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

======================================================================

이 모듈은 APL, Haskell 및 SML의 구성물들에서 영감을 얻은 여러 *이터레
이터* 빌딩 블록을 구현합니다. 각각을 파이썬에 적합한 형태로 개선했습니
다.

이 모듈은 자체적으로 혹은 조합하여 유용한 빠르고 메모리 효율적인 도구
의 핵심 집합을 표준화합니다. 함께 모여, 순수 파이썬에서 간결하고 효율
적으로 특수화된 도구를 구성할 수 있도록 하는 "이터레이터 대수(iterator
algebra)"를 형성합니다.

예를 들어, SML은 테이블 화 도구를 제공합니다: 시퀀스 "f(0), f(1), ..."
를 생성하는 "tabulate(f)". "map()"과 "count()"를 결합하여 "map(f,
count())"를 형성해서 파이썬에서도 같은 효과를 얻을 수 있습니다.

These tools and their built-in counterparts also work well with the
high-speed functions in the "operator" module.  For example, the
multiplication operator can be mapped across two vectors to form an
efficient dot-product: "sum(starmap(operator.mul, zip(vec1, vec2,
strict=True)))".

**무한 이터레이터:**

+--------------------+-------------------+---------------------------------------------------+-------------------------------------------+
| 이터레이터         | 인자              | 결과                                              | 예                                        |
|====================|===================|===================================================|===========================================|
| "count()"          | [start[, step]]   | start, start+step, start+2*step, ...              | "count(10) → 10 11 12 13 14 ..."          |
+--------------------+-------------------+---------------------------------------------------+-------------------------------------------+
| "cycle()"          | p                 | p0, p1, ... plast, p0, p1, ...                    | "cycle('ABCD') → A B C D A B C D ..."     |
+--------------------+-------------------+---------------------------------------------------+-------------------------------------------+
| "repeat()"         | elem [,n]         | elem, elem, elem, ... 끝없이 또는 최대 n 번       | "repeat(10, 3) → 10 10 10"                |
+--------------------+-------------------+---------------------------------------------------+-------------------------------------------+

**가장 짧은 입력 시퀀스에서 종료되는 이터레이터:**

+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| 이터레이터                   | 인자                         | 결과                                              | 예                                                            |
|==============================|==============================|===================================================|===============================================================|
| "accumulate()"               | p [,func]                    | p0, p0+p1, p0+p1+p2, ...                          | "accumulate([1,2,3,4,5]) → 1 3 6 10 15"                       |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "batched()"                  | p, n                         | (p0, p1, ..., p_n-1), ...                         | "batched('ABCDEFG', n=3) → ABC DEF G"                         |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "chain()"                    | p, q, ...                    | p0, p1, ... plast, q0, q1, ...                    | "chain('ABC', 'DEF') → A B C D E F"                           |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "chain.from_iterable()"      | iterable                     | p0, p1, ... plast, q0, q1, ...                    | "chain.from_iterable(['ABC', 'DEF']) → A B C D E F"           |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "compress()"                 | data, selectors              | (d[0] if s[0]), (d[1] if s[1]), ...               | "compress('ABCDEF', [1,0,1,0,1,1]) → A C E F"                 |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "dropwhile()"                | predicate, seq               | seq[n], seq[n+1], starting when predicate fails   | "dropwhile(lambda x: x<5, [1,4,6,3,8]) → 6 3 8"               |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "filterfalse()"              | predicate, seq               | elements of seq where predicate(elem) fails       | "filterfalse(lambda x: x<5, [1,4,6,3,8]) → 6 8"               |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "groupby()"                  | iterable[, key]              | key(v)의 값으로 그룹화된 서브 이터레이터들        |                                                               |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "islice()"                   | seq, [start,] stop [, step]  | seq[start:stop:step]의 요소들                     | "islice('ABCDEFG', 2, None) → C D E F G"                      |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "pairwise()"                 | iterable                     | (p[0], p[1]), (p[1], p[2])                        | "pairwise('ABCDEFG') → AB BC CD DE EF FG"                     |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "starmap()"                  | func, seq                    | func(*seq[0]), func(*seq[1]), ...                 | "starmap(pow, [(2,5), (3,2), (10,3)]) → 32 9 1000"            |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "takewhile()"                | predicate, seq               | seq[0], seq[1], until predicate fails             | "takewhile(lambda x: x<5, [1,4,6,3,8]) → 1 4"                 |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "tee()"                      | it, n                        | it1, it2, ... itn 하나의 이터레이터를 n개의 이터  |                                                               |
|                              |                              | 레이터로 나눕니다                                 |                                                               |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+
| "zip_longest()"              | p, q, ...                    | (p[0], q[0]), (p[1], q[1]), ...                   | "zip_longest('ABCD', 'xy', fillvalue='-') → Ax By C- D-"      |
+------------------------------+------------------------------+---------------------------------------------------+---------------------------------------------------------------+

