def commit_after(func):
def _commit_after(func, *args, **kwargs):
rval = func(*args, **kwargs)
db.session.commit()
return rval
return decorator.decorate(func, _commit_after)
def _register(f):
"""Registration function"""
allow_types = (string_types, int, float)
fkey = key + "." + f.__name__ + ".pkl"
if fkey not in Cache.cache_by_key:
Cache.cache_by_key[fkey] = Cache(fkey)
cache = Cache.cache_by_key[fkey]
cargs = tuple(x.cell_contents for x in f.__closure__)
cargs = (len(cargs),) + cargs
def _memoized_f(func, *args, **kwargs):
assert not kwargs, "Only allow positional call"
key = cargs + args
for arg in key:
if isinstance(arg, tuple):
for x in arg:
assert isinstance(x, allow_types)
else:
assert isinstance(arg, allow_types)
if key in cache.cache:
print("Use memoize {0}{1}".format(fkey, key))
return cache.cache[key]
res = func(*args)
cache.cache[key] = res
cache.dirty = True
return res
return decorate(f, _memoized_f)
def memoize(f):
"""
A simple memoize implementation. It works by adding a .cache dictionary
to the decorated function. The cache will grow indefinitely, so it is
your responsability to clear it, if needed.
"""
f.cache = {}
return decorate(f, _memoize)
def model_create_single(method):
""" Decorate a method that takes a dictionary and creates a single record.
The method may be called with either a single dict or a list of dicts::
record = model.create(vals)
records = model.create([vals, ...])
"""
wrapper = decorate(method, _model_create_single)
wrapper._api = 'model_create'
return wrapper
def model_create_single(method):
""" Decorate a method that takes a dictionary and creates a single record.
The method may be called with either a single dict or a list of dicts::
record = model.create(vals)
records = model.create([vals, ...])
"""
wrapper = decorate(method, _model_create_single)
wrapper._api = 'model_create'
return wrapper
def fdecorate(fdefault):
"""Wrap a target generic function, overriding the previous
default that was set for the generic function.
Parameters
----------
fdefault : function
The default function.
Returns
-------
fgeneric : function
A wrapped generic function.
"""
generic_func_node.set_default(fdefault, allow_override=True)
def register(key, func=None, override=True):
"""Register function to be the dispatch function.
Parameters
----------
key : str or list of str
The key to be registered.
func : function
The function to be registered.
override : bool, optional
Whether override existing registration.
Returns
-------
The register function is necessary.
"""
def _do_reg(myf):
generic_func_node.register(myf, key, override)
return myf
if func:
return _do_reg(func)
return _do_reg
def dispatch_func(func, *args, **kwargs):
#pylint: disable=unused-argument
"""The wrapped dispath function"""
if kwargs:
raise RuntimeError(
"Keyword arguments cannot be used when invoking generic_func %s"
% func_name)
return generic_func_node(*args)
fresult = decorate(fdefault, dispatch_func)
fresult.register = register
return fresult
def model_create_single(method):
""" Decorate a method that takes a dictionary and creates a single record.
The method may be called with either a single dict or a list of dicts::
record = model.create(vals)
records = model.create([vals, ...])
"""
_create_logger.warning("The model %s is not overriding the create method in batch", method.__module__)
wrapper = decorate(method, _model_create_single)
wrapper._api = 'model_create'
return wrapper
def inner(fun):
""" This one is not returned """
# set the base basepath, really need to refactor to Class
_basepath = basepath
if _basepath is None:
_basepath = 'things'
_basepath = os.path.join(_basepath, '{}:{}'.format(reg.sub('', os.path.basename(inspect.getmodule(fun).__file__)), fun.__name__)) # basically use pwd
if not os.path.exists(os.path.dirname(_basepath)):
_mkdir(os.path.dirname(_basepath))
def _inner(fun, *args, **kwargs):
""" this one is returned """
_path = _basepath
if type(_path) is str:
tmp = '_'.join(default_namer(fun, args, kwargs))
if tmp == '':
tmp = '__noargs__' # TODO is this the only place ... yikes
tmp = '{}.things'.format(tmp) if tmp else '.things'
# what about '/' in tmp ?
_path = os.path.join(_path, tmp) # save locally, could get weird with this default
if hasattr(_path, '__call__'):
_path = _path(**kwargs) # in case you want to do something else?
