
"""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		:						Optional[int] =					logging.getLogger(__name__)


def     lowerCamelCase_    (	)     ->			List[str]:


					'''simple docstring'''
					__a:						Union[str, Any]		    =					argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset.""" )
					parser.add_argument(
					    """--dataset_name"""	,				type=lowerCamelCase_	,				default="""wikitext"""	,				help="""Name of the training. Explore datasets at: hf.co/datasets."""	,				)
					parser.add_argument(
					    """--dataset_config"""	,				type=lowerCamelCase_	,				default="""wikitext-103-raw-v1"""	,				help="""Configuration name of the dataset.""" )
					parser.add_argument(
					    """--batch_size"""	,				type=lowerCamelCase_	,				default=10_00	,				help="""Batch size during training."""	,				)
					parser.add_argument(
					    """--vocab_size"""	,				type=lowerCamelCase_	,				default=1_00_48	,				help="""Size of the desired vocabulary."""	,				)
					parser.add_argument(
					    """--limit"""	,				default=lowerCamelCase_	,				type=lowerCamelCase_	,				help="""Limit the number of shards (used for debugging)."""	,				)
					parser.add_argument(
					    """--export_to_hub"""	,				action="""store_true"""	,				)

					__a:						Tuple		    =					parser.parse_args()
					return args


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					__a:						Optional[int]		    =					datasets.load_dataset(args.dataset_name	,				args.dataset_config	,				split="""train""" )

					if args.limit is not None:
										__a:						Any		    =					min(len(lowerCamelCase_ )	,				args.limit )
										__a:						Dict		    =					dataset.select(range(lowerCamelCase_ ) )
										logger.info(f"""Limiting the dataset to {args.limit} entries.""" )

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

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

					# Prepare the trainer.
					__a:						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=lowerCamelCase_ )
					logger.info("""Tokenizer training complete!""" )

					__a:						Union[str, Any]		    =					tokenizer.token_to_id("""[CLS]""" )
					__a:						Dict		    =					tokenizer.token_to_id("""[SEP]""" )
					__a:						Tuple		    =					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),
					    ]	,				)
					__a:						Union[str, Any]		    =					decoders.Metaspace()

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


if __name__ == "__main__":
			lowercase		:						Tuple =					parse_args()
			main(args)


"""simple docstring"""
lowercase		:						str =					{
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					__a:						Optional[int]		    =					str(lowerCamelCase_ ).strip()

					# Exceptions
					if not binary_str:
										raise ValueError("""Empty string was passed to the function""" )
					__a:						List[Any]		    =					binary_str[0] == """-"""
					__a:						List[str]		    =					binary_str[1:] if is_negative else binary_str
					if not all(char in """01""" for char in binary_str ):
										raise ValueError("""Non-binary value was passed to the function""" )

					__a:						Any		    =					(
					    """0""" * (4 * (divmod(len(lowerCamelCase_ )	,				4 )[0] + 1) - len(lowerCamelCase_ )) + binary_str
					)

					__a:						int		    =					[]
					for x in range(0	,				len(lowerCamelCase_ )	,				4 ):
										hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]] )
					__a:						Tuple		    =					"""0x""" + """""".join(lowerCamelCase_ )

					return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
			from doctest import testmod

			testmod()
