mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-10 19:57:17 +03:00
84e06f9fb7
Improve GoldParse NER alignment by including all cases where the start and end of the NER span can be aligned, regardless of internal tokenization differences. To do this, convert BILUO tags to character offsets, check start/end alignment with `doc.char_span()`, and assign the BILUO tags for the aligned spans. Alignment for `O/-` tags is handled through the one-to-one and multi alignments.
820 lines
26 KiB
Python
820 lines
26 KiB
Python
# coding: utf8
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from __future__ import unicode_literals, print_function
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import os
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import importlib
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import re
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from pathlib import Path
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import random
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from collections import OrderedDict
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from thinc.neural._classes.model import Model
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from thinc.neural.ops import NumpyOps
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import functools
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import itertools
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import numpy.random
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import numpy
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import srsly
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import catalogue
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import sys
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try:
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import jsonschema
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except ImportError:
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jsonschema = None
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try:
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import cupy.random
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except ImportError:
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cupy = None
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from .symbols import ORTH
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from .compat import cupy, CudaStream, path2str, basestring_, unicode_
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from .compat import import_file
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from .errors import Errors, Warnings, deprecation_warning
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_data_path = Path(__file__).parent / "data"
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_PRINT_ENV = False
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OOV_RANK = numpy.iinfo(numpy.uint64).max
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class registry(object):
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languages = catalogue.create("spacy", "languages", entry_points=True)
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architectures = catalogue.create("spacy", "architectures", entry_points=True)
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lookups = catalogue.create("spacy", "lookups", entry_points=True)
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factories = catalogue.create("spacy", "factories", entry_points=True)
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displacy_colors = catalogue.create("spacy", "displacy_colors", entry_points=True)
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def set_env_log(value):
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global _PRINT_ENV
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_PRINT_ENV = value
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def lang_class_is_loaded(lang):
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"""Check whether a Language class is already loaded. Language classes are
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loaded lazily, to avoid expensive setup code associated with the language
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data.
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lang (unicode): Two-letter language code, e.g. 'en'.
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RETURNS (bool): Whether a Language class has been loaded.
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"""
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return lang in registry.languages
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def get_lang_class(lang):
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"""Import and load a Language class.
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lang (unicode): Two-letter language code, e.g. 'en'.
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RETURNS (Language): Language class.
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"""
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# Check if language is registered / entry point is available
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if lang in registry.languages:
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return registry.languages.get(lang)
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else:
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try:
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module = importlib.import_module(".lang.%s" % lang, "spacy")
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except ImportError as err:
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raise ImportError(Errors.E048.format(lang=lang, err=err))
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set_lang_class(lang, getattr(module, module.__all__[0]))
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return registry.languages.get(lang)
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def set_lang_class(name, cls):
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"""Set a custom Language class name that can be loaded via get_lang_class.
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name (unicode): Name of Language class.
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cls (Language): Language class.
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"""
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registry.languages.register(name, func=cls)
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def get_data_path(require_exists=True):
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"""Get path to spaCy data directory.
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require_exists (bool): Only return path if it exists, otherwise None.
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RETURNS (Path or None): Data path or None.
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"""
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if not require_exists:
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return _data_path
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else:
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return _data_path if _data_path.exists() else None
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def set_data_path(path):
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"""Set path to spaCy data directory.
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path (unicode or Path): Path to new data directory.
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"""
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global _data_path
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_data_path = ensure_path(path)
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def make_layer(arch_config):
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arch_func = registry.architectures.get(arch_config["arch"])
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return arch_func(arch_config["config"])
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def ensure_path(path):
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"""Ensure string is converted to a Path.
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path: Anything. If string, it's converted to Path.
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RETURNS: Path or original argument.
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"""
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if isinstance(path, basestring_):
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return Path(path)
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else:
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return path
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def load_language_data(path):
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"""Load JSON language data using the given path as a base. If the provided
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path isn't present, will attempt to load a gzipped version before giving up.
