# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2026)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


"""@st.cache_resource implementation."""

from __future__ import annotations

import math
import threading
from collections.abc import Callable, Sequence
from typing import (
    TYPE_CHECKING,
    Any,
    Final,
    TypeAlias,
    TypeVar,
    overload,
)

from typing_extensions import ParamSpec

import streamlit as st
from streamlit import config
from streamlit.errors import StreamlitValueError
from streamlit.logger import get_logger
from streamlit.runtime.caching import cache_utils
from streamlit.runtime.caching.cache_errors import CacheKeyNotFoundError
from streamlit.runtime.caching.cache_type import CacheType
from streamlit.runtime.caching.cache_utils import (
    Cache,
    CachedFunc,
    CachedFuncInfo,
    CacheScope,
    OnRelease,
    RefreshMode,
    get_session_id_or_throw,
    make_cached_func_wrapper,
    validate_refresh_mode,
)
from streamlit.runtime.caching.cached_message_replay import (
    CachedMessageReplayContext,
    CachedResult,
    MsgData,
)
from streamlit.runtime.caching.ttl_cleanup_cache import TTLCleanupCache
from streamlit.runtime.metrics_util import gather_metrics
from streamlit.runtime.stats import (
    CACHE_MEMORY_FAMILY,
    CacheStat,
    StatsProvider,
    group_cache_stats,
)
from streamlit.time_util import time_to_seconds

if TYPE_CHECKING:
    from datetime import timedelta

    from streamlit.runtime.caching.hashing import HashFuncsDict

_LOGGER: Final = get_logger(__name__)


CACHE_RESOURCE_MESSAGE_REPLAY_CTX = CachedMessageReplayContext(CacheType.RESOURCE)

ValidateFunc: TypeAlias = Callable[[Any], bool]


def _equal_validate_funcs(a: ValidateFunc | None, b: ValidateFunc | None) -> bool:
    """True if the two validate functions are equal for the purposes of
    determining whether a given function cache needs to be recreated.
    """
    # To "properly" test for function equality here, we'd need to compare function bytecode.
    # For performance reasons, We've decided not to do that for now.
    return (a is None and b is None) or (a is not None and b is not None)


def _no_op_release(ignored: Any) -> None:
    """No-op OnRelease function."""


class ResourceCaches(StatsProvider):
    """Manages all ResourceCache instances."""

    def __init__(self) -> None:
        self._caches_lock = threading.Lock()
        # Map of session IDs to map of function keys to caches.
        self._function_caches: dict[str | None, dict[str, ResourceCache[Any]]] = {}

    @property
    def stats_families(self) -> Sequence[str]:
        return (CACHE_MEMORY_FAMILY,)

    def get_cache(
        self,
        key: str,
        display_name: str,
        max_entries: int | float | None,
        ttl: float | timedelta | str | None,
        validate: ValidateFunc | None,
        on_release: OnRelease,
        scope: CacheScope = "global",
        refresh_mode: RefreshMode = "foreground",
    ) -> ResourceCache[Any]:
        """Return the mem cache for the given key.

        If it doesn't exist, create a new one with the given params.

        Raises
        ------
        StreamlitAPIException
            Raised when ``scope`` is ``"session"`` and there is no thread-local run
            context.
        """
        if max_entries is None:
            max_entries = math.inf

        # The user-facing freshness ttl. In background mode the underlying cache uses a
        # hard-eviction ttl of 2*ttl and tracks freshness separately via stored_at.
        fresh_ttl_seconds = time_to_seconds(ttl)
        hard_ttl_seconds = (
            fresh_ttl_seconds * cache_utils.BACKGROUND_REFRESH_TTL_MULTIPLIER
            if refresh_mode == "background"
            else fresh_ttl_seconds
        )

        # Fetch the session ID. Note that this will throw an exception if there is no
        # session associated with the current thread.
        session_id: str | None
        if scope == "global":
            session_id = None
        else:
            session_id = get_session_id_or_throw()

        # Get the existing cache, if it exists, and validate that its params
        # haven't changed.
        with self._caches_lock:
            session_caches = self._function_caches.get(session_id)
            if session_caches is None:
                session_caches = self._function_caches[session_id] = {}

            cache = session_caches.get(key)
            if (
                cache is not None
                and cache.fresh_ttl_seconds == fresh_ttl_seconds
                and cache.max_entries == max_entries
                and cache.refresh_mode == refresh_mode
                and _equal_validate_funcs(cache.validate, validate)
            ):
                return cache

