--- rest25/library/itertools.rst	2008-12-19 16:58:07.000000000 +0900
+++ rest262/library/itertools.rst	2009-04-15 11:13:50.000000000 +0900
@@ -1,518 +1,759 @@
 
 :mod:`itertools` --- Functions creating iterators for efficient looping
 =======================================================================
 
 .. module:: itertools
    :synopsis: Functions creating iterators for efficient looping.
 .. moduleauthor:: Raymond Hettinger <python@rcn.com>
 .. sectionauthor:: Raymond Hettinger <python@rcn.com>
 
 
+.. testsetup::
+
+   from itertools import *
+
 .. versionadded:: 2.3
 
-This module implements a number of iterator building blocks inspired by
-constructs from the Haskell and SML programming languages.  Each has been recast
-in a form suitable for Python.
+This module implements a number of :term:`iterator` building blocks inspired
+by constructs from APL, Haskell, and SML.  Each has been recast in a form
+suitable for Python.
 
 The module standardizes a core set of fast, memory efficient tools that are
-useful by themselves or in combination.  Standardization helps avoid the
-readability and reliability problems which arise when many different individuals
-create their own slightly varying implementations, each with their own quirks
-and naming conventions.
-
-The tools are designed to combine readily with one another.  This makes it easy
-to construct more specialized tools succinctly and efficiently in pure Python.
+useful by themselves or in combination.  Together, they form an "iterator
+algebra" making it possible to construct specialized tools succinctly and
+efficiently in pure Python.
 
 For instance, SML provides a tabulation tool: ``tabulate(f)`` which produces a
 sequence ``f(0), f(1), ...``.  This toolbox provides :func:`imap` and
-:func:`count` which can be combined to form ``imap(f, count())`` and produce an
+:func:`count` which can be combined to form ``imap(f, count())`` to produce an
 equivalent result.
 
-Likewise, the functional tools are designed to work well with the high-speed
-functions provided by the :mod:`operator` module.
-
-The module author welcomes suggestions for other basic building blocks to be
-added to future versions of the module.
-
-Whether cast in pure python form or compiled code, tools that use iterators are
-more memory efficient (and faster) than their list based counterparts. Adopting
-the principles of just-in-time manufacturing, they create data when and where
-needed instead of consuming memory with the computer equivalent of "inventory".
-
-The performance advantage of iterators becomes more acute as the number of
-elements increases -- at some point, lists grow large enough to severely impact
-memory cache performance and start running slowly.
-
-
-.. seealso::
-
-   The Standard ML Basis Library, `The Standard ML Basis Library
-   <http://www.standardml.org/Basis/>`_.
-
-   Haskell, A Purely Functional Language, `Definition of Haskell and the Standard
-   Libraries <http://www.haskell.org/definition/>`_.
+These tools and their built-in counterparts also work well with the high-speed
+functions in the :mod:`operator` module.  For example, the multiplication
+operator can be mapped across two vectors to form an efficient dot-product:
+``sum(imap(operator.mul, vector1, vector2))``.
+
+
+**Infinite Iterators:**
+
+==================  =================       =================================================               =========================================
+Iterator            Arguments               Results                                                         Example
+==================  =================       =================================================               =========================================
+:func:`count`       start                   start, start+1, start+2, ...                                    ``count(10) --> 10 11 12 13 14 ...``
+:func:`cycle`       p                       p0, p1, ... plast, p0, p1, ...                                  ``cycle('ABCD') --> A B C D A B C D ...``
+:func:`repeat`      elem [,n]               elem, elem, elem, ... endlessly or up to n times                ``repeat(10, 3) --> 10 10 10``
+==================  =================       =================================================               =========================================
+
+**Iterators terminating on the shortest input sequence:**
+
+====================    ============================    =================================================   =============================================================
+Iterator                Arguments                       Results                                             Example
+====================    ============================    =================================================   =============================================================
+:func:`chain`           p, q, ...                       p0, p1, ... plast, q0, q1, ...                      ``chain('ABC', 'DEF') --> A B C D E F``
+:func:`dropwhile`       pred, seq                       seq[n], seq[n+1], starting when pred fails          ``dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1``
+:func:`groupby`         iterable[, keyfunc]             sub-iterators grouped by value of keyfunc(v)
+:func:`ifilter`         pred, seq                       elements of seq where pred(elem) is True            ``ifilter(lambda x: x%2, range(10)) --> 1 3 5 7 9``
+:func:`ifilterfalse`    pred, seq                       elements of seq where pred(elem) is False           ``ifilterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8``
+:func:`islice`          seq, [start,] stop [, step]     elements from seq[start:stop:step]                  ``islice('ABCDEFG', 2, None) --> C D E F G``
+:func:`imap`            func, p, q, ...                 func(p0, q0), func(p1, q1), ...                     ``imap(pow, (2,3,10), (5,2,3)) --> 32 9 1000``
+:func:`starmap`         func, seq                       func(\*seq[0]), func(\*seq[1]), ...                 ``starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000``
+:func:`tee`             it, n                           it1, it2 , ... itn  splits one iterator into n
+:func:`takewhile`       pred, seq                       seq[0], seq[1], until pred fails                    ``takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4``
+:func:`izip`            p, q, ...                       (p[0], q[0]), (p[1], q[1]), ...                     ``izip('ABCD', 'xy') --> Ax By``
+:func:`izip_longest`    p, q, ...                       (p[0], q[0]), (p[1], q[1]), ...                     ``izip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-``
+====================    ============================    =================================================   =============================================================
+
+**Combinatoric generators:**
+
+==============================================   ====================       =============================================================
+Iterator                                         Arguments                  Results
+==============================================   ====================       =============================================================
+:func:`product`                                  p, q, ... [repeat=1]       cartesian product, equivalent to a nested for-loop
+:func:`permutations`                             p[, r]                     r-length tuples, all possible orderings, no repeated elements
+:func:`combinations`                             p[, r]                     r-length tuples, in sorted order, no repeated elements
+|
+``product('ABCD', repeat=2)``                                               ``AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD``
+``permutations('ABCD', 2)``                                                 ``AB AC AD BA BC BD CA CB CD DA DB DC``
+``combinations('ABCD', 2)``                                                 ``AB AC AD BC BD CD``
+==============================================   ====================       =============================================================
 