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path (unicode / Path): The data to load.
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RETURNS: The loaded data.
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"""
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path = ensure_path(path)
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if path.exists():
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return srsly.read_json(path)
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path = path.with_suffix(path.suffix + ".gz")
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if path.exists():
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return srsly.read_gzip_json(path)
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raise ValueError(Errors.E160.format(path=path2str(path)))
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def get_module_path(module):
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if not hasattr(module, "__module__"):
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raise ValueError(Errors.E169.format(module=repr(module)))
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return Path(sys.modules[module.__module__].__file__).parent
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def load_model(name, **overrides):
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"""Load a model from a shortcut link, package or data path.
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name (unicode): Package name, shortcut link or model path.
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**overrides: Specific overrides, like pipeline components to disable.
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RETURNS (Language): `Language` class with the loaded model.
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"""
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data_path = get_data_path()
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if not data_path or not data_path.exists():
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raise IOError(Errors.E049.format(path=path2str(data_path)))
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if isinstance(name, basestring_): # in data dir / shortcut
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if name in set([d.name for d in data_path.iterdir()]):
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return load_model_from_link(name, **overrides)
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if is_package(name): # installed as package
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return load_model_from_package(name, **overrides)
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if Path(name).exists(): # path to model data directory
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return load_model_from_path(Path(name), **overrides)
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elif hasattr(name, "exists"): # Path or Path-like to model data
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return load_model_from_path(name, **overrides)
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raise IOError(Errors.E050.format(name=name))
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def load_model_from_link(name, **overrides):
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"""Load a model from a shortcut link, or directory in spaCy data path."""
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path = get_data_path() / name / "__init__.py"
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try:
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cls = import_file(name, path)
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except AttributeError:
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raise IOError(Errors.E051.format(name=name))
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return cls.load(**overrides)
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def load_model_from_package(name, **overrides):
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"""Load a model from an installed package."""
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cls = importlib.import_module(name)
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return cls.load(**overrides)
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def load_model_from_path(model_path, meta=False, **overrides):
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"""Load a model from a data directory path. Creates Language class with
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pipeline from meta.json and then calls from_disk() with path."""
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if not meta:
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meta = get_model_meta(model_path)
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# Support language factories registered via entry points (e.g. custom
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# language subclass) while keeping top-level language identifier "lang"
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lang = meta.get("lang_factory", meta["lang"])
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cls = get_lang_class(lang)
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nlp = cls(meta=meta, **overrides)
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pipeline = meta.get("pipeline", [])
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factories = meta.get("factories", {})
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disable = overrides.get("disable", [])
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if pipeline is True:
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pipeline = nlp.Defaults.pipe_names
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elif pipeline in (False, None):
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pipeline = []
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for name in pipeline:
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if name not in disable:
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config = meta.get("pipeline_args", {}).get(name, {})
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factory = factories.get(name, name)
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component = nlp.create_pipe(factory, config=config)
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nlp.add_pipe(component, name=name)
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return nlp.from_disk(model_path, exclude=disable)
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def load_model_from_init_py(init_file, **overrides):
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"""Helper function to use in the `load()` method of a model package's
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__init__.py.
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init_file (unicode): Path to model's __init__.py, i.e. `__file__`.
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**overrides: Specific overrides, like pipeline components to disable.
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RETURNS (Language): `Language` class with loaded model.
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"""
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model_path = Path(init_file).parent
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meta = get_model_meta(model_path)
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data_dir = "%s_%s-%s" % (meta["lang"], meta["name"], meta["version"])
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data_path = model_path / data_dir
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if not model_path.exists():
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raise IOError(Errors.E052.format(path=path2str(data_path)))
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return load_model_from_path(data_path, meta, **overrides)
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def get_model_meta(path):
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"""Get model meta.json from a directory path and validate its contents.
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path (unicode or Path): Path to model directory.
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RETURNS (dict): The model's meta data.
