Refined the virtual ancestors management and the tests

author: Michele Simionato <michele.simionato@gmail.com> 2015-07-22 07:32:49 +0200
committer: Michele Simionato <michele.simionato@gmail.com> 2015-07-22 07:32:49 +0200
commit: 4b28eea14c0ab65e447cf2955c26bc9a8e5fd11b (patch)
tree: 3aabc01ff7a68063519ef60e33a23116177b6068
parent: 10e035eaf93997a63a30687f6fe33b918714ba61 (diff)
download: python-decorator-git-4b28eea14c0ab65e447cf2955c26bc9a8e5fd11b.tar.gz
6 files changed, 1646 insertions, 1304 deletions
diff --git a/.travis.yml b/.travis.yml
index 981d9e0..55a4f81 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,4 +12,4 @@ install:
   - python setup.py install
 
 script:
-  python test.py -v
+  python src/tests/test.py -v
diff --git a/documentation.pdf b/documentation.pdf
index b4e28f8..802956a 100644
--- a/documentation.pdf
+++ b/documentation.pdf
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+BT 1 0 0 1 0 14 Tm 1.545814 Tw 12 TL /F1 10 Tf 0 0 0 rg (Notice even in Python 3.5 ) Tj /F4 10 Tf 0 0 0 rg (inspect.getargspec ) Tj /F1 10 Tf 0 0 0 rg (and ) Tj /F4 10 Tf 0 0 0 rg (inspect.getfullargspec ) Tj /F1 10 Tf 0 0 0 rg (\(which are deprecated in that) Tj T* 0 Tw (release\) will give the wrong signature.) Tj T* ET
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 BT 1 0 0 1 0 2 Tm /F1 10 Tf 12 TL (and it that it has the correct signature:) Tj T* ET
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-BT 1 0 0 1 0 38 Tm 1.471235 Tw 12 TL /F1 10 Tf 0 0 0 rg (\(see bug report ) Tj 0 0 .501961 rg (1764286 ) Tj 0 0 0 rg (for an explanation of what is happening\). Unfortunately the bug is still there,) Tj T* 0 Tw .508409 Tw (even in Python 2.7 and 3.4 \(it will be fixed for Python 3.5, thought\). There is however a workaround. The) Tj T* 0 Tw .325984 Tw (decorated function has an attribute ) Tj /F3 10 Tf 0 0 0 rg (.__wrapped__) Tj /F1 10 Tf 0 0 0 rg (, pointing to the original function. The easy way to get) Tj T* 0 Tw (the source code is to call ) Tj /F3 10 Tf 0 0 0 rg (inspect.getsource ) Tj /F1 10 Tf 0 0 0 rg (on the undecorated function:) Tj T* ET
+BT 1 0 0 1 0 38 Tm .011098 Tw 12 TL /F1 10 Tf 0 0 0 rg (\(see bug report ) Tj 0 0 .501961 rg (1764286 ) Tj 0 0 0 rg (for an explanation of what is happening\). Unfortunately the bug is still there, in all) Tj T* 0 Tw 1.33436 Tw (versions of Python except Python 3.5, which is not yet released. There is however a workaround. The) Tj T* 0 Tw .325984 Tw (decorated function has an attribute ) Tj /F3 10 Tf 0 0 0 rg (.__wrapped__) Tj /F1 10 Tf 0 0 0 rg (, pointing to the original function. The easy way to get) Tj T* 0 Tw (the source code is to call ) Tj /F3 10 Tf 0 0 0 rg (inspect.getsource ) Tj /F1 10 Tf 0 0 0 rg (on the undecorated function:) Tj T* ET
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-BT 1 0 0 1 0 110 Tm 12 TL /F5 10 Tf 0 .501961 0 rg (class) Tj /F3 10 Tf 0 0 0 rg ( ) Tj /F5 10 Tf 0 0 1 rg (TailRecursive) Tj /F3 10 Tf 0 0 0 rg (\() Tj 0 .501961 0 rg (object) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (    ) Tj /F6 10 Tf .729412 .129412 .129412 rg (""") Tj T* (    tail_recursive decorator based on Kay Schluehr's recipe) Tj T* (    http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/496691) Tj T* (    with improvements by me and George Sakkis.) Tj T* (    """) Tj /F3 10 Tf 0 0 0 rg  T*  T* (    ) Tj /F5 10 Tf 0 .501961 0 rg (def) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 1 rg (__init__) Tj 0 0 0 rg (\() Tj 0 .501961 0 rg (self) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (func) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (        ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (func) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (func) Tj 0 0 0 rg  T* (        ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (firstcall) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (True) Tj 0 0 0 rg  T* ET
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-BT 1 0 0 1 0 218 Tm 12 TL /F3 10 Tf 0 0 0 rg (        ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (CONTINUE) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (object) Tj 0 0 0 rg (\(\)) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# sentinel) Tj /F3 10 Tf 0 0 0 rg  T*  T* (    ) Tj /F5 10 Tf 0 .501961 0 rg (def) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 1 rg (__call__) Tj 0 0 0 rg (\() Tj 0 .501961 0 rg (self) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (*) Tj 0 0 0 rg (args) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (**) Tj 0 0 0 rg (kwd) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (        ) Tj 0 0 0 rg (CONTINUE) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (CONTINUE) Tj 0 0 0 rg  T* (        ) Tj /F5 10 Tf 0 .501961 0 rg (if) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (firstcall) Tj 0 0 0 rg (:) Tj 0 0 0 rg  T* (            ) Tj 0 0 0 rg (func) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (func) Tj 0 0 0 rg  T* (            ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (firstcall) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (False) Tj 0 0 0 rg  T* (            ) Tj /F5 10 Tf 0 .501961 0 rg (try) Tj /F3 10 Tf 0 0 0 rg (:) Tj 0 0 0 rg  T* (                ) Tj /F5 10 Tf 0 .501961 0 rg (while) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 .501961 0 rg (True) Tj 0 0 0 rg (:) Tj 0 0 0 rg  T* (                    ) Tj 0 0 0 rg (result) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (func) Tj 0 0 0 rg (\() Tj .4 .4 .4 rg (*) Tj 0 0 0 rg (args) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (**) Tj 0 0 0 rg (kwd) Tj 0 0 0 rg (\)) Tj 0 0 0 rg  T* (                    ) Tj /F5 10 Tf 0 .501961 0 rg (if) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 0 rg (result) Tj 0 0 0 rg ( ) Tj /F5 10 Tf .666667 .133333 1 rg (is) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 0 rg (CONTINUE) Tj 0 0 0 rg (:) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# update arguments) Tj /F3 10 Tf 0 0 0 rg  T* (                        ) Tj 0 0 0 rg (args) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (kwd) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (argskwd) Tj 0 0 0 rg  T* (                    ) Tj /F5 10 Tf 0 .501961 0 rg (else) Tj /F3 10 Tf 0 0 0 rg (:) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# last call) Tj /F3 10 Tf 0 0 0 rg  T* (                        ) Tj /F5 10 Tf 0 .501961 0 rg (return) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 0 rg (result) Tj 0 0 0 rg  T* (            ) Tj /F5 10 Tf 0 .501961 0 rg (finally) Tj /F3 10 Tf 0 0 0 rg (:) Tj 0 0 0 rg  T* (                ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (firstcall) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (True) Tj 0 0 0 rg  T* (        ) Tj /F5 10 Tf 0 .501961 0 rg (else) Tj /F3 10 Tf 0 0 0 rg (:) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# return the arguments of the tail call) Tj /F3 10 Tf 0 0 0 rg  T* (            ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (argskwd) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (args) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (kwd) Tj 0 0 0 rg  T* (            ) Tj /F5 10 Tf 0 .501961 0 rg (return) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 0 rg (CONTINUE) Tj T* ET
