spaCy/spacy/lang/zh/__init__.py

import tempfile
import srsly
import warnings
from pathlib import Path
from collections import OrderedDict
from ...attrs import LANG
from ...errors import Warnings, Errors
from ...language import Language
from ...tokens import Doc
from ...util import DummyTokenizer
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
from ... import util


_PKUSEG_INSTALL_MSG = "install it with `pip install pkuseg==0.0.22` or from https://github.com/lancopku/pkuseg-python"


def try_jieba_import(segmenter):
    try:
        import jieba

        if segmenter == "jieba":
            # segment a short text to have jieba initialize its cache in advance
            list(jieba.cut("作为", cut_all=False))

        return jieba
    except ImportError:
        if segmenter == "jieba":
            msg = (
                "Jieba not installed. To use jieba, install it with `pip "
                " install jieba` or from https://github.com/fxsjy/jieba"
            )
            raise ImportError(msg)


def try_pkuseg_import(segmenter, pkuseg_model, pkuseg_user_dict):
    try:
        import pkuseg

        if pkuseg_model:
            return pkuseg.pkuseg(pkuseg_model, pkuseg_user_dict)
        elif segmenter == "pkuseg":
            msg = (
                "The Chinese word segmenter is 'pkuseg' but no pkuseg model "
                "was specified. Please provide the name of a pretrained model "
                "or the path to a model with "
                '`cfg = {"segmenter": "pkuseg", "pkuseg_model": name_or_path}; '
                'nlp = Chinese(meta={"tokenizer": {"config": cfg}})`'
            )
            raise ValueError(msg)
    except ImportError:
        if segmenter == "pkuseg":
            msg = "pkuseg not installed. To use pkuseg, " + _PKUSEG_INSTALL_MSG
            raise ImportError(msg)
    except FileNotFoundError:
        if segmenter == "pkuseg":
            msg = "Unable to load pkuseg model from: " + pkuseg_model
            raise FileNotFoundError(msg)


class ChineseTokenizer(DummyTokenizer):
    def __init__(self, cls, nlp=None, config={}):
        self.supported_segmenters = ("char", "jieba", "pkuseg")
        self.configure_segmenter(config)
        self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
        # remove relevant settings from config so they're not also saved in
        # Language.meta
        for key in ["segmenter", "pkuseg_model", "pkuseg_user_dict"]:
            if key in config:
                del config[key]
        self.tokenizer = Language.Defaults().create_tokenizer(nlp)

    def configure_segmenter(self, config):
        self.segmenter = "char"
        if "segmenter" in config:
            if config["segmenter"] in self.supported_segmenters:
                self.segmenter = config["segmenter"]
            else:
                warn_msg = Warnings.W103.format(
                    lang="Chinese",
                    segmenter=config["segmenter"],
                    supported=", ".join([repr(s) for s in self.supported_segmenters]),
                    default="'char' (character segmentation)",
                )
                warnings.warn(warn_msg)
        self.jieba_seg = try_jieba_import(self.segmenter)
        self.pkuseg_seg = try_pkuseg_import(
            self.segmenter,
            pkuseg_model=config.get("pkuseg_model", None),
            pkuseg_user_dict=config.get("pkuseg_user_dict", "default"),
        )

    def __call__(self, text):
        if self.segmenter == "jieba":
            words = list([x for x in self.jieba_seg.cut(text, cut_all=False) if x])
            (words, spaces) = util.get_words_and_spaces(words, text)
            return Doc(self.vocab, words=words, spaces=spaces)
        elif self.segmenter == "pkuseg":
            if self.pkuseg_seg is None:
                raise ValueError(Errors.E1000)
            words = self.pkuseg_seg.cut(text)
            (words, spaces) = util.get_words_and_spaces(words, text)
            return Doc(self.vocab, words=words, spaces=spaces)

        # warn if segmenter setting is not the only remaining option "char"
        if self.segmenter != "char":
            warn_msg = Warnings.W103.format(
                lang="Chinese",
                segmenter=self.segmenter,
                supported=", ".join([repr(s) for s in self.supported_segmenters]),
                default="'char' (character segmentation)",
            )
            warnings.warn(warn_msg)

