


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,
)
lowerCAmelCase__      		= logging.getLogger(__name__)
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       _A      :       Dict				    = git.Repo(search_parent_directories=UpperCamelCase__						)
       _A      :       Optional[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__     :  Union[str, Any]						):
       if params.n_gpu <= 0:
              _A      :       Union[str, Any]				    = 0
              _A      :       Dict				    = -1
              _A      :       Dict				    = True
              _A      :       str				    = False
              return

       assert torch.cuda.is_available()

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

              _A      :       Any				    = int(os.environ["WORLD_SIZE"]						)
              _A      :       List[Any]				    = int(os.environ["N_GPU_NODE"]						)
              _A      :       Any				    = int(os.environ["RANK"]						)

              # number of nodes / node ID
              _A      :       int				    = params.world_size // params.n_gpu_per_node
              _A      :       str				    = params.global_rank // params.n_gpu_per_node
              _A      :       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

              _A      :       Dict				    = 1
              _A      :       List[str]				    = 0
              _A      :       int				    = 0
              _A      :       List[Any]				    = 0
              _A      :       str				    = 1
              _A      :       Union[str, Any]				    = 1
              _A      :       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
       _A      :       str				    = params.node_id == 0 and params.local_rank == 0
       _A      :       Any				    = params.n_nodes > 1

       # summary
       _A      :       Optional[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__     :  Any						):
       np.random.seed(args.seed						)
       torch.manual_seed(args.seed						)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed						)




from __future__ import annotations
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  int						):
       _A      :       Dict				    = list(range(len(UpperCamelCase__						)						)						)
       _A      :       Any				    = [v / w for v, w in zip(UpperCamelCase__				,							UpperCamelCase__						)]
       index.sort(key=lambda UpperCamelCase__						: ratio[i]				,							reverse=UpperCamelCase__						)

       _A      :       float				    = 0
       _A      :       list[float]				    = [0] * len(UpperCamelCase__						)
       for i in index:
              if weight[i] <= capacity:
                     _A      :       Union[str, Any]				    = 1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     _A      :       Optional[Any]				    = capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()
