add ydb deps

1 files changed, 701 insertions, 0 deletions

diff --git a/contrib/tools/python/src/Modules/_heapqmodule.c b/contrib/tools/python/src/Modules/_heapqmodule.c
new file mode 100644
index 0000000000..5b0ef69154
--- /dev/null
+++ b/contrib/tools/python/src/Modules/_heapqmodule.c
@@ -0,0 +1,701 @@
+/* Drop in replacement for heapq.py
+
+C implementation derived directly from heapq.py in Py2.3
+which was written by Kevin O'Connor, augmented by Tim Peters,
+annotated by François Pinard, and converted to C by Raymond Hettinger.
+
+*/
+
+#include "Python.h"
+
+/* Older implementations of heapq used Py_LE for comparisons.  Now, it uses
+   Py_LT so it will match min(), sorted(), and bisect().  Unfortunately, some
+   client code (Twisted for example) relied on Py_LE, so this little function
+   restores compatibility by trying both.
+*/
+static int
+cmp_lt(PyObject *x, PyObject *y)
+{
+    int cmp;
+    static PyObject *lt = NULL;
+
+    if (lt == NULL) {
+        lt = PyString_FromString("__lt__");
+        if (lt == NULL)
+            return -1;
+    }
+    if (PyObject_HasAttr(x, lt))
+        return PyObject_RichCompareBool(x, y, Py_LT);
+    cmp = PyObject_RichCompareBool(y, x, Py_LE);
+    if (cmp != -1)
+        cmp = 1 - cmp;
+    return cmp;
+}
+
+static int
+_siftdown(PyListObject *heap, Py_ssize_t startpos, Py_ssize_t pos)
+{
+    PyObject *newitem, *parent;
+    Py_ssize_t parentpos, size;
+    int cmp;
+
+    assert(PyList_Check(heap));
+    size = PyList_GET_SIZE(heap);
+    if (pos >= size) {
+        PyErr_SetString(PyExc_IndexError, "index out of range");
+        return -1;
+    }
+
+    /* Follow the path to the root, moving parents down until finding
+       a place newitem fits. */
+    newitem = PyList_GET_ITEM(heap, pos);
+    while (pos > startpos) {
+        parentpos = (pos - 1) >> 1;
+        parent = PyList_GET_ITEM(heap, parentpos);
+        cmp = cmp_lt(newitem, parent);
+        if (cmp == -1)
+            return -1;
+        if (size != PyList_GET_SIZE(heap)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                            "list changed size during iteration");
+            return -1;
+        }
+        if (cmp == 0)
+            break;
+        parent = PyList_GET_ITEM(heap, parentpos);
+        newitem = PyList_GET_ITEM(heap, pos);
+        PyList_SET_ITEM(heap, parentpos, newitem);
+        PyList_SET_ITEM(heap, pos, parent);
+        pos = parentpos;
+    }
+    return 0;
+}
+
+static int
+_siftup(PyListObject *heap, Py_ssize_t pos)
+{
+    Py_ssize_t startpos, endpos, childpos, rightpos, limit;
+    PyObject *tmp1, *tmp2;
+    int cmp;
+
+    assert(PyList_Check(heap));
+    endpos = PyList_GET_SIZE(heap);
+    startpos = pos;
+    if (pos >= endpos) {
+        PyErr_SetString(PyExc_IndexError, "index out of range");
+        return -1;
+    }
+
+    /* Bubble up the smaller child until hitting a leaf. */
+    limit = endpos / 2;          /* smallest pos that has no child */
+    while (pos < limit) {
+        /* Set childpos to index of smaller child.   */
+        childpos = 2*pos + 1;    /* leftmost child position  */
+        rightpos = childpos + 1;
+        if (rightpos < endpos) {
+            cmp = cmp_lt(
+                PyList_GET_ITEM(heap, childpos),
+                PyList_GET_ITEM(heap, rightpos));
+            if (cmp == -1)
+                return -1;
+            if (cmp == 0)
+                childpos = rightpos;
+            if (endpos != PyList_GET_SIZE(heap)) {
+                PyErr_SetString(PyExc_RuntimeError,
+                                "list changed size during iteration");
+                return -1;
+            }
+        }
+        /* Move the smaller child up. */
+        tmp1 = PyList_GET_ITEM(heap, childpos);
+        tmp2 = PyList_GET_ITEM(heap, pos);
+        PyList_SET_ITEM(heap, childpos, tmp2);
