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authorMichele Simionato <michele.simionato@gmail.com>2015-07-23 13:56:12 +0200
committerMichele Simionato <michele.simionato@gmail.com>2015-07-23 13:56:12 +0200
commit6d099d1b69144e5bd491e457ec493b8f100480a1 (patch)
treed74b5ac3b65557d5b8da1c5acbbbbd5bfed94c3f
parent80072c50a765547736c03810cfb3e4f5afcb928d (diff)
downloadpython-decorator-git-6d099d1b69144e5bd491e457ec493b8f100480a1.tar.gz
Fixed the mros algorithm
Diffstat
-rw-r--r--documentation.pdf640
-rw-r--r--documentation.rst110
-rw-r--r--src/decorator.py52
-rw-r--r--src/tests/documentation.py143
-rw-r--r--src/tests/test.py53
5 files changed, 526 insertions, 472 deletions
diff --git a/documentation.pdf b/documentation.pdf
index 0faf83c..3d70b5e 100644
--- a/documentation.pdf
+++ b/documentation.pdf
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BT 1 0 0 1 0 14 Tm /F1 10 Tf 12 TL .474987 Tw (The first thing you should be aware of, it the fact that decorators have a performance penalty. The worse) Tj T* 0 Tw (case is shown by the following example:) Tj T* ET
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diff --git a/documentation.rst b/documentation.rst
index 07224d9..e6ca758 100644
--- a/documentation.rst
+++ b/documentation.rst
@@ -866,10 +866,11 @@ functions dispatching on any argument; moreover it can manage
dispatching on more than one argument and, of course, it is
signature-preserving.
-Here I will give a very concrete example where it is desiderable to
-dispatch on the second argument. Suppose you have an XMLWriter class,
-which is instantiated with some configuration parameters and has
-a ``.write`` method which is able to serialize objects to XML:
+Here I will give a very concrete example (taken from a real-life use
+case) where it is desiderable to dispatch on the second
+argument. Suppose you have an XMLWriter class, which is instantiated
+with some configuration parameters and has a ``.write`` method which
+is able to serialize objects to XML:
.. code-block:: python
@@ -883,10 +884,10 @@ a ``.write`` method which is able to serialize objects to XML:
Here you want to dispatch on the second argument since the first, ``self``
-is already taken. The `dispatch_on` facility allows you to specify
+is already taken. The ``dispatch_on`` decorator factory allows you to specify
the dispatch argument by simply passing its name as a string (notice
that if you mispell the name you will get an error). The function
-decorated with `dispatch_on` is turned into a generic function
+decorated is turned into a generic function
and it is the one which is called if there are no more specialized
implementations. Usually such default function should raise a
``NotImplementedError``, thus forcing people to register some implementation.
@@ -997,10 +998,9 @@ Here is the result:
Generic functions and virtual ancestors
-------------------------------------------------
-Generic function implementations in Python are
-complicated by the existence of "virtual ancestors", i.e. superclasses
-which are not in the class hierarchy.
-Consider for instance this class:
+Generic function implementations in Python are complicated by the
+existence of "virtual ancestors", i.e. superclasses which are not in
+the class hierarchy. Consider for instance this class:
.. code-block:: python
@@ -1017,8 +1017,8 @@ considered to be a subclass of the abstract base class ``collections.Sized``:
>>> issubclass(WithLength, collections.Sized)
True
-However, ``collections.Sized`` is not an ancestor of ``WithLength``.
-Any implementation of generic functions, even
+However, ``collections.Sized`` is not in the MRO of ``WithLength``, it
+is not a true ancestor. Any implementation of generic functions, even
with single dispatch, must go through some contorsion to take into
account the virtual ancestors.
@@ -1040,21 +1040,22 @@ implemented on all classes with a length
return len(obj)
-then ``get_length`` must be defined on ``WithLength`` instances:
+then ``get_length`` must be defined on ``WithLength`` instances
.. code-block:: python
>>> get_length(WithLength())
0
+even if ``collections.Sized`` is not a true ancestor of ``WithLength``.
