Elep

"""Imported from the recipes section of the itertools documentation.

All functions taken from the recipes section of the itertools library docs
[1]_.
Some backward-compatible usability improvements have been made.

.. [1] http://docs.python.org/library/itertools.html#recipes

"""
import math
import operator

from collections import deque
from collections.abc import Sized
from functools import partial, reduce
from itertools import (
    chain,
    combinations,
    compress,
    count,
    cycle,
    groupby,
    islice,
    product,
    repeat,
    starmap,
    tee,
    zip_longest,
)
from random import randrange, sample, choice
from sys import hexversion

__all__ = [
    'all_equal',
    'batched',
    'before_and_after',
    'consume',
    'convolve',
    'dotproduct',
    'first_true',
    'factor',
    'flatten',
    'grouper',
    'iter_except',
    'iter_index',
    'matmul',
    'ncycles',
    'nth',
    'nth_combination',
    'padnone',
    'pad_none',
    'pairwise',
    'partition',
    'polynomial_eval',
    'polynomial_from_roots',
    'polynomial_derivative',
    'powerset',
    'prepend',
    'quantify',
    'reshape',
    'random_combination_with_replacement',
    'random_combination',
    'random_permutation',
    'random_product',
    'repeatfunc',
    'roundrobin',
    'sieve',
    'sliding_window',
    'subslices',
    'sum_of_squares',
    'tabulate',
    'tail',
    'take',
    'totient',
    'transpose',
    'triplewise',
    'unique_everseen',
    'unique_justseen',
]

_marker = object()

# zip with strict is available for Python 3.10+
try:
    zip(strict=True)
except TypeError:
    _zip_strict = zip
else:
    _zip_strict = partial(zip, strict=True)

# math.sumprod is available for Python 3.12+
_sumprod = getattr(math, 'sumprod', lambda x, y: dotproduct(x, y))

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

>>> take(3, range(10))
        [0, 1, 2]

If there are fewer than *n* items in the iterable, all of them are
    returned.

>>> take(10, range(3))
        [0, 1, 2]

"""
    return list(islice(iterable, n))

def tabulate(function, start=0):
    """Return an iterator over the results of ``func(start)``,
    ``func(start + 1)``, ``func(start + 2)``...

*func* should be a function that accepts one integer argument.

If *start* is not specified it defaults to 0. It will be incremented each
    time the iterator is advanced.

>>> square = lambda x: x ** 2
        >>> iterator = tabulate(square, -3)
        >>> take(4, iterator)
        [9, 4, 1, 0]

"""
    return map(function, count(start))

def tail(n, iterable):
    """Return an iterator over the last *n* items of *iterable*.

>>> t = tail(3, 'ABCDEFG')
    >>> list(t)
    ['E', 'F', 'G']

"""
    # If the given iterable has a length, then we can use islice to get its
    # final elements. Note that if the iterable is not actually Iterable,
    # either islice or deque will throw a TypeError. This is why we don't
    # check if it is Iterable.
    if isinstance(iterable, Sized):
        yield from islice(iterable, max(0, len(iterable) - n), None)
    else:
        yield from iter(deque(iterable, maxlen=n))

def consume(iterator, n=None):
    """Advance *iterable* by *n* steps. If *n* is ``None``, consume it
    entirely.

Efficiently exhausts an iterator without returning values. Defaults to
    consuming the whole iterator, but an optional second argument may be
    provided to limit consumption.

>>> i = (x for x in range(10))
        >>> next(i)
        0
        >>> consume(i, 3)
        >>> next(i)
        4
        >>> consume(i)
        >>> next(i)
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
        StopIteration

If the iterator has fewer items remaining than the provided limit, the
    whole iterator will be consumed.

>>> i = (x for x in range(3))
        >>> consume(i, 5)
        >>> next(i)
        Traceback (most recent call last):
          File "<stdin>", line 1, in <module>
        StopIteration

"""
    # Use functions that consume iterators at C speed.
    if n is None:
        # feed the entire iterator into a zero-length deque
        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.

>>> l = range(10)
    >>> nth(l, 3)
    3
    >>> nth(l, 20, "zebra")
    'zebra'

"""
    return next(islice(iterable, n, None), default)

def all_equal(iterable):
    """
    Returns ``True`` if all the elements are equal to each other.

>>> all_equal('aaaa')
        True
        >>> all_equal('aaab')
        False

"""
    g = groupby(iterable)
    return next(g, True) and not next(g, False)

def quantify(iterable, pred=bool):
    """Return the how many times the predicate is true.

