+ }ij^RIt^RIt^RIHtHtHtHt^RIHt^RI H t ^RI H t H t HtHt^RIHtHt^RIHtHtHtHtHtHtHtHtHtHtHtHtH t H!t!H"t"^RIH#t#H$t$H%t%H&t&H't'H(t(H)t)H*t*H+t+H,t,^R IH-t-^R I.H/t/H0t0^R I1H1t1H2t2H3t3H4t4^R I5H6t6H7t7H8t8H9t9H:t:H;t;HH?t?H@t@^RIAHBtB^RICHDtDHEtEHFtFHGtGHHtHHItIHJtJHKtKHLtLHMtMHNtNHOtOHPtPHQtQ.RNRNRNRNRNRNRNRNRNRNRNRNRNRNRNRNR NR!NR"NR#NR$NR%NR&NR'NR(NR)NR*NR+NR,NR-NR.NR/NR0NR1NR2NR3NR4NR5NR6NR7NR8NR9NR:NR;NRNR?NR@NRANRBNRCNRDNRENRFNRGNRHNRINRJNRKNRLNRMNRNNRONRPNRQNRRNRSNRTNRUNRVNRWNRXNRYNRZNR[NR\NR]NR^NR_NR`NRaNRbNRcNRdNReNRfNRgNRhNRiNRjNRkNRlNRmNRnNRoNRpNRqNRrNRsNRtNRuNRvNRwNRxNRyNRzNR{NR|NR}NR~NRNRNRNRNRNtR^RIHStTERRltX]D3RltY]D3RltZ]D3Rlt[!RR\4t\Rt]Rt^Rt_Rt`ERRltaRtbERRltcERRltdERRlteERRltfRtgERRlthRtiERRltj!RR4tkERRltlRtmRtnERRltoRtpERRltqERRltrERRltsERRlttERRltuERRltvERRltwRtxERRltyERRltzERRlt{Rt|ERRlt}Rt~RRRR/RltERRltRtRt]]33RltERRltERRlt!RRU]P]EP4tERRltRt]R3RltRtRtRtRt!RRB4tRtRtERRlt]<3RR/Rllt!RR]4t!RRh4t!RRf4t]3RltERRltERRltERRlt]R3RltERRltRtERRlt!RRy4tERRltRtRtRtRtRtRtERRltRtRtRtERRRRR/RlltERRlt!RR]4t!RR4tRtERRltRtRtRtRtRtRtRtRt!RR%4tRtR]]3RR/RltERRltERRltERRltERRltRRR]D/RltR]R3RltRtRtRtRtERtERtERtERtERtERtERtERtERtER R/ER ltRR/ER ltRR/ER ltER tR# ]UdRRltVRtWRtTELi;i(N)Counter defaultdictdequeabc)Sequence)suppress)cached_propertypartialreducewraps)heapify heapreplace)chain combinationscompresscountcycle dropwhilegroupbyislice permutationsrepeatstarmap takewhiletee zip_longestproduct) combeexp factorialfloorfsumloglog1ppermtau)ceil)EmptyQueue)random randrangeshuffleuniform) attrgetteris_not itemgetterltmulnegsubgt) hexversionmaxsize) monotonic)_marker _zip_equalUnequalIterablesErrorconsume first_trueflattenis_primenthpowersetsievetakeunique_everseen all_equalbatched AbortThread SequenceViewr<adjacent all_uniquealways_iterablealways_reversibleargmaxargminbucket callback_iterchunked chunked_evencircular_shiftscollapsecombination_index"combination_with_replacement_indexconsecutive_groupsconstrained_batchesconsumer count_cycle countable derangementsdft differencedistinct_combinationsdistinct_permutations distributedivide doublestarmapduplicates_everseenduplicates_justseenclassify_unique exactly_nextract filter_except filter_mapfirst gray_productgroupby_transformichunkediequalsidftilen interleaveinterleave_evenlyinterleave_longestinterleave_randomly intersperse is_sortedislice_extendediterate iter_suppress join_mappingslastlocatelongest_common_prefixlstripmake_decorator map_exceptmap_if map_reduce mark_endsminmax nth_or_lastnth_permutation nth_prime nth_product nth_combination_with_replacement numeric_rangeoneonly outer_productpaddedpartial_product partitionspeekablepermutation_indexpowerset_of_sets product_indexraise_ repeat_each repeat_lastreplacerlocaterstrip run_lengthsampleseekableset_partitions side_effectsliced sort_together split_aftersplit_at split_before split_into split_whenspystaggerstrip strictly_n substringssubstrings_indexestakewhile_inclusive time_limitedunique_in_windowunique_to_eachunzip value_chainwindowedwindowed_complete with_iter zip_broadcast zip_equal zip_offset)sumprodc$V^8dQhR\/#)x)float)formats"e/Users/ahmad/.openclaw/workspace/my-crawler/.venv/lib/python3.14/site-packages/more_itertools/more.py __annotate__rsEcHVR,pWV, , pW, pW#3#)z1Split a float into two half-precision components.gA)rthilos& rdl_splitrs$ O a%[ Vv rc\V4wr#\V4wrEW$,pW%,W4,,pWg,pWh, V,W5,,p W3#)zLossless multiplication.)r) ryxx_hixx_loyy_hiyy_lopqzzzs && rdl_mulrsM{ {  M MEM ) E UQY &u rc \\\P!\\W444#N)r#r from_iterablemapr)rrs&&r _fsumprodrsE''FA(9:;;rFc aa\\\S\V44.4oV'dSf \R4hVV3RlpV!4#S#)aBreak *iterable* into lists of length *n*: >>> list(chunked([1, 2, 3, 4, 5, 6], 3)) [[1, 2, 3], [4, 5, 6]] By the default, the last yielded list will have fewer than *n* elements if the length of *iterable* is not divisible by *n*: >>> list(chunked([1, 2, 3, 4, 5, 6, 7, 8], 3)) [[1, 2, 3], [4, 5, 6], [7, 8]] To use a fill-in value instead, see the :func:`grouper` recipe. If the length of *iterable* is not divisible by *n* and *strict* is ``True``, then ``ValueError`` will be raised before the last list is yielded. z*n must not be None when using strict mode.c3`<"SF"p\V4S8wd \R4hVxK$ R#5i)ziterable is not divisible by n.Nlen ValueError)chunkiteratorns rretchunked..rets,!u:?$%FGG "+.)iterr rDr)iterablerstrictrrs&f& @rrRrRsE&GD!T(^4b9H 9IJ J  u rcFVFpVu# V\Jd \R4hV#)aReturn the first item of *iterable*, or *default* if *iterable* is empty. >>> first([0, 1, 2, 3]) 0 >>> first([], 'some default') 'some default' If *default* is not provided and there are no items in the iterable, raise ``ValueError``. :func:`first` is useful when you have a generator of expensive-to-retrieve values and want any arbitrary one. It is marginally shorter than ``next(iter(iterable), default)``. zKfirst() was called on an empty iterable, and no default value was provided.)r:r)rdefaultitems&& rrlrls4" ' 1   Nrc\V\4'd VR,#\VRR4'd\\ V44#\ V^R7R,# \ \\3dT\Jd \R4hTu#i;i)zReturn the last item of *iterable*, or *default* if *iterable* is empty. >>> last([0, 1, 2, 3]) 3 >>> last([], 'some default') 'some default' If *default* is not provided and there are no items in the iterable, raise ``ValueError``. __reversed__NmaxlenzJlast() was called on an empty iterable, and no default value was provided.) isinstancergetattrnextreversedr IndexError TypeError StopIterationr:r)rrs&&rr}r}s  h ) )B<  8^T 2 2*+ +Xa(,,  = 1 g 5  sA&AA-B  B c<\\W^,4VR7#)a?Return the nth or the last item of *iterable*, or *default* if *iterable* is empty. >>> nth_or_last([0, 1, 2, 3], 2) 2 >>> nth_or_last([0, 1], 2) 1 >>> nth_or_last([], 0, 'some default') 'some default' If *default* is not provided and there are no items in the iterable, raise ``ValueError``. r)r}r)rrrs&&&rrr,s xQ' 99rcZa]tRtRtoRtRtRtRt]3Rlt Rt Rt R t R t R tVtR #) ri=aXWrap an iterator to allow lookahead and prepending elements. Call :meth:`peek` on the result to get the value that will be returned by :func:`next`. This won't advance the iterator: >>> p = peekable(['a', 'b']) >>> p.peek() 'a' >>> next(p) 'a' Pass :meth:`peek` a default value to return that instead of raising ``StopIteration`` when the iterator is exhausted. >>> p = peekable([]) >>> p.peek('hi') 'hi' peekables also offer a :meth:`prepend` method, which "inserts" items at the head of the iterable: >>> p = peekable([1, 2, 3]) >>> p.prepend(10, 11, 12) >>> next(p) 10 >>> p.peek() 11 >>> list(p) [11, 12, 1, 2, 3] peekables can be indexed. Index 0 is the item that will be returned by :func:`next`, index 1 is the item after that, and so on: The values up to the given index will be cached. >>> p = peekable(['a', 'b', 'c', 'd']) >>> p[0] 'a' >>> p[1] 'b' >>> next(p) 'a' Negative indexes are supported, but be aware that they will cache the remaining items in the source iterator, which may require significant storage. To check whether a peekable is exhausted, check its truth value: >>> p = peekable(['a', 'b']) >>> if p: # peekable has items ... list(p) ['a', 'b'] >>> if not p: # peekable is exhausted ... list(p) [] cD\V4Vn\4VnR#r)r_itr_cacheselfrs&&r__init__peekable.__init__xs>g rcV#rrrs&r__iter__peekable.__iter__| rcLVP4R# \dR#i;iFTpeekrrs&r__bool__peekable.__bool__)  IIK    ##c VP'g0VPP\VP44VP^,# \dT\ JdhTu#i;i)zReturn the item that will be next returned from ``next()``. Return ``default`` if there are no items left. If ``default`` is not provided, raise ``StopIteration``. )rappendrrrr:)rrs&&rr  peekable.peeks]{{{  ""4>2 {{1~ ! g% s.AA0/A0c NVPP\V44R#)a\Stack up items to be the next ones returned from ``next()`` or ``self.peek()``. The items will be returned in first in, first out order:: >>> p = peekable([1, 2, 3]) >>> p.prepend(10, 11, 12) >>> next(p) 10 >>> list(p) [11, 12, 1, 2, 3] It is possible, by prepending items, to "resurrect" a peekable that previously raised ``StopIteration``. >>> p = peekable([]) >>> next(p) Traceback (most recent call last): ... StopIteration >>> p.prepend(1) >>> next(p) 1 >>> next(p) Traceback (most recent call last): ... StopIteration N)r extendleftr)ritemss&*rprependpeekable.prepends: x/rcVP'dVPP4#\VP4#r)rpopleftrrrs&r__next__peekable.__next__s, ;;;;;&&( (DHH~rcVPf^M VPpV^8d>VPf^M VPpVPf\M VPpMWV^8dFVPfRM VPpVPf\)^, M VPpM \ R4hV^8gV^8d'VP P VP4Mp\\W44^,\4p\VP 4pWV8d6VP P \VPWV, 44\VP 4V,#)Nzslice step cannot be zeror) stepstartstopr8rrextendrminmaxrrlist)rindexrrrr cache_lens&& r _get_slicepeekable._get_slices ZZ'qejj !8++-AEKKE$zz17 D AX ;;.BU[[E&+jj&8WHqLuzzD89 9 AI4!8 KK  txx (C$q('2ADKK(I~ ""6$((AM#BCDKK ''rc\V\4'dVPV4#\VP4pV^8d'VPP VP 4MCW8d>VPP \VP V^,V, 44VPV,#r)rslicer$rrrrr)rr"r#s&& r __getitem__peekable.__getitem__s eU # #??5) ) $ 19 KK  txx (   KK  vdhh I0EF G{{5!!r)rrN)__name__ __module__ __qualname____firstlineno____doc__rrr r:r rrr$r)__static_attributes____classdictcell__ __classdict__s@rrr=s=8t# 0> (4 " "rc0a\S4V3Rl4pV#)aDecorator that automatically advances a PEP-342-style "reverse iterator" to its first yield point so you don't have to call ``next()`` on it manually. >>> @consumer ... def tally(): ... i = 0 ... while True: ... print('Thing number %s is %s.' % (i, (yield))) ... i += 1 ... >>> t = tally() >>> t.send('red') Thing number 0 is red. >>> t.send('fish') Thing number 1 is fish. Without the decorator, you would have to call ``next(t)`` before ``t.send()`` could be used. c.<S!V/VBp\V4V#rr)argskwargsgenfuncs*, rwrapperconsumer..wrappersD#F# S  r)r )r:r;sf rrZrZs". 4[ NrcP\\\^4\V444#)aQReturn the number of items in *iterable*. For example, there are 168 prime numbers below 1,000: >>> ilen(sieve(1000)) 168 Equivalent to, but faster than:: def ilen(iterable): count = 0 for _ in iterable: count += 1 return count This fully consumes the iterable, so handle with care. )sumrrziprs&rrrrrs, xq 3x=1 22rc#"\\4;_uu_4VxV!V4pK +'giR#;i5i)aReturn ``start``, ``func(start)``, ``func(func(start))``, ... Produces an infinite iterator. To add a stopping condition, use :func:`take`, ``takewhile``, or :func:`takewhile_inclusive`:. >>> take(10, iterate(lambda x: 2*x, 1)) [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] >>> collatz = lambda x: 3*x + 1 if x%2==1 else x // 2 >>> list(takewhile_inclusive(lambda x: x!=1, iterate(collatz, 10))) [10, 5, 16, 8, 4, 2, 1] N)rr)r:rs&&rrzrzs. - KKE ! s>* ; >c#t"T;_uu_4pTRjxL RRR4R#L +'giR#;i5i)a&Wrap an iterable in a ``with`` statement, so it closes once exhausted. For example, this will close the file when the iterator is exhausted:: upper_lines = (line.upper() for line in with_iter(open('foo'))) Any context manager which returns an iterable is a candidate for ``with_iter``. Nr)context_managerrs& rrr/s* H  s 8$"$ 8$ 5 8c\V4pVF,pVF!pRV: RV: R2pT;'g \V4h Vu# T;'g \R4h)aReturn the first item from *iterable*, which is expected to contain only that item. Raise an exception if *iterable* is empty or has more than one item. :func:`one` is useful for ensuring that an iterable contains only one item. 