Publish 11 evaluated Astra histories with reproducible checkpoint artifacts (part 5)

.gitattributes

@@ -84,3 +84,15 @@ shared/package-files/05/05464db9f29f3e3c72bda50625b2c3ae15931b2a75dfea352a08400d
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+shared/package-files/07/074ce037474f72e2fc9945195ed4c16fb5461760294fcf879724257094ee1810/blob filter=lfs diff=lfs merge=lfs -text
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shared/package-files/06/0696e309979cee7fae3a3fdc226bbe701d629950b8a1e2fcac95308f16e63787/blob

@@ -0,0 +1,488 @@
+from .artist import Artist
+from .axes import Axes
+from .backend_bases import FigureCanvasBase, Event, MouseEvent, MouseButton
+from .collections import LineCollection
+from .figure import Figure
+from .lines import Line2D
+from .patches import Polygon, Rectangle
+from .text import Text
+
+import PIL.Image
+
+from collections.abc import Callable, Collection, Iterable, Sequence
+from typing import Any, Literal
+from numpy.typing import ArrayLike
+from .typing import ColorType
+import numpy as np
+
+class LockDraw:
+    def __init__(self) -> None: ...
+    def __call__(self, o: Any) -> None: ...
+    def release(self, o: Any) -> None: ...
+    def available(self, o: Any) -> bool: ...
+    def isowner(self, o: Any) -> bool: ...
+    def locked(self) -> bool: ...
+
+class Widget:
+    drawon: bool
+    eventson: bool
+    active: bool
+    def set_active(self, active: bool) -> None: ...
+    def get_active(self) -> None: ...
+    def ignore(self, event) -> bool: ...
+
+class AxesWidget(Widget):
+    ax: Axes
+    def __init__(self, ax: Axes) -> None: ...
+    @property
+    def canvas(self) -> FigureCanvasBase | None: ...
+    def connect_event(self, event: Event, callback: Callable) -> None: ...
+    def disconnect_events(self) -> None: ...
+
+class Button(AxesWidget):
+    label: Text
+    color: ColorType
+    hovercolor: ColorType
+    def __init__(
+        self,
+        ax: Axes,
+        label: str,
+        image: ArrayLike | PIL.Image.Image | None = ...,
+        color: ColorType = ...,
+        hovercolor: ColorType = ...,
+        *,
+        useblit: bool = ...
+    ) -> None: ...
+    def on_clicked(self, func: Callable[[Event], Any]) -> int: ...
+    def disconnect(self, cid: int) -> None: ...
+
+class SliderBase(AxesWidget):
+    orientation: Literal["horizontal", "vertical"]
+    closedmin: bool
+    closedmax: bool
+    valmin: float
+    valmax: float
+    valstep: float | ArrayLike | None
+    drag_active: bool
+    valfmt: str
+    def __init__(
+        self,
+        ax: Axes,
+        orientation: Literal["horizontal", "vertical"],
+        closedmin: bool,
+        closedmax: bool,
+        valmin: float,
+        valmax: float,
+        valfmt: str,
+        dragging: Slider | None,
+        valstep: float | ArrayLike | None,
+    ) -> None: ...
+    def disconnect(self, cid: int) -> None: ...
+    def reset(self) -> None: ...
+
+class Slider(SliderBase):
+    slidermin: Slider | None
+    slidermax: Slider | None
+    val: float
+    valinit: float
+    track: Rectangle
+    poly: Polygon
+    hline: Line2D
+    vline: Line2D
+    label: Text
+    valtext: Text
+    def __init__(
+        self,
+        ax: Axes,
+        label: str,
+        valmin: float,
+        valmax: float,
+        *,
+        valinit: float = ...,
+        valfmt: str | None = ...,
+        closedmin: bool = ...,
+        closedmax: bool = ...,
+        slidermin: Slider | None = ...,
+        slidermax: Slider | None = ...,
+        dragging: bool = ...,
+        valstep: float | ArrayLike | None = ...,
+        orientation: Literal["horizontal", "vertical"] = ...,
+        initcolor: ColorType = ...,
+        track_color: ColorType = ...,
+        handle_style: dict[str, Any] | None = ...,
+        **kwargs
+    ) -> None: ...
+    def set_val(self, val: float) -> None: ...
+    def on_changed(self, func: Callable[[float], Any]) -> int: ...
+
+class RangeSlider(SliderBase):
+    val: tuple[float, float]
+    valinit: tuple[float, float]
+    track: Rectangle
+    poly: Polygon
+    label: Text
+    valtext: Text
+    def __init__(
+        self,
+        ax: Axes,
+        label: str,
+        valmin: float,
+        valmax: float,
+        *,
+        valinit: tuple[float, float] | None = ...,
+        valfmt: str | None = ...,
+        closedmin: bool = ...,
+        closedmax: bool = ...,
+        dragging: bool = ...,
+        valstep: float | ArrayLike | None = ...,
+        orientation: Literal["horizontal", "vertical"] = ...,
+        track_color: ColorType = ...,
+        handle_style: dict[str, Any] | None = ...,
+        **kwargs
+    ) -> None: ...
+    def set_min(self, min: float) -> None: ...
+    def set_max(self, max: float) -> None: ...
+    def set_val(self, val: ArrayLike) -> None: ...
+    def on_changed(self, func: Callable[[tuple[float, float]], Any]) -> int: ...
