from typing import List, Union, Dict, Any, Optional, Iterable, Callable, Tuple from typing import Iterator, Type, Pattern, Generator, TYPE_CHECKING from types import ModuleType import os import importlib import importlib.util import re from pathlib import Path import thinc from thinc.api import NumpyOps, get_current_ops, Adam, Config, Optimizer import functools import itertools import numpy.random import numpy import srsly import catalogue import sys import warnings from packaging.specifiers import SpecifierSet, InvalidSpecifier from packaging.version import Version, InvalidVersion import subprocess from contextlib import contextmanager import tempfile import shutil import shlex import inspect import logging try: import cupy.random except ImportError: cupy = None try: # Python 3.8 import importlib.metadata as importlib_metadata except ImportError: import importlib_metadata # These are functions that were previously (v2.x) available from spacy.util # and have since moved to Thinc. We're importing them here so people's code # doesn't break, but they should always be imported from Thinc from now on, # not from spacy.util. from thinc.api import fix_random_seed, compounding, decaying # noqa: F401 from .symbols import ORTH from .compat import cupy, CudaStream, is_windows from .errors import Errors, Warnings, OLD_MODEL_SHORTCUTS from . import about if TYPE_CHECKING: # This lets us add type hints for mypy etc. without causing circular imports from .language import Language # noqa: F401 from .tokens import Doc, Span # noqa: F401 from .vocab import Vocab # noqa: F401 OOV_RANK = numpy.iinfo(numpy.uint64).max LEXEME_NORM_LANGS = ["da", "de", "el", "en", "id", "lb", "pt", "ru", "sr", "ta", "th"] # Default order of sections in the config.cfg. Not all sections needs to exist, # and additional sections are added at the end, in alphabetical order. # fmt: off CONFIG_SECTION_ORDER = ["paths", "variables", "system", "nlp", "components", "training", "pretraining"] # fmt: on logging.basicConfig() logger = logging.getLogger("spacy") class registry(thinc.registry): languages = catalogue.create("spacy", "languages", entry_points=True) architectures = catalogue.create("spacy", "architectures", entry_points=True) tokenizers = catalogue.create("spacy", "tokenizers", entry_points=True) lemmatizers = catalogue.create("spacy", "lemmatizers", entry_points=True) lookups = catalogue.create("spacy", "lookups", entry_points=True) displacy_colors = catalogue.create("spacy", "displacy_colors", entry_points=True) assets = catalogue.create("spacy", "assets", entry_points=True) # Callback functions used to manipulate nlp object etc. callbacks = catalogue.create("spacy", "callbacks") batchers = catalogue.create("spacy", "batchers", entry_points=True) readers = catalogue.create("spacy", "readers", entry_points=True) loggers = catalogue.create("spacy", "loggers", entry_points=True) # These are factories registered via third-party packages and the # spacy_factories entry point. This registry only exists so we can easily # load them via the entry points. The "true" factories are added via the # Language.factory decorator (in the spaCy code base and user code) and those # are the factories used to initialize components via registry.make_from_config. _entry_point_factories = catalogue.create("spacy", "factories", entry_points=True) factories = catalogue.create("spacy", "internal_factories") # This is mostly used to get a list of all installed models in the current # environment. spaCy models packaged with `spacy package` will "advertise" # themselves via entry points. models = catalogue.create("spacy", "models", entry_points=True) 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 __init__(self, *args, error: str = Errors.E095, **kwargs) -> None: """Initialize the frozen dict. Can be initialized with pre-defined values. error (str): The error message when user tries to assign to dict. """ super().__init__(*args, **kwargs) self.error = error def __setitem__(self, key, value): raise NotImplementedError(self.error) def pop(self, key, default=None): raise NotImplementedError(self.error) def update(self, other): raise NotImplementedError(self.error) class SimpleFrozenList(list): """Wrapper class around a list that lets us raise custom errors if certain attributes/methods are accessed. Mostly used for properties like Language.pipeline that return an immutable list (and that we don't want to convert to a tuple to not break too much backwards compatibility). If a user accidentally calls nlp.pipeline.append(), we can raise a more helpful error. """ def __init__(self, *args, error: str = Errors.E927) -> None: """Initialize the frozen list. error (str): The error message when user tries to mutate the list. """ self.error = error super().