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shared/package-files/0c/0c00bafaf4e827a7eac83842bd6efcff7c52acc4b74808ec32a253bdbdb43bd3/add_if_then_else.h

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+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+
+TORCH_API bool AddIfThenElseOp(std::shared_ptr<Graph>& graph);
+
+} // namespace jit
+} // namespace torch

shared/package-files/0c/0c02ab85a5006c26645efe48e107c8667cbc71cac2e994791233c6833d45b75a/fmax_meta_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor fmax(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & fmax_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & fmax_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace meta
+} // namespace at

shared/package-files/0c/0c04462503d275fff1c68227b8404128de24ba7d08ceef8b197f1cd7e862baf9/distance_regular.py

@@ -0,0 +1,231 @@
+"""
+=======================
+Distance-regular graphs
+=======================
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+from .distance_measures import diameter
+
+__all__ = [
+    "is_distance_regular",
+    "is_strongly_regular",
+    "intersection_array",
+    "global_parameters",
+]
+
+
+def is_distance_regular(G):
+    """Returns True if the graph is distance regular, False otherwise.
+
+    A connected graph G is distance-regular if for any nodes x,y
+    and any integers i,j=0,1,...,d (where d is the graph
+    diameter), the number of vertices at distance i from x and
+    distance j from y depends only on i,j and the graph distance
+    between x and y, independently of the choice of x and y.
+
+    Parameters
+    ----------
+    G: Networkx graph (undirected)
+
+    Returns
+    -------
+    bool
+      True if the graph is Distance Regular, False otherwise
+
+    Examples
+    --------
+    >>> G = nx.hypercube_graph(6)
+    >>> nx.is_distance_regular(G)
+    True
+
+    See Also
+    --------
+    intersection_array, global_parameters
+
+    Notes
+    -----
+    For undirected and simple graphs only
+
+    References
+    ----------
+    .. [1] Brouwer, A. E.; Cohen, A. M.; and Neumaier, A.
+        Distance-Regular Graphs. New York: Springer-Verlag, 1989.
+    .. [2] Weisstein, Eric W. "Distance-Regular Graph."
+        http://mathworld.wolfram.com/Distance-RegularGraph.html
+
+    """
+    try:
+        intersection_array(G)
+        return True
+    except nx.NetworkXError:
+        return False
+
+
+def global_parameters(b, c):
+    """Returns global parameters for a given intersection array.
+
+    Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d
+    such that for any 2 vertices x,y in G at a distance i=d(x,y), there
+    are exactly c_i neighbors of y at a distance of i-1 from x and b_i
+    neighbors of y at a distance of i+1 from x.
+
+    Thus, a distance regular graph has the global parameters,
+    [[c_0,a_0,b_0],[c_1,a_1,b_1],......,[c_d,a_d,b_d]] for the
+    intersection array  [b_0,b_1,.....b_{d-1};c_1,c_2,.....c_d]
+    where a_i+b_i+c_i=k , k= degree of every vertex.
+
+    Parameters
+    ----------
+    b : list
+
+    c : list
+
+    Returns
+    -------
+    iterable
+       An iterable over three tuples.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> b, c = nx.intersection_array(G)
+    >>> list(nx.global_parameters(b, c))
+    [(0, 0, 3), (1, 0, 2), (1, 1, 1), (1, 1, 1), (2, 0, 1), (3, 0, 0)]
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Global Parameters."
+       From MathWorld--A Wolfram Web Resource.
+       http://mathworld.wolfram.com/GlobalParameters.html
+
+    See Also
+    --------
+    intersection_array
+    """
+    return ((y, b[0] - x - y, x) for x, y in zip(b + [0], [0] + c))
+
+
+@not_implemented_for("directed", "multigraph")
+def intersection_array(G):
+    """Returns the intersection array of a distance-regular graph.
+
+    Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d
+    such that for any 2 vertices x,y in G at a distance i=d(x,y), there
+    are exactly c_i neighbors of y at a distance of i-1 from x and b_i
+    neighbors of y at a distance of i+1 from x.
+
+    A distance regular graph's intersection array is given by,
+    [b_0,b_1,.....b_{d-1};c_1,c_2,.....c_d]
+
+    Parameters
+    ----------
+    G: Networkx graph (undirected)
+
+    Returns
+    -------
+    b,c: tuple of lists
+
+    Examples
+    --------
+    >>> G = nx.icosahedral_graph()
+    >>> nx.intersection_array(G)
+    ([5, 2, 1], [1, 2, 5])
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Intersection Array."
+       From MathWorld--A Wolfram Web Resource.
+       http://mathworld.wolfram.com/IntersectionArray.html
+
+    See Also
+    --------
+    global_parameters
+    """
+    # test for regular graph (all degrees must be equal)
+    degree = iter(G.degree())
+    (_, k) = next(degree)
+    for _, knext in degree:
+        if knext != k:
+            raise nx.NetworkXError("Graph is not distance regular.")
+        k = knext
+    path_length = dict(nx.all_pairs_shortest_path_length(G))
+    diameter = max(max(path_length[n].values()) for n in path_length)
+    bint = {}  # 'b' intersection array
+    cint = {}  # 'c' intersection array
+    for u in G:
+        for v in G:
+            try:
+                i = path_length[u][v]
+            except KeyError as err:  # graph must be connected
+                raise nx.NetworkXError("Graph is not distance regular.") from err
+            # number of neighbors of v at a distance of i-1 from u
+            c = len([n for n in G[v] if path_length[n][u] == i - 1])
+            # number of neighbors of v at a distance of i+1 from u
+            b = len([n for n in G[v] if path_length[n][u] == i + 1])
+            # b,c are independent of u and v
+            if cint.get(i, c) != c or bint.get(i, b) != b:
+                raise nx.NetworkXError("Graph is not distance regular")
+            bint[i] = b
+            cint[i] = c
+    return (
+        [bint.get(j, 0) for j in range(diameter)],
+        [cint.get(j + 1, 0) for j in range(diameter)],
+    )
+
+
+# TODO There is a definition for directed strongly regular graphs.
+@not_implemented_for("directed", "multigraph")
+def is_strongly_regular(G):
+    """Returns True if and only if the given graph is strongly
+    regular.
+
+    An undirected graph is *strongly regular* if
+
+    * it is regular,
+    * each pair of adjacent vertices has the same number of neighbors in
+      common,
+    * each pair of nonadjacent vertices has the same number of neighbors
+      in common.
+
+    Each strongly regular graph is a distance-regular graph.
+    Conversely, if a distance-regular graph has diameter two, then it is
+    a strongly regular graph. For more information on distance-regular
+    graphs, see :func:`is_distance_regular`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    Returns
+    -------
+    bool
+        Whether `G` is strongly regular.
+
+    Examples
+    --------
+
+    The cycle graph on five vertices is strongly regular. It is
+    two-regular, each pair of adjacent vertices has no shared neighbors,
+    and each pair of nonadjacent vertices has one shared neighbor::
+
+        >>> G = nx.cycle_graph(5)
+        >>> nx.is_strongly_regular(G)
+        True
+
+    """
+    # Here is an alternate implementation based directly on the
+    # definition of strongly regular graphs:
+    #
+    #     return (all_equal(G.degree().values())
+    #             and all_equal(len(common_neighbors(G, u, v))
+    #                           for u, v in G.edges())
+    #             and all_equal(len(common_neighbors(G, u, v))
+    #                           for u, v in non_edges(G)))
+    #
+    # We instead use the fact that a distance-regular graph of diameter
+    # two is strongly regular.
+    return is_distance_regular(G) and diameter(G) == 2

shared/package-files/0c/0c05cad261bb0815b7eecb9bb3c62e4e48fffe64f7563c7dd18e8a391eb33570/__init__.py

@@ -0,0 +1,52 @@
+
+import sys
+import torch
+
+
+def is_available():
+    return hasattr(torch._C, "_dist_autograd_init")
+
+
+if is_available() and not torch._C._dist_autograd_init():
+    raise RuntimeError("Failed to initialize torch.distributed.autograd")
+
+if is_available():
+    from torch._C._distributed_autograd import (
+        get_gradients,
+        backward,
+        _init,
+        _new_context,
+        _release_context,
+        _get_max_id,
+        _is_valid_context,
+        _retrieve_context,
+        _current_context,
+        _get_debug_info,
+        DistAutogradContext,
+    )
+
+
+class context:
+    '''
+    Context object to wrap forward and backward passes when using
+    distributed autograd. The ``context_id`` generated in the ``with``
+    statement  is required to uniquely identify a distributed backward pass
+    on all workers. Each worker stores metadata associated with this
+    ``context_id``, which is required to correctly execute a distributed
+    autograd pass.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> import torch.distributed.autograd as dist_autograd
+        >>> with dist_autograd.context() as context_id:
+        >>>     t1 = torch.rand((3, 3), requires_grad=True)
+        >>>     t2 = torch.rand((3, 3), requires_grad=True)
+        >>>     loss = rpc.rpc_sync("worker1", torch.add, args=(t1, t2)).sum()
+        >>>     dist_autograd.backward(context_id, [loss])
+    '''
+    def __enter__(self):
+        self.autograd_context = _new_context()
+        return self.autograd_context._context_id()
+
+    def __exit__(self, type, value, traceback):
+        _release_context(self.autograd_context._context_id())

shared/package-files/0c/0c0634e1aaf55e2c7012c725ead69275d922e18feb62742757ccc5eb5fb3d596/test_describe.py

@@ -0,0 +1,417 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameDescribe:
+    def test_describe_bool_in_mixed_frame(self):
+        df = DataFrame(
+            {
+                "string_data": ["a", "b", "c", "d", "e"],
+                "bool_data": [True, True, False, False, False],
+                "int_data": [10, 20, 30, 40, 50],
+            }
+        )
+
+        # Integer data are included in .describe() output,
+        # Boolean and string data are not.
+        result = df.describe()
+        expected = DataFrame(
+            {"int_data": [5, 30, df.int_data.std(), 10, 20, 30, 40, 50]},
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # Top value is a boolean value that is False
+        result = df.describe(include=["bool"])
+
+        expected = DataFrame(
+            {"bool_data": [5, 2, False, 3]}, index=["count", "unique", "top", "freq"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_empty_object(self):
+        # GH#27183
+        df = DataFrame({"A": [None, None]}, dtype=object)
+        result = df.describe()
+        expected = DataFrame(
+            {"A": [0, 0, np.nan, np.nan]},
+            dtype=object,
+            index=["count", "unique", "top", "freq"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = df.iloc[:0].describe()
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_bool_frame(self):
+        # GH#13891
+        df = DataFrame(
+            {
+                "bool_data_1": [False, False, True, True],
+                "bool_data_2": [False, True, True, True],
+            }
+        )
+        result = df.describe()
+        expected = DataFrame(
+            {"bool_data_1": [4, 2, False, 2], "bool_data_2": [4, 2, True, 3]},
+            index=["count", "unique", "top", "freq"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {
+                "bool_data": [False, False, True, True, False],
+                "int_data": [0, 1, 2, 3, 4],
+            }
+        )
+        result = df.describe()
+        expected = DataFrame(
+            {"int_data": [5, 2, df.int_data.std(), 0, 1, 2, 3, 4]},
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {"bool_data": [False, False, True, True], "str_data": ["a", "b", "c", "a"]}
+        )
+        result = df.describe()
+        expected = DataFrame(
+            {"bool_data": [4, 2, False, 2], "str_data": [4, 3, "a", 2]},
+            index=["count", "unique", "top", "freq"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_categorical(self):
+        df = DataFrame({"value": np.random.default_rng(2).integers(0, 10000, 100)})
+        labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
+        cat_labels = Categorical(labels, labels)
+
+        df = df.sort_values(by=["value"], ascending=True)
+        df["value_group"] = pd.cut(
+            df.value, range(0, 10500, 500), right=False, labels=cat_labels
+        )
+        cat = df
+
+        # Categoricals should not show up together with numerical columns
+        result = cat.describe()
+        assert len(result.columns) == 1
+
+        # In a frame, describe() for the cat should be the same as for string
+        # arrays (count, unique, top, freq)
+
+        cat = Categorical(
+            ["a", "b", "b", "b"], categories=["a", "b", "c"], ordered=True
+        )
+        s = Series(cat)
+        result = s.describe()
+        expected = Series([4, 2, "b", 3], index=["count", "unique", "top", "freq"])
+        tm.assert_series_equal(result, expected)
+
+        cat = Series(Categorical(["a", "b", "c", "c"]))
+        df3 = DataFrame({"cat": cat, "s": ["a", "b", "c", "c"]})
+        result = df3.describe()
+        tm.assert_numpy_array_equal(result["cat"].values, result["s"].values)
+
+    def test_describe_empty_categorical_column(self):
+        # GH#26397
+        # Ensure the index of an empty categorical DataFrame column
+        # also contains (count, unique, top, freq)
+        df = DataFrame({"empty_col": Categorical([])})
+        result = df.describe()
+        expected = DataFrame(
+            {"empty_col": [0, 0, np.nan, np.nan]},
+            index=["count", "unique", "top", "freq"],
+            dtype="object",
+        )
+        tm.assert_frame_equal(result, expected)
+        # ensure NaN, not None
+        assert np.isnan(result.iloc[2, 0])
+        assert np.isnan(result.iloc[3, 0])
+
+    def test_describe_categorical_columns(self):
+        # GH#11558
+        columns = pd.CategoricalIndex(["int1", "int2", "obj"], ordered=True, name="XXX")
+        df = DataFrame(
+            {
+                "int1": [10, 20, 30, 40, 50],
+                "int2": [10, 20, 30, 40, 50],
+                "obj": ["A", 0, None, "X", 1],
+            },
+            columns=columns,
+        )
+        result = df.describe()
+
+        exp_columns = pd.CategoricalIndex(
+            ["int1", "int2"],
+            categories=["int1", "int2", "obj"],
+            ordered=True,
+            name="XXX",
+        )
+        expected = DataFrame(
+            {
+                "int1": [5, 30, df.int1.std(), 10, 20, 30, 40, 50],
+                "int2": [5, 30, df.int2.std(), 10, 20, 30, 40, 50],
+            },
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+            columns=exp_columns,
+        )
+
+        tm.assert_frame_equal(result, expected)
+        tm.assert_categorical_equal(result.columns.values, expected.columns.values)
+
+    def test_describe_datetime_columns(self):
+        columns = pd.DatetimeIndex(
+            ["2011-01-01", "2011-02-01", "2011-03-01"],
+            freq="MS",
+            tz="US/Eastern",
+            name="XXX",
+        )
+        df = DataFrame(
+            {
+                0: [10, 20, 30, 40, 50],
+                1: [10, 20, 30, 40, 50],
+                2: ["A", 0, None, "X", 1],
+            }
+        )
+        df.columns = columns
+        result = df.describe()
+
+        exp_columns = pd.DatetimeIndex(
+            ["2011-01-01", "2011-02-01"], freq="MS", tz="US/Eastern", name="XXX"
+        )
+        expected = DataFrame(
+            {
+                0: [5, 30, df.iloc[:, 0].std(), 10, 20, 30, 40, 50],
+                1: [5, 30, df.iloc[:, 1].std(), 10, 20, 30, 40, 50],
+            },
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+        expected.columns = exp_columns
+        tm.assert_frame_equal(result, expected)
+        assert result.columns.freq == "MS"
+        assert result.columns.tz == expected.columns.tz
+
+    def test_describe_timedelta_values(self):
+        # GH#6145
+        t1 = pd.timedelta_range("1 days", freq="D", periods=5)
+        t2 = pd.timedelta_range("1 hours", freq="h", periods=5)
+        df = DataFrame({"t1": t1, "t2": t2})
+
+        expected = DataFrame(
+            {
+                "t1": [
+                    5,
+                    pd.Timedelta("3 days"),
+                    df.iloc[:, 0].std(),
+                    pd.Timedelta("1 days"),
+                    pd.Timedelta("2 days"),
+                    pd.Timedelta("3 days"),
+                    pd.Timedelta("4 days"),
+                    pd.Timedelta("5 days"),
+                ],
+                "t2": [
+                    5,
+                    pd.Timedelta("3 hours"),
+                    df.iloc[:, 1].std(),
+                    pd.Timedelta("1 hours"),
+                    pd.Timedelta("2 hours"),
+                    pd.Timedelta("3 hours"),
+                    pd.Timedelta("4 hours"),
+                    pd.Timedelta("5 hours"),
+                ],
+            },
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+        )
+
+        result = df.describe()
+        tm.assert_frame_equal(result, expected)
+
+        exp_repr = (
+            "                              t1                         t2\n"
+            "count                          5                          5\n"
+            "mean             3 days 00:00:00            0 days 03:00:00\n"
+            "std    1 days 13:56:50.394919273  0 days 01:34:52.099788303\n"
+            "min              1 days 00:00:00            0 days 01:00:00\n"
+            "25%              2 days 00:00:00            0 days 02:00:00\n"
+            "50%              3 days 00:00:00            0 days 03:00:00\n"
+            "75%              4 days 00:00:00            0 days 04:00:00\n"
+            "max              5 days 00:00:00            0 days 05:00:00"
+        )
+        assert repr(result) == exp_repr
+
+    def test_describe_tz_values(self, tz_naive_fixture):
+        # GH#21332
+        tz = tz_naive_fixture
+        s1 = Series(range(5))
+        start = Timestamp(2018, 1, 1)
+        end = Timestamp(2018, 1, 5)
+        s2 = Series(date_range(start, end, tz=tz))
+        df = DataFrame({"s1": s1, "s2": s2})
+
+        expected = DataFrame(
+            {
+                "s1": [5, 2, 0, 1, 2, 3, 4, 1.581139],
+                "s2": [
+                    5,
+                    Timestamp(2018, 1, 3).tz_localize(tz),
+                    start.tz_localize(tz),
+                    s2[1],
+                    s2[2],
+                    s2[3],
+                    end.tz_localize(tz),
+                    np.nan,
+                ],
+            },
+            index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"],
+        )
+        result = df.describe(include="all")
+        tm.assert_frame_equal(result, expected)
+
+    def test_datetime_is_numeric_includes_datetime(self):
+        df = DataFrame({"a": date_range("2012", periods=3), "b": [1, 2, 3]})
+        result = df.describe()
+        expected = DataFrame(
+            {
+                "a": [
+                    3,
+                    Timestamp("2012-01-02"),
+                    Timestamp("2012-01-01"),
+                    Timestamp("2012-01-01T12:00:00"),
+                    Timestamp("2012-01-02"),
+                    Timestamp("2012-01-02T12:00:00"),
+                    Timestamp("2012-01-03"),
+                    np.nan,
+                ],
+                "b": [3, 2, 1, 1.5, 2, 2.5, 3, 1],
+            },
+            index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_tz_values2(self):
+        tz = "CET"
+        s1 = Series(range(5))
+        start = Timestamp(2018, 1, 1)
+        end = Timestamp(2018, 1, 5)
+        s2 = Series(date_range(start, end, tz=tz))
+        df = DataFrame({"s1": s1, "s2": s2})
+
+        s1_ = s1.describe()
+        s2_ = s2.describe()
+        idx = [
+            "count",
+            "mean",
+            "min",
+            "25%",
+            "50%",
+            "75%",
+            "max",
+            "std",
+        ]
+        expected = pd.concat([s1_, s2_], axis=1, keys=["s1", "s2"]).reindex(
+            idx, copy=False
+        )
+
+        result = df.describe(include="all")
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_percentiles_integer_idx(self):
+        # GH#26660
+        df = DataFrame({"x": [1]})
+        pct = np.linspace(0, 1, 10 + 1)
+        result = df.describe(percentiles=pct)
+
+        expected = DataFrame(
+            {"x": [1.0, 1.0, np.nan, 1.0, *(1.0 for _ in pct), 1.0]},
+            index=[
+                "count",
+                "mean",
+                "std",
+                "min",
+                "0%",
+                "10%",
+                "20%",
+                "30%",
+                "40%",
+                "50%",
+                "60%",
+                "70%",
+                "80%",
+                "90%",
+                "100%",
+                "max",
+            ],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_does_not_raise_error_for_dictlike_elements(self):
+        # GH#32409
+        df = DataFrame([{"test": {"a": "1"}}, {"test": {"a": "2"}}])
+        expected = DataFrame(
+            {"test": [2, 2, {"a": "1"}, 1]}, index=["count", "unique", "top", "freq"]
+        )
+        result = df.describe()
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("exclude", ["x", "y", ["x", "y"], ["x", "z"]])
+    def test_describe_when_include_all_exclude_not_allowed(self, exclude):
+        """
+        When include is 'all', then setting exclude != None is not allowed.
+        """
+        df = DataFrame({"x": [1], "y": [2], "z": [3]})
+        msg = "exclude must be None when include is 'all'"
+        with pytest.raises(ValueError, match=msg):
+            df.describe(include="all", exclude=exclude)
+
+    def test_describe_with_duplicate_columns(self):
+        df = DataFrame(
+            [[1, 1, 1], [2, 2, 2], [3, 3, 3]],
+            columns=["bar", "a", "a"],
+            dtype="float64",
+        )
+        result = df.describe()
+        ser = df.iloc[:, 0].describe()
+        expected = pd.concat([ser, ser, ser], keys=df.columns, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_ea_with_na(self, any_numeric_ea_dtype):
+        # GH#48778
+
+        df = DataFrame({"a": [1, pd.NA, pd.NA], "b": pd.NA}, dtype=any_numeric_ea_dtype)
+        result = df.describe()
+        expected = DataFrame(
+            {"a": [1.0, 1.0, pd.NA] + [1.0] * 5, "b": [0.0] + [pd.NA] * 7},
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+            dtype="Float64",
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_describe_exclude_pa_dtype(self):
+        # GH#52570
+        pa = pytest.importorskip("pyarrow")
+        df = DataFrame(
+            {
+                "a": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int8())),
+                "b": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int16())),
+                "c": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int32())),
+            }
+        )
+        result = df.describe(
+            include=pd.ArrowDtype(pa.int8()), exclude=pd.ArrowDtype(pa.int32())
+        )
+        expected = DataFrame(
+            {"a": [3, 2, 1, 1, 1.5, 2, 2.5, 3]},
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+            dtype=pd.ArrowDtype(pa.float64()),
+        )
+        tm.assert_frame_equal(result, expected)

shared/package-files/0c/0c065a2cdd070b66d5d80715cf2c6c2acaf13ad2c85dbfcd9ffe99f8b806a7cc/arima112_css_results.py

@@ -0,0 +1,1315 @@
+import numpy as np
+
+from statsmodels.tools.tools import Bunch
+
+llf = np.array([-244.3852892951])
+
+nobs = np.array([202])
+
+k = np.array([5])
+
+k_exog = np.array([1])
+
+sigma = np.array([.81130812929037])
+
+chi2 = np.array([73901.783883385])
+
+df_model = np.array([3])
+
+k_ar = np.array([1])
+
+k_ma = np.array([2])
+
+params = np.array([
+    .91963917600489,
+    -.89804855498306,
+    1.3032353997768,
+    .30230174935463,
+    .65822088065264])
+
+cov_params = np.array([
+    .00622027554245,
+    .00026933156699,
+    -.00014478009121,
+    -.00010527901395,
+    .00006880952561,
+    .00026933156699,
+    .0023661521973,
+    -.00263264462948,
+    -.00241927046074,
+    -.00069258998629,
+    -.00014478009121,
+    -.00263264462948,
+    .003637275855,
+    .0033625395431,
+    .00072507981262,
+    -.00010527901395,
+    -.00241927046074,
+    .0033625395431,
+    .00312013997649,
+    .00067166747844,
+    .00006880952561,
+    -.00069258998629,
+    .00072507981262,
+    .00067166747844,
+    .00108477120916]).reshape(5, 5)
+
+xb = np.array([
+    .91963917016983,
+    .91963917016983,
+    .69261693954468,
+    .76115423440933,
+    .63710719347,
+    .77478265762329,
+    .61527073383331,
+    .80501782894135,
+    .62182641029358,
+    .72158020734787,
+    .66619211435318,
+    .72980058193207,
+    .64935982227325,
+    .77660953998566,
+    .6069877743721,
+    .79818457365036,
+    .60795724391937,
+    .7583304643631,
+    .68147450685501,
+    .72765469551086,
+    .69699174165726,
+    .69102382659912,
+    .67753201723099,
+    .72617518901825,
+    .69977235794067,
+    .713603079319,
+    .72478419542313,
+    .68503832817078,
+    .75677126646042,
+    .79034942388535,
+    .68609654903412,
+    .83208250999451,
+    .60605573654175,
+    .80849212408066,
+    .69862711429596,
+    .80978256464005,
+    .7383074760437,
+    .78789436817169,
+    .79390448331833,
+    .79169547557831,
+    .76283228397369,
+    .87939429283142,
+    .75783687829971,
+    .85010063648224,
+    .80657452344894,
+    .86508285999298,
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+    .75351697206497,
+    .74047154188156,
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+    .73184186220169,
+    .7623735666275,
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+    1.2375881671906,
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+    1.2652103900909,
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+    1.0786435604095,
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+    1.0368362665176,
+    1.0712716579437,
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+    .88966482877731,
+    1.009087562561,
+    1.0887442827225,
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+    1.2735350131989,
+    1.0157470703125,
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+    1.3003809452057,
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+    2.4736785888672,
+    -.25176140666008,
+    1.9082181453705,
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+    2.1775946617126,
+    -.71973150968552,
+    -.37754261493683,
+    1.2329530715942,
+    1.127131819725])
+
+y = np.array([
+    np.nan,
+    29.899639129639,
+    29.842617034912,
+    30.111154556274,
+    30.007108688354,
+    30.314783096313,
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+
+resid = np.array([
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+    202,
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+    -244.3852892951,
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+
+results = Bunch(
+    llf=llf,
+    nobs=nobs,
+    k=k,
+    k_exog=k_exog,
+    sigma=sigma,
+    chi2=chi2,
+    df_model=df_model,
+    k_ar=k_ar,
+    k_ma=k_ma,
+    params=params,
+    cov_params=cov_params,
+    xb=xb,
+    y=y,
+    resid=resid,
+    yr=yr,
+    mse=mse,
+    stdp=stdp,
+    icstats=icstats
+)

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shared/package-files/0c/0c11a425195e3e8ba1e86ee002716cda3a0ec653c88187a7946c79402380e793/Conda.psm1

@@ -0,0 +1,271 @@
+param([parameter(Position=0,Mandatory=$false)] [Hashtable] $CondaModuleArgs=@{})
+
+# Defaults from before we had arguments.
+if (-not $CondaModuleArgs.ContainsKey('ChangePs1')) {
+    $CondaModuleArgs.ChangePs1 = $True
+}
+
+## ENVIRONMENT MANAGEMENT ######################################################
+
+<#
+    .SYNOPSIS
+        Obtains a list of valid conda environments.
+
+    .EXAMPLE
+        Get-CondaEnvironment
+
+    .EXAMPLE
+        genv
+#>
+function Get-CondaEnvironment {
+    [CmdletBinding()]
+    param();
+
+    begin {}
+
+    process {
+        # NB: the JSON output of conda env list does not include the names
+        #     of each env, so we need to parse the fragile output instead.
+        & $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA env list | `
+            Where-Object { -not $_.StartsWith("#") } | `
+            Where-Object { -not $_.Trim().Length -eq 0 } | `
+            ForEach-Object {
+                $envLine = $_ -split "\s+";
+                $Active = $envLine[1] -eq "*";
+                [PSCustomObject] @{
+                    Name = $envLine[0];
+                    Active = $Active;
+                    Path = if ($Active) {$envLine[2]} else {$envLine[1]};
+                } | Write-Output;
+            }
+    }
+
+    end {}
+}
+
+<#
+    .SYNOPSIS
+        Activates a conda environment, placing its commands and packages at
+        the head of $Env:PATH.
+
+    .EXAMPLE
+        Enter-CondaEnvironment base
+
+    .EXAMPLE
+        etenv base
+
+    .NOTES
+        This command does not currently support activating environments stored
+        in a non-standard location.
+#>
+function Enter-CondaEnvironment {
+    [CmdletBinding()]
+    param(
+        [Parameter(Mandatory=$false)][switch]$Stack,
+        [Parameter(Position=0)][string]$Name
+    );
+
+    begin {
+        If ($Stack) {
+            $activateCommand = (& $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA shell.powershell activate --stack $Name | Out-String);
+        } Else {
+            $activateCommand = (& $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA shell.powershell activate $Name | Out-String);
+        }
+
+        Write-Verbose "[conda shell.powershell activate $Name]`n$activateCommand";
+        Invoke-Expression -Command $activateCommand;
+    }
+
+    process {}
+
+    end {}
+
+}
+
+<#
+    .SYNOPSIS
+        Deactivates the current conda environment, if any.
+
+    .EXAMPLE
+        Exit-CondaEnvironment
+
+    .EXAMPLE
+        exenv
+#>
+function Exit-CondaEnvironment {
+    [CmdletBinding()]
+    param();
+
+    begin {
+        $deactivateCommand = (& $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA shell.powershell deactivate | Out-String);
+
+        # If deactivate returns an empty string, we have nothing more to do,
+        # so return early.
+        if ($deactivateCommand.Trim().Length -eq 0) {
+            return;
+        }
+        Write-Verbose "[conda shell.powershell deactivate]`n$deactivateCommand";
+        Invoke-Expression -Command $deactivateCommand;
+    }
+    process {}
+    end {}
+}
+
+## CONDA WRAPPER ###############################################################
+
+<#
+    .SYNOPSIS
+        conda is a tool for managing and deploying applications, environments
+        and packages.
+
+    .PARAMETER Command
+        Subcommand to invoke.
+
+    .EXAMPLE
+        conda install toolz
+#>
+function Invoke-Conda() {
+    # Don't use any explicit args here, we'll use $args and tab completion
+    # so that we can capture everything, INCLUDING short options (e.g. -n).
+    if ($Args.Count -eq 0) {
+        # No args, just call the underlying conda executable.
+        & $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA;
+    }
+    else {
+        $Command = $Args[0];
+        if ($Args.Count -ge 2) {
+            $OtherArgs = $Args[1..($Args.Count - 1)];
+        } else {
+            $OtherArgs = @();
+        }
+        switch ($Command) {
+            "activate" {
+                Enter-CondaEnvironment @OtherArgs;
+            }
+            "deactivate" {
+                Exit-CondaEnvironment;
+            }
+
+            default {
+                # There may be a command we don't know want to handle
+                # differently in the shell wrapper, pass it through
+                # verbatim.
+                & $Env:CONDA_EXE $Env:_CE_M $Env:_CE_CONDA $Command @OtherArgs;
+            }
+        }
+    }
+}
+
+## TAB COMPLETION ##############################################################
+# We borrow the approach used by posh-git, in which we override any existing
+# functions named TabExpansion, look for commands we can complete on, and then
+# default to the previously defined TabExpansion function for everything else.
+
+if (Test-Path Function:\TabExpansion) {
+    # Since this technique is common, we encounter an infinite loop if it's
+    # used more than once unless we give our backup a unique name.
+    Rename-Item Function:\TabExpansion CondaTabExpansionBackup
+}
+
+function Expand-CondaEnv() {
+    param(
+        [string]
+        $Filter
+    );
+
+    $ValidEnvs = Get-CondaEnvironment;
+    $ValidEnvs `
+        | Where-Object { $_.Name -like "$filter*" } `
+        | ForEach-Object { $_.Name } `
+        | Write-Output;
+    $ValidEnvs `
+        | Where-Object { $_.Path -like "$filter*" } `
+        | ForEach-Object { $_.Path } `
+        | Write-Output;
+
+}
+
+function Expand-CondaSubcommands() {
+    param(
+        [string]
+        $Filter
+    );
+
+    $ValidCommands = Invoke-Conda shell.powershell commands;
+
+    # Add in the commands defined within this wrapper, filter, sort, and return.
+    $ValidCommands + @('activate', 'deactivate') `
+        | Where-Object { $_ -like "$Filter*" } `
+        | Sort-Object `
+        | Write-Output;
+
+}
+
+function TabExpansion($line, $lastWord) {
+    $lastBlock = [regex]::Split($line, '[|;]')[-1].TrimStart()
+
+    switch -regex ($lastBlock) {
+        # Pull out conda commands we recognize first before falling through
+        # to the general patterns for conda itself.
+        "^conda activate (.*)" { Expand-CondaEnv $lastWord; break; }
+        "^etenv (.*)" { Expand-CondaEnv $lastWord; break; }
+
+        # If we got down to here, check arguments to conda itself.
+        "^conda (.*)" { Expand-CondaSubcommands $lastWord; break; }
+
+        # Finally, fall back on existing tab expansion.
+        default {
+            if (Test-Path Function:\CondaTabExpansionBackup) {
+                CondaTabExpansionBackup $line $lastWord
+            }
+        }
+    }
+}
+
+## PROMPT MANAGEMENT ###########################################################
+
+<#
+    .SYNOPSIS
+        Modifies the current prompt to show the currently activated conda
+        environment, if any.
+    .EXAMPLE
+        Add-CondaEnvironmentToPrompt
+
+        Causes the current session's prompt to display the currently activated
+        conda environment.
+#>
+if ($CondaModuleArgs.ChangePs1) {
+    # We use the same procedure to nest prompts as we did for nested tab completion.
+    if (Test-Path Function:\prompt) {
+        Rename-Item Function:\prompt CondaPromptBackup
+    } else {
+        function CondaPromptBackup() {
+            # Restore a basic prompt if the definition is missing.
+            "PS $($executionContext.SessionState.Path.CurrentLocation)$('>' * ($nestedPromptLevel + 1)) ";
+        }
+    }
+
+    function global:prompt() {
+        if ($Env:CONDA_PROMPT_MODIFIER) {
+            $Env:CONDA_PROMPT_MODIFIER | Write-Host -NoNewline
+        }
+        CondaPromptBackup;
+    }
+}
+
+## ALIASES #####################################################################
+
+New-Alias conda Invoke-Conda -Force
+New-Alias genv Get-CondaEnvironment -Force
+New-Alias etenv Enter-CondaEnvironment -Force
+New-Alias exenv Exit-CondaEnvironment -Force
+
+## EXPORTS ###################################################################
+
+Export-ModuleMember `
+    -Alias * `
+    -Function `
+        Invoke-Conda, `
+        Get-CondaEnvironment, `
+        Enter-CondaEnvironment, Exit-CondaEnvironment, `
+        TabExpansion, prompt

shared/package-files/0c/0c15552da76e6541d25a5289e5a0579b181d366fd88cbbd68328934dc42ad387/test_cdft_asymptotic.py

@@ -0,0 +1,49 @@
+# gh-14777 regression tests
+# Test stdtr and stdtrit with infinite df and large values of df
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+from scipy.special import stdtr, stdtrit, ndtr, ndtri
+
+
+def test_stdtr_vs_R_large_df():
+    df = [1e10, 1e12, 1e120, np.inf]
+    t = 1.
+    res = stdtr(df, t)
+    # R Code:
+    #   options(digits=20)
+    #   pt(1., c(1e10, 1e12, 1e120, Inf))
+    res_R = [0.84134474605644460343,
+             0.84134474606842180044,
+             0.84134474606854281475,
+             0.84134474606854292578]
+    assert_allclose(res, res_R, rtol=2e-15)
+    # last value should also agree with ndtr
+    assert_equal(res[3], ndtr(1.))
+
+
+def test_stdtrit_vs_R_large_df():
+    df = [1e10, 1e12, 1e120, np.inf]
+    p = 0.1
+    res = stdtrit(df, p)
+    # R Code:
+    #   options(digits=20)
+    #   qt(0.1, c(1e10, 1e12, 1e120, Inf))
+    res_R = [-1.2815515656292593150,
+             -1.2815515655454472466,
+             -1.2815515655446008125,
+             -1.2815515655446008125]
+    assert_allclose(res, res_R, rtol=1e-14, atol=1e-15)
+    # last value should also agree with ndtri
+    assert_equal(res[3], ndtri(0.1))
+
+
+def test_stdtr_stdtri_invalid():
+    # a mix of large and inf df with t/p equal to nan
+    df = [1e10, 1e12, 1e120, np.inf]
+    x = np.nan
+    res1 = stdtr(df, x)
+    res2 = stdtrit(df, x)
+    res_ex = 4*[np.nan]
+    assert_equal(res1, res_ex)
+    assert_equal(res2, res_ex)

shared/package-files/0c/0c1580ed02b4765ba89278a8b8e8b3675879a0021c5a2e3b37b8ba5224834625/console.pyi

@@ -0,0 +1,44 @@
+import code
+from typing import Any, Optional
+
+class HTMLStringO:
+    def __init__(self): ...
+    def isatty(self): ...
+    def close(self): ...
+    def flush(self): ...
+    def seek(self, n, mode: int = ...): ...
+    def readline(self): ...
+    def reset(self): ...
+    def write(self, x): ...
+    def writelines(self, x): ...
+
+class ThreadedStream:
+    @staticmethod
+    def push(): ...
+    @staticmethod
+    def fetch(): ...
+    @staticmethod
+    def displayhook(obj): ...
+    def __setattr__(self, name, value): ...
+    def __dir__(self): ...
+    def __getattribute__(self, name): ...
+
+class _ConsoleLoader:
+    def __init__(self): ...
+    def register(self, code, source): ...
+    def get_source_by_code(self, code): ...
+
+class _InteractiveConsole(code.InteractiveInterpreter):
+    globals: Any
+    more: Any
+    buffer: Any
+    def __init__(self, globals, locals): ...
+    def runsource(self, source): ...
+    def runcode(self, code): ...
+    def showtraceback(self): ...
+    def showsyntaxerror(self, filename: Optional[Any] = ...): ...
+    def write(self, data): ...
+
+class Console:
+    def __init__(self, globals: Optional[Any] = ..., locals: Optional[Any] = ...): ...
+    def eval(self, code): ...

shared/package-files/0c/0c15ad816413e201d7ee66f88998d185f0cc11f728586cc8ca710e050a76719e/random_clustered.py

@@ -0,0 +1,116 @@
+"""Generate graphs with given degree and triangle sequence.
+"""
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_clustered_graph"]
+
+
+@py_random_state(2)
+def random_clustered_graph(joint_degree_sequence, create_using=None, seed=None):
+    r"""Generate a random graph with the given joint independent edge degree and
+    triangle degree sequence.
+
+    This uses a configuration model-like approach to generate a random graph
+    (with parallel edges and self-loops) by randomly assigning edges to match
+    the given joint degree sequence.
+
+    The joint degree sequence is a list of pairs of integers of the form
+    $[(d_{1,i}, d_{1,t}), \dotsc, (d_{n,i}, d_{n,t})]$. According to this list,
+    vertex $u$ is a member of $d_{u,t}$ triangles and has $d_{u, i}$ other
+    edges. The number $d_{u,t}$ is the *triangle degree* of $u$ and the number
+    $d_{u,i}$ is the *independent edge degree*.
+
+    Parameters
+    ----------
+    joint_degree_sequence : list of integer pairs
+        Each list entry corresponds to the independent edge degree and
+        triangle degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiGraph
+        A graph with the specified degree sequence. Nodes are labeled
+        starting at 0 with an index corresponding to the position in
+        deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the independent edge degree sequence sum is not even
+        or the triangle degree sequence sum is not divisible by 3.
+
+    Notes
+    -----
+    As described by Miller [1]_ (see also Newman [2]_ for an equivalent
+    description).
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the
+    independent degree sequence does not have an even sum or the
+    triangle degree sequence sum is not divisible by 3.
+
+    This configuration model-like construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.  This
+    "finite-size effect" decreases as the size of the graph increases.
+
+    References
+    ----------
+    .. [1] Joel C. Miller. "Percolation and epidemics in random clustered
+           networks". In: Physical review. E, Statistical, nonlinear, and soft
+           matter physics 80 (2 Part 1 August 2009).
+    .. [2] M. E. J. Newman. "Random Graphs with Clustering".
+           In: Physical Review Letters 103 (5 July 2009)
+
+    Examples
+    --------
+    >>> deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
+    >>> G = nx.random_clustered_graph(deg)
+
+    To remove parallel edges:
+
+    >>> G = nx.Graph(G)
+
+    To remove self loops:
+
+    >>> G.remove_edges_from(nx.selfloop_edges(G))
+
+    """
+    # In Python 3, zip() returns an iterator. Make this into a list.
+    joint_degree_sequence = list(joint_degree_sequence)
+
+    N = len(joint_degree_sequence)
+    G = nx.empty_graph(N, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    ilist = []
+    tlist = []
+    for n in G:
+        degrees = joint_degree_sequence[n]
+        for icount in range(degrees[0]):
+            ilist.append(n)
+        for tcount in range(degrees[1]):
+            tlist.append(n)
+
+    if len(ilist) % 2 != 0 or len(tlist) % 3 != 0:
+        raise nx.NetworkXError("Invalid degree sequence")
+
+    seed.shuffle(ilist)
+    seed.shuffle(tlist)
+    while ilist:
+        G.add_edge(ilist.pop(), ilist.pop())
+    while tlist:
+        n1 = tlist.pop()
+        n2 = tlist.pop()
+        n3 = tlist.pop()
+        G.add_edges_from([(n1, n2), (n1, n3), (n2, n3)])
+    return G

shared/package-files/0c/0c15dd9c87b6f2fadb7f80be6abed981e1486b49060f8a43049e183fecf84074/nodata.arff

@@ -0,0 +1,11 @@
+@RELATION iris
+
+@ATTRIBUTE sepallength  REAL
+@ATTRIBUTE sepalwidth   REAL
+@ATTRIBUTE petallength  REAL
+@ATTRIBUTE petalwidth   REAL
+@ATTRIBUTE class    {Iris-setosa,Iris-versicolor,Iris-virginica}
+
+@DATA
+
+% This file has no data

shared/package-files/0c/0c1cd30fcdcc877a1a71f6737d4e9f42e182f3b82fb67186683b9ef2cf4cd16b/uuid.h

@@ -0,0 +1,63 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include "arrow/extension_type.h"
+
+namespace arrow::extension {
+
+/// \brief UuidArray stores array of UUIDs. Underlying storage type is
+/// FixedSizeBinary(16).
+class ARROW_EXPORT UuidArray : public ExtensionArray {
+ public:
+  using ExtensionArray::ExtensionArray;
+};
+
+/// \brief UuidType is a canonical arrow extension type for UUIDs.
+/// UUIDs are stored as FixedSizeBinary(16) with big-endian notation and this
+/// does not interpret the bytes in any way. Specific UUID version is not
+/// required or guaranteed.
+class ARROW_EXPORT UuidType : public ExtensionType {
+ public:
+  /// \brief Construct a UuidType.
+  UuidType() : ExtensionType(fixed_size_binary(16)) {}
+
+  std::string extension_name() const override { return "arrow.uuid"; }
+  std::string ToString(bool show_metadata = false) const override;
+
+  bool ExtensionEquals(const ExtensionType& other) const override;
+
+  /// Create a UuidArray from ArrayData
+  std::shared_ptr<Array> MakeArray(std::shared_ptr<ArrayData> data) const override;
+
+  Result<std::shared_ptr<DataType>> Deserialize(
+      std::shared_ptr<DataType> storage_type,
+      const std::string& serialized) const override;
+
+  std::string Serialize() const override { return ""; }
+
+  /// \brief Create a UuidType instance
+  static Result<std::shared_ptr<DataType>> Make() { return std::make_shared<UuidType>(); }
+
+  static bool IsSupportedStorageType(const std::shared_ptr<DataType>& storage_type);
+};
+
+/// \brief Return a UuidType instance.
+ARROW_EXPORT std::shared_ptr<DataType> uuid();
+
+}  // namespace arrow::extension

shared/package-files/0c/0c2152360d3ce31214f7ff496a3d8e61b6d9b4b85e0af08d02fe49d16d0ecfbe/hann_window_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,30 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor hann_window(int64_t window_length, at::TensorOptions options={});
+TORCH_API at::Tensor hann_window(int64_t window_length, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+TORCH_API at::Tensor & hann_window_out(at::Tensor & out, int64_t window_length);
+TORCH_API at::Tensor & hann_window_outf(int64_t window_length, at::Tensor & out);
+TORCH_API at::Tensor hann_window(int64_t window_length, bool periodic, at::TensorOptions options={});
+TORCH_API at::Tensor hann_window(int64_t window_length, bool periodic, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+TORCH_API at::Tensor & hann_window_out(at::Tensor & out, int64_t window_length, bool periodic);
+TORCH_API at::Tensor & hann_window_outf(int64_t window_length, bool periodic, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

shared/package-files/0c/0c215ffc3b7970db2e4962676100067c9af1d72d99d064224938746a34098630/test_to_timestamp.py