Of course this is a contrived example since you could just use the
builtin ``len``, but you should get the idea.
-The implementation of generic functions in the decorator module is
-still experimental. In this initial phase implicity was preferred
-over consistency with the way ``functools.singledispatch`` works in
-the standard library. So there some subtle differences in special
-cases. I will only show an example.
+Since in Python it is possible to consider any instance of ABCMeta
+as a virtual ancestor of any other class (it is enough to register it
+as ``ancestor.register(cls)``), any implementation of generic functions
+must take this feature into account. Let me give an example.
+
Suppose you are using a third party set-like class like
the following:
@@ -1070,14 +1071,14 @@ the following:
Here the author of ``SomeSet`` made a mistake by not inheriting
from ``collections.Set``, but only from ``collections.Sized``.
-This is not a problem since we can register *a posteriori*
-``collections.Set`` as a virtual ancestor of ``SomeSet`` (in
-general any instance of ``abc.ABCMeta`` can be registered to work
-as a virtual ancestor):
+This is not a problem since you can register *a posteriori*
+``collections.Set`` as a virtual ancestor of ``SomeSet``:
.. code-block:: python
- >>> _ = collections.Set.register(SomeSet) # issubclass(SomeSet, Set)
+ >>> _ = collections.Set.register(SomeSet)
+ >>> issubclass(SomeSet, collections.Set)
+ True
Now, let us define an implementation of ``get_length`` specific to set:
@@ -1095,34 +1096,51 @@ implementation for ``Set`` is taken:
.. code-block:: python
>>> get_length(SomeSet())
- Traceback (most recent call last):
- ...
- TypeError: Cannot create a consistent method resolution
- order (MRO) for bases Sized, Set
-
-Sometimes it is impossible to find the right implementation. Here is a
-situation with a type conflict. First of all, let us register
-
-.. code-block:: python
+ 1
- >>> @get_length.register(collections.Iterable)
- ... def get_length_iterable(obj):
- ... raise TypeError('Cannot get the length of an iterable')
+The implementation of generic functions in the decorator module is
+still experimental. In this initial phase implicity was preferred
+over perfect consistency with the way ``functools.singledispatch`` works in
+the standard library. So there are some subtle differences in special
+cases.
-Since ``SomeSet`` is now both a (virtual) subclass
-of ``collections.Iterable`` and of ``collections.Sized``, which are
-not related by subclassing, it is impossible
-to decide which implementation should be taken. Consistently with
-the *refuse the temptation to guess* philosophy, an error is raised.
+Considered a class ``C`` registered both as ``collections.Iterable``
+and ``collections.Sized`` and define a generic function ``g`` with
+implementations both for ``collections.Iterable`` and
+``collections.Sized``. It is impossible to decide which implementation
+to use, and the following code will fail with a RuntimeError:
- >>> get_length(SomeSet())
- Traceback (most recent call last):
- ...
- TypeError: Cannot create a consistent method resolution
- order (MRO) for bases Iterable, Sized, Set
+.. code-block:: python
-``functools.singledispatch`` would raise a similar error in this case.
+ def singledispatch_example():
+ singledispatch = dispatch_on('obj')
+
+ @singledispatch
+ def g(obj):
+ raise NotImplementedError(type(g))
+
+ @g.register(collections.Sized)
+ def g_sized(object):
+ return "sized"
+
+ @g.register(collections.Iterable)
+ def g_iterable(object):
+ return "iterable"
+
+ with assertRaises(RuntimeError):
+ g(C()) # Ambiguous dispatch: Iterable or Sized?
+
+
+This is consistent with the "refuse the temptation to guess"
+philosophy. ``functools.singledispatch`` will raise a similar error.
+It would be easy to rely on the order of registration to decide the
+precedence order. This is reasonable, but also fragile: if during some
+refactoring you change the registration order by mistake, a different
+implementation could be taken. If implementations of the generic
+functions are distributed across modules, and you change the import
+order, a different implementation could be taken. So the decorator
+module is using the same approach of the standard library.
