

from __future__ import annotations

import time

a		=list[tuple[int, int]]

a		=[
    [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		=[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right






class        A_		:
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Node | None):
								__lowerCamelCase				:				Tuple					       = pos_x
								__lowerCamelCase				:				List[str]					       = pos_y
								__lowerCamelCase				:				str					       = (pos_y, pos_x)
								__lowerCamelCase				:				str					       = goal_x
								__lowerCamelCase				:				int					       = goal_y
								__lowerCamelCase				:				List[Any]					       = parent






class        A_		:
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			tuple[int, int]   ,SCREAMING_SNAKE_CASE__		:			tuple[int, int]):
								__lowerCamelCase				:				Any					       = Node(start[1]   ,start[0]   ,goal[1]   ,goal[0]   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = Node(goal[1]   ,goal[0]   ,goal[1]   ,goal[0]   ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Union[str, Any]					       = [self.start]
								__lowerCamelCase				:				List[str]					       = False
							def     lowerCAmelCase     (		self		:			List[Any]):
								while self.node_queue:
									__lowerCamelCase				:				Any					       = self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
										__lowerCamelCase				:				Dict					       = True
										return self.retrace_path(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase				:				Tuple					       = self.get_successors(SCREAMING_SNAKE_CASE__)

									for node in successors:
										self.node_queue.append(SCREAMING_SNAKE_CASE__)

								if not self.reached:
									return [self.start.pos]
								return None
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Node):
								__lowerCamelCase				:				Union[str, Any]					       = []
								for action in delta:
									__lowerCamelCase				:				Optional[Any]					       = parent.pos_x + action[1]
									__lowerCamelCase				:				Optional[int]					       = 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   ,SCREAMING_SNAKE_CASE__))
								return successors


							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Node | None):
								__lowerCamelCase				:				List[Any]					       = node
								__lowerCamelCase				:				int					       = []
								while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									__lowerCamelCase				:				int					       = current_node.parent
								path.reverse()
								return path






class        A_		:
							def __init__(		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				int					       = BreadthFirstSearch(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = BreadthFirstSearch(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = False
							def     lowerCAmelCase     (		self		:			str):
								while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									__lowerCamelCase				:				Any					       = self.fwd_bfs.node_queue.pop(0)
									__lowerCamelCase				:				Any					       = self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
										__lowerCamelCase				:				List[str]					       = True
										return self.retrace_bidirectional_path(
										    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase				:				Optional[Any]					       = current_bwd_node
									__lowerCamelCase				:				int					       = current_fwd_node

									__lowerCamelCase				:				str					       = {
									    self.fwd_bfs: self.fwd_bfs.get_successors(SCREAMING_SNAKE_CASE__),
									    self.bwd_bfs: self.bwd_bfs.get_successors(SCREAMING_SNAKE_CASE__),
									}

									for bfs in [self.fwd_bfs, self.bwd_bfs]:
										for node in successors[bfs]:
											bfs.node_queue.append(SCREAMING_SNAKE_CASE__)

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


							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Node   ,SCREAMING_SNAKE_CASE__		:			Node):
								__lowerCamelCase				:				List[Any]					       = self.fwd_bfs.retrace_path(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				str					       = self.bwd_bfs.retrace_path(SCREAMING_SNAKE_CASE__)
								bwd_path.pop()
								bwd_path.reverse()
								__lowerCamelCase				:				List[Any]					       = fwd_path + bwd_path
								return path


if __name__ == "__main__":
					# all coordinates are given in format [y,x]
					import doctest

					doctest.testmod()
					a		=(0, 0)
					a		=(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					a		=time.time()
					a		=BreadthFirstSearch(init, goal)
					a		=bfs.search()
					a		=time.time() - start_bfs_time

					print("""Unidirectional BFS computation time : """, bfs_time)

					a		=time.time()
					a		=BidirectionalBreadthFirstSearch(init, goal)
					a		=bd_bfs.search()
					a		=time.time() - start_bd_bfs_time

					print("""Bidirectional BFS computation time : """, bd_bfs_time)










import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__A        =    data_utils.TransfoXLTokenizer
__A        =    data_utils.TransfoXLCorpus
__A        =    data_utils
__A        =    data_utils







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				if transfo_xl_dataset_file:
								# Convert a pre-processed corpus (see original TensorFlow repo)
								with open(UpperCamelCase__ ,     'rb'      ) as fp:
												__lowerCamelCase		       =    pickle.load(UpperCamelCase__ ,     encoding='latin1'      )
								# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
								print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""      )
								__lowerCamelCase		       =    corpus.vocab.__dict__
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

								__lowerCamelCase		       =    corpus.__dict__
								corpus_dict_no_vocab.pop('vocab' ,     UpperCamelCase__      )
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + CORPUS_NAME
								print(F"""Save dataset to {pytorch_dataset_dump_path}"""      )
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )

								print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""      )
								# Initialise PyTorch model
								if transfo_xl_config_file == "":
												__lowerCamelCase		       =    TransfoXLConfig()
								else:
												__lowerCamelCase		       =    TransfoXLConfig.from_json_file(UpperCamelCase__      )
								print(F"""Building PyTorch model from configuration: {config}"""      )
								__lowerCamelCase		       =    TransfoXLLMHeadModel(UpperCamelCase__      )

								__lowerCamelCase		       =    load_tf_weights_in_transfo_xl(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								# Save pytorch-model
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""Save PyTorch model to {os.path.abspath(UpperCamelCase__      )}"""      )
								torch.save(model.state_dict() ,     UpperCamelCase__      )
								print(F"""Save configuration file to {os.path.abspath(UpperCamelCase__      )}"""      )
								with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8'      ) as f:
												f.write(config.to_json_string()      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to the folder to store the PyTorch model or dataset/vocab.",
					)
					parser.add_argument(
					    "--tf_checkpoint_path",
					    default="",
					    type=str,
					    help="An optional path to a TensorFlow checkpoint path to be converted.",
					)
					parser.add_argument(
					    "--transfo_xl_config_file",
					    default="",
					    type=str,
					    help=(
					        "An optional config json file corresponding to the pre-trained BERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--transfo_xl_dataset_file",
					    default="",
					    type=str,
					    help="An optional dataset file to be converted in a vocabulary.",
					)
					__A        =    parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)


