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diff --git a/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/__init__.py b/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/__init__.py
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--- /dev/null
+++ b/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/__init__.py
@@ -0,0 +1,11 @@
+"""Connectivity and cut algorithms"""
+
+from .connectivity import *
+from .cuts import *
+from .edge_augmentation import *
+from .edge_kcomponents import *
+from .disjoint_paths import *
+from .kcomponents import *
+from .kcutsets import *
+from .stoerwagner import *
+from .utils import *
diff --git a/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/kcomponents.py b/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/kcomponents.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2f1ba289fb14a705ab6b80883d36f9cdcbed7f1
--- /dev/null
+++ b/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/kcomponents.py
@@ -0,0 +1,223 @@
+"""
+Moody and White algorithm for k-components
+"""
+
+from collections import defaultdict
+from itertools import combinations
+from operator import itemgetter
+
+import networkx as nx
+
+# Define the default maximum flow function.
+from networkx.algorithms.flow import edmonds_karp
+from networkx.utils import not_implemented_for
+
+default_flow_func = edmonds_karp
+
+__all__ = ["k_components"]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def k_components(G, flow_func=None):
+    r"""Returns the k-component structure of a graph G.
+
+    A `k`-component is a maximal subgraph of a graph G that has, at least,
+    node connectivity `k`: we need to remove at least `k` nodes to break it
+    into more components. `k`-components have an inherent hierarchical
+    structure because they are nested in terms of connectivity: a connected
+    graph can contain several 2-components, each of which can contain
+    one or more 3-components, and so forth.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    flow_func : function
+        Function to perform the underlying flow computations. Default value
+        :meth:`edmonds_karp`. This function performs better in sparse graphs with
+        right tailed degree distributions. :meth:`shortest_augmenting_path` will
+        perform better in denser graphs.
+
+    Returns
+    -------
+    k_components : dict
+        Dictionary with all connectivity levels `k` in the input Graph as keys
+        and a list of sets of nodes that form a k-component of level `k` as
+        values.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is directed.
+
+    Examples
+    --------
+    >>> # Petersen graph has 10 nodes and it is triconnected, thus all
+    >>> # nodes are in a single component on all three connectivity levels
+    >>> G = nx.petersen_graph()
+    >>> k_components = nx.k_components(G)
+
+    Notes
+    -----
+    Moody and White [1]_ (appendix A) provide an algorithm for identifying
+    k-components in a graph, which is based on Kanevsky's algorithm [2]_
+    for finding all minimum-size node cut-sets of a graph (implemented in
+    :meth:`all_node_cuts` function):
+
+        1. Compute node connectivity, k, of the input graph G.
+
+        2. Identify all k-cutsets at the current level of connectivity using
+           Kanevsky's algorithm.
+
+        3. Generate new graph components based on the removal of
+           these cutsets. Nodes in a cutset belong to both sides
+           of the induced cut.
+
+        4. If the graph is neither complete nor trivial, return to 1;
+           else end.
+
+    This implementation also uses some heuristics (see [3]_ for details)
+    to speed up the computation.
+
+    See also
+    --------
+    node_connectivity
+    all_node_cuts
+    biconnected_components : special case of this function when k=2
+    k_edge_components : similar to this function, but uses edge-connectivity
+        instead of node-connectivity
+
+    References
+    ----------
+    .. [1]  Moody, J. and D. White (2003). Social cohesion and embeddedness:
+            A hierarchical conception of social groups.
+            American Sociological Review 68(1), 103--28.
+            http://www2.asanet.org/journals/ASRFeb03MoodyWhite.pdf
+
+    .. [2]  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
+
+    .. [3]  Torrents, J. and F. Ferraro (2015). Structural Cohesion:
+            Visualization and Heuristics for Fast Computation.
+            https://arxiv.org/pdf/1503.04476v1
+
+    """
+    # Dictionary with connectivity level (k) as keys and a list of
+    # sets of nodes that form a k-component as values. Note that
+    # k-components can overlap (but only k - 1 nodes).
+    k_components = defaultdict(list)
+    # Define default flow function
+    if flow_func is None:
+        flow_func = default_flow_func
+    # Bicomponents as a base to check for higher order k-components
+    for component in nx.connected_components(G):
+        # isolated nodes have connectivity 0
+        comp = set(component)
+        if len(comp) > 1:
+            k_components[1].append(comp)
+    bicomponents = [G.subgraph(c) for c in nx.biconnected_components(G)]
+    for bicomponent in bicomponents:
+        bicomp = set(bicomponent)
+        # avoid considering dyads as bicomponents
+        if len(bicomp) > 2:
+            k_components[2].append(bicomp)
+    for B in bicomponents:
+        if len(B) <= 2:
+            continue
+        k = nx.node_connectivity(B, flow_func=flow_func)
+        if k > 2:
+            k_components[k].append(set(B))
+        # Perform cuts in a DFS like order.
