# See https://zulip.readthedocs.io/en/latest/subsystems/caching.html for docs import base64 import hashlib import logging import os import random import re import sys import time import traceback from functools import lru_cache, wraps from typing import ( TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, TypeVar, cast, ) from django.conf import settings from django.core.cache import cache as djcache from django.core.cache import caches from django.core.cache.backends.base import BaseCache from django.db.models import Q from django.http import HttpRequest from zerver.lib.utils import make_safe_digest, statsd, statsd_key if TYPE_CHECKING: # These modules have to be imported for type annotations but # they cannot be imported at runtime due to cyclic dependency. from zerver.models import Message, Realm, UserProfile MEMCACHED_MAX_KEY_LENGTH = 250 FuncT = TypeVar('FuncT', bound=Callable[..., object]) logger = logging.getLogger() class NotFoundInCache(Exception): pass remote_cache_time_start = 0.0 remote_cache_total_time = 0.0 remote_cache_total_requests = 0 def get_remote_cache_time() -> float: return remote_cache_total_time def get_remote_cache_requests() -> int: return remote_cache_total_requests def remote_cache_stats_start() -> None: global remote_cache_time_start remote_cache_time_start = time.time() def remote_cache_stats_finish() -> None: global remote_cache_total_time global remote_cache_total_requests global remote_cache_time_start remote_cache_total_requests += 1 remote_cache_total_time += (time.time() - remote_cache_time_start) def get_or_create_key_prefix() -> str: if settings.CASPER_TESTS: # This sets the prefix for the benefit of the Casper tests. # # Having a fixed key is OK since we don't support running # multiple copies of the casper tests at the same time anyway. return 'casper_tests:' elif settings.TEST_SUITE: # The Python tests overwrite KEY_PREFIX on each test, but use # this codepath as well, just to save running the more complex # code below for reading the normal key prefix. return 'django_tests_unused:' # directory `var` should exist in production os.makedirs(os.path.join(settings.DEPLOY_ROOT, "var"), exist_ok=True) filename = os.path.join(settings.DEPLOY_ROOT, "var", "remote_cache_prefix") try: fd = os.open(filename, os.O_CREAT | os.O_EXCL | os.O_RDWR, 0o444) random_hash = hashlib.sha256(str(random.getrandbits(256)).encode('utf-8')).digest() prefix = base64.b16encode(random_hash)[:32].decode('utf-8').lower() + ':' # This does close the underlying file with os.fdopen(fd, 'w') as f: f.write(prefix + "\n") except OSError: # The file already exists tries = 1 while tries < 10: with open(filename) as f: prefix = f.readline()[:-1] if len(prefix) == 33: break tries += 1 prefix = '' time.sleep(0.5) if not prefix: print("Could not read remote cache key prefix file") sys.exit(1) return prefix KEY_PREFIX: str = get_or_create_key_prefix() def bounce_key_prefix_for_testing(test_name: str) -> None: global KEY_PREFIX KEY_PREFIX = test_name + ':' + str(os.getpid()) + ':' # We are taking the hash of the KEY_PREFIX to decrease the size of the key. # Memcached keys should have a length of less than 250. KEY_PREFIX = hashlib.sha1(KEY_PREFIX.encode('utf-8')).hexdigest() + ":" def get_cache_backend(cache_name: Optional[str]) -> BaseCache: if cache_name is None: return djcache return caches[cache_name] def get_cache_with_key( keyfunc: Callable[..., str], cache_name: Optional[str]=None, ) -> Callable[[FuncT], FuncT]: """ The main goal of this function getting value from the cache like in the "cache_with_key". A cache value can contain any data including the "None", so here used exception for case if value isn't found in the cache. """ def decorator(func: FuncT) -> FuncT: @wraps(func) def func_with_caching(*args: object, **kwargs: object) -> object: key = keyfunc(*args, **kwargs) try: val = cache_get(key, cache_name=cache_name) except InvalidCacheKeyException: stack_trace = traceback.format_exc() log_invalid_cache_keys(stack_trace, [key]) val = None if val is not None: return val[0] raise NotFoundInCache() return