>>> quantify([True, False, True])
    2

"""
    return sum(map(pred, iterable))

def pad_none(iterable):
    """Returns the sequence of elements and then returns ``None`` indefinitely.

>>> take(5, pad_none(range(3)))
        [0, 1, 2, None, None]

Useful for emulating the behavior of the built-in :func:`map` function.

See also :func:`padded`.

"""
    return chain(iterable, repeat(None))

padnone = pad_none

def ncycles(iterable, n):
    """Returns the sequence elements *n* times

>>> list(ncycles(["a", "b"], 3))
    ['a', 'b', 'a', 'b', 'a', 'b']

"""
    return chain.from_iterable(repeat(tuple(iterable), n))

def dotproduct(vec1, vec2):
    """Returns the dot product of the two iterables.

>>> dotproduct([10, 10], [20, 20])
    400

"""
    return sum(map(operator.mul, vec1, vec2))

def flatten(listOfLists):
    """Return an iterator flattening one level of nesting in a list of lists.

>>> list(flatten([[0, 1], [2, 3]]))
        [0, 1, 2, 3]

See also :func:`collapse`, which can flatten multiple levels of nesting.

"""
    return chain.from_iterable(listOfLists)

def repeatfunc(func, times=None, *args):
    """Call *func* with *args* repeatedly, returning an iterable over the
    results.

If *times* is specified, the iterable will terminate after that many
    repetitions:

>>> from operator import add
        >>> times = 4
        >>> args = 3, 5
        >>> list(repeatfunc(add, times, *args))
        [8, 8, 8, 8]

If *times* is ``None`` the iterable will not terminate:

>>> from random import randrange
        >>> times = None
        >>> args = 1, 11
        >>> take(6, repeatfunc(randrange, times, *args))  # doctest:+SKIP
        [2, 4, 8, 1, 8, 4]

"""
    if times is None:
        return starmap(func, repeat(args))
    return starmap(func, repeat(args, times))

def _pairwise(iterable):
    """Returns an iterator of paired items, overlapping, from the original

>>> take(4, pairwise(count()))
    [(0, 1), (1, 2), (2, 3), (3, 4)]

On Python 3.10 and above, this is an alias for :func:`itertools.pairwise`.

"""
    a, b = tee(iterable)
    next(b, None)
    return zip(a, b)

try:
    from itertools import pairwise as itertools_pairwise
except ImportError:
    pairwise = _pairwise
else:

def pairwise(iterable):
        return itertools_pairwise(iterable)

pairwise.__doc__ = _pairwise.__doc__

class UnequalIterablesError(ValueError):
    def __init__(self, details=None):
        msg = 'Iterables have different lengths'
        if details is not None:
            msg += (': index 0 has length {}; index {} has length {}').format(
                *details
            )

super().__init__(msg)

def _zip_equal_generator(iterables):
    for combo in zip_longest(*iterables, fillvalue=_marker):
        for val in combo:
            if val is _marker:
                raise UnequalIterablesError()
        yield combo

def _zip_equal(*iterables):
    # Check whether the iterables are all the same size.
    try:
        first_size = len(iterables[0])
        for i, it in enumerate(iterables[1:], 1):
            size = len(it)
            if size != first_size:
                raise UnequalIterablesError(details=(first_size, i, size))
        # All sizes are equal, we can use the built-in zip.
        return zip(*iterables)
    # If any one of the iterables didn't have a length, start reading
    # them until one runs out.
    except TypeError:
        return _zip_equal_generator(iterables)

def grouper(iterable, n, incomplete='fill', fillvalue=None):
    """Group elements from *iterable* into fixed-length groups of length *n*.

>>> list(grouper('ABCDEF', 3))
    [('A', 'B', 'C'), ('D', 'E', 'F')]

The keyword arguments *incomplete* and *fillvalue* control what happens for
    iterables whose length is not a multiple of *n*.

When *incomplete* is `'fill'`, the last group will contain instances of
    *fillvalue*.

>>> list(grouper('ABCDEFG', 3, incomplete='fill', fillvalue='x'))
    [('A', 'B', 'C'), ('D', 'E', 'F'), ('G', 'x', 'x')]

When *incomplete* is `'ignore'`, the last group will not be emitted.