assert type(_path) is str
print("checking cache {}".format(_path))
if os.path.exists(_path) and not _inner._dirty:
d = from_dict_of_things(_path)
else:
d = fun(*args, **kwargs)
to_dict_of_things(d, _path)
_inner._dirty = False
return d
_inner = decorator.decorate(fun, _inner)
_inner._dirty = False
def list_caches():
""" list .things that have been computed """
# if you need to parse the filenames, you need to rewrite this using a better args, kwargs storing mechanisi and a hash
files = glob.glob('{}*.things'.format(_basepath))
return files
def load_all_caches():
""" load .things that have been computed """
files = glob.glob('{}*.things'.format(_basepath))
return {k: from_dict_of_things(k) for k in files}
def poison_cache():
_inner._dirty = True
def force_clean():
_inner._dirty = False
def is_dirty():
return _inner._dirty
_inner.poison_cache = poison_cache
_inner.is_dirty = is_dirty
_inner.force_clean = force_clean
_inner.load_all_caches = load_all_caches
_inner.list_caches = list_caches
_inner.basepath = _basepath
return _inner
def docannotate(func):
"""Annotate a function using information from its docstring or from type annotations.
If function has a type annotations then docstring types would be ignored.
The annotation actually happens at the time the function is first called
to improve startup time. For this function to work, the docstring must be
formatted correctly. You should use the typedargs pylint plugin to make
sure there are no errors in the docstring.
This decorator is also could be used on classes to annotate their __init__() method.
If decorator applied on a class then the priority for source of type info would be:
__init__() type annotations -> class docstring -> __init__() docstring
Class docstring would be taken if it contains any argument type information. __init__() docstring
would be ignored in this case.
"""
cls = None
if inspect.isclass(func):
cls = func
func = cls.__init__
func.class_docstring = cls.__doc__ if cls.__doc__ else ''
func.class_name = cls.__name__
func = annotated(func)
func.metadata.load_from_doc = True
if func.decorated:
if cls:
setattr(cls, '__init__', func)
return cls
return func
func.decorated = True
if cls:
setattr(cls, '__init__', decorate(func, _check_and_execute))
return cls
return decorate(func, _check_and_execute)
def leakcheck(fn):
# Compare the memory (after GC) before and after the test to determine
# if we leaked memory
def wrapper(fn, *args, **kwargs):
gc.collect()
mem_before = MemoryUsage()
rv = fn(*args, **kwargs)
gc.collect()
mem_after = MemoryUsage()
assert mem_before >= mem_after
return rv
return decorator.decorate(fn, wrapper)
def _param(func):
func = annotated(func)
valids = _parse_validators(validators)
func.metadata.add_param(name, None, type_name, valids, **kwargs)
# Only decorate the function once even if we have multiple param decorators
if func.decorated:
return func
func.decorated = True
return decorate(func, _check_and_execute)
def log(func, *args, **kwargs):
"""Add debug messages to logger."""
logger = logging.getLogger(func.__module__)
def decorator(self, *args, **kwargs):
logger.debug(f'Entering {func.__name__}')
result = func(*args, **kwargs)
logger.debug(result)
logger.debug(f'Exiting {func.__name__}')
return result
return decorate(func, decorator)
def fdecorate(fdefault):
"""Wrap a target generic function, overriding the previous
default that was set for the generic function.
Parameters
----------
fdefault : function
The default function.
Returns
-------
fgeneric : function
A wrapped generic function.
"""
generic_func_node.set_default(fdefault, allow_override=True)
def register(key, func=None, override=True):
"""Register function to be the dispatch function.
Parameters
----------
key : str or list of str
The key to be registered.
func : function
The function to be registered.
override : bool, optional
Whether override existing registration.
Returns
-------
The register function is necessary.
