two version of R2R are hereHEADmaster

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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/components/weakly_connected.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/components/weakly_connected.py
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+"""Weakly connected components."""
+
+import networkx as nx
+from networkx.utils.decorators import not_implemented_for
+
+__all__ = [
+    "number_weakly_connected_components",
+    "weakly_connected_components",
+    "is_weakly_connected",
+]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def weakly_connected_components(G):
+    """Generate weakly connected components of G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph
+
+    Returns
+    -------
+    comp : generator of sets
+        A generator of sets of nodes, one for each weakly connected
+        component of G.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is undirected.
+
+    Examples
+    --------
+    Generate a sorted list of weakly connected components, largest first.
+
+    >>> G = nx.path_graph(4, create_using=nx.DiGraph())
+    >>> nx.add_path(G, [10, 11, 12])
+    >>> [
+    ...     len(c)
+    ...     for c in sorted(nx.weakly_connected_components(G), key=len, reverse=True)
+    ... ]
+    [4, 3]
+
+    If you only want the largest component, it's more efficient to
+    use max instead of sort:
+
+    >>> largest_cc = max(nx.weakly_connected_components(G), key=len)
+
+    See Also
+    --------
+    connected_components
+    strongly_connected_components
+
+    Notes
+    -----
+    For directed graphs only.
+
+    """
+    seen = set()
+    n = len(G)  # must be outside the loop to avoid performance hit with graph views
+    for v in G:
+        if v not in seen:
+            c = set(_plain_bfs(G, n, v))
+            seen.update(c)
+            yield c
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def number_weakly_connected_components(G):
+    """Returns the number of weakly connected components in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph.
+
+    Returns
+    -------
+    n : integer
+        Number of weakly connected components
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is undirected.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(0, 1), (2, 1), (3, 4)])
+    >>> nx.number_weakly_connected_components(G)
+    2
+
+    See Also
+    --------
+    weakly_connected_components
+    number_connected_components
+    number_strongly_connected_components
+
+    Notes
+    -----
+    For directed graphs only.
+
+    """
+    return sum(1 for wcc in weakly_connected_components(G))
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def is_weakly_connected(G):
+    """Test directed graph for weak connectivity.
+
+    A directed graph is weakly connected if and only if the graph
+    is connected when the direction of the edge between nodes is ignored.
+
+    Note that if a graph is strongly connected (i.e. the graph is connected
+    even when we account for directionality), it is by definition weakly
+    connected as well.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        A directed graph.
+
+    Returns
+    -------
+    connected : bool
+        True if the graph is weakly connected, False otherwise.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is undirected.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(0, 1), (2, 1)])
+    >>> G.add_node(3)
+    >>> nx.is_weakly_connected(G)  # node 3 is not connected to the graph
+    False
+    >>> G.add_edge(2, 3)
+    >>> nx.is_weakly_connected(G)
+    True
+
+    See Also
+    --------
+    is_strongly_connected
+    is_semiconnected
+    is_connected
+    is_biconnected
+    weakly_connected_components
+
+    Notes
+    -----
+    For directed graphs only.
+
+    """
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept(
+            """Connectivity is undefined for the null graph."""
+        )
+
+    return len(next(weakly_connected_components(G))) == len(G)
+
+
+def _plain_bfs(G, n, source):
+    """A fast BFS node generator
+
+    The direction of the edge between nodes is ignored.
+
+    For directed graphs only.
+
+    """
+    Gsucc = G._succ
+    Gpred = G._pred
+    seen = {source}
+    nextlevel = [source]
+
+    yield source
+    while nextlevel:
+        thislevel = nextlevel
+        nextlevel = []
+        for v in thislevel:
+            for w in Gsucc[v]:
+                if w not in seen:
+                    seen.add(w)
+                    nextlevel.append(w)
+                    yield w
+            for w in Gpred[v]:
+                if w not in seen:
+                    seen.add(w)
+                    nextlevel.append(w)
+                    yield w
+            if len(seen) == n:
+                return