consolidate all couchjs stuff in one subdir

1 files changed, 0 insertions, 244 deletions

diff --git a/scons/scons-local-2.0.1/SCons/Memoize.py b/scons/scons-local-2.0.1/SCons/Memoize.py
deleted file mode 100644
index c76d6a52..00000000
--- a/scons/scons-local-2.0.1/SCons/Memoize.py
+++ /dev/null
@@ -1,244 +0,0 @@
-#
-# Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010 The SCons Foundation
-#
-# Permission is hereby granted, free of charge, to any person obtaining
-# a copy of this software and associated documentation files (the
-# "Software"), to deal in the Software without restriction, including
-# without limitation the rights to use, copy, modify, merge, publish,
-# distribute, sublicense, and/or sell copies of the Software, and to
-# permit persons to whom the Software is furnished to do so, subject to
-# the following conditions:
-#
-# The above copyright notice and this permission notice shall be included
-# in all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY
-# KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
-# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
-# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
-# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
-# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-#
-
-__revision__ = "src/engine/SCons/Memoize.py 5134 2010/08/16 23:02:40 bdeegan"
-
-__doc__ = """Memoizer
-
-A metaclass implementation to count hits and misses of the computed
-values that various methods cache in memory.
-
-Use of this modules assumes that wrapped methods be coded to cache their
-values in a consistent way.  Here is an example of wrapping a method
-that returns a computed value, with no input parameters:
-
-    memoizer_counters = []                                      # Memoization
-
-    memoizer_counters.append(SCons.Memoize.CountValue('foo'))   # Memoization
-
-    def foo(self):
-
-        try:                                                    # Memoization
-            return self._memo['foo']                            # Memoization
-        except KeyError:                                        # Memoization
-            pass                                                # Memoization
-
-        result = self.compute_foo_value()
-
-        self._memo['foo'] = result                              # Memoization
-
-        return result
-
-Here is an example of wrapping a method that will return different values
-based on one or more input arguments:
-
-    def _bar_key(self, argument):                               # Memoization
-        return argument                                         # Memoization
-
-    memoizer_counters.append(SCons.Memoize.CountDict('bar', _bar_key)) # Memoization
-
-    def bar(self, argument):
-
-        memo_key = argument                                     # Memoization
-        try:                                                    # Memoization
-            memo_dict = self._memo['bar']                       # Memoization
-        except KeyError:                                        # Memoization
-            memo_dict = {}                                      # Memoization
-            self._memo['dict'] = memo_dict                      # Memoization
-        else:                                                   # Memoization
-            try:                                                # Memoization
-                return memo_dict[memo_key]                      # Memoization
-            except KeyError:                                    # Memoization
-                pass                                            # Memoization
-
-        result = self.compute_bar_value(argument)
-
-        memo_dict[memo_key] = result                            # Memoization
-
-        return result
-
-At one point we avoided replicating this sort of logic in all the methods
-by putting it right into this module, but we've moved away from that at
-present (see the "Historical Note," below.).
-
-Deciding what to cache is tricky, because different configurations
-can have radically different performance tradeoffs, and because the
-tradeoffs involved are often so non-obvious.  Consequently, deciding
-whether or not to cache a given method will likely be more of an art than
-a science, but should still be based on available data from this module.
-Here are some VERY GENERAL guidelines about deciding whether or not to
-cache return values from a method that's being called a lot:
-
-    --  The first question to ask is, "Can we change the calling code
-        so this method isn't called so often?"  Sometimes this can be
-        done by changing the algorithm.  Sometimes the *caller* should
-        be memoized, not the method you're looking at.
-
-    --  The memoized function should be timed with multiple configurations
-        to make sure it doesn't inadvertently slow down some other
-        configuration.
-
-    --  When memoizing values based on a dictionary key composed of
-        input arguments, you don't need to use all of the arguments
-        if some of them don't affect the return values.
-
-Historical Note:  The initial Memoizer implementation actually handled
-the caching of values for the wrapped methods, based on a set of generic
-algorithms for computing hashable values based on the method's arguments.
