



import heapq
import sys

import numpy as np

SCREAMING_SNAKE_CASE     				=						tuple[int, int]



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self     ):
							UpperCAmelCase_						=				[]
							UpperCAmelCase_						=				set()



					def    A__		(			self     ):
							if not self.empty():
									return self.elements[0][0]
							else:
									return float("inf"     )



					def    A__		(			self     ):
							return len(self.elements     ) == 0



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							if item not in self.set:
									heapq.heappush(self.elements    ,	(priority, item)     )
									self.set.add(lowerCAmelCase     )
							else:
									# update
									# print("update", item)
									UpperCAmelCase_						=				[]
									((UpperCAmelCase_)					,						(UpperCAmelCase_))						=				heapq.heappop(self.elements     )
									while x != item:
											temp.append((pri, x)     )
											((UpperCAmelCase_)					,						(UpperCAmelCase_))						=				heapq.heappop(self.elements     )
									temp.append((priority, item)     )
									for pro, xxx in temp:
											heapq.heappush(self.elements    ,	(pro, xxx)     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							if item in self.set:
									self.set.remove(lowerCAmelCase     )
									UpperCAmelCase_						=				[]
									((UpperCAmelCase_)					,						(UpperCAmelCase_))						=				heapq.heappop(self.elements     )
									while x != item:
											temp.append((pro, x)     )
											((UpperCAmelCase_)					,						(UpperCAmelCase_))						=				heapq.heappop(self.elements     )
									for prito, yyy in temp:
											heapq.heappush(self.elements    ,	(prito, yyy)     )



					def    A__		(			self     ):
							return self.elements[0][1]



					def    A__		(			self     ):
							((UpperCAmelCase_)					,						(UpperCAmelCase_))						=				heapq.heappop(self.elements     )
							self.set.remove(lowerCAmelCase     )
							return (priority, item)







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Any:
		# euclidean distance
		UpperCAmelCase_						=				np.array(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				np.array(__SCREAMING_SNAKE_CASE )
		return np.linalg.norm(a - b )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							int:
		# integer division by time variable
		return consistent_heuristic(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) // t







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		# manhattan distance
		return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Any:
		UpperCAmelCase_						=				g_function[start] + Wa * heuristics[i](__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		return ans







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[str]:
		UpperCAmelCase_						=				np.chararray((n, n) )
		for i in range(__SCREAMING_SNAKE_CASE ):
				for j in range(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				"*"

		for i in range(__SCREAMING_SNAKE_CASE ):
				for j in range(__SCREAMING_SNAKE_CASE ):
						if (j, (n - 1) - i) in blocks:
								UpperCAmelCase_						=				"#"

		UpperCAmelCase_						=				"-"
		UpperCAmelCase_						=				back_pointer[goal]
		while x != start:
				((UpperCAmelCase_)					,						(UpperCAmelCase_))						=				x
				# print(x)
				UpperCAmelCase_						=				"-"
				UpperCAmelCase_						=				back_pointer[x]
		UpperCAmelCase_						=				"-"

		for i in range(__SCREAMING_SNAKE_CASE ):
				for j in range(__SCREAMING_SNAKE_CASE ):
						if (i, j) == (0, n - 1):
								print(grid[i][j]			,     end=" " )
								print("<-- End position"			,     end=" " )
						else:
								print(grid[i][j]			,     end=" " )
				print()
		print("^" )
		print("Start position" )
		print()
		print("# is an obstacle" )
		print("- is the path taken by algorithm" )
		print("PATH TAKEN BY THE ALGORITHM IS:-" )
		UpperCAmelCase_						=				back_pointer[goal]
		while x != start:
				print(__SCREAMING_SNAKE_CASE			,     end=" " )
				UpperCAmelCase_						=				back_pointer[x]
		print(__SCREAMING_SNAKE_CASE )
		sys.exit()







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Dict:
		if p[0] < 0 or p[0] > n - 1:
				return False
		if p[1] < 0 or p[1] > n - 1:
				return False
		return True







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )		->							Union[str, Any]:
		for itera in range(__SCREAMING_SNAKE_CASE ):
				open_list[itera].remove_element(__SCREAMING_SNAKE_CASE )
		# print("s", s)
		# print("j", j)
		((UpperCAmelCase_)					,						(UpperCAmelCase_))						=				s
		UpperCAmelCase_						=				(x - 1, y)
		UpperCAmelCase_						=				(x + 1, y)
		UpperCAmelCase_						=				(x, y + 1)
		UpperCAmelCase_						=				(x, y - 1)

		for neighbours in [left, right, up, down]:
				if neighbours not in blocks:
						if valid(__SCREAMING_SNAKE_CASE ) and neighbours not in visited:
								# print("neighbour", neighbours)
								visited.add(__SCREAMING_SNAKE_CASE )
								UpperCAmelCase_						=				-1
								UpperCAmelCase_						=				float("inf" )

