envibert_tokenizer.py

# !pip install sentencepiece==0.1.96 transformers==4.10.0
import sentencepiece as spm
import os
from transformers import PreTrainedTokenizer
from collections import Counter
from typing import List, Optional


class RobertaTokenizer(PreTrainedTokenizer):
    def __init__(
            self,
            pretrained_file,
            bos_token="<s>",
            eos_token="</s>",
            sep_token="</s>",
            cls_token="<s>",
            unk_token="<unk>",
            pad_token="<pad>",
            mask_token="<mask>",
            **kwargs
    ):
        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            **kwargs,
        )

        # load bpe model and vocab file
        sentencepiece_model = os.path.join(pretrained_file, 'sentencepiece.bpe.model')
        vocab_file = os.path.join(pretrained_file, 'dict.txt')
        self.sp_model = spm.SentencePieceProcessor()
        self.sp_model.Load(
            sentencepiece_model)  # please dont use anything from sp_model bcz it makes everything goes wrong

        self.bpe_dict = Dictionary().load(vocab_file)

        # Mimic fairseq token-to-id alignment for the first 4 token
        self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        self.fairseq_offset = 0

        self.fairseq_tokens_to_ids["<mask>"] = len(self.bpe_dict) + self.fairseq_offset
        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}

    def _tokenize(self, text):
        return self.sp_model.EncodeAsPieces(text)

    def _convert_token_to_id(self, token):
        """ Converts a token (str) in an id using the vocab. """
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        spm_id = self.bpe_dict.index(token)
        return spm_id

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.bpe_dict[index]

    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.

        This implementation does not add special tokens and this method should be overridden in a subclass.

        Args:
            token_ids_0 (:obj:`List[int]`): The first tokenized sequence.
            token_ids_1 (:obj:`List[int]`, `optional`): The second tokenized sequence.

        Returns:
            :obj:`List[int]`: The model input with special tokens.
        """
        return [self.cls_token_id] + token_ids_0 + [self.sep_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. XLM-RoBERTa 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.

        """

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

        return len(cls + token_ids_0 + sep) * [0]

    @property
    def vocab_size(self):
        return len(self.bpe_dict) + self.fairseq_offset + 1  # Add the <mask> token

    def get_vocab(self):
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab


class Dictionary(object):
    """A mapping from symbols to consecutive integers"""

    def __init__(
            self,
            pad='<pad>',
            eos='</s>',
            unk='<unk>',
            bos='<s>',
            extra_special_symbols=None,
    ):
        self.unk_word, self.pad_word, self.eos_word = unk, pad, eos
        self.symbols = []
        self.count = []
        self.indices = {}
        self.bos_index = self.add_symbol(bos)
        self.pad_index = self.add_symbol(pad)
        self.eos_index = self.add_symbol(eos)
        self.unk_index = self.add_symbol(unk)
        if extra_special_symbols:
            for s in extra_special_symbols:
                self.add_symbol(s)
        self.nspecial = len(self.symbols)

    def __eq__(self, other):
        return self.indices == other.indices

    def __getitem__(self, idx):
        if idx < len(self.symbols):
            return self.symbols[idx]
        return self.unk_word

    def __len__(self):
        """Returns the number of symbols in the dictionary"""
        return len(self.symbols)

    def __contains__(self, sym):
        return sym in self.indices

    def index(self, sym):
        """Returns the index of the specified symbol"""
        assert isinstance(sym, str)
        if sym in self.indices:
            return self.indices[sym]
        return self.unk_index

    def unk_string(self, escape=False):
        """Return unknown string, optionally escaped as: <<unk>>"""
        if escape:
            return '<{}>'.format(self.unk_word)
        else:
            return self.unk_word

    def add_symbol(self, word, n=1):
        """Adds a word to the dictionary"""
        if word in self.indices:
            idx = self.indices[word]
            self.count[idx] = self.count[idx] + n
            return idx
        else:
            idx = len(self.symbols)
            self.indices[word] = idx
            self.symbols.append(word)
            self.count.append(n)
            return idx

    def update(self, new_dict):
        """Updates counts from new dictionary."""
        for word in new_dict.symbols:
            idx2 = new_dict.indices[word]
            if word in self.indices:
                idx = self.indices[word]
                self.count[idx] = self.count[idx] + new_dict.count[idx2]
            else:
                idx = len(self.symbols)
                self.indices[word] = idx
                self.symbols.append(word)
                self.count.append(new_dict.count[idx2])

    def finalize(self, threshold=-1, nwords=-1, padding_factor=8):
        """Sort symbols by frequency in descending order, ignoring special ones.