-This collected caching logic nicely, but had two drawbacks:
-
-    Running arguments through a generic key-conversion mechanism is slower
-    (and less flexible) than just coding these things directly.  Since the
-    methods that need memoized values are generally performance-critical,
-    slowing them down in order to collect the logic isn't the right
-    tradeoff.
-
-    Use of the memoizer really obscured what was being called, because
-    all the memoized methods were wrapped with re-used generic methods.
-    This made it more difficult, for example, to use the Python profiler
-    to figure out how to optimize the underlying methods.
-"""
-
-import types
-
-# A flag controlling whether or not we actually use memoization.
-use_memoizer = None
-
-CounterList = []
-
-class Counter(object):
-    """
-    Base class for counting memoization hits and misses.
-
-    We expect that the metaclass initialization will have filled in
-    the .name attribute that represents the name of the function
-    being counted.
-    """
-    def __init__(self, method_name):
-        """
-        """
-        self.method_name = method_name
-        self.hit = 0
-        self.miss = 0
-        CounterList.append(self)
-    def display(self):
-        fmt = "    %7d hits %7d misses    %s()"
-        print fmt % (self.hit, self.miss, self.name)
-    def __cmp__(self, other):
-        try:
-            return cmp(self.name, other.name)
-        except AttributeError:
-            return 0
-
-class CountValue(Counter):
-    """
-    A counter class for simple, atomic memoized values.
-
-    A CountValue object should be instantiated in a class for each of
-    the class's methods that memoizes its return value by simply storing
-    the return value in its _memo dictionary.
-
-    We expect that the metaclass initialization will fill in the
-    .underlying_method attribute with the method that we're wrapping.
-    We then call the underlying_method method after counting whether
-    its memoized value has already been set (a hit) or not (a miss).
-    """
-    def __call__(self, *args, **kw):
-        obj = args[0]
-        if self.method_name in obj._memo:
-            self.hit = self.hit + 1
-        else:
-            self.miss = self.miss + 1
-        return self.underlying_method(*args, **kw)
-
-class CountDict(Counter):
-    """
-    A counter class for memoized values stored in a dictionary, with
-    keys based on the method's input arguments.
-
-    A CountDict object is instantiated in a class for each of the
-    class's methods that memoizes its return value in a dictionary,
-    indexed by some key that can be computed from one or more of
-    its input arguments.
-
-    We expect that the metaclass initialization will fill in the
-    .underlying_method attribute with the method that we're wrapping.
-    We then call the underlying_method method after counting whether the
-    computed key value is already present in the memoization dictionary
-    (a hit) or not (a miss).
-    """
-    def __init__(self, method_name, keymaker):
-        """
-        """
-        Counter.__init__(self, method_name)
-        self.keymaker = keymaker
-    def __call__(self, *args, **kw):
-        obj = args[0]
-        try:
-            memo_dict = obj._memo[self.method_name]
-        except KeyError:
-            self.miss = self.miss + 1
-        else:
-            key = self.keymaker(*args, **kw)
-            if key in memo_dict:
-                self.hit = self.hit + 1
-            else:
-                self.miss = self.miss + 1
-        return self.underlying_method(*args, **kw)
-
-class Memoizer(object):
-    """Object which performs caching of method calls for its 'primary'
-    instance."""
-
-    def __init__(self):
-        pass
-
-def Dump(title=None):
-    if title:
-        print title
-    CounterList.sort()
-    for counter in CounterList:
-        counter.display()
-
-class Memoized_Metaclass(type):
-    def __init__(cls, name, bases, cls_dict):
-        super(Memoized_Metaclass, cls).__init__(name, bases, cls_dict)
-
-        for counter in cls_dict.get('memoizer_counters', []):
-            method_name = counter.method_name
-
-            counter.name = cls.__name__ + '.' + method_name
-            counter.underlying_method = cls_dict[method_name]
-
-            replacement_method = types.MethodType(counter, None, cls)
-            setattr(cls, method_name, replacement_method)
-
-def EnableMemoization():
-    global use_memoizer
-    use_memoizer = 1
-
-# Local Variables:
-# tab-width:4
-# indent-tabs-mode:nil
-# End:
-# vim: set expandtab tabstop=4 shiftwidth=4: