


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       __lowerCamelCase   	=  None


__lowerCamelCase   	=  logging.get_logger(__name__)


__lowerCamelCase   	=  "▁"

__lowerCamelCase   	=  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase   	=  {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

__lowerCamelCase   	=  {
    "google/reformer-crime-and-punishment": 524288,
}


class 	a__						(    _UpperCAmelCase     ):
       lowerCamelCase__:  List[Any]				     =  VOCAB_FILES_NAMES
       lowerCamelCase__:  Union[str, Any]				     =  PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase__:  Tuple				     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase__:  Dict				     =  ["""input_ids""", """attention_mask"""]
       lowerCamelCase__:  Tuple				     =  ReformerTokenizer

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  str=None   ,				lowerCamelCase_   :  Any=None   ,				lowerCamelCase_   :  Optional[int]="</s>"   ,				lowerCamelCase_   :  List[Any]="<unk>"   ,				lowerCamelCase_   :  Tuple=[]   ,				**lowerCamelCase_   :  Union[str, Any]   ,				):
            super().__init__(
                lowercase_   ,				tokenizer_file=lowercase_   ,				eos_token=lowercase_   ,				unk_token=lowercase_   ,				additional_special_tokens=lowercase_   ,				**lowercase_   ,				)

            a_     :  int				      =     vocab_file
            a_     :  str				      =     False if not self.vocab_file else True

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[str] = None     ):
            if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                     """tokenizer."""     )

            if not os.path.isdir(lowercase_     ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                 return
            a_     :  List[Any]				      =     os.path.join(
                lowercase_   ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

            if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowercase_     ):
                 copyfile(self.vocab_file   ,				lowercase_     )

            return (out_vocab_file,)




'''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)