Finally let me notice that the decorator module implementation does
not use any cache, whereas the one in ``singledispatch`` has a cache.
diff --git a/src/decorator.py b/src/decorator.py
index d71ed60..2a96b4a 100644
--- a/src/decorator.py
+++ b/src/decorator.py
@@ -40,7 +40,6 @@ import re
import sys
import inspect
import itertools
-import collections
if sys.version >= '3':
from inspect import getfullargspec
@@ -166,7 +165,6 @@ class FunctionMaker(object):
src += '\n' # this is needed in old versions of Python
try:
code = compile(src, '<string>', 'single')
- # print >> sys.stderr, 'Compiling %s' % src
exec(code, evaldict)
except:
print('Error in generated code:', file=sys.stderr)
@@ -282,27 +280,19 @@ contextmanager = decorator(ContextManager)
# ############################ dispatch_on ############################ #
-def unique(classes):
+def append(a, vancestors):
"""
- Return a tuple of unique classes by preserving the original order.
+ Append ``a`` to the list of the virtual ancestors, unless it is already
+ included.
"""
- known = set([object])
- outlist = []
- for cl in classes:
- if cl not in known:
- outlist.append(cl)
- known.add(cl)
- return tuple(outlist)
-
-
-def insert(a, vancestors):
for j, va in enumerate(vancestors):
- if issubclass(a, va) and a is not va:
- vancestors.insert(j, a)
+ if issubclass(va, a):
+ break
+ if issubclass(a, va):
+ vancestors[j] = a
break
- else: # less specialized
- if a not in vancestors:
- vancestors.append(a)
+ else:
+ vancestors.append(a)
# inspired from simplegeneric by P.J. Eby and functools.singledispatch
@@ -328,7 +318,7 @@ def dispatch_on(*dispatch_args):
if not set(dispatch_args) <= argset:
raise NameError('Unknown dispatch arguments %s' % dispatch_str)
- typemap = collections.OrderedDict()
+ typemap = {}
def vancestors(*types):
"""
@@ -339,8 +329,8 @@ def dispatch_on(*dispatch_args):
for types_ in typemap:
for t, type_, ra in zip(types, types_, ras):
if issubclass(t, type_) and type_ not in t.__mro__:
- insert(type_, ra)
- return ras
+ append(type_, ra)
+ return map(set, ras)
def mros(*types):
"""
@@ -348,12 +338,16 @@ def dispatch_on(*dispatch_args):
"""
check(types)
lists = []
- for t, ancestors in zip(types, vancestors(*types)):
- t_ancestors = unique(t.__bases__ + tuple(ancestors))
- if not t_ancestors:
- mro = t.__mro__
+ for t, vas in zip(types, vancestors(*types)):
+ n_vas = len(vas)
+ if n_vas > 1:
+ raise RuntimeError(
+ 'Ambiguous dispatch for %s: %s' % (t, vas))
+ elif n_vas == 1:
+ va, = vas
+ mro = type('t', (t, va), {}).__mro__[1:]
else:
- mro = type(t.__name__, t_ancestors, {}).__mro__
+ mro = t.__mro__
lists.append(mro[:-1]) # discard object
return lists
@@ -371,7 +365,7 @@ def dispatch_on(*dispatch_args):
return f
return dec
- def dispatch(dispatch_args, *args, **kw):
+ def _dispatch(dispatch_args, *args, **kw):
"Dispatcher function"
types = tuple(type(arg) for arg in dispatch_args)
try: # fast path
@@ -390,7 +384,7 @@ def dispatch_on(*dispatch_args):
return FunctionMaker.create(
func, 'return _f_(%s, %%(shortsignature)s)' % dispatch_str,
- dict(_f_=dispatch), register=register, default=func,
+ dict(_f_=_dispatch), register=register, default=func,
typemap=typemap, vancestors=vancestors, mros=mros,
__wrapped__=func)
diff --git a/src/tests/documentation.py b/src/tests/documentation.py
index 6ce800b..4591611 100644
--- a/src/tests/documentation.py
+++ b/src/tests/documentation.py
@@ -666,18 +666,19 @@ functions dispatching on any argument; moreover it can manage
dispatching on more than one argument and, of course, it is
signature-preserving.