+        PyList_SET_ITEM(heap, pos, tmp1);
+        pos = childpos;
+    }
+    /* Bubble it up to its final resting place (by sifting its parents down). */
+    return _siftdown(heap, startpos, pos);
+}
+
+static PyObject *
+heappush(PyObject *self, PyObject *args)
+{
+    PyObject *heap, *item;
+
+    if (!PyArg_UnpackTuple(args, "heappush", 2, 2, &heap, &item))
+        return NULL;
+
+    if (!PyList_Check(heap)) {
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
+        return NULL;
+    }
+
+    if (PyList_Append(heap, item) == -1)
+        return NULL;
+
+    if (_siftdown((PyListObject *)heap, 0, PyList_GET_SIZE(heap)-1) == -1)
+        return NULL;
+    Py_INCREF(Py_None);
+    return Py_None;
+}
+
+PyDoc_STRVAR(heappush_doc,
+"heappush(heap, item) -> None. Push item onto heap, maintaining the heap invariant.");
+
+static PyObject *
+heappop(PyObject *self, PyObject *heap)
+{
+    PyObject *lastelt, *returnitem;
+    Py_ssize_t n;
+
+    if (!PyList_Check(heap)) {
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
+        return NULL;
+    }
+
+    /* # raises appropriate IndexError if heap is empty */
+    n = PyList_GET_SIZE(heap);
+    if (n == 0) {
+        PyErr_SetString(PyExc_IndexError, "index out of range");
+        return NULL;
+    }
+
+    lastelt = PyList_GET_ITEM(heap, n-1) ;
+    Py_INCREF(lastelt);
+    PyList_SetSlice(heap, n-1, n, NULL);
+    n--;
+
+    if (!n)
+        return lastelt;
+    returnitem = PyList_GET_ITEM(heap, 0);
+    PyList_SET_ITEM(heap, 0, lastelt);
+    if (_siftup((PyListObject *)heap, 0) == -1) {
+        Py_DECREF(returnitem);
+        return NULL;
+    }
+    return returnitem;
+}
+
+PyDoc_STRVAR(heappop_doc,
+"Pop the smallest item off the heap, maintaining the heap invariant.");
+
+static PyObject *
+heapreplace(PyObject *self, PyObject *args)
+{
+    PyObject *heap, *item, *returnitem;
+
+    if (!PyArg_UnpackTuple(args, "heapreplace", 2, 2, &heap, &item))
+        return NULL;
+
+    if (!PyList_Check(heap)) {
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
+        return NULL;
+    }
+
+    if (PyList_GET_SIZE(heap) < 1) {
+        PyErr_SetString(PyExc_IndexError, "index out of range");
+        return NULL;
+    }
+
+    returnitem = PyList_GET_ITEM(heap, 0);
+    Py_INCREF(item);
+    PyList_SET_ITEM(heap, 0, item);
+    if (_siftup((PyListObject *)heap, 0) == -1) {
+        Py_DECREF(returnitem);
+        return NULL;
+    }
+    return returnitem;
+}
+
+PyDoc_STRVAR(heapreplace_doc,
+"heapreplace(heap, item) -> value. Pop and return the current smallest value, and add the new item.\n\
+\n\
+This is more efficient than heappop() followed by heappush(), and can be\n\
+more appropriate when using a fixed-size heap.  Note that the value\n\
+returned may be larger than item!  That constrains reasonable uses of\n\
+this routine unless written as part of a conditional replacement:\n\n\
+    if item > heap[0]:\n\
+        item = heapreplace(heap, item)\n");
+
+static PyObject *
+heappushpop(PyObject *self, PyObject *args)
+{
+    PyObject *heap, *item, *returnitem;
+    int cmp;
+
+    if (!PyArg_UnpackTuple(args, "heappushpop", 2, 2, &heap, &item))
+        return NULL;
+
+    if (!PyList_Check(heap)) {
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
+        return NULL;
+    }
+
+    if (PyList_GET_SIZE(heap) < 1) {
+        Py_INCREF(item);
+        return item;
+    }
+
+    cmp = cmp_lt(PyList_GET_ITEM(heap, 0), item);
+    if (cmp == -1)
+        return NULL;
+    if (cmp == 0) {
+        Py_INCREF(item);
+        return item;
+    }
+
+    if (PyList_GET_SIZE(heap) == 0) {
+        PyErr_SetString(PyExc_IndexError, "index out of range");
+        return NULL;
+    }
+
+    returnitem = PyList_GET_ITEM(heap, 0);
+    Py_INCREF(item);
+    PyList_SET_ITEM(heap, 0, item);
+    if (_siftup((PyListObject *)heap, 0) == -1) {