**조합형 이터레이터:**

+------------------------------------------------+----------------------+---------------------------------------------------------------+
| 이터레이터                                     | 인자                 | 결과                                                          |
|================================================|======================|===============================================================|
| "product()"                                    | p, q, ... [repeat=1] | 데카르트 곱(cartesian product), 중첩된 for 루프와 동등합니다  |
+------------------------------------------------+----------------------+---------------------------------------------------------------+
| "permutations()"                               | p[, r]               | r-길이 튜플들, 모든 가능한 순서, 반복되는 요소 없음           |
+------------------------------------------------+----------------------+---------------------------------------------------------------+
| "combinations()"                               | p, r                 | r-길이 튜플들, 정렬된 순서, 반복되는 요소 없음                |
+------------------------------------------------+----------------------+---------------------------------------------------------------+
| "combinations_with_replacement()"              | p, r                 | r-길이 튜플들, 정렬된 순서, 반복되는 요소 있음                |
+------------------------------------------------+----------------------+---------------------------------------------------------------+

+------------------------------------------------+---------------------------------------------------------------+
| 예                                             | 결과                                                          |
|================================================|===============================================================|
| "product('ABCD', repeat=2)"                    | "AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD"             |
+------------------------------------------------+---------------------------------------------------------------+
| "permutations('ABCD', 2)"                      | "AB AC AD BA BC BD CA CB CD DA DB DC"                         |
+------------------------------------------------+---------------------------------------------------------------+
| "combinations('ABCD', 2)"                      | "AB AC AD BC BD CD"                                           |
+------------------------------------------------+---------------------------------------------------------------+
| "combinations_with_replacement('ABCD', 2)"     | "AA AB AC AD BB BC BD CC CD DD"                               |
+------------------------------------------------+---------------------------------------------------------------+


Itertool Functions
==================

The following module functions all construct and return iterators.
Some provide streams of infinite length, so they should only be
accessed by functions or loops that truncate the stream.

itertools.accumulate(iterable[, function, *, initial=None])

   Make an iterator that returns accumulated sums or accumulated
   results from other binary functions.

   The *function* defaults to addition.  The *function* should accept
   two arguments, an accumulated total and a value from the
   *iterable*.

   If an *initial* value is provided, the accumulation will start with
   that value and the output will have one more element than the input
   iterable.

   대략 다음과 동등합니다:

      def accumulate(iterable, function=operator.add, *, initial=None):
          'Return running totals'
          # accumulate([1,2,3,4,5]) → 1 3 6 10 15
          # accumulate([1,2,3,4,5], initial=100) → 100 101 103 106 110 115
          # accumulate([1,2,3,4,5], operator.mul) → 1 2 6 24 120

          iterator = iter(iterable)
          total = initial
          if initial is None:
              try:
                  total = next(iterator)
              except StopIteration:
                  return

          yield total
          for element in iterator:
              total = function(total, element)
              yield total

   The *function* argument can be set to "min()" for a running
   minimum, "max()" for a running maximum, or "operator.mul()" for a
   running product.  Amortization tables can be built by accumulating
   interest and applying payments:

      >>> data = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8]
      >>> list(accumulate(data, max))              # running maximum
      [3, 4, 6, 6, 6, 9, 9, 9, 9, 9]
      >>> list(accumulate(data, operator.mul))     # running product
      [3, 12, 72, 144, 144, 1296, 0, 0, 0, 0]

      # Amortize a 5% loan of 1000 with 10 annual payments of 90
      >>> update = lambda balance, payment: round(balance * 1.05) - payment
      >>> list(accumulate(repeat(90, 10), update, initial=1_000))
      [1000, 960, 918, 874, 828, 779, 728, 674, 618, 559, 497]

   최종 누적값만 반환하는 유사한 함수에 대해서는 "functools.reduce()"
   를 참조하십시오.

   Added in version 3.2.

   버전 3.3에서 변경: Added the optional *function* parameter.

   버전 3.8에서 변경: 선택적 *initial* 매개 변수를 추가했습니다.

itertools.batched(iterable, n)

   Batch data from the *iterable* into tuples of length *n*. The last
   batch may be shorter than *n*.