+
+class CheckButtons(AxesWidget):
+    labels: list[Text]
+    def __init__(
+        self,
+        ax: Axes,
+        labels: Sequence[str],
+        actives: Iterable[bool] | None = ...,
+        *,
+        useblit: bool = ...,
+        label_props: dict[str, Sequence[Any]] | None = ...,
+        frame_props: dict[str, Any] | None = ...,
+        check_props: dict[str, Any] | None = ...,
+    ) -> None: ...
+    def set_label_props(self, props: dict[str, Sequence[Any]]) -> None: ...
+    def set_frame_props(self, props: dict[str, Any]) -> None: ...
+    def set_check_props(self, props: dict[str, Any]) -> None: ...
+    def set_active(self, index: int, state: bool | None = ...) -> None: ...  # type: ignore[override]
+    def clear(self) -> None: ...
+    def get_status(self) -> list[bool]: ...
+    def get_checked_labels(self) -> list[str]: ...
+    def on_clicked(self, func: Callable[[str | None], Any]) -> int: ...
+    def disconnect(self, cid: int) -> None: ...
+
+class TextBox(AxesWidget):
+    label: Text
+    text_disp: Text
+    cursor_index: int
+    cursor: LineCollection
+    color: ColorType
+    hovercolor: ColorType
+    capturekeystrokes: bool
+    def __init__(
+        self,
+        ax: Axes,
+        label: str,
+        initial: str = ...,
+        *,
+        color: ColorType = ...,
+        hovercolor: ColorType = ...,
+        label_pad: float = ...,
+        textalignment: Literal["left", "center", "right"] = ...,
+    ) -> None: ...
+    @property
+    def text(self) -> str: ...
+    def set_val(self, val: str) -> None: ...
+    def begin_typing(self) -> None: ...
+    def stop_typing(self) -> None: ...
+    def on_text_change(self, func: Callable[[str], Any]) -> int: ...
+    def on_submit(self, func: Callable[[str], Any]) -> int: ...
+    def disconnect(self, cid: int) -> None: ...
+
+class RadioButtons(AxesWidget):
+    activecolor: ColorType
+    value_selected: str
+    labels: list[Text]
+    def __init__(
+        self,
+        ax: Axes,
+        labels: Iterable[str],
+        active: int = ...,
+        activecolor: ColorType | None = ...,
+        *,
+        useblit: bool = ...,
+        label_props: dict[str, Sequence[Any]] | None = ...,
+        radio_props: dict[str, Any] | None = ...,
+    ) -> None: ...
+    def set_label_props(self, props: dict[str, Sequence[Any]]) -> None: ...
+    def set_radio_props(self, props: dict[str, Any]) -> None: ...
+    def set_active(self, index: int) -> None: ...
+    def clear(self) -> None: ...
+    def on_clicked(self, func: Callable[[str | None], Any]) -> int: ...
+    def disconnect(self, cid: int) -> None: ...
+
+class SubplotTool(Widget):
+    figure: Figure
+    targetfig: Figure
+    buttonreset: Button
+    def __init__(self, targetfig: Figure, toolfig: Figure) -> None: ...
+
+class Cursor(AxesWidget):
+    visible: bool
+    horizOn: bool
+    vertOn: bool
+    useblit: bool
+    lineh: Line2D
+    linev: Line2D
+    background: Any
+    needclear: bool
+    def __init__(
+        self,
+        ax: Axes,
+        *,
+        horizOn: bool = ...,
+        vertOn: bool = ...,
+        useblit: bool = ...,
+        **lineprops
+    ) -> None: ...
+    def clear(self, event: Event) -> None: ...
+    def onmove(self, event: Event) -> None: ...
+
+class MultiCursor(Widget):
+    axes: Sequence[Axes]
+    horizOn: bool
+    vertOn: bool
+    visible: bool
+    useblit: bool
+    vlines: list[Line2D]
+    hlines: list[Line2D]
+    def __init__(
+        self,
+        canvas: Any,
+        axes: Sequence[Axes],
+        *,
+        useblit: bool = ...,
+        horizOn: bool = ...,
+        vertOn: bool = ...,
+        **lineprops
+    ) -> None: ...
+    def connect(self) -> None: ...
+    def disconnect(self) -> None: ...
+    def clear(self, event: Event) -> None: ...
+    def onmove(self, event: Event) -> None: ...
+
+class _SelectorWidget(AxesWidget):
+    onselect: Callable[[float, float], Any]
+    useblit: bool
+    background: Any
+    validButtons: list[MouseButton]
+    def __init__(
+        self,
+        ax: Axes,
+        onselect: Callable[[float, float], Any] | None = ...,
+        useblit: bool = ...,
+        button: MouseButton | Collection[MouseButton] | None = ...,
+        state_modifier_keys: dict[str, str] | None = ...,
+        use_data_coordinates: bool = ...,
+    ) -> None: ...
+    def update_background(self, event: Event) -> None: ...
+    def connect_default_events(self) -> None: ...
+    def ignore(self, event: Event) -> bool: ...
+    def update(self) -> None: ...
+    def press(self, event: Event) -> bool: ...
+    def release(self, event: Event) -> bool: ...
+    def onmove(self, event: Event) -> bool: ...
+    def on_scroll(self, event: Event) -> None: ...
+    def on_key_press(self, event: Event) -> None: ...
+    def on_key_release(self, event: Event) -> None: ...
+    def set_visible(self, visible: bool) -> None: ...
+    def get_visible(self) -> bool: ...
+    def clear(self) -> None: ...
+    @property
+    def artists(self) -> tuple[Artist]: ...
+    def set_props(self, **props) -> None: ...