+        Py_DECREF(returnitem);
+        return NULL;
+    }
+    return returnitem;
+}
+
+PyDoc_STRVAR(heappushpop_doc,
+"heappushpop(heap, item) -> value. Push item on the heap, then pop and return the smallest item\n\
+from the heap. The combined action runs more efficiently than\n\
+heappush() followed by a separate call to heappop().");
+
+static PyObject *
+heapify(PyObject *self, PyObject *heap)
+{
+    Py_ssize_t i, n;
+
+    if (!PyList_Check(heap)) {
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
+        return NULL;
+    }
+
+    n = PyList_GET_SIZE(heap);
+    /* Transform bottom-up.  The largest index there's any point to
+       looking at is the largest with a child index in-range, so must
+       have 2*i + 1 < n, or i < (n-1)/2.  If n is even = 2*j, this is
+       (2*j-1)/2 = j-1/2 so j-1 is the largest, which is n//2 - 1.  If
+       n is odd = 2*j+1, this is (2*j+1-1)/2 = j so j-1 is the largest,
+       and that's again n//2-1.
+    */
+    for (i=n/2-1 ; i>=0 ; i--)
+        if(_siftup((PyListObject *)heap, i) == -1)
+            return NULL;
+    Py_INCREF(Py_None);
+    return Py_None;
+}
+
+PyDoc_STRVAR(heapify_doc,
+"Transform list into a heap, in-place, in O(len(heap)) time.");
+
+static PyObject *
+nlargest(PyObject *self, PyObject *args)
+{
+    PyObject *heap=NULL, *elem, *iterable, *sol, *it, *oldelem;
+    Py_ssize_t i, n;
+    int cmp;
+
+    if (!PyArg_ParseTuple(args, "nO:nlargest", &n, &iterable))
+        return NULL;
+
+    it = PyObject_GetIter(iterable);
+    if (it == NULL)
+        return NULL;
+
+    heap = PyList_New(0);
+    if (heap == NULL)
+        goto fail;
+
+    for (i=0 ; i<n ; i++ ){
+        elem = PyIter_Next(it);
+        if (elem == NULL) {
+            if (PyErr_Occurred())
+                goto fail;
+            else
+                goto sortit;
+        }
+        if (PyList_Append(heap, elem) == -1) {
+            Py_DECREF(elem);
+            goto fail;
+        }
+        Py_DECREF(elem);
+    }
+    if (PyList_GET_SIZE(heap) == 0)
+        goto sortit;
+
+    for (i=n/2-1 ; i>=0 ; i--)
+        if(_siftup((PyListObject *)heap, i) == -1)
+            goto fail;
+
+    sol = PyList_GET_ITEM(heap, 0);
+    while (1) {
+        elem = PyIter_Next(it);
+        if (elem == NULL) {
+            if (PyErr_Occurred())
+                goto fail;
+            else
+                goto sortit;
+        }
+        cmp = cmp_lt(sol, elem);
+        if (cmp == -1) {
+            Py_DECREF(elem);
+            goto fail;
+        }
+        if (cmp == 0) {
+            Py_DECREF(elem);
+            continue;
+        }
+        oldelem = PyList_GET_ITEM(heap, 0);
+        PyList_SET_ITEM(heap, 0, elem);
+        Py_DECREF(oldelem);
+        if (_siftup((PyListObject *)heap, 0) == -1)
+            goto fail;
+        sol = PyList_GET_ITEM(heap, 0);
+    }
+sortit:
+    if (PyList_Sort(heap) == -1)
+        goto fail;
+    if (PyList_Reverse(heap) == -1)
+        goto fail;
+    Py_DECREF(it);
+    return heap;
+
+fail:
+    Py_DECREF(it);
+    Py_XDECREF(heap);
+    return NULL;
+}
+
+PyDoc_STRVAR(nlargest_doc,
+"Find the n largest elements in a dataset.\n\
+\n\
+Equivalent to:  sorted(iterable, reverse=True)[:n]\n");
+
+static int
+_siftdownmax(PyListObject *heap, Py_ssize_t startpos, Py_ssize_t pos)
+{
+    PyObject *newitem, *parent;
+    int cmp;
+    Py_ssize_t parentpos;
+
+    assert(PyList_Check(heap));
+    if (pos >= PyList_GET_SIZE(heap)) {
+        PyErr_SetString(PyExc_IndexError, "index out of range");
+        return -1;
+    }
+
+    newitem = PyList_GET_ITEM(heap, pos);
+    Py_INCREF(newitem);
+    /* Follow the path to the root, moving parents down until finding
+       a place newitem fits. */
+    while (pos > startpos){
+        parentpos = (pos - 1) >> 1;
+        parent = PyList_GET_ITEM(heap, parentpos);
