Restoring authorship annotation for <[email protected]>. Commit 1 of 2.

1 files changed, 559 insertions, 559 deletions

diff --git a/contrib/tools/python3/src/Modules/_heapqmodule.c b/contrib/tools/python3/src/Modules/_heapqmodule.c
index 4e85e046d38..5ba0f556b4a 100644
--- a/contrib/tools/python3/src/Modules/_heapqmodule.c
+++ b/contrib/tools/python3/src/Modules/_heapqmodule.c
@@ -1,13 +1,13 @@
-/* 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"
-
+/* 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" 
+ 
 #include "clinic/_heapqmodule.c.h"
 
 /*[clinic input]
@@ -15,73 +15,73 @@ 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];
+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);
+        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) {
+        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);
@@ -89,27 +89,27 @@ siftup(PyListObject *heap, Py_ssize_t pos)
             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);
-}
-
+            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
 
@@ -120,60 +120,60 @@ _heapq.heappush
 Push item onto heap, maintaining the heap invariant.
 [clinic start generated code]*/
 
-static PyObject *
+static PyObject * 
 _heapq_heappush_impl(PyObject *module, PyObject *heap, PyObject *item)
 /*[clinic end generated code: output=912c094f47663935 input=7913545cb5118842]*/
-{
-    if (!PyList_Check(heap)) {
-        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
-        return NULL;
-    }
-
-    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;
-
-    if (!PyList_Check(heap)) {
-        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
-        return NULL;
-    }
-
-    /* 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;
-}
-
+{ 
+    if (!PyList_Check(heap)) { 
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); 
+        return NULL; 
+    } 
+ 
+    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; 
+ 
+    if (!PyList_Check(heap)) { 
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); 
+        return NULL; 
+    } 
+ 
+    /* 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
 
@@ -183,38 +183,38 @@ _heapq.heappop
 Pop the smallest item off the heap, maintaining the heap invariant.
 [clinic start generated code]*/
 
-static PyObject *
+static PyObject * 
 _heapq_heappop(PyObject *module, PyObject *heap)
 /*[clinic end generated code: output=e1bbbc9866bce179 input=9bd36317b806033d]*/
-{
-    return heappop_internal(heap, siftup);
-}
-
-static PyObject *
+{ 
+    return heappop_internal(heap, siftup); 
+} 
+ 
+static PyObject * 
 heapreplace_internal(PyObject *heap, PyObject *item, int siftup_func(PyListObject *, Py_ssize_t))
-{
+{ 
     PyObject *returnitem;
-
-    if (!PyList_Check(heap)) {
-        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
-        return NULL;
-    }
-
-    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;
-}
-
+ 
+    if (!PyList_Check(heap)) { 
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); 
+        return NULL; 
+    } 
+ 
+    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
@@ -234,16 +234,16 @@ this routine unless written as part of a conditional replacement:
         item = heapreplace(heap, item)
 [clinic start generated code]*/
 
-static PyObject *
+static PyObject * 
 _heapq_heapreplace_impl(PyObject *module, PyObject *heap, PyObject *item)
 /*[clinic end generated code: output=82ea55be8fbe24b4 input=e57ae8f4ecfc88e3]*/
-{
+{ 
     return heapreplace_internal(heap, item, siftup);
-}
-
+} 
+ 
 /*[clinic input]
 _heapq.heappushpop
-
+ 
     heap: object
     item: object
     /
@@ -254,145 +254,145 @@ The combined action runs more efficiently than heappush() followed by
 a separate call to heappop().
 [clinic start generated code]*/
 
-static PyObject *
+static PyObject * 
 _heapq_heappushpop_impl(PyObject *module, PyObject *heap, PyObject *item)
 /*[clinic end generated code: output=67231dc98ed5774f input=eb48c90ba77b2214]*/
-{
+{ 
     PyObject *returnitem;
-    int cmp;
-
-    if (!PyList_Check(heap)) {
-        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
-        return NULL;
-    }
-
-    if (PyList_GET_SIZE(heap) == 0) {
-        Py_INCREF(item);
-        return item;
-    }
-
+    int cmp; 
+ 
+    if (!PyList_Check(heap)) { 
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); 
+        return NULL; 
+    } 
+ 
+    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;
-
-    if (!PyList_Check(heap)) {
-        PyErr_SetString(PyExc_TypeError, "heap argument must be a list");
-        return NULL;
-    }
-
-    /* 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;
-}
-
+    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; 
+ 
+    if (!PyList_Check(heap)) { 
+        PyErr_SetString(PyExc_TypeError, "heap argument must be a list"); 
+        return NULL; 
+    } 
+ 
+    /* 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
 