+BT 1 0 0 1 0 338 Tm 12 TL /F5 10 Tf 0 .501961 0 rg (class) Tj /F3 10 Tf 0 0 0 rg ( ) Tj /F5 10 Tf 0 0 1 rg (TailRecursive) Tj /F3 10 Tf 0 0 0 rg (\() Tj 0 .501961 0 rg (object) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (    ) Tj /F6 10 Tf .729412 .129412 .129412 rg (""") Tj T* (    tail_recursive decorator based on Kay Schluehr's recipe) Tj T* (    http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/496691) Tj T* (    with improvements by me and George Sakkis.) Tj T* (    """) Tj /F3 10 Tf 0 0 0 rg  T*  T* (    ) Tj /F5 10 Tf 0 .501961 0 rg (def) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 1 rg (__init__) Tj 0 0 0 rg (\() Tj 0 .501961 0 rg (self) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (func) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (        ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (func) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (func) Tj 0 0 0 rg  T* (        ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (firstcall) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (True) Tj 0 0 0 rg  T* (        ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (CONTINUE) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (object) Tj 0 0 0 rg (\(\)) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# sentinel) Tj /F3 10 Tf 0 0 0 rg  T*  T* (    ) Tj /F5 10 Tf 0 .501961 0 rg (def) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 1 rg (__call__) Tj 0 0 0 rg (\() Tj 0 .501961 0 rg (self) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (*) Tj 0 0 0 rg (args) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (**) Tj 0 0 0 rg (kwd) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (        ) Tj 0 0 0 rg (CONTINUE) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (CONTINUE) Tj 0 0 0 rg  T* (        ) Tj /F5 10 Tf 0 .501961 0 rg (if) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (firstcall) Tj 0 0 0 rg (:) Tj 0 0 0 rg  T* (            ) Tj 0 0 0 rg (func) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (func) Tj 0 0 0 rg  T* (            ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (firstcall) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (False) Tj 0 0 0 rg  T* (            ) Tj /F5 10 Tf 0 .501961 0 rg (try) Tj /F3 10 Tf 0 0 0 rg (:) Tj 0 0 0 rg  T* (                ) Tj /F5 10 Tf 0 .501961 0 rg (while) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 .501961 0 rg (True) Tj 0 0 0 rg (:) Tj 0 0 0 rg  T* (                    ) Tj 0 0 0 rg (result) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (func) Tj 0 0 0 rg (\() Tj .4 .4 .4 rg (*) Tj 0 0 0 rg (args) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (**) Tj 0 0 0 rg (kwd) Tj 0 0 0 rg (\)) Tj 0 0 0 rg  T* (                    ) Tj /F5 10 Tf 0 .501961 0 rg (if) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 0 rg (result) Tj 0 0 0 rg ( ) Tj /F5 10 Tf .666667 .133333 1 rg (is) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 0 rg (CONTINUE) Tj 0 0 0 rg (:) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# update arguments) Tj /F3 10 Tf 0 0 0 rg  T* (                        ) Tj 0 0 0 rg (args) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (kwd) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (argskwd) Tj 0 0 0 rg  T* (                    ) Tj /F5 10 Tf 0 .501961 0 rg (else) Tj /F3 10 Tf 0 0 0 rg (:) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# last call) Tj /F3 10 Tf 0 0 0 rg  T* (                        ) Tj /F5 10 Tf 0 .501961 0 rg (return) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 0 rg (result) Tj 0 0 0 rg  T* (            ) Tj /F5 10 Tf 0 .501961 0 rg (finally) Tj /F3 10 Tf 0 0 0 rg (:) Tj 0 0 0 rg  T* (                ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (firstcall) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 .501961 0 rg (True) Tj 0 0 0 rg  T* (        ) Tj /F5 10 Tf 0 .501961 0 rg (else) Tj /F3 10 Tf 0 0 0 rg (:) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# return the arguments of the tail call) Tj /F3 10 Tf 0 0 0 rg  T* (            ) Tj 0 .501961 0 rg (self) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (argskwd) Tj 0 0 0 rg ( ) Tj .4 .4 .4 rg (=) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (args) Tj 0 0 0 rg (,) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (kwd) Tj 0 0 0 rg  T* (            ) Tj /F5 10 Tf 0 .501961 0 rg (return) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 0 rg (CONTINUE) Tj T* ET
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-BT 1 0 0 1 0 74 Tm .11936 Tw 12 TL /F1 10 Tf 0 0 0 rg (There has been talk of implementing multiple dispatch \(i.e. generic\) functions in Python for over ten years.) Tj T* 0 Tw 3.96881 Tw (Last year for the first time something was done and now in Python 3.4 we have a decorator) Tj T* 0 Tw 1.049269 Tw /F4 10 Tf 0 0 0 rg (functools.singledispatch ) Tj /F1 10 Tf 0 0 0 rg (which can be used to implement generic functions. As the name implies, it has) Tj T* 0 Tw 1.474198 Tw (the restriction of being limited to single dispatch, i.e. it is able to dispatch on the first argument of the) Tj T* 0 Tw 2.516412 Tw (function only. The decorator module provide a decorator factory ) Tj /F4 10 Tf 0 0 0 rg (dispatch_on ) Tj /F1 10 Tf 0 0 0 rg (which can be used to) Tj T* 0 Tw .616905 Tw (implement generic functions dispatching on any argument; moreover it can manage dispatching on more) Tj T* 0 Tw (than one argument and, of course, it is signature-preserving.) Tj T* ET
+BT 1 0 0 1 0 74 Tm .11936 Tw 12 TL /F1 10 Tf 0 0 0 rg (There has been talk of implementing multiple dispatch \(i.e. generic\) functions in Python for over ten years.) Tj T* 0 Tw 3.96881 Tw (Last year for the first time something was done and now in Python 3.4 we have a decorator) Tj T* 0 Tw .294147 Tw /F3 10 Tf 0 0 0 rg (functools.singledispatch ) Tj /F1 10 Tf 0 0 0 rg (which can be used to implement generic functions. As the name implies,) Tj T* 0 Tw .063322 Tw (it has the restriction of being limited to single dispatch, i.e. it is able to dispatch on the first argument of the) Tj T* 0 Tw 1.423555 Tw (function only. The decorator module provide a decorator factory ) Tj /F3 10 Tf 0 0 0 rg (dispatch_on ) Tj /F1 10 Tf 0 0 0 rg (which can be used to) Tj T* 0 Tw .616905 Tw (implement generic functions dispatching on any argument; moreover it can manage dispatching on more) Tj T* 0 Tw (than one argument and, of course, it is signature-preserving.) Tj T* ET
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-BT 1 0 0 1 0 26 Tm 1.551163 Tw 12 TL /F1 10 Tf 0 0 0 rg (Here I will give a very concrete example where it is desiderable to dispatch on the second argument.) Tj T* 0 Tw .216412 Tw (Suppose you have an XMLWriter class, which is instantiated with some configuration parameters and has) Tj T* 0 Tw (a ) Tj /F4 10 Tf 0 0 0 rg (.write ) Tj /F1 10 Tf 0 0 0 rg (method which is able to serialize objects to XML:) Tj T* ET
+BT 1 0 0 1 0 26 Tm 1.551163 Tw 12 TL /F1 10 Tf 0 0 0 rg (Here I will give a very concrete example where it is desiderable to dispatch on the second argument.) Tj T* 0 Tw .216412 Tw (Suppose you have an XMLWriter class, which is instantiated with some configuration parameters and has) Tj T* 0 Tw (a ) Tj /F3 10 Tf 0 0 0 rg (.write ) Tj /F1 10 Tf 0 0 0 rg (method which is able to serialize objects to XML:) Tj T* ET
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diff --git a/documentation.rst b/documentation.rst
index 107055b..ac4b152 100644
--- a/documentation.rst
+++ b/documentation.rst
@@ -4,7 +4,7 @@ The ``decorator`` module
 