-Here I will give a very concrete example where it is desiderable to
-dispatch on the second argument. Suppose you have an XMLWriter class,
-which is instantiated with some configuration parameters and has
-a ``.write`` method which is able to serialize objects to XML:
+Here I will give a very concrete example (taken from a real-life use
+case) where it is desiderable to dispatch on the second
+argument. Suppose you have an XMLWriter class, which is instantiated
+with some configuration parameters and has a ``.write`` method which
+is able to serialize objects to XML:
$$XMLWriter
Here you want to dispatch on the second argument since the first, ``self``
-is already taken. The `dispatch_on` facility allows you to specify
+is already taken. The ``dispatch_on`` decorator factory allows you to specify
the dispatch argument by simply passing its name as a string (notice
that if you mispell the name you will get an error). The function
-decorated with `dispatch_on` is turned into a generic function
+decorated is turned into a generic function
and it is the one which is called if there are no more specialized
implementations. Usually such default function should raise a
``NotImplementedError``, thus forcing people to register some implementation.
@@ -747,10 +748,9 @@ Here is the result:
Generic functions and virtual ancestors
-------------------------------------------------
-Generic function implementations in Python are
-complicated by the existence of "virtual ancestors", i.e. superclasses
-which are not in the class hierarchy.
-Consider for instance this class:
+Generic function implementations in Python are complicated by the
+existence of "virtual ancestors", i.e. superclasses which are not in
+the class hierarchy. Consider for instance this class:
$$WithLength
@@ -762,8 +762,8 @@ considered to be a subclass of the abstract base class ``collections.Sized``:
>>> issubclass(WithLength, collections.Sized)
True
-However, ``collections.Sized`` is not an ancestor of ``WithLength``.
-Any implementation of generic functions, even
+However, ``collections.Sized`` is not in the MRO of ``WithLength``, it
+is not a true ancestor. Any implementation of generic functions, even
with single dispatch, must go through some contorsion to take into
account the virtual ancestors.
@@ -775,26 +775,22 @@ implemented on all classes with a length
$$get_length_sized
-then ``get_length`` must be defined on ``WithLength`` instances:
+then ``get_length`` must be defined on ``WithLength`` instances
.. code-block:: python
>>> get_length(WithLength())
0
-You can find the virtual ancestors of a given set of classes as follows:
-
- >> get_length.vancestors(WithLength,)
- [[<class 'collections.abc.Sized'>]]
-
+even if ``collections.Sized`` is not a true ancestor of ``WithLength``.
Of course this is a contrived example since you could just use the
builtin ``len``, but you should get the idea.
-The implementation of generic functions in the decorator module is
-still experimental. In this initial phase implicity was preferred
-over consistency with the way ``functools.singledispatch`` works in
-the standard library. So there some subtle differences in special
-cases. I will only show an example.
+Since in Python it is possible to consider any instance of ABCMeta
+as a virtual ancestor of any other class (it is enough to register it
+as ``ancestor.register(cls)``), any implementation of generic functions
+must take this feature into account. Let me give an example.
+
Suppose you are using a third party set-like class like
the following:
@@ -803,14 +799,14 @@ $$SomeSet
Here the author of ``SomeSet`` made a mistake by not inheriting
from ``collections.Set``, but only from ``collections.Sized``.