"""
def _do_reg(myf):
generic_func_node.register(myf, key, override)
return myf
if func:
return _do_reg(func)
return _do_reg
def dispatch_func(func, *args, **kwargs):
#pylint: disable=unused-argument
"""The wrapped dispath function"""
if kwargs:
raise RuntimeError(
"Keyword arguments cannot be used when invoking generic_func %s" % func_name)
return generic_func_node(*args)
fresult = decorate(fdefault, dispatch_func)
fresult.fdefault = fdefault
fresult.register = register
return fresult
def with_mitmproxy(*args, **kwargs):
'''Decorator to abstract fixture recording and scenario setup for the E2E tests with mitmproxy.
Args:
scenario_name (Optional[str]): scenario name, by default test name without 'test_' prefix
should_record (Optional[bool]): is recording new interactions allowed during test run,
by default `False`
*args (List[Callable]): list of length of either zero or one: decorated function.
This is to allow the decorator to function both with and without calling it
with parameters: if args is present, we can deduce that the decorator was used
without parameters.
Returns:
Callable: a decorator the can be used both with and without calling brackets
(if all params should be default)
'''
scenario_name = kwargs.pop('scenario_name', None)
should_record = kwargs.pop('should_record', False)
if not args:
assert not kwargs, 'Parameters %s not expected' % kwargs
def _with_mitmproxy(func, *args, **kwargs):
def ommit_test_in_name(name):
if name.startswith('test_'):
return name[5:]
mitmproxy_url = environ.get('MITMPROXY_HOST',
'http://mitm-manager.local')
mitm_client = MITMClient(mitmproxy_url)
final_scenario_name = scenario_name or ommit_test_in_name(
func.__name__)
try:
mitm_client.set_scenario(final_scenario_name)
if should_record:
mitm_client.start_recording()
func(*args, **kwargs)
except Exception:
raise
finally:
mitm_client.stop_recording()
mitm_client.set_scenario('default')
def _decorator(func):
return decorate(func, _with_mitmproxy)
if args:
return decorate(args[0], _with_mitmproxy)
return _decorator
def decorate(func, fwrapped):
"""A wrapper call of decorator package, differs to call time
Parameters
----------
func : function
The original function
fwrapped : function
The wrapped function
"""
import decorator
return decorator.decorate(func, fwrapped)
def outer(wrapped):
@functools.wraps(wrapped)
def wrapper(self, *args, **kwargs):
params = inspect.getcallargs(wrapped, *args, **kwargs)
for arg_name, validators in to_validate.items():
for validator in validators:
params[arg_name], errs = validator(
arg_name, params.get(arg_name, None))
if errs:
raise ValidationError(errs)
return wrapped(**params)
return decorator.decorate(wrapped, wrapper)
def leakcheck(fn):
# Compare the memory (after GC) before and after the test to determine
# if we leaked memory
def wrapper(fn, *args, **kwargs):
gc.collect()
mem_before = MemoryUsage()
rv = fn(*args, **kwargs)
gc.collect()
mem_after = MemoryUsage()
assert mem_before >= mem_after
return rv
return decorator.decorate(fn, wrapper)
def decorate(func, fwrapped):
"""A wrapper call of decorator package, differs to call time
Parameters
----------
func : function
The original function
fwrapped : function
The wrapped function
"""
import decorator
return decorator.decorate(func, fwrapped)
def with_mitmproxy(*args, **kwargs):
'''Decorator to abstract fixture recording and scenario setup for the E2E tests with mitmproxy.
Args:
scenario_name (Optional[str]): scenario name, by default test name without 'test_' prefix
should_record (Optional[bool]): is recording new interactions allowed during test run,
by default `False`
*args (List[Callable]): list of length of either zero or one: decorated function.
This is to allow the decorator to function both with and without calling it
with parameters: if args is present, we can deduce that the decorator was used
without parameters.