 
 .. _itertools-functions:
 
 Itertool functions
 ------------------
 
 The following module functions all construct and return iterators. Some provide
 streams of infinite length, so they should only be accessed by functions or
 loops that truncate the stream.
 
 
 .. function:: chain(*iterables)
 
    Make an iterator that returns elements from the first iterable until it is
    exhausted, then proceeds to the next iterable, until all of the iterables are
    exhausted.  Used for treating consecutive sequences as a single sequence.
    Equivalent to::
 
       def chain(*iterables):
+          # chain('ABC', 'DEF') --> A B C D E F
+          for it in iterables:
+              for element in it:
+                  yield element
+
+
+.. function:: itertools.chain.from_iterable(iterable)
+
+   Alternate constructor for :func:`chain`.  Gets chained inputs from a
+   single iterable argument that is evaluated lazily.  Equivalent to::
+
+      @classmethod
+      def from_iterable(iterables):
+          # chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
           for it in iterables:
               for element in it:
                   yield element
 
+   .. versionadded:: 2.6
+
+
+.. function:: combinations(iterable, r)
+
+   Return *r* length subsequences of elements from the input *iterable*.
+
+   Combinations are emitted in lexicographic sort order.  So, if the
+   input *iterable* is sorted, the combination tuples will be produced
+   in sorted order.
+
+   Elements are treated as unique based on their position, not on their
+   value.  So if the input elements are unique, there will be no repeat
+   values in each combination.
+
+   Equivalent to::
+
+        def combinations(iterable, r):
+            # combinations('ABCD', 2) --> AB AC AD BC BD CD
+            # combinations(range(4), 3) --> 012 013 023 123
+            pool = tuple(iterable)
+            n = len(pool)
+            if r > n:
+                return
+            indices = range(r)
+            yield tuple(pool[i] for i in indices)
+            while True:
+                for i in reversed(range(r)):
+                    if indices[i] != i + n - r:
+                        break
+                else:
+                    return
+                indices[i] += 1
+                for j in range(i+1, r):
+                    indices[j] = indices[j-1] + 1
+                yield tuple(pool[i] for i in indices)
+
+   The code for :func:`combinations` can be also expressed as a subsequence
+   of :func:`permutations` after filtering entries where the elements are not
+   in sorted order (according to their position in the input pool)::
+
+        def combinations(iterable, r):
+            pool = tuple(iterable)
+            n = len(pool)
+            for indices in permutations(range(n), r):
+                if sorted(indices) == list(indices):
+                    yield tuple(pool[i] for i in indices)
+
+   The number of items returned is ``n! / r! / (n-r)!`` when ``0 <= r <= n``
+   or zero when ``r > n``.
+
+   .. versionadded:: 2.6
 