cast(FuncT, func_with_caching) # https://github.com/python/mypy/issues/1927 return decorator def cache_with_key( keyfunc: Callable[..., str], cache_name: Optional[str]=None, timeout: Optional[int]=None, with_statsd_key: Optional[str]=None, ) -> Callable[[FuncT], FuncT]: """Decorator which applies Django caching to a function. Decorator argument is a function which computes a cache key from the original function's arguments. You are responsible for avoiding collisions with other uses of this decorator or other uses of caching.""" def decorator(func: FuncT) -> FuncT: @wraps(func) def func_with_caching(*args: object, **kwargs: object) -> object: key = keyfunc(*args, **kwargs) try: val = cache_get(key, cache_name=cache_name) except InvalidCacheKeyException: stack_trace = traceback.format_exc() log_invalid_cache_keys(stack_trace, [key]) return func(*args, **kwargs) extra = "" if cache_name == 'database': extra = ".dbcache" if with_statsd_key is not None: metric_key = with_statsd_key else: metric_key = statsd_key(key) status = "hit" if val is not None else "miss" statsd.incr(f"cache{extra}.{metric_key}.{status}") # Values are singleton tuples so that we can distinguish # a result of None from a missing key. if val is not None: return val[0] val = func(*args, **kwargs) cache_set(key, val, cache_name=cache_name, timeout=timeout) return val return cast(FuncT, func_with_caching) # https://github.com/python/mypy/issues/1927 return decorator class InvalidCacheKeyException(Exception): pass def log_invalid_cache_keys(stack_trace: str, key: List[str]) -> None: logger.warning( "Invalid cache key used: %s\nStack trace: %s\n", key, stack_trace, ) def validate_cache_key(key: str) -> None: if not key.startswith(KEY_PREFIX): key = KEY_PREFIX + key # Theoretically memcached can handle non-ascii characters # and only "control" characters are strictly disallowed, see: # https://github.com/memcached/memcached/blob/master/doc/protocol.txt # However, limiting the characters we allow in keys simiplifies things, # and anyway we use make_safe_digest when forming some keys to ensure # the resulting keys fit the regex below. # The regex checks "all characters between ! and ~ in the ascii table", # which happens to be the set of all "nice" ascii characters. if not bool(re.fullmatch(r"([!-~])+", key)): raise InvalidCacheKeyException("Invalid characters in the cache key: " + key) if len(key) > MEMCACHED_MAX_KEY_LENGTH: raise InvalidCacheKeyException(f"Cache key too long: {key} Length: {len(key)}") def cache_set(key: str, val: Any, cache_name: Optional[str]=None, timeout: Optional[int]=None) -> None: final_key = KEY_PREFIX + key validate_cache_key(final_key) remote_cache_stats_start() cache_backend = get_cache_backend(cache_name) cache_backend.set(final_key, (val,), timeout=timeout) remote_cache_stats_finish() def cache_get(key: str, cache_name: Optional[str]=None) -> Any: final_key = KEY_PREFIX + key validate_cache_key(final_key) remote_cache_stats_start() cache_backend = get_cache_backend(cache_name) ret = cache_backend.get(final_key) remote_cache_stats_finish() return ret def cache_get_many(keys: List[str], cache_name: Optional[str]=None) -> Dict[str, Any]: keys = [KEY_PREFIX + key for key in keys] for key in keys: validate_cache_key(key) remote_cache_stats_start() ret = get_cache_backend(cache_name).get_many(keys) remote_cache_stats_finish() return {key[len(KEY_PREFIX):]: value for key, value in ret.items()} def safe_cache_get_many(keys: List[str], cache_name: Optional[str]=None) -> Dict[str, Any]: """Variant of cache_get_many that drops any keys that fail validation, rather than throwing an exception visible to the caller.""" try: # Almost always the keys will all be correct, so we just try # to do normal cache_get_many to avoid the overhead of # validating all the keys here. return cache_get_many(keys, cache_name) except InvalidCacheKeyException: stack_trace = traceback.format_exc() good_keys, bad_keys = filter_good_and_bad_keys(keys) log_invalid_cache_keys(stack_trace, bad_keys) return cache_get_many(good_keys, cache_name) def cache_set_many(items: Dict[str, Any], cache_name: Optional[str]=None, timeout: