o iS@sNdZddlZddlmZmZddlmZddlmZddl m Z m Z m Z ddl mZmZddlmZmZmZmZmZmZmZmZmZmZmZmZmZmZdd lm Z m!Z!m"Z"m#Z#dd l$m%Z%m&Z&m'Z'm(Z(m)Z)dd lm*Z*m+Z+m,Z,dd l-m.Z.gd Z/e0Z1ze2ddWn e3ye2Z4Ynwe e2ddZ4zddlm5Z6Wn e7yddZ6Ynwz ddl m8Z8m9Z9Wn e7ydZ:YnwdZ:ddZ;dddZdddZ?ddd Z@eAfd!d"ZBd#d$ZCeCZDd%d&ZEd'd(ZFd)d*ZGdd+d,ZHd-d.ZIzdd/lmJZKWn e7yeIZJYn wd0d1ZJeIjeJ_Gd2d3d3eLZMd4d5ZNd6d7ZOdd9d:ZPd;d<ZQd=d>ZRd?d@ZSddAdBZTddCdDZUddEdFZVddGdHZWddIdJZXdKdLdMdNZYddOdPZZdQdRZ[dSdTZ\dUdVZ]dWdXZ^dYdZZ_d[d\Z`d]d^Zad_d`ZbdadbZcdcddZddedfZedgdhZfddidjZgdkdlZhdddmdnZie.dokrddplmjZkdddqdrZjeijej_neiZjdsdtZlemenffdudvZodwdxZpdydzZqd{d|Zrd}d~ZsetehdZuddZvddZwddZxddZyddZzgdZ{e ddZ|ddZ}e~j*ZddZddZddZddZddZddZddddZdS)aImported 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 N) bisect_leftinsort)dequesuppress) lru_cachepartialreduce)heappush heappushpop) accumulatechain combinationscompresscountcyclegroupbyisliceproductrepeatstarmap takewhiletee zip_longest)prodcombisqrtgcd)mulnot_ itemgettergetitemindex) randrangesamplechoice) hexversion)2 all_equalbatchedbefore_and_afterconsumeconvolve dotproduct first_truefactorflattengrouperis_prime iter_except iter_indexloopsmatmul multinomialncyclesnthnth_combinationpadnonepad_nonepairwise partitionpolynomial_evalpolynomial_from_rootspolynomial_derivativepowersetprependquantifyreshape#random_combination_with_replacementrandom_combinationrandom_permutationrandom_product repeatfunc roundrobinrunning_mediansievesliding_window subslicessum_of_squarestabulatetailtaketotient transpose triplewiseuniqueunique_everseenunique_justseenTstrict)sumprodcCs t||SN)r,)xyr_\/var/www/html/karishye-ai-python/venv/lib/python3.10/site-packages/more_itertools/recipes.pyls ra) heappush_maxheappushpop_maxFcCtt||S)zReturn 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] )listr)niterabler_r_r`rRxs rRcCst|t|S)aReturn 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] )mapr)functionstartr_r_r`rPsrPcCsFzt|}Wntytt||dYSwt|td||dS)zReturn an iterator over the last *n* items of *iterable*. >>> t = tail(3, 'ABCDEFG') >>> list(t) ['E', 'F', 'G'] maxlenrN)len TypeErroriterrrmax)rfrgsizer_r_r`rQs   rQcCs.|dur t|dddStt|||ddS)aXAdvance *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 "", line 1, in 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 "", line 1, in StopIteration Nrrk)rnextr)iteratorrfr_r_r`r*s r*cCstt||d|S)zReturns the nth item or a default value. >>> l = range(10) >>> nth(l, 3) 3 >>> nth(l, 20, "zebra") 'zebra' N)rrr)rgrfdefaultr_r_r`r8s r8cCs,t||}|D] }|D]}dSdSdS)a Returns ``True`` if all the elements are equal to each other. >>> all_equal('aaaa') True >>> all_equal('aaab') False A function that accepts a single argument and returns a transformed version of each input item can be specified with *key*: >>> all_equal('AaaA', key=str.casefold) True >>> all_equal([1, 2, 3], key=lambda x: x < 10) True FT)r)rgkeyrsfirstsecondr_r_r`r's r'cCrd)zcReturn the how many times the predicate is true. >>> quantify([True, False, True]) 2 )sumrh)rgpredr_r_r`rCsrCcCst|tdS)aReturns 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`. N)r rrgr_r_r`r;s r;cCsttt||S)zvReturns the sequence elements *n* times >>> list(ncycles(["a", "b"], 3)) ['a', 'b', 'a', 'b', 'a', 'b'] )r from_iterablertuplergrfr_r_r`r7sr7cCsttt||S)zReturns