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 few 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 is only one item. See :func:`spy` or :func:`peekable` to check iterable contents less destructively. /Expected exactly one item in iterable, but got , , and perhaps more.z&too few items in iterable (expected 1)rr)r too_shorttoo_longrrlsecondmsgs&&& rrr>snXH~HFA%"*/1 --jo -     K Kz"JKKrcV!V!hrr) exceptionr7s&*rrrvs T rc#"VfRpVfRp\V4p^p\WA4FpVxV^, pK WQ8d V!V4R#VFpV!V^,4R# R#5i)aValidate that *iterable* has exactly *n* items and return them if it does. If it has fewer than *n* items, call function *too_short* with the actual number of items. If it has more than *n* items, call function *too_long* with the number ``n + 1``. >>> 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'] Nc*\\RV R24#)zToo few items in iterable (got )rr item_counts&rstrictly_n..sv -j\ ;( rc*\\RV R24#)z)Too many items in iterable (got at least rQrRrSs&rrUrVsf 7 |1 E' r)rr)rrrIrJitsentrs&&&& rrrzsy^   hB Dr     x$QsA&A(c a a aV3RlpRp\V4pVP4Rp\V4oVfSpVS8XdTM \W1R7p ^Tu;8dS8:d&MM"V'd V !V4#V 3RlV !X44#\V'dR4#R 4# \dRp\\4pTF)pYdP T4,P T4K+ TUu.uFqtP T4NK MuupippTP4TP 4U U u/uFwrT \T 4bK Muup p iup p o T 3Rlo EL1i;i) aYield successive distinct permutations of the elements in *iterable*. >>> sorted(distinct_permutations([1, 0, 1])) [(0, 1, 1), (1, 0, 1), (1, 1, 0)] Equivalent to yielding from ``set(permutations(iterable))``, except duplicates are not generated and thrown away. For larger input sequences this is much more efficient. Duplicate permutations arise when there are duplicated elements in the input iterable. The number of items returned is `n! / (x_1! * x_2! * ... * x_n!)`, where `n` is the total number of items input, and each `x_i` is the count of a distinct item in the input sequence. The function :func:`multinomial` computes this directly. If *r* is given, only the *r*-length permutations are yielded. >>> sorted(distinct_permutations([1, 0, 1], r=2)) [(0, 1), (1, 0), (1, 1)] >>> sorted(distinct_permutations(range(3), r=2)) [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)] *iterable* need not be sortable, but note that using equal (``x == y``) but non-identical (``id(x) != id(y)``) elements may produce surprising behavior. For example, ``1`` and ``True`` are equal but non-identical: >>> list(distinct_permutations([1, True, '3'])) # doctest: +SKIP [ (1, True, '3'), (1, '3', True), ('3', 1, True) ] >>> list(distinct_permutations([1, 2, '3'])) # doctest: +SKIP [ (1, 2, '3'), (1, '3', 2), (2, 1, '3'), (2, '3', 1), ('3', 1, 2), ('3', 2, 1) ] c3b<"\V4x\S^, RR4FpW,W^,,8gKM R#\S^, VR4FpW,W,8gKM VX,W,uW&W&VRVS, R1,W^,R%K5i)TNr)tuplerange)Aijsizes& r_full$distinct_permutations.._fulls(N4!8R,4!E(?- 4!8Q+4!$;, 1qtJAD!$?QX?+A!egJs=B/-B/78B/c3&"VRVWRr2\V^, RR4p\\V44p\V4xVR,pVFpW',V8dMW',pK R#VF-pW8,W',8gKW8,W',uW'&W8&M5 VF-pW(,W',8gKW(,W',uW'&W(&M W2RWq, R1,, pV^, pVRW, W1V, RuW'R%VR&K5i)N:NNNr)r]rr\) r^rheadtailright_head_indexesleft_tail_indexespivotr_r`s && r_partial'distinct_permutations.._partial srUAbEd"1q5"b1!#d),+ HE'7U?( '7TW$'+w$DGTW',Aw(+/7DG(, 152& &D FA $Wqu tEG} DHd1gsA?D.D9ADTFch<\;QJd.V3RlV4FNK 5#!V3RlV44#)c3H<"TFp\SV,4xK R#5irr6).0r"equivalent_itemss& r @distinct_permutations..permuted_items..Ds#;K%%e,--;Ks"r\)permuted_indicesrps&rpermuted_items-distinct_permutations..permuted_itemsCs55;K5 5;K rrec34<"TF pS!V4xK R#5irr)rortrus& rrq(distinct_permutations..Ss"(:$/00(:sr)r) r!sortrrr"rrrrr r)rrerbrkrsortable indices_dictrindiceskv algorithmrpruras&& @@@rraras?Z,0%=N NE & u:Dy t)'(*@I1}} U# #(1'(:  a ##U ##E "4( D T* + 2 24 8277;;t$77 4@4F4F4HI4HDAAuQxK4HI  s*BAE-D+E1E EEc\V4p\\\V444p\\ W!R7\ \ \ \ \\4\V4\ W1R7444#)ahYield successive derangements of the elements in *iterable*. A derangement is a permutation in which no element appears at its original index. In other words, a derangement is a permutation that has no fixed points. Suppose Alice, Bob, Carol, and Dave are playing Secret Santa. The code below outputs all of the different ways to assign gift recipients such that nobody is assigned to himself or herself: >>> for d in derangements(['Alice', 'Bob', 'Carol', 'Dave']): ... print(', '.join(d)) Bob, Alice, Dave, Carol Bob, Carol, Dave, Alice Bob, Dave, Alice, Carol Carol, Alice, Dave, Bob Carol, Dave, Alice, Bob Carol, Dave, Bob, Alice Dave, Alice, Bob, Carol Dave, Carol, Alice, Bob Dave, Carol, Bob, Alice If *r* is given, only the *r*-length derangements are yielded. >>> sorted(derangements(range(3), 2)) [(1, 0), (1, 2), (2, 0)] >>> sorted(derangements([0, 2, 3], 2)) [(2, 0), (2, 3), (3, 0)] Elements are treated as unique based on their position, not on their value. Consider the Secret Santa example with two *different* people who have the *same* name. Then there are two valid gift assignments even though it might appear that a person is assigned to themselves: >>> names = ['Alice', 'Bob', 'Bob'] >>> list(derangements(names)) [('Bob', 'Bob', 'Alice'), ('Bob', 'Alice', 'Bob')] To avoid confusion, make the inputs distinct: >>> deduped = [f'{name}{index}' for index, name in enumerate(names)] >>> list(derangements(deduped)) [('Bob1', 'Bob2', 'Alice0'), ('Bob2', 'Alice0', 'Bob1')] The number of derangements of a set of size *n* is known as the "subfactorial of n". For n > 0, the subfactorial is: ``round(math.factorial(n) / math.e)``. References: * Article: https://www.numberanalytics.com/blog/ultimate-guide-to-derangements-in-combinatorics * Sizes: https://oeis.org/A000166 rw) r\r]rrrrallrr0)rrexsyss&& rr]r][sWl xB uSW~ B R CS&.&*l26KLM rcV^8Xd \R4hV^8Xd!\\\V4V4^R4#\V.4p\ W4p\ \\W44^R44#)aIntersperse filler element *e* among the items in *iterable*, leaving *n* items between each filler element. >>> list(intersperse('!', [1, 2, 3, 4, 5])) [1, '!', 2, '!', 3, '!', 4, '!', 5] >>> list(intersperse(None, [1, 2, 3, 4, 5], n=2)) [1, 2, None, 3, 4, None, 5] z n must be > 0N)rrrsrrRr?)rrrfillerchunkss&&& rrwrwsi Av)) ajH5q$?? %vj8!TBCCrc LVUu.uFp\V4NK pp\\P!\ \ V444pVUu0uFqCV,^8XgKVkK ppVUu.uF"p\\ VPV44NK$ up#uupiuupiuupi)a{Return the elements from each of the input iterables that aren't in the other input iterables. For example, suppose you have a set of packages, each with a set of dependencies:: {'pkg_1': {'A', 'B'}, 'pkg_2': {'B', 'C'}, 'pkg_3': {'B', 'D'}} If you remove one package, which dependencies can also be removed? If ``pkg_1`` is removed, then ``A`` is no longer necessary - it is not associated with ``pkg_2`` or ``pkg_3``. Similarly, ``C`` is only needed for ``pkg_2``, and ``D`` is only needed for ``pkg_3``:: >>> unique_to_each({'A', 'B'}, {'B', 'C'}, {'B', 'D'}) [['A'], ['C'], ['D']] If there are duplicates in one input iterable that aren't in the others they will be duplicated in the output. Input order is preserved:: >>> unique_to_each("mississippi", "missouri") [['p', 'p'], ['o', 'u', 'r']] It is assumed that the elements of each iterable are hashable. )r!rrrrsetfilter __contains__) iterablesrXpoolcountselementuniquess* rrrs6 ) )yDHyD ) U((S$8 9F&,Ef7w10DwwfGE=A BTrD,,b1 2T BB *E BsB B B,(B!c#*"V^8d \R4hV^8XdRxR#V^8d \R4h\V4p\\WA4VR7pV'gR#\ V4V8d/\ V4V3V\ V4, ,,xR#\ V4xV3W18d V^, MV^, ,p\ VP\WF44p\Ws^, RV4Fp\ V4xK R#5i)aReturn a sliding window of width *n* over the given iterable. >>> all_windows = windowed([1, 2, 3, 4, 5], 3) >>> list(all_windows) [(1, 2, 3), (2, 3, 4), (3, 4, 5)] When the window is larger than the iterable, *fillvalue* is used in place of missing values: >>> list(windowed([1, 2, 3], 4)) [(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, '!')] To slide into the iterable's items, use :func:`chain` to add filler items to the left: >>> iterable = [1, 2, 3, 4] >>> n = 3 >>> padding = [None] * (n - 1) >>> list(windowed(chain(padding, iterable), 3)) [(None, None, 1), (None, 1, 2), (1, 2, 3), (2, 3, 4)] n must be >= 0Nzstep must be >= 1rr) rrrrrr\rrr) seqr fillvaluerrwindowpaddingr_s &&&& rrrs6 1u)**Av ax,--CyH6(&q 1F  6{QFm |q3v;?@@ -ltya!edQh?G h 8 9FF1HdD 1Fm2sDDc#".pVFpVPV4V3xK \V4p\V4p\^V^,4F.p\W4, ^,4FpWWT,xK K0 R#5i)a.Yield all of the substrings of *iterable*. >>> [''.join(s) for s in substrings('more')] ['m', 'o', 'r', 'e', 'mo', 'or', 're', 'mor', 'ore', 'more'] Note that non-string iterables can also be subdivided. >>> list(substrings([0, 1, 2])) [(0,), (1,), (2,), (0, 1), (1, 2), (0, 1, 2)] N)rr\rr])rrrrTrr_s& rrrsv C 4g  *CSJ1j1n %z~)*A!%. +&sA?Bcza\^\S4^,4pV'd \V4pV3RlV4#)aYield all substrings and their positions in *seq* The items yielded will be a tuple of the form ``(substr, i, j)``, where ``substr == seq[i:j]``. This function only works for iterables that support slicing, such as ``str`` objects. >>> for item in substrings_indexes('more'): ... print(item) ('m', 0, 1) ('o', 1, 2) ('r', 2, 3) ('e', 3, 4) ('mo', 0, 2) ('or', 1, 3) ('re', 2, 4) ('mor', 0, 3) ('ore', 1, 4) ('more', 0, 4) Set *reverse* to ``True`` to yield the same items in the opposite order. c3<"TFBp\\S4V, ^,4FpSW"V,W"V,3xK KD R#5iN)r]r)roLr_rs& rrq%substrings_indexes..EsA ,-quSX\A=M7N!QQE"7N"AsA A )r]rr)rreverseresf& rrr(s74 aSAA QK ,- rcFa]tRtRtoRtR RltRtRtRtRt R t Vt R#) rPiJarWrap *iterable* and return an object that buckets the iterable into child iterables based on a *key* function. >>> iterable = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2', 'b3'] >>> s = bucket(iterable, key=lambda x: x[0]) # Bucket by 1st character >>> sorted(list(s)) # Get the keys ['a', 'b', 'c'] >>> a_iterable = s['a'] >>> next(a_iterable) 'a1' >>> next(a_iterable) 'a2' >>> list(s['b']) ['b1', 'b2', 'b3'] The original iterable will be advanced and its items will be cached until they are used by the child iterables. This may require significant storage. By default, attempting to select a bucket to which no items belong will exhaust the iterable and cache all values. If you specify a *validator* function, selected buckets will instead be checked against it. >>> from itertools import count >>> it = count(1, 2) # Infinite sequence of odd numbers >>> key = lambda x: x % 10 # Bucket by last digit >>> validator = lambda x: x in {1, 3, 5, 7, 9} # Odd digits only >>> s = bucket(it, key=key, validator=validator) >>> 2 in s False >>> list(s[2]) [] Nc~\V4VnW n\\4VnT;'gRVnR#)cR#)Trrs&rrU!bucket.__init__..rs$rN)rr_keyrrr _validator)rrkey validators&&&&rrbucket.__init__ns-> !%( #77rcVPV4'gR#\W,4pVPV,PV4R# \dR#i;ir)rrr appendleftr)rvaluers&& rrbucket.__contains__tsUu%% 0 $D KK  ) )$ /   sA AAc# "VPV,'d&VPV,P4xK?\VP4pTP T4pY18XdTxKsTP T4'gKMTPT,PT4Kq \dR#i;i5i)z Helper to yield items from the parent iterator that match *value*. Items that don't match are stored in the local cache as they are encountered. N)rrrrrrrr)rrr item_values&& r _get_valuesbucket._get_valuess {{5!!kk%(0022#DHH~"&4J!*" 44 J/66t<)s4C&CB31C$C3 C>CCCcVPFOpVPV4pVPV4'gK-VPV,P V4KQ \ VP4#r)rrrrrr)rrrs& rrbucket.__iter__sTHHD4Jz** J'..t4 DKK  rchVPV4'g \R4#VPV4#)Nr)rrrrrs&&rr)bucket.__getitem__s+u%%8O&&r)rrrrr) r+r,r-r.r/rrrrr)r0r1r2s@rrPrPJs)!F8 =4!''rc6\V4wr#\W4V3#)alReturn a 2-tuple with a list containing the first *n* elements of *iterable*, and an iterator with the same items as *iterable*. This allows you to "look ahead" at the items in the iterable without advancing it. There is one item in the list by default: >>> iterable = 'abcdefg' >>> head, iterable = spy(iterable) >>> head ['a'] >>> list(iterable) ['a', 'b', 'c', 'd', 'e', 'f', 'g'] You may use unpacking to retrieve items instead of lists: >>> (head,), iterable = spy('abcdefg') >>> head 'a' >>> (first, second), iterable = spy('abcdefg', 2) >>> first 'a' >>> second 'b' The number of items requested can be larger than the number of items in the iterable: >>> iterable = [1, 2, 3, 4, 5] >>> head, iterable = spy(iterable, 10) >>> head [1, 2, 3, 4, 5] >>> list(iterable) [1, 2, 3, 4, 5] )rrD)rrrrs&& rrrsJ x=DA :q=rc<\P!\V!4#)aReturn a new iterable yielding from each iterable in turn, until the shortest is exhausted. >>> list(interleave([1, 2, 3], [4, 5], [6, 7, 8])) [1, 4, 6, 2, 5, 7] For a version that doesn't terminate after the shortest iterable is exhausted, see :func:`interleave_longest`. )rrr?rs*rrsrss   sI //rc'l"\VR\/FpVFpV\JgKVxK K R#5i)aWReturn a new iterable yielding from each iterable in turn, skipping any that are exhausted. >>> list(interleave_longest([1, 2, 3], [4, 5], [6, 7, 8])) [1, 4, 6, 2, 5, 7, 3, 8] This function produces the same output as :func:`roundrobin`, but may perform better for some inputs (in particular when the number of iterables is large). rN)rr:)rrrs* rrurus2988A9s44c#da"SfVUu.uFp\V4NK upoM$\V4\S48wd \R4h\S4p\\ V4V3RlRR7pVUu.uF pSV,NK ppVUu.uFp\ W,4NK ppV^,VR,rV^,VR,rW,.