__init__(*args) def append(self, *args, **kwargs): raise NotImplementedError(self.error) def clear(self, *args, **kwargs): raise NotImplementedError(self.error) def extend(self, *args, **kwargs): raise NotImplementedError(self.error) def insert(self, *args, **kwargs): raise NotImplementedError(self.error) def pop(self, *args, **kwargs): raise NotImplementedError(self.error) def remove(self, *args, **kwargs): raise NotImplementedError(self.error) def reverse(self, *args, **kwargs): raise NotImplementedError(self.error) def sort(self, *args, **kwargs): raise NotImplementedError(self.error) def lang_class_is_loaded(lang: str) -> bool: """Check whether a Language class is already loaded. Language classes are loaded lazily, to avoid expensive setup code associated with the language data. lang (str): Two-letter language code, e.g. 'en'. RETURNS (bool): Whether a Language class has been loaded. """ return lang in registry.languages def get_lang_class(lang: str) -> "Language": """Import and load a Language class. lang (str): Two-letter language code, e.g. 'en'. RETURNS (Language): Language class. """ # Check if language is registered / entry point is available if lang in registry.languages: return registry.languages.get(lang) else: try: module = importlib.import_module(f".lang.{lang}", "spacy") except ImportError as err: raise ImportError(Errors.E048.format(lang=lang, err=err)) from err set_lang_class(lang, getattr(module, module.__all__[0])) return registry.languages.get(lang) def set_lang_class(name: str, cls: Type["Language"]) -> None: """Set a custom Language class name that can be loaded via get_lang_class. name (str): Name of Language class. cls (Language): Language class. """ registry.languages.register(name, func=cls) def ensure_path(path: Any) -> Any: """Ensure string is converted to a Path. path (Any): Anything. If string, it's converted to Path. RETURNS: Path or original argument. """ if isinstance(path, str): return Path(path) else: return path def load_language_data(path: Union[str, Path]) -> Union[dict, list]: """Load JSON language data using the given path as a base. If the provided path isn't present, will attempt to load a gzipped version before giving up. path (str / Path): The data to load. RETURNS: The loaded data. """ path = ensure_path(path) if path.exists(): return srsly.read_json(path) path = path.with_suffix(path.suffix + ".gz") if path.exists(): return srsly.read_gzip_json(path) raise ValueError(Errors.E160.format(path=path)) def get_module_path(module: ModuleType) -> Path: """Get the path of a Python module. module (ModuleType): The Python module. RETURNS (Path): The path. """ if not hasattr(module, "__module__"): raise ValueError(Errors.E169.format(module=repr(module))) return Path(sys.modules[module.__module__].__file__).parent def load_vectors_into_model( nlp: "Language", name: Union[str, Path], *, add_strings=True ) -> None: """Load word vectors from an installed model or path into a model instance.""" vectors_nlp = load_model(name) nlp.vocab.vectors = vectors_nlp.vocab.vectors if add_strings: # I guess we should add the strings from the vectors_nlp model? # E.g. if someone does a similarity query, they might expect the strings. for key in nlp.vocab.vectors.key2row: if key in vectors_nlp.vocab.strings: nlp.vocab.strings.add(vectors_nlp.vocab.strings[key]) def load_model( name: Union[str, Path], *, vocab: Union["Vocab", bool] = True, disable: Iterable[str] = SimpleFrozenList(), exclude: Iterable[str] = SimpleFrozenList(), config: Union[Dict[str, Any], Config] = SimpleFrozenDict(), ) -> "Language": """Load a model from a package or data path. name (str): Package name or model path. vocab (Vocab / True): Optional vocab to pass in on initialization. If True, a new Vocab object will be created. disable (Iterable[str]): Names of pipeline components to disable. config (Dict[str, Any] / Config): Config overrides as nested dict or dict keyed by section values in dot notation. RETURNS (Language): The loaded nlp object. """ kwargs = {"vocab": vocab, "disable": disable, "exclude": exclude, "config": config} if isinstance(name, str): # name or string path if name.startswith("blank:"): # shortcut for blank model return get_lang_class(name.replace("blank:", ""))() if is_package(name): # installed as package return load_model_from_package(name, **kwargs) if Path(name).exists(): # path to model data directory return load_model_from_path(Path(name), **kwargs) elif hasattr(name, "exists"): # Path or Path-like to model data return load_model_from_path(name, **kwargs) if name in OLD_MODEL_SHORTCUTS: raise IOError(Errors.E941.format(name=name, full=OLD_MODEL_SHORTCUTS[name])) raise IOError(Errors.E050.format(name=name)) def load_model_from_package( name: str, *, vocab: Union["Vocab", bool] = True, disable: Iterable[str] = SimpleFrozenList(), exclude: Iterable[str] = SimpleFrozenList(), config: Union[Dict[str, Any], Config] = SimpleFrozenDict(), ) -> "Language": """Load a model from an installed package. name (str): The package name. vocab (Vocab / True): Optional vocab to pass in on initialization. If True, a new Vocab object will be created. disable (Iterable[str]): Names of pipeline components to disable. Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling nlp.enable_pipe. exclude (Iterable[str]): Names of pipeline components to exclude. Excluded components won't be loaded. config (Dict[str, Any] / Config): Config overrides as nested dict or dict keyed by section values in dot notation. RETURNS (Language): The loaded nlp object. """ cls = importlib.import_module(name) return cls.load(vocab=vocab, disable=disable, exclude=exclude, config=config) def load_model_from_path( model_path: Union[str, Path], *, meta: Optional[Dict[str, Any]] = None, vocab: Union["Vocab", bool] = True, disable: Iterable[str] = SimpleFrozenList(), exclude: Iterable[str] = SimpleFrozenList(), config: Union[Dict[str, Any], Config] = SimpleFrozenDict(), ) -> "Language": """Load a model from a data directory path. Creates Language class with pipeline from config.cfg and then calls from_disk() with path. name (str): Package name or model path. meta (Dict[str, Any]): Optional model meta. vocab (Vocab / True): Optional vocab to pass in on initialization. If True, a new Vocab object will be created. disable (Iterable[str]): Names of pipeline components to disable. Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling nlp.enable_pipe. exclude (Iterable[str]): Names of pipeline components to exclude. Excluded components won't be loaded. config (Dict[str, Any] / Config): Config overrides as nested dict or dict keyed by section values in dot notation. RETURNS (Language): The loaded nlp object. """ if not model_path.exists(): raise IOError(Errors.E052.format(path=model_path)) if not meta: meta = get_model_meta(model_path) config_path = model_path / "config.cfg" config = load_config(config_path, overrides=dict_to_dot(config)) nlp, _ = load_model_from_config( config, vocab=vocab, disable=disable, exclude=exclude ) return nlp.from_disk(model_path, exclude=exclude) def load_model_from_config( config: Union[Dict[str, Any], Config], *, vocab: Union["Vocab", bool] = True, disable: Iterable[str] = SimpleFrozenList(), exclude: Iterable[str] = SimpleFrozenList(), auto_fill: bool = False, validate: bool = True, ) -> Tuple["Language", Config]: """Create an nlp object from a config. Expects the full config file including a section "nlp" containing the settings for the nlp object. name (str): Package name or model path. meta (Dict[str, Any]): Optional model meta. vocab (Vocab / True): Optional vocab to pass in on initialization. If True, a new Vocab object will be created. disable (Iterable[str]): Names of pipeline components to disable. Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling nlp.enable_pipe. exclude (Iterable[str]): Names of pipeline components to exclude. Excluded components won't be loaded. auto_fill (bool): Whether to auto-fill config with missing defaults. validate (bool): Whether to show config validation errors. RETURNS (Language): The loaded nlp object. """ if "nlp" not in config: raise ValueError(Errors.E985.format(config=config)) nlp_config = config["nlp"] if "lang" not in nlp_config or nlp_config["lang"] is None: raise ValueError(Errors.E993.format(config=nlp_config)) # This will automatically handle all codes registered via the languages # registry, including custom subclasses provided via entry points lang_cls = get_lang_class(nlp_config["lang"]) nlp = lang_cls.from_config( config, vocab=vocab, disable=disable, exclude=exclude, auto_fill=auto_fill, validate=validate, ) return nlp, nlp.resolved def load_model_from_init_py( init_file: Union[Path, str], *, vocab: Union["Vocab", bool] = True, disable: Iterable[str] = SimpleFrozenList(), exclude: Iterable[str] = SimpleFrozenList(), config: Union[Dict[str, Any], Config] = SimpleFrozenDict(), ) -> "Language": """Helper function to use in the `load()` method of a model package's __init__.py. vocab (Vocab / True): Optional vocab to pass in on initialization. If True, a new Vocab object will be created. disable (Iterable[str]): Names of pipeline components to disable. Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling nlp.enable_pipe. exclude (Iterable[str]): Names of pipeline components to exclude. Excluded components won't be loaded. config (Dict[str, Any] / Config): Config overrides as nested dict or dict keyed by section values in dot notation. RETURNS (Language): The loaded nlp object. """ model_path = Path(init_file).parent meta = get_model_meta(model_path) data_dir = f"{meta['lang']}_{meta['name']}-{meta['version']}" data_path = model_path / data_dir if not model_path.exists(): raise IOError(Errors.E052.format(path=data_path)) return load_model_from_path( data_path, vocab=vocab, meta=meta, disable=disable, exclude=exclude, config=config, ) def load_config( path: Union[str, Path], overrides: Dict[str, Any] = SimpleFrozenDict(), interpolate: bool = False, ) -> Config: """Load a config file. Takes care of path validation and section order. path (Union[str, Path]): Path to the config file. overrides: (Dict[str, Any]): Config overrides as nested dict or dict keyed by section values in dot notation. interpolate (bool): Whether to interpolate and resolve variables. RETURNS (Config): The loaded config. """ config_path = ensure_path(path) if not config_path.exists() or not config_path.is_file(): raise IOError(Errors.E053.format(path=config_path, name="config.cfg")) return Config(section_order=CONFIG_SECTION_ORDER).from_disk( config_path, overrides=overrides, interpolate=interpolate ) def load_config_from_str( text: str, overrides: Dict[str, Any] = SimpleFrozenDict(), interpolate: bool = False ): """Load a full config from a string. Wrapper around Thinc's Config.from_str. text (str): The string config to load. interpolate (bool): Whether to interpolate and resolve variables. RETURNS (Config): The loaded config. """ return Config(section_order=CONFIG_SECTION_ORDER).from_str( text, overrides=overrides, interpolate=interpolate, ) def get_installed_models() -> List[str]: """List all model packages currently installed in the environment. RETURNS (List[str]): The string names of the models. """ return list(registry.models.get_all().keys()) def get_package_version(name: str) -> Optional[str]: """Get the version of an installed package. Typically used to get model package versions. name (str): The name of the installed Python package. RETURNS (str / None): The version or None if package not installed. """ try: return importlib_metadata.version(name) except importlib_metadata.PackageNotFoundError: return None def is_compatible_version( version: str, constraint: str, prereleases: bool = True ) -> Optional[bool]: """Check if a version (e.g. "2.0.0") is compatible given a version constraint (e.g. ">=1.9.0,<2.2.1"). If the constraint is a specific version, it's interpreted as =={version}. version (str): The version to check. constraint (str): The constraint string. prereleases (bool): Whether to allow prereleases. If set to False, prerelease versions will be considered incompatible. RETURNS (bool / None): Whether the version is compatible, or None if the version or constraint are invalid. """ # Handle cases where exact version is provided as constraint if constraint[0].isdigit(): constraint = f"=={constraint}" try: spec = SpecifierSet(constraint) version = Version(version) except (InvalidSpecifier, InvalidVersion): return None spec.prereleases = prereleases return version in spec def is_unconstrained_version( constraint: str, prereleases: bool = True ) -> Optional[bool]: # We have an exact version, this is the ultimate constrained version if constraint[0].isdigit(): return False try: spec = SpecifierSet(constraint) except InvalidSpecifier: return None spec.prereleases = prereleases specs = [sp for sp in spec] # We only have one version spec and it defines > or >= if len(specs) == 1 and specs[0].operator in (">", ">="): return True # One specifier is exact version if any(sp.operator in ("==") for sp in specs): return False has_upper = any(sp.operator in ("<", "<=") for sp in specs) has_lower = any(sp.operator in (">", ">=") for sp in specs) # We have a version spec that defines an upper and lower bound if has_upper and has_lower: return False # Everything else, like only an upper version, only a lower version etc. return True def get_model_version_range(spacy_version: str) -> str: """Generate a version range like >=1.2.3,<1.3.0 based on a given spaCy version. Models are always compatible across patch versions but not across minor or major versions. """ release = Version(spacy_version).release return f">={spacy_version},<{release[0]}.