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"""
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model_path = ensure_path(path)
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if not model_path.exists():
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raise IOError(Errors.E052.format(path=path2str(model_path)))
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meta_path = model_path / "meta.json"
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if not meta_path.is_file():
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raise IOError(Errors.E053.format(path=meta_path))
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meta = srsly.read_json(meta_path)
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for setting in ["lang", "name", "version"]:
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if setting not in meta or not meta[setting]:
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raise ValueError(Errors.E054.format(setting=setting))
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return meta
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def is_package(name):
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"""Check if string maps to a package installed via pip.
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name (unicode): Name of package.
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RETURNS (bool): True if installed package, False if not.
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"""
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import pkg_resources
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name = name.lower() # compare package name against lowercase name
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packages = pkg_resources.working_set.by_key.keys()
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for package in packages:
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if package.lower().replace("-", "_") == name:
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return True
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return False
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def get_package_path(name):
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"""Get the path to an installed package.
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name (unicode): Package name.
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RETURNS (Path): Path to installed package.
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"""
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name = name.lower() # use lowercase version to be safe
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# Here we're importing the module just to find it. This is worryingly
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# indirect, but it's otherwise very difficult to find the package.
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pkg = importlib.import_module(name)
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return Path(pkg.__file__).parent
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def is_in_jupyter():
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"""Check if user is running spaCy from a Jupyter notebook by detecting the
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IPython kernel. Mainly used for the displaCy visualizer.
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RETURNS (bool): True if in Jupyter, False if not.
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"""
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# https://stackoverflow.com/a/39662359/6400719
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try:
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shell = get_ipython().__class__.__name__
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if shell == "ZMQInteractiveShell":
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return True # Jupyter notebook or qtconsole
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except NameError:
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return False # Probably standard Python interpreter
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return False
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def get_component_name(component):
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if hasattr(component, "name"):
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return component.name
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if hasattr(component, "__name__"):
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return component.__name__
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if hasattr(component, "__class__") and hasattr(component.__class__, "__name__"):
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return component.__class__.__name__
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return repr(component)
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def get_cuda_stream(require=False, non_blocking=True):
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if CudaStream is None:
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return None
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elif isinstance(Model.ops, NumpyOps):
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return None
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else:
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return CudaStream(non_blocking=non_blocking)
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def get_async(stream, numpy_array):
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if cupy is None:
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return numpy_array
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else:
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array = cupy.ndarray(numpy_array.shape, order="C", dtype=numpy_array.dtype)
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array.set(numpy_array, stream=stream)
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return array
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def env_opt(name, default=None):
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if type(default) is float:
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type_convert = float
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else:
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type_convert = int
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if "SPACY_" + name.upper() in os.environ:
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value = type_convert(os.environ["SPACY_" + name.upper()])
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if _PRINT_ENV:
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print(name, "=", repr(value), "via", "$SPACY_" + name.upper())
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return value
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elif name in os.environ:
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value = type_convert(os.environ[name])
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if _PRINT_ENV:
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print(name, "=", repr(value), "via", "$" + name)
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return value
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else:
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if _PRINT_ENV:
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print(name, "=", repr(default), "by default")
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return default
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def read_regex(path):
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path = ensure_path(path)
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with path.open(encoding="utf8") as file_:
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entries = file_.read().split("\n")
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expression = "|".join(
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["^" + re.escape(piece) for piece in entries if piece.strip()]
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)
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return re.compile(expression)
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def compile_prefix_regex(entries):
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"""Compile a sequence of prefix rules into a regex object.
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entries (tuple): The prefix rules, e.g. spacy.lang.punctuation.TOKENIZER_PREFIXES.
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RETURNS (regex object): The regex object. to be used for Tokenizer.prefix_search.
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"""
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if "(" in entries:
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# Handle deprecated data
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expression = "|".join(
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["^" + re.escape(piece) for piece in entries if piece.strip()]
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)
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return re.compile(expression)
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else:
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expression = "|".join(["^" + piece for piece in entries if piece.strip()])
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return re.compile(expression)
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def compile_suffix_regex(entries):
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"""Compile a sequence of suffix rules into a regex object.
