


"""simple docstring"""






from __future__ import annotations

__snake_case		=       list[tuple[int, int]]

__snake_case		=       [
    [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],
]

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


class     __lowerCamelCase  :

					'''simple docstring'''


					def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							Optional[Any]:
										_a    	=				pos_x
										_a    	=				pos_y
										_a    	=				(pos_y, pos_x)
										_a    	=				goal_x
										_a    	=				goal_y
										_a    	=				g_cost
										_a    	=				parent
										_a    	=				self.calculate_heuristic()


					def 							_UpperCAmelCase      (      self							)       ->							float:
										_a    	=				abs(self.pos_x - self.goal_x							)
										_a    	=				abs(self.pos_y - self.goal_y							)
										return dx + dy





					def __lt__(      self    ,				__UpperCAmelCase							)       ->							bool:
										return self.f_cost < other.f_cost







class     __lowerCamelCase  :

					'''simple docstring'''


					def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							str:
										_a    	=				Node(start[1]    ,				start[0]    ,				goal[1]    ,				goal[0]    ,				0    ,				_lowerCamelCase							)
										_a    	=				Node(goal[1]    ,				goal[0]    ,				goal[1]    ,				goal[0]    ,				99999    ,				_lowerCamelCase							)

										_a    	=				[self.start]
										_a    	=				[]

										_a    	=				False


					def 							_UpperCAmelCase      (      self							)       ->							Path | None:





										while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															_a    	=				self.open_nodes.pop(0							)

															if current_node.pos == self.target.pos:
																				_a    	=				True
																				return self.retrace_path(_lowerCamelCase							)

															self.closed_nodes.append(_lowerCamelCase							)
															_a    	=				self.get_successors(_lowerCamelCase							)

															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(_lowerCamelCase							)
																				else:
																									# retrieve the best current path
																									_a    	=				self.open_nodes.pop(self.open_nodes.index(_lowerCamelCase							)							)

																									if child_node.g_cost < better_node.g_cost:
																														self.open_nodes.append(_lowerCamelCase							)
																									else:
																														self.open_nodes.append(_lowerCamelCase							)

										if not self.reached:
															return [self.start.pos]
										return None


					def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							list[Node]:
										_a    	=				[]
										for action in delta:
															_a    	=				parent.pos_x + action[1]
															_a    	=				parent.pos_y + action[0]

															if not (0 <= pos_x <= len(grid[0]							) - 1 and 0 <= pos_y <= len(_lowerCamelCase							) - 1):
																				continue

															if grid[pos_y][pos_x] != 0:
																				continue

															successors.append(
															    Node(
															        _lowerCamelCase    ,				_lowerCamelCase    ,				self.target.pos_y    ,				self.target.pos_x    ,				parent.g_cost + 1    ,				_lowerCamelCase    ,				)							)
										return successors





					def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Path:
										_a    	=				node
										_a    	=				[]
										while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)							)
															_a    	=				current_node.parent
										path.reverse()
										return path


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

			print('''------''')

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

						for elem in grid:
									print(elem)



"""simple docstring"""






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     _a    	=				BertConfig.from_json_file(_lowerCAmelCase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     _a    	=				BertForPreTraining(_lowerCAmelCase       )

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     torch.save(model.state_dict(),     _lowerCAmelCase       )


if __name__ == "__main__":
   __snake_case		=       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(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case		=       parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)