						if valid(__SCREAMING_SNAKE_CASE ) and g_function[neighbours] > g_function[s] + 1:
								UpperCAmelCase_						=				g_function[s] + 1
								UpperCAmelCase_						=				s
								if neighbours not in close_list_anchor:
										open_list[0].put(__SCREAMING_SNAKE_CASE			,     key(__SCREAMING_SNAKE_CASE			,     0			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) )
										if neighbours not in close_list_inad:
												for var in range(1			,     __SCREAMING_SNAKE_CASE ):
														if key(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) <= Wa * key(
														    __SCREAMING_SNAKE_CASE			,     0			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
																open_list[j].put(
																    __SCREAMING_SNAKE_CASE			,     key(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) )







def snake_case__  (			)		->							Any:
		UpperCAmelCase_						=				[]
		for x in range(1			,     5 ):
				for y in range(1			,     6 ):
						some_list.append((x, y) )

		for x in range(15			,     20 ):
				some_list.append((x, 17) )

		for x in range(10			,     19 ):
				for y in range(1			,     15 ):
						some_list.append((x, y) )

    # L block
		for x in range(1			,     4 ):
				for y in range(12			,     19 ):
						some_list.append((x, y) )
		for x in range(3			,     13 ):
				for y in range(16			,     19 ):
						some_list.append((x, y) )
		return some_list


SCREAMING_SNAKE_CASE     				=						{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

SCREAMING_SNAKE_CASE     				=						[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
SCREAMING_SNAKE_CASE     				=						make_common_ground()


SCREAMING_SNAKE_CASE     				=						blocks_blk
# hyper parameters
SCREAMING_SNAKE_CASE     				=						1
SCREAMING_SNAKE_CASE     				=						1
SCREAMING_SNAKE_CASE     				=						20
SCREAMING_SNAKE_CASE     				=						3  # one consistent and two other inconsistent

# start and end destination
SCREAMING_SNAKE_CASE     				=						(0, 0)
SCREAMING_SNAKE_CASE     				=						(n - 1, n - 1)

SCREAMING_SNAKE_CASE     				=						1







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Any:
		UpperCAmelCase_						=				{start: 0, goal: float("inf" )}
		UpperCAmelCase_						=				{start: -1, goal: -1}
		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				set()

		for i in range(__SCREAMING_SNAKE_CASE ):
				open_list.append(PriorityQueue() )
				open_list[i].put(__SCREAMING_SNAKE_CASE			,     key(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) )

		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				[]
		while open_list[0].minkey() < float("inf" ):
				for i in range(1			,     __SCREAMING_SNAKE_CASE ):
						# print(open_list[0].minkey(), open_list[i].minkey())
						if open_list[i].minkey() <= Wa * open_list[0].minkey():
								global t
								t += 1
								if g_function[goal] <= open_list[i].minkey():
										if g_function[goal] < float("inf" ):
												do_something(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
								else:
										UpperCAmelCase_					,						UpperCAmelCase_						=				open_list[i].top_show()
										visited.add(__SCREAMING_SNAKE_CASE )
										expand_state(
										    __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )
										close_list_inad.append(__SCREAMING_SNAKE_CASE )
						else:
								if g_function[goal] <= open_list[0].minkey():
										if g_function[goal] < float("inf" ):
												do_something(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
								else:
										UpperCAmelCase_						=				open_list[0].top_show()
										visited.add(__SCREAMING_SNAKE_CASE )
										expand_state(
										    __SCREAMING_SNAKE_CASE			,     0			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )
										close_list_anchor.append(__SCREAMING_SNAKE_CASE )
		print("No path found to goal" )
		print()
		for i in range(n - 1			,     -1			,     -1 ):
				for j in range(__SCREAMING_SNAKE_CASE ):
						if (j, i) in blocks:
								print("#"			,     end=" " )
						elif (j, i) in back_pointer:
								if (j, i) == (n - 1, n - 1):
										print("*"			,     end=" " )
								else:
										print("-"			,     end=" " )
						else:
								print("*"			,     end=" " )
						if (j, i) == (n - 1, n - 1):
								print("<-- End position"			,     end=" " )
				print()
		print("^" )
		print("Start position" )
		print()
		print("# is an obstacle" )
		print("- is the path taken by algorithm" )


if __name__ == "__main__":
			multi_a_star(start, goal, n_heuristic)






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		# Initialise PyTorch model
		UpperCAmelCase_						=				MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		print(f'''Building PyTorch model from configuration: {config}''' )
		UpperCAmelCase_						=				MobileBertForPreTraining(__SCREAMING_SNAKE_CASE )
		# Load weights from tf checkpoint
		UpperCAmelCase_						=				load_tf_weights_in_mobilebert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}''' )
		torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_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(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT 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."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)