 :Author: Michele Simionato
 :E-mail: michele.simionato@gmail.com
-:Version: 4.0.0 (2015-07-21)
+:Version: 4.0.0 (2015-07-22)
 :Supports: Python 2.6, 2.7, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5
 :Download page: http://pypi.python.org/pypi/decorator/4.0.0
 :Installation: ``pip install decorator``
@@ -12,46 +12,54 @@ The ``decorator`` module
 
 .. contents::
 
-Compatibility notes
+Introduction
 -----------------------------------------
 
 The decorator module is over ten years old, but still alive and
-kicking.  It is used by several frameworks and has been stable for a
-*long* time.  Even version 4.0 is compatible with the past, except for
-one thing: support for Python 2.4 and 2.5 has been dropped. That
-decision made it possible to use a single code base both for Python
-2.X and Python 3.X. This is a *huge* bonus, since I could remove over
-2,000 lines of duplicated documentation. Having to maintain separate
-docs for Python 2 and Python 3 effectively stopped any development on
-the module for several years. Moreover, it is now trivial to
-distribute the module as a wheel since 2to3 is no more required.
-
-This version supports all Python releases from 2.6 up to 3.5.  If
-you need to support ancient versions of Python, stick with the
-decorator module version 3.4.2.
+kicking. It is used by several frameworks (IPython, scipy,
+pyramid,...) and has been stable for a *long* time. It is your best
+option if you want to preserve the signature of decorated functions in
+a consistent way across Python releases. Version 4.0 is fully
+compatible with the past, except for one thing: support for Python 2.4
+and 2.5 has been dropped. That decision made it possible to use a
+single code base both for Python 2.X and Python 3.X. This is a *huge*
+bonus, since I could remove over 2,000 lines of duplicated
+documentation/doctests. Having to maintain separate docs for Python 2 and
+Python 3 effectively stopped any development on the module for several
+years. Moreover, it is now trivial to distribute the module as a wheel
+since 2to3 is no more required. Since Python 2.5 has been released 9
+years ago, I felt that it was reasonable to drop the support for it. If you
+need to support ancient versions of Python, stick with the decorator
+module version 3.4.2.  This version supports all Python releases from
+2.6 up to 3.5, which currently is still in beta status.
 
