Module molcrawl.compounds.utils.tokenizer

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 tokenize_smiles(
    smi: List[str],
    tokenizer: SmilesTokenizer = None,
    max_smiles_len=256,
    log_output=True,
):
    tokens = tokenizer.encode(smi)
    if len(tokens) > max_smiles_len:
        if log_output:
            print(f"Removing to long {smi} with smiles len of {len(tokens)} ")
        return None

    return tokens

class BasicSmilesTokenizer(object):
    """

    Run basic SMILES tokenization using a regex pattern developed by Schwaller et. al. This tokenizer is to be used
    when a tokenizer that does not require the transformers library by HuggingFace is required.

    Examples
    --------
    >>> from deepchem.feat.smiles_tokenizer import BasicSmilesTokenizer
    >>> tokenizer = BasicSmilesTokenizer()
    >>> print(tokenizer.tokenize("CC(=O)OC1=CC=CC=C1C(=O)O"))
    ['C', 'C', '(', '=', 'O', ')', 'O', 'C', '1', '=', 'C', 'C', '=', 'C', 'C', '=', 'C', '1', 'C', '(', '=', 'O', ')', 'O']


    References
    ----------
    .. [1]  Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee
            ACS Central Science 2019 5 (9): Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
            1572-1583 DOI: 10.1021/acscentsci.9b00576

    """

    def __init__(self, regex_pattern: str = SMI_REGEX_PATTERN):
        """Constructs a BasicSMILESTokenizer.
        Parameters
        ----------

        regex: string
            SMILES token regex

        """
        self.regex_pattern = regex_pattern
        self.regex = re.compile(self.regex_pattern)

    def tokenize(self, text):
        """Basic Tokenization of a SMILES."""
        tokens = [token for token in self.regex.findall(text)]
        return tokens

class BertTokenizer:  # type: ignore
    def __init__(self, *args, **kwargs):
        raise ModuleNotFoundError("transformers is required to use SmilesTokenizer")

class CompoundsTokenizer(UnTrainableTokenizer, SmilesTokenizer):
    def __init__(self, vocab_file: str, max_len: int = 256):
        self.max_len = max_len
        UnTrainableTokenizer.__init__(self)
        SmilesTokenizer.__init__(self, vocab_file)

    def tokenize_text(self, text: str, verbose: bool = False):
        tokens = self.encode(text, padding="max_length", truncation=True, max_length=self.max_len)
        if len(tokens) > self.max_len:
            if verbose:
                logger.info(f"Removing to long {text} with len of {len(tokens)} ")
            return None

        return tokens

class ScaffoldsTokenizer(CompoundsTokenizer):
    def tokenize_text(self, text: str, verbose: bool = False):
        return super().tokenize_text(prepare_scaffolds(text), verbose=verbose)

class SmilesTokenizer(BertTokenizer):
    """
    Creates the SmilesTokenizer class. The tokenizer heavily inherits from the BertTokenizer
    implementation found in Huggingface's transformers library. It runs a WordPiece tokenization
    algorithm over SMILES strings using the tokenisation SMILES regex developed by Schwaller et. al.

    Please see https://github.com/huggingface/transformers
    and https://github.com/rxn4chemistry/rxnfp for more details.

    References
    ----------
    .. [1]  Schwaller, Philippe; Probst, Daniel; Vaucher, Alain C.; Nair, Vishnu H; Kreutter, David;
            Laino, Teodoro; et al. (2019): Mapping the Space of Chemical Reactions using Attention-Based Neural
            Networks. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.9897365.v3

    Notes
    ----
    This class requires huggingface's transformers and tokenizers libraries to be installed.
    """

    def __init__(self, vocab_file, **kwargs):
        """Constructs a SmilesTokenizer.

        Parameters
        ----------
        vocab_file: str
            Path to a SMILES character per line vocabulary file.
            Default vocab file is found in deepchem/feat/tests/data/vocab.txt
        """

        super().__init__(vocab_file, **kwargs)

        self.sos = "[CLS]"
        self.eos = "[SEP]"

        if not os.path.isfile(vocab_file):
            raise ValueError("Can't find a vocab file at path '{}'.".format(vocab_file))
        self.vocab = load_vocab(vocab_file)
        self.highest_unused_index = max([i for i, v in enumerate(self.vocab.keys()) if v.startswith("[unused")])
        self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
        self.basic_tokenizer = BasicSmilesTokenizer()

    @property
    def eos_token_id(self):
        return self.added_tokens_encoder[self.eos]

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

    @property
    def vocab_list(self):
        return list(self.vocab.keys())

    def _tokenize(self, text: str):
        """
        Tokenize a string into a list of tokens.