+    def set_handle_props(self, **handle_props) -> None: ...
+    def add_state(self, state: str) -> None: ...
+    def remove_state(self, state: str) -> None: ...
+
+class SpanSelector(_SelectorWidget):
+    snap_values: ArrayLike | None
+    onmove_callback: Callable[[float, float], Any]
+    minspan: float
+    grab_range: float
+    drag_from_anywhere: bool
+    ignore_event_outside: bool
+    def __init__(
+        self,
+        ax: Axes,
+        onselect: Callable[[float, float], Any],
+        direction: Literal["horizontal", "vertical"],
+        *,
+        minspan: float = ...,
+        useblit: bool = ...,
+        props: dict[str, Any] | None = ...,
+        onmove_callback: Callable[[float, float], Any] | None = ...,
+        interactive: bool = ...,
+        button: MouseButton | Collection[MouseButton] | None = ...,
+        handle_props: dict[str, Any] | None = ...,
+        grab_range: float = ...,
+        state_modifier_keys: dict[str, str] | None = ...,
+        drag_from_anywhere: bool = ...,
+        ignore_event_outside: bool = ...,
+        snap_values: ArrayLike | None = ...,
+    ) -> None: ...
+    def new_axes(
+        self,
+        ax: Axes,
+        *,
+        _props: dict[str, Any] | None = ...,
+        _init: bool = ...,
+    ) -> None: ...
+    def connect_default_events(self) -> None: ...
+    @property
+    def direction(self) -> Literal["horizontal", "vertical"]: ...
+    @direction.setter
+    def direction(self, direction: Literal["horizontal", "vertical"]) -> None: ...
+    @property
+    def extents(self) -> tuple[float, float]: ...
+    @extents.setter
+    def extents(self, extents: tuple[float, float]) -> None: ...
+
+class ToolLineHandles:
+    ax: Axes
+    def __init__(
+        self,
+        ax: Axes,
+        positions: ArrayLike,
+        direction: Literal["horizontal", "vertical"],
+        *,
+        line_props: dict[str, Any] | None = ...,
+        useblit: bool = ...,
+    ) -> None: ...
+    @property
+    def artists(self) -> tuple[Line2D]: ...
+    @property
+    def positions(self) -> list[float]: ...
+    @property
+    def direction(self) -> Literal["horizontal", "vertical"]: ...
+    def set_data(self, positions: ArrayLike) -> None: ...
+    def set_visible(self, value: bool) -> None: ...
+    def set_animated(self, value: bool) -> None: ...
+    def remove(self) -> None: ...
+    def closest(self, x: float, y: float) -> tuple[int, float]: ...
+
+class ToolHandles:
+    ax: Axes
+    def __init__(
+        self,
+        ax: Axes,
+        x: ArrayLike,
+        y: ArrayLike,
+        *,
+        marker: str = ...,
+        marker_props: dict[str, Any] | None = ...,
+        useblit: bool = ...,
+    ) -> None: ...
+    @property
+    def x(self) -> ArrayLike: ...
+    @property
+    def y(self) -> ArrayLike: ...
+    @property
+    def artists(self) -> tuple[Line2D]: ...
+    def set_data(self, pts: ArrayLike, y: ArrayLike | None = ...) -> None: ...
+    def set_visible(self, val: bool) -> None: ...
+    def set_animated(self, val: bool) -> None: ...
+    def closest(self, x: float, y: float) -> tuple[int, float]: ...
+
+class RectangleSelector(_SelectorWidget):
+    drag_from_anywhere: bool
+    ignore_event_outside: bool
+    minspanx: float
+    minspany: float
+    spancoords: Literal["data", "pixels"]
+    grab_range: float
+    def __init__(
+        self,
+        ax: Axes,
+        onselect: Callable[[MouseEvent, MouseEvent], Any] | None = ...,
+        *,
+        minspanx: float = ...,
+        minspany: float = ...,
+        useblit: bool = ...,
+        props: dict[str, Any] | None = ...,
+        spancoords: Literal["data", "pixels"] = ...,
+        button: MouseButton | Collection[MouseButton] | None = ...,
+        grab_range: float = ...,
+        handle_props: dict[str, Any] | None = ...,
+        interactive: bool = ...,
+        state_modifier_keys: dict[str, str] | None = ...,
+        drag_from_anywhere: bool = ...,
+        ignore_event_outside: bool = ...,
+        use_data_coordinates: bool = ...,
+    ) -> None: ...
+    @property
+    def corners(self) -> tuple[np.ndarray, np.ndarray]: ...
+    @property
+    def edge_centers(self) -> tuple[np.ndarray, np.ndarray]: ...
+    @property
+    def center(self) -> tuple[float, float]: ...
+    @property
+    def extents(self) -> tuple[float, float, float, float]: ...
+    @extents.setter
+    def extents(self, extents: tuple[float, float, float, float]) -> None: ...
+    @property
+    def rotation(self) -> float: ...
+    @rotation.setter
+    def rotation(self, value: float) -> None: ...
+    @property
+    def geometry(self) -> np.ndarray: ...
+
+class EllipseSelector(RectangleSelector): ...
+
+class LassoSelector(_SelectorWidget):
+    verts: None | list[tuple[float, float]]
+    def __init__(
+        self,
+        ax: Axes,
+        onselect: Callable[[list[tuple[float, float]]], Any] | None = ...,
+        *,
+        useblit: bool = ...,
+        props: dict[str, Any] | None = ...,
+        button: MouseButton | Collection[MouseButton] | None = ...,
+    ) -> None: ...
