import sys

import argparse

def get_argument_parser():

parser = argparse.ArgumentParser()

# Required_parameter

parser.add_argument("--config-file", "--cf",

help="pointer to the configuration file of the experiment", type=str, required=True)

parser.add_argument("--output_dir", default=None, type=str, required=True,

help="The output directory where the model checkpoints will be written.")

# Optional Params

parser.add_argument("--max_seq_length", default=512, type=int,

help="The maximum total input sequence length after WordPiece tokenization. Sequences "

"longer than this will be truncated, and sequences shorter than this will be padded.")

parser.add_argument("--max_predictions_per_seq", "--max_pred", default=80, type=int,

help="The maximum number of masked tokens in a sequence to be predicted.")

parser.add_argument('--seed',

type=int,

default=42,

help="random seed for initialization")

parser.add_argument("--do_lower_case",

default=True,

action='store_true',

help="Whether to lower case the input text. True for uncased models, False for cased models.")

parser.add_argument("--local_rank",

type=int,

default=-1,

help="local_rank for distributed training on gpus")

parser.add_argument('--use_pretrain',

default=False,

action='store_true',

help="Whether to use Bert Pretrain Weights or not")

parser.add_argument('--refresh_bucket_size',

type=int,

default=1,

help="This param makes sure that a certain task is repeated for this time steps to \

optimise on the back propogation speed with APEX's DistributedDataParallel")

parser.add_argument('--finetune',

default=False,

action='store_true',

help="Whether to finetune only")

parser.add_argument('--load_training_checkpoint', '--load_cp',

type=str,

default=None,

help="This is the path to the TAR file which contains model+opt state_dict() checkpointed.")

parser.add_argument('--load_checkpoint_id', '--load_cp_id',

type=str,

default=None,

help='Checkpoint identifier to load from checkpoint path')

parser.add_argument('--job_name',

type=str,

default=None,

help="This is the path to store the output and TensorBoard results.")

parser.add_argument('--rewarmup',

default=False,

action='store_true',

help='Rewarmup learning rate after resuming from a checkpoint')

parser.add_argument('--max_steps',

type=int,

default=sys.maxsize,

help='Maximum number of training steps of effective batch size to complete.')

parser.add_argument('--max_steps_per_epoch',

type=int,

default=sys.maxsize,

help='Maximum number of training steps of effective batch size within an epoch to complete.')

parser.add_argument('--print_steps',

type=int,

default=100,

help='Interval to print training details.')

parser.add_argument('--data_path_prefix',

type=str,

default="",

help="Path to prefix data loading, helpful for AML and other environments")

return parser

def is_time_to_exit(args, epoch_steps=0, global_steps=0):

return (epoch_steps >= args.max_steps_per_epoch) or \

(global_steps >= args.max_steps)