Optional[int]=None) -> None: new_items = {} for key in items: new_key = KEY_PREFIX + key validate_cache_key(new_key) new_items[new_key] = items[key] items = new_items remote_cache_stats_start() get_cache_backend(cache_name).set_many(items, timeout=timeout) remote_cache_stats_finish() def safe_cache_set_many(items: Dict[str, Any], cache_name: Optional[str]=None, timeout: Optional[int]=None) -> None: """Variant of cache_set_many that drops saving any keys that fail validation, rather than throwing an exception visible to the caller.""" try: # Almost always the keys will all be correct, so we just try # to do normal cache_set_many to avoid the overhead of # validating all the keys here. return cache_set_many(items, cache_name, timeout) except InvalidCacheKeyException: stack_trace = traceback.format_exc() good_keys, bad_keys = filter_good_and_bad_keys(list(items.keys())) log_invalid_cache_keys(stack_trace, bad_keys) good_items = {key: items[key] for key in good_keys} return cache_set_many(good_items, cache_name, timeout) def cache_delete(key: str, cache_name: Optional[str]=None) -> None: final_key = KEY_PREFIX + key validate_cache_key(final_key) remote_cache_stats_start() get_cache_backend(cache_name).delete(final_key) remote_cache_stats_finish() def cache_delete_many(items: Iterable[str], cache_name: Optional[str]=None) -> None: keys = [KEY_PREFIX + item for item in items] for key in keys: validate_cache_key(key) remote_cache_stats_start() get_cache_backend(cache_name).delete_many(keys) remote_cache_stats_finish() def filter_good_and_bad_keys(keys: List[str]) -> Tuple[List[str], List[str]]: good_keys = [] bad_keys = [] for key in keys: try: validate_cache_key(key) good_keys.append(key) except InvalidCacheKeyException: bad_keys.append(key) return good_keys, bad_keys # Generic_bulk_cached fetch and its helpers. We start with declaring # a few type variables that help define its interface. # Type for the cache's keys; will typically be int or str. ObjKT = TypeVar('ObjKT') # Type for items to be fetched from the database (e.g. a Django model object) ItemT = TypeVar('ItemT') # Type for items to be stored in the cache (e.g. a dictionary serialization). # Will equal ItemT unless a cache_transformer is specified. CacheItemT = TypeVar('CacheItemT') # Type for compressed items for storage in the cache. For # serializable objects, will be the object; if encoded, bytes. CompressedItemT = TypeVar('CompressedItemT') # Required Arguments are as follows: # * object_ids: The list of object ids to look up # * cache_key_function: object_id => cache key # * query_function: [object_ids] => [objects from database] # * setter: Function to call before storing items to cache (e.g. compression) # * extractor: Function to call on items returned from cache # (e.g. decompression). Should be the inverse of the setter # function. # * id_fetcher: Function mapping an object from database => object_id # (in case we're using a key more complex than obj.id) # * cache_transformer: Function mapping an object from database => # value for cache (in case the values that we're caching are some # function of the objects, not the objects themselves) def generic_bulk_cached_fetch( cache_key_function: Callable[[ObjKT], str], query_function: Callable[[List[ObjKT]], Iterable[ItemT]], object_ids: Sequence[ObjKT], *, extractor: Callable[[CompressedItemT], CacheItemT], setter: Callable[[CacheItemT], CompressedItemT], id_fetcher: Callable[[ItemT], ObjKT], cache_transformer: Callable[[ItemT], CacheItemT], ) -> Dict[ObjKT, CacheItemT]: if len(object_ids) == 0: # Nothing to fetch. return {} cache_keys: Dict[ObjKT, str] = {} for object_id in object_ids: cache_keys[object_id] = cache_key_function(object_id) cached_objects_compressed: Dict[str, Tuple[CompressedItemT]] = safe_cache_get_many( [cache_keys[object_id] for object_id in object_ids], ) cached_objects: Dict[str, CacheItemT] = {} for (key, val) in cached_objects_compressed.items(): cached_objects[key] = extractor(cached_objects_compressed[key][0]) needed_ids = [object_id for object_id in object_ids if cache_keys[object_id] not in cached_objects] # Only