"itertools" --- Functions creating iterators for efficient looping
******************************************************************

New in version 2.3.

This module implements a number of *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.  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), ...".  The same effect can be
achieved in Python by combining "imap()" and "count()" to form
"imap(f, count())".

These tools and their built-in counterparts also work well with the
high-speed functions in the "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                                   |
+====================+===================+===================================================+===========================================+
| "count()"          | start, [step]     | start, start+step, start+2*step, ...              | "count(10) --> 10 11 12 13 14 ..."        |
+--------------------+-------------------+---------------------------------------------------+-------------------------------------------+
| "cycle()"          | p                 | p0, p1, ... plast, p0, p1, ...                    | "cycle('ABCD') --> A B C D A B C D ..."   |
+--------------------+-------------------+---------------------------------------------------+-------------------------------------------+
| "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                                                       |
+======================+==============================+===================================================+===============================================================+
| "chain()"            | p, q, ...                    | p0, p1, ... plast, q0, q1, ...                    | "chain('ABC', 'DEF') --> A B C D E F"                         |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "compress()"         | data, selectors              | (d[0] if s[0]), (d[1] if s[1]), ...               | "compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F"               |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "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"             |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "groupby()"          | iterable[, keyfunc]          | sub-iterators grouped by value of keyfunc(v)      |                                                               |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "ifilter()"          | pred, seq                    | elements of seq where pred(elem) is true          | "ifilter(lambda x: x%2, range(10)) --> 1 3 5 7 9"             |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "ifilterfalse()"     | pred, seq                    | elements of seq where pred(elem) is false         | "ifilterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8"        |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "islice()"           | seq, [start,] stop [, step]  | elements from seq[start:stop:step]                | "islice('ABCDEFG', 2, None) --> C D E F G"                    |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "imap()"             | func, p, q, ...              | func(p0, q0), func(p1, q1), ...                   | "imap(pow, (2,3,10), (5,2,3)) --> 32 9 1000"                  |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "starmap()"          | func, seq                    | func(*seq[0]), func(*seq[1]), ...                 | "starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000"          |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "tee()"              | it, n                        | it1, it2, ... itn  splits one iterator into n     |                                                               |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "takewhile()"        | pred, seq                    | seq[0], seq[1], until pred fails                  | "takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4"               |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "izip()"             | p, q, ...                    | (p[0], q[0]), (p[1], q[1]), ...                   | "izip('ABCD', 'xy') --> Ax By"                                |
+----------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "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                                                       |
+================================================+======================+===============================================================+
| "product()"                                    | p, q, ... [repeat=1] | cartesian product, equivalent to a nested for-loop            |
+------------------------------------------------+----------------------+---------------------------------------------------------------+
| "permutations()"                               | p[, r]               | r-length tuples, all possible orderings, no repeated elements |
+------------------------------------------------+----------------------+---------------------------------------------------------------+
| "combinations()"                               | p, r                 | r-length tuples, in sorted order, no repeated elements        |
+------------------------------------------------+----------------------+---------------------------------------------------------------+
| "combinations_with_replacement()"              | p, r                 | r-length tuples, in sorted order, with 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"                                           |
+------------------------------------------------+----------------------+---------------------------------------------------------------+
| "combinations_with_replacement('ABCD', 2)"     |                      | "AA AB AC AD BB BC BD CC CD DD"                               |
+------------------------------------------------+----------------------+---------------------------------------------------------------+


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.

itertools.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. Roughly equivalent to:

      def chain(*iterables):
          # chain('ABC', 'DEF') --> A B C D E F
          for it in iterables:
              for element in it:
                  yield element

classmethod chain.from_iterable(iterable)

   Alternate constructor for "chain()".  Gets chained inputs from a
   single iterable argument that is evaluated lazily.  Roughly
   equivalent to:

      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

   New in version 2.6.

itertools.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.

   Roughly 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 "combinations()" can be also expressed as a
   subsequence of "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".

   New in version 2.6.

itertools.combinations_with_replacement(iterable, r)

   Return *r* length subsequences of elements from the input
   *iterable* allowing individual elements to be repeated more than
   once.

   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, the generated
   combinations will also be unique.

   Roughly equivalent to:

      def combinations_with_replacement(iterable, r):
          # combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC
          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)

   The code for "combinations_with_replacement()" can be also
   expressed as a subsequence of "product()" after filtering entries
   where the elements are not in sorted order (according to their
   position in the input pool):

      def combinations_with_replacement(iterable, r):
          pool = tuple(iterable)
          n = len(pool)
          for indices in product(range(n), repeat=r):
              if sorted(indices) == list(indices):
                  yield tuple(pool[i] for i in indices)

   The number of items returned is "(n+r-1)! / r! / (n-1)!" when "n >
   0".

