





"""simple docstring"""




import string
from math import logaa






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str					, _lowercase		:					str      )				->int:






	'''simple docstring'''






	a   :	Tuple			=       document.translate(
	    str.maketrans(""					, ""					, string.punctuation      )      ).replace("\n"					, ""      )
	a   :	Union[str, Any]			=       document_without_punctuation.split(" "      )  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str					, _lowercase		:					str      )				->tuple[int, int]:






	'''simple docstring'''






	a   :	Any			=       corpus.lower().translate(
	    str.maketrans(""					, ""					, string.punctuation      )      )  # strip all punctuation and replace it with ''
	a   :	int			=       corpus_without_punctuation.split("\n"      )
	a   :	Dict			=       term.lower()
	return (len([doc for doc in docs if term in doc]      ), len(_lowercase      ))






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int					, _lowercase		:					Union[str, Any]=False      )				->float:






	'''simple docstring'''






	if smoothing:
		if n == 0:
			raise ValueError("log10(0) is undefined."      )
		return round(1 + logaa(n / (1 + df)      )					, 3      )

	if df == 0:
		raise ZeroDivisionError("df must be > 0"      )
	elif n == 0:
		raise ValueError("log10(0) is undefined."      )
	return round(logaa(n / df      )					, 3      )







def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int      )				->float:






	'''simple docstring'''






	return round(tf * idf					, 3      )









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


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

a		:				Dict  			=    time.time()
a		:				Tuple  			=    load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

a		:				Dict  			=    time.time()
a		:				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''')

a		:				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''')