{release[1] + 1}.0" def get_base_version(version: str) -> str: """Generate the base version without any prerelease identifiers. version (str): The version, e.g. "3.0.0.dev1". RETURNS (str): The base version, e.g. "3.0.0". """ return Version(version).base_version def load_meta(path: Union[str, Path]) -> Dict[str, Any]: """Load a model meta.json from a path and validate its contents. path (Union[str, Path]): Path to meta.json. RETURNS (Dict[str, Any]): The loaded meta. """ path = ensure_path(path) if not path.parent.exists(): raise IOError(Errors.E052.format(path=path.parent)) if not path.exists() or not path.is_file(): raise IOError(Errors.E053.format(path=path, name="meta.json")) meta = srsly.read_json(path) for setting in ["lang", "name", "version"]: if setting not in meta or not meta[setting]: raise ValueError(Errors.E054.format(setting=setting)) if "spacy_version" in meta: if not is_compatible_version(about.__version__, meta["spacy_version"]): warn_msg = Warnings.W095.format( model=f"{meta['lang']}_{meta['name']}", model_version=meta["version"], version=meta["spacy_version"], current=about.__version__, ) warnings.warn(warn_msg) if is_unconstrained_version(meta["spacy_version"]): warn_msg = Warnings.W094.format( model=f"{meta['lang']}_{meta['name']}", model_version=meta["version"], version=meta["spacy_version"], example=get_model_version_range(about.__version__), ) warnings.warn(warn_msg) return meta def get_model_meta(path: Union[str, Path]) -> Dict[str, Any]: """Get model meta.json from a directory path and validate its contents. path (str / Path): Path to model directory. RETURNS (Dict[str, Any]): The model's meta data. """ model_path = ensure_path(path) return load_meta(model_path / "meta.json") def is_package(name: str) -> bool: """Check if string maps to a package installed via pip. name (str): Name of package. RETURNS (bool): True if installed package, False if not. """ try: importlib_metadata.distribution(name) return True except: # noqa: E722 return False def get_package_path(name: str) -> Path: """Get the path to an installed package. name (str): Package name. RETURNS (Path): Path to installed package. """ name = name.lower() # use lowercase version to be safe # Here we're importing the module just to find it. This is worryingly # indirect, but it's otherwise very difficult to find the package. pkg = importlib.import_module(name) return Path(pkg.__file__).parent def split_command(command: str) -> List[str]: """Split a string command using shlex. Handles platform compatibility. command (str) : The command to split RETURNS (List[str]): The split command. """ return shlex.split(command, posix=not is_windows) def join_command(command: List[str]) -> str: """Join a command using shlex. shlex.join is only available for Python 3.8+, so we're using a workaround here. command (List[str]): The command to join. RETURNS (str): The joined command """ return " ".join(shlex.quote(cmd) for cmd in command) def run_command(command: Union[str, List[str]], *, capture=False, stdin=None) -> None: """Run a command on the command line as a subprocess. If the subprocess returns a non-zero exit code, a system exit is performed. command (str / List[str]): The command. If provided as a string, the string will be split using shlex.split. """ if isinstance(command, str): command = split_command(command) try: ret = subprocess.run( command, env=os.environ.copy(), input=stdin, encoding="utf8", check=True, stdout=subprocess.PIPE if capture else None, stderr=subprocess.PIPE if capture else None, ) except FileNotFoundError: raise FileNotFoundError( Errors.E970.format(str_command=" ".join(command), tool=command[0]) ) from None if ret.returncode != 0: sys.exit(ret.returncode) return ret @contextmanager def working_dir(path: Union[str, Path]) -> None: """Change current working directory and returns to previous on exit. path (str / Path): The directory to navigate to. YIELDS (Path): The absolute path to the current working directory. This should be used if the block needs to perform actions within the working directory, to prevent mismatches with relative paths. """ prev_cwd = Path.cwd() current = Path(path).resolve() os.chdir(str(current)) try: yield current finally: os.chdir(str(prev_cwd)) @contextmanager def