
"""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


UpperCamelCase			:       Union[str, Any]									=				logging.get_logger(__name__)

UpperCamelCase			:       List[str]									=				{"vocab_file": "spiece.model"}

UpperCamelCase			:       Any									=				{
    "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
UpperCamelCase			:       str									=				{
    "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,
}

UpperCamelCase			:       Dict									=				"▁"

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		VOCAB_FILES_NAMES
     lowercase		     =		PRETRAINED_VOCAB_FILES_MAP
     lowercase		     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase		     =		["input_ids", "attention_mask"]





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

      '''simple docstring'''




      if extra_ids > 0 and additional_special_tokens is None:
       __UpperCamelCase       						= [F'<extra_id_{i}>' for i in range(__UpperCAmelCase   )]
      elif extra_ids > 0 and additional_special_tokens is not None:
       # Check that we have the right number of extra_id special tokens
       __UpperCamelCase       						= len(set(filter(lambda __UpperCAmelCase   : bool('extra_id' in str(__UpperCAmelCase   )   )			,  __UpperCAmelCase   )   )   )
       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       						= legacy
      __UpperCamelCase       						= {} if sp_model_kwargs is None else sp_model_kwargs

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

      __UpperCamelCase       						= vocab_file
      __UpperCamelCase       						= extra_ids

      __UpperCamelCase       						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(__UpperCAmelCase   )





     @staticmethod
     def 		UpperCAmelCase      (			__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
       __UpperCamelCase       						= 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.'			,  __UpperCAmelCase			,  )

      return max_model_length





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= {self.convert_ids_to_tokens(__UpperCAmelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False   ):

      '''simple docstring'''




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

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if len(__UpperCAmelCase   ) > 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 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




      __UpperCamelCase       						= [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 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




      __UpperCamelCase       						= self._add_eos_if_not_present(__UpperCAmelCase   )
      if token_ids_a is None:
       return token_ids_a
      else:
       __UpperCamelCase       						= self._add_eos_if_not_present(__UpperCAmelCase   )
       return token_ids_a + token_ids_a





     def __getstate__(			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.__dict__.copy()
      __UpperCamelCase       						= None
      return state





     def __setstate__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= d

      # for backward compatibility
      if not hasattr(self			,  'sp_model_kwargs'   ):
       __UpperCamelCase       						= {}

      __UpperCamelCase       						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      if not self.legacy:
       __UpperCamelCase       						= SPIECE_UNDERLINE + text.replace(__UpperCAmelCase			,  ' '   )
      return super().tokenize(__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      if not self.legacy:
       __UpperCamelCase       						= text.startswith(__UpperCAmelCase   )
       if is_first:
        __UpperCamelCase       						= text[1:]

      __UpperCamelCase       						= self.sp_model.encode(__UpperCAmelCase			,  out_type=__UpperCAmelCase   )

      if not self.legacy and not is_first and not text.startswith(' '   ) and tokens[0].startswith(__UpperCAmelCase   ):
       __UpperCamelCase       						= ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
      return tokens





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if token.startswith('<extra_id_'   ):
       __UpperCamelCase       						= re.match(R'<extra_id_(\d+)>'			,  __UpperCAmelCase   )
       __UpperCamelCase       						= int(match.group(1   )   )
       return self.vocab_size - num - 1
      return self.sp_model.piece_to_id(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if index < self.sp_model.get_piece_size():
       __UpperCamelCase       						= self.sp_model.IdToPiece(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= F'<extra_id_{self.vocab_size - 1 - index}>'
      return token





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= []
      __UpperCamelCase       						= ''
      __UpperCamelCase       						= 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(__UpperCAmelCase   ) + token
        __UpperCamelCase       						= True
        __UpperCamelCase       						= []
       else:
        current_sub_tokens.append(__UpperCAmelCase   )
        __UpperCamelCase       						= False
      out_string += self.sp_model.decode(__UpperCAmelCase   )
      return out_string.strip()







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




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

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(__UpperCAmelCase   ) and os.path.isfile(self.vocab_file   ):
       copyfile(self.vocab_file			,  __UpperCAmelCase   )
      elif not os.path.isfile(self.vocab_file   ):
       with open(__UpperCAmelCase			,  'wb'   ) as fi:
        __UpperCamelCase       						= self.sp_model.serialized_model_proto()
        fi.write(__UpperCAmelCase   )

      return (out_vocab_file,)





"""simple docstring"""

def 							A		(       snake_case   :int	)   ->			bool:
 return str(snake_case	) == str(snake_case	)[::-1]






def 							A		(       snake_case   :int	)   ->			int:
 return int(snake_case	) + int(str(snake_case	)[::-1]	)






def 							A		(       snake_case   :int = 1_0_0_0_0	)   ->			int:
 __UpperCamelCase       						= []
 for num in range(1       ,      snake_case	):
  __UpperCamelCase       						= 0
  __UpperCamelCase       						= num
  while iterations < 5_0:
   __UpperCamelCase       						= sum_reverse(snake_case	)
   iterations += 1
   if is_palindrome(snake_case	):
    break
  else:
   lychrel_nums.append(snake_case	)
 return len(snake_case	)


if __name__ == "__main__":
      print(f'''{solution() = }''')