>>> list(grouper('ABCDEFG', 3, incomplete='ignore', fillvalue='x'))
    [('A', 'B', 'C'), ('D', 'E', 'F')]

When *incomplete* is `'strict'`, a subclass of `ValueError` will be raised.

>>> it = grouper('ABCDEFG', 3, incomplete='strict')
    >>> list(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
    Traceback (most recent call last):
    ...
    UnequalIterablesError

"""
    args = [iter(iterable)] * n
    if incomplete == 'fill':
        return zip_longest(*args, fillvalue=fillvalue)
    if incomplete == 'strict':
        return _zip_equal(*args)
    if incomplete == 'ignore':
        return zip(*args)
    else:
        raise ValueError('Expected fill, strict, or ignore')

def roundrobin(*iterables):
    """Yields an item from each iterable, alternating between them.

>>> list(roundrobin('ABC', 'D', 'EF'))
        ['A', 'D', 'E', 'B', 'F', 'C']

This function produces the same output as :func:`interleave_longest`, but
    may perform better for some inputs (in particular when the number of
    iterables is small).

"""
    # 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 partition(pred, iterable):
    """
    Returns a 2-tuple of iterables derived from the input iterable.
    The first yields the items that have ``pred(item) == False``.
    The second yields the items that have ``pred(item) == True``.

>>> is_odd = lambda x: x % 2 != 0
        >>> iterable = range(10)
        >>> even_items, odd_items = partition(is_odd, iterable)
        >>> list(even_items), list(odd_items)
        ([0, 2, 4, 6, 8], [1, 3, 5, 7, 9])

If *pred* is None, :func:`bool` is used.

>>> iterable = [0, 1, False, True, '', ' ']
        >>> false_items, true_items = partition(None, iterable)
        >>> list(false_items), list(true_items)
        ([0, False, ''], [1, True, ' '])

"""
    if pred is None:
        pred = bool

t1, t2, p = tee(iterable, 3)
    p1, p2 = tee(map(pred, p))
    return (compress(t1, map(operator.not_, p1)), compress(t2, p2))

def powerset(iterable):
    """Yields all possible subsets of the iterable.

>>> list(powerset([1, 2, 3]))
        [(), (1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]

:func:`powerset` will operate on iterables that aren't :class:`set`
    instances, so repeated elements in the input will produce repeated elements
    in the output. Use :func:`unique_everseen` on the input to avoid generating
    duplicates:

>>> seq = [1, 1, 0]
        >>> list(powerset(seq))
        [(), (1,), (1,), (0,), (1, 1), (1, 0), (1, 0), (1, 1, 0)]
        >>> from more_itertools import unique_everseen
        >>> list(powerset(unique_everseen(seq)))
        [(), (1,), (0,), (1, 0)]

"""
    s = list(iterable)
    return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))

def unique_everseen(iterable, key=None):
    """
    Yield unique elements, preserving order.

>>> list(unique_everseen('AAAABBBCCDAABBB'))
        ['A', 'B', 'C', 'D']
        >>> list(unique_everseen('ABBCcAD', str.lower))
        ['A', 'B', 'C', 'D']

Sequences with a mix of hashable and unhashable items can be used.
    The function will be slower (i.e., `O(n^2)`) for unhashable items.

Remember that ``list`` objects are unhashable - you can use the *key*
    parameter to transform the list to a tuple (which is hashable) to
    avoid a slowdown.

>>> iterable = ([1, 2], [2, 3], [1, 2])
        >>> list(unique_everseen(iterable))  # Slow
        [[1, 2], [2, 3]]
        >>> list(unique_everseen(iterable, key=tuple))  # Faster
        [[1, 2], [2, 3]]

Similarly, you may want to convert unhashable ``set`` objects with
    ``key=frozenset``. For ``dict`` objects,
    ``key=lambda x: frozenset(x.items())`` can be used.

"""
    seenset = set()
    seenset_add = seenset.add
    seenlist = []
    seenlist_add = seenlist.append
    use_key = key is not None

for element in iterable:
        k = key(element) if use_key else element
        try:
            if k not in seenset:
                seenset_add(k)
                yield element
        except TypeError:
            if k not in seenlist:
                seenlist_add(k)
                yield element

def unique_justseen(iterable, key=None):
    """Yields elements in order, ignoring serial duplicates

>>> list(unique_justseen('AAAABBBCCDAABBB'))
    ['A', 'B', 'C', 'D', 'A', 'B']
    >>> list(unique_justseen('ABBCcAD', str.lower))
    ['A', 'B', 'C', 'A', 'D']

"""
    if key is None:
        return map(operator.itemgetter(0), groupby(iterable))

return map(next, map(operator.itemgetter(1), groupby(iterable, key)))

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

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

>>> l = [0, 1, 2]
        >>> list(iter_except(l.pop, IndexError))
        [2, 1, 0]

Multiple exceptions can be specified as a stopping condition:

>>> l = [1, 2, 3, '...', 4, 5, 6]
        >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError)))
        [7, 6, 5]
        >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError)))
        [4, 3, 2]
        >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError)))
        []

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

def first_true(iterable, default=None, pred=None):
    """
    Returns the first true value in the iterable.