+
+class PolygonSelector(_SelectorWidget):
+    grab_range: float
+    def __init__(
+        self,
+        ax: Axes,
+        onselect: Callable[[ArrayLike, ArrayLike], Any] | None = ...,
+        *,
+        useblit: bool = ...,
+        props: dict[str, Any] | None = ...,
+        handle_props: dict[str, Any] | None = ...,
+        grab_range: float = ...,
+        draw_bounding_box: bool = ...,
+        box_handle_props: dict[str, Any] | None = ...,
+        box_props: dict[str, Any] | None = ...
+    ) -> None: ...
+    def onmove(self, event: Event) -> bool: ...
+    @property
+    def verts(self) -> list[tuple[float, float]]: ...
+    @verts.setter
+    def verts(self, xys: Sequence[tuple[float, float]]) -> None: ...
+
+class Lasso(AxesWidget):
+    useblit: bool
+    background: Any
+    verts: list[tuple[float, float]] | None
+    line: Line2D
+    callback: Callable[[list[tuple[float, float]]], Any]
+    def __init__(
+        self,
+        ax: Axes,
+        xy: tuple[float, float],
+        callback: Callable[[list[tuple[float, float]]], Any],
+        *,
+        useblit: bool = ...,
+        props: dict[str, Any] | None = ...,
+    ) -> None: ...
+    def onrelease(self, event: Event) -> None: ...
+    def onmove(self, event: Event) -> None: ...

shared/package-files/06/069723f7ea5e8585b6d2f9f8298f7ca400d07790348330d4b94936cb8f07cf23/blob

@@ -0,0 +1,139 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.tests.support import linux_only, override_config
+from numba.core.errors import NumbaPerformanceWarning
+import warnings
+
+
+@skip_on_cudasim('cudasim does not raise performance warnings')
+class TestWarnings(CUDATestCase):
+    def test_inefficient_launch_configuration(self):
+        @cuda.jit
+        def kernel():
+            pass
+
+        with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
+            with warnings.catch_warnings(record=True) as w:
+                kernel[1, 1]()
+
+        self.assertEqual(w[0].category, NumbaPerformanceWarning)
+        self.assertIn('Grid size', str(w[0].message))
+        self.assertIn('low occupancy', str(w[0].message))
+
+    def test_efficient_launch_configuration(self):
+        @cuda.jit
+        def kernel():
+            pass
+
+        with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
+            with warnings.catch_warnings(record=True) as w:
+                kernel[256, 256]()
+
+        self.assertEqual(len(w), 0)
+
+    def test_warn_on_host_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        arr_f32 = np.zeros(N, dtype=np.float32)
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](arr_f32, N)
+
+        self.assertEqual(w[0].category, NumbaPerformanceWarning)
+        self.assertIn('Host array used in CUDA kernel will incur',
+                      str(w[0].message))
+        self.assertIn('copy overhead', str(w[0].message))
+
+    def test_pinned_warn_on_host_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        ary = cuda.pinned_array(N, dtype=np.float32)
+
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](ary, N)
+
+        self.assertEqual(w[0].category, NumbaPerformanceWarning)
+        self.assertIn('Host array used in CUDA kernel will incur',
+                      str(w[0].message))
+        self.assertIn('copy overhead', str(w[0].message))
+
+    def test_nowarn_on_mapped_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        ary = cuda.mapped_array(N, dtype=np.float32)
+
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](ary, N)
+
+        self.assertEqual(len(w), 0)
+
+    @linux_only
+    def test_nowarn_on_managed_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        ary = cuda.managed_array(N, dtype=np.float32)
+
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](ary, N)
+
+        self.assertEqual(len(w), 0)
+
+    def test_nowarn_on_device_array(self):
+        @cuda.jit
+        def foo(r, x):
+            r[0] = x + 1
+
+        N = 10
+        ary = cuda.device_array(N, dtype=np.float32)
+
+        with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
+            with warnings.catch_warnings(record=True) as w:
+                foo[1, N](ary, N)
+
+        self.assertEqual(len(w), 0)
+
+    def test_warn_on_debug_and_opt(self):
+        with warnings.catch_warnings(record=True) as w:
+            cuda.jit(debug=True, opt=True)
+
+        self.assertEqual(len(w), 1)
+        self.assertIn('not supported by CUDA', str(w[0].message))
+
+    def test_warn_on_debug_and_opt_default(self):
+        with warnings.catch_warnings(record=True) as w:
+            cuda.jit(debug=True)
+
+        self.assertEqual(len(w), 1)
+        self.assertIn('not supported by CUDA', str(w[0].message))
+
+    def test_no_warn_on_debug_and_no_opt(self):
+        with warnings.catch_warnings(record=True) as w:
+            cuda.jit(debug=True, opt=False)
+
+        self.assertEqual(len(w), 0)
+
+    def test_no_warn_with_no_debug_and_opt_kwargs(self):
+        with warnings.catch_warnings(record=True) as w:
+            cuda.jit()
+
+        self.assertEqual(len(w), 0)
+
+
+if __name__ == '__main__':
+    unittest.main()

shared/package-files/06/069ad718312002b3c0a162ef04ff183de7efe6fbca3f338dcc1d5b4ab467cda1/blob

@@ -0,0 +1,133 @@
+"""Tests for the minimum dependencies in README.rst and pyproject.toml"""
+
+import os
+import re
+from collections import defaultdict
+from pathlib import Path
+
+import pytest
+
+import sklearn
+from sklearn._min_dependencies import dependent_packages
+from sklearn.utils.fixes import parse_version
+
+min_depencies_tag_to_packages_without_version = defaultdict(list)
+for package, (min_version, extras) in dependent_packages.items():
+    for extra in extras.split(", "):
+        min_depencies_tag_to_packages_without_version[extra].append(package)
+
+min_dependencies_tag_to_pyproject_section = {
+    "build": "build-system.requires",
+    "install": "project.dependencies",
+}
+for tag in min_depencies_tag_to_packages_without_version:
+    min_dependencies_tag_to_pyproject_section[tag] = (
+        f"project.optional-dependencies.{tag}"
+    )
+
+
+def test_min_dependencies_readme():
+    # Test that the minimum dependencies in the README.rst file are
+    # consistent with the minimum dependencies defined at the file:
+    # sklearn/_min_dependencies.py
+
+    pattern = re.compile(
+        r"\.\. \|"
+        r"([A-Za-z-]+)"
+        r"MinVersion\| replace::"
+        r"( [0-9]+\.[0-9]+(\.[0-9]+)?)"
