



'''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


lowerCamelCase__	  =		logging.getLogger(__name__)





def 				_SCREAMING_SNAKE_CASE(					)		->str:






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

							_lowercase							:		Tuple				=   parser.parse_args()
							return args





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any]  )		->List[str]:






							'''simple docstring'''
							_lowercase							:		Any				=   datasets.load_dataset(args.dataset_name ,       args.dataset_config ,       split='''train'''  )

							if args.limit is not None:
														_lowercase							:		List[str]				=   min(len(snake_case_  ) ,       args.limit  )
														_lowercase							:		Optional[Any]				=   dataset.select(range(snake_case_  )  )
														logger.info(F"Limiting the dataset to {args.limit} entries."  )

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

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

							# Prepare the trainer.
							_lowercase							:		Any				=   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=snake_case_  )
							logger.info('''Tokenizer training complete!'''  )

							_lowercase							:		List[Any]				=   tokenizer.token_to_id('''[CLS]'''  )
							_lowercase							:		Optional[int]				=   tokenizer.token_to_id('''[SEP]'''  )
							_lowercase							:		str				=   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							:		Dict				=   decoders.Metaspace()

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


if __name__ == "__main__":
						lowerCamelCase__	  =		parse_args()
						main(args)








'''simple docstring'''






import warnings

from .generation import FlaxGenerationMixin

class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		# warning at import time
		warnings.warn(
		    'Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'     ,		__A     ,		)