Returns:
Callable: a decorator the can be used both with and without calling brackets
(if all params should be default)
'''
scenario_name = kwargs.pop('scenario_name', None)
should_record = kwargs.pop('should_record', False)
if not args:
assert not kwargs, 'Parameters %s not expected' % kwargs
def _with_mitmproxy(func, *args, **kwargs):
def ommit_test_in_name(name):
if name.startswith('test_'):
return name[5:]
mitmproxy_url = environ.get('MITMPROXY_HOST', 'http://mitm-manager.local')
mitm_client = MITMClient(mitmproxy_url)
final_scenario_name = scenario_name or ommit_test_in_name(func.__name__)
try:
mitm_client.set_scenario(final_scenario_name)
if should_record:
mitm_client.start_recording()
func(*args, **kwargs)
except Exception:
raise
finally:
mitm_client.stop_recording()
mitm_client.set_scenario('default')
def _decorator(func):
return decorate(func, _with_mitmproxy)
if args:
return decorate(args[0], _with_mitmproxy)
return _decorator
def module_lock(self, f):
def do_function(func, module, *args, **kwargs):
with self.local_lock:
module_name = module.__class__.__name__
if module_name not in self.m_lock:
m_lock = self.m_lock[module_name] = Lock()
else:
m_lock = self.m_lock[module_name]
with m_lock:
result = func(module, *args, **kwargs)
return result
return decorate(f, do_function)
def function_lock(self, f):
def do_function(func, module, *args, **kwargs):
with self.local_lock:
lock_name = module.__class__.__name__ + "-" + func.__name__
if lock_name not in self.f_lock:
f_lock = self.f_lock[lock_name] = Lock()
else:
f_lock = self.f_lock[lock_name]
with f_lock:
result = func(module, *args, **kwargs)
return result
return decorate(f, do_function)
def fun_wrap(func):
def do_function(*args, **kwargs):
with self.local_lock:
if name not in self.n_lock:
n_lock = self.n_lock[name] = Lock()
else:
n_lock = self.n_lock[name]
with n_lock:
result = func(
*args[1:],
**kwargs) # XXX, too hard to write this line.
return result
return decorate(func, do_function)
def deprecated(*optional_message):
"""This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used.
Parameters
----------
*optional_message : str
an optional user level hint which should indicate which feature to use otherwise.
"""
def _deprecated(func, *args, **kw):
try:
caller_stack = stack()[1:]
while len(caller_stack) > 0:
frame = caller_stack.pop(0)
filename = frame[1]
# skip callee frames if they are other decorators or this file(func)
if 'decorator' in filename or __file__ in filename:
continue
else: break
lineno = frame[2]
# avoid cyclic references!
del caller_stack, frame
except OSError: # eg. os.getcwd() fails in conda-test, since cwd gets deleted.
filename = 'unknown'
lineno = -1
user_msg = 'Call to deprecated function "%s". Called from %s line %i. %s' \
% (func.__name__, filename, lineno, msg)
warnings.warn_explicit(
user_msg,
category=PyEMMA_DeprecationWarning,
filename=filename,
lineno=lineno
)
return func(*args, **kw)
# add deprecation notice to func docstring:
if len(optional_message) == 1 and callable(optional_message[0]):
# this is the function itself, decorate!
msg = ""
return decorate(optional_message[0], _deprecated)
else:
# actually got a message (or empty parenthesis)
msg = optional_message[0] if len(optional_message) > 0 else ""
return decorator(_deprecated)
def __call__(self, f):
def g(obj, *args, **kw):
obj = args[0]
if not hasattr(obj, 'cachers'):
obj.cachers = {}
caches = obj.cachers
if f in caches:
cacher = caches[f]
else:
cacher = caches[f] = Cacher(f, self.limit, self.ignore_args, self.force_kwargs)
return cacher(*args, **kw)
g.__name__ = f.__name__
g.__doc__ = f.__doc__
return decorator.decorate(f, g)
def rlock(func):
"""
Place a threading recursive lock (Rlock) on the wrapped function
"""