\V 4,p \ S4p V 'd\V 4xV ^,p \W4UUu.uF wrW, NK p pp\V 4F?wppV^8gK\W,4xV ^,p W;;,V, uu&KA KR#uupi \d \R4hi;iuupiuupiuuppi5i)a Interleave multiple iterables so that their elements are evenly distributed throughout the output sequence. >>> iterables = [1, 2, 3, 4, 5], ['a', 'b'] >>> list(interleave_evenly(iterables)) [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 ``__len__()`` can be used by manually specifying lengths with *lengths*: >>> from itertools import combinations, repeat >>> iterables = [combinations(range(4), 2), ['a', 'b', 'c']] >>> lengths = [4 * (4 - 1) // 2, 3] >>> list(interleave_evenly(iterables, lengths=lengths)) [(0, 1), (0, 2), 'a', (0, 3), (1, 2), 'b', (1, 3), (2, 3), 'c'] Based on Bresenham's algorithm. Nz^Iterable lengths could not be determined automatically. Specify them with the lengths keyword.z,Mismatching number of iterables and lengths.c<SV,#rr)r_lengthss&rrU#interleave_evenly..s 71:rTrr:rNN) rrrsortedr]rr>rr? enumerate)rrrXdimslengths_permuter_ lengths_desc iters_desc delta_primarydeltas_secondary iter_primaryiters_secondaryerrorsto_yieldrdeltae_s&f rrtrts0 )232s2w3G Y3w< 'GHH wo$y|$oJ>'31o|B7G#$.qM:b>/# $s+;'< Ks]F0FFFAF03F F0 F%%AF05$F0F*,F0 8F0FFF0c'"VUu.uFp\V4NK ppV'd+\\V44p\W#,4xK2R#uupi \dTR,Y#&TRKUi;i5i)aRepeatedly select one of the input *iterables* at random and yield the next item from it. >>> iterables = [1, 2, 3], 'abc', (True, False, None) >>> list(interleave_randomly(*iterables)) # doctest: +SKIP ['a', 'b', 1, 'c', True, False, None, 2, 3] The relative order of the items in each input iterable will preserved. Note the sequences of items with this property are not equally likely to be generated. Nr)rr,rrr)rr iteratorsidxs* rrvrv5sn#,,)Qa)I, I' y~& & -  &r]IN"  s7A6A A6A6A A6A3/A62A33A6c#"\4pVP^\V^434V'dVP4pVwrVVeWR8d TRjxL K2VFrp\ V\ \ 34'gVe\ Wq4'dVxK:\V4pVPV4VPV^,V34K KR#L \d TxKi;i5i)aFlatten an iterable with multiple levels of nesting (e.g., a list of lists of tuples) into non-iterable types. >>> iterable = [(1, 2), ([3, 4], [[5], [6]])] >>> list(collapse(iterable)) [1, 2, 3, 4, 5, 6] Binary and text strings are not considered iterable and will not be collapsed. 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 ['a', 'b', 'c', 'd'] >>> list(collapse(iterable, levels=1)) # Only one level flattened ['a', ['b'], 'c', ['d']] N) rrrrrstrbytesrr) r base_typelevelsstack node_grouplevelnodesnodetrees &&& rrUrULs4 GE a!,-. ]]_ !   %.   D$e --&Jt,G,G  :D $$Z0$$eai%67%  !Js;/C0#C0CAC0 C$5C0C-)C0,C--C0c#"VeV!4VfVFpV!V4VxK M$\W4FpV!V4TRjxL K Ve V!4R#R#L Te T!4ii;i5i)aInvoke *func* on each item in *iterable* (or on each *chunk_size* group of items) before yielding the item. `func` must be a function that takes a single argument. Its return value will be discarded. *before* and *after* are optional functions that take no arguments. They 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 >>> it = [u'a', u'b', u'c'] >>> consume(side_effect(func, it, before=before, after=after)) >>> f.closed True N)rR)r:r chunk_sizebeforeafterrrs&&&&& rrrs{X   H   T  !!6U    7   G !   G s/A/AAAA A/AA,,A/caaa\\VV3Rl\^S444oV'dVV3RlpV!4#S#)a@Yield slices of length *n* from the sequence *seq*. >>> list(sliced((1, 2, 3, 4, 5, 6), 3)) [(1, 2, 3), (4, 5, 6)] By the default, the last yielded slice will have fewer than *n* elements 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 ``True``, then ``ValueError`` will be raised before the last slice is yielded. This function will only work for iterables that support slicing. For non-sliceable iterables, see :func:`chunked`. c3:<"TFpSWS,xK R#5irr)ror_rrs& rrqsliced..sC{!s11u~{sc3`<"SF"p\V4S8wd \R4hVxK$ R#5i)zseq is not divisible by n.Nr)_slicerrs rrsliced..rets-"v;!#$%ABB #r)rrr)rrrrrsff& @rrrs3(CuQ{CDH   u rc#"V^8Xd\V4xR#.p\V4pVFWpV!V4'd6VxV'dV.xV^8Xd\V4xR#.pV^,pKFVPV4KY VxR#5i)aYield lists of items from *iterable*, where each list is delimited by an item where callable *pred* returns ``True``. >>> list(split_at('abcdcba', lambda x: x == 'b')) [['a'], ['c', 'd', 'c'], ['a']] >>> list(split_at(range(10), lambda n: n % 2 == 1)) [[0], [2], [4], [6], [8], []] At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, then there is no limit on the number of splits: >>> list(split_at(range(10), lambda n: n % 2 == 1, maxsplit=2)) [[0], [2], [4, 5, 6, 7, 8, 9]] By default, the delimiting items are not included in the output. To include them, set *keep_separator* to ``True``. >>> list(split_at('abcdcba', lambda x: x == 'b', keep_separator=True)) [['a'], ['b'], ['c', 'd', 'c'], ['b'], ['a']] Nr!rr)rpredmaxsplitkeep_separatorbufrXrs&&&& rrrs.1}8n C hB ::If 1}2hC MH JJt  Is AB AB c#"V^8Xd\V4xR#.p\V4pVFJpV!V4'd)V'd!VxV^8Xd V.VOxR#.pV^,pVPV4KL V'dVxR#R#5i)a4Yield lists of items from *iterable*, where each list ends just before an item for which callable *pred* returns ``True``: >>> list(split_before('OneTwo', lambda s: s.isupper())) [['O', 'n', 'e'], ['T', 'w', 'o']] >>> list(split_before(range(10), lambda n: n % 3 == 0)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, then there is no limit on the number of splits: >>> list(split_before(range(10), lambda n: n % 3 == 0, maxsplit=2)) [[0, 1, 2], [3, 4, 5], [6, 7, 8, 9]] NrrrrrrXrs&&& rrrs 1}8n C hB ::#I1}kbk!C MH 4   s>BBc#8"V^8Xd\V4xR#.p\V4pVF^pVPV4V!V4'gK$V'gK.VxV^8Xd\V4pV'dVxR#.pV^,pK` V'dVxR#R#5i)a3Yield lists of items from *iterable*, where each list ends with an item where callable *pred* returns ``True``: >>> list(split_after('one1two2', lambda s: s.isdigit())) [['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]] Nrrs&&& rrr6s"1}8n C hB 4 ::##I1}2hIC MH   sAB B;BBc#."V^8Xd\V4xR#\V4p\V4pT.pTFDpT!YF4'd!TxT^8Xd T.TOxR#.pT^,pTP T4TpKF TxR# \dR#i;i5i)aSplit *iterable* into pieces based on the output of *pred*. *pred* should be a function that takes successive pairs of items and returns ``True`` if the iterable should be split in between them. For example, to find runs of increasing numbers, split the iterable when element ``i`` is larger than element ``i + 1``: >>> 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, then there is no limit on the number of splits: >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], ... lambda x, y: x > y, maxsplit=2)) [[1, 2, 3, 3], [2, 5], [2, 4, 2]] N)r!rrrr)rrrrXcur_itemr next_items&&& rrr\s&1}8n hB8 *C  $ $I1} &2&&C MH 9 I! s("B BAB BBBBc#"\V4pVF-pVf\V4xR#\\W#44xK/ R#5i)aYield a list of sequential items from *iterable* of length 'n' for each integer 'n' in *sizes*. >>> list(split_into([1,2,3,4,5,6], [1,2,3])) [[1], [2, 3], [4, 5, 6]] If the sum of *sizes* is smaller than the length of *iterable*, then the remaining items of *iterable* will not be returned. >>> list(split_into([1,2,3,4,5,6], [2,3])) [[1, 2], [3, 4, 5]] If the sum of *sizes* is larger than the length of *iterable*, fewer items will be returned in the iteration that overruns the *iterable* and further lists will be empty: >>> list(split_into([1,2,3,4], [1,2,3,4])) [[1], [2, 3], [4], []] When a ``None`` object is encountered in *sizes*, the returned list will contain items up to the end of *iterable* the same way that :func:`itertools.slice` does: >>> list(split_into([1,2,3,4,5,6,7,8,9,0], [2,3,None])) [[1, 2], [3, 4, 5], [6, 7, 8, 9, 0]] :func:`split_into` can be useful for grouping a series of items where the sizes of the groups are not uniform. An example would be where in a row from a table, multiple columns represent elements of the same feature (e.g. a point represented by x,y,z) but, the format is not the same for all columns. N)rr!r)rsizesrXras&& rrrs<F hB <r(N vb'( ( sAAcaaa\V4o\S\V44oSfS#S^8d \R4hV'd$VVV3Rlp\P!V!44#\\ SS4S4#)aYield the elements from *iterable*, followed by *fillvalue*, such that at least *n* items are emitted. >>> list(padded([1, 2, 3], '?', 5)) [1, 2, 3, '?', '?'] If *next_multiple* is ``True``, *fillvalue* will be emitted until the number of items emitted is a multiple of *n*: >>> list(padded([1, 2, 3, 4], n=3, next_multiple=True)) [1, 2, 3, 4, None, None] If *n* is ``None``, *fillvalue* will be emitted indefinitely. To create an *iterable* of exactly size *n*, you can truncate with :func:`islice`. >>> list(islice(padded([1, 2, 3], '?'), 5)) [1, 2, 3, '?', '?'] >>> list(islice(padded([1, 2, 3, 4, 5, 6, 7, 8], '?'), 5)) [1, 2, 3, 4, 5] n must be at least 1c3V<"SFpV3x\SS^, 4xK R#5ir)r)rlriterator_with_repeatrs rslice_generatorpadded..slice_generators)!h11q599"s&))rrrrrr)rrr next_multiplerrrs&&f& @@rrrss0H~H 6)+<=y## Q/00  : ""?#455V0!4h??rc ^\P!\\V\V444#)z|Repeat each element in *iterable* *n* times. >>> list(repeat_each('ABC', 3)) ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'] )rrrr)rrs&&rrrs"   s68VAY? @@rc#z"\pVFpVxK V\JdTMTp\V4RjxL R#L5i)a After the *iterable* is exhausted, keep yielding its last element. >>> list(islice(repeat_last(range(3)), 5)) [0, 1, 2, 2, 2] If the iterable is empty, yield *default* forever:: >>> list(islice(repeat_last(range(0), 42), 5)) [42, 42, 42, 42, 42] N)r:r)rrrfinals&& rrrs5 D wGDEe}s 0;9;c V^8d \R4h\W4p\V4UUu.uFwr4\WCRV4NK upp#uuppi)aLDistribute the items from *iterable* among *n* smaller iterables. >>> group_1, group_2 = distribute(2, [1, 2, 3, 4, 5, 6]) >>> list(group_1) [1, 3, 5] >>> list(group_2) [2, 4, 6] If the length of *iterable* is not evenly divisible by *n*, then the length of the returned iterables will not be identical: >>> 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], [], []] This function uses :func:`itertools.tee` and may require significant storage. If you need the order items in the smaller iterables to match the original iterable, see :func:`divide`. rN)rrrr)rrchildrenr"rXs&& rrbrbsK< 1u/008H8A(8K L8K95F2dA &8K LL LsAc J\V\V44p\VRVRVRV/#)a#Yield tuples whose elements are offset from *iterable*. The amount by which the `i`-th item in each tuple is offset is given by the `i`-th item in *offsets*. >>> list(stagger([0, 1, 2, 3])) [(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)] By default, ``None`` will be used to replace offsets beyond the end of the sequence. Specify *fillvalue* to use some other value. offsetslongestr)rrr)rrrrrs&&&& rrr$s<*8S\*H   " ,3 ?H rc`\R8d\P!R\4\ V!#)a``zip`` the input *iterables* together but raise ``UnequalIterablesError`` if they aren't all the same length. >>> it_1 = range(3) >>> it_2 = iter('abc') >>> list(zip_equal(it_1, it_2)) [(0, 'a'), (1, 'b'), (2, 'c')] >>> it_1 = range(3) >>> it_2 = iter('abcd') >>> list(zip_equal(it_1, it_2)) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... more_itertools.more.UnequalIterablesError: Iterables have different lengths i zwzip_equal will be removed in a future version of more-itertools. Use the builtin zip function with strict=True instead.)r7warningswarnDeprecationWarningr;rs*rrr@s/$Y '    y !!rrrc t\V4\V48wd \R4h.p\W04FhwrVV^8d(VP\ \ W&)4V44K3V^8dVP\ WVR44KWVPV4Kj V'd \VRV/#\V!#)a``zip`` the input *iterables* together, but offset the `i`-th iterable by the `i`-th item in *offsets*. >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1))) [('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')] By default, ``None`` will be used to replace offsets beyond the end of the sequence. Specify *fillvalue* to use some other value. z,Number of iterables and offsets didn't matchNr)rrr?rrrrr)rrrr staggeredrXrs$$$* rrr_s* 9~W%GHHIY( q5   U6)R#8"= > U   VB40 1   R )I;;;  ?rc aaaSf \V!pM<\V4p\V4^8XdV^,oVV3RlpM\V!oVV3RlpV'd\M\p\V!