Publish 11 evaluated Astra histories with reproducible checkpoint artifacts (part 33)

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shared/package-files/9b/9b0ff4cbf4f33264cfdb0515fe87c0c1c8a1d11ff29a1524401ac7063f64fe03/blob

@@ -0,0 +1,324 @@
+"""
+Public testing utilities.
+
+See also _lib._testing for additional private testing utilities.
+"""
+
+from __future__ import annotations
+
+import contextlib
+from collections.abc import Callable, Iterable, Iterator, Sequence
+from functools import wraps
+from types import ModuleType
+from typing import TYPE_CHECKING, Any, ParamSpec, TypeVar, cast
+
+from ._lib._utils._compat import is_dask_namespace, is_jax_namespace
+
+__all__ = ["lazy_xp_function", "patch_lazy_xp_functions"]
+
+if TYPE_CHECKING:  # pragma: no cover
+    # TODO import override from typing (requires Python >=3.12)
+    import pytest
+    from dask.typing import Graph, Key, SchedulerGetCallable
+    from typing_extensions import override
+
+else:
+    # Sphinx hacks
+    SchedulerGetCallable = object
+
+    def override(func: object) -> object:
+        return func
+
+
+P = ParamSpec("P")
+T = TypeVar("T")
+
+_ufuncs_tags: dict[object, dict[str, Any]] = {}  # type: ignore[explicit-any]
+
+
+def lazy_xp_function(  # type: ignore[explicit-any]
+    func: Callable[..., Any],
+    *,
+    allow_dask_compute: int = 0,
+    jax_jit: bool = True,
+    static_argnums: int | Sequence[int] | None = None,
+    static_argnames: str | Iterable[str] | None = None,
+) -> None:  # numpydoc ignore=GL07
+    """
+    Tag a function to be tested on lazy backends.
+
+    Tag a function so that when any tests are executed with ``xp=jax.numpy`` the
+    function is replaced with a jitted version of itself, and when it is executed with
+    ``xp=dask.array`` the function will raise if it attempts to materialize the graph.
+    This will be later expanded to provide test coverage for other lazy backends.
+
+    In order for the tag to be effective, the test or a fixture must call
+    :func:`patch_lazy_xp_functions`.
+
+    Parameters
+    ----------
+    func : callable
+        Function to be tested.
+    allow_dask_compute : int, optional
+        Number of times `func` is allowed to internally materialize the Dask graph. This
+        is typically triggered by ``bool()``, ``float()``, or ``np.asarray()``.
+
+        Set to 1 if you are aware that `func` converts the input parameters to NumPy and
+        want to let it do so at least for the time being, knowing that it is going to be
+        extremely detrimental for performance.
+
+        If a test needs values higher than 1 to pass, it is a canary that the conversion
+        to NumPy/bool/float is happening multiple times, which translates to multiple
+        computations of the whole graph. Short of making the function fully lazy, you
+        should at least add explicit calls to ``np.asarray()`` early in the function.
+        *Note:* the counter of `allow_dask_compute` resets after each call to `func`, so
+        a test function that invokes `func` multiple times should still work with this
+        parameter set to 1.
+
+        Default: 0, meaning that `func` must be fully lazy and never materialize the
+        graph.
+    jax_jit : bool, optional
+        Set to True to replace `func` with ``jax.jit(func)`` after calling the
+        :func:`patch_lazy_xp_functions` test helper with ``xp=jax.numpy``. Set to False
+        if `func` is only compatible with eager (non-jitted) JAX. Default: True.
+    static_argnums : int | Sequence[int], optional
+        Passed to jax.jit. Positional arguments to treat as static (compile-time
+        constant). Default: infer from `static_argnames` using
+        `inspect.signature(func)`.
+    static_argnames : str | Iterable[str], optional
+        Passed to jax.jit. Named arguments to treat as static (compile-time constant).
+        Default: infer from `static_argnums` using `inspect.signature(func)`.
+
+    See Also
+    --------
+    patch_lazy_xp_functions : Companion function to call from the test or fixture.
+    jax.jit : JAX function to compile a function for performance.
+
+    Examples
+    --------
+    In ``test_mymodule.py``::
+
+      from array_api_extra.testing import lazy_xp_function from mymodule import myfunc
+
+      lazy_xp_function(myfunc)
+
+      def test_myfunc(xp):
+          a = xp.asarray([1, 2])
+          # When xp=jax.numpy, this is the same as `b = jax.jit(myfunc)(a)`
+          # When xp=dask.array, crash on compute() or persist()
+          b = myfunc(a)
+
+    Notes
+    -----
+    In order for this tag to be effective, the test function must be imported into the
+    test module globals without its namespace; alternatively its namespace must be
+    declared in a ``lazy_xp_modules`` list in the test module globals.
+
+    Example 1::
+
+      from mymodule import myfunc
+
+      lazy_xp_function(myfunc)
+
+      def test_myfunc(xp):
+          x = myfunc(xp.asarray([1, 2]))
+
+    Example 2::
+
+      import mymodule
+
+      lazy_xp_modules = [mymodule]
+      lazy_xp_function(mymodule.myfunc)
+
+      def test_myfunc(xp):
+          x = mymodule.myfunc(xp.asarray([1, 2]))
+
+    A test function can circumvent this monkey-patching system by using a namespace
+    outside of the two above patterns. You need to sanitize your code to make sure this
+    only happens intentionally.
+
+    Example 1::
+
+      import mymodule
+      from mymodule import myfunc
+
+      lazy_xp_function(myfunc)
+
+      def test_myfunc(xp):
+          a = xp.asarray([1, 2])
+          b = myfunc(a)  # This is wrapped when xp=jax.numpy or xp=dask.array
+          c = mymodule.myfunc(a)  # This is not
+
+    Example 2::
+
+      import mymodule
+
+      class naked:
+          myfunc = mymodule.myfunc
+
+      lazy_xp_modules = [mymodule]
+      lazy_xp_function(mymodule.myfunc)
+
+      def test_myfunc(xp):
+          a = xp.asarray([1, 2])
+          b = mymodule.myfunc(a)  # This is wrapped when xp=jax.numpy or xp=dask.array
+          c = naked.myfunc(a)  # This is not
+    """
+    tags = {
+        "allow_dask_compute": allow_dask_compute,
+        "jax_jit": jax_jit,
+        "static_argnums": static_argnums,
+        "static_argnames": static_argnames,
+    }
+    try:
+        func._lazy_xp_function = tags  # type: ignore[attr-defined]  # pylint: disable=protected-access  # pyright: ignore[reportFunctionMemberAccess]
+    except AttributeError:  # @cython.vectorize
+        _ufuncs_tags[func] = tags
+
+
+def patch_lazy_xp_functions(
+    request: pytest.FixtureRequest, monkeypatch: pytest.MonkeyPatch, *, xp: ModuleType
+) -> None:
+    """
+    Test lazy execution of functions tagged with :func:`lazy_xp_function`.
+
+    If ``xp==jax.numpy``, search for all functions which have been tagged with
+    :func:`lazy_xp_function` in the globals of the module that defines the current test,
+    as well as in the ``lazy_xp_modules`` list in the globals of the same module,
+    and wrap them with :func:`jax.jit`. Unwrap them at the end of the test.
+
+    If ``xp==dask.array``, wrap the functions with a decorator that disables
+    ``compute()`` and ``persist()`` and ensures that exceptions and warnings are raised
+    eagerly.
+
+    This function should be typically called by your library's `xp` fixture that runs
+    tests on multiple backends::
+
+        @pytest.fixture(params=[numpy, array_api_strict, jax.numpy, dask.array])
+        def xp(request, monkeypatch):
+            patch_lazy_xp_functions(request, monkeypatch, xp=request.param)
+            return request.param
+
+    but it can be otherwise be called by the test itself too.
+
+    Parameters
+    ----------
+    request : pytest.FixtureRequest
+        Pytest fixture, as acquired by the test itself or by one of its fixtures.
+    monkeypatch : pytest.MonkeyPatch
+        Pytest fixture, as acquired by the test itself or by one of its fixtures.
+    xp : array_namespace
+        Array namespace to be tested.
+
+    See Also
+    --------
+    lazy_xp_function : Tag a function to be tested on lazy backends.
+    pytest.FixtureRequest : `request` test function parameter.
+    """
+    mod = cast(ModuleType, request.module)
+    mods = [mod, *cast(list[ModuleType], getattr(mod, "lazy_xp_modules", []))]
+
+    def iter_tagged() -> (  # type: ignore[explicit-any]
+        Iterator[tuple[ModuleType, str, Callable[..., Any], dict[str, Any]]]
+    ):
+        for mod in mods:
+            for name, func in mod.__dict__.items():
+                tags: dict[str, Any] | None = None  # type: ignore[explicit-any]
+                with contextlib.suppress(AttributeError):
+                    tags = func._lazy_xp_function  # pylint: disable=protected-access
+                if tags is None:
+                    with contextlib.suppress(KeyError, TypeError):
+                        tags = _ufuncs_tags[func]
+                if tags is not None:
+                    yield mod, name, func, tags
+
+    if is_dask_namespace(xp):
+        for mod, name, func, tags in iter_tagged():
+            n = tags["allow_dask_compute"]
+            wrapped = _dask_wrap(func, n)
+            monkeypatch.setattr(mod, name, wrapped)
+
+    elif is_jax_namespace(xp):
+        import jax
+
+        for mod, name, func, tags in iter_tagged():
+            if tags["jax_jit"]:
+                # suppress unused-ignore to run mypy in -e lint as well as -e dev
+                wrapped = cast(  # type: ignore[explicit-any]
+                    Callable[..., Any],
+                    jax.jit(
+                        func,
+                        static_argnums=tags["static_argnums"],
+                        static_argnames=tags["static_argnames"],
+                    ),
+                )
+                monkeypatch.setattr(mod, name, wrapped)
+
+
+class CountingDaskScheduler(SchedulerGetCallable):
+    """
+    Dask scheduler that counts how many times `dask.compute` is called.
+
+    If the number of times exceeds 'max_count', it raises an error.
+    This is a wrapper around Dask's own 'synchronous' scheduler.
+
+    Parameters
+    ----------
+    max_count : int
+        Maximum number of allowed calls to `dask.compute`.
+    msg : str
+        Assertion to raise when the count exceeds `max_count`.
+    """
+
+    count: int
+    max_count: int
+    msg: str
+
+    def __init__(self, max_count: int, msg: str):  # numpydoc ignore=GL08
+        self.count = 0
+        self.max_count = max_count
+        self.msg = msg
+
+    @override
+    def __call__(self, dsk: Graph, keys: Sequence[Key] | Key, **kwargs: Any) -> Any:  # type: ignore[decorated-any,explicit-any] # numpydoc ignore=GL08
+        import dask
+
+        self.count += 1
+        # This should yield a nice traceback to the
+        # offending line in the user's code
+        assert self.count <= self.max_count, self.msg
+
+        return dask.get(dsk, keys, **kwargs)  # type: ignore[attr-defined,no-untyped-call] # pyright: ignore[reportPrivateImportUsage]
+
+
+def _dask_wrap(
+    func: Callable[P, T], n: int
+) -> Callable[P, T]:  # numpydoc ignore=PR01,RT01
+    """
+    Wrap `func` to raise if it attempts to call `dask.compute` more than `n` times.
+
+    After the function returns, materialize the graph in order to re-raise exceptions.
+    """
+    import dask
+
+    func_name = getattr(func, "__name__", str(func))
+    n_str = f"only up to {n}" if n else "no"
+    msg = (
+        f"Called `dask.compute()` or `dask.persist()` {n + 1} times, "
+        f"but {n_str} calls are allowed. Set "
+        f"`lazy_xp_function({func_name}, allow_dask_compute={n + 1})` "
+        "to allow for more (but note that this will harm performance). "
+    )
+
+    @wraps(func)
+    def wrapper(*args: P.args, **kwargs: P.kwargs) -> T:  # numpydoc ignore=GL08
+        scheduler = CountingDaskScheduler(n, msg)
+        with dask.config.set({"scheduler": scheduler}):  # pyright: ignore[reportPrivateImportUsage]
+            out = func(*args, **kwargs)
+
+        # Block until the graph materializes and reraise exceptions. This allows
+        # `pytest.raises` and `pytest.warns` to work as expected. Note that this would
+        # not work on scheduler='distributed', as it would not block.
+        return dask.persist(out, scheduler="threads")[0]  # type: ignore[attr-defined,no-untyped-call,func-returns-value,index]  # pyright: ignore[reportPrivateImportUsage]
+
+    return wrapper

shared/package-files/9b/9b1066af79ac4795bb4446ba33581ace72de0ee3bd979905840badd5598bda7a/blob

@@ -0,0 +1,28 @@
+#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 _sparse_mm_reduce_impl {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, const at::Tensor &, c10::string_view);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_sparse_mm_reduce_impl")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor)")
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, const at::Tensor & other, c10::string_view reduce);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::string_view reduce);
+};
+
+}} // namespace at::_ops

shared/package-files/9b/9b110614ea0eefae7c0d74748c47bf4ed50dd144a134754cef1a24705b9fb377/blob

@@ -0,0 +1,11 @@
+#pragma once
+#include <ATen/detail/CUDAHooksInterface.h>
+namespace at::cuda {
+// Forward-declares at::cuda::NVRTC
+struct NVRTC;
+
+namespace detail {
+extern NVRTC lazyNVRTC;
+} // namespace detail
+
+}  // namespace at::cuda

shared/package-files/9b/9b149709fc0432cdc59b496b4e33cc35dafc99321bf7afd9092fe0400027a576/blob

@@ -0,0 +1,39 @@
+"""
+Shared result type for the AR/ARMA parameter estimation functions in
+statsmodels.tsa.arima.estimators.
+
+Author: Chad Fulton
+License: BSD-3
+"""
+from typing import NamedTuple
+
+
+class ARMAEstimationResult(NamedTuple):
+    """
+    Result of an ARIMA parameter estimator.
+
+    Common to :func:`~statsmodels.tsa.arima.estimators.burg.burg`,
+    :func:`~statsmodels.tsa.arima.estimators.gls.gls`,
+    :func:`~statsmodels.tsa.arima.estimators.hannan_rissanen.hannan_rissanen`,
+    :func:`~statsmodels.tsa.arima.estimators.innovations.innovations`,
+    :func:`~statsmodels.tsa.arima.estimators.innovations.innovations_mle`,
+    :func:`~statsmodels.tsa.arima.estimators.yule_walker.yule_walker`,
+    :func:`~statsmodels.tsa.arima.estimators.durbin_levinson.durbin_levinson`,
+    and :func:`~statsmodels.tsa.arima.estimators.statespace.statespace`.
+
+    Parameters
+    ----------
+    parameters : SARIMAXParams or list of SARIMAXParams
+        Contains the parameter estimates from the final iteration. A list
+        of ``SARIMAXParams`` objects for :func:`innovations` and
+        :func:`durbin_levinson`, which estimate at multiple orders; a
+        single ``SARIMAXParams`` object for all other estimators.
+    other_results : Bunch
+        Estimator-specific ancillary results, always including at least
+        ``spec``, the ``SARIMAXSpecification`` instance corresponding to
+        the input arguments. See the individual estimator's docstring for
+        the full set of components.
+    """
+
+    parameters: object
+    other_results: object

shared/package-files/9b/9b14c96a807865ee74964fb64e0568a66aeb6ee41b12a52c6707372b51fcc3e4/blob

@@ -0,0 +1,118 @@
+from sympy.core.symbol import symbols, Dummy
+from sympy.matrices.expressions.applyfunc import ElementwiseApplyFunction
+from sympy.core.function import Lambda
+from sympy.functions.elementary.exponential import exp
+from sympy.functions.elementary.trigonometric import sin
+from sympy.matrices.dense import Matrix
+from sympy.matrices.expressions.matexpr import MatrixSymbol
+from sympy.matrices.expressions.matmul import MatMul
+from sympy.simplify.simplify import simplify
+
+
+X = MatrixSymbol("X", 3, 3)
+Y = MatrixSymbol("Y", 3, 3)
+
+k = symbols("k")
+Xk = MatrixSymbol("X", k, k)
+
+Xd = X.as_explicit()
+
+x, y, z, t = symbols("x y z t")
+
+
+def test_applyfunc_matrix():
+    x = Dummy('x')
+    double = Lambda(x, x**2)
+
+    expr = ElementwiseApplyFunction(double, Xd)
+    assert isinstance(expr, ElementwiseApplyFunction)
+    assert expr.doit() == Xd.applyfunc(lambda x: x**2)
+    assert expr.shape == (3, 3)
+    assert expr.func(*expr.args) == expr
+    assert simplify(expr) == expr
+    assert expr[0, 0] == double(Xd[0, 0])
+
+    expr = ElementwiseApplyFunction(double, X)
+    assert isinstance(expr, ElementwiseApplyFunction)
+    assert isinstance(expr.doit(), ElementwiseApplyFunction)
+    assert expr == X.applyfunc(double)
+    assert expr.func(*expr.args) == expr
+
+    expr = ElementwiseApplyFunction(exp, X*Y)
+    assert expr.expr == X*Y
+    assert expr.function.dummy_eq(Lambda(x, exp(x)))
+    assert expr.dummy_eq((X*Y).applyfunc(exp))
+    assert expr.func(*expr.args) == expr
+
+    assert isinstance(X*expr, MatMul)
+    assert (X*expr).shape == (3, 3)
+    Z = MatrixSymbol("Z", 2, 3)
+    assert (Z*expr).shape == (2, 3)
+
+    expr = ElementwiseApplyFunction(exp, Z.T)*ElementwiseApplyFunction(exp, Z)
+    assert expr.shape == (3, 3)
+    expr = ElementwiseApplyFunction(exp, Z)*ElementwiseApplyFunction(exp, Z.T)
+    assert expr.shape == (2, 2)
+
+    M = Matrix([[x, y], [z, t]])
+    expr = ElementwiseApplyFunction(sin, M)
+    assert isinstance(expr, ElementwiseApplyFunction)
+    assert expr.function.dummy_eq(Lambda(x, sin(x)))
+    assert expr.expr == M
+    assert expr.doit() == M.applyfunc(sin)
+    assert expr.doit() == Matrix([[sin(x), sin(y)], [sin(z), sin(t)]])
+    assert expr.func(*expr.args) == expr
+
+    expr = ElementwiseApplyFunction(double, Xk)
+    assert expr.doit() == expr
+    assert expr.subs(k, 2).shape == (2, 2)
+    assert (expr*expr).shape == (k, k)
+    M = MatrixSymbol("M", k, t)
+    expr2 = M.T*expr*M
+    assert isinstance(expr2, MatMul)
+    assert expr2.args[1] == expr
+    assert expr2.shape == (t, t)
+    expr3 = expr*M
+    assert expr3.shape == (k, t)
+
+    expr1 = ElementwiseApplyFunction(lambda x: x+1, Xk)
+    expr2 = ElementwiseApplyFunction(lambda x: x, Xk)
+    assert expr1 != expr2
+
+
+def test_applyfunc_entry():
+
+    af = X.applyfunc(sin)
+    assert af[0, 0] == sin(X[0, 0])
+
+    af = Xd.applyfunc(sin)
+    assert af[0, 0] == sin(X[0, 0])
+
+
+def test_applyfunc_as_explicit():
+
+    af = X.applyfunc(sin)
+    assert af.as_explicit() == Matrix([
+        [sin(X[0, 0]), sin(X[0, 1]), sin(X[0, 2])],
+        [sin(X[1, 0]), sin(X[1, 1]), sin(X[1, 2])],
+        [sin(X[2, 0]), sin(X[2, 1]), sin(X[2, 2])],
+    ])
+
+
+def test_applyfunc_transpose():
+
+    af = Xk.applyfunc(sin)
+    assert af.T.dummy_eq(Xk.T.applyfunc(sin))
+
+
+def test_applyfunc_shape_11_matrices():
+    M = MatrixSymbol("M", 1, 1)
+
+    double = Lambda(x, x*2)
+
+    expr = M.applyfunc(sin)
+    assert isinstance(expr, ElementwiseApplyFunction)
+
+    expr = M.applyfunc(double)
+    assert isinstance(expr, MatMul)
+    assert expr == 2*M

shared/package-files/9b/9b152deb0e3d095c45b1ce7937e25dc5c1d9e3aaf9bee11eb5b5444f590ab862/model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b152deb0e3d095c45b1ce7937e25dc5c1d9e3aaf9bee11eb5b5444f590ab862
+size 2235917054

shared/package-files/9b/9b156a37c540d4ea39994bad9664abd0a4bd57acfa72ffec72409c36a4532f2c/blob

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b156a37c540d4ea39994bad9664abd0a4bd57acfa72ffec72409c36a4532f2c
+size 417616

shared/package-files/9b/9b15c668d6a6afe9d1ce1a3f5571f71da43266871a1cd698fb4913043d6d8c24/blob

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+#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 cpu {
+
+TORCH_API at::Tensor & logical_or_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & logical_or_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at

shared/package-files/9b/9b16504660713d61693f7c7ebfe677239a640f93fc572807f3739fba203a2cdd/blob

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shared/package-files/9b/9b1676a9f2beda4d06310328bd464e716ce1fd334045af3425b820b172c51c83/blob

@@ -0,0 +1,163 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+import pandas._config.config as cf
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestIndexRendering:
+    def test_repr_is_valid_construction_code(self):
+        # for the case of Index, where the repr is traditional rather than
+        # stylized
+        idx = Index(["a", "b"])
+        res = eval(repr(idx))
+        tm.assert_index_equal(res, idx)
+
+    @pytest.mark.xfail(using_string_dtype(), reason="repr different")
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            # ASCII
+            # short
+            (
+                Index(["a", "bb", "ccc"]),
+                """Index(['a', 'bb', 'ccc'], dtype='object')""",
+            ),
+            # multiple lines
+            (
+                Index(["a", "bb", "ccc"] * 10),
+                "Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', "
+                "'bb', 'ccc', 'a', 'bb', 'ccc',\n"
+                "       'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', "
+                "'bb', 'ccc', 'a', 'bb', 'ccc',\n"
+                "       'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],\n"
+                "      dtype='object')",
+            ),
+            # truncated
+            (
+                Index(["a", "bb", "ccc"] * 100),
+                "Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',\n"
+                "       ...\n"
+                "       'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],\n"
+                "      dtype='object', length=300)",
+            ),
+            # Non-ASCII
+            # short
+            (
+                Index(["あ", "いい", "ううう"]),
+                """Index(['あ', 'いい', 'ううう'], dtype='object')""",
+            ),
+            # multiple lines
+            (
+                Index(["あ", "いい", "ううう"] * 10),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう'],\n"
+                    "      dtype='object')"
+                ),
+            ),
+            # truncated
+            (
+                Index(["あ", "いい", "ううう"] * 100),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
+                    "'あ', 'いい', 'ううう', 'あ',\n"
+                    "       ...\n"
+                    "       'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう'],\n"
+                    "      dtype='object', length=300)"
+                ),
+            ),
+        ],
+    )
+    def test_string_index_repr(self, index, expected):
+        result = repr(index)
+        assert result == expected
+
+    @pytest.mark.xfail(using_string_dtype(), reason="repr different")
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            # short
+            (
+                Index(["あ", "いい", "ううう"]),
+                ("Index(['あ', 'いい', 'ううう'], dtype='object')"),
+            ),
+            # multiple lines
+            (
+                Index(["あ", "いい", "ううう"] * 10),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ', 'いい', 'ううう'],\n"
+                    "      dtype='object')"
+                    ""
+                ),
+            ),
+            # truncated
+            (
+                Index(["あ", "いい", "ううう"] * 100),
+                (
+                    "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
+                    "'ううう', 'あ', 'いい', 'ううう',\n"
+                    "       'あ',\n"
+                    "       ...\n"
+                    "       'ううう', 'あ', 'いい', 'ううう', 'あ', "
+                    "'いい', 'ううう', 'あ', 'いい',\n"
+                    "       'ううう'],\n"
+                    "      dtype='object', length=300)"
+                ),
+            ),
+        ],
+    )
+    def test_string_index_repr_with_unicode_option(self, index, expected):
+        # Enable Unicode option -----------------------------------------
+        with cf.option_context("display.unicode.east_asian_width", True):
+            result = repr(index)
+            assert result == expected
+
+    def test_repr_summary(self):
+        with cf.option_context("display.max_seq_items", 10):
+            result = repr(Index(np.arange(1000)))
+            assert len(result) < 200
+            assert "..." in result
+
+    def test_summary_bug(self):
+        # GH#3869
+        ind = Index(["{other}%s", "~:{range}:0"], name="A")
+        result = ind._summary()
+        # shouldn't be formatted accidentally.
+        assert "~:{range}:0" in result
+        assert "{other}%s" in result
+
+    def test_index_repr_bool_nan(self):
+        # GH32146
+        arr = Index([True, False, np.nan], dtype=object)
+        msg = "Index.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            exp1 = arr.format()
+        out1 = ["True", "False", "NaN"]
+        assert out1 == exp1
+
+        exp2 = repr(arr)
+        out2 = "Index([True, False, nan], dtype='object')"
+        assert out2 == exp2
+
+    def test_format_different_scalar_lengths(self):
+        # GH#35439
+        idx = Index(["aaaaaaaaa", "b"])
+        expected = ["aaaaaaaaa", "b"]
+        msg = r"Index\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert idx.format() == expected

shared/package-files/9b/9b16c7223d8223a77cbb4db2ee2fcc0dc2008526d6d3256a2e2fb3f2a95820fe/blob

@@ -0,0 +1,3 @@
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+oid sha256:9b16c7223d8223a77cbb4db2ee2fcc0dc2008526d6d3256a2e2fb3f2a95820fe
+size 341496

shared/package-files/9b/9b17a60c7c785a7977fa093caca7705eb5730377a032a677f0500e979e459679/blob

@@ -0,0 +1,123 @@
+# ### Convenience Functions to be moved to kerneltools ####
+import numpy as np
+
+
+def forrt(X, m=None):
+    """
+    RFFT with order like Munro (1976) FORTT routine
+
+    Parameters
+    ----------
+    X : ndarray
+        The array to transform.
+    m : int, optional
+        The length used for the FFT. If None, ``len(X)`` is used.
+
+    Returns
+    -------
+    ndarray
+        The real and imaginary parts of the RFFT, concatenated in the
+        Munro (1976) FORTT order.
+    """
+    if m is None:
+        m = len(X)
+    y = np.fft.rfft(X, m) / m
+    return np.r_[y.real, y[1:-1].imag]
+
+
+def revrt(X, m=None):
+    """
+    Inverse of forrt, equivalent to Munro (1976) REVRT routine
+
+    Parameters
+    ----------
+    X : ndarray
+        The array, in the Munro (1976) FORTT order, to invert.
+    m : int, optional
+        The length used for the inverse FFT. If None, ``len(X)`` is used.
+
+    Returns
+    -------
+    ndarray
+        The inverse real FFT of `X`.
+    """
+    if m is None:
+        m = len(X)
+    i = int(m // 2 + 1)
+    y = X[:i] + np.r_[0, X[i:], 0] * 1j
+    return np.fft.irfft(y) * m
+
+
+def silverman_transform(bw, M, RANGE):
+    """
+    FFT of Gaussian kernel following to Silverman AS 176
+
+    Parameters
+    ----------
+    bw : float
+        The bandwidth used in the Gaussian kernel.
+    M : int
+        The number of grid points.
+    RANGE : float
+        The range of the grid over which the kernel is evaluated.
+
+    Returns
+    -------
+    ndarray
+        The FFT of the Gaussian kernel evaluated at the grid points.
+
+    Notes
+    -----
+    Underflow is intentional as a dampener.
+    """
+    J = np.arange(M / 2 + 1)
+    FAC1 = 2 * (np.pi * bw / RANGE) ** 2
+    JFAC = J**2 * FAC1
+    BC = 1 - 1.0 / 3 * (J * 1.0 / M * np.pi) ** 2
+    FAC = np.exp(-JFAC) / BC
+    kern_est = np.r_[FAC, FAC[1:-1]]
+    return kern_est
+
+
+def counts(x, v):
+    """
+    Counts the number of elements of x that fall within the grid points v
+
+    Parameters
+    ----------
+    x : array_like
+        The data whose elements are counted.
+    v : array_like
+        The grid points (bin edges) used to bin `x`.
+
+    Returns
+    -------
+    ndarray
+        The number of elements of `x` falling in each bin defined by `v`.
+
+    Notes
+    -----
+    Using np.digitize and np.bincount
+    """
+    idx = np.digitize(x, v)
+    return np.bincount(idx, minlength=len(v))
+
+
+def kdesum(x, axis=0):
+    """
+    Computes the sum of pairwise differences of x along the given axis
+
+    Parameters
+    ----------
+    x : array_like
+        The data for which pairwise differences are summed.
+    axis : int, optional
+        The axis along which the differences ``x[i] - x`` are summed.
+
+    Returns
+    -------
+    ndarray
+        The array of summed pairwise differences, one entry per element
+        of `x`.
+    """
+    return np.asarray([np.sum(x[i] - x, axis) for i in range(len(x))])

shared/package-files/9b/9b18078f5451cc903075e2ffd8a901d0db1e90d593b42adae53ee8664882709f/blob

@@ -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/addr_ops.h>
+
+namespace at {
+
+
+// aten::addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
+inline at::Tensor addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
+    return at::_ops::addr::call(self, vec1, vec2, beta, alpha);
+}
+
+// aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & addr_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
+    return at::_ops::addr_out::call(self, vec1, vec2, beta, alpha, out);
+}
+// aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & addr_outf(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
+    return at::_ops::addr_out::call(self, vec1, vec2, beta, alpha, out);
+}
+
+}

shared/package-files/9b/9b185eeba218c0a82e52dc46741cc08f676740e184182013738b2589c1fe99cb/blob

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b185eeba218c0a82e52dc46741cc08f676740e184182013738b2589c1fe99cb
+size 105276

shared/package-files/9b/9b191f143dab35f83c9e7d69a7fe35dd2d2e09d617c9d3d605ccfd8a5806900d/blob

@@ -0,0 +1,30 @@
+import numpy.distutils.fcompiler
+from numpy.testing import assert_
+
+
+intel_32bit_version_strings = [
+    ("Intel(R) Fortran Intel(R) 32-bit Compiler Professional for applications"
+     "running on Intel(R) 32, Version 11.1", '11.1'),
+]
+
+intel_64bit_version_strings = [
+    ("Intel(R) Fortran IA-64 Compiler Professional for applications"
+     "running on IA-64, Version 11.0", '11.0'),
+    ("Intel(R) Fortran Intel(R) 64 Compiler Professional for applications"
+     "running on Intel(R) 64, Version 11.1", '11.1')
+]
+
+class TestIntelFCompilerVersions:
+    def test_32bit_version(self):
+        fc = numpy.distutils.fcompiler.new_fcompiler(compiler='intel')
+        for vs, version in intel_32bit_version_strings:
+            v = fc.version_match(vs)
+            assert_(v == version)
+
+
+class TestIntelEM64TFCompilerVersions:
+    def test_64bit_version(self):
+        fc = numpy.distutils.fcompiler.new_fcompiler(compiler='intelem')
+        for vs, version in intel_64bit_version_strings:
+            v = fc.version_match(vs)
+            assert_(v == version)

shared/package-files/9b/9b19609c87d0a3fecb06f22824bc0f37d67a64ad4847019250d0ff2509d160d0/blob

@@ -0,0 +1,25 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.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 <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void _foreach_sigmoid_out(at::TensorList self, at::TensorList out);
+TORCH_API ::std::vector<at::Tensor> foreach_tensor_sigmoid_slow(at::TensorList self);
+TORCH_API void foreach_tensor_sigmoid_slow_(at::TensorList self);
+TORCH_API ::std::vector<at::Tensor> foreach_tensor_sigmoid_cuda(at::TensorList self);
+TORCH_API void foreach_tensor_sigmoid_cuda_(at::TensorList self);
+} // namespace native
+} // namespace at

shared/package-files/9b/9b19d299da335885dc0cb64dcc28a6f809d3c9e82d9c09c43b9058eb4dc332c1/ring_fragments.py