+        cmp = cmp_lt(parent, newitem);
+        if (cmp == -1) {
+            Py_DECREF(newitem);
+            return -1;
+        }
+        if (cmp == 0)
+            break;
+        Py_INCREF(parent);
+        Py_DECREF(PyList_GET_ITEM(heap, pos));
+        PyList_SET_ITEM(heap, pos, parent);
+        pos = parentpos;
+    }
+    Py_DECREF(PyList_GET_ITEM(heap, pos));
+    PyList_SET_ITEM(heap, pos, newitem);
+    return 0;
+}
+
+static int
+_siftupmax(PyListObject *heap, Py_ssize_t pos)
+{
+    Py_ssize_t startpos, endpos, childpos, rightpos, limit;
+    int cmp;
+    PyObject *newitem, *tmp;
+
+    assert(PyList_Check(heap));
+    endpos = PyList_GET_SIZE(heap);
+    startpos = pos;
+    if (pos >= endpos) {
+        PyErr_SetString(PyExc_IndexError, "index out of range");
+        return -1;
+    }
+    newitem = PyList_GET_ITEM(heap, pos);
+    Py_INCREF(newitem);
+
+    /* Bubble up the smaller child until hitting a leaf. */
+    limit = endpos / 2;          /* smallest pos that has no child */
+    while (pos < limit) {
+        /* Set childpos to index of smaller child.   */
+        childpos = 2*pos + 1;    /* leftmost child position  */
+        rightpos = childpos + 1;
+        if (rightpos < endpos) {
+            cmp = cmp_lt(
+                PyList_GET_ITEM(heap, rightpos),
+                PyList_GET_ITEM(heap, childpos));
+            if (cmp == -1) {
+                Py_DECREF(newitem);
+                return -1;
+            }
+            if (cmp == 0)
+                childpos = rightpos;
+        }
+        /* Move the smaller child up. */
+        tmp = PyList_GET_ITEM(heap, childpos);
+        Py_INCREF(tmp);
+        Py_DECREF(PyList_GET_ITEM(heap, pos));
+        PyList_SET_ITEM(heap, pos, tmp);
+        pos = childpos;
+    }
+
+    /* The leaf at pos is empty now.  Put newitem there, and bubble
+       it up to its final resting place (by sifting its parents down). */
+    Py_DECREF(PyList_GET_ITEM(heap, pos));
+    PyList_SET_ITEM(heap, pos, newitem);
+    return _siftdownmax(heap, startpos, pos);
+}
+
+static PyObject *
+nsmallest(PyObject *self, PyObject *args)
+{
+    PyObject *heap=NULL, *elem, *iterable, *los, *it, *oldelem;
+    Py_ssize_t i, n;
+    int cmp;
+
+    if (!PyArg_ParseTuple(args, "nO:nsmallest", &n, &iterable))
+        return NULL;
+
+    it = PyObject_GetIter(iterable);
+    if (it == NULL)
+        return NULL;
+
+    heap = PyList_New(0);
+    if (heap == NULL)
+        goto fail;
+
+    for (i=0 ; i<n ; i++ ){
+        elem = PyIter_Next(it);
+        if (elem == NULL) {
+            if (PyErr_Occurred())
+                goto fail;
+            else
+                goto sortit;
+        }
+        if (PyList_Append(heap, elem) == -1) {
+            Py_DECREF(elem);
+            goto fail;
+        }
+        Py_DECREF(elem);
+    }
+    n = PyList_GET_SIZE(heap);
+    if (n == 0)
+        goto sortit;
+
+    for (i=n/2-1 ; i>=0 ; i--)
+        if(_siftupmax((PyListObject *)heap, i) == -1)
+            goto fail;
+
+    los = PyList_GET_ITEM(heap, 0);
+    while (1) {
+        elem = PyIter_Next(it);
+        if (elem == NULL) {
+            if (PyErr_Occurred())
+                goto fail;
+            else
+                goto sortit;
+        }
+        cmp = cmp_lt(elem, los);
+        if (cmp == -1) {
+            Py_DECREF(elem);
+            goto fail;
+        }
+        if (cmp == 0) {
+            Py_DECREF(elem);
+            continue;
+        }
+
+        oldelem = PyList_GET_ITEM(heap, 0);
+        PyList_SET_ITEM(heap, 0, elem);
+        Py_DECREF(oldelem);
+        if (_siftupmax((PyListObject *)heap, 0) == -1)
+            goto fail;
+        los = PyList_GET_ITEM(heap, 0);
+    }
+
+sortit:
+    if (PyList_Sort(heap) == -1)
+        goto fail;
+    Py_DECREF(it);
+    return heap;
+
+fail:
