Lib/functools.py

"""functools.py - Tools for working with functions and callable objects
"""
# Python module wrapper for _functools C module
# to allow utilities written in Python to be added
# to the functools module.
# Written by Nick Coghlan <ncoghlan at gmail.com>
# and Raymond Hettinger <python at rcn.com>
#   Copyright (C) 2006-2010 Python Software Foundation.
# See C source code for _functools credits/copyright

__all__ = ['update_wrapper', 'wraps', 'WRAPPER_ASSIGNMENTS', 'WRAPPER_UPDATES',
           'total_ordering', 'cmp_to_key', 'lru_cache', 'reduce', 'partial']

from _functools import partial, reduce
from collections import namedtuple
try:
    from _thread import RLock
except:
    class RLock:
        'Dummy reentrant lock for builds without threads'
        def __enter__(self): pass
        def __exit__(self, exctype, excinst, exctb): pass


################################################################################
### update_wrapper() and wraps() decorator
################################################################################

# update_wrapper() and wraps() are tools to help write
# wrapper functions that can handle naive introspection

WRAPPER_ASSIGNMENTS = ('__module__', '__name__', '__qualname__', '__doc__',
                       '__annotations__')
WRAPPER_UPDATES = ('__dict__',)
def update_wrapper(wrapper,
                   wrapped,
                   assigned = WRAPPER_ASSIGNMENTS,
                   updated = WRAPPER_UPDATES):
    """Update a wrapper function to look like the wrapped function

       wrapper is the function to be updated
       wrapped is the original function
       assigned is a tuple naming the attributes assigned directly
       from the wrapped function to the wrapper function (defaults to
       functools.WRAPPER_ASSIGNMENTS)
       updated is a tuple naming the attributes of the wrapper that
       are updated with the corresponding attribute from the wrapped
       function (defaults to functools.WRAPPER_UPDATES)
    """
    wrapper.__wrapped__ = wrapped
    for attr in assigned:
        try:
            value = getattr(wrapped, attr)
        except AttributeError:
            pass
        else:
            setattr(wrapper, attr, value)
    for attr in updated:
        getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
    # Return the wrapper so this can be used as a decorator via partial()
    return wrapper

def wraps(wrapped,
          assigned = WRAPPER_ASSIGNMENTS,
          updated = WRAPPER_UPDATES):
    """Decorator factory to apply update_wrapper() to a wrapper function

       Returns a decorator that invokes update_wrapper() with the decorated
       function as the wrapper argument and the arguments to wraps() as the
       remaining arguments. Default arguments are as for update_wrapper().
       This is a convenience function to simplify applying partial() to
       update_wrapper().
    """
    return partial(update_wrapper, wrapped=wrapped,
                   assigned=assigned, updated=updated)


################################################################################
### total_ordering class decorator
################################################################################

def total_ordering(cls):
    """Class decorator that fills in missing ordering methods"""
    convert = {
        '__lt__': [('__gt__', lambda self, other: not (self < other or self == other)),
                   ('__le__', lambda self, other: self < other or self == other),
                   ('__ge__', lambda self, other: not self < other)],
        '__le__': [('__ge__', lambda self, other: not self <= other or self == other),
                   ('__lt__', lambda self, other: self <= other and not self == other),
                   ('__gt__', lambda self, other: not self <= other)],
        '__gt__': [('__lt__', lambda self, other: not (self > other or self == other)),
                   ('__ge__', lambda self, other: self > other or self == other),
                   ('__le__', lambda self, other: not self > other)],
        '__ge__': [('__le__', lambda self, other: (not self >= other) or self == other),
                   ('__gt__', lambda self, other: self >= other and not self == other),
                   ('__lt__', lambda self, other: not self >= other)]
    }
    # Find user-defined comparisons (not those inherited from object).
    roots = [op for op in convert if getattr(cls, op, None) is not getattr(object, op, None)]
    if not roots:
        raise ValueError('must define at least one ordering operation: < > <= >=')
    root = max(roots)       # prefer __lt__ to __le__ to __gt__ to __ge__
    for opname, opfunc in convert[root]:
        if opname not in roots:
            opfunc.__name__ = opname
            opfunc.__doc__ = getattr(int, opname).__doc__
            setattr(cls, opname, opfunc)
    return cls


################################################################################
### cmp_to_key() function converter
################################################################################

def cmp_to_key(mycmp):
    """Convert a cmp= function into a key= function"""
    class K(object):
        __slots__ = ['obj']
        def __init__(self, obj):
            self.obj = obj
        def __lt__(self, other):
            return mycmp(self.obj, other.obj) < 0
        def __gt__(self, other):
            return mycmp(self.obj, other.obj) > 0
        def __eq__(self, other):
            return mycmp(self.obj, other.obj) == 0
        def __le__(self, other):
            return mycmp(self.obj, other.obj) <= 0
        def __ge__(self, other):
            return mycmp(self.obj, other.obj) >= 0
        def __ne__(self, other):
            return mycmp(self.obj, other.obj) != 0
        __hash__ = None
    return K

try:
    from _functools import cmp_to_key
except ImportError:
    pass


################################################################################
### LRU Cache function decorator
################################################################################

_CacheInfo = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])

class _HashedSeq(list):
    """ This class guarantees that hash() will be called no more than once
        per element.  This is important because the lru_cache() will hash
        the key multiple times on a cache miss.

