





'''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,
)
__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.getLogger(__name__)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   git.Repo(search_parent_directories=__lowercase       )
     _UpperCAmelCase     							=   {
         "repo_id": str(__lowercase       ),
         "repo_sha": str(repo.head.object.hexsha       ),
         "repo_branch": str(repo.active_branch       ),
     }

     with open(os.path.join(__lowercase			,     "git_log.json"       )			,     "w"       ) as f:
          json.dump(__lowercase			,     __lowercase			,     indent=4       )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple       )		->       Tuple:


     '''simple docstring'''



     if params.n_gpu <= 0:
          _UpperCAmelCase     							=   0
          _UpperCAmelCase     							=   -1
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          return

     assert torch.cuda.is_available()

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

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

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

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

     # summary
     _UpperCAmelCase     							=   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_							(					__lowercase							:      Union[str, Any]       )		->       str:


     '''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"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   List[str]							  =	['onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""onnx"""]						)