 .. function:: count([n])
 
    Make an iterator that returns consecutive integers starting with *n*. If not
-   specified *n* defaults to zero.   Does not currently support python long
-   integers.  Often used as an argument to :func:`imap` to generate consecutive
-   data points. Also, used with :func:`izip` to add sequence numbers.  Equivalent
-   to::
+   specified *n* defaults to zero.   Often used as an argument to :func:`imap` to
+   generate consecutive data points. Also, used with :func:`izip` to add sequence
+   numbers.  Equivalent to::
 
       def count(n=0):
+          # count(10) --> 10 11 12 13 14 ...
           while True:
               yield n
               n += 1
 
-   Note, :func:`count` does not check for overflow and will return negative numbers
-   after exceeding ``sys.maxint``.  This behavior may change in the future.
-
 
 .. function:: cycle(iterable)
 
    Make an iterator returning elements from the iterable and saving a copy of each.
    When the iterable is exhausted, return elements from the saved copy.  Repeats
    indefinitely.  Equivalent to::
 
       def cycle(iterable):
+          # cycle('ABCD') --> A B C D A B C D A B C D ...
           saved = []
           for element in iterable:
               yield element
               saved.append(element)
           while saved:
               for element in saved:
                     yield element
 
    Note, this member of the toolkit may require significant auxiliary storage
    (depending on the length of the iterable).
 
 
 .. function:: dropwhile(predicate, iterable)
 
    Make an iterator that drops elements from the iterable as long as the predicate
    is true; afterwards, returns every element.  Note, the iterator does not produce
-   *any* output until the predicate is true, so it may have a lengthy start-up
-   time.  Equivalent to::
+   *any* output until the predicate first becomes false, so it may have a lengthy
+   start-up time.  Equivalent to::
 
       def dropwhile(predicate, iterable):
+          # dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
           iterable = iter(iterable)
           for x in iterable:
               if not predicate(x):
                   yield x
                   break
           for x in iterable:
               yield x
 
 
 .. function:: groupby(iterable[, key])
 
    Make an iterator that returns consecutive keys and groups from the *iterable*.
    The *key* is a function computing a key value for each element.  If not
    specified or is ``None``, *key* defaults to an identity function and returns
    the element unchanged.  Generally, the iterable needs to already be sorted on
    the same key function.
 
+   The operation of :func:`groupby` is similar to the ``uniq`` filter in Unix.  It
+   generates a break or new group every time the value of the key function changes
+   (which is why it is usually necessary to have sorted the data using the same key
+   function).  That behavior differs from SQL's GROUP BY which aggregates common
+   elements regardless of their input order.
+
    The returned group is itself an iterator that shares the underlying iterable
    with :func:`groupby`.  Because the source is shared, when the :func:`groupby`
    object is advanced, the previous group is no longer visible.  So, if that data
    is needed later, it should be stored as a list::
 
       groups = []
       uniquekeys = []
+      data = sorted(data, key=keyfunc)
       for k, g in groupby(data, keyfunc):
           groups.append(list(g))      # Store group iterator as a list
           uniquekeys.append(k)
 
    :func:`groupby` is equivalent to::
 
       class groupby(object):
+          # [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
+          # [list(g) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
           def __init__(self, iterable, key=None):
               if key is None:
                   key = lambda x: x
               self.keyfunc = key
               self.it = iter(iterable)
-              self.tgtkey = self.currkey = self.currvalue = xrange(0)
+              self.tgtkey = self.currkey = self.currvalue = object()
           def __iter__(self):
               return self
           def next(self):
               while self.currkey == self.tgtkey:
-                  self.currvalue = self.it.next() # Exit on StopIteration
+                  self.currvalue = next(self.it)    # Exit on StopIteration
                   self.currkey = self.keyfunc(self.currvalue)
               self.tgtkey = self.currkey
               return (self.currkey, self._grouper(self.tgtkey))
           def _grouper(self, tgtkey):
               while self.currkey == tgtkey:
                   yield self.currvalue
-                  self.currvalue = self.it.next() # Exit on StopIteration
+                  self.currvalue = next(self.it)    # Exit on StopIteration
                   self.currkey = self.keyfunc(self.currvalue)
 
