
'''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,
)
UpperCAmelCase    					=   logging.getLogger(__name__)

def      A (       A_							:					str       ):
      snake_case       :     Any =    git.Repo(search_parent_directories=A_       )
      snake_case       :     List[str] =    {
          '''repo_id''': str(A_       ),
          '''repo_sha''': str(repo.head.object.hexsha       ),
          '''repo_branch''': str(repo.active_branch       ),
      }

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

def      A (       A_							:					Tuple       ):
      if params.n_gpu <= 0:
            snake_case       :     Tuple =    0
            snake_case       :     Dict =    -1
            snake_case       :     Union[str, Any] =    True
            snake_case       :     int =    False
            return

      assert torch.cuda.is_available()

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

            snake_case       :     Dict =    int(os.environ['''WORLD_SIZE''']       )
            snake_case       :     Optional[int] =    int(os.environ['''N_GPU_NODE''']       )
            snake_case       :     Optional[int] =    int(os.environ['''RANK''']       )

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

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

      # summary
      snake_case       :     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      A (       A_							:					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'''





from __future__ import annotations

def      A (       A_							:					list[int]    ,							A_							:					int       ):
						snake_case       :     list[list[int]] =    []
						snake_case       :     list[int] =    []
						snake_case       :     int =    0
						snake_case       :     int =    sum(A_       )
						create_state_space_tree(A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_       )
						return result

def      A (       A_							:					list[int]    ,							A_							:					int    ,							A_							:					int    ,							A_							:					list[int]    ,							A_							:					list[list[int]]    ,							A_							:					int    ,							):
						if sum(A_       ) > max_sum or (remaining_nums_sum + sum(A_       )) < max_sum:
												return
						if sum(A_       ) == max_sum:
												result.append(A_       )
												return
						for index in range(A_    ,							len(A_       )       ):
												create_state_space_tree(
												    A_    ,							A_    ,							index + 1    ,							[*path, nums[index]]    ,							A_    ,							remaining_nums_sum - nums[index]    ,							)
UpperCAmelCase    					=   [3, 34, 4, 12, 5, 2]
UpperCAmelCase    					=   9
UpperCAmelCase    					=   generate_sum_of_subsets_soln(nums, max_sum)





print(*result)

