two version of R2R are here HEAD master

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diff --git a/.venv/lib/python3.12/site-packages/networkx/utils/misc.py b/.venv/lib/python3.12/site-packages/networkx/utils/misc.py
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+"""
+Miscellaneous Helpers for NetworkX.
+
+These are not imported into the base networkx namespace but
+can be accessed, for example, as
+
+>>> import networkx
+>>> networkx.utils.make_list_of_ints({1, 2, 3})
+[1, 2, 3]
+>>> networkx.utils.arbitrary_element({5, 1, 7})  # doctest: +SKIP
+1
+"""
+
+import random
+import sys
+import uuid
+import warnings
+from collections import defaultdict, deque
+from collections.abc import Iterable, Iterator, Sized
+from itertools import chain, tee
+
+import networkx as nx
+
+__all__ = [
+    "flatten",
+    "make_list_of_ints",
+    "dict_to_numpy_array",
+    "arbitrary_element",
+    "pairwise",
+    "groups",
+    "create_random_state",
+    "create_py_random_state",
+    "PythonRandomInterface",
+    "PythonRandomViaNumpyBits",
+    "nodes_equal",
+    "edges_equal",
+    "graphs_equal",
+    "_clear_cache",
+]
+
+
+# some cookbook stuff
+# used in deciding whether something is a bunch of nodes, edges, etc.
+# see G.add_nodes and others in Graph Class in networkx/base.py
+
+
+def flatten(obj, result=None):
+    """Return flattened version of (possibly nested) iterable object."""
+    if not isinstance(obj, Iterable | Sized) or isinstance(obj, str):
+        return obj
+    if result is None:
+        result = []
+    for item in obj:
+        if not isinstance(item, Iterable | Sized) or isinstance(item, str):
+            result.append(item)
+        else:
+            flatten(item, result)
+    return tuple(result)
+
+
+def make_list_of_ints(sequence):
+    """Return list of ints from sequence of integral numbers.
+
+    All elements of the sequence must satisfy int(element) == element
+    or a ValueError is raised. Sequence is iterated through once.
+
+    If sequence is a list, the non-int values are replaced with ints.
+    So, no new list is created
+    """
+    if not isinstance(sequence, list):
+        result = []
+        for i in sequence:
+            errmsg = f"sequence is not all integers: {i}"
+            try:
+                ii = int(i)
+            except ValueError:
+                raise nx.NetworkXError(errmsg) from None
+            if ii != i:
+                raise nx.NetworkXError(errmsg)
+            result.append(ii)
+        return result
+    # original sequence is a list... in-place conversion to ints
+    for indx, i in enumerate(sequence):
+        errmsg = f"sequence is not all integers: {i}"
+        if isinstance(i, int):
+            continue
+        try:
+            ii = int(i)
+        except ValueError:
+            raise nx.NetworkXError(errmsg) from None
+        if ii != i:
+            raise nx.NetworkXError(errmsg)
+        sequence[indx] = ii
+    return sequence
+
+
+def dict_to_numpy_array(d, mapping=None):
+    """Convert a dictionary of dictionaries to a numpy array
+    with optional mapping."""
+    try:
+        return _dict_to_numpy_array2(d, mapping)
+    except (AttributeError, TypeError):
+        # AttributeError is when no mapping was provided and v.keys() fails.
+        # TypeError is when a mapping was provided and d[k1][k2] fails.
+        return _dict_to_numpy_array1(d, mapping)
+
+
+def _dict_to_numpy_array2(d, mapping=None):
+    """Convert a dictionary of dictionaries to a 2d numpy array
+    with optional mapping.
+
+    """
+    import numpy as np
+
+    if mapping is None:
+        s = set(d.keys())
+        for k, v in d.items():
+            s.update(v.keys())
+        mapping = dict(zip(s, range(len(s))))
+    n = len(mapping)
+    a = np.zeros((n, n))
+    for k1, i in mapping.items():
+        for k2, j in mapping.items():
+            try:
+                a[i, j] = d[k1][k2]
+            except KeyError:
+                pass
+    return a
+
+
+def _dict_to_numpy_array1(d, mapping=None):
+    """Convert a dictionary of numbers to a 1d numpy array with optional mapping."""
+    import numpy as np
+
+    if mapping is None:
+        s = set(d.keys())
+        mapping = dict(zip(s, range(len(s))))
+    n = len(mapping)
+    a = np.zeros(n)
+    for k1, i in mapping.items():
+        i = mapping[k1]
+        a[i] = d[k1]
+    return a
+
+
+def arbitrary_element(iterable):
+    """Returns an arbitrary element of `iterable` without removing it.
