import os
import importlib
import importlib.util
import re
from pathlib import Path
import random
from typing import List
import thinc
from thinc.api import NumpyOps, get_current_ops, Adam, require_gpu, Config
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


try:
    import cupy.random
except ImportError:
    cupy = None

try:  # Python 3.8
    import importlib.metadata as importlib_metadata
except ImportError:
    import importlib_metadata

from .symbols import ORTH
from .compat import cupy, CudaStream
from .errors import Errors, Warnings
from . import about


_PRINT_ENV = False
OOV_RANK = numpy.iinfo(numpy.uint64).max


class registry(thinc.registry):
    languages = catalogue.create("spacy", "languages", entry_points=True)
    architectures = catalogue.create("spacy", "architectures", entry_points=True)
    lookups = catalogue.create("spacy", "lookups", entry_points=True)
    factories = catalogue.create("spacy", "factories", entry_points=True)
    displacy_colors = catalogue.create("spacy", "displacy_colors", entry_points=True)
    assets = catalogue.create("spacy", "assets", entry_points=True)
    # 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)


def set_env_log(value):
    global _PRINT_ENV
    _PRINT_ENV = value


def lang_class_is_loaded(lang):
    """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):
    """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))
        set_lang_class(lang, getattr(module, module.__all__[0]))
    return registry.languages.get(lang)


def set_lang_class(name, cls):
    """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):
    """Ensure string is converted to a Path.

    path: 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):
    """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):
    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, **overrides):
    """Load a model from a package or data path.

    name (str): Package name or model path.
    **overrides: Specific overrides, like pipeline components to disable.
    RETURNS (Language): `Language` class with the loaded model.
    """
    if isinstance(name, str):  # name or string path
        if is_package(name):  # installed as package
            return load_model_from_package(name, **overrides)
        if Path(name).exists():  # path to model data directory
            return load_model_from_path(Path(name), **overrides)
    elif hasattr(name, "exists"):  # Path or Path-like to model data
        return load_model_from_path(name, **overrides)
    raise IOError(Errors.E050.format(name=name))


def load_model_from_package(name, **overrides):
    """Load a model from an installed package."""
    cls = importlib.import_module(name)
    return cls.load(**overrides)


def load_model_from_path(model_path, meta=False, **overrides):
    """Load a model from a data directory path. Creates Language class with
    pipeline from meta.json and then calls from_disk() with path."""
    if not meta:
        meta = get_model_meta(model_path)
    nlp_config = get_model_config(model_path)
    if nlp_config.get("nlp", None):
        return load_model_from_config(nlp_config["nlp"])

    # Support language factories registered via entry points (e.g. custom
    # language subclass) while keeping top-level language identifier "lang"
    lang = meta.get("lang_factory", meta["lang"])
    cls = get_lang_class(lang)
    nlp = cls(meta=meta, **overrides)
    pipeline = meta.get("pipeline", [])
    factories = meta.get("factories", {})
    disable = overrides.get("disable", [])
    if pipeline is True:
        pipeline = nlp.Defaults.pipe_names
    elif pipeline in (False, None):
        pipeline = []
    for name in pipeline:
        if name not in disable:
            config = meta.get("pipeline_args", {}).get(name, {})
            config.update(overrides)
            factory = factories.get(name, name)
            if nlp_config.get(name, None):
                model_config = nlp_config[name]["model"]
                config["model"] = model_config
            component = nlp.create_pipe(factory, config=config)
            nlp.add_pipe(component, name=name)
    return nlp.from_disk(model_path, exclude=disable)


def load_model_from_config(nlp_config):
    if "name" in nlp_config:
        nlp = load_model(**nlp_config)
    elif "lang" in nlp_config:
        lang_class = get_lang_class(nlp_config["lang"])
        nlp = lang_class()
    else:
        raise ValueError(Errors.E993)
    if "pipeline" in nlp_config:
        for name, component_cfg in nlp_config["pipeline"].items():
            factory = component_cfg.pop("factory")
            component = nlp.create_pipe(factory, config=component_cfg)
            nlp.add_pipe(component, name=name)
    return nlp


def load_model_from_init_py(init_file, **overrides):
    """Helper function to use in the `load()` method of a model package's
    __init__.py.

    init_file (str): Path to model's __init__.py, i.e. `__file__`.
    **overrides: Specific overrides, like pipeline components to disable.
    RETURNS (Language): `Language` class with loaded model.
    """
    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, meta, **overrides)


def get_installed_models():
    """List all model packages currently installed in the environment.