            # The params changed: detach the old cache so any in-flight background
            # refresh is discarded rather than written back to a replaced cache.
            if cache is not None:
                cache.mark_detached()

            # Create a new cache object and put it in our dict
            _LOGGER.debug("Creating new ResourceCache (key=%s)", key)
            cache = ResourceCache(
                key=key,
                display_name=display_name,
                max_entries=max_entries,
                ttl_seconds=hard_ttl_seconds,
                fresh_ttl_seconds=fresh_ttl_seconds,
                validate=validate,
                on_release=on_release,
                refresh_mode=refresh_mode,
            )
            self._function_caches[session_id][key] = cache
            return cache

    def clear_session(self, session_id: str) -> None:
        """Clears all caches for the given session ID."""
        # Hold the lock while removing the cache, but release it while clearing.
        with self._caches_lock:
            session_caches = self._function_caches.get(session_id)
            if session_caches is not None:
                del self._function_caches[session_id]

        if session_caches is not None:
            for cache in session_caches.values():
                # Detach so a background refresh that completes after the session
                # ended is discarded (and its produced resource released).
                cache.mark_detached()
                cache.clear()

    def clear_all(self) -> None:
        """Clear all resource caches."""
        # Hold the lock long enough to copy the caches.
        with self._caches_lock:
            caches = [
                cache
                for caches in self._function_caches.values()
                for cache in caches.values()
            ]
            self._function_caches = {}

        # Clear each cache to ensure any on_release functions are called.
        for cache in caches:
            cache.mark_detached()
            cache.clear()

    def get_stats(
        self,
        family_names: Sequence[str] | None = None,  # noqa: ARG002
    ) -> dict[str, list[CacheStat]]:
        function_caches: list[ResourceCache[Any]]
        with self._caches_lock:
            # Shallow-clone our caches. We don't want to hold the global
            # lock during stats-gathering.
            function_caches = [
                cache
                for caches in self._function_caches.values()
                for cache in caches.values()
            ]

        stats: list[CacheStat] = []
        for cache in function_caches:
            cache_stats = cache.get_stats()
            for family_stats in cache_stats.values():
                stats.extend(family_stats)
        if not stats:
            return {}
        # In general, get_stats methods need to be able to return only requested stat
        # families, but this method only returns a single family, and we're guaranteed
        # that it was one of those requested if we make it here.
        return {CACHE_MEMORY_FAMILY: group_cache_stats(stats)}


# Singleton ResourceCaches instance
_resource_caches = ResourceCaches()


def clear_session_cache(session_id: str) -> None:
    """Clears all caches for the given session ID."""
    _resource_caches.clear_session(session_id)


def get_resource_cache_stats_provider() -> StatsProvider:
    """Return the StatsProvider for all @st.cache_resource functions."""
    return _resource_caches


P = ParamSpec("P")
R = TypeVar("R")


class CachedResourceFuncInfo(CachedFuncInfo[P, R]):
    """Implements the CachedFuncInfo interface for @st.cache_resource."""

    def __init__(
        self,
        func: Callable[P, R],
        show_spinner: bool | str,
        max_entries: int | None,
        ttl: float | timedelta | str | None,
        validate: ValidateFunc | None,
        hash_funcs: HashFuncsDict | None = None,
        show_time: bool = False,
        on_release: OnRelease | None = None,
        scope: CacheScope = "global",
        refresh_mode: RefreshMode = "foreground",
    ) -> None:
        super().__init__(
            func,
            hash_funcs=hash_funcs,
            show_spinner=show_spinner,
            show_time=show_time,
            scope=scope,
            refresh_mode=refresh_mode,
        )
        self.max_entries = max_entries
        self.ttl = ttl
        self.validate = validate
        self.on_release = on_release or _no_op_release

    @property
    def cache_type(self) -> CacheType:
        return CacheType.RESOURCE

    @property
    def cached_message_replay_ctx(self) -> CachedMessageReplayContext:
        return CACHE_RESOURCE_MESSAGE_REPLAY_CTX

    def get_function_cache(self, function_key: str) -> Cache[R]:
        return _resource_caches.get_cache(
            key=function_key,
            display_name=self.display_name,
            max_entries=self.max_entries,
            ttl=self.ttl,
            validate=self.validate,
            on_release=self.on_release,
            scope=self.scope,
            refresh_mode=self.refresh_mode,
        )


class CacheResourceAPI:
    """Implements the public st.cache_resource API: the @st.cache_resource decorator,
    and st.cache_resource.clear().
    """

    def __init__(self, decorator_metric_name: str) -> None:
        """Create a CacheResourceAPI instance.

