




'''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


UpperCAmelCase_    :					Dict					      =						logging.getLogger(__name__)







def      _UpperCamelCase	()->				Tuple:




     '''simple docstring'''





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

     __snake_case         =					parser.parse_args()
     return args







def      _UpperCamelCase	(_lowerCamelCase							:       List[str]		)->				List[str]:




     '''simple docstring'''





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

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

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

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

     # Prepare the trainer.
     __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=A__		)
     logger.info('''Tokenizer training complete!'''		)

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

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


if __name__ == "__main__":
     UpperCAmelCase_    :					int					      =						parse_args()
     main(args)












'''simple docstring'''



def      _UpperCamelCase	(_lowerCamelCase							:       list				,		_lowerCamelCase							:       int		)->				int:




     '''simple docstring'''





     for index, item in enumerate(_lowerCamelCase		):
          if item == target:
               return index
     return -1







def      _UpperCamelCase	(_lowerCamelCase							:       list				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       int		)->				int:




     '''simple docstring'''





     if not (0 <= high < len(_lowerCamelCase		) and 0 <= low < len(_lowerCamelCase		)):
          raise Exception('''Invalid upper or lower bound!'''		)
     if high < low:
          return -1
     if sequence[low] == target:
          return low
     if sequence[high] == target:
          return high
     return rec_linear_search(_lowerCamelCase				,		low + 1				,		high - 1				,		_lowerCamelCase		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[Any]					      =						input('''Enter numbers separated by comma:\n''').strip()
     UpperCAmelCase_    :					Optional[int]					      =						[int(item.strip()) for item in user_input.split(''',''')]

     UpperCAmelCase_    :					List[str]					      =						int(input('''Enter a single number to be found in the list:\n''').strip())
     UpperCAmelCase_    :					Optional[int]					      =						linear_search(sequence, target)
     if result != -1:
          print(F"""linear_search({sequence}, {target}) = {result}""")
     else:
          print(F"""{target} was not found in {sequence}""")






