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        >>> list(chunked([1, 2, 3, 4, 5, 6, 7, 8], 3))
        [[1, 2, 3], [4, 5, 6], [7, 8]]

    To use a fill-in value instead, see the :func:`grouper` recipe.

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        'a'

    Pass :meth:`peek` a default value to return that instead of raising
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        >>> p = peekable([])
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        'hi'

    peekables also offer a :meth:`prepend` method, which "inserts" items
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        >>> p = peekable([1, 2, 3])
        >>> p.prepend(10, 11, 12)
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        10
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        [11, 12, 1, 2, 3]

    peekables can be indexed. Index 0 is the item that will be returned by
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        >>> p[0]
        'a'
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        'b'
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        'a'

    Negative indexes are supported, but be aware that they will cache the
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        ...     i = 0
        ...     while True:
        ...         print('Thing number %s is %s.' % (i, (yield)))
        ...         i += 1
        ...
        >>> t = tally()
        >>> t.send('red')
        Thing number 0 is red.
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        Thing number 1 is fish.

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    For example, it can be used to retrieve the result of a database query
    that is expected to return a single row.

    If *iterable* is empty, ``ValueError`` will be raised. You may specify a
    different exception with the *too_short* keyword:

        >>> it = []
        >>> one(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        ValueError: too many items in iterable (expected 1)'
        >>> too_short = IndexError('too few items')
        >>> one(it, too_short=too_short)  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        IndexError: too few items

    Similarly, if *iterable* contains more than one item, ``ValueError`` will
    be raised. You may specify a different exception with the *too_long*
    keyword:

        >>> it = ['too', 'many']
        >>> one(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        ValueError: Expected exactly one item in iterable, but got 'too',
        'many', and perhaps more.
        >>> too_long = RuntimeError
        >>> one(it, too_long=too_long)  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        RuntimeError

    Note that :func:`one` attempts to advance *iterable* twice to ensure there
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    *too_long* with the first ``n + 1`` items.

        >>> iterable = ['a', 'b', 'c', 'd']
        >>> n = 4
        >>> list(strictly_n(iterable, n))
        ['a', 'b', 'c', 'd']

    Note that the returned iterable must be consumed in order for the check to
    be made.

    By default, *too_short* and *too_long* are functions that raise
    ``ValueError``.

        >>> list(strictly_n('ab', 3))  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        ValueError: too few items in iterable (got 2)

        >>> list(strictly_n('abc', 2))  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        ValueError: too many items in iterable (got at least 3)

    You can instead supply functions that do something else.
    *too_short* will be called with the number of items in *iterable*.
    *too_long* will be called with `n + 1`.

        >>> def too_short(item_count):
        ...     raise RuntimeError
        >>> it = strictly_n('abcd', 6, too_short=too_short)
        >>> list(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        RuntimeError

        >>> def too_long(item_count):
        ...     print('The boss is going to hear about this')
        >>> it = strictly_n('abcdef', 4, too_long=too_long)
        >>> list(it)
        The boss is going to hear about this
        ['a', 'b', 'c', 'd']

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    generated and thrown away. For larger input sequences this is much more
    efficient.

    Duplicate permutations arise when there are duplicated elements in the
    input iterable. The number of items returned is
    `n! / (x_1! * x_2! * ... * x_n!)`, where `n` is the total number of
    items input, and each `x_i` is the count of a distinct item in the input
    sequence.

    If *r* is given, only the *r*-length permutations are yielded.

        >>> sorted(distinct_permutations([1, 0, 1], r=2))
        [(0, 1), (1, 0), (1, 1)]
        >>> sorted(distinct_permutations(range(3), r=2))
        [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]

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    If you remove one package, which dependencies can also be removed?

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        >>> unique_to_each({'A', 'B'}, {'B', 'C'}, {'B', 'D'})
        [['A'], ['C'], ['D']]

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        [(1, 2, 3, None)]

    Each window will advance in increments of *step*:

        >>> list(windowed([1, 2, 3, 4, 5, 6], 3, fillvalue='!', step=2))
        [(1, 2, 3), (3, 4, 5), (5, 6, '!')]