@@ -0,0 +1,42 @@
+"""Bounded two-ring-bond extension to the unchanged single-cut chemistry."""
+from itertools import combinations
+import numpy as np
+from rdkit import Chem
+from graph_fragments import fragment_formulae as single_cut,INDEX,ELEMENTS,LOSSES
+MAX_RING_BONDS=32
+
+def fragment_formulae(smiles):
+    base=single_cut(smiles)
+    if base is None:return None
+    mol=Chem.MolFromSmiles(str(smiles));atoms=list(mol.GetAtoms())
+    bonds=[(b.GetBeginAtomIdx(),b.GetEndAtomIdx()) for b in mol.GetBonds() if b.IsInRing()]
+    result=dict(base);result['ring_bonds']=len(bonds);result['ring_cut_pairs']=0
+    result['ring_budget_skipped']=len(bonds)>MAX_RING_BONDS
+    if len(bonds)>MAX_RING_BONDS:return result
+    vectors=np.zeros((len(atoms),len(ELEMENTS)),np.int64)
+    for i,a in enumerate(atoms):
+        vectors[i,INDEX[a.GetAtomicNum()]]=1;vectors[i,INDEX[1]]=a.GetTotalNumHs()
+    whole=vectors.sum(0);adjacency=[set(n.GetIdx() for n in a.GetNeighbors()) for a in atoms]
+    direct=set(base['direct']);count=0
+    for (a,b),(c,d) in combinations(bonds,2):
+        removed={(a,b),(b,a),(c,d),(d,c)};component={a};todo=[a]
+        while todo:
+            j=todo.pop()
+            for k in adjacency[j]:
+                if (j,k) in removed or k in component:continue
+                component.add(k);todo.append(k)
+        if len(component)==len(atoms):continue
+        left=vectors[sorted(component)].sum(0)
+        for side in (left,whole-left):
+            for shift in (-1,0,1):
+                value=side.copy();value[INDEX[1]]+=shift
+                if value[INDEX[1]]>=0:direct.add(tuple(value.tolist()))
+        count+=1
+    extended=set(direct)
+    for formula in direct:
+        for loss in LOSSES:
+            value=tuple(a-b for a,b in zip(formula,loss))
+            if min(value)>=0 and sum(value)>0:extended.add(value)
+    result.update(direct=sorted(direct),losses=sorted(extended),ring_cut_pairs=count)
+    assert set(base['direct']).issubset(direct)
+    return result

shared/package-files/9b/9b218384eec238b9a6a2e721b23454e7b950a579c96a1cea69f7d79b26b8afa7/blob

@@ -0,0 +1,12 @@
+from typing import Any, Optional
+
+from django.db.models.base import Model
+
+NOT_PROVIDED: Any
+
+class FieldCacheMixin:
+    def get_cache_name(self) -> str: ...
+    def get_cached_value(self, instance: Model, default: Any = ...) -> Optional[Model]: ...
+    def is_cached(self, instance: Model) -> bool: ...
+    def set_cached_value(self, instance: Model, value: Optional[Model]) -> None: ...
+    def delete_cached_value(self, instance: Model) -> None: ...

shared/package-files/9b/9b23374ef43c10d49db0b72110877569dbd906b56d2c6747ef951c3c3152e868/blob

@@ -0,0 +1,522 @@
+#pragma once
+/*
+   AVX implementation of sin, cos, sincos, exp and log
+
+   Based on "sse_mathfun.h", by Julien Pommier
+   http://gruntthepeon.free.fr/ssemath/
+
+   Copyright (C) 2012 Giovanni Garberoglio
+   Interdisciplinary Laboratory for Computational Science (LISC)
+   Fondazione Bruno Kessler and University of Trento
+   via Sommarive, 18
+   I-38123 Trento (Italy)
+
+  This software is provided 'as-is', without any express or implied
+  warranty.  In no event will the authors be held liable for any damages
+  arising from the use of this software.
+
+  Permission is granted to anyone to use this software for any purpose,
+  including commercial applications, and to alter it and redistribute it
+  freely, subject to the following restrictions:
+
+  1. The origin of this software must not be misrepresented; you must not
+     claim that you wrote the original software. If you use this software
+     in a product, an acknowledgment in the product documentation would be
+     appreciated but is not required.
+  2. Altered source versions must be plainly marked as such, and must not be
+     misrepresented as being the original software.
+  3. This notice may not be removed or altered from any source distribution.
+
+  (this is the zlib license)
+*/
+
+#include <ATen/native/cpu/Intrinsics.h>
+
+/* The original source of this file has been modified. */
+#if defined(CPU_CAPABILITY_AVX2)
+
+#if defined(__GNUC__)
+# define ALIGN32_BEG __attribute__((aligned(32)))
+#elif defined(_WIN32)
+# define ALIGN32_BEG __declspec(align(32))
+#endif
+
+typedef __m256  v8sf; // vector of 8 float (avx2)
+typedef __m256i v8si; // vector of 8 int   (avx2)
+
+/* declare some AVX constants -- why can't I figure a better way to do that? */
+#define _PS256_CONST(Name, Val)                                            \
+  static const ALIGN32_BEG float _ps256_##Name[8] = { Val, Val, Val, Val, Val, Val, Val, Val }
+#define _PI32_CONST256(Name, Val)                                            \
+  static const ALIGN32_BEG int _pi32_256_##Name[8] = { Val, Val, Val, Val, Val, Val, Val, Val }
+#define _PS256_CONST_TYPE(Name, Type, Val)                                 \
+  static const ALIGN32_BEG Type _ps256_##Name[8] = { Val, Val, Val, Val, Val, Val, Val, Val }
+
+_PS256_CONST(1  , 1.0f);
+_PS256_CONST(0p5, 0.5f);
+/* the smallest non denormalized float number */
+_PS256_CONST_TYPE(min_norm_pos, int, 0x00800000);
+_PS256_CONST_TYPE(mant_mask, int, 0x7f800000);
+_PS256_CONST_TYPE(inv_mant_mask, int, ~0x7f800000);
+
+_PS256_CONST_TYPE(sign_mask, int, (int)0x80000000);
+_PS256_CONST_TYPE(inv_sign_mask, int, ~0x80000000);
+
+_PI32_CONST256(0, 0);
+_PI32_CONST256(1, 1);
+_PI32_CONST256(inv1, ~1);
+_PI32_CONST256(2, 2);
+_PI32_CONST256(4, 4);
+_PI32_CONST256(0x7f, 0x7f);
+
+_PS256_CONST(cephes_SQRTHF, 0.707106781186547524);
+_PS256_CONST(cephes_log_p0, 7.0376836292E-2);
+_PS256_CONST(cephes_log_p1, - 1.1514610310E-1);
+_PS256_CONST(cephes_log_p2, 1.1676998740E-1);
+_PS256_CONST(cephes_log_p3, - 1.2420140846E-1);
+_PS256_CONST(cephes_log_p4, + 1.4249322787E-1);
+_PS256_CONST(cephes_log_p5, - 1.6668057665E-1);
+_PS256_CONST(cephes_log_p6, + 2.0000714765E-1);
+_PS256_CONST(cephes_log_p7, - 2.4999993993E-1);
+_PS256_CONST(cephes_log_p8, + 3.3333331174E-1);
+_PS256_CONST(cephes_log_q1, -2.12194440e-4);
+_PS256_CONST(cephes_log_q2, 0.693359375);
+
+
+/* natural logarithm computed for 8 simultaneous float
+   return NaN for x <= 0
+*/
+inline v8sf log256_ps(v8sf x) {
+  v8si imm0;
+  v8sf one = *(v8sf*)_ps256_1;
+
+  //v8sf invalid_mask = _mm256_cmple_ps(x, _mm256_setzero_ps());
+  v8sf invalid_mask = _mm256_cmp_ps(x, _mm256_setzero_ps(), _CMP_LE_OS);
+
+  x = _mm256_max_ps(x, *(v8sf*)_ps256_min_norm_pos);  /* cut off denormalized stuff */
+
+  // can be done with AVX2
+  imm0 = _mm256_srli_epi32(_mm256_castps_si256(x), 23);
+
+  /* keep only the fractional part */
+  x = _mm256_and_ps(x, *(v8sf*)_ps256_inv_mant_mask);
+  x = _mm256_or_ps(x, *(v8sf*)_ps256_0p5);
+
+  // this is again another AVX2 instruction
+  imm0 = _mm256_sub_epi32(imm0, *(v8si*)_pi32_256_0x7f);
+  v8sf e = _mm256_cvtepi32_ps(imm0);
+
+  e = _mm256_add_ps(e, one);
+
+  /* part2:
+     if( x < SQRTHF ) {
+       e -= 1;
+       x = x + x - 1.0;
+     } else { x = x - 1.0; }
+  */
+  //v8sf mask = _mm256_cmplt_ps(x, *(v8sf*)_ps256_cephes_SQRTHF);
+  v8sf mask = _mm256_cmp_ps(x, *(v8sf*)_ps256_cephes_SQRTHF, _CMP_LT_OS);
+  v8sf tmp = _mm256_and_ps(x, mask);
+  x = _mm256_sub_ps(x, one);
+  e = _mm256_sub_ps(e, _mm256_and_ps(one, mask));
+  x = _mm256_add_ps(x, tmp);
+
+  v8sf z = _mm256_mul_ps(x,x);
+
+  v8sf y = *(v8sf*)_ps256_cephes_log_p0;
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p1);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p2);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p3);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p4);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p5);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p6);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p7);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_log_p8);
+  y = _mm256_mul_ps(y, x);
+
+  y = _mm256_mul_ps(y, z);
+
+  tmp = _mm256_mul_ps(e, *(v8sf*)_ps256_cephes_log_q1);
+  y = _mm256_add_ps(y, tmp);
+
+
+  tmp = _mm256_mul_ps(z, *(v8sf*)_ps256_0p5);
+  y = _mm256_sub_ps(y, tmp);
+
+  tmp = _mm256_mul_ps(e, *(v8sf*)_ps256_cephes_log_q2);
+  x = _mm256_add_ps(x, y);
+  x = _mm256_add_ps(x, tmp);
+  x = _mm256_or_ps(x, invalid_mask); // negative arg will be NAN
+  return x;
+}
+
+_PS256_CONST(exp_hi,        88.3762626647949f);
+_PS256_CONST(exp_lo,        -88.3762626647949f);
+
+_PS256_CONST(cephes_LOG2EF, 1.44269504088896341);
+_PS256_CONST(cephes_exp_C1, 0.693359375);
+_PS256_CONST(cephes_exp_C2, -2.12194440e-4);
+
+_PS256_CONST(cephes_exp_p0, 1.9875691500E-4);
+_PS256_CONST(cephes_exp_p1, 1.3981999507E-3);
+_PS256_CONST(cephes_exp_p2, 8.3334519073E-3);
+_PS256_CONST(cephes_exp_p3, 4.1665795894E-2);
+_PS256_CONST(cephes_exp_p4, 1.6666665459E-1);
+_PS256_CONST(cephes_exp_p5, 5.0000001201E-1);
+
+inline v8sf exp256_ps(v8sf x) {
+  v8sf tmp = _mm256_setzero_ps(), fx;
+  v8si imm0;
+  v8sf one = *(v8sf*)_ps256_1;
+
+  x = _mm256_min_ps(x, *(v8sf*)_ps256_exp_hi);
+  x = _mm256_max_ps(x, *(v8sf*)_ps256_exp_lo);
+
+  /* express exp(x) as exp(g + n*log(2)) */
+  fx = _mm256_mul_ps(x, *(v8sf*)_ps256_cephes_LOG2EF);
+  fx = _mm256_add_ps(fx, *(v8sf*)_ps256_0p5);
+
+  /* how to perform a floorf with SSE: just below */
+  //imm0 = _mm256_cvttps_epi32(fx);
+  //tmp  = _mm256_cvtepi32_ps(imm0);
+
+  tmp = _mm256_floor_ps(fx);
+
+  /* if greater, subtract 1 */
+  //v8sf mask = _mm256_cmpgt_ps(tmp, fx);
+  v8sf mask = _mm256_cmp_ps(tmp, fx, _CMP_GT_OS);
+  mask = _mm256_and_ps(mask, one);
+  fx = _mm256_sub_ps(tmp, mask);
+
+  tmp = _mm256_mul_ps(fx, *(v8sf*)_ps256_cephes_exp_C1);
+  v8sf z = _mm256_mul_ps(fx, *(v8sf*)_ps256_cephes_exp_C2);
+  x = _mm256_sub_ps(x, tmp);
+  x = _mm256_sub_ps(x, z);
+
+  z = _mm256_mul_ps(x,x);
+
+  v8sf y = *(v8sf*)_ps256_cephes_exp_p0;
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p1);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p2);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p3);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p4);
+  y = _mm256_mul_ps(y, x);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_cephes_exp_p5);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, x);
+  y = _mm256_add_ps(y, one);
+
+  /* build 2^n */
+  imm0 = _mm256_cvttps_epi32(fx);
+  // another two AVX2 instructions
+  imm0 = _mm256_add_epi32(imm0, *(v8si*)_pi32_256_0x7f);
+  imm0 = _mm256_slli_epi32(imm0, 23);
+  v8sf pow2n = _mm256_castsi256_ps(imm0);
+  y = _mm256_mul_ps(y, pow2n);
+  return y;
+}
+
+_PS256_CONST(minus_cephes_DP1, -0.78515625);
+_PS256_CONST(minus_cephes_DP2, -2.4187564849853515625e-4);
+_PS256_CONST(minus_cephes_DP3, -3.77489497744594108e-8);
+_PS256_CONST(sincof_p0, -1.9515295891E-4);
+_PS256_CONST(sincof_p1,  8.3321608736E-3);
+_PS256_CONST(sincof_p2, -1.6666654611E-1);
+_PS256_CONST(coscof_p0,  2.443315711809948E-005);
+_PS256_CONST(coscof_p1, -1.388731625493765E-003);
+_PS256_CONST(coscof_p2,  4.166664568298827E-002);
+_PS256_CONST(cephes_FOPI, 1.27323954473516); // 4 / M_PI
+
+
+/* evaluation of 8 sines at onces using AVX intrinsics
+
+   The code is the exact rewriting of the cephes sinf function.
+   Precision is excellent as long as x < 8192 (I did not bother to
+   take into account the special handling they have for greater values
+   -- it does not return garbage for arguments over 8192, though, but
+   the extra precision is missing).
+
+   Note that it is such that sinf((float)M_PI) = 8.74e-8, which is the
+   surprising but correct result.
+
+*/
+inline v8sf sin256_ps(v8sf x) { // any x
+  v8sf xmm1, xmm2 = _mm256_setzero_ps(), xmm3, sign_bit, y;
+  v8si imm0, imm2;
+
+  sign_bit = x;
+  /* take the absolute value */
+  x = _mm256_and_ps(x, *(v8sf*)_ps256_inv_sign_mask);
+  /* extract the sign bit (upper one) */
+  sign_bit = _mm256_and_ps(sign_bit, *(v8sf*)_ps256_sign_mask);
+
+  /* scale by 4/Pi */
+  y = _mm256_mul_ps(x, *(v8sf*)_ps256_cephes_FOPI);
+
+  /*
+    Here we start a series of integer operations, which are in the
+    realm of AVX2.
+    If we don't have AVX, let's perform them using SSE2 directives
+  */
+
+  /* store the integer part of y in mm0 */
+  imm2 = _mm256_cvttps_epi32(y);
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  // another two AVX2 instruction
+  imm2 = _mm256_add_epi32(imm2, *(v8si*)_pi32_256_1);
+  imm2 = _mm256_and_si256(imm2, *(v8si*)_pi32_256_inv1);
+  y = _mm256_cvtepi32_ps(imm2);
+
+  /* get the swap sign flag */
+  imm0 = _mm256_and_si256(imm2, *(v8si*)_pi32_256_4);
+  imm0 = _mm256_slli_epi32(imm0, 29);
+  /* get the polynom selection mask
+     there is one polynom for 0 <= x <= Pi/4
+     and another one for Pi/4<x<=Pi/2
+
+     Both branches will be computed.
+  */
+  imm2 = _mm256_and_si256(imm2, *(v8si*)_pi32_256_2);
+  imm2 = _mm256_cmpeq_epi32(imm2,*(v8si*)_pi32_256_0);
+
+  v8sf swap_sign_bit = _mm256_castsi256_ps(imm0);
+  v8sf poly_mask = _mm256_castsi256_ps(imm2);
+  sign_bit = _mm256_xor_ps(sign_bit, swap_sign_bit);
+
+  /* The magic pass: "Extended precision modular arithmetic"
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v8sf*)_ps256_minus_cephes_DP1;
+  xmm2 = *(v8sf*)_ps256_minus_cephes_DP2;
+  xmm3 = *(v8sf*)_ps256_minus_cephes_DP3;
+  xmm1 = _mm256_mul_ps(y, xmm1);
+  xmm2 = _mm256_mul_ps(y, xmm2);
+  xmm3 = _mm256_mul_ps(y, xmm3);
+  x = _mm256_add_ps(x, xmm1);
+  x = _mm256_add_ps(x, xmm2);
+  x = _mm256_add_ps(x, xmm3);
+
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  y = *(v8sf*)_ps256_coscof_p0;
+  v8sf z = _mm256_mul_ps(x,x);
+
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p1);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p2);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_mul_ps(y, z);
+  v8sf tmp = _mm256_mul_ps(z, *(v8sf*)_ps256_0p5);
+  y = _mm256_sub_ps(y, tmp);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_1);
+
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v8sf y2 = *(v8sf*)_ps256_sincof_p0;
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p1);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p2);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_mul_ps(y2, x);
+  y2 = _mm256_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */
+  xmm3 = poly_mask;
+  y2 = _mm256_and_ps(xmm3, y2); //, xmm3);
+  y = _mm256_andnot_ps(xmm3, y);
+  y = _mm256_add_ps(y,y2);
+  /* update the sign */
+  y = _mm256_xor_ps(y, sign_bit);
+
+  return y;
+}
+
+/* almost the same as sin_ps */
+inline v8sf cos256_ps(v8sf x) { // any x
+  v8sf xmm1, xmm2 = _mm256_setzero_ps(), xmm3, y;
+  v8si imm0, imm2;
+
+  /* take the absolute value */
+  x = _mm256_and_ps(x, *(v8sf*)_ps256_inv_sign_mask);
+
+  /* scale by 4/Pi */
+  y = _mm256_mul_ps(x, *(v8sf*)_ps256_cephes_FOPI);
+
+  /* store the integer part of y in mm0 */
+  imm2 = _mm256_cvttps_epi32(y);
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  imm2 = _mm256_add_epi32(imm2, *(v8si*)_pi32_256_1);
+  imm2 = _mm256_and_si256(imm2, *(v8si*)_pi32_256_inv1);
+  y = _mm256_cvtepi32_ps(imm2);
+  imm2 = _mm256_sub_epi32(imm2, *(v8si*)_pi32_256_2);
+
+  /* get the swap sign flag */
+  imm0 =  _mm256_andnot_si256(imm2, *(v8si*)_pi32_256_4);
+  imm0 = _mm256_slli_epi32(imm0, 29);
+  /* get the polynom selection mask */
+  imm2 = _mm256_and_si256(imm2, *(v8si*)_pi32_256_2);
+  imm2 = _mm256_cmpeq_epi32(imm2, *(v8si*)_pi32_256_0);
+
+  v8sf sign_bit = _mm256_castsi256_ps(imm0);
+  v8sf poly_mask = _mm256_castsi256_ps(imm2);
+
+  /* The magic pass: "Extended precision modular arithmetic"
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v8sf*)_ps256_minus_cephes_DP1;
+  xmm2 = *(v8sf*)_ps256_minus_cephes_DP2;
+  xmm3 = *(v8sf*)_ps256_minus_cephes_DP3;
+  xmm1 = _mm256_mul_ps(y, xmm1);
+  xmm2 = _mm256_mul_ps(y, xmm2);
+  xmm3 = _mm256_mul_ps(y, xmm3);
+  x = _mm256_add_ps(x, xmm1);
+  x = _mm256_add_ps(x, xmm2);
+  x = _mm256_add_ps(x, xmm3);
+
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  y = *(v8sf*)_ps256_coscof_p0;
+  v8sf z = _mm256_mul_ps(x,x);
+
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p1);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p2);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_mul_ps(y, z);
+  v8sf tmp = _mm256_mul_ps(z, *(v8sf*)_ps256_0p5);
+  y = _mm256_sub_ps(y, tmp);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_1);
+
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v8sf y2 = *(v8sf*)_ps256_sincof_p0;
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p1);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p2);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_mul_ps(y2, x);
+  y2 = _mm256_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */
+  xmm3 = poly_mask;
+  y2 = _mm256_and_ps(xmm3, y2); //, xmm3);
+  y = _mm256_andnot_ps(xmm3, y);
+  y = _mm256_add_ps(y,y2);
+  /* update the sign */
+  y = _mm256_xor_ps(y, sign_bit);
+
+  return y;
+}
+
+/* since sin256_ps and cos256_ps are almost identical, sincos256_ps could replace both of them..
+   it is almost as fast, and gives you a free cosine with your sine */
+inline void sincos256_ps(v8sf x, v8sf *s, v8sf *c) {
+
+  v8sf xmm1, xmm2, xmm3 = _mm256_setzero_ps(), sign_bit_sin, y;
+  v8si imm0, imm2, imm4;
+
+  sign_bit_sin = x;
+  /* take the absolute value */
+  x = _mm256_and_ps(x, *(v8sf*)_ps256_inv_sign_mask);
+  /* extract the sign bit (upper one) */
+  sign_bit_sin = _mm256_and_ps(sign_bit_sin, *(v8sf*)_ps256_sign_mask);
+
+  /* scale by 4/Pi */
+  y = _mm256_mul_ps(x, *(v8sf*)_ps256_cephes_FOPI);
+
+  /* store the integer part of y in imm2 */
+  imm2 = _mm256_cvttps_epi32(y);
+
+  /* j=(j+1) & (~1) (see the cephes sources) */
+  imm2 = _mm256_add_epi32(imm2, *(v8si*)_pi32_256_1);
+  imm2 = _mm256_and_si256(imm2, *(v8si*)_pi32_256_inv1);
+
+  y = _mm256_cvtepi32_ps(imm2);
+  imm4 = imm2;
+
+  /* get the swap sign flag for the sine */
+  imm0 = _mm256_and_si256(imm2, *(v8si*)_pi32_256_4);
+  imm0 = _mm256_slli_epi32(imm0, 29);
+  //v8sf swap_sign_bit_sin = _mm256_castsi256_ps(imm0);
+
+  /* get the polynom selection mask for the sine*/
+  imm2 = _mm256_and_si256(imm2, *(v8si*)_pi32_256_2);
+  imm2 = _mm256_cmpeq_epi32(imm2, *(v8si*)_pi32_256_0);
+  //v8sf poly_mask = _mm256_castsi256_ps(imm2);
+
+  v8sf swap_sign_bit_sin = _mm256_castsi256_ps(imm0);
+  v8sf poly_mask = _mm256_castsi256_ps(imm2);
+
+  /* The magic pass: "Extended precision modular arithmetic"
+     x = ((x - y * DP1) - y * DP2) - y * DP3; */
+  xmm1 = *(v8sf*)_ps256_minus_cephes_DP1;
+  xmm2 = *(v8sf*)_ps256_minus_cephes_DP2;
+  xmm3 = *(v8sf*)_ps256_minus_cephes_DP3;
+  xmm1 = _mm256_mul_ps(y, xmm1);
+  xmm2 = _mm256_mul_ps(y, xmm2);
+  xmm3 = _mm256_mul_ps(y, xmm3);
+  x = _mm256_add_ps(x, xmm1);
+  x = _mm256_add_ps(x, xmm2);
+  x = _mm256_add_ps(x, xmm3);
+
+  imm4 = _mm256_sub_epi32(imm4, *(v8si*)_pi32_256_2);
+  imm4 =  _mm256_andnot_si256(imm4, *(v8si*)_pi32_256_4);
+  imm4 = _mm256_slli_epi32(imm4, 29);
+
+  v8sf sign_bit_cos = _mm256_castsi256_ps(imm4);
+
+  sign_bit_sin = _mm256_xor_ps(sign_bit_sin, swap_sign_bit_sin);
+
+  /* Evaluate the first polynom  (0 <= x <= Pi/4) */
+  v8sf z = _mm256_mul_ps(x,x);
+  y = *(v8sf*)_ps256_coscof_p0;
+
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p1);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_coscof_p2);
+  y = _mm256_mul_ps(y, z);
+  y = _mm256_mul_ps(y, z);
+  v8sf tmp = _mm256_mul_ps(z, *(v8sf*)_ps256_0p5);
+  y = _mm256_sub_ps(y, tmp);
+  y = _mm256_add_ps(y, *(v8sf*)_ps256_1);
+
+  /* Evaluate the second polynom  (Pi/4 <= x <= 0) */
+
+  v8sf y2 = *(v8sf*)_ps256_sincof_p0;
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p1);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_add_ps(y2, *(v8sf*)_ps256_sincof_p2);
+  y2 = _mm256_mul_ps(y2, z);
+  y2 = _mm256_mul_ps(y2, x);
+  y2 = _mm256_add_ps(y2, x);
+
+  /* select the correct result from the two polynoms */
+  xmm3 = poly_mask;
+  v8sf ysin2 = _mm256_and_ps(xmm3, y2);
+  v8sf ysin1 = _mm256_andnot_ps(xmm3, y);
+  y2 = _mm256_sub_ps(y2,ysin2);
+  y = _mm256_sub_ps(y, ysin1);
+
+  xmm1 = _mm256_add_ps(ysin1,ysin2);
+  xmm2 = _mm256_add_ps(y,y2);
+
+  /* update the sign */
+  *s = _mm256_xor_ps(xmm1, sign_bit_sin);
+  *c = _mm256_xor_ps(xmm2, sign_bit_cos);
+}
+
+#endif // CPU_CAPABILITY_AVX2

shared/package-files/9b/9b27bd125063a5d81de94b64d9628603c25d7ad3736eb823440180d76271bf83/blob

@@ -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 quantized_gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+
+} // namespace compositeimplicitautograd
+} // namespace at

shared/package-files/9b/9b2824a306502b0a6f1290bd19fd1fcd2869bb52c21a5c33a1bc9480da5752dd/blob

