"""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,
)
__SCREAMING_SNAKE_CASE    	=logging.getLogger(__name__)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				List[str]													=	git.Repo(search_parent_directories=__lowercase      )
     lowercase_	:				List[Any]													=	{
         'repo_id': str(__lowercase      ),
         'repo_sha': str(repo.head.object.hexsha      ),
         'repo_branch': str(repo.active_branch      ),
     }

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple      ):



     if params.n_gpu <= 0:
          lowercase_	:				Dict													=	0
          lowercase_	:				str													=	-1
          lowercase_	:				Optional[Any]													=	True
          lowercase_	:				Optional[Any]													=	False
          return

     assert torch.cuda.is_available()

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

          lowercase_	:				List[Any]													=	int(os.environ['WORLD_SIZE']      )
          lowercase_	:				Tuple													=	int(os.environ['N_GPU_NODE']      )
          lowercase_	:				Dict													=	int(os.environ['RANK']      )

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

          lowercase_	:				Any													=	1
          lowercase_	:				Union[str, Any]													=	0
          lowercase_	:				Optional[Any]													=	0
          lowercase_	:				List[Any]													=	0
          lowercase_	:				List[str]													=	1
          lowercase_	:				str													=	1
          lowercase_	:				List[Any]													=	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
     lowercase_	:				Tuple													=	params.node_id == 0 and params.local_rank == 0
     lowercase_	:				Optional[int]													=	params.n_nodes > 1

     # summary
     lowercase_	:				Optional[int]													=	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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     np.random.seed(args.seed      )
     torch.manual_seed(args.seed      )
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed      )








'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       int:


     '''simple docstring'''



     if not isinstance(__lowercase			,     __lowercase       ) or number < 0:
          raise ValueError("Input must be a non-negative integer"       )

     _UpperCAmelCase     							=   0
     while number:
          # This way we arrive at next set bit (next 1) instead of looping
          # through each bit and checking for 1s hence the
          # loop won't run 32 times it will only run the number of `1` times
          number &= number - 1
          count += 1
     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()