 What's new
 ---------------------
 
 Since now there is a single manual for all Python versions, I took the
 occasion for overhauling the documentation. Therefore, even if you are
-an old time user, you may want to read the manual again, since several
-examples have been improved.  A new utility function ``decorate(func,
-caller)` has been added, doing the same job that in the past was done
+an old time user, you may want to read the docs again, since several
+examples have been improved. The packaging has been improved and now
+I am also distributing the code in wheel format. The integration
+with setuptools has been improved and now you can use
+``python setup.py test`` to run the tests.
+A new utility function ``decorate(func,
+caller)`` has been added, doing the same job that in the past was done
 by ``decorator(caller, func)``. The old functionality is still there
 for compatibility sake, but it is deprecated and not documented
 anymore.
 
-Apart from that, there are no changes. There is a new experimental
-feature, though. The decorator module now include an implementation
-of generic (multiple dispatch) functions. The API is designed to
-mimic the one of `functools.singledispatch` but the implementation
-is much simpler and more general; moreover it preserves the signature of
-the decorated functions. For the moment it is there to exemplify
-the power of the module. In the future it could change and/or be
-enhanced/optimized; on the other hand, it could even become
-deprecated. Such is the fate of experimental features. In any case
-it is only 40 lines of code. Take it as food for thought.
+Apart from that, there is a new experimental feature. The decorator
+module now includes an implementation of generic (multiple dispatch)
+functions. The API is designed to mimic the one of
+`functools.singledispatch` but the implementation is much simpler and
+more general; moreover all the decorators involved preserve the
+signature of the decorated functions. For the moment the facility is
+there mostly to exemplify the power of the module. In the future it
+could change and/or be enhanced/optimized; on the other hand, it could
+even become deprecated. Such is the fate of experimental features. In
+any case it is very short and compact, so you can extract it for
+your own use. Take it as food for thought.
 
 Usefulness of decorators
 ------------------------------------------------
@@ -195,8 +203,7 @@ argument, you will get an error:
 
 Notice even in Python 3.5 `inspect.getargspec` and
 `inspect.getfullargspec` (which are deprecated in that release) will
-give the wrong signature. `inspect.signature` will return the right
-signature on the surface.
+give the wrong signature.
 
 
 The solution
@@ -694,8 +701,8 @@ source code which is probably not what you want:
  <BLANKLINE>
 
 (see bug report 1764286_ for an explanation of what is happening).
-Unfortunately the bug is still there, even in Python 2.7 and 3.4 (it
-will be fixed for Python 3.5, thought).  There is however a
+Unfortunately the bug is still there, in all versions of Python except
+Python 3.5, which is not yet released. There is however a
 workaround. The decorated function has an attribute ``.__wrapped__``,
 pointing to the original function. The easy way to get the source code
 is to call ``inspect.getsource`` on the undecorated function:
@@ -834,11 +841,11 @@ Multiple dispatch
 There has been talk of implementing multiple dispatch (i.e. generic)
 functions in Python for over ten years. Last year for the first time
 something was done and now in Python 3.4 we have a decorator
-`functools.singledispatch` which can be used to implement generic
+``functools.singledispatch`` which can be used to implement generic
 functions. As the name implies, it has the restriction of being
 limited to single dispatch, i.e. it is able to dispatch on the first
 argument of the function only.  The decorator module provide a
-decorator factory `dispatch_on` which can be used to implement generic
+decorator factory ``dispatch_on`` which can be used to implement generic
 functions dispatching on any argument; moreover it can manage
 dispatching on more than one argument and, of course, it is
 signature-preserving.
@@ -846,7 +853,7 @@ 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:
+a ``.write`` method which is able to serialize objects to XML:
 
 .. code-block:: python
 
@@ -859,14 +866,14 @@ a `.write` method which is able to serialize objects to XML:
          raise NotImplementedError(type(obj))
 
 
-Here you want to dispatch on the second argument since the first, `self`
+Here you want to dispatch on the second argument since the first, ``self``
 is already taken. The `dispatch_on` facility 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
 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.
+``NotImplementedError``, thus forcing people to register some implementation.
 The registration can be done with a decorator:
 
 .. code-block:: python
@@ -908,8 +915,8 @@ Rock-Paper-Scissor example:
 
 
 I have added an ordinal to the Rock-Paper-Scissor classes to simplify
-the implementation. The idea is to define a generic function `win(a,
-b)` of two arguments corresponding to the moves of the first and
+the implementation. The idea is to define a generic function ``win(a,
+b)`` of two arguments corresponding to the moves of the first and
 second player respectively. The moves are instances of the classes
 Rock, Paper and Scissors; Paper wins over Rock, Scissor wins over
 Paper and Rock wins over Scissor. The function will return +1 for a
@@ -971,6 +978,9 @@ Here is the result:
  >>> win(Scissor(), Rock())
  -1
 
+Generics and Abstract Base Classes
+-------------------------------------------------
+
 Generic functions implementations in Python are
 complicated by the existence of "virtual ancestors", i.e. superclasses
 which are not in the class hierarchy.
@@ -978,18 +988,20 @@ Consider for instance this class:
 
 .. code-block:: python
 
- class WithLength(object):
+ class WithLength(collections.Sized):
      def __len__(self):
          return 0
 
 
-This class defines a `__len__` method and as such is
-considered to be a subclass of the abstract base class `Sized`:
+This class defines a ``__len__`` method and as such is
+considered to be a subclass of the abstract base class ``Sized``:
+
+.. code-block:: python
 