@@ -0,0 +1,413 @@
+from __future__ import annotations
+
+import abc
+from functools import lru_cache
+from typing import TYPE_CHECKING, Any, ClassVar, Final, Protocol, TypeVar, cast, overload
+
+import polars as pl
+
+from narwhals._duration import Interval
+from narwhals._utils import (
+    Implementation,
+    Version,
+    _DeferredIterable,
+    _StoresCompliant,
+    _StoresNative,
+    deep_getattr,
+    isinstance_or_issubclass,
+)
+from narwhals.exceptions import (
+    ColumnNotFoundError,
+    ComputeError,
+    DuplicateError,
+    InvalidOperationError,
+    NarwhalsError,
+    ShapeError,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Iterable, Iterator, Mapping
+
+    from typing_extensions import TypeIs
+
+    from narwhals._compliant.typing import Accessor
+    from narwhals._polars.dataframe import Method
+    from narwhals._polars.expr import PolarsExpr
+    from narwhals._polars.series import PolarsSeries
+    from narwhals.dtypes import DType
+    from narwhals.typing import IntoDType
+
+    T = TypeVar("T")
+    NativeT = TypeVar(
+        "NativeT", bound="pl.DataFrame | pl.LazyFrame | pl.Series | pl.Expr"
+    )
+
+NativeT_co = TypeVar("NativeT_co", "pl.Series", "pl.Expr", covariant=True)
+CompliantT_co = TypeVar("CompliantT_co", "PolarsSeries", "PolarsExpr", covariant=True)
+CompliantT = TypeVar("CompliantT", "PolarsSeries", "PolarsExpr")
+
+BACKEND_VERSION = Implementation.POLARS._backend_version()
+"""Static backend version for `polars`."""
+
+SERIES_ACCEPTS_PD_INDEX: Final[bool] = BACKEND_VERSION >= (0, 20, 7)
+"""`pl.Series(values: pd.Index)` fixed in https://github.com/pola-rs/polars/pull/14087"""
+
+RESPECT_JOIN_NULL_SEMI_ANTI: Final[bool] = BACKEND_VERSION >= (0, 20, 22)
+"""Polars < 0.20.22 treats `null == null` as a match in semi/anti joins.
+
+Dropping rows with null keys on the right removes these spurious matches,
+which is always valid since narwhals treats nulls as non-matching.
+
+Fixed in https://github.com/pola-rs/polars/pull/15696
+"""
+
+SERIES_RESPECTS_DTYPE: Final[bool] = BACKEND_VERSION >= (0, 20, 26)
+"""`pl.Series(dtype=...)` fixed in https://github.com/pola-rs/polars/pull/15962
+
+Includes `SERIES_ACCEPTS_PD_INDEX`.
+"""
+
+HAS_INT_128: Final[bool] = BACKEND_VERSION >= (1, 18, 0)
+"""https://github.com/pola-rs/polars/pull/20232"""
+
+FROM_DICTS_ACCEPTS_MAPPINGS: Final[bool] = BACKEND_VERSION >= (1, 30, 0)
+"""`pl.from_dicts(data: Iterable[Mapping[str, Any]])` since https://github.com/pola-rs/polars/pull/22638"""
+
+HAS_UINT_128: Final[bool] = BACKEND_VERSION >= (1, 34, 0)
+"""https://github.com/pola-rs/polars/pull/24346"""
+
+HAS_FLOAT_16: Final[bool] = BACKEND_VERSION >= (1, 36, 0)
+"""https://github.com/pola-rs/polars/pull/25185"""
+
+BINARY_ADD_UPCASTS_DECIMAL_TO_FLOAT: Final[bool] = BACKEND_VERSION >= (1, 34, 0)
+"""Polars >= 1.34 upcasts `Decimal + float` to `Float64` (matching `int`/`Float32` promotion).
+
+https://github.com/pola-rs/polars/pull/24594
+"""
+
+
+@overload
+def extract_native(obj: _StoresNative[NativeT]) -> NativeT: ...
+@overload
+def extract_native(obj: T) -> T: ...
+def extract_native(obj: _StoresNative[NativeT] | T) -> NativeT | T:
+    return obj.native if _is_compliant_polars(obj) else cast("T", obj)
+
+
+def _is_compliant_polars(
+    obj: _StoresNative[NativeT] | Any,
+) -> TypeIs[_StoresNative[NativeT]]:
+    from narwhals._polars.dataframe import PolarsDataFrame, PolarsLazyFrame
+    from narwhals._polars.expr import PolarsExpr
+    from narwhals._polars.series import PolarsSeries
+
+    return isinstance(obj, (PolarsDataFrame, PolarsLazyFrame, PolarsSeries, PolarsExpr))
+
+
+def extract_args_kwargs(
+    args: Iterable[Any], kwds: Mapping[str, Any], /
+) -> tuple[Iterator[Any], dict[str, Any]]:
+    it_args = (extract_native(arg) for arg in args)
+    return it_args, {k: extract_native(v) for k, v in kwds.items()}
+
+
+@lru_cache(maxsize=128)
+def native_to_narwhals_dtype(  # noqa: C901, PLR0912
+    dtype: pl.DataType, version: Version
+) -> DType:
+    dtypes = version.dtypes
+    if dtype == pl.Float64:
+        return dtypes.Float64()
+    if dtype == pl.Float32:
+        return dtypes.Float32()
+    if HAS_FLOAT_16 and dtype == pl.Float16:
+        return dtypes.Float16()
+    if HAS_INT_128 and dtype == pl.Int128:
+        return dtypes.Int128()
+    if dtype == pl.Int64:
+        return dtypes.Int64()
+    if dtype == pl.Int32:
+        return dtypes.Int32()
+    if dtype == pl.Int16:
+        return dtypes.Int16()
+    if dtype == pl.Int8:
+        return dtypes.Int8()
+    if HAS_UINT_128 and dtype == pl.UInt128:
+        return dtypes.UInt128()
+    if dtype == pl.UInt64:
+        return dtypes.UInt64()
+    if dtype == pl.UInt32:
+        return dtypes.UInt32()
+    if dtype == pl.UInt16:
+        return dtypes.UInt16()
+    if dtype == pl.UInt8:
+        return dtypes.UInt8()
+    if dtype == pl.String:
+        return dtypes.String()
+    if dtype == pl.Boolean:
+        return dtypes.Boolean()
+    if dtype == pl.Object:
+        return dtypes.Object()
+    if isinstance(dtype, pl.Categorical):
+        return dtypes.Categorical()
+    if isinstance(dtype, pl.Enum):
+        if version is Version.V1:
+            return dtypes.Enum()  # type: ignore[call-arg]
+        categories = _DeferredIterable(dtype.categories.to_list)
+        return dtypes.Enum(categories)
+    if dtype == pl.Date:
+        return dtypes.Date()
+    if isinstance(dtype, pl.Datetime):
+        return dtypes.Datetime(dtype.time_unit, dtype.time_zone)
+    if isinstance(dtype, pl.Duration):
+        return dtypes.Duration(dtype.time_unit)
+    if isinstance(dtype, pl.Struct):
+        fields = [
+            dtypes.Field(name, native_to_narwhals_dtype(tp, version))
+            for name, tp in dtype
+        ]
+        return dtypes.Struct(fields)
+    if isinstance(dtype, pl.List):
+        return dtypes.List(native_to_narwhals_dtype(dtype.inner, version))
+    if isinstance(dtype, pl.Array):
+        outer_shape = dtype.width if BACKEND_VERSION < (0, 20, 30) else dtype.size  # type: ignore[attr-defined]
+        return dtypes.Array(native_to_narwhals_dtype(dtype.inner, version), outer_shape)
+    if isinstance(dtype, pl.Decimal):
+        return dtypes.Decimal(dtype.precision, dtype.scale)
+    if dtype == pl.Time:
+        return dtypes.Time()
+    if dtype == pl.Binary:
+        return dtypes.Binary()
+    return dtypes.Unknown()
+
+
+dtypes = Version.MAIN.dtypes
+
+
+def _version_dependent_dtypes() -> dict[type[DType], pl.DataType]:
+    if not HAS_INT_128:  # pragma: no cover
+        return {}
+    nw_to_pl: dict[type[DType], pl.DataType] = {dtypes.Int128: pl.Int128()}
+    nw_to_pl = nw_to_pl | {dtypes.UInt128: pl.UInt128()} if HAS_UINT_128 else nw_to_pl
+    return nw_to_pl | {dtypes.Float16: pl.Float16()} if HAS_FLOAT_16 else nw_to_pl
+
+
+NW_TO_PL_DTYPES: Mapping[type[DType], pl.DataType] = {
+    dtypes.Float64: pl.Float64(),
+    dtypes.Float32: pl.Float32(),
+    dtypes.Binary: pl.Binary(),
+    dtypes.String: pl.String(),
+    dtypes.Boolean: pl.Boolean(),
+    dtypes.Categorical: pl.Categorical(),
+    dtypes.Date: pl.Date(),
+    dtypes.Time: pl.Time(),
+    dtypes.Int8: pl.Int8(),
+    dtypes.Int16: pl.Int16(),
+    dtypes.Int32: pl.Int32(),
+    dtypes.Int64: pl.Int64(),
+    dtypes.UInt8: pl.UInt8(),
+    dtypes.UInt16: pl.UInt16(),
+    dtypes.UInt32: pl.UInt32(),
+    dtypes.UInt64: pl.UInt64(),
+    dtypes.Object: pl.Object(),
+    dtypes.Unknown: pl.Unknown(),
+    **_version_dependent_dtypes(),
+}
+
+
+def narwhals_to_native_dtype(  # noqa: C901
+    dtype: IntoDType, version: Version
+) -> pl.DataType:
+    dtypes = version.dtypes
+    base_type = dtype.base_type()
+    if pl_type := NW_TO_PL_DTYPES.get(base_type):
+        return pl_type
+    if isinstance_or_issubclass(dtype, dtypes.Enum):
+        if version is Version.V1:
+            msg = "Converting to Enum is not supported in narwhals.stable.v1"
+            raise NotImplementedError(msg)
+        if isinstance(dtype, dtypes.Enum):
+            return pl.Enum(dtype.categories)
+        msg = "Can not cast / initialize Enum without categories present"
+        raise ValueError(msg)
+    if isinstance_or_issubclass(dtype, dtypes.Datetime):
+        return pl.Datetime(dtype.time_unit, dtype.time_zone)  # type: ignore[arg-type]
+    if isinstance_or_issubclass(dtype, dtypes.Duration):
+        return pl.Duration(dtype.time_unit)  # type: ignore[arg-type]
+    if isinstance_or_issubclass(dtype, dtypes.List):
+        return pl.List(narwhals_to_native_dtype(dtype.inner, version))
+    if isinstance_or_issubclass(dtype, dtypes.Struct):
+        fields = [
+            pl.Field(field.name, narwhals_to_native_dtype(field.dtype, version))
+            for field in dtype.fields
+        ]
+        return pl.Struct(fields)
+    if isinstance_or_issubclass(dtype, dtypes.Array):  # pragma: no cover
+        size = dtype.size
+        kwargs = {"width": size} if BACKEND_VERSION < (0, 20, 30) else {"shape": size}
+        return pl.Array(narwhals_to_native_dtype(dtype.inner, version), **kwargs)
+    if isinstance_or_issubclass(dtype, dtypes.Decimal):
+        return pl.Decimal(dtype.precision, dtype.scale)
+    return pl.Unknown()  # pragma: no cover
+
+
+def _is_polars_exception(exception: Exception) -> bool:
+    if BACKEND_VERSION >= (1,):
+        # Old versions of Polars didn't have PolarsError.
+        return isinstance(exception, pl.exceptions.PolarsError)
+    # Last attempt, for old Polars versions.
+    return "polars.exceptions" in str(type(exception))  # pragma: no cover
+
+
+def _is_cudf_exception(exception: Exception) -> bool:
+    # These exceptions are raised when running polars on GPUs via cuDF
+    return str(exception).startswith("CUDF failure")
+
+
+def catch_polars_exception(exception: Exception) -> NarwhalsError | Exception:
+    if isinstance(exception, pl.exceptions.ColumnNotFoundError):
+        return ColumnNotFoundError(str(exception))
+    if isinstance(exception, pl.exceptions.ShapeError):
+        return ShapeError(str(exception))
+    if isinstance(exception, pl.exceptions.InvalidOperationError):
+        return InvalidOperationError(str(exception))
+    if isinstance(exception, pl.exceptions.DuplicateError):
+        return DuplicateError(str(exception))
+    if isinstance(exception, pl.exceptions.ComputeError):
+        return ComputeError(str(exception))
+    if _is_polars_exception(exception) or _is_cudf_exception(exception):
+        return NarwhalsError(str(exception))  # pragma: no cover
+    # Just return exception as-is.
+    return exception
+
+
+class PolarsAnyNamespace(
+    _StoresCompliant[CompliantT_co],
+    _StoresNative[NativeT_co],
+    Protocol[CompliantT_co, NativeT_co],
+):
+    _accessor: ClassVar[Accessor]
+
+    def __getattr__(self, attr: str) -> Callable[..., CompliantT_co]:
+        def func(*args: Any, **kwargs: Any) -> CompliantT_co:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            method = deep_getattr(self.native, self._accessor, attr)
+            return self.compliant._with_native(method(*pos, **kwds))
+
+        return func
+
+
+class PolarsDateTimeNamespace(PolarsAnyNamespace[CompliantT, NativeT_co]):
+    _accessor: ClassVar[Accessor] = "dt"
+
+    def truncate(self, every: str) -> CompliantT:
+        # Ensure consistent error message is raised.
+        Interval.parse(every)
+        return self.__getattr__("truncate")(every)
+
+    def offset_by(self, by: str) -> CompliantT:
+        # Ensure consistent error message is raised.
+        Interval.parse_no_constraints(by)
+        return self.__getattr__("offset_by")(by)
+
+    to_string: Method[CompliantT]
+    replace_time_zone: Method[CompliantT]
+    convert_time_zone: Method[CompliantT]
+    timestamp: Method[CompliantT]
+    date: Method[CompliantT]
+    year: Method[CompliantT]
+    month: Method[CompliantT]
+    day: Method[CompliantT]
+    hour: Method[CompliantT]
+    minute: Method[CompliantT]
+    second: Method[CompliantT]
+    millisecond: Method[CompliantT]
+    microsecond: Method[CompliantT]
+    nanosecond: Method[CompliantT]
+    ordinal_day: Method[CompliantT]
+    weekday: Method[CompliantT]
+    total_minutes: Method[CompliantT]
+    total_seconds: Method[CompliantT]
+    total_milliseconds: Method[CompliantT]
+    total_microseconds: Method[CompliantT]
+    total_nanoseconds: Method[CompliantT]
+
+
+class PolarsStringNamespace(PolarsAnyNamespace[CompliantT, NativeT_co]):
+    _accessor: ClassVar[Accessor] = "str"
+
+    # NOTE: Use `abstractmethod` if we have defs to implement, but also `Method` usage
+    @abc.abstractmethod
+    def to_titlecase(self) -> CompliantT: ...
+
+    @abc.abstractmethod
+    def zfill(self, width: int) -> CompliantT: ...
+
+    @abc.abstractmethod
+    def slice(self, offset: int, length: int | None) -> CompliantT: ...
+
+    len_chars: Method[CompliantT]
+    replace: Method[CompliantT]
+    replace_all: Method[CompliantT]
+    strip_chars: Method[CompliantT]
+    strip_chars_start: Method[CompliantT]
+    strip_chars_end: Method[CompliantT]
+    starts_with: Method[CompliantT]
+    ends_with: Method[CompliantT]
+    contains: Method[CompliantT]
+    split: Method[CompliantT]
+    to_date: Method[CompliantT]
+    to_datetime: Method[CompliantT]
+    to_time: Method[CompliantT]
+    to_lowercase: Method[CompliantT]
+    to_uppercase: Method[CompliantT]
+    pad_start: Method[CompliantT]
+    pad_end: Method[CompliantT]
+
+
+def native_get_categories(native: pl.Series) -> pl.Series:
+    # NOTE: Polars deprecated `cat.get_categories` in v1.44 and removed it
+    # in v2.0, so we use the workaround they suggest.
+    # See https://github.com/narwhals-dev/narwhals/issues/3895.
+    #
+    # For `Enum`, the declared categories are already unique, ordered, and
+    # null-free, so we can return `dtype.categories` directly.
+    dtype = native.dtype
+    if isinstance(dtype, pl.Enum):
+        return dtype.categories
+    return native.unique(maintain_order=True).drop_nulls().cast(pl.String)
+
+
+class PolarsCatNamespace(PolarsAnyNamespace[CompliantT, NativeT_co]):
+    _accessor: ClassVar[Accessor] = "cat"
+
+    @abc.abstractmethod
+    def get_categories(self) -> CompliantT: ...
+
+
+class PolarsListNamespace(PolarsAnyNamespace[CompliantT, NativeT_co]):
+    _accessor: ClassVar[Accessor] = "list"
+
+    @abc.abstractmethod
+    def len(self) -> CompliantT: ...
+
+    get: Method[CompliantT]
+
+    unique: Method[CompliantT]
+
+    max: Method[CompliantT]
+
+    mean: Method[CompliantT]
+
+    median: Method[CompliantT]
+
+    min: Method[CompliantT]
+
+    sort: Method[CompliantT]
+
+    sum: Method[CompliantT]
+
+
+class PolarsStructNamespace(PolarsAnyNamespace[CompliantT, NativeT_co]):
+    _accessor: ClassVar[Accessor] = "struct"
+    field: Method[CompliantT]

shared/package-files/9b/9b29fc93ae95aef6672fa47a556e880cf4df3baf15c636c9f14a7922e4a314be/blob

@@ -0,0 +1,88 @@
+// 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 <cstdint>
+
+#include "arrow/util/bit_util.h"
+#include "arrow/util/bitmap_reader.h"
+
+namespace arrow {
+namespace internal {
+
+// A function that visits each bit in a bitmap and calls a visitor function with a
+// boolean representation of that bit. This is intended to be analogous to
+// GenerateBits.
+template <class Visitor>
+void VisitBits(const uint8_t* bitmap, int64_t start_offset, int64_t length,
+               Visitor&& visit) {
+  BitmapReader reader(bitmap, start_offset, length);
+  for (int64_t index = 0; index < length; ++index) {
+    visit(reader.IsSet());
+    reader.Next();
+  }
+}
+
+// Like VisitBits(), but unrolls its main loop for better performance.
+template <class Visitor>
+void VisitBitsUnrolled(const uint8_t* bitmap, int64_t start_offset, int64_t length,
+                       Visitor&& visit) {
+  if (length == 0) {
+    return;
+  }
+
+  // Start by visiting any bits preceding the first full byte.
+  int64_t num_bits_before_full_bytes =
+      bit_util::RoundUpToMultipleOf8(start_offset) - start_offset;
+  // Truncate num_bits_before_full_bytes if it is greater than length.
+  if (num_bits_before_full_bytes > length) {
+    num_bits_before_full_bytes = length;
+  }
+  // Use the non loop-unrolled VisitBits since we don't want to add branches
+  VisitBits<Visitor>(bitmap, start_offset, num_bits_before_full_bytes, visit);
+
+  // Shift the start pointer to the first full byte and compute the
+  // number of full bytes to be read.
+  const uint8_t* first_full_byte = bitmap + bit_util::CeilDiv(start_offset, 8);
+  const int64_t num_full_bytes = (length - num_bits_before_full_bytes) / 8;
+
+  // Iterate over each full byte of the input bitmap and call the visitor in
+  // a loop-unrolled manner.
+  for (int64_t byte_index = 0; byte_index < num_full_bytes; ++byte_index) {
+    // Get the current bit-packed byte value from the bitmap.
+    const uint8_t byte = *(first_full_byte + byte_index);
+
+    // Execute the visitor function on each bit of the current byte.
+    visit(bit_util::GetBitFromByte(byte, 0));
+    visit(bit_util::GetBitFromByte(byte, 1));
+    visit(bit_util::GetBitFromByte(byte, 2));
+    visit(bit_util::GetBitFromByte(byte, 3));
+    visit(bit_util::GetBitFromByte(byte, 4));
+    visit(bit_util::GetBitFromByte(byte, 5));
+    visit(bit_util::GetBitFromByte(byte, 6));
+    visit(bit_util::GetBitFromByte(byte, 7));
+  }
+
+  // Write any leftover bits in the last byte.
+  const int64_t num_bits_after_full_bytes = (length - num_bits_before_full_bytes) % 8;
+  VisitBits<Visitor>(first_full_byte + num_full_bytes, 0, num_bits_after_full_bytes,
+                     visit);
+}
+
+}  // namespace internal
+}  // namespace arrow

shared/package-files/9b/9b2c1a62c96e69385c72c6f7d5499d217c1773c48436f016e83412ea582aace6/blob

@@ -0,0 +1,57 @@
+#pragma once
+
+#include <functional>
+#include <memory>
+
+#include <c10/core/Device.h>
+#include <c10/util/strong_type.h>
+#include <torch/csrc/Export.h>
+
+struct CUevent_st;
+
+namespace torch {
+namespace profiler {
+namespace impl {
+
+// ----------------------------------------------------------------------------
+// -- Annotation --------------------------------------------------------------
+// ----------------------------------------------------------------------------
+using ProfilerEventStub = std::shared_ptr<CUevent_st>;
+using ProfilerVoidEventStub = std::shared_ptr<void>;
+
+struct TORCH_API ProfilerStubs {
+  virtual void record(
+      c10::DeviceIndex* device,
+      ProfilerVoidEventStub* event,
+      int64_t* cpu_ns) const = 0;
+  virtual float elapsed(
+      const ProfilerVoidEventStub* event,
+      const ProfilerVoidEventStub* event2) const = 0;
+  virtual void mark(const char* name) const = 0;
+  virtual void rangePush(const char* name) const = 0;
+  virtual void rangePop() const = 0;
+  virtual bool enabled() const {
+    return false;
+  }
+  virtual void onEachDevice(std::function<void(int)> op) const = 0;
+  virtual void synchronize() const = 0;
+  virtual ~ProfilerStubs();
+};
+
+TORCH_API void registerCUDAMethods(ProfilerStubs* stubs);
+TORCH_API const ProfilerStubs* cudaStubs();
+TORCH_API void registerITTMethods(ProfilerStubs* stubs);
+TORCH_API const ProfilerStubs* ittStubs();
+TORCH_API void registerPrivateUse1Methods(ProfilerStubs* stubs);
+TORCH_API const ProfilerStubs* privateuse1Stubs();
+
+using vulkan_id_t = strong::type<
+    int64_t,
+    struct _VulkanID,
+    strong::regular,
+    strong::convertible_to<int64_t>,
+    strong::hashable>;
+
+} // namespace impl
+} // namespace profiler
+} // namespace torch

shared/package-files/9b/9b2e55e0f1d3df31cb2b442f6a712a427eb98c9e7f9cd572eae97fd301d14f5f/blob

@@ -0,0 +1,140 @@
+""" Functions related to graph covers."""
+
+from functools import partial
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import arbitrary_element, not_implemented_for
+
+__all__ = ["min_edge_cover", "is_edge_cover"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def min_edge_cover(G, matching_algorithm=None):
+    """Returns the min cardinality edge cover of the graph as a set of edges.
+
+    A smallest edge cover can be found in polynomial time by finding
+    a maximum matching and extending it greedily so that all nodes
+    are covered. This function follows that process. A maximum matching
+    algorithm can be specified for the first step of the algorithm.
+    The resulting set may return a set with one 2-tuple for each edge,
+    (the usual case) or with both 2-tuples `(u, v)` and `(v, u)` for
+    each edge. The latter is only done when a bipartite matching algorithm
+    is specified as `matching_algorithm`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    matching_algorithm : function
+        A function that returns a maximum cardinality matching for `G`.
+        The function must take one input, the graph `G`, and return
+        either a set of edges (with only one direction for the pair of nodes)
+        or a dictionary mapping each node to its mate. If not specified,
+        :func:`~networkx.algorithms.matching.max_weight_matching` is used.
+        Common bipartite matching functions include
+        :func:`~networkx.algorithms.bipartite.matching.hopcroft_karp_matching`
+        or
+        :func:`~networkx.algorithms.bipartite.matching.eppstein_matching`.
+
+    Returns
+    -------
+    min_cover : set
+
+        A set of the edges in a minimum edge cover in the form of tuples.
+        It contains only one of the equivalent 2-tuples `(u, v)` and `(v, u)`
+        for each edge. If a bipartite method is used to compute the matching,
+        the returned set contains both the 2-tuples `(u, v)` and `(v, u)`
+        for each edge of a minimum edge cover.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> sorted(nx.min_edge_cover(G))
+    [(2, 1), (3, 0)]
+
+    Notes
+    -----
+    An edge cover of a graph is a set of edges such that every node of
+    the graph is incident to at least one edge of the set.
+    The minimum edge cover is an edge covering of smallest cardinality.
+
+    Due to its implementation, the worst-case running time of this algorithm
+    is bounded by the worst-case running time of the function
+    ``matching_algorithm``.
+
+    Minimum edge cover for `G` can also be found using the `min_edge_covering`
+    function in :mod:`networkx.algorithms.bipartite.covering` which is
+    simply this function with a default matching algorithm of
+    :func:`~networkx.algorithms.bipartite.matching.hopcraft_karp_matching`
+    """
+    if len(G) == 0:
+        return set()
+    if nx.number_of_isolates(G) > 0:
+        # ``min_cover`` does not exist as there is an isolated node
+        raise nx.NetworkXException(
+            "Graph has a node with no edge incident on it, " "so no edge cover exists."
+        )
+    if matching_algorithm is None:
+        matching_algorithm = partial(nx.max_weight_matching, maxcardinality=True)
+    maximum_matching = matching_algorithm(G)
+    # ``min_cover`` is superset of ``maximum_matching``
+    try:
+        # bipartite matching algs return dict so convert if needed
+        min_cover = set(maximum_matching.items())
+        bipartite_cover = True
+    except AttributeError:
+        min_cover = maximum_matching
+        bipartite_cover = False
+    # iterate for uncovered nodes
+    uncovered_nodes = set(G) - {v for u, v in min_cover} - {u for u, v in min_cover}
+    for v in uncovered_nodes:
+        # Since `v` is uncovered, each edge incident to `v` will join it
+        # with a covered node (otherwise, if there were an edge joining
+        # uncovered nodes `u` and `v`, the maximum matching algorithm
+        # would have found it), so we can choose an arbitrary edge
+        # incident to `v`. (This applies only in a simple graph, not a
+        # multigraph.)
+        u = arbitrary_element(G[v])
+        min_cover.add((u, v))
+        if bipartite_cover:
+            min_cover.add((v, u))
+    return min_cover
+
+
+@not_implemented_for("directed")
+def is_edge_cover(G, cover):
+    """Decides whether a set of edges is a valid edge cover of the graph.
+
+    Given a set of edges, whether it is an edge covering can
+    be decided if we just check whether all nodes of the graph
+    has an edge from the set, incident on it.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected bipartite graph.
+
+    cover : set
+        Set of edges to be checked.
+
+    Returns
+    -------
+    bool
+        Whether the set of edges is a valid edge cover of the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> cover = {(2, 1), (3, 0)}
+    >>> nx.is_edge_cover(G, cover)
+    True
+
+    Notes
+    -----
+    An edge cover of a graph is a set of edges such that every node of
+    the graph is incident to at least one edge of the set.
+    """
+    return set(G) <= set(chain.from_iterable(cover))

shared/package-files/9b/9b2f91be34024fbcf645f6ef92460e5f944ca6a16268b79478ab904b2934d357/blob

@@ -0,0 +1,52 @@
+# created by tools/loadICU.tcl -- do not edit
+namespace eval ::tcl::clock {
+    ::msgcat::mcset el DAYS_OF_WEEK_ABBREV [list \
+        "\u039a\u03c5\u03c1"\
+        "\u0394\u03b5\u03c5"\
+        "\u03a4\u03c1\u03b9"\
+        "\u03a4\u03b5\u03c4"\
+        "\u03a0\u03b5\u03bc"\
+        "\u03a0\u03b1\u03c1"\
+        "\u03a3\u03b1\u03b2"]
+    ::msgcat::mcset el DAYS_OF_WEEK_FULL [list \
+        "\u039a\u03c5\u03c1\u03b9\u03b1\u03ba\u03ae"\
+        "\u0394\u03b5\u03c5\u03c4\u03ad\u03c1\u03b1"\
+        "\u03a4\u03c1\u03af\u03c4\u03b7"\
+        "\u03a4\u03b5\u03c4\u03ac\u03c1\u03c4\u03b7"\
+        "\u03a0\u03ad\u03bc\u03c0\u03c4\u03b7"\
+        "\u03a0\u03b1\u03c1\u03b1\u03c3\u03ba\u03b5\u03c5\u03ae"\
+        "\u03a3\u03ac\u03b2\u03b2\u03b1\u03c4\u03bf"]
+    ::msgcat::mcset el MONTHS_ABBREV [list \
+        "\u0399\u03b1\u03bd"\
+        "\u03a6\u03b5\u03b2"\
+        "\u039c\u03b1\u03c1"\
+        "\u0391\u03c0\u03c1"\
+        "\u039c\u03b1\u03ca"\
+        "\u0399\u03bf\u03c5\u03bd"\
+        "\u0399\u03bf\u03c5\u03bb"\
+        "\u0391\u03c5\u03b3"\
+        "\u03a3\u03b5\u03c0"\
+        "\u039f\u03ba\u03c4"\
+        "\u039d\u03bf\u03b5"\
+        "\u0394\u03b5\u03ba"\
+        ""]
+    ::msgcat::mcset el MONTHS_FULL [list \
+        "\u0399\u03b1\u03bd\u03bf\u03c5\u03ac\u03c1\u03b9\u03bf\u03c2"\
+        "\u03a6\u03b5\u03b2\u03c1\u03bf\u03c5\u03ac\u03c1\u03b9\u03bf\u03c2"\
+        "\u039c\u03ac\u03c1\u03c4\u03b9\u03bf\u03c2"\
+        "\u0391\u03c0\u03c1\u03af\u03bb\u03b9\u03bf\u03c2"\
+        "\u039c\u03ac\u03ca\u03bf\u03c2"\
+        "\u0399\u03bf\u03cd\u03bd\u03b9\u03bf\u03c2"\
+        "\u0399\u03bf\u03cd\u03bb\u03b9\u03bf\u03c2"\
+        "\u0391\u03cd\u03b3\u03bf\u03c5\u03c3\u03c4\u03bf\u03c2"\
+        "\u03a3\u03b5\u03c0\u03c4\u03ad\u03bc\u03b2\u03c1\u03b9\u03bf\u03c2"\
+        "\u039f\u03ba\u03c4\u03ce\u03b2\u03c1\u03b9\u03bf\u03c2"\
+        "\u039d\u03bf\u03ad\u03bc\u03b2\u03c1\u03b9\u03bf\u03c2"\
+        "\u0394\u03b5\u03ba\u03ad\u03bc\u03b2\u03c1\u03b9\u03bf\u03c2"\
+        ""]
+    ::msgcat::mcset el AM "\u03c0\u03bc"
+    ::msgcat::mcset el PM "\u03bc\u03bc"
+    ::msgcat::mcset el DATE_FORMAT "%e/%m/%Y"
+    ::msgcat::mcset el TIME_FORMAT_12 "%l:%M:%S %P"
+    ::msgcat::mcset el DATE_TIME_FORMAT "%e/%m/%Y %l:%M:%S %P %z"
+}

shared/package-files/9b/9b2ff15cb3d217ce7ef0b06547f16743aef6d5363072c505bff85d03e769596e/blob

@@ -0,0 +1,360 @@
+Metadata-Version: 2.1
+Name: pyOpenSSL
+Version: 24.0.0
+Summary: Python wrapper module around the OpenSSL library
+Home-page: https://pyopenssl.org/
+Author: The pyOpenSSL developers
+Author-email: cryptography-dev@python.org
+License: Apache License, Version 2.0
+Project-URL: Source, https://github.com/pyca/pyopenssl
+Classifier: Development Status :: 6 - Mature
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Security :: Cryptography
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: System :: Networking
+Requires-Python: >=3.7
+License-File: LICENSE
+Requires-Dist: cryptography <43,>=41.0.5
+Provides-Extra: docs
+Requires-Dist: sphinx !=5.2.0,!=5.2.0.post0,!=7.2.5 ; extra == 'docs'
+Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
+Provides-Extra: test
+Requires-Dist: flaky ; extra == 'test'
+Requires-Dist: pretend ; extra == 'test'
+Requires-Dist: pytest >=3.0.1 ; extra == 'test'
+
+========================================================
+pyOpenSSL -- A Python wrapper around the OpenSSL library
+========================================================
+
+.. image:: https://readthedocs.org/projects/pyopenssl/badge/?version=stable
+   :target: https://pyopenssl.org/en/stable/
+   :alt: Stable Docs
+
+.. image:: https://github.com/pyca/pyopenssl/workflows/CI/badge.svg?branch=main
+   :target: https://github.com/pyca/pyopenssl/actions?query=workflow%3ACI+branch%3Amain
+
+.. image:: https://codecov.io/github/pyca/pyopenssl/branch/main/graph/badge.svg
+   :target: https://codecov.io/github/pyca/pyopenssl
+   :alt: Test coverage
+
+**Note:** The Python Cryptographic Authority **strongly suggests** the use of `pyca/cryptography`_
+where possible. If you are using pyOpenSSL for anything other than making a TLS connection
+**you should move to cryptography and drop your pyOpenSSL dependency**.
+
+High-level wrapper around a subset of the OpenSSL library. Includes
+
+* ``SSL.Connection`` objects, wrapping the methods of Python's portable sockets
+* Callbacks written in Python
+* Extensive error-handling mechanism, mirroring OpenSSL's error codes
+
+... and much more.
+
+You can find more information in the documentation_.
+Development takes place on GitHub_.
+
+
+Discussion
+==========
+
+If you run into bugs, you can file them in our `issue tracker`_.
+
+We maintain a cryptography-dev_ mailing list for both user and development discussions.
+
+You can also join ``#pyca`` on ``irc.libera.chat`` to ask questions or get involved.
+
+
+.. _documentation: https://pyopenssl.org/
+.. _`issue tracker`: https://github.com/pyca/pyopenssl/issues
+.. _cryptography-dev: https://mail.python.org/mailman/listinfo/cryptography-dev
+.. _GitHub: https://github.com/pyca/pyopenssl
+.. _`pyca/cryptography`: https://github.com/pyca/cryptography
+
+
+Release Information
+===================
+
+24.0.0 (2024-01-22)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Changes:
+^^^^^^^^
+
+- Added ``OpenSSL.SSL.Connection.get_selected_srtp_profile`` to determine which SRTP profile was negotiated.
+  `#1279 <https://github.com/pyca/pyopenssl/pull/1279>`_.
+
+23.3.0 (2023-10-25)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Dropped support for Python 3.6.
+- The minimum ``cryptography`` version is now 41.0.5.
+- Removed ``OpenSSL.crypto.loads_pkcs7`` and ``OpenSSL.crypto.loads_pkcs12`` which had been deprecated for 3 years.
+- Added ``OpenSSL.SSL.OP_LEGACY_SERVER_CONNECT`` to allow legacy insecure renegotiation between OpenSSL and unpatched servers.
+  `#1234 <https://github.com/pyca/pyopenssl/pull/1234>`_.
+
+Deprecations:
+^^^^^^^^^^^^^
+
+- Deprecated ``OpenSSL.crypto.PKCS12`` (which was intended to have been deprecated at the same time as ``OpenSSL.crypto.load_pkcs12``).
+- Deprecated ``OpenSSL.crypto.NetscapeSPKI``.
+- Deprecated ``OpenSSL.crypto.CRL``
+- Deprecated ``OpenSSL.crypto.Revoked``
+- Deprecated ``OpenSSL.crypto.load_crl`` and ``OpenSSL.crypto.dump_crl``
+- Deprecated ``OpenSSL.crypto.sign`` and ``OpenSSL.crypto.verify``
+- Deprecated ``OpenSSL.crypto.X509Extension``
+
+Changes:
+^^^^^^^^
+
+- Changed ``OpenSSL.crypto.X509Store.add_crl`` to also accept
+  ``cryptography``'s ``x509.CertificateRevocationList`` arguments in addition
+  to the now deprecated ``OpenSSL.crypto.CRL`` arguments.
+- Fixed ``test_set_default_verify_paths`` test so that it is skipped if no
+  network connection is available.
+
+23.2.0 (2023-05-30)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Removed ``X509StoreFlags.NOTIFY_POLICY``.
+  `#1213 <https://github.com/pyca/pyopenssl/pull/1213>`_.
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Changes:
+^^^^^^^^
+
+- ``cryptography`` maximum version has been increased to 41.0.x.
+- Invalid versions are now rejected in ``OpenSSL.crypto.X509Req.set_version``.
+- Added ``X509VerificationCodes`` to ``OpenSSL.SSL``.
+  `#1202 <https://github.com/pyca/pyopenssl/pull/1202>`_.
+
+23.1.1 (2023-03-28)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Changes:
+^^^^^^^^
+
+- Worked around an issue in OpenSSL 3.1.0 which caused `X509Extension.get_short_name` to raise an exception when no short name was known to OpenSSL.
+  `#1204 <https://github.com/pyca/pyopenssl/pull/1204>`_.
+
+23.1.0 (2023-03-24)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Changes:
+^^^^^^^^
+
+- ``cryptography`` maximum version has been increased to 40.0.x.
+- Add ``OpenSSL.SSL.Connection.DTLSv1_get_timeout`` and ``OpenSSL.SSL.Connection.DTLSv1_handle_timeout``
+  to support DTLS timeouts `#1180 <https://github.com/pyca/pyopenssl/pull/1180>`_.
+
+23.0.0 (2023-01-01)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Changes:
+^^^^^^^^
+
+- Add ``OpenSSL.SSL.X509StoreFlags.PARTIAL_CHAIN`` constant to allow for users
+  to perform certificate verification on partial certificate chains.
+  `#1166 <https://github.com/pyca/pyopenssl/pull/1166>`_
+- ``cryptography`` maximum version has been increased to 39.0.x.
+
+22.1.0 (2022-09-25)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Remove support for SSLv2 and SSLv3.
+- The minimum ``cryptography`` version is now 38.0.x (and we now pin releases
+  against ``cryptography`` major versions to prevent future breakage)
+- The ``OpenSSL.crypto.X509StoreContextError`` exception has been refactored,
+  changing its internal attributes.
+  `#1133 <https://github.com/pyca/pyopenssl/pull/1133>`_
+
+Deprecations:
+^^^^^^^^^^^^^
+
+- ``OpenSSL.SSL.SSLeay_version`` is deprecated in favor of
+  ``OpenSSL.SSL.OpenSSL_version``. The constants ``OpenSSL.SSL.SSLEAY_*`` are
+  deprecated in favor of ``OpenSSL.SSL.OPENSSL_*``.
+
+Changes:
+^^^^^^^^
+
+- Add ``OpenSSL.SSL.Connection.set_verify`` and ``OpenSSL.SSL.Connection.get_verify_mode``
+  to override the context object's verification flags.
+  `#1073 <https://github.com/pyca/pyopenssl/pull/1073>`_
+- Add ``OpenSSL.SSL.Connection.use_certificate`` and ``OpenSSL.SSL.Connection.use_privatekey``
+  to set a certificate per connection (and not just per context) `#1121 <https://github.com/pyca/pyopenssl/pull/1121>`_.
+
+22.0.0 (2022-01-29)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Drop support for Python 2.7.
+  `#1047 <https://github.com/pyca/pyopenssl/pull/1047>`_
+- The minimum ``cryptography`` version is now 35.0.
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Changes:
+^^^^^^^^
+
+- Expose wrappers for some `DTLS
+  <https://en.wikipedia.org/wiki/Datagram_Transport_Layer_Security>`_
+  primitives. `#1026 <https://github.com/pyca/pyopenssl/pull/1026>`_
+
+21.0.0 (2021-09-28)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- The minimum ``cryptography`` version is now 3.3.
+- Drop support for Python 3.5
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Changes:
+^^^^^^^^
+
+- Raise an error when an invalid ALPN value is set.
+  `#993 <https://github.com/pyca/pyopenssl/pull/993>`_
+- Added ``OpenSSL.SSL.Context.set_min_proto_version`` and ``OpenSSL.SSL.Context.set_max_proto_version``
+  to set the minimum and maximum supported TLS version `#985 <https://github.com/pyca/pyopenssl/pull/985>`_.
+- Updated ``to_cryptography`` and ``from_cryptography`` methods to support an upcoming release of ``cryptography`` without raising deprecation warnings.
+  `#1030 <https://github.com/pyca/pyopenssl/pull/1030>`_
+
+20.0.1 (2020-12-15)
+-------------------
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Changes:
+^^^^^^^^
+
+- Fixed compatibility with OpenSSL 1.1.0.
+
+20.0.0 (2020-11-27)
+-------------------
+
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- The minimum ``cryptography`` version is now 3.2.
+- Remove deprecated ``OpenSSL.tsafe`` module.
+- Removed deprecated ``OpenSSL.SSL.Context.set_npn_advertise_callback``, ``OpenSSL.SSL.Context.set_npn_select_callback``, and ``OpenSSL.SSL.Connection.get_next_proto_negotiated``.
+- Drop support for Python 3.4
+- Drop support for OpenSSL 1.0.1 and 1.0.2
+
+Deprecations:
+^^^^^^^^^^^^^
+
+- Deprecated ``OpenSSL.crypto.loads_pkcs7`` and ``OpenSSL.crypto.loads_pkcs12``.
+
+Changes:
+^^^^^^^^
+
+- Added a new optional ``chain`` parameter to ``OpenSSL.crypto.X509StoreContext()``
+  where additional untrusted certificates can be specified to help chain building.
+  `#948 <https://github.com/pyca/pyopenssl/pull/948>`_
+- Added ``OpenSSL.crypto.X509Store.load_locations`` to set trusted
+  certificate file bundles and/or directories for verification.
+  `#943 <https://github.com/pyca/pyopenssl/pull/943>`_
+- Added ``Context.set_keylog_callback`` to log key material.
+  `#910 <https://github.com/pyca/pyopenssl/pull/910>`_
+- Added ``OpenSSL.SSL.Connection.get_verified_chain`` to retrieve the
+  verified certificate chain of the peer.
+  `#894 <https://github.com/pyca/pyopenssl/pull/894>`_.
+- Make verification callback optional in ``Context.set_verify``.
+  If omitted, OpenSSL's default verification is used.
+  `#933 <https://github.com/pyca/pyopenssl/pull/933>`_
+- Fixed a bug that could truncate or cause a zero-length key error due to a
+  null byte in private key passphrase in ``OpenSSL.crypto.load_privatekey``
+  and ``OpenSSL.crypto.dump_privatekey``.
+  `#947 <https://github.com/pyca/pyopenssl/pull/947>`_
+
+19.1.0 (2019-11-18)
+-------------------
+
+
+Backward-incompatible changes:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Removed deprecated ``ContextType``, ``ConnectionType``, ``PKeyType``, ``X509NameType``, ``X509ReqType``, ``X509Type``, ``X509StoreType``, ``CRLType``, ``PKCS7Type``, ``PKCS12Type``, and ``NetscapeSPKIType`` aliases.
+  Use the classes without the ``Type`` suffix instead.
+  `#814 <https://github.com/pyca/pyopenssl/pull/814>`_
+- The minimum ``cryptography`` version is now 2.8 due to issues on macOS with a transitive dependency.
+  `#875 <https://github.com/pyca/pyopenssl/pull/875>`_
+
+Deprecations:
+^^^^^^^^^^^^^
+
+- Deprecated ``OpenSSL.SSL.Context.set_npn_advertise_callback``, ``OpenSSL.SSL.Context.set_npn_select_callback``, and ``OpenSSL.SSL.Connection.get_next_proto_negotiated``.
+  ALPN should be used instead.
+  `#820 <https://github.com/pyca/pyopenssl/pull/820>`_
+
+
+Changes:
+^^^^^^^^
+
+- Support ``bytearray`` in ``SSL.Connection.send()`` by using cffi's from_buffer.
+  `#852 <https://github.com/pyca/pyopenssl/pull/852>`_
+- The ``OpenSSL.SSL.Context.set_alpn_select_callback`` can return a new ``NO_OVERLAPPING_PROTOCOLS`` sentinel value
+  to allow a TLS handshake to complete without an application protocol.
+
+`Full changelog <https://pyopenssl.org/en/stable/changelog.html>`_.
+

shared/package-files/9b/9b34108030dba74e01d372b226ba2cb0ba7c62aef6449ce3a7edc0adf8db307f/blob

