



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

def  _lowerCAmelCase      (   lowercase_						):
						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  _lowerCAmelCase      (   lowercase_						):
						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  _lowerCAmelCase      (   lowercase_						):
						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 warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,*_a      :     List[str]							,**_a      :     Any       ):


     '''simple docstring'''






     warnings.warn(
         'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
         ' Please use VideoMAEImageProcessor instead.'							,_a							,)
     super().__init__(*_a							,**_a       )



