




"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase = 6008_5147_5143							)			->			str:


			"""simple docstring"""

			try:
						__UpperCAmelCase	: Optional[Any]		=					int(a_							)
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."							)
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."							)
			__UpperCAmelCase	: List[str]		=					2
			__UpperCAmelCase	: Tuple		=					0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						__UpperCAmelCase	: Optional[Any]		=					i
						while n % i == 0:
									__UpperCAmelCase	: Union[str, Any]		=					n // i
						i += 1
			return int(a_							)


if __name__ == "__main__":
					print(f'''{solution() = }''')








"""simple docstring"""





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   =       logging.getLogger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Any:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					git.Repo(search_parent_directories=UpperCamelCase							)
			__UpperCAmelCase	: Any		=					{
			    "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    (			UpperCamelCase							)			->			str:


			"""simple docstring"""
			if params.n_gpu <= 0:
						__UpperCAmelCase	: str		=					0
						__UpperCAmelCase	: Dict		=					-1
						__UpperCAmelCase	: Tuple		=					True
						__UpperCAmelCase	: List[str]		=					False
						return

			assert torch.cuda.is_available()

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

						__UpperCAmelCase	: Optional[int]		=					int(os.environ["WORLD_SIZE"]							)
						__UpperCAmelCase	: Union[str, Any]		=					int(os.environ["N_GPU_NODE"]							)
						__UpperCAmelCase	: Optional[int]		=					int(os.environ["RANK"]							)

						# number of nodes / node ID
						__UpperCAmelCase	: int		=					params.world_size // params.n_gpu_per_node
						__UpperCAmelCase	: Optional[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	: Union[str, Any]		=					1
						__UpperCAmelCase	: Dict		=					0
						__UpperCAmelCase	: List[Any]		=					0
						__UpperCAmelCase	: Tuple		=					0
						__UpperCAmelCase	: Any		=					1
						__UpperCAmelCase	: 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	: Union[str, Any]		=					params.node_id == 0 and params.local_rank == 0
			__UpperCAmelCase	: Dict		=					params.n_nodes > 1

			# summary
			__UpperCAmelCase	: 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 					_UpperCamelCase    (			UpperCamelCase							)			->			Tuple:


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


