



"""simple docstring"""







from __future__ import annotations

import random
import string





class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_ = None   ):
			_lowercase				: Optional[int]     =   passcode or self.__passcode_creator()
			_lowercase				: str     =   self.__make_key_list()
			_lowercase				: Optional[int]     =   self.__make_shift_key()






	def __str__(     self   ):
			return "".join(self.__passcode   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			for i in range(1				,len(UpperCAmelCase_   )				,2   ):
					iterlist[i] *= -1
			return iterlist






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   string.ascii_letters + string.digits
			_lowercase				: Any     =   [random.choice(UpperCAmelCase_   ) for _ in range(random.randint(10				,20   )   )]
			return password






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Dict     =   (
			    string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
			)

			_lowercase				: List[Any]     =   []

			# creates points known as breakpoints to break the key_list_options at those
			# points and pivot each substring
			_lowercase				: Optional[int]     =   sorted(set(self.__passcode   )   )
			_lowercase				: list[str]     =   []

			# algorithm for creating a new shuffled list, keys_l, out of key_list_options
			for i in key_list_options:
					temp_list.extend(UpperCAmelCase_   )

					# checking breakpoints at which to pivot temporary sublist and add it into
					# keys_l
					if i in breakpoints or i == key_list_options[-1]:
							keys_l.extend(temp_list[::-1]   )
							temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
			return keys_l






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Union[str, Any]     =   sum(self.__neg_pos([ord(UpperCAmelCase_   ) for x in self.__passcode]   )   )
			return num if num > 0 else len(self.__passcode   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Dict     =   """"""

			# decoding shift like Caesar cipher algorithm implementing negative shift or
			# reverse shift or left shift
			for i in encoded_message:
					_lowercase				: Dict     =   self.__key_list.index(UpperCAmelCase_   )
					decoded_message += self.__key_list[
					    (position - self.__shift_key) % -len(self.__key_list   )
					]

			return decoded_message






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   """"""

			# encoding shift like Caesar cipher algorithm implementing positive shift or
			# forward shift or right shift
			for i in plaintext:
					_lowercase				: Union[str, Any]     =   self.__key_list.index(UpperCAmelCase_   )
					encoded_message += self.__key_list[
					    (position + self.__shift_key) % len(self.__key_list   )
					]

			return encoded_message







def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase = "Hello, this is a modified Caesar cipher"      ):
		_lowercase				: Optional[Any]     =   ShuffledShiftCipher()
		return cipa.decrypt(cipa.encrypt(__UpperCAmelCase      )      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()







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)

