
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						      =				logging.get_logger(__name__)

_A						      =				{"vocab_file": "spiece.model"}

_A						      =				{
    "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						      =				{
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

_A						      =				"▁"







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							List[str]     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							Union[str, Any]     =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							Any     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							Union[str, Any]     =	["input_ids", "attention_mask"]





     def __init__(self   :					Tuple       ,					_A   :					str       ,					_A   :					Tuple="</s>"       ,					_A   :					Dict="<unk>"       ,					_A   :					int="<pad>"       ,					_A   :					Optional[int]=1_0_0       ,					_A   :					Optional[Any]=None       ,					_A   :					Optional[Dict[str, Any]] = None       ,					_A   :					int=True       ,					**_A   :					List[Any]       ,					)		->		None:
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
             snake_case				  =      [f'<extra_id_{i}>' for i in range(_A     )]
         elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra_id special tokens
             snake_case				  =      len(set(filter(lambda _A     : bool("extra_id" in str(_A     )     )       ,					_A     )     )     )
             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"     )

         snake_case				  =      legacy
         snake_case				  =      {} if sp_model_kwargs is None else sp_model_kwargs

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

         snake_case				  =      vocab_file
         snake_case				  =      extra_ids

         snake_case				  =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(_A     )





     @staticmethod
     def 	UpperCAmelCase(_A   :					Any       ,					_A   :					Dict       ,					_A   :					Optional[int]     )		->		Tuple:
         if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
             snake_case				  =      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."       ,					_A       ,					)

         return max_model_length





     @property
     def 	UpperCAmelCase(self   :					Tuple     )		->		int:
         return self.sp_model.get_piece_size() + self._extra_ids





     def 	UpperCAmelCase(self   :					List[Any]     )		->		Dict:
         snake_case				  =      {self.convert_ids_to_tokens(_A     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					List[int]       ,					_A   :					Optional[List[int]] = None       ,					_A   :					bool = False     )		->		List[int]:
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_A       ,					token_ids_a=_A       ,					already_has_special_tokens=_A     )

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





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Any:
         return list(
             set(filter(lambda _A     : bool(re.search(r"<extra_id_\d+>"       ,					_A     )     ) is not None       ,					self.additional_special_tokens     )     )     )





     def 	UpperCAmelCase(self   :					Any     )		->		List[str]:
         return [self._convert_token_to_id(_A     ) for token in self.get_sentinel_tokens()]





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					List[int]     )		->		List[int]:
         if len(_A     ) > 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   :					Dict       ,					_A   :					List[int]       ,					_A   :					Optional[List[int]] = None     )		->		List[int]:
         snake_case				  =      [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   :					int       ,					_A   :					List[int]       ,					_A   :					Optional[List[int]] = None     )		->		List[int]:
         snake_case				  =      self._add_eos_if_not_present(_A     )
         if token_ids_a is None:
             return token_ids_a
         else:
             snake_case				  =      self._add_eos_if_not_present(_A     )
             return token_ids_a + token_ids_a





     def __getstate__(self   :					Dict     )		->		Optional[Any]:
         snake_case				  =      self.__dict__.copy()
         snake_case				  =      None
         return state





     def __setstate__(self   :					Tuple       ,					_A   :					Dict     )		->		str:
         snake_case				  =      d

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

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





     def 	UpperCAmelCase(self   :					Any       ,					_A   :					"TextInput"       ,					**_A   :					Union[str, Any]     )		->		List[str]:
         # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
         # the beginning of the text
         if not self.legacy:
             snake_case				  =      SPIECE_UNDERLINE + text.replace(_A       ,					" "     )
         return super().tokenize(_A       ,					**_A     )





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					Optional[Any]       ,					**_A   :					Union[str, Any]     )		->		Optional[Any]:
         if not self.legacy:
             snake_case				  =      text.startswith(_A     )
             if is_first:
                 snake_case				  =      text[1:]

         snake_case				  =      self.sp_model.encode(_A       ,					out_type=_A     )

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





     def 	UpperCAmelCase(self   :					int       ,					_A   :					str     )		->		Tuple:
         if token.startswith("<extra_id_"     ):
             snake_case				  =      re.match(r"<extra_id_(\d+)>"       ,					_A     )
             snake_case				  =      int(match.group(1     )     )
             return self.vocab_size - num - 1
         return self.sp_model.piece_to_id(_A     )





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Any     )		->		Optional[Any]:
         if index < self.sp_model.get_piece_size():
             snake_case				  =      self.sp_model.IdToPiece(_A     )
         else:
             snake_case				  =      f'<extra_id_{self.vocab_size - 1 - index}>'
         return token





     def 	UpperCAmelCase(self   :					int       ,					_A   :					int     )		->		int:
         snake_case				  =      []
         snake_case				  =      ""
         snake_case				  =      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(_A     ) + token
                 snake_case				  =      True
                 snake_case				  =      []
             else:
                 current_sub_tokens.append(_A     )
                 snake_case				  =      False
         out_string += self.sp_model.decode(_A     )
         return out_string.strip()





     def 	UpperCAmelCase(self   :					Any       ,					_A   :					str       ,					_A   :					Optional[str] = None     )		->		Tuple[str]:
         if not os.path.isdir(_A     ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
             return
         snake_case				  =      os.path.join(
             _A       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

         return (out_vocab_file,)



import os

# Precomputes a list of the 100 first triangular numbers
_A						      =				[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]



def 		lowercase_    (     )							->							Tuple:
    """simple docstring"""

    snake_case				  =      os.path.dirname(os.path.realpath(A__	)	)
    snake_case				  =      os.path.join(A__				,	"words.txt"	)

    snake_case				  =      ""
    with open(A__	) as f:
        snake_case				  =      f.readline()

    snake_case				  =      [word.strip("\""	) for word in words.strip("\r\n"	).split(","	)]
    snake_case				  =      [
        word
        for word in [sum(ord(A__	) - 64 for x in word	) for word in words]
        if word in TRIANGULAR_NUMBERS
    ]
    return len(A__	)


if __name__ == "__main__":
   print(solution())