->>> issubclass(WithLength, collections.Sized)
-True
+ >>> issubclass(WithLength, collections.Sized)
+ True
 
-However, `collections.Sized` is not an ancestor of `WithLenght`.
+However, ``collections.Sized`` is not an ancestor of ``WithLenght``.
 Any implementation of generic functions, even
 with single dispatch, must go through some contorsion to take into
 account the virtual ancestors.
@@ -1012,16 +1024,52 @@ implemented on all classes with a lenght
      return len(obj)
 
 
-then `get_length` must be defined on `WithLength` instances:
+then ``get_length`` must be defined on ``WithLength`` instances:
 
->>> get_length(WithLength())
-0
+.. code-block:: python
+
+ >>> get_length(WithLength())
+ 0
+
+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
-marked as experimental because it may
-fail in some corner cases. Also, the implementation does not even
-attempt to use a cache, so it is not as fast as it could be.
-Simplicity was the paramount concern of this implementation.
+marked as experimental because it may change in the future.
+Simplicity was preferred over consistency with the way
+``functools.singledispatch`` works in the standard library.
+For instance, suppose we define ``WithLength`` as a virtual
+subclass of ``collections.Set``:
+
+.. code-block:: python
+
+ >>> _ = collections.Set.register(WithLength)  # issubclass(WithLength, Set)
+
+Now, let us define an implementation of ``get_length`` specific to set:
+
+.. code-block:: python
+
+ >>> @get_length.register(collections.Set)
+ ... def get_length_set(obj):
+ ...     return 1
+
+The current implementation first check in the MRO and then look
+for abstract bases classes; since ``WithLength`` inherits directly
+from ``collections.Sized`` that implementation is found first:
+
+.. code-block:: python
+
+ >>> get_length(WithLength())
+ 0
+
+Generic functions implemented via ``functools.singledispatch`` use
+a more sophisticated lookup algorithm; in particular they are able
+to discern that a ``Set`` is a ``Sized`` object, so the most
+specialized implementation is the one for ``Set`` and the result should be
+1, not 0.
+
+Finally let me notice that the current implementation does not use any
+cache, whereas the one in ``singledispatch`` has a cache.
 
 Caveats and limitations
 -------------------------------------------
@@ -1108,7 +1156,7 @@ the module and releasing new versions.
 In the present implementation, decorators generated by ``decorator``
 can only be used on user-defined Python functions or methods, not on generic
 callable objects, nor on built-in functions, due to limitations of the
-``inspect`` module in the standard library.
+``inspect`` module in the standard library, especially for Python 2.X.
 
 There is a restriction on the names of the arguments: for instance,
 if try to call an argument ``_call_`` or ``_func_``
diff --git a/src/decorator.py b/src/decorator.py
index fc41c85..4b3fc09 100644
--- a/src/decorator.py
+++ b/src/decorator.py
@@ -295,15 +295,13 @@ class _ABCManager(object):
         keeping the partial ordering.
         """
         abcs = self.abcs[i]
-        if not abcs:
-            abcs.append(a)
-            return
         for j, abc in enumerate(abcs):
-            if issubclass(a, abc):
+            if issubclass(a, abc) and a is not abc:
                 abcs.insert(j, a)
                 break
         else:  # less specialized
-            abcs.append(a)
+            if a not in abcs:
+                abcs.append(a)
 
     def get_abcs(self, types):
         """
@@ -313,11 +311,10 @@ class _ABCManager(object):
             pass
         abclist = [Sentinel for _ in self.indices]
         for i, t, abcs in zip(self.indices, types, self.abcs):
-            mro = t.__mro__
             for new in abcs:
                 if issubclass(t, new):
                     old = abclist[i]
-                    if old is Sentinel or issubclass(new, old) or new in mro:
+                    if old is Sentinel or issubclass(new, old):
                         abclist[i] = new
                     elif issubclass(old, new):
                         pass
@@ -345,7 +342,7 @@ def dispatch_on(*dispatch_args):
         if not set(dispatch_args) <= argset:
             raise NameError('Unknown dispatch arguments %s' % dispatch_str)
 
-        typemap = {(object,) * len(dispatch_args): func}
+        typemap = {}
         abcman = _ABCManager(len(dispatch_args))
 
         def register(*types):
@@ -355,15 +352,15 @@ def dispatch_on(*dispatch_args):
                                 (len(dispatch_args), len(types)))
 
             def dec(f):
-                typemap[types] = f
                 n_args = len(getfullargspec(f).args)
                 if n_args < len(dispatch_args):
                     raise TypeError(
                         '%s has not enough arguments (got %d, expected %d)' %
                         (f, n_args, len(dispatch_args)))
                 for i, t, abc in zip(abcman.indices, types, abcman.abcs):
-                    if inspect.isabstract(t):
+                    if hasattr(type(t), 'register'):  # instance of ABCMeta
                         abcman.insert(i, t)
+                typemap[types] = f
                 return f
             return dec
 
@@ -374,13 +371,17 @@ def dispatch_on(*dispatch_args):
                 return typemap[types](*args, **kw)
             except KeyError:
                 pass
-            for types in itertools.product(*(t.__mro__ for t in types)):
-                f = typemap.get(types)
-                if f is None and abcman.abcs:  # look in the ABCs
-                    print(abcman.get_abcs(types))
-                    f = typemap.get(abcman.get_abcs(types))
+            for types_ in itertools.product(*(t.__mro__ for t in types)):
+                f = typemap.get(types_)
+                if f is not None:
+                    return f(*args, **kw)
+
+            # else look at the ABCs
+            if abcman.abcs:
+                f = typemap.get(abcman.get_abcs(types))
                 if f is not None:
                     return f(*args, **kw)
+
             # else call the default implementation
             return func(*args, **kw)
 
diff --git a/src/tests/documentation.py b/src/tests/documentation.py
index 21d60de..d552cc6 100644
--- a/src/tests/documentation.py
+++ b/src/tests/documentation.py
@@ -14,46 +14,54 @@ The ``decorator`` module
 