+        cuts = list(nx.all_node_cuts(B, k=k, flow_func=flow_func))
+        stack = [(k, _generate_partition(B, cuts, k))]
+        while stack:
+            (parent_k, partition) = stack[-1]
+            try:
+                nodes = next(partition)
+                C = B.subgraph(nodes)
+                this_k = nx.node_connectivity(C, flow_func=flow_func)
+                if this_k > parent_k and this_k > 2:
+                    k_components[this_k].append(set(C))
+                cuts = list(nx.all_node_cuts(C, k=this_k, flow_func=flow_func))
+                if cuts:
+                    stack.append((this_k, _generate_partition(C, cuts, this_k)))
+            except StopIteration:
+                stack.pop()
+
+    # This is necessary because k-components may only be reported at their
+    # maximum k level. But we want to return a dictionary in which keys are
+    # connectivity levels and values list of sets of components, without
+    # skipping any connectivity level. Also, it's possible that subsets of
+    # an already detected k-component appear at a level k. Checking for this
+    # in the while loop above penalizes the common case. Thus we also have to
+    # _consolidate all connectivity levels in _reconstruct_k_components.
+    return _reconstruct_k_components(k_components)
+
+
+def _consolidate(sets, k):
+    """Merge sets that share k or more elements.
+
+    See: http://rosettacode.org/wiki/Set_consolidation
+
+    The iterative python implementation posted there is
+    faster than this because of the overhead of building a
+    Graph and calling nx.connected_components, but it's not
+    clear for us if we can use it in NetworkX because there
+    is no licence for the code.
+
+    """
+    G = nx.Graph()
+    nodes = dict(enumerate(sets))
+    G.add_nodes_from(nodes)
+    G.add_edges_from(
+        (u, v) for u, v in combinations(nodes, 2) if len(nodes[u] & nodes[v]) >= k
+    )
+    for component in nx.connected_components(G):
+        yield set.union(*[nodes[n] for n in component])
+
+
+def _generate_partition(G, cuts, k):
+    def has_nbrs_in_partition(G, node, partition):
+        return any(n in partition for n in G[node])
+
+    components = []
+    nodes = {n for n, d in G.degree() if d > k} - {n for cut in cuts for n in cut}
+    H = G.subgraph(nodes)
+    for cc in nx.connected_components(H):
+        component = set(cc)
+        for cut in cuts:
+            for node in cut:
+                if has_nbrs_in_partition(G, node, cc):
+                    component.add(node)
+        if len(component) < G.order():
+            components.append(component)
+    yield from _consolidate(components, k + 1)
+
+
+def _reconstruct_k_components(k_comps):
+    result = {}
+    max_k = max(k_comps)
+    for k in reversed(range(1, max_k + 1)):
+        if k == max_k:
+            result[k] = list(_consolidate(k_comps[k], k))
+        elif k not in k_comps:
+            result[k] = list(_consolidate(result[k + 1], k))
+        else:
+            nodes_at_k = set.union(*k_comps[k])
+            to_add = [c for c in result[k + 1] if any(n not in nodes_at_k for n in c)]
+            if to_add:
+                result[k] = list(_consolidate(k_comps[k] + to_add, k))
+            else:
+                result[k] = list(_consolidate(k_comps[k], k))
+    return result
+
+
+def build_k_number_dict(kcomps):
+    result = {}
+    for k, comps in sorted(kcomps.items(), key=itemgetter(0)):
+        for comp in comps:
+            for node in comp:
+                result[node] = k
+    return result
diff --git a/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/kcutsets.py b/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/kcutsets.py
new file mode 100644
index 0000000000000000000000000000000000000000..de26f4c5d85f42312a811509b7a9b92cd5db952c
--- /dev/null
+++ b/minigpt2/lib/python3.10/site-packages/networkx/algorithms/connectivity/kcutsets.py
@@ -0,0 +1,235 @@
+"""
+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
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+"""Unit tests for the beam search functions."""
+
+import pytest
+
+import networkx as nx
+
+
+def test_narrow():
+    """Tests that a narrow beam width may cause an incomplete search."""
+    # In this search, we enqueue only the neighbor 3 at the first
+    # step, then only the neighbor 2 at the second step. Once at
+    # node 2, the search chooses node 3, since it has a higher value
+    # than node 1, but node 3 has already been visited, so the
+    # search terminates.
+    G = nx.cycle_graph(4)
+    edges = nx.bfs_beam_edges(G, source=0, value=lambda n: n, width=1)
+    assert list(edges) == [(0, 3), (3, 2)]
+
+
+@pytest.mark.parametrize("width", (2, None))
+def test_wide(width):
+    """All nodes are searched when `width` is None or >= max degree"""
+    G = nx.cycle_graph(4)
+    edges = nx.bfs_beam_edges(G, source=0, value=lambda n: n, width=width)
+    assert list(edges) == [(0, 3), (0, 1), (3, 2)]