add ydb deps

1 files changed, 709 insertions, 0 deletions

diff --git a/contrib/tools/python3/src/Modules/_heapqmodule.c b/contrib/tools/python3/src/Modules/_heapqmodule.c
new file mode 100644
index 0000000000..3dbaaa0a0d
--- /dev/null
+++ b/contrib/tools/python3/src/Modules/_heapqmodule.c
@@ -0,0 +1,709 @@
+/* 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.
+
+*/
+
+#ifndef Py_BUILD_CORE_BUILTIN
+#  define Py_BUILD_CORE_MODULE 1
+#endif
+
+#include "Python.h"
+#include "pycore_list.h"          // _PyList_ITEMS()
+
+#include "clinic/_heapqmodule.c.h"
+
+
+/*[clinic input]
+module _heapq
+[clinic start generated code]*/
+/*[clinic end generated code: output=da39a3ee5e6b4b0d input=d7cca0a2e4c0ceb3]*/
+
+static int
+siftdown(PyListObject *heap, Py_ssize_t startpos, Py_ssize_t pos)
+{
+    PyObject *newitem, *parent, **arr;
+    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. */
+    arr = _PyList_ITEMS(heap);
+    newitem = arr[pos];
+    while (pos > startpos) {
+        parentpos = (pos - 1) >> 1;
+        parent = arr[parentpos];
+        Py_INCREF(newitem);
+        Py_INCREF(parent);
+        cmp = PyObject_RichCompareBool(newitem, parent, Py_LT);
+        Py_DECREF(parent);
+        Py_DECREF(newitem);
+        if (cmp < 0)
+            return -1;
+        if (size != PyList_GET_SIZE(heap)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                            "list changed size during iteration");
+            return -1;
+        }
+        if (cmp == 0)
+            break;
+        arr = _PyList_ITEMS(heap);
+        parent = arr[parentpos];
+        newitem = arr[pos];
+        arr[parentpos] = newitem;
+        arr[pos] = parent;
+        pos = parentpos;
+    }
+    return 0;
+}
+
+static int
+siftup(PyListObject *heap, Py_ssize_t pos)
+{
+    Py_ssize_t startpos, endpos, childpos, limit;
+    PyObject *tmp1, *tmp2, **arr;
+    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. */
+    arr = _PyList_ITEMS(heap);
+    limit = endpos >> 1;         /* smallest pos that has no child */
+    while (pos < limit) {
+        /* Set childpos to index of smaller child.   */
+        childpos = 2*pos + 1;    /* leftmost child position  */
+        if (childpos + 1 < endpos) {
+            PyObject* a = arr[childpos];
+            PyObject* b = arr[childpos + 1];
+            Py_INCREF(a);
+            Py_INCREF(b);
+            cmp = PyObject_RichCompareBool(a, b, Py_LT);
+            Py_DECREF(a);
+            Py_DECREF(b);
+            if (cmp < 0)
+                return -1;
+            childpos += ((unsigned)cmp ^ 1);   /* increment when cmp==0 */
+            arr = _PyList_ITEMS(heap);         /* arr may have changed */
+            if (endpos != PyList_GET_SIZE(heap)) {
+                PyErr_SetString(PyExc_RuntimeError,
+                                "list changed size during iteration");
+                return -1;
+            }
+        }
+        /* Move the smaller child up. */
+        tmp1 = arr[childpos];
+        tmp2 = arr[pos];
+        arr[childpos] = tmp2;
+        arr[pos] = tmp1;
+        pos = childpos;
+    }
+    /* Bubble it up to its final resting place (by sifting its parents down). */
+    return siftdown(heap, startpos, pos);
+}
+
+/*[clinic input]
+_heapq.heappush
+
+    heap: object(subclass_of='&PyList_Type')
+    item: object
+    /
+
+Push item onto heap, maintaining the heap invariant.
