




from __future__ import annotations

import time

UpperCAmelCase_		:				Optional[int]									=     list[tuple[int, int]]

UpperCAmelCase_		:				Optional[int]									=     [
    [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],
]

UpperCAmelCase_		:				Dict									=     [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right




class 		__A      :

						def __init__(		self   :Dict    , __snake_case   :int    , __snake_case   :int    , __snake_case   :int    , __snake_case   :int    , __snake_case   :Node | None ):
										'''simple docstring'''

										__magic_name__	:	str								=pos_x
										__magic_name__	:	Any								=pos_y
										__magic_name__	:	List[Any]								=(pos_y, pos_x)
										__magic_name__	:	Optional[int]								=goal_x
										__magic_name__	:	Optional[Any]								=goal_y
										__magic_name__	:	str								=parent




class 		__A      :

						def __init__(		self   :Optional[Any]    , __snake_case   :tuple[int, int]    , __snake_case   :tuple[int, int] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=Node(start[1]    , start[0]    , goal[1]    , goal[0]    , __snake_case )
										__magic_name__	:	int								=Node(goal[1]    , goal[0]    , goal[1]    , goal[0]    , __snake_case )

										__magic_name__	:	List[str]								=[self.start]
										__magic_name__	:	List[Any]								=False

						def     A__						(		self   :int ):
										'''simple docstring'''

										while self.node_queue:
														__magic_name__	:	Optional[int]								=self.node_queue.pop(0 )

														if current_node.pos == self.target.pos:
																		__magic_name__	:	Tuple								=True
																		return self.retrace_path(__snake_case )

														__magic_name__	:	Optional[int]								=self.get_successors(__snake_case )

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

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

						def     A__						(		self   :Optional[int]    , __snake_case   :Node ):
										'''simple docstring'''

										__magic_name__	:	int								=[]
										for action in delta:
														__magic_name__	:	List[str]								=parent.pos_x + action[1]
														__magic_name__	:	str								=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    , __snake_case ) )
										return successors


						def     A__						(		self   :Optional[Any]    , __snake_case   :Node | None ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=node
										__magic_name__	:	Optional[Any]								=[]
										while current_node is not None:
														path.append((current_node.pos_y, current_node.pos_x) )
														__magic_name__	:	Union[str, Any]								=current_node.parent
										path.reverse()
										return path




class 		__A      :

						def __init__(		self   :List[str]    , __snake_case   :List[str]    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=BreadthFirstSearch(__snake_case    , __snake_case )
										__magic_name__	:	str								=BreadthFirstSearch(__snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=False

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
														__magic_name__	:	List[Any]								=self.fwd_bfs.node_queue.pop(0 )
														__magic_name__	:	Dict								=self.bwd_bfs.node_queue.pop(0 )

														if current_bwd_node.pos == current_fwd_node.pos:
																		__magic_name__	:	Tuple								=True
																		return self.retrace_bidirectional_path(
																		    __snake_case    , __snake_case )

														__magic_name__	:	Tuple								=current_bwd_node
														__magic_name__	:	List[Any]								=current_fwd_node

														__magic_name__	:	Any								={
														    self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case ),
														    self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case ),
														}

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

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


						def     A__						(		self   :List[str]    , __snake_case   :Node    , __snake_case   :Node ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.fwd_bfs.retrace_path(__snake_case )
										__magic_name__	:	List[Any]								=self.bwd_bfs.retrace_path(__snake_case )
										bwd_path.pop()
										bwd_path.reverse()
										__magic_name__	:	Optional[int]								=fwd_path + bwd_path
										return path


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

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

							UpperCAmelCase_		:				Tuple									=     time.time()
							UpperCAmelCase_		:				int									=     BreadthFirstSearch(init, goal)
							UpperCAmelCase_		:				Optional[int]									=     bfs.search()
							UpperCAmelCase_		:				Tuple									=     time.time() - start_bfs_time

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

							UpperCAmelCase_		:				List[str]									=     time.time()
							UpperCAmelCase_		:				str									=     BidirectionalBreadthFirstSearch(init, goal)
							UpperCAmelCase_		:				Optional[Any]									=     bd_bfs.search()
							UpperCAmelCase_		:				str									=     time.time() - start_bd_bfs_time

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





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase_		:				str									=     "3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
							import torch

							print("Torch version:", torch.__version__)
							print("Cuda available:", torch.cuda.is_available())
							print("Cuda version:", torch.version.cuda)
							print("CuDNN version:", torch.backends.cudnn.version())
							print("Number of GPUs available:", torch.cuda.device_count())
							print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
							print("Torch version:", None)

try:
							import deepspeed

							print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
							print("DeepSpeed version:", None)

try:
							import tensorflow as tf

							print("TensorFlow version:", tf.__version__)
							print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
							print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
							print("TensorFlow version:", None)