 .. contents::
 
-Compatibility notes
+Introduction
 -----------------------------------------
 
 The decorator module is over ten years old, but still alive and
-kicking.  It is used by several frameworks and has been stable for a
-*long* time.  Even version 4.0 is compatible with the past, except for
-one thing: support for Python 2.4 and 2.5 has been dropped. That
-decision made it possible to use a single code base both for Python
-2.X and Python 3.X. This is a *huge* bonus, since I could remove over
-2,000 lines of duplicated documentation. Having to maintain separate
-docs for Python 2 and Python 3 effectively stopped any development on
-the module for several years. Moreover, it is now trivial to
-distribute the module as a wheel since 2to3 is no more required.
-
-This version supports all Python releases from 2.6 up to 3.5.  If
-you need to support ancient versions of Python, stick with the
-decorator module version 3.4.2.
+kicking. It is used by several frameworks (IPython, scipy,
+pyramid,...) and has been stable for a *long* time. It is your best
+option if you want to preserve the signature of decorated functions in
+a consistent way across Python releases. Version 4.0 is fully
+compatible with the past, except for one thing: support for Python 2.4
+and 2.5 has been dropped. That decision made it possible to use a
+single code base both for Python 2.X and Python 3.X. This is a *huge*
+bonus, since I could remove over 2,000 lines of duplicated
+documentation/doctests. Having to maintain separate docs for Python 2 and
+Python 3 effectively stopped any development on the module for several
+years. Moreover, it is now trivial to distribute the module as a wheel
+since 2to3 is no more required. Since Python 2.5 has been released 9
+years ago, I felt that it was reasonable to drop the support for it. If you
+need to support ancient versions of Python, stick with the decorator
+module version 3.4.2.  This version supports all Python releases from
+2.6 up to 3.5, which currently is still in beta status.
 
 What's new
 ---------------------
 
 Since now there is a single manual for all Python versions, I took the
 occasion for overhauling the documentation. Therefore, even if you are
-an old time user, you may want to read the manual again, since several
-examples have been improved.  A new utility function ``decorate(func,
-caller)` has been added, doing the same job that in the past was done
+an old time user, you may want to read the docs again, since several
+examples have been improved. The packaging has been improved and now
+I am also distributing the code in wheel format. The integration
+with setuptools has been improved and now you can use
+``python setup.py test`` to run the tests.
+A new utility function ``decorate(func,
+caller)`` has been added, doing the same job that in the past was done
 by ``decorator(caller, func)``. The old functionality is still there
 for compatibility sake, but it is deprecated and not documented
 anymore.
 
-Apart from that, there are no changes. There is a new experimental
-feature, though. The decorator module now include an implementation
-of generic (multiple dispatch) functions. The API is designed to
-mimic the one of `functools.singledispatch` but the implementation
-is much simpler and more general; moreover it preserves the signature of
-the decorated functions. For the moment it is there to exemplify
-the power of the module. In the future it could change and/or be
-enhanced/optimized; on the other hand, it could even become
-deprecated. Such is the fate of experimental features. In any case
-it is only 40 lines of code. Take it as food for thought.
+Apart from that, there is a new experimental feature. The decorator
+module now includes an implementation of generic (multiple dispatch)
+functions. The API is designed to mimic the one of
+`functools.singledispatch` but the implementation is much simpler and
+more general; moreover all the decorators involved preserve the
+signature of the decorated functions. For the moment the facility is
+there mostly to exemplify the power of the module. In the future it
+could change and/or be enhanced/optimized; on the other hand, it could
+even become deprecated. Such is the fate of experimental features. In
+any case it is very short and compact, so you can extract it for
+your own use. Take it as food for thought.
 
 Usefulness of decorators
 ------------------------------------------------
@@ -176,8 +184,7 @@ argument, you will get an error:
 
 Notice even in Python 3.5 `inspect.getargspec` and
 `inspect.getfullargspec` (which are deprecated in that release) will
-give the wrong signature. `inspect.signature` will return the right
-signature on the surface.
+give the wrong signature.
 
 
 The solution
@@ -553,8 +560,8 @@ $$example
  <BLANKLINE>
 
 (see bug report 1764286_ for an explanation of what is happening).
-Unfortunately the bug is still there, even in Python 2.7 and 3.4 (it
-will be fixed for Python 3.5, thought).  There is however a
+Unfortunately the bug is still there, in all versions of Python except
+Python 3.5, which is not yet released. There is however a
 workaround. The decorated function has an attribute ``.__wrapped__``,
 pointing to the original function. The easy way to get the source code
 is to call ``inspect.getsource`` on the undecorated function:
@@ -634,11 +641,11 @@ Multiple dispatch
 There has been talk of implementing multiple dispatch (i.e. generic)
 functions in Python for over ten years. Last year for the first time
 something was done and now in Python 3.4 we have a decorator
-`functools.singledispatch` which can be used to implement generic
+``functools.singledispatch`` which can be used to implement generic
 functions. As the name implies, it has the restriction of being
 limited to single dispatch, i.e. it is able to dispatch on the first
 argument of the function only.  The decorator module provide a
-decorator factory `dispatch_on` which can be used to implement generic
+decorator factory ``dispatch_on`` which can be used to implement generic
 functions dispatching on any argument; moreover it can manage
 dispatching on more than one argument and, of course, it is
 signature-preserving.
@@ -646,18 +653,18 @@ 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:
+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`
+Here you want to dispatch on the second argument since the first, ``self``
 is already taken. The `dispatch_on` facility 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
 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.
+``NotImplementedError``, thus forcing people to register some implementation.
 The registration can be done with a decorator:
 
 $$writefloat
@@ -682,8 +689,8 @@ $$Paper
 $$Scissor
 
 I have added an ordinal to the Rock-Paper-Scissor classes to simplify
-the implementation. The idea is to define a generic function `win(a,
-b)` of two arguments corresponding to the moves of the first and
+the implementation. The idea is to define a generic function ``win(a,
+b)`` of two arguments corresponding to the moves of the first and
 second player respectively. The moves are instances of the classes
 Rock, Paper and Scissors; Paper wins over Rock, Scissor wins over
 Paper and Rock wins over Scissor. The function will return +1 for a
@@ -721,6 +728,9 @@ Here is the result:
  >>> win(Scissor(), Rock())
  -1
 