# This lock will be instantiated at function creation time, i.e. at the
# time the Python interpreter sees the decorated function the very
# first time - this lock thus exists once for each decorated function.
_rlock = threading.RLock()
def _locked_f(f, *args, **kwargs):
with _rlock:
return func(*args, **kwargs)
return decorate(func, _locked_f)
def outer(wrapped):
@functools.wraps(wrapped)
def wrapper(self, *args, **kwargs):
params = inspect.getcallargs(wrapped, *args, **kwargs)
for arg_name, validators in to_validate.items():
for validator in validators:
params[arg_name], errs = validator(arg_name,
params.get(
arg_name, None))
if errs:
raise ValidationError(errs)
return wrapped(**params)
return decorator.decorate(wrapped, wrapper)
def exception_improved_logging(f):
""" decorator to increment the error message with the name of the class in which it appears """
# useful when using inherited class
def _exception_handling(f, self, *args, **kwargs):
try:
return f(self, *args, **kwargs)
except Exception as e:
raise type(e)(str(e) +
" (within %s)" % self.__class__).with_traceback(
sys.exc_info()[2])
return decorate(f, _exception_handling)
def decorator(func):
assert_typed(func)
def _ensure_series_options(func, *a, **kw):
res = func(*a, **kw)
if isinstance(res, pd.Series):
if not getattr(res, 'options', None):
res.options = {}
return res
dec = decorate(func, _ensure_series_options)
FUNCS[name] = dec
return dec
def __call__(self, f):
self.f = f
def g(obj, *args, **kw):
obj = args[0]
if not hasattr(obj, 'cache'):
obj.cache = FunctionCache()
cache = obj.cache
if self.f in cache:
cacher = cache[self.f]
else:
cacher = cache[self.f] = Cacher(self.f, self.limit, self.ignore_args, self.force_kwargs, cacher_enabled=cache.caching_enabled)
return cacher(*args, **kw)
g.__name__ = f.__name__
g.__doc__ = f.__doc__
return decorator.decorate(self.f, g)
def decorator(func):
def _ensure_finder_keys(func, *a, **kw):
res = func(*a, **kw)
for name, meta in res.items():
if meta is None:
# underlying series is void, must be
# register_formula(..., reject_unknown=False)
continue
missing = _KEYS - set(meta.keys())
if len(missing):
warn(f'{name} has missing metadata keys ({missing})')
return res
dec = decorate(func, _ensure_finder_keys)
FINDERS[name] = dec
return dec
def inner(f):
f_ = decorator.decorate(f, _memoize)
keys = dict(active=active, copy_numpy=copy_numpy)
f.keys = keys
f_.keys = keys
def reset():
cache = {}
f.cache = cache
f_.cache = cache
reset()
f_.reset = reset
return f_
def memoized(f):
"""Decorate a function to cache its return value the first time it is
called.
If called later with the same arguments, the cached value is returned
(not reevaluated).
"""
f.cache = {}
def drop_cache():
logger.debug('Dropping cache for %s', f)
f.cache = {}
f.drop_cache = drop_cache
return decorate(f, _memoize)
def docannotate(func):
"""Annotate a function using information from its docstring.
The annotation actually happens at the time the function is first called
to improve startup time. For this function to work, the docstring must be
formatted correctly. You should use the typedargs pylint plugin to make
sure there are no errors in the docstring.
"""
func = annotated(func)
func.metadata.load_from_doc = True
if func.decorated:
return func
func.decorated = True
return decorate(func, _check_and_execute)
def _decorate_polyfill(func, caller):
"""
decorate(func, caller) decorates a function using a caller.
"""
try:
from decorator import decorate
return decorate(func, caller)
except ImportError:
from decorator import FunctionMaker
evaldict = dict(_call_=caller, _func_=func)
fun = FunctionMaker.create(func,
"return _call_(_func_, %(shortsignature)s)",
evaldict,
__wrapped__=func)
if hasattr(func, '__qualname__'):
fun.__qualname__ = func.__qualname__
return fun
def logging_scope(name, *wargs, **wkwargs):
"""
A decorator to add the decorated function as a new logging scope, with name `name`.
All additional arguments are passed to `StatusLogger._scope_enter`. Current
supported keyword arguments are "timed", in which case when the scope closes,
the duration of the call is shown.