        Args:
            - threshold defines the minimum word count
            - nwords defines the total number of words in the final dictionary,
                including special symbols
            - padding_factor can be used to pad the dictionary size to be a
                multiple of 8, which is important on some hardware (e.g., Nvidia
                Tensor Cores).
        """
        if nwords <= 0:
            nwords = len(self)

        new_indices = dict(zip(self.symbols[:self.nspecial], range(self.nspecial)))
        new_symbols = self.symbols[:self.nspecial]
        new_count = self.count[:self.nspecial]

        c = Counter(dict(sorted(zip(self.symbols[self.nspecial:], self.count[self.nspecial:]))))
        for symbol, count in c.most_common(nwords - self.nspecial):
            if count >= threshold:
                new_indices[symbol] = len(new_symbols)
                new_symbols.append(symbol)
                new_count.append(count)
            else:
                break

        threshold_nwords = len(new_symbols)
        if padding_factor > 1:
            i = 0
            while threshold_nwords % padding_factor != 0:
                symbol = 'madeupword{:04d}'.format(i)
                new_indices[symbol] = len(new_symbols)
                new_symbols.append(symbol)
                new_count.append(0)
                i += 1
                threshold_nwords += 1

        assert len(new_symbols) % padding_factor == 0
        assert len(new_symbols) == len(new_indices)

        self.count = list(new_count)
        self.symbols = list(new_symbols)
        self.indices = new_indices

    def bos(self):
        """Helper to get index of beginning-of-sentence symbol"""
        return self.bos_index

    def pad(self):
        """Helper to get index of pad symbol"""
        return self.pad_index

    def eos(self):
        """Helper to get index of end-of-sentence symbol"""
        return self.eos_index

    def unk(self):
        """Helper to get index of unk symbol"""
        return self.unk_index

    @classmethod
    def load(cls, f):
        """Loads the dictionary from a text file with the format:

        ```
        <symbol0> <count0>
        <symbol1> <count1>
        ...
        ```
        """
        d = cls()
        d.add_from_file(f)
        return d

    def add_from_file(self, f):
        """
        Loads a pre-existing dictionary from a text file and adds its symbols
        to this instance.
        """
        if isinstance(f, str):
            try:
                with open(f, 'r', encoding='utf-8') as fd:
                    self.add_from_file(fd)
            except FileNotFoundError as fnfe:
                raise fnfe
            except UnicodeError:
                raise Exception("Incorrect encoding detected in {}, please "
                                "rebuild the dataset".format(f))
            return

        lines = f.readlines()
        indices_start_line = self._load_meta(lines)
        for line in lines[indices_start_line:]:
            idx = line.rfind(' ')
            if idx == -1:
                raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
            word = line[:idx]
            count = int(line[idx + 1:])
            self.indices[word] = len(self.symbols)
            self.symbols.append(word)
            self.count.append(count)

    def _save(self, f, kv_iterator):
        if isinstance(f, str):
            os.makedirs(os.path.dirname(f), exist_ok=True)
            with open(f, 'w', encoding='utf-8') as fd:
                return self.save(fd)
        for k, v in kv_iterator:
            print('{} {}'.format(k, v), file=f)

    def _get_meta(self):
        return [], []

    def _load_meta(self, lines):
        return 0

    def save(self, f):
        """Stores dictionary into a text file"""
        ex_keys, ex_vals = self._get_meta()
        self._save(f, zip(ex_keys + self.symbols[self.nspecial:], ex_vals + self.count[self.nspecial:]))