        Parameters
        ----------
        decorator_metric_name
            The metric name to record for decorator usage.
        """

        # Parameterize the decorator metric name.
        # (Ignore spurious mypy complaints - https://github.com/python/mypy/issues/2427)
        self._decorator = gather_metrics(decorator_metric_name, self._decorator)  # type: ignore

    # Type-annotate the decorator function.
    # (See https://mypy.readthedocs.io/en/stable/generics.html#decorator-factories)

    # Bare decorator usage
    @overload
    def __call__(self, func: Callable[P, R]) -> CachedFunc[P, R]: ...

    # Decorator with arguments
    @overload
    def __call__(
        self,
        *,
        ttl: float | timedelta | str | None = None,
        max_entries: int | None = None,
        show_spinner: bool | str = True,
        show_time: bool = False,
        validate: ValidateFunc | None = None,
        hash_funcs: HashFuncsDict | None = None,
        on_release: OnRelease | None = None,
        scope: CacheScope = "global",
        refresh_mode: RefreshMode = "foreground",
    ) -> Callable[[Callable[P, R]], CachedFunc[P, R]]: ...

    def __call__(
        self,
        func: Callable[P, R] | None = None,
        *,
        ttl: float | timedelta | str | None = None,
        max_entries: int | None = None,
        show_spinner: bool | str = True,
        show_time: bool = False,
        validate: ValidateFunc | None = None,
        hash_funcs: HashFuncsDict | None = None,
        on_release: OnRelease | None = None,
        scope: CacheScope = "global",
        refresh_mode: RefreshMode = "foreground",
    ) -> CachedFunc[P, R] | Callable[[Callable[P, R]], CachedFunc[P, R]]:
        return self._decorator(  # ty: ignore[missing-argument]
            func,  # ty: ignore[invalid-argument-type]
            ttl=ttl,
            max_entries=max_entries,
            show_spinner=show_spinner,
            show_time=show_time,
            validate=validate,
            hash_funcs=hash_funcs,
            on_release=on_release,
            scope=scope,
            refresh_mode=refresh_mode,
        )

    def _decorator(
        self,
        func: Callable[P, R] | None,
        *,
        ttl: float | timedelta | str | None,
        max_entries: int | None,
        show_spinner: bool | str,
        show_time: bool = False,
        validate: ValidateFunc | None,
        hash_funcs: HashFuncsDict | None = None,
        on_release: OnRelease | None = None,
        scope: CacheScope = "global",
        refresh_mode: RefreshMode = "foreground",
    ) -> CachedFunc[P, R] | Callable[[Callable[P, R]], CachedFunc[P, R]]:
        """Decorator to cache functions that return resource objects (e.g. database connections, ML models).

        Cached objects can be global or session-scoped. Global resources are
        shared across all users, sessions, and reruns. Session-scoped resources are
        scoped to the current session and are removed when the session disconnects.
        Global resources must be thread-safe. If thread safety is an issue,
        consider using a session-scoped cache or storing the resource in
        ``st.session_state`` instead.

        You can clear a function's cache with ``func.clear()`` or clear the entire
        cache with ``st.cache_resource.clear()``.

        A function's arguments must be hashable to cache it. Streamlit makes a
        best effort to hash a variety of objects, but the fallback hashing method
        requires that the argument be pickleable, also. If you have an unhashable
        argument (like a database connection) or an argument you want to exclude
        from caching, use an underscore prefix in the argument name. In this case,
        Streamlit will return a cached value when all other arguments match a
        previous function call. Alternatively, you can declare custom hashing
        functions with ``hash_funcs``.

        Objects cached by ``st.cache_resource`` act like singletons and can
        mutate. To cache data and return copies, use ``st.cache_data`` instead.
        To learn more about caching, see `Caching overview
        <https://docs.streamlit.io/develop/concepts/architecture/caching>`_.

        .. warning::
            Async objects are not officially supported in Streamlit. Caching
            async objects or objects that reference async objects may have
            unintended consequences. For example, Streamlit may close event
            loops in its normal operation and make the cached object raise an
            ``Event loop closed`` error.