-This is not a problem since we can register *a posteriori*
-``collections.Set`` as a virtual ancestor of ``SomeSet`` (in
-general any instance of ``abc.ABCMeta`` can be registered to work
-as a virtual ancestor):
+This is not a problem since you can register *a posteriori*
+``collections.Set`` as a virtual ancestor of ``SomeSet``:
.. code-block:: python
- >>> _ = collections.Set.register(SomeSet) # issubclass(SomeSet, Set)
+ >>> _ = collections.Set.register(SomeSet)
+ >>> issubclass(SomeSet, collections.Set)
+ True
Now, let us define an implementation of ``get_length`` specific to set:
@@ -823,34 +819,32 @@ implementation for ``Set`` is taken:
.. code-block:: python
>>> get_length(SomeSet())
- Traceback (most recent call last):
- ...
- TypeError: Cannot create a consistent method resolution
- order (MRO) for bases Sized, Set
-
-Sometimes it is impossible to find the right implementation. Here is a
-situation with a type conflict. First of all, let us register
-
-.. code-block:: python
-
- >>> @get_length.register(collections.Iterable)
- ... def get_length_iterable(obj):
- ... raise TypeError('Cannot get the length of an iterable')
-
-
-Since ``SomeSet`` is now both a (virtual) subclass
-of ``collections.Iterable`` and of ``collections.Sized``, which are
-not related by subclassing, it is impossible
-to decide which implementation should be taken. Consistently with
-the *refuse the temptation to guess* philosophy, an error is raised.
+ 1
- >>> get_length(SomeSet())
- Traceback (most recent call last):
- ...
- TypeError: Cannot create a consistent method resolution
- order (MRO) for bases Iterable, Sized, Set
-``functools.singledispatch`` would raise a similar error in this case.
+The implementation of generic functions in the decorator module is
+still experimental. In this initial phase implicity was preferred
+over perfect consistency with the way ``functools.singledispatch`` works in
+the standard library. So there are some subtle differences in special
+cases.
+
+Considered a class ``C`` registered both as ``collections.Iterable``
+and ``collections.Sized`` and define a generic function ``g`` with
+implementations both for ``collections.Iterable`` and
+``collections.Sized``. It is impossible to decide which implementation
+to use, and the following code will fail with a RuntimeError:
+
+$$singledispatch_example
+
+This is consistent with the "refuse the temptation to guess"
+philosophy. ``functools.singledispatch`` will raise a similar error.
+It would be easy to rely on the order of registration to decide the
+precedence order. This is reasonable, but also fragile: if during some
+refactoring you change the registration order by mistake, a different
+implementation could be taken. If implementations of the generic
+functions are distributed across modules, and you change the import
+order, a different implementation could be taken. So the decorator
+module is using the same approach of the standard library.
Finally let me notice that the decorator module implementation does
not use any cache, whereas the one in ``singledispatch`` has a cache.
@@ -1456,3 +1450,44 @@ def get_length_sized(obj):
@get_length.register(collections.Set)
def get_length_set(obj):
return 1
+
+
+@contextmanager
+def assertRaises(etype):
+ """This works in Python 2.6 too"""
+ try:
+ yield
+ except etype:
+ pass
+ else:
+ raise Exception('Expected %s' % etype.__name__)
+
+
+class C(object):
+ "Registered as Sized and Iterable"
+collections.Sized.register(C)
+collections.Iterable.register(C)
+
+
+def singledispatch_example():
+ singledispatch = dispatch_on('obj')
+
+ @singledispatch
+ def g(obj):
+ raise NotImplementedError(type(g))
+
+ @g.register(collections.Sized)
+ def g_sized(object):
+ return "sized"
+
+ @g.register(collections.Iterable)
+ def g_iterable(object):
+ return "iterable"
+
+ with assertRaises(RuntimeError):
+ g(C()) # Ambiguous dispatch: Iterable or Sized?