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    >>> for item in substrings_indexes('more'):
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    ('m', 0, 1)
    ('o', 1, 2)
    ('r', 2, 3)
    ('e', 3, 4)
    ('mo', 0, 2)
    ('or', 1, 3)
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        >>> a_iterable = s['a']
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        >>> next(a_iterable)
        'a2'
        >>> list(s['b'])
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    The number of items requested can be larger than the number of items in
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    [1, 2, 'a', 3, 4, 'b', 5]

    >>> iterables = [[1, 2, 3], [4, 5], [6, 7, 8]]
    >>> list(interleave_evenly(iterables))
    [1, 6, 4, 2, 7, 3, 8, 5]

    This function requires iterables of known length. Iterables without
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    >>> from itertools import combinations, repeat
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    >>> lengths = [4 * (4 - 1) // 2, 3]
    >>> list(interleave_evenly(iterables, lengths=lengths))
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        [1, 2, 3, 4, 5, 6]

    Binary and text strings are not considered iterable and
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    To avoid collapsing other types, specify *base_type*:

        >>> iterable = ['ab', ('cd', 'ef'), ['gh', 'ij']]
        >>> list(collapse(iterable, base_type=tuple))
        ['ab', ('cd', 'ef'), 'gh', 'ij']

    Specify *levels* to stop flattening after a certain level:

    >>> iterable = [('a', ['b']), ('c', ['d'])]
    >>> list(collapse(iterable))  # Fully flattened
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    >>> list(collapse(iterable, levels=1))  # Only one level flattened
    ['a', ['b'], 'c', ['d']]

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    will be executed before iteration starts and after it ends, respectively.

    `side_effect` can be used for logging, updating progress bars, or anything
    that is not functionally "pure."

    Emitting a status message:

        >>> from more_itertools import consume
        >>> func = lambda item: print('Received {}'.format(item))
        >>> consume(side_effect(func, range(2)))
        Received 0
        Received 1

    Operating on chunks of items:

        >>> pair_sums = []
        >>> func = lambda chunk: pair_sums.append(sum(chunk))
        >>> list(side_effect(func, [0, 1, 2, 3, 4, 5], 2))
        [0, 1, 2, 3, 4, 5]
        >>> list(pair_sums)
        [1, 5, 9]

    Writing to a file-like object:

        >>> from io import StringIO
        >>> from more_itertools import consume
        >>> f = StringIO()
        >>> func = lambda x: print(x, file=f)
        >>> before = lambda: print(u'HEADER', file=f)
        >>> after = f.close
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    if the length of *seq* is not divisible by *n*:

    >>> list(sliced((1, 2, 3, 4, 5, 6, 7, 8), 3))
    [(1, 2, 3), (4, 5, 6), (7, 8)]

    If the length of *seq* is not divisible by *n* and *strict* is
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        >>> list(split_at(range(10), lambda n: n % 2 == 1))
        [[0], [2], [4], [6], [8], []]

    At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
    then there is no limit on the number of splits:

        >>> list(split_at(range(10), lambda n: n % 2 == 1, maxsplit=2))
        [[0], [2], [4, 5, 6, 7, 8, 9]]

    By default, the delimiting items are not included in the output.
    To include them, set *keep_separator* to ``True``.

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        [['O', 'n', 'e'], ['T', 'w', 'o']]

        >>> list(split_before(range(10), lambda n: n % 3 == 0))
        [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]

    At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
    then there is no limit on the number of splits:

        >>> list(split_before(range(10), lambda n: n % 3 == 0, maxsplit=2))
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        [['o', 'n', 'e', '1'], ['t', 'w', 'o', '2']]

        >>> list(split_after(range(10), lambda n: n % 3 == 0))
        [[0], [1, 2, 3], [4, 5, 6], [7, 8, 9]]

    At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
    then there is no limit on the number of splits:

        >>> list(split_after(range(10), lambda n: n % 3 == 0, maxsplit=2))
        [[0], [1, 2, 3], [4, 5, 6, 7, 8, 9]]

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        >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], lambda x, y: x > y))
        [[1, 2, 3, 3], [2, 5], [2, 4], [2]]

    At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
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        >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2],
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    If the sum of *sizes* is smaller than the length of *iterable*, then the
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        >>> list(split_into([1,2,3,4,5,6], [2,3]))
        [[1, 2], [3, 4, 5]]

    If the sum of *sizes* is larger than the length of *iterable*, fewer items
    will be returned in the iteration that overruns *iterable* and further
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        >>> list(split_into([1,2,3,4], [1,2,3,4]))
        [[1], [2, 3], [4], []]

    When a ``None`` object is encountered in *sizes*, the returned list will
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        >>> list(split_into([1,2,3,4,5,6,7,8,9,0], [2,3,None]))
        [[1, 2], [3, 4, 5], [6, 7, 8, 9, 0]]

    :func:`split_into` can be useful for grouping a series of items where the
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        >>> list(group_2)
        [2, 4, 6]

    If the length of *iterable* is not evenly divisible by *n*, then the
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        >>> children = distribute(3, [1, 2, 3, 4, 5, 6, 7])
        >>> [list(c) for c in children]
        [[1, 4, 7], [2, 5], [3, 6]]

    If the length of *iterable* is smaller than *n*, then the last returned
    iterables will be empty:

        >>> children = distribute(5, [1, 2, 3])
        >>> [list(c) for c in children]
        [[1], [2], [3], [], []]