    .. versionadded:: 2.4
 
 
 .. function:: ifilter(predicate, iterable)
 
    Make an iterator that filters elements from iterable returning only those for
    which the predicate is ``True``. If *predicate* is ``None``, return the items
    that are true. Equivalent to::
 
       def ifilter(predicate, iterable):
+          # ifilter(lambda x: x%2, range(10)) --> 1 3 5 7 9
           if predicate is None:
               predicate = bool
           for x in iterable:
               if predicate(x):
                   yield x
 
 
 .. function:: ifilterfalse(predicate, iterable)
 
    Make an iterator that filters elements from iterable returning only those for
    which the predicate is ``False``. If *predicate* is ``None``, return the items
    that are false. Equivalent to::
 
       def ifilterfalse(predicate, iterable):
+          # ifilterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
           if predicate is None:
               predicate = bool
           for x in iterable:
               if not predicate(x):
                   yield x
 
 
 .. function:: imap(function, *iterables)
 
    Make an iterator that computes the function using arguments from each of the
    iterables.  If *function* is set to ``None``, then :func:`imap` returns the
    arguments as a tuple.  Like :func:`map` but stops when the shortest iterable is
    exhausted instead of filling in ``None`` for shorter iterables.  The reason for
    the difference is that infinite iterator arguments are typically an error for
    :func:`map` (because the output is fully evaluated) but represent a common and
    useful way of supplying arguments to :func:`imap`. Equivalent to::
 
       def imap(function, *iterables):
+          # imap(pow, (2,3,10), (5,2,3)) --> 32 9 1000
           iterables = map(iter, iterables)
           while True:
-              args = [i.next() for i in iterables]
+              args = [next(it) for it in iterables]
               if function is None:
                   yield tuple(args)
               else:
                   yield function(*args)
 
 
 .. function:: islice(iterable, [start,] stop [, step])
 
    Make an iterator that returns selected elements from the iterable. If *start* is
    non-zero, then elements from the iterable are skipped until start is reached.
    Afterward, elements are returned consecutively unless *step* is set higher than
    one which results in items being skipped.  If *stop* is ``None``, then iteration
    continues until the iterator is exhausted, if at all; otherwise, it stops at the
    specified position.  Unlike regular slicing, :func:`islice` does not support
    negative values for *start*, *stop*, or *step*.  Can be used to extract related
    fields from data where the internal structure has been flattened (for example, a
    multi-line report may list a name field on every third line).  Equivalent to::
 
       def islice(iterable, *args):
+          # islice('ABCDEFG', 2) --> A B
+          # islice('ABCDEFG', 2, 4) --> C D
+          # islice('ABCDEFG', 2, None) --> C D E F G
+          # islice('ABCDEFG', 0, None, 2) --> A C E G
           s = slice(*args)
           it = iter(xrange(s.start or 0, s.stop or sys.maxint, s.step or 1))
-          nexti = it.next()
+          nexti = next(it)
           for i, element in enumerate(iterable):
               if i == nexti:
                   yield element
-                  nexti = it.next()          
+                  nexti = next(it)
 
    If *start* is ``None``, then iteration starts at zero. If *step* is ``None``,
    then the step defaults to one.
 
    .. versionchanged:: 2.5
       accept ``None`` values for default *start* and *step*.
 
 
 .. function:: izip(*iterables)
 
    Make an iterator that aggregates elements from each of the iterables. Like
    :func:`zip` except that it returns an iterator instead of a list.  Used for
    lock-step iteration over several iterables at a time.  Equivalent to::
 
       def izip(*iterables):
+          # izip('ABCD', 'xy') --> Ax By
           iterables = map(iter, iterables)
           while iterables:
-              result = [it.next() for it in iterables]
-              yield tuple(result)
+              yield yield tuple(map(next, iterables))
 
    .. versionchanged:: 2.4
       When no iterables are specified, returns a zero length iterator instead of
       raising a :exc:`TypeError` exception.
 