@@ -0,0 +1,142 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    NaT,
+    PeriodIndex,
+    Timedelta,
+    Timestamp,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestToTimestamp:
+    def test_to_timestamp_non_contiguous(self):
+        # GH#44100
+        dti = date_range("2021-10-18", periods=9, freq="D")
+        pi = dti.to_period()
+
+        result = pi[::2].to_timestamp()
+        expected = dti[::2]
+        tm.assert_index_equal(result, expected)
+
+        result = pi._data[::2].to_timestamp()
+        expected = dti._data[::2]
+        # TODO: can we get the freq to round-trip?
+        tm.assert_datetime_array_equal(result, expected, check_freq=False)
+
+        result = pi[::-1].to_timestamp()
+        expected = dti[::-1]
+        tm.assert_index_equal(result, expected)
+
+        result = pi._data[::-1].to_timestamp()
+        expected = dti._data[::-1]
+        tm.assert_datetime_array_equal(result, expected, check_freq=False)
+
+        result = pi[::2][::-1].to_timestamp()
+        expected = dti[::2][::-1]
+        tm.assert_index_equal(result, expected)
+
+        result = pi._data[::2][::-1].to_timestamp()
+        expected = dti._data[::2][::-1]
+        tm.assert_datetime_array_equal(result, expected, check_freq=False)
+
+    def test_to_timestamp_freq(self):
+        idx = period_range("2017", periods=12, freq="Y-DEC")
+        result = idx.to_timestamp()
+        expected = date_range("2017", periods=12, freq="YS-JAN")
+        tm.assert_index_equal(result, expected)
+
+    def test_to_timestamp_pi_nat(self):
+        # GH#7228
+        index = PeriodIndex(["NaT", "2011-01", "2011-02"], freq="M", name="idx")
+
+        result = index.to_timestamp("D")
+        expected = DatetimeIndex(
+            [NaT, datetime(2011, 1, 1), datetime(2011, 2, 1)],
+            dtype="M8[ns]",
+            name="idx",
+        )
+        tm.assert_index_equal(result, expected)
+        assert result.name == "idx"
+
+        result2 = result.to_period(freq="M")
+        tm.assert_index_equal(result2, index)
+        assert result2.name == "idx"
+
+        result3 = result.to_period(freq="3M")
+        exp = PeriodIndex(["NaT", "2011-01", "2011-02"], freq="3M", name="idx")
+        tm.assert_index_equal(result3, exp)
+        assert result3.freqstr == "3M"
+
+        msg = "Frequency must be positive, because it represents span: -2Y"
+        with pytest.raises(ValueError, match=msg):
+            result.to_period(freq="-2Y")
+
+    def test_to_timestamp_preserve_name(self):
+        index = period_range(freq="Y", start="1/1/2001", end="12/1/2009", name="foo")
+        assert index.name == "foo"
+
+        conv = index.to_timestamp("D")
+        assert conv.name == "foo"
+
+    def test_to_timestamp_quarterly_bug(self):
+        years = np.arange(1960, 2000).repeat(4)
+        quarters = np.tile(list(range(1, 5)), 40)
+
+        pindex = PeriodIndex.from_fields(year=years, quarter=quarters)
+
+        stamps = pindex.to_timestamp("D", "end")
+        expected = DatetimeIndex([x.to_timestamp("D", "end") for x in pindex])
+        tm.assert_index_equal(stamps, expected)
+        assert stamps.freq == expected.freq
+
+    def test_to_timestamp_pi_mult(self):
+        idx = PeriodIndex(["2011-01", "NaT", "2011-02"], freq="2M", name="idx")
+
+        result = idx.to_timestamp()
+        expected = DatetimeIndex(
+            ["2011-01-01", "NaT", "2011-02-01"], dtype="M8[ns]", name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = idx.to_timestamp(how="E")
+        expected = DatetimeIndex(
+            ["2011-02-28", "NaT", "2011-03-31"], dtype="M8[ns]", name="idx"
+        )
+        expected = expected + Timedelta(1, "D") - Timedelta(1, "ns")
+        tm.assert_index_equal(result, expected)
+
+    def test_to_timestamp_pi_combined(self):
+        idx = period_range(start="2011", periods=2, freq="1D1h", name="idx")
+
+        result = idx.to_timestamp()
+        expected = DatetimeIndex(
+            ["2011-01-01 00:00", "2011-01-02 01:00"], dtype="M8[ns]", name="idx"
+        )
+        tm.assert_index_equal(result, expected)
+
+        result = idx.to_timestamp(how="E")
+        expected = DatetimeIndex(
+            ["2011-01-02 00:59:59", "2011-01-03 01:59:59"], name="idx", dtype="M8[ns]"
+        )
+        expected = expected + Timedelta(1, "s") - Timedelta(1, "ns")
+        tm.assert_index_equal(result, expected)
+
+        result = idx.to_timestamp(how="E", freq="h")
+        expected = DatetimeIndex(
+            ["2011-01-02 00:00", "2011-01-03 01:00"], dtype="M8[ns]", name="idx"
+        )
+        expected = expected + Timedelta(1, "h") - Timedelta(1, "ns")
+        tm.assert_index_equal(result, expected)
+
+    def test_to_timestamp_1703(self):
+        index = period_range("1/1/2012", periods=4, freq="D")
+
+        result = index.to_timestamp()
+        assert result[0] == Timestamp("1/1/2012")

shared/package-files/0c/0c21d293265526a2b3535f6de026771f36d49019c5edb86d4bdeba473f7188f9/ViewFuncs.h

@@ -0,0 +1,953 @@
+#pragma once
+
+// @generated from ../tools/autograd/templates/ViewFuncs.h
+
+#include <torch/library.h>
+#include <torch/csrc/autograd/variable.h>
+#include <c10/core/SymIntArrayRef.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Operators.h>
+#else
+#include <ATen/ops/_conj_ops.h>
+#include <ATen/ops/_indices_ops.h>
+#include <ATen/ops/_neg_view_ops.h>
+#include <ATen/ops/_nested_get_values_ops.h>
+#include <ATen/ops/_nested_view_from_buffer_ops.h>
+#include <ATen/ops/_nested_view_from_jagged_ops.h>
+#include <ATen/ops/_reshape_alias_ops.h>
+#include <ATen/ops/_test_autograd_multiple_dispatch_view_ops.h>
+#include <ATen/ops/_values_ops.h>
+#include <ATen/ops/alias_ops.h>
+#include <ATen/ops/as_strided_ops.h>
+#include <ATen/ops/ccol_indices_ops.h>
+#include <ATen/ops/chunk_ops.h>
+#include <ATen/ops/col_indices_ops.h>
+#include <ATen/ops/crow_indices_ops.h>
+#include <ATen/ops/diagonal_ops.h>
+#include <ATen/ops/expand_ops.h>
+#include <ATen/ops/indices_ops.h>
+#include <ATen/ops/narrow_ops.h>
+#include <ATen/ops/permute_ops.h>
+#include <ATen/ops/row_indices_ops.h>
+#include <ATen/ops/select_ops.h>
+#include <ATen/ops/slice_ops.h>
+#include <ATen/ops/slice_inverse_ops.h>
+#include <ATen/ops/split_ops.h>
+#include <ATen/ops/split_with_sizes_ops.h>
+#include <ATen/ops/squeeze_ops.h>
+#include <ATen/ops/squeeze_ops.h>
+#include <ATen/ops/squeeze_ops.h>
+#include <ATen/ops/t_ops.h>
+#include <ATen/ops/transpose_ops.h>
+#include <ATen/ops/unbind_ops.h>
+#include <ATen/ops/unfold_ops.h>
+#include <ATen/ops/unsqueeze_ops.h>
+#include <ATen/ops/values_ops.h>
+#include <ATen/ops/view_ops.h>
+#include <ATen/ops/view_ops.h>
+#include <ATen/ops/view_as_complex_ops.h>
+#include <ATen/ops/view_as_real_ops.h>
+#endif
+
+namespace torch::autograd::generated {
+
+using at::Scalar;
+using at::Tensor;
+using at::IntArrayRef;
+using at::ArrayRef;
+using at::Type;
+using at::ScalarType;
+using c10::optional;
+using c10::fmap;
+
+#define _CONJ_VIEW_FUNC_AVAILABLE
+struct _ConjViewFunc : public torch::autograd::ViewFunc {
+  _ConjViewFunc() 
+  {};
+  virtual ~_ConjViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _INDICES_VIEW_FUNC_AVAILABLE
+struct _IndicesViewFunc : public torch::autograd::ViewFunc {
+  _IndicesViewFunc() 
+  {};
+  virtual ~_IndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _NEG_VIEW_VIEW_FUNC_AVAILABLE
+struct _NegViewViewFunc : public torch::autograd::ViewFunc {
+  _NegViewViewFunc() 
+  {};
+  virtual ~_NegViewViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _NESTED_GET_VALUES_VIEW_FUNC_AVAILABLE
+struct _NestedGetValuesViewFunc : public torch::autograd::ViewFunc {
+  _NestedGetValuesViewFunc() 
+  {};
+  virtual ~_NestedGetValuesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _NESTED_VIEW_FROM_BUFFER_VIEW_FUNC_AVAILABLE
+struct _NestedViewFromBufferViewFunc : public torch::autograd::ViewFunc {
+  _NestedViewFromBufferViewFunc(const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) : nested_size(nested_size), nested_strides(nested_strides), offsets(offsets)
+  {};
+  virtual ~_NestedViewFromBufferViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  at::Tensor nested_size;
+  at::Tensor nested_strides;
+  at::Tensor offsets;
+};
+
+#define _NESTED_VIEW_FROM_JAGGED_VIEW_FUNC_AVAILABLE
+struct _NestedViewFromJaggedViewFunc : public torch::autograd::ViewFunc {
+  _NestedViewFromJaggedViewFunc(const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional<at::Tensor> & lengths, int64_t ragged_idx) : offsets(offsets), dummy(dummy), lengths(lengths), ragged_idx(ragged_idx)
+  {};
+  virtual ~_NestedViewFromJaggedViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  at::Tensor offsets;
+  at::Tensor dummy;
+  c10::optional<at::Tensor> lengths;
+  int64_t ragged_idx;
+};
+
+#define _RESHAPE_ALIAS_VIEW_FUNC_AVAILABLE
+struct _ReshapeAliasViewFunc : public torch::autograd::ViewFunc {
+  _ReshapeAliasViewFunc(c10::SymIntArrayRef size, c10::SymIntArrayRef stride) : size(size.vec()), stride(stride.vec())
+  {};
+  virtual ~_ReshapeAliasViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> size;
+  ::std::vector<c10::SymInt> stride;
+};
+
+#define _TEST_AUTOGRAD_MULTIPLE_DISPATCH_VIEW_VIEW_FUNC_AVAILABLE
+struct _TestAutogradMultipleDispatchViewViewFunc : public torch::autograd::ViewFunc {
+  _TestAutogradMultipleDispatchViewViewFunc() 
+  {};
+  virtual ~_TestAutogradMultipleDispatchViewViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define _VALUES_VIEW_FUNC_AVAILABLE
+struct _ValuesViewFunc : public torch::autograd::ViewFunc {
+  _ValuesViewFunc() 
+  {};
+  virtual ~_ValuesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define ALIAS_VIEW_FUNC_AVAILABLE
+struct AliasViewFunc : public torch::autograd::ViewFunc {
+  AliasViewFunc() 
+  {};
+  virtual ~AliasViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define AS_STRIDED_VIEW_FUNC_AVAILABLE
+struct AsStridedViewFunc : public torch::autograd::ViewFunc {
+  AsStridedViewFunc(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) : size(size.vec()), stride(stride.vec()), storage_offset(storage_offset)
+  {};
+  virtual ~AsStridedViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> size;
+  ::std::vector<c10::SymInt> stride;
+  c10::optional<c10::SymInt> storage_offset;
+};
+
+#define CCOL_INDICES_VIEW_FUNC_AVAILABLE
+struct CcolIndicesViewFunc : public torch::autograd::ViewFunc {
+  CcolIndicesViewFunc() 
+  {};
+  virtual ~CcolIndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define CHUNK_VIEW_FUNC_AVAILABLE
+struct ChunkViewFunc : public torch::autograd::ViewFunc {
+  ChunkViewFunc(int64_t chunks, int64_t dim, int64_t view_idx) : chunks(chunks), dim(dim), view_idx(view_idx)
+  {};
+  virtual ~ChunkViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t chunks;
+  int64_t dim;
+  int64_t view_idx;
+};
+
+#define COL_INDICES_VIEW_FUNC_AVAILABLE
+struct ColIndicesViewFunc : public torch::autograd::ViewFunc {
+  ColIndicesViewFunc() 
+  {};
+  virtual ~ColIndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define CROW_INDICES_VIEW_FUNC_AVAILABLE
+struct CrowIndicesViewFunc : public torch::autograd::ViewFunc {
+  CrowIndicesViewFunc() 
+  {};
+  virtual ~CrowIndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define DIAGONAL_VIEW_FUNC_AVAILABLE
+struct DiagonalViewFunc : public torch::autograd::ViewFunc {
+  DiagonalViewFunc(int64_t offset, int64_t dim1, int64_t dim2) : offset(offset), dim1(dim1), dim2(dim2)
+  {};
+  virtual ~DiagonalViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t offset;
+  int64_t dim1;
+  int64_t dim2;
+};
+
+#define EXPAND_VIEW_FUNC_AVAILABLE
+struct ExpandViewFunc : public torch::autograd::ViewFunc {
+  ExpandViewFunc(c10::SymIntArrayRef size, bool implicit) : size(size.vec()), implicit(implicit)
+  {};
+  virtual ~ExpandViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> size;
+  bool implicit;
+};
+
+#define INDICES_VIEW_FUNC_AVAILABLE
+struct IndicesViewFunc : public torch::autograd::ViewFunc {
+  IndicesViewFunc() 
+  {};
+  virtual ~IndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define NARROW_VIEW_FUNC_AVAILABLE
+struct NarrowViewFunc : public torch::autograd::ViewFunc {
+  NarrowViewFunc(int64_t dim, c10::SymInt start, c10::SymInt length) : dim(dim), start(start), length(length)
+  {};
+  virtual ~NarrowViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+  c10::SymInt start;
+  c10::SymInt length;
+};
+
+#define PERMUTE_VIEW_FUNC_AVAILABLE
+struct PermuteViewFunc : public torch::autograd::ViewFunc {
+  PermuteViewFunc(at::IntArrayRef dims) : dims(dims.vec())
+  {};
+  virtual ~PermuteViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<int64_t> dims;
+};
+
+#define ROW_INDICES_VIEW_FUNC_AVAILABLE
+struct RowIndicesViewFunc : public torch::autograd::ViewFunc {
+  RowIndicesViewFunc() 
+  {};
+  virtual ~RowIndicesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define SELECT_INT_VIEW_FUNC_AVAILABLE
+struct SelectIntViewFunc : public torch::autograd::ViewFunc {
+  SelectIntViewFunc(int64_t dim, c10::SymInt index) : dim(dim), index(index)
+  {};
+  virtual ~SelectIntViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+  c10::SymInt index;
+};
+
+#define SLICE_TENSOR_VIEW_FUNC_AVAILABLE
+struct SliceTensorViewFunc : public torch::autograd::ViewFunc {
+  SliceTensorViewFunc(int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) : dim(dim), start(start), end(end), step(step)
+  {};
+  virtual ~SliceTensorViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+  c10::optional<c10::SymInt> start;
+  c10::optional<c10::SymInt> end;
+  c10::SymInt step;
+};
+
+#define SLICE_INVERSE_VIEW_FUNC_AVAILABLE
+struct SliceInverseViewFunc : public torch::autograd::ViewFunc {
+  SliceInverseViewFunc(const at::Tensor & src, int64_t dim, c10::optional<c10::SymInt> start, c10::optional<c10::SymInt> end, c10::SymInt step) : src(src), dim(dim), start(start), end(end), step(step)
+  {};
+  virtual ~SliceInverseViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  at::Tensor src;
+  int64_t dim;
+  c10::optional<c10::SymInt> start;
+  c10::optional<c10::SymInt> end;
+  c10::SymInt step;
+};
+
+#define SPLIT_TENSOR_VIEW_FUNC_AVAILABLE
+struct SplitTensorViewFunc : public torch::autograd::ViewFunc {
+  SplitTensorViewFunc(c10::SymInt split_size, int64_t dim, int64_t view_idx) : split_size(split_size), dim(dim), view_idx(view_idx)
+  {};
+  virtual ~SplitTensorViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  c10::SymInt split_size;
+  int64_t dim;
+  int64_t view_idx;
+};
+
+#define SPLIT_WITH_SIZES_VIEW_FUNC_AVAILABLE
+struct SplitWithSizesViewFunc : public torch::autograd::ViewFunc {
+  SplitWithSizesViewFunc(c10::SymIntArrayRef split_sizes, int64_t dim, int64_t view_idx) : split_sizes(split_sizes.vec()), dim(dim), view_idx(view_idx)
+  {};
+  virtual ~SplitWithSizesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> split_sizes;
+  int64_t dim;
+  int64_t view_idx;
+};
+
+#define SQUEEZE_VIEW_FUNC_AVAILABLE
+struct SqueezeViewFunc : public torch::autograd::ViewFunc {
+  SqueezeViewFunc() 
+  {};
+  virtual ~SqueezeViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define SQUEEZE_DIM_VIEW_FUNC_AVAILABLE
+struct SqueezeDimViewFunc : public torch::autograd::ViewFunc {
+  SqueezeDimViewFunc(int64_t dim) : dim(dim)
+  {};
+  virtual ~SqueezeDimViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+};
+
+#define SQUEEZE_DIMS_VIEW_FUNC_AVAILABLE
+struct SqueezeDimsViewFunc : public torch::autograd::ViewFunc {
+  SqueezeDimsViewFunc(at::IntArrayRef dim) : dim(dim.vec())
+  {};
+  virtual ~SqueezeDimsViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<int64_t> dim;
+};
+
+#define T_VIEW_FUNC_AVAILABLE
+struct TViewFunc : public torch::autograd::ViewFunc {
+  TViewFunc() 
+  {};
+  virtual ~TViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define TRANSPOSE_INT_VIEW_FUNC_AVAILABLE
+struct TransposeIntViewFunc : public torch::autograd::ViewFunc {
+  TransposeIntViewFunc(int64_t dim0, int64_t dim1) : dim0(dim0), dim1(dim1)
+  {};
+  virtual ~TransposeIntViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim0;
+  int64_t dim1;
+};
+
+#define UNBIND_INT_VIEW_FUNC_AVAILABLE
+struct UnbindIntViewFunc : public torch::autograd::ViewFunc {
+  UnbindIntViewFunc(int64_t dim, int64_t view_idx) : dim(dim), view_idx(view_idx)
+  {};
+  virtual ~UnbindIntViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+  int64_t view_idx;
+};
+
+#define UNFOLD_VIEW_FUNC_AVAILABLE
+struct UnfoldViewFunc : public torch::autograd::ViewFunc {
+  UnfoldViewFunc(int64_t dimension, int64_t size, int64_t step) : dimension(dimension), size(size), step(step)
+  {};
+  virtual ~UnfoldViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dimension;
+  int64_t size;
+  int64_t step;
+};
+
+#define UNSQUEEZE_VIEW_FUNC_AVAILABLE
+struct UnsqueezeViewFunc : public torch::autograd::ViewFunc {
+  UnsqueezeViewFunc(int64_t dim) : dim(dim)
+  {};
+  virtual ~UnsqueezeViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  int64_t dim;
+};
+
+#define VALUES_VIEW_FUNC_AVAILABLE
+struct ValuesViewFunc : public torch::autograd::ViewFunc {
+  ValuesViewFunc() 
+  {};
+  virtual ~ValuesViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define VIEW_VIEW_FUNC_AVAILABLE
+struct ViewViewFunc : public torch::autograd::ViewFunc {
+  ViewViewFunc(c10::SymIntArrayRef size) : size(size.vec())
+  {};
+  virtual ~ViewViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  ::std::vector<c10::SymInt> size;
+};
+
+#define VIEW_DTYPE_VIEW_FUNC_AVAILABLE
+struct ViewDtypeViewFunc : public torch::autograd::ViewFunc {
+  ViewDtypeViewFunc(at::ScalarType dtype) : dtype(dtype)
+  {};
+  virtual ~ViewDtypeViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+  at::ScalarType dtype;
+};
+
+#define VIEW_AS_COMPLEX_VIEW_FUNC_AVAILABLE
+struct ViewAsComplexViewFunc : public torch::autograd::ViewFunc {
+  ViewAsComplexViewFunc() 
+  {};
+  virtual ~ViewAsComplexViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+#define VIEW_AS_REAL_VIEW_FUNC_AVAILABLE
+struct ViewAsRealViewFunc : public torch::autograd::ViewFunc {
+  ViewAsRealViewFunc() 
+  {};
+  virtual ~ViewAsRealViewFunc() override {};
+  virtual std::vector<c10::SymInt> get_symints() const override;
+  virtual size_t num_symints() const override;
+  virtual std::vector<at::Tensor> get_tensors() const override;
+  virtual size_t num_tensors() const override;
+  virtual at::Tensor operator()(const at::Tensor&) const override;
+  virtual std::unique_ptr<ViewFunc> clone_and_set(
+      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
+      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;
+
+protected:
+  virtual void set_symints(std::vector<c10::SymInt>) override;
+  virtual void set_tensors(std::vector<at::Tensor>) override;
+
+private:
+
+};
+
+} // namespace torch::autograd::generated

shared/package-files/0c/0c228e0a4cf2e60236e37d2b6b49d892dd86323096d27188a6125edd358d782b/ts_backend_impl.h

@@ -0,0 +1,52 @@
+#pragma once
+
+#include <torch/csrc/lazy/backend/backend_interface.h>
+
+namespace torch {
+namespace lazy {
+
+class TORCH_API TSData : public torch::lazy::BackendData {
+ public:
+  TSData(const at::Scalar& scalar, const torch::lazy::BackendDevice& device)
+      : torch::lazy::BackendData(device, torch::lazy::Shape(scalar.type(), {})),
+        scalar(scalar) {}
+
+  TSData(
+      const at::Tensor& data,
+      const torch::lazy::Shape& shape,
+      const torch::lazy::BackendDevice& device)
+      : torch::lazy::BackendData(device, shape), data_(data) {}
+
+  TSData(
+      const torch::lazy::Shape& shape,
+      const torch::lazy::BackendDevice& device)
+      : torch::lazy::BackendData(device, shape) {}
+
+  Handle GetHandle() override {
+    return reinterpret_cast<int64_t>(this);
+  }
+
+  void Assign(const torch::lazy::BackendData& data) override {
+    data_ = static_cast<const TSData&>(data).data_;
+  }
+
+  bool HasValue() const override {
+    return data_.defined();
+  }
+
+  at::Tensor data() {
+    return data_;
+  }
+
+  c10::optional<at::Scalar> scalar;
+
+ private:
+  at::Tensor data_;
+};
+
+TORCH_API torch::lazy::BackendImplInterface* GetTSBackendImpl();
+
+TORCH_API void InitTorchScriptBackend();
+
+} // namespace lazy
+} // namespace torch

shared/package-files/0c/0c22e0d886b5d782dca4f5371425bc1cc3990a49f92bb9217d744c7f667d5b22/dialogs.py

@@ -0,0 +1,107 @@
+"""
+Collection of reusable components for building full screen applications.
+"""
+from __future__ import annotations
+
+from typing import Sequence
+
+from prompt_toolkit.filters import has_completions, has_focus
+from prompt_toolkit.formatted_text import AnyFormattedText
+from prompt_toolkit.key_binding.bindings.focus import focus_next, focus_previous
+from prompt_toolkit.key_binding.key_bindings import KeyBindings
+from prompt_toolkit.layout.containers import (
+    AnyContainer,
+    DynamicContainer,
+    HSplit,
+    VSplit,
+)
+from prompt_toolkit.layout.dimension import AnyDimension
+from prompt_toolkit.layout.dimension import Dimension as D
+
+from .base import Box, Button, Frame, Shadow
+
+__all__ = [
+    "Dialog",
+]
+
+
+class Dialog:
+    """
+    Simple dialog window. This is the base for input dialogs, message dialogs
+    and confirmation dialogs.
+
+    Changing the title and body of the dialog is possible at runtime by
+    assigning to the `body` and `title` attributes of this class.
+
+    :param body: Child container object.
+    :param title: Text to be displayed in the heading of the dialog.
+    :param buttons: A list of `Button` widgets, displayed at the bottom.
+    """
+
+    def __init__(
+        self,
+        body: AnyContainer,
+        title: AnyFormattedText = "",
+        buttons: Sequence[Button] | None = None,
+        modal: bool = True,
+        width: AnyDimension = None,
+        with_background: bool = False,
+    ) -> None:
+        self.body = body
+        self.title = title
+
+        buttons = buttons or []
+
+        # When a button is selected, handle left/right key bindings.
+        buttons_kb = KeyBindings()
+        if len(buttons) > 1:
+            first_selected = has_focus(buttons[0])
+            last_selected = has_focus(buttons[-1])
+
+            buttons_kb.add("left", filter=~first_selected)(focus_previous)
+            buttons_kb.add("right", filter=~last_selected)(focus_next)
+
+        frame_body: AnyContainer
+        if buttons:
+            frame_body = HSplit(
+                [
+                    # Add optional padding around the body.
+                    Box(
+                        body=DynamicContainer(lambda: self.body),
+                        padding=D(preferred=1, max=1),
+                        padding_bottom=0,
+                    ),
+                    # The buttons.
+                    Box(
+                        body=VSplit(buttons, padding=1, key_bindings=buttons_kb),
+                        height=D(min=1, max=3, preferred=3),
+                    ),
+                ]
+            )
+        else:
+            frame_body = body
+
+        # Key bindings for whole dialog.
+        kb = KeyBindings()
+        kb.add("tab", filter=~has_completions)(focus_next)
+        kb.add("s-tab", filter=~has_completions)(focus_previous)
+
+        frame = Shadow(
+            body=Frame(
+                title=lambda: self.title,
+                body=frame_body,
+                style="class:dialog.body",
+                width=(None if with_background is None else width),
+                key_bindings=kb,
+                modal=modal,
+            )
+        )
+
+        self.container: Box | Shadow
+        if with_background:
+            self.container = Box(body=frame, style="class:dialog", width=width)
+        else:
+            self.container = frame
+
+    def __pt_container__(self) -> AnyContainer:
+        return self.container

shared/package-files/0c/0c23c1c4d58ff81bdd4ff4448fafdae138d4acd69feac0aafd7a31537a446df7/cython_blas.pxd

@@ -0,0 +1,169 @@
+"""
+This file was generated by _generate_pyx.py.
+Do not edit this file directly.
+"""
+
+# Within scipy, these wrappers can be used via relative or absolute cimport.
+# Examples:
+# from ..linalg cimport cython_blas
+# from scipy.linalg cimport cython_blas
+# cimport scipy.linalg.cython_blas as cython_blas
+# cimport ..linalg.cython_blas as cython_blas
+
+# Within SciPy, if BLAS functions are needed in C/C++/Fortran,
+# these wrappers should not be used.
+# The original libraries should be linked directly.
+
+ctypedef float s
+ctypedef double d
+ctypedef float complex c
+ctypedef double complex z
+
+cdef void caxpy(int *n, c *ca, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef void ccopy(int *n, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef c cdotc(int *n, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef c cdotu(int *n, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef void cgbmv(char *trans, int *m, int *n, int *kl, int *ku, c *alpha, c *a, int *lda, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void cgemm(char *transa, char *transb, int *m, int *n, int *k, c *alpha, c *a, int *lda, c *b, int *ldb, c *beta, c *c, int *ldc) noexcept nogil
+cdef void cgemv(char *trans, int *m, int *n, c *alpha, c *a, int *lda, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void cgerc(int *m, int *n, c *alpha, c *x, int *incx, c *y, int *incy, c *a, int *lda) noexcept nogil
+cdef void cgeru(int *m, int *n, c *alpha, c *x, int *incx, c *y, int *incy, c *a, int *lda) noexcept nogil
+cdef void chbmv(char *uplo, int *n, int *k, c *alpha, c *a, int *lda, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void chemm(char *side, char *uplo, int *m, int *n, c *alpha, c *a, int *lda, c *b, int *ldb, c *beta, c *c, int *ldc) noexcept nogil
+cdef void chemv(char *uplo, int *n, c *alpha, c *a, int *lda, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void cher(char *uplo, int *n, s *alpha, c *x, int *incx, c *a, int *lda) noexcept nogil
+cdef void cher2(char *uplo, int *n, c *alpha, c *x, int *incx, c *y, int *incy, c *a, int *lda) noexcept nogil
+cdef void cher2k(char *uplo, char *trans, int *n, int *k, c *alpha, c *a, int *lda, c *b, int *ldb, s *beta, c *c, int *ldc) noexcept nogil
+cdef void cherk(char *uplo, char *trans, int *n, int *k, s *alpha, c *a, int *lda, s *beta, c *c, int *ldc) noexcept nogil
+cdef void chpmv(char *uplo, int *n, c *alpha, c *ap, c *x, int *incx, c *beta, c *y, int *incy) noexcept nogil
+cdef void chpr(char *uplo, int *n, s *alpha, c *x, int *incx, c *ap) noexcept nogil
+cdef void chpr2(char *uplo, int *n, c *alpha, c *x, int *incx, c *y, int *incy, c *ap) noexcept nogil
+cdef void crotg(c *ca, c *cb, s *c, c *s) noexcept nogil
+cdef void cscal(int *n, c *ca, c *cx, int *incx) noexcept nogil
+cdef void csrot(int *n, c *cx, int *incx, c *cy, int *incy, s *c, s *s) noexcept nogil
+cdef void csscal(int *n, s *sa, c *cx, int *incx) noexcept nogil
+cdef void cswap(int *n, c *cx, int *incx, c *cy, int *incy) noexcept nogil
+cdef void csymm(char *side, char *uplo, int *m, int *n, c *alpha, c *a, int *lda, c *b, int *ldb, c *beta, c *c, int *ldc) noexcept nogil
+cdef void csyr2k(char *uplo, char *trans, int *n, int *k, c *alpha, c *a, int *lda, c *b, int *ldb, c *beta, c *c, int *ldc) noexcept nogil
+cdef void csyrk(char *uplo, char *trans, int *n, int *k, c *alpha, c *a, int *lda, c *beta, c *c, int *ldc) noexcept nogil
+cdef void ctbmv(char *uplo, char *trans, char *diag, int *n, int *k, c *a, int *lda, c *x, int *incx) noexcept nogil
+cdef void ctbsv(char *uplo, char *trans, char *diag, int *n, int *k, c *a, int *lda, c *x, int *incx) noexcept nogil
+cdef void ctpmv(char *uplo, char *trans, char *diag, int *n, c *ap, c *x, int *incx) noexcept nogil
+cdef void ctpsv(char *uplo, char *trans, char *diag, int *n, c *ap, c *x, int *incx) noexcept nogil
+cdef void ctrmm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, c *alpha, c *a, int *lda, c *b, int *ldb) noexcept nogil
+cdef void ctrmv(char *uplo, char *trans, char *diag, int *n, c *a, int *lda, c *x, int *incx) noexcept nogil
+cdef void ctrsm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, c *alpha, c *a, int *lda, c *b, int *ldb) noexcept nogil
+cdef void ctrsv(char *uplo, char *trans, char *diag, int *n, c *a, int *lda, c *x, int *incx) noexcept nogil
+cdef d dasum(int *n, d *dx, int *incx) noexcept nogil
+cdef void daxpy(int *n, d *da, d *dx, int *incx, d *dy, int *incy) noexcept nogil
+cdef d dcabs1(z *z) noexcept nogil
+cdef void dcopy(int *n, d *dx, int *incx, d *dy, int *incy) noexcept nogil
+cdef d ddot(int *n, d *dx, int *incx, d *dy, int *incy) noexcept nogil
+cdef void dgbmv(char *trans, int *m, int *n, int *kl, int *ku, d *alpha, d *a, int *lda, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dgemm(char *transa, char *transb, int *m, int *n, int *k, d *alpha, d *a, int *lda, d *b, int *ldb, d *beta, d *c, int *ldc) noexcept nogil
+cdef void dgemv(char *trans, int *m, int *n, d *alpha, d *a, int *lda, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dger(int *m, int *n, d *alpha, d *x, int *incx, d *y, int *incy, d *a, int *lda) noexcept nogil
+cdef d dnrm2(int *n, d *x, int *incx) noexcept nogil
+cdef void drot(int *n, d *dx, int *incx, d *dy, int *incy, d *c, d *s) noexcept nogil
+cdef void drotg(d *da, d *db, d *c, d *s) noexcept nogil
+cdef void drotm(int *n, d *dx, int *incx, d *dy, int *incy, d *dparam) noexcept nogil
+cdef void drotmg(d *dd1, d *dd2, d *dx1, d *dy1, d *dparam) noexcept nogil
+cdef void dsbmv(char *uplo, int *n, int *k, d *alpha, d *a, int *lda, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dscal(int *n, d *da, d *dx, int *incx) noexcept nogil
+cdef d dsdot(int *n, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef void dspmv(char *uplo, int *n, d *alpha, d *ap, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dspr(char *uplo, int *n, d *alpha, d *x, int *incx, d *ap) noexcept nogil
+cdef void dspr2(char *uplo, int *n, d *alpha, d *x, int *incx, d *y, int *incy, d *ap) noexcept nogil
+cdef void dswap(int *n, d *dx, int *incx, d *dy, int *incy) noexcept nogil
+cdef void dsymm(char *side, char *uplo, int *m, int *n, d *alpha, d *a, int *lda, d *b, int *ldb, d *beta, d *c, int *ldc) noexcept nogil
+cdef void dsymv(char *uplo, int *n, d *alpha, d *a, int *lda, d *x, int *incx, d *beta, d *y, int *incy) noexcept nogil
+cdef void dsyr(char *uplo, int *n, d *alpha, d *x, int *incx, d *a, int *lda) noexcept nogil
+cdef void dsyr2(char *uplo, int *n, d *alpha, d *x, int *incx, d *y, int *incy, d *a, int *lda) noexcept nogil
+cdef void dsyr2k(char *uplo, char *trans, int *n, int *k, d *alpha, d *a, int *lda, d *b, int *ldb, d *beta, d *c, int *ldc) noexcept nogil
+cdef void dsyrk(char *uplo, char *trans, int *n, int *k, d *alpha, d *a, int *lda, d *beta, d *c, int *ldc) noexcept nogil
+cdef void dtbmv(char *uplo, char *trans, char *diag, int *n, int *k, d *a, int *lda, d *x, int *incx) noexcept nogil
+cdef void dtbsv(char *uplo, char *trans, char *diag, int *n, int *k, d *a, int *lda, d *x, int *incx) noexcept nogil
+cdef void dtpmv(char *uplo, char *trans, char *diag, int *n, d *ap, d *x, int *incx) noexcept nogil
+cdef void dtpsv(char *uplo, char *trans, char *diag, int *n, d *ap, d *x, int *incx) noexcept nogil
+cdef void dtrmm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, d *alpha, d *a, int *lda, d *b, int *ldb) noexcept nogil
+cdef void dtrmv(char *uplo, char *trans, char *diag, int *n, d *a, int *lda, d *x, int *incx) noexcept nogil
+cdef void dtrsm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, d *alpha, d *a, int *lda, d *b, int *ldb) noexcept nogil
+cdef void dtrsv(char *uplo, char *trans, char *diag, int *n, d *a, int *lda, d *x, int *incx) noexcept nogil
+cdef d dzasum(int *n, z *zx, int *incx) noexcept nogil
+cdef d dznrm2(int *n, z *x, int *incx) noexcept nogil
+cdef int icamax(int *n, c *cx, int *incx) noexcept nogil
+cdef int idamax(int *n, d *dx, int *incx) noexcept nogil
+cdef int isamax(int *n, s *sx, int *incx) noexcept nogil
+cdef int izamax(int *n, z *zx, int *incx) noexcept nogil
+cdef bint lsame(char *ca, char *cb) noexcept nogil
+cdef s sasum(int *n, s *sx, int *incx) noexcept nogil
+cdef void saxpy(int *n, s *sa, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef s scasum(int *n, c *cx, int *incx) noexcept nogil
+cdef s scnrm2(int *n, c *x, int *incx) noexcept nogil
+cdef void scopy(int *n, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef s sdot(int *n, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef s sdsdot(int *n, s *sb, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef void sgbmv(char *trans, int *m, int *n, int *kl, int *ku, s *alpha, s *a, int *lda, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void sgemm(char *transa, char *transb, int *m, int *n, int *k, s *alpha, s *a, int *lda, s *b, int *ldb, s *beta, s *c, int *ldc) noexcept nogil
+cdef void sgemv(char *trans, int *m, int *n, s *alpha, s *a, int *lda, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void sger(int *m, int *n, s *alpha, s *x, int *incx, s *y, int *incy, s *a, int *lda) noexcept nogil
+cdef s snrm2(int *n, s *x, int *incx) noexcept nogil
+cdef void srot(int *n, s *sx, int *incx, s *sy, int *incy, s *c, s *s) noexcept nogil
+cdef void srotg(s *sa, s *sb, s *c, s *s) noexcept nogil
+cdef void srotm(int *n, s *sx, int *incx, s *sy, int *incy, s *sparam) noexcept nogil
+cdef void srotmg(s *sd1, s *sd2, s *sx1, s *sy1, s *sparam) noexcept nogil
+cdef void ssbmv(char *uplo, int *n, int *k, s *alpha, s *a, int *lda, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void sscal(int *n, s *sa, s *sx, int *incx) noexcept nogil
+cdef void sspmv(char *uplo, int *n, s *alpha, s *ap, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void sspr(char *uplo, int *n, s *alpha, s *x, int *incx, s *ap) noexcept nogil
+cdef void sspr2(char *uplo, int *n, s *alpha, s *x, int *incx, s *y, int *incy, s *ap) noexcept nogil
+cdef void sswap(int *n, s *sx, int *incx, s *sy, int *incy) noexcept nogil
+cdef void ssymm(char *side, char *uplo, int *m, int *n, s *alpha, s *a, int *lda, s *b, int *ldb, s *beta, s *c, int *ldc) noexcept nogil
+cdef void ssymv(char *uplo, int *n, s *alpha, s *a, int *lda, s *x, int *incx, s *beta, s *y, int *incy) noexcept nogil
+cdef void ssyr(char *uplo, int *n, s *alpha, s *x, int *incx, s *a, int *lda) noexcept nogil
+cdef void ssyr2(char *uplo, int *n, s *alpha, s *x, int *incx, s *y, int *incy, s *a, int *lda) noexcept nogil
+cdef void ssyr2k(char *uplo, char *trans, int *n, int *k, s *alpha, s *a, int *lda, s *b, int *ldb, s *beta, s *c, int *ldc) noexcept nogil
+cdef void ssyrk(char *uplo, char *trans, int *n, int *k, s *alpha, s *a, int *lda, s *beta, s *c, int *ldc) noexcept nogil
+cdef void stbmv(char *uplo, char *trans, char *diag, int *n, int *k, s *a, int *lda, s *x, int *incx) noexcept nogil
+cdef void stbsv(char *uplo, char *trans, char *diag, int *n, int *k, s *a, int *lda, s *x, int *incx) noexcept nogil
+cdef void stpmv(char *uplo, char *trans, char *diag, int *n, s *ap, s *x, int *incx) noexcept nogil
+cdef void stpsv(char *uplo, char *trans, char *diag, int *n, s *ap, s *x, int *incx) noexcept nogil
+cdef void strmm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, s *alpha, s *a, int *lda, s *b, int *ldb) noexcept nogil
+cdef void strmv(char *uplo, char *trans, char *diag, int *n, s *a, int *lda, s *x, int *incx) noexcept nogil
+cdef void strsm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, s *alpha, s *a, int *lda, s *b, int *ldb) noexcept nogil
+cdef void strsv(char *uplo, char *trans, char *diag, int *n, s *a, int *lda, s *x, int *incx) noexcept nogil
+cdef void zaxpy(int *n, z *za, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef void zcopy(int *n, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef z zdotc(int *n, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef z zdotu(int *n, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef void zdrot(int *n, z *cx, int *incx, z *cy, int *incy, d *c, d *s) noexcept nogil
+cdef void zdscal(int *n, d *da, z *zx, int *incx) noexcept nogil
+cdef void zgbmv(char *trans, int *m, int *n, int *kl, int *ku, z *alpha, z *a, int *lda, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zgemm(char *transa, char *transb, int *m, int *n, int *k, z *alpha, z *a, int *lda, z *b, int *ldb, z *beta, z *c, int *ldc) noexcept nogil
+cdef void zgemv(char *trans, int *m, int *n, z *alpha, z *a, int *lda, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zgerc(int *m, int *n, z *alpha, z *x, int *incx, z *y, int *incy, z *a, int *lda) noexcept nogil
+cdef void zgeru(int *m, int *n, z *alpha, z *x, int *incx, z *y, int *incy, z *a, int *lda) noexcept nogil
+cdef void zhbmv(char *uplo, int *n, int *k, z *alpha, z *a, int *lda, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zhemm(char *side, char *uplo, int *m, int *n, z *alpha, z *a, int *lda, z *b, int *ldb, z *beta, z *c, int *ldc) noexcept nogil
+cdef void zhemv(char *uplo, int *n, z *alpha, z *a, int *lda, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zher(char *uplo, int *n, d *alpha, z *x, int *incx, z *a, int *lda) noexcept nogil
+cdef void zher2(char *uplo, int *n, z *alpha, z *x, int *incx, z *y, int *incy, z *a, int *lda) noexcept nogil
+cdef void zher2k(char *uplo, char *trans, int *n, int *k, z *alpha, z *a, int *lda, z *b, int *ldb, d *beta, z *c, int *ldc) noexcept nogil
+cdef void zherk(char *uplo, char *trans, int *n, int *k, d *alpha, z *a, int *lda, d *beta, z *c, int *ldc) noexcept nogil
+cdef void zhpmv(char *uplo, int *n, z *alpha, z *ap, z *x, int *incx, z *beta, z *y, int *incy) noexcept nogil
+cdef void zhpr(char *uplo, int *n, d *alpha, z *x, int *incx, z *ap) noexcept nogil
+cdef void zhpr2(char *uplo, int *n, z *alpha, z *x, int *incx, z *y, int *incy, z *ap) noexcept nogil
+cdef void zrotg(z *ca, z *cb, d *c, z *s) noexcept nogil
+cdef void zscal(int *n, z *za, z *zx, int *incx) noexcept nogil
+cdef void zswap(int *n, z *zx, int *incx, z *zy, int *incy) noexcept nogil
+cdef void zsymm(char *side, char *uplo, int *m, int *n, z *alpha, z *a, int *lda, z *b, int *ldb, z *beta, z *c, int *ldc) noexcept nogil
+cdef void zsyr2k(char *uplo, char *trans, int *n, int *k, z *alpha, z *a, int *lda, z *b, int *ldb, z *beta, z *c, int *ldc) noexcept nogil
+cdef void zsyrk(char *uplo, char *trans, int *n, int *k, z *alpha, z *a, int *lda, z *beta, z *c, int *ldc) noexcept nogil
+cdef void ztbmv(char *uplo, char *trans, char *diag, int *n, int *k, z *a, int *lda, z *x, int *incx) noexcept nogil
+cdef void ztbsv(char *uplo, char *trans, char *diag, int *n, int *k, z *a, int *lda, z *x, int *incx) noexcept nogil
+cdef void ztpmv(char *uplo, char *trans, char *diag, int *n, z *ap, z *x, int *incx) noexcept nogil
+cdef void ztpsv(char *uplo, char *trans, char *diag, int *n, z *ap, z *x, int *incx) noexcept nogil
+cdef void ztrmm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, z *alpha, z *a, int *lda, z *b, int *ldb) noexcept nogil
+cdef void ztrmv(char *uplo, char *trans, char *diag, int *n, z *a, int *lda, z *x, int *incx) noexcept nogil
+cdef void ztrsm(char *side, char *uplo, char *transa, char *diag, int *m, int *n, z *alpha, z *a, int *lda, z *b, int *ldb) noexcept nogil
+cdef void ztrsv(char *uplo, char *trans, char *diag, int *n, z *a, int *lda, z *x, int *incx) noexcept nogil

shared/package-files/0c/0c26e96fb439c6485b0e2206edf3b80b4a45e0ccdd7c14f298cb22bab5fd1401/digamma.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/digamma_ops.h>
+
+namespace at {
+
+
+// aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & digamma_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::digamma_out::call(self, out);
+}
+// aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & digamma_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::digamma_out::call(self, out);
+}
+
+// aten::digamma(Tensor self) -> Tensor
+inline at::Tensor digamma(const at::Tensor & self) {
+    return at::_ops::digamma::call(self);
+}
+
+}

shared/package-files/0c/0c2d4669bc3b13d6f7073c1d029c6513aa0433bde7990a18c41b9c2fc8c0a005/test_tracing.py