+    )
+
+    readme_path = Path(sklearn.__file__).parent.parent
+    readme_file = readme_path / "README.rst"
+
+    if not os.path.exists(readme_file):
+        # Skip the test if the README.rst file is not available.
+        # For instance, when installing scikit-learn from wheels
+        pytest.skip("The README.rst file is not available.")
+
+    with readme_file.open("r") as f:
+        for line in f:
+            matched = pattern.match(line)
+
+            if not matched:
+                continue
+
+            package, version = matched.group(0), matched.group(1)
+            package = package.lower()
+
+            if package in dependent_packages:
+                version = parse_version(version)
+                min_version = parse_version(dependent_packages[package][0])
+
+                assert version == min_version, f"{package} has a mismatched version"
+
+
+def check_pyproject_section(
+    pyproject_section, min_dependencies_tag, skip_version_check_for=None
+):
+    # tomllib is available in Python 3.11
+    tomllib = pytest.importorskip("tomllib")
+
+    if skip_version_check_for is None:
+        skip_version_check_for = []
+
+    expected_packages = min_depencies_tag_to_packages_without_version[
+        min_dependencies_tag
+    ]
+
+    root_directory = Path(sklearn.__file__).parent.parent
+    pyproject_toml_path = root_directory / "pyproject.toml"
+
+    if not pyproject_toml_path.exists():
+        # Skip the test if the pyproject.toml file is not available.
+        # For instance, when installing scikit-learn from wheels
+        pytest.skip("pyproject.toml is not available.")
+
+    with pyproject_toml_path.open("rb") as f:
+        pyproject_toml = tomllib.load(f)
+
+    pyproject_section_keys = pyproject_section.split(".")
+    info = pyproject_toml
+    for key in pyproject_section_keys:
+        info = info[key]
+
+    pyproject_build_min_versions = {}
+    for requirement in info:
+        if ">=" in requirement:
+            package, version = requirement.split(">=")
+        elif "==" in requirement:
+            package, version = requirement.split("==")
+        else:
+            raise NotImplementedError(
+                f"{requirement} not supported yet in this test. "
+                "Only >= and == are supported for version requirements"
+            )
+
+        pyproject_build_min_versions[package] = version
+
+    assert sorted(pyproject_build_min_versions) == sorted(expected_packages)
+
+    for package, version in pyproject_build_min_versions.items():
+        version = parse_version(version)
+        expected_min_version = parse_version(dependent_packages[package][0])
+        if package in skip_version_check_for:
+            continue
+
+        assert version == expected_min_version, f"{package} has a mismatched version"
+
+
+@pytest.mark.parametrize(
+    "min_dependencies_tag, pyproject_section",
+    min_dependencies_tag_to_pyproject_section.items(),
+)
+def test_min_dependencies_pyproject_toml(pyproject_section, min_dependencies_tag):
+    """Check versions in pyproject.toml is consistent with _min_dependencies."""