   Loops over the input iterable and accumulates data into tuples up
   to size *n*.  The input is consumed lazily, just enough to fill a
   batch. The result is yielded as soon as the batch is full or when
   the input iterable is exhausted:

      >>> flattened_data = ['roses', 'red', 'violets', 'blue', 'sugar', 'sweet']
      >>> unflattened = list(batched(flattened_data, 2))
      >>> unflattened
      [('roses', 'red'), ('violets', 'blue'), ('sugar', 'sweet')]

   대략 다음과 동등합니다:

      def batched(iterable, n):
          # batched('ABCDEFG', 3) → ABC DEF G
          if n < 1:
              raise ValueError('n must be at least one')
          iterator = iter(iterable)
          while batch := tuple(islice(iterator, n)):
              yield batch

   Added in version 3.12.

itertools.chain(*iterables)

   Make an iterator that returns elements from the first iterable
   until it is exhausted, then proceeds to the next iterable, until
   all of the iterables are exhausted.  Used for treating consecutive
   sequences as a single sequence. Roughly equivalent to:

      def chain(*iterables):
          # chain('ABC', 'DEF') → A B C D E F
          for iterable in iterables:
              yield from iterable

classmethod chain.from_iterable(iterable)

   "chain()"의 대체 생성자. 게으르게 평가되는 단일 이터러블 인자에서
   연쇄 입력을 가져옵니다. 대략 다음과 동등합니다:

      def from_iterable(iterables):
          # chain.from_iterable(['ABC', 'DEF']) → A B C D E F
          for iterable in iterables:
              yield from iterable

itertools.combinations(iterable, r)

   입력 *iterable*에서 요소의 길이 *r* 서브 시퀀스들을 반환합니다.

   The output is a subsequence of "product()" keeping only entries
   that are subsequences of the *iterable*.  The length of the output
   is given by "math.comb()" which computes "n! / r! / (n - r)!" when
   "0 ≤ r ≤ n" or zero when "r > n".

   The combination tuples are emitted in lexicographic order according
   to the order of the input *iterable*. If the input *iterable* is
   sorted, the output tuples will be produced in sorted order.

   Elements are treated as unique based on their position, not on
   their value.  If the input elements are unique, there will be no
   repeated values within each combination.

   대략 다음과 동등합니다:

      def combinations(iterable, r):
          # combinations('ABCD', 2) → AB AC AD BC BD CD
          # combinations(range(4), 3) → 012 013 023 123

          pool = tuple(iterable)
          n = len(pool)
          if r > n:
              return
          indices = list(range(r))

          yield tuple(pool[i] for i in indices)
          while True:
              for i in reversed(range(r)):
                  if indices[i] != i + n - r:
                      break
              else:
                  return
              indices[i] += 1
              for j in range(i+1, r):
                  indices[j] = indices[j-1] + 1
              yield tuple(pool[i] for i in indices)

itertools.combinations_with_replacement(iterable, r)

   입력 *iterable*에서 요소의 길이 *r* 서브 시퀀스들을 반환하는데, 개
   별 요소를 두 번 이상 반복할 수 있습니다.

   The output is a subsequence of "product()" that keeps only entries
   that are subsequences (with possible repeated elements) of the
   *iterable*.  The number of subsequence returned is "(n + r - 1)! /
   r! / (n - 1)!" when "n > 0".

   The combination tuples are emitted in lexicographic order according
   to the order of the input *iterable*. if the input *iterable* is
   sorted, the output tuples will be produced in sorted order.

   Elements are treated as unique based on their position, not on
   their value.  If the input elements are unique, the generated
   combinations will also be unique.

   대략 다음과 동등합니다:

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

          pool = tuple(iterable)
          n = len(pool)
          if not n and r:
              return
          indices = [0] * r

          yield tuple(pool[i] for i in indices)
          while True:
              for i in reversed(range(r)):
                  if indices[i] != n - 1:
                      break
              else:
                  return
              indices[i:] = [indices[i] + 1] * (r - i)
              yield tuple(pool[i] for i in indices)

   Added in version 3.1.

itertools.compress(data, selectors)

   Make an iterator that returns elements from *data* where the
   corresponding element in *selectors* is true.  Stops when either
   the *data* or *selectors* iterables have been exhausted.  Roughly
   equivalent to:

      def compress(data, selectors):
          # compress('ABCDEF', [1,0,1,0,1,1]) → A C E F
          return (datum for datum, selector in zip(data, selectors) if selector)

   Added in version 3.1.

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

   Make an iterator that returns evenly spaced values beginning with
   *start*. Can be used with "map()" to generate consecutive data
   points or with "zip()" to add sequence numbers.  Roughly equivalent
   to:

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

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

   버전 3.1에서 변경: *step* 인자를 추가하고 정수가 아닌 인자를 허용했
   습니다.

itertools.cycle(iterable)

   Make an iterator returning elements from the *iterable* and saving
   a copy of each.  When the iterable is exhausted, return elements
   from the saved copy.  Repeats indefinitely.  Roughly equivalent to:

      def cycle(iterable):
          # cycle('ABCD') → A B C D A B C D A B C D ...
          saved = []
          for element in iterable:
              yield element
              saved.append(element)
          while saved:
              for element in saved:
                  yield element

   This itertool may require significant auxiliary storage (depending
   on the length of the iterable).

itertools.dropwhile(predicate, iterable)

