


'''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,
)
snake_case							: List[Any]      =   logging.getLogger(__name__)






def 							__lowercase     (				__lowerCAmelCase							:       str     ):
							a__    =  git.Repo(search_parent_directories=__lowerCAmelCase     )
							a__    =  {
							    '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 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]     ):
							if params.n_gpu <= 0:
														a__    =  0
														a__    =  -1
														a__    =  True
														a__    =  False
														return

							assert torch.cuda.is_available()

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

														a__    =  int(os.environ['WORLD_SIZE']     )
														a__    =  int(os.environ['N_GPU_NODE']     )
														a__    =  int(os.environ['RANK']     )

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

														a__    =  1
														a__    =  0
														a__    =  0
														a__    =  0
														a__    =  1
														a__    =  1
														a__    =  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
							a__    =  params.node_id == 0 and params.local_rank == 0
							a__    =  params.n_nodes > 1

							# summary
							a__    =  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     (				__lowerCAmelCase							:       Dict     ):
							np.random.seed(args.seed     )
							torch.manual_seed(args.seed     )
							if args.n_gpu > 0:
														torch.cuda.manual_seed_all(args.seed     )








def 							__lowercase     (				__lowerCAmelCase							:       int = 2_0_0     ):
							a__    =  [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
							a__    =  [0] * (pence + 1)
							a__    =  1  # base case: 1 way to make 0 pence

							for coin in coins:
														for i in range(__lowerCAmelCase      ,					pence + 1      ,					1     ):
																					number_of_ways[i] += number_of_ways[i - coin]
							return number_of_ways[pence]


if __name__ == "__main__":
						assert solution(2_00) == 7_36_82




