tokenization_bertweet_fast.py

# coding=utf-8
# Copyright (c) 2020, VinAI Research and the HuggingFace Inc. team.
# Copyright 2018 The Open AI Team Authors and The 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 classes for BERTweet"""

import os
from collections import defaultdict
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

from transformers.tokenization_utils_base import EncodingFast

from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
from transformers.utils import logging
from .tokenization_bertweet import BertweetTokenizer


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
    "tokenizer_file": "tokenizer.json",
}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "vinai/bertweet-base": "https://huggingface.co./vinai/bertweet-base/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/bertweet-base": "https://huggingface.co./vinai/bertweet-base/resolve/main/bpe.codes",
    },
    "tokenizer_file": {
        "vinai/bertweet-base": "https://huggingface.co./vinai/bertweet-base/resolve/main/tokenizer.json",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "vinai/bertweet-base": 128,
}


class BertweetTokenizerFast(PreTrainedTokenizerFast):
    """
    Construct a "Fast" BPE tokenizer for BERTweet (backed by HuggingFace's *tokenizers* library).

    Peculiarities:

    - uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of
      a punctuation character will be treated separately.

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

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        merges_file (`str`):
            Path to the merges file.
    """

    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"]
    slow_tokenizer_class = BertweetTokenizer

    def __init__(
        self,
        vocab_file=None,
        merges_file=None,
        tokenizer_file=None,
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        **kwargs
    ):
        super().__init__(
            vocab_file,
            merges_file,
            tokenizer_file=tokenizer_file,
            bos_token=bos_token,
            eos_token=eos_token,
            sep_token=sep_token,
            cls_token=cls_token,
            unk_token=unk_token,
            pad_token=pad_token,
            mask_token=mask_token,
            **kwargs,
        )

        self.vocab_file = vocab_file
        self.merges_file = merges_file
        self.can_save_slow_tokenizer = False if not self.vocab_file else True

    def get_added_vocab_hacking(self):
        """
        Returns the added tokens in the vocabulary as a dictionary of token to index.

        Returns:
            `Dict[str, int], Dict[int, int]`: The added tokens, and their original and new ids
        """
        base_vocab_size = self._tokenizer.get_vocab_size(with_added_tokens=False)
        full_vocab_size = self._tokenizer.get_vocab_size(with_added_tokens=True)
        if full_vocab_size == base_vocab_size:
            return {}, {}

        # Tokens in added_vocab should have ids that are equal to or larger than the size of base_vocab
        added_vocab = dict(
            (self._tokenizer.id_to_token(index), index + 1 - base_vocab_size + self.mask_token_id)
            for index in range(base_vocab_size, full_vocab_size)
        )

        id_mapping = dict((index, self._tokenizer.token_to_id(tok)) for tok, index in added_vocab.items())

        return added_vocab, id_mapping

    def _decode(
        self,
        token_ids: Union[int, List[int]],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        **kwargs
    ) -> str:
        self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)

        if isinstance(token_ids, int):
            token_ids = [token_ids]

        # Mapping ids into their original values
        _, id_mapping = self.get_added_vocab_hacking()
        if len(id_mapping) > 0:
            token_ids = [id_mapping[id] if id in id_mapping else id for id in token_ids]

        text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)

        if clean_up_tokenization_spaces:
            clean_text = self.clean_up_tokenization(text)
            return clean_text
        else:
            return text

    def _convert_encoding(
        self,
        encoding: EncodingFast,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
    ) -> Tuple[Dict[str, Any], List[EncodingFast]]:
        """
        Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list
        of encodings, take care of building a batch from overflowing tokens.

        Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are
        lists (overflows) of lists (tokens).

        Output shape: (overflows, sequence length)
        """
        if return_token_type_ids is None:
            return_token_type_ids = "token_type_ids" in self.model_input_names
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        if return_overflowing_tokens and encoding.overflowing is not None:
            encodings = [encoding] + encoding.overflowing
        else:
            encodings = [encoding]

        encoding_dict = defaultdict(list)
        added_vocab, _ = self.get_added_vocab_hacking()
        for e in encodings:
            # encoding_dict["input_ids"].append(e.ids)
            # Reassign ids of tokens due to the hacking strategy
            ids = []
            for id, token in zip(e.ids, e.tokens):
                if id <= self.mask_token_id:
                    ids.append(id)
                else:
                    if token.strip() in added_vocab:
                        ids.append(added_vocab[token.strip()])
                    else:
                        ids.append(self.unk_token_id)

            encoding_dict["input_ids"].append(ids)

            if return_token_type_ids:
                encoding_dict["token_type_ids"].append(e.type_ids)
            if return_attention_mask:
                encoding_dict["attention_mask"].append(e.attention_mask)
            if return_special_tokens_mask:
                encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
            if return_offsets_mapping:
                encoding_dict["offset_mapping"].append(e.offsets)
            if return_length:
                # encoding_dict["length"].append(len(e.ids))
                encoding_dict["length"].append(len(ids))

        return encoding_dict, encodings

    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 BERTweet sequence has the following format:

        - single sequence: `<s> X </s>`
        - pair of sequences: `<s> A </s></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.
        """

        if token_ids_1 is None:
            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
        cls = [self.cls_token_id]
        sep = [self.sep_token_id]
        return cls + token_ids_0 + sep + sep + token_ids_1 + sep

    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
            )

        if token_ids_1 is None:
            return [1] + ([0] * len(token_ids_0)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]

    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. BERTweet 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.

        """

        sep = [self.sep_token_id]
        cls = [self.cls_token_id]

        if token_ids_1 is None:
            return len(cls + token_ids_0 + sep) * [0]
        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not self.can_save_slow_tokenizer:
            raise ValueError(
                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                "tokenizer."
            )

        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory.")
            return

        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        out_merges_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
            copyfile(self.vocab_file, out_vocab_file)

        if os.path.abspath(self.merges_file) != os.path.abspath(out_merges_file):
            copyfile(self.merges_file, out_merges_file)

        return (out_vocab_file, out_merges_file)