   Make an iterator that drops elements from the *iterable* while the
   *predicate* is true and afterwards returns every element.  Roughly
   equivalent to:

      def dropwhile(predicate, iterable):
          # dropwhile(lambda x: x<5, [1,4,6,3,8]) → 6 3 8

          iterator = iter(iterable)
          for x in iterator:
              if not predicate(x):
                  yield x
                  break

          for x in iterator:
              yield x

   Note this does not produce *any* output until the predicate first
   becomes false, so this itertool may have a lengthy start-up time.

itertools.filterfalse(predicate, iterable)

   Make an iterator that filters elements from the *iterable*
   returning only those for which the *predicate* returns a false
   value.  If *predicate* is "None", returns the items that are false.
   Roughly equivalent to:

      def filterfalse(predicate, iterable):
          # filterfalse(lambda x: x<5, [1,4,6,3,8]) → 6 8
          if predicate is None:
              predicate = bool
          for x in iterable:
              if not predicate(x):
                  yield x

itertools.groupby(iterable, key=None)

   *iterable*에서 연속적인 키와 그룹을 반환하는 이터레이터를 만듭니다.
   *key*는 각 요소의 키값을 계산하는 함수입니다. 지정되지 않거나
   "None"이면, *key*의 기본값은 항등함수(identity function)이고 요소를
   변경하지 않고 반환합니다. 일반적으로, iterable은 같은 키 함수로 이
   미 정렬되어 있어야 합니다.

   "groupby()"의 작동은 유닉스의 "uniq" 필터와 유사합니다. 키 함수의
   값이 변경될 때마다 중단(break)이나 새 그룹을 생성합니다 (이것이 일
   반적으로 같은 키 함수를 사용하여 데이터를 정렬해야 하는 이유입니다
   ). 이 동작은 입력 순서와 관계없이 공통 요소를 집계하는 SQL의 GROUP
   BY와 다릅니다.

   반환되는 그룹 자체는 "groupby()"와 하부 이터러블(iterable)을 공유하
   는 이터레이터입니다. 소스가 공유되므로, "groupby()" 객체가 진행하면
   , 이전 그룹은 이 더는 보이지 않게 됩니다. 따라서, 나중에 데이터가
   필요하면, 리스트로 저장해야 합니다:

      groups = []
      uniquekeys = []
      data = sorted(data, key=keyfunc)
      for k, g in groupby(data, keyfunc):
          groups.append(list(g))      # Store group iterator as a list
          uniquekeys.append(k)

   "groupby()"는 대략 다음과 동등합니다:

      def groupby(iterable, key=None):
          # [k for k, g in groupby('AAAABBBCCDAABBB')] → A B C D A B
          # [list(g) for k, g in groupby('AAAABBBCCD')] → AAAA BBB CC D

          keyfunc = (lambda x: x) if key is None else key
          iterator = iter(iterable)
          exhausted = False

          def _grouper(target_key):
              nonlocal curr_value, curr_key, exhausted
              yield curr_value
              for curr_value in iterator:
                  curr_key = keyfunc(curr_value)
                  if curr_key != target_key:
                      return
                  yield curr_value
              exhausted = True

          try:
              curr_value = next(iterator)
          except StopIteration:
              return
          curr_key = keyfunc(curr_value)

          while not exhausted:
              target_key = curr_key
              curr_group = _grouper(target_key)
              yield curr_key, curr_group
              if curr_key == target_key:
                  for _ in curr_group:
                      pass

itertools.islice(iterable, stop)
itertools.islice(iterable, start, stop[, step])

   Make an iterator that returns selected elements from the iterable.
   Works like sequence slicing but does not support negative values
   for *start*, *stop*, or *step*.

   If *start* is zero or "None", iteration starts at zero.  Otherwise,
   elements from the iterable are skipped until *start* is reached.

   If *stop* is "None", iteration continues until the iterator is
   exhausted, if at all.  Otherwise, it stops at the specified
   position.

   If *step* is "None", the step defaults to one.  Elements are
   returned consecutively unless *step* is set higher than one which
   results in items being skipped.

   대략 다음과 동등합니다:

      def islice(iterable, *args):
          # islice('ABCDEFG', 2) → A B
          # islice('ABCDEFG', 2, 4) → C D
          # islice('ABCDEFG', 2, None) → C D E F G
          # islice('ABCDEFG', 0, None, 2) → A C E G

          s = slice(*args)
          start = 0 if s.start is None else s.start
          stop = s.stop
          step = 1 if s.step is None else s.step
          if start < 0 or (stop is not None and stop < 0) or step <= 0:
              raise ValueError

          indices = count() if stop is None else range(max(start, stop))
          next_i = start
          for i, element in zip(indices, iterable):
              if i == next_i:
                  yield element
                  next_i += step

itertools.pairwise(iterable)

   Return successive overlapping pairs taken from the input
   *iterable*.