        # split into individual characters
        words = list(text)
        (words, spaces) = util.get_words_and_spaces(words, text)
        return Doc(self.vocab, words=words, spaces=spaces)

    def pkuseg_update_user_dict(self, words, reset=False):
        if self.segmenter == "pkuseg":
            if reset:
                try:
                    import pkuseg

                    self.pkuseg_seg.preprocesser = pkuseg.Preprocesser(None)
                except ImportError:
                    if self.segmenter == "pkuseg":
                        msg = (
                            "pkuseg not installed: unable to reset pkuseg "
                            "user dict. Please " + _PKUSEG_INSTALL_MSG
                        )
                        raise ImportError(msg)
            for word in words:
                self.pkuseg_seg.preprocesser.insert(word.strip(), "")
        else:
            warn_msg = Warnings.W104.format(target="pkuseg", current=self.segmenter)
            warnings.warn(warn_msg)

    def _get_config(self):
        config = OrderedDict((("segmenter", self.segmenter),))
        return config

    def _set_config(self, config={}):
        self.configure_segmenter(config)

    def to_bytes(self, **kwargs):
        pkuseg_features_b = b""
        pkuseg_weights_b = b""
        pkuseg_processors_data = None
        if self.pkuseg_seg:
            with tempfile.TemporaryDirectory() as tempdir:
                self.pkuseg_seg.feature_extractor.save(tempdir)
                self.pkuseg_seg.model.save(tempdir)
                tempdir = Path(tempdir)
                with open(tempdir / "features.pkl", "rb") as fileh:
                    pkuseg_features_b = fileh.read()
                with open(tempdir / "weights.npz", "rb") as fileh:
                    pkuseg_weights_b = fileh.read()
            pkuseg_processors_data = (
                _get_pkuseg_trie_data(self.pkuseg_seg.preprocesser.trie),
                self.pkuseg_seg.postprocesser.do_process,
                sorted(list(self.pkuseg_seg.postprocesser.common_words)),
                sorted(list(self.pkuseg_seg.postprocesser.other_words)),
            )
        serializers = OrderedDict(
            (
                ("cfg", lambda: srsly.json_dumps(self._get_config())),
                ("pkuseg_features", lambda: pkuseg_features_b),
                ("pkuseg_weights", lambda: pkuseg_weights_b),
                (
                    "pkuseg_processors",
                    lambda: srsly.msgpack_dumps(pkuseg_processors_data),
                ),
            )
        )
        return util.to_bytes(serializers, [])

    def from_bytes(self, data, **kwargs):
        pkuseg_data = {"features_b": b"", "weights_b": b"", "processors_data": None}

        def deserialize_pkuseg_features(b):
            pkuseg_data["features_b"] = b

        def deserialize_pkuseg_weights(b):
            pkuseg_data["weights_b"] = b

        def deserialize_pkuseg_processors(b):
            pkuseg_data["processors_data"] = srsly.msgpack_loads(b)

        deserializers = OrderedDict(
            (
                ("cfg", lambda b: self._set_config(srsly.json_loads(b))),
                ("pkuseg_features", deserialize_pkuseg_features),
                ("pkuseg_weights", deserialize_pkuseg_weights),
                ("pkuseg_processors", deserialize_pkuseg_processors),
            )
        )
        util.from_bytes(data, deserializers, [])