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entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
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RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
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"""
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expression = "|".join([piece + "$" for piece in entries if piece.strip()])
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return re.compile(expression)
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def compile_infix_regex(entries):
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"""Compile a sequence of infix rules into a regex object.
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entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
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RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
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"""
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expression = "|".join([piece for piece in entries if piece.strip()])
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return re.compile(expression)
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def add_lookups(default_func, *lookups):
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"""Extend an attribute function with special cases. If a word is in the
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lookups, the value is returned. Otherwise the previous function is used.
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default_func (callable): The default function to execute.
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*lookups (dict): Lookup dictionary mapping string to attribute value.
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RETURNS (callable): Lexical attribute getter.
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"""
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# This is implemented as functools.partial instead of a closure, to allow
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# pickle to work.
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return functools.partial(_get_attr_unless_lookup, default_func, lookups)
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def _get_attr_unless_lookup(default_func, lookups, string):
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for lookup in lookups:
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if string in lookup:
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return lookup[string]
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return default_func(string)
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def update_exc(base_exceptions, *addition_dicts):
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"""Update and validate tokenizer exceptions. Will overwrite exceptions.
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base_exceptions (dict): Base exceptions.
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*addition_dicts (dict): Exceptions to add to the base dict, in order.
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RETURNS (dict): Combined tokenizer exceptions.
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"""
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exc = dict(base_exceptions)
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for additions in addition_dicts:
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for orth, token_attrs in additions.items():
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if not all(isinstance(attr[ORTH], unicode_) for attr in token_attrs):
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raise ValueError(Errors.E055.format(key=orth, orths=token_attrs))
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described_orth = "".join(attr[ORTH] for attr in token_attrs)
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if orth != described_orth:
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raise ValueError(Errors.E056.format(key=orth, orths=described_orth))
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exc.update(additions)
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exc = expand_exc(exc, "'", "’")
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return exc
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def expand_exc(excs, search, replace):
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"""Find string in tokenizer exceptions, duplicate entry and replace string.
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For example, to add additional versions with typographic apostrophes.
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excs (dict): Tokenizer exceptions.
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search (unicode): String to find and replace.
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replace (unicode): Replacement.
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RETURNS (dict): Combined tokenizer exceptions.
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"""
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def _fix_token(token, search, replace):
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fixed = dict(token)
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fixed[ORTH] = fixed[ORTH].replace(search, replace)
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return fixed
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new_excs = dict(excs)
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for token_string, tokens in excs.items():
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if search in token_string:
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new_key = token_string.replace(search, replace)
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new_value = [_fix_token(t, search, replace) for t in tokens]
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new_excs[new_key] = new_value
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return new_excs
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def normalize_slice(length, start, stop, step=None):
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if not (step is None or step == 1):
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raise ValueError(Errors.E057)
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if start is None:
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start = 0
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elif start < 0:
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start += length
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start = min(length, max(0, start))
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if stop is None:
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stop = length
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elif stop < 0:
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stop += length
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stop = min(length, max(start, stop))
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return start, stop
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def minibatch(items, size=8):
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"""Iterate over batches of items. `size` may be an iterator,
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so that batch-size can vary on each step.
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"""
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if isinstance(size, int):
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size_ = itertools.repeat(size)
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else:
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size_ = size
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items = iter(items)
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while True:
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batch_size = next(size_)
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batch = list(itertools.islice(items, int(batch_size)))
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if len(batch) == 0:
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break
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yield list(batch)
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def compounding(start, stop, compound):
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"""Yield an infinite series of compounding values. Each time the
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generator is called, a value is produced by multiplying the previous
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value by the compound rate.
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EXAMPLE:
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>>> sizes = compounding(1., 10., 1.5)
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>>> assert next(sizes) == 1.
