text: # coding=utf-8
  
  """ Tokenization class for model T5. Taken from Huggingface Transformers"""
  
  
  import os
  import re
  import warnings
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  
  import sentencepiece as spm
  
  
  VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
  
  PRETRAINED_VOCAB_FILES_MAP = {
      "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",
      }
  }
  
  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "t5-small": 512,
      "t5-base": 512,
      "t5-large": 512,
      "t5-3b": 512,
      "t5-11b": 512,
  }
  
  
  class EncDecTokenizer():
      """
      Construct a T5 tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__.
  
      This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
      Users should refer to this superclass for more information regarding those methods.
  
      Args:
          vocab_file (:obj:`str`):
              `SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
              contains the vocabulary necessary to instantiate a tokenizer.
          eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
              The end of sequence token.
  
              .. note::
  
                  When building a sequence using special tokens, this is not the token that is used for the end of
                  sequence. The token used is the :obj:`sep_token`.
          unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
              The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
              token instead.
          pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
              The token used for padding, for example when batching sequences of different lengths.
          extra_ids (:obj:`int`, `optional`, defaults to 100):
              Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
              accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
              indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
              like in T5 preprocessing see `here
              <https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__).
          additional_special_tokens (:obj:`List[str]`, `optional`):
              Additional special tokens used by the tokenizer.
          sp_model_kwargs (:obj:`dict`, `optional`):
              Will be passed to the ``SentencePieceProcessor.__init__()`` method. The `Python wrapper for SentencePiece
              <https://github.com/google/sentencepiece/tree/master/python>`__ can be used, among other things, to set:
  
              - ``enable_sampling``: Enable subword regularization.
              - ``nbest_size``: Sampling parameters for unigram. Invalid for BPE-Dropout.
  
                - ``nbest_size = {0,1}``: No sampling is performed.
                - ``nbest_size > 1``: samples from the nbest_size results.
                - ``nbest_size < 0``: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
                  using forward-filtering-and-backward-sampling algorithm.
  
              - ``alpha``: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
                BPE-dropout.
  
      Attributes:
          sp_model (:obj:`SentencePieceProcessor`):
              The `SentencePiece` processor that is used for every conversion (string, tokens and IDs).
      """
  
      vocab_files_names = VOCAB_FILES_NAMES
      pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
      max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      model_input_names = ["input_ids", "attention_mask"]
  
      def __init__(
          self,
          vocab_file,
          eos_token="</s>",
          unk_token="<unk>",
          pad_token="<pad>",
          extra_ids=100,
          extra_extra_ids=28,
          additional_special_tokens=None,
          sp_model_kwargs: Optional[Dict[str, Any]] = None,
          **kwargs
      ) -> None:
          # Add extra_ids to the special token list
          if extra_ids > 0 and additional_special_tokens is None:
              additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
          elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
              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"
                  )
  
          self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
  
          self.eos_token = eos_token
          self.unk_token = unk_token
          self.pad_token = pad_token
          self.extra_ids = extra_ids
          self.additional_sepcial_tokens = additional_special_tokens 
  
          self.vocab_file = vocab_file
          self._extra_ids = extra_ids
          self._extra_extra_ids = extra_extra_ids
  
          self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(vocab_file)
  
          self.eod_id = self.convert_token_to_id(self.eos_token)
          self.unk_id = self.convert_token_to_id(self.unk_token)
          self.pad_id = self.convert_token_to_id(self.pad_token)
  
  
      @property
      def real_vocab_size(self):
          return self.sp_model.get_piece_size() + self._extra_ids
  
      @property
      def vocab_size(self):
          return self.sp_model.get_piece_size() + self._extra_ids + self._extra_extra_ids
  
      def get_vocab(self):
          vocab = {self.convert_ids_to_tokens(i): i for i in range(self.real_vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab
  
      def get_special_tokens_mask(
          self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
      ) -> List[int]:
          """
          Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
          special tokens using the tokenizer ``prepare_for_model`` method.
  
