


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				:    int       				=     logging.getLogger(__name__)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:List[str] 							=       git.Repo(search_parent_directories=UpperCAmelCase_						)
      a						:Tuple 							=       {
          '''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 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      if params.n_gpu <= 0:
            a						:Dict 							=       0
            a						:Tuple 							=       -1
            a						:Dict 							=       True
            a						:Optional[int] 							=       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						:Union[str, Any] 							=       int(os.environ['''N_GPU_NODE''']						)
            a						:Tuple 							=       int(os.environ['''RANK''']						)

            # number of nodes / node ID
            a						:List[Any] 							=       params.world_size // params.n_gpu_per_node
            a						:str 							=       params.global_rank // params.n_gpu_per_node
            a						:Tuple 							=       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						:Any 							=       1
            a						:Any 							=       0
            a						:Tuple 							=       0
            a						:Any 							=       0
            a						:str 							=       1
            a						:str 							=       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						:Optional[int] 							=       params.node_id == 0 and params.local_rank == 0
      a						:Optional[Any] 							=       params.n_nodes > 1

      # summary
      a						:List[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 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





      np.random.seed(args.seed						)
      torch.manual_seed(args.seed						)
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed						)









def 				__lowerCamelCase						( UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      if len(UpperCAmelCase_						) != len(UpperCAmelCase_						):
            raise ValueError('''The length of profit and weight must be same.'''						)
      if max_weight <= 0:
            raise ValueError('''max_weight must greater than zero.'''						)
      if any(p < 0 for p in profit						):
            raise ValueError('''Profit can not be negative.'''						)
      if any(w < 0 for w in weight						):
            raise ValueError('''Weight can not be negative.'''						)

      # List created to store profit gained for the 1kg in case of each weight
      # respectively.  Calculate and append profit/weight for each element.
      a						:Optional[int] 							=       [p / w for p, w in zip(UpperCAmelCase_ ,  UpperCAmelCase_						)]

      # Creating a copy of the list and sorting profit/weight in ascending order
      a						:List[Any] 							=       sorted(UpperCAmelCase_						)

      # declaring useful variables
      a						:Dict 							=       len(UpperCAmelCase_						)
      a						:Tuple 							=       0
      a						:List[Any] 							=       0
      a						:str 							=       0

      # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
      while limit <= max_weight and i < length:
            # flag value for encountered greatest element in sorted_profit_by_weight
            a						:List[Any] 							=       sorted_profit_by_weight[length - i - 1]
            a						:Optional[Any] 							=       profit_by_weight.index(UpperCAmelCase_						)
            a						:Optional[int] 							=       -1

            # check if the weight encountered is less than the total weight
            # encountered before.
            if max_weight - limit >= weight[index]:
                  limit += weight[index]
                  # Adding profit gained for the given weight 1 ===
                  # weight[index]/weight[index]
                  gain += 1 * profit[index]
            else:
                  # Since the weight encountered is greater than limit, therefore take the
                  # required number of remaining kgs and calculate profit for it.
                  # weight remaining / weight[index]
                  gain += (max_weight - limit) / weight[index] * profit[index]
                  break
            i += 1
      return gain


if __name__ == "__main__":
     print(
         '''Input profits, weights, and then max_weight (all positive ints) separated by '''
         '''spaces.'''
     )

     snake_case				:    Union[str, Any]       				=     [int(x) for x in input('''Input profits separated by spaces: ''').split()]
     snake_case				:    Tuple       				=     [int(x) for x in input('''Input weights separated by spaces: ''').split()]
     snake_case				:    str       				=     int(input('''Max weight allowed: '''))

     # Function Call
     calc_profit(profit, weight, max_weight)