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        [(None, 0, 1), (0, 1, 2), (1, 2, 3)]
        >>> list(stagger(range(8), offsets=(0, 2, 4)))
        [(0, 2, 4), (1, 3, 5), (2, 4, 6), (3, 5, 7)]

    By default, the sequence will end when the final element of a tuple is the
    last item in the iterable. To continue until the first element of a tuple
    is the last item in the iterable, set *longest* to ``True``::

        >>> list(stagger([0, 1, 2, 3], longest=True))
        [(None, 0, 1), (0, 1, 2), (1, 2, 3), (2, 3, None), (3, None, None)]

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        [(0, 'a'), (1, 'b'), (2, 'c')]

        >>> it_1 = range(3)
        >>> it_2 = iter('abcd')
        >>> list(zip_equal(it_1, it_2)) # doctest: +IGNORE_EXCEPTION_DETAIL
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        [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e')]

    This can be used as a lightweight alternative to SciPy or pandas to analyze
    data sets in which some series have a lead or lag relationship.

    By default, the sequence will end when the shortest iterable is exhausted.
    To continue until the longest iterable is exhausted, set *longest* to
    ``True``.

        >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1), longest=True))
        [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e'), (None, 'f')]

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    columns are used for sorting.

    By default, all iterables are sorted using the ``0``-th iterable::

        >>> iterables = [(4, 3, 2, 1), ('a', 'b', 'c', 'd')]
        >>> sort_together(iterables)
        [(1, 2, 3, 4), ('d', 'c', 'b', 'a')]

    Set a different key list to sort according to another iterable.
    Specifying multiple keys dictates how ties are broken::

        >>> iterables = [(3, 1, 2), (0, 1, 0), ('c', 'b', 'a')]
        >>> sort_together(iterables, key_list=(1, 2))
        [(2, 3, 1), (0, 0, 1), ('a', 'c', 'b')]

    To sort by a function of the elements of the iterable, pass a *key*
    function. Its arguments are the elements of the iterables corresponding to
    the key list::

        >>> names = ('a', 'b', 'c')
        >>> lengths = (1, 2, 3)
        >>> widths = (5, 2, 1)
        >>> def area(length, width):
        ...     return length * width
        >>> sort_together([names, lengths, widths], key_list=(1, 2), key=area)
        [('c', 'b', 'a'), (3, 2, 1), (1, 2, 5)]

    Set *reverse* to ``True`` to sort in descending order.

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        [(3, 2, 1), ('a', 'b', 'c')]

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    length of the remaining elements.

        >>> iterable = [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
        >>> letters, numbers = unzip(iterable)
        >>> list(letters)
        ['a', 'b', 'c', 'd']
        >>> list(numbers)
        [1, 2, 3, 4]

    This is similar to using ``zip(*iterable)``, but it avoids reading
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        >>> list(group_2)
        [4, 5, 6]

    If the length of *iterable* is not evenly divisible by *n*, then the
    length of the returned iterables will not be identical:

        >>> children = divide(3, [1, 2, 3, 4, 5, 6, 7])
        >>> [list(c) for c in children]
        [[1, 2, 3], [4, 5], [6, 7]]

    If the length of the iterable is smaller than n, then the last returned
    iterables will be empty:

        >>> children = divide(5, [1, 2, 3])
        >>> [list(c) for c in children]
        [[1], [2], [3], [], []]

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    which does not first pull the iterable into memory.

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    If *obj* is not iterable, return a one-item iterable containing *obj*::

        >>> obj = 1
        >>> list(always_iterable(obj))
        [1]

    If *obj* is ``None``, return an empty iterable:

        >>> obj = None
        >>> list(always_iterable(None))
        []

    By default, binary and text strings are not considered iterable::

        >>> obj = 'foo'
        >>> list(always_iterable(obj))
        ['foo']

    If *base_type* is set, objects for which ``isinstance(obj, base_type)``
    returns ``True`` won't be considered iterable.

        >>> obj = {'a': 1}
        >>> list(always_iterable(obj))  # Iterate over the dict's keys
        ['a']
        >>> list(always_iterable(obj, base_type=dict))  # Treat dicts as a unit
        [{'a': 1}]

    Set *base_type* to ``None`` to avoid any special handling and treat objects
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        >>> list(adjacent(lambda x: x == 3, range(6)))
        [(False, 0), (False, 1), (True, 2), (True, 3), (True, 4), (False, 5)]

    Set *distance* to change what counts as adjacent. For example, to find
    whether items are two places away from a ``3``:

        >>> list(adjacent(lambda x: x == 3, range(6), distance=2))
        [(False, 0), (True, 1), (True, 2), (True, 3), (True, 4), (True, 5)]

    This is useful for contextualizing the results of a search function.
    For example, a code comparison tool might want to identify lines that
    have changed, but also surrounding lines to give the viewer of the diff
    context.