"""
def logging_scope(func, *args, **kwargs):
logger._scope_enter(name, *wargs, **wkwargs)
success = True
try:
f = func(*args, **kwargs)
return f
except Exception as e:
success = False
raise_with_traceback(e)
finally:
logger._scope_exit(success)
return lambda func: decorate(func, logging_scope)
def functioncache_decorator(function):
# make sure cache is loaded
if not hasattr(function, '_db'):
cache_name = (_get_cache_name(function), type(backend))
if cache_name in OPEN_DBS:
function._db = OPEN_DBS[cache_name]
else:
backend.setup(function)
function._db = backend
OPEN_DBS[cache_name] = function._db
function_with_cache._db = function._db
function._seconds_of_validity = seconds_of_validity
function._fail_silently = fail_silently
function._ignore_instance = ignore_instance
function._function_key = function_key
return decorate(function, function_with_cache)
def functioncache_decorator(function):
# make sure cache is loaded
if not hasattr(function, '_db'):
cache_name = (_get_cache_name(function), type(backend))
if cache_name in OPEN_DBS:
function._db = OPEN_DBS[cache_name]
else:
backend.setup(function)
function._db = backend
OPEN_DBS[cache_name] = function._db
function_with_cache._db = function._db
function._seconds_of_validity = seconds_of_validity
function._fail_silently = fail_silently
function._ignore_instance = ignore_instance
function._function_key = function_key
return decorate(function, function_with_cache)
def check_process(func):
"""Decorator for ``TestRemoteRobot.test_...`` methods
to check if the external ``RemoteRobot`` process is still running,
which is passed to every test method as ``process`` fixture.
"""
def caller(func, self, process, *args, **kwargs):
cls = type(self)
# ensure that process was not recreated for some reason
if hasattr(cls, 'process'):
assert process is cls.process, \
"RemoteRobot process was recreated. " \
"Check `process` fixture."
cls.process = process
# polling returns None as long as process is running
assert process.poll() is None, \
"RemoteRobot process is not running (anymore)."
return func(self, process, *args, **kwargs)
return decorate(func, caller)
def logging_scope(name, *wargs, **wkwargs):
"""
A decorator to add the decorated function as a new logging scope, with name `name`.
All additional arguments are passed to `StatusLogger._scope_enter`. Current
supported keyword arguments are "timed", in which case when the scope closes,
the duration of the call is shown.
"""
def logging_scope(func, *args, **kwargs):
logger._scope_enter(name, *wargs, **wkwargs)
success = True
try:
f = func(*args, **kwargs)
return f
except Exception as e:
success = False
raise_with_traceback(e)
finally:
logger._scope_exit(success)
return lambda func: decorate(func, logging_scope)
def cache_decorator(user_func):
if to_str is compat.string_type:
# backwards compatible
key_generator = function_key_generator(namespace, user_func)
else:
key_generator = function_key_generator(namespace,
user_func,
to_str=to_str)
def refresh(*arg, **kw):
"""
Like invalidate, but regenerates the value instead
"""
key = key_generator(*arg, **kw)
value = user_func(*arg, **kw)
self.set(key, value)
return value
def invalidate(*arg, **kw):
key = key_generator(*arg, **kw)
self.delete(key)
def set_(value, *arg, **kw):
key = key_generator(*arg, **kw)
self.set(key, value)
def get(*arg, **kw):
key = key_generator(*arg, **kw)
return self.get(key)
user_func.set = set_
user_func.invalidate = invalidate
user_func.get = get
user_func.refresh = refresh
user_func.key_generator = key_generator
user_func.original = user_func
# Use `decorate` to preserve the signature of :param:`user_func`.
return decorate(
user_func,
functools.partial(get_or_create_for_user_func, key_generator))
def __call__(self, f):
self.f = f
def g(obj, *args, **kw):
obj = args[0]
if not hasattr(obj, 'cache'):
obj.cache = FunctionCache()
cache = obj.cache
if self.f in cache:
cacher = cache[self.f]
else:
cacher = cache[self.f] = Cacher(self.f, self.limit, self.ignore_args, self.force_kwargs, cacher_enabled=cache.caching_enabled)
return cacher(*args, **kw)
g.__name__ = f.__name__
g.__doc__ = f.__doc__
try: # readthedocs weirdness.........
from decorator import decorate # @UnresolvedImport
g = decorate(self.f, g)
except (AttributeError, ImportError):
pass
return g
def distributionMethod(method):
"""Decorator for wrapping a method so that it can take distributions as arguments."""
