
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 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			git.Repo(search_parent_directories=lowerCamelCase__    )
				lowerCamelCase_						=			{
				    "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 			lowerCamelCase_ (				lowerCamelCase__    ):
				if params.n_gpu <= 0:
								lowerCamelCase_						=			0
								lowerCamelCase_						=			-1
								lowerCamelCase_						=			True
								lowerCamelCase_						=			False
								return

				assert torch.cuda.is_available()

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

								lowerCamelCase_						=			int(os.environ["WORLD_SIZE"]    )
								lowerCamelCase_						=			int(os.environ["N_GPU_NODE"]    )
								lowerCamelCase_						=			int(os.environ["RANK"]    )

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

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

				# summary
				lowerCamelCase_						=			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 			lowerCamelCase_ (				lowerCamelCase__    ):
				np.random.seed(args.seed    )
				torch.manual_seed(args.seed    )
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed    )




def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[0 for i in range(r + 1    )]
				# nc0 = 1
				lowerCamelCase_						=			1
				for i in range(1	, n + 1    ):
								# to compute current row from previous row.
								lowerCamelCase_						=			min(lowerCamelCase__	, lowerCamelCase__    )
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]





print(binomial_coefficient(n=1_0, r=5))