   The number of 2-tuples in the output iterator will be one fewer
   than the number of inputs.  It will be empty if the input iterable
   has fewer than two values.

   대략 다음과 동등합니다:

      def pairwise(iterable):
          # pairwise('ABCDEFG') → AB BC CD DE EF FG
          iterator = iter(iterable)
          a = next(iterator, None)
          for b in iterator:
              yield a, b
              a = b

   Added in version 3.10.

itertools.permutations(iterable, r=None)

   Return successive *r* length permutations of elements from the
   *iterable*.

   *r*이 지정되지 않았거나 "None"이면, *r*의 기본값은 *iterable*의 길
   이이며 가능한 모든 최대 길이 순열이 생성됩니다.

   The output is a subsequence of "product()" where entries with
   repeated elements have been filtered out.  The length of the output
   is given by "math.perm()" which computes "n! / (n - r)!" when "0 ≤
   r ≤ n" or zero when "r > n".

   The permutation tuples are emitted in lexicographic order according
   to the order of the input *iterable*.  If the input *iterable* is
   sorted, the output tuples will be produced in sorted order.

   Elements are treated as unique based on their position, not on
   their value.  If the input elements are unique, there will be no
   repeated values within a permutation.

   대략 다음과 동등합니다:

      def permutations(iterable, r=None):
          # permutations('ABCD', 2) → AB AC AD BA BC BD CA CB CD DA DB DC
          # permutations(range(3)) → 012 021 102 120 201 210

          pool = tuple(iterable)
          n = len(pool)
          r = n if r is None else r
          if r > n:
              return

          indices = list(range(n))
          cycles = list(range(n, n-r, -1))
          yield tuple(pool[i] for i in indices[:r])

          while n:
              for i in reversed(range(r)):
                  cycles[i] -= 1
                  if cycles[i] == 0:
                      indices[i:] = indices[i+1:] + indices[i:i+1]
                      cycles[i] = n - i
                  else:
                      j = cycles[i]
                      indices[i], indices[-j] = indices[-j], indices[i]
                      yield tuple(pool[i] for i in indices[:r])
                      break
              else:
                  return

itertools.product(*iterables, repeat=1)

   Cartesian product of input iterables.

   대략 제너레이터 표현식에서의 중첩된 for-루프와 동등합니다. 예를 들
   어, "product(A, B)"는 "((x,y) for x in A for y in B)"와 같은 것을
   반환합니다.

   중첩된 루프는 매 이터레이션마다 가장 오른쪽 요소가 진행되는 주행 거
   리계처럼 순환합니다. 이 패턴은 사전식 순서를 만들어서 입력의 이터러
   블들이 정렬되어 있다면, 곱(product) 튜플이 정렬된 순서로 방출됩니다
   .

   이터러블의 자신과의 곱을 계산하려면, 선택적 *repeat* 키워드 인자를
   사용하여 반복 횟수를 지정하십시오. 예를 들어, "product(A,
   repeat=4)"는 "product(A, A, A, A)"와 같은 것을 뜻합니다.

   이 함수는 실제 구현이 메모리에 중간 결과를 쌓지 않는다는 점을 제외
   하고 다음 코드와 대략 동등합니다:

      def product(*iterables, repeat=1):
          # product('ABCD', 'xy') → Ax Ay Bx By Cx Cy Dx Dy
          # product(range(2), repeat=3) → 000 001 010 011 100 101 110 111

          pools = [tuple(pool) for pool in iterables] * repeat

          result = [[]]
          for pool in pools:
              result = [x+[y] for x in result for y in pool]

          for prod in result:
              yield tuple(prod)

   "product()"가 실행되기 전에, 입력 이터러블을 완전히 소비하여, 곱을
   생성하기 위해 값의 풀(pool)을 메모리에 유지합니다. 따라서, 유한 입
   력에만 유용합니다.

itertools.repeat(object[, times])

   Make an iterator that returns *object* over and over again. Runs
   indefinitely unless the *times* argument is specified.

   대략 다음과 동등합니다:

      def repeat(object, times=None):
          # repeat(10, 3) → 10 10 10
          if times is None:
              while True:
                  yield object
          else:
              for i in range(times):
                  yield object

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

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

itertools.starmap(function, iterable)

   Make an iterator that computes the *function* using arguments
   obtained from the *iterable*.  Used instead of "map()" when
   argument parameters have already been "pre-zipped" into tuples.

   The difference between "map()" and "starmap()" parallels the
   distinction between "function(a,b)" and "function(*c)". Roughly
   equivalent to:

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

itertools.takewhile(predicate, iterable)

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

      def takewhile(predicate, iterable):
          # takewhile(lambda x: x<5, [1,4,6,3,8]) → 1 4
          for x in iterable:
              if not predicate(x):
                  break
              yield x

   Note, the element that first fails the predicate condition is
   consumed from the input iterator and there is no way to access it.
   This could be an issue if an application wants to further consume
   the input iterator after *takewhile* has been run to exhaustion.
   To work around this problem, consider using more-itertools
   before_and_after() instead.

itertools.tee(iterable, n=2)

   단일 iterable에서 *n* 개의 독립 이터레이터를 반환합니다.