@@ -0,0 +1,182 @@
+from io import StringIO
+import logging
+
+import unittest
+from numba.core import tracing
+
+logger = logging.getLogger('trace')
+logger.setLevel(logging.INFO)
+
+# Make sure tracing is enabled
+orig_trace = tracing.trace
+tracing.trace = tracing.dotrace
+
+class CapturedTrace:
+    """Capture the trace temporarily for validation."""
+
+    def __init__(self):
+        self.buffer = StringIO()
+        self.handler = logging.StreamHandler(self.buffer)
+    def __enter__(self):
+        self._handlers = logger.handlers
+        self.buffer = StringIO()
+        logger.handlers = [logging.StreamHandler(self.buffer)]
+    def __exit__(self, type, value, traceback):
+        logger.handlers = self._handlers
+    def getvalue(self):
+
+        # Depending on how the tests are run, object names may be
+        # qualified by their containing module.
+        # Remove that to make the trace output independent from the testing mode.
+        log = self.buffer.getvalue()
+        log = log.replace(__name__ + '.','')
+        return log
+
+class Class(object):
+
+    @tracing.trace
+    @classmethod
+    def class_method(cls):
+        pass
+
+    @tracing.trace
+    @staticmethod
+    def static_method():
+        pass
+
+    __test = None
+
+    def _test_get(self):
+        return self.__test
+
+    def _test_set(self, value):
+        self.__test = value
+
+    test = tracing.trace(property(_test_get, _test_set))
+        
+    @tracing.trace
+    def method(self, some, other='value', *args, **kwds):
+        pass
+
+    def __repr__(self):
+        """Generate a deterministic string for testing."""
+        return '<Class instance>'
+
+class Class2(object):
+    @classmethod
+    def class_method(cls):
+        pass
+
+    @staticmethod
+    def static_method():
+        pass
+
+    __test = None
+    @property
+    def test(self):
+        return self.__test
+    @test.setter
+    def test(self, value):
+        self.__test = value
+
+    def method(self):
+        pass
+
+    def __str__(self):
+        return 'Test(' + str(self.test) + ')'
+
+    def __repr__(self):
+        """Generate a deterministic string for testing."""
+        return '<Class2 instance>'
+
+
+@tracing.trace
+def test(x, y, z = True):
+    a = x + y
+    b = x * y
+    if z: return a
+    else: return b
+
+class TestTracing(unittest.TestCase):
+
+    def __init__(self, *args):
+        super(TestTracing, self).__init__(*args)
+
+    def setUp(self):
+        self.capture = CapturedTrace()
+
+    def tearDown(self):
+        del self.capture
+        
+    def test_method(self):
+
+        with self.capture:
+            Class().method('foo', bar='baz')
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class.method(self=<Class instance>, some='foo', other='value', bar='baz')\n" +
+                         "<< Class.method\n")
+
+    def test_class_method(self):
+
+        with self.capture:
+            Class.class_method()
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class.class_method(cls=<class 'Class'>)\n" +
+                         "<< Class.class_method\n")
+
+    def test_static_method(self):
+
+        with self.capture:
+            Class.static_method()
+        self.assertEqual(self.capture.getvalue(),
+                         ">> static_method()\n" +
+                         "<< static_method\n")
+
+
+    def test_property(self):
+
+        with self.capture:
+            test = Class()
+            test.test = 1
+            assert 1 == test.test
+        self.assertEqual(self.capture.getvalue(),
+                         ">> Class._test_set(self=<Class instance>, value=1)\n" +
+                         "<< Class._test_set\n" +
+                         ">> Class._test_get(self=<Class instance>)\n" +
+                         "<< Class._test_get -> 1\n")
+
+    def test_function(self):
+
+        with self.capture:
+            test(5, 5)
+            test(5, 5, False)
+        self.assertEqual(self.capture.getvalue(),
+                         ">> test(x=5, y=5, z=True)\n" +
+                         "<< test -> 10\n" +
+                         ">> test(x=5, y=5, z=False)\n" +
+                         "<< test -> 25\n")
+
+    @unittest.skip("recursive decoration not yet implemented")
+    def test_injected(self):
+
+        with self.capture:
+            tracing.trace(Class2, recursive=True)
+            Class2.class_method()
+            Class2.static_method()
+            test = Class2()
+            test.test = 1
+            assert 1 == test.test
+            test.method()
+
+            self.assertEqual(self.capture.getvalue(),
+                         ">> Class2.class_method(cls=<type 'Class2'>)\n" +
+                         "<< Class2.class_method\n"
+                         ">> static_method()\n"
+                         "<< static_method\n")
+
+            
+# Reset tracing to its original value
+tracing.trace = orig_trace
+
+if __name__ == '__main__':
+    unittest.main()

shared/package-files/0c/0c38936b6626aa40a1a4bccaafcd27f9939f2405d66fa618074444474d77747c/msvcrt.pyi

@@ -0,0 +1,24 @@
+import sys
+from typing import Text
+
+# This module is only available on Windows
+if sys.platform == "win32":
+    LK_LOCK: int
+    LK_NBLCK: int
+    LK_NBRLCK: int
+    LK_RLCK: int
+    LK_UNLCK: int
+    def locking(__fd: int, __mode: int, __nbytes: int) -> None: ...
+    def setmode(__fd: int, __mode: int) -> int: ...
+    def open_osfhandle(__handle: int, __flags: int) -> int: ...
+    def get_osfhandle(__fd: int) -> int: ...
+    def kbhit() -> bool: ...
+    def getch() -> bytes: ...
+    def getwch() -> Text: ...
+    def getche() -> bytes: ...
+    def getwche() -> Text: ...
+    def putch(__char: bytes) -> None: ...
+    def putwch(__unicode_char: Text) -> None: ...
+    def ungetch(__char: bytes) -> None: ...
+    def ungetwch(__unicode_char: Text) -> None: ...
+    def heapmin() -> None: ...

shared/package-files/0c/0c38a332607070a1ab616daa6f0cfc5b7bc3dd4abac295fff70edf60a6275763/textfmts.py