the dot product of the two iterables. >>> dotproduct([10, 15, 12], [0.65, 0.80, 1.25]) 33.5 >>> 10 * 0.65 + 15 * 0.80 + 12 * 1.25 33.5 In Python 3.12 and later, use ``math.sumprod()`` instead. )rxrhr)vec1vec2r_r_r`r,s r,cCs t|S)zReturn 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. )r r{) listOfListsr_r_r`r/+s r/cGs&|dur t|t|St|t||S)aGCall *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] N)rr)functimesargsr_r_r`rI7srIcCs t|\}}t|dt||S)zReturns 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`. Nrrrzip)rgabr_r_r` _pairwiseRs  r)r<cCst|Sr\)itertools_pairwiserzr_r_r`r<fsr<cseZdZdfdd ZZS)UnequalIterablesErrorNcs*d}|dur |dj|7}t|dS)Nz Iterables have different lengthsz/: index 0 has length {}; index {} has length {})formatsuper__init__)selfdetailsmsg __class__r_r`rms zUnequalIterablesError.__init__r\)__name__ __module__ __qualname__r __classcell__r_r_rr`rlsrccs8t|dtiD]}|D] }|turtq |VqdS)N fillvalue)r_markerr) iterablescombovalr_r_r`_zip_equal_generatorwsrcGsnz)t|d}t|dddD]\}}t|}||kr$t|||fdqt|WSty6t|YSw)Nr)r)rm enumeraterrrnr)r first_sizeiitrqr_r_r` _zip_equals    rfillcCsLt|g|}|dkrt|d|iS|dkrt|S|dkr"t|Std)aGroup 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. >>> iterator = grouper('ABCDEFG', 3, incomplete='strict') >>> list(iterator) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... UnequalIterablesError rrrZignorez Expected fill, strict, or ignore)rorrr ValueError)rgrf incompleter iteratorsr_r_r`r0sr0cgsDtt|}tt|ddD]}tt||}tt|EdHqdS)aGVisit input iterables in a cycle until each is exhausted. >>> 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). rN)rhrorangermrrrr)rr num_activer_r_r`rJs  rJcCsH|durt}t|d\}}}tt||\}}t|tt|t||fS)a 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, ' ']) N)boolrrhrr)ryrgt1t2pp1p2r_r_r`r=s r=cs,t|tfddttdDS)a1Yields 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. >>> seq = [1, 1, 0] >>> list(powerset(seq)) [(), (1,), (1,), (0,), (1, 1), (1, 0), (1, 0), (1, 1, 0)] For a variant that efficiently yields actual :class:`set` instances, see :func:`powerset_of_sets`. c3|]}t|VqdSr\)r).0rsr_r` zpowerset..r)rer r{rrmrzr_rr`rAs$rAc cst}|j}g}|j}|du}|D]+}|r||n|}z ||vr(|||VWqty=||vr;|||VYqwdS)a 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. N)setaddappendrn) rgruseenset seenset_addseenlist seenlist_adduse_keyelementkr_r_r`rWs( rWcCs4|dur ttdt|Sttttdt||S)zYields 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'] Nrr)rhr rrr)rgrur_r_r`rX's rXcCst|||d}t||dS)aYields unique elements in sorted order. >>> list(unique([[1, 2], [3, 4], [1, 2]])) [[1, 2], [3, 4]] *key* and *reverse* are passed to :func:`sorted`. >>> list(unique('ABBcCAD', str.casefold)) ['A', 'B', 'c', 'D'] >>> list(unique('ABBcCAD', str.casefold, reverse=True)) ['D', 'c', 'B', 'A'] The elements in *iterable* need not be hashable, but they must be comparable for sorting to work. )rureverse)ru)sortedrX)rgrur sequencedr_r_r`rV6s rVccs<t||dur|V |Vq1swYdS)aYields 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))) [] Nr)r exceptionrvr_r_r`r2Js r2cCstt|||S)a 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' )rrfilter)rgrtryr_r_r`r-gsr-rrcGs$dd|D|}tdd|DS)aDraw 