tokenization_gzip_bert.py

# coding=utf-8
# Copyright 2021 Javier de la Rosa, T5 Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for model GzipBERT."""

import gzip
import warnings
from typing import Dict, List, Optional, Tuple

from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
from transformers.utils import logging


logger = logging.get_logger(__name__)


class GzipBertTokenizer(PreTrainedTokenizer):
    """
    Construct a GzipBert tokenizer. GzipBert simply uses raw bytes utf-8 encoding.

    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
    this superclass for more information regarding those methods.

    Args:
        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sequence token.

            <Tip>

            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 `sep_token`.

            </Tip>

        unk_token (`str`, *optional*, defaults to `"<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 (`str`, *optional*, defaults to `"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        mask_token (`str`, *optional*, defaults to `"<mask>"`):
            The token used for padding, for example when batching sequences of different lengths.
        extra_ids (`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 ByT5 preprocessing see
            [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
        additional_special_tokens (`List[str]`, *optional*):
            Additional special tokens used by the tokenizer.
    """

    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        eos_token="</s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        extra_ids=0,
        additional_special_tokens=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 GzipBertTokenizer. In this case the additional_special_tokens must include the"
                    " extra_ids tokens"
                )
        elif extra_ids == 0 and additional_special_tokens is None:
            additional_special_tokens = []

        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
        mask_token = AddedToken(mask_token, lstrip=False, rstrip=False) if isinstance(mask_token, str) else mask_token

        super().__init__(
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            mask_token=mask_token,
            extra_ids=extra_ids,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )

        self._extra_ids = extra_ids

        self._utf_vocab_size = 2**8  # utf is 8 bits

        # define special tokens dict
        self.special_tokens_encoder: Dict[int, str] = {
            self.pad_token: 0,
            self.eos_token: 1,
            self.unk_token: 2,
            self.mask_token: 3,
        }
        self._num_special_tokens = len(self.special_tokens_encoder)
        n = len(additional_special_tokens)
        for i, token in enumerate(additional_special_tokens):
            self.special_tokens_encoder[token] = self.vocab_size + i - n
        self.special_tokens_decoder: Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}

    @property
    def vocab_size(self):
        return self._utf_vocab_size + self._num_special_tokens + self._extra_ids

    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 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            `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. GzipBert does not
        make use of token type ids, therefore a list of zeros is returned.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `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 (`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [input IDs](../glossary#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 _tokenize(self, text: str) -> List[str]:
        """Take as input a string and return a list of bytes (str) for binary gzip content"""
        tokens = [chr(i) for i in gzip.compress(bytes(text, 'utf-8'))]
        return tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        if token in self.special_tokens_encoder:
            token_id = self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
            token_id = self.added_tokens_encoder[token]
        elif len(token) != 1:
            token_id = self.unk_token_id
        else:
            token_id = ord(token) + self._num_special_tokens
        return token_id

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        if index in self.special_tokens_decoder:
            token = self.special_tokens_decoder[index]
        else:
            token = chr(index - self._num_special_tokens)
        return token

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        bstring = b""
        for token in tokens:
            if token in self.special_tokens_decoder:
                tok_string = self.special_tokens_decoder[token].encode("utf-8")
            elif token in self.added_tokens_decoder:
                tok_string = self.special_tokens_decoder[token].encode("utf-8")
            elif token in self.special_tokens_encoder:
                tok_string = token.encode("utf-8")
            elif token in self.added_tokens_encoder:
                tok_string = token.encode("utf-8")
            else:
                tok_string = bytes([ord(token)])
            bstring += tok_string
        string = gzip.decompress(bstring).decode("utf8")
        return string

    # GzipBertTokenizer has no vocab file
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        return ()