@@ -0,0 +1,431 @@
+"""
+    pygments.lexers.textfmts
+    ~~~~~~~~~~~~~~~~~~~~~~~~
+
+    Lexers for various text formats.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import re
+
+from pygments.lexers import guess_lexer, get_lexer_by_name
+from pygments.lexer import RegexLexer, bygroups, default, include
+from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
+    Number, Generic, Literal, Punctuation
+from pygments.util import ClassNotFound
+
+__all__ = ['IrcLogsLexer', 'TodotxtLexer', 'HttpLexer', 'GettextLexer',
+           'NotmuchLexer', 'KernelLogLexer']
+
+
+class IrcLogsLexer(RegexLexer):
+    """
+    Lexer for IRC logs in *irssi*, *xchat* or *weechat* style.
+    """
+
+    name = 'IRC logs'
+    aliases = ['irc']
+    filenames = ['*.weechatlog']
+    mimetypes = ['text/x-irclog']
+
+    flags = re.VERBOSE | re.MULTILINE
+    timestamp = r"""
+        (
+          # irssi / xchat and others
+          (?: \[|\()?                  # Opening bracket or paren for the timestamp
+            (?:                        # Timestamp
+                (?: (?:\d{1,4} [-/])*  # Date as - or /-separated groups of digits
+                    (?:\d{1,4})
+                 [T ])?                # Date/time separator: T or space
+                (?: \d?\d [:.])*       # Time as :/.-separated groups of 1 or 2 digits
+                    (?: \d?\d)
+            )
+          (?: \]|\))?\s+               # Closing bracket or paren for the timestamp
+        |
+          # weechat
+          \d{4}\s\w{3}\s\d{2}\s        # Date
+          \d{2}:\d{2}:\d{2}\s+         # Time + Whitespace
+        |
+          # xchat
+          \w{3}\s\d{2}\s               # Date
+          \d{2}:\d{2}:\d{2}\s+         # Time + Whitespace
+        )?
+    """
+    tokens = {
+        'root': [
+            # log start/end
+            (r'^\*\*\*\*(.*)\*\*\*\*$', Comment),
+            # hack
+            ("^" + timestamp + r'(\s*<[^>]*>\s*)$', bygroups(Comment.Preproc, Name.Tag)),
+            # normal msgs
+            ("^" + timestamp + r"""
+                (\s*<.*?>\s*)          # Nick """,
+             bygroups(Comment.Preproc, Name.Tag), 'msg'),
+            # /me msgs
+            ("^" + timestamp + r"""
+                (\s*[*]\s+)            # Star
+                (\S+\s+.*?\n)          # Nick + rest of message """,
+             bygroups(Comment.Preproc, Keyword, Generic.Inserted)),
+            # join/part msgs
+            ("^" + timestamp + r"""
+                (\s*(?:\*{3}|<?-[!@=P]?->?)\s*)  # Star(s) or symbols
+                (\S+\s+)                     # Nick + Space
+                (.*?\n)                         # Rest of message """,
+             bygroups(Comment.Preproc, Keyword, String, Comment)),
+            (r"^.*?\n", Text),
+        ],
+        'msg': [
+            (r"\S+:(?!//)", Name.Attribute),  # Prefix
+            (r".*\n", Text, '#pop'),
+        ],
+    }
+
+
+class GettextLexer(RegexLexer):
+    """
+    Lexer for Gettext catalog files.
+
+    .. versionadded:: 0.9
+    """
+    name = 'Gettext Catalog'
+    aliases = ['pot', 'po']
+    filenames = ['*.pot', '*.po']
+    mimetypes = ['application/x-gettext', 'text/x-gettext', 'text/gettext']
+
+    tokens = {
+        'root': [
+            (r'^#,\s.*?$', Keyword.Type),
+            (r'^#:\s.*?$', Keyword.Declaration),
+            # (r'^#$', Comment),
+            (r'^(#|#\.\s|#\|\s|#~\s|#\s).*$', Comment.Single),
+            (r'^(")([A-Za-z-]+:)(.*")$',
+             bygroups(String, Name.Property, String)),
+            (r'^".*"$', String),
+            (r'^(msgid|msgid_plural|msgstr|msgctxt)(\s+)(".*")$',
+             bygroups(Name.Variable, Text, String)),
+            (r'^(msgstr\[)(\d)(\])(\s+)(".*")$',
+             bygroups(Name.Variable, Number.Integer, Name.Variable, Text, String)),
+        ]
+    }
+
+
+class HttpLexer(RegexLexer):
+    """
+    Lexer for HTTP sessions.
+
+    .. versionadded:: 1.5
+    """
+
+    name = 'HTTP'
+    aliases = ['http']
+
+    flags = re.DOTALL
+
+    def get_tokens_unprocessed(self, text, stack=('root',)):
+        """Reset the content-type state."""
+        self.content_type = None
+        return RegexLexer.get_tokens_unprocessed(self, text, stack)
+
+    def header_callback(self, match):
+        if match.group(1).lower() == 'content-type':
+            content_type = match.group(5).strip()
+            if ';' in content_type:
+                content_type = content_type[:content_type.find(';')].strip()
+            self.content_type = content_type
+        yield match.start(1), Name.Attribute, match.group(1)
+        yield match.start(2), Text, match.group(2)
+        yield match.start(3), Operator, match.group(3)
+        yield match.start(4), Text, match.group(4)
+        yield match.start(5), Literal, match.group(5)
+        yield match.start(6), Text, match.group(6)
+
+    def continuous_header_callback(self, match):
+        yield match.start(1), Text, match.group(1)
+        yield match.start(2), Literal, match.group(2)
+        yield match.start(3), Text, match.group(3)
+
+    def content_callback(self, match):
+        content_type = getattr(self, 'content_type', None)
+        content = match.group()
+        offset = match.start()
+        if content_type:
+            from pygments.lexers import get_lexer_for_mimetype
+            possible_lexer_mimetypes = [content_type]
+            if '+' in content_type:
+                # application/calendar+xml can be treated as application/xml
+                # if there's not a better match.
+                general_type = re.sub(r'^(.*)/.*\+(.*)$', r'\1/\2',
+                                      content_type)
+                possible_lexer_mimetypes.append(general_type)
+
+            for i in possible_lexer_mimetypes:
+                try:
+                    lexer = get_lexer_for_mimetype(i)
+                except ClassNotFound:
+                    pass
+                else:
+                    for idx, token, value in lexer.get_tokens_unprocessed(content):
+                        yield offset + idx, token, value
+                    return
+        yield offset, Text, content
+
+    tokens = {
+        'root': [
+            (r'(GET|POST|PUT|DELETE|HEAD|OPTIONS|TRACE|PATCH|CONNECT)( +)([^ ]+)( +)'
+             r'(HTTP)(/)(1\.[01]|2(?:\.0)?|3)(\r?\n|\Z)',
+             bygroups(Name.Function, Text, Name.Namespace, Text,
+                      Keyword.Reserved, Operator, Number, Text),
+             'headers'),
+            (r'(HTTP)(/)(1\.[01]|2(?:\.0)?|3)( +)(\d{3})(?:( +)([^\r\n]*))?(\r?\n|\Z)',
+             bygroups(Keyword.Reserved, Operator, Number, Text, Number, Text,
+                      Name.Exception, Text),
+             'headers'),
+        ],
+        'headers': [
+            (r'([^\s:]+)( *)(:)( *)([^\r\n]+)(\r?\n|\Z)', header_callback),
+            (r'([\t ]+)([^\r\n]+)(\r?\n|\Z)', continuous_header_callback),
+            (r'\r?\n', Text, 'content')
+        ],
+        'content': [
+            (r'.+', content_callback)
+        ]
+    }
+
+    def analyse_text(text):
+        return text.startswith(('GET /', 'POST /', 'PUT /', 'DELETE /', 'HEAD /',
+                                'OPTIONS /', 'TRACE /', 'PATCH /', 'CONNECT '))
+
+
+class TodotxtLexer(RegexLexer):
+    """
+    Lexer for Todo.txt todo list format.
+
+    .. versionadded:: 2.0
+    """
+
+    name = 'Todotxt'
+    url = 'http://todotxt.com/'
+    aliases = ['todotxt']
+    # *.todotxt is not a standard extension for Todo.txt files; including it
+    # makes testing easier, and also makes autodetecting file type easier.
+    filenames = ['todo.txt', '*.todotxt']
+    mimetypes = ['text/x-todo']
+
+    # Aliases mapping standard token types of Todo.txt format concepts
+    CompleteTaskText = Operator  # Chosen to de-emphasize complete tasks
+    IncompleteTaskText = Text    # Incomplete tasks should look like plain text
+
+    # Priority should have most emphasis to indicate importance of tasks
+    Priority = Generic.Heading
+    # Dates should have next most emphasis because time is important
+    Date = Generic.Subheading
+
+    # Project and context should have equal weight, and be in different colors
+    Project = Generic.Error
+    Context = String
+
+    # If tag functionality is added, it should have the same weight as Project
+    # and Context, and a different color. Generic.Traceback would work well.
+
+    # Regex patterns for building up rules; dates, priorities, projects, and
+    # contexts are all atomic
+    # TODO: Make date regex more ISO 8601 compliant
+    date_regex = r'\d{4,}-\d{2}-\d{2}'
+    priority_regex = r'\([A-Z]\)'
+    project_regex = r'\+\S+'
+    context_regex = r'@\S+'
+
+    # Compound regex expressions
+    complete_one_date_regex = r'(x )(' + date_regex + r')'
+    complete_two_date_regex = (complete_one_date_regex + r'( )(' +
+                               date_regex + r')')
+    priority_date_regex = r'(' + priority_regex + r')( )(' + date_regex + r')'
+
+    tokens = {
+        # Should parse starting at beginning of line; each line is a task
+        'root': [
+            # Complete task entry points: two total:
+            # 1. Complete task with two dates
+            (complete_two_date_regex, bygroups(CompleteTaskText, Date,
+                                               CompleteTaskText, Date),
+             'complete'),
+            # 2. Complete task with one date
+            (complete_one_date_regex, bygroups(CompleteTaskText, Date),
+             'complete'),
+
+            # Incomplete task entry points: six total:
+            # 1. Priority plus date
+            (priority_date_regex, bygroups(Priority, IncompleteTaskText, Date),
+             'incomplete'),
+            # 2. Priority only
+            (priority_regex, Priority, 'incomplete'),
+            # 3. Leading date
+            (date_regex, Date, 'incomplete'),
+            # 4. Leading context
+            (context_regex, Context, 'incomplete'),
+            # 5. Leading project
+            (project_regex, Project, 'incomplete'),
+            # 6. Non-whitespace catch-all
+            (r'\S+', IncompleteTaskText, 'incomplete'),
+        ],
+
+        # Parse a complete task
+        'complete': [
+            # Newline indicates end of task, should return to root
+            (r'\s*\n', CompleteTaskText, '#pop'),
+            # Tokenize contexts and projects
+            (context_regex, Context),
+            (project_regex, Project),
+            # Tokenize non-whitespace text
+            (r'\S+', CompleteTaskText),
+            # Tokenize whitespace not containing a newline
+            (r'\s+', CompleteTaskText),
+        ],
+
+        # Parse an incomplete task
+        'incomplete': [
+            # Newline indicates end of task, should return to root
+            (r'\s*\n', IncompleteTaskText, '#pop'),
+            # Tokenize contexts and projects
+            (context_regex, Context),
+            (project_regex, Project),
+            # Tokenize non-whitespace text
+            (r'\S+', IncompleteTaskText),
+            # Tokenize whitespace not containing a newline
+            (r'\s+', IncompleteTaskText),
+        ],
+    }
+
+
+class NotmuchLexer(RegexLexer):
+    """
+    For Notmuch email text format.
+
+    .. versionadded:: 2.5
+
+    Additional options accepted:
+
+    `body_lexer`
+        If given, highlight the contents of the message body with the specified
+        lexer, else guess it according to the body content (default: ``None``).
+    """
+
+    name = 'Notmuch'
+    url = 'https://notmuchmail.org/'
+    aliases = ['notmuch']
+
+    def _highlight_code(self, match):
+        code = match.group(1)
+
+        try:
+            if self.body_lexer:
+                lexer = get_lexer_by_name(self.body_lexer)
+            else:
+                lexer = guess_lexer(code.strip())
+        except ClassNotFound:
+            lexer = get_lexer_by_name('text')
+
+        yield from lexer.get_tokens_unprocessed(code)
+
+    tokens = {
+        'root': [
+            (r'\fmessage\{\s*', Keyword, ('message', 'message-attr')),
+        ],
+        'message-attr': [
+            (r'(\s*id:\s*)(\S+)', bygroups(Name.Attribute, String)),
+            (r'(\s*(?:depth|match|excluded):\s*)(\d+)',
+             bygroups(Name.Attribute, Number.Integer)),
+            (r'(\s*filename:\s*)(.+\n)',
+             bygroups(Name.Attribute, String)),
+            default('#pop'),
+        ],
+        'message': [
+            (r'\fmessage\}\n', Keyword, '#pop'),
+            (r'\fheader\{\n', Keyword, 'header'),
+            (r'\fbody\{\n', Keyword, 'body'),
+        ],
+        'header': [
+            (r'\fheader\}\n', Keyword, '#pop'),
+            (r'((?:Subject|From|To|Cc|Date):\s*)(.*\n)',
+             bygroups(Name.Attribute, String)),
+            (r'(.*)(\s*\(.*\))(\s*\(.*\)\n)',
+             bygroups(Generic.Strong, Literal, Name.Tag)),
+        ],
+        'body': [
+            (r'\fpart\{\n', Keyword, 'part'),
+            (r'\f(part|attachment)\{\s*', Keyword, ('part', 'part-attr')),
+            (r'\fbody\}\n', Keyword, '#pop'),
+        ],
+        'part-attr': [
+            (r'(ID:\s*)(\d+)', bygroups(Name.Attribute, Number.Integer)),
+            (r'(,\s*)((?:Filename|Content-id):\s*)([^,]+)',
+             bygroups(Punctuation, Name.Attribute, String)),
+            (r'(,\s*)(Content-type:\s*)(.+\n)',
+             bygroups(Punctuation, Name.Attribute, String)),
+            default('#pop'),
+        ],
+        'part': [
+            (r'\f(?:part|attachment)\}\n', Keyword, '#pop'),
+            (r'\f(?:part|attachment)\{\s*', Keyword, ('#push', 'part-attr')),
+            (r'^Non-text part: .*\n', Comment),
+            (r'(?s)(.*?(?=\f(?:part|attachment)\}\n))', _highlight_code),
+        ],
+    }
+
+    def analyse_text(text):
+        return 1.0 if text.startswith('\fmessage{') else 0.0
+
+    def __init__(self, **options):
+        self.body_lexer = options.get('body_lexer', None)
+        RegexLexer.__init__(self, **options)
+
+
+class KernelLogLexer(RegexLexer):
+    """
+    For Linux Kernel log ("dmesg") output.
+
+    .. versionadded:: 2.6
+    """
+    name = 'Kernel log'
+    aliases = ['kmsg', 'dmesg']
+    filenames = ['*.kmsg', '*.dmesg']
+
+    tokens = {
+        'root': [
+            (r'^[^:]+:debug : (?=\[)', Text, 'debug'),
+            (r'^[^:]+:info  : (?=\[)', Text, 'info'),
+            (r'^[^:]+:warn  : (?=\[)', Text, 'warn'),
+            (r'^[^:]+:notice: (?=\[)', Text, 'warn'),
+            (r'^[^:]+:err   : (?=\[)', Text, 'error'),
+            (r'^[^:]+:crit  : (?=\[)', Text, 'error'),
+            (r'^(?=\[)', Text, 'unknown'),
+        ],
+        'unknown': [
+            (r'^(?=.+(warning|notice|audit|deprecated))', Text, 'warn'),
+            (r'^(?=.+(error|critical|fail|Bug))', Text, 'error'),
+            default('info'),
+        ],
+        'base': [
+            (r'\[[0-9. ]+\] ', Number),
+            (r'(?<=\] ).+?:', Keyword),
+            (r'\n', Text, '#pop'),
+        ],
+        'debug': [
+            include('base'),
+            (r'.+\n', Comment, '#pop')
+        ],
+        'info': [
+            include('base'),
+            (r'.+\n', Text, '#pop')
+        ],
+        'warn': [
+            include('base'),
+            (r'.+\n', Generic.Strong, '#pop')
+        ],
+        'error': [
+            include('base'),
+            (r'.+\n', Generic.Error, '#pop')
+        ]
+    }

shared/package-files/0c/0c3b8cc2b2263040b0e8c85f9dc9f6c4c3a390570a515d4ed79f7bfdf485480b/chunked_builder.h

@@ -0,0 +1,68 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <memory>
+#include <vector>
+
+#include "arrow/status.h"
+#include "arrow/type_fwd.h"
+#include "arrow/util/type_fwd.h"
+#include "arrow/util/visibility.h"
+
+namespace arrow {
+namespace json {
+
+class PromotionGraph;
+
+class ARROW_EXPORT ChunkedArrayBuilder {
+ public:
+  virtual ~ChunkedArrayBuilder() = default;
+
+  /// Spawn a task that will try to convert and insert the given JSON block
+  virtual void Insert(int64_t block_index,
+                      const std::shared_ptr<Field>& unconverted_field,
+                      const std::shared_ptr<Array>& unconverted) = 0;
+
+  /// Return the final chunked array.
+  /// Every chunk must be inserted before this is called!
+  virtual Status Finish(std::shared_ptr<ChunkedArray>* out) = 0;
+
+  /// Finish current task group and substitute a new one
+  virtual Status ReplaceTaskGroup(
+      const std::shared_ptr<arrow::internal::TaskGroup>& task_group) = 0;
+
+ protected:
+  explicit ChunkedArrayBuilder(
+      const std::shared_ptr<arrow::internal::TaskGroup>& task_group)
+      : task_group_(task_group) {}
+
+  std::shared_ptr<arrow::internal::TaskGroup> task_group_;
+};
+
+/// create a chunked builder
+///
+/// if unexpected fields and promotion need to be handled, promotion_graph must be
+/// non-null
+ARROW_EXPORT Status MakeChunkedArrayBuilder(
+    const std::shared_ptr<arrow::internal::TaskGroup>& task_group, MemoryPool* pool,
+    const PromotionGraph* promotion_graph, const std::shared_ptr<DataType>& type,
+    std::shared_ptr<ChunkedArrayBuilder>* out);
+
+}  // namespace json
+}  // namespace arrow

shared/package-files/0c/0c3bf5261f951c3c1c6373588b8fbd4a9c7fe7dbba9face51bb58c8b74ad989b/distro.py

@@ -0,0 +1,1107 @@
+# Copyright 2015,2016 Nir Cohen
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+The ``distro`` package (``distro`` stands for Linux Distribution) provides
+information about the Linux distribution it runs on, such as a reliable
+machine-readable distro ID, or version information.
+
+It is a renewed alternative implementation for Python's original
+:py:func:`platform.linux_distribution` function, but it provides much more
+functionality. An alternative implementation became necessary because Python
+3.5 deprecated this function, and Python 3.7 is expected to remove it
+altogether. Its predecessor function :py:func:`platform.dist` was already
+deprecated since Python 2.6 and is also expected to be removed in Python 3.7.
+Still, there are many cases in which access to Linux distribution information
+is needed. See `Python issue 1322 <https://bugs.python.org/issue1322>`_ for
+more information.
+"""
+
+import os
+import re
+import sys
+import json
+import shlex
+import logging
+import argparse
+import subprocess
+
+
+if not sys.platform.startswith('linux'):
+    raise ImportError('Unsupported platform: {0}'.format(sys.platform))
+
+_UNIXCONFDIR = os.environ.get('UNIXCONFDIR', '/etc')
+_OS_RELEASE_BASENAME = 'os-release'
+
+#: Translation table for normalizing the "ID" attribute defined in os-release
+#: files, for use by the :func:`distro.id` method.
+#:
+#: * Key: Value as defined in the os-release file, translated to lower case,
+#:   with blanks translated to underscores.
+#:
+#: * Value: Normalized value.
+NORMALIZED_OS_ID = {}
+
+#: Translation table for normalizing the "Distributor ID" attribute returned by
+#: the lsb_release command, for use by the :func:`distro.id` method.
+#:
+#: * Key: Value as returned by the lsb_release command, translated to lower
+#:   case, with blanks translated to underscores.
+#:
+#: * Value: Normalized value.
+NORMALIZED_LSB_ID = {
+    'enterpriseenterprise': 'oracle',  # Oracle Enterprise Linux
+    'redhatenterpriseworkstation': 'rhel',  # RHEL 6, 7 Workstation
+    'redhatenterpriseserver': 'rhel',  # RHEL 6, 7 Server
+}
+
+#: Translation table for normalizing the distro ID derived from the file name
+#: of distro release files, for use by the :func:`distro.id` method.
+#:
+#: * Key: Value as derived from the file name of a distro release file,
+#:   translated to lower case, with blanks translated to underscores.
+#:
+#: * Value: Normalized value.
+NORMALIZED_DISTRO_ID = {
+    'redhat': 'rhel',  # RHEL 6.x, 7.x
+}
+
+# Pattern for content of distro release file (reversed)
+_DISTRO_RELEASE_CONTENT_REVERSED_PATTERN = re.compile(
+    r'(?:[^)]*\)(.*)\()? *(?:STL )?([\d.+\-a-z]*\d) *(?:esaeler *)?(.+)')
+
+# Pattern for base file name of distro release file
+_DISTRO_RELEASE_BASENAME_PATTERN = re.compile(
+    r'(\w+)[-_](release|version)$')
+
+# Base file names to be ignored when searching for distro release file
+_DISTRO_RELEASE_IGNORE_BASENAMES = (
+    'debian_version',
+    'lsb-release',
+    'oem-release',
+    _OS_RELEASE_BASENAME,
+    'system-release'
+)
+
+
+def linux_distribution(full_distribution_name=True):
+    """
+    Return information about the current Linux distribution as a tuple
+    ``(id_name, version, codename)`` with items as follows:
+
+    * ``id_name``:  If *full_distribution_name* is false, the result of
+      :func:`distro.id`. Otherwise, the result of :func:`distro.name`.
+
+    * ``version``:  The result of :func:`distro.version`.
+
+    * ``codename``:  The result of :func:`distro.codename`.
+
+    The interface of this function is compatible with the original
+    :py:func:`platform.linux_distribution` function, supporting a subset of
+    its parameters.
+
+    The data it returns may not exactly be the same, because it uses more data
+    sources than the original function, and that may lead to different data if
+    the Linux distribution is not consistent across multiple data sources it
+    provides (there are indeed such distributions ...).
+
+    Another reason for differences is the fact that the :func:`distro.id`
+    method normalizes the distro ID string to a reliable machine-readable value
+    for a number of popular Linux distributions.
+    """
+    return _distro.linux_distribution(full_distribution_name)
+
+
+def id():
+    """
+    Return the distro ID of the current Linux distribution, as a
+    machine-readable string.
+
+    For a number of Linux distributions, the returned distro ID value is
+    *reliable*, in the sense that it is documented and that it does not change
+    across releases of the distribution.
+
+    This package maintains the following reliable distro ID values:
+
+    ==============  =========================================
+    Distro ID       Distribution
+    ==============  =========================================
+    "ubuntu"        Ubuntu
+    "debian"        Debian
+    "rhel"          RedHat Enterprise Linux
+    "centos"        CentOS
+    "fedora"        Fedora
+    "sles"          SUSE Linux Enterprise Server
+    "opensuse"      openSUSE
+    "amazon"        Amazon Linux
+    "arch"          Arch Linux
+    "cloudlinux"    CloudLinux OS
+    "exherbo"       Exherbo Linux
+    "gentoo"        GenToo Linux
+    "ibm_powerkvm"  IBM PowerKVM
+    "kvmibm"        KVM for IBM z Systems
+    "linuxmint"     Linux Mint
+    "mageia"        Mageia
+    "mandriva"      Mandriva Linux
+    "parallels"     Parallels
+    "pidora"        Pidora
+    "raspbian"      Raspbian
+    "oracle"        Oracle Linux (and Oracle Enterprise Linux)
+    "scientific"    Scientific Linux
+    "slackware"     Slackware
+    "xenserver"     XenServer
+    ==============  =========================================
+
+    If you have a need to get distros for reliable IDs added into this set,
+    or if you find that the :func:`distro.id` function returns a different
+    distro ID for one of the listed distros, please create an issue in the
+    `distro issue tracker`_.
+
+    **Lookup hierarchy and transformations:**
+
+    First, the ID is obtained from the following sources, in the specified
+    order. The first available and non-empty value is used:
+
+    * the value of the "ID" attribute of the os-release file,
+
+    * the value of the "Distributor ID" attribute returned by the lsb_release
+      command,
+
+    * the first part of the file name of the distro release file,
+
+    The so determined ID value then passes the following transformations,
+    before it is returned by this method:
+
+    * it is translated to lower case,
+
+    * blanks (which should not be there anyway) are translated to underscores,
+
+    * a normalization of the ID is performed, based upon
+      `normalization tables`_. The purpose of this normalization is to ensure
+      that the ID is as reliable as possible, even across incompatible changes
+      in the Linux distributions. A common reason for an incompatible change is
+      the addition of an os-release file, or the addition of the lsb_release
+      command, with ID values that differ from what was previously determined
+      from the distro release file name.
+    """
+    return _distro.id()
+
+
+def name(pretty=False):
+    """
+    Return the name of the current Linux distribution, as a human-readable
+    string.
+
+    If *pretty* is false, the name is returned without version or codename.
+    (e.g. "CentOS Linux")
+
+    If *pretty* is true, the version and codename are appended.
+    (e.g. "CentOS Linux 7.1.1503 (Core)")
+
+    **Lookup hierarchy:**
+
+    The name is obtained from the following sources, in the specified order.
+    The first available and non-empty value is used:
+
+    * If *pretty* is false:
+
+      - the value of the "NAME" attribute of the os-release file,
+
+      - the value of the "Distributor ID" attribute returned by the lsb_release
+        command,
+
+      - the value of the "<name>" field of the distro release file.
+
+    * If *pretty* is true:
+
+      - the value of the "PRETTY_NAME" attribute of the os-release file,
+
+      - the value of the "Description" attribute returned by the lsb_release
+        command,
+
+      - the value of the "<name>" field of the distro release file, appended
+        with the value of the pretty version ("<version_id>" and "<codename>"
+        fields) of the distro release file, if available.
+    """
+    return _distro.name(pretty)
+
+
+def version(pretty=False, best=False):
+    """
+    Return the version of the current Linux distribution, as a human-readable
+    string.
+
+    If *pretty* is false, the version is returned without codename (e.g.
+    "7.0").
+
+    If *pretty* is true, the codename in parenthesis is appended, if the
+    codename is non-empty (e.g. "7.0 (Maipo)").
+
+    Some distributions provide version numbers with different precisions in
+    the different sources of distribution information. Examining the different
+    sources in a fixed priority order does not always yield the most precise
+    version (e.g. for Debian 8.2, or CentOS 7.1).
+
+    The *best* parameter can be used to control the approach for the returned
+    version:
+
+    If *best* is false, the first non-empty version number in priority order of
+    the examined sources is returned.
+
+    If *best* is true, the most precise version number out of all examined
+    sources is returned.
+
+    **Lookup hierarchy:**
+
+    In all cases, the version number is obtained from the following sources.
+    If *best* is false, this order represents the priority order:
+
+    * the value of the "VERSION_ID" attribute of the os-release file,
+    * the value of the "Release" attribute returned by the lsb_release
+      command,
+    * the version number parsed from the "<version_id>" field of the first line
+      of the distro release file,
+    * the version number parsed from the "PRETTY_NAME" attribute of the
+      os-release file, if it follows the format of the distro release files.
+    * the version number parsed from the "Description" attribute returned by
+      the lsb_release command, if it follows the format of the distro release
+      files.
+    """
+    return _distro.version(pretty, best)
+
+
+def version_parts(best=False):
+    """
+    Return the version of the current Linux distribution as a tuple
+    ``(major, minor, build_number)`` with items as follows:
+
+    * ``major``:  The result of :func:`distro.major_version`.
+
+    * ``minor``:  The result of :func:`distro.minor_version`.
+
+    * ``build_number``:  The result of :func:`distro.build_number`.
+
+    For a description of the *best* parameter, see the :func:`distro.version`
+    method.
+    """
+    return _distro.version_parts(best)
+
+
+def major_version(best=False):
+    """
+    Return the major version of the current Linux distribution, as a string,
+    if provided.
+    Otherwise, the empty string is returned. The major version is the first
+    part of the dot-separated version string.
+
+    For a description of the *best* parameter, see the :func:`distro.version`
+    method.
+    """
+    return _distro.major_version(best)
+
+
+def minor_version(best=False):
+    """
+    Return the minor version of the current Linux distribution, as a string,
+    if provided.
+    Otherwise, the empty string is returned. The minor version is the second
+    part of the dot-separated version string.
+
+    For a description of the *best* parameter, see the :func:`distro.version`
+    method.
+    """
+    return _distro.minor_version(best)
+
+
+def build_number(best=False):
+    """
+    Return the build number of the current Linux distribution, as a string,
+    if provided.
+    Otherwise, the empty string is returned. The build number is the third part
+    of the dot-separated version string.
+
+    For a description of the *best* parameter, see the :func:`distro.version`
+    method.
+    """
+    return _distro.build_number(best)
+
+
+def like():
+    """
+    Return a space-separated list of distro IDs of distributions that are
+    closely related to the current Linux distribution in regards to packaging
+    and programming interfaces, for example distributions the current
+    distribution is a derivative from.
+
+    **Lookup hierarchy:**
+
+    This information item is only provided by the os-release file.
+    For details, see the description of the "ID_LIKE" attribute in the
+    `os-release man page
+    <http://www.freedesktop.org/software/systemd/man/os-release.html>`_.
+    """
+    return _distro.like()
+
+
+def codename():
+    """
+    Return the codename for the release of the current Linux distribution,
+    as a string.
+
+    If the distribution does not have a codename, an empty string is returned.
+
+    Note that the returned codename is not always really a codename. For
+    example, openSUSE returns "x86_64". This function does not handle such
+    cases in any special way and just returns the string it finds, if any.
+
+    **Lookup hierarchy:**
+
+    * the codename within the "VERSION" attribute of the os-release file, if
+      provided,
+
+    * the value of the "Codename" attribute returned by the lsb_release
+      command,
+
+    * the value of the "<codename>" field of the distro release file.
+    """
+    return _distro.codename()
+
+
+def info(pretty=False, best=False):
+    """
+    Return certain machine-readable information items about the current Linux
+    distribution in a dictionary, as shown in the following example:
+
+    .. sourcecode:: python
+
+        {
+            'id': 'rhel',
+            'version': '7.0',
+            'version_parts': {
+                'major': '7',
+                'minor': '0',
+                'build_number': ''
+            },
+            'like': 'fedora',
+            'codename': 'Maipo'
+        }
+
+    The dictionary structure and keys are always the same, regardless of which
+    information items are available in the underlying data sources. The values
+    for the various keys are as follows:
+
+    * ``id``:  The result of :func:`distro.id`.
+
+    * ``version``:  The result of :func:`distro.version`.
+
+    * ``version_parts -> major``:  The result of :func:`distro.major_version`.
+
+    * ``version_parts -> minor``:  The result of :func:`distro.minor_version`.
+
+    * ``version_parts -> build_number``:  The result of
+      :func:`distro.build_number`.
+
+    * ``like``:  The result of :func:`distro.like`.
+
+    * ``codename``:  The result of :func:`distro.codename`.
+
+    For a description of the *pretty* and *best* parameters, see the
+    :func:`distro.version` method.
+    """
+    return _distro.info(pretty, best)
+
+
+def os_release_info():
+    """
+    Return a dictionary containing key-value pairs for the information items
+    from the os-release file data source of the current Linux distribution.
+
+    See `os-release file`_ for details about these information items.
+    """
+    return _distro.os_release_info()
+
+
+def lsb_release_info():
+    """
+    Return a dictionary containing key-value pairs for the information items
+    from the lsb_release command data source of the current Linux distribution.
+
+    See `lsb_release command output`_ for details about these information
+    items.
+    """
+    return _distro.lsb_release_info()
+
+
+def distro_release_info():
+    """
+    Return a dictionary containing key-value pairs for the information items
+    from the distro release file data source of the current Linux distribution.
+
+    See `distro release file`_ for details about these information items.
+    """
+    return _distro.distro_release_info()
+
+
+def os_release_attr(attribute):
+    """
+    Return a single named information item from the os-release file data source
+    of the current Linux distribution.
+
+    Parameters:
+
+    * ``attribute`` (string): Key of the information item.
+
+    Returns:
+
+    * (string): Value of the information item, if the item exists.
+      The empty string, if the item does not exist.
+
+    See `os-release file`_ for details about these information items.
+    """
+    return _distro.os_release_attr(attribute)
+
+
+def lsb_release_attr(attribute):
+    """
+    Return a single named information item from the lsb_release command output
+    data source of the current Linux distribution.
+
+    Parameters:
+
+    * ``attribute`` (string): Key of the information item.
+
+    Returns:
+
+    * (string): Value of the information item, if the item exists.
+      The empty string, if the item does not exist.
+
+    See `lsb_release command output`_ for details about these information
+    items.
+    """
+    return _distro.lsb_release_attr(attribute)
+
+
+def distro_release_attr(attribute):
+    """
+    Return a single named information item from the distro release file
+    data source of the current Linux distribution.
+
+    Parameters:
+
+    * ``attribute`` (string): Key of the information item.
+
+    Returns:
+
+    * (string): Value of the information item, if the item exists.
+      The empty string, if the item does not exist.
+
+    See `distro release file`_ for details about these information items.
+    """
+    return _distro.distro_release_attr(attribute)
+
+
+class LinuxDistribution(object):
+    """
+    Provides information about a Linux distribution.
+
+    This package creates a private module-global instance of this class with
+    default initialization arguments, that is used by the
+    `consolidated accessor functions`_ and `single source accessor functions`_.
+    By using default initialization arguments, that module-global instance
+    returns data about the current Linux distribution (i.e. the distro this
+    package runs on).
+
+    Normally, it is not necessary to create additional instances of this class.
+    However, in situations where control is needed over the exact data sources
+    that are used, instances of this class can be created with a specific
+    distro release file, or a specific os-release file, or without invoking the
+    lsb_release command.
+    """
+
+    def __init__(self,
+                 include_lsb=True,
+                 os_release_file='',
+                 distro_release_file=''):
+        """
+        The initialization method of this class gathers information from the
+        available data sources, and stores that in private instance attributes.
+        Subsequent access to the information items uses these private instance
+        attributes, so that the data sources are read only once.
+
+        Parameters:
+
+        * ``include_lsb`` (bool): Controls whether the
+          `lsb_release command output`_ is included as a data source.
+
+          If the lsb_release command is not available in the program execution
+          path, the data source for the lsb_release command will be empty.
+
+        * ``os_release_file`` (string): The path name of the
+          `os-release file`_ that is to be used as a data source.
+
+          An empty string (the default) will cause the default path name to
+          be used (see `os-release file`_ for details).
+
+          If the specified or defaulted os-release file does not exist, the
+          data source for the os-release file will be empty.
+
+        * ``distro_release_file`` (string): The path name of the
+          `distro release file`_ that is to be used as a data source.
+
+          An empty string (the default) will cause a default search algorithm
+          to be used (see `distro release file`_ for details).
+
+          If the specified distro release file does not exist, or if no default
+          distro release file can be found, the data source for the distro
+          release file will be empty.
+
+        Public instance attributes:
+
+        * ``os_release_file`` (string): The path name of the
+          `os-release file`_ that is actually used as a data source. The
+          empty string if no distro release file is used as a data source.
+
+        * ``distro_release_file`` (string): The path name of the
+          `distro release file`_ that is actually used as a data source. The
+          empty string if no distro release file is used as a data source.
+
+        Raises:
+
+        * :py:exc:`IOError`: Some I/O issue with an os-release file or distro
+          release file.
+
+        * :py:exc:`subprocess.CalledProcessError`: The lsb_release command had
+          some issue (other than not being available in the program execution
+          path).
+
+        * :py:exc:`UnicodeError`: A data source has unexpected characters or
+          uses an unexpected encoding.
+        """
+        self.os_release_file = os_release_file or \
+            os.path.join(_UNIXCONFDIR, _OS_RELEASE_BASENAME)
+        self.distro_release_file = distro_release_file or ''  # updated later
+        self._os_release_info = self._get_os_release_info()
+        self._lsb_release_info = self._get_lsb_release_info() \
+            if include_lsb else {}
+        self._distro_release_info = self._get_distro_release_info()
+
+    def __repr__(self):
+        """Return repr of all info
+        """
+        return \
+            "LinuxDistribution(" \
+            "os_release_file={0!r}, " \
+            "distro_release_file={1!r}, " \
+            "_os_release_info={2!r}, " \
+            "_lsb_release_info={3!r}, " \
+            "_distro_release_info={4!r})".format(
+                self.os_release_file,
+                self.distro_release_file,
+                self._os_release_info,
+                self._lsb_release_info,
+                self._distro_release_info)
+
+    def linux_distribution(self, full_distribution_name=True):
+        """
+        Return information about the Linux distribution that is compatible
+        with Python's :func:`platform.linux_distribution`, supporting a subset
+        of its parameters.
+
+        For details, see :func:`distro.linux_distribution`.
+        """
+        return (
+            self.name() if full_distribution_name else self.id(),
+            self.version(),
+            self.codename()
+        )
+
+    def id(self):
+        """Return the distro ID of the Linux distribution, as a string.
+
+        For details, see :func:`distro.id`.
+        """
+        def normalize(distro_id, table):
+            distro_id = distro_id.lower().replace(' ', '_')
+            return table.get(distro_id, distro_id)
+
+        distro_id = self.os_release_attr('id')
+        if distro_id:
+            return normalize(distro_id, NORMALIZED_OS_ID)
+
+        distro_id = self.lsb_release_attr('distributor_id')
+        if distro_id:
+            return normalize(distro_id, NORMALIZED_LSB_ID)
+
+        distro_id = self.distro_release_attr('id')
+        if distro_id:
+            return normalize(distro_id, NORMALIZED_DISTRO_ID)
+
+        return ''
+
+    def name(self, pretty=False):
+        """
+        Return the name of the Linux distribution, as a string.
+
+        For details, see :func:`distro.name`.
+        """
+        name = self.os_release_attr('name') \
+            or self.lsb_release_attr('distributor_id') \
+            or self.distro_release_attr('name')
+        if pretty:
+            name = self.os_release_attr('pretty_name') \
+                or self.lsb_release_attr('description')
+            if not name:
+                name = self.distro_release_attr('name')
+                version = self.version(pretty=True)
+                if version:
+                    name = name + ' ' + version
+        return name or ''
+
+    def version(self, pretty=False, best=False):
+        """
+        Return the version of the Linux distribution, as a string.
+
+        For details, see :func:`distro.version`.
+        """
+        versions = [
+            self.os_release_attr('version_id'),
+            self.lsb_release_attr('release'),
+            self.distro_release_attr('version_id'),
+            self._parse_distro_release_content(
+                self.os_release_attr('pretty_name')).get('version_id', ''),
+            self._parse_distro_release_content(
+                self.lsb_release_attr('description')).get('version_id', '')
+        ]
+        version = ''
+        if best:
+            # This algorithm uses the last version in priority order that has
+            # the best precision. If the versions are not in conflict, that
+            # does not matter; otherwise, using the last one instead of the
+            # first one might be considered a surprise.
+            for v in versions:
+                if v.count(".") > version.count(".") or version == '':
+                    version = v
+        else:
+            for v in versions:
+                if v != '':
+                    version = v
+                    break
+        if pretty and version and self.codename():
+            version = u'{0} ({1})'.format(version, self.codename())
+        return version
+
+    def version_parts(self, best=False):
+        """
+        Return the version of the Linux distribution, as a tuple of version
+        numbers.
+
+        For details, see :func:`distro.version_parts`.
+        """
+        version_str = self.version(best=best)
+        if version_str:
+            version_regex = re.compile(r'(\d+)\.?(\d+)?\.?(\d+)?')
+            matches = version_regex.match(version_str)
+            if matches:
+                major, minor, build_number = matches.groups()
+                return major, minor or '', build_number or ''
+        return '', '', ''
+
+    def major_version(self, best=False):
+        """
+        Return the major version number of the current distribution.
+
+        For details, see :func:`distro.major_version`.
+        """
+        return self.version_parts(best)[0]
+
+    def minor_version(self, best=False):
+        """
+        Return the minor version number of the Linux distribution.
+
+        For details, see :func:`distro.minor_version`.
+        """
+        return self.version_parts(best)[1]
+
+    def build_number(self, best=False):
+        """
+        Return the build number of the Linux distribution.
+
+        For details, see :func:`distro.build_number`.
+        """
+        return self.version_parts(best)[2]
+
+    def like(self):
+        """
+        Return the IDs of distributions that are like the Linux distribution.
+
+        For details, see :func:`distro.like`.
+        """
+        return self.os_release_attr('id_like') or ''
+
+    def codename(self):
+        """
+        Return the codename of the Linux distribution.
+
+        For details, see :func:`distro.codename`.
+        """
+        return self.os_release_attr('codename') \
+            or self.lsb_release_attr('codename') \
+            or self.distro_release_attr('codename') \
+            or ''
+
+    def info(self, pretty=False, best=False):
+        """
+        Return certain machine-readable information about the Linux
+        distribution.
+
+        For details, see :func:`distro.info`.
+        """
+        return dict(
+            id=self.id(),
+            version=self.version(pretty, best),
+            version_parts=dict(
+                major=self.major_version(best),
+                minor=self.minor_version(best),
+                build_number=self.build_number(best)
+            ),
+            like=self.like(),
+            codename=self.codename(),
+        )
+
+    def os_release_info(self):
+        """
+        Return a dictionary containing key-value pairs for the information
+        items from the os-release file data source of the Linux distribution.
+
+        For details, see :func:`distro.os_release_info`.
+        """
+        return self._os_release_info
+
+    def lsb_release_info(self):
+        """
+        Return a dictionary containing key-value pairs for the information
+        items from the lsb_release command data source of the Linux
+        distribution.
+
+        For details, see :func:`distro.lsb_release_info`.
+        """
+        return self._lsb_release_info
+
+    def distro_release_info(self):
+        """
+        Return a dictionary containing key-value pairs for the information
+        items from the distro release file data source of the Linux
+        distribution.
+
+        For details, see :func:`distro.distro_release_info`.
+        """
+        return self._distro_release_info
+
+    def os_release_attr(self, attribute):
+        """
+        Return a single named information item from the os-release file data
+        source of the Linux distribution.
+
+        For details, see :func:`distro.os_release_attr`.
+        """
+        return self._os_release_info.get(attribute, '')
+
+    def lsb_release_attr(self, attribute):
+        """
+        Return a single named information item from the lsb_release command
+        output data source of the Linux distribution.
+
+        For details, see :func:`distro.lsb_release_attr`.
+        """
+        return self._lsb_release_info.get(attribute, '')
+
+    def distro_release_attr(self, attribute):
+        """
+        Return a single named information item from the distro release file
+        data source of the Linux distribution.
+
+        For details, see :func:`distro.distro_release_attr`.
+        """
+        return self._distro_release_info.get(attribute, '')
+
+    def _get_os_release_info(self):
+        """
+        Get the information items from the specified os-release file.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        if os.path.isfile(self.os_release_file):
+            with open(self.os_release_file) as release_file:
+                return self._parse_os_release_content(release_file)
+        return {}
+
+    @staticmethod
+    def _parse_os_release_content(lines):
+        """
+        Parse the lines of an os-release file.
+
+        Parameters:
+
+        * lines: Iterable through the lines in the os-release file.
+                 Each line must be a unicode string or a UTF-8 encoded byte
+                 string.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        props = {}
+        lexer = shlex.shlex(lines, posix=True)
+        lexer.whitespace_split = True
+
+        # The shlex module defines its `wordchars` variable using literals,
+        # making it dependent on the encoding of the Python source file.
+        # In Python 2.6 and 2.7, the shlex source file is encoded in
+        # 'iso-8859-1', and the `wordchars` variable is defined as a byte
+        # string. This causes a UnicodeDecodeError to be raised when the
+        # parsed content is a unicode object. The following fix resolves that
+        # (... but it should be fixed in shlex...):
+        if sys.version_info[0] == 2 and isinstance(lexer.wordchars, bytes):
+            lexer.wordchars = lexer.wordchars.decode('iso-8859-1')
+
+        tokens = list(lexer)
+        for token in tokens:
+            # At this point, all shell-like parsing has been done (i.e.
+            # comments processed, quotes and backslash escape sequences
+            # processed, multi-line values assembled, trailing newlines
+            # stripped, etc.), so the tokens are now either:
+            # * variable assignments: var=value
+            # * commands or their arguments (not allowed in os-release)
+            if '=' in token:
+                k, v = token.split('=', 1)
+                if isinstance(v, bytes):
+                    v = v.decode('utf-8')
+                props[k.lower()] = v
+                if k == 'VERSION':
+                    # this handles cases in which the codename is in
+                    # the `(CODENAME)` (rhel, centos, fedora) format
+                    # or in the `, CODENAME` format (Ubuntu).
+                    codename = re.search(r'(\(\D+\))|,(\s+)?\D+', v)
+                    if codename:
+                        codename = codename.group()
+                        codename = codename.strip('()')
+                        codename = codename.strip(',')
+                        codename = codename.strip()
+                        # codename appears within paranthese.
+                        props['codename'] = codename
+                    else:
+                        props['codename'] = ''
+            else:
+                # Ignore any tokens that are not variable assignments
+                pass
+        return props
+
+    def _get_lsb_release_info(self):
+        """
+        Get the information items from the lsb_release command output.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        cmd = 'lsb_release -a'
+        # conda customization: On Ubuntu 17.10, lsb_release calls the
+        # system Python and it will not find our custom sysconfigdata
+        env = os.environ.copy()
+        if '_PYTHON_SYSCONFIGDATA_NAME' in env:
+            del env['_PYTHON_SYSCONFIGDATA_NAME']
+        process = subprocess.Popen(
+            cmd,
+            shell=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=env)
+        stdout, stderr = process.communicate()
+        stdout, stderr = stdout.decode('utf-8'), stderr.decode('utf-8')
+        code = process.returncode
+        if code == 0:
+            content = stdout.splitlines()
+            return self._parse_lsb_release_content(content)
+        elif code == 127:  # Command not found
+            return {}
+        else:
+            if sys.version_info[:2] >= (3, 5):
+                raise subprocess.CalledProcessError(code, cmd, stdout, stderr)
+            elif sys.version_info[:2] >= (2, 7):
+                raise subprocess.CalledProcessError(code, cmd, stdout)
+            elif sys.version_info[:2] == (2, 6):
+                raise subprocess.CalledProcessError(code, cmd)
+
+    @staticmethod
+    def _parse_lsb_release_content(lines):
+        """
+        Parse the output of the lsb_release command.
+
+        Parameters:
+
+        * lines: Iterable through the lines of the lsb_release output.
+                 Each line must be a unicode string or a UTF-8 encoded byte
+                 string.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        props = {}
+        for line in lines:
+            line = line.decode('utf-8') if isinstance(line, bytes) else line
+            kv = line.strip('\n').split(':', 1)
+            if len(kv) != 2:
+                # Ignore lines without colon.
+                continue
+            k, v = kv
+            props.update({k.replace(' ', '_').lower(): v.strip()})
+        return props
+
+    def _get_distro_release_info(self):
+        """
+        Get the information items from the specified distro release file.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        if self.distro_release_file:
+            # If it was specified, we use it and parse what we can, even if
+            # its file name or content does not match the expected pattern.
+            distro_info = self._parse_distro_release_file(
+                self.distro_release_file)
+            basename = os.path.basename(self.distro_release_file)
+            # The file name pattern for user-specified distro release files
+            # is somewhat more tolerant (compared to when searching for the
+            # file), because we want to use what was specified as best as
+            # possible.
+            match = _DISTRO_RELEASE_BASENAME_PATTERN.match(basename)
+            if match:
+                distro_info['id'] = match.group(1)
+            return distro_info
+        else:
+            try:
+                basenames = os.listdir(_UNIXCONFDIR)
+                # We sort for repeatability in cases where there are multiple
+                # distro specific files; e.g. CentOS, Oracle, Enterprise all
+                # containing `redhat-release` on top of their own.
+                basenames.sort()
+            except OSError:
+                # This may occur when /etc is not readable but we can't be
+                # sure about the *-release files. Check common entries of
+                # /etc for information. If they turn out to not be there the
+                # error is handled in `_parse_distro_release_file()`.
+                basenames = ['SuSE-release',
+                             'arch-release',
+                             'base-release',
+                             'centos-release',
+                             'fedora-release',
+                             'gentoo-release',
+                             'mageia-release',
+                             'manjaro-release',
+                             'oracle-release',
+                             'redhat-release',
+                             'sl-release',
+                             'slackware-version']
+            for basename in basenames:
+                if basename in _DISTRO_RELEASE_IGNORE_BASENAMES:
+                    continue
+                match = _DISTRO_RELEASE_BASENAME_PATTERN.match(basename)
+                if match:
+                    filepath = os.path.join(_UNIXCONFDIR, basename)
+                    distro_info = self._parse_distro_release_file(filepath)
+                    if 'name' in distro_info:
+                        # The name is always present if the pattern matches
+                        self.distro_release_file = filepath
+                        distro_info['id'] = match.group(1)
+                        return distro_info
+            return {}
+
+    def _parse_distro_release_file(self, filepath):
+        """
+        Parse a distro release file.
+
+        Parameters:
+
+        * filepath: Path name of the distro release file.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        try:
+            with open(filepath) as fp:
+                # Only parse the first line. For instance, on SLES there
+                # are multiple lines. We don't want them...
+                return self._parse_distro_release_content(fp.readline())
+        except (OSError, IOError):
+            # Ignore not being able to read a specific, seemingly version
+            # related file.
+            # See https://github.com/nir0s/distro/issues/162
+            return {}
+
+    @staticmethod
+    def _parse_distro_release_content(line):
+        """
+        Parse a line from a distro release file.
+
+        Parameters:
+        * line: Line from the distro release file. Must be a unicode string
+                or a UTF-8 encoded byte string.
+
+        Returns:
+            A dictionary containing all information items.
+        """
+        if isinstance(line, bytes):
+            line = line.decode('utf-8')
+        matches = _DISTRO_RELEASE_CONTENT_REVERSED_PATTERN.match(
+            line.strip()[::-1])
+        distro_info = {}
+        if matches:
+            # regexp ensures non-None
+            distro_info['name'] = matches.group(3)[::-1]
+            if matches.group(2):
+                distro_info['version_id'] = matches.group(2)[::-1]
+            if matches.group(1):
+                distro_info['codename'] = matches.group(1)[::-1]
+        elif line:
+            distro_info['name'] = line.strip()
+        return distro_info
+
+
+_distro = LinuxDistribution()
+
+
+def main():
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.DEBUG)
+    logger.addHandler(logging.StreamHandler(sys.stdout))
+
+    parser = argparse.ArgumentParser(description="Linux distro info tool")
+    parser.add_argument(
+        '--json',
+        '-j',
+        help="Output in machine readable format",
+        action="store_true")
+    args = parser.parse_args()
+
+    if args.json:
+        logger.info(json.dumps(info(), indent=4, sort_keys=True))
+    else:
+        logger.info('Name: %s', name(pretty=True))
+        distribution_version = version(pretty=True)
+        logger.info('Version: %s', distribution_version)
+        distribution_codename = codename()
+        logger.info('Codename: %s', distribution_codename)
+
+
+if __name__ == '__main__':
+    main()

shared/package-files/0c/0c3d6692d733af00fbf4d4770346c07a05d64a577fe3bae941f26d66bd3d31d9/maxcut.py

@@ -0,0 +1,111 @@
+import networkx as nx
+from networkx.utils.decorators import not_implemented_for, py_random_state
+
+__all__ = ["randomized_partitioning", "one_exchange"]
+
+
+@not_implemented_for("directed", "multigraph")
+@py_random_state(1)
+def randomized_partitioning(G, seed=None, p=0.5, weight=None):
+    """Compute a random partitioning of the graph nodes and its cut value.
+