    The predicate function will only be called once for each item in the
    iterable.

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      *iterable* after grouping
    * *reducefunc* is a function that transforms each group of items

    >>> iterable = 'aAAbBBcCC'
    >>> keyfunc = lambda k: k.upper()
    >>> valuefunc = lambda v: v.lower()
    >>> reducefunc = lambda g: ''.join(g)
    >>> list(groupby_transform(iterable, keyfunc, valuefunc, reducefunc))
    [('A', 'aaa'), ('B', 'bbb'), ('C', 'ccc')]

    Each optional argument defaults to an identity function if not specified.

    :func:`groupby_transform` is useful when grouping elements of an iterable
    using a separate iterable as the key. To do this, :func:`zip` the iterables
    and pass a *keyfunc* that extracts the first element and a *valuefunc*
    that extracts the second element::

        >>> from operator import itemgetter
        >>> keys = [0, 0, 1, 1, 1, 2, 2, 2, 3]
        >>> values = 'abcdefghi'
        >>> iterable = zip(keys, values)
        >>> grouper = groupby_transform(iterable, itemgetter(0), itemgetter(1))
        >>> [(k, ''.join(g)) for k, g in grouper]
        [(0, 'ab'), (1, 'cde'), (2, 'fgh'), (3, 'i')]

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        >>> list(numeric_range(3.5))
        [0.0, 1.0, 2.0, 3.0]

    With only *start* and *stop* specified, *step* defaults to ``1``. The
    output items will match the type of *start*:

        >>> from decimal import Decimal
        >>> start = Decimal('2.1')
        >>> stop = Decimal('5.1')
        >>> list(numeric_range(start, stop))
        [Decimal('2.1'), Decimal('3.1'), Decimal('4.1')]

    With *start*, *stop*, and *step*  specified the output items will match
    the type of ``start + step``:

        >>> from fractions import Fraction
        >>> start = Fraction(1, 2)  # Start at 1/2
        >>> stop = Fraction(5, 2)  # End at 5/2
        >>> step = Fraction(1, 2)  # Count by 1/2
        >>> list(numeric_range(start, stop, step))
        [Fraction(1, 2), Fraction(1, 1), Fraction(3, 2), Fraction(2, 1)]

    If *step* is zero, ``ValueError`` is raised. Negative steps are supported:

        >>> list(numeric_range(3, -1, -1.0))
        [3.0, 2.0, 1.0, 0.0]

    Be aware of the limitations of floating point numbers; the representation
    of the yielded numbers may be surprising.

    ``datetime.datetime`` objects can be used for *start* and *stop*, if *step*
    is a ``datetime.timedelta`` object:

        >>> import datetime
        >>> start = datetime.datetime(2019, 1, 1)
        >>> stop = datetime.datetime(2019, 1, 3)
        >>> step = datetime.timedelta(days=1)
        >>> items = iter(numeric_range(start, stop, step))
        >>> next(items)
        datetime.datetime(2019, 1, 1, 0, 0)
        >>> next(items)
        datetime.datetime(2019, 1, 2, 0, 0)

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        [10, 11, 12]
        [20]
        [30, 31, 32, 33]
        [40]

    For finding runs of adjacent letters, try using the :meth:`index` method
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        >>> iterable = 'abcdfgilmnop'
        >>> ordering = ascii_lowercase.index
        >>> for group in consecutive_groups(iterable, ordering):
        ...     print(list(group))
        ['a', 'b', 'c', 'd']
        ['f', 'g']
        ['i']
        ['l', 'm', 'n', 'o', 'p']

    Each group of consecutive items is an iterator that shares it source with
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        >>> saved_groups = []
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    *func* defaults to :func:`operator.sub`, but other functions can be
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        >>> iterable = [1, 2, 6, 24, 120]
        >>> func = lambda x, y: x // y
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    If the *initial* keyword is set, the first element will be skipped when
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        >>> seq = ['0', '1', '2']
        >>> view = SequenceView(seq)
        >>> view
        SequenceView(['0', '1', '2'])
        >>> seq.append('3')
        >>> view
        SequenceView(['0', '1', '2', '3'])

    Sequence views support indexing, slicing, and length queries. They act
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    To "reset" an iterator, seek to ``0``:

        >>> from itertools import count
        >>> it = seekable((str(n) for n in count()))
        >>> next(it), next(it), next(it)
        ('0', '1', '2')
        >>> it.seek(0)
        >>> next(it), next(it), next(it)
        ('0', '1', '2')
        >>> next(it)
        '3'

    You can also seek forward:

        >>> it = seekable((str(n) for n in range(20)))
        >>> it.seek(10)
        >>> next(it)
        '10'
        >>> it.relative_seek(-2)  # Seeking relative to the current position
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        '9'
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        ('0', '1', '2')