-   Note, the left-to-right evaluation order of the iterables is guaranteed. This
-   makes possible an idiom for clustering a data series into n-length groups using
-   ``izip(*[iter(s)]*n)``.  For data that doesn't fit n-length groups exactly, the
-   last tuple can be pre-padded with fill values using ``izip(*[chain(s,
-   [None]*(n-1))]*n)``.
-
-   Note, when :func:`izip` is used with unequal length inputs, subsequent iteration
-   over the longer iterables cannot reliably be continued after :func:`izip`
-   terminates.  Potentially, up to one entry will be missing from each of the left-
-   over iterables. This occurs because a value is fetched from each iterator in-
-   turn, but the process ends when one of the iterators terminates.  This leaves
-   the last fetched values in limbo (they cannot be returned in a final, incomplete
-   tuple and they are cannot be pushed back into the iterator for retrieval with
-   ``it.next()``).  In general, :func:`izip` should only be used with unequal
-   length inputs when you don't care about trailing, unmatched values from the
-   longer iterables.
+   The left-to-right evaluation order of the iterables is guaranteed. This
+   makes possible an idiom for clustering a data series into n-length groups
+   using ``izip(*[iter(s)]*n)``.
+
+   :func:`izip` should only be used with unequal length inputs when you don't
+   care about trailing, unmatched values from the longer iterables.  If those
+   values are important, use :func:`izip_longest` instead.
+
+
+.. function:: izip_longest(*iterables[, fillvalue])
+
+   Make an iterator that aggregates elements from each of the iterables. If the
+   iterables are of uneven length, missing values are filled-in with *fillvalue*.
+   Iteration continues until the longest iterable is exhausted.  Equivalent to::
+
+      def izip_longest(*args, **kwds):
+          # izip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
+          fillvalue = kwds.get('fillvalue')
+          def sentinel(counter = ([fillvalue]*(len(args)-1)).pop):
+              yield counter()         # yields the fillvalue, or raises IndexError
+          fillers = repeat(fillvalue)
+          iters = [chain(it, sentinel(), fillers) for it in args]
+          try:
+              for tup in izip(*iters):
+                  yield tup
+          except IndexError:
+              pass
+
+   If one of the iterables is potentially infinite, then the
+   :func:`izip_longest` function should be wrapped with something that limits
+   the number of calls (for example :func:`islice` or :func:`takewhile`).  If
+   not specified, *fillvalue* defaults to ``None``.
+
+   .. versionadded:: 2.6
+
+.. function:: permutations(iterable[, r])
+
+   Return successive *r* length permutations of elements in the *iterable*.
+
+   If *r* is not specified or is ``None``, then *r* defaults to the length
+   of the *iterable* and all possible full-length permutations
+   are generated.
+
+   Permutations are emitted in lexicographic sort order.  So, if the
+   input *iterable* is sorted, the permutation tuples will be produced
+   in sorted order.
+
+   Elements are treated as unique based on their position, not on their
+   value.  So if the input elements are unique, there will be no repeat
+   values in each permutation.
+
+   Equivalent to::
+
+        def permutations(iterable, r=None):
+            # permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC
+            # permutations(range(3)) --> 012 021 102 120 201 210
+            pool = tuple(iterable)
+            n = len(pool)
+            r = n if r is None else r
+            if r > n:
+                return
+            indices = range(n)
+            cycles = range(n, n-r, -1)
+            yield tuple(pool[i] for i in indices[:r])
+            while n:
+                for i in reversed(range(r)):
+                    cycles[i] -= 1
+                    if cycles[i] == 0:
+                        indices[i:] = indices[i+1:] + indices[i:i+1]
+                        cycles[i] = n - i
+                    else:
+                        j = cycles[i]
+                        indices[i], indices[-j] = indices[-j], indices[i]
+                        yield tuple(pool[i] for i in indices[:r])
+                        break
+                else:
+                    return
+
+   The code for :func:`permutations` can be also expressed as a subsequence of
+   :func:`product`, filtered to exclude entries with repeated elements (those
+   from the same position in the input pool)::
+
+        def permutations(iterable, r=None):
+            pool = tuple(iterable)
+            n = len(pool)
+            r = n if r is None else r
+            for indices in product(range(n), repeat=r):
+                if len(set(indices)) == r:
+                    yield tuple(pool[i] for i in indices)
+
+   The number of items returned is ``n! / (n-r)!`` when ``0 <= r <= n``
+   or zero when ``r > n``.
+
+   .. versionadded:: 2.6
+
+.. function:: product(*iterables[, repeat])
+
+   Cartesian product of input iterables.
+
+   Equivalent to nested for-loops in a generator expression. For example,
+   ``product(A, B)`` returns the same as ``((x,y) for x in A for y in B)``.
+
+   The nested loops cycle like an odometer with the rightmost element advancing
+   on every iteration.  This pattern creates a lexicographic ordering so that if
+   the input's iterables are sorted, the product tuples are emitted in sorted
+   order.
+
+   To compute the product of an iterable with itself, specify the number of
+   repetitions with the optional *repeat* keyword argument.  For example,
+   ``product(A, repeat=4)`` means the same as ``product(A, A, A, A)``.
+
+   This function is equivalent to the following code, except that the
+   actual implementation does not build up intermediate results in memory::
+
+       def product(*args, **kwds):
+           # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
+           # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
+           pools = map(tuple, args) * kwds.get('repeat', 1)
+           result = [[]]
+           for pool in pools:
+               result = [x+[y] for x in result for y in pool]
+           for prod in result:
+               yield tuple(prod)
 