    RETURNS (list): The string names of the models.
    """
    return list(registry.models.get_all().keys())


def get_package_version(name):
    """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, constraint, prereleases=True):
    """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 get_model_version_range(spacy_version):
    """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):
    """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_config(path, create_objects=False):
    """Load a Thinc-formatted config file, optionally filling in objects where
    the config references registry entries. See "Thinc config files" for details.

    path (str / Path): Path to the config file
    create_objects (bool): Whether to automatically create objects when the config
        references registry entries. Defaults to False.

    RETURNS (dict): The objects from the config file.
    """
    config = thinc.config.Config().from_disk(path)
    if create_objects:
        return registry.make_from_config(config, validate=True)
    else:
        return config


def load_config_from_str(string, create_objects=False):
    """Load a Thinc-formatted config, optionally filling in objects where
    the config references registry entries. See "Thinc config files" for details.

    string (str / Path): Text contents of the config file.
    create_objects (bool): Whether to automatically create objects when the config
        references registry entries. Defaults to False.

    RETURNS (dict): The objects from the config file.
    """
    config = thinc.config.Config().from_str(string)
    if create_objects:
        return registry.make_from_config(config, validate=True)
    else:
        return config


def get_model_meta(path):
    """Get model meta.json from a directory path and validate its contents.

    path (str / Path): Path to model directory.
    RETURNS (dict): The model's meta data.
    """
    model_path = ensure_path(path)
    if not model_path.exists():
        raise IOError(Errors.E052.format(path=model_path))
    meta_path = model_path / "meta.json"
    if not meta_path.is_file():
        raise IOError(Errors.E053.format(path=meta_path, name="meta.json"))
    meta = srsly.read_json(meta_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"]):
            warnings.warn(
                Warnings.W095.format(
                    model=f"{meta['lang']}_{meta['name']}",
                    model_version=meta["version"],
                    version=meta["spacy_version"],
                    current=about.__version__,
                )
            )
    return meta


def get_model_config(path):
    """Get the model's config from a directory path.

    path (str / Path): Path to model directory.
    RETURNS (Config): The model's config data.
    """
    model_path = ensure_path(path)
    if not model_path.exists():
        raise IOError(Errors.E052.format(path=model_path))
    config_path = model_path / "config.cfg"
    # model directories are allowed not to have config files ?
    if not config_path.is_file():
        return Config({})
        # raise IOError(Errors.E053.format(path=config_path, name="config.cfg"))
    return Config().from_disk(config_path)


def is_package(name):
    """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):
    """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 is_in_jupyter():
    """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_component_name(component):
    if hasattr(component, "name"):
        return component.name
    if hasattr(component, "__name__"):
        return component.__name__
    if hasattr(component, "__class__") and hasattr(component.__class__, "__name__"):
        return component.__class__.__name__
    return repr(component)


def get_cuda_stream(require=False, non_blocking=True):
    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 eg2doc(example):
    """Get a Doc object from an Example (or if it's a Doc, use it directly)"""
    # Put the import here to avoid circular import problems
    from .tokens.doc import Doc

    return example if isinstance(example, Doc) else example.doc


def env_opt(name, default=None):
    if type(default) is float:
        type_convert = float
    else:
        type_convert = int
    if "SPACY_" + name.upper() in os.environ:
        value = type_convert(os.environ["SPACY_" + name.upper()])
        if _PRINT_ENV:
            print(name, "=", repr(value), "via", "$SPACY_" + name.upper())
        return value
    elif name in os.environ:
        value = type_convert(os.environ[name])
        if _PRINT_ENV:
            print(name, "=", repr(value), "via", "$" + name)
        return value
    else:
        if _PRINT_ENV:
            print(name, "=", repr(default), "by default")
        return default


def read_regex(path):
    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):
    """Compile a sequence of prefix rules into a regex object.