    Call :meth:`peek` to look ahead one item without advancing the iterator:

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        >>> it.peek()
        '1'
        >>> list(it)
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        >>> it.peek(default='empty')
        'empty'

    Before the iterator is at its end, calling :func:`bool` on it will return
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        >>> it = seekable('5678')
        >>> bool(it)
        True
        >>> list(it)
        ['5', '6', '7', '8']
        >>> bool(it)
        False

    You may view the contents of the cache with the :meth:`elements` method.
    That returns a :class:`SequenceView`, a view that updates automatically:

        >>> it = seekable((str(n) for n in range(10)))
        >>> next(it), next(it), next(it)
        ('0', '1', '2')
        >>> elements = it.elements()
        >>> elements
        SequenceView(['0', '1', '2'])
        >>> next(it)
        '3'
        >>> elements
        SequenceView(['0', '1', '2', '3'])

    By default, the cache grows as the source iterable progresses, so beware of
    wrapping very large or infinite iterables. Supply *maxlen* to limit the
    size of the cache (this of course limits how far back you can seek).

        >>> from itertools import count
        >>> it = seekable((str(n) for n in count()), maxlen=2)
        >>> next(it), next(it), next(it), next(it)
        ('0', '1', '2', '3')
        >>> list(it.elements())
        ['2', '3']
        >>> it.seek(0)
        >>> next(it), next(it), next(it), next(it)
        ('2', '3', '4', '5')
        >>> next(it)
        '6'

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        [('a', 1), ('b', 2), ('c', 3), ('d', 4)]

    :func:`run_length.decode` decompresses an iterable that was previously
    compressed with run-length encoding. It yields the items of the
    decompressed iterable:

        >>> compressed = [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
        >>> list(run_length.decode(compressed))
        ['a', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd', 'd']

    c�&�d�t|�D�S)Nc3�<K�|]\}}|t|�f���y�wr�)r`)r@r�r�s   r�rBz$run_length.encode.<locals>.<genexpr>,s����;���A��D��G��;�s�r�rs r��encodezrun_length.encode*s��;���):�;�;r�c�:�tjd�|D��S)Nc3�:K�|]\}}t||����y�wr�)r)r@r�r�s   r�rBz$run_length.decode.<locals>.<genexpr>0s����"E�D�A�q�6�!�Q�<�"E�s�)rr,rs r��decodezrun_length.decode.s���"�"�"E�H�"E�E�Er�N)r�r�r�r��staticmethodr>rAr�r�r�r�r�s1���&�<��<��F��Fr�r�c	�L�tt|dzt||���|k(S)a�Return ``True`` if exactly ``n`` items in the iterable are ``True``
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        >>> exactly_n([True, True, False], 1)
        False
        >>> exactly_n([0, 1, 2, 3, 4, 5], 3, lambda x: x < 3)
        True

    The iterable will be advanced until ``n + 1`` truthy items are encountered,
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    )r�r9r�r�r)r��lsts  r�rGrGEs-���x�.�C���C��(�5��:�s�3�x�8�9�9r�c������fd�}|S)a�Return a decorator version of *wrapping_func*, which is a function that
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    definition. This can augment what the function returns without changing the
    function's code.

    For example, to produce a decorator version of :func:`chunked`:

        >>> from more_itertools import chunked
        >>> chunker = make_decorator(chunked, result_index=0)
        >>> @chunker(3)
        ... def iter_range(n):
        ...     return iter(range(n))
        ...
        >>> list(iter_range(9))
        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]

    To only allow truthy items to be returned:

        >>> truth_serum = make_decorator(filter, result_index=1)
        >>> @truth_serum(bool)
        ... def boolean_test():
        ...     return [0, 1, '', ' ', False, True]
        ...
        >>> list(boolean_test())
        [1, ' ', True]

    The :func:`peekable` and :func:`seekable` wrappers make for practical
    decorators:

        >>> from more_itertools import peekable
        >>> peekable_function = make_decorator(peekable)
        >>> @peekable_function()
        ... def str_range(*args):
        ...     return (str(x) for x in range(*args))
        ...
        >>> it = str_range(1, 20, 2)
        >>> next(it), next(it), next(it)
        ('1', '3', '5')
        >>> it.peek()
        '7'
        >>> next(it)
        '7'

    c���������fd�}|S)Nc���������fd�}|S)Nc�^���|i|��}t��}|j�|��|i���Sr�)r��insert)	r�r��result�wrapping_args_�f�result_index�
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        >>> keyfunc = lambda x: x.upper()
        >>> result = map_reduce('abbccc', keyfunc)
        >>> sorted(result.items())
        [('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]

    Specifying *valuefunc* transforms the categorized items:

