:     Optional[Any] = None		,   _a:     str = None		,   )->		Union[str, Any]:
      '''simple docstring'''


      if config_name_or_path is None:
            lowerCamelCase__           =		'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

      if generator_tokenizer_name_or_path is None:
            lowerCamelCase__           =		generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            lowerCamelCase__           =		question_encoder_name_or_path

      lowerCamelCase__           =		RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

      # Save model.
      lowerCamelCase__           =		RagConfig.from_pretrained(__snake_case							)
      lowerCamelCase__           =		AutoConfig.from_pretrained(__snake_case							)
      lowerCamelCase__           =		AutoConfig.from_pretrained(__snake_case							)

      lowerCamelCase__           =		gen_config
      lowerCamelCase__           =		question_encoder_config

      lowerCamelCase__           =		model_class.from_pretrained_question_encoder_generator(
          __snake_case		,   __snake_case		,   config=__snake_case							)
      rag_model.save_pretrained(__snake_case							)

      # Sanity check.
      model_class.from_pretrained(__snake_case							)

      # Save tokenizers.
      lowerCamelCase__           =		AutoTokenizer.from_pretrained(__snake_case							)
      gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'							)
      lowerCamelCase__           =		AutoTokenizer.from_pretrained(__snake_case							)
      question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'							)


if __name__ == "__main__":
   _snake_case          =							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``"
       ),
   )

   _snake_case          =							parser.parse_args()

   _snake_case          =							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,
   )






"""simple docstring"""


from __future__ import annotations

_snake_case          =							[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def   snake_case						(				_a:     list[list[int]]		,   _a:     list[int]		,   _a:     list[int]		,   _a:     int		,   _a:     list[list[int]]		,   )->		tuple[list[list[int]], list[list[int]]]:
      '''simple docstring'''


      lowerCamelCase__           =		[
          [0 for col in range(len(grid[0]							)							)] for row in range(len(_a							)							)
      ]  # the reference grid
      lowerCamelCase__           =		1
      lowerCamelCase__           =		[
          [0 for col in range(len(grid[0]							)							)] for row in range(len(_a							)							)
      ]  # the action grid

      lowerCamelCase__           =		init[0]
      lowerCamelCase__           =		init[1]
      lowerCamelCase__           =		0
      lowerCamelCase__           =		g + heuristic[x][y]  # cost from starting cell to destination cell
      lowerCamelCase__           =		[[f, g, x, y]]

      lowerCamelCase__           =		False  # flag that is set when search is complete
      lowerCamelCase__           =		False  # flag set if we can't find expand

      while not found and not resign:
            if len(_a							) == 0:
                  raise ValueError('Algorithm is unable to find solution'							)
            else:  # to choose the least costliest action so as to move closer to the goal
                  cell.sort()
                  cell.reverse()
                  lowerCamelCase__           =		cell.pop()
                  lowerCamelCase__           =		next_cell[2]
                  lowerCamelCase__           =		next_cell[3]
                  lowerCamelCase__           =		next_cell[1]

                  if x == goal[0] and y == goal[1]:
                        lowerCamelCase__           =		True
                  else:
                        for i in range(len(_a							)							):  # to try out different valid actions
                              lowerCamelCase__           =		x + DIRECTIONS[i][0]
                              lowerCamelCase__           =		y + DIRECTIONS[i][1]
                              if xa >= 0 and xa < len(_a							) and ya >= 0 and ya < len(grid[0]							):
                                    if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                          lowerCamelCase__           =		g + cost
                                          lowerCamelCase__           =		ga + heuristic[xa][ya]
                                          cell.append([fa, ga, xa, ya]							)
                                          lowerCamelCase__           =		1
                                          lowerCamelCase__           =		i
      lowerCamelCase__           =		[]
      lowerCamelCase__           =		goal[0]
      lowerCamelCase__           =		goal[1]
      invpath.append([x, y]							)  # we get the reverse path from here
      while x != init[0] or y != init[1]:
            lowerCamelCase__           =		x - DIRECTIONS[action[x][y]][0]
            lowerCamelCase__           =		y - DIRECTIONS[action[x][y]][1]
            lowerCamelCase__           =		xa
            lowerCamelCase__           =		ya
            invpath.append([x, y]							)

      lowerCamelCase__           =		[]
      for i in range(len(_a							)							):
            path.append(invpath[len(_a							) - 1 - i]							)
      return path, action


if __name__ == "__main__":
   _snake_case          =							[
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
       [0, 1, 0, 0, 0, 0],
       [0, 1, 0, 0, 1, 0],
       [0, 0, 0, 0, 1, 0],
   ]

   _snake_case          =							[0, 0]
   # all coordinates are given in format [y,x]
   _snake_case          =							[len(grid) - 1, len(grid[0]) - 1]
   _snake_case          =							1

   # the cost map which pushes the path closer to the goal
   _snake_case          =							[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
   for i in range(len(grid)):
      for j in range(len(grid[0])):
         _snake_case          =							abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            _snake_case          =							99

   _snake_case      ,					_snake_case          =							search(grid, init, goal, cost, heuristic)

   print("ACTION MAP")
   for i in range(len(action)):
      print(action[i])

   for i in range(len(path)):
      print(path[i])



