'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			['''image_processor''', '''tokenizer''']
			UpperCamelCase__							=			'''AutoImageProcessor'''
			UpperCamelCase__							=			'''AutoTokenizer'''







			def __init__(      self							:    str							,     lowercase_							:    Dict=None							,     lowercase_							:    Optional[int]=None							,     **lowercase_							:    int       ):
										lowercase_   :     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_							,     )
																	lowercase_   :     str									=			kwargs.pop("""feature_extractor"""       )

										lowercase_   :     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_       )
										lowercase_   :     Union[str, Any]									=			self.image_processor
										lowercase_   :     Any									=			False







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

										lowercase_   :     Tuple									=			kwargs.pop("""images"""							,     lowercase_       )
										lowercase_   :     Optional[Any]									=			kwargs.pop("""text"""							,     lowercase_       )
										if len(lowercase_       ) > 0:
																	lowercase_   :     Dict									=			args[0]
																	lowercase_   :     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:
																	lowercase_   :     Optional[Any]									=			self.image_processor(lowercase_							,     *lowercase_							,     **lowercase_       )
										if text is not None:
																	lowercase_   :     str									=			self.tokenizer(lowercase_							,     **lowercase_       )

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







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







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







			@contextmanager
			def       SCREAMING_SNAKE_CASE_	(      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."""       )
										lowercase_   :     str									=			True
										lowercase_   :     Dict									=			self.tokenizer
										yield
										lowercase_   :     Dict									=			self.image_processor
										lowercase_   :     Tuple									=			False







			@property
			def       SCREAMING_SNAKE_CASE_	(      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       SCREAMING_SNAKE_CASE_	(      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)