\ V!V!WSR7!4#)aReturn the input iterables sorted together, with *key_list* as the priority for sorting. All iterables are trimmed to the length of the shortest one. This can be used like the sorting function in a spreadsheet. If each iterable represents a column of data, the key list determines which 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. >>> sort_together([(1, 2, 3), ('c', 'b', 'a')], reverse=True) [(3, 2, 1), ('a', 'b', 'c')] If the *strict* keyword argument is ``True``, then ``UnequalIterablesError`` will be raised if any of the iterables have different lengths. c"<S!VS,4#rr) zipped_itemsr key_offsets&rrUsort_together..sL4L0Mrc<S!S!V4!#rr)r  get_key_itemsrs&rrUr s|,1rr)r1r!rrr?r) rkey_listrrr key_argumentzipperrr s &&f&& @@rrrs}\ {"8, > x=A "!JML'1ML!YcF vy)|MN rc\V4wrV'gR#V^,p\V\V44p\;QJd.R\ V44FNK 5#!R\ V444#)aThe inverse of :func:`zip`, this function disaggregates the elements of the zipped *iterable*. The ``i``-th iterable contains the ``i``-th element from each element of the zipped iterable. The first element is used to determine the 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 *iterable* into memory. Note, however, that this function uses :func:`itertools.tee` and thus may require significant storage. c3l"TF*wr\\\V4V4\4xK, R#5ir)r{rr1r)ror_rXs& rrqunzip..s.)EA c*Q-,j99)s24r)rrrr\r)rrfrs& rrrsn(]ND  7DHc$i(I 5y)55y) rcPV^8d \R4hVR,Tp\\ V4V4wr4.p^p\ ^V^,4F9pTpYgV8:d V^,MT, pVP \W(V44K; V# \d\T4pLi;i)a.Divide the elements from *iterable* into *n* parts, maintaining order. >>> group_1, group_2 = divide(2, [1, 2, 3, 4, 5, 6]) >>> list(group_1) [1, 2, 3] >>> 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], [], []] This function will exhaust the iterable before returning. If order is not important, see :func:`distribute`, which does not first pull the iterable into memory. r:NrN)rrr\divmodrr]rr) rrrrrerrr_rs && rrcrcs: 1u/00  #c(A DA C D 1a!e_ aAQ& 4$() J Hos B B%$B%cVf \R4#Ve\W4'd \V34#\V4# \d\T34u#i;i)aIf *obj* is iterable, return an iterator over its items:: >>> obj = (1, 2, 3) >>> list(always_iterable(obj)) [1, 2, 3] 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 Python considers iterable as iterable: >>> obj = 'foo' >>> list(always_iterable(obj, base_type=None)) ['f', 'o', 'o'] r)rrr)objrs&&rrLrL+sYR {Bx:c#=#=SF|Cy SF|s =AAcV^8d \R4h\V4wr4R.V,p\V\W4V4p\\\ V^V,^,44p\ Wt4#)a+Return an iterable over `(bool, item)` tuples where the `item` is drawn from *iterable* and the `bool` indicates whether that item satisfies the *predicate* or is adjacent to an item that does. For example, to find whether items are adjacent to a ``3``:: >>> 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. See also :func:`groupby_transform`, which can be used with this function to group ranges of items with the same `bool` value. zdistance must be at least 0F)rrrranyrr?) predicaterdistancei1i2rselectedadjacent_to_selecteds&&& rrJrJ`sj:!|677 ]FBg GWc)0':HsHXq8|a7G$HI # ((rcpaa\W4pS'd V3RlV4pS'd V3RlV4pV#)aAn extension of :func:`itertools.groupby` that can apply transformations to the grouped data. * *keyfunc* is a function computing a key value for each item in *iterable* * *valuefunc* is a function that transforms the individual items from *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')] Note that the order of items in the iterable is significant. Only adjacent items are grouped together, so if you don't want any duplicate groups, you should sort the iterable by the key function. c3D<"TFwrV\SV43xK R#5ir)r)ror~g valuefuncs& rrq$groupby_transform..s6#$!3y!$%# c3<<"TFwrVS!V43xK R#5irr)ror~r$ reducefuncs& rrqr&s2cda:a=!csr)rkeyfuncr%r)rs&&ff rrnrns/H ( $C6#62c2 Jrca]tRtRtoRt]!]!^^44tRtRt Rt Rt Rt Rt R tR t]R 4tR tR tRtRtRtRtRtVtR#)riaAn extension of the built-in ``range()`` function whose arguments can be any orderable numeric type. With only *stop* specified, *start* defaults to ``0`` and *step* defaults to ``1``. The output items will match the type of *stop*: >>> 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) c\V4pV^8Xd\VwVn\VP4!^4Vn\VPVP, 4!^4VnMV^8XdBVwVnVn\VPVP, 4!^4VnM@V^8XdVwVnVnVnM#V^8Xd\ RV 24h\ RV 24h\VP4!^4VnVPVP 8Xd \R4hVPVP 8VnR#)rz0numeric_range expected at least 1 argument, got z0numeric_range expected at most 3 arguments, got z&numeric_range() arg 3 must not be zeroN) r_stoptype_start_stepr_zeror_growing)rr7argcs&* rrnumeric_range.__init__s4y 19 MTZtzz*1-DKdjj4;;67:DJ QY&* #DKdjj4;;67:DJ QY26 /DKTZ QYB4&I B4&I $**%a( :: #EF F TZZ/ rcVP'dVPVP8#VPVP8#r)r3r0r.rs&rr numeric_range.__bool__ s1 ===;;+ +;;+ +rcVP'd[VPTu;8:dVP8d5MR#WP, VP,VP8H#R#VPTu;8dVP8d7MR#VPV, VP),VP8H#R#F)r3r0r.r1r2)relems&&rrnumeric_range.__contains__ s ==={{d/TZZ/  {{*djj8DJJFF0 {{d/TZZ/ d* {;tzzIIrct\V\4'd\V4'*p\V4'*pV'g V'd T;'dT#VPVP8H;'dEVPVP8H;'d$VP R4VP R48H#R#)rFr)rrboolr0r1 _get_by_index)rother empty_self empty_others&& r__eq__numeric_range.__eq__ s e] + +!$ZJ"5k/K[!11k1KK5<</JJ ekk1JJ**2.%2E2Eb2II rc.\V\4'dVPV4#\V\4'Ed8VPf VP MVPVP ,pVP eVP VP)8:dVPpMCVP VP8dVPpMVPVP 4pVPeVPVP8dVPpMDVPVP)8:dVPpMVPVP4p\W4V4#\R\V4P 24h)Nz6numeric range indices must be integers or slices, not )rintr>r(rr1r_lenr0r.rrrr/r+)rrrrrs&& rr)numeric_range.__getitem__" s% c3  %%c* * U # #!$!14::sxx$**7LDyy CII$))$; dii' **3995xx388tyy#8zzdiiZ'{{))#((3 d3 3++/9+=+=*>@ rcV'd2\VPVPR4VP34#VP#rr)hashr0r>r1 _EMPTY_HASHrs&r__hash__numeric_range.__hash__= s6 d&8&8&.D s%B'Q$++TZZ00's14)rr3rr r6r.r2)rvaluessf rrnumeric_range.__iter__C sHB%'B ===WR4f= =WR4f= =rcVP#r)rFrs&r__len__numeric_range.__len__J s yyrcVVP'd&VPpVPpVPpM%VPpVPpVP)pW!, pW@P8:d^#\ WC4wrV\ V4\ W`P8g4,#r')r3r0r.r1r2rrE)rrrrrrres& rrFnumeric_range._lenM s} ===KKE::D::DJJE;;DJJ;D< zz !()DAq6CZZ00 0rcT\VPVPVP33#r)rr0r.r1rs&r __reduce__numeric_range.__reduce__^ s t{{DJJ CCCrcVP^8Xd RVP: RVP: R2#RVP: RVP: RVP: R2#)rznumeric_range(rFrQ)r1r0r.rs&r__repr__numeric_range.__repr__a sS ::?#DKK?"TZZN!D DT[[O2djj^2djj^1 M rc\\VPR4VPVP, VP)44#rI)rrr>r0r1rs&rrnumeric_range.__reversed__h s< ""2& djj(@4::+   rc\W94#r)rErs&&rrnumeric_range.counto s5=!!rcVP'dkVPTu;8:dVP8dGMM\WP, VP4wr#W0P 8Xd \ V4#MkVPTu;8dVP8dIMME\VPV, VP)4wr#W0P 8Xd \ V4#\V R24h)z is not in numeric range)r3r0r.rr1r2rEr)rrrres&& rr"numeric_range.indexr s ==={{e0djj0ekk14::> ?q6M{{e0djj0dkkE1DJJ;? ?q6ME7":;<numeric_range._get_by_index sD q5 NA q5ANFG G{{Q^++r)r3r0r1r.r2N)r+r,r-r.r/rJr]rKrr rrBr)rLrrTr rFrYr\rrr"r>r0r1r2s@rrrs}1fuQ{#K02,  6$ >11 D  " =,,rc\V4pV'g \R4#Vf \4M \V4p\ \ V\ V44\V44#)a Cycle through the items from *iterable* up to *n* times, yielding the number of completed cycles along with each item. If *n* is omitted the process repeats indefinitely. >>> list(count_cycle('AB', 3)) [(0, 'A'), (0, 'B'), (1, 'A'), (1, 'B'), (2, 'A'), (2, 'B')] r)r\rrr]r?rrr)rrrcounters&& rr[r[ sF /C BxegaG {7CH-uSz ::rc#p"\V4pVF pRpVFpVRV3xTpRpK VRV3xK" R#5i)a Yield 3-tuples of the form ``(is_first, is_last, item)``. >>> list(mark_ends('ABC')) [(True, False, 'A'), (False, False, 'B'), (False, True, 'C')] Use this when looping over an iterable to take special action on its first and/or last items: >>> iterable = ['Header', 100, 200, 'Footer'] >>> total = 0 >>> for is_first, is_last, item in mark_ends(iterable): ... if is_first: ... continue # Skip the header ... if is_last: ... continue # Skip the footer ... total += item >>> print(total) 300 TFN)r)rrXarlbs& rrr sM( hB A/ !AET1n s46cVf\\4\W44#V^8d \R4h\ W\ R7p\\4\ W44#)a3Yield the index of each item in *iterable* for which *pred* returns ``True``. *pred* defaults to :func:`bool`, which will select truthy items: >>> list(locate([0, 1, 1, 0, 1, 0, 0])) [1, 2, 4] Set *pred* to a custom function to, e.g., find the indexes for a particular item. >>> list(locate(['a', 'b', 'c', 'b'], lambda x: x == 'b')) [1, 3] 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(locate(iterable, pred=pred, window_size=3)) [1, 5, 9] Use with :func:`seekable` to find indexes and then retrieve the associated items: >>> from itertools import count >>> from more_itertools import seekable >>> source = (3 * n + 1 if (n % 2) else n // 2 for n in count()) >>> it = seekable(source) >>> pred = lambda x: x > 100 >>> indexes = locate(it, pred=pred) >>> i = next(indexes) >>> it.seek(i) >>> next(it) 106 zwindow size must be at least 1r)rrrrrr:r)rr window_sizerXs&&& rr~r~ sRLT!455Q9:: (7 ;B EGWT. //rc>R\\\V!44#)zYield elements of the longest common prefix among given *iterables*. >>> ''.join(longest_common_prefix(['abcd', 'abc', 'abf'])) 'ab' c32"TF q^,xK R#5irNr)rocs& rrq(longest_common_prefix.. s @?QaDD?s)rrFr?rs&rrr s A)IsI? @@rc\W4#)aYield the items from *iterable*, but strip any from the beginning for which *pred* returns ``True``. For example, to remove a set of items from the start of an iterable: >>> iterable = (None, False, None, 1, 2, None, 3, False, None) >>> pred = lambda x: x in {None, False, ''} >>> list(lstrip(iterable, pred)) [1, 2, None, 3, False, None] This function is analogous to to :func:`str.lstrip`, and is essentially an wrapper for :func:`itertools.dropwhile`. )rrrs&&rrr s T $$rc#".pVPpVPpVF0pV!V4'd V!V4KTRjxL V!4VxK2 R#L5i)aYield the items from *iterable*, but strip any from the end for which *pred* returns ``True``. For example, to remove a set of items from the end of an iterable: >>> iterable = (None, False, None, 1, 2, None, 3, False, None) >>> pred = lambda x: x in {None, False, ''} >>> list(rstrip(iterable, pred)) [None, False, None, 1, 2, None, 3] This function is analogous to :func:`str.rstrip`. N)rclear)rrcache cache_append cache_clearrs&& rrr sP E<>> iterable = (None, False, None, 1, 2, None, 3, False, None) >>> pred = lambda x: x in {None, False, ''} >>> list(strip(iterable, pred)) [1, 2, None, 3] This function is analogous to :func:`str.strip`. )rrrts&&rrr s &($ //rc<a]tRtRtoRtRtRtRtRtRt Vt R#) ryi- aAn extension of :func:`itertools.islice` that supports negative values for *stop*, *start*, and *step*. >>> iterator = iter('abcdefgh') >>> list(islice_extended(iterator, -4, -1)) ['e', 'f', 'g'] Slices with negative values require some caching of *iterable*, but this function takes care to minimize the amount of memory required. For example, you can use a negative step with an infinite iterator: >>> from itertools import count >>> list(islice_extended(count(), 110, 99, -2)) [110, 108, 106, 104, 102, 100] You can also use slice notation directly: >>> iterator = map(str, count()) >>> it = islice_extended(iterator)[10:20:2] >>> list(it) ['10', '12', '14', '16', '18'] cl\V4pV'd\V\V!4VnR#W0nR#r)r_islice_helperr( _iterator)rrr7rXs&&* rrislice_extended.__init__G s% (^ +Bt =DNNrcV#rrrs&rrislice_extended.__iter__N rrc,\VP4#rrr~rs&rrislice_extended.__next__Q DNN##rc\V\4'd \\VPV44#\ R4h)z4islice_extended.__getitem__ argument must be a slice)rr(ryr}r~r)rrs&&rr)islice_extended.__getitem__T s1 c5 ! !">$..