


from __future__ import annotations

_lowercase    :    List[Any]			     =  [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def    lowerCamelCase__   ( A     :    list[list[int]]   ,     A     :    list[int]   ,     A     :    list[int]   ,     A     :    int   ,     A     :    list[list[int]]   ,     ):




						'''simple docstring'''






						UpperCAmelCase         =			[
						    [0 for col in range(len(grid[0]  )  )] for row in range(len(A  )  )
						]  # the reference grid
						UpperCAmelCase         =			1
						UpperCAmelCase         =			[
						    [0 for col in range(len(grid[0]  )  )] for row in range(len(A  )  )
						]  # the action grid

						UpperCAmelCase         =			init[0]
						UpperCAmelCase         =			init[1]
						UpperCAmelCase         =			0
						UpperCAmelCase         =			g + heuristic[x][y]  # cost from starting cell to destination cell
						UpperCAmelCase         =			[[f, g, x, y]]

						UpperCAmelCase         =			False  # flag that is set when search is complete
						UpperCAmelCase         =			False  # flag set if we can't find expand

						while not found and not resign:
												if len(A  ) == 0:
																		raise ValueError('''Algorithm is unable to find solution'''  )
												else:  # to choose the least costliest action so as to move closer to the goal
																		cell.sort()
																		cell.reverse()
																		UpperCAmelCase         =			cell.pop()
																		UpperCAmelCase         =			next_cell[2]
																		UpperCAmelCase         =			next_cell[3]
																		UpperCAmelCase         =			next_cell[1]

																		if x == goal[0] and y == goal[1]:
																								UpperCAmelCase         =			True
																		else:
																								for i in range(len(A  )  ):  # to try out different valid actions
																														UpperCAmelCase         =			x + DIRECTIONS[i][0]
																														UpperCAmelCase         =			y + DIRECTIONS[i][1]
																														if xa >= 0 and xa < len(A  ) and ya >= 0 and ya < len(grid[0]  ):
																																				if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																																										UpperCAmelCase         =			g + cost
																																										UpperCAmelCase         =			ga + heuristic[xa][ya]
																																										cell.append([fa, ga, xa, ya]  )
																																										UpperCAmelCase         =			1
																																										UpperCAmelCase         =			i
						UpperCAmelCase         =			[]
						UpperCAmelCase         =			goal[0]
						UpperCAmelCase         =			goal[1]
						invpath.append([x, y]  )  # we get the reverse path from here
						while x != init[0] or y != init[1]:
												UpperCAmelCase         =			x - DIRECTIONS[action[x][y]][0]
												UpperCAmelCase         =			y - DIRECTIONS[action[x][y]][1]
												UpperCAmelCase         =			xa
												UpperCAmelCase         =			ya
												invpath.append([x, y]  )

						UpperCAmelCase         =			[]
						for i in range(len(A  )  ):
												path.append(invpath[len(A  ) - 1 - i]  )
						return path, action


if __name__ == "__main__":
	_lowercase    :    Tuple			     =  [
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 1, 0],
	    [0, 0, 0, 0, 1, 0],
	]

	_lowercase    :    Optional[Any]			     =  [0, 0]
	# all coordinates are given in format [y,x]
	_lowercase    :    Any			     =  [len(grid) - 1, len(grid[0]) - 1]
	_lowercase    :    str			     =  1

	# the cost map which pushes the path closer to the goal
	_lowercase    :    Optional[int]			     =  [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
	for i in range(len(grid)):
		for j in range(len(grid[0])):
			_lowercase    :    Optional[Any]			     =  abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				_lowercase    :    str			     =  99

	_lowercase    :    int			     =  search(grid, init, goal, cost, heuristic)

	print("""ACTION MAP""")
	for i in range(len(action)):
		print(action[i])

	for i in range(len(path)):
		print(path[i])









'''simple docstring'''



# 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


_lowercase    :    Union[str, Any]			     =  """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)