+   .. versionadded:: 2.6
 
 .. function:: repeat(object[, times])
 
    Make an iterator that returns *object* over and over again. Runs indefinitely
    unless the *times* argument is specified. Used as argument to :func:`imap` for
-   invariant parameters to the called function.  Also used with :func:`izip` to
-   create an invariant part of a tuple record.  Equivalent to::
+   invariant function parameters.  Also used with :func:`izip` to create constant
+   fields in a tuple record.  Equivalent to::
 
       def repeat(object, times=None):
+          # repeat(10, 3) --> 10 10 10
           if times is None:
               while True:
                   yield object
           else:
               for i in xrange(times):
                   yield object
 
 
 .. function:: starmap(function, iterable)
 
-   Make an iterator that computes the function using arguments tuples obtained from
+   Make an iterator that computes the function using arguments obtained from
    the iterable.  Used instead of :func:`imap` when argument parameters are already
    grouped in tuples from a single iterable (the data has been "pre-zipped").  The
    difference between :func:`imap` and :func:`starmap` parallels the distinction
    between ``function(a,b)`` and ``function(*c)``. Equivalent to::
 
       def starmap(function, iterable):
-          iterable = iter(iterable)
-          while True:
-              yield function(*iterable.next())
-
+          # starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
+          for args in iterable:
+              yield function(*args)
+
+   .. versionchanged:: 2.6
+      Previously, :func:`starmap` required the function arguments to be tuples.
+      Now, any iterable is allowed.
 
 .. function:: takewhile(predicate, iterable)
 
    Make an iterator that returns elements from the iterable as long as the
    predicate is true.  Equivalent to::
 
       def takewhile(predicate, iterable):
+          # takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
           for x in iterable:
               if predicate(x):
                   yield x
               else:
                   break
 
 
 .. function:: tee(iterable[, n=2])
 
-   Return *n* independent iterators from a single iterable. The case where ``n==2``
-   is equivalent to::
-
-      def tee(iterable):
-          def gen(next, data={}, cnt=[0]):
-              for i in count():
-                  if i == cnt[0]:
-                      item = data[i] = next()
-                      cnt[0] += 1
-                  else:
-                      item = data.pop(i)
-                  yield item
-          it = iter(iterable)
-          return (gen(it.next), gen(it.next))
-
-   Note, once :func:`tee` has made a split, the original *iterable* should not be
-   used anywhere else; otherwise, the *iterable* could get advanced without the tee
-   objects being informed.
+   Return *n* independent iterators from a single iterable.  Equivalent to::
 
-   Note, this member of the toolkit may require significant auxiliary storage
-   (depending on how much temporary data needs to be stored). In general, if one
-   iterator is going to use most or all of the data before the other iterator, it
-   is faster to use :func:`list` instead of :func:`tee`.
+        def tee(iterable, n=2):
+            it = iter(iterable)
+            deques = [collections.deque() for i in range(n)]
+            def gen(mydeque):
+                while True:
+                    if not mydeque:             # when the local deque is empty
+                        newval = next(it)       # fetch a new value and
+                        for d in deques:        # load it to all the deques
+                            d.append(newval)
+                    yield mydeque.popleft()
+            return tuple(gen(d) for d in deques)
+
+   Once :func:`tee` has made a split, the original *iterable* should not be
+   used anywhere else; otherwise, the *iterable* could get advanced without
+   the tee objects being informed.
+
+   This itertool may require significant auxiliary storage (depending on how
+   much temporary data needs to be stored). In general, if one iterator uses
+   most or all of the data before another iterator starts, it is faster to use
+   :func:`list` instead of :func:`tee`.
 
