




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__      :			int           =					logging.getLogger(__name__)

def 					SCREAMING_SNAKE_CASE							(   )			->     Tuple:
     a      =      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=10_00		,					help="Batch size during training."		,					)
     parser.add_argument(
         "--vocab_size"		,					type=__UpperCamelCase		,					default=1_00_48		,					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"		,					)

     a      =      parser.parse_args()
     return args

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     Union[str, Any]:
     a      =      datasets.load_dataset(args.dataset_name		,					args.dataset_config		,					split="train")

     if args.limit is not None:
          a      =      min(len(__UpperCamelCase)		,					args.limit)
          a      =      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.
     a      =      Tokenizer(Unigram())
     a      =      normalizers.Sequence([normalizers.Replace("``"		,					"\""), normalizers.Replace("''"		,					"\"")])
     a      =      pre_tokenizers.Metaspace()

     # Prepare the trainer.
     a      =      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!")

     a      =      tokenizer.token_to_id("[CLS]")
     a      =      tokenizer.token_to_id("[SEP]")
     a      =      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      =      decoders.Metaspace()

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


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







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase__      :			Union[str, Any]           =					logging.get_logger(__name__)




class 			a__       (					UpperCamelCase__      ):







     def __init__(	self  ,		*A  ,		**A )  ->    None:



          '''simple docstring'''



          warnings.warn(
              "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV2ImageProcessor instead."  ,		A  ,		)
          super().__init__(*A  ,		**A )

