text: 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
  from thinc.api import ConfigValidationError, Model
  import functools
  import itertools
  import numpy.random
  import numpy
  import srsly
  import catalogue
  from catalogue import RegistryError, Registry
  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
  DEFAULT_OOV_PROB = -20
  LEXEME_NORM_LANGS = ["cs", "da", "de", "el", "en", "id", "lb", "mk", "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", "corpora", "training", "pretraining", "initialize"]
  # fmt: on
  
  
  logger = logging.getLogger("spacy")
  logger_stream_handler = logging.StreamHandler()
  logger_stream_handler.setFormatter(
      logging.Formatter("[%(asctime)s] [%(levelname)s] %(message)s")
  )
  logger.addHandler(logger_stream_handler)
  
  
  class ENV_VARS:
      CONFIG_OVERRIDES = "SPACY_CONFIG_OVERRIDES"
      PROJECT_USE_GIT_VERSION = "SPACY_PROJECT_USE_GIT_VERSION"
  
  
  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)
      misc = catalogue.create("spacy", "misc", entry_points=True)
      # Callback functions used to manipulate nlp object etc.
      callbacks = catalogue.create("spacy", "callbacks", entry_points=True)
      batchers = catalogue.create("spacy", "batchers", entry_points=True)
      readers = catalogue.create("spacy", "readers", entry_points=True)
      augmenters = catalogue.create("spacy", "augmenters", 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.resolve.
      _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)
      cli = catalogue.create("spacy", "cli", entry_points=True)
  
      @classmethod
      def get_registry_names(cls) -> List[str]:
          """List all available registries."""
          names = []
          for name, value in inspect.getmembers(cls):
              if not name.startswith("_") and isinstance(value, Registry):
                  names.append(name)
          return sorted(names)
  
      @classmethod
      def get(cls, registry_name: str, func_name: str) -> Callable:
          """Get a registered function from the registry."""
          # We're overwriting this classmethod so we're able to provide more
          # specific error messages and implement a fallback to spacy-legacy.
          if not hasattr(cls, registry_name):
              names = ", ".join(cls.get_registry_names()) or "none"
              raise RegistryError(Errors.E892.format(name=registry_name, available=names))
          reg = getattr(cls, registry_name)
          try:
              func = reg.get(func_name)
          except RegistryError:
              if func_name.startswith("spacy."):
                  legacy_name = func_name.replace("spacy.", "spacy-legacy.")
                  try:
                      return reg.get(legacy_name)
                  except catalogue.RegistryError:
                      pass
              available = ", ".join(sorted(reg.get_all().keys())) or "none"
              raise RegistryError(
                  Errors.E893.format(
                      name=func_name, reg_name=registry_name, available=available
                  )
              ) from None
          return func
  
      @classmethod
      def has(cls, registry_name: str, func_name: str) -> bool:
          """Check whether a function is available in a registry."""
          if not hasattr(cls, registry_name):
              return False
          reg = getattr(cls, registry_name)
          if func_name.startswith("spacy."):
              legacy_name = func_name.replace("spacy.", "spacy-legacy.")
              return func_name in reg or legacy_name in reg
          return func_name in reg
  
  
  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_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,
  ) -> "Language":
      """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
  
  
  def get_sourced_components(
      config: Union[Dict[str, Any], Config]
  ) -> Dict[str, Dict[str, Any]]:
      """RETURNS (List[str]): All sourced components in the original config,
      e.g. {"source": "en_core_web_sm"}. If the config contains a key
      "factory", we assume it refers to a component factory.
      """
      return {
          name: cfg
          for name, cfg in config.get("components", {}).items()
          if "factory" not in cfg and "source" in cfg
      }
  
  
  def resolve_dot_names(config: Config, dot_names: List[Optional[str]]) -> Tuple[Any]:
      """Resolve one or more "dot notation" names, e.g. corpora.train.
      The paths could point anywhere into the config, so we don't know which
      top-level section we'll be looking within.
  
