
"""simple docstring"""




from __future__ import annotations

A__      :	Optional[Any]          =    [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def 							a__ (				lowerCAmelCase	:       list[list[int]]       ,    lowerCAmelCase	:       list[int]       ,    lowerCAmelCase	:       list[int]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       list[list[int]]       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						[
         [0 for col in range(len(grid[0]		)		)] for row in range(len(lowerCAmelCase		)		)
     ]  # the reference grid
     UpperCAmelCase__							: Tuple 							=						1
     UpperCAmelCase__							: List[str] 							=						[
         [0 for col in range(len(grid[0]		)		)] for row in range(len(lowerCAmelCase		)		)
     ]  # the action grid

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

     UpperCAmelCase__							: Any 							=						False  # flag that is set when search is complete
     UpperCAmelCase__							: Tuple 							=						False  # flag set if we can't find expand

     while not found and not resign:
          if len(lowerCAmelCase		) == 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()
               UpperCAmelCase__							: List[Any] 							=						cell.pop()
               UpperCAmelCase__							: Dict 							=						next_cell[2]
               UpperCAmelCase__							: Tuple 							=						next_cell[3]
               UpperCAmelCase__							: Dict 							=						next_cell[1]

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

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


if __name__ == "__main__":
    A__      :	int          =    [
        [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__      :	Tuple          =    [0, 0]
    # all coordinates are given in format [y,x]
    A__      :	List[Any]          =    [len(grid) - 1, len(grid[0]) - 1]
    A__      :	List[Any]          =    1

    # the cost map which pushes the path closer to the goal
    A__      :	int          =    [[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__      :	List[Any]          =    abs(i - goal[0]) + abs(j - goal[1])
            if grid[i][j] == 1:
                # added extra penalty in the heuristic map
                A__      :	int          =    99

    A__      :	Optional[int]          =    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

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

     # Load weights from numpy
     load_tf_weights_in_gpta(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # Save pytorch-model
     UpperCAmelCase__							: Optional[int] 							=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__							: Any 							=						pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(F"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,    lowerCAmelCase		)
     print(F"Save configuration file to {pytorch_config_dump_path}"		)
     with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
    A__      :	Optional[int]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