   New in version 2.7.

itertools.compress(data, selectors)

   Make an iterator that filters elements from *data* returning only
   those that have a corresponding element in *selectors* that
   evaluates to "True". Stops when either the *data* or *selectors*
   iterables has been exhausted. Roughly equivalent to:

      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)

   New in version 2.7.

itertools.count(start=0, step=1)

   Make an iterator that returns evenly spaced values starting with
   *n*. Often used as an argument to "imap()" to generate consecutive
   data points. Also, used with "izip()" to add sequence numbers.
   Equivalent to:

      def count(start=0, step=1):
          # count(10) --> 10 11 12 13 14 ...
          # count(2.5, 0.5) -> 2.5 3.0 3.5 ...
          n = start
          while True:
              yield n
              n += step

   When counting with floating point numbers, better accuracy can
   sometimes be achieved by substituting multiplicative code such as:
   "(start + step * i for i in count())".

   Changed in version 2.7: added *step* argument and allowed non-
   integer arguments.

itertools.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.  Roughly 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).

itertools.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
   first becomes false, so it may have a lengthy start-up time.
   Roughly 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

itertools.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 "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 "groupby()".  Because the source is shared, when the
   "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)

   "groupby()" is roughly 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 = object()
          def __iter__(self):
              return self
          def next(self):
              while self.currkey == self.tgtkey:
                  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 = next(self.it)    # Exit on StopIteration
                  self.currkey = self.keyfunc(self.currvalue)

   New in version 2.4.

itertools.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. Roughly 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

itertools.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. Roughly 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

itertools.imap(function, *iterables)

   Make an iterator that computes the function using arguments from
   each of the iterables.  If *function* is set to "None", then
   "imap()" returns the arguments as a tuple.  Like "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 "map()"
   (because the output is fully evaluated) but represent a common and
   useful way of supplying arguments to "imap()". Roughly equivalent
   to:

      def imap(function, *iterables):
          # imap(pow, (2,3,10), (5,2,3)) --> 32 9 1000
          iterables = map(iter, iterables)
          while True:
              args = [next(it) for it in iterables]
              if function is None:
                  yield tuple(args)
              else:
                  yield function(*args)

itertools.islice(iterable, stop)
itertools.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, "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).  Roughly 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)
          start, stop, step = s.start or 0, s.stop or sys.maxint, s.step or 1
          it = iter(xrange(start, stop, step)))
          try:
              nexti = next(it)
          except StopIteration:
              # Consume *iterable* up to the *start* position.
              for i, element in izip(xrange(start), iterable):
                  pass
              return
          try:
              for i, element in enumerate(iterable):
                  if i == nexti:
                      yield element
                      nexti = next(it)
          except StopIteration:
              # Consume to *stop*.
              for i, element in izip(xrange(i + 1, stop), iterable):
                  pass

   If *start* is "None", then iteration starts at zero. If *step* is
   "None", then the step defaults to one.

   Changed in version 2.5: accept "None" values for default *start*
   and *step*.

itertools.izip(*iterables)

   Make an iterator that aggregates elements from each of the
   iterables. Like "zip()" except that it returns an iterator instead
   of a list.  Used for lock-step iteration over several iterables at
   a time.  Roughly equivalent to:

      def izip(*iterables):
          # izip('ABCD', 'xy') --> Ax By
          iterators = map(iter, iterables)
          while iterators:
              yield tuple(map(next, iterators))

   Changed in version 2.4: When no iterables are specified, returns a
   zero length iterator instead of raising a "TypeError" exception.

   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)".

   "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 "izip_longest()"
   instead.

itertools.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.  Roughly equivalent to:

      class ZipExhausted(Exception):
          pass

      def izip_longest(*args, **kwds):
          # izip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
          fillvalue = kwds.get('fillvalue')
          counter = [len(args) - 1]
          def sentinel():
              if not counter[0]:
                  raise ZipExhausted
              counter[0] -= 1
              yield fillvalue
          fillers = repeat(fillvalue)
          iterators = [chain(it, sentinel(), fillers) for it in args]
          try:
              while iterators:
                  yield tuple(map(next, iterators))
          except ZipExhausted:
              pass

   If one of the iterables is potentially infinite, then the
   "izip_longest()" function should be wrapped with something that
   limits the number of calls (for example "islice()" or
   "takewhile()").  If not specified, *fillvalue* defaults to "None".

   New in version 2.6.

itertools.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.

   Roughly 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 "permutations()" can be also expressed as a
   subsequence of "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".

   New in version 2.6.

itertools.product(*iterables[, repeat])

   Cartesian product of input iterables.