+    A partitioning is calculated by observing each node
+    and deciding to add it to the partition with probability `p`,
+    returning a random cut and its corresponding value (the
+    sum of weights of edges connecting different partitions).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    p : scalar
+        Probability for each node to be part of the first partition.
+        Should be in [0,1]
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    cut_size : scalar
+        Value of the minimum cut.
+
+    partition : pair of node sets
+        A partitioning of the nodes that defines a minimum cut.
+    """
+    cut = {node for node in G.nodes() if seed.random() < p}
+    cut_size = nx.algorithms.cut_size(G, cut, weight=weight)
+    partition = (cut, G.nodes - cut)
+    return cut_size, partition
+
+
+def _swap_node_partition(cut, node):
+    return cut - {node} if node in cut else cut.union({node})
+
+
+@not_implemented_for("directed", "multigraph")
+@py_random_state(2)
+def one_exchange(G, initial_cut=None, seed=None, weight=None):
+    """Compute a partitioning of the graphs nodes and the corresponding cut value.
+
+    Use a greedy one exchange strategy to find a locally maximal cut
+    and its value, it works by finding the best node (one that gives
+    the highest gain to the cut value) to add to the current cut
+    and repeats this process until no improvement can be made.
+
+    Parameters
+    ----------
+    G : networkx Graph
+        Graph to find a maximum cut for.
+
+    initial_cut : set
+        Cut to use as a starting point. If not supplied the algorithm
+        starts with an empty cut.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    cut_value : scalar
+        Value of the maximum cut.
+
+    partition : pair of node sets
+        A partitioning of the nodes that defines a maximum cut.
+    """
+    if initial_cut is None:
+        initial_cut = set()
+    cut = set(initial_cut)
+    current_cut_size = nx.algorithms.cut_size(G, cut, weight=weight)
+    while True:
+        nodes = list(G.nodes())
+        # Shuffling the nodes ensures random tie-breaks in the following call to max
+        seed.shuffle(nodes)
+        best_node_to_swap = max(
+            nodes,
+            key=lambda v: nx.algorithms.cut_size(
+                G, _swap_node_partition(cut, v), weight=weight
+            ),
+            default=None,
+        )
+        potential_cut = _swap_node_partition(cut, best_node_to_swap)
+        potential_cut_size = nx.algorithms.cut_size(G, potential_cut, weight=weight)
+
+        if potential_cut_size > current_cut_size:
+            cut = potential_cut
+            current_cut_size = potential_cut_size
+        else:
+            break
+
+    partition = (cut, G.nodes - cut)
+    return current_cut_size, partition

shared/package-files/0c/0c3e225c027aebf0653a8e3bff13cb34bd0968bf1f084d5f506af347c8440e55/_standard_gamma_grad_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _standard_gamma_grad {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_standard_gamma_grad")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_standard_gamma_grad(Tensor self, Tensor output) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & output);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & output);
+};
+
+struct TORCH_API _standard_gamma_grad_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_standard_gamma_grad")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_standard_gamma_grad.out(Tensor self, Tensor output, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & output, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & output, at::Tensor & out);
+};
+
+}} // namespace at::_ops

shared/package-files/0c/0c4aa958f21646900836e9bf94a761f3e16f2acc7ef09f939c672e6ebc7303a9/compat.pyi

@@ -0,0 +1,39 @@
+import sys
+from io import TextIOWrapper
+from typing import Any, Optional, Tuple
+
+PY2: bool
+PY3: bool
+WIN: bool
+string_types: Tuple[
+    str,
+]
+integer_types: Tuple[
+    int,
+]
+class_types: Tuple[
+    type,
+]
+text_type = str
+binary_type = bytes
+long = int
+
+def unquote_bytes_to_wsgi(bytestring: bytes) -> str: ...
+def text_(s: str, encoding: str = ..., errors: str = ...) -> str: ...
+def tostr(s: str) -> str: ...
+def tobytes(s: str) -> bytes: ...
+
+exec_: Any
+
+def reraise(tp: Any, value: BaseException, tb: Optional[str] = ...) -> None: ...
+
+MAXINT: int
+HAS_IPV6: bool
+IPPROTO_IPV6: int
+IPV6_V6ONLY: int
+
+def set_nonblocking(fd: TextIOWrapper) -> None: ...
+
+ResourceWarning: Warning
+
+def qualname(cls: Any) -> str: ...

shared/package-files/0c/0c51168439117527a818479f180221ffba8cdd9b04ec71a03065582d006b69c7/pipe.py

@@ -0,0 +1,490 @@
+# Copyright 2019 Kakao Brain
+#
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+"""The Pipe interface."""
+from collections import OrderedDict
+from typing import TYPE_CHECKING, Any, Iterable, Iterator, List, Optional, Union, Sequence, Tuple, cast
+
+import torch
+from torch import Tensor, nn
+from torch.distributed.rpc import RRef
+import torch.autograd
+import torch.cuda
+
+from . import microbatch
+from .batchnorm import DeferredBatchNorm
+from .pipeline import Pipeline
+from .skip.layout import inspect_skip_layout
+from .skip.skippable import verify_skippables
+from .stream import AbstractStream, new_stream
+
+__all__ = ["Pipe", "BalanceError", "PipeSequential", "WithDevice"]
+
+
+Device = Union[torch.device, int, str]
+Devices = Union[Iterable[Device], List[Device]]
+
+Tensors = Sequence[Tensor]
+TensorOrTensors = Union[Tensor, Tensors]
+
+if TYPE_CHECKING:
+    # Typechecking: nn.Module is not a Generic
+    Module = nn.Module[TensorOrTensors]  # type: ignore[type-arg]
+    NamedModules = OrderedDict[str, Module]
+else:
+    Module = nn.Module
+    NamedModules = OrderedDict
+
+
+def _recommend_auto_balance(message: str) -> str:
+    """Expands a message with recommendation to :mod:`torchpipe.balance`."""
+    return f"""{message}
+
+If your model is still under development, its optimal balance would change
+frequently. In this case, we highly recommend 'torch.distributed.pipeline.sync.balance' for
+naive automatic balancing:
+
+  from torch.distributed.pipeline.sync import Pipe
+  from torch.distributed.pipeline.sync.balance import balance_by_time
+
+  partitions = torch.cuda.device_count()
+  sample = torch.empty(...)
+  balance = balance_by_time(partitions, model, sample)
+
+  model = Pipe(model, balance, ...)
+"""
+
+
+def _verify_module(module: nn.Sequential) -> None:
+    if not isinstance(module, nn.Sequential):
+        raise TypeError("module must be nn.Sequential to be partitioned")
+
+    named_children = list(module.named_children())
+    if len(named_children) != len(module):
+        raise ValueError("module with duplicate children is not supported")
+
+
+def _verify_splitting(
+    module: nn.Sequential, partitions: List[nn.Sequential], devices: List[torch.device]
+) -> None:
+    num_parameters = len(list(module.parameters()))
+    num_child_parameters = sum(len(list(child.parameters())) for child in module.children())
+    if num_parameters == num_child_parameters:
+        return
+
+    for i in range(len(partitions)):
+        for j in range(i + 1, len(partitions)):
+            parti = partitions[i]
+            partj = partitions[j]
+            if devices[i] == devices[j]:
+                continue
+            for p in parti.parameters():
+                for q in partj.parameters():
+                    if p is q:
+                        raise ValueError("module with duplicate parameters on distinct devices is not supported")
+
+
+class BalanceError(ValueError):
+    pass
+
+
+def _retrieve_device(module: nn.Module) -> torch.device:
+    """Validates all parameters in the Module have the same device and returns
+    the appropriate device.
+
+    Args:
+        An ``nn.Module`` to process.
+
+    Returns:
+        ``torch.Device`` for the entire module.
+
+    Raises:
+        ValueError:
+            If devices for ``nn.Module`` parameters are not all same.
+    """
+
+    device = None
+    for parameter in module.parameters():
+        if device is None:
+            device = parameter.device
+        elif device != parameter.device:
+            raise ValueError(
+                f'nn.Module: {module}, should have all parameters on a single device,'
+                ' please use .to() to place the module on a single device')
+
+    return device if device is not None else torch.device("cpu")
+
+
+class PipeSequential(nn.Sequential):
+    """
+    Pipe variant of ``nn.Sequential`` which supports multiple inputs.
+    """
+
+    def forward(self, *inputs):
+        for module in self:
+            if isinstance(inputs, Tuple):  # type: ignore[arg-type]
+                inputs = module(*inputs)
+            else:
+                # Don't expand single variables (ex: lists/Tensor)
+                inputs = module(inputs)
+        return inputs
+
+
+class WithDevice(nn.Module):
+    """
+    Wraps an ``nn.Module`` which is part of ``nn.Sequential`` passed into :class:`Pipe`
+    that overrides the device for that module. In cases where :class:`Pipe`
+    can't implicitly determine the device for the module and places it on CPU,
+    this wrapper can be used to override the implicit behavior and explicitly
+    specify which device a module should run on.
+
+    The provided module is also moved to the given device via ``.to(device)``
+    by :class:`Pipe`
+
+    Args:
+        module(:class:`torch.nn.Module`): The module to be wrapped.
+        device(:class:`torch.device`): The device to run the module on.
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> fc1 = nn.Linear(16, 8).cuda(0)
+        >>> fc2 = nn.Linear(8, 4).cuda(1)
+        >>> dropout = nn.Dropout()
+        >>>
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
+        >>> # Dropout does not have any parameters/buffers, but we want to
+        >>> # run it on cuda:1 to avoid any GPU to CPU transfers.
+        >>> model = nn.Sequential(fc1, fc2, WithDevice(dropout, 'cuda:1'))
+        >>> # xdoctest: +SKIP("Needs RPC framework init")
+        >>> model = Pipe(model, chunks=8)
+    """
+    def __init__(self, module: nn.Module, device: torch.device):
+        super().__init__()
+        self._module = module
+        self._device = torch.device(device)
+
+    def forward(self, *args, **kwargs):
+        return self._module(*args, **kwargs)
+
+    @property
+    def module(self):
+        return self._module
+
+    @property
+    def device(self):
+        return self._device
+
+
+def _assemble_partition(modules: List[nn.Module]):
+    modules_list: List[nn.Module] = []
+    for module in modules:
+        if isinstance(module, nn.Sequential):
+            modules_list.extend(module.children())
+        else:
+            modules_list.append(module)
+    return PipeSequential(*modules_list)
+
+
+def _split_module(modules: nn.Sequential) -> Tuple[List[nn.Sequential], List[torch.device]]:
+    partitions = []
+    devices = []
+
+    current_partition = []
+    current_device = None
+    for name, module in modules.named_children():
+        if isinstance(module, WithDevice):
+            # Process device override and move module to appropriate device.
+            device = module.device
+            module = module.module
+            module.to(device)
+        else:
+            device = _retrieve_device(module)
+        if current_device is not None and (current_device != device or device.type == 'cpu'):
+            partitions.append(_assemble_partition(current_partition))
+            devices.append(current_device)
+            current_partition = []
+        current_device = device
+        current_partition.append(module)
+
+    if current_device is not None:
+        partitions.append(_assemble_partition(current_partition))
+        devices.append(current_device)
+
+    partitions = cast(List[nn.Sequential], nn.ModuleList(partitions))
+
+    return partitions, devices
+
+
+MOVING_DENIED = TypeError("denied to move parameters and buffers, because Pipe should manage device placement")
+
+
+class Pipe(Module):
+    """Wraps an arbitrary :class:`nn.Sequential <torch.nn.Sequential>` module
+    to train on using synchronous pipeline parallelism. If the module requires
+    lots of memory and doesn't fit on a single GPU, pipeline parallelism is a
+    useful technique to employ for training.
+
+    The implementation is based on the torchgpipe_ paper.
+
+    .. _torchgpipe: https://arxiv.org/abs/2004.09910
+
+    Pipe combines pipeline parallelism with checkpointing to reduce peak
+    memory required to train while minimizing device under-utilization.
+
+    You should place all the modules on the appropriate devices and wrap them
+    into an :class:`nn.Sequential <torch.nn.Sequential>` module defining the
+    desired order of execution. If a module does not contain any
+    parameters/buffers, it is assumed this module should be executed on CPU
+    and appropriate input tensors to the module are moved to CPU before
+    execution. This behavior can be overridden by the :class:`WithDevice`
+    wrapper which can be used to explicitly specify which device a module
+    should run on.
+
+    Args:
+        module (:class:`nn.Sequential <torch.nn.Sequential>`):
+            sequential module to be parallelized using pipelining. Each module
+            in the sequence has to have all of its parameters on a single
+            device. Each module in the sequence has to either be an nn.Module
+            or :class:`nn.Sequential <torch.nn.Sequential>` (to combine multiple
+            sequential modules on a single device)
+        chunks (int):
+            number of micro-batches (default: ``1``)
+        checkpoint (str):
+            when to enable checkpointing, one of ``'always'``,
+            ``'except_last'``, or ``'never'`` (default: ``'except_last'``).
+            ``'never'`` disables checkpointing completely, ``'except_last'``
+            enables checkpointing for all micro-batches except the last one
+            and ``'always'`` enables checkpointing for all micro-batches.
+        deferred_batch_norm (bool):
+            whether to use deferred ``BatchNorm`` moving statistics (default:
+            :data:`False`). If set to :data:`True`, we track statistics across
+            multiple micro-batches to update the running statistics per
+            mini-batch.
+
+    Raises:
+        TypeError:
+            the module is not a :class:`nn.Sequential <torch.nn.Sequential>`.
+        ValueError:
+            invalid arguments
+
+    Example::
+        Pipeline of two FC layers across GPUs 0 and 1.
+
+        >>> # Need to initialize RPC framework first.
+        >>> # xdoctest: +SKIP
+        >>> os.environ['MASTER_ADDR'] = 'localhost'
+        >>> os.environ['MASTER_PORT'] = '29500'
+        >>> torch.distributed.rpc.init_rpc('worker', rank=0, world_size=1)
+        >>>
+        >>> # Build pipe.
+        >>> fc1 = nn.Linear(16, 8).cuda(0)
+        >>> fc2 = nn.Linear(8, 4).cuda(1)
+        >>> model = nn.Sequential(fc1, fc2)
+        >>> model = Pipe(model, chunks=8)
+        >>> input = torch.rand(16, 16).cuda(0)
+        >>> output_rref = model(input)
+
+    .. note::
+        You can wrap a :class:`Pipe` model with
+        :class:`torch.nn.parallel.DistributedDataParallel` only when the
+        checkpoint parameter of :class:`Pipe` is ``'never'``.
+
+    .. note::
+        :class:`Pipe` only supports intra-node pipelining currently, but
+        will be expanded to support inter-node pipelining in the future.
+        The forward function returns an :class:`~torch.distributed.rpc.RRef`
+        to allow for inter-node pipelining in the future, where the output
+        might be on a remote host. For intra-node pipelining you can use
+        :meth:`~torch.distributed.rpc.RRef.local_value` to retrieve the
+        output locally.
+
+    .. warning::
+        :class:`Pipe` is experimental and subject to change.
+    """
+
+    def __init__(
+        self,
+        module: nn.Sequential,
+        chunks: int = 1,
+        checkpoint: str = "except_last",
+        deferred_batch_norm: bool = False,
+    ) -> None:
+        super().__init__()
+
+        # Check if RPC framework is initialized.
+        if not torch.distributed.rpc._is_current_rpc_agent_set():
+            raise RuntimeError(
+                'Please initialize RPC framework for Pipe using '
+                'torch.distributed.rpc.init_rpc')
+
+        chunks = int(chunks)
+        checkpoint = str(checkpoint)
+
+        if chunks <= 0:
+            raise ValueError("number of chunks must be positive integer")
+        if checkpoint not in ["always", "except_last", "never"]:
+            raise ValueError("checkpoint is not one of 'always', 'except_last', or 'never'")
+
+        _verify_module(module)
+
+        # Verify if the underlying skippable modules satisfy integrity. The
+        # integrity can be verified before forward() because it is static.
+        verify_skippables(module)
+
+        self.chunks = chunks
+        self.checkpoint = checkpoint
+
+        if deferred_batch_norm:
+            module = DeferredBatchNorm.convert_deferred_batch_norm(module, chunks)
+
+        self.partitions, self.devices = _split_module(module)
+        _verify_splitting(module, self.partitions, self.devices)
+
+        self._copy_streams: List[List[AbstractStream]] = []
+        self._skip_layout = inspect_skip_layout(self.partitions)
+
+        # Separate CUDA streams for copy.
+        copy_streams = self._ensure_copy_streams()
+
+        # The micro-batch index where the checkpointing stops.
+        checkpoint_stop = {"always": self.chunks, "except_last": self.chunks - 1, "never": 0}[self.checkpoint]
+
+        self.pipeline = Pipeline(self.partitions, self.devices, copy_streams, self._skip_layout, checkpoint_stop)
+
+    def __len__(self) -> int:
+        """Counts the length of the underlying sequential module."""
+        return sum(len(p) for p in self.partitions)
+
+    def __getitem__(self, index: int) -> nn.Module:
+        """Gets a layer in the underlying sequential module."""
+        partitions = self.partitions
+        if index < 0:
+            partitions = partitions[::-1]
+
+        for partition in partitions:
+            try:
+                return partition[index]
+            except IndexError:
+                pass
+
+            shift = len(partition)
+
+            if index < 0:
+                index += shift
+            else:
+                index -= shift
+
+        raise IndexError
+
+    def __iter__(self) -> Iterator[nn.Module]:
+        """Iterates over children of the underlying sequential module."""
+        for partition in self.partitions:
+            yield from partition
+
+    # Pipe should manage the device of each partition.
+    # Deny cuda(), cpu(), and to() with device, by TypeError.
+    def cuda(self, device: Optional[Device] = None) -> "Pipe":
+        raise MOVING_DENIED
+
+    def cpu(self) -> "Pipe":
+        raise MOVING_DENIED
+
+    def to(self, *args: Any, **kwargs: Any) -> "Pipe":
+        # Deny these usages:
+        #
+        # - to(device[, dtype, non_blocking])
+        # - to(tensor[, non_blocking])
+        #
+        # But allow this:
+        #
+        # - to(dtype[, non_blocking])
+        #
+        if "device" in kwargs or "tensor" in kwargs:
+            raise MOVING_DENIED
+
+        if args:
+            if isinstance(args[0], (torch.device, int, str)):
+                raise MOVING_DENIED
+            if torch.is_tensor(args[0]):
+                raise MOVING_DENIED
+
+        return super().to(*args, **kwargs)
+
+    def _ensure_copy_streams(self) -> List[List[AbstractStream]]:
+        """Ensures that :class:`Pipe` caches CUDA streams for copy.
+
+        It's worth to cache CUDA streams although PyTorch already manages a
+        pool of pre-allocated CUDA streams, because it may reduce GPU memory
+        fragmentation when the number of micro-batches is small.
+
+        """
+        if not self._copy_streams:
+            for device in self.devices:
+                self._copy_streams.append([new_stream(device) for _ in range(self.chunks)])
+
+        return self._copy_streams
+
+    def forward(self, *inputs) -> RRef:
+        """
+        Processes a single input mini-batch through the pipe and returns an
+        :class:`~torch.distributed.rpc.RRef` pointing to the output.
+        :class:`Pipe` is a fairly transparent module wrapper. It doesn't
+        modify the input and output signature of the underlying module. But
+        there's type restriction. Input and output have to contain at least one
+        tensor. This restriction is applied at partition boundaries too.
+
+        The sequence of inputs are fed into the first stage of the pipeline as
+        ``*inputs``. As a result the positional args for this function should
+        match the positional args for the first stage of the pipeline. The same
+        condition applies for output of one stage of the pipeline which is the
+        input for the next stage.
+
+        The input tensor is split into multiple micro-batches based on the
+        ``chunks`` parameter used to initialize :class:`Pipe`. The batch size
+        is assumed to be the first dimension of the tensor and if the batch
+        size is less than ``chunks``, the number of micro-batches is equal to
+        the batch size.
+
+        Only tensors are split into multiple micro-batches, non-Tensor inputs
+        are just replicated as-is in each micro-batch. For non-Tensor outputs
+        in the last stage of the pipeline, they are aggregated as a ``List``
+        and returned the user. For example, if you have 2 micro-batches
+        returning the integer 5, the user would receive the consolidated
+        output of `[5, 5]`
+
+        All the input tensors need to be on the same device as the first
+        partition of the pipeline.
+
+        If a tensor is wrapped with the :class:`NoChunk` wrapper, the tensor
+        is not split across micro-batches and is replicated as-is similar to
+        non-tensors.
+
+        Args:
+            inputs: input mini-batch
+
+        Returns:
+            :class:`~torch.distributed.rpc.RRef` to the output of the mini-batch
+
+        Raises:
+            TypeError: input doesn't contain at least one tensor
+
+        """
+        first_partition_device = self.devices[0] if len(self.devices) != 0 else torch.device("cpu")
+        microbatch.check(first_partition_device, *inputs)
+
+        if not self.devices:
+            # Empty sequential module is not illegal.
+            return RRef(*inputs)
+
+        # Divide a mini-batch into micro-batches.
+        batches = microbatch.scatter(*inputs, chunks=self.chunks)
+
+        # Run pipeline parallelism.
+        self.pipeline.run(batches)
+
+        # Merge the micro-batches into one mini-batch.
+        output = microbatch.gather(batches)
+        return RRef(output)

shared/package-files/0c/0c5230e53eb4f80a1d08664f0b06cccdb111a441774c46c0685242b86cfb90ea/vecm_e6_jmulti_cili.txt

@@ -0,0 +1,149 @@
+*** Mon, 25 Jul 2016 21:32:41 ***
+VEC REPRESENTATION
+endogenous variables:     Dp R 
+exogenous variables:       
+deterministic variables:  CONST TREND 
+endogenous lags (diffs):  3 
+exogenous lags:           0 
+sample range:             [1973 Q2, 1998 Q4], T = 103
+estimation procedure:     One stage. Johansen approach 
+
+
+Lagged endogenous term:
+=======================
+              d(Dp)      d(R)  
+------------------------------
+d(Dp)(t-1)|   -0.531    -0.324  
+          |   (0.151)   (0.131) 
+          |   {0.000}   {0.013} 
+          |  [-3.514]  [-2.479] 
+d(R) (t-1)|    0.051     0.260  
+          |   (0.117)   (0.101) 
+          |   {0.660}   {0.010} 
+          |   [0.440]   [2.581] 
+d(Dp)(t-2)|   -0.665    -0.202  
+          |   (0.103)   (0.089) 
+          |   {0.000}   {0.024} 
+          |  [-6.427]  [-2.254] 
+d(R) (t-2)|    0.122     0.017  
+          |   (0.116)   (0.101) 
+          |   {0.295}   {0.863} 
+          |   [1.047]   [0.172] 
+d(Dp)(t-3)|   -0.808    -0.071  
+          |   (0.055)   (0.047) 
+          |   {0.000}   {0.137} 
+          | [-14.712]  [-1.487] 
+d(R) (t-3)|   -0.050     0.226  
+          |   (0.114)   (0.098) 
+          |   {0.659}   {0.022} 
+          |  [-0.441]   [2.295] 
+------------------------------
+
+
+
+
+Loading coefficients:
+=====================
+             d(Dp)      d(R)  
+-----------------------------
+ec1(t-1)|   -0.619     0.426  
+        |   (0.198)   (0.171) 
+        |   {0.002}   {0.013} 
+        |  [-3.127]   [2.490] 
+-----------------------------
+
+Estimated cointegration relation(s):
+====================================
+           ec1(t-1)  
+--------------------
+Dp(t-1)   |    1.000  
+          |   (0.000) 
+          |   {0.000} 
+          |   [0.000] 
+R (t-1)   |   -0.281  
+          |   (0.063) 
+          |   {0.000} 
+          |  [-4.429] 
+CONST     |    0.013  
+          |   (0.006) 
+          |   {0.032} 
+          |   [2.150] 
+TREND(t-1)|    0.000  
+          |   (0.000) 
+          |   {0.901} 
+          |  [-0.124] 
+--------------------
+
+
+
+VAR REPRESENTATION
+
+modulus of the eigenvalues of the reverse characteristic polynomial:
+|z| = ( 1.0095     1.0117     1.0117     1.0000     1.3224     1.3224     1.7259     1.7259     )
+
+Legend:
+=======
+              Equation 1   Equation 2  ...
+------------------------------------------
+Variable 1 | Coefficient          ...
+           | (Std. Dev.)
+           | {p - Value}
+           | [t - Value]
+Variable 2 |         ...
+...
+------------------------------------------
+
+
+Lagged endogenous term:
+=======================
+                Dp         R  
+-----------------------------
+ Dp(t-1)|   -0.150     0.102  
+        |   (0.249)   (0.215) 
+        |   {0.547}   {0.634} 
+        |  [-0.603]   [0.475] 
+ R (t-1)|    0.225     1.140  
+        |   (0.129)   (0.112) 
+        |   {0.081}   {0.000} 
+        |   [1.745]  [10.216] 
+ Dp(t-2)|   -0.134     0.122  
+        |   (0.055)   (0.048) 
+        |   {0.016}   {0.011} 
+        |  [-2.415]   [2.552] 
+ R (t-2)|    0.071    -0.243  
+        |   (0.170)   (0.147) 
+        |   {0.678}   {0.099} 
+        |   [0.415]  [-1.649] 
+ Dp(t-3)|   -0.143     0.131  
+        |   (0.056)   (0.048) 
+        |   {0.010}   {0.007} 
+        |  [-2.561]   [2.716] 
+ R (t-3)|   -0.172     0.208  
+        |   (0.170)   (0.147) 
+        |   {0.312}   {0.157} 
+        |  [-1.011]   [1.415] 
+ Dp(t-4)|    0.808     0.071  
+        |   (0.055)   (0.047) 
+        |   {0.000}   {0.137} 
+        |  [14.712]   [1.487] 
+ R (t-4)|    0.050    -0.226  
+        |   (0.114)   (0.098) 
+        |   {0.659}   {0.022} 
+        |   [0.441]  [-2.295] 
+-----------------------------
+
+
+Deterministic term:
+===================
+                    Dp         R  
+---------------------------------
+CONST     (t)|   -0.008     0.006  
+             |   (0.000)   (0.000) 
+             |   {0.000}   {0.000} 
+             |   [0.000]   [0.000] 
+TREND(t-1)(t)|    0.000     0.000  
+             |   (0.000)   (0.000) 
+             |   {0.000}   {0.000} 
+             |   [0.000]   [0.000] 
+---------------------------------
+

shared/package-files/0c/0c527a38a57a811c38a7d3ae55fcb510d4de2c909b612d86ba9a70a499f04064/test_backend.py

@@ -0,0 +1,98 @@
+from functools import partial
+
+import numpy as np
+import scipy.fft
+from scipy.fft import _fftlog, _pocketfft, set_backend
+from scipy.fft.tests import mock_backend
+
+from numpy.testing import assert_allclose, assert_equal
+import pytest
+
+fnames = ('fft', 'fft2', 'fftn',
+          'ifft', 'ifft2', 'ifftn',
+          'rfft', 'rfft2', 'rfftn',
+          'irfft', 'irfft2', 'irfftn',
+          'dct', 'idct', 'dctn', 'idctn',
+          'dst', 'idst', 'dstn', 'idstn',
+          'fht', 'ifht')
+
+np_funcs = (np.fft.fft, np.fft.fft2, np.fft.fftn,
+            np.fft.ifft, np.fft.ifft2, np.fft.ifftn,
+            np.fft.rfft, np.fft.rfft2, np.fft.rfftn,
+            np.fft.irfft, np.fft.irfft2, np.fft.irfftn,
+            np.fft.hfft, _pocketfft.hfft2, _pocketfft.hfftn,  # np has no hfftn
+            np.fft.ihfft, _pocketfft.ihfft2, _pocketfft.ihfftn,
+            _pocketfft.dct, _pocketfft.idct, _pocketfft.dctn, _pocketfft.idctn,
+            _pocketfft.dst, _pocketfft.idst, _pocketfft.dstn, _pocketfft.idstn,
+            # must provide required kwargs for fht, ifht
+            partial(_fftlog.fht, dln=2, mu=0.5),
+            partial(_fftlog.ifht, dln=2, mu=0.5))
+
+funcs = (scipy.fft.fft, scipy.fft.fft2, scipy.fft.fftn,
+         scipy.fft.ifft, scipy.fft.ifft2, scipy.fft.ifftn,
+         scipy.fft.rfft, scipy.fft.rfft2, scipy.fft.rfftn,
+         scipy.fft.irfft, scipy.fft.irfft2, scipy.fft.irfftn,
+         scipy.fft.hfft, scipy.fft.hfft2, scipy.fft.hfftn,
+         scipy.fft.ihfft, scipy.fft.ihfft2, scipy.fft.ihfftn,
+         scipy.fft.dct, scipy.fft.idct, scipy.fft.dctn, scipy.fft.idctn,
+         scipy.fft.dst, scipy.fft.idst, scipy.fft.dstn, scipy.fft.idstn,
+         # must provide required kwargs for fht, ifht
+         partial(scipy.fft.fht, dln=2, mu=0.5),
+         partial(scipy.fft.ifht, dln=2, mu=0.5))
+
+mocks = (mock_backend.fft, mock_backend.fft2, mock_backend.fftn,
+         mock_backend.ifft, mock_backend.ifft2, mock_backend.ifftn,
+         mock_backend.rfft, mock_backend.rfft2, mock_backend.rfftn,
+         mock_backend.irfft, mock_backend.irfft2, mock_backend.irfftn,
+         mock_backend.hfft, mock_backend.hfft2, mock_backend.hfftn,
+         mock_backend.ihfft, mock_backend.ihfft2, mock_backend.ihfftn,
+         mock_backend.dct, mock_backend.idct,
+         mock_backend.dctn, mock_backend.idctn,
+         mock_backend.dst, mock_backend.idst,
+         mock_backend.dstn, mock_backend.idstn,
+         mock_backend.fht, mock_backend.ifht)
+
+
+@pytest.mark.parametrize("func, np_func, mock", zip(funcs, np_funcs, mocks))
+def test_backend_call(func, np_func, mock):
+    x = np.arange(20).reshape((10,2))
+    answer = np_func(x.astype(np.float64))
+    assert_allclose(func(x), answer, atol=1e-10)
+
+    with set_backend(mock_backend, only=True):
+        mock.number_calls.c = 0
+        y = func(x)
+        assert_equal(y, mock.return_value)
+        assert_equal(mock.number_calls.c, 1)
+
+    assert_allclose(func(x), answer, atol=1e-10)
+
+
+plan_funcs = (scipy.fft.fft, scipy.fft.fft2, scipy.fft.fftn,
+              scipy.fft.ifft, scipy.fft.ifft2, scipy.fft.ifftn,
+              scipy.fft.rfft, scipy.fft.rfft2, scipy.fft.rfftn,
+              scipy.fft.irfft, scipy.fft.irfft2, scipy.fft.irfftn,
+              scipy.fft.hfft, scipy.fft.hfft2, scipy.fft.hfftn,
+              scipy.fft.ihfft, scipy.fft.ihfft2, scipy.fft.ihfftn)
+
+plan_mocks = (mock_backend.fft, mock_backend.fft2, mock_backend.fftn,
+              mock_backend.ifft, mock_backend.ifft2, mock_backend.ifftn,
+              mock_backend.rfft, mock_backend.rfft2, mock_backend.rfftn,
+              mock_backend.irfft, mock_backend.irfft2, mock_backend.irfftn,
+              mock_backend.hfft, mock_backend.hfft2, mock_backend.hfftn,
+              mock_backend.ihfft, mock_backend.ihfft2, mock_backend.ihfftn)
+
+
+@pytest.mark.parametrize("func, mock", zip(plan_funcs, plan_mocks))
+def test_backend_plan(func, mock):
+    x = np.arange(20).reshape((10, 2))
+
+    with pytest.raises(NotImplementedError, match='precomputed plan'):
+        func(x, plan='foo')
+
+    with set_backend(mock_backend, only=True):
+        mock.number_calls.c = 0
+        y = func(x, plan='foo')
+        assert_equal(y, mock.return_value)
+        assert_equal(mock.number_calls.c, 1)
+        assert_equal(mock.last_args.l[1]['plan'], 'foo')

shared/package-files/0c/0c52c03003c5cd7d832358800048d563f9ccf53ba86260a37b79bb435ec0d045/test_formats.py

@@ -0,0 +1,350 @@
+from contextlib import nullcontext
+from datetime import (
+    datetime,
+    time,
+)
+import locale
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    PeriodIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+def get_local_am_pm():
+    """Return the AM and PM strings returned by strftime in current locale."""
+    am_local = time(1).strftime("%p")
+    pm_local = time(13).strftime("%p")
+    return am_local, pm_local
+
+
+def test_get_values_for_csv():
+    index = PeriodIndex(["2017-01-01", "2017-01-02", "2017-01-03"], freq="D")
+
+    # First, with no arguments.
+    expected = np.array(["2017-01-01", "2017-01-02", "2017-01-03"], dtype=object)
+
+    result = index._get_values_for_csv()
+    tm.assert_numpy_array_equal(result, expected)
+
+    # No NaN values, so na_rep has no effect
+    result = index._get_values_for_csv(na_rep="pandas")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # Make sure date formatting works
+    expected = np.array(["01-2017-01", "01-2017-02", "01-2017-03"], dtype=object)
+
+    result = index._get_values_for_csv(date_format="%m-%Y-%d")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # NULL object handling should work
+    index = PeriodIndex(["2017-01-01", pd.NaT, "2017-01-03"], freq="D")
+    expected = np.array(["2017-01-01", "NaT", "2017-01-03"], dtype=object)
+
+    result = index._get_values_for_csv(na_rep="NaT")
+    tm.assert_numpy_array_equal(result, expected)
+
+    expected = np.array(["2017-01-01", "pandas", "2017-01-03"], dtype=object)
+
+    result = index._get_values_for_csv(na_rep="pandas")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+class TestPeriodIndexRendering:
+    def test_format_empty(self):
+        # GH#35712
+        empty_idx = PeriodIndex([], freq="Y")
+        msg = r"PeriodIndex\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert empty_idx.format() == []
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert empty_idx.format(name=True) == [""]
+
+    @pytest.mark.parametrize("method", ["__repr__", "__str__"])
+    def test_representation(self, method):
+        # GH#7601
+        idx1 = PeriodIndex([], freq="D")
+        idx2 = PeriodIndex(["2011-01-01"], freq="D")
+        idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
+        idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
+        idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
+        idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="h")
+        idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
+        idx8 = pd.period_range("2013Q1", periods=2, freq="Q")
+        idx9 = pd.period_range("2013Q1", periods=3, freq="Q")
+        idx10 = PeriodIndex(["2011-01-01", "2011-02-01"], freq="3D")
+
+        exp1 = "PeriodIndex([], dtype='period[D]')"
+
+        exp2 = "PeriodIndex(['2011-01-01'], dtype='period[D]')"
+
+        exp3 = "PeriodIndex(['2011-01-01', '2011-01-02'], dtype='period[D]')"
+
+        exp4 = (
+            "PeriodIndex(['2011-01-01', '2011-01-02', '2011-01-03'], "
+            "dtype='period[D]')"
+        )
+
+        exp5 = "PeriodIndex(['2011', '2012', '2013'], dtype='period[Y-DEC]')"
+
+        exp6 = (
+            "PeriodIndex(['2011-01-01 09:00', '2012-02-01 10:00', 'NaT'], "
+            "dtype='period[h]')"
+        )
+
+        exp7 = "PeriodIndex(['2013Q1'], dtype='period[Q-DEC]')"
+
+        exp8 = "PeriodIndex(['2013Q1', '2013Q2'], dtype='period[Q-DEC]')"
+
+        exp9 = "PeriodIndex(['2013Q1', '2013Q2', '2013Q3'], dtype='period[Q-DEC]')"
+
+        exp10 = "PeriodIndex(['2011-01-01', '2011-02-01'], dtype='period[3D]')"
+
+        for idx, expected in zip(
+            [idx1, idx2, idx3, idx4, idx5, idx6, idx7, idx8, idx9, idx10],
+            [exp1, exp2, exp3, exp4, exp5, exp6, exp7, exp8, exp9, exp10],
+        ):
+            result = getattr(idx, method)()
+            assert result == expected
+
+    # TODO: These are Series.__repr__ tests
+    def test_representation_to_series(self):
+        # GH#10971
+        idx1 = PeriodIndex([], freq="D")
+        idx2 = PeriodIndex(["2011-01-01"], freq="D")
+        idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
+        idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
+        idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
+        idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="h")
+
+        idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
+        idx8 = pd.period_range("2013Q1", periods=2, freq="Q")
+        idx9 = pd.period_range("2013Q1", periods=3, freq="Q")
+
+        exp1 = """Series([], dtype: period[D])"""
+
+        exp2 = """0    2011-01-01
+dtype: period[D]"""
+
+        exp3 = """0    2011-01-01
+1    2011-01-02
+dtype: period[D]"""
+
+        exp4 = """0    2011-01-01
+1    2011-01-02
+2    2011-01-03
+dtype: period[D]"""
+
+        exp5 = """0    2011
+1    2012
+2    2013
+dtype: period[Y-DEC]"""
+
+        exp6 = """0    2011-01-01 09:00
+1    2012-02-01 10:00
+2                 NaT
+dtype: period[h]"""
+
+        exp7 = """0    2013Q1
+dtype: period[Q-DEC]"""
+
+        exp8 = """0    2013Q1
+1    2013Q2
+dtype: period[Q-DEC]"""
+
+        exp9 = """0    2013Q1
+1    2013Q2
+2    2013Q3
+dtype: period[Q-DEC]"""
+
+        for idx, expected in zip(
+            [idx1, idx2, idx3, idx4, idx5, idx6, idx7, idx8, idx9],
+            [exp1, exp2, exp3, exp4, exp5, exp6, exp7, exp8, exp9],
+        ):
+            result = repr(Series(idx))
+            assert result == expected
+
+    def test_summary(self):
+        # GH#9116
+        idx1 = PeriodIndex([], freq="D")
+        idx2 = PeriodIndex(["2011-01-01"], freq="D")
+        idx3 = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
+        idx4 = PeriodIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
+        idx5 = PeriodIndex(["2011", "2012", "2013"], freq="Y")
+        idx6 = PeriodIndex(["2011-01-01 09:00", "2012-02-01 10:00", "NaT"], freq="h")
+
+        idx7 = pd.period_range("2013Q1", periods=1, freq="Q")
+        idx8 = pd.period_range("2013Q1", periods=2, freq="Q")
+        idx9 = pd.period_range("2013Q1", periods=3, freq="Q")
+
+        exp1 = """PeriodIndex: 0 entries
+Freq: D"""
+
+        exp2 = """PeriodIndex: 1 entries, 2011-01-01 to 2011-01-01
+Freq: D"""
+
+        exp3 = """PeriodIndex: 2 entries, 2011-01-01 to 2011-01-02
+Freq: D"""
+
+        exp4 = """PeriodIndex: 3 entries, 2011-01-01 to 2011-01-03
+Freq: D"""
+
+        exp5 = """PeriodIndex: 3 entries, 2011 to 2013
+Freq: Y-DEC"""
+
+        exp6 = """PeriodIndex: 3 entries, 2011-01-01 09:00 to NaT
+Freq: h"""
+
+        exp7 = """PeriodIndex: 1 entries, 2013Q1 to 2013Q1
+Freq: Q-DEC"""
+
+        exp8 = """PeriodIndex: 2 entries, 2013Q1 to 2013Q2
+Freq: Q-DEC"""
+
+        exp9 = """PeriodIndex: 3 entries, 2013Q1 to 2013Q3
+Freq: Q-DEC"""
+
+        for idx, expected in zip(
+            [idx1, idx2, idx3, idx4, idx5, idx6, idx7, idx8, idx9],
+            [exp1, exp2, exp3, exp4, exp5, exp6, exp7, exp8, exp9],
+        ):
+            result = idx._summary()
+            assert result == expected
+
+
+class TestPeriodIndexFormat:
+    def test_period_format_and_strftime_default(self):
+        per = PeriodIndex([datetime(2003, 1, 1, 12), None], freq="h")
+
+        # Default formatting
+        msg = "PeriodIndex.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format()
+        assert formatted[0] == "2003-01-01 12:00"  # default: minutes not shown
+        assert formatted[1] == "NaT"
+        # format is equivalent to strftime(None)...
+        assert formatted[0] == per.strftime(None)[0]
+        assert per.strftime(None)[1] is np.nan  # ...except for NaTs
+
+        # Same test with nanoseconds freq
+        per = pd.period_range("2003-01-01 12:01:01.123456789", periods=2, freq="ns")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format()
+        assert (formatted == per.strftime(None)).all()
+        assert formatted[0] == "2003-01-01 12:01:01.123456789"
+        assert formatted[1] == "2003-01-01 12:01:01.123456790"
+
+    def test_period_custom(self):
+        # GH#46252 custom formatting directives %l (ms) and %u (us)
+        msg = "PeriodIndex.format is deprecated"
+
+        # 3 digits
+        per = pd.period_range("2003-01-01 12:01:01.123", periods=2, freq="ms")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+        assert formatted[0] == "03 12:01:01 (ms=123 us=123000 ns=123000000)"
+        assert formatted[1] == "03 12:01:01 (ms=124 us=124000 ns=124000000)"
+
+        # 6 digits
+        per = pd.period_range("2003-01-01 12:01:01.123456", periods=2, freq="us")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+        assert formatted[0] == "03 12:01:01 (ms=123 us=123456 ns=123456000)"
+        assert formatted[1] == "03 12:01:01 (ms=123 us=123457 ns=123457000)"
+
+        # 9 digits
+        per = pd.period_range("2003-01-01 12:01:01.123456789", periods=2, freq="ns")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = per.format(date_format="%y %I:%M:%S (ms=%l us=%u ns=%n)")
+        assert formatted[0] == "03 12:01:01 (ms=123 us=123456 ns=123456789)"
+        assert formatted[1] == "03 12:01:01 (ms=123 us=123456 ns=123456790)"
+
+    def test_period_tz(self):
+        # Formatting periods created from a datetime with timezone.
+        msg = r"PeriodIndex\.format is deprecated"
+        # This timestamp is in 2013 in Europe/Paris but is 2012 in UTC
+        dt = pd.to_datetime(["2013-01-01 00:00:00+01:00"], utc=True)
+
+        # Converting to a period looses the timezone information
+        # Since tz is currently set as utc, we'll see 2012
+        with tm.assert_produces_warning(UserWarning, match="will drop timezone"):
+            per = dt.to_period(freq="h")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert per.format()[0] == "2012-12-31 23:00"
+
+        # If tz is currently set as paris before conversion, we'll see 2013
+        dt = dt.tz_convert("Europe/Paris")
+        with tm.assert_produces_warning(UserWarning, match="will drop timezone"):
+            per = dt.to_period(freq="h")
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert per.format()[0] == "2013-01-01 00:00"
+
+    @pytest.mark.parametrize(
+        "locale_str",
+        [
+            pytest.param(None, id=str(locale.getlocale())),
+            "it_IT.utf8",
+            "it_IT",  # Note: encoding will be 'ISO8859-1'
+            "zh_CN.utf8",
+            "zh_CN",  # Note: encoding will be 'gb2312'
+        ],
+    )
+    def test_period_non_ascii_fmt(self, locale_str):
+        # GH#46468 non-ascii char in input format string leads to wrong output
+
+        # Skip if locale cannot be set
+        if locale_str is not None and not tm.can_set_locale(locale_str, locale.LC_ALL):
+            pytest.skip(f"Skipping as locale '{locale_str}' cannot be set on host.")
+
+        # Change locale temporarily for this test.
+        with tm.set_locale(locale_str, locale.LC_ALL) if locale_str else nullcontext():
+            # Scalar
+            per = pd.Period("2018-03-11 13:00", freq="h")
+            assert per.strftime("%y é") == "18 é"
+
+            # Index
+            per = pd.period_range("2003-01-01 01:00:00", periods=2, freq="12h")
+            msg = "PeriodIndex.format is deprecated"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                formatted = per.format(date_format="%y é")
+            assert formatted[0] == "03 é"
+            assert formatted[1] == "03 é"
+
+    @pytest.mark.parametrize(
+        "locale_str",
+        [
+            pytest.param(None, id=str(locale.getlocale())),
+            "it_IT.utf8",
+            "it_IT",  # Note: encoding will be 'ISO8859-1'
+            "zh_CN.utf8",
+            "zh_CN",  # Note: encoding will be 'gb2312'
+        ],
+    )
+    def test_period_custom_locale_directive(self, locale_str):
+        # GH#46319 locale-specific directive leads to non-utf8 c strftime char* result
+
+        # Skip if locale cannot be set
+        if locale_str is not None and not tm.can_set_locale(locale_str, locale.LC_ALL):
+            pytest.skip(f"Skipping as locale '{locale_str}' cannot be set on host.")
+
+        # Change locale temporarily for this test.
+        with tm.set_locale(locale_str, locale.LC_ALL) if locale_str else nullcontext():
+            # Get locale-specific reference
+            am_local, pm_local = get_local_am_pm()
+
+            # Scalar
+            per = pd.Period("2018-03-11 13:00", freq="h")
+            assert per.strftime("%p") == pm_local
+
+            # Index
+            per = pd.period_range("2003-01-01 01:00:00", periods=2, freq="12h")
+            msg = "PeriodIndex.format is deprecated"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                formatted = per.format(date_format="%y %I:%M:%S%p")
+            assert formatted[0] == f"03 01:00:00{am_local}"
+            assert formatted[1] == f"03 01:00:00{pm_local}"

shared/package-files/0c/0c56d1cc902aa31c536cb523a2357e1eec061df3a70003caa4e7415c54705d2e/adaptive.h

@@ -0,0 +1,109 @@
+#pragma once
+
+#include <torch/nn/cloneable.h>
+#include <torch/nn/functional/activation.h>
+#include <torch/nn/module.h>
+#include <torch/nn/modules/container/modulelist.h>
+#include <torch/nn/modules/container/sequential.h>
+#include <torch/nn/modules/linear.h>
+#include <torch/nn/options/adaptive.h>
+
+namespace torch {
+namespace nn {
+
+/// The output of a single invocation of an AdaptiveLogSoftmaxWithLoss
+/// module's `forward()` method.
+struct TORCH_API ASMoutput {
+  ASMoutput(Tensor output_, double loss_);
+
+  /// Tensor containing computed target log probabilities for each example
+  Tensor output;
+
+  /// Scalar representing the computed negative log likelihood loss
+  double loss;
+};
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AdaptiveLogSoftmaxWithLoss
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Efficient softmax approximation as described in
+/// `Efficient softmax approximation for GPUs`_ by Edouard Grave, Armand Joulin,
+/// Moustapha Cissé, David Grangier, and Hervé Jégou.
+/// See
+/// https://pytorch.org/docs/master/nn.html#torch.nn.AdaptiveLogSoftmaxWithLoss
+/// to learn about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::AdaptiveLogSoftmaxWithLossOptions`
+/// class to learn what constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// AdaptiveLogSoftmaxWithLoss model(AdaptiveLogSoftmaxWithLossOptions(8, 10,
+/// {4, 8}).div_value(2.).head_bias(true));
+/// ```
+class TORCH_API AdaptiveLogSoftmaxWithLossImpl
+    : public Cloneable<AdaptiveLogSoftmaxWithLossImpl> {
+ public:
+  AdaptiveLogSoftmaxWithLossImpl(
+      int64_t in_features,
+      int64_t n_classes,
+      std::vector<int64_t> cutoffs)
+      : AdaptiveLogSoftmaxWithLossImpl(AdaptiveLogSoftmaxWithLossOptions(
+            in_features,
+            n_classes,
+            cutoffs)) {}
+
+  explicit AdaptiveLogSoftmaxWithLossImpl(
+      AdaptiveLogSoftmaxWithLossOptions options_);
+
+  ASMoutput forward(const Tensor& input, const Tensor& target);
+
+  void reset() override;
+
+  void reset_parameters();
+
+  /// Pretty prints the `AdaptiveLogSoftmaxWithLoss` module into the given
+  /// `stream`.
+  void pretty_print(std::ostream& stream) const override;
+
+  /// Given input tensor, and output of `head`, computes the log of the full
+  /// distribution
+  Tensor _get_full_log_prob(const Tensor& input, const Tensor& head_output);
+
+  /// Computes log probabilities for all n_classes
+  Tensor log_prob(const Tensor& input);
+
+  /// This is equivalent to `log_pob(input).argmax(1)` but is more efficient in
+  /// some cases
+  Tensor predict(const Tensor& input);
+
+  /// The options with which this `Module` was constructed
+  AdaptiveLogSoftmaxWithLossOptions options;
+
+  /// Cutoffs used to assign targets to their buckets. It should be an ordered
+  /// Sequence of integers sorted in the increasing order
+  std::vector<int64_t> cutoffs;
+
+  int64_t shortlist_size;
+
+  /// Number of clusters
+  int64_t n_clusters;
+
+  /// Output size of head classifier
+  int64_t head_size;
+
+  Linear head = nullptr;
+
+  ModuleList tail;
+};
+
+/// A `ModuleHolder` subclass for `AdaptiveLogSoftmaxWithLossImpl`.
+/// See the documentation for `AdaptiveLogSoftmaxWithLossImpl` class to learn
+/// what methods it provides, and examples of how to use
+/// `AdaptiveLogSoftmaxWithLoss` with
+/// `torch::nn::AdaptiveLogSoftmaxWithLossOptions`. See the documentation for
+/// `ModuleHolder` to learn about PyTorch's module storage semantics.
+TORCH_MODULE(AdaptiveLogSoftmaxWithLoss);
+
+} // namespace nn
+} // namespace torch

shared/package-files/0c/0c5a3106c098456aa876635a0cd3e742b6534d789a4afc0d664c2bc9b7bb725d/test_tools.py

@@ -0,0 +1,52 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Period,
+    PeriodIndex,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestPeriodRepresentation:
+    """
+    Wish to match NumPy units
+    """
+
+    @pytest.mark.parametrize(
+        "freq, base_date",
+        [
+            ("W-THU", "1970-01-01"),
+            ("D", "1970-01-01"),
+            ("B", "1970-01-01"),
+            ("h", "1970-01-01"),
+            ("min", "1970-01-01"),
+            ("s", "1970-01-01"),
+            ("ms", "1970-01-01"),
+            ("us", "1970-01-01"),
+            ("ns", "1970-01-01"),
+            ("M", "1970-01"),
+            ("Y", 1970),
+        ],
+    )
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    @pytest.mark.filterwarnings(
+        "ignore:Period with BDay freq is deprecated:FutureWarning"
+    )
+    def test_freq(self, freq, base_date):
+        rng = period_range(start=base_date, periods=10, freq=freq)
+        exp = np.arange(10, dtype=np.int64)
+
+        tm.assert_numpy_array_equal(rng.asi8, exp)
+
+
+class TestPeriodIndexConversion:
+    def test_tolist(self):
+        index = period_range(freq="Y", start="1/1/2001", end="12/1/2009")
+        rs = index.tolist()
+        for x in rs:
+            assert isinstance(x, Period)
+
+        recon = PeriodIndex(rs)
+        tm.assert_index_equal(index, recon)

shared/package-files/0c/0c5bdff5e2602627390d41edda5ac992c4aee57e9e5d17eb877d2de720154b36/gdscript.py

@@ -0,0 +1,188 @@
+"""
+    pygments.lexers.gdscript
+    ~~~~~~~~~~~~~~~~~~~~~~~~
+
+    Lexer for GDScript.
+
+    Modified by Daniel J. Ramirez <djrmuv@gmail.com> based on the original
+    python.py.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import re
+
+from pygments.lexer import RegexLexer, include, bygroups, default, words, \
+    combined
+from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
+    Number, Punctuation, Whitespace
+
+__all__ = ["GDScriptLexer"]
+
+
+class GDScriptLexer(RegexLexer):
+    """
+    For GDScript source code.
+    """
+
+    name = "GDScript"
+    url = 'https://www.godotengine.org'
+    aliases = ["gdscript", "gd"]
+    filenames = ["*.gd"]
+    mimetypes = ["text/x-gdscript", "application/x-gdscript"]
+
+    def innerstring_rules(ttype):
+        return [
+            # the old style '%s' % (...) string formatting
+            (r"%(\(\w+\))?[-#0 +]*([0-9]+|[*])?(\.([0-9]+|[*]))?"
+             "[hlL]?[E-GXc-giorsux%]",
+             String.Interpol),
+            # backslashes, quotes and formatting signs must be parsed one at a time
+            (r'[^\\\'"%\n]+', ttype),
+            (r'[\'"\\]', ttype),
+            # unhandled string formatting sign
+            (r"%", ttype),
+            # newlines are an error (use "nl" state)
+        ]
+
+    tokens = {
+        "root": [
+            (r"\n", Whitespace),
+            (r'^(\s*)([rRuUbB]{,2})("""(?:.|\n)*?""")',
+             bygroups(Whitespace, String.Affix, String.Doc)),
+            (r"^(\s*)([rRuUbB]{,2})('''(?:.|\n)*?''')",
+             bygroups(Whitespace, String.Affix, String.Doc)),
+            (r"[^\S\n]+", Whitespace),
+            (r"#.*$", Comment.Single),
+            (r"[]{}:(),;[]", Punctuation),
+            (r"(\\)(\n)", bygroups(Text, Whitespace)),
+            (r"\\", Text),
+            (r"(in|and|or|not)\b", Operator.Word),
+            (r"!=|==|<<|>>|&&|\+=|-=|\*=|/=|%=|&=|\|=|\|\||[-~+/*%=<>&^.!|$]",
+             Operator),
+            include("keywords"),
+            (r"(func)(\s+)", bygroups(Keyword, Whitespace), "funcname"),
+            (r"(class)(\s+)", bygroups(Keyword, Whitespace), "classname"),
+            include("builtins"),
+            ('([rR]|[uUbB][rR]|[rR][uUbB])(""")',
+             bygroups(String.Affix, String.Double),
+             "tdqs"),
+            ("([rR]|[uUbB][rR]|[rR][uUbB])(''')",
+             bygroups(String.Affix, String.Single),
+             "tsqs"),
+            ('([rR]|[uUbB][rR]|[rR][uUbB])(")',
+             bygroups(String.Affix, String.Double),
+             "dqs"),
+            ("([rR]|[uUbB][rR]|[rR][uUbB])(')",
+             bygroups(String.Affix, String.Single),
+             "sqs"),
+            ('([uUbB]?)(""")',
+             bygroups(String.Affix, String.Double),
+             combined("stringescape", "tdqs")),
+            ("([uUbB]?)(''')",
+             bygroups(String.Affix, String.Single),
+             combined("stringescape", "tsqs")),
+            ('([uUbB]?)(")',
+             bygroups(String.Affix, String.Double),
+             combined("stringescape", "dqs")),
+            ("([uUbB]?)(')",
+             bygroups(String.Affix, String.Single),
+             combined("stringescape", "sqs")),
+            include("name"),
+            include("numbers"),
+        ],
+        "keywords": [
+            (words(("and", "in", "not", "or", "as", "breakpoint", "class",
+                    "class_name", "extends", "is", "func", "setget", "signal",
+                    "tool", "const", "enum", "export", "onready", "static",
+                    "var", "break", "continue", "if", "elif", "else", "for",
+                    "pass", "return", "match", "while", "remote", "master",
+                    "puppet", "remotesync", "mastersync", "puppetsync"),
+                   suffix=r"\b"), Keyword),
+        ],
+        "builtins": [
+            (words(("Color8", "ColorN", "abs", "acos", "asin", "assert", "atan",
+                    "atan2", "bytes2var", "ceil", "char", "clamp", "convert",
+                    "cos", "cosh", "db2linear", "decimals", "dectime", "deg2rad",
+                    "dict2inst", "ease", "exp", "floor", "fmod", "fposmod",
+                    "funcref", "hash", "inst2dict", "instance_from_id", "is_inf",
+                    "is_nan", "lerp", "linear2db", "load", "log", "max", "min",
+                    "nearest_po2", "pow", "preload", "print", "print_stack",
+                    "printerr", "printraw", "prints", "printt", "rad2deg",
+                    "rand_range", "rand_seed", "randf", "randi", "randomize",
+                    "range", "round", "seed", "sign", "sin", "sinh", "sqrt",
+                    "stepify", "str", "str2var", "tan", "tan", "tanh",
+                    "type_exist", "typeof", "var2bytes", "var2str", "weakref",
+                    "yield"), prefix=r"(?<!\.)", suffix=r"\b"),
+             Name.Builtin),
+            (r"((?<!\.)(self|false|true)|(PI|TAU|NAN|INF)" r")\b",
+             Name.Builtin.Pseudo),
+            (words(("bool", "int", "float", "String", "NodePath", "Vector2",
+                    "Rect2", "Transform2D", "Vector3", "Rect3", "Plane", "Quat",
+                    "Basis", "Transform", "Color", "RID", "Object", "NodePath",
+                    "Dictionary", "Array", "PackedByteArray", "PackedInt32Array",
+                    "PackedInt64Array", "PackedFloat32Array", "PackedFloat64Array",
+                    "PackedStringArray", "PackedVector2Array", "PackedVector3Array",
+                    "PackedColorArray", "null", "void"),
+                   prefix=r"(?<!\.)", suffix=r"\b"),
+             Name.Builtin.Type),
+        ],
+        "numbers": [
+            (r"(\d+\.\d*|\d*\.\d+)([eE][+-]?[0-9]+)?j?", Number.Float),
+            (r"\d+[eE][+-]?[0-9]+j?", Number.Float),
+            (r"0[xX][a-fA-F0-9]+", Number.Hex),
+            (r"\d+j?", Number.Integer),
+        ],
+        "name": [(r"[a-zA-Z_]\w*", Name)],
+        "funcname": [(r"[a-zA-Z_]\w*", Name.Function, "#pop"), default("#pop")],
+        "classname": [(r"[a-zA-Z_]\w*", Name.Class, "#pop")],
+        "stringescape": [
+            (
+                r'\\([\\abfnrtv"\']|\n|N\{.*?\}|u[a-fA-F0-9]{4}|'
+                r"U[a-fA-F0-9]{8}|x[a-fA-F0-9]{2}|[0-7]{1,3})",
+                String.Escape,
+            )
+        ],
+        "strings-single": innerstring_rules(String.Single),
+        "strings-double": innerstring_rules(String.Double),
+        "dqs": [
+            (r'"', String.Double, "#pop"),
+            (r'\\\\|\\"|\\\n', String.Escape),  # included here for raw strings
+            include("strings-double"),
+        ],
+        "sqs": [
+            (r"'", String.Single, "#pop"),
+            (r"\\\\|\\'|\\\n", String.Escape),  # included here for raw strings
+            include("strings-single"),
+        ],
+        "tdqs": [
+            (r'"""', String.Double, "#pop"),
+            include("strings-double"),
+            (r"\n", Whitespace),
+        ],
+        "tsqs": [
+            (r"'''", String.Single, "#pop"),
+            include("strings-single"),
+            (r"\n", Whitespace),
+        ],
+    }
+
+    def analyse_text(text):
+        score = 0.0
+
+        if re.search(
+            r"func (_ready|_init|_input|_process|_unhandled_input)", text
+        ):
+            score += 0.8
+
+        if re.search(
+            r"(extends |class_name |onready |preload|load|setget|func [^_])",
+            text
+        ):
+            score += 0.4
+
+        if re.search(r"(var|const|enum|export|signal|tool)", text):
+            score += 0.2
+
+        return min(score, 1.0)

