





"""simple docstring"""





from __future__ import annotations

lowerCAmelCase__   	=	list[tuple[int, int]]

lowerCAmelCase__   	=	[
    [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],
]

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





class 				_lowerCamelCase					:


						def __init__(self      ,							__a      ,							__a      ,							__a      ,							__a      ,							__a      ,							__a      ,							)       ->      Union[str, Any]:
								UpperCamelCase      =		pos_x
								UpperCamelCase      =		pos_y
								UpperCamelCase      =		(pos_y, pos_x)
								UpperCamelCase      =		goal_x
								UpperCamelCase      =		goal_y
								UpperCamelCase      =		g_cost
								UpperCamelCase      =		parent
								UpperCamelCase      =		self.calculate_heuristic()


						def 			snake_case_      (self						)       ->      Tuple:
								UpperCamelCase      =		abs(self.pos_x - self.goal_x						)
								UpperCamelCase      =		abs(self.pos_y - self.goal_y						)
								return dx + dy




						def __lt__(self      ,							__a						)       ->      List[Any]:
								return self.f_cost < other.f_cost





class 				_lowerCamelCase					:


						def __init__(self      ,							__a      ,							__a						)       ->      Dict:
								UpperCamelCase      =		Node(start[1]      ,							start[0]      ,							goal[1]      ,							goal[0]      ,							0      ,							__SCREAMING_SNAKE_CASE						)
								UpperCamelCase      =		Node(goal[1]      ,							goal[0]      ,							goal[1]      ,							goal[0]      ,							9_99_99      ,							__SCREAMING_SNAKE_CASE						)

								UpperCamelCase      =		[self.start]
								UpperCamelCase      =		[]

								UpperCamelCase      =		False


						def 			snake_case_      (self						)       ->      Optional[int]:

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

										if current_node.pos == self.target.pos:
												UpperCamelCase      =		True
												return self.retrace_path(__SCREAMING_SNAKE_CASE						)

										self.closed_nodes.append(__SCREAMING_SNAKE_CASE						)
										UpperCamelCase      =		self.get_successors(__SCREAMING_SNAKE_CASE						)

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

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

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


						def 			snake_case_      (self      ,							__a						)       ->      List[str]:
								UpperCamelCase      =		[]
								for action in delta:
										UpperCamelCase      =		parent.pos_x + action[1]
										UpperCamelCase      =		parent.pos_y + action[0]

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

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

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




						def 			snake_case_      (self      ,							__a						)       ->      Any:
								UpperCamelCase      =		node
								UpperCamelCase      =		[]
								while current_node is not None:
										path.append((current_node.pos_y, current_node.pos_x)						)
										UpperCamelCase      =		current_node.parent
								path.reverse()
								return path


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

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

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

												for elem in grid:
																		print(elem)











import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				LxmertConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)
    __a						       =				LxmertForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

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


if __name__ == "__main__":
 __snake_case						:List[Any]      		=     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 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.'''
 )
 __snake_case						:Optional[Any]      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