##FG GNOOr)r~N) r+r,r-r.r/rrrr)r0r1r2s@rryry- s%2 $PPrc#"VPpVPpVP^8Xd \R4hVP;'g^pV^8EdVf^MTpV^8d\ \ V^4V)R7pV'dVR,^,M^p\ Wb,^4pVfTpM%V^8d \W64pM\ Wc,^4pW, p V ^8:dR#\V 4F;p W,^8XdVP4^,xK+VP4K= R#VeV^8d\\WV4R4\ \W)4V)R7p\ V4FFwrW,^8XdVP4xMVP4VPV 4KH R#\WW44RjxL R#VfRMTpVeV^8d|V)^, p \ \ V^4V R7pV'dVR,^,M^pV^8dY#rM\W&, R4Rr\V4WxV1,F wrV xK R#Ve V^,p \\W V 4R4V^8dTpRp M#Vf RpV^,p MRpW#, p V ^8:dR#\\W 44pWWRV1,RjxL R#ELL 5i)rz1step argument must be a non-zero integer or None.Nrr)rrrrrrr rr]rrrrr!) rXsrrrrwlen_iterr_r`rr"rms && rr}r}[ s GGE 66Dvv{LMM 66;;QD axm% 19)B*E6:E',uRy|!HH$a(A|'+AAvq<1$ --/!,,MMO"TAX 5)4 0&U+TE:E(} <1$ --/)MMO T" -b4 4 4}5  4!8 A)B*15E',uRy|!H qy15+R0$1#E{1t844   51HVB1%t,qyAIL6'EWW~ % %[ 5Z &s,AK% FK%K DK%K#K%#K%cd\V4# \d\\T44u#i;i)aAn extension of :func:`reversed` that supports all iterables, not just those which implement the ``Reversible`` or ``Sequence`` protocols. >>> print(*always_reversible(x for x in range(3))) 2 1 0 If the iterable is already reversible, this function returns the result of :func:`reversed()`. If the iterable is not reversible, this function will cache the remaining items in the iterable and yield them in reverse order, which may require significant storage. )rrr!r@s&rrMrM s0(!! (X''(s //c#a"SfRpMV3Rlp\\V4VR7Fwr4\\^4V4xK R#5i)aYield groups of consecutive items using :func:`itertools.groupby`. The *ordering* function determines whether two items are adjacent by returning their position. By default, the ordering function is the identity function. This is suitable for finding runs of numbers: >>> iterable = [1, 10, 11, 12, 20, 30, 31, 32, 33, 40] >>> for group in consecutive_groups(iterable): ... print(list(group)) [1] [10, 11, 12] [20] [30, 31, 32, 33] [40] To find runs of adjacent letters, apply :func:`ord` function to convert letters to ordinals. >>> iterable = 'abcdfgilmnop' >>> ordering = ord >>> for group in consecutive_groups(iterable, ordering): ... print(list(group)) ['a', 'b', 'c', 'd'] ['f', 'g'] ['i'] ['l', 'm', 'n', 'o', 'p'] Each group of consecutive items is an iterator that shares it source with *iterable*. When an an output group is advanced, the previous group is no longer available unless its elements are copied (e.g., into a ``list``). >>> iterable = [1, 2, 11, 12, 21, 22] >>> saved_groups = [] >>> for group in consecutive_groups(iterable): ... saved_groups.append(list(group)) # Copy group elements >>> saved_groups [[1, 2], [11, 12], [21, 22]] Nc0V^,V^,, #r'rrs&rrU$consecutive_groups.. s!qt rc><V^,S!V^,4, #r'r)rorderings&rrUr s!x!~-rr)rrrr1)rrrr~r$s&f rrXrX sBR#- (+5*Q-##6sAA initialc\V4wr4\V4.pTe.p\ T\ YT44# \d\.4u#i;i)aThis function is the inverse of :func:`itertools.accumulate`. By default it will compute the first difference of *iterable* using :func:`operator.sub`: >>> from itertools import accumulate >>> iterable = accumulate([0, 1, 2, 3, 4]) # produces 0, 1, 3, 6, 10 >>> list(difference(iterable)) [0, 1, 2, 3, 4] *func* defaults to :func:`operator.sub`, but other functions can be specified. They will be applied as follows:: A, B, C, D, ... --> A, func(B, A), func(C, B), func(D, C), ... For example, to do progressive division: >>> iterable = [1, 2, 6, 24, 120] >>> func = lambda x, y: x // y >>> list(difference(iterable, func)) [1, 2, 3, 4, 5] If the *initial* keyword is set, the first element will be skipped when computing successive differences. >>> it = [10, 11, 13, 16] # from accumulate([1, 2, 3], initial=10) >>> list(difference(it, initial=10)) [1, 2, 3] )rrrrrr)rr:rrirjrls&&$ rr_r_ sY< x=DAa  DQ (( Bxs 7AAc<a]tRtRtoRtRtRtRtRtRt Vt R#) rIi5 aReturn a read-only view of the sequence object *target*. :class:`SequenceView` objects are analogous to Python's built-in "dictionary view" types. They provide a dynamic view of a sequence's items, meaning that when the sequence updates, so does the view. >>> 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 like the underlying sequence, except they don't allow assignment: >>> view[1] '1' >>> view[1:-1] ['1', '2'] >>> len(view) 4 Sequence views are useful as an alternative to copying, as they don't require (much) extra storage. cJ\V\4'g\hWnR#r)rrr_target)rtargets&&rrSequenceView.__init__S s&(++O rc(VPV,#rr)rr"s&&rr)SequenceView.__getitem__X s||E""rc,\VP4#r)rrrs&rrTSequenceView.__len__[ s4<<  rcPVPP RVP: R2#)(rQ) __class__r+rrs&rr\SequenceView.__repr__^ s%..))*!DLL+;1==rrN) r+r,r-r.r/rr)rTr\r0r1r2s@rrIrI5 s#: #!>>rc^a]tRtRtoRtR RltRtRtRt] 3Rlt R t R t R t R tVtR#)rib an Wrap an iterator to allow for seeking backward and forward. This progressively caches the items in the source iterable so they can be re-visited. Call :meth:`seek` with an index to seek to that position in the source iterable. 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') You can also seek forward: >>> it = seekable((str(n) for n in range(20))) >>> it.seek(10) >>> next(it) '10' >>> it.seek(20) # Seeking past the end of the source isn't a problem >>> list(it) [] >>> it.seek(0) # Resetting works even after hitting the end >>> next(it) '0' Call :meth:`relative_seek` to seek relative to the source iterator's current position. >>> it = seekable((str(n) for n in range(20))) >>> next(it), next(it), next(it) ('0', '1', '2') >>> it.relative_seek(2) >>> next(it) '5' >>> it.relative_seek(-3) # Source is at '6', we move back to '3' >>> next(it) '3' >>> it.relative_seek(-3) # Source is at '4', we move back to '1' >>> next(it) '1' Call :meth:`peek` to look ahead one item without advancing the iterator: >>> it = seekable('1234') >>> it.peek() '1' >>> list(it) ['1', '2', '3', '4'] >>> it.peek(default='empty') 'empty' Before the iterator is at its end, calling :func:`bool` on it will return ``True``. After it will return ``False``: >>> 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' Ncn\V4VnVf .VnM\.V4VnRVnR#r)r_sourcerr_index)rrrs&&&rrseekable.__init__ s.H~ >DKF+DK rcV#rrrs&rrseekable.__iter__ rrc$VPe:VPVP,pV;P^, unV#\VP4pVPP V4V# \d RTnLFi;ir)rrrrrrrrs& rrseekable.__next__ su ;; " {{4;;/ q  DLL! 4   #"  #sA::BBcLVP4R# \dR#i;irrrs&rr seekable.__bool__ r r c\V4pTPf\ TP 4TnT;P^,unT# \dT\JdhTu#i;ir)rrr:rrr)rrpeekeds&& rr  seekable.peek s` $ZF ;; dkk*DK q   '!N s AA+*A+c,\VP4#r)rIrrs&relementsseekable.elements sDKK((rcrWnV\VP4, pV^8d\W4R#R#rp)rrrr=)rr" remainders&& rseek seekable.seek s. C ,, q= D $ rcVPf\VP4VnVP\ VPV,^44R#r)rrrrr )rrs&&r relative_seekseekable.relative_seek s8 ;; dkk*DK #dkkE)1-.r)rrrr)r+r,r-r.r/rrrr r:r rrrr0r1r2s@rrrb s>bH # )% //rcDa]tRtRtoRt]R4t]R4tRtVt R#)ri af :func:`run_length.encode` compresses an iterable with run-length encoding. It yields groups of repeated items with the count of how many times they were repeated: >>> uncompressed = 'abbcccdddd' >>> list(run_length.encode(uncompressed)) [('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&R\V44#)c3@"TFwrV\V43xK R#5ir)rr)ror~r$s& rrq$run_length.encode.. s;):DG ):sr*r@s&rencoderun_length.encode s;):;;rcJ\P!\\V44#r)rrrrr@s&rdecoderun_length.decode s""768#<==rrN) r+r,r-r.r/ staticmethodrrr0r1r2s@rrr s2&<<>>rcR\\\W 4V^,44V8H#)aReturn ``True`` if exactly ``n`` items in the iterable are ``True`` according to the *predicate* function. >>> exactly_n([True, True, False], 2) True >>> 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, so avoid calling it on infinite iterables. )rrrr)rrrs&&&rrhrh s$ vi2AE: ;q @@rc#""\V4pV^8Xd \R4hVPV4V)p\V4pV\P !W14,p\ RV4F!pVPV4\V4xK# R#5i)aYield the circular shifts of *iterable*. >>> list(circular_shifts(range(4))) [(0, 1, 2, 3), (1, 2, 3, 0), (2, 3, 0, 1), (3, 0, 1, 2)] Set *steps* to the number of places to rotate to the left (or to the right if negative). Defaults to 1. >>> list(circular_shifts(range(4), 2)) [(0, 1, 2, 3), (2, 3, 0, 1)] >>> list(circular_shifts(range(4), -1)) [(0, 1, 2, 3), (3, 0, 1, 2), (2, 3, 0, 1), (1, 2, 3, 0)] z"Steps should be a non-zero integerN)rrrotatermathgcdrr\)rstepsbufferrrs&& rrTrT2 sx 8_F z=>> MM% FE F A$((1 A D!_ eFmsB BcaaVV3RlpV#)aZReturn a decorator version of *wrapping_func*, which is a function that modifies an iterable. *result_index* is the position in that function's signature where the iterable goes. This lets you use itertools on the "production end," i.e. at function definition. This can augment what the function returns without changing the function's code. For example, to produce a decorator version of :func:`chunked`: >>> from more_itertools import chunked >>> chunker = make_decorator(chunked, result_index=0) >>> @chunker(3) ... def iter_range(n): ... return iter(range(n)) ... >>> list(iter_range(9)) [[0, 1, 2], [3, 4, 5], [6, 7, 8]] To only allow truthy items to be returned: >>> truth_serum = make_decorator(filter, result_index=1) >>> @truth_serum(bool) ... def boolean_test(): ... return [0, 1, '', ' ', False, True] ... >>> list(boolean_test()) [1, ' ', True] The :func:`peekable` and :func:`seekable` wrappers make for practical decorators: >>> from more_itertools import peekable >>> peekable_function = make_decorator(peekable) >>> @peekable_function() ... def str_range(*args): ... return (str(x) for x in range(*args)) ... >>> it = str_range(1, 20, 2) >>> next(it), next(it), next(it) ('1', '3', '5') >>> it.peek() '7' >>> next(it) '7' c<aaVVVV3RlpV#)c<aVVVVV3RlpV#)c^<S!V/VBp\S4pVPSV4S!V/SB#r)r!insert) r7r8resultwrapping_args_f result_index wrapping_args wrapping_funcwrapping_kwargss *, r inner_wrapperOmake_decorator..decorator..outer_wrapper..inner_wrapper s<D+F+!%m!4%%lF;$nHHHrr)rrrrrrsf r outer_wrapper8make_decorator..decorator..outer_wrapper s I I ! rr)rrrrrsjl r decorator!make_decorator..decorator s ! !rr)rrrsff rrrP sf  rc:\\4pVf*VF"pV!V4pWF,PV4K$ M0VF*pV!V4pV!V4pWF,PV4K, Ve$VP4FwrhV!V4WF&K RVnV#)aCReturn a dictionary that maps the items in *iterable* to categories defined by *keyfunc*, transforms them with *valuefunc*, and then summarizes them by category with *reducefunc*. *valuefunc* defaults to the identity function if it is unspecified. If *reducefunc* is unspecified, no summarization takes place: >>> keyfunc = lambda x: x.upper() >>> result = map_reduce('abbccc', keyfunc) >>> sorted(result.items()) [('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])] Specifying *valuefunc* transforms the categorized items: >>> keyfunc = lambda x: x.upper() >>> valuefunc = lambda x: 1 >>> result = map_reduce('abbccc', keyfunc, valuefunc) >>> sorted(result.items()) [('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])] Specifying *reducefunc* summarizes the categorized items: >>> keyfunc = lambda x: x.upper() >>> valuefunc = lambda x: 1 >>> reducefunc = sum >>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc) >>> sorted(result.items()) [('A', 1), ('B', 2), ('C', 3)] You may want to filter the input iterable before applying the map/reduce procedure: >>> all_items = range(30) >>> items = [x for x in all_items if 10 <= x <= 20] # Filter >>> keyfunc = lambda x: x % 2 # Evens map to 0; odds to 1 >>> categories = map_reduce(items, keyfunc=keyfunc) >>> sorted(categories.items()) [(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])] >>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum) >>> sorted(summaries.items()) [(0, 90), (1, 75)] Note that all items in the iterable are gathered into a list before the summarization step, which may require significant storage. The returned object is a :obj:`collections.defaultdict` with the ``default_factory`` set to ``None``, such that it behaves like a normal dictionary. N)rr!rrdefault_factory) rr+r%r)rrrr value_lists &&&& rrr sh d CD$-C HOOD ! D$-CdOE HOOE " "yy{OC!*-CH +C Jrc aVf,\V4oV3Rl\\V4V44#\\ \WV444# \dL+i;i)a+Yield the index of each item in *iterable* for which *pred* returns ``True``, starting from the right and moving left. *pred* defaults to :func:`bool`, which will select truthy items: >>> list(rlocate([0, 1, 1, 0, 1, 0, 0])) # Truthy at 1, 2, and 4 [4, 2, 1] Set *pred* to a custom function to, e.g., find the indexes for a particular item: >>> iterator = iter('abcb') >>> pred = lambda x: x == 'b' >>> list(rlocate(iterator, 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. See :func:`locate` to for other example applications. c3D<"TFpSV, ^, xK R#5irr)ror_rs& rrqrlocate.. sO.NHqL1$$.Nr')rr~rrr!)rrrmrs&&&@rrr s^B 8}HOfXh5G.NO O D <= >>   s)A AAc#"V^8d \R4h\V4p\V\\V^, 44p\ WT4p^pVFhpV!V!'d2VeWs8d(V^, pTRjxL \ Wd^, 4K@V'gKJV^,\JgK]V^,xKj R#LF5i)aYield the items from *iterable*, replacing the items for which *pred* returns ``True`` with the items from the iterable *substitutes*. >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1] >>> 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] zwindow_size must be at least 1N)rr\rrr:rr=) rr substitutesrrmrXwindowsrws &&&&& rrr s:Q9:: $K xq9 :Br'G A  88 19Q&&&q1 1!A$g%A$J#'sA5C7B>8CC-Cc#"\V4p\V4p\\^V44F5p\ RV,W23,4UUu.uF wr4WVNK uppxK7 R#uuppi5i)zYield all possible order-preserving partitions of *iterable*. >>> iterable = 'abc' >>> for part in partitions(iterable): ... print([''.join(p) for p in part]) ['abc'] ['a', 'bc'] ['ab', 'c'] ['a', 'b', 'c'] This is unrelated to :func:`partition`. Nr')r!rrBr]r?)rsequencerr_r`s& rrrC s]H~H H A eAqk "),TAXq4x)@A)@x!