If no true value is found, returns *default*

If *pred* is not None, returns the first item for which
    ``pred(item) == True`` .

>>> first_true(range(10))
        1
        >>> first_true(range(10), pred=lambda x: x > 5)
        6
        >>> first_true(range(10), default='missing', pred=lambda x: x > 9)
        'missing'

"""
    return next(filter(pred, iterable), default)

def random_product(*args, repeat=1):
    """Draw an item at random from each of the input iterables.

>>> random_product('abc', range(4), 'XYZ')  # doctest:+SKIP
        ('c', 3, 'Z')

If *repeat* is provided as a keyword argument, that many items will be
    drawn from each iterable.

>>> random_product('abcd', range(4), repeat=2)  # doctest:+SKIP
        ('a', 2, 'd', 3)

This equivalent to taking a random selection from
    ``itertools.product(*args, **kwarg)``.

"""
    pools = [tuple(pool) for pool in args] * repeat
    return tuple(choice(pool) for pool in pools)

def random_permutation(iterable, r=None):
    """Return a random *r* length permutation of the elements in *iterable*.

If *r* is not specified or is ``None``, then *r* defaults to the length of
    *iterable*.

>>> random_permutation(range(5))  # doctest:+SKIP
        (3, 4, 0, 1, 2)

This equivalent to taking a random selection from
    ``itertools.permutations(iterable, r)``.

"""
    pool = tuple(iterable)
    r = len(pool) if r is None else r
    return tuple(sample(pool, r))

def random_combination(iterable, r):
    """Return a random *r* length subsequence of the elements in *iterable*.

>>> random_combination(range(5), 3)  # doctest:+SKIP
        (2, 3, 4)

This equivalent to taking a random selection from
    ``itertools.combinations(iterable, r)``.

"""
    pool = tuple(iterable)
    n = len(pool)
    indices = sorted(sample(range(n), r))
    return tuple(pool[i] for i in indices)

def random_combination_with_replacement(iterable, r):
    """Return a random *r* length subsequence of elements in *iterable*,
    allowing individual elements to be repeated.

>>> random_combination_with_replacement(range(3), 5) # doctest:+SKIP
        (0, 0, 1, 2, 2)

This equivalent to taking a random selection from
    ``itertools.combinations_with_replacement(iterable, r)``.

"""
    pool = tuple(iterable)
    n = len(pool)
    indices = sorted(randrange(n) for i in range(r))
    return tuple(pool[i] for i in indices)

def nth_combination(iterable, r, index):
    """Equivalent to ``list(combinations(iterable, r))[index]``.

The subsequences of *iterable* that are of length *r* can be ordered
    lexicographically. :func:`nth_combination` computes the subsequence at
    sort position *index* directly, without computing the previous
    subsequences.

>>> nth_combination(range(5), 3, 5)
        (0, 3, 4)

``ValueError`` will be raised If *r* is negative or greater than the length
    of *iterable*.
    ``IndexError`` will be raised if the given *index* is invalid.
    """
    pool = tuple(iterable)
    n = len(pool)
    if (r < 0) or (r > n):
        raise ValueError

c = 1
    k = min(r, n - r)
    for i in range(1, k + 1):
        c = c * (n - k + i) // i

if index < 0:
        index += c

if (index < 0) or (index >= c):
        raise IndexError

result = []
    while r:
        c, n, r = c * r // n, n - 1, r - 1
        while index >= c:
            index -= c
            c, n = c * (n - r) // n, n - 1
        result.append(pool[-1 - n])

return tuple(result)

def prepend(value, iterator):
    """Yield *value*, followed by the elements in *iterator*.

>>> value = '0'
        >>> iterator = ['1', '2', '3']
        >>> list(prepend(value, iterator))
        ['0', '1', '2', '3']

To prepend multiple values, see :func:`itertools.chain`
    or :func:`value_chain`.