   대략 다음과 동등합니다:

      def tee(iterable, n=2):
          if n < 0:
              raise ValueError
          if n == 0:
              return ()
          iterator = _tee(iterable)
          result = [iterator]
          for _ in range(n - 1):
              result.append(_tee(iterator))
          return tuple(result)

      class _tee:

          def __init__(self, iterable):
              it = iter(iterable)
              if isinstance(it, _tee):
                  self.iterator = it.iterator
                  self.link = it.link
              else:
                  self.iterator = it
                  self.link = [None, None]

          def __iter__(self):
              return self

          def __next__(self):
              link = self.link
              if link[1] is None:
                  link[0] = next(self.iterator)
                  link[1] = [None, None]
              value, self.link = link
              return value

   "tee" iterators are not threadsafe. A "RuntimeError" may be raised
   when simultaneously using iterators returned by the same "tee()"
   call, even if the original *iterable* is threadsafe.

   이 이터레이터 도구에는 상당한 보조 기억 장치가 필요할 수 있습니다 (
   일시적으로 저장해야 하는 데이터양에 따라 다릅니다). 일반적으로, 다
   른 이터레이터가 시작하기 전에 하나의 이터레이터가 대부분이나 모든
   데이터를 사용하면, "tee()" 대신 "list()"를 사용하는 것이 더 빠릅니
   다.

itertools.zip_longest(*iterables, fillvalue=None)

   Make an iterator that aggregates elements from each of the
   *iterables*.

   If the iterables are of uneven length, missing values are filled-in
   with *fillvalue*.  If not specified, *fillvalue* defaults to
   "None".

   Iteration continues until the longest iterable is exhausted.

   대략 다음과 동등합니다:

      def zip_longest(*iterables, fillvalue=None):
          # zip_longest('ABCD', 'xy', fillvalue='-') → Ax By C- D-

          iterators = list(map(iter, iterables))
          num_active = len(iterators)
          if not num_active:
              return

          while True:
              values = []
              for i, iterator in enumerate(iterators):
                  try:
                      value = next(iterator)
                  except StopIteration:
                      num_active -= 1
                      if not num_active:
                          return
                      iterators[i] = repeat(fillvalue)
                      value = fillvalue
                  values.append(value)
              yield tuple(values)

   If one of the iterables is potentially infinite, then the
   "zip_longest()" function should be wrapped with something that
   limits the number of calls (for example "islice()" or
   "takewhile()").


Itertools 조리법
================

이 섹션에서는 기존 itertools를 빌딩 블록으로 사용하여 확장 도구 집합을
만드는 방법을 보여줍니다.

The primary purpose of the itertools recipes is educational.  The
recipes show various ways of thinking about individual tools — for
example, that "chain.from_iterable" is related to the concept of
flattening.  The recipes also give ideas about ways that the tools can
be combined — for example, how "starmap()" and "repeat()" can work
together.  The recipes also show patterns for using itertools with the
"operator" and "collections" modules as well as with the built-in
itertools such as "map()", "filter()", "reversed()", and
"enumerate()".

A secondary purpose of the recipes is to serve as an incubator.  The
"accumulate()", "compress()", and "pairwise()" itertools started out
as recipes.  Currently, the "sliding_window()", "iter_index()", and
"sieve()" recipes are being tested to see whether they prove their
worth.

Substantially all of these recipes and many, many others can be
installed from the more-itertools project found on the Python Package
Index:

   python -m pip install more-itertools

Many of the recipes offer the same high performance as the underlying
toolset. Superior memory performance is kept by processing elements
one at a time rather than bringing the whole iterable into memory all
at once. Code volume is kept small by linking the tools together in a
functional style.  High speed is retained by preferring "vectorized"
building blocks over the use of for-loops and *generators* which incur
interpreter overhead.

   import collections
   import contextlib
   import functools
   import math
   import operator
   import random

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

   def prepend(value, iterable):
       "Prepend a single value in front of an iterable."
       # prepend(1, [2, 3, 4]) → 1 2 3 4
       return chain([value], iterable)

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

   def repeatfunc(func, times=None, *args):
       "Repeat calls to func with specified arguments."
       if times is None:
           return starmap(func, repeat(args))
       return starmap(func, repeat(args, times))

   def flatten(list_of_lists):
       "Flatten one level of nesting."
       return chain.from_iterable(list_of_lists)