        >>> keyfunc = lambda x: x.upper()
        >>> valuefunc = lambda x: 1
        >>> result = map_reduce('abbccc', keyfunc, valuefunc)
        >>> sorted(result.items())
        [('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]

    Specifying *reducefunc* summarizes the categorized items:

        >>> keyfunc = lambda x: x.upper()
        >>> valuefunc = lambda x: 1
        >>> reducefunc = sum
        >>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc)
        >>> sorted(result.items())
        [('A', 1), ('B', 2), ('C', 3)]

    You may want to filter the input iterable before applying the map/reduce
    procedure:

        >>> all_items = range(30)
        >>> items = [x for x in all_items if 10 <= x <= 20]  # Filter
        >>> keyfunc = lambda x: x % 2  # Evens map to 0; odds to 1
        >>> categories = map_reduce(items, keyfunc=keyfunc)
        >>> sorted(categories.items())
        [(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
        >>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum)
        >>> sorted(summaries.items())
        [(0, 90), (1, 75)]

    Note that all items in the iterable are gathered into a list before the
    summarization step, which may require significant storage.

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        [4, 2, 1]

    Set *pred* to a custom function to, e.g., find the indexes for a particular
    item:

        >>> iterable = iter('abcb')
        >>> pred = lambda x: x == 'b'
        >>> list(rlocate(iterable, pred))
        [3, 1]

    If *window_size* is given, then the *pred* function will be called with
    that many items. This enables searching for sub-sequences:

        >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
        >>> pred = lambda *args: args == (1, 2, 3)
        >>> list(rlocate(iterable, pred=pred, window_size=3))
        [9, 5, 1]

    Beware, this function won't return anything for infinite iterables.
    If *iterable* is reversible, ``rlocate`` will reverse it and search from
    the right. Otherwise, it will search from the left and return the results
    in reverse order.

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        >>> pred = lambda x: x == 0
        >>> substitutes = (2, 3)
        >>> list(replace(iterable, pred, substitutes))
        [1, 1, 2, 3, 1, 1, 2, 3, 1, 1]

    If *count* is given, the number of replacements will be limited:

        >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1, 0]
        >>> pred = lambda x: x == 0
        >>> substitutes = [None]
        >>> list(replace(iterable, pred, substitutes, count=2))
        [1, 1, None, 1, 1, None, 1, 1, 0]

    Use *window_size* to control the number of items passed as arguments to
    *pred*. This allows for locating and replacing subsequences.

        >>> iterable = [0, 1, 2, 5, 0, 1, 2, 5]
        >>> window_size = 3
        >>> pred = lambda *args: args == (0, 1, 2)  # 3 items passed to pred
        >>> substitutes = [3, 4] # Splice in these items
        >>> list(replace(iterable, pred, substitutes, window_size=window_size))
        [3, 4, 5, 3, 4, 5]

    r1zwindow_size must be at least 1rN)r�rrr2r�r5)	r�r��substitutesrrr�windowsr��ws	         r�r�r��s�����:�Q���9�:�:���$�K�
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    ['a', 'b', 'c']

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    ...     print([''.join(p) for p in part])
    ['a', 'bc']
    ['ab', 'c']
    ['b', 'ac']


    If *k* is not given, every set partition is generated.

    >>> iterable = 'abc'
    >>> for part in set_partitions(iterable):
    ...     print([''.join(p) for p in part])
    ['abc']
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    ['a', 'b', 'c']

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    Yield items from *iterable* until *limit_seconds* have passed.
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    >>> def generator():
    ...     yield 1
    ...     yield 2
    ...     sleep(0.2)
    ...     yield 3
    >>> iterable = time_limited(0.1, generator())
    >>> list(iterable)
    [1, 2]
    >>> iterable.timed_out
    True

    Note that the time is checked before each item is yielded, and iteration
    stops if  the time elapsed is greater than *limit_seconds*. If your time
    limit is 1 second, but it takes 2 seconds to generate the first item from
    the iterable, the function will run for 2 seconds and not yield anything.
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    c�~�|dkrtd��||_t|�|_t	�|_d|_y)Nrzlimit_seconds must be positiveF)r��
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    >>> only([], default='missing')
    'missing'
    >>> only([1])
    1
    >>> only([1, 2])  # doctest: +IGNORE_EXCEPTION_DETAIL
    Traceback (most recent call last):
    ...
    ValueError: Expected exactly one item in iterable, but got 1, 2,
     and perhaps more.'
    >>> only([1, 2], too_long=TypeError)  # doctest: +IGNORE_EXCEPTION_DETAIL
    Traceback (most recent call last):
    ...
    TypeError

    Note that :func:`only` attempts to advance *iterable* twice to ensure there
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    won't be stored in memory.
    If they are read out of order, :func:`itertools.tee` is used to cache
    elements as necessary.

