






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   :							List[str]						= logging.getLogger(__name__)



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   List[str]:





    """simple docstring"""

    __A       =						git.Repo(search_parent_directories=__lowercase     )
    __A       =						{
        """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       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   List[Any]:





    """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       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   int:





    """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_deit import DeiTImageProcessor


__a   :							Any						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Tuple			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         warnings.warn(
             """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use DeiTImageProcessor instead."""			,						UpperCamelCase_			,						)
         super().__init__(*UpperCamelCase_			,						**UpperCamelCase_					)




