two version of R2R are hereHEADmaster

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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/connectivity/kcutsets.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/connectivity/kcutsets.py
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+"""
+Kanevsky all minimum node k cutsets algorithm.
+"""
+
+import copy
+from collections import defaultdict
+from itertools import combinations
+from operator import itemgetter
+
+import networkx as nx
+from networkx.algorithms.flow import (
+    build_residual_network,
+    edmonds_karp,
+    shortest_augmenting_path,
+)
+
+from .utils import build_auxiliary_node_connectivity
+
+default_flow_func = edmonds_karp
+
+
+__all__ = ["all_node_cuts"]
+
+
+@nx._dispatchable
+def all_node_cuts(G, k=None, flow_func=None):
+    r"""Returns all minimum k cutsets of an undirected graph G.
+
+    This implementation is based on Kanevsky's algorithm [1]_ for finding all
+    minimum-size node cut-sets of an undirected graph G; ie the set (or sets)
+    of nodes of cardinality equal to the node connectivity of G. Thus if
+    removed, would break G into two or more connected components.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    k : Integer
+        Node connectivity of the input graph. If k is None, then it is
+        computed. Default value: None.
+
+    flow_func : function
+        Function to perform the underlying flow computations. Default value is
+        :func:`~networkx.algorithms.flow.edmonds_karp`. This function performs
+        better in sparse graphs with right tailed degree distributions.
+        :func:`~networkx.algorithms.flow.shortest_augmenting_path` will
+        perform better in denser graphs.
+
+
+    Returns
+    -------
+    cuts : a generator of node cutsets
+        Each node cutset has cardinality equal to the node connectivity of
+        the input graph.
+
+    Examples
+    --------
+    >>> # A two-dimensional grid graph has 4 cutsets of cardinality 2
+    >>> G = nx.grid_2d_graph(5, 5)
+    >>> cutsets = list(nx.all_node_cuts(G))
+    >>> len(cutsets)
+    4
+    >>> all(2 == len(cutset) for cutset in cutsets)
+    True
+    >>> nx.node_connectivity(G)
+    2
+
+    Notes
+    -----
+    This implementation is based on the sequential algorithm for finding all
+    minimum-size separating vertex sets in a graph [1]_. The main idea is to
+    compute minimum cuts using local maximum flow computations among a set
+    of nodes of highest degree and all other non-adjacent nodes in the Graph.
+    Once we find a minimum cut, we add an edge between the high degree
+    node and the target node of the local maximum flow computation to make
+    sure that we will not find that minimum cut again.
+
+    See also
+    --------
+    node_connectivity
+    edmonds_karp
+    shortest_augmenting_path
+
+    References
+    ----------
+    .. [1]  Kanevsky, A. (1993). Finding all minimum-size separating vertex
+            sets in a graph. Networks 23(6), 533--541.
+            http://onlinelibrary.wiley.com/doi/10.1002/net.3230230604/abstract
+
+    """
+    if not nx.is_connected(G):
+        raise nx.NetworkXError("Input graph is disconnected.")
+
+    # Address some corner cases first.
+    # For complete Graphs
+
+    if nx.density(G) == 1:
+        yield from ()
+        return
+
+    # Initialize data structures.
+    # Keep track of the cuts already computed so we do not repeat them.
+    seen = []
+    # Even-Tarjan reduction is what we call auxiliary digraph
+    # for node connectivity.
+    H = build_auxiliary_node_connectivity(G)
+    H_nodes = H.nodes  # for speed
+    mapping = H.graph["mapping"]
+    # Keep a copy of original predecessors, H will be modified later.
+    # Shallow copy is enough.
+    original_H_pred = copy.copy(H._pred)
+    R = build_residual_network(H, "capacity")
+    kwargs = {"capacity": "capacity", "residual": R}
+    # Define default flow function
+    if flow_func is None:
+        flow_func = default_flow_func
+    if flow_func is shortest_augmenting_path:
+        kwargs["two_phase"] = True
+    # Begin the actual algorithm
+    # step 1: Find node connectivity k of G
+    if k is None:
+        k = nx.node_connectivity(G, flow_func=flow_func)
+    # step 2:
+    # Find k nodes with top degree, call it X:
+    X = {n for n, d in sorted(G.degree(), key=itemgetter(1), reverse=True)[:k]}
+    # Check if X is a k-node-cutset
+    if _is_separating_set(G, X):
+        seen.append(X)
+        yield X
+
+    for x in X:
+        # step 3: Compute local connectivity flow of x with all other
+        # non adjacent nodes in G
+        non_adjacent = set(G) - {x} - set(G[x])
+        for v in non_adjacent:
+            # step 4: compute maximum flow in an Even-Tarjan reduction H of G
+            # and step 5: build the associated residual network R
+            R = flow_func(H, f"{mapping[x]}B", f"{mapping[v]}A", **kwargs)
+            flow_value = R.graph["flow_value"]
+
+            if flow_value == k:
+                # Find the nodes incident to the flow.