make_tempdir() -> Generator[Path, None, None]: """Execute a block in a temporary directory and remove the directory and its contents at the end of the with block. YIELDS (Path): The path of the temp directory. """ d = Path(tempfile.mkdtemp()) yield d try: shutil.rmtree(str(d)) except PermissionError as e: warnings.warn(Warnings.W091.format(dir=d, msg=e)) def is_cwd(path: Union[Path, str]) -> bool: """Check whether a path is the current working directory. path (Union[Path, str]): The directory path. RETURNS (bool): Whether the path is the current working directory. """ return str(Path(path).resolve()).lower() == str(Path.cwd().resolve()).lower() def is_in_jupyter() -> bool: """Check if user is running spaCy from a Jupyter notebook by detecting the IPython kernel. Mainly used for the displaCy visualizer. RETURNS (bool): True if in Jupyter, False if not. """ # https://stackoverflow.com/a/39662359/6400719 try: shell = get_ipython().__class__.__name__ if shell == "ZMQInteractiveShell": return True # Jupyter notebook or qtconsole except NameError: return False # Probably standard Python interpreter return False def get_object_name(obj: Any) -> str: """Get a human-readable name of a Python object, e.g. a pipeline component. obj (Any): The Python object, typically a function or class. RETURNS (str): A human-readable name. """ if hasattr(obj, "name"): return obj.name if hasattr(obj, "__name__"): return obj.__name__ if hasattr(obj, "__class__") and hasattr(obj.__class__, "__name__"): return obj.__class__.__name__ return repr(obj) def is_same_func(func1: Callable, func2: Callable) -> bool: """Approximately decide whether two functions are the same, even if their identity is different (e.g. after they have been live reloaded). Mostly used in the @Language.component and @Language.factory decorators to decide whether to raise if a factory already exists. Allows decorator to run multiple times with the same function. func1 (Callable): The first function. func2 (Callable): The second function. RETURNS (bool): Whether it's the same function (most likely). """ if not callable(func1) or not callable(func2): return False same_name = func1.__qualname__ == func2.__qualname__ same_file = inspect.getfile(func1) == inspect.getfile(func2) same_code = inspect.getsourcelines(func1) == inspect.getsourcelines(func2) return same_name and same_file and same_code def get_cuda_stream( require: bool = False, non_blocking: bool = True ) -> Optional[CudaStream]: ops = get_current_ops() if CudaStream is None: return None elif isinstance(ops, NumpyOps): return None else: return CudaStream(non_blocking=non_blocking) def get_async(stream, numpy_array): if cupy is None: return numpy_array else: array = cupy.ndarray(numpy_array.shape, order="C", dtype=numpy_array.dtype) array.set(numpy_array, stream=stream) return array def read_regex(path: Union[str, Path]) -> Pattern: path = ensure_path(path) with path.open(encoding="utf8") as file_: entries = file_.read().split("\n") expression = "|".join( ["^" + re.escape(piece) for piece in entries if piece.strip()] ) return re.compile(expression) def compile_prefix_regex(entries: Iterable[Union[str, Pattern]]) -> Pattern: """Compile a sequence of prefix rules into a regex object. entries (Iterable[Union[str, Pattern]]): The prefix rules, e.g. spacy.lang.punctuation.TOKENIZER_PREFIXES. RETURNS (Pattern): The regex object. to be used for Tokenizer.prefix_search. """ expression = "|".join(["^" + piece for piece in entries if piece.strip()]) return re.compile(expression) def compile_suffix_regex(entries: Iterable[Union[str, Pattern]]) -> Pattern: """Compile a sequence of suffix rules into a regex object. entries (Iterable[Union[str, Pattern]]): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES. RETURNS (Pattern): The regex object. to be used for Tokenizer.suffix_search. """ expression = "|".join([piece + "$" for piece in entries if piece.strip()]) return re.compile(expression) def compile_infix_regex(entries: Iterable[Union[str, Pattern]]) -> Pattern: """Compile a sequence of infix rules into a regex object. entries (Iterable[Union[str, Pattern]]): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES. RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer. """ expression = "|".join([piece for piece in entries if piece.strip()]) return re.compile(expression) def add_lookups(default_func: Callable[[str], Any], *lookups) -> Callable[[str], Any]: """Extend an attribute function with special cases. If a word is in the lookups, the value is returned. Otherwise the previous function