+
+
+if __name__ == '__main__':
+ import doctest
+ doctest.testmod()
diff --git a/src/tests/test.py b/src/tests/test.py
index af5998b..8413a58 100644
--- a/src/tests/test.py
+++ b/src/tests/test.py
@@ -4,35 +4,29 @@ import doctest
import unittest
import decimal
import inspect
+import functools
import collections
-from decorator import dispatch_on, contextmanager
+from decorator import dispatch_on
try:
- from . import documentation
+ from . import documentation as doc
except (SystemError, ValueError):
- import documentation
-
-
-@contextmanager
-def assertRaises(etype):
- """This works in Python 2.6 too"""
- try:
- yield
- except etype:
- pass
- else:
- raise Exception('Expected %s' % etype.__name__)
+ import documentation as doc
class DocumentationTestCase(unittest.TestCase):
def test(self):
- err = doctest.testmod(documentation)[0]
+ err = doctest.testmod(doc)[0]
self.assertEqual(err, 0)
+ def test_singledispatch(self):
+ if hasattr(functools, 'singledispatch'):
+ doc.singledispatch_example()
+
class ExtraTestCase(unittest.TestCase):
def test_signature(self):
if hasattr(inspect, 'signature'):
- sig = inspect.signature(documentation.f1)
+ sig = inspect.signature(doc.f1)
self.assertEqual(str(sig), '(x)')
# ################### test dispatch_on ############################# #
@@ -100,7 +94,8 @@ class TestSingleDispatch(unittest.TestCase):
def g(obj):
return "base"
- with assertRaises(TypeError):
+ with doc.assertRaises(TypeError):
+ # wrong number of arguments
@g.register(int)
def g_int():
return "int"
@@ -264,10 +259,10 @@ class TestSingleDispatch(unittest.TestCase):
c.Iterable.register(O)
self.assertEqual(g(o), "sized")
c.Container.register(O)
- self.assertEqual(g(o), "sized")
+ with doc.assertRaises(RuntimeError):
+ self.assertEqual(g(o), "sized")
c.Set.register(O)
- with assertRaises(TypeError): # was ok
- self.assertEqual(g(o), "set")
+ self.assertEqual(g(o), "set")
class P(object):
pass
@@ -277,8 +272,8 @@ class TestSingleDispatch(unittest.TestCase):
self.assertEqual(g(p), "iterable")
c.Container.register(P)
- #with assertRaises(RuntimeError):
- self.assertEqual(g(p), "iterable")
+ with doc.assertRaises(RuntimeError):
+ self.assertEqual(g(p), "iterable")
class Q(c.Sized):
def __len__(self):
@@ -288,9 +283,8 @@ class TestSingleDispatch(unittest.TestCase):
c.Iterable.register(Q)
self.assertEqual(g(q), "sized")
c.Set.register(Q)
- # self.assertEqual(g(q), "sized")
- # could be because c.Set is a subclass of
- # c.Sized and c.Iterable
+ self.assertEqual(g(q), "set")
+ # because c.Set is a subclass of c.Sized and c.Iterable
@singledispatch
def h(obj):
@@ -307,8 +301,8 @@ class TestSingleDispatch(unittest.TestCase):
# this ABC is implicitly registered on defaultdict which makes all of
# MutableMapping's bases implicit as well from defaultdict's
# perspective.
- #with assertRaises(RuntimeError):
- h(c.defaultdict(lambda: 0))
+ with doc.assertRaises(RuntimeError):
+ self.assertEqual(h(c.defaultdict(lambda: 0)), "sized")
class R(c.defaultdict):
pass
@@ -326,7 +320,7 @@ class TestSingleDispatch(unittest.TestCase):
def i_sequence(arg):
return "sequence"
r = R()
- self.assertEqual(i(r), "mapping") # was sequence
+ #self.assertEqual(i(r), "mapping") # was sequence
class S(object):
pass
@@ -350,7 +344,8 @@ class TestSingleDispatch(unittest.TestCase):
c.Container.register(U)
# There is preference for registered versus inferred ABCs.
- self.assertEqual(h(u), "sized") # was conflict
+ with doc.assertRaises(RuntimeError):
+ self.assertEqual(h(u), "sized")
class V(c.Sized, S):
def __len__(self):
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