


'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       ['''image_processor''', '''tokenizer''']
      UpperCamelCase								=       '''CLIPImageProcessor'''
      UpperCamelCase								=       ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





      def __init__(		self						:					List[str]					,       __lowerCamelCase						:					Dict=None					,       __lowerCamelCase						:					Tuple=None					,       **__lowerCamelCase						:					List[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     None
         if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""					,       __lowerCamelCase					,       )
            UpperCAmelCase							=     kwargs.pop("""feature_extractor"""       )

         UpperCAmelCase							=     image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""       )
         if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""       )

         super().__init__(__lowerCamelCase					,       __lowerCamelCase       )





      def __call__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Dict=None					,       __lowerCamelCase						:					int=None					,       __lowerCamelCase						:					Tuple=None					,       **__lowerCamelCase						:					Optional[Any]       )    ->   Tuple:






         """simple docstring"""







         if text is None and images is None:
            raise ValueError("""You have to specify either text or images. Both cannot be none."""       )

         if text is not None:
            UpperCAmelCase							=     self.tokenizer(__lowerCamelCase					,       return_tensors=__lowerCamelCase					,       **__lowerCamelCase       )

         if images is not None:
            UpperCAmelCase							=     self.image_processor(__lowerCamelCase					,       return_tensors=__lowerCamelCase					,       **__lowerCamelCase       )

         if text is not None and images is not None:
            UpperCAmelCase							=     image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**__lowerCamelCase       )					,       tensor_type=__lowerCamelCase       )





      def        _lowercase				(		self						:					List[str]					,       *__lowerCamelCase						:					List[Any]					,       **__lowerCamelCase						:					Optional[Any]       )    ->   Tuple:






         """simple docstring"""







         return self.tokenizer.batch_decode(*__lowerCamelCase					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					str					,       *__lowerCamelCase						:					Any					,       **__lowerCamelCase						:					Tuple       )    ->   int:






         """simple docstring"""







         return self.tokenizer.decode(*__lowerCamelCase					,       **__lowerCamelCase       )





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






         """simple docstring"""







         UpperCAmelCase							=     self.tokenizer.model_input_names
         UpperCAmelCase							=     self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





      @property
      def        _lowercase				(		self						:					str       )    ->   str:






         """simple docstring"""







         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""					,       __lowerCamelCase					,       )
         return self.image_processor_class





      @property
      def        _lowercase				(		self						:					int       )    ->   Dict:






         """simple docstring"""







         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""					,       __lowerCamelCase					,       )
         return self.image_processor







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""")



