




from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    ArgumentParser("Transformers CLI tool",						usage="transformers-cli <command> [<args>]"     )
			a					=    parser.add_subparsers(help="transformers-cli command helpers"     )

			# Register commands
			ConvertCommand.register_subcommand(A     )
			DownloadCommand.register_subcommand(A     )
			EnvironmentCommand.register_subcommand(A     )
			RunCommand.register_subcommand(A     )
			ServeCommand.register_subcommand(A     )
			UserCommands.register_subcommand(A     )
			AddNewModelCommand.register_subcommand(A     )
			AddNewModelLikeCommand.register_subcommand(A     )
			LfsCommands.register_subcommand(A     )
			PTtoTFCommand.register_subcommand(A     )

			# Let's go
			a					=    parser.parse_args()

			if not hasattr(A,						"func"     ):
						parser.print_help()
						exit(1     )

			# Run
			a					=    args.func(A     )
			service.run()


if __name__ == "__main__":
		main()












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






