

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,
)
SCREAMING_SNAKE_CASE_        =      logging.getLogger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       Tuple:
				_UpperCAmelCase       :		Optional[Any] 		=    git.Repo(search_parent_directories=lowerCAmelCase )
				_UpperCAmelCase       :		List[str] 		=    {
				    "repo_id": str(lowerCAmelCase ),
				    "repo_sha": str(repo.head.object.hexsha ),
				    "repo_branch": str(repo.active_branch ),
				}

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       Dict:
				if params.n_gpu <= 0:
								_UpperCAmelCase       :		List[str] 		=    0
								_UpperCAmelCase       :		Any 		=    -1
								_UpperCAmelCase       :		str 		=    True
								_UpperCAmelCase       :		str 		=    False
								return

				assert torch.cuda.is_available()

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

								_UpperCAmelCase       :		Tuple 		=    int(os.environ["WORLD_SIZE"] )
								_UpperCAmelCase       :		Optional[Any] 		=    int(os.environ["N_GPU_NODE"] )
								_UpperCAmelCase       :		List[str] 		=    int(os.environ["RANK"] )

								# number of nodes / node ID
								_UpperCAmelCase       :		List[Any] 		=    params.world_size // params.n_gpu_per_node
								_UpperCAmelCase       :		int 		=    params.global_rank // params.n_gpu_per_node
								_UpperCAmelCase       :		Tuple 		=    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

								_UpperCAmelCase       :		Tuple 		=    1
								_UpperCAmelCase       :		Any 		=    0
								_UpperCAmelCase       :		List[str] 		=    0
								_UpperCAmelCase       :		str 		=    0
								_UpperCAmelCase       :		Dict 		=    1
								_UpperCAmelCase       :		List[str] 		=    1
								_UpperCAmelCase       :		Dict 		=    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
				_UpperCAmelCase       :		Optional[Any] 		=    params.node_id == 0 and params.local_rank == 0
				_UpperCAmelCase       :		List[str] 		=    params.n_nodes > 1

				# summary
				_UpperCAmelCase       :		Optional[Any] 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       Any:
				np.random.seed(args.seed )
				torch.manual_seed(args.seed )
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed )





from __future__ import annotations

import numpy as np

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[float] )   ->       Dict:
				return np.maximum(0  ,      lowerCAmelCase )


if __name__ == "__main__":
					print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


