




"""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,
)
a							      =     logging.getLogger(__name__)






def 		_snake_case	(   _snake_case				:   str				)       ->   int:






      '''simple docstring'''


      _A     =  git.Repo(search_parent_directories=_snake_case				)
      _A     =  {
          'repo_id': str(_snake_case				),
          'repo_sha': str(repo.head.object.hexsha				),
          'repo_branch': str(repo.active_branch				),
      }

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






def 		_snake_case	(   _snake_case				:   List[str]				)       ->   str:






      '''simple docstring'''


      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 		_snake_case	(   _snake_case				:   Any				)       ->   Union[str, 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				)







"""simple docstring"""

import math






def 		_snake_case	(   _snake_case				:   float							,    _snake_case				:   float				)       ->   float:






      '''simple docstring'''


      if (
          not isinstance(_snake_case							,    (int, float)				)
          or power_factor < -1
          or power_factor > 1
      ):
            raise ValueError('power_factor must be a valid float value between -1 and 1.'				)
      return apparent_power * power_factor






def 		_snake_case	(   _snake_case				:   float							,    _snake_case				:   float				)       ->   float:






      '''simple docstring'''


      if (
          not isinstance(_snake_case							,    (int, float)				)
          or power_factor < -1
          or power_factor > 1
      ):
            raise ValueError('power_factor must be a valid float value between -1 and 1.'				)
      return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