    entries (tuple): The prefix rules, e.g. spacy.lang.punctuation.TOKENIZER_PREFIXES.
    RETURNS (regex object): The regex object. to be used for Tokenizer.prefix_search.
    """
    if "(" in entries:
        # Handle deprecated data
        expression = "|".join(
            ["^" + re.escape(piece) for piece in entries if piece.strip()]
        )
        return re.compile(expression)
    else:
        expression = "|".join(["^" + piece for piece in entries if piece.strip()])
        return re.compile(expression)


def compile_suffix_regex(entries):
    """Compile a sequence of suffix rules into a regex object.

    entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
    RETURNS (regex object): 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):
    """Compile a sequence of infix rules into a regex object.

    entries (tuple): 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, *lookups):
    """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, lookups, string):
    for lookup in lookups:
        if string in lookup:
            return lookup[string]
    return default_func(string)


def update_exc(base_exceptions, *addition_dicts):
    """Update and validate tokenizer exceptions. Will overwrite exceptions.

    base_exceptions (dict): Base exceptions.
    *addition_dicts (dict): Exceptions to add to the base dict, in order.
    RETURNS (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, search, replace):
    """Find string in tokenizer exceptions, duplicate entry and replace string.
    For example, to add additional versions with typographic apostrophes.

    excs (dict): Tokenizer exceptions.
    search (str): String to find and replace.
    replace (str): Replacement.
    RETURNS (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, start, stop, step=None):
    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 minibatch(items, size=8):
    """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 compounding(start, stop, compound):
    """Yield an infinite series of compounding values. Each time the
    generator is called, a value is produced by multiplying the previous
    value by the compound rate.

    EXAMPLE:
      >>> sizes = compounding(1., 10., 1.5)
      >>> assert next(sizes) == 1.
      >>> assert next(sizes) == 1 * 1.5
      >>> assert next(sizes) == 1.5 * 1.5
    """

    def clip(value):
        return max(value, stop) if (start > stop) else min(value, stop)

    curr = float(start)
    while True:
        yield clip(curr)
        curr *= compound


def stepping(start, stop, steps):
    """Yield an infinite series of values that step from a start value to a
    final value over some number of steps. Each step is (stop-start)/steps.

    After the final value is reached, the generator continues yielding that
    value.

    EXAMPLE:
      >>> sizes = stepping(1., 200., 100)
      >>> assert next(sizes) == 1.
      >>> assert next(sizes) == 1 * (200.-1.) / 100
      >>> assert next(sizes) == 1 + (200.-1.) / 100 + (200.-1.) / 100
    """

    def clip(value):
        return max(value, stop) if (start > stop) else min(value, stop)

    curr = float(start)
    while True:
        yield clip(curr)
        curr += (stop - start) / steps


def decaying(start, stop, decay):
    """Yield an infinite series of linearly decaying values."""

    curr = float(start)
    while True:
        yield max(curr, stop)
        curr -= decay


def minibatch_by_words(examples, size, count_words=len, tolerance=0.2, discard_oversize=False):
    """Create minibatches of roughly a given number of words. If any examples
    are longer than the specified batch length, they will appear in a batch by
    themselves, or be discarded if discard_oversize=True."""
    if isinstance(size, int):
        size_ = itertools.repeat(size)
    elif isinstance(size, List):
        size_ = iter(size)
    else:
        size_ = size

    target_size = next(size_)
    tol_size = target_size * tolerance
    batch = []
    overflow = []
    batch_size = 0
    overflow_size = 0

    for example in examples:
        n_words = count_words(example.doc)
        # if the current example exceeds the maximum batch size, it is returned separately
        # but only if discard_oversize=False.
        if n_words > target_size + tol_size:
            if not discard_oversize:
                yield [example]