@@ -402,80 +402,80 @@ _heapq.heapify
 Transform list into a heap, in-place, in O(len(heap)) time.
 [clinic start generated code]*/
 
-static PyObject *
+static PyObject * 
 _heapq_heapify(PyObject *module, PyObject *heap)
 /*[clinic end generated code: output=11483f23627c4616 input=872c87504b8de970]*/
-{
-    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];
+{ 
+    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);
+        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) {
+        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);
@@ -483,27 +483,27 @@ siftup_max(PyListObject *heap, Py_ssize_t pos)
             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);
-}
-
+            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
@@ -514,16 +514,16 @@ _heapq._heappop_max
 Maxheap variant of heappop.
 [clinic start generated code]*/
 
-static PyObject *
+static PyObject * 
 _heapq__heappop_max(PyObject *module, PyObject *heap)
 /*[clinic end generated code: output=acd30acf6384b13c input=62ede3ba9117f541]*/
-{
-    return heappop_internal(heap, siftup_max);
-}
-
+{ 
+    return heappop_internal(heap, siftup_max); 
+} 
+ 
 /*[clinic input]
 _heapq._heapreplace_max
-
+ 
     heap: object
     item: object
     /
@@ -531,31 +531,31 @@ _heapq._heapreplace_max
 Maxheap variant of heapreplace.
 [clinic start generated code]*/
 
-static PyObject *
+static PyObject * 
 _heapq__heapreplace_max_impl(PyObject *module, PyObject *heap,
                              PyObject *item)
 /*[clinic end generated code: output=8ad7545e4a5e8adb input=6d8f25131e0f0e5f]*/
-{
+{ 
     return heapreplace_internal(heap, item, siftup_max);
-}
-
+} 
+ 
 /*[clinic input]
 _heapq._heapify_max
-
+ 
     heap: object
     /
 
 Maxheap variant of heapify.
 [clinic start generated code]*/
 
-static PyObject *
+static PyObject * 
 _heapq__heapify_max(PyObject *module, PyObject *heap)
 /*[clinic end generated code: output=1c6bb6b60d6a2133 input=cdfcc6835b14110d]*/
-{
-    return heapify_internal(heap, siftup_max);
-}
-
-static PyMethodDef heapq_methods[] = {
+{ 
+    return heapify_internal(heap, siftup_max); 
+} 
+ 
+static PyMethodDef heapq_methods[] = { 
     _HEAPQ_HEAPPUSH_METHODDEF
     _HEAPQ_HEAPPUSHPOP_METHODDEF
     _HEAPQ_HEAPPOP_METHODDEF
@@ -565,134 +565,134 @@ static PyMethodDef heapq_methods[] = {
     _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");
-
-
+}; 
+ 
+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)
 {
@@ -709,20 +709,20 @@ static struct PyModuleDef_Slot heapq_slots[] = {
     {0, NULL}
 };
 
-static struct PyModuleDef _heapqmodule = {
-    PyModuleDef_HEAD_INIT,
-    "_heapq",
-    module_doc,
+static struct PyModuleDef _heapqmodule = { 
+    PyModuleDef_HEAD_INIT, 
+    "_heapq", 
+    module_doc, 
     0,
-    heapq_methods,
+    heapq_methods, 
     heapq_slots,
-    NULL,
-    NULL,
-    NULL
-};
-
-PyMODINIT_FUNC
-PyInit__heapq(void)
-{
+    NULL, 
+    NULL, 
+    NULL 
+}; 
+ 
+PyMODINIT_FUNC 
+PyInit__heapq(void) 
+{ 
     return PyModuleDef_Init(&_heapqmodule);
-}
+}