@@ -0,0 +1,188 @@
+try:
+    from ctypes import c_float, c_int, c_double
+except ImportError:
+    pass
+
+import pyglet.gl as pgl
+from sympy.core import S
+
+
+def get_model_matrix(array_type=c_float, glGetMethod=pgl.glGetFloatv):
+    """
+    Returns the current modelview matrix.
+    """
+    m = (array_type*16)()
+    glGetMethod(pgl.GL_MODELVIEW_MATRIX, m)
+    return m
+
+
+def get_projection_matrix(array_type=c_float, glGetMethod=pgl.glGetFloatv):
+    """
+    Returns the current modelview matrix.
+    """
+    m = (array_type*16)()
+    glGetMethod(pgl.GL_PROJECTION_MATRIX, m)
+    return m
+
+
+def get_viewport():
+    """
+    Returns the current viewport.
+    """
+    m = (c_int*4)()
+    pgl.glGetIntegerv(pgl.GL_VIEWPORT, m)
+    return m
+
+
+def get_direction_vectors():
+    m = get_model_matrix()
+    return ((m[0], m[4], m[8]),
+            (m[1], m[5], m[9]),
+            (m[2], m[6], m[10]))
+
+
+def get_view_direction_vectors():
+    m = get_model_matrix()
+    return ((m[0], m[1], m[2]),
+            (m[4], m[5], m[6]),
+            (m[8], m[9], m[10]))
+
+
+def get_basis_vectors():
+    return ((1, 0, 0), (0, 1, 0), (0, 0, 1))
+
+
+def screen_to_model(x, y, z):
+    m = get_model_matrix(c_double, pgl.glGetDoublev)
+    p = get_projection_matrix(c_double, pgl.glGetDoublev)
+    w = get_viewport()
+    mx, my, mz = c_double(), c_double(), c_double()
+    pgl.gluUnProject(x, y, z, m, p, w, mx, my, mz)
+    return float(mx.value), float(my.value), float(mz.value)
+
+
+def model_to_screen(x, y, z):
+    m = get_model_matrix(c_double, pgl.glGetDoublev)
+    p = get_projection_matrix(c_double, pgl.glGetDoublev)
+    w = get_viewport()
+    mx, my, mz = c_double(), c_double(), c_double()
+    pgl.gluProject(x, y, z, m, p, w, mx, my, mz)
+    return float(mx.value), float(my.value), float(mz.value)
+
+
+def vec_subs(a, b):
+    return tuple(a[i] - b[i] for i in range(len(a)))
+
+
+def billboard_matrix():
+    """
+    Removes rotational components of
+    current matrix so that primitives
+    are always drawn facing the viewer.
+
+    |1|0|0|x|
+    |0|1|0|x|
+    |0|0|1|x| (x means left unchanged)
+    |x|x|x|x|
+    """
+    m = get_model_matrix()
+    # XXX: for i in range(11): m[i] = i ?
+    m[0] = 1
+    m[1] = 0
+    m[2] = 0
+    m[4] = 0
+    m[5] = 1
+    m[6] = 0
+    m[8] = 0
+    m[9] = 0
+    m[10] = 1
+    pgl.glLoadMatrixf(m)
+
+
+def create_bounds():
+    return [[S.Infinity, S.NegativeInfinity, 0],
+            [S.Infinity, S.NegativeInfinity, 0],
+            [S.Infinity, S.NegativeInfinity, 0]]
+
+
+def update_bounds(b, v):
+    if v is None:
+        return
+    for axis in range(3):
+        b[axis][0] = min([b[axis][0], v[axis]])
+        b[axis][1] = max([b[axis][1], v[axis]])
+
+
+def interpolate(a_min, a_max, a_ratio):
+    return a_min + a_ratio * (a_max - a_min)
+
+
+def rinterpolate(a_min, a_max, a_value):
+    a_range = a_max - a_min
+    if a_max == a_min:
+        a_range = 1.0
+    return (a_value - a_min) / float(a_range)
+
+
+def interpolate_color(color1, color2, ratio):
+    return tuple(interpolate(color1[i], color2[i], ratio) for i in range(3))
+
+
+def scale_value(v, v_min, v_len):
+    return (v - v_min) / v_len
+
+
+def scale_value_list(flist):
+    v_min, v_max = min(flist), max(flist)
+    v_len = v_max - v_min
+    return [scale_value(f, v_min, v_len) for f in flist]
+
+
+def strided_range(r_min, r_max, stride, max_steps=50):
+    o_min, o_max = r_min, r_max
+    if abs(r_min - r_max) < 0.001:
+        return []
+    try:
+        range(int(r_min - r_max))
+    except (TypeError, OverflowError):
+        return []
+    if r_min > r_max:
+        raise ValueError("r_min cannot be greater than r_max")
+    r_min_s = (r_min % stride)
+    r_max_s = stride - (r_max % stride)
+    if abs(r_max_s - stride) < 0.001:
+        r_max_s = 0.0
+    r_min -= r_min_s
+    r_max += r_max_s
+    r_steps = int((r_max - r_min)/stride)
+    if max_steps and r_steps > max_steps:
+        return strided_range(o_min, o_max, stride*2)
+    return [r_min] + [r_min + e*stride for e in range(1, r_steps + 1)] + [r_max]
+
+
+def parse_option_string(s):
+    if not isinstance(s, str):
+        return None
+    options = {}
+    for token in s.split(';'):
+        pieces = token.split('=')
+        if len(pieces) == 1:
+            option, value = pieces[0], ""
+        elif len(pieces) == 2:
+            option, value = pieces
+        else:
+            raise ValueError("Plot option string '%s' is malformed." % (s))
+        options[option.strip()] = value.strip()
+    return options
+
+
+def dot_product(v1, v2):
+    return sum(v1[i]*v2[i] for i in range(3))
+
+
+def vec_sub(v1, v2):
+    return tuple(v1[i] - v2[i] for i in range(3))
+
+
+def vec_mag(v):
+    return sum(v[i]**2 for i in range(3))**(0.5)

shared/package-files/9b/9b3bb70cade8184ee8eca3239aa7e6762ad6be906e15e8ea42ed5629b8abdafa/blob

@@ -0,0 +1,200 @@
+/* Translated from Cython into C++ by SciPy developers in 2024.
+ * Original header with Copyright information appears below.
+ */
+
+/* Implementation of sin/cos/sinh/cosh integrals for complex arguments
+ *
+ * Sources
+ * [1] Fredrik Johansson and others. mpmath: a Python library for
+ *     arbitrary-precision floating-point arithmetic (version 0.19),
+ *     December 2013. http://mpmath.org/.
+ * [2] NIST, "Digital Library of Mathematical Functions",
+ *     https://dlmf.nist.gov/
+ */
+
+#pragma once
+
+#include "config.h"
+#include "error.h"
+
+#include "expint.h"
+#include "cephes/const.h"
+#include "cephes/sici.h"
+#include "cephes/shichi.h"
+
+namespace xsf {
+namespace detail {
+    
+    XSF_HOST_DEVICE inline void sici_power_series(int sgn, std::complex<double> z,
+						       std::complex<double> *s, std::complex<double> *c) {
+	/* DLMF 6.6.5 and 6.6.6. If sgn = -1 computes si/ci, and if sgn = 1
+	 * computes shi/chi.
+	 */        
+	std::complex<double> fac = z;
+	*s = fac;
+	*c = 0;
+	std::complex<double> term1, term2;
+	for (int n = 1; n < 100; n++) {
+	    fac *= static_cast<double>(sgn)*z/(2.0*n);
+	    term2 = fac/(2.0*n);
+	    *c += term2;
+	    fac *= z/(2.0*n + 1.0);
+	    term1 = fac/(2.0*n + 1.0);
+	    *s += term1;
+	    constexpr double tol = std::numeric_limits<double>::epsilon();
+	    if (std::abs(term1) < tol*std::abs(*s) && std::abs(term2) < tol*std::abs(*c)) {
+		break;
+	    }
+	}
+    }
+
+}
+
+    
+XSF_HOST_DEVICE inline int sici(std::complex<double> z,
+				    std::complex<double> *si, std::complex<double> *ci) {
+    /* Compute sin/cos integrals at complex arguments. The algorithm
+     * largely follows that of [1].
+     */
+
+    constexpr double EULER = xsf::cephes::detail::SCIPY_EULER;
+
+    if (z == std::numeric_limits<double>::infinity()) {
+        *si = M_PI_2;
+        *ci = 0;
+        return 0;
+    }
+    if (z == -std::numeric_limits<double>::infinity()) {
+	*si = -M_PI_2;
+        *ci = {0.0, M_PI};
+        return 0;
+    }
+
+    if (std::abs(z) < 0.8) {
+        // Use the series to avoid cancellation in si
+	detail::sici_power_series(-1, z, si, ci);
+
+        if (z == 0.0) {
+            set_error("sici", SF_ERROR_DOMAIN, NULL);
+            *ci = {-std::numeric_limits<double>::infinity(), std::numeric_limits<double>::quiet_NaN()};
+        } else {
+            *ci += EULER + std::log(z);
+	}
+        return 0;
+    }
+    
+    // DLMF 6.5.5/6.5.6 plus DLMF 6.4.4/6.4.6/6.4.7
+    std::complex<double> jz = std::complex<double>(0.0, 1.0) * z;
+    std::complex<double> term1 = expi(jz);
+    std::complex<double> term2 = expi(-jz);
+    *si = std::complex<double>(0.0, -0.5)*(term1 - term2);
+    *ci = 0.5*(term1 + term2);
+    if (z.real() == 0) {
+        if (z.imag() > 0) {
+            *ci += std::complex<double>(0.0, M_PI_2);
+	} else if (z.imag() < 0) {
+            *ci -= std::complex<double>(0.0, M_PI_2);
+	}
+    } else if (z.real() > 0) {
+        *si -= M_PI_2;
+    } else {
+        *si += M_PI_2;
+        if (z.imag() >= 0) {
+            *ci += std::complex<double>(0.0, M_PI);
+        } else {
+            *ci -= std::complex<double>(0.0, M_PI);
+	}
+    }
+    return 0;
+}
+
+XSF_HOST_DEVICE inline int sici(std::complex<float> z,
+				    std::complex<float> *si_f, std::complex<float> *ci_f) {
+    std::complex<double> si;
+    std::complex<double> ci;
+    int res = sici(z, &si, &ci);
+    *si_f = si;
+    *ci_f = ci;
+    return res;
+}
+
+XSF_HOST_DEVICE inline int shichi(std::complex<double> z,
+				       std::complex<double> *shi, std::complex<double> *chi) {
+    /* Compute sinh/cosh integrals at complex arguments. The algorithm
+     * largely follows that of [1].
+     */
+    constexpr double EULER = xsf::cephes::detail::SCIPY_EULER;
+    if (z == std::numeric_limits<double>::infinity()) {
+        *shi = std::numeric_limits<double>::infinity();
+        *chi = std::numeric_limits<double>::infinity();
+        return 0;
+    }
+    if (z == -std::numeric_limits<double>::infinity()) {
+        *shi = -std::numeric_limits<double>::infinity();
+        *chi = std::numeric_limits<double>::infinity();
+        return 0;
+    }
+    if (std::abs(z) < 0.8) {
+        // Use the series to avoid cancellation in shi
+	detail::sici_power_series(1, z, shi, chi);
+        if (z == 0.0) {
+            set_error("shichi", SF_ERROR_DOMAIN, NULL);
+            *chi = {-std::numeric_limits<double>::infinity(), std::numeric_limits<double>::quiet_NaN()};
+        } else {
+            *chi += EULER + std::log(z);
+	}
+	return 0;
+    }
+
+    std::complex<double> term1 = expi(z);
+    std::complex<double> term2 = expi(-z);
+    *shi = 0.5*(term1 - term2);
+    *chi = 0.5*(term1 + term2);
+    if (z.imag() > 0) {
+        *shi -= std::complex<double>(0.0, 0.5*M_PI);
+        *chi += std::complex<double>(0.0, 0.5*M_PI);
+    } else if (z.imag() < 0) {
+        *shi += std::complex<double>(0.0, 0.5*M_PI);
+        *chi -= std::complex<double>(0.0, 0.5*M_PI);
+    } else if (z.real() < 0) {
+        *chi += std::complex<double>(0.0, M_PI);
+    }
+    return 0;
+}
+
+XSF_HOST_DEVICE inline int shichi(std::complex<float> z,
+				    std::complex<float> *shi_f, std::complex<float> *chi_f) {
+    std::complex<double> shi;
+    std::complex<double> chi;
+    int res = shichi(z, &shi, &chi);
+    *shi_f = shi;
+    *chi_f = chi;
+    return res;
+}
+
+XSF_HOST_DEVICE inline int sici(double x, double *si, double *ci) {
+    return cephes::sici(x, si, ci);
+}
+
+XSF_HOST_DEVICE inline int shichi(double x, double *shi, double *chi) {
+    return cephes::shichi(x, shi, chi);
+}
+
+XSF_HOST_DEVICE inline int sici(float x, float *si_f, float *ci_f) {
+    double si;
+    double ci;
+    int res = cephes::sici(x, &si, &ci);
+    *si_f = si;
+    *ci_f = ci;
+    return res;
+}
+
+XSF_HOST_DEVICE inline int shichi(float x, float *shi_f, float *chi_f) {
+    double shi;
+    double chi;
+    int res = cephes::shichi(x, &shi, &chi);
+    *shi_f = shi;
+    *chi_f = chi;
+    return res;
+}
+}

shared/package-files/9b/9b3e92596047cab99a939229b56d656a7862d617eacf9291d7fa6f6c95f80e36/blob

@@ -0,0 +1,570 @@
+from collections import defaultdict
+
+import numpy as np
+
+import statsmodels.base.model as base
+import statsmodels.base.wrapper as wrap
+from statsmodels.formula.formulatools import advance_eval_env
+from statsmodels.genmod import families
+from statsmodels.genmod.families import links, varfuncs
+from statsmodels.genmod.generalized_linear_model import GLM
+import statsmodels.regression.linear_model as lm
+from statsmodels.tools._decorators import cache_readonly
+
+
+class QIFCovariance:
+    """
+    A covariance model for quadratic inference function regression.
+
+    The mat method returns a basis matrix B such that the inverse
+    of the working covariance lies in the linear span of the
+    basis matrices.
+
+    Subclasses should set the number of basis matrices `num_terms`,
+    so that `mat(d, j)` for j=0, ..., num_terms-1 gives the basis
+    of dimension d.
+    """
+
+    def mat(self, dim, term):
+        """
+        Returns the term'th basis matrix, which is a dim x dim
+        matrix.
+
+        Parameters
+        ----------
+        dim : int
+            The dimension of the basis matrix.
+        term : int
+            The index of the basis matrix to return, ranging from 0
+            to `num_terms` - 1.
+
+        Returns
+        -------
+        ndarray or None
+            The dim x dim basis matrix for `term`, or None if `term`
+            is greater than or equal to `num_terms`.
+        """
+        raise NotImplementedError
+
+
+class QIFIndependence(QIFCovariance):
+    """
+    Independent working covariance for QIF regression.  This covariance
+    model gives identical results to GEE with the independence working
+    covariance.  When using QIFIndependence as the working covariance,
+    the QIF value will be zero, and cannot be used for chi^2 testing, or
+    for model selection using AIC, BIC, etc.
+    """
+
+    def __init__(self):
+        self.num_terms = 1
+
+    def mat(self, dim, term):
+        if term == 0:
+            return np.eye(dim)
+        else:
+            return None
+
+
+class QIFExchangeable(QIFCovariance):
+    """
+    Exchangeable working covariance for QIF regression.
+    """
+
+    def __init__(self):
+        self.num_terms = 2
+
+    def mat(self, dim, term):
+        if term == 0:
+            return np.eye(dim)
+        elif term == 1:
+            return np.ones((dim, dim))
+        else:
+            return None
+
+
+class QIFAutoregressive(QIFCovariance):
+    """
+    Autoregressive working covariance for QIF regression.
+    """
+
+    def __init__(self):
+        self.num_terms = 3
+
+    def mat(self, dim, term):
+
+        if dim < 3:
+            msg = ("Groups must have size at least 3 for "
+                   "autoregressive covariance.")
+            raise ValueError(msg)
+
+        if term == 0:
+            return np.eye(dim)
+        elif term == 1:
+            mat = np.zeros((dim, dim))
+            mat.flat[1::(dim+1)] = 1
+            mat += mat.T
+            return mat
+        elif term == 2:
+            mat = np.zeros((dim, dim))
+            mat[0, 0] = 1
+            mat[dim-1, dim-1] = 1
+            return mat
+        else:
+            return None
+
+
+class QIF(base.Model):
+    """
+    Fit a regression model using quadratic inference functions (QIF).
+
+    QIF is an alternative to GEE that can be more efficient, and that
+    offers different approaches for model selection and inference.
+
+    Parameters
+    ----------
+    endog : array_like
+        The dependent variables of the regression.
+    exog : array_like
+        The independent variables of the regression.
+    groups : array_like
+        Labels indicating which group each observation belongs to.
+        Observations in different groups should be independent.
+    family : genmod family, optional
+        An instance of a GLM family. The default is Gaussian.
+    cov_struct : QIFCovariance instance, optional
+        An instance of a QIFCovariance. The default is
+        QIFIndependence.
+    missing : str, optional
+        Available options are 'none', 'drop', and 'raise'. If 'none', no nan
+        checking is done. If 'drop', any observations with nans are dropped.
+        If 'raise', an error is raised.
+
+    References
+    ----------
+    A. Qu, B. Lindsay, B. Li (2000).  Improving Generalized Estimating
+    Equations using Quadratic Inference Functions, Biometrika 87:4.
+    www.jstor.org/stable/2673612
+    """
+
+    def __init__(self, endog, exog, groups, family=None,
+                 cov_struct=None, missing="none", **kwargs):
+
+        # Handle the family argument
+        if family is None:
+            family = families.Gaussian()
+        elif not issubclass(family.__class__, families.Family):
+            raise ValueError("QIF: `family` must be a genmod "
+                             "family instance")
+        self.family = family
+
+        self._fit_history = defaultdict(list)
+
+        # Handle the cov_struct argument
+        if cov_struct is None:
+            cov_struct = QIFIndependence()
+        elif not isinstance(cov_struct, QIFCovariance):
+            raise ValueError(
+                "QIF: `cov_struct` must be a QIFCovariance instance")
+        self.cov_struct = cov_struct
+
+        groups = np.asarray(groups)
+
+        super().__init__(endog, exog, groups=groups,
+                         missing=missing, **kwargs)
+
+        self.group_names = list(set(groups))
+        self.nobs = len(self.endog)
+
+        groups_ix = defaultdict(list)
+        for i, g in enumerate(groups):
+            groups_ix[g].append(i)
+        self.groups_ix = [groups_ix[na] for na in self.group_names]
+
+        self._check_args(groups)
+
+    def _check_args(self, groups):
+
+        if len(groups) != len(self.endog):
+            msg = "QIF: groups and endog should have the same length"
+            raise ValueError(msg)
+
+        if len(self.endog) != self.exog.shape[0]:
+            msg = ("QIF: the length of endog should be equal to the "
+                   "number of rows of exog.")
+            raise ValueError(msg)
+
+    def objective(self, params):
+        """
+        Calculate the QIF objective function and its gradient.
+
+        Parameters
+        ----------
+        params : array_like
+            The model parameters at which the objective function and
+            its gradient are evaluated.
+
+        Returns
+        -------
+        qif : float
+            The value of the QIF objective function.
+        grad : ndarray
+            The gradient vector of the QIF objective function.
+        cmat : ndarray
+            The estimated covariance matrix of the estimating
+            equations.
+        gn : ndarray
+            The moment vector, i.e., the average of the estimating
+            equations over the groups.
+        gn_deriv : ndarray
+            The gradients of each estimating equation with
+            respect to the parameter.
+        """
+
+        endog = self.endog
+        exog = self.exog
+        lpr = np.dot(exog, params)
+        mean = self.family.link.inverse(lpr)
+        va = self.family.variance(mean)
+
+        # Mean derivative
+        idl = self.family.link.inverse_deriv(lpr)
+        idl2 = self.family.link.inverse_deriv2(lpr)
+        vd = self.family.variance.deriv(mean)
+
+        m = self.cov_struct.num_terms
+        p = exog.shape[1]
+
+        d = p * m
+        gn = np.zeros(d)
+        gi = np.zeros(d)
+        gi_deriv = np.zeros((d, p))
+        gn_deriv = np.zeros((d, p))
+        cn_deriv = [0] * p
+        cmat = np.zeros((d, d))
+
+        fastvar = self.family.variance is varfuncs.constant
+        fastlink = isinstance(
+            self.family.link,
+            # TODO: Remove links.identity after deprecation final
+            (links.Identity, links.identity)
+        )
+
+        for ix in self.groups_ix:
+            sd = np.sqrt(va[ix])
+            resid = endog[ix] - mean[ix]
+            sresid = resid / sd
+            deriv = exog[ix, :] * idl[ix, None]
+
+            jj = 0
+            for j in range(m):
+                # The derivative of each term in (5) of Qu et al.
+                # There are four terms involving beta in a product.
+                # Iterated application of the product rule gives
+                # the gradient as a sum of four terms.
+                c = self.cov_struct.mat(len(ix), j)
+                crs1 = np.dot(c, sresid) / sd
+                gi[jj:jj+p] = np.dot(deriv.T, crs1)
+                crs2 = np.dot(c, -deriv / sd[:, None]) / sd[:, None]
+                gi_deriv[jj:jj+p, :] = np.dot(deriv.T, crs2)
+                if not (fastlink and fastvar):
+                    for k in range(p):
+                        m1 = np.dot(exog[ix, :].T,
+                                    idl2[ix] * exog[ix, k] * crs1)
+                        if not fastvar:
+                            vx = -0.5 * vd[ix] * deriv[:, k] / va[ix]**1.5
+                            m2 = np.dot(deriv.T, vx * np.dot(c, sresid))
+                            m3 = np.dot(deriv.T, np.dot(c, vx * resid) / sd)
+                        else:
+                            m2, m3 = 0, 0
+                        gi_deriv[jj:jj+p, k] += m1 + m2 + m3
+                jj += p
+
+            for j in range(p):
+                u = np.outer(gi, gi_deriv[:, j])
+                cn_deriv[j] += u + u.T
+
+            gn += gi
+            gn_deriv += gi_deriv
+
+            cmat += np.outer(gi, gi)
+
+        ngrp = len(self.groups_ix)
+        gn /= ngrp
+        gn_deriv /= ngrp
+        cmat /= ngrp**2
+
+        qif = np.dot(gn, np.linalg.solve(cmat, gn))
+
+        gcg = np.zeros(p)
+        for j in range(p):
+            cn_deriv[j] /= len(self.groups_ix)**2
+            u = np.linalg.solve(cmat, cn_deriv[j]).T
+            u = np.linalg.solve(cmat, u)
+            gcg[j] = np.dot(gn, np.dot(u, gn))
+
+        grad = 2 * np.dot(gn_deriv.T, np.linalg.solve(cmat, gn)) - gcg
+
+        return qif, grad, cmat, gn, gn_deriv
+
+    def estimate_scale(self, params):
+        """
+        Estimate the dispersion/scale.
+
+        The scale parameter for binomial and Poisson families is
+        fixed at 1, otherwise it is estimated from the data.
+
+        Parameters
+        ----------
+        params : array_like
+            The regression coefficients at which the scale is
+            estimated.
+
+        Returns
+        -------
+        float
+            The estimated scale parameter.
+        """
+
+        if isinstance(self.family, (families.Binomial, families.Poisson)):
+            return 1.
+
+        if hasattr(self, "ddof_scale"):
+            ddof_scale = self.ddof_scale
+        else:
+            ddof_scale = self.exog[1]
+
+        lpr = np.dot(self.exog, params)
+        mean = self.family.link.inverse(lpr)
+        resid = self.endog - mean
+        scale = np.sum(resid**2) / (self.nobs - ddof_scale)
+
+        return scale
+
+    @classmethod
+    def from_formula(cls, formula, groups, data, subset=None,
+                     *args, **kwargs):
+        """
+        Create a QIF model instance from a formula and dataframe.
+
+        Parameters
+        ----------
+        formula : str or generic Formula object
+            The formula specifying the model
+        groups : array_like or str
+            Array of grouping labels.  If a string, this is the name
+            of a variable in `data` that contains the grouping labels.
+        data : array_like
+            The data for the model.
+        subset : array_like, optional
+            An array_like object of booleans, integers, or index
+            values that indicate the subset of the data to use when
+            fitting the model.
+
+        Returns
+        -------
+        model : QIF model instance
+        """
+
+        if isinstance(groups, str):
+            groups = data[groups]
+        advance_eval_env(kwargs)
+        model = super().from_formula(
+            formula, data, *args,  subset=subset, groups=groups, **kwargs
+        )
+
+        return model
+
+    def fit(self, maxiter=100, start_params=None, tol=1e-6, gtol=1e-4,
+            ddof_scale=None):
+        """
+        Fit a GLM to correlated data using QIF.
+
+        Parameters
+        ----------
+        maxiter : int, optional
+            Maximum number of iterations.
+        start_params : array_like, optional
+            Starting values
+        tol : float, optional
+            Convergence threshold for difference of successive
+            estimates.
+        gtol : float, optional
+            Convergence threshold for gradient.
+        ddof_scale : int, optional
+            Degrees of freedom for the scale parameter
+
+        Returns
+        -------
+        QIFResults object
+        """
+
+        if ddof_scale is None:
+            self.ddof_scale = self.exog.shape[1]
+        else:
+            self.ddof_scale = ddof_scale
+
+        if start_params is None:
+            model = GLM(self.endog, self.exog, family=self.family)
+            result = model.fit()
+            params = result.params
+        else:
+            params = start_params
+
+        for _ in range(maxiter):
+
+            qif, grad, cmat, _, gn_deriv = self.objective(params)
+
+            gnorm = np.sqrt(np.sum(grad * grad))
+            self._fit_history["qif"].append(qif)
+            self._fit_history["gradnorm"].append(gnorm)
+
+            if gnorm < gtol:
+                break
+
+            cjac = 2 * np.dot(gn_deriv.T, np.linalg.solve(cmat, gn_deriv))
+            step = np.linalg.solve(cjac, grad)
+
+            snorm = np.sqrt(np.sum(step * step))
+            self._fit_history["stepnorm"].append(snorm)
+            if snorm < tol:
+                break
+            params -= step
+
+        vcov = np.dot(gn_deriv.T, np.linalg.solve(cmat, gn_deriv))
+        vcov = np.linalg.inv(vcov)
+        scale = self.estimate_scale(params)
+
+        rslt = QIFResults(self, params, vcov / scale, scale)
+        rslt.fit_history = self._fit_history
+        self._fit_history = defaultdict(list)
+
+        return QIFResultsWrapper(rslt)
+
+
+class QIFResults(base.LikelihoodModelResults):
+    """
+    Results class for QIF Regression
+
+    Attributes
+    ----------
+    qif : float
+        The value of the QIF objective function at the estimated
+        parameters.
+    """
+    def __init__(self, model, params, cov_params, scale,
+                 use_t=False, **kwds):
+
+        super().__init__(
+            model, params, normalized_cov_params=cov_params,
+            scale=scale)
+
+        self.qif, _, _, _, _ = self.model.objective(params)
+
+    @cache_readonly
+    def aic(self):
+        """
+        An AIC-like statistic for models fit using QIF.
+        """
+        if isinstance(self.model.cov_struct, QIFIndependence):
+            msg = "AIC not available with QIFIndependence covariance"
+            raise ValueError(msg)
+        df = self.model.exog.shape[1]
+        return self.qif + 2*df
+
+    @cache_readonly
+    def bic(self):
+        """
+        A BIC-like statistic for models fit using QIF.
+        """
+        if isinstance(self.model.cov_struct, QIFIndependence):
+            msg = "BIC not available with QIFIndependence covariance"
+            raise ValueError(msg)
+        df = self.model.exog.shape[1]
+        return self.qif + np.log(self.model.nobs)*df
+
+    @cache_readonly
+    def fittedvalues(self):
+        """
+        Returns the fitted values from the model.
+        """
+        return self.model.family.link.inverse(
+                np.dot(self.model.exog, self.params))
+
+    def summary(self, yname=None, xname=None, title=None, alpha=.05):
+        """
+        Summarize the QIF regression results
+
+        Parameters
+        ----------
+        yname : str, optional
+            Default is `y`
+        xname : list of str, optional
+            Names for the exogenous variables, default is `var_#` where `#`
+            is the 0-based index of the regressor. Must match the number of
+            parameters in the model
+        title : str, optional
+            Title for the top table. If not None, then this replaces
+            the default title
+        alpha : float, optional
+            significance level for the confidence intervals
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be
+            printed or converted to various output formats.
+
+        See Also
+        --------
+        statsmodels.iolib.summary.Summary : class to hold summary results
+        """
+
+        top_left = [("Dep. Variable:", None),
+                    ("Method:", ["QIF"]),
+                    ("Family:", [self.model.family.__class__.__name__]),
+                    ("Covariance structure:",
+                     [self.model.cov_struct.__class__.__name__]),
+                    ("Date:", None),
+                    ("Time:", None),
+                    ]
+
+        NY = [len(y) for y in self.model.groups_ix]
+
+        top_right = [("No. Observations:", [sum(NY)]),
+                     ("No. clusters:", [len(NY)]),
+                     ("Min. cluster size:", [min(NY)]),
+                     ("Max. cluster size:", [max(NY)]),
+                     ("Mean cluster size:", [f"{np.mean(NY):.1f}"]),
+                     ("Scale:", [f"{self.scale:.3f}"]),
+                     ]
+
+        if title is None:
+            title = self.model.__class__.__name__ + " " +\
+                "Regression Results"
+
+        # Override the exog variable names if xname is provided as an
+        # argument.
+        if xname is None:
+            xname = self.model.exog_names
+
+        if yname is None:
+            yname = self.model.endog_names
+
+        # Create summary table instance
+        from statsmodels.iolib.summary import Summary
+        smry = Summary()
+        smry.add_table_2cols(self, gleft=top_left, gright=top_right,
+                             yname=yname, xname=xname,
+                             title=title)
+        smry.add_table_params(self, yname=yname, xname=xname,
+                              alpha=alpha, use_t=False)
+
+        return smry
+
+
+class QIFResultsWrapper(lm.RegressionResultsWrapper):
+    pass
+
+
+wrap.populate_wrapper(QIFResultsWrapper, QIFResults)

shared/package-files/9b/9b45a809e914c434a5fedb1bcfe7f9bd9ee4471fd5d828bcafa59cd50b502507/blob