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>>> assert next(sizes) == 1 * 1.5
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>>> assert next(sizes) == 1.5 * 1.5
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"""
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def clip(value):
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return max(value, stop) if (start > stop) else min(value, stop)
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curr = float(start)
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||
while True:
|
||
yield clip(curr)
|
||
curr *= compound
|
||
|
||
|
||
def stepping(start, stop, steps):
|
||
"""Yield an infinite series of values that step from a start value to a
|
||
final value over some number of steps. Each step is (stop-start)/steps.
|
||
|
||
After the final value is reached, the generator continues yielding that
|
||
value.
|
||
|
||
EXAMPLE:
|
||
>>> sizes = stepping(1., 200., 100)
|
||
>>> assert next(sizes) == 1.
|
||
>>> assert next(sizes) == 1 * (200.-1.) / 100
|
||
>>> assert next(sizes) == 1 + (200.-1.) / 100 + (200.-1.) / 100
|
||
"""
|
||
|
||
def clip(value):
|
||
return max(value, stop) if (start > stop) else min(value, stop)
|
||
|
||
curr = float(start)
|
||
while True:
|
||
yield clip(curr)
|
||
curr += (stop - start) / steps
|
||
|
||
|
||
def decaying(start, stop, decay):
|
||
"""Yield an infinite series of linearly decaying values."""
|
||
|
||
curr = float(start)
|
||
while True:
|
||
yield max(curr, stop)
|
||
curr -= decay
|
||
|
||
|
||
def minibatch_by_words(items, size, tuples=True, count_words=len):
|
||
"""Create minibatches of a given number of words."""
|
||
if isinstance(size, int):
|
||
size_ = itertools.repeat(size)
|
||
else:
|
||
size_ = size
|
||
items = iter(items)
|
||
while True:
|
||
batch_size = next(size_)
|
||
batch = []
|
||
while batch_size >= 0:
|
||
try:
|
||
if tuples:
|
||
doc, gold = next(items)
|
||
else:
|
||
doc = next(items)
|
||
except StopIteration:
|
||
if batch:
|
||
yield batch
|
||
return
|
||
batch_size -= count_words(doc)
|
||
if tuples:
|
||
batch.append((doc, gold))
|
||
else:
|
||
batch.append(doc)
|
||
if batch:
|
||
yield batch
|
||
|
||
|
||
def itershuffle(iterable, bufsize=1000):
|
||
"""Shuffle an iterator. This works by holding `bufsize` items back
|
||
and yielding them sometime later. Obviously, this is not unbiased –
|
||
but should be good enough for batching. Larger bufsize means less bias.
|
||
From https://gist.github.com/andres-erbsen/1307752
|
||
|
||
iterable (iterable): Iterator to shuffle.
|
||
bufsize (int): Items to hold back.
|
||
YIELDS (iterable): The shuffled iterator.
|
||
"""
|
||
iterable = iter(iterable)
|
||
buf = []
|
||
try:
|
||
while True:
|
||
for i in range(random.randint(1, bufsize - len(buf))):
|
||
buf.append(next(iterable))
|
||
random.shuffle(buf)
|
||
for i in range(random.randint(1, bufsize)):
|
||
if buf:
|
||
yield buf.pop()
|
||
else:
|
||
break
|
||
except StopIteration:
|
||
random.shuffle(buf)
|
||
while buf:
|
||
yield buf.pop()
|
||
raise StopIteration
|
||
|
||
|
||
def filter_spans(spans):
|
||
"""Filter a sequence of spans and remove duplicates or overlaps. Useful for
|
||
creating named entities (where one token can only be part of one entity) or
|
||
when merging spans with `Retokenizer.merge`. When spans overlap, the (first)
|
||
longest span is preferred over shorter spans.
|
||
|
||
spans (iterable): The spans to filter.
|
||
RETURNS (list): The filtered spans.