      We resolve the whole top-level section, although we could resolve less --
      we could find the lowest part of the tree.
      """
      # TODO: include schema?
      resolved = {}
      output = []
      errors = []
      for name in dot_names:
          if name is None:
              output.append(name)
          else:
              section = name.split(".")[0]
              # We want to avoid resolving the same thing twice
              if section not in resolved:
                  if registry.is_promise(config[section]):
                      # Otherwise we can't resolve [corpus] if it's a promise
                      result = registry.resolve({"config": config[section]})["config"]
                  else:
                      result = registry.resolve(config[section])
                  resolved[section] = result
              try:
                  output.append(dot_to_object(resolved, name))
              except KeyError:
                  msg = f"not a valid section reference: {name}"
                  errors.append({"loc": name.split("."), "msg": msg})
      if errors:
          raise ConfigValidationError(config=config, errors=errors)
      return tuple(output)
  
  
  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 or "-" to read from stdin.
      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)
      config = Config(section_order=CONFIG_SECTION_ORDER)
      if str(config_path) == "-":  # read from standard input
          return config.from_str(
              sys.stdin.read(), overrides=overrides, interpolate=interpolate
          )
      else:
          if not config_path or not config_path.exists() or not config_path.is_file():
              raise IOError(Errors.E053.format(path=config_path, name="config.cfg"))
          return config.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 get_minor_version(version: str) -> Optional[str]:
      """Get the major + minor version (without patch or prerelease identifiers).
  
      version (str): The version.
      RETURNS (str): The major + minor version or None if version is invalid.
      """
      try:
          v = Version(version)
      except (TypeError, InvalidVersion):
          return None
      return f"{v.major}.{v.minor}"
  
  
  def is_minor_version_match(version_a: str, version_b: str) -> bool:
      """Compare two versions and check if they match in major and minor, without
      patch or prerelease identifiers. Used internally for compatibility checks
      that should be insensitive to patch releases.
  
      version_a (str): The first version
      version_b (str): The second version.
      RETURNS (bool): Whether the versions match.
      """
      a = get_minor_version(version_a)
      b = get_minor_version(version_b)
      return a is not None and b is not None and a == b
  
  
  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.parent, 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 replace_model_node(model: Model, target: Model, replacement: Model) -> None:
      """Replace a node within a model with a new one, updating refs.
  
      model (Model): The parent model.
      target (Model): The target node.
      replacement (Model): The node to replace the target with.
      """
      # Place the node into the sublayers
      for node in model.walk():
          if target in node.layers:
              node.layers[node.layers.index(target)] = replacement
      # Now fix any node references
      for node in model.walk():
          for ref_name in node.ref_names:
              if node.maybe_get_ref(ref_name) is target:
                  node.set_ref(ref_name, replacement)
  
  
  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]],
      *,
      stdin: Optional[Any] = None,
      capture: bool = False,
  ) -> Optional[subprocess.CompletedProcess]:
      """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.
      stdin (Optional[Any]): stdin to read from or None.
      capture (bool): Whether to capture the output and errors. If False,
          the stdout and stderr will not be redirected, and if there's an error,
          sys.exit will be called with the return code. You should use capture=False
          when you want to turn over execution to the command, and capture=True
          when you want to run the command more like a function.
      RETURNS (Optional[CompletedProcess]): The process object.
      """
      if isinstance(command, str):
          cmd_list = split_command(command)
          cmd_str = command
      else:
          cmd_list = command
          cmd_str = " ".join(command)
      try:
          ret = subprocess.run(
              cmd_list,
              env=os.environ.copy(),
              input=stdin,
              encoding="utf8",
              check=False,
              stdout=subprocess.PIPE if capture else None,
              stderr=subprocess.STDOUT if capture else None,
          )
      except FileNotFoundError:
          # Indicates the *command* wasn't found, it's an error before the command
          # is run.
          raise FileNotFoundError(
              Errors.E970.format(str_command=cmd_str, tool=cmd_list[0])
          ) from None
      if ret.returncode != 0 and capture:
          message = f"Error running command:\n\n{cmd_str}\n\n"
          message += f"Subprocess exited with status {ret.returncode}"
          if ret.stdout is not None:
              message += f"\n\nProcess log (stdout and stderr):\n\n"
              message += ret.stdout
          error = subprocess.SubprocessError(message)
          error.ret = ret
          error.command = cmd_str
          raise error
      elif 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") and obj.name is not None:
          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
      if not hasattr(func1, "__qualname__") or not hasattr(func2, "__qualname__"):
          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.
      """
      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("&", "&amp;")
      text = text.replace("<", "&lt;")
      text = text.replace(">", "&gt;")
      text = text.replace('"', "&quot;")
      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 set_dot_to_object(config: Config, section: str, value: Any) -> None:
      """Update a config at a given position from a dot notation.
  