+Generics and Abstract Base Classes
+-------------------------------------------------
+
 Generic functions implementations in Python are
 complicated by the existence of "virtual ancestors", i.e. superclasses
 which are not in the class hierarchy.
@@ -728,13 +738,15 @@ Consider for instance this class:
 
 $$WithLength
 
-This class defines a `__len__` method and as such is
-considered to be a subclass of the abstract base class `Sized`:
+This class defines a ``__len__`` method and as such is
+considered to be a subclass of the abstract base class ``Sized``:
+
+.. code-block:: python
 
->>> issubclass(WithLength, collections.Sized)
-True
+ >>> issubclass(WithLength, collections.Sized)
+ True
 
-However, `collections.Sized` is not an ancestor of `WithLenght`.
+However, ``collections.Sized`` is not an ancestor of ``WithLenght``.
 Any implementation of generic functions, even
 with single dispatch, must go through some contorsion to take into
 account the virtual ancestors.
@@ -747,16 +759,52 @@ implemented on all classes with a lenght
 
 $$get_length_sized
 
-then `get_length` must be defined on `WithLength` instances:
+then ``get_length`` must be defined on ``WithLength`` instances:
 
->>> get_length(WithLength())
-0
+.. code-block:: python
+
+ >>> get_length(WithLength())
+ 0
+
+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
-marked as experimental because it may
-fail in some corner cases. Also, the implementation does not even
-attempt to use a cache, so it is not as fast as it could be.
-Simplicity was the paramount concern of this implementation.
+marked as experimental because it may change in the future.
+Simplicity was preferred over consistency with the way
+``functools.singledispatch`` works in the standard library.
+For instance, suppose we define ``WithLength`` as a virtual
+subclass of ``collections.Set``:
+
+.. code-block:: python
+
+ >>> _ = collections.Set.register(WithLength)  # issubclass(WithLength, Set)
+
+Now, let us define an implementation of ``get_length`` specific to set:
+
+.. code-block:: python
+
+ >>> @get_length.register(collections.Set)
+ ... def get_length_set(obj):
+ ...     return 1
+
+The current implementation first check in the MRO and then look
+for abstract bases classes; since ``WithLength`` inherits directly
+from ``collections.Sized`` that implementation is found first:
+
+.. code-block:: python
+
+ >>> get_length(WithLength())
+ 0
+
+Generic functions implemented via ``functools.singledispatch`` use
+a more sophisticated lookup algorithm; in particular they are able
+to discern that a ``Set`` is a ``Sized`` object, so the most
+specialized implementation is the one for ``Set`` and the result should be
+1, not 0.
+
+Finally let me notice that the current implementation does not use any
+cache, whereas the one in ``singledispatch`` has a cache.
 
 Caveats and limitations
 -------------------------------------------
@@ -843,7 +891,7 @@ the module and releasing new versions.
 In the present implementation, decorators generated by ``decorator``
 can only be used on user-defined Python functions or methods, not on generic
 callable objects, nor on built-in functions, due to limitations of the
-``inspect`` module in the standard library.
+``inspect`` module in the standard library, especially for Python 2.X.
 
 There is a restriction on the names of the arguments: for instance,
 if try to call an argument ``_call_`` or ``_func_``
@@ -1326,7 +1374,7 @@ def winPaperScissor(a, b):
     return -1
 
 
-class WithLength(object):
+class WithLength(collections.Sized):
     def __len__(self):
         return 0
 
diff --git a/src/tests/test.py b/src/tests/test.py
index 57f6a06..872d771 100644
--- a/src/tests/test.py
+++ b/src/tests/test.py
@@ -14,7 +14,8 @@ except (SystemError, ValueError):
 
 class DocumentationTestCase(unittest.TestCase):
     def test(self):
-        doctest.testmod(documentation)
+        err = doctest.testmod(documentation)[0]
+        self.assertEqual(err, 0)
 
 
 class ExtraTestCase(unittest.TestCase):
@@ -232,11 +233,16 @@ class TestSingleDispatch(unittest.TestCase):
         self.assertEqual(g(s), "concrete-set")
         self.assertEqual(g(f), "frozen-set")
         self.assertEqual(g(t), "tuple")
-
-        self.assertEqual(
-            [abc.__name__ for abc in g.abcs[0]],
-            ['MutableMapping', 'MutableSequence', 'MutableSet', 'Mapping',
-             'Sequence', 'Set', 'Sized'])
+        if hasattr(c, 'ChainMap'):
+            self.assertEqual(
+                [abc.__name__ for abc in g.abcs[0]],
+                ['ChainMap', 'MutableMapping', 'MutableSequence', 'MutableSet',
+                 'Mapping', 'Sequence', 'Set', 'Sized'])
+        else:
+            self.assertEqual(
+                [abc.__name__ for abc in g.abcs[0]],
+                ['MutableMapping', 'MutableSequence', 'MutableSet',
+                 'Mapping', 'Sequence', 'Set', 'Sized'])
 
     def test_mro_conflicts(self):
         c = collections
@@ -258,14 +264,13 @@ class TestSingleDispatch(unittest.TestCase):
         c.Iterable.register(O)
         self.assertEqual(g(o), "sized")   # because it's explicitly in __mro__
         c.Container.register(O)
-        return
         self.assertEqual(g(o), "sized")   # see above: Sized is in __mro__
         c.Set.register(O)
-        self.assertEqual(g(o), "set")
-        # because c.Set is a subclass of
+        self.assertEqual(g(o), "sized")
+        # could be set because c.Set is a subclass of
         # c.Sized and c.Container
 