|
||
"""
|
||
get_sort_key = lambda span: (span.end - span.start, -span.start)
|
||
sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
|
||
result = []
|
||
seen_tokens = set()
|
||
for span in sorted_spans:
|
||
# Check for end - 1 here because boundaries are inclusive
|
||
if span.start not in seen_tokens and span.end - 1 not in seen_tokens:
|
||
result.append(span)
|
||
seen_tokens.update(range(span.start, span.end))
|
||
result = sorted(result, key=lambda span: span.start)
|
||
return result
|
||
|
||
|
||
def to_bytes(getters, exclude):
|
||
serialized = OrderedDict()
|
||
for key, getter in getters.items():
|
||
# Split to support file names like meta.json
|
||
if key.split(".")[0] not in exclude:
|
||
serialized[key] = getter()
|
||
return srsly.msgpack_dumps(serialized)
|
||
|
||
|
||
def from_bytes(bytes_data, setters, exclude):
|
||
msg = srsly.msgpack_loads(bytes_data)
|
||
for key, setter in setters.items():
|
||
# Split to support file names like meta.json
|
||
if key.split(".")[0] not in exclude and key in msg:
|
||
setter(msg[key])
|
||
return msg
|
||
|
||
|
||
def to_disk(path, writers, exclude):
|
||
path = ensure_path(path)
|
||
if not path.exists():
|
||
path.mkdir()
|
||
for key, writer in writers.items():
|
||
# Split to support file names like meta.json
|
||
if key.split(".")[0] not in exclude:
|
||
writer(path / key)
|
||
return path
|
||
|
||
|
||
def from_disk(path, readers, exclude):
|
||
path = ensure_path(path)
|
||
for key, reader in readers.items():
|
||
# Split to support file names like meta.json
|
||
if key.split(".")[0] not in exclude:
|
||
reader(path / key)
|
||
return path
|
||
|
||
|
||
def minify_html(html):
|
||
"""Perform a template-specific, rudimentary HTML minification for displaCy.
|
||
Disclaimer: NOT a general-purpose solution, only removes indentation and
|
||
newlines.
|
||
|
||
html (unicode): Markup to minify.
|
||
RETURNS (unicode): "Minified" HTML.
|
||
"""
|
||
return html.strip().replace(" ", "").replace("\n", "")
|
||
|
||
|
||
def escape_html(text):
|
||
"""Replace <, >, &, " with their HTML encoded representation. Intended to
|
||
prevent HTML errors in rendered displaCy markup.
|
||
|
||
text (unicode): The original text.
|
||
RETURNS (unicode): Equivalent text to be safely used within HTML.
|
||
"""
|
||
text = text.replace("&", "&")
|
||
text = text.replace("<", "<")
|
||
text = text.replace(">", ">")
|
||
text = text.replace('"', """)
|
||
return text
|
||
|
||
|
||
def use_gpu(gpu_id):
|
||
try:
|
||
import cupy.cuda.device
|
||
except ImportError:
|
||
return None
|
||
from thinc.neural.ops import CupyOps
|
||
|
||
device = cupy.cuda.device.Device(gpu_id)
|
||
device.use()
|
||
Model.ops = CupyOps()
|
||
Model.Ops = CupyOps
|
||
return device
|
||
|
||
|
||
def fix_random_seed(seed=0):
|
||
random.seed(seed)
|
||
numpy.random.seed(seed)
|
||
if cupy is not None:
|
||
cupy.random.seed(seed)
|
||
|
||
|
||
def get_json_validator(schema):
|
||
# We're using a helper function here to make it easier to change the
|
||
# validator that's used (e.g. different draft implementation), without
|
||
# having to change it all across the codebase.
|
||
# TODO: replace with (stable) Draft6Validator, if available
|
||
if jsonschema is None:
|
||
raise ValueError(Errors.E136)
|
||
return jsonschema.Draft4Validator(schema)
|
||
|
||
|
||
def validate_schema(schema):
|
||
"""Validate a given schema. This just checks if the schema itself is valid."""