@@ -0,0 +1,884 @@
+#pragma once
+
+#include <ATen/Context.h>
+#include <c10/core/DeviceType.h>
+#include <torch/csrc/autograd/autograd.h>
+#include <torch/csrc/autograd/edge.h>
+#include <torch/csrc/autograd/function.h>
+#include <torch/csrc/autograd/generated/variable_factories.h>
+#include <torch/csrc/autograd/variable.h>
+#include <torch/csrc/jit/api/compilation_unit.h>
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/jit/frontend/error_report.h>
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/mobile/register_ops_common_utils.h>
+#include <torch/csrc/jit/runtime/custom_operator.h>
+#include <torch/csrc/jit/runtime/graph_executor.h>
+#include <torch/csrc/jit/runtime/jit_exception.h>
+#include <torch/csrc/jit/runtime/logging.h>
+#include <torch/csrc/jit/runtime/operator.h>
+#include <torch/csrc/jit/runtime/print_handler.h>
+#include <torch/csrc/jit/runtime/profiling_record.h>
+#include <torch/csrc/jit/runtime/vararg_functions.h>
+#include <torch/csrc/jit/serialization/pickle.h>
+
+#include <ATen/ExpandUtils.h>
+#include <ATen/Parallel.h>
+#include <ATen/WrapDimUtils.h>
+#include <ATen/core/Dict.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/ivalue.h>
+#include <c10/core/Device.h>
+#include <c10/core/thread_pool.h>
+#include <c10/util/SmallVector.h>
+#include <c10/util/irange.h>
+#include <c10/util/string_utils.h>
+
+namespace torch::jit {
+constexpr inline c10::AliasAnalysisKind aliasAnalysisFromSchema() {
+  return c10::AliasAnalysisKind::FROM_SCHEMA;
+}
+
+constexpr inline c10::AliasAnalysisKind aliasAnalysisConservative() {
+  return c10::AliasAnalysisKind::CONSERVATIVE;
+}
+
+constexpr inline c10::AliasAnalysisKind aliasAnalysisSpecialCase() {
+  return c10::AliasAnalysisKind::INTERNAL_SPECIAL_CASE;
+}
+
+template <class T>
+c10::List<T> make_result_list(const TypePtr& elemType) {
+  return c10::List<T>();
+}
+
+template <>
+c10::impl::GenericList make_result_list<IValue>(const TypePtr& elemType);
+
+// As described in https://docs.python.org/3/library/functions.html#round
+// When a number is exactly halfway between two integers, python builtin round
+// function will round to even number. We use round(x/2)*2 to handle the
+// special halfway case. For positive 'x', round(x/2)*2 =
+// round((x_e + x_r)/2)*2 = x_e + round(x_r/2)*2, where x_e is an even integer,
+// x_r is either 0.5 of 1.5, round(x_r/2)*2 results a 0 or 2, so the final
+// result will always be a even number. Due to symmetricity, it also applies to
+// negative cases.
+inline double round_to_even(double a) {
+  return a - std::floor(a) == 0.5 ? (std::round(a * 0.5) * 2.0) : std::round(a);
+}
+
+// using the rules from python_arg_parser FunctionParameter::check
+// tensor cannot have grad set, tensor must be 0 dim,
+// and if the dest is an int the source must be integral type
+void checkImplicitTensorToNum(const at::Tensor& t, bool toInt);
+
+static C10_UNUSED int64_t floordiv(int64_t a, int64_t b) {
+  if (b == 0) {
+    throw std::runtime_error("division by 0");
+  }
+  if ((a > 0) == (b > 0)) {
+    // simple case, both have same sign
+    return a / b;
+  } else {
+    // in python division rounds down, it doesn't not truncate like in c++
+    auto r = lldiv(a, b);
+    return (r.rem) ? r.quot - 1 : r.quot;
+  }
+}
+TORCH_API void checkDoubleInRange(double a);
+static C10_UNUSED int64_t floor(double a) {
+  checkDoubleInRange(a);
+  return std::floor(a);
+}
+static C10_UNUSED int64_t ceil(double a) {
+  checkDoubleInRange(a);
+  return std::ceil(a);
+}
+
+static C10_UNUSED int64_t gcd(int64_t a, int64_t b) {
+  while (b != 0) {
+    int64_t r = a % b;
+    a = b;
+    b = r;
+  }
+  // in python gcd returns non-negative values
+  return std::abs(a);
+}
+
+int64_t partProduct(int n, int m);
+
+void loop(int n, int64_t& p, int64_t& r);
+
+int nminussumofbits(int v);
+
+int64_t factorial(int n);
+static const double degToRad = std::acos(-1.0) / 180.0;
+static const double radToDeg = 180.0 / std::acos(-1.0);
+double degrees(double x);
+double radians(double x);
+
+// Convert an python index (which may be negative) into an index usable for a
+// C++ container
+
+// Equivalent to list.at(idx)
+template <typename T>
+decltype(auto) getItem(const c10::List<T>& list, int64_t idx) {
+  const int64_t list_size = list.size();
+  const int64_t normalized_idx = normalizeIndex(idx, list_size);
+  if (normalized_idx < 0 || normalized_idx >= list_size) {
+    throw std::out_of_range("list index out of range");
+  }
+  return list.get(normalized_idx);
+}
+
+template <typename T>
+void setItem(const c10::List<T>& list, int64_t idx, T&& value) {
+  const int64_t list_size = list.size();
+  const int64_t normalized_idx = normalizeIndex(idx, list_size);
+  if (normalized_idx < 0 || normalized_idx >= list_size) {
+    throw std::out_of_range("list index out of range");
+  }
+  list.set(normalized_idx, std::forward<T>(value));
+}
+
+void listAppend(Stack& stack);
+
+void listReverse(Stack& stack);
+
+template <typename T>
+void minList(Stack& stack) {
+  c10::List<T> a = pop(stack).to<c10::List<T>>();
+  c10::List<T> b = pop(stack).to<c10::List<T>>();
+
+  size_t min_size = std::min(a.size(), b.size());
+  for (const auto i : c10::irange(min_size)) {
+    if (a[i] == b[i]) {
+      continue;
+    }
+
+    push(stack, a[i] < b[i] ? a : b);
+    return;
+  }
+
+  push(stack, b.size() < a.size() ? b : a);
+}
+
+template <typename T>
+void maxList(Stack& stack) {
+  c10::List<T> a = pop(stack).to<c10::List<T>>();
+  c10::List<T> b = pop(stack).to<c10::List<T>>();
+
+  size_t min_size = std::min(a.size(), b.size());
+  for (const auto i : c10::irange(min_size)) {
+    if (a[i] == b[i]) {
+      continue;
+    }
+
+    push(stack, a[i] > b[i] ? a : b);
+    return;
+  }
+
+  push(stack, b.size() > a.size() ? b : a);
+}
+
+void listPopImpl(Stack& stack, const char* empty_message);
+
+void listPop(Stack& stack);
+
+void listClear(Stack& stack);
+
+void listDelete(Stack& stack);
+
+void listInsert(Stack& stack);
+
+template <typename T>
+void listRemove(Stack& stack) {
+  T elem = pop(stack).to<T>();
+  c10::List<T> list = pop(stack).to<c10::List<T>>();
+
+  auto pos = std::find(list.begin(), list.end(), elem);
+
+  if (pos != list.end()) {
+    list.erase(pos);
+  } else {
+    AT_ERROR("list.remove(x): x not in list");
+  }
+}
+
+template <typename T>
+void listMin(Stack& stack) {
+  c10::List<T> list = pop(stack).to<c10::List<T>>();
+  size_t list_size = list.size();
+  if (list_size == 0) {
+    throw std::runtime_error("min() arg is an empty sequence");
+  }
+
+  T min_elem = list[0];
+  for (const auto i : c10::irange(1, list_size)) {
+    T elem = list[i];
+    min_elem = elem < min_elem ? elem : min_elem;
+  }
+
+  stack.push_back(min_elem);
+}
+
+template <typename T>
+void listMax(Stack& stack) {
+  c10::List<T> list = pop(stack).to<c10::List<T>>();
+  size_t list_size = list.size();
+  if (list_size == 0) {
+    throw std::runtime_error("max() arg is an empty sequence");
+  }
+
+  T max_elem = list[0];
+  for (const auto i : c10::irange(1, list_size)) {
+    T elem = list[i];
+    max_elem = elem > max_elem ? elem : max_elem;
+  }
+
+  stack.push_back(max_elem);
+}
+
+template <>
+void listRemove<at::Tensor>(Stack& stack);
+
+template <typename T>
+void listIndex(Stack& stack) {
+  T elem = pop(stack).to<T>();
+  c10::List<T> list = pop(stack).to<c10::List<T>>();
+
+  auto pos = std::find(list.begin(), list.end(), elem);
+
+  if (pos != list.end()) {
+    push(stack, static_cast<int64_t>(std::distance(list.begin(), pos)));
+  } else {
+    AT_ERROR("'", elem, "' is not in list");
+  }
+}
+
+template <>
+void listIndex<at::Tensor>(Stack& stack);
+
+template <typename T>
+void listCount(Stack& stack) {
+  T elem = pop(stack).to<T>();
+  c10::List<T> list = pop(stack).to<c10::List<T>>();
+
+  const int64_t count = std::count(list.begin(), list.end(), elem);
+  push(stack, count);
+}
+
+template <>
+void listCount<at::Tensor>(Stack& stack);
+
+void listExtend(Stack& stack);
+
+void listCopy(Stack& stack);
+
+void listSelect(Stack& stack);
+
+void listLen(Stack& stack);
+
+template <typename T>
+void listEq(Stack& stack) {
+  c10::List<T> b = pop(stack).to<c10::List<T>>();
+  c10::List<T> a = pop(stack).to<c10::List<T>>();
+  push(stack, a == b);
+}
+
+template <typename T>
+void listNe(Stack& stack) {
+  c10::List<T> b = pop(stack).to<c10::List<T>>();
+  c10::List<T> a = pop(stack).to<c10::List<T>>();
+  push(stack, a != b);
+}
+
+inline bool tensor_list_equal(
+    const c10::List<at::Tensor>& a,
+    const c10::List<at::Tensor>& b) {
+  if (a.size() != b.size()) {
+    return false;
+  }
+
+  for (const auto i : c10::irange(a.size())) {
+    const at::Tensor& a_element = a[i];
+    const at::Tensor& b_element = b[i];
+    // This preserves Python's semantics, which uses eq() to compare two
+    // elements, then passes the result to bool().
+    // see: https://docs.python.org/3.4/reference/datamodel.html#object.__ge__
+    const auto cmp_result = a_element.eq(b_element);
+    if (!at::native::is_nonzero(cmp_result)) {
+      return false;
+    }
+  }
+
+  return true;
+}
+
+// Specialization for at::Tensor, since it doesn't define operator==
+template <>
+void listEq<at::Tensor>(Stack& stack);
+
+// Specialization for at::Tensor, since it doesn't define operator==
+template <>
+void listNe<at::Tensor>(Stack& stack);
+
+void listList(Stack& stack);
+
+template <typename T>
+void listContains(Stack& stack) {
+  auto key = pop(stack).to<T>();
+  auto list = pop(stack).to<c10::List<T>>();
+  // NOLINTNEXTLINE(performance-implicit-conversion-in-loop)
+  for (const T& item : list) {
+    if (item == key) {
+      push(stack, true);
+      return;
+    }
+  }
+  push(stack, false);
+}
+
+void listAdd(Stack& stack);
+
+void listInplaceAdd(Stack& stack);
+
+void listMulIntLeftInPlace(Stack& stack);
+
+void listMulIntLeft(Stack& stack);
+
+void listMulIntRight(Stack& stack);
+
+void listSlice(Stack& stack);
+
+template <typename T>
+void listSort(Stack& stack) {
+  bool reverse = pop(stack).toBool();
+  c10::List<T> list = pop(stack).to<c10::List<T>>();
+  std::sort(list.begin(), list.end(), [reverse](const T& a, const T& b) {
+    // FBCode errors without this check - "strict weak ordering"
+    // TODO: remove when possible, since it just slows down
+    // sorting and doesn't do anything useful
+    if (a == b) {
+      return false;
+    }
+    return (a < b) != reverse;
+  });
+}
+
+// Specialization for at::Tensor
+template <>
+void listSort<at::Tensor>(Stack& stack);
+
+template <typename T>
+void listCopyAndSort(Stack& stack) {
+  c10::List<T> list = pop(stack).to<c10::List<T>>();
+  auto list_copied = list.copy();
+  std::sort(list_copied.begin(), list_copied.end(), [](const T& a, const T& b) {
+    // "strict weak ordering" issue - see other sort
+    if (a == b) {
+      return false;
+    }
+    return a < b;
+  });
+  push(stack, list_copied);
+}
+
+// Specialization for at::Tensor
+template <>
+void listCopyAndSort<at::Tensor>(Stack& stack);
+
+void listSetItem(Stack& stack);
+
+struct OperatorGeneratorArgs {
+  const char* schema_str;
+  bool isOperationCreator;
+  union {
+    void (*operation)(Stack&);
+    OperationCreator operationCreator;
+  };
+  AliasAnalysisKind aliasAnalysis;
+
+  explicit constexpr OperatorGeneratorArgs(
+      torch::detail::SelectiveStr<true> schema_str,
+      void (*op)(Stack&),
+      AliasAnalysisKind aa)
+      : schema_str(schema_str),
+        isOperationCreator(false),
+        operation(op),
+        aliasAnalysis(aa) {}
+
+  explicit constexpr OperatorGeneratorArgs(
+      torch::detail::SelectiveStr<true> schema_str,
+      OperationCreator opCreator,
+      AliasAnalysisKind aa)
+      : schema_str(schema_str),
+        isOperationCreator(true),
+        operationCreator(opCreator),
+        aliasAnalysis(aa) {}
+
+  template <typename... Args>
+  explicit constexpr OperatorGeneratorArgs(
+      torch::detail::SelectiveStr<false>,
+      Args...)
+      : schema_str(nullptr),
+        isOperationCreator(false),
+        operation(nullptr),
+        aliasAnalysis(AliasAnalysisKind::INTERNAL_SPECIAL_CASE) {}
+};
+
+#define DEFINE_GENERIC_BINARY_OP(                                             \
+    aten_op, op, int_float_result, complex_result)                            \
+  OperatorGeneratorArgs(                                                      \
+      TORCH_SELECTIVE_SCHEMA(#aten_op                                         \
+                             ".int_int(int a, int b) -> " #int_float_result), \
+      [](Stack& stack) {                                                      \
+        int64_t a, b;                                                         \
+        pop(stack, a, b);                                                     \
+        push(stack, op);                                                      \
+      },                                                                      \
+      aliasAnalysisFromSchema()),                                             \
+      OperatorGeneratorArgs(                                                  \
+          TORCH_SELECTIVE_SCHEMA(                                             \
+              #aten_op                                                        \
+              ".float_float(float a, float b) -> " #int_float_result),        \
+          [](Stack& stack) {                                                  \
+            double a, b;                                                      \
+            pop(stack, a, b);                                                 \
+            push(stack, op);                                                  \
+          },                                                                  \
+          aliasAnalysisFromSchema()),                                         \
+      OperatorGeneratorArgs(                                                  \
+          TORCH_SELECTIVE_SCHEMA(                                             \
+              #aten_op                                                        \
+              ".complex_complex(complex a, complex b) -> " #complex_result),  \
+          [](Stack& stack) {                                                  \
+            c10::complex<double> a, b;                                        \
+            pop(stack, a, b);                                                 \
+            push(stack, op);                                                  \
+          },                                                                  \
+          aliasAnalysisFromSchema())
+
+// define implementations for primitive number ops
+#define DEFINE_GENERIC_OP(aten_op, int_op, float_op, int_result, float_result) \
+  OperatorGeneratorArgs(                                                       \
+      TORCH_SELECTIVE_SCHEMA(#aten_op ".int(int a, int b) -> " #int_result),   \
+      [](Stack& stack) {                                                       \
+        int64_t a, b;                                                          \
+        pop(stack, a, b);                                                      \
+        push(stack, int_op);                                                   \
+      },                                                                       \
+      aliasAnalysisFromSchema()),                                              \
+      OperatorGeneratorArgs(                                                   \
+          TORCH_SELECTIVE_SCHEMA(                                              \
+              #aten_op ".float(float a, float b) -> " #float_result),          \
+          [](Stack& stack) {                                                   \
+            double a, b;                                                       \
+            pop(stack, a, b);                                                  \
+            push(stack, float_op);                                             \
+          },                                                                   \
+          aliasAnalysisFromSchema())
+
+#define DEFINE_INT_FLOAT_OP(aten_op, op, result)                            \
+  OperatorGeneratorArgs(                                                    \
+      TORCH_SELECTIVE_SCHEMA(#aten_op                                       \
+                             ".int_float(int a, float b) -> " #result),     \
+      [](Stack& stack) {                                                    \
+        int64_t a;                                                          \
+        double b;                                                           \
+        pop(stack, a, b);                                                   \
+        push(stack, op);                                                    \
+      },                                                                    \
+      aliasAnalysisFromSchema()),                                           \
+      OperatorGeneratorArgs(                                                \
+          TORCH_SELECTIVE_SCHEMA(#aten_op                                   \
+                                 ".float_int(float a, int b) -> " #result), \
+          [](Stack& stack) {                                                \
+            double a;                                                       \
+            int64_t b;                                                      \
+            pop(stack, a, b);                                               \
+            push(stack, op);                                                \
+          },                                                                \
+          aliasAnalysisFromSchema())
+
+#define DEFINE_INT_OP(aten_op, op)                                  \
+  OperatorGeneratorArgs(                                            \
+      TORCH_SELECTIVE_SCHEMA(#aten_op ".int(int a, int b) -> int"), \
+      [](Stack& stack) {                                            \
+        int64_t a, b;                                               \
+        pop(stack, a, b);                                           \
+        push(stack, op); /* NOLINT(hicpp-signed-bitwise) */         \
+      },                                                            \
+      aliasAnalysisFromSchema())
+
+#define DEFINE_STR_CMP_OP(aten_op, op)                               \
+  OperatorGeneratorArgs(                                             \
+      TORCH_SELECTIVE_SCHEMA(#aten_op ".str(str a, str b) -> bool"), \
+      [](Stack& stack) {                                             \
+        auto b = pop(stack).toStringRef();                           \
+        auto a = pop(stack).toStringRef();                           \
+        push(stack, op);                                             \
+      },                                                             \
+      aliasAnalysisFromSchema())
+
+// define a primitive op over Scalar operands.
+// it's necessary to register this overload following
+// int/float variations to avoid trapping Scalar args
+// in unintended implicit conversions
+#define DEFINE_SCALAR_BINARY_OP_AVOID_COLLISION_GENERIC(          \
+    aten_op, int_op, float_op, result, string_val)                \
+  OperatorGeneratorArgs(                                          \
+      TORCH_SELECTIVE_SCHEMA(#aten_op string_val                  \
+                             "(Scalar a, Scalar b) -> " #result), \
+      [](Stack& stack) {                                          \
+        IValue x, y;                                              \
+        pop(stack, x, y);                                         \
+        if (x.isDouble()) {                                       \
+          if (y.isDouble()) {                                     \
+            double a = x.toDouble();                              \
+            double b = y.toDouble();                              \
+            push(stack, float_op);                                \
+          } else {                                                \
+            double a = x.toDouble();                              \
+            int64_t b = y.toInt();                                \
+            push(stack, float_op);                                \
+          }                                                       \
+        } else {                                                  \
+          if (y.isDouble()) {                                     \
+            int64_t a = x.toInt();                                \
+            double b = y.toDouble();                              \
+            push(stack, float_op);                                \
+          } else {                                                \
+            int64_t a = x.toInt();                                \
+            int64_t b = y.toInt();                                \
+            push(stack, int_op);                                  \
+          }                                                       \
+        }                                                         \
+      },                                                          \
+      aliasAnalysisFromSchema())
+
+#define DEFINE_SCALAR_BINARY_OP(aten_op, int_op, float_op, result) \
+  DEFINE_SCALAR_BINARY_OP_AVOID_COLLISION_GENERIC(                 \
+      aten_op, int_op, float_op, result, "")
+
+#define DEFINE_SCALAR_BINARY_OP_AVOID_COLLISION(   \
+    aten_op, int_op, float_op, result)             \
+  DEFINE_SCALAR_BINARY_OP_AVOID_COLLISION_GENERIC( \
+      aten_op, int_op, float_op, result, ".Scalar_Scalar")
+
+#define DEFINE_BINARY_OP(aten_op, op)             \
+  DEFINE_GENERIC_OP(aten_op, op, op, int, float), \
+      DEFINE_INT_FLOAT_OP(aten_op, op, float),    \
+      DEFINE_SCALAR_BINARY_OP(aten_op, op, op, Scalar)
+
+#define DEFINE_BINARY_FLOAT_OP(aten_op, op)         \
+  DEFINE_GENERIC_OP(aten_op, op, op, float, float), \
+      DEFINE_INT_FLOAT_OP(aten_op, op, float),      \
+      DEFINE_SCALAR_BINARY_OP(aten_op, op, op, float)
+
+#define DEFINE_COMPARISON_OP(aten_op, op)             \
+  DEFINE_GENERIC_OP(aten_op, op, op, bool, bool),     \
+      DEFINE_INT_FLOAT_OP(aten_op, op, bool),         \
+      DEFINE_SCALAR_BINARY_OP(aten_op, op, op, bool), \
+      DEFINE_STR_CMP_OP(aten_op, op)
+
+#define DEFINE_UNARY_INT_OP(aten_op, op, result)                  \
+  OperatorGeneratorArgs(                                          \
+      TORCH_SELECTIVE_SCHEMA(#aten_op ".int(int a) -> " #result), \
+      [](Stack& stack) {                                          \
+        int64_t a;                                                \
+        pop(stack, a);                                            \
+        push(stack, op);                                          \
+      },                                                          \
+      aliasAnalysisFromSchema())
+
+#define DEFINE_UNARY_FLOAT_OP(aten_op, op, result)                    \
+  OperatorGeneratorArgs(                                              \
+      TORCH_SELECTIVE_SCHEMA(#aten_op ".float(float a) -> " #result), \
+      [](Stack& stack) {                                              \
+        double a;                                                     \
+        pop(stack, a);                                                \
+        push(stack, op);                                              \
+      },                                                              \
+      aliasAnalysisFromSchema())
+
+#define DEFINE_UNARY_OP(aten_op, op, int_result, float_result)            \
+  DEFINE_UNARY_INT_OP(aten_op, op, int_result),                           \
+      DEFINE_UNARY_FLOAT_OP(aten_op, op, float_result),                   \
+      OperatorGeneratorArgs(                                              \
+          TORCH_SELECTIVE_SCHEMA(#aten_op ".Scalar(Scalar a) -> Scalar"), \
+          [](Stack& stack) {                                              \
+            IValue x;                                                     \
+            pop(stack, x);                                                \
+            if (x.isDouble()) {                                           \
+              double a = x.toDouble();                                    \
+              push(stack, static_cast<float_result>(op));                 \
+            } else {                                                      \
+              int64_t a = x.toInt();                                      \
+              push(stack, static_cast<int_result>(op));                   \
+            }                                                             \
+          },                                                              \
+          aliasAnalysisFromSchema())
+#define DEFINE_BOOL_OP(aten_op, op)                                     \
+  OperatorGeneratorArgs(                                                \
+      TORCH_SELECTIVE_SCHEMA(#aten_op ".bool(bool a, bool b) -> bool"), \
+      [](Stack& stack) {                                                \
+        bool a, b;                                                      \
+        pop(stack, a, b);                                               \
+        push(stack, op);                                                \
+      },                                                                \
+      aliasAnalysisFromSchema())
+#define DEFINE_STRING_OP(op_name, string_op, result)                    \
+  OperatorGeneratorArgs(                                                \
+      TORCH_SELECTIVE_SCHEMA(#op_name ".str(str a, str b) ->" #result), \
+      [](Stack& stack) {                                                \
+        auto b = pop(stack).toStringRef();                              \
+        auto a = pop(stack).toStringRef();                              \
+        push(stack, string_op);                                         \
+      },                                                                \
+      aliasAnalysisFromSchema())
+
+//-----------------------------------------------------------------------------
+//-----------------------------------------------------------------------------
+//-----------------------------------------------------------------------------
+//-----------------------------------------------------------------------------
+#define DEFINE_UNARY_COMPLEX_OP(aten_op, op, result)                      \
+  OperatorGeneratorArgs(                                                  \
+      TORCH_SELECTIVE_SCHEMA(#aten_op ".complex(complex a) -> " #result), \
+      [](Stack& stack) {                                                  \
+        c10::complex<double> a;                                           \
+        pop(stack, a);                                                    \
+        push(stack, op);                                                  \
+      },                                                                  \
+      aliasAnalysisFromSchema())
+
+// Some complex unary ops (like abs, angle) return real valued output, but most
+// other unary ops return complex valued output. So, this macro is used in the
+// former case where we can explicitly pass complex_result_cast argument, which
+// is set to c10::complex<float> in the macro `DEFINE_UNARY_OP_WITH_COMPLEX`
+// defined below.
+#define DEFINE_UNARY_OP_WITH_COMPLEX_CAST(                                \
+    aten_op,                                                              \
+    op,                                                                   \
+    int_result,                                                           \
+    float_result,                                                         \
+    complex_result,                                                       \
+    complex_result_cast)                                                  \
+  DEFINE_UNARY_INT_OP(aten_op, op, int_result),                           \
+      DEFINE_UNARY_FLOAT_OP(aten_op, op, float_result),                   \
+      DEFINE_UNARY_COMPLEX_OP(aten_op, op, complex_result),               \
+      OperatorGeneratorArgs(                                              \
+          TORCH_SELECTIVE_SCHEMA(#aten_op ".Scalar(Scalar a) -> Scalar"), \
+          [](Stack& stack) {                                              \
+            IValue x;                                                     \
+            pop(stack, x);                                                \
+            if (x.isDouble()) {                                           \
+              double a = x.toDouble();                                    \
+              push(stack, static_cast<float_result>(op));                 \
+            } else if (x.isComplexDouble()) {                             \
+              c10::complex<double> a = x.toComplexDouble();               \
+              push(stack, static_cast<complex_result_cast>(op));          \
+            } else {                                                      \
+              int64_t a = x.toInt();                                      \
+              push(stack, static_cast<int_result>(op));                   \
+            }                                                             \
+          },                                                              \
+          aliasAnalysisFromSchema())
+
+#define DEFINE_UNARY_OP_WITH_COMPLEX(aten_op, op, int_result, float_result) \
+  DEFINE_UNARY_OP_WITH_COMPLEX_CAST(                                        \
+      aten_op, op, int_result, float_result, complex, c10::complex<double>)
+
+#define DEFINE_GENERIC_OP_WITH_COMPLEX(                                       \
+    aten_op,                                                                  \
+    int_op,                                                                   \
+    float_op,                                                                 \
+    complex_op,                                                               \
+    int_result,                                                               \
+    float_result,                                                             \
+    complex_result)                                                           \
+  OperatorGeneratorArgs(                                                      \
+      TORCH_SELECTIVE_SCHEMA(#aten_op ".int(int a, int b) -> " #int_result),  \
+      [](Stack& stack) {                                                      \
+        int64_t a, b;                                                         \
+        pop(stack, a, b);                                                     \
+        push(stack, int_op);                                                  \
+      },                                                                      \
+      aliasAnalysisFromSchema()),                                             \
+      OperatorGeneratorArgs(                                                  \
+          TORCH_SELECTIVE_SCHEMA(                                             \
+              #aten_op ".complex(complex a, complex b) -> " #complex_result), \
+          [](Stack& stack) {                                                  \
+            c10::complex<double> a, b;                                        \
+            pop(stack, a, b);                                                 \
+            push(stack, complex_op);                                          \
+          },                                                                  \
+          aliasAnalysisFromSchema()),                                         \
+      OperatorGeneratorArgs(                                                  \
+          TORCH_SELECTIVE_SCHEMA(                                             \
+              #aten_op ".float(float a, float b) -> " #float_result),         \
+          [](Stack& stack) {                                                  \
+            double a, b;                                                      \
+            pop(stack, a, b);                                                 \
+            push(stack, float_op);                                            \
+          },                                                                  \
+          aliasAnalysisFromSchema())
+
+#define DEFINE_INT_COMPLEX_OP(aten_op, op, result)                          \
+  OperatorGeneratorArgs(                                                    \
+      TORCH_SELECTIVE_SCHEMA(#aten_op                                       \
+                             ".int_complex(int a, complex b) -> " #result), \
+      [](Stack& stack) {                                                    \
+        int64_t a;                                                          \
+        c10::complex<double> b;                                             \
+        pop(stack, a, b);                                                   \
+        push(stack, op);                                                    \
+      },                                                                    \
+      aliasAnalysisFromSchema()),                                           \
+      OperatorGeneratorArgs(                                                \
+          TORCH_SELECTIVE_SCHEMA(                                           \
+              #aten_op ".complex_int(complex a, int b) -> " #result),       \
+          [](Stack& stack) {                                                \
+            c10::complex<double> a;                                         \
+            int64_t b;                                                      \
+            pop(stack, a, b);                                               \
+            push(stack, op);                                                \
+          },                                                                \
+          aliasAnalysisFromSchema())
+
+#define DEFINE_FLOAT_COMPLEX_OP(aten_op, op, result)                      \
+  OperatorGeneratorArgs(                                                  \
+      TORCH_SELECTIVE_SCHEMA(                                             \
+          #aten_op ".float_complex(float a, complex b) -> " #result),     \
+      [](Stack& stack) {                                                  \
+        double a;                                                         \
+        c10::complex<double> b;                                           \
+        pop(stack, a, b);                                                 \
+        push(stack, op);                                                  \
+      },                                                                  \
+      aliasAnalysisFromSchema()),                                         \
+      OperatorGeneratorArgs(                                              \
+          TORCH_SELECTIVE_SCHEMA(                                         \
+              #aten_op ".complex_float(complex a, float b) -> " #result), \
+          [](Stack& stack) {                                              \
+            c10::complex<double> a;                                       \
+            double b;                                                     \
+            pop(stack, a, b);                                             \
+            push(stack, op);                                              \
+          },                                                              \
+          aliasAnalysisFromSchema())
+
+#define DEFINE_SCALAR_BINARY_OP_WITH_COMPLEX_AVOID_COLLISION_GENERIC( \
+    aten_op, int_op, float_op, complex_op, result, string_val)        \
+  OperatorGeneratorArgs(                                              \
+      TORCH_SELECTIVE_SCHEMA(#aten_op string_val                      \
+                             "(Scalar a, Scalar b) -> " #result),     \
+      [](Stack& stack) {                                              \
+        IValue x, y;                                                  \
+        pop(stack, x, y);                                             \
+        if (x.isComplexDouble()) {                                    \
+          c10::complex<double> a = x.toComplexDouble();               \
+          if (y.isComplexDouble()) {                                  \
+            c10::complex<double> b = y.toComplexDouble();             \
+            push(stack, complex_op);                                  \
+          } else if (y.isDouble()) {                                  \
+            double b = y.toDouble();                                  \
+            push(stack, complex_op);                                  \
+          } else {                                                    \
+            int64_t b = y.toInt();                                    \
+            push(stack, complex_op);                                  \
+          }                                                           \
+        } else if (x.isDouble()) {                                    \
+          double a = x.toDouble();                                    \
+          if (y.isComplexDouble()) {                                  \
+            c10::complex<double> b = y.toComplexDouble();             \
+            push(stack, complex_op);                                  \
+          } else if (y.isDouble()) {                                  \
+            double b = y.toDouble();                                  \
+            push(stack, float_op);                                    \
+          } else {                                                    \
+            int64_t b = y.toInt();                                    \
+            push(stack, float_op);                                    \
+          }                                                           \
+        } else {                                                      \
+          int64_t a = x.toInt();                                      \
+          if (y.isComplexDouble()) {                                  \
+            c10::complex<double> b = y.toComplexDouble();             \
+            push(stack, complex_op);                                  \
+          } else if (y.isDouble()) {                                  \
+            double b = y.toDouble();                                  \
+            push(stack, float_op);                                    \
+          } else {                                                    \
+            int64_t b = y.toInt();                                    \
+            push(stack, int_op);                                      \
+          }                                                           \
+        }                                                             \
+      },                                                              \
+      aliasAnalysisFromSchema())
+
+#define DEFINE_SCALAR_BINARY_OP_WITH_COMPLEX_WITHOUT_INT_COMPLEX_PAIR(     \
+    aten_op, int_op, float_op, complex_op, result)                         \
+  OperatorGeneratorArgs(                                                   \
+      TORCH_SELECTIVE_SCHEMA(#aten_op "(Scalar a, Scalar b) -> " #result), \
+      [](Stack& stack) {                                                   \
+        IValue x, y;                                                       \
+        pop(stack, x, y);                                                  \
+        if (x.isComplexDouble()) {                                         \
+          c10::complex<double> a = x.toComplexDouble();                    \
+          if (y.isComplexDouble()) {                                       \
+            c10::complex<double> b = y.toComplexDouble();                  \
+            push(stack, complex_op);                                       \
+          } else if (y.isDouble()) {                                       \
+            double b = y.toDouble();                                       \
+            push(stack, complex_op);                                       \
+          }                                                                \
+        } else if (x.isDouble()) {                                         \
+          double a = x.toDouble();                                         \
+          if (y.isComplexDouble()) {                                       \
+            c10::complex<double> b = y.toComplexDouble();                  \
+            push(stack, complex_op);                                       \
+          } else if (y.isDouble()) {                                       \
+            double b = y.toDouble();                                       \
+            push(stack, float_op);                                         \
+          } else {                                                         \
+            int64_t b = y.toInt();                                         \
+            push(stack, float_op);                                         \
+          }                                                                \
+        } else {                                                           \
+          int64_t a = x.toInt();                                           \
+          if (y.isDouble()) {                                              \
+            double b = y.toDouble();                                       \
+            push(stack, float_op);                                         \
+          } else if (y.isInt()) {                                          \
+            int64_t b = y.toInt();                                         \
+            push(stack, int_op);                                           \
+          }                                                                \
+        }                                                                  \
+      },                                                                   \
+      aliasAnalysisFromSchema())
+
+#define DEFINE_SCALAR_BINARY_OP_WITH_COMPLEX(                   \
+    aten_op, int_op, float_op, complex_op, result)              \
+  DEFINE_SCALAR_BINARY_OP_WITH_COMPLEX_AVOID_COLLISION_GENERIC( \
+      aten_op, int_op, float_op, complex_op, result, "")
+
+#define DEFINE_BINARY_OP_WITH_COMPLEX(aten_op, op)                          \
+  DEFINE_GENERIC_OP_WITH_COMPLEX(aten_op, op, op, op, int, float, complex), \
+      DEFINE_INT_COMPLEX_OP(aten_op, op, complex),                          \
+      DEFINE_FLOAT_COMPLEX_OP(aten_op, op, complex),                        \
+      DEFINE_INT_FLOAT_OP(aten_op, op, float),                              \
+      DEFINE_SCALAR_BINARY_OP_WITH_COMPLEX(aten_op, op, op, op, Scalar)
+
+#define DEFINE_COMPARISON_OP_WITH_COMPLEX(aten_op, op)                   \
+  DEFINE_GENERIC_OP_WITH_COMPLEX(aten_op, op, op, op, bool, bool, bool), \
+      DEFINE_INT_FLOAT_OP(aten_op, op, bool),                            \
+      DEFINE_FLOAT_COMPLEX_OP(aten_op, op, bool),                        \
+      DEFINE_SCALAR_BINARY_OP_WITH_COMPLEX_WITHOUT_INT_COMPLEX_PAIR(     \
+          aten_op, op, op, op, bool),                                    \
+      DEFINE_STR_CMP_OP(aten_op, op)
+
+TORCH_API at::Generator make_generator_for_device(
+    c10::Device device,
+    c10::optional<int64_t> seed = c10::nullopt);
+
+} // namespace torch::jit

