
from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A     :	Tuple    = 0

A     :	Any    = [
    [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],
]

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

A     :	str    = tuple[int, int]







class 			__A:




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     )    ->      List[Any]:


								'''simple docstring'''




								__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()
								__a										=    self.g_cost + self.h_cost




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.pos_x - self.goal_x
								__a										=    self.pos_y - self.goal_y
								if HEURISTIC == 1:
												return abs(_snake_case				) + abs(_snake_case				)
								else:
												return sqrt(dy**2 + dx**2				)




				def __lt__(     self		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								return self.f_cost < other.f_cost







class 			__A:




				def __init__(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    Node(start[1]		,     start[0]		,     goal[1]		,     goal[0]		,     0		,     _snake_case				)
								__a										=    Node(goal[1]		,     goal[0]		,     goal[1]		,     goal[0]		,     99_999		,     _snake_case				)

								__a										=    [self.start]
								__a										=    []

								__a										=    False




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								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:
																return self.retrace_path(_snake_case				)

												self.closed_nodes.append(_snake_case				)
												__a										=    self.get_successors(_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(_snake_case				)
																else:
																				# retrieve the best current path
																				__a										=    self.open_nodes.pop(self.open_nodes.index(_snake_case				)				)

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

								return [self.start.pos]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__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(_snake_case				) - 1):
																continue

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								__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







class 			__A:




				def __init__(     self		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    AStar(_snake_case		,     _snake_case				)
								__a										=    AStar(_snake_case		,     _snake_case				)
								__a										=    False




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
												self.fwd_astar.open_nodes.sort()
												self.bwd_astar.open_nodes.sort()
												__a										=    self.fwd_astar.open_nodes.pop(0				)
												__a										=    self.bwd_astar.open_nodes.pop(0				)

												if current_bwd_node.pos == current_fwd_node.pos:
																return self.retrace_bidirectional_path(
																    _snake_case		,     _snake_case				)

												self.fwd_astar.closed_nodes.append(_snake_case				)
												self.bwd_astar.closed_nodes.append(_snake_case				)

												__a										=    current_bwd_node
												__a										=    current_fwd_node

												__a										=    {
												    self.fwd_astar: self.fwd_astar.get_successors(_snake_case				),
												    self.bwd_astar: self.bwd_astar.get_successors(_snake_case				),
												}

												for astar in [self.fwd_astar, self.bwd_astar]:
																for child_node in successors[astar]:
																				if child_node in astar.closed_nodes:
																								continue

																				if child_node not in astar.open_nodes:
																								astar.open_nodes.append(_snake_case				)
																				else:
																								# retrieve the best current path
																								__a										=    astar.open_nodes.pop(
																								    astar.open_nodes.index(_snake_case				)				)

																								if child_node.g_cost < better_node.g_cost:
																												astar.open_nodes.append(_snake_case				)
																								else:
																												astar.open_nodes.append(_snake_case				)

								return [self.fwd_astar.start.pos]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.fwd_astar.retrace_path(_snake_case				)
								__a										=    self.bwd_astar.retrace_path(_snake_case				)
								bwd_path.pop()
								bwd_path.reverse()
								__a										=    fwd_path + bwd_path
								return path


if __name__ == "__main__":
					# all coordinates are given in format [y,x]
					A     :	Optional[Any]    = (0, 0)
					A     :	List[Any]    = (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					A     :	str    = time.time()
					A     :	List[str]    = AStar(init, goal)
					A     :	Optional[Any]    = a_star.search()
					A     :	Optional[int]    = time.time() - start_time
					print(F"AStar execution time = {end_time:f} seconds")

					A     :	Optional[int]    = time.time()
					A     :	Optional[Any]    = BidirectionalAStar(init, goal)
					A     :	Tuple    = time.time() - bd_start_time
					print(F"BidirectionalAStar execution time = {bd_end_time:f} seconds")







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				lowercase__    =			(
				    ('''layer.''', '''layer_'''),
				    ('''word_embeddings.weight''', '''word_embeddings'''),
				    ('''position_embeddings.weight''', '''position_embeddings'''),
				    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
				    ('''.''', '''/'''),
				    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
				    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
				    ('''weight''', '''kernel'''),
				)

				if not os.path.isdir(lowerCamelCase_				):
								os.makedirs(lowerCamelCase_				)

				lowercase__    =			model.state_dict()

				def to_tf_var_name(lowerCamelCase_				):
								for patt, repl in iter(lowerCamelCase_				):
												lowercase__    =			name.replace(lowerCamelCase_		,   lowerCamelCase_				)
								return F"""bert/{name}"""

				def create_tf_var(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):
								lowercase__    =			tf.dtypes.as_dtype(tensor.dtype				)
								lowercase__    =			tf.get_variable(dtype=lowerCamelCase_		,   shape=tensor.shape		,   name=lowerCamelCase_		,   initializer=tf.zeros_initializer()				)
								session.run(tf.variables_initializer([tf_var]				)				)
								session.run(lowerCamelCase_				)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												lowercase__    =			to_tf_var_name(lowerCamelCase_				)
												lowercase__    =			state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose				):
																lowercase__    =			torch_tensor.T
												lowercase__    =			create_tf_var(tensor=lowerCamelCase_		,   name=lowerCamelCase_		,   session=lowerCamelCase_				)
												tf.keras.backend.set_value(lowerCamelCase_		,   lowerCamelCase_				)
												lowercase__    =			session.run(lowerCamelCase_				)
												print(F"""Successfully created {tf_name}: {np.allclose(lowerCamelCase_		,   lowerCamelCase_				)}"""				)

								lowercase__    =			tf.train.Saver(tf.trainable_variables()				)
								saver.save(lowerCamelCase_		,   os.path.join(lowerCamelCase_		,   model_name.replace('''-'''		,   '''_'''				) + '''.ckpt'''				)				)






def   a			(						lowerCamelCase_=None				):






				'''simple docstring'''
				lowercase__    =			argparse.ArgumentParser()
				parser.add_argument('''--model_name'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''model name e.g. bert-base-uncased'''				)
				parser.add_argument(
				    '''--cache_dir'''		,   type=lowerCamelCase_		,   default=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''Directory containing pytorch model'''				)
				parser.add_argument('''--pytorch_model_path'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''/path/to/<pytorch-model-name>.bin'''				)
				parser.add_argument('''--tf_cache_dir'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''Directory in which to save tensorflow model'''				)
				lowercase__    =			parser.parse_args(lowerCamelCase_				)

				lowercase__    =			BertModel.from_pretrained(
				    pretrained_model_name_or_path=args.model_name		,   state_dict=torch.load(args.pytorch_model_path				)		,   cache_dir=args.cache_dir		,   )

				convert_pytorch_checkpoint_to_tf(model=lowerCamelCase_		,   ckpt_dir=args.tf_cache_dir		,   model_name=args.model_name				)


if __name__ == "__main__":
							main()