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, repeat=repeat)``. cSsg|]}t|qSr_r|rpoolr_r_r` sz"random_product..css|]}t|VqdSr\)r%rr_r_r`rz!random_product..r)rrpoolsr_r_r`rH{srHcCs*t|}|dur t|n|}tt||S)abReturn 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)``. N)r|rmr$)rgrrr_r_r`rGs rGcs8t|t}ttt||}tfdd|DS)zReturn a random *r* length subsequence of the elements in *iterable*. >>> random_combination(range(5), 3) # doctest:+SKIP (2, 3, 4) This equivalent to taking a random selection from ``itertools.combinations(iterable, r)``. c3|]}|VqdSr\r_rrrr_r`rrz%random_combination..)r|rmrr$r)rgrrfindicesr_rr`rFs rFcs@t|ttfddt|D}tfdd|DS)aSReturn 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)``. c3s|]}tVqdSr\)r#rrfr_r`rrz6random_combination_with_replacement..c3rr\r_rrr_r`rr)r|rmrr)rgrrr_)rfrr`rEs rEc Cst|}t|}|dks||krtd}t|||}td|dD] }|||||}q"|dkr7||7}|dks?||krAtg}|ry||||d|d}}}||krn||8}|||||d}}||ksY||d||sEt|S)aEquivalent 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. rrr)r|rmrminr IndexErrorr) rgrr"rrfcrrresultr_r_r`r9s, r9cCs t|g|S)aYield *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`. )r )valuersr_r_r`rBs rBccsbt|ddd}t|}tdg|d|}t|td|dD] }||t||Vq!dS)u}Discrete linear convolution of two iterables. Equivalent to polynomial multiplication. For example, multiplying ``(x² -x - 20)`` by ``(x - 3)`` gives ``(x³ -4x² -17x + 60)``. >>> list(convolve([1, -1, -20], [1, -3])) [1, -4, -17, 60] Examples of popular kinds of kernels: * The kernel ``[0.25, 0.25, 0.25, 0.25]`` computes a moving average. For image data, this blurs the image and reduces noise. * The kernel ``[1/2, 0, -1/2]`` estimates the first derivative of a function evaluated at evenly spaced inputs. * The kernel ``[1, -2, 1]`` estimates the second derivative of a function evaluated at evenly spaced inputs. Convolutions are mathematically commutative; however, the inputs are evaluated differently. The signal is consumed lazily and can be infinite. The kernel is fully consumed before the calculations begin. Supports all numeric types: int, float, complex, Decimal, Fraction. References: * Article: https://betterexplained.com/articles/intuitive-convolution/ * Video by 3Blue1Brown: https://www.youtube.com/watch?v=KuXjwB4LzSA Nrrrkr)r|rmrr rr_sumprod)signalkernelrfwindowr]r_r_r`r+s# r+cCs(t|\}}tt||t|}||fS)aA 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. )rrrr) predicatertruesafterr_r_r`r)&s r)cCs:t|d\}}}t|dt|dt|dt|||S)zReturn overlapping triplets from *iterable*. >>> list(triplewise('ABCDE')) [('A', 'B', 'C'), ('B', 'C', 'D'), ('C', 'D', 'E')] rNr)rgrrt3r_r_r`rU9s     rUcCs6t||}t|D] \}}tt|||dq t|Sr\)rrrrrr)rgrfrrrsr_r_r`_sliding_window_isliceIs rccsBt|}tt||d|d}|D] }||t|VqdS)Nrrk)rorrrr|)rgrfrsrr]r_r_r`_sliding_window_dequeQs  rcCsR|dkr t||S|dkrt||S|dkrt|S|dkr"t|Std|)aYReturn 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`. rzn should be at least one, not )rrr<rrr}r_r_r`rMZs  rMcCs4t|}ttttt|dd}ttt||S)zReturn 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. rr) rerslicerrrmrhr!r)rgseqslicesr_r_r`rNss rNcCs(dg}|D] }tt|d| f}q|S)ukCompute a polynomial's coefficients from its roots. >>> roots = [5, -4, 3] # (x - 5) * (x + 4) * (x - 3) >>> polynomial_from_roots(roots) # x³ - 4 x² - 17 x + 60 [1, -4, -17, 60] Note that polynomial coefficients are specified in