})@AA#AsA A/A)A/c#~aaa"\V4p\V4pVeV^8d \R4hW8dR#SeSM^oSeSMToSS8dR#V3RloVf<\^V^,4F"p\ VV3RlS!WA44RjxL K$ R#\ VV3RlS!WA44RjxL R#L+L5i)aO Yield the set partitions of *iterable* into *k* parts. Set partitions are not order-preserving. >>> iterable = 'abc' >>> for part in set_partitions(iterable, 2): ... 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'] ['a', 'bc'] ['ab', 'c'] ['b', 'ac'] ['a', 'b', 'c'] if *min_size* and/or *max_size* are given, the minimum and/or maximum size per block in partition is set. >>> iterable = 'abc' >>> for part in set_partitions(iterable, min_size=2): ... print([''.join(p) for p in part]) ['abc'] >>> for part in set_partitions(iterable, max_size=2): ... print([''.join(p) for p in part]) ['a', 'bc'] ['ab', 'c'] ['b', 'ac'] ['a', 'b', 'c'] Nz6Can't partition in a negative or zero number of groupsc3t<"\V4pV^8XdV.xR#W!8XdVUu.uFq3.NK upxR#VvrES!WQ^, 4F pV..VOxK S!WQ4FKp\\V44F0pVRVV.Wg,,.,Wg^,R,xK2 KM R#uupi5ir)rr]) rr~rrrMrr_set_partitions_helpers && rr-set_partitions..set_partitions_helper s F 6#I V !"13" "EA*1!e4siQi5*10s1vABQ%A3:,.q57;;'1 #s$B8 B3BB8c|<\;QJd VV3RlV4F 'dK R# R#!VV3RlV44#)c3b<"TF$pS\V4u;8*;'dS8*MuxK& R#5irrrobkmax_sizemin_sizes& rrq3set_partitions.... s%J"h#b'==X==,/FTrrrrs&rrU set_partitions.. s(##JJ##J#J#JJJrc|<\;QJd VV3RlV4F 'dK R# R#!VV3RlV44#)c3b<"TF$pS\V4u;8*;'dS8*MuxK& R#5irrrs& rrqr s%FAb(c"g9999ArFTrrs&rrUr s(ccFAFccFcFcFAFFr)r!rrr]r)rr~rrrrrs&&ff @rrrW sN XA AA} q5H U #/xQH#/xQH( < yq!a%AJ%a+  !  F !! '      s$BB= B9$B=2B;3B=;B=c6a]tRtRtoRtRtRtRtRtVt R#)ri a  Yield items from *iterable* until *limit_seconds* have passed. If the time limit expires before all items have been yielded, the ``timed_out`` parameter will be set to ``True``. >>> from time import sleep >>> 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. As a special case, when *limit_seconds* is zero, the iterator never returns anything. cV^8d \R4hWn\V4Vn\ 4VnRVnR#)rzlimit_seconds must be positiveFN)r limit_secondsrr~r9 _start_time timed_out)rrrs&&&rrtime_limited.__init__ s8 1 => >*h$;rcV#rrrs&rrtime_limited.__iter__ rrcVP^8XdRVn\h\VP4p\ 4VP , VP8dRVn\hV#)rT)rrrrr~r9rrs& rrtime_limited.__next__ sX    "!DN DNN# ;)) )D,>,> >!DN  r)r~rrrN r+r,r-r.r/rrrr0r1r2s@rrr s4  rc\V4pVF,pVF!pRV: RV: R2pT;'g \V4h Vu# V#)aIf *iterable* has only one item, return it. If it has zero items, return *default*. If it has more than one item, raise the exception given by *too_long*, which is ``ValueError`` by default. >>> 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 is only one item. See :func:`spy` or :func:`peekable` to check iterable contents less destructively. rErFrGrH)rrrJrrlrKrLs&&& rrr s^2H~HFA%"*/1 --jo -    Nrcbaa\4o\W4oVV3RlpRVV3RllpV!4V3#)c3<"\\4;_uu_4S'dSP4xK\S4xK, +'giR#;i5iTN)rrrr)rwrsr generator_ichunk..generators8 m $ $--/)u+% % $ $sA A#A A  Ac<VfSPS4\S4#V\S4, pV^8dSP\SV44\V\S44#r)rrrr)rto_cacherwrs& rmaterialize_next!_ichunk..materialize_next sZ 9 LL u: s5z> a< LLx0 11c%j!!rr)rr)rrr rrwrs&& @@r_ichunkr s3 GE 8 E& " " K) **rc#v"\V4p\W!4wr4V!4'gR#VxV!R4K+5i)agBreak *iterable* into sub-iterables with *n* elements each. :func:`ichunked` is like :func:`chunked`, but it yields iterables instead of lists. If the sub-iterables are read in order, the elements of *iterable* 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] >>> list(c_1) [0, 1, 2, 3] >>> list(c_3) [8, 9, 10, 11] N)rr)rrrrrs&& rroros=*H~H ")("6 !!   s79c V\\\\VR\ 4/44#)aReturn ``True`` if all given *iterables* are equal to each other, which means that they contain the same elements in the same order. The function is useful for comparing iterables of different data types or iterables that do not support equality checks. >>> iequals("abc", ['a', 'b', 'c'], ('a', 'b', 'c'), iter("abc")) True >>> iequals("abc", "acb") False Not to be confused with :func:`all_equal`, which checks whether all elements of iterable are equal to each other. r)rrrFrobjectrs*rrprpAs"" s9k9IIJ KKrc # "V^8d \R4hV^8XdRxR#\V4p\\V4\ ^4R7.pR.V,p^pV'd\ VR,4wrgYtT&T^,T8Xd\T4xK>TP\\Y&^,RT^,4\ ^4R74T^, pKR# \ dTP4T^,pKi;i5i)a*Yield the distinct combinations of *r* items taken from *iterable*. >>> list(distinct_combinations([0, 0, 1], 2)) [(0, 0), (0, 1)] Equivalent to ``set(combinations(iterable))``, except duplicates are not generated and thrown away. For larger input sequences this is much more efficient. zr must be non-negativeNrrr) rr\rErr1rrpopr)rrer generators current_comborcur_idxrs&& rr`r`Us 1u122 a ?D!)D/z!}EFJFQJM E  jn-JG !e 19> & &   dQ;=17Q;?"1   QJE#   NN  QJE  s+AD"C#6A-D#$D D D  Dc'V"VFpV!V4VxK R# TdK i;i5i)aYield the items from *iterable* for which the *validator* function does not raise one of the specified *exceptions*. *validator* is called for each item in *iterable*. It should be a function that accepts one argument and raises an exception if that item is not valid. >>> iterable = ['1', '2', 'three', '4', None] >>> list(filter_except(int, iterable, ValueError, TypeError)) ['1', '2', '4'] If an exception other than one given by *exceptions* is raised by *validator*, it is raised like normal. Nr)rr exceptionsrs&&* rrjrj}s7  dOJ    s) )&)&)c'R"VFpV!V4xK R# TdKi;i5i)aTransform each item from *iterable* with *function* and yield the result, unless *function* raises one of the specified *exceptions*. *function* is called to transform each item in *iterable*. It should accept one argument. >>> iterable = ['1', '2', 'three', '4', None] >>> list(map_except(int, iterable, ValueError, TypeError)) [1, 2, 4] If an exception other than one given by *exceptions* is raised by *function*, it is raised like normal. Nr)functionrrrs&&* rrrs3 4.    s' '$'$'c#"Vf&VFpV!V4'd V!V4MTxK R#VF#pV!V4'd V!V4MV!V4xK% R#5i)aEvaluate each item from *iterable* using *pred*. If the result is equivalent to ``True``, transform the item with *func* and yield it. Otherwise, transform the item with *func_else* and yield it. *pred*, *func*, and *func_else* should each be functions that accept one argument. By default, *func_else* is the identity function. >>> from math import sqrt >>> iterable = list(range(-5, 5)) >>> iterable [-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, ... lambda x: f'{sqrt(x):.2f}', lambda x: None)) [None, None, None, None, None, '0.00', '1.00', '1.41', '1.73', '2.00'] Nr)rrr: func_elsers&&&& rrrsO&D $T $t* 4D $T $t* $ ?sAAc \\W44pV'd\V4V8d \R4hRp\ \ 4;_uu_4V\ 4^V, ,,p\\\ 44\V)4, 4p\\WR44pWc\V4&Kq +'giM;i\T4T#)Sample larger than population?N) r!rrrrrr+r"r$r%rr,r-)rr~r reservoirWskiprs&&& r_sample_unweightedr)sVH()I #i.1$899 A -  a!e$ $AVXr23D6($78G&-il # !  I s A2C C cTRV4p\V\W@44pV'd\V4V8d \R4h\ V4V^,wrg\ \ 44V, p\W 4FwrW8doV^,wrg\W,4p \V ^4p \ V 4V , p \W]V 34V^,wrg\ \ 44V, pKyW,pK VU U u.uFwrV NK pp p \V4V#uup p i)c3V"TFp\\44V, xK! R#5ir)r$r+)roweights& rrq#_sample_weighted..s@f3vx=6))s')r$) rDr?rrr r$r+r r.rr-)rr~weightsr weight_keysr&smallest_weight_keyrweights_to_skipr,rt_wr_2 weight_keyrs&&&& r_sample_weightedr5s  A@KQK23I #i.1$899 I'q\&(m&99Ow1  $&/q\ " f23C#q/CSF*J  #8 9%.q\ " !&(m.AAO  %O2/8 8i*z7iC 8 CL J 9s D$cdaaa a Ro ^o VV VV 3Rlp\\4;_uu_4.p\V4FpVPV!^44K RRR4V'd\ X4V8d \ R4h\\4;_uu_4RpV\ 4^V, ,,p\\\ 44\V)4, 4pV!V4o S X\V4&Ke +'giL;i +'giM;i\X4T#)Nc<V^,S8d"VS, p\S4o\S4oK/SV^,,oS#rr6)r_rr population remainings&rfeed_sample_counted..feeds@!eiI A:&GV IQU rr$r%) rrr]rrrr+r"r$r%r,r-) r8r~rrr:r&rr'r(rr9s f&f& @@r_sample_countedr<sGI   -  qA   T!W % ! #i.1$899 -  a!e$ $AVXr23D4jG&-Iil # !  !  I s,C?A(D? D  D" rrc\V4pV^8d \R4hV^8Xd.#VeVe \R4hVe\V4p\WQW$4#Ve\V4p\ WQW44#\ WQV4#)aReturn a *k*-length list of elements chosen (without replacement) from the *iterable*. Similar to :func:`random.sample`, but works on inputs that aren't indexable (such as sets and dictionaries) and on inputs where the size isn't known in advance (such as generators). >>> iterable = range(100) >>> sample(iterable, 5) # doctest: +SKIP [81, 60, 96, 16, 4] For iterables with repeated elements, you may supply *counts* to indicate the repeats. >>> iterable = ['a', 'b'] >>> counts = [3, 4] # Equivalent to 'a', 'a', 'a', 'b', 'b', 'b', 'b' >>> sample(iterable, k=3, counts=counts) # doctest: +SKIP ['a', 'a', 'b'] An iterable with *weights* may be given: >>> iterable = range(100) >>> weights = (i * i + 1 for i in range(100)) >>> sampled = sample(iterable, 5, weights=weights) # doctest: +SKIP [79, 67, 74, 66, 78] Weighted selections are made without replacement. After an element is selected, it is removed from the pool and the relative weights of the other elements increase (this does not match the behavior of :func:`random.sample`'s *counts* parameter). Note that *weights* may not be used with *counts*. If the length of *iterable* is less than *k*, ``ValueError`` is raised if *strict* is ``True`` and all elements are returned (in shuffled order) if *strict* is ``False``. By default, the `Algorithm L `__ reservoir sampling technique is used. When *weights* are provided, `Algorithm A-ExpJ `__ is used instead. Notes on reproducibility: * The algorithms rely on inexact floating-point functions provided by the underlying math library (e.g. ``log``, ``log1p``, and ``pow``). Those functions can `produce slightly different results `_ on different builds. Accordingly, selections can vary across builds even for the same seed. * The algorithms loop over the input and make selections based on ordinal position, so selections from unordered collections (such as sets) won't reproduce across sessions on the same platform using the same seed. For example, this won't reproduce:: >> seed(8675309) >> sample(set('abcdefghijklmnopqrstuvwxyz'), 10) ['c', 'p', 'e', 'w', 's', 'a', 'j', 'd', 'n', 't'] zk must be non-negativez)weights and counts are mutually exclusive)rrrr5r<r))rr~r.rrrs&&&$$ rrr$sxH~H1u122Av v1CDD  w-W==  fxF;;"(v66rcVfTM \W4p\V4wrV\VR4V'dYVrVV'd\\\WV44#\ \\We44'*#)aReturns ``True`` if the items of iterable are in sorted order, and ``False`` otherwise. *key* and *reverse* have the same meaning that they do in the built-in :func:`sorted` function. >>> is_sorted(['1', '2', '3', '4', '5'], key=int) True >>> is_sorted([5, 4, 3, 1, 2], reverse=True) False If *strict*, tests for strict sorting, that is, returns ``False`` if equal elements are found: >>> is_sorted([1, 2, 2]) True >>> is_sorted([1, 2, 2], strict=True) False The function returns ``False`` after encountering the first out-of-order item, which means it may produce results that differ from the built-in :func:`sorted` function for objects with unusual comparison dynamics (like ``math.nan``). If there are no out-of-order items, the iterable is exhausted. N)rrrrr2r)rrrrrXrirjs&&&& rrxrxwsV0kC(:B r7DADM1!'3s2q} CSQ]1C-CCrc]tRtRtRtR#)rHirN)r+r,r-r.r0rrrrHrHsrcla]tRtRtoRtR RltRtRtRtRt ] R4t ] R 4t R t R tVtR #)rQiaUConvert a function that uses callbacks to an iterator. Let *func* be a function that takes a `callback` keyword argument. For example: >>> def func(callback=None): ... for i, c in [(1, 'a'), (2, 'b'), (3, 'c')]: ... if callback: ... callback(i, c) ... return 4 Use ``with callback_iter(func)`` to get an iterator over the parameters that are delivered to the callback. >>> with callback_iter(func) as it: ... for args, kwargs in it: ... print(args) (1, 'a') (2, 'b') (3, 'c') The function will be called in a background thread. The ``done`` property indicates whether it has completed execution. >>> it.done True If it completes successfully, its return value will be available in the ``result`` property. >>> it.result 4 Notes: * If the function uses some keyword argument besides ``callback``, supply *callback_kwd*. * If it finished executing, but raised an exception, accessing the ``result`` property will raise the same exception. * If it hasn't finished executing, accessing the ``result`` property from within the ``with`` block will raise ``RuntimeError``. * If it hasn't finished executing, accessing the ``result`` property from outside the ``with`` block will raise a ``more_itertools.AbortThread`` exception. * Provide *wait_seconds* to adjust how frequently the it is polled for output. cWnW nRVnRVnW0n\ R4P P^R7VnVP4Vn R#)FNzconcurrent.futures) max_workers) _func _callback_kwd_aborted_future _wait_seconds __import__futuresThreadPoolExecutor _executor_readerr~)rr: callback_kwd wait_secondss&&&&rrcallback_iter.