

import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__A     :					Dict     = logging.getLogger(__name__)




def     __UpperCamelCase			(	_A :				str						)							->int:



							"""simple docstring"""
							lowerCamelCase_				     =git.Repo(search_parent_directories=_UpperCAmelCase						)
							lowerCamelCase_				     ={
							    '''repo_id''': str(_UpperCAmelCase						),
							    '''repo_sha''': str(repo.head.object.hexsha						),
							    '''repo_branch''': str(repo.active_branch						),
							}

							with open(os.path.join(_UpperCAmelCase	,   """git_log.json"""						)	,   """w"""						) as f:
														json.dump(_UpperCAmelCase	,   _UpperCAmelCase	,   indent=4						)




def     __UpperCamelCase			(	_A :				Dict						)							->Union[str, Any]:



							"""simple docstring"""





							if params.n_gpu <= 0:
														lowerCamelCase_				     =0
														lowerCamelCase_				     =-1
														lowerCamelCase_				     =True
														lowerCamelCase_				     =False
														return

							assert torch.cuda.is_available()

							logger.info("""Initializing GPUs"""						)
							if params.n_gpu > 1:
														assert params.local_rank != -1

														lowerCamelCase_				     =int(os.environ["""WORLD_SIZE"""]						)
														lowerCamelCase_				     =int(os.environ["""N_GPU_NODE"""]						)
														lowerCamelCase_				     =int(os.environ["""RANK"""]						)

														# number of nodes / node ID
														lowerCamelCase_				     =params.world_size // params.n_gpu_per_node
														lowerCamelCase_				     =params.global_rank // params.n_gpu_per_node
														lowerCamelCase_				     =True

														assert params.n_nodes == int(os.environ["""N_NODES"""]						)
														assert params.node_id == int(os.environ["""NODE_RANK"""]						)

							# local job (single GPU)
							else:
														assert params.local_rank == -1

														lowerCamelCase_				     =1
														lowerCamelCase_				     =0
														lowerCamelCase_				     =0
														lowerCamelCase_				     =0
														lowerCamelCase_				     =1
														lowerCamelCase_				     =1
														lowerCamelCase_				     =False

							# sanity checks
							assert params.n_nodes >= 1
							assert 0 <= params.node_id < params.n_nodes
							assert 0 <= params.local_rank <= params.global_rank < params.world_size
							assert params.world_size == params.n_nodes * params.n_gpu_per_node

							# define whether this is the master process / if we are in multi-node distributed mode
							lowerCamelCase_				     =params.node_id == 0 and params.local_rank == 0
							lowerCamelCase_				     =params.n_nodes > 1

							# summary
							lowerCamelCase_				     =f'--- Global rank: {params.global_rank} - '
							logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes						)
							logger.info(PREFIX + """Node ID        : %i""" % params.node_id						)
							logger.info(PREFIX + """Local rank     : %i""" % params.local_rank						)
							logger.info(PREFIX + """World size     : %i""" % params.world_size						)
							logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node						)
							logger.info(PREFIX + """Master         : %s""" % str(params.is_master						)						)
							logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node						)						)
							logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu						)						)
							logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()						)

							# set GPU device
							torch.cuda.set_device(params.local_rank						)

							# initialize multi-GPU
							if params.multi_gpu:
														logger.info("""Initializing PyTorch distributed"""						)
														torch.distributed.init_process_group(
														    init_method="""env://"""	,   backend="""nccl"""	,   )






def     __UpperCamelCase			(	_A :				Union[str, Any]						)							->Any:



							"""simple docstring"""
							np.random.seed(args.seed						)
							torch.manual_seed(args.seed						)
							if args.n_gpu > 0:
														torch.cuda.manual_seed_all(args.seed						)





import numpy as np

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	(0, 0)
					__UpperCAmelCase    :     List[str]													=	None
					__UpperCAmelCase    :     int													=	0
					__UpperCAmelCase    :     Union[str, Any]													=	0
					__UpperCAmelCase    :     List[Any]													=	0




			def __eq__(   self							: Optional[int]       ,       a_							: List[Any] ):
					'''simple docstring'''





					return self.position == cell.position




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					print(self.position )

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: str       ,       a_							: Dict=(5, 5) ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	np.zeros(a_ )
					__UpperCAmelCase    :     List[Any]													=	world_size[0]
					__UpperCAmelCase    :     List[str]													=	world_size[1]




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					print(self.w )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	[
					    (-1, -1),
					    (-1, 0),
					    (-1, 1),
					    (0, -1),
					    (0, 1),
					    (1, -1),
					    (1, 0),
					    (1, 1),
					]
					__UpperCAmelCase    :     str													=	cell.position[0]
					__UpperCAmelCase    :     List[Any]													=	cell.position[1]
					__UpperCAmelCase    :     str													=	[]
					for n in neughbour_cord:
							__UpperCAmelCase    :     Optional[Any]													=	current_x + n[0]
							__UpperCAmelCase    :     Any													=	current_y + n[1]
							if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
									__UpperCAmelCase    :     int													=	Cell()
									__UpperCAmelCase    :     List[str]													=	(x, y)
									__UpperCAmelCase    :     List[Any]													=	cell
									neighbours.append(a_ )
					return neighbours

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		_open.append(_UpperCAmelCase						)

		while _open:
				__UpperCAmelCase    :     str													=	np.argmin([n.f for n in _open]						)
				__UpperCAmelCase    :     Optional[Any]													=	_open[min_f]
				_closed.append(_open.pop(_UpperCAmelCase						)						)
				if current == goal:
						break
				for n in world.get_neigbours(_UpperCAmelCase						):
						for c in _closed:
								if c == n:
										continue
						__UpperCAmelCase    :     Optional[Any]													=	current.g + 1
						__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	n.position
						__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	goal.position
						__UpperCAmelCase    :     Union[str, Any]													=	(ya - ya) ** 2 + (xa - xa) ** 2
						__UpperCAmelCase    :     Optional[Any]													=	n.h + n.g

						for c in _open:
								if c == n and c.f < n.f:
										continue
						_open.append(_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	[]
		while current.parent is not None:
				path.append(current.position						)
				__UpperCAmelCase    :     int													=	current.parent
		path.append(current.position						)
		return path[::-1]


if __name__ == "__main__":
				__A   			=Gridworld()
				# Start position and goal
				__A   			=Cell()
				__A   			=(0, 0)
				__A   			=Cell()
				__A   			=(4, 4)
				print(f'''path from {start.position} to {goal.position}''')
				__A   			=astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								__A   			=1
				print(world.w)





