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N)abs v/home/obispo/Crisostomo_bridge/mision_env/lib/python3.12/site-packages/streamlit/runtime/caching/cache_resource_api.py_equal_validate_funcsr,Ms) I #!t) I$)H1D=Icy)zNo-op OnRelease function.Nr()ignoreds r+_no_op_releaser0Vsr-cveZdZdZd dZed dZ d d dZd dZd dZ d ddZ y)ResourceCachesz$Manages all ResourceCache instances.cDtj|_i|_yN) threadingLock _caches_lock_function_cachesselfs r+__init__zResourceCaches.__init__]s%NN,QSr-ctfSr4)rr9s r+stats_familieszResourceCaches.stats_familiesbs #%%r-c |tj}t|}|dk(rd} n t} |j5|j j | } | ix} |j | <| j |} | ?| j|k(r0| j|k(r!t| j|r | cdddStjd|t||||||} | |j | |<| cdddS#1swYyxYw)aReturn 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. Nglobalz#Creating new ResourceCache (key=%s))key display_name max_entries ttl_secondsvalidate on_release)mathinfr"rr7r8getrCrBr,rDr%debug ResourceCache) r:r@rArBttlrDrEscoperC session_idsession_cachescaches r+ get_cachezResourceCaches.get_cachefs(  ((K%c* H J02J   !2266zBN%EGG!6!6z!B"&&s+E!%%4%%4)%..(C   MM? E!)''!% E6;D ! !* -c 23   sA7D=:DD c|j5|jj|}| |j|=ddd&|jD]}|j yy#1swY2xYwz+Clears all caches for the given session ID.N)r7r8rHvaluesclear)r:rMrNrOs r+ clear_sessionzResourceCaches.clear_sessionsz   6!2266zBN)))*5 6  %'..0    &  6 6s +A))A2c |j5|jjDcgc]}|jD]}|c}}}i|_dddD]}|jycc}}w#1swY'xYw)zClear all resource caches.N)r7r8rSrT)r:cachesrOs r+ clear_allzResourceCaches.clear_alls   '#33::<#]]_F %'D !  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Parameters ---------- decorator_metric_name The metric name to record for decorator usage. N)r _decorator)r:decorator_metric_names r+r;zCacheResourceAPI.__init__$s))>Pr-cyr4r()r:r|s r+__call__zCacheResourceAPI.__call__5sBEr-NTFr?rKrBryrzrDrxrErLcyr4r() r:rKrBryrzrDrxrErLs r+rzCacheResourceAPI.__call__9s8;r-c 6|j|||||||||  S)Nr)r r:r|rKrBryrzrDrxrErLs r+rzCacheResourceAPI.__call__Gs5 #%!!  r-)rzrxrErLc  dvrtd d|  fdStt|  S)a*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 `_. .. 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 `_. To upvote support for caching async functions, see GitHub issue `#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 `_, 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 `_, 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. Example ------- **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. 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