          Args:
              token_ids_0 (:obj:`List[int]`):
                  List of IDs.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
              already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  Whether or not the token list is already formatted with special tokens for the model.
  
          Returns:
              :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
          """
          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
              )
  
          # normal case: some special tokens
          if token_ids_1 is None:
              return ([0] * len(token_ids_0)) + [1]
          return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
  
      def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
          """Do not add eos again if user already added it."""
          if len(token_ids) > 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 create_token_type_ids_from_sequences(
          self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
      ) -> List[int]:
          """
          Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
          use of token type ids, therefore a list of zeros is returned.
  
          Args:
              token_ids_0 (:obj:`List[int]`):
                  List of IDs.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
  
          Returns:
              :obj:`List[int]`: List of zeros.
          """
          eos = [self.eos_token_id]
  
          if token_ids_1 is None:
              return len(token_ids_0 + eos) * [0]
          return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
  
      def build_inputs_with_special_tokens(
          self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
      ) -> List[int]:
          """
          Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
          adding special tokens. A sequence has the following format:
  
          - single sequence: ``X </s>``
          - pair of sequences: ``A </s> B </s>``
  
          Args:
              token_ids_0 (:obj:`List[int]`):
                  List of IDs to which the special tokens will be added.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
  
          Returns:
              :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
          """
          token_ids_0 = self._add_eos_if_not_present(token_ids_0)
          if token_ids_1 is None:
              return token_ids_0
          else:
              token_ids_1 = self._add_eos_if_not_present(token_ids_1)
              return token_ids_0 + token_ids_1
  
      def __getstate__(self):
          state = self.__dict__.copy()
          state["sp_model"] = None
          return state
  
      def __setstate__(self, d):
          self.__dict__ = d
  
          # for backward compatibility
          if not hasattr(self, "sp_model_kwargs"):
              self.sp_model_kwargs = {}
  
          self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(self.vocab_file)
  
      def tokenize(self, text: str) -> List[str]:
          """Take as input a string and return a list of strings (tokens) for words/sub-words"""
          return self.sp_model.encode(text, out_type=str)
  
      def convert_token_to_id(self, token):
          """Converts a token (str) in an id using the vocab."""
          if token.startswith("<extra_id_"):
              match = re.match(r"<extra_id_(\d+)>", token)
              num = int(match.group(1))
              return self.real_vocab_size - num - 1
          return self.sp_model.piece_to_id(token)
  
      def convert_id_to_token(self, index):
          """Converts an index (integer) in a token (str) using the vocab."""
          if index < self.sp_model.get_piece_size():
              token = self.sp_model.IdToPiece(index)
          else:
              token = f"<extra_id_{self.real_vocab_size - 1 - index}>"
          return token
  
      def convert_tokens_to_string(self, tokens):
          """Converts a sequence of tokens (string) in a single string."""
          current_sub_tokens = []
          out_string = ""
          for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.additional_sepcial_tokens:
                  out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " "
                  current_sub_tokens = []
              else:
                  current_sub_tokens.append(token)
          out_string += self.sp_model.decode_pieces(current_sub_tokens)
          return out_string.strip()
  
      def convert_tokens_to_ids(self, tokens):
          return [self.convert_token_to_id(x) for x in tokens]
  
      def convert_ids_to_tokens(self, ids):
          return [self.convert_id_to_token(x) for x in ids]
  
      def encode(self, text):
          return self.convert_tokens_to_ids(self.tokenize(text))
  
      def decode(self, ids):
          return self.convert_tokens_to_string(self.convert_ids_to_tokens(ids))
  
      def get_sentinel_id(self, idx):
          return self.real_vocab_size - idx - 1
  
      def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
          if not os.path.isdir(save_directory):
              return
          out_vocab_file = os.path.join(
              save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
          )
  
          if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
              copyfile(self.vocab_file, out_vocab_file)
  
          return (out_vocab_file,)
  