        if pkuseg_data["features_b"] and pkuseg_data["weights_b"]:
            with tempfile.TemporaryDirectory() as tempdir:
                tempdir = Path(tempdir)
                with open(tempdir / "features.pkl", "wb") as fileh:
                    fileh.write(pkuseg_data["features_b"])
                with open(tempdir / "weights.npz", "wb") as fileh:
                    fileh.write(pkuseg_data["weights_b"])
                try:
                    import pkuseg
                except ImportError:
                    raise ImportError(
                        "pkuseg not installed. To use this model, "
                        + _PKUSEG_INSTALL_MSG
                    )
                self.pkuseg_seg = pkuseg.pkuseg(str(tempdir))
            if pkuseg_data["processors_data"]:
                processors_data = pkuseg_data["processors_data"]
                (user_dict, do_process, common_words, other_words) = processors_data
                self.pkuseg_seg.preprocesser = pkuseg.Preprocesser(user_dict)
                self.pkuseg_seg.postprocesser.do_process = do_process
                self.pkuseg_seg.postprocesser.common_words = set(common_words)
                self.pkuseg_seg.postprocesser.other_words = set(other_words)

        return self

    def to_disk(self, path, **kwargs):
        path = util.ensure_path(path)

        def save_pkuseg_model(path):
            if self.pkuseg_seg:
                if not path.exists():
                    path.mkdir(parents=True)
                self.pkuseg_seg.model.save(path)
                self.pkuseg_seg.feature_extractor.save(path)

        def save_pkuseg_processors(path):
            if self.pkuseg_seg:
                data = (
                    _get_pkuseg_trie_data(self.pkuseg_seg.preprocesser.trie),
                    self.pkuseg_seg.postprocesser.do_process,
                    sorted(list(self.pkuseg_seg.postprocesser.common_words)),
                    sorted(list(self.pkuseg_seg.postprocesser.other_words)),
                )
                srsly.write_msgpack(path, data)

        serializers = OrderedDict(
            (
                ("cfg", lambda p: srsly.write_json(p, self._get_config())),
                ("pkuseg_model", lambda p: save_pkuseg_model(p)),
                ("pkuseg_processors", lambda p: save_pkuseg_processors(p)),
            )
        )
        return util.to_disk(path, serializers, [])

    def from_disk(self, path, **kwargs):
        path = util.ensure_path(path)

        def load_pkuseg_model(path):
            try:
                import pkuseg
            except ImportError:
                if self.segmenter == "pkuseg":
                    raise ImportError(
                        "pkuseg not installed. To use this model, "
                        + _PKUSEG_INSTALL_MSG
                    )
            if path.exists():
                self.pkuseg_seg = pkuseg.pkuseg(path)

        def load_pkuseg_processors(path):
            try:
                import pkuseg
            except ImportError:
                if self.segmenter == "pkuseg":
                    raise ImportError(self._pkuseg_install_msg)
            if self.segmenter == "pkuseg":
                data = srsly.read_msgpack(path)
                (user_dict, do_process, common_words, other_words) = data
                self.pkuseg_seg.preprocesser = pkuseg.Preprocesser(user_dict)
                self.pkuseg_seg.postprocesser.do_process = do_process
                self.pkuseg_seg.postprocesser.common_words = set(common_words)
                self.pkuseg_seg.postprocesser.other_words = set(other_words)

        serializers = OrderedDict(
            (
                ("cfg", lambda p: self._set_config(srsly.read_json(p))),
                ("pkuseg_model", lambda p: load_pkuseg_model(p)),
                ("pkuseg_processors", lambda p: load_pkuseg_processors(p)),
            )
        )
        util.from_disk(path, serializers, [])


class ChineseDefaults(Language.Defaults):
    lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
    lex_attr_getters.update(LEX_ATTRS)
    lex_attr_getters[LANG] = lambda text: "zh"
    tokenizer_exceptions = BASE_EXCEPTIONS
    stop_words = STOP_WORDS
    tag_map = TAG_MAP
    writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}

    @classmethod
    def create_tokenizer(cls, nlp=None, config={}):
        return ChineseTokenizer(cls, nlp, config=config)


class Chinese(Language):
    lang = "zh"
    Defaults = ChineseDefaults  # override defaults

    def make_doc(self, text):
        return self.tokenizer(text)


def _get_pkuseg_trie_data(node, path=""):
    data = []
    for c, child_node in sorted(node.children.items()):
        data.extend(_get_pkuseg_trie_data(child_node, path + c))
    if node.isword:
        data.append((path, node.usertag))
    return data


__all__ = ["Chinese"]