+
+    This is most useful for "peeking" at an arbitrary element of a set,
+    but can be used for any list, dictionary, etc., as well.
+
+    Parameters
+    ----------
+    iterable : `abc.collections.Iterable` instance
+        Any object that implements ``__iter__``, e.g. set, dict, list, tuple,
+        etc.
+
+    Returns
+    -------
+    The object that results from ``next(iter(iterable))``
+
+    Raises
+    ------
+    ValueError
+        If `iterable` is an iterator (because the current implementation of
+        this function would consume an element from the iterator).
+
+    Examples
+    --------
+    Arbitrary elements from common Iterable objects:
+
+    >>> nx.utils.arbitrary_element([1, 2, 3])  # list
+    1
+    >>> nx.utils.arbitrary_element((1, 2, 3))  # tuple
+    1
+    >>> nx.utils.arbitrary_element({1, 2, 3})  # set
+    1
+    >>> d = {k: v for k, v in zip([1, 2, 3], [3, 2, 1])}
+    >>> nx.utils.arbitrary_element(d)  # dict_keys
+    1
+    >>> nx.utils.arbitrary_element(d.values())  # dict values
+    3
+
+    `str` is also an Iterable:
+
+    >>> nx.utils.arbitrary_element("hello")
+    'h'
+
+    :exc:`ValueError` is raised if `iterable` is an iterator:
+
+    >>> iterator = iter([1, 2, 3])  # Iterator, *not* Iterable
+    >>> nx.utils.arbitrary_element(iterator)
+    Traceback (most recent call last):
+        ...
+    ValueError: cannot return an arbitrary item from an iterator
+
+    Notes
+    -----
+    This function does not return a *random* element. If `iterable` is
+    ordered, sequential calls will return the same value::
+
+        >>> l = [1, 2, 3]
+        >>> nx.utils.arbitrary_element(l)
+        1
+        >>> nx.utils.arbitrary_element(l)
+        1
+
+    """
+    if isinstance(iterable, Iterator):
+        raise ValueError("cannot return an arbitrary item from an iterator")
+    # Another possible implementation is ``for x in iterable: return x``.
+    return next(iter(iterable))
+
+
+# Recipe from the itertools documentation.
+def pairwise(iterable, cyclic=False):
+    "s -> (s0, s1), (s1, s2), (s2, s3), ..."
+    a, b = tee(iterable)
+    first = next(b, None)
+    if cyclic is True:
+        return zip(a, chain(b, (first,)))
+    return zip(a, b)
+
+
+def groups(many_to_one):
+    """Converts a many-to-one mapping into a one-to-many mapping.
+
+    `many_to_one` must be a dictionary whose keys and values are all
+    :term:`hashable`.
+
+    The return value is a dictionary mapping values from `many_to_one`
+    to sets of keys from `many_to_one` that have that value.
+
+    Examples
+    --------
+    >>> from networkx.utils import groups
+    >>> many_to_one = {"a": 1, "b": 1, "c": 2, "d": 3, "e": 3}
+    >>> groups(many_to_one)  # doctest: +SKIP
+    {1: {'a', 'b'}, 2: {'c'}, 3: {'e', 'd'}}
+    """
+    one_to_many = defaultdict(set)
+    for v, k in many_to_one.items():
+        one_to_many[k].add(v)
+    return dict(one_to_many)
+
+
+def create_random_state(random_state=None):
+    """Returns a numpy.random.RandomState or numpy.random.Generator instance
+    depending on input.
+
+    Parameters
+    ----------
+    random_state : int or NumPy RandomState or Generator instance, optional (default=None)
+        If int, return a numpy.random.RandomState instance set with seed=int.
+        if `numpy.random.RandomState` instance, return it.
+        if `numpy.random.Generator` instance, return it.
+        if None or numpy.random, return the global random number generator used
+        by numpy.random.
