



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_   :Tuple        =      logging.getLogger(__name__)
def        lowercase_							(A : str      ):
       snake_case__							:	Union[str, Any]   	=	git.Repo(search_parent_directories=A      )
       snake_case__							:	List[str]   	=	{
           'repo_id': str(A      ),
           'repo_sha': str(repo.head.object.hexsha      ),
           'repo_branch': str(repo.active_branch      ),
       }

       with open(os.path.join(A		,					'git_log.json'      )		,					'w'      ) as f:
              json.dump(A		,					A		,					indent=4      )
def        lowercase_							(A : Tuple      ):
       if params.n_gpu <= 0:
              snake_case__							:	Any   	=	0
              snake_case__							:	int   	=	-1
              snake_case__							:	List[str]   	=	True
              snake_case__							:	str   	=	False
              return

       assert torch.cuda.is_available()

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

              snake_case__							:	str   	=	int(os.environ['WORLD_SIZE']      )
              snake_case__							:	Any   	=	int(os.environ['N_GPU_NODE']      )
              snake_case__							:	Optional[int]   	=	int(os.environ['RANK']      )

              # number of nodes / node ID
              snake_case__							:	Dict   	=	params.world_size // params.n_gpu_per_node
              snake_case__							:	Optional[Any]   	=	params.global_rank // params.n_gpu_per_node
              snake_case__							:	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

              snake_case__							:	List[Any]   	=	1
              snake_case__							:	int   	=	0
              snake_case__							:	Optional[int]   	=	0
              snake_case__							:	str   	=	0
              snake_case__							:	Dict   	=	1
              snake_case__							:	str   	=	1
              snake_case__							:	int   	=	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
       snake_case__							:	Optional[Any]   	=	params.node_id == 0 and params.local_rank == 0
       snake_case__							:	str   	=	params.n_nodes > 1

       # summary
       snake_case__							:	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        lowercase_							(A : List[Any]      ):
       np.random.seed(args.seed      )
       torch.manual_seed(args.seed      )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed      )











from collections import deque

from .hash_table import HashTable




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], *_snake_case  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Optional[int]:
            super().__init__(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Dict					)   ->Dict:
            snake_case__							:	int   	=	deque([]					) if self.values[key] is None else self.values[key]
            self.values[key].appendleft(_snake_case					)
            snake_case__							:	Dict   	=	self.values[key]






     def    lowercase_   (       self  :  Any					)   ->Optional[Any]:
            return (
                sum(self.charge_factor - len(_snake_case					) for slot in self.values					)
                / self.size_table
                * self.charge_factor
            )






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  Optional[int]=None					)   ->Optional[Any]:
            if not (
                len(self.values[key]					) == self.charge_factor and self.values.count(_snake_case					) == 0
            ):
                   return key
            return super()._collision_resolution(_snake_case, _snake_case					)






