



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				{}
    __a						       =				tokenizer(example['''content''']   , truncation=_UpperCAmelCase				)['''input_ids''']
    __a						       =				len(example['''content''']				) / len(output['''input_ids''']				)
    return output


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

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

__snake_case						:Optional[Any]      		=     time.time()
__snake_case						:Tuple      		=     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')

__snake_case						: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')










from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     NestedDataStructureLike[PathLike]     ,		__SCREAMING_SNAKE_CASE							:     Optional[NamedSplit] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Features] = None     ,		__SCREAMING_SNAKE_CASE							:     str = None     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		**__SCREAMING_SNAKE_CASE							:     List[str]     ,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		split=__SCREAMING_SNAKE_CASE     ,		features=__SCREAMING_SNAKE_CASE     ,		cache_dir=__SCREAMING_SNAKE_CASE     ,		keep_in_memory=__SCREAMING_SNAKE_CASE     ,		streaming=__SCREAMING_SNAKE_CASE     ,		num_proc=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				path_or_paths if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a						       =				Text(
               cache_dir=__SCREAMING_SNAKE_CASE     ,		data_files=__SCREAMING_SNAKE_CASE     ,		features=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           if self.streaming:
               __a						       =				self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               __a						       =				None
               __a						       =				None
               __a						       =				None
               __a						       =				None

               self.builder.download_and_prepare(
                   download_config=__SCREAMING_SNAKE_CASE     ,		download_mode=__SCREAMING_SNAKE_CASE     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		base_path=__SCREAMING_SNAKE_CASE     ,		num_proc=self.num_proc     ,		)
               __a						       =				self.builder.as_dataset(
                   split=self.split     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		in_memory=self.keep_in_memory)
           return dataset