    """

    __slots__ = 'hashvalue'

    def __init__(self, tup, hash=hash):
        self[:] = tup
        self.hashvalue = hash(tup)

    def __hash__(self):
        return self.hashvalue

def _make_key(args, kwds, typed,
             kwd_mark = (object(),),
             fasttypes = {int, str, frozenset, type(None)},
             sorted=sorted, tuple=tuple, type=type, len=len):
    """Make a cache key from optionally typed positional and keyword arguments

    The key is constructed in a way that is flat as possible rather than
    as a nested structure that would take more memory.

    If there is only a single argument and its data type is known to cache
    its hash value, then that argument is returned without a wrapper.  This
    saves space and improves lookup speed.

    """
    key = args
    if kwds:
        sorted_items = sorted(kwds.items())
        key += kwd_mark
        for item in sorted_items:
            key += item
    if typed:
        key += tuple(type(v) for v in args)
        if kwds:
            key += tuple(type(v) for k, v in sorted_items)
    elif len(key) == 1 and type(key[0]) in fasttypes:
        return key[0]
    return _HashedSeq(key)

def lru_cache(maxsize=128, typed=False):
    """Least-recently-used cache decorator.

    If *maxsize* is set to None, the LRU features are disabled and the cache
    can grow without bound.

    If *typed* is True, arguments of different types will be cached separately.
    For example, f(3.0) and f(3) will be treated as distinct calls with
    distinct results.

    Arguments to the cached function must be hashable.

    View the cache statistics named tuple (hits, misses, maxsize, currsize)
    with f.cache_info().  Clear the cache and statistics with f.cache_clear().
    Access the underlying function with f.__wrapped__.

    See:  http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used

    """

    # Users should only access the lru_cache through its public API:
    #       cache_info, cache_clear, and f.__wrapped__
    # The internals of the lru_cache are encapsulated for thread safety and
    # to allow the implementation to change (including a possible C version).

    # Constants shared by all lru cache instances:
    sentinel = object()          # unique object used to signal cache misses
    make_key = _make_key         # build a key from the function arguments
    PREV, NEXT, KEY, RESULT = 0, 1, 2, 3   # names for the link fields

    def decorating_function(user_function):

        cache = {}
        hits = misses = currsize = 0
        full = False
        cache_get = cache.get    # bound method to lookup a key or return None
        lock = RLock()           # because linkedlist updates aren't threadsafe
        root = []                # root of the circular doubly linked list
        root[:] = [root, root, None, None]     # initialize by pointing to self

        if maxsize == 0:

            def wrapper(*args, **kwds):
                # No caching -- just a statistics update after a successful call
                nonlocal misses
                result = user_function(*args, **kwds)
                misses += 1
                return result

        elif maxsize is None:

            def wrapper(*args, **kwds):
                # Simple caching without ordering or size limit
                nonlocal hits, misses, currsize
                key = make_key(args, kwds, typed)
                result = cache_get(key, sentinel)
                if result is not sentinel:
                    hits += 1
                    return result
                result = user_function(*args, **kwds)
                cache[key] = result
                misses += 1
                currsize += 1
                return result

        else:

            def wrapper(*args, **kwds):
                # Size limited caching that tracks accesses by recency
                nonlocal root, hits, misses, currsize, full
                key = make_key(args, kwds, typed)
                with lock:
                    link = cache_get(key)
                    if link is not None:
                        # Move the link to the front of the circular queue
                        link_prev, link_next, _key, result = link
                        link_prev[NEXT] = link_next
                        link_next[PREV] = link_prev
                        last = root[PREV]
                        last[NEXT] = root[PREV] = link
                        link[PREV] = last
                        link[NEXT] = root
                        hits += 1
                        return result
                result = user_function(*args, **kwds)
                with lock:
                    if key in cache:
                        # Getting here means that this same key was added to the
                        # cache while the lock was released.  Since the link
                        # update is already done, we need only return the
                        # computed result and update the count of misses.
                        pass
                    elif full:
                        # Use the old root to store the new key and result.
                        oldroot = root
                        oldroot[KEY] = key
                        oldroot[RESULT] = result
                        # Empty the oldest link and make it the new root.
                        # Keep a reference to the old key and old result to
                        # prevent their ref counts from going to zero during the
                        # update. That will prevent potentially arbitrary object
                        # clean-up code (i.e. __del__) from running while we're
                        # still adjusting the links.
                        root = oldroot[NEXT]
                        oldkey = root[KEY]
                        oldresult = root[RESULT]
                        root[KEY] = root[RESULT] = None
                        # Now update the cache dictionary.
                        del cache[oldkey]
                        # Save the potentially reentrant cache[key] assignment
                        # for last, after the root and links have been put in
                        # a consistent state.
                        cache[key] = oldroot
                    else:
                        # Put result in a new link at the front of the queue.
                        last = root[PREV]
                        link = [last, root, key, result]
                        last[NEXT] = root[PREV] = cache[key] = link
                        currsize += 1
                        full = (currsize >= maxsize)
                    misses += 1
                return result

        def cache_info():
            """Report cache statistics"""
            with lock:
                return _CacheInfo(hits, misses, maxsize, currsize)

        def cache_clear():
            """Clear the cache and cache statistics"""
            nonlocal hits, misses, currsize, full
            with lock:
                cache.clear()
                root[:] = [root, root, None, None]
                hits = misses = currsize = 0
                full = False

        wrapper.cache_info = cache_info
        wrapper.cache_clear = cache_clear
        return update_wrapper(wrapper, user_function)

    return decorating_function