"""
    return chain([value], iterator)

def convolve(signal, kernel):
    """Convolve the iterable *signal* with the iterable *kernel*.

>>> signal = (1, 2, 3, 4, 5)
        >>> kernel = [3, 2, 1]
        >>> list(convolve(signal, kernel))
        [3, 8, 14, 20, 26, 14, 5]

Note: the input arguments are not interchangeable, as the *kernel*
    is immediately consumed and stored.

"""
    # This implementation intentionally doesn't match the one in the itertools
    # documentation.
    kernel = tuple(kernel)[::-1]
    n = len(kernel)
    window = deque([0], maxlen=n) * n
    for x in chain(signal, repeat(0, n - 1)):
        window.append(x)
        yield _sumprod(kernel, window)

def before_and_after(predicate, it):
    """A variant of :func:`takewhile` that allows complete access to the
    remainder of the iterator.

>>> it = iter('ABCdEfGhI')
         >>> all_upper, remainder = before_and_after(str.isupper, it)
         >>> ''.join(all_upper)
         'ABC'
         >>> ''.join(remainder) # takewhile() would lose the 'd'
         'dEfGhI'

Note that the first iterator must be fully consumed before the second
    iterator can generate valid results.
    """
    it = iter(it)
    transition = []

def true_iterator():
        for elem in it:
            if predicate(elem):
                yield elem
            else:
                transition.append(elem)
                return

# Note: this is different from itertools recipes to allow nesting
    # before_and_after remainders into before_and_after again. See tests
    # for an example.
    remainder_iterator = chain(transition, it)

return true_iterator(), remainder_iterator

def triplewise(iterable):
    """Return overlapping triplets from *iterable*.

>>> list(triplewise('ABCDE'))
    [('A', 'B', 'C'), ('B', 'C', 'D'), ('C', 'D', 'E')]

"""
    for (a, _), (b, c) in pairwise(pairwise(iterable)):
        yield a, b, c

def sliding_window(iterable, n):
    """Return a sliding window of width *n* over *iterable*.

>>> list(sliding_window(range(6), 4))
        [(0, 1, 2, 3), (1, 2, 3, 4), (2, 3, 4, 5)]

If *iterable* has fewer than *n* items, then nothing is yielded:

>>> list(sliding_window(range(3), 4))
        []

For a variant with more features, see :func:`windowed`.
    """
    it = iter(iterable)
    window = deque(islice(it, n - 1), maxlen=n)
    for x in it:
        window.append(x)
        yield tuple(window)

def subslices(iterable):
    """Return all contiguous non-empty subslices of *iterable*.

>>> list(subslices('ABC'))
        [['A'], ['A', 'B'], ['A', 'B', 'C'], ['B'], ['B', 'C'], ['C']]

This is similar to :func:`substrings`, but emits items in a different
    order.
    """
    seq = list(iterable)
    slices = starmap(slice, combinations(range(len(seq) + 1), 2))
    return map(operator.getitem, repeat(seq), slices)

def polynomial_from_roots(roots):
    """Compute a polynomial's coefficients from its roots.

>>> roots = [5, -4, 3]  # (x - 5) * (x + 4) * (x - 3)
    >>> polynomial_from_roots(roots)  # x^3 - 4 * x^2 - 17 * x + 60
    [1, -4, -17, 60]
    """
    factors = zip(repeat(1), map(operator.neg, roots))
    return list(reduce(convolve, factors, [1]))

def iter_index(iterable, value, start=0, stop=None):
    """Yield the index of each place in *iterable* that *value* occurs,
    beginning with index *start* and ending before index *stop*.

See :func:`locate` for a more general means of finding the indexes
    associated with particular values.

>>> list(iter_index('AABCADEAF', 'A'))
    [0, 1, 4, 7]
    >>> list(iter_index('AABCADEAF', 'A', 1))  # start index is inclusive
    [1, 4, 7]
    >>> list(iter_index('AABCADEAF', 'A', 1, 7))  # stop index is not inclusive
    [1, 4]
    """
    seq_index = getattr(iterable, 'index', None)
    if seq_index is None:
        # Slow path for general iterables
        it = islice(iterable, start, stop)
        for i, element in enumerate(it, start):
            if element is value or element == value:
                yield i
    else:
        # Fast path for sequences
        stop = len(iterable) if stop is None else stop
        i = start - 1
        try:
            while True:
                yield (i := seq_index(value, i + 1, stop))
        except ValueError:
            pass

def sieve(n):
    """Yield the primes less than n.