+    Py_DECREF(it);
+    Py_XDECREF(heap);
+    return NULL;
+}
+
+PyDoc_STRVAR(nsmallest_doc,
+"Find the n smallest elements in a dataset.\n\
+\n\
+Equivalent to:  sorted(iterable)[:n]\n");
+
+static PyMethodDef heapq_methods[] = {
+    {"heappush",        (PyCFunction)heappush,
+        METH_VARARGS,           heappush_doc},
+    {"heappushpop",     (PyCFunction)heappushpop,
+        METH_VARARGS,           heappushpop_doc},
+    {"heappop",         (PyCFunction)heappop,
+        METH_O,                 heappop_doc},
+    {"heapreplace",     (PyCFunction)heapreplace,
+        METH_VARARGS,           heapreplace_doc},
+    {"heapify",         (PyCFunction)heapify,
+        METH_O,                 heapify_doc},
+    {"nlargest",        (PyCFunction)nlargest,
+        METH_VARARGS,           nlargest_doc},
+    {"nsmallest",       (PyCFunction)nsmallest,
+        METH_VARARGS,           nsmallest_doc},
+    {NULL,              NULL}           /* sentinel */
+};
+
+PyDoc_STRVAR(module_doc,
+"Heap queue algorithm (a.k.a. priority queue).\n\
+\n\
+Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\
+all k, counting elements from 0.  For the sake of comparison,\n\
+non-existing elements are considered to be infinite.  The interesting\n\
+property of a heap is that a[0] is always its smallest element.\n\
+\n\
+Usage:\n\
+\n\
+heap = []            # creates an empty heap\n\
+heappush(heap, item) # pushes a new item on the heap\n\
+item = heappop(heap) # pops the smallest item from the heap\n\
+item = heap[0]       # smallest item on the heap without popping it\n\
+heapify(x)           # transforms list into a heap, in-place, in linear time\n\
+item = heapreplace(heap, item) # pops and returns smallest item, and adds\n\
+                               # new item; the heap size is unchanged\n\
+\n\
+Our API differs from textbook heap algorithms as follows:\n\
+\n\
+- We use 0-based indexing.  This makes the relationship between the\n\
+  index for a node and the indexes for its children slightly less\n\
+  obvious, but is more suitable since Python uses 0-based indexing.\n\
+\n\
+- Our heappop() method returns the smallest item, not the largest.\n\
+\n\
+These two make it possible to view the heap as a regular Python list\n\
+without surprises: heap[0] is the smallest item, and heap.sort()\n\
+maintains the heap invariant!\n");
+
+
+PyDoc_STRVAR(__about__,
+"Heap queues\n\
+\n\
+[explanation by François Pinard]\n\
+\n\
+Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\n\
+all k, counting elements from 0.  For the sake of comparison,\n\
+non-existing elements are considered to be infinite.  The interesting\n\
+property of a heap is that a[0] is always its smallest element.\n"
+"\n\
+The strange invariant above is meant to be an efficient memory\n\
+representation for a tournament.  The numbers below are `k', not a[k]:\n\
+\n\
+                                   0\n\
+\n\
+                  1                                 2\n\
+\n\
+          3               4                5               6\n\
+\n\
+      7       8       9       10      11      12      13      14\n\
+\n\
+    15 16   17 18   19 20   21 22   23 24   25 26   27 28   29 30\n\
+\n\
+\n\
+In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'.  In\n\
+a usual binary tournament we see in sports, each cell is the winner\n\
+over the two cells it tops, and we can trace the winner down the tree\n\
+to see all opponents s/he had.  However, in many computer applications\n\
+of such tournaments, we do not need to trace the history of a winner.\n\
+To be more memory efficient, when a winner is promoted, we try to\n\
+replace it by something else at a lower level, and the rule becomes\n\
+that a cell and the two cells it tops contain three different items,\n\
+but the top cell \"wins\" over the two topped cells.\n"
+"\n\