descending power order. Supports all numeric types: int, float, complex, Decimal, Fraction. r)rer+)rootspolyrootr_r_r`r?sr?ccst|dd}|dur(t|||}t||D]\}}||us"||kr%|VqdS|dur0t|n|}|d}tt |||d|}Vq<1sKwYdS)aYield the index of each place in *iterable* that *value* occurs, beginning with index *start* and ending before index *stop*. >>> 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] The behavior for non-scalar *values* matches the built-in Python types. >>> list(iter_index('ABCDABCD', 'AB')) [0, 4] >>> list(iter_index([0, 1, 2, 3, 0, 1, 2, 3], [0, 1])) [] >>> list(iter_index([[0, 1], [2, 3], [0, 1], [2, 3]], [0, 1])) [0, 2] See :func:`locate` for a more general means of finding the indexes associated with particular values. r"Nr)getattrrrrmrr)rgrrjstop seq_indexrsrrr_r_r`r3s    r3ccs|dkrdVd}td|d}t|d|t|ddD])}t|d|||EdHttt|||||||||||<||}qt|d|EdHdS)zeYield the primes less than n. >>> list(sieve(30)) [2, 3, 5, 7, 11, 13, 17, 19, 23, 29] rr)rrr)rN) bytearrayr3rbytesrmr)rfrjdatarr_r_r`rLs . rLccsd|dkr tdt|}tt||}r0|r"t||kr"td|Vtt||}sdSdS)aBatch 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`. rzn must be at least onezbatched(): incomplete batchN)rror|rrm)rgrfrZrsbatchr_r_r`_batcheds ri )r(cCst|||dS)NrY)itertools_batched)rgrfrZr_r_r`r(sr(cCst|S)a 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. ) _zip_strictrr_r_r`rTs rTcCs,zt|Wn tyYdSwt||S)z.Scalars are bytes, strings, and non-iterables.T)rorn isinstance)r stringliker_r_r` _is_scalars    rcCsRt|} zt|}Wn ty|YSwt|f|}t|r#|St|}q)z.Depth-first iterator over scalars in a tensor.)rorr StopIterationr rr{)tensorrsrr_r_r`_flatten_tensor s    rcCsDt|tr tt||S|^}}t|}ttt||}t||S)aChange the shape of a *matrix*. If *shape* is an integer, the matrix must be two dimensional and the shape is interpreted as the desired number of columns: >>> matrix = [(0, 1), (2, 3), (4, 5)] >>> cols = 3 >>> list(reshape(matrix, cols)) [(0, 1, 2), (3, 4, 5)] If *shape* is a tuple (or other iterable), the input matrix can have any number of dimensions. It will first be flattened and then rebuilt to the desired shape which can also be multidimensional: >>> matrix = [(0, 1), (2, 3), (4, 5)] # Start with a 3 x 2 matrix >>> list(reshape(matrix, (2, 3))) # Make a 2 x 3 matrix [(0, 1, 2), (3, 4, 5)] >>> list(reshape(matrix, (6,))) # Make a vector of length six [0, 1, 2, 3, 4, 5] >>> list(reshape(matrix, (2, 1, 3, 1))) # Make 2 x 1 x 3 x 1 tensor [(((0,), (1,), (2,)),), (((3,), (4,), (5,)),)] Each dimension is assumed to be uniform, either all arrays or all scalars. Flattening stops when the first value in a dimension is a scalar. Scalars are bytes, strings, and non-iterables. The reshape iterator stops when the requested shape is complete or when the input is exhausted, whichever comes first. ) rintr(r r{rr reversedr)matrixshape first_dimdims scalar_streamreshapedr_r_r`rDs ! rDcCs&t|d}tttt|t||S)a#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. Supports all numeric types: int, float, complex, Decimal, Fraction. r)rmr(rrrrT)m1m2rfr_r_r`r5@s r5cCstd|D]7}d}}d}|dkr4||||}||||}||||}t|||}|dks||kr<|Sqtd)Nrrzprime or under 5)rrr)rfrr]r^dr_r_r`_factor_pollardOsrccs|dkrdStD]}||s|V||}||rq g}|dkr$|gng}|D]}|dks2t|r8||q(t|}||||f7}q(t|EdHdS)aYield the prime factors of