
import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE )		-> List[str]:
		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			{}
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			tokenizer(example['content'] ,    truncation=SCREAMING_SNAKE_CASE_ )['input_ids']
		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			len(example['content'] ) / len(output['input_ids'] )
		return output


lowerCAmelCase__:    Optional[int]      = HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__:    Any      = parser.parse_args()
if args.num_workers is None:
		lowerCAmelCase__:    Any      = multiprocessing.cpu_count()
lowerCAmelCase__:    List[Any]      = AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

lowerCAmelCase__:    Tuple      = time.time()
lowerCAmelCase__:    Union[str, Any]      = 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__:    int      = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')













from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=A__    )
class 			__UpperCamelCase    (							A__    ):
     __A	:  str							  =   field(default="""language-modeling"""	, metadata={"""include_in_asdict_even_if_is_default""": True}    )

     __A	:  ClassVar[Features]							  =   Features({"""text""": Value("""string"""    )}    )
     __A	:  ClassVar[Features]							  =   Features({}    )
     __A	:  str							  =   "text"





     @property
     def 		UpperCamelCase(     self					):
        return {self.text_column: "text"}