|
||
validator = get_json_validator(schema)
|
||
validator.check_schema(schema)
|
||
|
||
|
||
def validate_json(data, validator):
|
||
"""Validate data against a given JSON schema (see https://json-schema.org).
|
||
|
||
data: JSON-serializable data to validate.
|
||
validator (jsonschema.DraftXValidator): The validator.
|
||
RETURNS (list): A list of error messages, if available.
|
||
"""
|
||
errors = []
|
||
for err in sorted(validator.iter_errors(data), key=lambda e: e.path):
|
||
if err.path:
|
||
err_path = "[{}]".format(" -> ".join([str(p) for p in err.path]))
|
||
else:
|
||
err_path = ""
|
||
msg = err.message + " " + err_path
|
||
if err.context: # Error has suberrors, e.g. if schema uses anyOf
|
||
suberrs = [" - {}".format(suberr.message) for suberr in err.context]
|
||
msg += ":\n{}".format("".join(suberrs))
|
||
errors.append(msg)
|
||
return errors
|
||
|
||
|
||
def get_serialization_exclude(serializers, exclude, kwargs):
|
||
"""Helper function to validate serialization args and manage transition from
|
||
keyword arguments (pre v2.1) to exclude argument.
|
||
"""
|
||
exclude = list(exclude)
|
||
# Split to support file names like meta.json
|
||
options = [name.split(".")[0] for name in serializers]
|
||
for key, value in kwargs.items():
|
||
if key in ("vocab",) and value is False:
|
||
deprecation_warning(Warnings.W015.format(arg=key))
|
||
exclude.append(key)
|
||
elif key.split(".")[0] in options:
|
||
raise ValueError(Errors.E128.format(arg=key))
|
||
# TODO: user warning?
|
||
return exclude
|
||
|
||
|
||
def get_words_and_spaces(words, text):
|
||
if "".join("".join(words).split()) != "".join(text.split()):
|
||
raise ValueError(Errors.E194.format(text=text, words=words))
|
||
text_words = []
|
||
text_spaces = []
|
||
text_pos = 0
|
||
# normalize words to remove all whitespace tokens
|
||
norm_words = [word for word in words if not word.isspace()]
|
||
# align words with text
|
||
for word in norm_words:
|
||
try:
|
||
word_start = text[text_pos:].index(word)
|
||
except ValueError:
|
||
raise ValueError(Errors.E194.format(text=text, words=words))
|
||
if word_start > 0:
|
||
text_words.append(text[text_pos:text_pos+word_start])
|
||
text_spaces.append(False)
|
||
text_pos += word_start
|
||
text_words.append(word)
|
||
text_spaces.append(False)
|
||
text_pos += len(word)
|
||
if text_pos < len(text) and text[text_pos] == " ":
|
||
text_spaces[-1] = True
|
||
text_pos += 1
|
||
if text_pos < len(text):
|
||
text_words.append(text[text_pos:])
|
||
text_spaces.append(False)
|
||
return (text_words, text_spaces)
|
||
|
||
|
||
class SimpleFrozenDict(dict):
|
||
"""Simplified implementation of a frozen dict, mainly used as default
|
||
function or method argument (for arguments that should default to empty
|
||
dictionary). Will raise an error if user or spaCy attempts to add to dict.
|
||
"""
|
||
|
||
def __setitem__(self, key, value):
|
||
raise NotImplementedError(Errors.E095)
|
||
|
||
def pop(self, key, default=None):
|
||
raise NotImplementedError(Errors.E095)
|
||
|
||
def update(self, other):
|
||
raise NotImplementedError(Errors.E095)
|
||
|
||
|
||
class DummyTokenizer(object):
|
||
# add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
|
||
# allow serialization (see #1557)
|
||
def to_bytes(self, **kwargs):
|
||
return b""
|
||
|
||
def from_bytes(self, _bytes_data, **kwargs):
|
||
return self
|
||
|
||
def to_disk(self, _path, **kwargs):
|
||
return None
|
||
|
||
def from_disk(self, _path, **kwargs):
|
||
return self
|