shared/package-files/0c/0c5c5c2d1ff01de4c6740e3d565f9dafc934cdd9bb2e592c07a0ea8f7643e588/well_known_types.pyi

@@ -0,0 +1,94 @@
+from datetime import datetime, timedelta
+from typing import Any as tAny, Dict, Optional, Text, Type
+
+class Error(Exception): ...
+class ParseError(Error): ...
+
+class Any:
+    type_url: tAny = ...
+    value: tAny = ...
+    def Pack(self, msg: tAny, type_url_prefix: bytes = ..., deterministic: Optional[tAny] = ...) -> None: ...
+    def Unpack(self, msg: tAny): ...
+    def TypeName(self): ...
+    def Is(self, descriptor: tAny): ...
+
+class Timestamp:
+    def ToJsonString(self) -> str: ...
+    seconds: int = ...
+    nanos: int = ...
+    def FromJsonString(self, value: str) -> None: ...
+    def GetCurrentTime(self) -> None: ...
+    def ToNanoseconds(self) -> int: ...
+    def ToMicroseconds(self) -> int: ...
+    def ToMilliseconds(self) -> int: ...
+    def ToSeconds(self) -> int: ...
+    def FromNanoseconds(self, nanos: int) -> None: ...
+    def FromMicroseconds(self, micros: int) -> None: ...
+    def FromMilliseconds(self, millis: int) -> None: ...
+    def FromSeconds(self, seconds: int) -> None: ...
+    def ToDatetime(self) -> datetime: ...
+    def FromDatetime(self, dt: datetime) -> None: ...
+
+class Duration:
+    def ToJsonString(self) -> str: ...
+    seconds: int = ...
+    nanos: int = ...
+    def FromJsonString(self, value: tAny) -> None: ...
+    def ToNanoseconds(self) -> int: ...
+    def ToMicroseconds(self) -> int: ...
+    def ToMilliseconds(self) -> int: ...
+    def ToSeconds(self) -> int: ...
+    def FromNanoseconds(self, nanos: int) -> None: ...
+    def FromMicroseconds(self, micros: int) -> None: ...
+    def FromMilliseconds(self, millis: int) -> None: ...
+    def FromSeconds(self, seconds: int) -> None: ...
+    def ToTimedelta(self) -> timedelta: ...
+    def FromTimedelta(self, td: timedelta) -> None: ...
+
+class FieldMask:
+    def ToJsonString(self) -> str: ...
+    def FromJsonString(self, value: tAny) -> None: ...
+    def IsValidForDescriptor(self, message_descriptor: tAny): ...
+    def AllFieldsFromDescriptor(self, message_descriptor: tAny) -> None: ...
+    def CanonicalFormFromMask(self, mask: tAny) -> None: ...
+    def Union(self, mask1: tAny, mask2: tAny) -> None: ...
+    def Intersect(self, mask1: tAny, mask2: tAny) -> None: ...
+    def MergeMessage(
+        self, source: tAny, destination: tAny, replace_message_field: bool = ..., replace_repeated_field: bool = ...
+    ) -> None: ...
+
+class _FieldMaskTree:
+    def __init__(self, field_mask: Optional[tAny] = ...) -> None: ...
+    def MergeFromFieldMask(self, field_mask: tAny) -> None: ...
+    def AddPath(self, path: tAny): ...
+    def ToFieldMask(self, field_mask: tAny) -> None: ...
+    def IntersectPath(self, path: tAny, intersection: tAny): ...
+    def AddLeafNodes(self, prefix: tAny, node: tAny) -> None: ...
+    def MergeMessage(self, source: tAny, destination: tAny, replace_message: tAny, replace_repeated: tAny) -> None: ...
+
+class Struct:
+    def __getitem__(self, key: tAny): ...
+    def __contains__(self, item: tAny): ...
+    def __setitem__(self, key: tAny, value: tAny) -> None: ...
+    def __delitem__(self, key: tAny) -> None: ...
+    def __len__(self): ...
+    def __iter__(self): ...
+    def keys(self): ...
+    def values(self): ...
+    def items(self): ...
+    def get_or_create_list(self, key: tAny): ...
+    def get_or_create_struct(self, key: tAny): ...
+    def update(self, dictionary: tAny) -> None: ...
+
+class ListValue:
+    def __len__(self): ...
+    def append(self, value: tAny) -> None: ...
+    def extend(self, elem_seq: tAny) -> None: ...
+    def __getitem__(self, index: tAny): ...
+    def __setitem__(self, index: tAny, value: tAny) -> None: ...
+    def __delitem__(self, key: tAny) -> None: ...
+    def items(self) -> None: ...
+    def add_struct(self): ...
+    def add_list(self): ...
+
+WKTBASES: Dict[Text, Type]

shared/package-files/0c/0c5c72165a73591652f7374186c8c82397c550e318f5b5c79f7108ac8be638ca/builder_decimal.h

@@ -0,0 +1,164 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include <memory>
+
+#include "arrow/array/array_decimal.h"
+#include "arrow/array/builder_base.h"
+#include "arrow/array/builder_binary.h"
+#include "arrow/array/data.h"
+#include "arrow/status.h"
+#include "arrow/type.h"
+#include "arrow/util/visibility.h"
+
+namespace arrow {
+
+/// \addtogroup numeric-builders
+///
+/// @{
+
+class ARROW_EXPORT Decimal32Builder : public FixedSizeBinaryBuilder {
+ public:
+  using TypeClass = Decimal32Type;
+  using ValueType = Decimal32;
+
+  explicit Decimal32Builder(const std::shared_ptr<DataType>& type,
+                            MemoryPool* pool = default_memory_pool(),
+                            int64_t alignment = kDefaultBufferAlignment);
+
+  using FixedSizeBinaryBuilder::Append;
+  using FixedSizeBinaryBuilder::AppendValues;
+  using FixedSizeBinaryBuilder::Reset;
+
+  Status Append(Decimal32 val);
+  void UnsafeAppend(Decimal32 val);
+  void UnsafeAppend(std::string_view val);
+
+  Status FinishInternal(std::shared_ptr<ArrayData>* out) override;
+
+  /// \cond FALSE
+  using ArrayBuilder::Finish;
+  /// \endcond
+
+  Status Finish(std::shared_ptr<Decimal32Array>* out) { return FinishTyped(out); }
+
+  std::shared_ptr<DataType> type() const override { return decimal_type_; }
+
+ protected:
+  std::shared_ptr<Decimal32Type> decimal_type_;
+};
+
+class ARROW_EXPORT Decimal64Builder : public FixedSizeBinaryBuilder {
+ public:
+  using TypeClass = Decimal64Type;
+  using ValueType = Decimal64;
+
+  explicit Decimal64Builder(const std::shared_ptr<DataType>& type,
+                            MemoryPool* pool = default_memory_pool(),
+                            int64_t alignment = kDefaultBufferAlignment);
+
+  using FixedSizeBinaryBuilder::Append;
+  using FixedSizeBinaryBuilder::AppendValues;
+  using FixedSizeBinaryBuilder::Reset;
+
+  Status Append(Decimal64 val);
+  void UnsafeAppend(Decimal64 val);
+  void UnsafeAppend(std::string_view val);
+
+  Status FinishInternal(std::shared_ptr<ArrayData>* out) override;
+
+  /// \cond FALSE
+  using ArrayBuilder::Finish;
+  /// \endcond
+
+  Status Finish(std::shared_ptr<Decimal64Array>* out) { return FinishTyped(out); }
+
+  std::shared_ptr<DataType> type() const override { return decimal_type_; }
+
+ protected:
+  std::shared_ptr<Decimal64Type> decimal_type_;
+};
+
+class ARROW_EXPORT Decimal128Builder : public FixedSizeBinaryBuilder {
+ public:
+  using TypeClass = Decimal128Type;
+  using ValueType = Decimal128;
+
+  explicit Decimal128Builder(const std::shared_ptr<DataType>& type,
+                             MemoryPool* pool = default_memory_pool(),
+                             int64_t alignment = kDefaultBufferAlignment);
+
+  using FixedSizeBinaryBuilder::Append;
+  using FixedSizeBinaryBuilder::AppendValues;
+  using FixedSizeBinaryBuilder::Reset;
+
+  Status Append(Decimal128 val);
+  void UnsafeAppend(Decimal128 val);
+  void UnsafeAppend(std::string_view val);
+
+  Status FinishInternal(std::shared_ptr<ArrayData>* out) override;
+
+  /// \cond FALSE
+  using ArrayBuilder::Finish;
+  /// \endcond
+
+  Status Finish(std::shared_ptr<Decimal128Array>* out) { return FinishTyped(out); }
+
+  std::shared_ptr<DataType> type() const override { return decimal_type_; }
+
+ protected:
+  std::shared_ptr<Decimal128Type> decimal_type_;
+};
+
+class ARROW_EXPORT Decimal256Builder : public FixedSizeBinaryBuilder {
+ public:
+  using TypeClass = Decimal256Type;
+  using ValueType = Decimal256;
+
+  explicit Decimal256Builder(const std::shared_ptr<DataType>& type,
+                             MemoryPool* pool = default_memory_pool(),
+                             int64_t alignment = kDefaultBufferAlignment);
+
+  using FixedSizeBinaryBuilder::Append;
+  using FixedSizeBinaryBuilder::AppendValues;
+  using FixedSizeBinaryBuilder::Reset;
+
+  Status Append(const Decimal256& val);
+  void UnsafeAppend(const Decimal256& val);
+  void UnsafeAppend(std::string_view val);
+
+  Status FinishInternal(std::shared_ptr<ArrayData>* out) override;
+
+  /// \cond FALSE
+  using ArrayBuilder::Finish;
+  /// \endcond
+
+  Status Finish(std::shared_ptr<Decimal256Array>* out) { return FinishTyped(out); }
+
+  std::shared_ptr<DataType> type() const override { return decimal_type_; }
+
+ protected:
+  std::shared_ptr<Decimal256Type> decimal_type_;
+};
+
+using DecimalBuilder = Decimal128Builder;
+
+/// @}
+
+}  // namespace arrow

shared/package-files/0c/0c5c98d884018f7af619992f6d121e64ed84118e3aeb16eec1ce0f4a1025e12c/test_classes.py