    .. versionadded:: 2.4
 
 
 .. _itertools-example:
 
 Examples
 --------
 
 The following examples show common uses for each tool and demonstrate ways they
-can be combined. ::
+can be combined.
 
-   >>> amounts = [120.15, 764.05, 823.14]
-   >>> for checknum, amount in izip(count(1200), amounts):
-   ...     print 'Check %d is for $%.2f' % (checknum, amount)
-   ...
-   Check 1200 is for $120.15
-   Check 1201 is for $764.05
-   Check 1202 is for $823.14
-
-   >>> import operator
-   >>> for cube in imap(operator.pow, xrange(1,5), repeat(3)):
-   ...    print cube
-   ...
-   1
-   8
-   27
-   64
-
-   >>> reportlines = ['EuroPython', 'Roster', '', 'alex', '', 'laura',
-                     '', 'martin', '', 'walter', '', 'mark']
-   >>> for name in islice(reportlines, 3, None, 2):
-   ...    print name.title()
-   ...
-   Alex
-   Laura
-   Martin
-   Walter
-   Mark
+.. doctest::
 
-   # Show a dictionary sorted and grouped by value
+   >>> # Show a dictionary sorted and grouped by value
    >>> from operator import itemgetter
    >>> d = dict(a=1, b=2, c=1, d=2, e=1, f=2, g=3)
    >>> di = sorted(d.iteritems(), key=itemgetter(1))
    >>> for k, g in groupby(di, key=itemgetter(1)):
    ...     print k, map(itemgetter(0), g)
    ...
    1 ['a', 'c', 'e']
    2 ['b', 'd', 'f']
    3 ['g']
 
-   # Find runs of consecutive numbers using groupby.  The key to the solution
-   # is differencing with a range so that consecutive numbers all appear in
-   # same group.
+   >>> # Find runs of consecutive numbers using groupby.  The key to the solution
+   >>> # is differencing with a range so that consecutive numbers all appear in
+   >>> # same group.
    >>> data = [ 1,  4,5,6, 10, 15,16,17,18, 22, 25,26,27,28]
    >>> for k, g in groupby(enumerate(data), lambda (i,x):i-x):
-   ...     print map(operator.itemgetter(1), g)
-   ... 
+   ...     print map(itemgetter(1), g)
+   ...
    [1]
    [4, 5, 6]
    [10]
    [15, 16, 17, 18]
    [22]
    [25, 26, 27, 28]
 
 
 
 .. _itertools-recipes:
 
 Recipes
 -------
 
 This section shows recipes for creating an extended toolset using the existing
 itertools as building blocks.
 
 The extended tools offer the same high performance as the underlying toolset.
 The superior memory performance is kept by processing elements one at a time
 rather than bringing the whole iterable into memory all at once. Code volume is
 kept small by linking the tools together in a functional style which helps
 eliminate temporary variables.  High speed is retained by preferring
-"vectorized" building blocks over the use of for-loops and generators which
-incur interpreter overhead. ::
+"vectorized" building blocks over the use of for-loops and :term:`generator`\s
+which incur interpreter overhead.
+
+.. testcode::
 
-   def take(n, seq):
-       return list(islice(seq, n))
+   def take(n, iterable):
+       "Return first n items of the iterable as a list"
+       return list(islice(iterable, n))
 
-   def enumerate(iterable):
-       return izip(count(), iterable)
+   def enumerate(iterable, start=0):
+       return izip(count(start), iterable)
 
-   def tabulate(function):
+   def tabulate(function, start=0):
        "Return function(0), function(1), ..."
-       return imap(function, count())
+       return imap(function, count(start))
 
-   def iteritems(mapping):
-       return izip(mapping.iterkeys(), mapping.itervalues())
+   def consume(iterator, n):
+       "Advance the iterator n-steps ahead. If n is none, consume entirely."
+       collections.deque(islice(iterator, n), maxlen=0)
 
-   def nth(iterable, n):
-       "Returns the nth item"
-       return list(islice(iterable, n, n+1))
-
-   def all(seq, pred=None):
-       "Returns True if pred(x) is true for every element in the iterable"
-       for elem in ifilterfalse(pred, seq):
-           return False
-       return True
-
-   def any(seq, pred=None):
-       "Returns True if pred(x) is true for at least one element in the iterable"
-       for elem in ifilter(pred, seq):
-           return True
-       return False
-
-   def no(seq, pred=None):
-       "Returns True if pred(x) is false for every element in the iterable"
-       for elem in ifilter(pred, seq):
-           return False
-       return True
-
-   def quantify(seq, pred=None):
-       "Count how many times the predicate is true in the sequence"
-       return sum(imap(pred, seq))
+   def nth(iterable, n, default=None):
+       "Returns the nth item or a default value"
+       return next(islice(iterable, n, None), default)
 
-   def padnone(seq):
+   def quantify(iterable, pred=bool):
+       "Count how many times the predicate is true"
+       return sum(imap(pred, iterable))
+
+   def padnone(iterable):
        """Returns the sequence elements and then returns None indefinitely.
 