+[clinic start generated code]*/
+
+static PyObject *
+_heapq_heappush_impl(PyObject *module, PyObject *heap, PyObject *item)
+/*[clinic end generated code: output=912c094f47663935 input=7c69611f3698aceb]*/
+{
+    if (PyList_Append(heap, item))
+        return NULL;
+
+    if (siftdown((PyListObject *)heap, 0, PyList_GET_SIZE(heap)-1))
+        return NULL;
+    Py_RETURN_NONE;
+}
+
+static PyObject *
+heappop_internal(PyObject *heap, int siftup_func(PyListObject *, Py_ssize_t))
+{
+    PyObject *lastelt, *returnitem;
+    Py_ssize_t n;
+
+    /* raises IndexError if the 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);
+    if (PyList_SetSlice(heap, n-1, n, NULL)) {
+        Py_DECREF(lastelt);
+        return NULL;
+    }
+    n--;
+
+    if (!n)
+        return lastelt;
+    returnitem = PyList_GET_ITEM(heap, 0);
+    PyList_SET_ITEM(heap, 0, lastelt);
+    if (siftup_func((PyListObject *)heap, 0)) {
+        Py_DECREF(returnitem);
+        return NULL;
+    }
+    return returnitem;
+}
+
+/*[clinic input]
+_heapq.heappop
+
+    heap: object(subclass_of='&PyList_Type')
+    /
+
+Pop the smallest item off the heap, maintaining the heap invariant.
+[clinic start generated code]*/
+
+static PyObject *
+_heapq_heappop_impl(PyObject *module, PyObject *heap)
+/*[clinic end generated code: output=96dfe82d37d9af76 input=91487987a583c856]*/
+{
+    return heappop_internal(heap, siftup);
+}
+
+static PyObject *
+heapreplace_internal(PyObject *heap, PyObject *item, int siftup_func(PyListObject *, Py_ssize_t))
+{
+    PyObject *returnitem;
+
+    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_func((PyListObject *)heap, 0)) {
+        Py_DECREF(returnitem);
+        return NULL;
+    }
+    return returnitem;
+}
+
+
+/*[clinic input]
+_heapq.heapreplace
+
+    heap: object(subclass_of='&PyList_Type')
+    item: object
+    /
+
+Pop and return the current smallest value, and add the new item.
+
+This is more efficient than heappop() followed by heappush(), and can be
+more appropriate when using a fixed-size heap.  Note that the value
+returned may be larger than item!  That constrains reasonable uses of
+this routine unless written as part of a conditional replacement:
+
+    if item > heap[0]:
+        item = heapreplace(heap, item)
+[clinic start generated code]*/
+
+static PyObject *
+_heapq_heapreplace_impl(PyObject *module, PyObject *heap, PyObject *item)
+/*[clinic end generated code: output=82ea55be8fbe24b4 input=719202ac02ba10c8]*/
+{
+    return heapreplace_internal(heap, item, siftup);
+}
+
+/*[clinic input]
+_heapq.heappushpop
+
+    heap: object(subclass_of='&PyList_Type')
+    item: object
+    /
+
+Push item on the heap, then pop and return the smallest item from the heap.
+
+The combined action runs more efficiently than heappush() followed by
+a separate call to heappop().
+[clinic start generated code]*/
+
+static PyObject *
+_heapq_heappushpop_impl(PyObject *module, PyObject *heap, PyObject *item)
+/*[clinic end generated code: output=67231dc98ed5774f input=5dc701f1eb4a4aa7]*/
+{
+    PyObject *returnitem;
+    int cmp;
+
+    if (PyList_GET_SIZE(heap) == 0) {
+        Py_INCREF(item);
+        return item;
+    }
+
+    PyObject* top = PyList_GET_ITEM(heap, 0);
+    Py_INCREF(top);
+    cmp = PyObject_RichCompareBool(top, item, Py_LT);
+    Py_DECREF(top);
+    if (cmp < 0)
+        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)) {
+        Py_DECREF(returnitem);
+        return NULL;
+    }
+    return returnitem;
+}
+
+static Py_ssize_t
+keep_top_bit(Py_ssize_t n)
+{
+    int i = 0;
+
+    while (n > 1) {
+        n >>= 1;
+        i++;
+    }
+    return n << i;
+}
+
+/* Cache friendly version of heapify()
+   -----------------------------------
+
+   Build-up a heap in O(n) time by performing siftup() operations
+   on nodes whose children are already heaps.
+
+   The simplest way is to sift the nodes in reverse order from
+   n//2-1 to 0 inclusive.  The downside is that children may be
+   out of cache by the time their parent is reached.