call query_function if there are some ids to fetch from the database: if len(needed_ids) > 0: db_objects = query_function(needed_ids) else: db_objects = [] items_for_remote_cache: Dict[str, Tuple[CompressedItemT]] = {} for obj in db_objects: key = cache_keys[id_fetcher(obj)] item = cache_transformer(obj) items_for_remote_cache[key] = (setter(item),) cached_objects[key] = item if len(items_for_remote_cache) > 0: safe_cache_set_many(items_for_remote_cache) return {object_id: cached_objects[cache_keys[object_id]] for object_id in object_ids if cache_keys[object_id] in cached_objects} def transformed_bulk_cached_fetch( cache_key_function: Callable[[ObjKT], str], query_function: Callable[[List[ObjKT]], Iterable[ItemT]], object_ids: Sequence[ObjKT], *, id_fetcher: Callable[[ItemT], ObjKT], cache_transformer: Callable[[ItemT], CacheItemT], ) -> Dict[ObjKT, CacheItemT]: return generic_bulk_cached_fetch( cache_key_function, query_function, object_ids, extractor=lambda obj: obj, setter=lambda obj: obj, id_fetcher=id_fetcher, cache_transformer=cache_transformer, ) def bulk_cached_fetch( cache_key_function: Callable[[ObjKT], str], query_function: Callable[[List[ObjKT]], Iterable[ItemT]], object_ids: Sequence[ObjKT], *, id_fetcher: Callable[[ItemT], ObjKT], ) -> Dict[ObjKT, ItemT]: return transformed_bulk_cached_fetch( cache_key_function, query_function, object_ids, id_fetcher=id_fetcher, cache_transformer=lambda obj: obj, ) def preview_url_cache_key(url: str) -> str: return f"preview_url:{make_safe_digest(url)}" def display_recipient_cache_key(recipient_id: int) -> str: return f"display_recipient_dict:{recipient_id}" def display_recipient_bulk_get_users_by_id_cache_key(user_id: int) -> str: # Cache key function for a function for bulk fetching users, used internally # by display_recipient code. return 'bulk_fetch_display_recipients:' + user_profile_by_id_cache_key(user_id) def user_profile_by_email_cache_key(email: str) -> str: # See the comment in zerver/lib/avatar_hash.py:gravatar_hash for why we # are proactively encoding email addresses even though they will # with high likelihood be ASCII-only for the foreseeable future. return f'user_profile_by_email:{make_safe_digest(email.strip())}' def user_profile_cache_key_id(email: str, realm_id: int) -> str: return f"user_profile:{make_safe_digest(email.strip())}:{realm_id}" def user_profile_cache_key(email: str, realm: 'Realm') -> str: return user_profile_cache_key_id(email, realm.id) def bot_profile_cache_key(email: str) -> str: return f"bot_profile:{make_safe_digest(email.strip())}" def user_profile_by_id_cache_key(user_profile_id: int) -> str: return f"user_profile_by_id:{user_profile_id}" def user_profile_by_api_key_cache_key(api_key: str) -> str: return f"user_profile_by_api_key:{api_key}" realm_user_dict_fields: List[str] = [ 'id', 'full_name', 'email', 'avatar_source', 'avatar_version', 'is_active', 'role', 'is_bot', 'realm_id', 'timezone', 'date_joined', 'bot_owner_id', 'delivery_email', 'bot_type', 'long_term_idle' ] def realm_user_dicts_cache_key(realm_id: int) -> str: return f"realm_user_dicts:{realm_id}" def get_realm_used_upload_space_cache_key(realm: 'Realm') -> str: return f'realm_used_upload_space:{realm.id}' def active_user_ids_cache_key(realm_id: int) -> str: return f"active_user_ids:{realm_id}" def active_non_guest_user_ids_cache_key(realm_id: int) -> str: return f"active_non_guest_user_ids:{realm_id}" bot_dict_fields: List[str] = [ 'api_key', 'avatar_source', 'avatar_version', 'bot_owner__id', 'bot_type', 'default_all_public_streams', 'default_events_register_stream__name', 'default_sending_stream__name', 'email', 'full_name', 'id', 'is_active', 'realm_id', ] def bot_dicts_in_realm_cache_key(realm: 'Realm') -> str: return f"bot_dicts_in_realm:{realm.id}" def get_stream_cache_key(stream_name: str, realm_id: int) -> str: return f"stream_by_realm_and_name:{realm_id}:{make_safe_digest(stream_name.strip().lower())}" def delete_user_profile_caches(user_profiles: Iterable['UserProfile']) -> None: # Imported here to avoid cyclic dependency. from zerver.lib.users import get_all_api_keys from