>>> list(sieve(30))
    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
    """
    if n > 2:
        yield 2
    start = 3
    data = bytearray((0, 1)) * (n // 2)
    limit = math.isqrt(n) + 1
    for p in iter_index(data, 1, start, limit):
        yield from iter_index(data, 1, start, p * p)
        data[p * p : n : p + p] = bytes(len(range(p * p, n, p + p)))
        start = p * p
    yield from iter_index(data, 1, start)

def _batched(iterable, n, *, strict=False):
    """Batch data into tuples of length *n*. If the number of items in
    *iterable* is not divisible by *n*:
    * The last batch will be shorter if *strict* is ``False``.
    * :exc:`ValueError` will be raised if *strict* is ``True``.

>>> list(batched('ABCDEFG', 3))
    [('A', 'B', 'C'), ('D', 'E', 'F'), ('G',)]

On Python 3.13 and above, this is an alias for :func:`itertools.batched`.
    """
    if n < 1:
        raise ValueError('n must be at least one')
    it = iter(iterable)
    while batch := tuple(islice(it, n)):
        if strict and len(batch) != n:
            raise ValueError('batched(): incomplete batch')
        yield batch

if hexversion >= 0x30D00A2:
    from itertools import batched as itertools_batched

def batched(iterable, n, *, strict=False):
        return itertools_batched(iterable, n, strict=strict)

else:
    batched = _batched

batched.__doc__ = _batched.__doc__

def transpose(it):
    """Swap the rows and columns of the input matrix.

>>> list(transpose([(1, 2, 3), (11, 22, 33)]))
    [(1, 11), (2, 22), (3, 33)]

The caller should ensure that the dimensions of the input are compatible.
    If the input is empty, no output will be produced.
    """
    return _zip_strict(*it)

def reshape(matrix, cols):
    """Reshape the 2-D input *matrix* to have a column count given by *cols*.

>>> matrix = [(0, 1), (2, 3), (4, 5)]
    >>> cols = 3
    >>> list(reshape(matrix, cols))
    [(0, 1, 2), (3, 4, 5)]
    """
    return batched(chain.from_iterable(matrix), cols)

def matmul(m1, m2):
    """Multiply two matrices.

>>> list(matmul([(7, 5), (3, 5)], [(2, 5), (7, 9)]))
    [(49, 80), (41, 60)]

The caller should ensure that the dimensions of the input matrices are
    compatible with each other.
    """
    n = len(m2[0])
    return batched(starmap(_sumprod, product(m1, transpose(m2))), n)

def factor(n):
    """Yield the prime factors of n.

>>> list(factor(360))
    [2, 2, 2, 3, 3, 5]
    """
    for prime in sieve(math.isqrt(n) + 1):
        while not n % prime:
            yield prime
            n //= prime
            if n == 1:
                return
    if n > 1:
        yield n

def polynomial_eval(coefficients, x):
    """Evaluate a polynomial at a specific value.

Example: evaluating x^3 - 4 * x^2 - 17 * x + 60 at x = 2.5:

>>> coefficients = [1, -4, -17, 60]
    >>> x = 2.5
    >>> polynomial_eval(coefficients, x)
    8.125
    """
    n = len(coefficients)
    if n == 0:
        return x * 0  # coerce zero to the type of x
    powers = map(pow, repeat(x), reversed(range(n)))
    return _sumprod(coefficients, powers)

def sum_of_squares(it):
    """Return the sum of the squares of the input values.

>>> sum_of_squares([10, 20, 30])
    1400
    """
    return _sumprod(*tee(it))

def polynomial_derivative(coefficients):
    """Compute the first derivative of a polynomial.

Example: evaluating the derivative of x^3 - 4 * x^2 - 17 * x + 60

>>> coefficients = [1, -4, -17, 60]
    >>> derivative_coefficients = polynomial_derivative(coefficients)
    >>> derivative_coefficients
    [3, -8, -17]
    """
    n = len(coefficients)
    powers = reversed(range(1, n))
    return list(map(operator.mul, coefficients, powers))

def totient(n):
    """Return the count of natural numbers up to *n* that are coprime with *n*.

>>> totient(9)
    6
    >>> totient(12)
    4
    """
    for p in unique_justseen(factor(n)):
        n = n // p * (p - 1)

return n