   Roughly 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 roughly 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)

   New in version 2.6.

itertools.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 "imap()" for invariant function parameters.  Also used
   with "izip()" to create constant fields in a tuple record.  Roughly
   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

   A common use for *repeat* is to supply a stream of constant values
   to *imap* or *zip*:

      >>> list(imap(pow, xrange(10), repeat(2)))
      [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

itertools.starmap(function, iterable)

   Make an iterator that computes the function using arguments
   obtained from the iterable.  Used instead of "imap()" when argument
   parameters are already grouped in tuples from a single iterable
   (the data has been "pre-zipped").  The difference between "imap()"
   and "starmap()" parallels the distinction between "function(a,b)"
   and "function(*c)". Roughly equivalent to:

      def starmap(function, iterable):
          # starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
          for args in iterable:
              yield function(*args)

   Changed in version 2.6: Previously, "starmap()" required the
   function arguments to be tuples. Now, any iterable is allowed.

itertools.takewhile(predicate, iterable)

   Make an iterator that returns elements from the iterable as long as
   the predicate is true.  Roughly 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

itertools.tee(iterable[, n=2])

   Return *n* independent iterators from a single iterable.  Roughly
   equivalent to:

      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 "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 "list()" instead of "tee()".

   New in version 2.4.


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 *generator*s which incur
interpreter overhead.

   def take(n, iterable):
       "Return first n items of the iterable as a list"
       return list(islice(iterable, n))

   def tabulate(function, start=0):
       "Return function(0), function(1), ..."
       return imap(function, count(start))

   def consume(iterator, n=None):
       "Advance the iterator n-steps ahead. If n is None, consume entirely."
       # Use functions that consume iterators at C speed.
       if n is None:
           # feed the entire iterator into a zero-length deque
           collections.deque(iterator, maxlen=0)
       else:
           # advance to the empty slice starting at position n
           next(islice(iterator, n, n), None)

   def nth(iterable, n, default=None):
       "Returns the nth item or a default value"
       return next(islice(iterable, n, None), default)

   def all_equal(iterable):
       "Returns True if all the elements are equal to each other"
       g = groupby(iterable)
       return next(g, True) and not next(g, False)

   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(iterable, repeat(None))

   def ncycles(iterable, n):
       "Returns the sequence elements n times"
       return chain.from_iterable(repeat(tuple(iterable), n))

   def dotproduct(vec1, vec2):
       return sum(imap(operator.mul, vec1, vec2))

   def flatten(listOfLists):
       "Flatten one level of nesting"
       return 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))
       return starmap(func, repeat(args, times))

   def pairwise(iterable):
       "s -> (s0,s1), (s1,s2), (s2, s3), ..."
       a, b = tee(iterable)
       next(b, None)
       return izip(a, b)

   def grouper(iterable, n, fillvalue=None):
       "Collect data into fixed-length chunks or blocks"
       # grouper('ABCDEFG', 3, '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 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 ifilterfalse(seen.__contains__, iterable):
               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)))

   def iter_except(func, exception, first=None):
       """ Call a function repeatedly until an exception is raised.

       Converts a call-until-exception interface to an iterator interface.
       Like __builtin__.iter(func, sentinel) but uses an exception instead
       of a sentinel to end the loop.

       Examples:
           bsddbiter = iter_except(db.next, bsddb.error, db.first)
           heapiter = iter_except(functools.partial(heappop, h), IndexError)
           dictiter = iter_except(d.popitem, KeyError)
           dequeiter = iter_except(d.popleft, IndexError)
           queueiter = iter_except(q.get_nowait, Queue.Empty)
           setiter = iter_except(s.pop, KeyError)

       """
       try:
           if first is not None:
               yield first()
           while 1:
               yield func()
       except exception:
           pass

   def random_product(*args, **kwds):
       "Random selection from itertools.product(*args, **kwds)"
       pools = map(tuple, args) * kwds.get('repeat', 1)
       return tuple(random.choice(pool) for pool in pools)

   def random_permutation(iterable, r=None):
       "Random selection from itertools.permutations(iterable, r)"
       pool = tuple(iterable)
       r = len(pool) if r is None else r
       return tuple(random.sample(pool, r))

   def random_combination(iterable, r):
       "Random selection from itertools.combinations(iterable, r)"
       pool = tuple(iterable)
       n = len(pool)
       indices = sorted(random.sample(xrange(n), r))
       return tuple(pool[i] for i in indices)

   def random_combination_with_replacement(iterable, r):
       "Random selection from itertools.combinations_with_replacement(iterable, r)"
       pool = tuple(iterable)
       n = len(pool)
       indices = sorted(random.randrange(n) for i in xrange(r))
       return tuple(pool[i] for i in indices)

   def tee_lookahead(t, i):
       """Inspect the i-th upcomping value from a tee object
          while leaving the tee object at its current position.

          Raise an IndexError if the underlying iterator doesn't
          have enough values.

       """
       for value in islice(t.__copy__(), i, None):
           return value
       raise IndexError(i)

Note, many of the above recipes can be optimized by replacing global
lookups with local variables defined as default values.  For example,
the *dotproduct* recipe can be written as:

   def dotproduct(vec1, vec2, sum=sum, imap=imap, mul=operator.mul):
       return sum(imap(mul, vec1, vec2))