+    # NumPy is more complex because build-time (>=1.25) and run-time (>=1.19.5)
+    # requirement currently don't match
+    skip_version_check_for = ["numpy"] if min_dependencies_tag == "build" else None
+    check_pyproject_section(
+        pyproject_section,
+        min_dependencies_tag,
+        skip_version_check_for=skip_version_check_for,
+    )

shared/package-files/06/069d4ee48ec2edb74e10982f4c730a0d8d39bd5c9f7d2cc97ff5322a179999ea/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/special_bessel_y0_ops.h>
+
+namespace at {
+
+
+// aten::special_bessel_y0(Tensor self) -> Tensor
+inline at::Tensor special_bessel_y0(const at::Tensor & self) {
+    return at::_ops::special_bessel_y0::call(self);
+}
+
+// aten::special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_bessel_y0_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_bessel_y0_out::call(self, out);
+}
+// aten::special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_bessel_y0_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_bessel_y0_out::call(self, out);
+}
+
+}

shared/package-files/06/069d8f07866ea9e33218850c0e8d5d0a08397899e08d6983aae65c6e82e582fa/blob

@@ -0,0 +1,472 @@
+from statsmodels.compat.platform import PLATFORM_WIN32
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import pandas as pd
+import pytest
+
+from statsmodels.multivariate.pca import PCA, pca
+from statsmodels.multivariate.tests.results.datamlw import (
+    data,
+    princomp1,
+    princomp2,
+)
+from statsmodels.tools.sm_exceptions import EstimationWarning, ValueWarning
+
+DECIMAL_5 = 0.00001
+
+
+class TestPCA:
+    @classmethod
+    def setup_class(cls):
+        rs = np.random.RandomState()
+        rs.seed(1234)
+        k = 3
+        n = 100
+        t = 200
+        lam = 2
+
+        norm_rng = rs.standard_normal
+        e = norm_rng((t, n))
+        f = norm_rng((t, k))
+        b = rs.standard_gamma(lam, size=(k, n)) / lam
+        cls.x = f.dot(b) + e
+        cls.x_copy = cls.x + 0.0
+        cls.rs = rs
+
+        k = 3
+        n = 300
+        t = 200
+        lam = 2
+
+        norm_rng = rs.standard_normal
+        e = norm_rng((t, n))
+        f = norm_rng((t, k))
+        b = rs.standard_gamma(lam, size=(k, n)) / lam
+        cls.x_wide = f.dot(b) + e
+
+    @pytest.mark.smoke
+    @pytest.mark.thread_unsafe(reason="Uses matplotlib")
+    @pytest.mark.matplotlib
+    def test_smoke_plot_and_repr(self, close_figures):
+        pc = PCA(self.x)
+        pc.plot_scree()
+        pc.plot_scree(ncomp=10)
+        pc.plot_scree(log_scale=False)
+        pc.plot_scree(cumulative=True)
+        pc.plot_rsquare()
+        pc.plot_rsquare(ncomp=5)
+        # Additional smoke test
+        pc.__repr__()
+        pc = PCA(self.x, standardize=False)
+        pc.__repr__()
+        pc = PCA(self.x, standardize=False, demean=False)
+        pc.__repr__()
+        pc = PCA(self.x, ncomp=2, gls=True)
+        assert "GLS" in pc.__repr__()
+        # Check data for no changes
+        assert_equal(self.x, pc.data)
+
+    def test_eig_svd_equiv(self):
+        # Test leading components since the tail end can differ
+        pc_eig = PCA(self.x)
+        pc_svd = PCA(self.x, method="svd")
+
+        assert_allclose(pc_eig.projection, pc_svd.projection)
+        assert_allclose(np.abs(pc_eig.factors[:, :2]), np.abs(pc_svd.factors[:, :2]))
+        assert_allclose(np.abs(pc_eig.coeff[:2, :]), np.abs(pc_svd.coeff[:2, :]))
+        assert_allclose(pc_eig.eigenvals, pc_svd.eigenvals)
+        assert_allclose(
+            np.abs(pc_eig.eigenvecs[:, :2]), np.abs(pc_svd.eigenvecs[:, :2])
+        )
+
+        pc_svd = PCA(self.x, method="svd", ncomp=2)
+        pc_nipals = PCA(self.x, method="nipals", ncomp=2)
+        assert_allclose(
+            np.abs(pc_nipals.factors), np.abs(pc_svd.factors), atol=DECIMAL_5
+        )
+        assert_allclose(np.abs(pc_nipals.coeff), np.abs(pc_svd.coeff), atol=DECIMAL_5)
+        assert_allclose(pc_nipals.eigenvals, pc_svd.eigenvals, atol=DECIMAL_5)
+        assert_allclose(
+            np.abs(pc_nipals.eigenvecs), np.abs(pc_svd.eigenvecs), atol=DECIMAL_5
+        )
+        # Check data for no changes
+        assert_equal(self.x, pc_svd.data)
+        # Check data for no changes
+        assert_equal(self.x, pc_eig.data)
+        # Check data for no changes
+        assert_equal(self.x, pc_nipals.data)
+
+    def test_options(self):
+        pc = PCA(self.x)
+        pc_no_norm = PCA(self.x, normalize=False)
+        assert_allclose(
+            pc.factors.dot(pc.coeff), pc_no_norm.factors.dot(pc_no_norm.coeff)
+        )
+        princomp = pc.factors
+        assert_allclose(princomp.T.dot(princomp), np.eye(100), atol=1e-5)
+        weights = pc_no_norm.coeff
+        assert_allclose(weights.T.dot(weights), np.eye(100), atol=1e-5)
+
+        pc_10 = PCA(self.x, ncomp=10)
+        assert_allclose(pc.factors[:, :10], pc_10.factors)
+        assert_allclose(pc.coeff[:10, :], pc_10.coeff)
+        assert_allclose(pc.rsquare[: (10 + 1)], pc_10.rsquare)
