
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,)



_A						      =				0  # The first color of the flag.
_A						      =				1  # The second color of the flag.
_A						      =				2  # The third color of the flag.
_A						      =				(red, white, blue)



def 		lowercase_    (     A__	)							->							list:
    """simple docstring"""

    if not sequence:
        return []
    if len(A__	) == 1:
        return list(A__	)
    snake_case				  =      0
    snake_case				  =      len(A__	) - 1
    snake_case				  =      0
    while mid <= high:
        if sequence[mid] == colors[0]:
            snake_case  ,							snake_case				  =      sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            snake_case  ,							snake_case				  =      sequence[high], sequence[mid]
            high -= 1
        else:
            snake_case				  =      F'The elements inside the sequence must contains only {colors} values'
            raise ValueError(A__	)
    return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _A						      =				input("Enter numbers separated by commas:\n").strip()
   _A						      =				[int(item.strip()) for item in user_input.split(",")]
   print(f"{dutch_national_flag_sort(unsorted)}")