@@ -0,0 +1,600 @@
+"""Test inter-conversion of different polynomial classes.
+
+This tests the convert and cast methods of all the polynomial classes.
+
+"""
+import operator as op
+from numbers import Number
+
+import pytest
+import numpy as np
+from numpy.polynomial import (
+    Polynomial, Legendre, Chebyshev, Laguerre, Hermite, HermiteE)
+from numpy.testing import (
+    assert_almost_equal, assert_raises, assert_equal, assert_,
+    )
+from numpy.polynomial.polyutils import RankWarning
+
+#
+# fixtures
+#
+
+classes = (
+    Polynomial, Legendre, Chebyshev, Laguerre,
+    Hermite, HermiteE
+    )
+classids = tuple(cls.__name__ for cls in classes)
+
+@pytest.fixture(params=classes, ids=classids)
+def Poly(request):
+    return request.param
+
+#
+# helper functions
+#
+random = np.random.random
+
+
+def assert_poly_almost_equal(p1, p2, msg=""):
+    try:
+        assert_(np.all(p1.domain == p2.domain))
+        assert_(np.all(p1.window == p2.window))
+        assert_almost_equal(p1.coef, p2.coef)
+    except AssertionError:
+        msg = f"Result: {p1}\nTarget: {p2}"
+        raise AssertionError(msg)
+
+
+#
+# Test conversion methods that depend on combinations of two classes.
+#
+
+Poly1 = Poly
+Poly2 = Poly
+
+
+def test_conversion(Poly1, Poly2):
+    x = np.linspace(0, 1, 10)
+    coef = random((3,))
+
+    d1 = Poly1.domain + random((2,))*.25
+    w1 = Poly1.window + random((2,))*.25
+    p1 = Poly1(coef, domain=d1, window=w1)
+
+    d2 = Poly2.domain + random((2,))*.25
+    w2 = Poly2.window + random((2,))*.25
+    p2 = p1.convert(kind=Poly2, domain=d2, window=w2)
+
+    assert_almost_equal(p2.domain, d2)
+    assert_almost_equal(p2.window, w2)
+    assert_almost_equal(p2(x), p1(x))
+
+
+def test_cast(Poly1, Poly2):
+    x = np.linspace(0, 1, 10)
+    coef = random((3,))
+
+    d1 = Poly1.domain + random((2,))*.25
+    w1 = Poly1.window + random((2,))*.25
+    p1 = Poly1(coef, domain=d1, window=w1)
+
+    d2 = Poly2.domain + random((2,))*.25
+    w2 = Poly2.window + random((2,))*.25
+    p2 = Poly2.cast(p1, domain=d2, window=w2)
+
+    assert_almost_equal(p2.domain, d2)
+    assert_almost_equal(p2.window, w2)
+    assert_almost_equal(p2(x), p1(x))
+
+
+#
+# test methods that depend on one class
+#
+
+
+def test_identity(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    x = np.linspace(d[0], d[1], 11)
+    p = Poly.identity(domain=d, window=w)
+    assert_equal(p.domain, d)
+    assert_equal(p.window, w)
+    assert_almost_equal(p(x), x)
+
+
+def test_basis(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly.basis(5, domain=d, window=w)
+    assert_equal(p.domain, d)
+    assert_equal(p.window, w)
+    assert_equal(p.coef, [0]*5 + [1])
+
+
+def test_fromroots(Poly):
+    # check that requested roots are zeros of a polynomial
+    # of correct degree, domain, and window.
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    r = random((5,))
+    p1 = Poly.fromroots(r, domain=d, window=w)
+    assert_equal(p1.degree(), len(r))
+    assert_equal(p1.domain, d)
+    assert_equal(p1.window, w)
+    assert_almost_equal(p1(r), 0)
+
+    # check that polynomial is monic
+    pdom = Polynomial.domain
+    pwin = Polynomial.window
+    p2 = Polynomial.cast(p1, domain=pdom, window=pwin)
+    assert_almost_equal(p2.coef[-1], 1)
+
+
+def test_bad_conditioned_fit(Poly):
+
+    x = [0., 0., 1.]
+    y = [1., 2., 3.]
+
+    # check RankWarning is raised
+    with pytest.warns(RankWarning) as record:
+        Poly.fit(x, y, 2)
+    assert record[0].message.args[0] == "The fit may be poorly conditioned"
+
+
+def test_fit(Poly):
+
+    def f(x):
+        return x*(x - 1)*(x - 2)
+    x = np.linspace(0, 3)
+    y = f(x)
+
+    # check default value of domain and window
+    p = Poly.fit(x, y, 3)
+    assert_almost_equal(p.domain, [0, 3])
+    assert_almost_equal(p(x), y)
+    assert_equal(p.degree(), 3)
+
+    # check with given domains and window
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly.fit(x, y, 3, domain=d, window=w)
+    assert_almost_equal(p(x), y)
+    assert_almost_equal(p.domain, d)
+    assert_almost_equal(p.window, w)
+    p = Poly.fit(x, y, [0, 1, 2, 3], domain=d, window=w)
+    assert_almost_equal(p(x), y)
+    assert_almost_equal(p.domain, d)
+    assert_almost_equal(p.window, w)
+
+    # check with class domain default
+    p = Poly.fit(x, y, 3, [])
+    assert_equal(p.domain, Poly.domain)
+    assert_equal(p.window, Poly.window)
+    p = Poly.fit(x, y, [0, 1, 2, 3], [])
+    assert_equal(p.domain, Poly.domain)
+    assert_equal(p.window, Poly.window)
+
+    # check that fit accepts weights.
+    w = np.zeros_like(x)
+    z = y + random(y.shape)*.25
+    w[::2] = 1
+    p1 = Poly.fit(x[::2], z[::2], 3)
+    p2 = Poly.fit(x, z, 3, w=w)
+    p3 = Poly.fit(x, z, [0, 1, 2, 3], w=w)
+    assert_almost_equal(p1(x), p2(x))
+    assert_almost_equal(p2(x), p3(x))
+
+
+def test_equal(Poly):
+    p1 = Poly([1, 2, 3], domain=[0, 1], window=[2, 3])
+    p2 = Poly([1, 1, 1], domain=[0, 1], window=[2, 3])
+    p3 = Poly([1, 2, 3], domain=[1, 2], window=[2, 3])
+    p4 = Poly([1, 2, 3], domain=[0, 1], window=[1, 2])
+    assert_(p1 == p1)
+    assert_(not p1 == p2)
+    assert_(not p1 == p3)
+    assert_(not p1 == p4)
+
+
+def test_not_equal(Poly):
+    p1 = Poly([1, 2, 3], domain=[0, 1], window=[2, 3])
+    p2 = Poly([1, 1, 1], domain=[0, 1], window=[2, 3])
+    p3 = Poly([1, 2, 3], domain=[1, 2], window=[2, 3])
+    p4 = Poly([1, 2, 3], domain=[0, 1], window=[1, 2])
+    assert_(not p1 != p1)
+    assert_(p1 != p2)
+    assert_(p1 != p3)
+    assert_(p1 != p4)
+
+
+def test_add(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 + p2
+    assert_poly_almost_equal(p2 + p1, p3)
+    assert_poly_almost_equal(p1 + c2, p3)
+    assert_poly_almost_equal(c2 + p1, p3)
+    assert_poly_almost_equal(p1 + tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) + p1, p3)
+    assert_poly_almost_equal(p1 + np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) + p1, p3)
+    assert_raises(TypeError, op.add, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.add, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.add, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.add, p1, Polynomial([0]))
+
+
+def test_sub(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 - p2
+    assert_poly_almost_equal(p2 - p1, -p3)
+    assert_poly_almost_equal(p1 - c2, p3)
+    assert_poly_almost_equal(c2 - p1, -p3)
+    assert_poly_almost_equal(p1 - tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) - p1, -p3)
+    assert_poly_almost_equal(p1 - np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) - p1, -p3)
+    assert_raises(TypeError, op.sub, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.sub, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.sub, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.sub, p1, Polynomial([0]))
+
+
+def test_mul(Poly):
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = p1 * p2
+    assert_poly_almost_equal(p2 * p1, p3)
+    assert_poly_almost_equal(p1 * c2, p3)
+    assert_poly_almost_equal(c2 * p1, p3)
+    assert_poly_almost_equal(p1 * tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c2) * p1, p3)
+    assert_poly_almost_equal(p1 * np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c2) * p1, p3)
+    assert_poly_almost_equal(p1 * 2, p1 * Poly([2]))
+    assert_poly_almost_equal(2 * p1, p1 * Poly([2]))
+    assert_raises(TypeError, op.mul, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.mul, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.mul, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.mul, p1, Polynomial([0]))
+
+
+def test_floordiv(Poly):
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    assert_poly_almost_equal(p4 // p2, p1)
+    assert_poly_almost_equal(p4 // c2, p1)
+    assert_poly_almost_equal(c4 // p2, p1)
+    assert_poly_almost_equal(p4 // tuple(c2), p1)
+    assert_poly_almost_equal(tuple(c4) // p2, p1)
+    assert_poly_almost_equal(p4 // np.array(c2), p1)
+    assert_poly_almost_equal(np.array(c4) // p2, p1)
+    assert_poly_almost_equal(2 // p2, Poly([0]))
+    assert_poly_almost_equal(p2 // 2, 0.5*p2)
+    assert_raises(
+        TypeError, op.floordiv, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(
+        TypeError, op.floordiv, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.floordiv, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.floordiv, p1, Polynomial([0]))
+
+
+def test_truediv(Poly):
+    # true division is valid only if the denominator is a Number and
+    # not a python bool.
+    p1 = Poly([1,2,3])
+    p2 = p1 * 5
+
+    for stype in np.ScalarType:
+        if not issubclass(stype, Number) or issubclass(stype, bool):
+            continue
+        s = stype(5)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for stype in (int, float):
+        s = stype(5)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for stype in [complex]:
+        s = stype(5, 0)
+        assert_poly_almost_equal(op.truediv(p2, s), p1)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for s in [tuple(), list(), dict(), bool(), np.array([1])]:
+        assert_raises(TypeError, op.truediv, p2, s)
+        assert_raises(TypeError, op.truediv, s, p2)
+    for ptype in classes:
+        assert_raises(TypeError, op.truediv, p2, ptype(1))
+
+
+def test_mod(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    assert_poly_almost_equal(p4 % p2, p3)
+    assert_poly_almost_equal(p4 % c2, p3)
+    assert_poly_almost_equal(c4 % p2, p3)
+    assert_poly_almost_equal(p4 % tuple(c2), p3)
+    assert_poly_almost_equal(tuple(c4) % p2, p3)
+    assert_poly_almost_equal(p4 % np.array(c2), p3)
+    assert_poly_almost_equal(np.array(c4) % p2, p3)
+    assert_poly_almost_equal(2 % p2, Poly([2]))
+    assert_poly_almost_equal(p2 % 2, Poly([0]))
+    assert_raises(TypeError, op.mod, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, op.mod, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, op.mod, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, op.mod, p1, Polynomial([0]))
+
+
+def test_divmod(Poly):
+    # This checks commutation, not numerical correctness
+    c1 = list(random((4,)) + .5)
+    c2 = list(random((3,)) + .5)
+    c3 = list(random((2,)) + .5)
+    p1 = Poly(c1)
+    p2 = Poly(c2)
+    p3 = Poly(c3)
+    p4 = p1 * p2 + p3
+    c4 = list(p4.coef)
+    quo, rem = divmod(p4, p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, c2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(c4, p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, tuple(c2))
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(tuple(c4), p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p4, np.array(c2))
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(np.array(c4), p2)
+    assert_poly_almost_equal(quo, p1)
+    assert_poly_almost_equal(rem, p3)
+    quo, rem = divmod(p2, 2)
+    assert_poly_almost_equal(quo, 0.5*p2)
+    assert_poly_almost_equal(rem, Poly([0]))
+    quo, rem = divmod(2, p2)
+    assert_poly_almost_equal(quo, Poly([0]))
+    assert_poly_almost_equal(rem, Poly([2]))
+    assert_raises(TypeError, divmod, p1, Poly([0], domain=Poly.domain + 1))
+    assert_raises(TypeError, divmod, p1, Poly([0], window=Poly.window + 1))
+    if Poly is Polynomial:
+        assert_raises(TypeError, divmod, p1, Chebyshev([0]))
+    else:
+        assert_raises(TypeError, divmod, p1, Polynomial([0]))
+
+
+def test_roots(Poly):
+    d = Poly.domain * 1.25 + .25
+    w = Poly.window
+    tgt = np.linspace(d[0], d[1], 5)
+    res = np.sort(Poly.fromroots(tgt, domain=d, window=w).roots())
+    assert_almost_equal(res, tgt)
+    # default domain and window
+    res = np.sort(Poly.fromroots(tgt).roots())
+    assert_almost_equal(res, tgt)
+
+
+def test_degree(Poly):
+    p = Poly.basis(5)
+    assert_equal(p.degree(), 5)
+
+
+def test_copy(Poly):
+    p1 = Poly.basis(5)
+    p2 = p1.copy()
+    assert_(p1 == p2)
+    assert_(p1 is not p2)
+    assert_(p1.coef is not p2.coef)
+    assert_(p1.domain is not p2.domain)
+    assert_(p1.window is not p2.window)
+
+
+def test_integ(Poly):
+    P = Polynomial
+    # Check defaults
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ())
+    p2 = P.cast(p0.integ(2))
+    assert_poly_almost_equal(p1, P([0, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([0, 0, 1, 1, 1]))
+    # Check with k
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ(k=1))
+    p2 = P.cast(p0.integ(2, k=[1, 1]))
+    assert_poly_almost_equal(p1, P([1, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([1, 1, 1, 1, 1]))
+    # Check with lbnd
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]))
+    p1 = P.cast(p0.integ(lbnd=1))
+    p2 = P.cast(p0.integ(2, lbnd=1))
+    assert_poly_almost_equal(p1, P([-9, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([6, -9, 1, 1, 1]))
+    # Check scaling
+    d = 2*Poly.domain
+    p0 = Poly.cast(P([1*2, 2*3, 3*4]), domain=d)
+    p1 = P.cast(p0.integ())
+    p2 = P.cast(p0.integ(2))
+    assert_poly_almost_equal(p1, P([0, 2, 3, 4]))
+    assert_poly_almost_equal(p2, P([0, 0, 1, 1, 1]))
+
+
+def test_deriv(Poly):
+    # Check that the derivative is the inverse of integration. It is
+    # assumes that the integration has been checked elsewhere.
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p1 = Poly([1, 2, 3], domain=d, window=w)
+    p2 = p1.integ(2, k=[1, 2])
+    p3 = p1.integ(1, k=[1])
+    assert_almost_equal(p2.deriv(1).coef, p3.coef)
+    assert_almost_equal(p2.deriv(2).coef, p1.coef)
+    # default domain and window
+    p1 = Poly([1, 2, 3])
+    p2 = p1.integ(2, k=[1, 2])
+    p3 = p1.integ(1, k=[1])
+    assert_almost_equal(p2.deriv(1).coef, p3.coef)
+    assert_almost_equal(p2.deriv(2).coef, p1.coef)
+
+
+def test_linspace(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    p = Poly([1, 2, 3], domain=d, window=w)
+    # check default domain
+    xtgt = np.linspace(d[0], d[1], 20)
+    ytgt = p(xtgt)
+    xres, yres = p.linspace(20)
+    assert_almost_equal(xres, xtgt)
+    assert_almost_equal(yres, ytgt)
+    # check specified domain
+    xtgt = np.linspace(0, 2, 20)
+    ytgt = p(xtgt)
+    xres, yres = p.linspace(20, domain=[0, 2])
+    assert_almost_equal(xres, xtgt)
+    assert_almost_equal(yres, ytgt)
+
+
+def test_pow(Poly):
+    d = Poly.domain + random((2,))*.25
+    w = Poly.window + random((2,))*.25
+    tgt = Poly([1], domain=d, window=w)
+    tst = Poly([1, 2, 3], domain=d, window=w)
+    for i in range(5):
+        assert_poly_almost_equal(tst**i, tgt)
+        tgt = tgt * tst
+    # default domain and window
+    tgt = Poly([1])
+    tst = Poly([1, 2, 3])
+    for i in range(5):
+        assert_poly_almost_equal(tst**i, tgt)
+        tgt = tgt * tst
+    # check error for invalid powers
+    assert_raises(ValueError, op.pow, tgt, 1.5)
+    assert_raises(ValueError, op.pow, tgt, -1)
+
+
+def test_call(Poly):
+    P = Polynomial
+    d = Poly.domain
+    x = np.linspace(d[0], d[1], 11)
+
+    # Check defaults
+    p = Poly.cast(P([1, 2, 3]))
+    tgt = 1 + x*(2 + 3*x)
+    res = p(x)
+    assert_almost_equal(res, tgt)
+
+
+def test_cutdeg(Poly):
+    p = Poly([1, 2, 3])
+    assert_raises(ValueError, p.cutdeg, .5)
+    assert_raises(ValueError, p.cutdeg, -1)
+    assert_equal(len(p.cutdeg(3)), 3)
+    assert_equal(len(p.cutdeg(2)), 3)
+    assert_equal(len(p.cutdeg(1)), 2)
+    assert_equal(len(p.cutdeg(0)), 1)
+
+
+def test_truncate(Poly):
+    p = Poly([1, 2, 3])
+    assert_raises(ValueError, p.truncate, .5)
+    assert_raises(ValueError, p.truncate, 0)
+    assert_equal(len(p.truncate(4)), 3)
+    assert_equal(len(p.truncate(3)), 3)
+    assert_equal(len(p.truncate(2)), 2)
+    assert_equal(len(p.truncate(1)), 1)
+
+
+def test_trim(Poly):
+    c = [1, 1e-6, 1e-12, 0]
+    p = Poly(c)
+    assert_equal(p.trim().coef, c[:3])
+    assert_equal(p.trim(1e-10).coef, c[:2])
+    assert_equal(p.trim(1e-5).coef, c[:1])
+
+
+def test_mapparms(Poly):
+    # check with defaults. Should be identity.
+    d = Poly.domain
+    w = Poly.window
+    p = Poly([1], domain=d, window=w)
+    assert_almost_equal([0, 1], p.mapparms())
+    #
+    w = 2*d + 1
+    p = Poly([1], domain=d, window=w)
+    assert_almost_equal([1, 2], p.mapparms())
+
+
+def test_ufunc_override(Poly):
+    p = Poly([1, 2, 3])
+    x = np.ones(3)
+    assert_raises(TypeError, np.add, p, x)
+    assert_raises(TypeError, np.add, x, p)
+
+
+#
+# Test class method that only exists for some classes
+#
+
+
+class TestInterpolate:
+
+    def f(self, x):
+        return x * (x - 1) * (x - 2)
+
+    def test_raises(self):
+        assert_raises(ValueError, Chebyshev.interpolate, self.f, -1)
+        assert_raises(TypeError, Chebyshev.interpolate, self.f, 10.)
+
+    def test_dimensions(self):
+        for deg in range(1, 5):
+            assert_(Chebyshev.interpolate(self.f, deg).degree() == deg)
+
+    def test_approximation(self):
+
+        def powx(x, p):
+            return x**p
+
+        x = np.linspace(0, 2, 10)
+        for deg in range(0, 10):
+            for t in range(0, deg + 1):
+                p = Chebyshev.interpolate(powx, deg, domain=[0, 2], args=(t,))
+                assert_almost_equal(p(x), powx(x, t), decimal=11)

shared/package-files/0c/0c5e8c0e2db9f345b7a68e92b90e246437a366cd2f089b02a3027ad847e05c85/upsample_linear1d_cuda_dispatch.h

@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales=c10::nullopt);
+TORCH_API at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales=c10::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales=c10::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out);
+TORCH_API at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales=c10::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional<double> scales, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at

shared/package-files/0c/0c5f2c66f5f8df9c0b965029eaebe9513826b36e477df135720292741281ab22/max_pool1d_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor max_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false);
+
+} // namespace compositeimplicitautograd
+} // namespace at

shared/package-files/0c/0c62da0125fd540f733823d1962e19ed355de7b928015a0605e2d67aec185073/multicomp.py

@@ -0,0 +1,87 @@
+"""
+
+Created on Fri Mar 30 18:27:25 2012
+Author: Josef Perktold
+"""
+
+from statsmodels.sandbox.stats.multicomp import MultiComparison, tukeyhsd
+
+__all__ = ["MultiComparison", "tukeyhsd"]
+
+
+def pairwise_tukeyhsd(endog, groups, alpha=0.05, use_var="equal"):
+    """
+    Calculate all pairwise comparisons with TukeyHSD or Games-Howell
+
+    Parameters
+    ----------
+    endog : array_like, 1d
+        response variable
+    groups : array_like, 1d
+        array with groups, can be string or integers
+    alpha : float, optional
+        significance level for the test
+    use_var : {"unequal", "equal"}, optional
+        If ``use_var`` is "equal", then the Tukey-hsd pvalues are returned.
+        Tukey-hsd assumes that (within) variances are the same across groups.
+        If ``use_var`` is "unequal", then the Games-Howell pvalues are
+        returned. This uses Welch's t-test for unequal variances with
+        Satterthwaite's corrected degrees of freedom for each pairwise
+        comparison.
+
+    Returns
+    -------
+    results : TukeyHSDResults instance
+        A results class containing relevant data and some post-hoc
+        calculations, including adjusted p-value.
+
+    See Also
+    --------
+    MultiComparison
+        Class for pairwise comparisons of multiple groups.
+    tukeyhsd
+        Compute simultaneous Tukey HSD comparisons from summary data.
+    statsmodels.sandbox.stats.multicomp.TukeyHSDResults
+        Results from a Tukey HSD comparison.
+
+    Notes
+    -----
+    The results include the following attributes and methods:
+
+    * ``reject`` is a boolean array indicating whether each comparison is
+      statistically significant.
+    * ``pvalues`` contains the adjusted p-values for each comparison.
+    * ``summary()`` returns a printable table that includes the reject column.
+    * ``summary_frame()`` returns a DataFrame with the comparison results.
+
+    This is just a wrapper around tukeyhsd method of MultiComparison.
+    Tukey-hsd is not robust to heteroscedasticity, i.e., variance differ across
+    groups, especially if group sizes also vary. In those cases, the actual
+    size (rejection rate under the Null hypothesis) might be far from the
+    nominal size of the test.
+    The Games-Howell method uses pairwise t-tests that are robust to differences
+    in variances and approximately maintains size unless samples are very
+    small.
+
+    .. versionadded:: 0.15
+
+        The `use_var` keyword and option for Games-Howell test.
+
+    Examples
+    --------
+    The reject decisions and adjusted p-values can be accessed directly from
+    the results instance.
+
+    >>> import numpy as np
+    >>> endog = np.array([1, 2, 3, 4, 5, 6])
+    >>> groups = np.array(["a", "a", "b", "b", "c", "c"])
+    >>> res = pairwise_tukeyhsd(endog, groups)
+    >>> res.reject
+    array([False,  True, False])
+    >>> res.pvalues.round(3)
+    array([0.129, 0.022, 0.129])
+
+    """
+
+    return MultiComparison(endog, groups).tukeyhsd(alpha=alpha,
+                                                   use_var=use_var)

shared/package-files/0c/0c6571e2379b940a9b69816133705f3d25b947ec0496d69fe8abbad37883f11c/libdeflate-targets.cmake

@@ -0,0 +1,103 @@
+# Generated by CMake
+
+if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
+   message(FATAL_ERROR "CMake >= 2.8.0 required")
+endif()
+if(CMAKE_VERSION VERSION_LESS "2.8.3")
+   message(FATAL_ERROR "CMake >= 2.8.3 required")
+endif()
+cmake_policy(PUSH)
+cmake_policy(VERSION 2.8.3...3.24)
+#----------------------------------------------------------------
+# Generated CMake target import file.
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Protect against multiple inclusion, which would fail when already imported targets are added once more.
+set(_cmake_targets_defined "")
+set(_cmake_targets_not_defined "")
+set(_cmake_expected_targets "")
+foreach(_cmake_expected_target IN ITEMS libdeflate::libdeflate_shared)
+  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
+  if(TARGET "${_cmake_expected_target}")
+    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
+  else()
+    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
+  endif()
+endforeach()
+unset(_cmake_expected_target)
+if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
+  unset(_cmake_targets_defined)
+  unset(_cmake_targets_not_defined)
+  unset(_cmake_expected_targets)
+  unset(CMAKE_IMPORT_FILE_VERSION)
+  cmake_policy(POP)
+  return()
+endif()
+if(NOT _cmake_targets_defined STREQUAL "")
+  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
+  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
+  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
+endif()
+unset(_cmake_targets_defined)
+unset(_cmake_targets_not_defined)
+unset(_cmake_expected_targets)
+
+
+# Compute the installation prefix relative to this file.
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+if(_IMPORT_PREFIX STREQUAL "/")
+  set(_IMPORT_PREFIX "")
+endif()
+
+# Create imported target libdeflate::libdeflate_shared
+add_library(libdeflate::libdeflate_shared SHARED IMPORTED)
+
+set_target_properties(libdeflate::libdeflate_shared PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "LIBDEFLATE_DLL"
+  INTERFACE_INCLUDE_DIRECTORIES "/opt/conda/include"
+)
+
+# Load information for each installed configuration.
+file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/libdeflate-targets-*.cmake")
+foreach(_cmake_config_file IN LISTS _cmake_config_files)
+  include("${_cmake_config_file}")
+endforeach()
+unset(_cmake_config_file)
+unset(_cmake_config_files)
+
+# Cleanup temporary variables.
+set(_IMPORT_PREFIX)
+
+# Loop over all imported files and verify that they actually exist
+foreach(_cmake_target IN LISTS _cmake_import_check_targets)
+  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+    if(NOT EXISTS "${_cmake_file}")
+      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+   \"${_cmake_file}\"
+but this file does not exist.  Possible reasons include:
+* The file was deleted, renamed, or moved to another location.
+* An install or uninstall procedure did not complete successfully.
+* The installation package was faulty and contained
+   \"${CMAKE_CURRENT_LIST_FILE}\"
+but not all the files it references.
+")
+    endif()
+  endforeach()
+  unset(_cmake_file)
+  unset("_cmake_import_check_files_for_${_cmake_target}")
+endforeach()
+unset(_cmake_target)
+unset(_cmake_import_check_targets)
+
+# This file does not depend on other imported targets which have
+# been exported from the same project but in a separate export set.
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
+cmake_policy(POP)

shared/package-files/0c/0c66cae5e8d23befe26d90e878ce51943358edaf19f1e192727ab7713850d49b/test_arrayfuncs.py

@@ -0,0 +1,40 @@
+import numpy as np
+import pytest
+
+from sklearn.utils._testing import assert_allclose
+from sklearn.utils.arrayfuncs import _all_with_any_reduction_axis_1, min_pos
+
+
+def test_min_pos():
+    # Check that min_pos returns a positive value and that it's consistent
+    # between float and double
+    X = np.random.RandomState(0).randn(100)
+
+    min_double = min_pos(X)
+    min_float = min_pos(X.astype(np.float32))
+
+    assert_allclose(min_double, min_float)
+    assert min_double >= 0
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+def test_min_pos_no_positive(dtype):
+    # Check that the return value of min_pos is the maximum representable
+    # value of the input dtype when all input elements are <= 0 (#19328)
+    X = np.full(100, -1.0).astype(dtype, copy=False)
+
+    assert min_pos(X) == np.finfo(dtype).max
+
+
+@pytest.mark.parametrize(
+    "dtype", [np.int16, np.int32, np.int64, np.float32, np.float64]
+)
+@pytest.mark.parametrize("value", [0, 1.5, -1])
+def test_all_with_any_reduction_axis_1(dtype, value):
+    # Check that return value is False when there is no row equal to `value`
+    X = np.arange(12, dtype=dtype).reshape(3, 4)
+    assert not _all_with_any_reduction_axis_1(X, value=value)
+
+    # Make a row equal to `value`
+    X[1, :] = value
+    assert _all_with_any_reduction_axis_1(X, value=value)

shared/package-files/0c/0c698b1a4e136e1906841889be76de352719ccb7679beea72685adf3786bcfeb/ivp.py

@@ -0,0 +1,755 @@
+import inspect
+import numpy as np
+from .bdf import BDF
+from .radau import Radau
+from .rk import RK23, RK45, DOP853
+from .lsoda import LSODA
+from scipy.optimize import OptimizeResult
+from .common import EPS, OdeSolution
+from .base import OdeSolver
+
+
+METHODS = {'RK23': RK23,
+           'RK45': RK45,
+           'DOP853': DOP853,
+           'Radau': Radau,
+           'BDF': BDF,
+           'LSODA': LSODA}
+
+
+MESSAGES = {0: "The solver successfully reached the end of the integration interval.",
+            1: "A termination event occurred."}
+
+
+class OdeResult(OptimizeResult):
+    pass
+
+
+def prepare_events(events):
+    """Standardize event functions and extract attributes."""
+    if callable(events):
+        events = (events,)
+
+    max_events = np.empty(len(events))
+    direction = np.empty(len(events))
+    for i, event in enumerate(events):
+        terminal = getattr(event, 'terminal', None)
+        direction[i] = getattr(event, 'direction', 0)
+
+        message = ('The `terminal` attribute of each event '
+                   'must be a boolean or positive integer.')
+        if terminal is None or terminal == 0:
+            max_events[i] = np.inf
+        elif int(terminal) == terminal and terminal > 0:
+            max_events[i] = terminal
+        else:
+            raise ValueError(message)
+
+    return events, max_events, direction
+
+
+def solve_event_equation(event, sol, t_old, t):
+    """Solve an equation corresponding to an ODE event.
+
+    The equation is ``event(t, y(t)) = 0``, here ``y(t)`` is known from an
+    ODE solver using some sort of interpolation. It is solved by
+    `scipy.optimize.brentq` with xtol=atol=4*EPS.
+
+    Parameters
+    ----------
+    event : callable
+        Function ``event(t, y)``.
+    sol : callable
+        Function ``sol(t)`` which evaluates an ODE solution between `t_old`
+        and  `t`.
+    t_old, t : float
+        Previous and new values of time. They will be used as a bracketing
+        interval.
+
+    Returns
+    -------
+    root : float
+        Found solution.
+    """
+    from scipy.optimize import brentq
+    return brentq(lambda t: event(t, sol(t)), t_old, t,
+                  xtol=4 * EPS, rtol=4 * EPS)
+
+
+def handle_events(sol, events, active_events, event_count, max_events,
+                  t_old, t):
+    """Helper function to handle events.
+
+    Parameters
+    ----------
+    sol : DenseOutput
+        Function ``sol(t)`` which evaluates an ODE solution between `t_old`
+        and  `t`.
+    events : list of callables, length n_events
+        Event functions with signatures ``event(t, y)``.
+    active_events : ndarray
+        Indices of events which occurred.
+    event_count : ndarray
+        Current number of occurrences for each event.
+    max_events : ndarray, shape (n_events,)
+        Number of occurrences allowed for each event before integration
+        termination is issued.
+    t_old, t : float
+        Previous and new values of time.
+
+    Returns
+    -------
+    root_indices : ndarray
+        Indices of events which take zero between `t_old` and `t` and before
+        a possible termination.
+    roots : ndarray
+        Values of t at which events occurred.
+    terminate : bool
+        Whether a terminal event occurred.
+    """
+    roots = [solve_event_equation(events[event_index], sol, t_old, t)
+             for event_index in active_events]
+
+    roots = np.asarray(roots)
+
+    if np.any(event_count[active_events] >= max_events[active_events]):
+        if t > t_old:
+            order = np.argsort(roots)
+        else:
+            order = np.argsort(-roots)
+        active_events = active_events[order]
+        roots = roots[order]
+        t = np.nonzero(event_count[active_events]
+                       >= max_events[active_events])[0][0]
+        active_events = active_events[:t + 1]
+        roots = roots[:t + 1]
+        terminate = True
+    else:
+        terminate = False
+
+    return active_events, roots, terminate
+
+
+def find_active_events(g, g_new, direction):
+    """Find which event occurred during an integration step.
+
+    Parameters
+    ----------
+    g, g_new : array_like, shape (n_events,)
+        Values of event functions at a current and next points.
+    direction : ndarray, shape (n_events,)
+        Event "direction" according to the definition in `solve_ivp`.
+
+    Returns
+    -------
+    active_events : ndarray
+        Indices of events which occurred during the step.
+    """
+    g, g_new = np.asarray(g), np.asarray(g_new)
+    up = (g <= 0) & (g_new >= 0)
+    down = (g >= 0) & (g_new <= 0)
+    either = up | down
+    mask = (up & (direction > 0) |
+            down & (direction < 0) |
+            either & (direction == 0))
+
+    return np.nonzero(mask)[0]
+
+
+def solve_ivp(fun, t_span, y0, method='RK45', t_eval=None, dense_output=False,
+              events=None, vectorized=False, args=None, **options):
+    """Solve an initial value problem for a system of ODEs.
+
+    This function numerically integrates a system of ordinary differential
+    equations given an initial value::
+
+        dy / dt = f(t, y)
+        y(t0) = y0
+
+    Here t is a 1-D independent variable (time), y(t) is an
+    N-D vector-valued function (state), and an N-D
+    vector-valued function f(t, y) determines the differential equations.
+    The goal is to find y(t) approximately satisfying the differential
+    equations, given an initial value y(t0)=y0.
+
+    Some of the solvers support integration in the complex domain, but note
+    that for stiff ODE solvers, the right-hand side must be
+    complex-differentiable (satisfy Cauchy-Riemann equations [11]_).
+    To solve a problem in the complex domain, pass y0 with a complex data type.
+    Another option always available is to rewrite your problem for real and
+    imaginary parts separately.
+
+    Parameters
+    ----------
+    fun : callable
+        Right-hand side of the system: the time derivative of the state ``y``
+        at time ``t``. The calling signature is ``fun(t, y)``, where ``t`` is a
+        scalar and ``y`` is an ndarray with ``len(y) = len(y0)``. Additional
+        arguments need to be passed if ``args`` is used (see documentation of
+        ``args`` argument). ``fun`` must return an array of the same shape as
+        ``y``. See `vectorized` for more information.
+    t_span : 2-member sequence
+        Interval of integration (t0, tf). The solver starts with t=t0 and
+        integrates until it reaches t=tf. Both t0 and tf must be floats
+        or values interpretable by the float conversion function.
+    y0 : array_like, shape (n,)
+        Initial state. For problems in the complex domain, pass `y0` with a
+        complex data type (even if the initial value is purely real).
+    method : string or `OdeSolver`, optional
+        Integration method to use:
+
+            * 'RK45' (default): Explicit Runge-Kutta method of order 5(4) [1]_.
+              The error is controlled assuming accuracy of the fourth-order
+              method, but steps are taken using the fifth-order accurate
+              formula (local extrapolation is done). A quartic interpolation
+              polynomial is used for the dense output [2]_. Can be applied in
+              the complex domain.
+            * 'RK23': Explicit Runge-Kutta method of order 3(2) [3]_. The error
+              is controlled assuming accuracy of the second-order method, but
+              steps are taken using the third-order accurate formula (local
+              extrapolation is done). A cubic Hermite polynomial is used for the
+              dense output. Can be applied in the complex domain.
+            * 'DOP853': Explicit Runge-Kutta method of order 8 [13]_.
+              Python implementation of the "DOP853" algorithm originally
+              written in Fortran [14]_. A 7-th order interpolation polynomial
+              accurate to 7-th order is used for the dense output.
+              Can be applied in the complex domain.
+            * 'Radau': Implicit Runge-Kutta method of the Radau IIA family of
+              order 5 [4]_. The error is controlled with a third-order accurate
+              embedded formula. A cubic polynomial which satisfies the
+              collocation conditions is used for the dense output.
+            * 'BDF': Implicit multi-step variable-order (1 to 5) method based
+              on a backward differentiation formula for the derivative
+              approximation [5]_. The implementation follows the one described
+              in [6]_. A quasi-constant step scheme is used and accuracy is
+              enhanced using the NDF modification. Can be applied in the
+              complex domain.
+            * 'LSODA': Adams/BDF method with automatic stiffness detection and
+              switching [7]_, [8]_. This is a wrapper of the Fortran solver
+              from ODEPACK.
+
+        Explicit Runge-Kutta methods ('RK23', 'RK45', 'DOP853') should be used
+        for non-stiff problems and implicit methods ('Radau', 'BDF') for
+        stiff problems [9]_. Among Runge-Kutta methods, 'DOP853' is recommended
+        for solving with high precision (low values of `rtol` and `atol`).
+
+        If not sure, first try to run 'RK45'. If it makes unusually many
+        iterations, diverges, or fails, your problem is likely to be stiff and
+        you should use 'Radau' or 'BDF'. 'LSODA' can also be a good universal
+        choice, but it might be somewhat less convenient to work with as it
+        wraps old Fortran code.
+
+        You can also pass an arbitrary class derived from `OdeSolver` which
+        implements the solver.
+    t_eval : array_like or None, optional
+        Times at which to store the computed solution, must be sorted and lie
+        within `t_span`. If None (default), use points selected by the solver.
+    dense_output : bool, optional
+        Whether to compute a continuous solution. Default is False.
+    events : callable, or list of callables, optional
+        Events to track. If None (default), no events will be tracked.
+        Each event occurs at the zeros of a continuous function of time and
+        state. Each function must have the signature ``event(t, y)`` where
+        additional argument have to be passed if ``args`` is used (see
+        documentation of ``args`` argument). Each function must return a
+        float. The solver will find an accurate value of `t` at which
+        ``event(t, y(t)) = 0`` using a root-finding algorithm. By default,
+        all zeros will be found. The solver looks for a sign change over
+        each step, so if multiple zero crossings occur within one step,
+        events may be missed. Additionally each `event` function might
+        have the following attributes:
+
+            terminal: bool or int, optional
+                When boolean, whether to terminate integration if this event occurs.
+                When integral, termination occurs after the specified the number of
+                occurrences of this event.
+                Implicitly False if not assigned.
+            direction: float, optional
+                Direction of a zero crossing. If `direction` is positive,
+                `event` will only trigger when going from negative to positive,
+                and vice versa if `direction` is negative. If 0, then either
+                direction will trigger event. Implicitly 0 if not assigned.
+
+        You can assign attributes like ``event.terminal = True`` to any
+        function in Python.
+    vectorized : bool, optional
+        Whether `fun` can be called in a vectorized fashion. Default is False.
+
+        If ``vectorized`` is False, `fun` will always be called with ``y`` of
+        shape ``(n,)``, where ``n = len(y0)``.
+
+        If ``vectorized`` is True, `fun` may be called with ``y`` of shape
+        ``(n, k)``, where ``k`` is an integer. In this case, `fun` must behave
+        such that ``fun(t, y)[:, i] == fun(t, y[:, i])`` (i.e. each column of
+        the returned array is the time derivative of the state corresponding
+        with a column of ``y``).
+
+        Setting ``vectorized=True`` allows for faster finite difference
+        approximation of the Jacobian by methods 'Radau' and 'BDF', but
+        will result in slower execution for other methods and for 'Radau' and
+        'BDF' in some circumstances (e.g. small ``len(y0)``).
+    args : tuple, optional
+        Additional arguments to pass to the user-defined functions.  If given,
+        the additional arguments are passed to all user-defined functions.
+        So if, for example, `fun` has the signature ``fun(t, y, a, b, c)``,
+        then `jac` (if given) and any event functions must have the same
+        signature, and `args` must be a tuple of length 3.
+    **options
+        Options passed to a chosen solver. All options available for already
+        implemented solvers are listed below.
+    first_step : float or None, optional
+        Initial step size. Default is `None` which means that the algorithm
+        should choose.
+    max_step : float, optional
+        Maximum allowed step size. Default is np.inf, i.e., the step size is not
+        bounded and determined solely by the solver.
+    rtol, atol : float or array_like, optional
+        Relative and absolute tolerances. The solver keeps the local error
+        estimates less than ``atol + rtol * abs(y)``. Here `rtol` controls a
+        relative accuracy (number of correct digits), while `atol` controls
+        absolute accuracy (number of correct decimal places). To achieve the
+        desired `rtol`, set `atol` to be smaller than the smallest value that
+        can be expected from ``rtol * abs(y)`` so that `rtol` dominates the
+        allowable error. If `atol` is larger than ``rtol * abs(y)`` the
+        number of correct digits is not guaranteed. Conversely, to achieve the
+        desired `atol` set `rtol` such that ``rtol * abs(y)`` is always smaller
+        than `atol`. If components of y have different scales, it might be
+        beneficial to set different `atol` values for different components by
+        passing array_like with shape (n,) for `atol`. Default values are
+        1e-3 for `rtol` and 1e-6 for `atol`.
+    jac : array_like, sparse_matrix, callable or None, optional
+        Jacobian matrix of the right-hand side of the system with respect
+        to y, required by the 'Radau', 'BDF' and 'LSODA' method. The
+        Jacobian matrix has shape (n, n) and its element (i, j) is equal to
+        ``d f_i / d y_j``.  There are three ways to define the Jacobian:
+
+            * If array_like or sparse_matrix, the Jacobian is assumed to
+              be constant. Not supported by 'LSODA'.
+            * If callable, the Jacobian is assumed to depend on both
+              t and y; it will be called as ``jac(t, y)``, as necessary.
+              Additional arguments have to be passed if ``args`` is
+              used (see documentation of ``args`` argument).
+              For 'Radau' and 'BDF' methods, the return value might be a
+              sparse matrix.
+            * If None (default), the Jacobian will be approximated by
+              finite differences.
+
+        It is generally recommended to provide the Jacobian rather than
+        relying on a finite-difference approximation.
+    jac_sparsity : array_like, sparse matrix or None, optional
+        Defines a sparsity structure of the Jacobian matrix for a finite-
+        difference approximation. Its shape must be (n, n). This argument
+        is ignored if `jac` is not `None`. If the Jacobian has only few
+        non-zero elements in *each* row, providing the sparsity structure
+        will greatly speed up the computations [10]_. A zero entry means that
+        a corresponding element in the Jacobian is always zero. If None
+        (default), the Jacobian is assumed to be dense.
+        Not supported by 'LSODA', see `lband` and `uband` instead.
+    lband, uband : int or None, optional
+        Parameters defining the bandwidth of the Jacobian for the 'LSODA'
+        method, i.e., ``jac[i, j] != 0 only for i - lband <= j <= i + uband``.
+        Default is None. Setting these requires your jac routine to return the
+        Jacobian in the packed format: the returned array must have ``n``
+        columns and ``uband + lband + 1`` rows in which Jacobian diagonals are
+        written. Specifically ``jac_packed[uband + i - j , j] = jac[i, j]``.
+        The same format is used in `scipy.linalg.solve_banded` (check for an
+        illustration).  These parameters can be also used with ``jac=None`` to
+        reduce the number of Jacobian elements estimated by finite differences.
+    min_step : float, optional
+        The minimum allowed step size for 'LSODA' method.
+        By default `min_step` is zero.
+
+    Returns
+    -------
+    Bunch object with the following fields defined:
+    t : ndarray, shape (n_points,)
+        Time points.
+    y : ndarray, shape (n, n_points)
+        Values of the solution at `t`.
+    sol : `OdeSolution` or None
+        Found solution as `OdeSolution` instance; None if `dense_output` was
+        set to False.
+    t_events : list of ndarray or None
+        Contains for each event type a list of arrays at which an event of
+        that type event was detected. None if `events` was None.
+    y_events : list of ndarray or None
+        For each value of `t_events`, the corresponding value of the solution.
+        None if `events` was None.
+    nfev : int
+        Number of evaluations of the right-hand side.
+    njev : int
+        Number of evaluations of the Jacobian.
+    nlu : int
+        Number of LU decompositions.
+    status : int
+        Reason for algorithm termination:
+
+            * -1: Integration step failed.
+            *  0: The solver successfully reached the end of `tspan`.
+            *  1: A termination event occurred.
+
+    message : string
+        Human-readable description of the termination reason.
+    success : bool
+        True if the solver reached the interval end or a termination event
+        occurred (``status >= 0``).
+
+    References
+    ----------
+    .. [1] J. R. Dormand, P. J. Prince, "A family of embedded Runge-Kutta
+           formulae", Journal of Computational and Applied Mathematics, Vol. 6,
+           No. 1, pp. 19-26, 1980.
+    .. [2] L. W. Shampine, "Some Practical Runge-Kutta Formulas", Mathematics
+           of Computation,, Vol. 46, No. 173, pp. 135-150, 1986.
+    .. [3] P. Bogacki, L.F. Shampine, "A 3(2) Pair of Runge-Kutta Formulas",
+           Appl. Math. Lett. Vol. 2, No. 4. pp. 321-325, 1989.
+    .. [4] E. Hairer, G. Wanner, "Solving Ordinary Differential Equations II:
+           Stiff and Differential-Algebraic Problems", Sec. IV.8.
+    .. [5] `Backward Differentiation Formula
+            <https://en.wikipedia.org/wiki/Backward_differentiation_formula>`_
+            on Wikipedia.
+    .. [6] L. F. Shampine, M. W. Reichelt, "THE MATLAB ODE SUITE", SIAM J. SCI.
+           COMPUTE., Vol. 18, No. 1, pp. 1-22, January 1997.
+    .. [7] A. C. Hindmarsh, "ODEPACK, A Systematized Collection of ODE
+           Solvers," IMACS Transactions on Scientific Computation, Vol 1.,
+           pp. 55-64, 1983.
+    .. [8] L. Petzold, "Automatic selection of methods for solving stiff and
+           nonstiff systems of ordinary differential equations", SIAM Journal
+           on Scientific and Statistical Computing, Vol. 4, No. 1, pp. 136-148,
+           1983.
+    .. [9] `Stiff equation <https://en.wikipedia.org/wiki/Stiff_equation>`_ on
+           Wikipedia.
+    .. [10] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of
+            sparse Jacobian matrices", Journal of the Institute of Mathematics
+            and its Applications, 13, pp. 117-120, 1974.
+    .. [11] `Cauchy-Riemann equations
+             <https://en.wikipedia.org/wiki/Cauchy-Riemann_equations>`_ on
+             Wikipedia.
+    .. [12] `Lotka-Volterra equations
+            <https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations>`_
+            on Wikipedia.
+    .. [13] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential
+            Equations I: Nonstiff Problems", Sec. II.
+    .. [14] `Page with original Fortran code of DOP853
+            <http://www.unige.ch/~hairer/software.html>`_.
+
+    Examples
+    --------
+    Basic exponential decay showing automatically chosen time points.
+
+    >>> import numpy as np
+    >>> from scipy.integrate import solve_ivp
+    >>> def exponential_decay(t, y): return -0.5 * y
+    >>> sol = solve_ivp(exponential_decay, [0, 10], [2, 4, 8])
+    >>> print(sol.t)
+    [ 0.          0.11487653  1.26364188  3.06061781  4.81611105  6.57445806
+      8.33328988 10.        ]
+    >>> print(sol.y)
+    [[2.         1.88836035 1.06327177 0.43319312 0.18017253 0.07483045
+      0.03107158 0.01350781]
+     [4.         3.7767207  2.12654355 0.86638624 0.36034507 0.14966091
+      0.06214316 0.02701561]
+     [8.         7.5534414  4.25308709 1.73277247 0.72069014 0.29932181
+      0.12428631 0.05403123]]
+
+    Specifying points where the solution is desired.
+
+    >>> sol = solve_ivp(exponential_decay, [0, 10], [2, 4, 8],
+    ...                 t_eval=[0, 1, 2, 4, 10])
+    >>> print(sol.t)
+    [ 0  1  2  4 10]
+    >>> print(sol.y)
+    [[2.         1.21305369 0.73534021 0.27066736 0.01350938]
+     [4.         2.42610739 1.47068043 0.54133472 0.02701876]
+     [8.         4.85221478 2.94136085 1.08266944 0.05403753]]
+
+    Cannon fired upward with terminal event upon impact. The ``terminal`` and
+    ``direction`` fields of an event are applied by monkey patching a function.
+    Here ``y[0]`` is position and ``y[1]`` is velocity. The projectile starts
+    at position 0 with velocity +10. Note that the integration never reaches
+    t=100 because the event is terminal.
+
+    >>> def upward_cannon(t, y): return [y[1], -0.5]
+    >>> def hit_ground(t, y): return y[0]
+    >>> hit_ground.terminal = True
+    >>> hit_ground.direction = -1
+    >>> sol = solve_ivp(upward_cannon, [0, 100], [0, 10], events=hit_ground)
+    >>> print(sol.t_events)
+    [array([40.])]
+    >>> print(sol.t)
+    [0.00000000e+00 9.99900010e-05 1.09989001e-03 1.10988901e-02
+     1.11088891e-01 1.11098890e+00 1.11099890e+01 4.00000000e+01]
+
+    Use `dense_output` and `events` to find position, which is 100, at the apex
+    of the cannonball's trajectory. Apex is not defined as terminal, so both
+    apex and hit_ground are found. There is no information at t=20, so the sol
+    attribute is used to evaluate the solution. The sol attribute is returned
+    by setting ``dense_output=True``. Alternatively, the `y_events` attribute
+    can be used to access the solution at the time of the event.
+
+    >>> def apex(t, y): return y[1]
+    >>> sol = solve_ivp(upward_cannon, [0, 100], [0, 10],
+    ...                 events=(hit_ground, apex), dense_output=True)
+    >>> print(sol.t_events)
+    [array([40.]), array([20.])]
+    >>> print(sol.t)
+    [0.00000000e+00 9.99900010e-05 1.09989001e-03 1.10988901e-02
+     1.11088891e-01 1.11098890e+00 1.11099890e+01 4.00000000e+01]
+    >>> print(sol.sol(sol.t_events[1][0]))
+    [100.   0.]
+    >>> print(sol.y_events)
+    [array([[-5.68434189e-14, -1.00000000e+01]]),
+     array([[1.00000000e+02, 1.77635684e-15]])]
+
+    As an example of a system with additional parameters, we'll implement
+    the Lotka-Volterra equations [12]_.
+
+    >>> def lotkavolterra(t, z, a, b, c, d):
+    ...     x, y = z
+    ...     return [a*x - b*x*y, -c*y + d*x*y]
+    ...
+
+    We pass in the parameter values a=1.5, b=1, c=3 and d=1 with the `args`
+    argument.
+
+    >>> sol = solve_ivp(lotkavolterra, [0, 15], [10, 5], args=(1.5, 1, 3, 1),
+    ...                 dense_output=True)
+
+    Compute a dense solution and plot it.
+
+    >>> t = np.linspace(0, 15, 300)
+    >>> z = sol.sol(t)
+    >>> import matplotlib.pyplot as plt
+    >>> plt.plot(t, z.T)
+    >>> plt.xlabel('t')
+    >>> plt.legend(['x', 'y'], shadow=True)
+    >>> plt.title('Lotka-Volterra System')
+    >>> plt.show()
+
+    A couple examples of using solve_ivp to solve the differential
+    equation ``y' = Ay`` with complex matrix ``A``.
+
+    >>> A = np.array([[-0.25 + 0.14j, 0, 0.33 + 0.44j],
+    ...               [0.25 + 0.58j, -0.2 + 0.14j, 0],
+    ...               [0, 0.2 + 0.4j, -0.1 + 0.97j]])
+
+    Solving an IVP with ``A`` from above and ``y`` as 3x1 vector:
+
+    >>> def deriv_vec(t, y):
+    ...     return A @ y
+    >>> result = solve_ivp(deriv_vec, [0, 25],
+    ...                    np.array([10 + 0j, 20 + 0j, 30 + 0j]),
+    ...                    t_eval=np.linspace(0, 25, 101))
+    >>> print(result.y[:, 0])
+    [10.+0.j 20.+0.j 30.+0.j]
+    >>> print(result.y[:, -1])
+    [18.46291039+45.25653651j 10.01569306+36.23293216j
+     -4.98662741+80.07360388j]
+
+    Solving an IVP with ``A`` from above with ``y`` as 3x3 matrix :
+
+    >>> def deriv_mat(t, y):
+    ...     return (A @ y.reshape(3, 3)).flatten()
+    >>> y0 = np.array([[2 + 0j, 3 + 0j, 4 + 0j],
+    ...                [5 + 0j, 6 + 0j, 7 + 0j],
+    ...                [9 + 0j, 34 + 0j, 78 + 0j]])
+
+    >>> result = solve_ivp(deriv_mat, [0, 25], y0.flatten(),
+    ...                    t_eval=np.linspace(0, 25, 101))
+    >>> print(result.y[:, 0].reshape(3, 3))
+    [[ 2.+0.j  3.+0.j  4.+0.j]
+     [ 5.+0.j  6.+0.j  7.+0.j]
+     [ 9.+0.j 34.+0.j 78.+0.j]]
+    >>> print(result.y[:, -1].reshape(3, 3))
+    [[  5.67451179 +12.07938445j  17.2888073  +31.03278837j
+        37.83405768 +63.25138759j]
+     [  3.39949503 +11.82123994j  21.32530996 +44.88668871j
+        53.17531184+103.80400411j]
+     [ -2.26105874 +22.19277664j -15.1255713  +70.19616341j
+       -38.34616845+153.29039931j]]
+
+
+    """
+    if method not in METHODS and not (
+            inspect.isclass(method) and issubclass(method, OdeSolver)):
+        raise ValueError(f"`method` must be one of {METHODS} or OdeSolver class.")
+
+    t0, tf = map(float, t_span)
+
+    if args is not None:
+        # Wrap the user's fun (and jac, if given) in lambdas to hide the
+        # additional parameters.  Pass in the original fun as a keyword
+        # argument to keep it in the scope of the lambda.
+        try:
+            _ = [*(args)]
+        except TypeError as exp:
+            suggestion_tuple = (
+                "Supplied 'args' cannot be unpacked. Please supply `args`"
+                f" as a tuple (e.g. `args=({args},)`)"
+            )
+            raise TypeError(suggestion_tuple) from exp
+
+        def fun(t, x, fun=fun):
+            return fun(t, x, *args)
+        jac = options.get('jac')
+        if callable(jac):
+            options['jac'] = lambda t, x: jac(t, x, *args)
+
+    if t_eval is not None:
+        t_eval = np.asarray(t_eval)
+        if t_eval.ndim != 1:
+            raise ValueError("`t_eval` must be 1-dimensional.")
+
+        if np.any(t_eval < min(t0, tf)) or np.any(t_eval > max(t0, tf)):
+            raise ValueError("Values in `t_eval` are not within `t_span`.")
+
+        d = np.diff(t_eval)
+        if tf > t0 and np.any(d <= 0) or tf < t0 and np.any(d >= 0):
+            raise ValueError("Values in `t_eval` are not properly sorted.")
+
+        if tf > t0:
+            t_eval_i = 0
+        else:
+            # Make order of t_eval decreasing to use np.searchsorted.
+            t_eval = t_eval[::-1]
+            # This will be an upper bound for slices.
+            t_eval_i = t_eval.shape[0]
+
+    if method in METHODS:
+        method = METHODS[method]
+
+    solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)
+
+    if t_eval is None:
+        ts = [t0]
+        ys = [y0]
+    elif t_eval is not None and dense_output:
+        ts = []
+        ti = [t0]
+        ys = []
+    else:
+        ts = []
+        ys = []
+
+    interpolants = []
+
+    if events is not None:
+        events, max_events, event_dir = prepare_events(events)
+        event_count = np.zeros(len(events))
+        if args is not None:
+            # Wrap user functions in lambdas to hide the additional parameters.
+            # The original event function is passed as a keyword argument to the
+            # lambda to keep the original function in scope (i.e., avoid the
+            # late binding closure "gotcha").
+            events = [lambda t, x, event=event: event(t, x, *args)
+                      for event in events]
+        g = [event(t0, y0) for event in events]
+        t_events = [[] for _ in range(len(events))]
+        y_events = [[] for _ in range(len(events))]
+    else:
+        t_events = None
+        y_events = None
+
+    status = None
+    while status is None:
+        message = solver.step()
+
+        if solver.status == 'finished':
+            status = 0
+        elif solver.status == 'failed':
+            status = -1
+            break
+
+        t_old = solver.t_old
+        t = solver.t
+        y = solver.y
+
+        if dense_output:
+            sol = solver.dense_output()
+            interpolants.append(sol)
+        else:
+            sol = None
+
+        if events is not None:
+            g_new = [event(t, y) for event in events]
+            active_events = find_active_events(g, g_new, event_dir)
+            if active_events.size > 0:
+                if sol is None:
+                    sol = solver.dense_output()
+
+                event_count[active_events] += 1
+                root_indices, roots, terminate = handle_events(
+                    sol, events, active_events, event_count, max_events,
+                    t_old, t)
+
+                for e, te in zip(root_indices, roots):
+                    t_events[e].append(te)
+                    y_events[e].append(sol(te))
+
+                if terminate:
+                    status = 1
+                    t = roots[-1]
+                    y = sol(t)
+
+            g = g_new
+
+        if t_eval is None:
+            donot_append = (len(ts) > 1 and
+                            ts[-1] == t and
+                            dense_output)
+            if not donot_append:
+                ts.append(t)
+                ys.append(y)
+            else:
+                if len(interpolants) > 0:
+                    interpolants.pop()
+        else:
+            # The value in t_eval equal to t will be included.
+            if solver.direction > 0:
+                t_eval_i_new = np.searchsorted(t_eval, t, side='right')
+                t_eval_step = t_eval[t_eval_i:t_eval_i_new]
+            else:
+                t_eval_i_new = np.searchsorted(t_eval, t, side='left')
+                # It has to be done with two slice operations, because
+                # you can't slice to 0th element inclusive using backward
+                # slicing.
+                t_eval_step = t_eval[t_eval_i_new:t_eval_i][::-1]
+
+            if t_eval_step.size > 0:
+                if sol is None:
+                    sol = solver.dense_output()
+                ts.append(t_eval_step)
+                ys.append(sol(t_eval_step))
+                t_eval_i = t_eval_i_new
+
+        if t_eval is not None and dense_output:
+            ti.append(t)
+
+    message = MESSAGES.get(status, message)
+
+    if t_events is not None:
+        t_events = [np.asarray(te) for te in t_events]
+        y_events = [np.asarray(ye) for ye in y_events]
+
+    if t_eval is None:
+        ts = np.array(ts)
+        ys = np.vstack(ys).T
+    elif ts:
+        ts = np.hstack(ts)
+        ys = np.hstack(ys)
+
+    if dense_output:
+        if t_eval is None:
+            sol = OdeSolution(
+                ts, interpolants, alt_segment=True if method in [BDF, LSODA] else False
+            )
+        else:
+            sol = OdeSolution(
+                ti, interpolants, alt_segment=True if method in [BDF, LSODA] else False
+            )
+    else:
+        sol = None
+
+    return OdeResult(t=ts, y=ys, sol=sol, t_events=t_events, y_events=y_events,
+                     nfev=solver.nfev, njev=solver.njev, nlu=solver.nlu,
+                     status=status, message=message, success=status >= 0)