shared/package-files/9b/9b484599c4f3bd9f2c65b6e759e10dc332b2b9fa44f735b0651f96088e247695/blob

@@ -0,0 +1,113 @@
+"""Tests for homomorphisms."""
+
+from sympy.core.singleton import S
+from sympy.polys.domains.rationalfield import QQ
+from sympy.abc import x, y
+from sympy.polys.agca import homomorphism
+from sympy.testing.pytest import raises
+
+
+def test_printing():
+    R = QQ.old_poly_ring(x)
+
+    assert str(homomorphism(R.free_module(1), R.free_module(1), [0])) == \
+        'Matrix([[0]]) : QQ[x]**1 -> QQ[x]**1'
+    assert str(homomorphism(R.free_module(2), R.free_module(2), [0, 0])) == \
+        'Matrix([                       \n[0, 0], : QQ[x]**2 -> QQ[x]**2\n[0, 0]])                       '
+    assert str(homomorphism(R.free_module(1), R.free_module(1) / [[x]], [0])) == \
+        'Matrix([[0]]) : QQ[x]**1 -> QQ[x]**1/<[x]>'
+    assert str(R.free_module(0).identity_hom()) == 'Matrix(0, 0, []) : QQ[x]**0 -> QQ[x]**0'
+
+def test_operations():
+    F = QQ.old_poly_ring(x).free_module(2)
+    G = QQ.old_poly_ring(x).free_module(3)
+    f = F.identity_hom()
+    g = homomorphism(F, F, [0, [1, x]])
+    h = homomorphism(F, F, [[1, 0], 0])
+    i = homomorphism(F, G, [[1, 0, 0], [0, 1, 0]])
+
+    assert f == f
+    assert f != g
+    assert f != i
+    assert (f != F.identity_hom()) is False
+    assert 2*f == f*2 == homomorphism(F, F, [[2, 0], [0, 2]])
+    assert f/2 == homomorphism(F, F, [[S.Half, 0], [0, S.Half]])
+    assert f + g == homomorphism(F, F, [[1, 0], [1, x + 1]])
+    assert f - g == homomorphism(F, F, [[1, 0], [-1, 1 - x]])
+    assert f*g == g == g*f
+    assert h*g == homomorphism(F, F, [0, [1, 0]])
+    assert g*h == homomorphism(F, F, [0, 0])
+    assert i*f == i
+    assert f([1, 2]) == [1, 2]
+    assert g([1, 2]) == [2, 2*x]
+
+    assert i.restrict_domain(F.submodule([x, x]))([x, x]) == i([x, x])
+    h1 = h.quotient_domain(F.submodule([0, 1]))
+    assert h1([1, 0]) == h([1, 0])
+    assert h1.restrict_domain(h1.domain.submodule([x, 0]))([x, 0]) == h([x, 0])
+
+    raises(TypeError, lambda: f/g)
+    raises(TypeError, lambda: f + 1)
+    raises(TypeError, lambda: f + i)
+    raises(TypeError, lambda: f - 1)
+    raises(TypeError, lambda: f*i)
+
+
+def test_creation():
+    F = QQ.old_poly_ring(x).free_module(3)
+    G = QQ.old_poly_ring(x).free_module(2)
+    SM = F.submodule([1, 1, 1])
+    Q = F / SM
+    SQ = Q.submodule([1, 0, 0])
+
+    matrix = [[1, 0], [0, 1], [-1, -1]]
+    h = homomorphism(F, G, matrix)
+    h2 = homomorphism(Q, G, matrix)
+    assert h.quotient_domain(SM) == h2
+    raises(ValueError, lambda: h.quotient_domain(F.submodule([1, 0, 0])))
+    assert h2.restrict_domain(SQ) == homomorphism(SQ, G, matrix)
+    raises(ValueError, lambda: h.restrict_domain(G))
+    raises(ValueError, lambda: h.restrict_codomain(G.submodule([1, 0])))
+    raises(ValueError, lambda: h.quotient_codomain(F))
+
+    im = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
+    for M in [F, SM, Q, SQ]:
+        assert M.identity_hom() == homomorphism(M, M, im)
+    assert SM.inclusion_hom() == homomorphism(SM, F, im)
+    assert SQ.inclusion_hom() == homomorphism(SQ, Q, im)
+    assert Q.quotient_hom() == homomorphism(F, Q, im)
+    assert SQ.quotient_hom() == homomorphism(SQ.base, SQ, im)
+
+    class conv:
+        def convert(x, y=None):
+            return x
+
+    class dummy:
+        container = conv()
+
+        def submodule(*args):
+            return None
+    raises(TypeError, lambda: homomorphism(dummy(), G, matrix))
+    raises(TypeError, lambda: homomorphism(F, dummy(), matrix))
+    raises(
+        ValueError, lambda: homomorphism(QQ.old_poly_ring(x, y).free_module(3), G, matrix))
+    raises(ValueError, lambda: homomorphism(F, G, [0, 0]))
+
+
+def test_properties():
+    R = QQ.old_poly_ring(x, y)
+    F = R.free_module(2)
+    h = homomorphism(F, F, [[x, 0], [y, 0]])
+    assert h.kernel() == F.submodule([-y, x])
+    assert h.image() == F.submodule([x, 0], [y, 0])
+    assert not h.is_injective()
+    assert not h.is_surjective()
+    assert h.restrict_codomain(h.image()).is_surjective()
+    assert h.restrict_domain(F.submodule([1, 0])).is_injective()
+    assert h.quotient_domain(
+        h.kernel()).restrict_codomain(h.image()).is_isomorphism()
+
+    R2 = QQ.old_poly_ring(x, y, order=(("lex", x), ("ilex", y))) / [x**2 + 1]
+    F = R2.free_module(2)
+    h = homomorphism(F, F, [[x, 0], [y, y + 1]])
+    assert h.is_isomorphism()

shared/package-files/9b/9b4b2ab4ef7b3c4bfcfff6eca7136078b7c168c81adab9d952790797eea37249/blob

@@ -0,0 +1,63 @@
+from typing import Any, Dict, Optional, TypeVar, Union
+
+from django.db.models.expressions import Combinable, CombinedExpression, Func, Value, _OutputField
+from django.db.models.lookups import Lookup
+
+from django.db.models import Field
+
+_Expression = Union[str, Combinable, "SearchQueryCombinable"]
+
+class SearchVectorExact(Lookup): ...
+class SearchVectorField(Field): ...
+class SearchQueryField(Field): ...
+
+class SearchVectorCombinable:
+    ADD: str = ...
+
+class SearchVector(SearchVectorCombinable, Func):
+    config: Optional[Any] = ...
+    def __init__(self, *expressions: _Expression, **extra: Any): ...
+
+class CombinedSearchVector(SearchVectorCombinable, CombinedExpression):
+    def __init__(
+        self, lhs, connector, rhs, config: Optional[_Expression] = ..., output_field: Optional[_OutputField] = ...
+    ): ...
+
+_T = TypeVar("_T", bound="SearchQueryCombinable")
+
+class SearchQueryCombinable:
+    BITAND: str = ...
+    BITOR: str = ...
+    def __or__(self: _T, other: SearchQueryCombinable) -> _T: ...
+    def __ror__(self: _T, other: SearchQueryCombinable) -> _T: ...
+    def __and__(self: _T, other: SearchQueryCombinable) -> _T: ...
+    def __rand__(self: _T, other: SearchQueryCombinable) -> _T: ...
+
+class SearchQuery(SearchQueryCombinable, Value):  # type: ignore
+    SEARCH_TYPES: Dict[str, str] = ...
+    def __init__(
+        self,
+        value: str,
+        output_field: Optional[_OutputField] = ...,
+        *,
+        config: Optional[_Expression] = ...,
+        invert: bool = ...,
+        search_type: str = ...
+    ): ...
+    def __invert__(self: _T) -> _T: ...
+
+class CombinedSearchQuery(SearchQueryCombinable, CombinedExpression):  # type: ignore
+    def __init__(
+        self, lhs, connector, rhs, config: Optional[_Expression] = ..., output_field: Optional[_OutputField] = ...
+    ) -> None: ...
+
+class SearchRank(Func):
+    def __init__(
+        self, vector: Union[SearchVector, _Expression], query: Union[SearchQuery, _Expression], **extra: Any
+    ) -> None: ...
+
+class TrigramBase(Func):
+    def __init__(self, expression: _Expression, string, **extra: Any) -> None: ...
+
+class TrigramSimilarity(TrigramBase): ...
+class TrigramDistance(TrigramBase): ...

shared/package-files/9b/9b4d6e6e077c5be4e16cbf790138fb75158aea70027ab99005cd0cac43cf42bf/blob

@@ -0,0 +1,68 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+def _check_feature_names(X, feature_names=None):
+    """Check feature names.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Input data.
+
+    feature_names : None or array-like of shape (n_names,), dtype=str
+        Feature names to check or `None`.
+
+    Returns
+    -------
+    feature_names : list of str
+        Feature names validated. If `feature_names` is `None`, then a list of
+        feature names is provided, i.e. the column names of a pandas dataframe
+        or a generic list of feature names (e.g. `["x0", "x1", ...]`) for a
+        NumPy array.
+    """
+    if feature_names is None:
+        if hasattr(X, "columns") and hasattr(X.columns, "tolist"):
+            # get the column names for a pandas dataframe
+            feature_names = X.columns.tolist()
+        else:
+            # define a list of numbered indices for a numpy array
+            feature_names = [f"x{i}" for i in range(X.shape[1])]
+    elif hasattr(feature_names, "tolist"):
+        # convert numpy array or pandas index to a list
+        feature_names = feature_names.tolist()
+    if len(set(feature_names)) != len(feature_names):
+        raise ValueError("feature_names should not contain duplicates.")
+
+    return feature_names
+
+
+def _get_feature_index(fx, feature_names=None):
+    """Get feature index.
+
+    Parameters
+    ----------
+    fx : int or str
+        Feature index or name.
+
+    feature_names : list of str, default=None
+        All feature names from which to search the indices.
+
+    Returns
+    -------
+    idx : int
+        Feature index.
+    """
+    if isinstance(fx, str):
+        if feature_names is None:
+            raise ValueError(
+                f"Cannot plot partial dependence for feature {fx!r} since "
+                "the list of feature names was not provided, neither as "
+                "column names of a pandas data-frame nor via the feature_names "
+                "parameter."
+            )
+        try:
+            return feature_names.index(fx)
+        except ValueError as e:
+            raise ValueError(f"Feature {fx!r} not in feature_names") from e
+    return fx

shared/package-files/9b/9b4fa9e614c6536132eee050ac1ecb48f813bd2d02f05afe480f5dd81017e8c9/blob

@@ -0,0 +1,687 @@
+import itertools
+
+from sympy.concrete.summations import Sum
+from sympy.core.add import Add
+from sympy.core.expr import Expr
+from sympy.core.function import expand as _expand
+from sympy.core.mul import Mul
+from sympy.core.relational import Eq
+from sympy.core.singleton import S
+from sympy.core.symbol import Symbol
+from sympy.integrals.integrals import Integral
+from sympy.logic.boolalg import Not
+from sympy.core.parameters import global_parameters
+from sympy.core.sorting import default_sort_key
+from sympy.core.sympify import _sympify
+from sympy.core.relational import Relational
+from sympy.logic.boolalg import Boolean
+from sympy.stats import variance, covariance
+from sympy.stats.rv import (RandomSymbol, pspace, dependent,
+                            given, sampling_E, RandomIndexedSymbol, is_random,
+                            PSpace, sampling_P, random_symbols)
+
+__all__ = ['Probability', 'Expectation', 'Variance', 'Covariance']
+
+
+@is_random.register(Expr)
+def _(x):
+    atoms = x.free_symbols
+    if len(atoms) == 1 and next(iter(atoms)) == x:
+        return False
+    return any(is_random(i) for i in atoms)
+
+@is_random.register(RandomSymbol)  # type: ignore
+def _(x):
+    return True
+
+
+class Probability(Expr):
+    """
+    Symbolic expression for the probability.
+
+    Examples
+    ========
+
+    >>> from sympy.stats import Probability, Normal
+    >>> from sympy import Integral
+    >>> X = Normal("X", 0, 1)
+    >>> prob = Probability(X > 1)
+    >>> prob
+    Probability(X > 1)
+
+    Integral representation:
+
+    >>> prob.rewrite(Integral)
+    Integral(sqrt(2)*exp(-_z**2/2)/(2*sqrt(pi)), (_z, 1, oo))
+
+    Evaluation of the integral:
+
+    >>> prob.evaluate_integral()
+    sqrt(2)*(-sqrt(2)*sqrt(pi)*erf(sqrt(2)/2) + sqrt(2)*sqrt(pi))/(4*sqrt(pi))
+    """
+    def __new__(cls, prob, condition=None, **kwargs):
+        prob = _sympify(prob)
+        if condition is None:
+            obj = Expr.__new__(cls, prob)
+        else:
+            condition = _sympify(condition)
+            obj = Expr.__new__(cls, prob, condition)
+        obj._condition = condition
+        return obj
+
+    def doit(self, **hints):
+        condition = self.args[0]
+        given_condition = self._condition
+        numsamples = hints.get('numsamples', False)
+        for_rewrite = not hints.get('for_rewrite', False)
+
+        if isinstance(condition, Not):
+            return S.One - self.func(condition.args[0], given_condition,
+                                    evaluate=for_rewrite).doit(**hints)
+
+        if condition.has(RandomIndexedSymbol):
+            return pspace(condition).probability(condition, given_condition,
+                                             evaluate=for_rewrite)
+
+        if isinstance(given_condition, RandomSymbol):
+            condrv = random_symbols(condition)
+            if len(condrv) == 1 and condrv[0] == given_condition:
+                from sympy.stats.frv_types import BernoulliDistribution
+                return BernoulliDistribution(self.func(condition).doit(**hints), 0, 1)
+            if any(dependent(rv, given_condition) for rv in condrv):
+                return Probability(condition, given_condition)
+            else:
+                return Probability(condition).doit()
+
+        if given_condition is not None and \
+                not isinstance(given_condition, (Relational, Boolean)):
+            raise ValueError("%s is not a relational or combination of relationals"
+                    % (given_condition))
+
+        if given_condition == False or condition is S.false:
+            return S.Zero
+        if not isinstance(condition, (Relational, Boolean)):
+            raise ValueError("%s is not a relational or combination of relationals"
+                    % (condition))
+        if condition is S.true:
+            return S.One
+
+        if numsamples:
+            return sampling_P(condition, given_condition, numsamples=numsamples)
+        if given_condition is not None:  # If there is a condition
+            # Recompute on new conditional expr
+            return Probability(given(condition, given_condition)).doit()
+
+        # Otherwise pass work off to the ProbabilitySpace
+        if pspace(condition) == PSpace():
+            return Probability(condition, given_condition)
+
+        result = pspace(condition).probability(condition)
+        if hasattr(result, 'doit') and for_rewrite:
+            return result.doit()
+        else:
+            return result
+
+    def _eval_rewrite_as_Integral(self, arg, condition=None, **kwargs):
+        return self.func(arg, condition=condition).doit(for_rewrite=True)
+
+    _eval_rewrite_as_Sum = _eval_rewrite_as_Integral
+
+    def evaluate_integral(self):
+        return self.rewrite(Integral).doit()
+
+
+class Expectation(Expr):
+    """
+    Symbolic expression for the expectation.
+
+    Examples
+    ========
+
+    >>> from sympy.stats import Expectation, Normal, Probability, Poisson
+    >>> from sympy import symbols, Integral, Sum
+    >>> mu = symbols("mu")
+    >>> sigma = symbols("sigma", positive=True)
+    >>> X = Normal("X", mu, sigma)
+    >>> Expectation(X)
+    Expectation(X)
+    >>> Expectation(X).evaluate_integral().simplify()
+    mu
+
+    To get the integral expression of the expectation:
+
+    >>> Expectation(X).rewrite(Integral)
+    Integral(sqrt(2)*X*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo))
+
+    The same integral expression, in more abstract terms:
+
+    >>> Expectation(X).rewrite(Probability)
+    Integral(x*Probability(Eq(X, x)), (x, -oo, oo))
+
+    To get the Summation expression of the expectation for discrete random variables:
+
+    >>> lamda = symbols('lamda', positive=True)
+    >>> Z = Poisson('Z', lamda)
+    >>> Expectation(Z).rewrite(Sum)
+    Sum(Z*lamda**Z*exp(-lamda)/factorial(Z), (Z, 0, oo))
+
+    This class is aware of some properties of the expectation:
+
+    >>> from sympy.abc import a
+    >>> Expectation(a*X)
+    Expectation(a*X)
+    >>> Y = Normal("Y", 1, 2)
+    >>> Expectation(X + Y)
+    Expectation(X + Y)
+
+    To expand the ``Expectation`` into its expression, use ``expand()``:
+
+    >>> Expectation(X + Y).expand()
+    Expectation(X) + Expectation(Y)
+    >>> Expectation(a*X + Y).expand()
+    a*Expectation(X) + Expectation(Y)
+    >>> Expectation(a*X + Y)
+    Expectation(a*X + Y)
+    >>> Expectation((X + Y)*(X - Y)).expand()
+    Expectation(X**2) - Expectation(Y**2)
+
+    To evaluate the ``Expectation``, use ``doit()``:
+
+    >>> Expectation(X + Y).doit()
+    mu + 1
+    >>> Expectation(X + Expectation(Y + Expectation(2*X))).doit()
+    3*mu + 1
+
+    To prevent evaluating nested ``Expectation``, use ``doit(deep=False)``
+
+    >>> Expectation(X + Expectation(Y)).doit(deep=False)
+    mu + Expectation(Expectation(Y))
+    >>> Expectation(X + Expectation(Y + Expectation(2*X))).doit(deep=False)
+    mu + Expectation(Expectation(Y + Expectation(2*X)))
+
+    """
+
+    def __new__(cls, expr, condition=None, **kwargs):
+        expr = _sympify(expr)
+        if expr.is_Matrix:
+            from sympy.stats.symbolic_multivariate_probability import ExpectationMatrix
+            return ExpectationMatrix(expr, condition)
+        if condition is None:
+            if not is_random(expr):
+                return expr
+            obj = Expr.__new__(cls, expr)
+        else:
+            condition = _sympify(condition)
+            obj = Expr.__new__(cls, expr, condition)
+        obj._condition = condition
+        return obj
+
+    def expand(self, **hints):
+        expr = self.args[0]
+        condition = self._condition
+
+        if not is_random(expr):
+            return expr
+
+        if isinstance(expr, Add):
+            return Add.fromiter(Expectation(a, condition=condition).expand()
+                    for a in expr.args)
+
+        expand_expr = _expand(expr)
+        if isinstance(expand_expr, Add):
+            return Add.fromiter(Expectation(a, condition=condition).expand()
+                    for a in expand_expr.args)
+
+        elif isinstance(expr, Mul):
+            rv = []
+            nonrv = []
+            for a in expr.args:
+                if is_random(a):
+                    rv.append(a)
+                else:
+                    nonrv.append(a)
+            return Mul.fromiter(nonrv)*Expectation(Mul.fromiter(rv), condition=condition)
+
+        return self
+
+    def doit(self, **hints):
+        deep = hints.get('deep', True)
+        condition = self._condition
+        expr = self.args[0]
+        numsamples = hints.get('numsamples', False)
+        for_rewrite = not hints.get('for_rewrite', False)
+
+        if deep:
+            expr = expr.doit(**hints)
+
+        if not is_random(expr) or isinstance(expr, Expectation):  # expr isn't random?
+            return expr
+        if numsamples:  # Computing by monte carlo sampling?
+            evalf = hints.get('evalf', True)
+            return sampling_E(expr, condition, numsamples=numsamples, evalf=evalf)
+
+        if expr.has(RandomIndexedSymbol):
+            return pspace(expr).compute_expectation(expr, condition)
+
+        # Create new expr and recompute E
+        if condition is not None:  # If there is a condition
+            return self.func(given(expr, condition)).doit(**hints)
+
+        # A few known statements for efficiency
+
+        if expr.is_Add:  # We know that E is Linear
+            return Add(*[self.func(arg, condition).doit(**hints)
+                    if not isinstance(arg, Expectation) else self.func(arg, condition)
+                         for arg in expr.args])
+        if expr.is_Mul:
+            if expr.atoms(Expectation):
+                return expr
+
+        if pspace(expr) == PSpace():
+            return self.func(expr)
+        # Otherwise case is simple, pass work off to the ProbabilitySpace
+        result = pspace(expr).compute_expectation(expr, evaluate=for_rewrite)
+        if hasattr(result, 'doit') and for_rewrite:
+            return result.doit(**hints)
+        else:
+            return result
+
+
+    def _eval_rewrite_as_Probability(self, arg, condition=None, **kwargs):
+        rvs = arg.atoms(RandomSymbol)
+        if len(rvs) > 1:
+            raise NotImplementedError()
+        if len(rvs) == 0:
+            return arg
+
+        rv = rvs.pop()
+        if rv.pspace is None:
+            raise ValueError("Probability space not known")
+
+        symbol = rv.symbol
+        if symbol.name[0].isupper():
+            symbol = Symbol(symbol.name.lower())
+        else :
+            symbol = Symbol(symbol.name + "_1")
+
+        if rv.pspace.is_Continuous:
+            return Integral(arg.replace(rv, symbol)*Probability(Eq(rv, symbol), condition), (symbol, rv.pspace.domain.set.inf, rv.pspace.domain.set.sup))
+        else:
+            if rv.pspace.is_Finite:
+                raise NotImplementedError
+            else:
+                return Sum(arg.replace(rv, symbol)*Probability(Eq(rv, symbol), condition), (symbol, rv.pspace.domain.set.inf, rv.pspace.set.sup))
+
+    def _eval_rewrite_as_Integral(self, arg, condition=None, **kwargs):
+        return self.func(arg, condition=condition).doit(deep=False, for_rewrite=True)
+
+    _eval_rewrite_as_Sum = _eval_rewrite_as_Integral # For discrete this will be Sum
+
+    def evaluate_integral(self):
+        return self.rewrite(Integral).doit()
+
+    evaluate_sum = evaluate_integral
+
+class Variance(Expr):
+    """
+    Symbolic expression for the variance.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols, Integral
+    >>> from sympy.stats import Normal, Expectation, Variance, Probability
+    >>> mu = symbols("mu", positive=True)
+    >>> sigma = symbols("sigma", positive=True)
+    >>> X = Normal("X", mu, sigma)
+    >>> Variance(X)
+    Variance(X)
+    >>> Variance(X).evaluate_integral()
+    sigma**2
+
+    Integral representation of the underlying calculations:
+
+    >>> Variance(X).rewrite(Integral)
+    Integral(sqrt(2)*(X - Integral(sqrt(2)*X*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo)))**2*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo))
+
+    Integral representation, without expanding the PDF:
+
+    >>> Variance(X).rewrite(Probability)
+    -Integral(x*Probability(Eq(X, x)), (x, -oo, oo))**2 + Integral(x**2*Probability(Eq(X, x)), (x, -oo, oo))
+
+    Rewrite the variance in terms of the expectation
+
+    >>> Variance(X).rewrite(Expectation)
+    -Expectation(X)**2 + Expectation(X**2)
+
+    Some transformations based on the properties of the variance may happen:
+
+    >>> from sympy.abc import a
+    >>> Y = Normal("Y", 0, 1)
+    >>> Variance(a*X)
+    Variance(a*X)
+
+    To expand the variance in its expression, use ``expand()``:
+
+    >>> Variance(a*X).expand()
+    a**2*Variance(X)
+    >>> Variance(X + Y)
+    Variance(X + Y)
+    >>> Variance(X + Y).expand()
+    2*Covariance(X, Y) + Variance(X) + Variance(Y)
+
+    """
+    def __new__(cls, arg, condition=None, **kwargs):
+        arg = _sympify(arg)
+
+        if arg.is_Matrix:
+            from sympy.stats.symbolic_multivariate_probability import VarianceMatrix
+            return VarianceMatrix(arg, condition)
+        if condition is None:
+            obj = Expr.__new__(cls, arg)
+        else:
+            condition = _sympify(condition)
+            obj = Expr.__new__(cls, arg, condition)
+        obj._condition = condition
+        return obj
+
+    def expand(self, **hints):
+        arg = self.args[0]
+        condition = self._condition
+
+        if not is_random(arg):
+            return S.Zero
+
+        if isinstance(arg, RandomSymbol):
+            return self
+        elif isinstance(arg, Add):
+            rv = []
+            for a in arg.args:
+                if is_random(a):
+                    rv.append(a)
+            variances = Add(*(Variance(xv, condition).expand() for xv in rv))
+            map_to_covar = lambda x: 2*Covariance(*x, condition=condition).expand()
+            covariances = Add(*map(map_to_covar, itertools.combinations(rv, 2)))
+            return variances + covariances
+        elif isinstance(arg, Mul):
+            nonrv = []
+            rv = []
+            for a in arg.args:
+                if is_random(a):
+                    rv.append(a)
+                else:
+                    nonrv.append(a**2)
+            if len(rv) == 0:
+                return S.Zero
+            return Mul.fromiter(nonrv)*Variance(Mul.fromiter(rv), condition)
+
+        # this expression contains a RandomSymbol somehow:
+        return self
+
+    def _eval_rewrite_as_Expectation(self, arg, condition=None, **kwargs):
+            e1 = Expectation(arg**2, condition)
+            e2 = Expectation(arg, condition)**2
+            return e1 - e2
+
+    def _eval_rewrite_as_Probability(self, arg, condition=None, **kwargs):
+        return self.rewrite(Expectation).rewrite(Probability)
+
+    def _eval_rewrite_as_Integral(self, arg, condition=None, **kwargs):
+        return variance(self.args[0], self._condition, evaluate=False)
+
+    _eval_rewrite_as_Sum = _eval_rewrite_as_Integral
+
+    def evaluate_integral(self):
+        return self.rewrite(Integral).doit()
+
+
+class Covariance(Expr):
+    """
+    Symbolic expression for the covariance.
+
+    Examples
+    ========
+
+    >>> from sympy.stats import Covariance
+    >>> from sympy.stats import Normal
+    >>> X = Normal("X", 3, 2)
+    >>> Y = Normal("Y", 0, 1)
+    >>> Z = Normal("Z", 0, 1)
+    >>> W = Normal("W", 0, 1)
+    >>> cexpr = Covariance(X, Y)
+    >>> cexpr
+    Covariance(X, Y)
+
+    Evaluate the covariance, `X` and `Y` are independent,
+    therefore zero is the result:
+
+    >>> cexpr.evaluate_integral()
+    0
+
+    Rewrite the covariance expression in terms of expectations:
+
+    >>> from sympy.stats import Expectation
+    >>> cexpr.rewrite(Expectation)
+    Expectation(X*Y) - Expectation(X)*Expectation(Y)
+
+    In order to expand the argument, use ``expand()``:
+
+    >>> from sympy.abc import a, b, c, d
+    >>> Covariance(a*X + b*Y, c*Z + d*W)
+    Covariance(a*X + b*Y, c*Z + d*W)
+    >>> Covariance(a*X + b*Y, c*Z + d*W).expand()
+    a*c*Covariance(X, Z) + a*d*Covariance(W, X) + b*c*Covariance(Y, Z) + b*d*Covariance(W, Y)
+
+    This class is aware of some properties of the covariance:
+
+    >>> Covariance(X, X).expand()
+    Variance(X)
+    >>> Covariance(a*X, b*Y).expand()
+    a*b*Covariance(X, Y)
+    """
+
+    def __new__(cls, arg1, arg2, condition=None, **kwargs):
+        arg1 = _sympify(arg1)
+        arg2 = _sympify(arg2)
+
+        if arg1.is_Matrix or arg2.is_Matrix:
+            from sympy.stats.symbolic_multivariate_probability import CrossCovarianceMatrix
+            return CrossCovarianceMatrix(arg1, arg2, condition)
+
+        if kwargs.pop('evaluate', global_parameters.evaluate):
+            arg1, arg2 = sorted([arg1, arg2], key=default_sort_key)
+
+        if condition is None:
+            obj = Expr.__new__(cls, arg1, arg2)
+        else:
+            condition = _sympify(condition)
+            obj = Expr.__new__(cls, arg1, arg2, condition)
+        obj._condition = condition
+        return obj
+
+    def expand(self, **hints):
+        arg1 = self.args[0]
+        arg2 = self.args[1]
+        condition = self._condition
+
+        if arg1 == arg2:
+            return Variance(arg1, condition).expand()
+
+        if not is_random(arg1):
+            return S.Zero
+        if not is_random(arg2):
+            return S.Zero
+
+        arg1, arg2 = sorted([arg1, arg2], key=default_sort_key)
+
+        if isinstance(arg1, RandomSymbol) and isinstance(arg2, RandomSymbol):
+            return Covariance(arg1, arg2, condition)
+
+        coeff_rv_list1 = self._expand_single_argument(arg1.expand())
+        coeff_rv_list2 = self._expand_single_argument(arg2.expand())
+
+        addends = [a*b*Covariance(*sorted([r1, r2], key=default_sort_key), condition=condition)
+                   for (a, r1) in coeff_rv_list1 for (b, r2) in coeff_rv_list2]
+        return Add.fromiter(addends)
+
+    @classmethod
+    def _expand_single_argument(cls, expr):
+        # return (coefficient, random_symbol) pairs:
+        if isinstance(expr, RandomSymbol):
+            return [(S.One, expr)]
+        elif isinstance(expr, Add):
+            outval = []
+            for a in expr.args:
+                if isinstance(a, Mul):
+                    outval.append(cls._get_mul_nonrv_rv_tuple(a))
+                elif is_random(a):
+                    outval.append((S.One, a))
+
+            return outval
+        elif isinstance(expr, Mul):
+            return [cls._get_mul_nonrv_rv_tuple(expr)]
+        elif is_random(expr):
+            return [(S.One, expr)]
+
+    @classmethod
+    def _get_mul_nonrv_rv_tuple(cls, m):
+        rv = []
+        nonrv = []
+        for a in m.args:
+            if is_random(a):
+                rv.append(a)
+            else:
+                nonrv.append(a)
+        return (Mul.fromiter(nonrv), Mul.fromiter(rv))
+
+    def _eval_rewrite_as_Expectation(self, arg1, arg2, condition=None, **kwargs):
+        e1 = Expectation(arg1*arg2, condition)
+        e2 = Expectation(arg1, condition)*Expectation(arg2, condition)
+        return e1 - e2
+
+    def _eval_rewrite_as_Probability(self, arg1, arg2, condition=None, **kwargs):
+        return self.rewrite(Expectation).rewrite(Probability)
+
+    def _eval_rewrite_as_Integral(self, arg1, arg2, condition=None, **kwargs):
+        return covariance(self.args[0], self.args[1], self._condition, evaluate=False)
+
+    _eval_rewrite_as_Sum = _eval_rewrite_as_Integral
+
+    def evaluate_integral(self):
+        return self.rewrite(Integral).doit()
+
+
+class Moment(Expr):
+    """
+    Symbolic class for Moment
+
+    Examples
+    ========
+
+    >>> from sympy import Symbol, Integral
+    >>> from sympy.stats import Normal, Expectation, Probability, Moment
+    >>> mu = Symbol('mu', real=True)
+    >>> sigma = Symbol('sigma', positive=True)
+    >>> X = Normal('X', mu, sigma)
+    >>> M = Moment(X, 3, 1)
+
+    To evaluate the result of Moment use `doit`:
+
+    >>> M.doit()
+    mu**3 - 3*mu**2 + 3*mu*sigma**2 + 3*mu - 3*sigma**2 - 1
+
+    Rewrite the Moment expression in terms of Expectation:
+
+    >>> M.rewrite(Expectation)
+    Expectation((X - 1)**3)
+
+    Rewrite the Moment expression in terms of Probability:
+
+    >>> M.rewrite(Probability)
+    Integral((x - 1)**3*Probability(Eq(X, x)), (x, -oo, oo))
+
+    Rewrite the Moment expression in terms of Integral:
+
+    >>> M.rewrite(Integral)
+    Integral(sqrt(2)*(X - 1)**3*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo))
+
+    """
+    def __new__(cls, X, n, c=0, condition=None, **kwargs):
+        X = _sympify(X)
+        n = _sympify(n)
+        c = _sympify(c)
+        if condition is not None:
+            condition = _sympify(condition)
+            return super().__new__(cls, X, n, c, condition)
+        else:
+            return super().__new__(cls, X, n, c)
+
+    def doit(self, **hints):
+        return self.rewrite(Expectation).doit(**hints)
+
+    def _eval_rewrite_as_Expectation(self, X, n, c=0, condition=None, **kwargs):
+        return Expectation((X - c)**n, condition)
+
+    def _eval_rewrite_as_Probability(self, X, n, c=0, condition=None, **kwargs):
+        return self.rewrite(Expectation).rewrite(Probability)
+
+    def _eval_rewrite_as_Integral(self, X, n, c=0, condition=None, **kwargs):
+        return self.rewrite(Expectation).rewrite(Integral)
+
+
+class CentralMoment(Expr):
+    """
+    Symbolic class Central Moment
+
+    Examples
+    ========
+
+    >>> from sympy import Symbol, Integral
+    >>> from sympy.stats import Normal, Expectation, Probability, CentralMoment
+    >>> mu = Symbol('mu', real=True)
+    >>> sigma = Symbol('sigma', positive=True)
+    >>> X = Normal('X', mu, sigma)
+    >>> CM = CentralMoment(X, 4)
+
+    To evaluate the result of CentralMoment use `doit`:
+
+    >>> CM.doit().simplify()
+    3*sigma**4
+
+    Rewrite the CentralMoment expression in terms of Expectation:
+
+    >>> CM.rewrite(Expectation)
+    Expectation((X - Expectation(X))**4)
+
+    Rewrite the CentralMoment expression in terms of Probability:
+
+    >>> CM.rewrite(Probability)
+    Integral((x - Integral(x*Probability(True), (x, -oo, oo)))**4*Probability(Eq(X, x)), (x, -oo, oo))
+
+    Rewrite the CentralMoment expression in terms of Integral:
+
+    >>> CM.rewrite(Integral)
+    Integral(sqrt(2)*(X - Integral(sqrt(2)*X*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo)))**4*exp(-(X - mu)**2/(2*sigma**2))/(2*sqrt(pi)*sigma), (X, -oo, oo))
+
+    """
+    def __new__(cls, X, n, condition=None, **kwargs):
+        X = _sympify(X)
+        n = _sympify(n)
+        if condition is not None:
+            condition = _sympify(condition)
+            return super().__new__(cls, X, n, condition)
+        else:
+            return super().__new__(cls, X, n)
+
+    def doit(self, **hints):
+        return self.rewrite(Expectation).doit(**hints)
+
+    def _eval_rewrite_as_Expectation(self, X, n, condition=None, **kwargs):
+        mu = Expectation(X, condition, **kwargs)
+        return Moment(X, n, mu, condition, **kwargs).rewrite(Expectation)
+
+    def _eval_rewrite_as_Probability(self, X, n, condition=None, **kwargs):
+        return self.rewrite(Expectation).rewrite(Probability)
+
+    def _eval_rewrite_as_Integral(self, X, n, condition=None, **kwargs):
+        return self.rewrite(Expectation).rewrite(Integral)

