
'''simple docstring'''





from __future__ import annotations

from typing import Any


class 			UpperCamelCase__	:






 def __init__(      self	:List[Any]						,					_A	:Any						,					_A	:Any      )					->      None:  # noqa: A002







       '''simple docstring'''



       __A													=      item
       __A													=      next


class 			UpperCamelCase__	:






 def __init__(      self	:Optional[int]      )					->      None:







       '''simple docstring'''



       __A													=      None
       __A													=      0






 def 						lowercase_    (      self	:Any						,					_A	:Any      )					->      None:







       '''simple docstring'''



       __A													=      Node(_A						,					self.head      )
       self.size += 1






 def 						lowercase_    (      self	:List[Any]      )					->      Any:







       '''simple docstring'''



       if self.head is None:
             return None
       else:
             __A													=      self.head.item
             __A													=      self.head.next
             self.size -= 1
             return item






 def 						lowercase_    (      self	:Optional[Any]      )					->      bool:







       '''simple docstring'''



       return self.head is None






 def __str__(      self	:List[str]      )					->      str:







       '''simple docstring'''



       if self.is_empty():
             return ""
       else:
             __A													=      self.head
             __A													=      ''
             __A													=      []
             while iterate:
                   item_list.append(str(iterate.item      )      )
                   __A													=      iterate.next

             __A													=      ' --> '.join(_A      )

             return item_str






 def __len__(      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       return self.size










from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






   for _ in tqdm(range(0							,  args.n_examples							,  A_      )      ):
      yield [next(A_      )[args.text_column] for _ in range(A_      )]


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)