    >>> from itertools import count
    >>> all_chunks = ichunked(count(), 4)
    >>> c_1, c_2, c_3 = next(all_chunks), next(all_chunks), next(all_chunks)
    >>> list(c_2)  # c_1's elements have been cached; c_3's haven't been
    [4, 5, 6, 7]
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    [8, 9, 10, 11]

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    >>> iequals("abc", ['a', 'b', 'c'], ('a', 'b', 'c'), iter("abc"))
    True

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    if that item is not valid.

    >>> iterable = ['1', '2', 'three', '4', None]
    >>> list(filter_except(int, iterable, ValueError, TypeError))
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    >>> list(map_except(int, iterable, ValueError, TypeError))
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    [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
    >>> list(map_if(iterable, lambda x: x > 3, lambda x: 'toobig'))
    [-5, -4, -3, -2, -1, 0, 1, 2, 3, 'toobig']
    >>> list(map_if(iterable, lambda x: x >= 0,
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    [81, 60, 96, 16, 4]

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    >>> weights = (i * i + 1 for i in range(100))
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    [79, 67, 74, 66, 78]

    The algorithm can also be used to generate weighted random permutations.
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    >>> weights = range(1, len(data) + 1)
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    If *strict*, tests for strict sorting, that is, returns ``False`` if equal
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        >>> nth_permutation('ghijk', 2, 5)
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    [[1, 2, 3], [4, 5], [6, 7]]
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t	|����(y�w)aA version of :func:`zip` that "broadcasts" any scalar
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    >>> iterable_1 = [1, 2, 3]
    >>> iterable_2 = ['a', 'b', 'c']
    >>> scalar = '_'
    >>> list(zip_broadcast(iterable_1, iterable_2, scalar))
    [(1, 'a', '_'), (2, 'b', '_'), (3, 'c', '_')]

    The *scalar_types* keyword argument determines what types are considered
    scalar. It is set to ``(str, bytes)`` by default. Set it to ``None`` to
    treat strings and byte strings as iterable:

    >>> list(zip_broadcast('abc', 0, 'xyz', scalar_types=None))
    [('a', 0, 'x'), ('b', 0, 'y'), ('c', 0, 'z')]

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cc<|j|��by�w)a�Yield the items from *iterable* that haven't been seen recently.
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        >>> n = 3
        >>> list(unique_in_window(iterable, n))
        [0, 1, 2, 3, 0]

    The *key* function, if provided, will be used to determine uniqueness:

        >>> list(unique_in_window('abAcda', 3, key=lambda x: x.lower()))
        ['a', 'b', 'c', 'd', 'a']

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    >>> list(duplicates_everseen('mississippi'))
    ['s', 'i', 's', 's', 'i', 'p', 'i']
    >>> list(duplicates_everseen('AaaBbbCccAaa', str.lower))
    ['a', 'a', 'b', 'b', 'c', 'c', 'A', 'a', 'a']

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    >>> list(duplicates_justseen('mississippi'))
    ['s', 's', 'p']
    >>> list(duplicates_justseen('AaaBbbCccAaa', str.lower))
    ['a', 'a', 'b', 'b', 'c', 'c', 'a', 'a']

    This function is analogous to :func:`unique_justseen`.

    c3�4K�|]\}}|D]}|����y�wr�r�)r@r;r�s   r�rBz&duplicates_justseen.<locals>.<genexpr>�s ����C���A��C�A�1�C�1�C�s�)r6r)r�rLs  r�rVrV�s���C���3�!7�C�C�Cr�c#�K�t�}g}|du}d}t|�D]>\}}|r||�n|}|xs||k7}	|}d}
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    For each element in the input iterable, return a 3-tuple consisting of:

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       ``True`` otherwise (i.e. the equivalent of :func:`unique_justseen`)
    3. ``False`` if this element has been seen anywhere in the input before,
       ``True`` otherwise (i.e. the equivalent of :func:`unique_everseen`)

    >>> list(classify_unique('otto'))    # doctest: +NORMALIZE_WHITESPACE
    [('o', True,  True),
     ('t', True,  True),
     ('t', False, False),
     ('o', True,  False)]

    This function is analogous to :func:`unique_everseen` and is subject to
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|}}�:||fS#t$r }|turt	d�|�|cYd}~Sd}~wwxYw)a�Returns both the smallest and largest items in an iterable
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        (1, 5)

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        (2, 6)

    If a *key* function is provided, it will be used to transform the input
    items for comparison.

        >>> minmax([5, 30], key=str)  # '30' sorts before '5'
        (30, 5)

    If a *default* value is provided, it will be returned if there are no
    input items.

        >>> minmax([], default=(0, 0))
        (0, 0)

    Otherwise ``ValueError`` is raised.

