import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


lowerCAmelCase			:							Optional[Any]     =["small", "medium", "large"]

lowerCAmelCase			:							Dict     ="lm_head.decoder.weight"
lowerCAmelCase			:							str     ="lm_head.weight"







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						torch.load(__A	)
    _lowerCamelCase     :		Tuple 						=						d.pop(__A	)
    os.makedirs(__A   , exist_ok=__A	)
    torch.save(__A   , os.path.join(__A   , __A	)	)


if __name__ == "__main__":
    lowerCAmelCase			:							Dict     =argparse.ArgumentParser()
    parser.add_argument("--dialogpt_path", default=".", type=str)
    lowerCAmelCase			:							Optional[int]     =parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        lowerCAmelCase			:							Union[str, Any]     =os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
        lowerCAmelCase			:							str     =F"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )





import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						ArgumentParser(
        description=(
            """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
        )	)

    # Optional arguments for the launch helper
    parser.add_argument("""--num_cores"""   , type=__A   , default=1   , help="""Number of TPU cores to use (1 or 8)."""	)

    # positional
    parser.add_argument(
        """training_script"""   , type=__A   , help=(
            """The full path to the single TPU training """
            """program/script to be launched in parallel, """
            """followed by all the arguments for the """
            """training script"""
        )   , )

    # rest from the training program
    parser.add_argument("""training_script_args"""   , nargs=__A	)

    return parser.parse_args()







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						parse_args()

    # Import training_script as a module.
    _lowerCamelCase     :		List[Any] 						=						Path(args.training_script	)
    sys.path.append(str(script_fpath.parent.resolve()	)	)
    _lowerCamelCase     :		Optional[Any] 						=						script_fpath.stem
    _lowerCamelCase     :		Dict 						=						importlib.import_module(__A	)

    # Patch sys.argv
    _lowerCamelCase     :		Union[str, Any] 						=						[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

    xmp.spawn(mod._mp_fn   , args=()   , nprocs=args.num_cores	)


if __name__ == "__main__":
    main()