is used. default_func (callable): The default function to execute. *lookups (dict): Lookup dictionary mapping string to attribute value. RETURNS (callable): Lexical attribute getter. """ # This is implemented as functools.partial instead of a closure, to allow # pickle to work. return functools.partial(_get_attr_unless_lookup, default_func, lookups) def _get_attr_unless_lookup( default_func: Callable[[str], Any], lookups: Dict[str, Any], string: str ) -> Any: for lookup in lookups: if string in lookup: return lookup[string] return default_func(string) def update_exc( base_exceptions: Dict[str, List[dict]], *addition_dicts ) -> Dict[str, List[dict]]: """Update and validate tokenizer exceptions. Will overwrite exceptions. base_exceptions (Dict[str, List[dict]]): Base exceptions. *addition_dicts (Dict[str, List[dict]]): Exceptions to add to the base dict, in order. RETURNS (Dict[str, List[dict]]): Combined tokenizer exceptions. """ exc = dict(base_exceptions) for additions in addition_dicts: for orth, token_attrs in additions.items(): if not all(isinstance(attr[ORTH], str) for attr in token_attrs): raise ValueError(Errors.E055.format(key=orth, orths=token_attrs)) described_orth = "".join(attr[ORTH] for attr in token_attrs) if orth != described_orth: raise ValueError(Errors.E056.format(key=orth, orths=described_orth)) exc.update(additions) exc = expand_exc(exc, "'", "’") return exc def expand_exc( excs: Dict[str, List[dict]], search: str, replace: str ) -> Dict[str, List[dict]]: """Find string in tokenizer exceptions, duplicate entry and replace string. For example, to add additional versions with typographic apostrophes. excs (Dict[str, List[dict]]): Tokenizer exceptions. search (str): String to find and replace. replace (str): Replacement. RETURNS (Dict[str, List[dict]]): Combined tokenizer exceptions. """ def _fix_token(token, search, replace): fixed = dict(token) fixed[ORTH] = fixed[ORTH].replace(search, replace) return fixed new_excs = dict(excs) for token_string, tokens in excs.items(): if search in token_string: new_key = token_string.replace(search, replace) new_value = [_fix_token(t, search, replace) for t in tokens] new_excs[new_key] = new_value return new_excs def normalize_slice( length: int, start: int, stop: int, step: Optional[int] = None ) -> Tuple[int, int]: if not (step is None or step == 1): raise ValueError(Errors.E057) if start is None: start = 0 elif start < 0: start += length start = min(length, max(0, start)) if stop is None: stop = length elif stop < 0: stop += length stop = min(length, max(start, stop)) return start, stop def filter_spans(spans: Iterable["Span"]) -> List["Span"]: """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[Span]): The spans to filter. RETURNS (List[Span]): 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: Dict[str, Callable[[], bytes]], exclude: Iterable[str]) -> bytes: return srsly.msgpack_dumps(to_dict(getters, exclude)) def from_bytes( bytes_data: bytes, setters: Dict[str, Callable[[bytes], Any]], exclude: Iterable[str], ) -> None: return from_dict(srsly.msgpack_loads(bytes_data), setters, exclude) def to_dict( getters: Dict[str, Callable[[], Any]], exclude: Iterable[str] ) -> Dict[str, Any]: serialized = {} 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 serialized def from_dict( msg: Dict[str, Any], setters: Dict[str, Callable[[Any], Any]], exclude: Iterable[str], ) -> Dict[str, Any]: 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: Union[str, Path], writers: Dict[str, Callable[[Path], None]], exclude: Iterable[str], ) -> Path: 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: Union[str, Path], readers: Dict[str, Callable[[Path], None]], exclude: Iterable[str], ) -> Path: 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 import_file(name: str, loc: Union[str, Path]) -> ModuleType: """Import module from a file. Used to load models from a directory. name (str): Name of module to load. loc (str / Path): Path to the file. RETURNS: The loaded module. """ loc = str(loc) spec = importlib.util.spec_from_file_location(name, str(loc)) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) return module def minify_html(html: str) -> str: """Perform a template-specific, rudimentary HTML minification for displaCy. Disclaimer: NOT a general-purpose solution, only removes indentation and newlines. html (str): Markup to minify. RETURNS (str): "Minified" HTML. """ return html.strip().replace(" ", "").replace("\n", "") def escape_html(text: str) -> str: """Replace <, >, &, " with their HTML encoded