


"""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,
)
lowerCAmelCase__      				=   logging.getLogger(__name__)
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							git.Repo(search_parent_directories=_snake_case				)
  lowerCAmelCase     :     List[Any]								=							{
      "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 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  if params.n_gpu <= 0:
    lowerCAmelCase     :     int								=							0
    lowerCAmelCase     :     Optional[Any]								=							-1
    lowerCAmelCase     :     List[Any]								=							True
    lowerCAmelCase     :     int								=							False
    return

  assert torch.cuda.is_available()

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

    lowerCAmelCase     :     str								=							int(os.environ["WORLD_SIZE"]				)
    lowerCAmelCase     :     str								=							int(os.environ["N_GPU_NODE"]				)
    lowerCAmelCase     :     str								=							int(os.environ["RANK"]				)

    # number of nodes / node ID
    lowerCAmelCase     :     Dict								=							params.world_size // params.n_gpu_per_node
    lowerCAmelCase     :     int								=							params.global_rank // params.n_gpu_per_node
    lowerCAmelCase     :     Dict								=							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

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

  # summary
  lowerCAmelCase     :     Tuple								=							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 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







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








import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_lowerCAmelCase					:			str  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  *__snake_case					,  **__snake_case					)   -> None:




						'''simple docstring'''
						warnings.warn(
						    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use CLIPImageProcessor instead.'					,  __snake_case					,  )
						super().__init__(*__snake_case					,  **__snake_case					)