+                E1 = flowed_edges = [
+                    (u, w) for (u, w, d) in R.edges(data=True) if d["flow"] != 0
+                ]
+                VE1 = incident_nodes = {n for edge in E1 for n in edge}
+                # Remove saturated edges form the residual network.
+                # Note that reversed edges are introduced with capacity 0
+                # in the residual graph and they need to be removed too.
+                saturated_edges = [
+                    (u, w, d)
+                    for (u, w, d) in R.edges(data=True)
+                    if d["capacity"] == d["flow"] or d["capacity"] == 0
+                ]
+                R.remove_edges_from(saturated_edges)
+                R_closure = nx.transitive_closure(R)
+                # step 6: shrink the strongly connected components of
+                # residual flow network R and call it L.
+                L = nx.condensation(R)
+                cmap = L.graph["mapping"]
+                inv_cmap = defaultdict(list)
+                for n, scc in cmap.items():
+                    inv_cmap[scc].append(n)
+                # Find the incident nodes in the condensed graph.
+                VE1 = {cmap[n] for n in VE1}
+                # step 7: Compute all antichains of L;
+                # they map to closed sets in H.
+                # Any edge in H that links a closed set is part of a cutset.
+                for antichain in nx.antichains(L):
+                    # Only antichains that are subsets of incident nodes counts.
+                    # Lemma 8 in reference.
+                    if not set(antichain).issubset(VE1):
+                        continue
+                    # Nodes in an antichain of the condensation graph of
+                    # the residual network map to a closed set of nodes that
+                    # define a node partition of the auxiliary digraph H
+                    # through taking all of antichain's predecessors in the
+                    # transitive closure.
+                    S = set()
+                    for scc in antichain:
+                        S.update(inv_cmap[scc])
+                    S_ancestors = set()
+                    for n in S:
+                        S_ancestors.update(R_closure._pred[n])
+                    S.update(S_ancestors)
+                    if f"{mapping[x]}B" not in S or f"{mapping[v]}A" in S:
+                        continue
+                    # Find the cutset that links the node partition (S,~S) in H
+                    cutset = set()
+                    for u in S:
+                        cutset.update((u, w) for w in original_H_pred[u] if w not in S)
+                    # The edges in H that form the cutset are internal edges
+                    # (ie edges that represent a node of the original graph G)
+                    if any(H_nodes[u]["id"] != H_nodes[w]["id"] for u, w in cutset):
+                        continue
+                    node_cut = {H_nodes[u]["id"] for u, _ in cutset}
+
+                    if len(node_cut) == k:
+                        # The cut is invalid if it includes internal edges of
+                        # end nodes. The other half of Lemma 8 in ref.
+                        if x in node_cut or v in node_cut:
+                            continue
+                        if node_cut not in seen:
+                            yield node_cut
+                            seen.append(node_cut)
+
+                # Add an edge (x, v) to make sure that we do not
+                # find this cutset again. This is equivalent
+                # of adding the edge in the input graph
+                # G.add_edge(x, v) and then regenerate H and R:
+                # Add edges to the auxiliary digraph.
+                # See build_residual_network for convention we used
+                # in residual graphs.
+                H.add_edge(f"{mapping[x]}B", f"{mapping[v]}A", capacity=1)
+                H.add_edge(f"{mapping[v]}B", f"{mapping[x]}A", capacity=1)
+                # Add edges to the residual network.
+                R.add_edge(f"{mapping[x]}B", f"{mapping[v]}A", capacity=1)
+                R.add_edge(f"{mapping[v]}A", f"{mapping[x]}B", capacity=0)
+                R.add_edge(f"{mapping[v]}B", f"{mapping[x]}A", capacity=1)
+                R.add_edge(f"{mapping[x]}A", f"{mapping[v]}B", capacity=0)
+
+                # Add again the saturated edges to reuse the residual network
+                R.add_edges_from(saturated_edges)
+
+
+def _is_separating_set(G, cut):
+    """Assumes that the input graph is connected"""
+    if len(cut) == len(G) - 1:
+        return True
+
+    H = nx.restricted_view(G, cut, [])
+    if nx.is_connected(H):
+        return False
+    return True