+        assert_allclose(pc.eigenvals[:10], pc_10.eigenvals)
+        assert_allclose(pc.eigenvecs[:, :10], pc_10.eigenvecs)
+
+        pc = PCA(self.x, standardize=False, normalize=False)
+        mu = self.x.mean(0)
+        xdm = self.x - mu
+        xpx = xdm.T.dot(xdm)
+        val, vec = np.linalg.eigh(xpx)
+        ind = np.argsort(val)
+        ind = ind[::-1]
+        val = val[ind]
+        vec = vec[:, ind]
+        assert_allclose(xdm, pc.transformed_data)
+        assert_allclose(val, pc.eigenvals)
+        assert_allclose(np.abs(vec), np.abs(pc.eigenvecs))
+        assert_allclose(np.abs(pc.factors), np.abs(xdm.dot(vec)))
+        assert_allclose(pc.projection, xdm + mu)
+
+        pc = PCA(self.x, standardize=False, demean=False, normalize=False)
+        x = self.x
+        xpx = x.T.dot(x)
+        val, vec = np.linalg.eigh(xpx)
+        ind = np.argsort(val)
+        ind = ind[::-1]
+        val = val[ind]
+        vec = vec[:, ind]
+        assert_allclose(x, pc.transformed_data)
+        assert_allclose(val, pc.eigenvals)
+        assert_allclose(np.abs(vec), np.abs(pc.eigenvecs))
+        assert_allclose(np.abs(pc.factors), np.abs(x.dot(vec)))
+
+    def test_against_reference(self):
+        # Test against MATLAB, which by default demeans but does not standardize
+        x = data.xo / 1000.0
+        pc = PCA(x, normalize=False, standardize=False)
+
+        ref = princomp1
+        assert_allclose(np.abs(pc.factors), np.abs(ref.factors))
+        assert_allclose(pc.factors.dot(pc.coeff) + x.mean(0), x)
+        assert_allclose(np.abs(pc.coeff), np.abs(ref.coef.T))
+        assert_allclose(pc.factors.dot(pc.coeff), ref.factors.dot(ref.coef.T))
+
+        pc = PCA(x[:20], normalize=False, standardize=False)
+        mu = x[:20].mean(0)
+        ref = princomp2
+        assert_allclose(np.abs(pc.factors), np.abs(ref.factors))
+        assert_allclose(pc.factors.dot(pc.coeff) + mu, x[:20])
+        assert_allclose(np.abs(pc.coeff), np.abs(ref.coef.T))
+        assert_allclose(pc.factors.dot(pc.coeff), ref.factors.dot(ref.coef.T))
+
+    @pytest.mark.thread_unsafe(reason="Issues and checks warnings")
+    def test_warnings(self):
+        with pytest.warns(ValueWarning, match="The requested number of components"):
+            PCA(self.x, ncomp=300)
+
+        rs = self.rs
+        x = rs.standard_normal((200, 1)) * np.ones(200)
+        with pytest.warns(EstimationWarning, match="Only"):
+            PCA(x, method="eig")
+
+    def test_errors(self):
+        with pytest.raises(ValueError):
+            PCA(self.x, method="unknown")
+        with pytest.raises(ValueError):
+            PCA(self.x, missing="unknown")
+        with pytest.raises(ValueError):
+            PCA(self.x, tol=2.0)
+        with pytest.raises(ValueError):
+            PCA(np.nan * np.ones((200, 100)), tol=2.0)
+
+    @pytest.mark.thread_unsafe(reason="Uses matplotlib")
+    @pytest.mark.matplotlib
+    def test_pandas(self, close_figures):
+        pc = PCA(pd.DataFrame(self.x))
+        pc1 = PCA(self.x)
+        assert_allclose(pc.factors.values, pc1.factors)
+        pc.plot_scree()
+        pc.plot_scree(ncomp=10)
+        pc.plot_scree(log_scale=False)
+        pc.plot_rsquare()
+        pc.plot_rsquare(ncomp=5)
+        pc.project(2)
+        PCA(pd.DataFrame(self.x), ncomp=4, gls=True)
+        PCA(pd.DataFrame(self.x), ncomp=4, standardize=False)
+
+    def test_gls_and_weights(self):
+        with pytest.raises(ValueError):
+            PCA(self.x, gls=True)
+        with pytest.raises(ValueError):
+            PCA(self.x, weights=np.array([1.0, 1.0]))
+
+        # Pre-standardize to make comparison simple
+        x = self.x - self.x.mean(0)
+        x = x / (x**2.0).mean(0)
+        pc_gls = PCA(x, ncomp=1, standardize=False, demean=False, gls=True)
+        pc = PCA(x, ncomp=1, standardize=False, demean=False)
+        errors = x - pc.projection
+        var = (errors**2.0).mean(0)
+        weights = 1.0 / var
+        weights = weights / np.sqrt((weights**2.0).mean())
+
+        assert_allclose(weights, pc_gls.weights)
+        assert_equal(x, pc_gls.data)
+        assert_equal(x, pc.data)
+
+        pc_weights = PCA(x, ncomp=1, standardize=False, demean=False, weights=weights)
+
+        assert_allclose(weights, pc_weights.weights)
+        assert_allclose(np.abs(pc_weights.factors), np.abs(pc_gls.factors))
+
+    @pytest.mark.slow
+    def test_wide(self):
+        pc = PCA(self.x_wide)
+        assert_equal(pc.factors.shape[1], self.x_wide.shape[0])
+        assert_equal(pc.eigenvecs.shape[1], min(np.array(self.x_wide.shape)))
+
+        pc = PCA(pd.DataFrame(self.x_wide))
+        assert_equal(pc.factors.shape[1], self.x_wide.shape[0])
+        assert_equal(pc.eigenvecs.shape[1], min(np.array(self.x_wide.shape)))
+
+    def test_projection(self):
+        pc = PCA(self.x, ncomp=5)
+        mu = self.x.mean(0)
+        demean_x = self.x - mu
+        coef = np.linalg.pinv(pc.factors).dot(demean_x)
+        direct = pc.factors.dot(coef)
+        assert_allclose(pc.projection, direct + mu)