+
+   A better way is to not wait for the children to go out of cache.
+   Once a sibling pair of child nodes have been sifted, immediately
+   sift their parent node (while the children are still in cache).
+
+   Both ways build child heaps before their parents, so both ways
+   do the exact same number of comparisons and produce exactly
+   the same heap.  The only difference is that the traversal
+   order is optimized for cache efficiency.
+*/
+
+static PyObject *
+cache_friendly_heapify(PyObject *heap, int siftup_func(PyListObject *, Py_ssize_t))
+{
+    Py_ssize_t i, j, m, mhalf, leftmost;
+
+    m = PyList_GET_SIZE(heap) >> 1;         /* index of first childless node */
+    leftmost = keep_top_bit(m + 1) - 1;     /* leftmost node in row of m */
+    mhalf = m >> 1;                         /* parent of first childless node */
+
+    for (i = leftmost - 1 ; i >= mhalf ; i--) {
+        j = i;
+        while (1) {
+            if (siftup_func((PyListObject *)heap, j))
+                return NULL;
+            if (!(j & 1))
+                break;
+            j >>= 1;
+        }
+    }
+
+    for (i = m - 1 ; i >= leftmost ; i--) {
+        j = i;
+        while (1) {
+            if (siftup_func((PyListObject *)heap, j))
+                return NULL;
+            if (!(j & 1))
+                break;
+            j >>= 1;
+        }
+    }
+    Py_RETURN_NONE;
+}
+
+static PyObject *
+heapify_internal(PyObject *heap, int siftup_func(PyListObject *, Py_ssize_t))
+{
+    Py_ssize_t i, n;
+
+    /* For heaps likely to be bigger than L1 cache, we use the cache
+       friendly heapify function.  For smaller heaps that fit entirely
+       in cache, we prefer the simpler algorithm with less branching.
+    */
+    n = PyList_GET_SIZE(heap);
+    if (n > 2500)
+        return cache_friendly_heapify(heap, siftup_func);
+
+    /* 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 >> 1) - 1 ; i >= 0 ; i--)
+        if (siftup_func((PyListObject *)heap, i))
+            return NULL;
+    Py_RETURN_NONE;
+}
+
+/*[clinic input]
+_heapq.heapify
+
+    heap: object(subclass_of='&PyList_Type')
+    /
+
+Transform list into a heap, in-place, in O(len(heap)) time.
+[clinic start generated code]*/
+
+static PyObject *
+_heapq_heapify_impl(PyObject *module, PyObject *heap)
+/*[clinic end generated code: output=e63a636fcf83d6d0 input=53bb7a2166febb73]*/
+{
+    return heapify_internal(heap, siftup);
+}
+
+static int
+siftdown_max(PyListObject *heap, Py_ssize_t startpos, Py_ssize_t pos)
+{
+    PyObject *newitem, *parent, **arr;
+    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. */
+    arr = _PyList_ITEMS(heap);
+    newitem = arr[pos];
+    while (pos > startpos) {
+        parentpos = (pos - 1) >> 1;
+        parent = arr[parentpos];
+        Py_INCREF(parent);
+        Py_INCREF(newitem);
+        cmp = PyObject_RichCompareBool(parent, newitem, Py_LT);
+        Py_DECREF(parent);
+        Py_DECREF(newitem);
+        if (cmp < 0)
+            return -1;
+        if (size != PyList_GET_SIZE(heap)) {
+            PyErr_SetString(PyExc_RuntimeError,
+                            "list changed size during iteration");
+            return -1;
+        }
+        if (cmp == 0)
+            break;
+        arr = _PyList_ITEMS(heap);
+        parent = arr[parentpos];
+        newitem = arr[pos];
+        arr[parentpos] = newitem;
+        arr[pos] = parent;
+        pos = parentpos;
+    }
+    return 0;
+}
+
+static int
+siftup_max(PyListObject *heap, Py_ssize_t pos)
+{
+    Py_ssize_t startpos, endpos, childpos, limit;
+    PyObject *tmp1, *tmp2, **arr;
