tokenization_dna.py

# coding=utf-8
# Copyright 2018 The Google AI Language 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."""


import collections
import logging
import os
import math
import unicodedata


from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast


logger = logging.getLogger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": os.getenv("VOCAB_NAME")}

PRETRAINED_VOCAB_FILES_MAP = {"vocab_file": {
                                    'dna' : os.getenv("VOCAB_PATH")
                                    }
                            }


PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {'dna': os.getenv("POSITIONAL_EMBEDDINGS_SIZE")}
PRETRAINED_INIT_CONFIGURATION = {'dna': {"do_lower_case": False}}

def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    with open(vocab_file, "r", encoding="utf-8") as reader:
        tokens = reader.readlines()
    for index, token in enumerate(tokens):
        token = token.rstrip("\n")
        vocab[token] = index
    return vocab


def whitespace_tokenize(text):
    """Runs basic whitespace cleaning and splitting on a piece of text."""
    text = text.strip()
    if not text:
        return []
    tokens = text.split()
    return tokens


class DNATokenizer(PreTrainedTokenizer):
    r"""
    Constructs a BertTokenizer.
    :class:`~transformers.BertTokenizer` runs end-to-end tokenization: punctuation splitting + wordpiece
    Args:
        vocab_file: Path to a one-wordpiece-per-line vocabulary file
        do_lower_case: Whether to lower case the input. Only has an effect when do_basic_tokenize=True
        do_basic_tokenize: Whether to do basic tokenization before wordpiece.
        max_len: An artificial maximum length to truncate tokenized sequences to; Effective maximum length is always the
            minimum of this value (if specified) and the underlying BERT model's sequence length.
        never_split: List of tokens which will never be split during tokenization. Only has an effect when
            do_basic_tokenize=True
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


    def __init__(
        self,
        vocab_file,
        do_lower_case=False,
        never_split=None,
        unk_token="[UNK]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
        tokenize_chinese_chars=True,
        **kwargs
    ):
        """Constructs a BertTokenizer.
        Args:
            **vocab_file**: Path to a one-wordpiece-per-line vocabulary file
            **do_lower_case**: (`optional`) boolean (default True)
                Whether to lower case the input
                Only has an effect when do_basic_tokenize=True
            **do_basic_tokenize**: (`optional`) boolean (default True)
                Whether to do basic tokenization before wordpiece.
            **never_split**: (`optional`) list of string
                List of tokens which will never be split during tokenization.
                Only has an effect when do_basic_tokenize=True
            **tokenize_chinese_chars**: (`optional`) boolean (default True)
                Whether to tokenize Chinese characters.
                This should likely be deactivated for Japanese:
                see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
        """
        super().__init__(
            unk_token=unk_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            **kwargs,
        )
        self.max_len_single_sentence = self.max_len - 2  # take into account special tokens
        self.max_len_sentences_pair = self.max_len - 3  # take into account special tokens

        if not os.path.isfile(vocab_file):
            raise ValueError(
                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
            )
        self.vocab = load_vocab(vocab_file)
        self.kmer = VOCAB_KMER[str(len(self.vocab))]
        self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
        self.basic_tokenizer = BasicTokenizer(
                do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=tokenize_chinese_chars
            )

    @property
    def vocab_size(self):
        return len(self.vocab)

    def _tokenize(self, text):
        split_tokens = []
        for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
                split_tokens.append(token)
        # print(split_tokens)
        return split_tokens

    def _convert_token_to_id(self, token):
        """ Converts a token (str) in an id using the vocab. """
        return self.vocab.get(token, self.vocab.get(self.unk_token))

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.ids_to_tokens.get(index, self.unk_token)

    def convert_tokens_to_string(self, tokens):
        """ Converts a sequence of tokens (string) in a single string. """
        out_string = " ".join(tokens).replace(" ##", "").strip()
        return out_string

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks
        by concatenating and adding special tokens.
        A BERT sequence has the following format:
            single sequence: [CLS] X [SEP]
            pair of sequences: [CLS] A [SEP] B [SEP]
        """
        cls = [self.cls_token_id]
        sep = [self.sep_token_id]

        if token_ids_1 is None:
            if len(token_ids_0) < 510:
                return cls + token_ids_0 + sep
            else:
                output = []
                num_pieces = int(len(token_ids_0)//510) + 1
                for i in range(num_pieces):
                    output.extend(cls + token_ids_0[510*i:min(len(token_ids_0), 510*(i+1))] + sep)
                return output

        return cls + token_ids_0 + sep + token_ids_1 + sep

    def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
        """
        Retrieves 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`` or ``encode_plus`` methods.
        Args:
            token_ids_0: list of ids (must not contain special tokens)
            token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
                for sequence pairs
            already_has_special_tokens: (default False) Set to True if the token list is already formated with
                special tokens for the model
        Returns:
            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
            if token_ids_1 is not None:
                raise ValueError(
                    "You should not supply a second sequence if the provided sequence of "
                    "ids is already formated with special tokens for the model."
                )
            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))

        if token_ids_1 is not None:
            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
        