def helper(wrapped, self, *args, **kwargs):
args = tuple(toDistribution(arg) for arg in args)
kwargs = {name: toDistribution(arg) for name, arg in kwargs.items()}
if any(
needsSampling(arg)
for arg in itertools.chain(args, kwargs.values())):
return MethodDistribution(method, self, args, kwargs)
elif any(
needsLazyEvaluation(arg)
for arg in itertools.chain(args, kwargs.values())):
# see analogous comment in distributionFunction
return makeDelayedFunctionCall(helper, (method, self) + args,
kwargs)
else:
return method(self, *args, **kwargs)
return unpacksDistributions(
decorator.decorate(method, helper, kwsyntax=True))
def _decorate_function_with_mapping(self, f, m, force_update: bool = False):
try:
# try to update the mapping of an already decorated function
mapping = f.schalter_mapping
if force_update:
mapping.update(m)
else:
updates = {
k for k in mapping.keys() & m.keys() if mapping[k][0] != m[k][0]
}
for k in updates:
# introduce a strict mode where this raises an error?
msg = (
"Replacing original mapping of argument '{}' "
"to config entry '{}' with new mapping to '{}'.".format(
k, m[k][0], mapping[k][0]
)
)
logger.warning("Function '{}': {}".format(f.__name__, msg))
mapping.update({k: v for k, v in m.items() if k not in updates})
f.schalter_f.__kwdefaults__ = f.__kwdefaults__
except AttributeError:
# first decoration of function. Save mapping and original function
setattr(f, "schalter_mapping", m)
decorated = decorate(f, self.make_call_decorated_function(m))
setattr(decorated, "schalter_f", f)
f = decorated
try:
if id(f.schalter_config) != id(self):
raise NotImplementedError()
except AttributeError:
setattr(f, "schalter_config", self)
return f
def memoize(func):
"""
Rather simple memoize implementation supporting arbitrary signature function
calls. Since it simply appends a caching dict to the function, it is neither
optimal in performance, nor does it cover all edge cases.
Implementation is shamelessly stolen from:
https://decorator.readthedocs.io/en/latest/tests.documentation.html#the-solution
"""
def _memoize(func, *args, **kwargs):
if kwargs:
key = args, frozenset(kwargs.items())
else:
key = args
cache = func.cache
if key not in cache:
cache[key] = func(*args, **kwargs)
return cache[key]
func.cache = {}
return decorate(func, _memoize)
def api_error_handle(func):
def func_wrapper(f, *args, **kwargs):
try:
return f(*args, **kwargs)
except RateLimitError as e:
raise APIQuotaError(str(e))
except TweepError as e:
if e.api_code == TWITTER_AUTOMATED_REQUEST_ERROR:
raise AutomatedRequestError
elif e.api_code == TWITTER_OVER_CAPACITY_ERROR:
raise OverCapacityError
elif e.api_code in [TWITTER_CHARACTER_LIMIT_ERROR_1, TWITTER_CHARACTER_LIMIT_ERROR_2]:
raise CharacterLimitError
elif e.api_code == TWITTER_DAILY_STATUS_UPDATE_LIMIT_ERROR:
raise DailyStatusUpdateError
elif e.api_code == TWITTER_STATUS_DUPLICATE_ERROR:
raise StatusDuplicateError
else:
raise
return decorate(func, func_wrapper)
def decorator_gridify(f):
arg_str = ', '.join([k for k in defaults])
signature = ''
for k, v in defaults.items():
signature = signature + "{} = {},".format(k, k)
scope = {**defaults}
scope['np'] = np
scope[f.__name__] = f
exec(
grid_wrapper_def.format(signature=signature,
arg_str=arg_str,
fname=f.__name__), scope)
wrapped = scope['wrapped']
wrapped.__name__ = f.__name__
wrapped.__doc__ = f.__doc__
return decorate(wrapped, decorator_wrapper)
def __call__(self, fdecl):
def tagged_fdecl(func, *args, **kwargs):
with self:
return func(*args, **kwargs)
return decorate(fdecl, tagged_fdecl)
def dispatcher(fworkload):
"""Wrap a workload dispatcher function.
Parameters
----------
fworkload : function
The workload extraction function from arguments.