n. >>> list(factor(360)) [2, 2, 2, 3, 3, 5] Finds small factors with trial division. Larger factors are either verified as prime with ``is_prime`` or split into smaller factors with Pollard's rho algorithm. rNri)_primes_below_211r1rrr)rfprimeprimestodofactr_r_r`r.bs"  r.cCs>t|}|dkrt|dSttt|tt|}t||S)aEvaluate a polynomial at a specific value. Computes with better numeric stability than Horner's method. Evaluate ``x^3 - 4 * x^2 - 17 * x + 60`` at ``x = 2.5``: >>> coefficients = [1, -4, -17, 60] >>> x = 2.5 >>> polynomial_eval(coefficients, x) 8.125 Note that polynomial coefficients are specified in descending power order. Supports all numeric types: int, float, complex, Decimal, Fraction. r)rmtyperhpowrrrr) coefficientsr]rfpowersr_r_r`r>s   r>cCs tt|S)zReturn the sum of the squares of the input values. >>> sum_of_squares([10, 20, 30]) 1400 Supports all numeric types: int, float, complex, Decimal, Fraction. )rrrr_r_r`rOs rOcCs&t|}ttd|}ttt||S)uCompute the first derivative of a polynomial. Evaluate the derivative of ``x³ - 4 x² - 17 x + 60``: >>> coefficients = [1, -4, -17, 60] >>> derivative_coefficients = polynomial_derivative(coefficients) >>> derivative_coefficients [3, -8, -17] Note that polynomial coefficients are specified in descending power order. Supports all numeric types: int, float, complex, Decimal, Fraction. r)rmrrrerhr)rrfrr_r_r`r@sr@cCs"tt|D]}|||8}q|S)uReturn the count of natural numbers up to *n* that are coprime with *n*. Euler's totient function φ(n) gives the number of totatives. Totative are integers k in the range 1 ≤ k ≤ n such that gcd(n, k) = 1. >>> n = 9 >>> totient(n) 6 >>> totatives = [x for x in range(1, n) if gcd(n, x) == 1] >>> totatives [1, 2, 4, 5, 7, 8] >>> len(totatives) 6 Reference: https://en.wikipedia.org/wiki/Euler%27s_totient_function )rr.)rfr r_r_r`rSsrS))i)r)i)I)ltT7)r=)lay)r iS_)l;n>)rrr )lp )rrrrrr)l)riEi$inii=ik)l%!Hn fW) rrrrrrrr%)cCsH|d|Ad}||?}d|>||kr|d@r|dks J||fS)z#Return s, d such that 2**s * d == nrr) bit_length)rfrrr_r_r` _shift_to_odds$r cCs|dkr|d@rd|kr|ksJJt|d\}}t|||}|dks.||dkr0dSt|dD]}|||}||dkrGdSq6dS)NrrTF)r rr)rfbaserrr]_r_r_r`_strong_probable_primes,   r#csdkrdvSd@r dr dr dr dr ds"d StD] \}}|kr.n q$fd d td D}tfd d |DS)aReturn ``True`` if *n* is prime and ``False`` otherwise. Basic examples: >>> is_prime(37) True >>> is_prime(3 * 13) False >>> is_prime(18_446_744_073_709_551_557) True Find the next prime over one billion: >>> next(filter(is_prime, count(10**9))) 1000000007 Generate random primes up to 200 bits and up to 60 decimal digits: >>> from random import seed, randrange, getrandbits >>> seed(18675309) >>> next(filter(is_prime, map(getrandbits, repeat(200)))) 893303929355758292373272075469392561129886005037663238028407 >>> next(filter(is_prime, map(randrange, repeat(10**60)))) 269638077304026462407872868003560484232362454342414618963649 This function is exact for values of *n* below 10**24. For larger inputs, the probabilistic Miller-Rabin primality test has a less than 1 in 2**128 chance of a false positive. r>rrrrrrrrrrrrFc3s|] }tddVqdS)rrN)_private_randrangerrr_r`r*szis_prime..