"""simple docstring"""







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

import torch_xla.distributed.xla_multiprocessing as xmp
def 						__UpperCAmelCase       (					)							->    Optional[int]:

							'''simple docstring'''





							__snake_case    :							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 						__UpperCAmelCase       (					)							->    Dict:

							'''simple docstring'''





							__snake_case    :							List[str]   	=				parse_args()

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

							# Patch sys.argv
							__snake_case    :							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()






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