        Useful for emulating the behavior of the built-in map() function.
        """
-       return chain(seq, repeat(None))
+       return chain(iterable, repeat(None))
 
-   def ncycles(seq, n):
+   def ncycles(iterable, n):
        "Returns the sequence elements n times"
-       return chain(*repeat(seq, n))
+       return chain.from_iterable(repeat(iterable, n))
 
    def dotproduct(vec1, vec2):
        return sum(imap(operator.mul, vec1, vec2))
 
    def flatten(listOfLists):
-       return list(chain(*listOfLists))
+       return list(chain.from_iterable(listOfLists))
 
    def repeatfunc(func, times=None, *args):
        """Repeat calls to func with specified arguments.
 
        Example:  repeatfunc(random.random)
        """
        if times is None:
            return starmap(func, repeat(args))
-       else:
-           return starmap(func, repeat(args, times))
+       return starmap(func, repeat(args, times))
 
    def pairwise(iterable):
        "s -> (s0,s1), (s1,s2), (s2, s3), ..."
        a, b = tee(iterable)
-       try:
-           b.next()
-       except StopIteration:
-           pass
+       next(b, None)
        return izip(a, b)
 
-   def grouper(n, iterable, padvalue=None):
-       "grouper(3, 'abcdefg', 'x') --> ('a','b','c'), ('d','e','f'), ('g','x','x')"
-       return izip(*[chain(iterable, repeat(padvalue, n-1))]*n)
-
-
-
+   def grouper(n, iterable, fillvalue=None):
+       "grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
+       args = [iter(iterable)] * n
+       return izip_longest(fillvalue=fillvalue, *args)
+
+   def roundrobin(*iterables):
+       "roundrobin('ABC', 'D', 'EF') --> A D E B F C"
+       # Recipe credited to George Sakkis
+       pending = len(iterables)
+       nexts = cycle(iter(it).next for it in iterables)
+       while pending:
+           try:
+               for next in nexts:
+                   yield next()
+           except StopIteration:
+               pending -= 1
+               nexts = cycle(islice(nexts, pending))
+
+   def powerset(iterable):
+       "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
+       s = list(iterable)
+       return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
+
+   def compress(data, selectors):
+       "compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F"
+       return (d for d, s in izip(data, selectors) if s)
+
+   def combinations_with_replacement(iterable, r):
+       "combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC"
+       # number items returned:  (n+r-1)! / r! / (n-1)!
+       pool = tuple(iterable)
+       n = len(pool)
+       if not n and r:
+           return
+       indices = [0] * r
+       yield tuple(pool[i] for i in indices)
+       while True:
+           for i in reversed(range(r)):
+               if indices[i] != n - 1:
+                   break
+           else:
+               return
+           indices[i:] = [indices[i] + 1] * (r - i)
+           yield tuple(pool[i] for i in indices)
+
+   def powerset(iterable):
+       "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
+       s = list(iterable)
+       return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
+
+   def unique_everseen(iterable, key=None):
+       "List unique elements, preserving order. Remember all elements ever seen."
+       # unique_everseen('AAAABBBCCDAABBB') --> A B C D
+       # unique_everseen('ABBCcAD', str.lower) --> A B C D
+       seen = set()
+       seen_add = seen.add
+       if key is None:
+           for element in iterable:
+               if element not in seen:
+                   seen_add(element)
+                   yield element
+       else:
+           for element in iterable:
+               k = key(element)
+               if k not in seen:
+                   seen_add(k)
+                   yield element
+
+   def unique_justseen(iterable, key=None):
+       "List unique elements, preserving order. Remember only the element just seen."
+       # unique_justseen('AAAABBBCCDAABBB') --> A B C D A B
+       # unique_justseen('ABBCcAD', str.lower) --> A B C A D
+       return imap(next, imap(itemgetter(1), groupby(iterable, key)))