            To upvote official ``asyncio`` support, see GitHub issue `#8488
            <https://github.com/streamlit/streamlit/issues/8488>`_. To upvote
            support for caching async functions, see GitHub issue `#8308
            <https://github.com/streamlit/streamlit/issues/8308>`_.

        Parameters
        ----------
        func : callable
            The function that creates the cached resource. Streamlit hashes the
            function's source code.

        ttl : float, timedelta, str, or None
            The maximum age of a returned entry from the cache. This can be one
            of the following values:

            - ``None`` if cache entries should never expire (default).
            - A number specifying the time in seconds.
            - A string specifying the time in a format supported by `Pandas's
              Timedelta constructor <https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.html>`_,
              e.g. ``"1D"``, ``"1.5 days"``, or ``"1h23s"``. Note that number strings
              without units are treated by Pandas as nanoseconds.
            - A ``timedelta`` object from `Python's built-in datetime library
              <https://docs.python.org/3/library/datetime.html#timedelta-objects>`_,
              e.g. ``timedelta(days=1)``.

            Changes to this value will trigger a new cache to be created.

        max_entries : int or None
            The maximum number of entries to keep in the cache, or None
            for an unbounded cache. When a new entry is added to a full cache,
            the oldest cached entry will be removed. Defaults to None.

            Changes to this value will trigger a new cache to be created.

        show_spinner : bool or str
            Enable the spinner. Default is True to show a spinner when there is
            a "cache miss" and the cached resource is being created. If string,
            value of show_spinner param will be used for spinner text.

        show_time : bool
            Whether to show the elapsed time next to the spinner text. If this is
            ``False`` (default), no time is displayed. If this is ``True``,
            elapsed time is displayed with a precision of 0.1 seconds. The time
            format is not configurable.

        validate : callable or None
            An optional validation function for cached resources. ``validate`` is called
            each time the cached value is accessed. It receives the cached value as
            its only parameter and it must return a boolean. If ``validate`` returns
            False, the current cached value is discarded, and the decorated function
            is called to compute a new value. This is useful e.g. to check the
            health of database connections.

        hash_funcs : dict or None
            Mapping of types or fully qualified names to hash functions.
            This is used to override the behavior of the hasher inside Streamlit's
            caching mechanism: when the hasher encounters an object, it will first
            check to see if its type matches a key in this dict and, if so, will use
            the provided function to generate a hash for it. See below for an example
            of how this can be used.

        on_release : callable or None
            A function to call when an entry is removed from the cache.
            The removed item will be provided to the function as an argument.

            This is only useful for caches that remove entries normally.
            Most commonly, this is used session-scoped caches to release
            per-session resources. This can also be used with ``max_entries``
            or ``ttl`` settings.

            TTL expiration only happens when expired resources are accessed.
            Therefore, don't rely on TTL expiration to guarantee timely cleanup.
            Also, expiration can happen on any script run. Ensure that
            ``on_release`` functions are thread-safe and don't rely on session
            state.

            The ``on_release`` function isn't guaranteed to be called when an
            app is shut down.

        scope : "global" or "session"
            The scope for the resource cache. If this is ``"global"`` (default),
            the resource is cached globally. If this is ``"session"``, the
            resource is removed from the cache when the session disconnects.

            Because a session-scoped cache is cleared when a session disconnects,
            an unstable network connection can cause the cache to populate
            multiple times in a single session. If this is a problem, you might
            consider adjusting the ``server.websocketPingInterval``
            configuration option.

        refresh_mode : "foreground" or "background"
            How to refresh a cached resource once its ``ttl`` expires. This can be
            one of the following:

            - ``"foreground"`` (default): When the ``ttl`` expires, the next access
              runs the cached function synchronously. The app rerun waits until the new
              resource is ready.
            - ``"background"``: Return the expired resource immediately and update it
              in the background. Streamlit can keep returning the expired resource for
              up to one additional ``ttl``. After that, the next call waits for a new
              resource. This mode requires a ``ttl``. If you set ``on_release``,
              Streamlit calls it for the old resource after a successful update.

            .. note::
                A function that refreshes in the background can't use session-specific
                features such as ``st.session_state``. Pass any required session values
                as arguments instead. The function also shouldn't contain Streamlit
                commands that display elements. Streamlit doesn't replay these elements
                for cached results and shows a warning when the function creates them.

        Examples
        --------
        **Example 1: Global cache**

        By default, an ``@st.cache_resource``-decorated function uses a global cache.

