


'''simple docstring'''





from argparse import ArgumentParser

from . import BaseTransformersCLICommand
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Union[str, Any]:
   return DownloadCommand(args.model  ,						args.cache_dir  ,						args.force  ,						args.trust_remote_code   )







class   __lowercase				(  __a  ):





      @staticmethod
      def        _lowercase				(		__lowerCamelCase						:					ArgumentParser       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     parser.add_parser("""download"""       )
         download_parser.add_argument(
             """--cache-dir"""					,       type=snake_case__					,       default=snake_case__					,       help="""Path to location to store the models"""       )
         download_parser.add_argument(
             """--force"""					,       action="""store_true"""					,       help="""Force the model to be download even if already in cache-dir"""       )
         download_parser.add_argument(
             """--trust-remote-code"""					,       action="""store_true"""					,       help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""					,       )
         download_parser.add_argument("""model"""					,       type=snake_case__					,       help="""Name of the model to download"""       )
         download_parser.set_defaults(func=snake_case__       )





      def __init__(		self						:					Tuple					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					bool					,       __lowerCamelCase						:					bool       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     model
         UpperCAmelCase							=     cache
         UpperCAmelCase							=     force
         UpperCAmelCase							=     trust_remote_code





      def        _lowercase				(		self						:					List[Any]       )    ->   List[Any]:






         """simple docstring"""







         from ..models.auto import AutoModel, AutoTokenizer

         AutoModel.from_pretrained(
             self._model					,       cache_dir=self._cache					,       force_download=self._force					,       trust_remote_code=self._trust_remote_code       )
         AutoTokenizer.from_pretrained(
             self._model					,       cache_dir=self._cache					,       force_download=self._force					,       trust_remote_code=self._trust_remote_code       )







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     tokenizer(example["""content"""]  ,						truncation=lowerCAmelCase_   )["""input_ids"""]
   UpperCAmelCase							=     len(example["""content"""]   ) / len(output["""input_ids"""]   )
   return output


__a     =			HfArgumentParser(PretokenizationArguments)
__a     =			parser.parse_args()
if args.num_workers is None:
 __a     =			multiprocessing.cpu_count()
__a     =			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

__a     =			time.time()
__a     =			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     =			time.time()
ds.push_to_hub(args.tokenized_data_repo)







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