+    """
+    import numpy as np
+
+    if random_state is None or random_state is np.random:
+        return np.random.mtrand._rand
+    if isinstance(random_state, np.random.RandomState):
+        return random_state
+    if isinstance(random_state, int):
+        return np.random.RandomState(random_state)
+    if isinstance(random_state, np.random.Generator):
+        return random_state
+    msg = (
+        f"{random_state} cannot be used to create a numpy.random.RandomState or\n"
+        "numpy.random.Generator instance"
+    )
+    raise ValueError(msg)
+
+
+class PythonRandomViaNumpyBits(random.Random):
+    """Provide the random.random algorithms using a numpy.random bit generator
+
+    The intent is to allow people to contribute code that uses Python's random
+    library, but still allow users to provide a single easily controlled random
+    bit-stream for all work with NetworkX. This implementation is based on helpful
+    comments and code from Robert Kern on NumPy's GitHub Issue #24458.
+
+    This implementation supersedes that of `PythonRandomInterface` which rewrote
+    methods to account for subtle differences in API between `random` and
+    `numpy.random`. Instead this subclasses `random.Random` and overwrites
+    the methods `random`, `getrandbits`, `getstate`, `setstate` and `seed`.
+    It makes them use the rng values from an input numpy `RandomState` or `Generator`.
+    Those few methods allow the rest of the `random.Random` methods to provide
+    the API interface of `random.random` while using randomness generated by
+    a numpy generator.
+    """
+
+    def __init__(self, rng=None):
+        try:
+            import numpy as np
+        except ImportError:
+            msg = "numpy not found, only random.random available."
+            warnings.warn(msg, ImportWarning)
+
+        if rng is None:
+            self._rng = np.random.mtrand._rand
+        else:
+            self._rng = rng
+
+        # Not necessary, given our overriding of gauss() below, but it's
+        # in the superclass and nominally public, so initialize it here.
+        self.gauss_next = None
+
+    def random(self):
+        """Get the next random number in the range 0.0 <= X < 1.0."""
+        return self._rng.random()
+
+    def getrandbits(self, k):
+        """getrandbits(k) -> x.  Generates an int with k random bits."""
+        if k < 0:
+            raise ValueError("number of bits must be non-negative")
+        numbytes = (k + 7) // 8  # bits / 8 and rounded up
+        x = int.from_bytes(self._rng.bytes(numbytes), "big")
+        return x >> (numbytes * 8 - k)  # trim excess bits
+
+    def getstate(self):
+        return self._rng.__getstate__()
+
+    def setstate(self, state):
+        self._rng.__setstate__(state)
+
+    def seed(self, *args, **kwds):
+        "Do nothing override method."
+        raise NotImplementedError("seed() not implemented in PythonRandomViaNumpyBits")
+
+
+##################################################################
+class PythonRandomInterface:
+    """PythonRandomInterface is included for backward compatibility
+    New code should use PythonRandomViaNumpyBits instead.
+    """
+
+    def __init__(self, rng=None):
+        try:
+            import numpy as np
+        except ImportError:
+            msg = "numpy not found, only random.random available."
+            warnings.warn(msg, ImportWarning)
+
+        if rng is None:
+            self._rng = np.random.mtrand._rand
+        else:
+            self._rng = rng
+
+    def random(self):
+        return self._rng.random()
+
+    def uniform(self, a, b):
+        return a + (b - a) * self._rng.random()
+
+    def randrange(self, a, b=None):
+        import numpy as np
+
+        if b is None:
+            a, b = 0, a
+        if b > 9223372036854775807:  # from np.iinfo(np.int64).max
+            tmp_rng = PythonRandomViaNumpyBits(self._rng)
+            return tmp_rng.randrange(a, b)
+
+        if isinstance(self._rng, np.random.Generator):
+            return self._rng.integers(a, b)
+        return self._rng.randint(a, b)
+
+    # NOTE: the numpy implementations of `choice` don't support strings, so
+    # this cannot be replaced with self._rng.choice
+    def choice(self, seq):
+        import numpy as np
+
+        if isinstance(self._rng, np.random.Generator):
+            idx = self._rng.integers(0, len(seq))
+        else:
+            idx = self._rng.randint(0, len(seq))
+        return seq[idx]
+
+    def gauss(self, mu, sigma):
+        return self._rng.normal(mu, sigma)
+
+    def shuffle(self, seq):
+        return self._rng.shuffle(seq)
+
+    #    Some methods don't match API for numpy RandomState.