zerver.models import is_cross_realm_bot_email keys = [] for user_profile in user_profiles: keys.append(user_profile_by_email_cache_key(user_profile.delivery_email)) keys.append(user_profile_by_id_cache_key(user_profile.id)) for api_key in get_all_api_keys(user_profile): keys.append(user_profile_by_api_key_cache_key(api_key)) keys.append(user_profile_cache_key(user_profile.email, user_profile.realm)) if user_profile.is_bot and is_cross_realm_bot_email(user_profile.email): # Handle clearing system bots from their special cache. keys.append(bot_profile_cache_key(user_profile.email)) cache_delete_many(keys) def delete_display_recipient_cache(user_profile: 'UserProfile') -> None: from zerver.models import Subscription # We need to import here to avoid cyclic dependency. recipient_ids = Subscription.objects.filter(user_profile=user_profile) recipient_ids = recipient_ids.values_list('recipient_id', flat=True) keys = [display_recipient_cache_key(rid) for rid in recipient_ids] keys.append(display_recipient_bulk_get_users_by_id_cache_key(user_profile.id)) cache_delete_many(keys) def changed(kwargs: Any, fields: List[str]) -> bool: if kwargs.get('update_fields') is None: # adds/deletes should invalidate the cache return True update_fields = set(kwargs['update_fields']) for f in fields: if f in update_fields: return True return False # Called by models.py to flush the user_profile cache whenever we save # a user_profile object def flush_user_profile(sender: Any, **kwargs: Any) -> None: user_profile = kwargs['instance'] delete_user_profile_caches([user_profile]) # Invalidate our active_users_in_realm info dict if any user has changed # the fields in the dict or become (in)active if changed(kwargs, realm_user_dict_fields): cache_delete(realm_user_dicts_cache_key(user_profile.realm_id)) if changed(kwargs, ['is_active']): cache_delete(active_user_ids_cache_key(user_profile.realm_id)) cache_delete(active_non_guest_user_ids_cache_key(user_profile.realm_id)) if changed(kwargs, ['role']): cache_delete(active_non_guest_user_ids_cache_key(user_profile.realm_id)) if changed(kwargs, ['email', 'full_name', 'id', 'is_mirror_dummy']): delete_display_recipient_cache(user_profile) # Invalidate our bots_in_realm info dict if any bot has # changed the fields in the dict or become (in)active if user_profile.is_bot and changed(kwargs, bot_dict_fields): cache_delete(bot_dicts_in_realm_cache_key(user_profile.realm)) # Called by models.py to flush various caches whenever we save # a Realm object. The main tricky thing here is that Realm info is # generally cached indirectly through user_profile objects. def flush_realm(sender: Any, **kwargs: Any) -> None: realm = kwargs['instance'] users = realm.get_active_users() delete_user_profile_caches(users) if realm.deactivated or (kwargs["update_fields"] is not None and "string_id" in kwargs['update_fields']): cache_delete(realm_user_dicts_cache_key(realm.id)) cache_delete(active_user_ids_cache_key(realm.id)) cache_delete(bot_dicts_in_realm_cache_key(realm)) cache_delete(realm_alert_words_cache_key(realm)) cache_delete(realm_alert_words_automaton_cache_key(realm)) cache_delete(active_non_guest_user_ids_cache_key(realm.id)) cache_delete(realm_rendered_description_cache_key(realm)) cache_delete(realm_text_description_cache_key(realm)) if changed(kwargs, ['description']): cache_delete(realm_rendered_description_cache_key(realm)) cache_delete(realm_text_description_cache_key(realm)) def realm_alert_words_cache_key(realm: 'Realm') -> str: return f"realm_alert_words:{realm.string_id}" def realm_alert_words_automaton_cache_key(realm: 'Realm') -> str: return f"realm_alert_words_automaton:{realm.string_id}" def realm_rendered_description_cache_key(realm: 'Realm') -> str: return f"realm_rendered_description:{realm.string_id}" def realm_text_description_cache_key(realm: 'Realm') -> str: return f"realm_text_description:{realm.string_id}" # Called by models.py to flush the stream cache whenever we save a stream # object. def flush_stream(sender: Any, **kwargs: Any) -> None: from zerver.models import UserProfile stream = kwargs['instance'] items_for_remote_cache = {} items_for_remote_cache[get_stream_cache_key(stream.name, stream.realm_id)] = (stream,) cache_set_many(items_for_remote_cache) if kwargs.get('update_fields') is None or 'name' in kwargs['update_fields'] and \ UserProfile.objects.filter( Q(default_sending_stream=stream) | Q(default_events_register_stream=stream)).exists(): cache_delete(bot_dicts_in_realm_cache_key(stream.realm)) def flush_used_upload_space_cache(sender: Any, **kwargs: Any) -> None: attachment = kwargs['instance'] if kwargs.get("created") is None or kwargs.get("created") is True: cache_delete(get_realm_used_upload_space_cache_key(attachment.owner.realm)) def to_dict_cache_key_id(message_id: int) -> str: return f'message_dict:{message_id}' def to_dict_cache_key(message: 'Message', realm_id: Optional[int]=None) -> str: return to_dict_cache_key_id(message.id) def open_graph_description_cache_key(content: Any, request: HttpRequest) -> str: return 'open_graph_description_path:{}'.format(make_safe_digest(request.META['PATH_INFO'])) def flush_message(sender: Any, **kwargs: Any) -> None: message = kwargs['instance'] cache_delete(to_dict_cache_key_id(message.id)) def flush_submessage(sender: Any, **kwargs: Any) -> None: submessage = kwargs['instance'] # submessages are not cached directly, they are part of their # parent messages message_id = submessage.message_id cache_delete(to_dict_cache_key_id(message_id)) DECORATOR = Callable[[Callable[..., Any]], Callable[..., Any]] def ignore_unhashable_lru_cache(maxsize: int=128, typed: bool=False) -> DECORATOR: """ This is a wrapper over lru_cache function. It adds following features on top of lru_cache: * It will not cache result of functions with unhashable arguments. * It will clear cache whenever zerver.lib.cache.KEY_PREFIX changes. """ internal_decorator = lru_cache(maxsize=maxsize, typed=typed) def decorator(user_function: Callable[..., Any]) -> Callable[..., Any]: if settings.DEVELOPMENT and not settings.TEST_SUITE: # nocoverage # In the development environment, we want every file # change to refresh the source files from disk. return user_function # Casting to Any since we're about to monkey-patch this. cache_enabled_user_function = cast(Any, internal_decorator(user_function)) def wrapper(*args: Any, **kwargs: Any) -> Any: if not hasattr(cache_enabled_user_function, 'key_prefix'): cache_enabled_user_function.key_prefix = KEY_PREFIX if cache_enabled_user_function.key_prefix != KEY_PREFIX: # Clear cache when cache.KEY_PREFIX changes. This is used in # tests. cache_enabled_user_function.cache_clear() cache_enabled_user_function.key_prefix = KEY_PREFIX try: return cache_enabled_user_function(*args, **kwargs) except TypeError: # args or kwargs contains an element which is unhashable. In # this case we don't cache the result. pass # Deliberately calling this function from outside of exception # handler to get a more descriptive traceback. Otherwise traceback # can include the exception from cached_enabled_user_function as # well. return user_function(*args, **kwargs) setattr(wrapper, 'cache_info', cache_enabled_user_function.cache_info) setattr(wrapper, 'cache_clear', cache_enabled_user_function.cache_clear) return wrapper return decorator def dict_to_items_tuple(user_function: Callable[..., Any]) -> Callable[..., Any]: """Wrapper that converts any dict args to dict item tuples.""" def dict_to_tuple(arg: Any) -> Any: if isinstance(arg, dict): return tuple(sorted(arg.items())) return arg def wrapper(*args: Any, **kwargs: Any) -> Any: new_args = (dict_to_tuple(arg) for arg in args) return user_function(*new_args, **kwargs) return wrapper def items_tuple_to_dict(user_function: Callable[..., Any]) -> Callable[..., Any]: """Wrapper that converts any dict items tuple args to dicts.""" def dict_items_to_dict(arg: Any) -> Any: if isinstance(arg, tuple): try: return dict(arg) except TypeError: pass return arg def wrapper(*args: Any, **kwargs: Any) -> Any: new_args = (dict_items_to_dict(arg) for arg in args) new_kwargs = {key: dict_items_to_dict(val) for key, val in kwargs.items()} return user_function(*new_args, **new_kwargs) return wrapper