


'''simple docstring'''


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 		lowerCAmelCase__	(	lowercase_		):




			def __init__(						self     :   Dict		,     lowerCamelCase__     :   Optional[Any]		,     lowerCamelCase__     :   Any = None		,     lowerCamelCase__     :   List[str] = None		,     lowerCamelCase__     :   int = True		,     lowerCamelCase__     :   Tuple = None		,     lowerCamelCase__     :   str = False		,     lowerCamelCase__     :   Optional[Any] = None		,     lowerCamelCase__     :   Dict = True		,     lowerCamelCase__     :   List[str] = "arrow"		,     **lowerCamelCase__     :   Dict		,     )      ->List[str]:



					'''simple docstring'''







					super().__init__(
					    split=lowerCamelCase__		,     features=lowerCamelCase__		,     cache_dir=lowerCamelCase__		,     keep_in_memory=lowerCamelCase__		,     streaming=lowerCamelCase__		,     **lowerCamelCase__		,     )
					_UpperCAmelCase						:				List[Any]	    =					load_from_cache_file
					_UpperCAmelCase						:				Any	    =					file_format
					_UpperCAmelCase						:				List[Any]	    =					Spark(
					    df=lowerCamelCase__		,     features=lowerCamelCase__		,     cache_dir=lowerCamelCase__		,     working_dir=lowerCamelCase__		,     **lowerCamelCase__		,     )






			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Any:



					'''simple docstring'''







					if self.streaming:
							return self.builder.as_streaming_dataset(split=self.split       )
					_UpperCAmelCase						:				int	    =					None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
					self.builder.download_and_prepare(
					    download_mode=lowerCamelCase__		,     file_format=self._file_format		,     )
					return self.builder.as_dataset(split=self.split       )






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


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

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

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   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''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


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