Returns
-------
fdispatcher : function
A wrapped dispatcher function, which will
dispatch based on DispatchContext and
the current workload.
"""
dispatch_dict = {}
func_name = fworkload.__name__
def register(key, func=None, override=False):
"""Register template function.
Parameters
----------
key : str or List of str
The template key to identify the template
under this dispatcher.
func : function
The function to be registered.
The first argument of the function is always
cfg returned by DispatchContext,
the rest arguments are the same as the fworkload.
override : bool
Whether override existing registration.
Returns
-------
The register function if necessary.
"""
if isinstance(key, str):
key = [key]
def _do_reg(myf):
for x in key:
if x in dispatch_dict and not override:
raise ValueError(
"Key %s is already registered for %s" % (x, func_name))
dispatch_dict[x] = myf
return myf
if func:
return _do_reg(func)
return _do_reg
def dispatch_func(func, *args, **kwargs):
"""The wrapped dispatch function"""
tgt = _target.current_target()
workload = func(*args, **kwargs)
cfg = DispatchContext.current.query(tgt, workload)
if cfg.is_fallback and not cfg.template_key:
# first try 'direct' template
if 'direct' in dispatch_dict:
return dispatch_dict['direct'](cfg, *args, **kwargs)
# otherwise pick a random template
for v in dispatch_dict.values():
return v(cfg, *args, **kwargs)
else:
return dispatch_dict[cfg.template_key](cfg, *args, **kwargs)
fdecorate = decorate(fworkload, dispatch_func)
fdecorate.register = register
return fdecorate
def decorate(func):
def wrapper(func, *args, **kwargs):
with block_timer(name, **tags):
result = func(*args, **kwargs)
return result
return decorator.decorate(func, wrapper)
def decorater(f):
for key, value in kwargs.items():
setattr(f, key, value)
return decorate(f, deco)
def memoize(f):
f.cache = {}
return decorate(f, _memoize)
def trace(f):
return decorate(f, _trace)
def memoize(f):
f.cache = weakref.WeakKeyDictionary()
return decorate(f, _memoize)
def _decorator(func):
return decorate(func, _with_mitmproxy)
def generic_func(fdefault):
"""Wrap a target generic function.
Generic function allows registeration of further functions
that can be dispatched on current target context.
If no registered dispatch is matched, the fdefault will be called.
Parameters
----------
fdefault : function
The default function.
Returns
-------
fgeneric : function
A wrapped generic function.
Example
-------
.. code-block:: python
import tvm
# wrap function as target generic
@tvm.target.generic_func
def my_func(a):
return a + 1
# register specialization of my_func under target cuda
@my_func.register("cuda")
def my_func_cuda(a):
return a + 2
# displays 3, because my_func is called
print(my_func(2))
# displays 4, because my_func_cuda is called
with tvm.target.cuda():
print(my_func(2))
"""
dispatch_dict = {}
func_name = fdefault.__name__
def register(key, func=None, override=False):
"""Register function to be the dispatch function.
Parameters
----------
key : str or list of str
The key to be registered.
func : function
The function to be registered.
override : bool
Whether override existing registeration.
Returns
-------
The register function is necessary.
"""
def _do_reg(myf):
key_list = [key] if isinstance(key, str) else key
for k in key_list:
if k in dispatch_dict and not override:
raise ValueError(
"Key is already registered for %s" % func_name)
dispatch_dict[k] = myf
return myf
if func:
return _do_reg(func)
return _do_reg
def dispatch_func(func, *args, **kwargs):
"""The wrapped dispath function"""
target = current_target()
if target is None:
return func(*args, **kwargs)
for k in target.keys:
if k in dispatch_dict:
return dispatch_dict[k](*args, **kwargs)
return func(*args, **kwargs)
fdecorate = decorate(fdefault, dispatch_func)
fdecorate.register = register
fdecorate.fdefault = fdefault
return fdecorate
def _versioned(f):
f.version = version
return decorator.decorate(f, __versioned)
def wrapper(func):
def new_init(func, self, *args, **kwargs):
func(self, *args, **kwargs)
self.name = module_name
return decorate(func, new_init, make_weak_signature(func))