-        class P:
+        class P(object):
             pass
         p = P()
         self.assertEqual(g(p), "base")
@@ -273,15 +278,9 @@ class TestSingleDispatch(unittest.TestCase):
         self.assertEqual(g(p), "iterable")
         c.Container.register(P)
 
-        with self.assertRaises(RuntimeError) as re_one:
+        with self.assertRaises(RuntimeError):
             g(p)
-        self.assertIn(
-            str(re_one.exception),
-            (("Ambiguous dispatch: <class 'collections.abc.Container'> "
-              "or <class 'collections.abc.Iterable'>"),
-             ("Ambiguous dispatch: <class 'collections.abc.Iterable'> "
-              "or <class 'collections.abc.Container'>")),
-        )
+
         class Q(c.Sized):
             def __len__(self):
                 return 0
@@ -290,46 +289,50 @@ class TestSingleDispatch(unittest.TestCase):
         c.Iterable.register(Q)
         self.assertEqual(g(q), "sized")   # because it's explicitly in __mro__
         c.Set.register(Q)
-        self.assertEqual(g(q), "set")     # because c.Set is a subclass of
-                                          # c.Sized and c.Iterable
-        @functools.singledispatch
-        def h(arg):
+        self.assertEqual(g(q), "sized")
+        # could be because c.Set is a subclass of
+        # c.Sized and c.Iterable
+
+        @singledispatch
+        def h(obj):
             return "base"
+
         @h.register(c.Sized)
-        def _(arg):
+        def h_sized(arg):
             return "sized"
+
         @h.register(c.Container)
-        def _(arg):
+        def h_container(arg):
             return "container"
         # Even though Sized and Container are explicit bases of MutableMapping,
         # this ABC is implicitly registered on defaultdict which makes all of
         # MutableMapping's bases implicit as well from defaultdict's
         # perspective.
-        with self.assertRaises(RuntimeError) as re_two:
+        with self.assertRaises(RuntimeError):
             h(c.defaultdict(lambda: 0))
-        self.assertIn(
-            str(re_two.exception),
-            (("Ambiguous dispatch: <class 'collections.abc.Container'> "
-              "or <class 'collections.abc.Sized'>"),
-             ("Ambiguous dispatch: <class 'collections.abc.Sized'> "
-              "or <class 'collections.abc.Container'>")),
-        )
+
         class R(c.defaultdict):
             pass
         c.MutableSequence.register(R)
-        @functools.singledispatch
-        def i(arg):
+
+        @singledispatch
+        def i(obj):
             return "base"
+
         @i.register(c.MutableMapping)
-        def _(arg):
+        def i_mapping(arg):
             return "mapping"
+
         @i.register(c.MutableSequence)
-        def _(arg):
+        def i_sequence(arg):
             return "sequence"
         r = R()
-        self.assertEqual(i(r), "sequence")
+        with self.assertRaises(RuntimeError):  # not for standardlib
+            self.assertEqual(i(r), "sequence")
+
         class S:
             pass
+
         class T(S, c.Sized):
             def __len__(self):
                 return 0
@@ -337,40 +340,42 @@ class TestSingleDispatch(unittest.TestCase):
         self.assertEqual(h(t), "sized")
         c.Container.register(T)
         self.assertEqual(h(t), "sized")   # because it's explicitly in the MRO
+
         class U:
             def __len__(self):
                 return 0
         u = U()
-        self.assertEqual(h(u), "sized")   # implicit Sized subclass inferred
-                                          # from the existence of __len__()
+        if sys.version >= '3':
+            self.assertEqual(h(u), "sized")
+            # implicit Sized subclass inferred
+            # from the existence of __len__()
+
         c.Container.register(U)
         # There is no preference for registered versus inferred ABCs.
-        with self.assertRaises(RuntimeError) as re_three:
+        with self.assertRaises(RuntimeError):
             h(u)
-        self.assertIn(
-            str(re_three.exception),
-            (("Ambiguous dispatch: <class 'collections.abc.Container'> "
-              "or <class 'collections.abc.Sized'>"),
-             ("Ambiguous dispatch: <class 'collections.abc.Sized'> "
-              "or <class 'collections.abc.Container'>")),
-        )
+
         class V(c.Sized, S):
             def __len__(self):
                 return 0
-        @functools.singledispatch
-        def j(arg):
+
+        @singledispatch
+        def j(obj):
             return "base"
+
         @j.register(S)
-        def _(arg):
+        def j_s(arg):
             return "s"
+
         @j.register(c.Container)
-        def _(arg):
+        def j_container(arg):
             return "container"
         v = V()
         self.assertEqual(j(v), "s")
         c.Container.register(V)
-        self.assertEqual(j(v), "container")   # because it ends up right after
-                                              # Sized in the MRO
+        self.assertEqual(j(v), "s")  # could be "container"
+        # because it ends up right after
+        # Sized in the MRO
 
 if __name__ == '__main__':
     unittest.main()
author	Michele Simionato <michele.simionato@gmail.com>	2015-07-22 07:32:49 +0200
committer	Michele Simionato <michele.simionato@gmail.com>	2015-07-22 07:32:49 +0200
commit	4b28eea14c0ab65e447cf2955c26bc9a8e5fd11b (patch)
tree	3aabc01ff7a68063519ef60e33a23116177b6068
parent	10e035eaf93997a63a30687f6fe33b918714ba61 (diff)
download	python-decorator-git-4b28eea14c0ab65e447cf2955c26bc9a8e5fd11b.tar.gz