        # add the example to the current batch if there's no overflow yet and it still fits
        elif overflow_size == 0 and (batch_size + n_words) <= target_size:
            batch.append(example)
            batch_size += n_words

        # add the example to the overflow buffer if it fits in the tolerance margin
        elif (batch_size + overflow_size + n_words) <= (target_size + tol_size):
            overflow.append(example)
            overflow_size += n_words

        # yield the previous batch and start a new one. The new one gets the overflow examples.
        else:
            yield batch
            target_size = next(size_)
            tol_size = target_size * tolerance
            batch = overflow
            batch_size = overflow_size
            overflow = []
            overflow_size = 0

            # this example still fits
            if (batch_size + n_words) <= target_size:
                batch.append(example)
                batch_size += n_words

            # this example fits in overflow
            elif (batch_size + n_words) <= (target_size + tol_size):
                overflow.append(example)
                overflow_size += n_words

            # this example does not fit with the previous overflow: start another new batch
            else:
                yield batch
                target_size = next(size_)
                tol_size = target_size * tolerance
                batch = [example]
                batch_size = n_words

    # yield the final batch
    if batch:
        batch.extend(overflow)
        yield batch


def itershuffle(iterable, bufsize=1000):
    """Shuffle an iterator. This works by holding `bufsize` items back
    and yielding them sometime later. Obviously, this is not unbiased –
    but should be good enough for batching. Larger bufsize means less bias.
    From https://gist.github.com/andres-erbsen/1307752

    iterable (iterable): Iterator to shuffle.
    bufsize (int): Items to hold back.
    YIELDS (iterable): The shuffled iterator.
    """
    iterable = iter(iterable)
    buf = []
    try:
        while True:
            for i in range(random.randint(1, bufsize - len(buf))):
                buf.append(next(iterable))
            random.shuffle(buf)
            for i in range(random.randint(1, bufsize)):
                if buf:
                    yield buf.pop()
                else:
                    break
    except StopIteration:
        random.shuffle(buf)
        while buf:
            yield buf.pop()
        raise StopIteration


def filter_spans(spans):
    """Filter a sequence of spans and remove duplicates or overlaps. Useful for
    creating named entities (where one token can only be part of one entity) or
    when merging spans with `Retokenizer.merge`. When spans overlap, the (first)
    longest span is preferred over shorter spans.

    spans (iterable): The spans to filter.
    RETURNS (list): The filtered spans.
    """
    get_sort_key = lambda span: (span.end - span.start, -span.start)
    sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
    result = []
    seen_tokens = set()
    for span in sorted_spans:
        # Check for end - 1 here because boundaries are inclusive
        if span.start not in seen_tokens and span.end - 1 not in seen_tokens:
            result.append(span)
        seen_tokens.update(range(span.start, span.end))
    result = sorted(result, key=lambda span: span.start)
    return result


def to_bytes(getters, exclude):
    serialized = {}
    for key, getter in getters.items():
        # Split to support file names like meta.json
        if key.split(".")[0] not in exclude:
            serialized[key] = getter()
    return srsly.msgpack_dumps(serialized)


def from_bytes(bytes_data, setters, exclude):
    msg = srsly.msgpack_loads(bytes_data)
    for key, setter in setters.items():
        # Split to support file names like meta.json
        if key.split(".")[0] not in exclude and key in msg:
            setter(msg[key])
    return msg


def to_disk(path, writers, exclude):
    path = ensure_path(path)
    if not path.exists():
        path.mkdir()
    for key, writer in writers.items():
        # Split to support file names like meta.json
        if key.split(".")[0] not in exclude:
            writer(path / key)
    return path


def from_disk(path, readers, exclude):
    path = ensure_path(path)
    for key, reader in readers.items():
        # Split to support file names like meta.json
        if key.split(".")[0] not in exclude:
            reader(path / key)
    return path


