

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      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'     )
     parser.add_argument(
         '--dataset_name',       type=_lowercase,       default='wikitext',       help='Name of the training. Explore datasets at: hf.co/datasets.',       )
     parser.add_argument(
         '--dataset_config',       type=_lowercase,       default='wikitext-103-raw-v1',       help='Configuration name of the dataset.'     )
     parser.add_argument(
         '--batch_size',       type=_lowercase,       default=1000,       help='Batch size during training.',       )
     parser.add_argument(
         '--vocab_size',       type=_lowercase,       default=10048,       help='Size of the desired vocabulary.',       )
     parser.add_argument(
         '--limit',       default=_lowercase,       type=_lowercase,       help='Limit the number of shards (used for debugging).',       )
     parser.add_argument(
         '--export_to_hub',       action='store_true',       )

     SCREAMING_SNAKE_CASE 		=parser.parse_args()
     return args







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=datasets.load_dataset(args.dataset_name,       args.dataset_config,       split='train'     )

     if args.limit is not None:
          SCREAMING_SNAKE_CASE 		=min(len(_lowercase     ),       args.limit     )
          SCREAMING_SNAKE_CASE 		=dataset.select(range(_lowercase     )     )
          logger.info(F'Limiting the dataset to {args.limit} entries.'     )

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

    # Prepare the tokenizer.
     SCREAMING_SNAKE_CASE 		=Tokenizer(Unigram()     )
     SCREAMING_SNAKE_CASE 		=normalizers.Sequence([normalizers.Replace('``',       '"'     ), normalizers.Replace('\'\'',       '"'     )]     )
     SCREAMING_SNAKE_CASE 		=pre_tokenizers.Metaspace()

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

     SCREAMING_SNAKE_CASE 		=tokenizer.token_to_id('[CLS]'     )
     SCREAMING_SNAKE_CASE 		=tokenizer.token_to_id('[SEP]'     )
     SCREAMING_SNAKE_CASE 		=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),
         ],       )
     SCREAMING_SNAKE_CASE 		=decoders.Metaspace()

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


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






'''simple docstring'''

from __future__ import annotations

import sys







def     __UpperCamelCase					(				_lowercase       )			->      int:
	if n < 0:
		raise ValueError('Negative arguments are not supported'       )
	return _fib(_lowercase       )[0]







def     __UpperCamelCase					(				_lowercase       )			->      tuple[int, int]:
	if n == 0:  # (F(0), F(1))
		return (0, 1)

	# F(2n) = F(n)[2F(n+1) − F(n)]
	# F(2n+1) = F(n+1)^2+F(n)^2
	_lowercase	,     _lowercase       :							List[str]  	=		_fib(n // 2       )
	_lowercase       :							Dict  	=		a * (b * 2 - a)
	_lowercase       :							Union[str, Any]  	=		a * a + b * b
	return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		_A					:     Optional[Any]      =int(sys.argv[1])
		print(F'''fibonacci({n}) is {fibonacci(n)}''')