shared/package-files/0c/0c6afc23568d55b3e9ac914f9c5361e3033e778aa5b58d3cc82835fc5c638679/difflib.py

@@ -0,0 +1,2056 @@
+"""
+Module difflib -- helpers for computing deltas between objects.
+
+Function get_close_matches(word, possibilities, n=3, cutoff=0.6):
+    Use SequenceMatcher to return list of the best "good enough" matches.
+
+Function context_diff(a, b):
+    For two lists of strings, return a delta in context diff format.
+
+Function ndiff(a, b):
+    Return a delta: the difference between `a` and `b` (lists of strings).
+
+Function restore(delta, which):
+    Return one of the two sequences that generated an ndiff delta.
+
+Function unified_diff(a, b):
+    For two lists of strings, return a delta in unified diff format.
+
+Class SequenceMatcher:
+    A flexible class for comparing pairs of sequences of any type.
+
+Class Differ:
+    For producing human-readable deltas from sequences of lines of text.
+
+Class HtmlDiff:
+    For producing HTML side by side comparison with change highlights.
+"""
+
+__all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher',
+           'Differ','IS_CHARACTER_JUNK', 'IS_LINE_JUNK', 'context_diff',
+           'unified_diff', 'diff_bytes', 'HtmlDiff', 'Match']
+
+from heapq import nlargest as _nlargest
+from collections import namedtuple as _namedtuple
+from types import GenericAlias
+
+Match = _namedtuple('Match', 'a b size')
+
+def _calculate_ratio(matches, length):
+    if length:
+        return 2.0 * matches / length
+    return 1.0
+
+class SequenceMatcher:
+
+    """
+    SequenceMatcher is a flexible class for comparing pairs of sequences of
+    any type, so long as the sequence elements are hashable.  The basic
+    algorithm predates, and is a little fancier than, an algorithm
+    published in the late 1980's by Ratcliff and Obershelp under the
+    hyperbolic name "gestalt pattern matching".  The basic idea is to find
+    the longest contiguous matching subsequence that contains no "junk"
+    elements (R-O doesn't address junk).  The same idea is then applied
+    recursively to the pieces of the sequences to the left and to the right
+    of the matching subsequence.  This does not yield minimal edit
+    sequences, but does tend to yield matches that "look right" to people.
+
+    SequenceMatcher tries to compute a "human-friendly diff" between two
+    sequences.  Unlike e.g. UNIX(tm) diff, the fundamental notion is the
+    longest *contiguous* & junk-free matching subsequence.  That's what
+    catches peoples' eyes.  The Windows(tm) windiff has another interesting
+    notion, pairing up elements that appear uniquely in each sequence.
+    That, and the method here, appear to yield more intuitive difference
+    reports than does diff.  This method appears to be the least vulnerable
+    to syncing up on blocks of "junk lines", though (like blank lines in
+    ordinary text files, or maybe "<P>" lines in HTML files).  That may be
+    because this is the only method of the 3 that has a *concept* of
+    "junk" <wink>.
+
+    Example, comparing two strings, and considering blanks to be "junk":
+
+    >>> s = SequenceMatcher(lambda x: x == " ",
+    ...                     "private Thread currentThread;",
+    ...                     "private volatile Thread currentThread;")
+    >>>
+
+    .ratio() returns a float in [0, 1], measuring the "similarity" of the
+    sequences.  As a rule of thumb, a .ratio() value over 0.6 means the
+    sequences are close matches:
+
+    >>> print(round(s.ratio(), 3))
+    0.866
+    >>>
+
+    If you're only interested in where the sequences match,
+    .get_matching_blocks() is handy:
+
+    >>> for block in s.get_matching_blocks():
+    ...     print("a[%d] and b[%d] match for %d elements" % block)
+    a[0] and b[0] match for 8 elements
+    a[8] and b[17] match for 21 elements
+    a[29] and b[38] match for 0 elements
+
+    Note that the last tuple returned by .get_matching_blocks() is always a
+    dummy, (len(a), len(b), 0), and this is the only case in which the last
+    tuple element (number of elements matched) is 0.
+
+    If you want to know how to change the first sequence into the second,
+    use .get_opcodes():
+
+    >>> for opcode in s.get_opcodes():
+    ...     print("%6s a[%d:%d] b[%d:%d]" % opcode)
+     equal a[0:8] b[0:8]
+    insert a[8:8] b[8:17]
+     equal a[8:29] b[17:38]
+
+    See the Differ class for a fancy human-friendly file differencer, which
+    uses SequenceMatcher both to compare sequences of lines, and to compare
+    sequences of characters within similar (near-matching) lines.
+
+    See also function get_close_matches() in this module, which shows how
+    simple code building on SequenceMatcher can be used to do useful work.
+
+    Timing:  Basic R-O is cubic time worst case and quadratic time expected
+    case.  SequenceMatcher is quadratic time for the worst case and has
+    expected-case behavior dependent in a complicated way on how many
+    elements the sequences have in common; best case time is linear.
+    """
+
+    def __init__(self, isjunk=None, a='', b='', autojunk=True):
+        """Construct a SequenceMatcher.
+
+        Optional arg isjunk is None (the default), or a one-argument
+        function that takes a sequence element and returns true iff the
+        element is junk.  None is equivalent to passing "lambda x: 0", i.e.
+        no elements are considered to be junk.  For example, pass
+            lambda x: x in " \\t"
+        if you're comparing lines as sequences of characters, and don't
+        want to synch up on blanks or hard tabs.
+
+        Optional arg a is the first of two sequences to be compared.  By
+        default, an empty string.  The elements of a must be hashable.  See
+        also .set_seqs() and .set_seq1().
+
+        Optional arg b is the second of two sequences to be compared.  By
+        default, an empty string.  The elements of b must be hashable. See
+        also .set_seqs() and .set_seq2().
+
+        Optional arg autojunk should be set to False to disable the
+        "automatic junk heuristic" that treats popular elements as junk
+        (see module documentation for more information).
+        """
+
+        # Members:
+        # a
+        #      first sequence
+        # b
+        #      second sequence; differences are computed as "what do
+        #      we need to do to 'a' to change it into 'b'?"
+        # b2j
+        #      for x in b, b2j[x] is a list of the indices (into b)
+        #      at which x appears; junk and popular elements do not appear
+        # fullbcount
+        #      for x in b, fullbcount[x] == the number of times x
+        #      appears in b; only materialized if really needed (used
+        #      only for computing quick_ratio())
+        # matching_blocks
+        #      a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k];
+        #      ascending & non-overlapping in i and in j; terminated by
+        #      a dummy (len(a), len(b), 0) sentinel
+        # opcodes
+        #      a list of (tag, i1, i2, j1, j2) tuples, where tag is
+        #      one of
+        #          'replace'   a[i1:i2] should be replaced by b[j1:j2]
+        #          'delete'    a[i1:i2] should be deleted
+        #          'insert'    b[j1:j2] should be inserted
+        #          'equal'     a[i1:i2] == b[j1:j2]
+        # isjunk
+        #      a user-supplied function taking a sequence element and
+        #      returning true iff the element is "junk" -- this has
+        #      subtle but helpful effects on the algorithm, which I'll
+        #      get around to writing up someday <0.9 wink>.
+        #      DON'T USE!  Only __chain_b uses this.  Use "in self.bjunk".
+        # bjunk
+        #      the items in b for which isjunk is True.
+        # bpopular
+        #      nonjunk items in b treated as junk by the heuristic (if used).
+
+        self.isjunk = isjunk
+        self.a = self.b = None
+        self.autojunk = autojunk
+        self.set_seqs(a, b)
+
+    def set_seqs(self, a, b):
+        """Set the two sequences to be compared.
+
+        >>> s = SequenceMatcher()
+        >>> s.set_seqs("abcd", "bcde")
+        >>> s.ratio()
+        0.75
+        """
+
+        self.set_seq1(a)
+        self.set_seq2(b)
+
+    def set_seq1(self, a):
+        """Set the first sequence to be compared.
+
+        The second sequence to be compared is not changed.
+
+        >>> s = SequenceMatcher(None, "abcd", "bcde")
+        >>> s.ratio()
+        0.75
+        >>> s.set_seq1("bcde")
+        >>> s.ratio()
+        1.0
+        >>>
+
+        SequenceMatcher computes and caches detailed information about the
+        second sequence, so if you want to compare one sequence S against
+        many sequences, use .set_seq2(S) once and call .set_seq1(x)
+        repeatedly for each of the other sequences.
+
+        See also set_seqs() and set_seq2().
+        """
+
+        if a is self.a:
+            return
+        self.a = a
+        self.matching_blocks = self.opcodes = None
+
+    def set_seq2(self, b):
+        """Set the second sequence to be compared.
+
+        The first sequence to be compared is not changed.
+
+        >>> s = SequenceMatcher(None, "abcd", "bcde")
+        >>> s.ratio()
+        0.75
+        >>> s.set_seq2("abcd")
+        >>> s.ratio()
+        1.0
+        >>>
+
+        SequenceMatcher computes and caches detailed information about the
+        second sequence, so if you want to compare one sequence S against
+        many sequences, use .set_seq2(S) once and call .set_seq1(x)
+        repeatedly for each of the other sequences.
+
+        See also set_seqs() and set_seq1().
+        """
+
+        if b is self.b:
+            return
+        self.b = b
+        self.matching_blocks = self.opcodes = None
+        self.fullbcount = None
+        self.__chain_b()
+
+    # For each element x in b, set b2j[x] to a list of the indices in
+    # b where x appears; the indices are in increasing order; note that
+    # the number of times x appears in b is len(b2j[x]) ...
+    # when self.isjunk is defined, junk elements don't show up in this
+    # map at all, which stops the central find_longest_match method
+    # from starting any matching block at a junk element ...
+    # b2j also does not contain entries for "popular" elements, meaning
+    # elements that account for more than 1 + 1% of the total elements, and
+    # when the sequence is reasonably large (>= 200 elements); this can
+    # be viewed as an adaptive notion of semi-junk, and yields an enormous
+    # speedup when, e.g., comparing program files with hundreds of
+    # instances of "return NULL;" ...
+    # note that this is only called when b changes; so for cross-product
+    # kinds of matches, it's best to call set_seq2 once, then set_seq1
+    # repeatedly
+
+    def __chain_b(self):
+        # Because isjunk is a user-defined (not C) function, and we test
+        # for junk a LOT, it's important to minimize the number of calls.
+        # Before the tricks described here, __chain_b was by far the most
+        # time-consuming routine in the whole module!  If anyone sees
+        # Jim Roskind, thank him again for profile.py -- I never would
+        # have guessed that.
+        # The first trick is to build b2j ignoring the possibility
+        # of junk.  I.e., we don't call isjunk at all yet.  Throwing
+        # out the junk later is much cheaper than building b2j "right"
+        # from the start.
+        b = self.b
+        self.b2j = b2j = {}
+
+        for i, elt in enumerate(b):
+            indices = b2j.setdefault(elt, [])
+            indices.append(i)
+
+        # Purge junk elements
+        self.bjunk = junk = set()
+        isjunk = self.isjunk
+        if isjunk:
+            for elt in b2j.keys():
+                if isjunk(elt):
+                    junk.add(elt)
+            for elt in junk: # separate loop avoids separate list of keys
+                del b2j[elt]
+
+        # Purge popular elements that are not junk
+        self.bpopular = popular = set()
+        n = len(b)
+        if self.autojunk and n >= 200:
+            ntest = n // 100 + 1
+            for elt, idxs in b2j.items():
+                if len(idxs) > ntest:
+                    popular.add(elt)
+            for elt in popular: # ditto; as fast for 1% deletion
+                del b2j[elt]
+
+    def find_longest_match(self, alo=0, ahi=None, blo=0, bhi=None):
+        """Find longest matching block in a[alo:ahi] and b[blo:bhi].
+
+        By default it will find the longest match in the entirety of a and b.
+
+        If isjunk is not defined:
+
+        Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where
+            alo <= i <= i+k <= ahi
+            blo <= j <= j+k <= bhi
+        and for all (i',j',k') meeting those conditions,
+            k >= k'
+            i <= i'
+            and if i == i', j <= j'
+
+        In other words, of all maximal matching blocks, return one that
+        starts earliest in a, and of all those maximal matching blocks that
+        start earliest in a, return the one that starts earliest in b.
+
+        >>> s = SequenceMatcher(None, " abcd", "abcd abcd")
+        >>> s.find_longest_match(0, 5, 0, 9)
+        Match(a=0, b=4, size=5)
+
+        If isjunk is defined, first the longest matching block is
+        determined as above, but with the additional restriction that no
+        junk element appears in the block.  Then that block is extended as
+        far as possible by matching (only) junk elements on both sides.  So
+        the resulting block never matches on junk except as identical junk
+        happens to be adjacent to an "interesting" match.
+
+        Here's the same example as before, but considering blanks to be
+        junk.  That prevents " abcd" from matching the " abcd" at the tail
+        end of the second sequence directly.  Instead only the "abcd" can
+        match, and matches the leftmost "abcd" in the second sequence:
+
+        >>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
+        >>> s.find_longest_match(0, 5, 0, 9)
+        Match(a=1, b=0, size=4)
+
+        If no blocks match, return (alo, blo, 0).
+
+        >>> s = SequenceMatcher(None, "ab", "c")
+        >>> s.find_longest_match(0, 2, 0, 1)
+        Match(a=0, b=0, size=0)
+        """
+
+        # CAUTION:  stripping common prefix or suffix would be incorrect.
+        # E.g.,
+        #    ab
+        #    acab
+        # Longest matching block is "ab", but if common prefix is
+        # stripped, it's "a" (tied with "b").  UNIX(tm) diff does so
+        # strip, so ends up claiming that ab is changed to acab by
+        # inserting "ca" in the middle.  That's minimal but unintuitive:
+        # "it's obvious" that someone inserted "ac" at the front.
+        # Windiff ends up at the same place as diff, but by pairing up
+        # the unique 'b's and then matching the first two 'a's.
+
+        a, b, b2j, isbjunk = self.a, self.b, self.b2j, self.bjunk.__contains__
+        if ahi is None:
+            ahi = len(a)
+        if bhi is None:
+            bhi = len(b)
+        besti, bestj, bestsize = alo, blo, 0
+        # find longest junk-free match
+        # during an iteration of the loop, j2len[j] = length of longest
+        # junk-free match ending with a[i-1] and b[j]
+        j2len = {}
+        nothing = []
+        for i in range(alo, ahi):
+            # look at all instances of a[i] in b; note that because
+            # b2j has no junk keys, the loop is skipped if a[i] is junk
+            j2lenget = j2len.get
+            newj2len = {}
+            for j in b2j.get(a[i], nothing):
+                # a[i] matches b[j]
+                if j < blo:
+                    continue
+                if j >= bhi:
+                    break
+                k = newj2len[j] = j2lenget(j-1, 0) + 1
+                if k > bestsize:
+                    besti, bestj, bestsize = i-k+1, j-k+1, k
+            j2len = newj2len
+
+        # Extend the best by non-junk elements on each end.  In particular,
+        # "popular" non-junk elements aren't in b2j, which greatly speeds
+        # the inner loop above, but also means "the best" match so far
+        # doesn't contain any junk *or* popular non-junk elements.
+        while besti > alo and bestj > blo and \
+              not isbjunk(b[bestj-1]) and \
+              a[besti-1] == b[bestj-1]:
+            besti, bestj, bestsize = besti-1, bestj-1, bestsize+1
+        while besti+bestsize < ahi and bestj+bestsize < bhi and \
+              not isbjunk(b[bestj+bestsize]) and \
+              a[besti+bestsize] == b[bestj+bestsize]:
+            bestsize += 1
+
+        # Now that we have a wholly interesting match (albeit possibly
+        # empty!), we may as well suck up the matching junk on each
+        # side of it too.  Can't think of a good reason not to, and it
+        # saves post-processing the (possibly considerable) expense of
+        # figuring out what to do with it.  In the case of an empty
+        # interesting match, this is clearly the right thing to do,
+        # because no other kind of match is possible in the regions.
+        while besti > alo and bestj > blo and \
+              isbjunk(b[bestj-1]) and \
+              a[besti-1] == b[bestj-1]:
+            besti, bestj, bestsize = besti-1, bestj-1, bestsize+1
+        while besti+bestsize < ahi and bestj+bestsize < bhi and \
+              isbjunk(b[bestj+bestsize]) and \
+              a[besti+bestsize] == b[bestj+bestsize]:
+            bestsize = bestsize + 1
+
+        return Match(besti, bestj, bestsize)
+
+    def get_matching_blocks(self):
+        """Return list of triples describing matching subsequences.
+
+        Each triple is of the form (i, j, n), and means that
+        a[i:i+n] == b[j:j+n].  The triples are monotonically increasing in
+        i and in j.  New in Python 2.5, it's also guaranteed that if
+        (i, j, n) and (i', j', n') are adjacent triples in the list, and
+        the second is not the last triple in the list, then i+n != i' or
+        j+n != j'.  IOW, adjacent triples never describe adjacent equal
+        blocks.
+
+        The last triple is a dummy, (len(a), len(b), 0), and is the only
+        triple with n==0.
+
+        >>> s = SequenceMatcher(None, "abxcd", "abcd")
+        >>> list(s.get_matching_blocks())
+        [Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)]
+        """
+
+        if self.matching_blocks is not None:
+            return self.matching_blocks
+        la, lb = len(self.a), len(self.b)
+
+        # This is most naturally expressed as a recursive algorithm, but
+        # at least one user bumped into extreme use cases that exceeded
+        # the recursion limit on their box.  So, now we maintain a list
+        # ('queue`) of blocks we still need to look at, and append partial
+        # results to `matching_blocks` in a loop; the matches are sorted
+        # at the end.
+        queue = [(0, la, 0, lb)]
+        matching_blocks = []
+        while queue:
+            alo, ahi, blo, bhi = queue.pop()
+            i, j, k = x = self.find_longest_match(alo, ahi, blo, bhi)
+            # a[alo:i] vs b[blo:j] unknown
+            # a[i:i+k] same as b[j:j+k]
+            # a[i+k:ahi] vs b[j+k:bhi] unknown
+            if k:   # if k is 0, there was no matching block
+                matching_blocks.append(x)
+                if alo < i and blo < j:
+                    queue.append((alo, i, blo, j))
+                if i+k < ahi and j+k < bhi:
+                    queue.append((i+k, ahi, j+k, bhi))
+        matching_blocks.sort()
+
+        # It's possible that we have adjacent equal blocks in the
+        # matching_blocks list now.  Starting with 2.5, this code was added
+        # to collapse them.
+        i1 = j1 = k1 = 0
+        non_adjacent = []
+        for i2, j2, k2 in matching_blocks:
+            # Is this block adjacent to i1, j1, k1?
+            if i1 + k1 == i2 and j1 + k1 == j2:
+                # Yes, so collapse them -- this just increases the length of
+                # the first block by the length of the second, and the first
+                # block so lengthened remains the block to compare against.
+                k1 += k2
+            else:
+                # Not adjacent.  Remember the first block (k1==0 means it's
+                # the dummy we started with), and make the second block the
+                # new block to compare against.
+                if k1:
+                    non_adjacent.append((i1, j1, k1))
+                i1, j1, k1 = i2, j2, k2
+        if k1:
+            non_adjacent.append((i1, j1, k1))
+
+        non_adjacent.append( (la, lb, 0) )
+        self.matching_blocks = list(map(Match._make, non_adjacent))
+        return self.matching_blocks
+
+    def get_opcodes(self):
+        """Return list of 5-tuples describing how to turn a into b.
+
+        Each tuple is of the form (tag, i1, i2, j1, j2).  The first tuple
+        has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the
+        tuple preceding it, and likewise for j1 == the previous j2.
+
+        The tags are strings, with these meanings:
+
+        'replace':  a[i1:i2] should be replaced by b[j1:j2]
+        'delete':   a[i1:i2] should be deleted.
+                    Note that j1==j2 in this case.
+        'insert':   b[j1:j2] should be inserted at a[i1:i1].
+                    Note that i1==i2 in this case.
+        'equal':    a[i1:i2] == b[j1:j2]
+
+        >>> a = "qabxcd"
+        >>> b = "abycdf"
+        >>> s = SequenceMatcher(None, a, b)
+        >>> for tag, i1, i2, j1, j2 in s.get_opcodes():
+        ...    print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
+        ...           (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2])))
+         delete a[0:1] (q) b[0:0] ()
+          equal a[1:3] (ab) b[0:2] (ab)
+        replace a[3:4] (x) b[2:3] (y)
+          equal a[4:6] (cd) b[3:5] (cd)
+         insert a[6:6] () b[5:6] (f)
+        """
+
+        if self.opcodes is not None:
+            return self.opcodes
+        i = j = 0
+        self.opcodes = answer = []
+        for ai, bj, size in self.get_matching_blocks():
+            # invariant:  we've pumped out correct diffs to change
+            # a[:i] into b[:j], and the next matching block is
+            # a[ai:ai+size] == b[bj:bj+size].  So we need to pump
+            # out a diff to change a[i:ai] into b[j:bj], pump out
+            # the matching block, and move (i,j) beyond the match
+            tag = ''
+            if i < ai and j < bj:
+                tag = 'replace'
+            elif i < ai:
+                tag = 'delete'
+            elif j < bj:
+                tag = 'insert'
+            if tag:
+                answer.append( (tag, i, ai, j, bj) )
+            i, j = ai+size, bj+size
+            # the list of matching blocks is terminated by a
+            # sentinel with size 0
+            if size:
+                answer.append( ('equal', ai, i, bj, j) )
+        return answer
+
+    def get_grouped_opcodes(self, n=3):
+        """ Isolate change clusters by eliminating ranges with no changes.
+
+        Return a generator of groups with up to n lines of context.
+        Each group is in the same format as returned by get_opcodes().
+
+        >>> from pprint import pprint
+        >>> a = list(map(str, range(1,40)))
+        >>> b = a[:]
+        >>> b[8:8] = ['i']     # Make an insertion
+        >>> b[20] += 'x'       # Make a replacement
+        >>> b[23:28] = []      # Make a deletion
+        >>> b[30] += 'y'       # Make another replacement
+        >>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))
+        [[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],
+         [('equal', 16, 19, 17, 20),
+          ('replace', 19, 20, 20, 21),
+          ('equal', 20, 22, 21, 23),
+          ('delete', 22, 27, 23, 23),
+          ('equal', 27, 30, 23, 26)],
+         [('equal', 31, 34, 27, 30),
+          ('replace', 34, 35, 30, 31),
+          ('equal', 35, 38, 31, 34)]]
+        """
+
+        codes = self.get_opcodes()
+        if not codes:
+            codes = [("equal", 0, 1, 0, 1)]
+        # Fixup leading and trailing groups if they show no changes.
+        if codes[0][0] == 'equal':
+            tag, i1, i2, j1, j2 = codes[0]
+            codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2
+        if codes[-1][0] == 'equal':
+            tag, i1, i2, j1, j2 = codes[-1]
+            codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n)
+
+        nn = n + n
+        group = []
+        for tag, i1, i2, j1, j2 in codes:
+            # End the current group and start a new one whenever
+            # there is a large range with no changes.
+            if tag == 'equal' and i2-i1 > nn:
+                group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n)))
+                yield group
+                group = []
+                i1, j1 = max(i1, i2-n), max(j1, j2-n)
+            group.append((tag, i1, i2, j1 ,j2))
+        if group and not (len(group)==1 and group[0][0] == 'equal'):
+            yield group
+
+    def ratio(self):
+        """Return a measure of the sequences' similarity (float in [0,1]).
+
+        Where T is the total number of elements in both sequences, and
+        M is the number of matches, this is 2.0*M / T.
+        Note that this is 1 if the sequences are identical, and 0 if
+        they have nothing in common.
+
+        .ratio() is expensive to compute if you haven't already computed
+        .get_matching_blocks() or .get_opcodes(), in which case you may
+        want to try .quick_ratio() or .real_quick_ratio() first to get an
+        upper bound.
+
+        >>> s = SequenceMatcher(None, "abcd", "bcde")
+        >>> s.ratio()
+        0.75
+        >>> s.quick_ratio()
+        0.75
+        >>> s.real_quick_ratio()
+        1.0
+        """
+
+        matches = sum(triple[-1] for triple in self.get_matching_blocks())
+        return _calculate_ratio(matches, len(self.a) + len(self.b))
+
+    def quick_ratio(self):
+        """Return an upper bound on ratio() relatively quickly.
+
+        This isn't defined beyond that it is an upper bound on .ratio(), and
+        is faster to compute.
+        """
+
+        # viewing a and b as multisets, set matches to the cardinality
+        # of their intersection; this counts the number of matches
+        # without regard to order, so is clearly an upper bound
+        if self.fullbcount is None:
+            self.fullbcount = fullbcount = {}
+            for elt in self.b:
+                fullbcount[elt] = fullbcount.get(elt, 0) + 1
+        fullbcount = self.fullbcount
+        # avail[x] is the number of times x appears in 'b' less the
+        # number of times we've seen it in 'a' so far ... kinda
+        avail = {}
+        availhas, matches = avail.__contains__, 0
+        for elt in self.a:
+            if availhas(elt):
+                numb = avail[elt]
+            else:
+                numb = fullbcount.get(elt, 0)
+            avail[elt] = numb - 1
+            if numb > 0:
+                matches = matches + 1
+        return _calculate_ratio(matches, len(self.a) + len(self.b))
+
+    def real_quick_ratio(self):
+        """Return an upper bound on ratio() very quickly.
+
+        This isn't defined beyond that it is an upper bound on .ratio(), and
+        is faster to compute than either .ratio() or .quick_ratio().
+        """
+
+        la, lb = len(self.a), len(self.b)
+        # can't have more matches than the number of elements in the
+        # shorter sequence
+        return _calculate_ratio(min(la, lb), la + lb)
+
+    __class_getitem__ = classmethod(GenericAlias)
+
+
+def get_close_matches(word, possibilities, n=3, cutoff=0.6):
+    """Use SequenceMatcher to return list of the best "good enough" matches.
+
+    word is a sequence for which close matches are desired (typically a
+    string).
+
+    possibilities is a list of sequences against which to match word
+    (typically a list of strings).
+
+    Optional arg n (default 3) is the maximum number of close matches to
+    return.  n must be > 0.
+
+    Optional arg cutoff (default 0.6) is a float in [0, 1].  Possibilities
+    that don't score at least that similar to word are ignored.
+
+    The best (no more than n) matches among the possibilities are returned
+    in a list, sorted by similarity score, most similar first.
+
+    >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
+    ['apple', 'ape']
+    >>> import keyword as _keyword
+    >>> get_close_matches("wheel", _keyword.kwlist)
+    ['while']
+    >>> get_close_matches("Apple", _keyword.kwlist)
+    []
+    >>> get_close_matches("accept", _keyword.kwlist)
+    ['except']
+    """
+
+    if not n >  0:
+        raise ValueError("n must be > 0: %r" % (n,))
+    if not 0.0 <= cutoff <= 1.0:
+        raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,))
+    result = []
+    s = SequenceMatcher()
+    s.set_seq2(word)
+    for x in possibilities:
+        s.set_seq1(x)
+        if s.real_quick_ratio() >= cutoff and \
+           s.quick_ratio() >= cutoff and \
+           s.ratio() >= cutoff:
+            result.append((s.ratio(), x))
+
+    # Move the best scorers to head of list
+    result = _nlargest(n, result)
+    # Strip scores for the best n matches
+    return [x for score, x in result]
+
+
+def _keep_original_ws(s, tag_s):
+    """Replace whitespace with the original whitespace characters in `s`"""
+    return ''.join(
+        c if tag_c == " " and c.isspace() else tag_c
+        for c, tag_c in zip(s, tag_s)
+    )
+
+
+
+class Differ:
+    r"""
+    Differ is a class for comparing sequences of lines of text, and
+    producing human-readable differences or deltas.  Differ uses
+    SequenceMatcher both to compare sequences of lines, and to compare
+    sequences of characters within similar (near-matching) lines.
+
+    Each line of a Differ delta begins with a two-letter code:
+
+        '- '    line unique to sequence 1
+        '+ '    line unique to sequence 2
+        '  '    line common to both sequences
+        '? '    line not present in either input sequence
+
+    Lines beginning with '? ' attempt to guide the eye to intraline
+    differences, and were not present in either input sequence.  These lines
+    can be confusing if the sequences contain tab characters.
+
+    Note that Differ makes no claim to produce a *minimal* diff.  To the
+    contrary, minimal diffs are often counter-intuitive, because they synch
+    up anywhere possible, sometimes accidental matches 100 pages apart.
+    Restricting synch points to contiguous matches preserves some notion of
+    locality, at the occasional cost of producing a longer diff.
+
+    Example: Comparing two texts.
+
+    First we set up the texts, sequences of individual single-line strings
+    ending with newlines (such sequences can also be obtained from the
+    `readlines()` method of file-like objects):
+
+    >>> text1 = '''  1. Beautiful is better than ugly.
+    ...   2. Explicit is better than implicit.
+    ...   3. Simple is better than complex.
+    ...   4. Complex is better than complicated.
+    ... '''.splitlines(keepends=True)
+    >>> len(text1)
+    4
+    >>> text1[0][-1]
+    '\n'
+    >>> text2 = '''  1. Beautiful is better than ugly.
+    ...   3.   Simple is better than complex.
+    ...   4. Complicated is better than complex.
+    ...   5. Flat is better than nested.
+    ... '''.splitlines(keepends=True)
+
+    Next we instantiate a Differ object:
+
+    >>> d = Differ()
+
+    Note that when instantiating a Differ object we may pass functions to
+    filter out line and character 'junk'.  See Differ.__init__ for details.
+
+    Finally, we compare the two:
+
+    >>> result = list(d.compare(text1, text2))
+
+    'result' is a list of strings, so let's pretty-print it:
+
+    >>> from pprint import pprint as _pprint
+    >>> _pprint(result)
+    ['    1. Beautiful is better than ugly.\n',
+     '-   2. Explicit is better than implicit.\n',
+     '-   3. Simple is better than complex.\n',
+     '+   3.   Simple is better than complex.\n',
+     '?     ++\n',
+     '-   4. Complex is better than complicated.\n',
+     '?            ^                     ---- ^\n',
+     '+   4. Complicated is better than complex.\n',
+     '?           ++++ ^                      ^\n',
+     '+   5. Flat is better than nested.\n']
+
+    As a single multi-line string it looks like this:
+
+    >>> print(''.join(result), end="")
+        1. Beautiful is better than ugly.
+    -   2. Explicit is better than implicit.
+    -   3. Simple is better than complex.
+    +   3.   Simple is better than complex.
+    ?     ++
+    -   4. Complex is better than complicated.
+    ?            ^                     ---- ^
+    +   4. Complicated is better than complex.
+    ?           ++++ ^                      ^
+    +   5. Flat is better than nested.
+    """
+
+    def __init__(self, linejunk=None, charjunk=None):
+        """
+        Construct a text differencer, with optional filters.
+
+        The two optional keyword parameters are for filter functions:
+
+        - `linejunk`: A function that should accept a single string argument,
+          and return true iff the string is junk. The module-level function
+          `IS_LINE_JUNK` may be used to filter out lines without visible
+          characters, except for at most one splat ('#').  It is recommended
+          to leave linejunk None; the underlying SequenceMatcher class has
+          an adaptive notion of "noise" lines that's better than any static
+          definition the author has ever been able to craft.
+
+        - `charjunk`: A function that should accept a string of length 1. The
+          module-level function `IS_CHARACTER_JUNK` may be used to filter out
+          whitespace characters (a blank or tab; **note**: bad idea to include
+          newline in this!).  Use of IS_CHARACTER_JUNK is recommended.
+        """
+
+        self.linejunk = linejunk
+        self.charjunk = charjunk
+
+    def compare(self, a, b):
+        r"""
+        Compare two sequences of lines; generate the resulting delta.
+
+        Each sequence must contain individual single-line strings ending with
+        newlines. Such sequences can be obtained from the `readlines()` method
+        of file-like objects.  The delta generated also consists of newline-
+        terminated strings, ready to be printed as-is via the writelines()
+        method of a file-like object.
+
+        Example:
+
+        >>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True),
+        ...                                'ore\ntree\nemu\n'.splitlines(True))),
+        ...       end="")
+        - one
+        ?  ^
+        + ore
+        ?  ^
+        - two
+        - three
+        ?  -
+        + tree
+        + emu
+        """
+
+        cruncher = SequenceMatcher(self.linejunk, a, b)
+        for tag, alo, ahi, blo, bhi in cruncher.get_opcodes():
+            if tag == 'replace':
+                g = self._fancy_replace(a, alo, ahi, b, blo, bhi)
+            elif tag == 'delete':
+                g = self._dump('-', a, alo, ahi)
+            elif tag == 'insert':
+                g = self._dump('+', b, blo, bhi)
+            elif tag == 'equal':
+                g = self._dump(' ', a, alo, ahi)
+            else:
+                raise ValueError('unknown tag %r' % (tag,))
+
+            yield from g
+
+    def _dump(self, tag, x, lo, hi):
+        """Generate comparison results for a same-tagged range."""
+        for i in range(lo, hi):
+            yield '%s %s' % (tag, x[i])
+
+    def _plain_replace(self, a, alo, ahi, b, blo, bhi):
+        assert alo < ahi and blo < bhi
+        # dump the shorter block first -- reduces the burden on short-term
+        # memory if the blocks are of very different sizes
+        if bhi - blo < ahi - alo:
+            first  = self._dump('+', b, blo, bhi)
+            second = self._dump('-', a, alo, ahi)
+        else:
+            first  = self._dump('-', a, alo, ahi)
+            second = self._dump('+', b, blo, bhi)
+
+        for g in first, second:
+            yield from g
+
+    def _fancy_replace(self, a, alo, ahi, b, blo, bhi):
+        r"""
+        When replacing one block of lines with another, search the blocks
+        for *similar* lines; the best-matching pair (if any) is used as a
+        synch point, and intraline difference marking is done on the
+        similar pair. Lots of work, but often worth it.
+
+        Example:
+
+        >>> d = Differ()
+        >>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1,
+        ...                            ['abcdefGhijkl\n'], 0, 1)
+        >>> print(''.join(results), end="")
+        - abcDefghiJkl
+        ?    ^  ^  ^
+        + abcdefGhijkl
+        ?    ^  ^  ^
+        """
+
+        # don't synch up unless the lines have a similarity score of at
+        # least cutoff; best_ratio tracks the best score seen so far
+        best_ratio, cutoff = 0.74, 0.75
+        cruncher = SequenceMatcher(self.charjunk)
+        eqi, eqj = None, None   # 1st indices of equal lines (if any)
+
+        # search for the pair that matches best without being identical
+        # (identical lines must be junk lines, & we don't want to synch up
+        # on junk -- unless we have to)
+        for j in range(blo, bhi):
+            bj = b[j]
+            cruncher.set_seq2(bj)
+            for i in range(alo, ahi):
+                ai = a[i]
+                if ai == bj:
+                    if eqi is None:
+                        eqi, eqj = i, j
+                    continue
+                cruncher.set_seq1(ai)
+                # computing similarity is expensive, so use the quick
+                # upper bounds first -- have seen this speed up messy
+                # compares by a factor of 3.
+                # note that ratio() is only expensive to compute the first
+                # time it's called on a sequence pair; the expensive part
+                # of the computation is cached by cruncher
+                if cruncher.real_quick_ratio() > best_ratio and \
+                      cruncher.quick_ratio() > best_ratio and \
+                      cruncher.ratio() > best_ratio:
+                    best_ratio, best_i, best_j = cruncher.ratio(), i, j
+        if best_ratio < cutoff:
+            # no non-identical "pretty close" pair
+            if eqi is None:
+                # no identical pair either -- treat it as a straight replace
+                yield from self._plain_replace(a, alo, ahi, b, blo, bhi)
+                return
+            # no close pair, but an identical pair -- synch up on that
+            best_i, best_j, best_ratio = eqi, eqj, 1.0
+        else:
+            # there's a close pair, so forget the identical pair (if any)
+            eqi = None
+
+        # a[best_i] very similar to b[best_j]; eqi is None iff they're not
+        # identical
+
+        # pump out diffs from before the synch point
+        yield from self._fancy_helper(a, alo, best_i, b, blo, best_j)
+
+        # do intraline marking on the synch pair
+        aelt, belt = a[best_i], b[best_j]
+        if eqi is None:
+            # pump out a '-', '?', '+', '?' quad for the synched lines
+            atags = btags = ""
+            cruncher.set_seqs(aelt, belt)
+            for tag, ai1, ai2, bj1, bj2 in cruncher.get_opcodes():
+                la, lb = ai2 - ai1, bj2 - bj1
+                if tag == 'replace':
+                    atags += '^' * la
+                    btags += '^' * lb
+                elif tag == 'delete':
+                    atags += '-' * la
+                elif tag == 'insert':
+                    btags += '+' * lb
+                elif tag == 'equal':
+                    atags += ' ' * la
+                    btags += ' ' * lb
+                else:
+                    raise ValueError('unknown tag %r' % (tag,))
+            yield from self._qformat(aelt, belt, atags, btags)
+        else:
+            # the synch pair is identical
+            yield '  ' + aelt
+
+        # pump out diffs from after the synch point
+        yield from self._fancy_helper(a, best_i+1, ahi, b, best_j+1, bhi)
+
+    def _fancy_helper(self, a, alo, ahi, b, blo, bhi):
+        g = []
+        if alo < ahi:
+            if blo < bhi:
+                g = self._fancy_replace(a, alo, ahi, b, blo, bhi)
+            else:
+                g = self._dump('-', a, alo, ahi)
+        elif blo < bhi:
+            g = self._dump('+', b, blo, bhi)
+
+        yield from g
+
+    def _qformat(self, aline, bline, atags, btags):
+        r"""
+        Format "?" output and deal with tabs.
+
+        Example:
+
+        >>> d = Differ()
+        >>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n',
+        ...                      '  ^ ^  ^      ', '  ^ ^  ^      ')
+        >>> for line in results: print(repr(line))
+        ...
+        '- \tabcDefghiJkl\n'
+        '? \t ^ ^  ^\n'
+        '+ \tabcdefGhijkl\n'
+        '? \t ^ ^  ^\n'
+        """
+        atags = _keep_original_ws(aline, atags).rstrip()
+        btags = _keep_original_ws(bline, btags).rstrip()
+
+        yield "- " + aline
+        if atags:
+            yield f"? {atags}\n"
+
+        yield "+ " + bline
+        if btags:
+            yield f"? {btags}\n"
+
+# With respect to junk, an earlier version of ndiff simply refused to
+# *start* a match with a junk element.  The result was cases like this:
+#     before: private Thread currentThread;
+#     after:  private volatile Thread currentThread;
+# If you consider whitespace to be junk, the longest contiguous match
+# not starting with junk is "e Thread currentThread".  So ndiff reported
+# that "e volatil" was inserted between the 't' and the 'e' in "private".
+# While an accurate view, to people that's absurd.  The current version
+# looks for matching blocks that are entirely junk-free, then extends the
+# longest one of those as far as possible but only with matching junk.
+# So now "currentThread" is matched, then extended to suck up the
+# preceding blank; then "private" is matched, and extended to suck up the
+# following blank; then "Thread" is matched; and finally ndiff reports
+# that "volatile " was inserted before "Thread".  The only quibble
+# remaining is that perhaps it was really the case that " volatile"
+# was inserted after "private".  I can live with that <wink>.
+
+import re
+
+def IS_LINE_JUNK(line, pat=re.compile(r"\s*(?:#\s*)?$").match):
+    r"""
+    Return True for ignorable line: iff `line` is blank or contains a single '#'.
+
+    Examples:
+
+    >>> IS_LINE_JUNK('\n')
+    True
+    >>> IS_LINE_JUNK('  #   \n')
+    True
+    >>> IS_LINE_JUNK('hello\n')
+    False
+    """
+
+    return pat(line) is not None
+
+def IS_CHARACTER_JUNK(ch, ws=" \t"):
+    r"""
+    Return True for ignorable character: iff `ch` is a space or tab.
+
+    Examples:
+
+    >>> IS_CHARACTER_JUNK(' ')
+    True
+    >>> IS_CHARACTER_JUNK('\t')
+    True
+    >>> IS_CHARACTER_JUNK('\n')
+    False
+    >>> IS_CHARACTER_JUNK('x')
+    False
+    """
+
+    return ch in ws
+
+
+########################################################################
+###  Unified Diff
+########################################################################
+
+def _format_range_unified(start, stop):
+    'Convert range to the "ed" format'
+    # Per the diff spec at http://www.unix.org/single_unix_specification/
+    beginning = start + 1     # lines start numbering with one
+    length = stop - start
+    if length == 1:
+        return '{}'.format(beginning)
+    if not length:
+        beginning -= 1        # empty ranges begin at line just before the range
+    return '{},{}'.format(beginning, length)
+
+def unified_diff(a, b, fromfile='', tofile='', fromfiledate='',
+                 tofiledate='', n=3, lineterm='\n'):
+    r"""
+    Compare two sequences of lines; generate the delta as a unified diff.
+
+    Unified diffs are a compact way of showing line changes and a few
+    lines of context.  The number of context lines is set by 'n' which
+    defaults to three.
+
+    By default, the diff control lines (those with ---, +++, or @@) are
+    created with a trailing newline.  This is helpful so that inputs
+    created from file.readlines() result in diffs that are suitable for
+    file.writelines() since both the inputs and outputs have trailing
+    newlines.
+
+    For inputs that do not have trailing newlines, set the lineterm
+    argument to "" so that the output will be uniformly newline free.
+
+    The unidiff format normally has a header for filenames and modification
+    times.  Any or all of these may be specified using strings for
+    'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
+    The modification times are normally expressed in the ISO 8601 format.
+
+    Example:
+
+    >>> for line in unified_diff('one two three four'.split(),
+    ...             'zero one tree four'.split(), 'Original', 'Current',
+    ...             '2005-01-26 23:30:50', '2010-04-02 10:20:52',
+    ...             lineterm=''):
+    ...     print(line)                 # doctest: +NORMALIZE_WHITESPACE
+    --- Original        2005-01-26 23:30:50
+    +++ Current         2010-04-02 10:20:52
+    @@ -1,4 +1,4 @@
+    +zero
+     one
+    -two
+    -three
+    +tree
+     four
+    """
+
+    _check_types(a, b, fromfile, tofile, fromfiledate, tofiledate, lineterm)
+    started = False
+    for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n):
+        if not started:
+            started = True
+            fromdate = '\t{}'.format(fromfiledate) if fromfiledate else ''
+            todate = '\t{}'.format(tofiledate) if tofiledate else ''
+            yield '--- {}{}{}'.format(fromfile, fromdate, lineterm)
+            yield '+++ {}{}{}'.format(tofile, todate, lineterm)
+
+        first, last = group[0], group[-1]
+        file1_range = _format_range_unified(first[1], last[2])
+        file2_range = _format_range_unified(first[3], last[4])
+        yield '@@ -{} +{} @@{}'.format(file1_range, file2_range, lineterm)
+
+        for tag, i1, i2, j1, j2 in group:
+            if tag == 'equal':
+                for line in a[i1:i2]:
+                    yield ' ' + line
+                continue
+            if tag in {'replace', 'delete'}:
+                for line in a[i1:i2]:
+                    yield '-' + line
+            if tag in {'replace', 'insert'}:
+                for line in b[j1:j2]:
+                    yield '+' + line
+
+
+########################################################################
+###  Context Diff
+########################################################################
+
+def _format_range_context(start, stop):
+    'Convert range to the "ed" format'
+    # Per the diff spec at http://www.unix.org/single_unix_specification/
+    beginning = start + 1     # lines start numbering with one
+    length = stop - start
+    if not length:
+        beginning -= 1        # empty ranges begin at line just before the range
+    if length <= 1:
+        return '{}'.format(beginning)
+    return '{},{}'.format(beginning, beginning + length - 1)
+
+# See http://www.unix.org/single_unix_specification/
+def context_diff(a, b, fromfile='', tofile='',
+                 fromfiledate='', tofiledate='', n=3, lineterm='\n'):
+    r"""
+    Compare two sequences of lines; generate the delta as a context diff.
+
+    Context diffs are a compact way of showing line changes and a few
+    lines of context.  The number of context lines is set by 'n' which
+    defaults to three.
+
+    By default, the diff control lines (those with *** or ---) are
+    created with a trailing newline.  This is helpful so that inputs
+    created from file.readlines() result in diffs that are suitable for
+    file.writelines() since both the inputs and outputs have trailing
+    newlines.
+
+    For inputs that do not have trailing newlines, set the lineterm
+    argument to "" so that the output will be uniformly newline free.
+
+    The context diff format normally has a header for filenames and
+    modification times.  Any or all of these may be specified using
+    strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
+    The modification times are normally expressed in the ISO 8601 format.
+    If not specified, the strings default to blanks.
+
+    Example:
+
+    >>> print(''.join(context_diff('one\ntwo\nthree\nfour\n'.splitlines(True),
+    ...       'zero\none\ntree\nfour\n'.splitlines(True), 'Original', 'Current')),
+    ...       end="")
+    *** Original
+    --- Current
+    ***************
+    *** 1,4 ****
+      one
+    ! two
+    ! three
+      four
+    --- 1,4 ----
+    + zero
+      one
+    ! tree
+      four
+    """
+
+    _check_types(a, b, fromfile, tofile, fromfiledate, tofiledate, lineterm)
+    prefix = dict(insert='+ ', delete='- ', replace='! ', equal='  ')
+    started = False
+    for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n):
+        if not started:
+            started = True
+            fromdate = '\t{}'.format(fromfiledate) if fromfiledate else ''
+            todate = '\t{}'.format(tofiledate) if tofiledate else ''
+            yield '*** {}{}{}'.format(fromfile, fromdate, lineterm)
+            yield '--- {}{}{}'.format(tofile, todate, lineterm)
+
+        first, last = group[0], group[-1]
+        yield '***************' + lineterm
+
+        file1_range = _format_range_context(first[1], last[2])
+        yield '*** {} ****{}'.format(file1_range, lineterm)
+
+        if any(tag in {'replace', 'delete'} for tag, _, _, _, _ in group):
+            for tag, i1, i2, _, _ in group:
+                if tag != 'insert':
+                    for line in a[i1:i2]:
+                        yield prefix[tag] + line
+
+        file2_range = _format_range_context(first[3], last[4])
+        yield '--- {} ----{}'.format(file2_range, lineterm)
+
+        if any(tag in {'replace', 'insert'} for tag, _, _, _, _ in group):
+            for tag, _, _, j1, j2 in group:
+                if tag != 'delete':
+                    for line in b[j1:j2]:
+                        yield prefix[tag] + line
+
+def _check_types(a, b, *args):
+    # Checking types is weird, but the alternative is garbled output when
+    # someone passes mixed bytes and str to {unified,context}_diff(). E.g.
+    # without this check, passing filenames as bytes results in output like
+    #   --- b'oldfile.txt'
+    #   +++ b'newfile.txt'
+    # because of how str.format() incorporates bytes objects.
+    if a and not isinstance(a[0], str):
+        raise TypeError('lines to compare must be str, not %s (%r)' %
+                        (type(a[0]).__name__, a[0]))
+    if b and not isinstance(b[0], str):
+        raise TypeError('lines to compare must be str, not %s (%r)' %
+                        (type(b[0]).__name__, b[0]))
+    for arg in args:
+        if not isinstance(arg, str):
+            raise TypeError('all arguments must be str, not: %r' % (arg,))
+
+def diff_bytes(dfunc, a, b, fromfile=b'', tofile=b'',
+               fromfiledate=b'', tofiledate=b'', n=3, lineterm=b'\n'):
+    r"""
+    Compare `a` and `b`, two sequences of lines represented as bytes rather
+    than str. This is a wrapper for `dfunc`, which is typically either
+    unified_diff() or context_diff(). Inputs are losslessly converted to
+    strings so that `dfunc` only has to worry about strings, and encoded
+    back to bytes on return. This is necessary to compare files with
+    unknown or inconsistent encoding. All other inputs (except `n`) must be
+    bytes rather than str.
+    """
+    def decode(s):
+        try:
+            return s.decode('ascii', 'surrogateescape')
+        except AttributeError as err:
+            msg = ('all arguments must be bytes, not %s (%r)' %
+                   (type(s).__name__, s))
+            raise TypeError(msg) from err
+    a = list(map(decode, a))
+    b = list(map(decode, b))
+    fromfile = decode(fromfile)
+    tofile = decode(tofile)
+    fromfiledate = decode(fromfiledate)
+    tofiledate = decode(tofiledate)
+    lineterm = decode(lineterm)
+
+    lines = dfunc(a, b, fromfile, tofile, fromfiledate, tofiledate, n, lineterm)
+    for line in lines:
+        yield line.encode('ascii', 'surrogateescape')
+
+def ndiff(a, b, linejunk=None, charjunk=IS_CHARACTER_JUNK):
+    r"""
+    Compare `a` and `b` (lists of strings); return a `Differ`-style delta.
+
+    Optional keyword parameters `linejunk` and `charjunk` are for filter
+    functions, or can be None:
+
+    - linejunk: A function that should accept a single string argument and
+      return true iff the string is junk.  The default is None, and is
+      recommended; the underlying SequenceMatcher class has an adaptive
+      notion of "noise" lines.
+
+    - charjunk: A function that accepts a character (string of length
+      1), and returns true iff the character is junk. The default is
+      the module-level function IS_CHARACTER_JUNK, which filters out
+      whitespace characters (a blank or tab; note: it's a bad idea to
+      include newline in this!).
+
+    Tools/scripts/ndiff.py is a command-line front-end to this function.
+
+    Example:
+
+    >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
+    ...              'ore\ntree\nemu\n'.splitlines(keepends=True))
+    >>> print(''.join(diff), end="")
+    - one
+    ?  ^
+    + ore
+    ?  ^
+    - two
+    - three
+    ?  -
+    + tree
+    + emu
+    """
+    return Differ(linejunk, charjunk).compare(a, b)
+
+def _mdiff(fromlines, tolines, context=None, linejunk=None,
+           charjunk=IS_CHARACTER_JUNK):
+    r"""Returns generator yielding marked up from/to side by side differences.
+
+    Arguments:
+    fromlines -- list of text lines to compared to tolines
+    tolines -- list of text lines to be compared to fromlines
+    context -- number of context lines to display on each side of difference,
+               if None, all from/to text lines will be generated.
+    linejunk -- passed on to ndiff (see ndiff documentation)
+    charjunk -- passed on to ndiff (see ndiff documentation)
+
+    This function returns an iterator which returns a tuple:
+    (from line tuple, to line tuple, boolean flag)
+
+    from/to line tuple -- (line num, line text)
+        line num -- integer or None (to indicate a context separation)
+        line text -- original line text with following markers inserted:
+            '\0+' -- marks start of added text
+            '\0-' -- marks start of deleted text
+            '\0^' -- marks start of changed text
+            '\1' -- marks end of added/deleted/changed text
+
+    boolean flag -- None indicates context separation, True indicates
+        either "from" or "to" line contains a change, otherwise False.
+
+    This function/iterator was originally developed to generate side by side
+    file difference for making HTML pages (see HtmlDiff class for example
+    usage).
+
+    Note, this function utilizes the ndiff function to generate the side by
+    side difference markup.  Optional ndiff arguments may be passed to this
+    function and they in turn will be passed to ndiff.
+    """
+    import re
+
+    # regular expression for finding intraline change indices
+    change_re = re.compile(r'(\++|\-+|\^+)')
+
+    # create the difference iterator to generate the differences
+    diff_lines_iterator = ndiff(fromlines,tolines,linejunk,charjunk)
+
+    def _make_line(lines, format_key, side, num_lines=[0,0]):
+        """Returns line of text with user's change markup and line formatting.
+
+        lines -- list of lines from the ndiff generator to produce a line of
+                 text from.  When producing the line of text to return, the
+                 lines used are removed from this list.
+        format_key -- '+' return first line in list with "add" markup around
+                          the entire line.
+                      '-' return first line in list with "delete" markup around
+                          the entire line.
+                      '?' return first line in list with add/delete/change
+                          intraline markup (indices obtained from second line)
+                      None return first line in list with no markup
+        side -- indice into the num_lines list (0=from,1=to)
+        num_lines -- from/to current line number.  This is NOT intended to be a
+                     passed parameter.  It is present as a keyword argument to
+                     maintain memory of the current line numbers between calls
+                     of this function.
+
+        Note, this function is purposefully not defined at the module scope so
+        that data it needs from its parent function (within whose context it
+        is defined) does not need to be of module scope.
+        """
+        num_lines[side] += 1
+        # Handle case where no user markup is to be added, just return line of
+        # text with user's line format to allow for usage of the line number.
+        if format_key is None:
+            return (num_lines[side],lines.pop(0)[2:])
+        # Handle case of intraline changes
+        if format_key == '?':
+            text, markers = lines.pop(0), lines.pop(0)
+            # find intraline changes (store change type and indices in tuples)
+            sub_info = []
+            def record_sub_info(match_object,sub_info=sub_info):
+                sub_info.append([match_object.group(1)[0],match_object.span()])
+                return match_object.group(1)
+            change_re.sub(record_sub_info,markers)
+            # process each tuple inserting our special marks that won't be
+            # noticed by an xml/html escaper.
+            for key,(begin,end) in reversed(sub_info):
+                text = text[0:begin]+'\0'+key+text[begin:end]+'\1'+text[end:]
+            text = text[2:]
+        # Handle case of add/delete entire line
+        else:
+            text = lines.pop(0)[2:]
+            # if line of text is just a newline, insert a space so there is
+            # something for the user to highlight and see.
+            if not text:
+                text = ' '
+            # insert marks that won't be noticed by an xml/html escaper.
+            text = '\0' + format_key + text + '\1'
+        # Return line of text, first allow user's line formatter to do its
+        # thing (such as adding the line number) then replace the special
+        # marks with what the user's change markup.
+        return (num_lines[side],text)
+
+    def _line_iterator():
+        """Yields from/to lines of text with a change indication.
+
+        This function is an iterator.  It itself pulls lines from a
+        differencing iterator, processes them and yields them.  When it can
+        it yields both a "from" and a "to" line, otherwise it will yield one
+        or the other.  In addition to yielding the lines of from/to text, a
+        boolean flag is yielded to indicate if the text line(s) have
+        differences in them.
+
+        Note, this function is purposefully not defined at the module scope so
+        that data it needs from its parent function (within whose context it
+        is defined) does not need to be of module scope.
+        """
+        lines = []
+        num_blanks_pending, num_blanks_to_yield = 0, 0
+        while True:
+            # Load up next 4 lines so we can look ahead, create strings which
+            # are a concatenation of the first character of each of the 4 lines
+            # so we can do some very readable comparisons.
+            while len(lines) < 4:
+                lines.append(next(diff_lines_iterator, 'X'))
+            s = ''.join([line[0] for line in lines])
+            if s.startswith('X'):
+                # When no more lines, pump out any remaining blank lines so the
+                # corresponding add/delete lines get a matching blank line so
+                # all line pairs get yielded at the next level.
+                num_blanks_to_yield = num_blanks_pending
+            elif s.startswith('-?+?'):
+                # simple intraline change
+                yield _make_line(lines,'?',0), _make_line(lines,'?',1), True
+                continue
+            elif s.startswith('--++'):
+                # in delete block, add block coming: we do NOT want to get
+                # caught up on blank lines yet, just process the delete line
+                num_blanks_pending -= 1
+                yield _make_line(lines,'-',0), None, True
+                continue
+            elif s.startswith(('--?+', '--+', '- ')):
+                # in delete block and see an intraline change or unchanged line
+                # coming: yield the delete line and then blanks
+                from_line,to_line = _make_line(lines,'-',0), None
+                num_blanks_to_yield,num_blanks_pending = num_blanks_pending-1,0
+            elif s.startswith('-+?'):
+                # intraline change
+                yield _make_line(lines,None,0), _make_line(lines,'?',1), True
+                continue
+            elif s.startswith('-?+'):
+                # intraline change
+                yield _make_line(lines,'?',0), _make_line(lines,None,1), True
+                continue
+            elif s.startswith('-'):
+                # delete FROM line
+                num_blanks_pending -= 1
+                yield _make_line(lines,'-',0), None, True
+                continue
+            elif s.startswith('+--'):
+                # in add block, delete block coming: we do NOT want to get
+                # caught up on blank lines yet, just process the add line
+                num_blanks_pending += 1
+                yield None, _make_line(lines,'+',1), True
+                continue
+            elif s.startswith(('+ ', '+-')):
+                # will be leaving an add block: yield blanks then add line
+                from_line, to_line = None, _make_line(lines,'+',1)
+                num_blanks_to_yield,num_blanks_pending = num_blanks_pending+1,0
+            elif s.startswith('+'):
+                # inside an add block, yield the add line
+                num_blanks_pending += 1
+                yield None, _make_line(lines,'+',1), True
+                continue
+            elif s.startswith(' '):
+                # unchanged text, yield it to both sides
+                yield _make_line(lines[:],None,0),_make_line(lines,None,1),False
+                continue
+            # Catch up on the blank lines so when we yield the next from/to
+            # pair, they are lined up.
+            while(num_blanks_to_yield < 0):
+                num_blanks_to_yield += 1
+                yield None,('','\n'),True
+            while(num_blanks_to_yield > 0):
+                num_blanks_to_yield -= 1
+                yield ('','\n'),None,True
+            if s.startswith('X'):
+                return
+            else:
+                yield from_line,to_line,True
+
+    def _line_pair_iterator():
+        """Yields from/to lines of text with a change indication.
+
+        This function is an iterator.  It itself pulls lines from the line
+        iterator.  Its difference from that iterator is that this function
+        always yields a pair of from/to text lines (with the change
+        indication).  If necessary it will collect single from/to lines
+        until it has a matching pair from/to pair to yield.
+
+        Note, this function is purposefully not defined at the module scope so
+        that data it needs from its parent function (within whose context it
+        is defined) does not need to be of module scope.
+        """
+        line_iterator = _line_iterator()
+        fromlines,tolines=[],[]
+        while True:
+            # Collecting lines of text until we have a from/to pair
+            while (len(fromlines)==0 or len(tolines)==0):
+                try:
+                    from_line, to_line, found_diff = next(line_iterator)
+                except StopIteration:
+                    return
+                if from_line is not None:
+                    fromlines.append((from_line,found_diff))
+                if to_line is not None:
+                    tolines.append((to_line,found_diff))
+            # Once we have a pair, remove them from the collection and yield it
+            from_line, fromDiff = fromlines.pop(0)
+            to_line, to_diff = tolines.pop(0)
+            yield (from_line,to_line,fromDiff or to_diff)
+
+    # Handle case where user does not want context differencing, just yield
+    # them up without doing anything else with them.
+    line_pair_iterator = _line_pair_iterator()
+    if context is None:
+        yield from line_pair_iterator
+    # Handle case where user wants context differencing.  We must do some
+    # storage of lines until we know for sure that they are to be yielded.
+    else:
+        context += 1
+        lines_to_write = 0
+        while True:
+            # Store lines up until we find a difference, note use of a
+            # circular queue because we only need to keep around what
+            # we need for context.
+            index, contextLines = 0, [None]*(context)
+            found_diff = False
+            while(found_diff is False):
+                try:
+                    from_line, to_line, found_diff = next(line_pair_iterator)
+                except StopIteration:
+                    return
+                i = index % context
+                contextLines[i] = (from_line, to_line, found_diff)
+                index += 1
+            # Yield lines that we have collected so far, but first yield
+            # the user's separator.
+            if index > context:
+                yield None, None, None
+                lines_to_write = context
+            else:
+                lines_to_write = index
+                index = 0
+            while(lines_to_write):
+                i = index % context
+                index += 1
+                yield contextLines[i]
+                lines_to_write -= 1
+            # Now yield the context lines after the change
+            lines_to_write = context-1
+            try:
+                while(lines_to_write):
+                    from_line, to_line, found_diff = next(line_pair_iterator)
+                    # If another change within the context, extend the context
+                    if found_diff:
+                        lines_to_write = context-1
+                    else:
+                        lines_to_write -= 1
+                    yield from_line, to_line, found_diff
+            except StopIteration:
+                # Catch exception from next() and return normally
+                return
+
+
+_file_template = """
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+          "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+
+<html>
+
+<head>
+    <meta http-equiv="Content-Type"
+          content="text/html; charset=%(charset)s" />
+    <title></title>
+    <style type="text/css">%(styles)s
+    </style>
+</head>
+
+<body>
+    %(table)s%(legend)s
+</body>
+
+</html>"""
+
+_styles = """
+        table.diff {font-family:Courier; border:medium;}
+        .diff_header {background-color:#e0e0e0}
+        td.diff_header {text-align:right}
+        .diff_next {background-color:#c0c0c0}
+        .diff_add {background-color:#aaffaa}
+        .diff_chg {background-color:#ffff77}
+        .diff_sub {background-color:#ffaaaa}"""
+
+_table_template = """
+    <table class="diff" id="difflib_chg_%(prefix)s_top"
+           cellspacing="0" cellpadding="0" rules="groups" >
+        <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup>
+        <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup>
+        %(header_row)s
+        <tbody>
+%(data_rows)s        </tbody>
+    </table>"""
+
+_legend = """
+    <table class="diff" summary="Legends">
+        <tr> <th colspan="2"> Legends </th> </tr>
+        <tr> <td> <table border="" summary="Colors">
+                      <tr><th> Colors </th> </tr>
+                      <tr><td class="diff_add">&nbsp;Added&nbsp;</td></tr>
+                      <tr><td class="diff_chg">Changed</td> </tr>
+                      <tr><td class="diff_sub">Deleted</td> </tr>
+                  </table></td>
+             <td> <table border="" summary="Links">
+                      <tr><th colspan="2"> Links </th> </tr>
+                      <tr><td>(f)irst change</td> </tr>
+                      <tr><td>(n)ext change</td> </tr>
+                      <tr><td>(t)op</td> </tr>
+                  </table></td> </tr>
+    </table>"""
+
+class HtmlDiff(object):
+    """For producing HTML side by side comparison with change highlights.
+
+    This class can be used to create an HTML table (or a complete HTML file
+    containing the table) showing a side by side, line by line comparison
+    of text with inter-line and intra-line change highlights.  The table can
+    be generated in either full or contextual difference mode.
+
+    The following methods are provided for HTML generation:
+
+    make_table -- generates HTML for a single side by side table
+    make_file -- generates complete HTML file with a single side by side table
+
+    See tools/scripts/diff.py for an example usage of this class.
+    """
+
+    _file_template = _file_template
+    _styles = _styles
+    _table_template = _table_template
+    _legend = _legend
+    _default_prefix = 0
+
+    def __init__(self,tabsize=8,wrapcolumn=None,linejunk=None,
+                 charjunk=IS_CHARACTER_JUNK):
+        """HtmlDiff instance initializer
+
+        Arguments:
+        tabsize -- tab stop spacing, defaults to 8.
+        wrapcolumn -- column number where lines are broken and wrapped,
+            defaults to None where lines are not wrapped.
+        linejunk,charjunk -- keyword arguments passed into ndiff() (used by
+            HtmlDiff() to generate the side by side HTML differences).  See
+            ndiff() documentation for argument default values and descriptions.
+        """
+        self._tabsize = tabsize
+        self._wrapcolumn = wrapcolumn
+        self._linejunk = linejunk
+        self._charjunk = charjunk
+
+    def make_file(self, fromlines, tolines, fromdesc='', todesc='',
+                  context=False, numlines=5, *, charset='utf-8'):
+        """Returns HTML file of side by side comparison with change highlights
+
+        Arguments:
+        fromlines -- list of "from" lines
+        tolines -- list of "to" lines
+        fromdesc -- "from" file column header string
+        todesc -- "to" file column header string
+        context -- set to True for contextual differences (defaults to False
+            which shows full differences).
+        numlines -- number of context lines.  When context is set True,
+            controls number of lines displayed before and after the change.
+            When context is False, controls the number of lines to place
+            the "next" link anchors before the next change (so click of
+            "next" link jumps to just before the change).
+        charset -- charset of the HTML document
+        """
+
+        return (self._file_template % dict(
+            styles=self._styles,
+            legend=self._legend,
+            table=self.make_table(fromlines, tolines, fromdesc, todesc,
+                                  context=context, numlines=numlines),
+            charset=charset
+        )).encode(charset, 'xmlcharrefreplace').decode(charset)
+
+    def _tab_newline_replace(self,fromlines,tolines):
+        """Returns from/to line lists with tabs expanded and newlines removed.
+
+        Instead of tab characters being replaced by the number of spaces
+        needed to fill in to the next tab stop, this function will fill
+        the space with tab characters.  This is done so that the difference
+        algorithms can identify changes in a file when tabs are replaced by
+        spaces and vice versa.  At the end of the HTML generation, the tab
+        characters will be replaced with a nonbreakable space.
+        """
+        def expand_tabs(line):
+            # hide real spaces
+            line = line.replace(' ','\0')
+            # expand tabs into spaces
+            line = line.expandtabs(self._tabsize)
+            # replace spaces from expanded tabs back into tab characters
+            # (we'll replace them with markup after we do differencing)
+            line = line.replace(' ','\t')
+            return line.replace('\0',' ').rstrip('\n')
+        fromlines = [expand_tabs(line) for line in fromlines]
+        tolines = [expand_tabs(line) for line in tolines]
+        return fromlines,tolines
+
+    def _split_line(self,data_list,line_num,text):
+        """Builds list of text lines by splitting text lines at wrap point
+
+        This function will determine if the input text line needs to be
+        wrapped (split) into separate lines.  If so, the first wrap point
+        will be determined and the first line appended to the output
+        text line list.  This function is used recursively to handle
+        the second part of the split line to further split it.
+        """
+        # if blank line or context separator, just add it to the output list
+        if not line_num:
+            data_list.append((line_num,text))
+            return
+
+        # if line text doesn't need wrapping, just add it to the output list
+        size = len(text)
+        max = self._wrapcolumn
+        if (size <= max) or ((size -(text.count('\0')*3)) <= max):
+            data_list.append((line_num,text))
+            return
+
+        # scan text looking for the wrap point, keeping track if the wrap
+        # point is inside markers
+        i = 0
+        n = 0
+        mark = ''
+        while n < max and i < size:
+            if text[i] == '\0':
+                i += 1
+                mark = text[i]
+                i += 1
+            elif text[i] == '\1':
+                i += 1
+                mark = ''
+            else:
+                i += 1
+                n += 1
+
+        # wrap point is inside text, break it up into separate lines
+        line1 = text[:i]
+        line2 = text[i:]
+
+        # if wrap point is inside markers, place end marker at end of first
+        # line and start marker at beginning of second line because each
+        # line will have its own table tag markup around it.
+        if mark:
+            line1 = line1 + '\1'
+            line2 = '\0' + mark + line2
+
+        # tack on first line onto the output list
+        data_list.append((line_num,line1))
+
+        # use this routine again to wrap the remaining text
+        self._split_line(data_list,'>',line2)
+
+    def _line_wrapper(self,diffs):
+        """Returns iterator that splits (wraps) mdiff text lines"""
+
+        # pull from/to data and flags from mdiff iterator
+        for fromdata,todata,flag in diffs:
+            # check for context separators and pass them through
+            if flag is None:
+                yield fromdata,todata,flag
+                continue
+            (fromline,fromtext),(toline,totext) = fromdata,todata
+            # for each from/to line split it at the wrap column to form
+            # list of text lines.
+            fromlist,tolist = [],[]
+            self._split_line(fromlist,fromline,fromtext)
+            self._split_line(tolist,toline,totext)
+            # yield from/to line in pairs inserting blank lines as
+            # necessary when one side has more wrapped lines
+            while fromlist or tolist:
+                if fromlist:
+                    fromdata = fromlist.pop(0)
+                else:
+                    fromdata = ('',' ')
+                if tolist:
+                    todata = tolist.pop(0)
+                else:
+                    todata = ('',' ')
+                yield fromdata,todata,flag
+
+    def _collect_lines(self,diffs):
+        """Collects mdiff output into separate lists
+
+        Before storing the mdiff from/to data into a list, it is converted
+        into a single line of text with HTML markup.
+        """
+
+        fromlist,tolist,flaglist = [],[],[]
+        # pull from/to data and flags from mdiff style iterator
+        for fromdata,todata,flag in diffs:
+            try:
+                # store HTML markup of the lines into the lists
+                fromlist.append(self._format_line(0,flag,*fromdata))
+                tolist.append(self._format_line(1,flag,*todata))
+            except TypeError:
+                # exceptions occur for lines where context separators go
+                fromlist.append(None)
+                tolist.append(None)
+            flaglist.append(flag)
+        return fromlist,tolist,flaglist
+
+    def _format_line(self,side,flag,linenum,text):
+        """Returns HTML markup of "from" / "to" text lines
+
+        side -- 0 or 1 indicating "from" or "to" text
+        flag -- indicates if difference on line
+        linenum -- line number (used for line number column)
+        text -- line text to be marked up
+        """
+        try:
+            linenum = '%d' % linenum
+            id = ' id="%s%s"' % (self._prefix[side],linenum)
+        except TypeError:
+            # handle blank lines where linenum is '>' or ''
+            id = ''
+        # replace those things that would get confused with HTML symbols
+        text=text.replace("&","&amp;").replace(">","&gt;").replace("<","&lt;")
+
+        # make space non-breakable so they don't get compressed or line wrapped
+        text = text.replace(' ','&nbsp;').rstrip()
+
+        return '<td class="diff_header"%s>%s</td><td nowrap="nowrap">%s</td>' \
+               % (id,linenum,text)
+
+    def _make_prefix(self):
+        """Create unique anchor prefixes"""
+
+        # Generate a unique anchor prefix so multiple tables
+        # can exist on the same HTML page without conflicts.
+        fromprefix = "from%d_" % HtmlDiff._default_prefix
+        toprefix = "to%d_" % HtmlDiff._default_prefix
+        HtmlDiff._default_prefix += 1
+        # store prefixes so line format method has access
+        self._prefix = [fromprefix,toprefix]
+
+    def _convert_flags(self,fromlist,tolist,flaglist,context,numlines):
+        """Makes list of "next" links"""
+
+        # all anchor names will be generated using the unique "to" prefix
+        toprefix = self._prefix[1]
+
+        # process change flags, generating middle column of next anchors/links
+        next_id = ['']*len(flaglist)
+        next_href = ['']*len(flaglist)
+        num_chg, in_change = 0, False
+        last = 0
+        for i,flag in enumerate(flaglist):
+            if flag:
+                if not in_change:
+                    in_change = True
+                    last = i
+                    # at the beginning of a change, drop an anchor a few lines
+                    # (the context lines) before the change for the previous
+                    # link
+                    i = max([0,i-numlines])
+                    next_id[i] = ' id="difflib_chg_%s_%d"' % (toprefix,num_chg)
+                    # at the beginning of a change, drop a link to the next
+                    # change
+                    num_chg += 1
+                    next_href[last] = '<a href="#difflib_chg_%s_%d">n</a>' % (
+                         toprefix,num_chg)
+            else:
+                in_change = False
+        # check for cases where there is no content to avoid exceptions
+        if not flaglist:
+            flaglist = [False]
+            next_id = ['']
+            next_href = ['']
+            last = 0
+            if context:
+                fromlist = ['<td></td><td>&nbsp;No Differences Found&nbsp;</td>']
+                tolist = fromlist
+            else:
+                fromlist = tolist = ['<td></td><td>&nbsp;Empty File&nbsp;</td>']
+        # if not a change on first line, drop a link
+        if not flaglist[0]:
+            next_href[0] = '<a href="#difflib_chg_%s_0">f</a>' % toprefix
+        # redo the last link to link to the top
+        next_href[last] = '<a href="#difflib_chg_%s_top">t</a>' % (toprefix)
+
+        return fromlist,tolist,flaglist,next_href,next_id
+
+    def make_table(self,fromlines,tolines,fromdesc='',todesc='',context=False,
+                   numlines=5):
+        """Returns HTML table of side by side comparison with change highlights
+
+        Arguments:
+        fromlines -- list of "from" lines
+        tolines -- list of "to" lines
+        fromdesc -- "from" file column header string
+        todesc -- "to" file column header string
+        context -- set to True for contextual differences (defaults to False
+            which shows full differences).
+        numlines -- number of context lines.  When context is set True,
+            controls number of lines displayed before and after the change.
+            When context is False, controls the number of lines to place
+            the "next" link anchors before the next change (so click of
+            "next" link jumps to just before the change).
+        """
+
+        # make unique anchor prefixes so that multiple tables may exist
+        # on the same page without conflict.
+        self._make_prefix()
+
+        # change tabs to spaces before it gets more difficult after we insert
+        # markup
+        fromlines,tolines = self._tab_newline_replace(fromlines,tolines)
+
+        # create diffs iterator which generates side by side from/to data
+        if context:
+            context_lines = numlines
+        else:
+            context_lines = None
+        diffs = _mdiff(fromlines,tolines,context_lines,linejunk=self._linejunk,
+                      charjunk=self._charjunk)
+
+        # set up iterator to wrap lines that exceed desired width
+        if self._wrapcolumn:
+            diffs = self._line_wrapper(diffs)
+
+        # collect up from/to lines and flags into lists (also format the lines)
+        fromlist,tolist,flaglist = self._collect_lines(diffs)
+
+        # process change flags, generating middle column of next anchors/links
+        fromlist,tolist,flaglist,next_href,next_id = self._convert_flags(
+            fromlist,tolist,flaglist,context,numlines)
+
+        s = []
+        fmt = '            <tr><td class="diff_next"%s>%s</td>%s' + \
+              '<td class="diff_next">%s</td>%s</tr>\n'
+        for i in range(len(flaglist)):
+            if flaglist[i] is None:
+                # mdiff yields None on separator lines skip the bogus ones
+                # generated for the first line
+                if i > 0:
+                    s.append('        </tbody>        \n        <tbody>\n')
+            else:
+                s.append( fmt % (next_id[i],next_href[i],fromlist[i],
+                                           next_href[i],tolist[i]))
+        if fromdesc or todesc:
+            header_row = '<thead><tr>%s%s%s%s</tr></thead>' % (
+                '<th class="diff_next"><br /></th>',
+                '<th colspan="2" class="diff_header">%s</th>' % fromdesc,
+                '<th class="diff_next"><br /></th>',
+                '<th colspan="2" class="diff_header">%s</th>' % todesc)
+        else:
+            header_row = ''
+
+        table = self._table_template % dict(
+            data_rows=''.join(s),
+            header_row=header_row,
+            prefix=self._prefix[1])
+
+        return table.replace('\0+','<span class="diff_add">'). \
+                     replace('\0-','<span class="diff_sub">'). \
+                     replace('\0^','<span class="diff_chg">'). \
+                     replace('\1','</span>'). \
+                     replace('\t','&nbsp;')
+
+del re
+
+def restore(delta, which):
+    r"""
+    Generate one of the two sequences that generated a delta.
+
+    Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract
+    lines originating from file 1 or 2 (parameter `which`), stripping off line
+    prefixes.
+
+    Examples:
+
+    >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
+    ...              'ore\ntree\nemu\n'.splitlines(keepends=True))
+    >>> diff = list(diff)
+    >>> print(''.join(restore(diff, 1)), end="")
+    one
+    two
+    three
+    >>> print(''.join(restore(diff, 2)), end="")
+    ore
+    tree
+    emu
+    """
+    try:
+        tag = {1: "- ", 2: "+ "}[int(which)]
+    except KeyError:
+        raise ValueError('unknown delta choice (must be 1 or 2): %r'
+                           % which) from None
+    prefixes = ("  ", tag)
+    for line in delta:
+        if line[:2] in prefixes:
+            yield line[2:]
+
+def _test():
+    import doctest, difflib
+    return doctest.testmod(difflib)
+
+if __name__ == "__main__":
+    _test()