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

   def loops(n):
       "Loop n times. Like range(n) but without creating integers."
       # for _ in loops(100): ...
       return repeat(None, n)

   def tail(n, iterable):
       "Return an iterator over the last n items."
       # tail(3, 'ABCDEFG') → E F G
       return iter(collections.deque(iterable, maxlen=n))

   def consume(iterator, n=None):
       "Advance the iterator n-steps ahead. If n is None, consume entirely."
       # Use functions that consume iterators at C speed.
       if n is None:
           collections.deque(iterator, maxlen=0)
       else:
           next(islice(iterator, n, n), None)

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

   def quantify(iterable, predicate=bool):
       "Given a predicate that returns True or False, count the True results."
       return sum(map(predicate, iterable))

   def first_true(iterable, default=False, predicate=None):
       "Returns the first true value or the *default* if there is no true value."
       # first_true([a,b,c], x) → a or b or c or x
       # first_true([a,b], x, f) → a if f(a) else b if f(b) else x
       return next(filter(predicate, iterable), default)

   def all_equal(iterable, key=None):
       "Returns True if all the elements are equal to each other."
       # all_equal('4٤௪౪໔', key=int) → True
       return len(take(2, groupby(iterable, key))) <= 1

   def unique_justseen(iterable, key=None):
       "Yield unique elements, preserving order. Remember only the element just seen."
       # unique_justseen('AAAABBBCCDAABBB') → A B C D A B
       # unique_justseen('ABBcCAD', str.casefold) → A B c A D
       if key is None:
           return map(operator.itemgetter(0), groupby(iterable))
       return map(next, map(operator.itemgetter(1), groupby(iterable, key)))

   def unique_everseen(iterable, key=None):
       "Yield unique elements, preserving order. Remember all elements ever seen."
       # unique_everseen('AAAABBBCCDAABBB') → A B C D
       # unique_everseen('ABBcCAD', str.casefold) → A B c D
       seen = set()
       if key is None:
           for element in filterfalse(seen.__contains__, iterable):
               seen.add(element)
               yield element
       else:
           for element in iterable:
               k = key(element)
               if k not in seen:
                   seen.add(k)
                   yield element

   def unique(iterable, key=None, reverse=False):
      "Yield unique elements in sorted order. Supports unhashable inputs."
      # unique([[1, 2], [3, 4], [1, 2]]) → [1, 2] [3, 4]
      return unique_justseen(sorted(iterable, key=key, reverse=reverse), key=key)

   def sliding_window(iterable, n):
       "Collect data into overlapping fixed-length chunks or blocks."
       # sliding_window('ABCDEFG', 4) → ABCD BCDE CDEF DEFG
       iterator = iter(iterable)
       window = collections.deque(islice(iterator, n - 1), maxlen=n)
       for x in iterator:
           window.append(x)
           yield tuple(window)

   def grouper(iterable, n, *, incomplete='fill', fillvalue=None):
       "Collect data into non-overlapping fixed-length chunks or blocks."
       # grouper('ABCDEFG', 3, fillvalue='x') → ABC DEF Gxx
       # grouper('ABCDEFG', 3, incomplete='strict') → ABC DEF ValueError
       # grouper('ABCDEFG', 3, incomplete='ignore') → ABC DEF
       iterators = [iter(iterable)] * n
       match incomplete:
           case 'fill':
               return zip_longest(*iterators, fillvalue=fillvalue)
           case 'strict':
               return zip(*iterators, strict=True)
           case 'ignore':
               return zip(*iterators)
           case _:
               raise ValueError('Expected fill, strict, or ignore')

   def roundrobin(*iterables):
       "Visit input iterables in a cycle until each is exhausted."
       # roundrobin('ABC', 'D', 'EF') → A D E B F C
       # Algorithm credited to George Sakkis
       iterators = map(iter, iterables)
       for num_active in range(len(iterables), 0, -1):
           iterators = cycle(islice(iterators, num_active))
           yield from map(next, iterators)

   def partition(predicate, iterable):
       """Partition entries into false entries and true entries.

       If *predicate* is slow, consider wrapping it with functools.lru_cache().
       """
       # partition(is_odd, range(10)) → 0 2 4 6 8   and  1 3 5 7 9
       t1, t2 = tee(iterable)
       return filterfalse(predicate, t1), filter(predicate, t2)

   def subslices(seq):
       "Return all contiguous non-empty subslices of a sequence."
       # subslices('ABCD') → A AB ABC ABCD B BC BCD C CD D
       slices = starmap(slice, combinations(range(len(seq) + 1), 2))
       return map(operator.getitem, repeat(seq), slices)

   def iter_index(iterable, value, start=0, stop=None):
       "Return indices where a value occurs in a sequence or iterable."
       # iter_index('AABCADEAF', 'A') → 0 1 4 7
       seq_index = getattr(iterable, 'index', None)
       if seq_index is None:
           iterator = islice(iterable, start, stop)
           for i, element in enumerate(iterator, start):
               if element is value or element == value:
                   yield i
       else:
           stop = len(iterable) if stop is None else stop
           i = start
           with contextlib.suppress(ValueError):
               while True:
                   yield (i := seq_index(value, i, stop))
                   i += 1

   def iter_except(func, exception, first=None):
       "Convert a call-until-exception interface to an iterator interface."
       # iter_except(d.popitem, KeyError) → non-blocking dictionary iterator
       with contextlib.suppress(exception):
           if first is not None:
               yield first()
           while True:
               yield func()

