two version of R2R are hereHEADmaster

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diff --git a/.venv/lib/python3.12/site-packages/networkx/utils/mapped_queue.py b/.venv/lib/python3.12/site-packages/networkx/utils/mapped_queue.py
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+"""Priority queue class with updatable priorities."""
+
+import heapq
+
+__all__ = ["MappedQueue"]
+
+
+class _HeapElement:
+    """This proxy class separates the heap element from its priority.
+
+    The idea is that using a 2-tuple (priority, element) works
+    for sorting, but not for dict lookup because priorities are
+    often floating point values so round-off can mess up equality.
+
+    So, we need inequalities to look at the priority (for sorting)
+    and equality (and hash) to look at the element to enable
+    updates to the priority.
+
+    Unfortunately, this class can be tricky to work with if you forget that
+    `__lt__` compares the priority while `__eq__` compares the element.
+    In `greedy_modularity_communities()` the following code is
+    used to check that two _HeapElements differ in either element or priority:
+
+        if d_oldmax != row_max or d_oldmax.priority != row_max.priority:
+
+    If the priorities are the same, this implementation uses the element
+    as a tiebreaker. This provides compatibility with older systems that
+    use tuples to combine priority and elements.
+    """
+
+    __slots__ = ["priority", "element", "_hash"]
+
+    def __init__(self, priority, element):
+        self.priority = priority
+        self.element = element
+        self._hash = hash(element)
+
+    def __lt__(self, other):
+        try:
+            other_priority = other.priority
+        except AttributeError:
+            return self.priority < other
+        # assume comparing to another _HeapElement
+        if self.priority == other_priority:
+            try:
+                return self.element < other.element
+            except TypeError as err:
+                raise TypeError(
+                    "Consider using a tuple, with a priority value that can be compared."
+                )
+        return self.priority < other_priority
+
+    def __gt__(self, other):
+        try:
+            other_priority = other.priority
+        except AttributeError:
+            return self.priority > other
+        # assume comparing to another _HeapElement
+        if self.priority == other_priority:
+            try:
+                return self.element > other.element
+            except TypeError as err:
+                raise TypeError(
+                    "Consider using a tuple, with a priority value that can be compared."
+                )
+        return self.priority > other_priority
+
+    def __eq__(self, other):
+        try:
+            return self.element == other.element
+        except AttributeError:
+            return self.element == other
+
+    def __hash__(self):
+        return self._hash
+
+    def __getitem__(self, indx):
+        return self.priority if indx == 0 else self.element[indx - 1]
+
+    def __iter__(self):
+        yield self.priority
+        try:
+            yield from self.element
+        except TypeError:
+            yield self.element
+
+    def __repr__(self):
+        return f"_HeapElement({self.priority}, {self.element})"
+
+
+class MappedQueue:
+    """The MappedQueue class implements a min-heap with removal and update-priority.
+
+    The min heap uses heapq as well as custom written _siftup and _siftdown
+    methods to allow the heap positions to be tracked by an additional dict
+    keyed by element to position. The smallest element can be popped in O(1) time,
+    new elements can be pushed in O(log n) time, and any element can be removed
+    or updated in O(log n) time. The queue cannot contain duplicate elements
+    and an attempt to push an element already in the queue will have no effect.
+
+    MappedQueue complements the heapq package from the python standard
+    library. While MappedQueue is designed for maximum compatibility with
+    heapq, it adds element removal, lookup, and priority update.
+
+    Parameters
+    ----------
+    data : dict or iterable
+
+    Examples
+    --------
+
+    A `MappedQueue` can be created empty, or optionally, given a dictionary
+    of initial elements and priorities.  The methods `push`, `pop`,
+    `remove`, and `update` operate on the queue.
+
+    >>> colors_nm = {"red": 665, "blue": 470, "green": 550}
+    >>> q = MappedQueue(colors_nm)
+    >>> q.remove("red")
+    >>> q.update("green", "violet", 400)
+    >>> q.push("indigo", 425)
+    True
+    >>> [q.pop().element for i in range(len(q.heap))]
+    ['violet', 'indigo', 'blue']
+
+    A `MappedQueue` can also be initialized with a list or other iterable. The priority is assumed
+    to be the sort order of the items in the list.
+
+    >>> q = MappedQueue([916, 50, 4609, 493, 237])
+    >>> q.remove(493)
+    >>> q.update(237, 1117)
+    >>> [q.pop() for i in range(len(q.heap))]
+    [50, 916, 1117, 4609]
+
+    An exception is raised if the elements are not comparable.
+
+    >>> q = MappedQueue([100, "a"])
+    Traceback (most recent call last):
+    ...
+    TypeError: '<' not supported between instances of 'int' and 'str'
+
+    To avoid the exception, use a dictionary to assign priorities to the elements.