        >>> import streamlit as st
        >>>
        >>> @st.cache_resource
        ... def get_database_session(url):
        ...     # Create a database session object that points to the URL.
        ...     return session
        >>>
        >>> s1 = get_database_session(SESSION_URL_1)
        >>> # Actually executes the function, since this is the first time it was
        >>> # encountered.
        >>>
        >>> s2 = get_database_session(SESSION_URL_1)
        >>> # Does not execute the function. Instead, returns its previously computed
        >>> # value. This means that now the connection object in s1 is the same as in s2.
        >>>
        >>> s3 = get_database_session(SESSION_URL_2)
        >>> # This is a different URL, so the function executes.

        **Example 2: Session-scoped cache**

        By passing ``scope="session"``, an ``@st.cache_resource``-decorated function
        uses a session-scoped cache. You can also use ``on_release`` to clean up
        resources when they are no longer needed.

        >>> import streamlit as st
        >>>
        >>> @st.cache_resource(scope="session", on_release=lambda sess: sess.close())
        ... def get_database_session(url):
        ...     # Create a database session object that points to the URL.
        ...     return session

        **Example 3: Unhashable arguments**

        By default, all parameters to a cached function must be hashable.
        Any parameter whose name begins with ``_`` will not be hashed. You can use
        this as an "escape hatch" for parameters that are not hashable:

        >>> import streamlit as st
        >>>
        >>> @st.cache_resource
        ... def get_database_session(_sessionmaker, url):
        ...     # Create a database connection object that points to the URL.
        ...     return connection
        >>>
        >>> s1 = get_database_session(create_sessionmaker(), DATA_URL_1)
        >>> # Actually executes the function, since this is the first time it was
        >>> # encountered.
        >>>
        >>> s2 = get_database_session(create_sessionmaker(), DATA_URL_1)
        >>> # Does not execute the function. Instead, returns its previously computed
        >>> # value - even though the _sessionmaker parameter was different
        >>> # in both calls.

        **Example 4: Clearing a cache**

        A cached function's cache can be procedurally cleared:

        >>> import streamlit as st
        >>>
        >>> @st.cache_resource
        ... def get_database_session(_sessionmaker, url):
        ...     # Create a database connection object that points to the URL.
        ...     return connection
        >>>
        >>> get_database_session.clear(_sessionmaker, "https://streamlit.io/")
        >>> # Clear the cached entry for the arguments provided.
        >>>
        >>> get_database_session.clear()
        >>> # Clear all cached entries for this function.

        **Example 5: Custom hashing**

        To override the default hashing behavior, pass a custom hash function.
        You can do that by mapping a type (e.g. ``Person``) to a hash
        function (``str``) like this:

        >>> import streamlit as st
        >>> from pydantic import BaseModel
        >>>
        >>> class Person(BaseModel):
        ...     name: str
        >>>
        >>> @st.cache_resource(hash_funcs={Person: str})
        ... def get_person_name(person: Person):
        ...     return person.name

        Alternatively, you can map the type's fully-qualified name
        (e.g. ``"__main__.Person"``) to the hash function instead:

        >>> import streamlit as st
        >>> from pydantic import BaseModel
        >>>
        >>> class Person(BaseModel):
        ...     name: str
        >>>
        >>> @st.cache_resource(hash_funcs={"__main__.Person": str})
        ... def get_person_name(person: Person):
        ...     return person.name

        """

        if scope not in {"global", "session"}:
            raise StreamlitValueError("scope", ["'global'", "'session'"])

        validate_refresh_mode(
            refresh_mode, time_to_seconds(ttl, coerce_none_to_inf=False)
        )

        # Support passing the params via function decorator, e.g.
        # @st.cache_resource(show_spinner=False)
        if func is None:
            return lambda f: make_cached_func_wrapper(
                CachedResourceFuncInfo(
                    func=f,
                    show_spinner=show_spinner,
                    show_time=show_time,
                    max_entries=max_entries,
                    ttl=ttl,
                    validate=validate,
                    hash_funcs=hash_funcs,
                    on_release=on_release,
                    scope=scope,
                    refresh_mode=refresh_mode,
                )
            )

        return make_cached_func_wrapper(
            CachedResourceFuncInfo(
                func=func,
                show_spinner=show_spinner,
                show_time=show_time,
                max_entries=max_entries,
                ttl=ttl,
                validate=validate,
                hash_funcs=hash_funcs,
                on_release=on_release,
                scope=scope,
                refresh_mode=refresh_mode,
            )
        )