        Parameters
        ----------
        text: str
            Input string sequence to be tokenized.
        """

        split_tokens = [token for token in self.basic_tokenizer.tokenize(text)]
        return split_tokens

    def _convert_token_to_id(self, token):
        """
        Converts a token (str/unicode) in an id using the vocab.

        Parameters
        ----------
        token: str
            String token from a larger sequence to be converted to a numerical id.
        """

        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 (string/unicode) using the vocab.

        Parameters
        ----------
        index: int
            Integer index to be converted back to a string-based token as part of a larger sequence.
        """

        return self.ids_to_tokens.get(index, self.unk_token)

    def convert_tokens_to_string(self, tokens: List[str]):
        """Converts a sequence of tokens (string) in a single string.

        Parameters
        ----------
        tokens: List[str]
            List of tokens for a given string sequence.

        Returns
        -------
        out_string: str
            Single string from combined tokens.
        """

        out_string: str = " ".join(tokens).replace(" ##", "").strip()
        return out_string

    def add_special_tokens_ids_single_sequence(self, token_ids: List[int]):
        """
        Adds special tokens to the a sequence for sequence classification tasks.
        A BERT sequence has the following format: [CLS] X [SEP]

        Parameters
        ----------

        token_ids: list[int]
            list of tokenized input ids. Can be obtained using the encode or encode_plus methods.
        """

        return [self.cls_token_id] + token_ids + [self.sep_token_id]

    def add_special_tokens_single_sequence(self, tokens: List[str]):
        """
        Adds special tokens to the a sequence for sequence classification tasks.
        A BERT sequence has the following format: [CLS] X [SEP]

        Parameters
        ----------
        tokens: List[str]
            List of tokens for a given string sequence.

        """
        return [self.cls_token] + tokens + [self.sep_token]

    def add_special_tokens_ids_sequence_pair(self, token_ids_0: List[int], token_ids_1: List[int]) -> List[int]:
        """
        Adds special tokens to a sequence pair for sequence classification tasks.
        A BERT sequence pair has the following format: [CLS] A [SEP] B [SEP]

        Parameters
        ----------
        token_ids_0: List[int]
            List of ids for the first string sequence in the sequence pair (A).

        token_ids_1: List[int]
            List of tokens for the second string sequence in the sequence pair (B).
        """

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

        return cls + token_ids_0 + sep + token_ids_1 + sep

    def add_padding_tokens(self, token_ids: List[int], length: int, right: bool = True) -> List[int]:
        """
        Adds padding tokens to return a sequence of length max_length.
        By default padding tokens are added to the right of the sequence.

        Parameters
        ----------
        token_ids: list[int]
            list of tokenized input ids. Can be obtained using the encode or encode_plus methods.

        length: int

        right: bool (True by default)

        Returns
        ----------
        token_ids :
            list of tokenized input ids. Can be obtained using the encode or encode_plus methods.

        padding: int
            Integer to be added as padding token

        """
        padding = [self.pad_token_id] * (length - len(token_ids))

        if right:
            return token_ids + padding
        else:
            return padding + token_ids

    def save_vocabulary(self, vocab_path: str):  # -> tuple[str]: doctest issue raised with this return type annotation
        """
        Save the tokenizer vocabulary to a file.

        Parameters
        ----------
        vocab_path: obj: str
            The directory in which to save the SMILES character per line vocabulary file.
            Default vocab file is found in deepchem/feat/tests/data/vocab.txt

        Returns
        ----------
        vocab_file: :obj:`Tuple(str)`:
            Paths to the files saved.
            tuple with string to a SMILES character per line vocabulary file.
            Default vocab file is found in deepchem/feat/tests/data/vocab.txt

        """
        index = 0
        if os.path.isdir(vocab_path):
            vocab_file = os.path.join(vocab_path, "vocab.txt")
        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:
                    index = token_index
                writer.write(token + "\n")
                index += 1
        return (vocab_file,)