+If this heap invariant is protected at all time, index 0 is clearly\n\
+the overall winner.  The simplest algorithmic way to remove it and\n\
+find the \"next\" winner is to move some loser (let's say cell 30 in the\n\
+diagram above) into the 0 position, and then percolate this new 0 down\n\
+the tree, exchanging values, until the invariant is re-established.\n\
+This is clearly logarithmic on the total number of items in the tree.\n\
+By iterating over all items, you get an O(n ln n) sort.\n"
+"\n\
+A nice feature of this sort is that you can efficiently insert new\n\
+items while the sort is going on, provided that the inserted items are\n\
+not \"better\" than the last 0'th element you extracted.  This is\n\
+especially useful in simulation contexts, where the tree holds all\n\
+incoming events, and the \"win\" condition means the smallest scheduled\n\
+time.  When an event schedule other events for execution, they are\n\
+scheduled into the future, so they can easily go into the heap.  So, a\n\
+heap is a good structure for implementing schedulers (this is what I\n\
+used for my MIDI sequencer :-).\n"
+"\n\
+Various structures for implementing schedulers have been extensively\n\
+studied, and heaps are good for this, as they are reasonably speedy,\n\
+the speed is almost constant, and the worst case is not much different\n\
+than the average case.  However, there are other representations which\n\
+are more efficient overall, yet the worst cases might be terrible.\n"
+"\n\
+Heaps are also very useful in big disk sorts.  You most probably all\n\
+know that a big sort implies producing \"runs\" (which are pre-sorted\n\
+sequences, which size is usually related to the amount of CPU memory),\n\
+followed by a merging passes for these runs, which merging is often\n\
+very cleverly organised[1].  It is very important that the initial\n\
+sort produces the longest runs possible.  Tournaments are a good way\n\
+to that.  If, using all the memory available to hold a tournament, you\n\
+replace and percolate items that happen to fit the current run, you'll\n\
+produce runs which are twice the size of the memory for random input,\n\
+and much better for input fuzzily ordered.\n"
+"\n\
+Moreover, if you output the 0'th item on disk and get an input which\n\
+may not fit in the current tournament (because the value \"wins\" over\n\
+the last output value), it cannot fit in the heap, so the size of the\n\
+heap decreases.  The freed memory could be cleverly reused immediately\n\
+for progressively building a second heap, which grows at exactly the\n\
+same rate the first heap is melting.  When the first heap completely\n\
+vanishes, you switch heaps and start a new run.  Clever and quite\n\
+effective!\n\
+\n\
+In a word, heaps are useful memory structures to know.  I use them in\n\
+a few applications, and I think it is good to keep a `heap' module\n\
+around. :-)\n"
+"\n\
+--------------------\n\
+[1] The disk balancing algorithms which are current, nowadays, are\n\
+more annoying than clever, and this is a consequence of the seeking\n\
+capabilities of the disks.  On devices which cannot seek, like big\n\
+tape drives, the story was quite different, and one had to be very\n\
+clever to ensure (far in advance) that each tape movement will be the\n\
+most effective possible (that is, will best participate at\n\
+\"progressing\" the merge).  Some tapes were even able to read\n\
+backwards, and this was also used to avoid the rewinding time.\n\
+Believe me, real good tape sorts were quite spectacular to watch!\n\
+From all times, sorting has always been a Great Art! :-)\n");
+
+PyMODINIT_FUNC
+init_heapq(void)
+{
+    PyObject *m;
+
+    m = Py_InitModule3("_heapq", heapq_methods, module_doc);
+    if (m == NULL)
+        return;
+    PyModule_AddObject(m, "__about__", PyString_FromString(__about__));
+}
+