    @gather_metrics("clear_resource_caches")
    def clear(self) -> None:
        """Clear all cache_resource caches."""
        _resource_caches.clear_all()


class ResourceCache(Cache[R]):
    """Manages cached values for a single st.cache_resource function."""

    def __init__(
        self,
        key: str,
        max_entries: float,
        ttl_seconds: float,
        validate: ValidateFunc | None,
        display_name: str,
        on_release: OnRelease,
        fresh_ttl_seconds: float | None = None,
        refresh_mode: RefreshMode = "foreground",
    ) -> None:
        super().__init__()

        def wrapped_on_release(result: CachedResult[R]) -> None:
            # Note that exceptions raised here will bubble out to the calling scope,
            # which will then treat them as user script errors.
            # This is also how exceptions thrown when generating cache values are
            # treated.
            on_release(result.value)

        self.key = key
        self.display_name = display_name
        self._mem_cache: TTLCleanupCache[str, CachedResult[R]] = TTLCleanupCache(
            maxsize=max_entries,
            # In background mode this is the hard-eviction bound (2*ttl); freshness
            # within the fresh window is tracked separately via stored_at.
            ttl=ttl_seconds,
            timer=cache_utils.TTLCACHE_TIMER,
            on_release=wrapped_on_release,
        )
        self._mem_cache_lock = threading.Lock()
        self.validate = validate
        self.refresh_mode = refresh_mode
        # The user-facing freshness ttl (equals ttl_seconds in foreground mode).
        self.fresh_ttl_seconds = (
            fresh_ttl_seconds if fresh_ttl_seconds is not None else ttl_seconds
        )
        # The raw user on_release, used to release a freshly produced resource that is
        # discarded by an orphaned background refresh (the mem cache's own hook only
        # fires on eviction, not on discard).
        self._user_on_release = on_release

    @property
    def max_entries(self) -> float:
        return self._mem_cache.maxsize

    @property
    def ttl_seconds(self) -> float:
        return self._mem_cache.ttl

    def _is_stale(self, result: CachedResult[R]) -> bool:
        """Whether a present entry is in the stale grace window ``[ttl, 2*ttl)``."""
        # Unlike DataCache, no ``fresh_ttl_seconds is None`` guard is needed here:
        # resource caches always resolve it to a float (ttl uses coerce_none_to_inf).
        if self.refresh_mode != "background" or result.stored_at is None:
            return False
        return (
            cache_utils.TTLCACHE_TIMER() - result.stored_at
        ) >= self.fresh_ttl_seconds

    def read_result(self, value_key: str) -> CachedResult[R]:
        """Read a value and associated messages from the cache.
        Raise `CacheKeyNotFoundError` if the value doesn't exist.
        """
        with self._mem_cache_lock:
            if value_key not in self._mem_cache:
                # key does not exist in cache.
                raise CacheKeyNotFoundError()

            result = self._mem_cache[value_key]

            if self.validate is not None and not self.validate(result.value):
                # Validate failed: delete the entry and raise an error.
                del self._mem_cache[value_key]
                raise CacheKeyNotFoundError()

            return result

    @gather_metrics("_cache_resource_object")
    def write_result(self, value_key: str, value: R, messages: list[MsgData]) -> None:
        """Write a value and associated messages to the cache."""
        main_id = st._main._id
        sidebar_id = st.sidebar._id

        # stored_at is only needed (and only consulted) in background mode.
        stored_at = (
            cache_utils.TTLCACHE_TIMER() if self.refresh_mode == "background" else None
        )
        with self._mem_cache_lock:
            self._mem_cache[value_key] = CachedResult(
                value, messages, main_id, sidebar_id, stored_at=stored_at
            )

    def write_background_refresh_result(
        self,
        value_key: str,
        value: R,
        *,
        expected_generation: int,
        expected_key_generation: int,
    ) -> None:
        """Write back a background-refreshed resource unless it is orphaned.