+    #    Commented out versions are not used by NetworkX
+
+    def sample(self, seq, k):
+        return self._rng.choice(list(seq), size=(k,), replace=False)
+
+    def randint(self, a, b):
+        import numpy as np
+
+        if b > 9223372036854775807:  # from np.iinfo(np.int64).max
+            tmp_rng = PythonRandomViaNumpyBits(self._rng)
+            return tmp_rng.randint(a, b)
+
+        if isinstance(self._rng, np.random.Generator):
+            return self._rng.integers(a, b + 1)
+        return self._rng.randint(a, b + 1)
+
+    #    exponential as expovariate with 1/argument,
+    def expovariate(self, scale):
+        return self._rng.exponential(1 / scale)
+
+    #    pareto as paretovariate with 1/argument,
+    def paretovariate(self, shape):
+        return self._rng.pareto(shape)
+
+
+#    weibull as weibullvariate multiplied by beta,
+#    def weibullvariate(self, alpha, beta):
+#        return self._rng.weibull(alpha) * beta
+#
+#    def triangular(self, low, high, mode):
+#        return self._rng.triangular(low, mode, high)
+#
+#    def choices(self, seq, weights=None, cum_weights=None, k=1):
+#        return self._rng.choice(seq
+
+
+def create_py_random_state(random_state=None):
+    """Returns a random.Random instance depending on input.
+
+    Parameters
+    ----------
+    random_state : int or random number generator or None (default=None)
+        - If int, return a `random.Random` instance set with seed=int.
+        - If `random.Random` instance, return it.
+        - If None or the `np.random` package, return the global random number
+          generator used by `np.random`.
+        - If an `np.random.Generator` instance, or the `np.random` package, or
+          the global numpy random number generator, then return it.
+          wrapped in a `PythonRandomViaNumpyBits` class.
+        - If a `PythonRandomViaNumpyBits` instance, return it.
+        - If a `PythonRandomInterface` instance, return it.
+        - If a `np.random.RandomState` instance and not the global numpy default,
+          return it wrapped in `PythonRandomInterface` for backward bit-stream
+          matching with legacy code.
+
+    Notes
+    -----
+    - A diagram intending to illustrate the relationships behind our support
+      for numpy random numbers is called
+      `NetworkX Numpy Random Numbers <https://excalidraw.com/#room=b5303f2b03d3af7ccc6a,e5ZDIWdWWCTTsg8OqoRvPA>`_.
+    - More discussion about this support also appears in
+      `gh-6869#comment <https://github.com/networkx/networkx/pull/6869#issuecomment-1944799534>`_.
+    - Wrappers of numpy.random number generators allow them to mimic the Python random
+      number generation algorithms. For example, Python can create arbitrarily large
+      random ints, and the wrappers use Numpy bit-streams with CPython's random module
+      to choose arbitrarily large random integers too.
+    - We provide two wrapper classes:
+      `PythonRandomViaNumpyBits` is usually what you want and is always used for
+      `np.Generator` instances. But for users who need to recreate random numbers
+      produced in NetworkX 3.2 or earlier, we maintain the `PythonRandomInterface`
+      wrapper as well. We use it only used if passed a (non-default) `np.RandomState`
+      instance pre-initialized from a seed. Otherwise the newer wrapper is used.
+    """
+    if random_state is None or random_state is random:
+        return random._inst
+    if isinstance(random_state, random.Random):
+        return random_state
+    if isinstance(random_state, int):
+        return random.Random(random_state)
+
+    try:
+        import numpy as np
+    except ImportError:
+        pass
+    else:
+        if isinstance(random_state, PythonRandomInterface | PythonRandomViaNumpyBits):
+            return random_state
+        if isinstance(random_state, np.random.Generator):
+            return PythonRandomViaNumpyBits(random_state)
+        if random_state is np.random:
+            return PythonRandomViaNumpyBits(np.random.mtrand._rand)
+
+        if isinstance(random_state, np.random.RandomState):
+            if random_state is np.random.mtrand._rand:
+                return PythonRandomViaNumpyBits(random_state)
+            # Only need older interface if specially constructed RandomState used
+            return PythonRandomInterface(random_state)
+
+    msg = f"{random_state} cannot be used to generate a random.Random instance"
+    raise ValueError(msg)
+
+
+def nodes_equal(nodes1, nodes2):
+    """Check if nodes are equal.
+
+    Equality here means equal as Python objects.
+    Node data must match if included.
+    The order of nodes is not relevant.
+
+    Parameters
+    ----------
+    nodes1, nodes2 : iterables of nodes, or (node, datadict) tuples
+
+    Returns
+    -------
+    bool
+        True if nodes are equal, False otherwise.
