


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  Optional[int]				     =  ["""image_processor""", """tokenizer"""]
       lowerCamelCase__:  Optional[Any]				     =  """AutoImageProcessor"""
       lowerCamelCase__:  Optional[Any]				     =  """AutoTokenizer"""

       def __init__( self   :  str   ,				lowerCamelCase_   :  Dict=None   ,				lowerCamelCase_   :  Optional[int]=None   ,				**lowerCamelCase_   :  int     ):
            a_     :  List[str]				      =     None
            if "feature_extractor" in kwargs:
                 warnings.warn(
                     """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                     """ instead."""   ,				lowercase_   ,				)
                 a_     :  str				      =     kwargs.pop("""feature_extractor"""     )

            a_     :  List[Any]				      =     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__(lowercase_   ,				lowercase_     )
            a_     :  Union[str, Any]				      =     self.image_processor
            a_     :  Any				      =     False

       def __call__( self   :  Tuple   ,				*lowerCamelCase_   :  Dict   ,				**lowerCamelCase_   :  int     ):
            # For backward compatibility
            if self._in_target_context_manager:
                 return self.current_processor(*lowercase_   ,				**lowercase_     )

            a_     :  Tuple				      =     kwargs.pop("""images"""   ,				lowercase_     )
            a_     :  Optional[Any]				      =     kwargs.pop("""text"""   ,				lowercase_     )
            if len(lowercase_     ) > 0:
                 a_     :  Dict				      =     args[0]
                 a_     :  str				      =     args[1:]

            if images is None and text is None:
                 raise ValueError("""You need to specify either an `images` or `text` input to process."""     )

            if images is not None:
                 a_     :  Optional[Any]				      =     self.image_processor(lowercase_   ,				*lowercase_   ,				**lowercase_     )
            if text is not None:
                 a_     :  str				      =     self.tokenizer(lowercase_   ,				**lowercase_     )

            if text is None:
                 return inputs
            elif images is None:
                 return encodings
            else:
                 a_     :  Union[str, Any]				      =     encodings["""input_ids"""]
                 return inputs

       def     UpperCAmelCase( self   :  int   ,				*lowerCamelCase_   :  Union[str, Any]   ,				**lowerCamelCase_   :  List[Any]     ):
            return self.tokenizer.batch_decode(*lowercase_   ,				**lowercase_     )

       def     UpperCAmelCase( self   :  str   ,				*lowerCamelCase_   :  Optional[Any]   ,				**lowerCamelCase_   :  Optional[Any]     ):
            return self.tokenizer.decode(*lowercase_   ,				**lowercase_     )

       @contextmanager
       def     UpperCAmelCase( self   :  List[Any]     ):
            warnings.warn(
                """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
                """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
                """your images inputs, or in a separate call."""     )
            a_     :  str				      =     True
            a_     :  Dict				      =     self.tokenizer
            yield
            a_     :  Dict				      =     self.image_processor
            a_     :  Tuple				      =     False

       @property
       def     UpperCAmelCase( self   :  List[str]     ):
            warnings.warn(
                """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,				lowercase_   ,				)
            return self.image_processor_class

       @property
       def     UpperCAmelCase( self   :  str     ):
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,				lowercase_   ,				)
            return self.image_processor




'''simple docstring'''




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_lowercase		:					str													=		logging.getLogger(__name__)




def 				lowerCamelCase      ( )     -> Tuple:
							lowercase_   :     Any									=			argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""					)
							parser.add_argument(
							    """--dataset_name""" , type=UpperCAmelCase__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , )
							parser.add_argument(
							    """--dataset_config""" , type=UpperCAmelCase__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset."""					)
							parser.add_argument(
							    """--batch_size""" , type=UpperCAmelCase__ , default=1000 , help="""Batch size during training.""" , )
							parser.add_argument(
							    """--vocab_size""" , type=UpperCAmelCase__ , default=10048 , help="""Size of the desired vocabulary.""" , )
							parser.add_argument(
							    """--limit""" , default=UpperCAmelCase__ , type=UpperCAmelCase__ , help="""Limit the number of shards (used for debugging).""" , )
							parser.add_argument(
							    """--export_to_hub""" , action="""store_true""" , )

							lowercase_   :     List[Any]									=			parser.parse_args()
							return args




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int]					)     -> List[Any]:
							lowercase_   :     Any									=			datasets.load_dataset(args.dataset_name , args.dataset_config , split="""train"""					)

							if args.limit is not None:
														lowercase_   :     Optional[Any]									=			min(len(UpperCAmelCase__					) , args.limit					)
														lowercase_   :     Dict									=			dataset.select(range(UpperCAmelCase__					)					)
														logger.info(F'''Limiting the dataset to {args.limit} entries.'''					)

							def batch_iterator():
														for i in range(0 , len(UpperCAmelCase__					) , args.batch_size					):
																					yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
							lowercase_   :     Optional[int]									=			Tokenizer(Unigram()					)
							lowercase_   :     Optional[int]									=			normalizers.Sequence([normalizers.Replace("""``""" , """\""""					), normalizers.Replace("""''""" , """\""""					)]					)
							lowercase_   :     List[Any]									=			pre_tokenizers.Metaspace()

							# Prepare the trainer.
							lowercase_   :     str									=			UnigramTrainer(
							    unk_token="""<unk>""" , special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""] , vocab_size=args.vocab_size , )

							logger.info("""Training the tokenizer."""					)
							tokenizer.train_from_iterator(batch_iterator() , trainer=UpperCAmelCase__					)
							logger.info("""Tokenizer training complete!"""					)

							lowercase_   :     Dict									=			tokenizer.token_to_id("""[CLS]"""					)
							lowercase_   :     Any									=			tokenizer.token_to_id("""[SEP]"""					)
							lowercase_   :     Optional[Any]									=			processors.TemplateProcessing(
							    single="""[CLS]:0 $A:0 [SEP]:0""" , pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1""" , special_tokens=[
							        ("""[CLS]""", cls_token_id),
							        ("""[SEP]""", sep_token_id),
							    ] , )
							lowercase_   :     int									=			decoders.Metaspace()

							if args.export_to_hub:
														logger.info("""Exporting the trained tokenzier to Hub."""					)
														lowercase_   :     Any									=			AlbertTokenizerFast(tokenizer_object=UpperCAmelCase__					)
														new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""					)


if __name__ == "__main__":
						_lowercase		:					Dict													=		parse_args()
						main(args)