shared/package-files/9b/9b542cdd392345b80afdab68e0f7056548da6fee5a1315a7bf918d1b54bd957b/blob

@@ -0,0 +1,11 @@
+from .optimizer import Optimizer, ParamsT
+
+class Adadelta(Optimizer):
+    def __init__(
+        self,
+        params: ParamsT,
+        lr: float = ...,
+        rho: float = ...,
+        eps: float = ...,
+        weight_decay: float = ...,
+    ) -> None: ...

shared/package-files/9b/9b5b3703806bbd17835f93e58836a5639dd884f7ef875b3ec7879f6b57e521a1/blob

@@ -0,0 +1,95 @@
+"""A variety of linear models."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# See http://scikit-learn.sourceforge.net/modules/sgd.html and
+# http://scikit-learn.sourceforge.net/modules/linear_model.html for
+# complete documentation.
+
+from ._base import LinearRegression
+from ._bayes import ARDRegression, BayesianRidge
+from ._coordinate_descent import (
+    ElasticNet,
+    ElasticNetCV,
+    Lasso,
+    LassoCV,
+    MultiTaskElasticNet,
+    MultiTaskElasticNetCV,
+    MultiTaskLasso,
+    MultiTaskLassoCV,
+    enet_path,
+    lasso_path,
+)
+from ._glm import GammaRegressor, PoissonRegressor, TweedieRegressor
+from ._huber import HuberRegressor
+from ._least_angle import (
+    Lars,
+    LarsCV,
+    LassoLars,
+    LassoLarsCV,
+    LassoLarsIC,
+    lars_path,
+    lars_path_gram,
+)
+from ._logistic import LogisticRegression, LogisticRegressionCV
+from ._omp import (
+    OrthogonalMatchingPursuit,
+    OrthogonalMatchingPursuitCV,
+    orthogonal_mp,
+    orthogonal_mp_gram,
+)
+from ._passive_aggressive import PassiveAggressiveClassifier, PassiveAggressiveRegressor
+from ._perceptron import Perceptron
+from ._quantile import QuantileRegressor
+from ._ransac import RANSACRegressor
+from ._ridge import Ridge, RidgeClassifier, RidgeClassifierCV, RidgeCV, ridge_regression
+from ._stochastic_gradient import SGDClassifier, SGDOneClassSVM, SGDRegressor
+from ._theil_sen import TheilSenRegressor
+
+__all__ = [
+    "ARDRegression",
+    "BayesianRidge",
+    "ElasticNet",
+    "ElasticNetCV",
+    "GammaRegressor",
+    "HuberRegressor",
+    "Lars",
+    "LarsCV",
+    "Lasso",
+    "LassoCV",
+    "LassoLars",
+    "LassoLarsCV",
+    "LassoLarsIC",
+    "LinearRegression",
+    "LogisticRegression",
+    "LogisticRegressionCV",
+    "MultiTaskElasticNet",
+    "MultiTaskElasticNetCV",
+    "MultiTaskLasso",
+    "MultiTaskLassoCV",
+    "OrthogonalMatchingPursuit",
+    "OrthogonalMatchingPursuitCV",
+    "PassiveAggressiveClassifier",
+    "PassiveAggressiveRegressor",
+    "Perceptron",
+    "PoissonRegressor",
+    "QuantileRegressor",
+    "RANSACRegressor",
+    "Ridge",
+    "RidgeCV",
+    "RidgeClassifier",
+    "RidgeClassifierCV",
+    "SGDClassifier",
+    "SGDOneClassSVM",
+    "SGDRegressor",
+    "TheilSenRegressor",
+    "TweedieRegressor",
+    "enet_path",
+    "lars_path",
+    "lars_path_gram",
+    "lasso_path",
+    "orthogonal_mp",
+    "orthogonal_mp_gram",
+    "ridge_regression",
+]

shared/package-files/9b/9b62149e65ae88c6dd4da6bc3fd68cac9355d5be78045d9faf3270495fdc5451/blob

@@ -0,0 +1,160 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from narwhals._compliant.any_namespace import StringNamespace
+from narwhals._pandas_like.utils import (
+    PandasLikeSeriesNamespace,
+    align_and_extract_native,
+    get_dtype_backend,
+    is_dtype_pyarrow,
+)
+from narwhals._utils import str_slice_stop
+
+if TYPE_CHECKING:
+    from narwhals._pandas_like.series import PandasLikeSeries
+
+
+class PandasLikeSeriesStringNamespace(
+    PandasLikeSeriesNamespace, StringNamespace["PandasLikeSeries"]
+):
+    def _with_native_bool(self, result: Any) -> PandasLikeSeries:
+        # See https://narwhals-dev.github.io/narwhals/concepts/boolean/.
+        if (
+            result.dtype == object
+            and get_dtype_backend(self.native.dtype, self.implementation) is None
+        ):
+            not_na = result.notna()
+            all_valid = not_na.all()
+            if not all_valid and self.implementation._backend_version() < (3,):
+                result.where(not_na, False, inplace=True)
+                result = result.astype(bool, copy=False)
+            elif not all_valid:
+                result = result.where(not_na, False).astype(bool)
+            else:  # pragma: no cover
+                pass
+        return self.with_native(result)
+
+    def len_chars(self) -> PandasLikeSeries:
+        result = self.native.str.len()
+        if result.dtype == object:
+            # pandas returns `object` instead of a number for an all-null `object` column.
+            result = self.implementation.to_native_namespace().to_numeric(result)
+        return self.with_native(result)
+
+    def replace(
+        self, value: PandasLikeSeries, pattern: str, *, literal: bool, n: int
+    ) -> PandasLikeSeries:
+        if n == 0:
+            return self.compliant
+        _, value_native = align_and_extract_native(self.compliant, value)
+        if not isinstance(value_native, str):
+            msg = f"{self.compliant._implementation} backed `.str.replace` only supports str replacement values"
+            raise TypeError(msg)
+        series = self.native.str.replace(
+            pat=pattern, repl=value_native, n=n, regex=not literal
+        )
+        return self.with_native(series)
+
+    def replace_all(
+        self, value: PandasLikeSeries, pattern: str, *, literal: bool
+    ) -> PandasLikeSeries:
+        return self.replace(value, pattern, literal=literal, n=-1)
+
+    def strip_chars(self, characters: str | None) -> PandasLikeSeries:
+        return self.with_native(self.native.str.strip(characters))
+
+    def strip_chars_start(self, characters: str) -> PandasLikeSeries:
+        return self.with_native(self.native.str.lstrip(characters))
+
+    def strip_chars_end(self, characters: str) -> PandasLikeSeries:
+        return self.with_native(self.native.str.rstrip(characters))
+
+    def starts_with(self, prefix: PandasLikeSeries) -> PandasLikeSeries:
+        _, prefix_native = align_and_extract_native(self.compliant, prefix)
+        if not isinstance(prefix_native, str):
+            msg = f"`.str.starts_with` only supports prefix values for {self.compliant._implementation} backend"
+            raise TypeError(msg)
+        return self._with_native_bool(self.native.str.startswith(prefix_native))
+
+    def ends_with(self, suffix: PandasLikeSeries) -> PandasLikeSeries:
+        _, suffix_native = align_and_extract_native(self.compliant, suffix)
+        if not isinstance(suffix_native, str):
+            msg = f"`.str.ends_with` only supports suffix values for {self.compliant._implementation} backend"
+            raise TypeError(msg)
+        return self._with_native_bool(self.native.str.endswith(suffix_native))
+
+    def contains(self, pattern: PandasLikeSeries, *, literal: bool) -> PandasLikeSeries:
+        _, pattern_native = align_and_extract_native(self.compliant, pattern)
+        if not isinstance(pattern_native, str):
+            msg = f"`.str.contains` only supports str pattern values for {self.compliant._implementation} backend"
+            raise TypeError(msg)
+        return self._with_native_bool(
+            self.native.str.contains(pat=pattern_native, regex=not literal)
+        )
+
+    def slice(self, offset: int, length: int | None) -> PandasLikeSeries:
+        stop = str_slice_stop(offset, length)
+        return self.with_native(self.native.str.slice(start=offset, stop=stop))
+
+    def split(self, by: str) -> PandasLikeSeries:
+        implementation = self.implementation
+        if not implementation.is_cudf() and not is_dtype_pyarrow(self.native.dtype):
+            msg = (
+                "This operation requires a pyarrow-backed series. "
+                "Please refer to https://narwhals-dev.github.io/narwhals/api-reference/narwhals/#narwhals.maybe_convert_dtypes "
+                "and ensure you are using dtype_backend='pyarrow'. "
+                "Additionally, make sure you have pandas version 1.5+ and pyarrow installed. "
+            )
+            raise TypeError(msg)
+        return self.with_native(self.native.str.split(pat=by))
+
+    def to_datetime(self, format: str | None) -> PandasLikeSeries:
+        # If we know inputs are timezone-aware, we can pass `utc=True` for better performance.
+        if format and any(x in format for x in ("%z", "Z")):
+            return self.with_native(self._to_datetime(format, utc=True))
+        result = self.with_native(self._to_datetime(format, utc=False))
+        if (tz := getattr(result.dtype, "time_zone", None)) and tz != "UTC":
+            return result.dt.convert_time_zone("UTC")
+        return result
+
+    def _to_datetime(self, format: str | None, *, utc: bool) -> Any:
+        result = self.implementation.to_native_namespace().to_datetime(
+            self.native, format=format, utc=utc
+        )
+        return (
+            result.convert_dtypes(dtype_backend="pyarrow")
+            if is_dtype_pyarrow(self.native.dtype)
+            else result
+        )
+
+    def to_date(self, format: str | None) -> PandasLikeSeries:
+        return self.to_datetime(format=format).dt.date()
+
+    def to_time(self, format: str | None) -> PandasLikeSeries:
+        time_dtype = self.version.dtypes.Time()
+        return self.with_native(self._to_datetime(format, utc=False)).cast(time_dtype)
+
+    def to_uppercase(self) -> PandasLikeSeries:
+        return self.with_native(self.native.str.upper())
+
+    def to_lowercase(self) -> PandasLikeSeries:
+        return self.with_native(self.native.str.lower())
+
+    def to_titlecase(self) -> PandasLikeSeries:
+        return self.with_native(self.native.str.title())
+
+    def zfill(self, width: int) -> PandasLikeSeries:
+        if width == 0:
+            return self.compliant
+        return self.with_native(self.native.str.zfill(width))
+
+    def pad_start(self, length: int, fill_char: str) -> PandasLikeSeries:
+        return self.with_native(
+            self.native.str.pad(width=length, fillchar=fill_char, side="left")
+        )
+
+    def pad_end(self, length: int, fill_char: str) -> PandasLikeSeries:
+        return self.with_native(
+            self.native.str.pad(width=length, fillchar=fill_char, side="right")
+        )

shared/package-files/9b/9b633e2d6aa5d803d19bb099de8163f7b49408a1bfaea3fe3e7d519e15dda808/blob

@@ -0,0 +1,50 @@
+#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 ldexp_Tensor {
+  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::ldexp")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "ldexp.Tensor(Tensor self, Tensor other) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API ldexp_ {
+  using schema = at::Tensor & (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::ldexp_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "ldexp_(Tensor(a!) self, Tensor other) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API ldexp_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::ldexp")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+}} // namespace at::_ops

shared/package-files/9b/9b65eeebf6d708da9966136f37d3d3eef925cdce7587f22da457eeee1f8ba83e/blob

@@ -0,0 +1,557 @@
+from __future__ import annotations
+
+import ctypes
+import re
+from typing import Any
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import SettingWithCopyError
+
+import pandas as pd
+from pandas.core.interchange.dataframe_protocol import (
+    Buffer,
+    Column,
+    ColumnNullType,
+    DataFrame as DataFrameXchg,
+    DtypeKind,
+)
+from pandas.core.interchange.utils import (
+    ArrowCTypes,
+    Endianness,
+)
+
+_NP_DTYPES: dict[DtypeKind, dict[int, Any]] = {
+    DtypeKind.INT: {8: np.int8, 16: np.int16, 32: np.int32, 64: np.int64},
+    DtypeKind.UINT: {8: np.uint8, 16: np.uint16, 32: np.uint32, 64: np.uint64},
+    DtypeKind.FLOAT: {32: np.float32, 64: np.float64},
+    DtypeKind.BOOL: {1: bool, 8: bool},
+}
+
+
+def from_dataframe(df, allow_copy: bool = True) -> pd.DataFrame:
+    """
+    Build a ``pd.DataFrame`` from any DataFrame supporting the interchange protocol.
+
+    .. note::
+
+       For new development, we highly recommend using the Arrow C Data Interface
+       alongside the Arrow PyCapsule Interface instead of the interchange protocol.
+       From pandas 2.3 onwards, `from_dataframe` uses the PyCapsule Interface,
+       only falling back to the interchange protocol if that fails.
+
+    .. warning::
+
+        Due to severe implementation issues, we recommend only considering using the
+        interchange protocol in the following cases:
+
+        - converting to pandas: for pandas >= 2.0.3
+        - converting from pandas: for pandas >= 3.0.0
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+        Object supporting the interchange protocol, i.e. `__dataframe__` method.
+    allow_copy : bool, default: True
+        Whether to allow copying the memory to perform the conversion
+        (if false then zero-copy approach is requested).
+
+    Returns
+    -------
+    pd.DataFrame
+
+    Examples
+    --------
+    >>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
+    >>> interchange_object = df_not_necessarily_pandas.__dataframe__()
+    >>> interchange_object.column_names()
+    Index(['A', 'B'], dtype='object')
+    >>> df_pandas = (pd.api.interchange.from_dataframe
+    ...              (interchange_object.select_columns_by_name(['A'])))
+    >>> df_pandas
+         A
+    0    1
+    1    2
+
+    These methods (``column_names``, ``select_columns_by_name``) should work
+    for any dataframe library which implements the interchange protocol.
+    """
+    if isinstance(df, pd.DataFrame):
+        return df
+
+    if hasattr(df, "__arrow_c_stream__"):
+        try:
+            pa = import_optional_dependency("pyarrow", min_version="14.0.0")
+        except ImportError:
+            # fallback to _from_dataframe
+            pass
+        else:
+            try:
+                return pa.table(df).to_pandas(zero_copy_only=not allow_copy)
+            except pa.ArrowInvalid as e:
+                raise RuntimeError(e) from e
+
+    if not hasattr(df, "__dataframe__"):
+        raise ValueError("`df` does not support __dataframe__")
+
+    return _from_dataframe(
+        df.__dataframe__(allow_copy=allow_copy), allow_copy=allow_copy
+    )
+
+
+def _from_dataframe(df: DataFrameXchg, allow_copy: bool = True):
+    """
+    Build a ``pd.DataFrame`` from the DataFrame interchange object.
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+        Object supporting the interchange protocol, i.e. `__dataframe__` method.
+    allow_copy : bool, default: True
+        Whether to allow copying the memory to perform the conversion
+        (if false then zero-copy approach is requested).
+
+    Returns
+    -------
+    pd.DataFrame
+    """
+    pandas_dfs = []
+    for chunk in df.get_chunks():
+        pandas_df = protocol_df_chunk_to_pandas(chunk)
+        pandas_dfs.append(pandas_df)
+
+    if not allow_copy and len(pandas_dfs) > 1:
+        raise RuntimeError(
+            "To join chunks a copy is required which is forbidden by allow_copy=False"
+        )
+    if not pandas_dfs:
+        pandas_df = protocol_df_chunk_to_pandas(df)
+    elif len(pandas_dfs) == 1:
+        pandas_df = pandas_dfs[0]
+    else:
+        pandas_df = pd.concat(pandas_dfs, axis=0, ignore_index=True, copy=False)
+
+    index_obj = df.metadata.get("pandas.index", None)
+    if index_obj is not None:
+        pandas_df.index = index_obj
+
+    return pandas_df
+
+
+def protocol_df_chunk_to_pandas(df: DataFrameXchg) -> pd.DataFrame:
+    """
+    Convert interchange protocol chunk to ``pd.DataFrame``.
+
+    Parameters
+    ----------
+    df : DataFrameXchg
+
+    Returns
+    -------
+    pd.DataFrame
+    """
+    columns: dict[str, Any] = {}
+    buffers = []  # hold on to buffers, keeps memory alive
+    for name in df.column_names():
+        if not isinstance(name, str):
+            raise ValueError(f"Column {name} is not a string")
+        if name in columns:
+            raise ValueError(f"Column {name} is not unique")
+        col = df.get_column_by_name(name)
+        dtype = col.dtype[0]
+        if dtype in (
+            DtypeKind.INT,
+            DtypeKind.UINT,
+            DtypeKind.FLOAT,
+            DtypeKind.BOOL,
+        ):
+            columns[name], buf = primitive_column_to_ndarray(col)
+        elif dtype == DtypeKind.CATEGORICAL:
+            columns[name], buf = categorical_column_to_series(col)
+        elif dtype == DtypeKind.STRING:
+            columns[name], buf = string_column_to_ndarray(col)
+        elif dtype == DtypeKind.DATETIME:
+            columns[name], buf = datetime_column_to_ndarray(col)
+        else:
+            raise NotImplementedError(f"Data type {dtype} not handled yet")
+
+        buffers.append(buf)
+
+    pandas_df = pd.DataFrame(columns)
+    pandas_df.attrs["_INTERCHANGE_PROTOCOL_BUFFERS"] = buffers
+    return pandas_df
+
+
+def primitive_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
+    """
+    Convert a column holding one of the primitive dtypes to a NumPy array.
+
+    A primitive type is one of: int, uint, float, bool.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    buffers = col.get_buffers()
+
+    data_buff, data_dtype = buffers["data"]
+    data = buffer_to_ndarray(
+        data_buff, data_dtype, offset=col.offset, length=col.size()
+    )
+
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def categorical_column_to_series(col: Column) -> tuple[pd.Series, Any]:
+    """
+    Convert a column holding categorical data to a pandas Series.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of pd.Series holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    categorical = col.describe_categorical
+
+    if not categorical["is_dictionary"]:
+        raise NotImplementedError("Non-dictionary categoricals not supported yet")
+
+    cat_column = categorical["categories"]
+    if hasattr(cat_column, "_col"):
+        # Item "Column" of "Optional[Column]" has no attribute "_col"
+        # Item "None" of "Optional[Column]" has no attribute "_col"
+        categories = np.array(cat_column._col)  # type: ignore[union-attr]
+    else:
+        raise NotImplementedError(
+            "Interchanging categorical columns isn't supported yet, and our "
+            "fallback of using the `col._col` attribute (a ndarray) failed."
+        )
+    buffers = col.get_buffers()
+
+    codes_buff, codes_dtype = buffers["data"]
+    codes = buffer_to_ndarray(
+        codes_buff, codes_dtype, offset=col.offset, length=col.size()
+    )
+
+    # Doing module in order to not get ``IndexError`` for
+    # out-of-bounds sentinel values in `codes`
+    if len(categories) > 0:
+        values = categories[codes % len(categories)]
+    else:
+        values = codes
+
+    cat = pd.Categorical(
+        values, categories=categories, ordered=categorical["is_ordered"]
+    )
+    data = pd.Series(cat)
+
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
+    """
+    Convert a column holding string data to a NumPy array.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    null_kind, sentinel_val = col.describe_null
+
+    if null_kind not in (
+        ColumnNullType.NON_NULLABLE,
+        ColumnNullType.USE_BITMASK,
+        ColumnNullType.USE_BYTEMASK,
+    ):
+        raise NotImplementedError(
+            f"{null_kind} null kind is not yet supported for string columns."
+        )
+
+    buffers = col.get_buffers()
+
+    assert buffers["offsets"], "String buffers must contain offsets"
+    # Retrieve the data buffer containing the UTF-8 code units
+    data_buff, _ = buffers["data"]
+    # We're going to reinterpret the buffer as uint8, so make sure we can do it safely
+    assert col.dtype[2] in (
+        ArrowCTypes.STRING,
+        ArrowCTypes.LARGE_STRING,
+    )  # format_str == utf-8
+    # Convert the buffers to NumPy arrays. In order to go from STRING to
+    # an equivalent ndarray, we claim that the buffer is uint8 (i.e., a byte array)
+    data_dtype = (
+        DtypeKind.UINT,
+        8,
+        ArrowCTypes.UINT8,
+        Endianness.NATIVE,
+    )
+    # Specify zero offset as we don't want to chunk the string data
+    data = buffer_to_ndarray(data_buff, data_dtype, offset=0, length=data_buff.bufsize)
+
+    # Retrieve the offsets buffer containing the index offsets demarcating
+    # the beginning and the ending of each string
+    offset_buff, offset_dtype = buffers["offsets"]
+    # Offsets buffer contains start-stop positions of strings in the data buffer,
+    # meaning that it has more elements than in the data buffer, do `col.size() + 1`
+    # here to pass a proper offsets buffer size
+    offsets = buffer_to_ndarray(
+        offset_buff, offset_dtype, offset=col.offset, length=col.size() + 1
+    )
+
+    null_pos = None
+    if null_kind in (ColumnNullType.USE_BITMASK, ColumnNullType.USE_BYTEMASK):
+        validity = buffers["validity"]
+        if validity is not None:
+            valid_buff, valid_dtype = validity
+            null_pos = buffer_to_ndarray(
+                valid_buff, valid_dtype, offset=col.offset, length=col.size()
+            )
+            if sentinel_val == 0:
+                null_pos = ~null_pos
+
+    # Assemble the strings from the code units
+    str_list: list[None | float | str] = [None] * col.size()
+    for i in range(col.size()):
+        # Check for missing values
+        if null_pos is not None and null_pos[i]:
+            str_list[i] = np.nan
+            continue
+
+        # Extract a range of code units
+        units = data[offsets[i] : offsets[i + 1]]
+
+        # Convert the list of code units to bytes
+        str_bytes = bytes(units)
+
+        # Create the string
+        string = str_bytes.decode(encoding="utf-8")
+
+        # Add to our list of strings
+        str_list[i] = string
+
+    if using_string_dtype():
+        res = pd.Series(str_list, dtype="str")
+    else:
+        res = np.asarray(str_list, dtype="object")  # type: ignore[assignment]
+
+    return res, buffers  # type: ignore[return-value]
+
+
+def parse_datetime_format_str(format_str, data) -> pd.Series | np.ndarray:
+    """Parse datetime `format_str` to interpret the `data`."""
+    # timestamp 'ts{unit}:tz'
+    timestamp_meta = re.match(r"ts([smun]):(.*)", format_str)
+    if timestamp_meta:
+        unit, tz = timestamp_meta.group(1), timestamp_meta.group(2)
+        if unit != "s":
+            # the format string describes only a first letter of the unit, so
+            # add one extra letter to convert the unit to numpy-style:
+            # 'm' -> 'ms', 'u' -> 'us', 'n' -> 'ns'
+            unit += "s"
+        data = data.astype(f"datetime64[{unit}]")
+        if tz != "":
+            data = pd.Series(data).dt.tz_localize("UTC").dt.tz_convert(tz)
+        return data
+
+    # date 'td{Days/Ms}'
+    date_meta = re.match(r"td([Dm])", format_str)
+    if date_meta:
+        unit = date_meta.group(1)
+        if unit == "D":
+            # NumPy doesn't support DAY unit, so converting days to seconds
+            # (converting to uint64 to avoid overflow)
+            data = (data.astype(np.uint64) * (24 * 60 * 60)).astype("datetime64[s]")
+        elif unit == "m":
+            data = data.astype("datetime64[ms]")
+        else:
+            raise NotImplementedError(f"Date unit is not supported: {unit}")
+        return data
+
+    raise NotImplementedError(f"DateTime kind is not supported: {format_str}")
+
+
+def datetime_column_to_ndarray(col: Column) -> tuple[np.ndarray | pd.Series, Any]:
+    """
+    Convert a column holding DateTime data to a NumPy array.
+
+    Parameters
+    ----------
+    col : Column
+
+    Returns
+    -------
+    tuple
+        Tuple of np.ndarray holding the data and the memory owner object
+        that keeps the memory alive.
+    """
+    buffers = col.get_buffers()
+
+    _, col_bit_width, format_str, _ = col.dtype
+    dbuf, _ = buffers["data"]
+    # Consider dtype being `uint` to get number of units passed since the 01.01.1970
+
+    data = buffer_to_ndarray(
+        dbuf,
+        (
+            DtypeKind.INT,
+            col_bit_width,
+            getattr(ArrowCTypes, f"INT{col_bit_width}"),
+            Endianness.NATIVE,
+        ),
+        offset=col.offset,
+        length=col.size(),
+    )
+
+    data = parse_datetime_format_str(format_str, data)  # type: ignore[assignment]
+    data = set_nulls(data, col, buffers["validity"])
+    return data, buffers
+
+
+def buffer_to_ndarray(
+    buffer: Buffer,
+    dtype: tuple[DtypeKind, int, str, str],
+    *,
+    length: int,
+    offset: int = 0,
+) -> np.ndarray:
+    """
+    Build a NumPy array from the passed buffer.
+
+    Parameters
+    ----------
+    buffer : Buffer
+        Buffer to build a NumPy array from.
+    dtype : tuple
+        Data type of the buffer conforming protocol dtypes format.
+    offset : int, default: 0
+        Number of elements to offset from the start of the buffer.
+    length : int, optional
+        If the buffer is a bit-mask, specifies a number of bits to read
+        from the buffer. Has no effect otherwise.
+
+    Returns
+    -------
+    np.ndarray
+
+    Notes
+    -----
+    The returned array doesn't own the memory. The caller of this function is
+    responsible for keeping the memory owner object alive as long as
+    the returned NumPy array is being used.
+    """
+    kind, bit_width, _, _ = dtype
+
+    column_dtype = _NP_DTYPES.get(kind, {}).get(bit_width, None)
+    if column_dtype is None:
+        raise NotImplementedError(f"Conversion for {dtype} is not yet supported.")
+
+    # TODO: No DLPack yet, so need to construct a new ndarray from the data pointer
+    # and size in the buffer plus the dtype on the column. Use DLPack as NumPy supports
+    # it since https://github.com/numpy/numpy/pull/19083
+    ctypes_type = np.ctypeslib.as_ctypes_type(column_dtype)
+
+    if bit_width == 1:
+        assert length is not None, "`length` must be specified for a bit-mask buffer."
+        pa = import_optional_dependency("pyarrow")
+        arr = pa.BooleanArray.from_buffers(
+            pa.bool_(),
+            length,
+            [None, pa.foreign_buffer(buffer.ptr, length)],
+            offset=offset,
+        )
+        return np.asarray(arr)
+    else:
+        data_pointer = ctypes.cast(
+            buffer.ptr + (offset * bit_width // 8), ctypes.POINTER(ctypes_type)
+        )
+        if length > 0:
+            return np.ctypeslib.as_array(data_pointer, shape=(length,))
+        return np.array([], dtype=ctypes_type)
+
+
+def set_nulls(
+    data: np.ndarray | pd.Series,
+    col: Column,
+    validity: tuple[Buffer, tuple[DtypeKind, int, str, str]] | None,
+    allow_modify_inplace: bool = True,
+):
+    """
+    Set null values for the data according to the column null kind.
+
+    Parameters
+    ----------
+    data : np.ndarray or pd.Series
+        Data to set nulls in.
+    col : Column
+        Column object that describes the `data`.
+    validity : tuple(Buffer, dtype) or None
+        The return value of ``col.buffers()``. We do not access the ``col.buffers()``
+        here to not take the ownership of the memory of buffer objects.
+    allow_modify_inplace : bool, default: True
+        Whether to modify the `data` inplace when zero-copy is possible (True) or always
+        modify a copy of the `data` (False).
+
+    Returns
+    -------
+    np.ndarray or pd.Series
+        Data with the nulls being set.
+    """
+    if validity is None:
+        return data
+    null_kind, sentinel_val = col.describe_null
+    null_pos = None
+
+    if null_kind == ColumnNullType.USE_SENTINEL:
+        null_pos = pd.Series(data) == sentinel_val
+    elif null_kind in (ColumnNullType.USE_BITMASK, ColumnNullType.USE_BYTEMASK):
+        assert validity, "Expected to have a validity buffer for the mask"
+        valid_buff, valid_dtype = validity
+        null_pos = buffer_to_ndarray(
+            valid_buff, valid_dtype, offset=col.offset, length=col.size()
+        )
+        if sentinel_val == 0:
+            null_pos = ~null_pos
+    elif null_kind in (ColumnNullType.NON_NULLABLE, ColumnNullType.USE_NAN):
+        pass
+    else:
+        raise NotImplementedError(f"Null kind {null_kind} is not yet supported.")
+
+    if null_pos is not None and np.any(null_pos):
+        if not allow_modify_inplace:
+            data = data.copy()
+        try:
+            data[null_pos] = None
+        except TypeError:
+            # TypeError happens if the `data` dtype appears to be non-nullable
+            # in numpy notation (bool, int, uint). If this happens,
+            # cast the `data` to nullable float dtype.
+            data = data.astype(float)
+            data[null_pos] = None
+        except SettingWithCopyError:
+            # `SettingWithCopyError` may happen for datetime-like with missing values.
+            data = data.copy()
+            data[null_pos] = None
+
+    return data

shared/package-files/9b/9b66f7e1cf75ec85b9a3e43fe936081e5b0af6549494d8b2ac84d3507ff3c1ec/blob