+    """
+    nlist1 = list(nodes1)
+    nlist2 = list(nodes2)
+    try:
+        d1 = dict(nlist1)
+        d2 = dict(nlist2)
+    except (ValueError, TypeError):
+        d1 = dict.fromkeys(nlist1)
+        d2 = dict.fromkeys(nlist2)
+    return d1 == d2
+
+
+def edges_equal(edges1, edges2):
+    """Check if edges are equal.
+
+    Equality here means equal as Python objects.
+    Edge data must match if included.
+    The order of the edges is not relevant.
+
+    Parameters
+    ----------
+    edges1, edges2 : iterables of with u, v nodes as
+        edge tuples (u, v), or
+        edge tuples with data dicts (u, v, d), or
+        edge tuples with keys and data dicts (u, v, k, d)
+
+    Returns
+    -------
+    bool
+        True if edges are equal, False otherwise.
+    """
+    from collections import defaultdict
+
+    d1 = defaultdict(dict)
+    d2 = defaultdict(dict)
+    c1 = 0
+    for c1, e in enumerate(edges1):
+        u, v = e[0], e[1]
+        data = [e[2:]]
+        if v in d1[u]:
+            data = d1[u][v] + data
+        d1[u][v] = data
+        d1[v][u] = data
+    c2 = 0
+    for c2, e in enumerate(edges2):
+        u, v = e[0], e[1]
+        data = [e[2:]]
+        if v in d2[u]:
+            data = d2[u][v] + data
+        d2[u][v] = data
+        d2[v][u] = data
+    if c1 != c2:
+        return False
+    # can check one direction because lengths are the same.
+    for n, nbrdict in d1.items():
+        for nbr, datalist in nbrdict.items():
+            if n not in d2:
+                return False
+            if nbr not in d2[n]:
+                return False
+            d2datalist = d2[n][nbr]
+            for data in datalist:
+                if datalist.count(data) != d2datalist.count(data):
+                    return False
+    return True
+
+
+def graphs_equal(graph1, graph2):
+    """Check if graphs are equal.
+
+    Equality here means equal as Python objects (not isomorphism).
+    Node, edge and graph data must match.
+
+    Parameters
+    ----------
+    graph1, graph2 : graph
+
+    Returns
+    -------
+    bool
+        True if graphs are equal, False otherwise.
+    """
+    return (
+        graph1.adj == graph2.adj
+        and graph1.nodes == graph2.nodes
+        and graph1.graph == graph2.graph
+    )
+
+
+def _clear_cache(G):
+    """Clear the cache of a graph (currently stores converted graphs).
+
+    Caching is controlled via ``nx.config.cache_converted_graphs`` configuration.
+    """
+    if cache := getattr(G, "__networkx_cache__", None):
+        cache.clear()
+
+
+def check_create_using(create_using, *, directed=None, multigraph=None, default=None):
+    """Assert that create_using has good properties
+
+    This checks for desired directedness and multi-edge properties.
+    It returns `create_using` unless that is `None` when it returns
+    the optionally specified default value.
+
+    Parameters
+    ----------
+    create_using : None, graph class or instance
+        The input value of create_using for a function.
+    directed : None or bool
+        Whether to check `create_using.is_directed() == directed`.
+        If None, do not assert directedness.
+    multigraph : None or bool
+        Whether to check `create_using.is_multigraph() == multigraph`.
+        If None, do not assert multi-edge property.
+    default : None or graph class
+        The graph class to return if create_using is None.
+
+    Returns
+    -------
+    create_using : graph class or instance
+        The provided graph class or instance, or if None, the `default` value.
+
+    Raises
+    ------
+    NetworkXError
+        When `create_using` doesn't match the properties specified by `directed`
+        or `multigraph` parameters.
+    """
+    if default is None:
+        default = nx.Graph
+    G = create_using if create_using is not None else default
+
+    G_directed = G.is_directed(None) if isinstance(G, type) else G.is_directed()
+    G_multigraph = G.is_multigraph(None) if isinstance(G, type) else G.is_multigraph()
+
+    if directed is not None:
+        if directed and not G_directed:
+            raise nx.NetworkXError("create_using must be directed")
+        if not directed and G_directed:
+            raise nx.NetworkXError("create_using must not be directed")
+
+    if multigraph is not None:
+        if multigraph and not G_multigraph:
+            raise nx.NetworkXError("create_using must be a multi-graph")
+        if not multigraph and G_multigraph:
+            raise nx.NetworkXError("create_using must not be a multi-graph")
+    return G