__init__sU )  )#  '$$$3 rcV#rrrs&r __enter__callback_iter.__enter__rrcHRVnVPP4R#r )rErKshutdown)rexc_type exc_value tracebacks&&&&r__exit__callback_iter.__exit__s  !rcV#rrrs&rrcallback_iter.__iter__rrc,\VP4#rrrs&rrcallback_iter.__next__rrcVVPfR#VPP4#)NF)rFdoners&rr_callback_iter.dones" << ||  ""rcpVP'g \R4hVPP4#)zFunction has not yet completed)r_ RuntimeErrorrFrrs&rrcallback_iter.results*yyy?@ @||""$$rc#6aa"\4oVV3RlpSPP!SP3/SPV/BSnSP SPR7pSP4VxSP P4'gKT.pSP4pSP4TPT4K5 \dLgi;i \dMi;iSP4TRjxL R#5i)cf<SP'd \R4hSPW34R#)zcanceled by userN)rErHput)r7r8rrs*,rcallback'callback_iter._reader..callbacks&}}}!"455 EE4. !r)timeoutN)r*rKsubmitrCrDrFgetrG task_doner)r_ get_nowaitrjoin)rrgrr9rsf @rrLcallback_iter._readers G " ~~,, JJ --x8   uuT%7%7u8  ||  ""  '||~   &#     s`A DC,1D"D'C+7#D C(%D'C((D+ C96D8C99DDD)rErDrKrCrFr~rGN)rgg?)r+r,r-r.r/rrQrXrrpropertyr_rrLr0r1r2s@rrQrQsV0d ("$## %% ##rc#"V^8d \R4h\V4p\V4pW8d \R4h\W1, ^,4F#pVRVpW$WA,pW$V,RpWVV3xK% R#5i)a Yield ``(beginning, middle, end)`` tuples, where: * Each ``middle`` has *n* items from *iterable* * Each ``beginning`` has the items before the ones in ``middle`` * Each ``end`` has the items after the ones in ``middle`` >>> iterable = range(7) >>> n = 3 >>> for beginning, middle, end in windowed_complete(iterable, n): ... print(beginning, middle, end) () (0, 1, 2) (3, 4, 5, 6) (0,) (1, 2, 3) (4, 5, 6) (0, 1) (2, 3, 4) (5, 6) (0, 1, 2) (3, 4, 5) (6,) (0, 1, 2, 3) (4, 5, 6) () Note that *n* must be at least 0 and most equal to the length of *iterable*. This function will exhaust the iterable and may require significant storage. rzn must be <= len(seq)N)rr\rr])rrrrar_ beginningmiddleends&& rrrs0 1u)** /C s8Dx011 48a< G a%'l$$ !sA;A=c\4pVPp.pVPpV'd \W4MTFpWb9dR#V!V4K R# \dYd9dR#T!T4K;i;i)a Returns ``True`` if all the elements of *iterable* are unique (no two elements are equal). >>> all_unique('ABCB') False If a *key* function is specified, it will be used to make comparisons. >>> all_unique('ABCb') True >>> all_unique('ABCb', str.lower) False The function returns as soon as the first non-unique element is encountered. Iterables with a mix of hashable and unhashable items can be used, but the function will be slower for unhashable items. FT)raddrrr)rrseenset seenset_addseenlist seenlist_addrs&& rrKrKBsu&eG++KH??L),3s%(: "!  ;   ""  ! "sAAA6*A65A6c\\\\V444p\\\V44p\ \ V4pV^8d W, p^Tu;8:dV8g\h\h.p\W#4F+wrgVPW`V,,4W,pK- \\V44#)aeEquivalent to ``list(product(*args))[index]``. The products of *args* can be ordered lexicographically. :func:`nth_product` computes the product at sort position *index* without computing the previous products. >>> nth_product(8, range(2), range(2), range(2), range(2)) (1, 0, 0, 0) ``IndexError`` will be raised if the given *index* is invalid. ) r!rr\rrr r3rr?r)r"r7poolsnsrqrrrs&* rrres UHTN+ ,E c#uo BsBA qy  >>  Fu> d19o& " &! ""rcN\V4p\V4pVeW8XdT\V4rQM(^Tu;8:dV8g\h\h\ WA4pV^8gQhV^8d W%, p^Tu;8:dV8g\ h\ h^.V,pW8dV\V4,V,MTp\ ^V^,4F;p\Wx4wry^WH, u;8:dV8dMM WWH, &V^8XgK;M \\VPV44#)aEquivalent to ``list(permutations(iterable, r))[index]``` The subsequences of *iterable* that are of length *r* where order is important can be ordered lexicographically. :func:`nth_permutation` computes the subsequence at sort position *index* directly, without computing the previous subsequences. >>> nth_permutation('ghijk', 2, 5) ('h', 'i') ``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. ) r!rr!rr&rr]rr\rr) rrer"rrrqrrdr_s &&& rrrs >D D AyAF)A,1 !ZaZ J q5L5 qy  >> S1WF%&U ! !A 1a!e_a| >>15M 6   TXXv& ''rc\V4p\V4pV^8gW8d\h\WA,^, V4pV^8d W%, pV^8gW%8d\h.p^pV'dfV^,pV^8d=\WA,^, V4pW(8dMV^,pV^, pW(,pKCVP W7,4Km\V4#)a;Equivalent to ``list(combinations_with_replacement(iterable, r))[index]``. The subsequences with repetition of *iterable* that are of length *r* can be ordered lexicographically. :func:`nth_combination_with_replacement` computes the subsequence at sort position *index* directly, without computing the previous subsequences with replacement. >>> nth_combination_with_replacement(range(5), 3, 5) (0, 1, 1) ``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. )r\rrrrr) rrer"rrrqrr_ num_combss &&& rrrs" ?D D A A15 QUQYA qy   uz F A  Q1fQUQY*I  FA FA  E dg =rc'"VF0p\V\\34'dVxK%TRjxL K2 R#L \d TxKJi;i5i)ajYield all arguments passed to the function in the same order in which they were passed. If an argument itself is iterable then iterate over its values. >>> list(value_chain(1, 2, 3, [4, 5, 6])) [1, 2, 3, 4, 5, 6] Binary and text strings are not considered iterable and are emitted as-is: >>> list(value_chain('12', '34', ['56', '78'])) ['12', '34', '56', '78'] Pre- or postpend a single element to an iterable: >>> list(value_chain(1, [2, 3, 4, 5, 6])) [1, 2, 3, 4, 5, 6] >>> list(value_chain([1, 2, 3, 4, 5], 6)) [1, 2, 3, 4, 5, 6] Multiple levels of nesting are not flattened. N)rrrr)r7rs* rrrsP0 ec5\ * *K       K s1(A=;=A=A AAAc^p\W\R7FWwr4V\Jg V\Jd \R4h\V4pV\ V4,VP V4,pKY V#)afEquivalent to ``list(product(*args)).index(element)`` The products of *args* can be ordered lexicographically. :func:`product_index` computes the first index of *element* without computing the previous products. >>> product_index([8, 2], range(10), range(5)) 42 ``ValueError`` will be raised if the given *element* isn't in the product of *args*. rlz element is not a product of args)rr:rr\rr")rr7r"rrs&* rrrsa Ew@ <47??@ @T{D !DJJqM1 A Lrc\V4p\VR4wr#Vf^#.p\V4pVF4wrgWs8XgK VPV4\VR4wrVfMTpK6 \R4h\ WVR3R7wri^p \\ V4^R7F'wrWl, p W8:gKV \ W4, p K) \ V^,V^,4V , #)aEquivalent to ``list(combinations(iterable, r)).index(element)`` The subsequences of *iterable* that are of length *r* can be ordered lexicographically. :func:`combination_index` computes the index of the first *element*, without computing the previous combinations. >>> combination_index('adf', 'abcdefg') 10 ``ValueError`` will be raised if the given *element* isn't one of the combinations of *iterable*. Nz(element is not a combination of iterabler)rNN)rrrrr}rr) rrr~rindexesrrrtmprr"r_r`s && rrVrVs G  &DAyG X D 6 NN1 '<0FC{CDD $i (DA E(7+15 E 6 T!Z E6 Aq1u  %%rcl\V4p\V4p\V4p\VR4wr4Vf^#.p\V4p\V4F9wrxW8Xd)VP V4\VR4wrV fMT pK.VeK9M \ R4h\V4p^.V,p VFp W;;,^, uu&K ^p ^p \ ^V4FXpWV^, ,, p W',^, V, V , pWs, pW8:gKFV \W4, p KZ V #)aEquivalent to ``list(combinations_with_replacement(iterable, r)).index(element)`` The subsequences with repetition of *iterable* that are of length *r* can be ordered lexicographically. :func:`combination_with_replacement_index` computes the index of the first *element*, without computing the previous combinations with replacement. >>> combination_with_replacement_index('adf', 'abcdefg') 20 ``ValueError`` will be raised if the given *element* isn't one of the combinations with replacement of *iterable*. z9element is not a combination with replacement of iterabler)r\rrrrrr]r)rrlr~rrrrrr occupationsrr"cumulative_sumr`r_s&& rrWrWDs(GnG G A G  &DAyG ?D$f NN1 '<0FC{ 9   G   D A#'K ! EN 1a[a!e,, EAIMN * E 6 T!Z E  Lrc^p\V4p\\\V4RR4V4F'wrEVP V4pW$,V,pW6K) V#)aEquivalent to ``list(permutations(iterable, r)).index(element)``` The subsequences of *iterable* that are of length *r* where order is important can be ordered lexicographically. :func:`permutation_index` computes the index of the first *element* directly, without computing the previous permutations. >>> permutation_index([1, 3, 2], range(5)) 19 ``ValueError`` will be raised if the given *element* isn't one of the permutations of *iterable*. r)r!r?r]rr")rrr"rr_rres&& rrr}sV E >DE#d)R,g6 JJqM A  G7 Lrc6a]tRtRtoRtRtRtRtRtVt R#)r\iaWrap *iterable* and keep a count of how many items have been consumed. The ``items_seen`` attribute starts at ``0`` and increments as the iterable is consumed: >>> iterable = map(str, range(10)) >>> it = countable(iterable) >>> it.items_seen 0 >>> next(it), next(it) ('0', '1') >>> list(it) ['2', '3', '4', '5', '6', '7', '8', '9'] >>> it.items_seen 10 c4\V4Vn^VnR#rp)rr~ items_seenrs&&rrcountable.__init__shrcV#rrrs&rrcountable.__iter__rrcb\VP4pV;P^, unV#r)rr~rrs& rrcountable.__next__s#DNN# 1 r)r~rNrr2s@rr\r\s"rc#b"\V4pV^, V^, ,p\\W#44p\\VPV4VRV4FpVRVxVRV1K V'gR#\ V4p\ Wa4wrxYx^8d^M^,p \ Wi4wrxYx^8d^M^,p V ^, p WkV ,, p W,p V ^8d \^W4FpWNW,xK V ^8d"\WV 4FpWNW,xK R#R#5i)aeBreak *iterable* into lists of approximately length *n*. Items are distributed such the lengths of the lists differ by at most 1 item. >>> iterable = [1, 2, 3, 4, 5, 6, 7] >>> n = 3 >>> list(chunked_even(iterable, n)) # List lengths: 3, 2, 2 [[1, 2, 3], [4, 5], [6, 7]] >>> list(chunked(iterable, n)) # List lengths: 3, 3, 1 [[1, 2, 3], [4, 5, 6], [7]] N)rr!rrrrrr])rrr min_bufferrrlengthrre num_lists full_size partial_sizenum_fullpartial_start_idxr_s&& rrSrSs.H~Ha%AE"J &. /FC x0!T1 =Raj 2A2J>  [F & DAa%QQ'I & $DAa%QQ'Iq=Ly00H!,1}q+7AQ]+ +8a(,?AQ-. .@s A3D/6B9D/ scalar_typesc'a"V3Rlp\V4pV'gR#R.V,p..rv\V4FDwrV!V 4'dWV&KVP\V 44VPV4KF V'g\ V4xR#V'd\ M\ p V !V!F'p \ W{4FwqV&K \ V4xK) R#5i)aA version of :func:`zip` that "broadcasts" any scalar (i.e., non-iterable) items into output tuples. >>> 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')] If the *strict* keyword argument is ``True``, then ``UnequalIterablesError`` will be raised if any of the iterables have different lengths. c|<S'd\VS4'dR#\V4R# \dR#i;i)TF)rrr)rrs&r is_scalar zip_broadcast..is_scalars: JsL99  I  s , ;;N)rrrrr\r;r?) rrobjectsrranew_itemriterable_positionsr_rrrs d$* rrrs, w>QK   T#Y '  % %a ( % Gn!ZsF "!"4;NA{ <Ho#sBCC?Cc#~"V^8:d \R4h\VR7p\\4pVRJpVFp\ V4V8Xd.V^,pWG,^8XdWGMWG;;,^,uu&V'd V!V4MTpW9dVxWH;;,^, uu&VP V4K R#5i)aYield the items from *iterable* that haven't been seen recently. *n* is the size of the lookback window. >>> iterable = [0, 1, 0, 2, 3, 0] >>> 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'] The items in *iterable* must be hashable. zn must be greater than 0rN)rrrrErr) rrrrruse_keyr to_discardr~s &&& rrrs" Av344 !_F  FoG v;! J!Q&&"a'" CId ?J Q  as A;B=>?B=c#"\4p.pVRJpVF3pV'd V!V4MTpWb9dVPV4K/VxK5 R# \d#Yc9dTPT4K^TxKei;i5i)a_Yield duplicate elements after their first appearance. >>> 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'] This function is analogous to :func:`unique_everseen` and is subject to the same performance considerations. N)rrvrr)rrseen_set seen_listrrr~s&& rrereFswuHIoG#CL   Q  !  #  s@(A>AA>AA>"A;0A>3A;7A>:A;;A>c8\R\W444#)aYields serially-duplicate elements after their first appearance. >>> 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:"TFwrVFqxK K R#5irr)rorr$s& rrq&duplicates_justseen..osC!7A11!7s)r?rrrs&&rrfrfds C!7C CCrc#@"\4p.pVRJpRp\V4FNwrgV'd V!V4MTpV'*;'gWX8gp TpRp W9dVPV4Rp YyT 3xKP R# \dY9dTP T4Rp L2i;i5i)aClassify each element in terms of its uniqueness. For each element in the input iterable, return a 3-tuple consisting of: 1. The element itself 2. ``False`` if the element is equal to the one preceding it in the input, ``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 the same performance considerations. NFT)rrrvrr) rrrrrpreviousr_rr~is_unique_justseenis_unique_everseens && rrgrgrs*uHIoGH) #CL!"U33hm" *  Q%)" +===* *!  #%)" *s0AB BA4( B4$BBBBrrcV'dV.VO5MTp\V4p\V4;rgTf4\ YUTR7FwrY8dYrY8dT pYz8gKT pK! Yg3#T!T4;r\ YUTR7F3wrT!T 4T!T 4rY8dYY3wrrY8dYrY8gK1YrK5 Yg3# \d#pT\Jd \ R4ThTuRp?#Rp?ii;i)aReturns both the smallest and largest items from an iterable or from two or more arguments. >>> minmax([3, 1, 5]) (1, 5) >>> minmax(4, 2, 6) (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 makes a single pass over the input elements and takes care to minimize the number of comparisons made during processing. Note that unlike the builtin ``max`` function, which always returns the first item with the maximum value, this function may return another item when there are ties. This function is based on the `recipe `__ by Raymond Hettinger. z[`minmax()` argument is an empty iterable. Provide a `default` value to suppress this error.Nrl)rrrr:rr)iterable_or_valuerrothersrrXrrexcrrlo_keyhi_keyx_keyy_keys&$$* rrrsF06!+F+;LH hBr( {"5DAu1vv 6* 6Mb'!"5DAq63q65}%&5%7"e~F~F6 6MC  g D  s B00 C;CCCTc#"V^8:d \R4h.p^p^pVFpV!V4p V'dW8d \R4hWr8Hp W,V8p V'd2V 'g V 'd"\V4xVP4^p^pVPV4Wi, pV^, pK V'd\V4xR#R#5i)a Yield batches of items from *iterable* with a combined size limited by *max_size*. >>> iterable = [b'12345', b'123', b'12345678', b'1', b'1', b'12', b'1'] >>> 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*. z&maximum size must be greater than zerozitem size exceeds maximum sizeN)rr\rvr) rr max_countget_lenrbatch batch_size batch_countritem_len reached_count reached_sizes &&&&& rrYrYs.1}ABB EJK4= h)=> >#0 ,x7 LM,  KKMJK T q " El sAC C(AC7Cc'aa"\;QJd.RV4FNK 5M !RV44o\S4pSFp\V4^8gK\R4h ^.V,o\\ V^,44p^.V,p\;QJd!.VV3Rl\ V44FNK 5M!VV3Rl\ V444xV^,p^V^&WQ8XdR#SV,WE,,SV&SV,^8Xg(SV,\SV,4^, 8XgKWE,)WE&W5^,,W5&V^,W5^,&K5i)a:Like :func:`itertools.product`, but return tuples in an order such that only one element in the generated tuple changes from one iteration 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 `this section `__ of Donald Knuth's *The Art of Computer Programming*. c38"TFp\V4xK R#5irrs)rors& rrqgray_product..+s6Iq%((Isz)each iterable must have two or more itemsc3R<"TFpSV,SV,,xK R#5irr)ror_ri all_iterabless& rrqr9s$J4IqM!$QqT**4Is$'N)r\rrr!r])riterable_countrror`rirs* @@rrmrms& E6I6EE6I66M'N! x=1 HI I" nA U>A% &'A nA eJE.4IJeeJE.4IJJJ aD!  tad{! Q419!M!$4 5 99D5ADU8AD1uA!eHsA E,AE,BE,<0E,c'"\\\V44pVUu.uFp\V4NK pp\ T4x\ T4FwrBTFY4&\ T4xK K R#uupi \dR#i;i5i)aYields tuples containing one item from each iterator, with subsequent tuples changing a single item at a time by advancing each iterator until it is exhausted. This sequence guarantees every value in each iterable is output at least once without generating all possible combinations. This may be useful, for example, when testing an expensive function. >>> list(partial_product('AB', 'C', 'DEF')) [('A', 'C', 'D'), ('B', 'C', 'D'), ('B', 'C', 'E'), ('B', 'C', 'F')] N)r!rrrrr\r)rrrXprodr_s* rrrEs~Sy)*I#,-9RR9- +9%DG+ & . s7B A7A2A7;B 2A77 BB BB c#J"VFpVxV!V4'dKR# R#5i)zA variant of :func:`takewhile` that yields one additional element. >>> list(takewhile_inclusive(lambda x: x < 5, [1, 4, 6, 4, 1])) [1, 4, 6] :func:`takewhile` would return ``[1, 4]``. Nr)rrrs&& rrr^s"|| s##c |aaa\V4p\\VVV3Rl\W44\ V4R7#)asA generalized outer product that applies a binary function to all pairs of items. Returns a 2D matrix with ``len(xs)`` rows and ``len(ys)`` columns. Also accepts ``*args`` and ``**kwargs`` that are passed to ``func``. 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'] >>> pair_counts = Counter(zip(xs, ys)) >>> count_rows = lambda x, y: pair_counts[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<S!W.SO5/SB#rr)rrr7r:r8s&&rrUouter_product..sT!888r)r)r\rGrrr)r:rrr7r8sf&&jlrrrls14 rB 8'"/J b' rc'D"TRjxL R#L TdR#i;i5i)aYield each of the items from *iterable*. If the iteration raises one of the specified *exceptions*, that exception will be suppressed and iteration will stop. >>> from itertools import chain >>> 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)rrs&*rr{r{s$" s(    c#D"VFpV!V4pVfKVxK R#5i)zApply *func* to every element of *iterable*, yielding only those which are not ``None``. >>> elems = ['1', 'a', '2', 'b', '3'] >>> list(filter_map(lambda s: int(s) if s.isnumeric() else None, elems)) [1, 2, 3] Nr)r:rrrs&& rrkrks$ G =Gs  c a\\P\\\ V4444o\ P!V3Rl\\S4^,444#)a\Yields all possible subsets of the iterable. >>> list(powerset_of_sets([1, 2, 3])) # doctest: +SKIP [set(), {1}, {2}, {3}, {1, 2}, {1, 3}, {2, 3}, {1, 2, 3}] >>> list(powerset_of_sets([1, 1, 0])) # doctest: +SKIP [set(), {1}, {0}, {0, 1}] :func:`powerset_of_sets` takes care to minimize the number of hash operations performed. c3t<"TF-p\\4P\SV44xK/ R#5ir)rrunionr)roresetss& rrq#powerset_of_sets..s/%A  \$233%s58) r\dictfromkeysr frozensetr?rrr]r)rrs&@rrrsP s9c(m<= >D   s4y1}% rc \\4pVP4F)wr#VP4FwrEWQV,V&K K+ \V4#)a Joins multiple mappings together using their common keys. >>> user_scores = {'elliot': 50, 'claris': 60} >>> user_times = {'elliot': 30, 'claris': 40} >>> join_mappings(score=user_scores, time=user_times) {'elliot': {'score': 50, 'time': 30}, 'claris': {'score': 60, 'time': 40}} )rrr) field_to_mapr field_namemappingrrs, rr|r|sN d C+113 !--/JC#(HZ * 4 9rc v\R4p\R4p\\W 4\\\W0444p\\W!4\W144p\\W 4\W044p\\W14\W!44p\ \ WE4\ Wg44#)zTHigh precision sumprod() for complex numbers. Used by :func:`dft` and :func:`idft`. realimag)r/rrr4complexr)v1v2rrr1r2rrs&& r_complex_sumprodrs f D f D s4}c#s4}5 6B s4}c$m ,B s4}c$m ,B s4}c$m ,B 9R$i&7 88rc#J"\V4p\V4Uu.uF(p\W!, \,R,,NK* pp\V4F=p\V4Uu.uFq#WB,V,,NK pp\ W4xK? R#uupiuupi5i)aIDiscrete Fourier Transform. *xarr* is a sequence of complex numbers. Yields the components of the corresponding transformed output vector. >>> import cmath >>> xarr = [1, 2-1j, -1j, -1+2j] # time domain >>> Xarr = [2, -2-2j, -2j, 4+4j] # frequency domain >>> magnitudes, phases = zip(*map(cmath.polar, Xarr)) >>> all(map(cmath.isclose, dft(xarr), Xarr)) True Inputs are restricted to numeric types that can add and multiply with a complex number. This includes int, float, complex, and Fraction, but excludes Decimal. See :func:`idft` for the inverse Discrete Fourier Transform. Nyrr]rr'r)xarrNrroots_of_unityr~coeffss& rr^r^s" D A8=aA1aAECK#-..NA 1X5:1X>X++X>t,,B>sB#.B B#&BB#c#X"\V4p\V4Uu.uF(p\W!, \,R,,NK* pp\V4FDp\V4Uu.uFq#WB,V,,NK pp\ W4V, xKF R#uupiuupi5i)aInverse Discrete Fourier Transform. *Xarr* is a sequence of complex numbers. Yields the components of the corresponding inverse-transformed output vector. >>> import cmath >>> xarr = [1, 2-1j, -1j, -1+2j] # time domain >>> Xarr = [2, -2-2j, -2j, 4+4j] # frequency domain >>> all(map(cmath.isclose, idft(Xarr), xarr)) True Inputs are restricted to numeric types that can add and multiply with a complex number. This includes int, float, complex, and Fraction, but excludes Decimal. See :func:`dft` for the Discrete Fourier Transform. y?Nr)Xarrrrrr~rs& rrqrqs" D A7X++X>t,q00A>sB*.B  B*&B%&B*c#4"VF pV!R/VBxK R#5i)aApply *func* to every item of *iterable* by dictionary unpacking the item into *func*. The difference between :func:`itertools.starmap` and :func:`doublestarmap` parallels the distinction between ``func(*a)`` and ``func(**a)``. >>> iterable = [{'a': 1, 'b': 2}, {'a': 40, 'b': 60}] >>> list(doublestarmap(lambda a, b: a + b, iterable)) [3, 100] ``TypeError`` will be raised if *func*'s signature doesn't match the mapping contained in *iterable* or if *iterable* does not contain mappings. Nrr)r:rrs&& rrdrdslTlsc&V^8d\hV^8d VRV,3#V\V\V4,4,pW, pVR8d@WR\\V44R, \V4, , ,,pW!3#)z:Bounds for the nth prime (counting from 1): lb < p_n < ub.g@i r%g@)rr$)r upper_bound lower_bounds& r_nth_prime_boundsr&s} 1u1u4!8}c!c!f*o%K/KG|C3s1v;#4A">>??  ##r approximatec\V^,4wr#V'dVR8:d\\\V44V4#\ W#,^, 4^,p\ \ V^R7\R7#)a8Return the nth prime (counting from 0). >>> nth_prime(0) 2 >>> nth_prime(100) 547 If *approximate* is set to True, will return a prime close to the nth prime. The estimation is much faster than computing an exact result. >>> nth_prime(200_000_000, approximate=True) # Exact result is 4222234763 4217820427 i@B)r)r)rrArCr(r"r>rr@)rrlbubodds&$ rrr9s] q1u %FB !y.5b?A&& A  "C eCa(x 88rclVe \W4p\\V4\^4R7^,#)a Index of the first occurrence of a minimum value in an iterable. >>> argmin('efghabcdijkl') 4 >>> argmin([3, 2, 1, 0, 4, 2, 1, 0]) 3 For example, look up a label corresponding to the position of a value that minimizes a cost function:: >>> def cost(x): ... "Days for a wound to heal given a subject's age." ... return x**2 - 20*x + 150 ... >>> labels = ['homer', 'marge', 'bart', 'lisa', 'maggie'] >>> ages = [ 35, 30, 10, 9, 1 ] # Fastest healing family member >>> labels[argmin(ages, key=cost)] 'bart' # Age with fastest healing >>> min(ages, key=cost) 10 r)rrrr1rs&$rrOrOSs.8 s% y" 1 6q 99rclVe \W4p\\V4\^4R7^,#)u Index of the first occurrence of a maximum value in an iterable. >>> argmax('abcdefghabcd') 7 >>> argmax([0, 1, 2, 3, 3, 2, 1, 0]) 3 For example, identify the best machine learning model:: >>> models = ['svm', 'random forest', 'knn', 'naïve bayes'] >>> accuracy = [ 68, 61, 84, 72 ] # Most accurate model >>> models[argmax(accuracy)] 'knn' # Best accuracy >>> max(accuracy) 84 r)rr rr1rs&$rrNrNts.. s% y" 1 6q 99rc#"\V4p\\V\444pV'd!V^,^,^8d \ R4h/pRp^pVF\wrxWu, p V 'd \ \ W)^, R44p TpX WH&Wd9gK@VPV4xV^, pK& R# \d \T4hi;i5i)aYield values at the specified indices. Example: >>> data = 'abcdefghijklmnopqrstuvwxyz' >>> list(extract(data, [7, 4, 11, 11, 14])) ['h', 'e', 'l', 'l', 'o'] The *iterable* is consumed lazily and can be infinite. The *indices* are consumed immediately and must be finite. Raises ``IndexError`` if an index lies beyond the iterable. Raises ``ValueError`` for negative indices. zIndices must be non-negativeNr) rrr?rrrrrrr) rr}rindex_and_positionriterator_position next_to_emitr"orderadvancers && rriris H~HGUW 5603A6:788 FL* +  (VHk4@A!&  $**\* * A L+ ! ( '' (s$A.C1B= C"C=CCr9rrr)Nr)NNN)rF)r)NNF)r))rrrFNr')r'NFF)NFF)rr collectionsrrrrcollections.abcr contextlibr functoolsr r r r heapqr r itertoolsrrrrrrrrrrrrrrrrrr r!r"r#r$r%r&r'r(queuer)r*r+r,r-r.operatorr/r0r1r2r3r4r5r6sysr7r8timer9recipesr:r;r<r=r>r?r@rArBrCrDrErFrG__all__rr ImportErrorrrrRrlr}rrrZrrrzrrrrrar]rwrrrrrPrrsrurtrvrUrrrrrrrrrrrbrrrrrrcrrrLrJrnHashablerr[rr=r~rrrrryr}rMrXr_rIrrrhrTrrrrrrrrrrorpr`rjrrr)r5r<rrx BaseExceptionrHrQrrKrrrrrrVrWrr\rSrrrerfrgrrrYrmrrrr{rkrr|rr^rqrdrrrOrNrirrrrs  88$==&"MLL66   $"u u u u  u   u   u u  u  u  u u u u u u  !u ")#u $%u &'u ()u *+u ,-u ./u 0 1u 23u 45u 67u 89u : ;u <=u >?u @Au BCu DEu FGu HIu JKu L Mu NOu PQu RSu TUu V Wu X Yu Z[u \]u ^_u `au bcu deu fgu hiu jku lmu n ou p qu rsu t uu vwu xyu z {u |}u ~u @ Au BCu DEu FGu HIu J'Ku LMu N Ou P Qu RSu T Uu VWu XYu Z[u \]u ^_u `au b cu deu fgu hiu jku l mu nou p qu rsu tuu vwu x yu z{u |}u ~u @Au BCu DEu F Gu HIu J Ku LMu NOu PQu RSu TUu VWu XYu Z [u \]u ^_u `au bcu deu fgu hiu p<): F$6#8&-:"b"b"J@32 ( 5LpHVS$l;|D4CB7t!4D]']'@&R 0$?+D.9x:zD)X F#L*Z*)Z*@ZA&"MJ8">$E$T$NFR&R0f%(<2j$)N*ZQ,CLL#,,Q,h ; <D-0`A%$40"+P+P\i&X($/$d"')d')T*>8*>Z^/^/B>>:&*A$)(X+\D2(&V6r0@//d5#u5e5p$N< D'>TJ4JJ\#'D/d'T2 B. $$ 9-010$$&994:D:B:D:8's^<