    This function is based on the
    `recipe <http://code.activestate.com/recipes/577916/>`__ by
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    >>> list(constrained_batches(iterable, 10))
    [(b'12345', b'123'), (b'12345678', b'1', b'1'), (b'12', b'1')]

    If a *max_count* is supplied, the number of items per batch is also
    limited:

    >>> iterable = [b'12345', b'123', b'12345678', b'1', b'1', b'12', b'1']
    >>> list(constrained_batches(iterable, 10, max_count = 2))
    [(b'12345', b'123'), (b'12345678', b'1'), (b'1', b'12'), (b'1',)]

    If a *get_len* function is supplied, use that instead of :func:`len` to
    determine item size.

    If *strict* is ``True``, raise ``ValueError`` if any single item is bigger
    than *max_size*. Otherwise, allow single items to exceed *max_size*.
    rz&maximum size must be greater than zerozitem size exceeds maximum sizer1N)r�rrr�)r��max_size�	max_count�get_lenr��batch�
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    to the next.

        >>> list(gray_product('AB','CD'))
        [('A', 'C'), ('B', 'C'), ('B', 'D'), ('A', 'D')]

    This function consumes all of the input iterables before producing output.
    If any of the input iterables have fewer than two items, ``ValueError``
    is raised.

    For information on the algorithm, see
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    c3�2K�|]}t|����y�wr�)rr^s  r�rBzgray_product.<locals>.<genexpr>�s����6�q�%��(�6�s�rz)each iterable must have two or more itemsrr1c3�4�K�|]}�|�|���y�wr�r�)r@rr��
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        >>> list(partial_product('AB', 'C', 'DEF'))
        [('A', 'C', 'D'), ('B', 'C', 'D'), ('B', 'C', 'E'), ('B', 'C', 'F')]
    N)r�r-r�r�r�rrd)r1�	iteratorsr�prodrs     r�r|r|�s������S��y�)�*�I��#,�-�R��R��-��-���+���9�%����2��	�D��G���+��	���.������s7�B�A2�A-�A2�8B�-A2�2	A>�;B�=A>�>Bc#�6K�|D]}|��||�r�yy�w)z�A variant of :func:`takewhile` that yields one additional element.

        >>> list(takewhile_inclusive(lambda x: x < 5, [1, 4, 6, 4, 1]))
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    Nr�)r�r�rIs   r�r�r��s&�����������|���s��c	�|����t|�}tt���fd�t||��t	|���S)a�A generalized outer product that applies a binary function to all
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    Multiplication table:

    >>> list(outer_product(mul, range(1, 4), range(1, 6)))
    [(1, 2, 3, 4, 5), (2, 4, 6, 8, 10), (3, 6, 9, 12, 15)]

    Cross tabulation:

    >>> xs = ['A', 'B', 'A', 'A', 'B', 'B', 'A', 'A', 'B', 'B']
    >>> ys = ['X', 'X', 'X', 'Y', 'Z', 'Z', 'Y', 'Y', 'Z', 'Z']
    >>> rows = list(zip(xs, ys))
    >>> count_rows = lambda x, y: rows.count((x, y))
    >>> list(outer_product(count_rows, sorted(set(xs)), sorted(set(ys))))
    [(2, 3, 0), (1, 0, 4)]

    Usage with ``*args`` and ``**kwargs``:

    >>> animals = ['cat', 'wolf', 'mouse']
    >>> list(outer_product(min, animals, animals, key=len))
    [('cat', 'cat', 'cat'), ('cat', 'wolf', 'wolf'), ('cat', 'wolf', 'mouse')]
    c����||g���i���Sr�r�)rIr�r�r�r�s  ���r�rzouter_product.<locals>.<lambda>	s���T�!�Q�8��8��8�r�)r�)rr<rrr�)r��xs�ysr�r�s`  ``r�rzrz�s3���4
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�b�'��r�c'�<K�	|Ed{���y7�#|$rYywxYw�w)a.Yield each of the items from *iterable*. If the iteration raises one of
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    >>> def breaks_at_five(x):
    ...     while True:
    ...         if x >= 5:
    ...             raise RuntimeError
    ...         yield x
    ...         x += 1
    >>> it_1 = iter_suppress(breaks_at_five(1), RuntimeError)
    >>> it_2 = iter_suppress(breaks_at_five(2), RuntimeError)
    >>> list(chain(it_1, it_2))
    [1, 2, 3, 4, 2, 3, 4]
    Nr�)r�r�s  r�rhrhs%����"���������s(����������c#�8K�|D]}||�}|��|���y�w)z�Apply *func* to every element of *iterable*, yielding only those which
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    [1, 2, 3]
    Nr�)r�r�rIr�s    r�rZrZ%s*���������G���=��G��s��)Fr�r�)r1rr)NNN)r�F)r�)NNF)r))r�rr1FN)r�NF)NFF)�r��collectionsrrrr�collections.abcr�	functoolsrr	r
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