def import_file(name, loc):
    """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):
    """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):
    """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 use_gpu(gpu_id):
    return require_gpu(gpu_id)


def fix_random_seed(seed=0):
    random.seed(seed)
    numpy.random.seed(seed)
    if cupy is not None:
        cupy.random.seed(seed)


def get_serialization_exclude(serializers, exclude, kwargs):
    """Helper function to validate serialization args and manage transition from
    keyword arguments (pre v2.1) to exclude argument.
    """
    exclude = list(exclude)
    # Split to support file names like meta.json
    options = [name.split(".")[0] for name in serializers]
    for key, value in kwargs.items():
        if key in ("vocab",) and value is False:
            warnings.warn(Warnings.W015.format(arg=key), DeprecationWarning)
            exclude.append(key)
        elif key.split(".")[0] in options:
            raise ValueError(Errors.E128.format(arg=key))
        # TODO: user warning?
    return exclude


def get_words_and_spaces(words, text):
    if "".join("".join(words).split()) != "".join(text.split()):
        raise ValueError(Errors.E194.format(text=text, words=words))
    text_words = []
    text_spaces = []
    text_pos = 0
    # normalize words to remove all whitespace tokens
    norm_words = [word for word in words if not word.isspace()]
    # align words with text
    for word in norm_words:
        try:
            word_start = text[text_pos:].index(word)
        except ValueError:
            raise ValueError(Errors.E194.format(text=text, words=words))
        if word_start > 0:
            text_words.append(text[text_pos : text_pos + word_start])
            text_spaces.append(False)
            text_pos += word_start
        text_words.append(word)
        text_spaces.append(False)
        text_pos += len(word)
        if text_pos < len(text) and text[text_pos] == " ":
            text_spaces[-1] = True
            text_pos += 1
    if text_pos < len(text):
        text_words.append(text[text_pos:])
        text_spaces.append(False)
    return (text_words, text_spaces)


class SimpleFrozenDict(dict):
    """Simplified implementation of a frozen dict, mainly used as default
    function or method argument (for arguments that should default to empty
    dictionary). Will raise an error if user or spaCy attempts to add to dict.
    """

    def __setitem__(self, key, value):
        raise NotImplementedError(Errors.E095)

    def pop(self, key, default=None):
        raise NotImplementedError(Errors.E095)

    def update(self, other):
        raise NotImplementedError(Errors.E095)


class DummyTokenizer(object):
    # add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
    # allow serialization (see #1557)
    def to_bytes(self, **kwargs):
        return b""

    def from_bytes(self, _bytes_data, **kwargs):
        return self

    def to_disk(self, _path, **kwargs):
        return None

    def from_disk(self, _path, **kwargs):
        return self


def link_vectors_to_models(vocab):
    vectors = vocab.vectors
    if vectors.name is None:
        vectors.name = VECTORS_KEY
        if vectors.data.size != 0:
            warnings.warn(Warnings.W020.format(shape=vectors.data.shape))
    for word in vocab:
        if word.orth in vectors.key2row:
            word.rank = vectors.key2row[word.orth]
        else:
            word.rank = 0


VECTORS_KEY = "spacy_pretrained_vectors"


def create_default_optimizer():
    learn_rate = env_opt("learn_rate", 0.001)
    beta1 = env_opt("optimizer_B1", 0.9)
    beta2 = env_opt("optimizer_B2", 0.999)
    eps = env_opt("optimizer_eps", 1e-8)
    L2 = env_opt("L2_penalty", 1e-6)
    grad_clip = env_opt("grad_norm_clip", 10.0)
    L2_is_weight_decay = env_opt("L2_is_weight_decay", False)
    optimizer = Adam(
        learn_rate,
        L2=L2,
        beta1=beta1,
        beta2=beta2,
        eps=eps,
        grad_clip=grad_clip,
        L2_is_weight_decay=L2_is_weight_decay,
    )
    return optimizer