



"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
      from ...tokenization_utils_base import TextInput
from ...utils import logging


A:   Optional[Any]								=		logging.get_logger(__name__)

A:   Optional[Any]								=		{'vocab_file': 'spiece.model'}

A:   str								=		{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A:   Tuple								=		{
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}

A:   Optional[int]								=		'▁'



class       SCREAMING_SNAKE_CASE__  (					__lowercase ):
   __lowerCAmelCase  :   Any						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   str						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   int						=						['input_ids', 'attention_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="</s>"      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE=100      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE=True      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      None:





    '''simple docstring'''




    if extra_ids > 0 and additional_special_tokens is None:
     UpperCAmelCase	:					Optional[int]	=	[F"<extra_id_{i}>" for i in range(_SCREAMING_SNAKE_CASE						)]
    elif extra_ids > 0 and additional_special_tokens is not None:
     # Check that we have the right number of extra_id special tokens
     UpperCAmelCase	:					Union[str, Any]	=	len(set(filter(lambda _SCREAMING_SNAKE_CASE						: bool("""extra_id""" in str(_SCREAMING_SNAKE_CASE						)						)      ,     _SCREAMING_SNAKE_CASE						)						)						)
     if extra_tokens != extra_ids:
      raise ValueError(
          F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
          """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
          """ tokens"""						)
    if legacy:
     logger.warning_once(
         F"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
         """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""						)

    UpperCAmelCase	:					Union[str, Any]	=	legacy
    UpperCAmelCase	:					Dict	=	{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        eos_token=_SCREAMING_SNAKE_CASE      ,     unk_token=_SCREAMING_SNAKE_CASE      ,     pad_token=_SCREAMING_SNAKE_CASE      ,     extra_ids=_SCREAMING_SNAKE_CASE      ,     additional_special_tokens=_SCREAMING_SNAKE_CASE      ,     sp_model_kwargs=self.sp_model_kwargs      ,     legacy=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )

    UpperCAmelCase	:					List[str]	=	vocab_file
    UpperCAmelCase	:					str	=	extra_ids

    UpperCAmelCase	:					Dict	=	spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(_SCREAMING_SNAKE_CASE						)

   @staticmethod
   def 	SCREAMING_SNAKE_CASE     (   _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
     UpperCAmelCase	:					Any	=	TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
     if init_max_model_length is not None and init_max_model_length != max_model_length:
      return init_max_model_length
     elif init_max_model_length is None:
      warnings.warn(
          """This tokenizer was incorrectly instantiated with a model max length of"""
          F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
          """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
          """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
          F" {pretrained_model_name_or_path} automatically truncating your input to"
          F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
          F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
          """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
          """ instantiate this tokenizer with `model_max_length` set to your preferred value."""      ,     _SCREAMING_SNAKE_CASE      ,     )

    return max_model_length

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return self.sp_model.get_piece_size() + self._extra_ids

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





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False						)  ->      List[int]:





    '''simple docstring'''




    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=_SCREAMING_SNAKE_CASE      ,     token_ids_a=_SCREAMING_SNAKE_CASE      ,     already_has_special_tokens=_SCREAMING_SNAKE_CASE						)

    # normal case: some special tokens
    if token_ids_a is None:
     return ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1]
    return ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1]

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    return list(
        set(filter(lambda _SCREAMING_SNAKE_CASE						: bool(re.search(r"""<extra_id_\d+>"""      ,     _SCREAMING_SNAKE_CASE						)						) is not None      ,     self.additional_special_tokens						)						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    return [self._convert_token_to_id(_SCREAMING_SNAKE_CASE						) for token in self.get_sentinel_tokens()]

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[int]:





    '''simple docstring'''




    if len(_SCREAMING_SNAKE_CASE						) > 0 and token_ids[-1] == self.eos_token_id:
     warnings.warn(
         F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
         """ eos tokens being added."""						)
     return token_ids
    else:
     return token_ids + [self.eos_token_id]

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	[self.eos_token_id]

    if token_ids_a is None:
     return len(token_ids_a + eos						) * [0]
    return len(token_ids_a + eos + token_ids_a + eos						) * [0]