      config (Config): The config.
      section (str): The dot notation of the section in the config.
      value (Any): The value to set in the config.
      """
      component = config
      parts = section.split(".")
      for i, item in enumerate(parts):
          try:
              if i == len(parts) - 1:
                  component[item] = value
              else:
                  component = component[item]
          except (KeyError, TypeError):
              raise KeyError(Errors.E952.format(name=section)) from None
  
  
  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]],
      overrides: Dict[str, Optional[Union[float, int]]] = SimpleFrozenDict(),
  ) -> 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.
      overrides (Dict[str, Optional[Union[float, int]]]): Existing scores that
          should be preserved.
      RETURNS (Dict[str, float]): The combined and normalized weights.
      """
      # We first need to extract all None/null values for score weights that
      # shouldn't be shown in the table *or* be weighted
      result = {}
      all_weights = []
      for w_dict in weights:
          filtered_weights = {}
          for key, value in w_dict.items():
              value = overrides.get(key, value)
              if value is None:
                  result[key] = None
              else:
                  filtered_weights[key] = value
          all_weights.append(filtered_weights)
      for w_dict in all_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():
              if total == 0:
                  weight = 0.0
              else:
                  weight = round(value / total / len(all_weights), 2)
              prev_weight = result.get(key, 0.0)
              prev_weight = 0.0 if prev_weight is None else prev_weight
              result[key] = prev_weight + weight
      return result
  
  
  class DummyTokenizer:
      def __call__(self, text):
          raise NotImplementedError
  
      def pipe(self, texts, **kwargs):
          for text in texts:
              yield self(text)
  
      # 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)
  
  
  def is_cython_func(func: Callable) -> bool:
      """Slightly hacky check for whether a callable is implemented in Cython.
      Can be used to implement slightly different behaviors, especially around
      inspecting and parameter annotations. Note that this will only return True
      for actual cdef functions and methods, not regular Python functions defined
      in Python modules.
  
      func (Callable): The callable to check.
      RETURNS (bool): Whether the callable is Cython (probably).
      """
      attr = "__pyx_vtable__"
      if hasattr(func, attr):  # function or class instance
          return True
      # https://stackoverflow.com/a/55767059
      if (
          hasattr(func, "__qualname__")
          and hasattr(func, "__module__")
          and func.__module__ in sys.modules
      ):  # method
          cls_func = vars(sys.modules[func.__module__])[func.__qualname__.split(".")[0]]
          return hasattr(cls_func, attr)
      return False
  
  
  def check_bool_env_var(env_var: str) -> bool:
      """Convert the value of an environment variable to a boolean. Add special
      check for "0" (falsy) and consider everything else truthy, except unset.
  
      env_var (str): The name of the environment variable to check.
      RETURNS (bool): Its boolean value.
      """
      value = os.environ.get(env_var, False)
      if value == "0":
          return False
      return bool(value)
  
  
  def _pipe(docs, proc, name, default_error_handler, kwargs):
      if hasattr(proc, "pipe"):
          yield from proc.pipe(docs, **kwargs)
      else:
          # We added some args for pipe that __call__ doesn't expect.
          kwargs = dict(kwargs)
          error_handler = default_error_handler
          if hasattr(proc, "get_error_handler"):
              error_handler = proc.get_error_handler()
          for arg in ["batch_size"]:
              if arg in kwargs:
                  kwargs.pop(arg)
          for doc in docs:
              try:
                  doc = proc(doc, **kwargs)
                  yield doc
              except Exception as e:
                  error_handler(name, proc, [doc], e)
  
  
  def raise_error(proc_name, proc, docs, e):
      raise e
  
  
  def ignore_error(proc_name, proc, docs, e):
      pass
  
  
  def warn_if_jupyter_cupy():
      """Warn about require_gpu if a jupyter notebook + cupy + mismatched
      contextvars vs. thread ops are detected
      """
      if is_in_jupyter():
          from thinc.backends.cupy_ops import CupyOps
  
          if CupyOps.xp is not None:
              from thinc.backends import contextvars_eq_thread_ops
  
              if not contextvars_eq_thread_ops():
                  warnings.warn(Warnings.W111)
  
  
  def check_lexeme_norms(vocab, component_name):
      lexeme_norms = vocab.lookups.get_table("lexeme_norm", {})
      if len(lexeme_norms) == 0 and vocab.lang in LEXEME_NORM_LANGS:
          langs = ", ".join(LEXEME_NORM_LANGS)
          logger.debug(Warnings.W033.format(model=component_name, langs=langs))
  