+    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. */
+    arr = _PyList_ITEMS(heap);
+    limit = endpos >> 1;         /* smallest pos that has no child */
+    while (pos < limit) {
+        /* Set childpos to index of smaller child.   */
+        childpos = 2*pos + 1;    /* leftmost child position  */
+        if (childpos + 1 < endpos) {
+            PyObject* a = arr[childpos + 1];
+            PyObject* b = arr[childpos];
+            Py_INCREF(a);
+            Py_INCREF(b);
+            cmp = PyObject_RichCompareBool(a, b, Py_LT);
+            Py_DECREF(a);
+            Py_DECREF(b);
+            if (cmp < 0)
+                return -1;
+            childpos += ((unsigned)cmp ^ 1);   /* increment when cmp==0 */
+            arr = _PyList_ITEMS(heap);         /* arr may have changed */
+            if (endpos != PyList_GET_SIZE(heap)) {
+                PyErr_SetString(PyExc_RuntimeError,
+                                "list changed size during iteration");
+                return -1;
+            }
+        }
+        /* Move the smaller child up. */
+        tmp1 = arr[childpos];
+        tmp2 = arr[pos];
+        arr[childpos] = tmp2;
+        arr[pos] = tmp1;
+        pos = childpos;
+    }
+    /* Bubble it up to its final resting place (by sifting its parents down). */
+    return siftdown_max(heap, startpos, pos);
+}
+
+
+/*[clinic input]
+_heapq._heappop_max
+
+    heap: object(subclass_of='&PyList_Type')
+    /
+
+Maxheap variant of heappop.
+[clinic start generated code]*/
+
+static PyObject *
+_heapq__heappop_max_impl(PyObject *module, PyObject *heap)
+/*[clinic end generated code: output=9e77aadd4e6a8760 input=362c06e1c7484793]*/
+{
+    return heappop_internal(heap, siftup_max);
+}
+
+/*[clinic input]
+_heapq._heapreplace_max
+
+    heap: object(subclass_of='&PyList_Type')
+    item: object
+    /
+
+Maxheap variant of heapreplace.
+[clinic start generated code]*/
+
+static PyObject *
+_heapq__heapreplace_max_impl(PyObject *module, PyObject *heap,
+                             PyObject *item)
+/*[clinic end generated code: output=8ad7545e4a5e8adb input=f2dd27cbadb948d7]*/
+{
+    return heapreplace_internal(heap, item, siftup_max);
+}
+
+/*[clinic input]
+_heapq._heapify_max
+
+    heap: object(subclass_of='&PyList_Type')
+    /
+
+Maxheap variant of heapify.
+[clinic start generated code]*/
+
+static PyObject *
+_heapq__heapify_max_impl(PyObject *module, PyObject *heap)
+/*[clinic end generated code: output=2cb028beb4a8b65e input=c1f765ee69f124b8]*/
+{
+    return heapify_internal(heap, siftup_max);
+}
+
+static PyMethodDef heapq_methods[] = {
+    _HEAPQ_HEAPPUSH_METHODDEF
+    _HEAPQ_HEAPPUSHPOP_METHODDEF
+    _HEAPQ_HEAPPOP_METHODDEF
+    _HEAPQ_HEAPREPLACE_METHODDEF
+    _HEAPQ_HEAPIFY_METHODDEF
+    _HEAPQ__HEAPPOP_MAX_METHODDEF
+    _HEAPQ__HEAPIFY_MAX_METHODDEF
+    _HEAPQ__HEAPREPLACE_MAX_METHODDEF
+    {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 Fran\xc3\xa7ois 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");
+
+
+static int
+heapq_exec(PyObject *m)
+{
+    PyObject *about = PyUnicode_FromString(__about__);
+    if (PyModule_AddObject(m, "__about__", about) < 0) {
+        Py_DECREF(about);
+        return -1;
+    }
+    return 0;
+}
+
+static struct PyModuleDef_Slot heapq_slots[] = {
+    {Py_mod_exec, heapq_exec},
+    {0, NULL}
+};
+
+static struct PyModuleDef _heapqmodule = {
+    PyModuleDef_HEAD_INIT,
+    "_heapq",
+    module_doc,
+    0,
+    heapq_methods,
+    heapq_slots,
+    NULL,
+    NULL,
+    NULL
+};
+
+PyMODINIT_FUNC
+PyInit__heapq(void)
+{
+    return PyModuleDef_Init(&_heapqmodule);
+}