




"""simple docstring"""


from __future__ import annotations

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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		)						->    tuple[list[list[int]], list[list[int]]]:
      lowerCAmelCase__      :			Dict   =	[
          [0 for col in range(len(grid[0]				)				)] for row in range(len(__UpperCAmelCase				)				)
      ]  # the reference grid
      lowerCAmelCase__      :			Tuple   =	1
      lowerCAmelCase__      :			Optional[int]   =	[
          [0 for col in range(len(grid[0]				)				)] for row in range(len(__UpperCAmelCase				)				)
      ]  # the action grid

      lowerCAmelCase__      :			Optional[Any]   =	init[0]
      lowerCAmelCase__      :			Any   =	init[1]
      lowerCAmelCase__      :			Optional[int]   =	0
      lowerCAmelCase__      :			Dict   =	g + heuristic[x][y]  # cost from starting cell to destination cell
      lowerCAmelCase__      :			Dict   =	[[f, g, x, y]]

      lowerCAmelCase__      :			List[Any]   =	False  # flag that is set when search is complete
      lowerCAmelCase__      :			List[str]   =	False  # flag set if we can't find expand

      while not found and not resign:
            if len(__UpperCAmelCase				) == 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__      :			List[Any]   =	cell.pop()
                  lowerCAmelCase__      :			Optional[int]   =	next_cell[2]
                  lowerCAmelCase__      :			Tuple   =	next_cell[3]
                  lowerCAmelCase__      :			Optional[int]   =	next_cell[1]

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

      lowerCAmelCase__      :			List[Any]   =	[]
      for i in range(len(__UpperCAmelCase				)				):
            path.append(invpath[len(__UpperCAmelCase				) - 1 - i]				)
      return path, action


if __name__ == "__main__":
  _A													=      [
      [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],
  ]

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

  # the cost map which pushes the path closer to the goal
  _A													=      [[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])):
      _A													=      abs(i - goal[0]) + abs(j - goal[1])
      if grid[i][j] == 1:
        # added extra penalty in the heuristic map
        _A													=      9_9

  _A						,	_A													=      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])











"""simple docstring"""


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      # Initialise PyTorch model
      lowerCAmelCase__      :			int   =	TaConfig.from_json_file(__UpperCAmelCase				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			Optional[int]   =	TaForConditionalGeneration(__UpperCAmelCase				)

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

      # Save pytorch-model
      print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
      model.save_pretrained(__UpperCAmelCase				)


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _A													=      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