The following recipes have a more mathematical flavor:

   def powerset(iterable):
       "powerset([1,2,3]) → () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
       s = list(iterable)
       return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

   def sum_of_squares(iterable):
       "Add up the squares of the input values."
       # sum_of_squares([10, 20, 30]) → 1400
       return math.sumprod(*tee(iterable))

   def reshape(matrix, cols):
       "Reshape a 2-D matrix to have a given number of columns."
       # reshape([(0, 1), (2, 3), (4, 5)], 3) →  (0, 1, 2), (3, 4, 5)
       return batched(chain.from_iterable(matrix), cols)

   def transpose(matrix):
       "Swap the rows and columns of a 2-D matrix."
       # transpose([(1, 2, 3), (11, 22, 33)]) → (1, 11) (2, 22) (3, 33)
       return zip(*matrix, strict=True)

   def matmul(m1, m2):
       "Multiply two matrices."
       # matmul([(7, 5), (3, 5)], [(2, 5), (7, 9)]) → (49, 80), (41, 60)
       n = len(m2[0])
       return batched(starmap(math.sumprod, product(m1, transpose(m2))), n)

   def convolve(signal, kernel):
       """Discrete linear convolution of two iterables.
       Equivalent to polynomial multiplication.

       Convolutions are mathematically commutative; however, the inputs are
       evaluated differently.  The signal is consumed lazily and can be
       infinite. The kernel is fully consumed before the calculations begin.

       Article:  https://betterexplained.com/articles/intuitive-convolution/
       Video:    https://www.youtube.com/watch?v=KuXjwB4LzSA
       """
       # convolve([1, -1, -20], [1, -3]) → 1 -4 -17 60
       # convolve(data, [0.25, 0.25, 0.25, 0.25]) → Moving average (blur)
       # convolve(data, [1/2, 0, -1/2]) → 1st derivative estimate
       # convolve(data, [1, -2, 1]) → 2nd derivative estimate
       kernel = tuple(kernel)[::-1]
       n = len(kernel)
       padded_signal = chain(repeat(0, n-1), signal, repeat(0, n-1))
       windowed_signal = sliding_window(padded_signal, n)
       return map(math.sumprod, repeat(kernel), windowed_signal)

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

          (x - 5) (x + 4) (x - 3)  expands to:   x³ -4x² -17x + 60
       """
       # polynomial_from_roots([5, -4, 3]) → [1, -4, -17, 60]
       factors = zip(repeat(1), map(operator.neg, roots))
       return list(functools.reduce(convolve, factors, [1]))

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

       Computes with better numeric stability than Horner's method.
       """
       # Evaluate x³ -4x² -17x + 60 at x = 5
       # polynomial_eval([1, -4, -17, 60], x=5) → 0
       n = len(coefficients)
       if not n:
           return type(x)(0)
       powers = map(pow, repeat(x), reversed(range(n)))
       return math.sumprod(coefficients, powers)

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

          f(x)  =  x³ -4x² -17x + 60
          f'(x) = 3x² -8x  -17
       """
       # polynomial_derivative([1, -4, -17, 60]) → [3, -8, -17]
       n = len(coefficients)
       powers = reversed(range(1, n))
       return list(map(operator.mul, coefficients, powers))

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

   def is_prime(n):
       "Return True if n is prime."
       # is_prime(1_000_000_000_000_403) → True
       return n > 1 and all(n % p for p in sieve(math.isqrt(n) + 1))

   def factor(n):
       "Prime factors of n."
       # factor(99) → 3 3 11
       # factor(1_000_000_000_000_007) → 47 59 360620266859
       # factor(1_000_000_000_000_403) → 1000000000000403
       for prime in sieve(math.isqrt(n) + 1):
           while not n % prime:
               yield prime
               n //= prime
               if n == 1:
                   return
       if n > 1:
           yield n

   def totient(n):
       "Count of natural numbers up to n that are coprime to n."
       # https://mathworld.wolfram.com/TotientFunction.html
       # totient(12) → 4 because len([1, 5, 7, 11]) == 4
       for prime in set(factor(n)):
           n -= n // prime
       return n

   def multinomial(*counts):
       "Number of distinct arrangements of a multiset."
       # Counter('abracadabra').values() -> 5 2 1 1 2
       # multinomial(5, 2, 1, 1, 2) → 83160
       return math.prod(map(math.comb, accumulate(counts), counts))