representation. Intended to prevent HTML errors in rendered displaCy markup. text (str): The original text. RETURNS (str): Equivalent text to be safely used within HTML. """ text = text.replace("&", "&") text = text.replace("<", "<") text = text.replace(">", ">") text = text.replace('"', """) return text def get_words_and_spaces( words: Iterable[str], text: str ) -> Tuple[List[str], List[bool]]: """Given a list of words and a text, reconstruct the original tokens and return a list of words and spaces that can be used to create a Doc. This can help recover destructive tokenization that didn't preserve any whitespace information. words (Iterable[str]): The words. text (str): The original text. RETURNS (Tuple[List[str], List[bool]]): The words and spaces. """ 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)) from None 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) def copy_config(config: Union[Dict[str, Any], Config]) -> Config: """Deep copy a Config. Will raise an error if the config contents are not JSON-serializable. config (Config): The config to copy. RETURNS (Config): The copied config. """ try: return Config(config).copy() except ValueError: raise ValueError(Errors.E961.format(config=config)) from None def dot_to_dict(values: Dict[str, Any]) -> Dict[str, dict]: """Convert dot notation to a dict. For example: {"token.pos": True, "token._.xyz": True} becomes {"token": {"pos": True, "_": {"xyz": True }}}. values (Dict[str, Any]): The key/value pairs to convert. RETURNS (Dict[str, dict]): The converted values. """ result = {} for key, value in values.items(): path = result parts = key.lower().split(".") for i, item in enumerate(parts): is_last = i == len(parts) - 1 path = path.setdefault(item, value if is_last else {}) return result def dict_to_dot(obj: Dict[str, dict]) -> Dict[str, Any]: """Convert dot notation to a dict. For example: {"token": {"pos": True, "_": {"xyz": True }}} becomes {"token.pos": True, "token._.xyz": True}. values (Dict[str, dict]): The dict to convert. RETURNS (Dict[str, Any]): The key/value pairs. """ return {".".join(key): value for key, value in walk_dict(obj)} def dot_to_object(config: Config, section: str): """Convert dot notation of a "section" to a specific part of the Config. e.g. "training.optimizer" would return the Optimizer object. Throws an error if the section is not defined in this config. config (Config): The config. section (str): The dot notation of the section in the config. RETURNS: The object denoted by the section """ component = config parts = section.split(".") for item in parts: try: component = component[item] except (KeyError, TypeError): raise KeyError(Errors.E952.format(name=section)) from None return component def walk_dict( node: Dict[str, Any], parent: List[str] = [] ) -> Iterator[Tuple[List[str], Any]]: """Walk a dict and yield the path and values of the leaves.""" for key, value in node.items(): key_parent = [*parent, key] if isinstance(value, dict): yield from walk_dict(value, key_parent) else: yield (key_parent, value) def get_arg_names(func: Callable) -> List[str]: """Get a list of all named arguments of a function (regular, keyword-only). func (Callable): The function RETURNS (List[str]): The argument names. """ argspec = inspect.getfullargspec(func) return list(set([*argspec.args, *argspec.kwonlyargs])) def combine_score_weights(weights: List[Dict[str, float]]) -> Dict[str, float]: """Combine and normalize score weights defined by components, e.g. {"ents_r": 0.2, "ents_p": 0.3, "ents_f": 0.5} and {"some_other_score": 1.0}. weights (List[dict]): The weights defined by the components. RETURNS (Dict[str, float]): The combined and normalized weights. """ result = {} for w_dict in weights: # We need to account for weights that don't sum to 1.0 and normalize # the score weights accordingly, then divide score by the number of # components. total = sum(w_dict.values()) for key, value in w_dict.items(): weight = round(value / total / len(weights), 2) result[key] = result.get(key, 0.0) + weight return result class DummyTokenizer: # 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 def create_default_optimizer() -> Optimizer: return Adam() def minibatch(items, size): """Iterate over batches of items. `size` may be an iterator, so that batch-size can vary on each step. """ if isinstance(size, int): size_ = itertools.repeat(size) else: size_ = size items = iter(items) while True: batch_size = next(size_) batch = list(itertools.islice(items, int(batch_size))) if len(batch) == 0: break yield list(batch)