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE						)
    if token_ids_a is None:
     return token_ids_a
    else:
     UpperCAmelCase	:					Optional[Any]	=	self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE						)
     return token_ids_a + token_ids_a

   def __getstate__(   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.__dict__.copy()
    UpperCAmelCase	:					Union[str, Any]	=	None
    return state

   def __setstate__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	d

    # for backward compatibility
    if not hasattr(self      ,     """sp_model_kwargs"""						):
     UpperCAmelCase	:					Dict	=	{}

    UpperCAmelCase	:					Dict	=	spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    if not self.legacy:
     UpperCAmelCase	:					int	=	SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE      ,     """ """						)
    return super().tokenize(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    if not self.legacy:
     UpperCAmelCase	:					Union[str, Any]	=	text.startswith(_SCREAMING_SNAKE_CASE						)
     if is_first:
      UpperCAmelCase	:					Tuple	=	text[1:]

    UpperCAmelCase	:					Optional[int]	=	self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,     out_type=_SCREAMING_SNAKE_CASE						)

    if not self.legacy and not is_first and not text.startswith(""" """						) and tokens[0].startswith(_SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Optional[Any]	=	([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
    return tokens

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    if token.startswith("""<extra_id_"""						):
     UpperCAmelCase	:					Optional[int]	=	re.match(r"""<extra_id_(\d+)>"""      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					str	=	int(match.group(1						)						)
     return self.vocab_size - num - 1
    return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




    if index < self.sp_model.get_piece_size():
     UpperCAmelCase	:					List[Any]	=	self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Any	=	F"<extra_id_{self.vocab_size - 1 - index}>"
    return token

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	[]
    UpperCAmelCase	:					str	=	""
    UpperCAmelCase	:					List[str]	=	False
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      if not prev_is_special:
       out_string += " "
      out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE						) + token
      UpperCAmelCase	:					int	=	True
      UpperCAmelCase	:					List[Any]	=	[]
     else:
      current_sub_tokens.append(_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Optional[int]	=	False
    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE						)
    return out_string.strip()




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Tuple[str]:





    '''simple docstring'''




    if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
     logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
     return
    UpperCAmelCase	:					int	=	os.path.join(
        _SCREAMING_SNAKE_CASE      ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

    if os.path.abspath(self.vocab_file						) != os.path.abspath(_SCREAMING_SNAKE_CASE						) and os.path.isfile(self.vocab_file						):
     copyfile(self.vocab_file      ,     _SCREAMING_SNAKE_CASE						)
    elif not os.path.isfile(self.vocab_file						):
     with open(_SCREAMING_SNAKE_CASE      ,     """wb"""						) as fi:
      UpperCAmelCase	:					List[str]	=	self.sp_model.serialized_model_proto()
      fi.write(_SCREAMING_SNAKE_CASE						)

    return (out_vocab_file,)


import math




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							__UpperCamelCase	:   List[Any]			   =					input("Enter message: ")
							__UpperCamelCase	:   Optional[int]			   =					int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
							__UpperCamelCase	:   str			   =					input("Encryption/Decryption [e/d]: ")

							if mode.lower().startswith("e"):
														__UpperCamelCase	:   List[str]			   =					encrypt_message(_lowerCamelCase						,  _lowerCamelCase)
							elif mode.lower().startswith("d"):
														__UpperCamelCase	:   Dict			   =					decrypt_message(_lowerCamelCase						,  _lowerCamelCase)

							# Append pipe symbol (vertical bar) to identify spaces at the end.
							print(F'Output:\n{text + "|"}')




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					[""] * key
							for col in range(_lowerCamelCase):
														__UpperCamelCase	:   Any			   =					col
														while pointer < len(_lowerCamelCase):
																					cipher_text[col] += message[pointer]
																					pointer += key
							return "".join(_lowerCamelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			str)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					math.ceil(len(_lowerCamelCase) / key)
							__UpperCamelCase	:   Any			   =					key
							__UpperCamelCase	:   str			   =					(num_cols * num_rows) - len(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					[""] * num_cols
							__UpperCamelCase	:   Dict			   =					0
							__UpperCamelCase	:   int			   =					0

							for symbol in message:
														plain_text[col] += symbol
														col += 1

														if (
														    (col == num_cols)
														    or (col == num_cols - 1)
														    and (row >= num_rows - num_shaded_boxes)
														):
																					__UpperCamelCase	:   List[Any]			   =					0
																					row += 1

							return "".join(_lowerCamelCase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()