        if len(token_ids_0) < 510:
            return [1] + ([0] * len(token_ids_0)) + [1]
        else:
            output = []
            num_pieces = int(len(token_ids_0)//510) + 1
            for i in range(num_pieces):
                output.extend([1] + ([0] * (min(len(token_ids_0), 510*(i+1))-510*i)) + [1])
            return output
            return [1] + ([0] * len(token_ids_0)) + [1]

    def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
        """
        Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
        A BERT sequence pair mask has the following format:
        0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence
        if token_ids_1 is None, only returns the first portion of the mask (0's).
        """
        sep = [self.sep_token_id]
        cls = [self.cls_token_id]
        if token_ids_1 is None:
            if len(token_ids_0) < 510:
                return len(cls + token_ids_0 + sep) * [0]
            else:
                num_pieces = int(len(token_ids_0)//510) + 1
                return (len(cls + token_ids_0 + sep) + 2*(num_pieces-1)) * [0]
        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

    def save_vocabulary(self, vocab_path):
        """Save the tokenizer vocabulary to a directory or file."""
        index = 0
        if os.path.isdir(vocab_path):
            vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["vocab_file"])
        else:
            vocab_file = vocab_path
        with open(vocab_file, "w", encoding="utf-8") as writer:
            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    logger.warning(
                        "Saving vocabulary to {}: vocabulary indices are not consecutive."
                        " Please check that the vocabulary is not corrupted!".format(vocab_file)
                    )
                    index = token_index
                writer.write(token + "\n")
                index += 1
        return (vocab_file,)


class BasicTokenizer(object):
    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""

    def __init__(self, do_lower_case=False, never_split=None, tokenize_chinese_chars=True):
        """ Constructs a BasicTokenizer.
        Args:
            **do_lower_case**: Whether to lower case the input.
            **never_split**: (`optional`) list of str
                Kept for backward compatibility purposes.
                Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
                List of token not to split.
            **tokenize_chinese_chars**: (`optional`) boolean (default True)
                Whether to tokenize Chinese characters.
                This should likely be deactivated for Japanese:
                see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
        """
        if never_split is None:
            never_split = []
        self.do_lower_case = do_lower_case
        self.never_split = never_split
        self.tokenize_chinese_chars = tokenize_chinese_chars

    def tokenize(self, text, never_split=None):
        """ Basic Tokenization of a piece of text.
            Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer.
        Args:
            **never_split**: (`optional`) list of str
                Kept for backward compatibility purposes.
                Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
                List of token not to split.
        """
        never_split = self.never_split + (never_split if never_split is not None else [])
        text = self._clean_text(text)
        # This was added on November 1st, 2018 for the multilingual and Chinese
        # models. This is also applied to the English models now, but it doesn't
        # matter since the English models were not trained on any Chinese data
        # and generally don't have any Chinese data in them (there are Chinese
        # characters in the vocabulary because Wikipedia does have some Chinese
        # words in the English Wikipedia.).
        orig_tokens = whitespace_tokenize(text)
        split_tokens = []
        for token in orig_tokens:
            if token not in never_split:
                token = self._run_strip_accents(token)
            split_tokens.extend(self._run_split_on_punc(token, never_split))

        output_tokens = whitespace_tokenize(" ".join(split_tokens))
        return output_tokens

    def _run_strip_accents(self, text):
        """Strips accents from a piece of text."""
        text = unicodedata.normalize("NFD", text)
        output = []
        for char in text:
            cat = unicodedata.category(char)
            if cat == "Mn":
                continue
            output.append(char)
        return "".join(output)

    def _run_split_on_punc(self, text, never_split=None):
        """Splits punctuation on a piece of text."""
        if never_split is not None and text in never_split:
            return [text]
        chars = list(text)
        i = 0
        start_new_word = True
        output = []
        while i < len(chars):
            char = chars[i]
            if _is_punctuation(char):
                output.append([char])
                start_new_word = True
            else:
                if start_new_word:
                    output.append([])
                start_new_word = False
                output[-1].append(char)
            i += 1

        return ["".join(x) for x in output]



    def _clean_text(self, text):
        """Performs invalid character removal and whitespace cleanup on text."""
        output = []
        for char in text:
            cp = ord(char)
            if cp == 0 or cp == 0xFFFD or _is_control(char):
                continue
            if _is_whitespace(char):
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)


def _is_whitespace(char):
    """Checks whether `chars` is a whitespace character."""
    # \t, \n, and \r are technically contorl characters but we treat them
    # as whitespace since they are generally considered as such.
    if char == " " or char == "\t" or char == "\n" or char == "\r":
        return True
    cat = unicodedata.category(char)
    if cat == "Zs":
        return True
    return False


def _is_control(char):
    """Checks whether `chars` is a control character."""
    # These are technically control characters but we count them as whitespace
    # characters.
    if char == "\t" or char == "\n" or char == "\r":
        return False
    cat = unicodedata.category(char)
    if cat.startswith("C"):
        return True
    return False


def _is_punctuation(char):
    """Checks whether `chars` is a punctuation character."""
    cp = ord(char)
    # We treat all non-letter/number ASCII as punctuation.
    # Characters such as "^", "$", and "`" are not in the Unicode
    # Punctuation class but we treat them as punctuation anyways, for
    # consistency.
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    cat = unicodedata.category(char)
    if cat.startswith("P"):
        return True
    return False