+
+    >>> q = MappedQueue({100: 0, "a": 1})
+
+    References
+    ----------
+    .. [1] Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001).
+       Introduction to algorithms second edition.
+    .. [2] Knuth, D. E. (1997). The art of computer programming (Vol. 3).
+       Pearson Education.
+    """
+
+    def __init__(self, data=None):
+        """Priority queue class with updatable priorities."""
+        if data is None:
+            self.heap = []
+        elif isinstance(data, dict):
+            self.heap = [_HeapElement(v, k) for k, v in data.items()]
+        else:
+            self.heap = list(data)
+        self.position = {}
+        self._heapify()
+
+    def _heapify(self):
+        """Restore heap invariant and recalculate map."""
+        heapq.heapify(self.heap)
+        self.position = {elt: pos for pos, elt in enumerate(self.heap)}
+        if len(self.heap) != len(self.position):
+            raise AssertionError("Heap contains duplicate elements")
+
+    def __len__(self):
+        return len(self.heap)
+
+    def push(self, elt, priority=None):
+        """Add an element to the queue."""
+        if priority is not None:
+            elt = _HeapElement(priority, elt)
+        # If element is already in queue, do nothing
+        if elt in self.position:
+            return False
+        # Add element to heap and dict
+        pos = len(self.heap)
+        self.heap.append(elt)
+        self.position[elt] = pos
+        # Restore invariant by sifting down
+        self._siftdown(0, pos)
+        return True
+
+    def pop(self):
+        """Remove and return the smallest element in the queue."""
+        # Remove smallest element
+        elt = self.heap[0]
+        del self.position[elt]
+        # If elt is last item, remove and return
+        if len(self.heap) == 1:
+            self.heap.pop()
+            return elt
+        # Replace root with last element
+        last = self.heap.pop()
+        self.heap[0] = last
+        self.position[last] = 0
+        # Restore invariant by sifting up
+        self._siftup(0)
+        # Return smallest element
+        return elt
+
+    def update(self, elt, new, priority=None):
+        """Replace an element in the queue with a new one."""
+        if priority is not None:
+            new = _HeapElement(priority, new)
+        # Replace
+        pos = self.position[elt]
+        self.heap[pos] = new
+        del self.position[elt]
+        self.position[new] = pos
+        # Restore invariant by sifting up
+        self._siftup(pos)
+
+    def remove(self, elt):
+        """Remove an element from the queue."""
+        # Find and remove element
+        try:
+            pos = self.position[elt]
+            del self.position[elt]
+        except KeyError:
+            # Not in queue
+            raise
+        # If elt is last item, remove and return
+        if pos == len(self.heap) - 1:
+            self.heap.pop()
+            return
+        # Replace elt with last element
+        last = self.heap.pop()
+        self.heap[pos] = last
+        self.position[last] = pos
+        # Restore invariant by sifting up
+        self._siftup(pos)
+
+    def _siftup(self, pos):
+        """Move smaller child up until hitting a leaf.
+
+        Built to mimic code for heapq._siftup
+        only updating position dict too.
+        """
+        heap, position = self.heap, self.position
+        end_pos = len(heap)
+        startpos = pos
+        newitem = heap[pos]
+        # Shift up the smaller child until hitting a leaf
+        child_pos = (pos << 1) + 1  # start with leftmost child position
+        while child_pos < end_pos:
+            # Set child_pos to index of smaller child.
+            child = heap[child_pos]
+            right_pos = child_pos + 1
+            if right_pos < end_pos:
+                right = heap[right_pos]
+                if not child < right:
+                    child = right
+                    child_pos = right_pos
+            # Move the smaller child up.
+            heap[pos] = child
+            position[child] = pos
+            pos = child_pos
+            child_pos = (pos << 1) + 1
+        # pos is a leaf position. Put newitem there, and bubble it up
+        # to its final resting place (by sifting its parents down).
+        while pos > 0:
+            parent_pos = (pos - 1) >> 1
+            parent = heap[parent_pos]
+            if not newitem < parent:
+                break
+            heap[pos] = parent
+            position[parent] = pos
+            pos = parent_pos
+        heap[pos] = newitem
+        position[newitem] = pos
+
+    def _siftdown(self, start_pos, pos):
+        """Restore invariant. keep swapping with parent until smaller.
+
+        Built to mimic code for heapq._siftdown
+        only updating position dict too.
+        """
+        heap, position = self.heap, self.position
+        newitem = heap[pos]
+        # Follow the path to the root, moving parents down until finding a place
+        # newitem fits.
+        while pos > start_pos:
+            parent_pos = (pos - 1) >> 1
+            parent = heap[parent_pos]
+            if not newitem < parent:
+                break
+            heap[pos] = parent
+            position[parent] = pos
+            pos = parent_pos
+        heap[pos] = newitem
+        position[newitem] = pos