@@ -0,0 +1,353 @@
+"""This is invoked in a subprocess to call the build backend hooks.
+
+It expects:
+- Command line args: hook_name, control_dir
+- Environment variables:
+      PEP517_BUILD_BACKEND=entry.point:spec
+      PEP517_BACKEND_PATH=paths (separated with os.pathsep)
+- control_dir/input.json:
+  - {"kwargs": {...}}
+
+Results:
+- control_dir/output.json
+  - {"return_val": ...}
+"""
+import json
+import os
+import os.path
+import re
+import shutil
+import sys
+import traceback
+from glob import glob
+from importlib import import_module
+from os.path import join as pjoin
+
+# This file is run as a script, and `import wrappers` is not zip-safe, so we
+# include write_json() and read_json() from wrappers.py.
+
+
+def write_json(obj, path, **kwargs):
+    with open(path, 'w', encoding='utf-8') as f:
+        json.dump(obj, f, **kwargs)
+
+
+def read_json(path):
+    with open(path, encoding='utf-8') as f:
+        return json.load(f)
+
+
+class BackendUnavailable(Exception):
+    """Raised if we cannot import the backend"""
+    def __init__(self, traceback):
+        self.traceback = traceback
+
+
+class BackendInvalid(Exception):
+    """Raised if the backend is invalid"""
+    def __init__(self, message):
+        self.message = message
+
+
+class HookMissing(Exception):
+    """Raised if a hook is missing and we are not executing the fallback"""
+    def __init__(self, hook_name=None):
+        super().__init__(hook_name)
+        self.hook_name = hook_name
+
+
+def contained_in(filename, directory):
+    """Test if a file is located within the given directory."""
+    filename = os.path.normcase(os.path.abspath(filename))
+    directory = os.path.normcase(os.path.abspath(directory))
+    return os.path.commonprefix([filename, directory]) == directory
+
+
+def _build_backend():
+    """Find and load the build backend"""
+    # Add in-tree backend directories to the front of sys.path.
+    backend_path = os.environ.get('PEP517_BACKEND_PATH')
+    if backend_path:
+        extra_pathitems = backend_path.split(os.pathsep)
+        sys.path[:0] = extra_pathitems
+
+    ep = os.environ['PEP517_BUILD_BACKEND']
+    mod_path, _, obj_path = ep.partition(':')
+    try:
+        obj = import_module(mod_path)
+    except ImportError:
+        raise BackendUnavailable(traceback.format_exc())
+
+    if backend_path:
+        if not any(
+            contained_in(obj.__file__, path)
+            for path in extra_pathitems
+        ):
+            raise BackendInvalid("Backend was not loaded from backend-path")
+
+    if obj_path:
+        for path_part in obj_path.split('.'):
+            obj = getattr(obj, path_part)
+    return obj
+
+
+def _supported_features():
+    """Return the list of options features supported by the backend.
+
+    Returns a list of strings.
+    The only possible value is 'build_editable'.
+    """
+    backend = _build_backend()
+    features = []
+    if hasattr(backend, "build_editable"):
+        features.append("build_editable")
+    return features
+
+
+def get_requires_for_build_wheel(config_settings):
+    """Invoke the optional get_requires_for_build_wheel hook
+
+    Returns [] if the hook is not defined.
+    """
+    backend = _build_backend()
+    try:
+        hook = backend.get_requires_for_build_wheel
+    except AttributeError:
+        return []
+    else:
+        return hook(config_settings)
+
+
+def get_requires_for_build_editable(config_settings):
+    """Invoke the optional get_requires_for_build_editable hook
+
+    Returns [] if the hook is not defined.
+    """
+    backend = _build_backend()
+    try:
+        hook = backend.get_requires_for_build_editable
+    except AttributeError:
+        return []
+    else:
+        return hook(config_settings)
+
+
+def prepare_metadata_for_build_wheel(
+        metadata_directory, config_settings, _allow_fallback):
+    """Invoke optional prepare_metadata_for_build_wheel
+
+    Implements a fallback by building a wheel if the hook isn't defined,
+    unless _allow_fallback is False in which case HookMissing is raised.
+    """
+    backend = _build_backend()
+    try:
+        hook = backend.prepare_metadata_for_build_wheel
+    except AttributeError:
+        if not _allow_fallback:
+            raise HookMissing()
+    else:
+        return hook(metadata_directory, config_settings)
+    # fallback to build_wheel outside the try block to avoid exception chaining
+    # which can be confusing to users and is not relevant
+    whl_basename = backend.build_wheel(metadata_directory, config_settings)
+    return _get_wheel_metadata_from_wheel(whl_basename, metadata_directory,
+                                          config_settings)
+
+
+def prepare_metadata_for_build_editable(
+        metadata_directory, config_settings, _allow_fallback):
+    """Invoke optional prepare_metadata_for_build_editable
+
+    Implements a fallback by building an editable wheel if the hook isn't
+    defined, unless _allow_fallback is False in which case HookMissing is
+    raised.
+    """
+    backend = _build_backend()
+    try:
+        hook = backend.prepare_metadata_for_build_editable
+    except AttributeError:
+        if not _allow_fallback:
+            raise HookMissing()
+        try:
+            build_hook = backend.build_editable
+        except AttributeError:
+            raise HookMissing(hook_name='build_editable')
+        else:
+            whl_basename = build_hook(metadata_directory, config_settings)
+            return _get_wheel_metadata_from_wheel(whl_basename,
+                                                  metadata_directory,
+                                                  config_settings)
+    else:
+        return hook(metadata_directory, config_settings)
+
+
+WHEEL_BUILT_MARKER = 'PEP517_ALREADY_BUILT_WHEEL'
+
+
+def _dist_info_files(whl_zip):
+    """Identify the .dist-info folder inside a wheel ZipFile."""
+    res = []
+    for path in whl_zip.namelist():
+        m = re.match(r'[^/\\]+-[^/\\]+\.dist-info/', path)
+        if m:
+            res.append(path)
+    if res:
+        return res
+    raise Exception("No .dist-info folder found in wheel")
+
+
+def _get_wheel_metadata_from_wheel(
+        whl_basename, metadata_directory, config_settings):
+    """Extract the metadata from a wheel.
+
+    Fallback for when the build backend does not
+    define the 'get_wheel_metadata' hook.
+    """
+    from zipfile import ZipFile
+    with open(os.path.join(metadata_directory, WHEEL_BUILT_MARKER), 'wb'):
+        pass  # Touch marker file
+
+    whl_file = os.path.join(metadata_directory, whl_basename)
+    with ZipFile(whl_file) as zipf:
+        dist_info = _dist_info_files(zipf)
+        zipf.extractall(path=metadata_directory, members=dist_info)
+    return dist_info[0].split('/')[0]
+
+
+def _find_already_built_wheel(metadata_directory):
+    """Check for a wheel already built during the get_wheel_metadata hook.
+    """
+    if not metadata_directory:
+        return None
+    metadata_parent = os.path.dirname(metadata_directory)
+    if not os.path.isfile(pjoin(metadata_parent, WHEEL_BUILT_MARKER)):
+        return None
+
+    whl_files = glob(os.path.join(metadata_parent, '*.whl'))
+    if not whl_files:
+        print('Found wheel built marker, but no .whl files')
+        return None
+    if len(whl_files) > 1:
+        print('Found multiple .whl files; unspecified behaviour. '
+              'Will call build_wheel.')
+        return None
+
+    # Exactly one .whl file
+    return whl_files[0]
+
+
+def build_wheel(wheel_directory, config_settings, metadata_directory=None):
+    """Invoke the mandatory build_wheel hook.
+
+    If a wheel was already built in the
+    prepare_metadata_for_build_wheel fallback, this
+    will copy it rather than rebuilding the wheel.
+    """
+    prebuilt_whl = _find_already_built_wheel(metadata_directory)
+    if prebuilt_whl:
+        shutil.copy2(prebuilt_whl, wheel_directory)
+        return os.path.basename(prebuilt_whl)
+
+    return _build_backend().build_wheel(wheel_directory, config_settings,
+                                        metadata_directory)
+
+
+def build_editable(wheel_directory, config_settings, metadata_directory=None):
+    """Invoke the optional build_editable hook.
+
+    If a wheel was already built in the
+    prepare_metadata_for_build_editable fallback, this
+    will copy it rather than rebuilding the wheel.
+    """
+    backend = _build_backend()
+    try:
+        hook = backend.build_editable
+    except AttributeError:
+        raise HookMissing()
+    else:
+        prebuilt_whl = _find_already_built_wheel(metadata_directory)
+        if prebuilt_whl:
+            shutil.copy2(prebuilt_whl, wheel_directory)
+            return os.path.basename(prebuilt_whl)
+
+        return hook(wheel_directory, config_settings, metadata_directory)
+
+
+def get_requires_for_build_sdist(config_settings):
+    """Invoke the optional get_requires_for_build_wheel hook
+
+    Returns [] if the hook is not defined.
+    """
+    backend = _build_backend()
+    try:
+        hook = backend.get_requires_for_build_sdist
+    except AttributeError:
+        return []
+    else:
+        return hook(config_settings)
+
+
+class _DummyException(Exception):
+    """Nothing should ever raise this exception"""
+
+
+class GotUnsupportedOperation(Exception):
+    """For internal use when backend raises UnsupportedOperation"""
+    def __init__(self, traceback):
+        self.traceback = traceback
+
+
+def build_sdist(sdist_directory, config_settings):
+    """Invoke the mandatory build_sdist hook."""
+    backend = _build_backend()
+    try:
+        return backend.build_sdist(sdist_directory, config_settings)
+    except getattr(backend, 'UnsupportedOperation', _DummyException):
+        raise GotUnsupportedOperation(traceback.format_exc())
+
+
+HOOK_NAMES = {
+    'get_requires_for_build_wheel',
+    'prepare_metadata_for_build_wheel',
+    'build_wheel',
+    'get_requires_for_build_editable',
+    'prepare_metadata_for_build_editable',
+    'build_editable',
+    'get_requires_for_build_sdist',
+    'build_sdist',
+    '_supported_features',
+}
+
+
+def main():
+    if len(sys.argv) < 3:
+        sys.exit("Needs args: hook_name, control_dir")
+    hook_name = sys.argv[1]
+    control_dir = sys.argv[2]
+    if hook_name not in HOOK_NAMES:
+        sys.exit("Unknown hook: %s" % hook_name)
+    hook = globals()[hook_name]
+
+    hook_input = read_json(pjoin(control_dir, 'input.json'))
+
+    json_out = {'unsupported': False, 'return_val': None}
+    try:
+        json_out['return_val'] = hook(**hook_input['kwargs'])
+    except BackendUnavailable as e:
+        json_out['no_backend'] = True
+        json_out['traceback'] = e.traceback
+    except BackendInvalid as e:
+        json_out['backend_invalid'] = True
+        json_out['backend_error'] = e.message
+    except GotUnsupportedOperation as e:
+        json_out['unsupported'] = True
+        json_out['traceback'] = e.traceback
+    except HookMissing as e:
+        json_out['hook_missing'] = True
+        json_out['missing_hook_name'] = e.hook_name or hook_name
+
+    write_json(json_out, pjoin(control_dir, 'output.json'), indent=2)
+
+
+if __name__ == '__main__':
+    main()

shared/package-files/9b/9b6742f1d21425a930997735c01c838096af851948a118f4923bade4c777e9e7/blob

@@ -0,0 +1,24 @@
+#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 _upsample_bilinear2d_aa(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor _upsample_bilinear2d_aa_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional<at::ArrayRef<double>> scale_factors);
+
+} // namespace compositeimplicitautograd
+} // namespace at

shared/package-files/9b/9b6cdb3f80bbf7a16f415b15566b175028b3f0fae3d8efa94f9e6b6c70363700/blob

@@ -0,0 +1,25 @@
+#pragma once
+
+#include <Python.h>
+
+// @generated from ../tools/autograd/templates/python_functions.h
+
+// Python bindings for automatically generated autograd functions
+
+namespace torch { namespace autograd { namespace generated {
+
+void initialize_autogenerated_functions_0(PyObject* module);
+void initialize_autogenerated_functions_1(PyObject* module);
+void initialize_autogenerated_functions_2(PyObject* module);
+void initialize_autogenerated_functions_3(PyObject* module);
+void initialize_autogenerated_functions_4(PyObject* module);
+
+inline void initialize_autogenerated_functions(PyObject* module) {
+  initialize_autogenerated_functions_0(module);
+  initialize_autogenerated_functions_1(module);
+  initialize_autogenerated_functions_2(module);
+  initialize_autogenerated_functions_3(module);
+  initialize_autogenerated_functions_4(module);
+}
+
+}}} // namespace torch::autograd::generated

shared/package-files/9b/9b6d58df002d41cfa38ba55e6fa93f33983a034672148e1e81c853767c21fa94/blob

@@ -0,0 +1,11 @@
+"""For when pip wants to check the date or time.
+"""
+
+import datetime
+
+
+def today_is_later_than(year: int, month: int, day: int) -> bool:
+    today = datetime.date.today()
+    given = datetime.date(year, month, day)
+
+    return today > given

shared/package-files/9b/9b6e400eb85440ec64ab66b4ac111546585740c9ca61fd156400d7153cbad9f4/blob

@@ -0,0 +1,335 @@
+# history.tcl --
+#
+# Implementation of the history command.
+#
+# Copyright (c) 1997 Sun Microsystems, Inc.
+#
+# See the file "license.terms" for information on usage and redistribution of
+# this file, and for a DISCLAIMER OF ALL WARRANTIES.
+#
+
+# The tcl::history array holds the history list and some additional
+# bookkeeping variables.
+#
+# nextid	the index used for the next history list item.
+# keep		the max size of the history list
+# oldest	the index of the oldest item in the history.
+
+namespace eval ::tcl {
+    variable history
+    if {![info exists history]} {
+	array set history {
+	    nextid	0
+	    keep	20
+	    oldest	-20
+	}
+    }
+
+    namespace ensemble create -command ::tcl::history -map {
+	add	::tcl::HistAdd
+	change	::tcl::HistChange
+	clear	::tcl::HistClear
+	event	::tcl::HistEvent
+	info	::tcl::HistInfo
+	keep	::tcl::HistKeep
+	nextid	::tcl::HistNextID
+	redo	::tcl::HistRedo
+    }
+}
+
+# history --
+#
+#	This is the main history command.  See the man page for its interface.
+#	This does some argument checking and calls the helper ensemble in the
+#	tcl namespace.
+
+proc ::history {args} {
+    # If no command given, we're doing 'history info'. Can't be done with an
+    # ensemble unknown handler, as those don't fire when no subcommand is
+    # given at all.
+
+    if {![llength $args]} {
+	set args info
+    }
+
+    # Tricky stuff needed to make stack and errors come out right!
+    tailcall apply {arglist {tailcall history {*}$arglist} ::tcl} $args
+}
+
+# (unnamed) --
+#
+#	Callback when [::history] is destroyed. Destroys the implementation.
+#
+# Parameters:
+#	oldName    what the command was called.
+#	newName    what the command is now called (an empty string).
+#	op	   the operation (= delete).
+#
+# Results:
+#	none
+#
+# Side Effects:
+#	The implementation of the [::history] command ceases to exist.
+
+trace add command ::history delete [list apply {{oldName newName op} {
+    variable history
+    unset -nocomplain history
+    foreach c [info procs ::tcl::Hist*] {
+	rename $c {}
+    }
+    rename ::tcl::history {}
+} ::tcl}]
+
+# tcl::HistAdd --
+#
+#	Add an item to the history, and optionally eval it at the global scope
+#
+# Parameters:
+#	event		the command to add
+#	exec		(optional) a substring of "exec" causes the command to
+#			be evaled.
+# Results:
+# 	If executing, then the results of the command are returned
+#
+# Side Effects:
+#	Adds to the history list
+
+proc ::tcl::HistAdd {event {exec {}}} {
+    variable history
+
+    if {
+	[prefix longest {exec {}} $exec] eq ""
+	&& [llength [info level 0]] == 3
+    } then {
+	return -code error "bad argument \"$exec\": should be \"exec\""
+    }
+
+    # Do not add empty commands to the history
+    if {[string trim $event] eq ""} {
+	return ""
+    }
+
+    # Maintain the history
+    set history([incr history(nextid)]) $event
+    unset -nocomplain history([incr history(oldest)])
+
+    # Only execute if 'exec' (or non-empty prefix of it) given
+    if {$exec eq ""} {
+	return ""
+    }
+    tailcall eval $event
+}
+
+# tcl::HistKeep --
+#
+#	Set or query the limit on the length of the history list
+#
+# Parameters:
+#	limit	(optional) the length of the history list
+#
+# Results:
+#	If no limit is specified, the current limit is returned
+#
+# Side Effects:
+#	Updates history(keep) if a limit is specified
+
+proc ::tcl::HistKeep {{count {}}} {
+    variable history
+    if {[llength [info level 0]] == 1} {
+	return $history(keep)
+    }
+    if {![string is integer -strict $count] || ($count < 0)} {
+	return -code error "illegal keep count \"$count\""
+    }
+    set oldold $history(oldest)
+    set history(oldest) [expr {$history(nextid) - $count}]
+    for {} {$oldold <= $history(oldest)} {incr oldold} {
+	unset -nocomplain history($oldold)
+    }
+    set history(keep) $count
+}
+
+# tcl::HistClear --
+#
+#	Erase the history list
+#
+# Parameters:
+#	none
+#
+# Results:
+#	none
+#
+# Side Effects:
+#	Resets the history array, except for the keep limit
+
+proc ::tcl::HistClear {} {
+    variable history
+    set keep $history(keep)
+    unset history
+    array set history [list \
+	nextid	0	\
+	keep	$keep	\
+	oldest	-$keep	\
+    ]
+}
+
+# tcl::HistInfo --
+#
+#	Return a pretty-printed version of the history list
+#
+# Parameters:
+#	num	(optional) the length of the history list to return
+#
+# Results:
+#	A formatted history list
+
+proc ::tcl::HistInfo {{count {}}} {
+    variable history
+    if {[llength [info level 0]] == 1} {
+	set count [expr {$history(keep) + 1}]
+    } elseif {![string is integer -strict $count]} {
+	return -code error "bad integer \"$count\""
+    }
+    set result {}
+    set newline ""
+    for {set i [expr {$history(nextid) - $count + 1}]} \
+	    {$i <= $history(nextid)} {incr i} {
+	if {![info exists history($i)]} {
+	    continue
+	}
+        set cmd [string map [list \n \n\t] [string trimright $history($i) \ \n]]
+	append result $newline[format "%6d  %s" $i $cmd]
+	set newline \n
+    }
+    return $result
+}
+
+# tcl::HistRedo --
+#
+#	Fetch the previous or specified event, execute it, and then replace
+#	the current history item with that event.
+#
+# Parameters:
+#	event	(optional) index of history item to redo.  Defaults to -1,
+#		which means the previous event.
+#
+# Results:
+#	Those of the command being redone.
+#
+# Side Effects:
+#	Replaces the current history list item with the one being redone.
+
+proc ::tcl::HistRedo {{event -1}} {
+    variable history
+
+    set i [HistIndex $event]
+    if {$i == $history(nextid)} {
+	return -code error "cannot redo the current event"
+    }
+    set cmd $history($i)
+    HistChange $cmd 0
+    tailcall eval $cmd
+}
+
+# tcl::HistIndex --
+#
+#	Map from an event specifier to an index in the history list.
+#
+# Parameters:
+#	event	index of history item to redo.
+#		If this is a positive number, it is used directly.
+#		If it is a negative number, then it counts back to a previous
+#		event, where -1 is the most recent event.
+#		A string can be matched, either by being the prefix of a
+#		command or by matching a command with string match.
+#
+# Results:
+#	The index into history, or an error if the index didn't match.
+
+proc ::tcl::HistIndex {event} {
+    variable history
+    if {![string is integer -strict $event]} {
+	for {set i [expr {$history(nextid)-1}]} {[info exists history($i)]} \
+		{incr i -1} {
+	    if {[string match $event* $history($i)]} {
+		return $i
+	    }
+	    if {[string match $event $history($i)]} {
+		return $i
+	    }
+	}
+	return -code error "no event matches \"$event\""
+    } elseif {$event <= 0} {
+	set i [expr {$history(nextid) + $event}]
+    } else {
+	set i $event
+    }
+    if {$i <= $history(oldest)} {
+	return -code error "event \"$event\" is too far in the past"
+    }
+    if {$i > $history(nextid)} {
+	return -code error "event \"$event\" hasn't occured yet"
+    }
+    return $i
+}
+
+# tcl::HistEvent --
+#
+#	Map from an event specifier to the value in the history list.
+#
+# Parameters:
+#	event	index of history item to redo.  See index for a description of
+#		possible event patterns.
+#
+# Results:
+#	The value from the history list.
+
+proc ::tcl::HistEvent {{event -1}} {
+    variable history
+    set i [HistIndex $event]
+    if {![info exists history($i)]} {
+	return ""
+    }
+    return [string trimright $history($i) \ \n]
+}
+
+# tcl::HistChange --
+#
+#	Replace a value in the history list.
+#
+# Parameters:
+#	newValue  The new value to put into the history list.
+#	event	  (optional) index of history item to redo.  See index for a
+#		  description of possible event patterns.  This defaults to 0,
+#		  which specifies the current event.
+#
+# Side Effects:
+#	Changes the history list.
+
+proc ::tcl::HistChange {newValue {event 0}} {
+    variable history
+    set i [HistIndex $event]
+    set history($i) $newValue
+}
+
+# tcl::HistNextID --
+#
+#	Returns the number of the next history event.
+#
+# Parameters:
+#	None.
+#
+# Side Effects:
+#	None.
+
+proc ::tcl::HistNextID {} {
+    variable history
+    return [expr {$history(nextid) + 1}]
+}
+
+return
+
+# Local Variables:
+# mode: tcl
+# fill-column: 78
+# End:

shared/package-files/9b/9b6fd8df3c9d5193733e9bbcf27f303cd5aa6b17da01330a128ff30205ccab26/blob

@@ -0,0 +1,166 @@
+from __future__ import annotations
+
+import dataclasses
+import inspect
+from types import FunctionType
+from typing import Any
+from typing import final
+from typing import Generic
+from typing import TypeVar
+import warnings
+
+
+class PytestWarning(UserWarning):
+    """Base class for all warnings emitted by pytest."""
+
+    __module__ = "pytest"
+
+
+@final
+class PytestAssertRewriteWarning(PytestWarning):
+    """Warning emitted by the pytest assert rewrite module."""
+
+    __module__ = "pytest"
+
+
+@final
+class PytestCacheWarning(PytestWarning):
+    """Warning emitted by the cache plugin in various situations."""
+
+    __module__ = "pytest"
+
+
+@final
+class PytestConfigWarning(PytestWarning):
+    """Warning emitted for configuration issues."""
+
+    __module__ = "pytest"
+
+
+@final
+class PytestCollectionWarning(PytestWarning):
+    """Warning emitted when pytest is not able to collect a file or symbol in a module."""
+
+    __module__ = "pytest"
+
+
+class PytestDeprecationWarning(PytestWarning, DeprecationWarning):
+    """Warning class for features that will be removed in a future version."""
+
+    __module__ = "pytest"
+
+
+class PytestRemovedIn9Warning(PytestDeprecationWarning):
+    """Warning class for features that will be removed in pytest 9."""
+
+    __module__ = "pytest"
+
+
+class PytestReturnNotNoneWarning(PytestWarning):
+    """Warning emitted when a test function is returning value other than None."""
+
+    __module__ = "pytest"
+
+
+@final
+class PytestExperimentalApiWarning(PytestWarning, FutureWarning):
+    """Warning category used to denote experiments in pytest.
+
+    Use sparingly as the API might change or even be removed completely in a
+    future version.
+    """
+
+    __module__ = "pytest"
+
+    @classmethod
+    def simple(cls, apiname: str) -> PytestExperimentalApiWarning:
+        return cls(f"{apiname} is an experimental api that may change over time")
+
+
+@final
+class PytestUnhandledCoroutineWarning(PytestReturnNotNoneWarning):
+    """Warning emitted for an unhandled coroutine.
+
+    A coroutine was encountered when collecting test functions, but was not
+    handled by any async-aware plugin.
+    Coroutine test functions are not natively supported.
+    """
+
+    __module__ = "pytest"
+
+
+@final
+class PytestUnknownMarkWarning(PytestWarning):
+    """Warning emitted on use of unknown markers.
+
+    See :ref:`mark` for details.
+    """
+
+    __module__ = "pytest"
+
+
+@final
+class PytestUnraisableExceptionWarning(PytestWarning):
+    """An unraisable exception was reported.
+
+    Unraisable exceptions are exceptions raised in :meth:`__del__ <object.__del__>`
+    implementations and similar situations when the exception cannot be raised
+    as normal.
+    """
+
+    __module__ = "pytest"
+
+
+@final
+class PytestUnhandledThreadExceptionWarning(PytestWarning):
+    """An unhandled exception occurred in a :class:`~threading.Thread`.
+
+    Such exceptions don't propagate normally.
+    """
+
+    __module__ = "pytest"
+
+
+_W = TypeVar("_W", bound=PytestWarning)
+
+
+@final
+@dataclasses.dataclass
+class UnformattedWarning(Generic[_W]):
+    """A warning meant to be formatted during runtime.
+
+    This is used to hold warnings that need to format their message at runtime,
+    as opposed to a direct message.
+    """
+
+    category: type[_W]
+    template: str
+
+    def format(self, **kwargs: Any) -> _W:
+        """Return an instance of the warning category, formatted with given kwargs."""
+        return self.category(self.template.format(**kwargs))
+
+
+def warn_explicit_for(method: FunctionType, message: PytestWarning) -> None:
+    """
+    Issue the warning :param:`message` for the definition of the given :param:`method`
+
+    this helps to log warnings for functions defined prior to finding an issue with them
+    (like hook wrappers being marked in a legacy mechanism)
+    """
+    lineno = method.__code__.co_firstlineno
+    filename = inspect.getfile(method)
+    module = method.__module__
+    mod_globals = method.__globals__
+    try:
+        warnings.warn_explicit(
+            message,
+            type(message),
+            filename=filename,
+            module=module,
+            registry=mod_globals.setdefault("__warningregistry__", {}),
+            lineno=lineno,
+        )
+    except Warning as w:
+        # If warnings are errors (e.g. -Werror), location information gets lost, so we add it to the message.
+        raise type(w)(f"{w}\n at {filename}:{lineno}") from None

shared/package-files/9b/9b717166deba6db15d053b26793d1c34c4a756495065bafecb0657dd9461794f/blob

@@ -0,0 +1,57 @@
+from .module import Module
+from .. import functional as F
+
+from torch import Tensor
+
+__all__ = ['ChannelShuffle']
+
+class ChannelShuffle(Module):
+    r"""Divides and rearranges the channels in a tensor.
+
+    This operation divides the channels in a tensor of shape :math:`(*, C , H, W)`
+    into g groups and rearranges them as :math:`(*, \frac{C}{g}, g, H, W)`,
+    while keeping the original tensor shape.
+
+    Args:
+        groups (int): number of groups to divide channels in.
+
+    Examples::
+
+        >>> # xdoctest: +IGNORE_WANT("FIXME: incorrect want")
+        >>> channel_shuffle = nn.ChannelShuffle(2)
+        >>> input = torch.randn(1, 4, 2, 2)
+        >>> print(input)
+        [[[[1, 2],
+           [3, 4]],
+          [[5, 6],
+           [7, 8]],
+          [[9, 10],
+           [11, 12]],
+          [[13, 14],
+           [15, 16]],
+         ]]
+        >>> output = channel_shuffle(input)
+        >>> print(output)
+        [[[[1, 2],
+           [3, 4]],
+          [[9, 10],
+           [11, 12]],
+          [[5, 6],
+           [7, 8]],
+          [[13, 14],
+           [15, 16]],
+         ]]
+    """
+
+    __constants__ = ['groups']
+    groups: int
+
+    def __init__(self, groups: int) -> None:
+        super().__init__()
+        self.groups = groups
+
+    def forward(self, input: Tensor) -> Tensor:
+        return F.channel_shuffle(input, self.groups)
+
+    def extra_repr(self) -> str:
+        return f'groups={self.groups}'

shared/package-files/9b/9b7207dba4c2ccb0e94bc72dbf324a2c0f31e35e2eaf98e55f7b1d4f8b01e26f/blob

@@ -0,0 +1,50 @@
+from collections.abc import Generator
+from contextlib import contextmanager
+import pathlib
+import tempfile
+
+import pytest
+
+from pandas.io.pytables import HDFStore
+
+tables = pytest.importorskip("tables")
+# set these parameters so we don't have file sharing
+tables.parameters.MAX_NUMEXPR_THREADS = 1
+tables.parameters.MAX_BLOSC_THREADS = 1
+tables.parameters.MAX_THREADS = 1
+
+
+def safe_close(store):
+    try:
+        if store is not None:
+            store.close()
+    except OSError:
+        pass
+
+
+# contextmanager to ensure the file cleanup
+@contextmanager
+def ensure_clean_store(
+    path, mode="a", complevel=None, complib=None, fletcher32=False
+) -> Generator[HDFStore, None, None]:
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        tmp_path = pathlib.Path(tmpdirname, path)
+        with HDFStore(
+            tmp_path,
+            mode=mode,
+            complevel=complevel,
+            complib=complib,
+            fletcher32=fletcher32,
+        ) as store:
+            yield store
+
+
+def _maybe_remove(store, key):
+    """
+    For tests using tables, try removing the table to be sure there is
+    no content from previous tests using the same table name.
+    """
+    try:
+        store.remove(key)
+    except (ValueError, KeyError):
+        pass