        On a successful write the replaced resource's ``on_release`` fires, unless the
        refresh returned the same object that is now cached (then it is left intact).
        On a discard the freshly produced resource is released so it doesn't leak. Any
        ``on_release`` failure is logged rather than propagated so it can't turn a
        successful compute into a failed refresh.
        """
        # st._main and st.sidebar are process-global DeltaGenerator singletons, so
        # reading their _id is safe here on the background refresh thread even though
        # it has no ScriptRunContext.
        main_id = st._main._id
        sidebar_id = st.sidebar._id

        discard = False
        replaced_value: R | None = None
        replaced_present = False
        with self._mem_cache_lock:
            if (
                self._refresh_is_orphaned(
                    value_key,
                    expected_generation=expected_generation,
                    expected_key_generation=expected_key_generation,
                )
                or value_key not in self._mem_cache
            ):
                # Detached cache, whole-cache or per-key clear, or the entry was
                # hard-evicted / LRU-evicted / cleared: discard the refresh.
                discard = True
            else:
                # Store the new resource first, capturing the replaced one to release
                # afterwards. Storing before releasing (rather than safe_del first)
                # ensures a raising on_release can neither drop the entry nor leak the
                # freshly built resource. __setitem__ does not fire on_release, so we
                # release the replaced resource explicitly below.
                replaced_value = self._mem_cache[value_key].value
                replaced_present = True
                self._mem_cache[value_key] = CachedResult(
                    value,
                    [],
                    main_id,
                    sidebar_id,
                    stored_at=cache_utils.TTLCACHE_TIMER(),
                )

        if discard:
            # Release the orphaned resource outside the lock (user code may block). The
            # compute itself succeeded, so a failing on_release must not propagate (it
            # would otherwise be treated as a failed refresh and start a cooldown); log
            # it instead.
            try:
                self._user_on_release(value)
            except Exception:
                _LOGGER.warning(
                    "on_release raised while releasing a discarded resource during a "
                    "background cache refresh.",
                    exc_info=True,
                )
        elif replaced_present and replaced_value is not value:
            # Release the replaced resource outside the lock (user code may block). A
            # failing on_release must not undo the successful swap, so log it rather
            # than propagate (which would otherwise mark the refresh failed while the
            # new value is already stored). We skip the release entirely when the
            # refresh returned the same object that is now cached (e.g. a process-wide
            # singleton), so we don't tear down the live cached resource.
            try:
                self._user_on_release(replaced_value)
            except Exception:
                _LOGGER.warning(
                    "on_release raised while releasing a replaced resource during a "
                    "background cache refresh.",
                    exc_info=True,
                )

    def _clear(self, key: str | None = None) -> None:
        with self._mem_cache_lock:
            if key is None:
                # Clear the whole cache.
                # TTLCleanupCache will stop a clear() execution when an exception is
                # thrown by an on_release. To ensure that our clear() actually flushes
                # the cache and calls all cleanup functions, we clear each item
                # individually. We also collect exceptions for logging.
                errors: list[Exception] = []
                while len(self._mem_cache) > 0:
                    try:
                        # TTLCleanupCache only reliably calls on_release for popitem -
                        # so just use that.
                        self._mem_cache.popitem()
                    except Exception as e:  # noqa: PERF203 (we require a tight scope)
                        errors.append(e)

                # Log all errors encountered at warning. This could potentially result in a
                # lot of log spam in the worst case - but for resources, a huge cache is very
                # unlikely.
                for error in errors:
                    _LOGGER.warning("Error clearing resource cache: %s", error)
            elif key in self._mem_cache:
                # Note: This code path does not seem to be reachable through public APIs.
                self._mem_cache.safe_del(key)

    def get_stats(
        self, _family_names: Sequence[str] | None = None
    ) -> dict[str, list[CacheStat]]:
        # Shallow clone our cache. Computing item sizes is potentially
        # expensive, and we want to minimize the time we spend holding
        # the lock.
        with self._mem_cache_lock:
            cache_entries = list(self._mem_cache.values())

        if not cache_entries:
            return {}

        if config.get_option("server.enableExpensiveMemoryStats"):
            from streamlit.runtime.stats import safe_sizeof

            byte_lengths = [safe_sizeof(entry) for entry in cache_entries]
        else:
            # Use a cheap item-count proxy instead of traversing the cached
            # resource objects, which can be very expensive.
            byte_lengths = [len(cache_entries)]

        stats = [
            CacheStat(
                category_name="st_cache_resource",
                cache_name=self.display_name,
                byte_length=byte_length,
            )
            for byte_length in byte_lengths
        ]

        # In general, get_stats methods need to be able to return only requested stat
        # families, but this method only returns a single family, and we're guaranteed
        # that it was one of those requested if we make it here.
        return {CACHE_MEMORY_FAMILY: stats}