@c3rr\)r#)rr!rr_r`r,r)_perfect_testsrall)rflimitbasesr_rr`r1s!0 r1cCs td|S)zReturns an iterable with *n* elements for efficient looping. Like ``range(n)`` but doesn't create integers. >>> i = 0 >>> for _ in loops(5): ... i += 1 >>> i 5 Nrrr_r_r`r4/s r4cGstttt||S)uNumber of distinct arrangements of a multiset. The expression ``multinomial(3, 4, 2)`` has several equivalent interpretations: * In the expansion of ``(a + b + c)⁹``, the coefficient of the ``a³b⁴c²`` term is 1260. * There are 1260 distinct ways to arrange 9 balls consisting of 3 reds, 4 greens, and 2 blues. * There are 1260 unique ways to place 9 distinct objects into three bins with sizes 3, 4, and 2. The :func:`multinomial` function computes the length of :func:`distinct_permutations`. For example, there are 83,160 distinct anagrams of the word "abracadabra": >>> from more_itertools import distinct_permutations, ilen >>> ilen(distinct_permutations('abracadabra')) 83160 This can be computed directly from the letter counts, 5a 2b 2r 1c 1d: >>> from collections import Counter >>> list(Counter('abracadabra').values()) [5, 2, 2, 1, 1] >>> multinomial(5, 2, 2, 1, 1) 83160 A binomial coefficient is a special case of multinomial where there are only two categories. For example, the number of ways to arrange 12 balls with 5 reds and 7 blues is ``multinomial(5, 7)`` or ``math.comb(12, 5)``. Likewise, factorial is a special case of multinomial where the multiplicities are all just 1 so that ``multinomial(1, 1, 1, 1, 1, 1, 1) == math.factorial(7)``. Reference: https://en.wikipedia.org/wiki/Multinomial_theorem )rrhrr )countsr_r_r`r6=s*r6ccsv|j}g}g}tt% t|t|||dVt|t|||d|ddVq1s4wYdS)z.Non-windowed running_median() for Python 3.14+TrrN)__next__rrrbr r rcrsreadlohir_r_r`#_running_median_minheap_and_maxheapjs  r0ccs~|j}g}g}tt) t|t|| |d Vt|t||  |d|ddVq1s8wYdS)zDBackport of non-windowed running_median() for Python 3.13 and prior.TrrN)r+rrr r r,r_r_r`_running_median_minheap_onlyzs  r1ccst}g}|D]9}||t||t||kr$t||}||=t|}|d}|d@r4||n ||d||dVqdS)z+Yield median of values in a sliding window.rrN)rrrrmrpopleft)rsrlrorderedr]rrfmr_r_r`_running_median_windoweds   ,r5rkcCsFt|}|durt|}|dkrtdt||Stst|St|S)aDCumulative median of values seen so far or values in a sliding window. Set *maxlen* to a positive integer to specify the maximum size of the sliding window. The default of *None* is equivalent to an unbounded window. For example: >>> list(running_median([5.0, 9.0, 4.0, 12.0, 8.0, 9.0])) [5.0, 7.0, 5.0, 7.0, 8.0, 8.5] >>> list(running_median([5.0, 9.0, 4.0, 12.0, 8.0, 9.0], maxlen=3)) [5.0, 7.0, 5.0, 9.0, 8.0, 9.0] Supports numeric types such as int, float, Decimal, and Fraction, but not complex numbers which are unorderable. On version Python 3.13 and prior, max-heaps are simulated with negative values. The negation causes Decimal inputs to apply context rounding, making the results slightly different than that obtained by statistics.median(). NrzWindow size should be positive)ror"rr5_max_heap_availabler1r0)rgrlrsr_r_r`rKs rK)rr\)rN)NF)NN)rN)__doc__randombisectrr collectionsr contextlibr functoolsrrr heapqr r itertoolsr r rrrrrrrrrrrrmathrrrroperatorrrr r!r"r#r$r%sysr&__all__objectrrrnrr[r ImportErrorrbrcr6rRrPrQr*r8r'rrCr;r:r7r,r/rIrr<rrrrrr0rJr=rArWrXrVr2r-rHrGrFrEr9rBr+r)rUrrrMrNr?r3rLrr(rrTstrrrrrDr5rr|r r.r>rOr@rSr&r r#Randomr$r1r4r6r0r1r5rKr_r_r_r`s   @ 5        (      (  -    *+  )     ) !   0-