+
+        pc = PCA(self.x, standardize=False, ncomp=5)
+        coef = np.linalg.pinv(pc.factors).dot(demean_x)
+        direct = pc.factors.dot(coef)
+        assert_allclose(pc.projection, direct + mu)
+
+        pc = PCA(self.x, standardize=False, demean=False, ncomp=5)
+        coef = np.linalg.pinv(pc.factors).dot(self.x)
+        direct = pc.factors.dot(coef)
+        assert_allclose(pc.projection, direct)
+
+        pc = PCA(self.x, ncomp=5, gls=True)
+        mu = self.x.mean(0)
+        demean_x = self.x - mu
+        coef = np.linalg.pinv(pc.factors).dot(demean_x)
+        direct = pc.factors.dot(coef)
+        assert_allclose(pc.projection, direct + mu)
+
+        pc = PCA(self.x, standardize=False, ncomp=5)
+        coef = np.linalg.pinv(pc.factors).dot(demean_x)
+        direct = pc.factors.dot(coef)
+        assert_allclose(pc.projection, direct + mu)
+
+        pc = PCA(self.x, standardize=False, demean=False, ncomp=5, gls=True)
+        coef = np.linalg.pinv(pc.factors).dot(self.x)
+        direct = pc.factors.dot(coef)
+        assert_allclose(pc.projection, direct)
+
+        # Test error for too many factors
+        project = pc.project
+        with pytest.raises(ValueError):
+            project(6)
+
+    @pytest.mark.skipif(PLATFORM_WIN32, reason="Windows 32-bit")
+    def test_replace_missing(self):
+        x = self.x.copy()
+        x[::5, ::7] = np.nan
+
+        pc = PCA(x, missing="drop-row")
+        x_dropped_row = x[np.logical_not(np.any(np.isnan(x), 1))]
+        pc_dropped = PCA(x_dropped_row)
+        assert_allclose(pc.projection, pc_dropped.projection)
+        assert_equal(x, pc.data)
+
+        pc = PCA(x, missing="drop-col")
+        x_dropped_col = x[:, np.logical_not(np.any(np.isnan(x), 0))]
+        pc_dropped = PCA(x_dropped_col)
+        assert_allclose(pc.projection, pc_dropped.projection)
+        assert_equal(x, pc.data)
+
+        pc = PCA(x, missing="drop-min")
+        if x_dropped_row.size > x_dropped_col.size:
+            x_dropped_min = x_dropped_row
+        else:
+            x_dropped_min = x_dropped_col
+        pc_dropped = PCA(x_dropped_min)
+        assert_allclose(pc.projection, pc_dropped.projection)
+        assert_equal(x, pc.data)
+
+        pc = PCA(x, ncomp=3, missing="fill-em")
+        missing = np.isnan(x)
+        mu = np.nanmean(x, axis=0)
+        errors = x - mu
+        sigma = np.sqrt(np.nanmean(errors**2, axis=0))
+        x_std = errors / sigma
+        x_std[missing] = 0.0
+        last = x_std[missing]
+        delta = 1.0
+        count = 0
+        while delta > 5e-8:
+            pc_temp = PCA(x_std, ncomp=3, standardize=False, demean=False)
+            x_std[missing] = pc_temp.projection[missing]
+            current = x_std[missing]
+            diff = current - last
+            delta = np.sqrt(np.sum(diff**2)) / np.sqrt(np.sum(current**2))
+            last = current
+            count += 1
+        x = self.x + 0.0
+        projection = pc_temp.projection * sigma + mu
+        x[missing] = projection[missing]
+        assert_allclose(pc._adjusted_data, x)
+        # Check data for no changes
+        assert_equal(self.x, self.x_copy)
+
+        x = self.x
+        pc = PCA(x)
+        pc_dropped = PCA(x, missing="drop-row")
+        assert_allclose(pc.projection, pc_dropped.projection, atol=DECIMAL_5)
+
+        pc_dropped = PCA(x, missing="drop-col")
+        assert_allclose(pc.projection, pc_dropped.projection, atol=DECIMAL_5)
+
+        pc_dropped = PCA(x, missing="drop-min")
+        assert_allclose(pc.projection, pc_dropped.projection, atol=DECIMAL_5)
+
+        pc = PCA(x, ncomp=3)
+        pc_dropped = PCA(x, ncomp=3, missing="fill-em")
+        assert_allclose(pc.projection, pc_dropped.projection, atol=DECIMAL_5)
+
+        # Test too many missing for missing='fill-em'
+        x = self.x.copy()
+        x[:, :] = np.nan
+        with pytest.raises(ValueError):
+            PCA(x, missing="drop-row")
+        with pytest.raises(ValueError):
+            PCA(x, missing="drop-col")
+        with pytest.raises(ValueError):
+            PCA(x, missing="drop-min")
+        with pytest.raises(ValueError):
+            PCA(x, missing="fill-em")
+
+    def test_rsquare(self):
+        x = self.x + 0.0
+        mu = x.mean(0)
+        x_demean = x - mu
+        std = np.std(x, 0)
+        x_std = x_demean / std
+
+        pc = PCA(self.x)
+        nvar = x.shape[1]
+        rsquare = np.zeros(nvar + 1)
+        tss = np.sum(x_std**2)
+        for i in range(nvar + 1):
+            errors = x_std - pc.project(i, transform=False, unweight=False)
+            rsquare[i] = 1.0 - np.sum(errors**2) / tss
+        assert_allclose(rsquare, pc.rsquare)
+
+        pc = PCA(self.x, standardize=False)
+        tss = np.sum(x_demean**2)
+        for i in range(nvar + 1):
+            errors = x_demean - pc.project(i, transform=False, unweight=False)
+            rsquare[i] = 1.0 - np.sum(errors**2) / tss
+        assert_allclose(rsquare, pc.rsquare)
+
+        pc = PCA(self.x, standardize=False, demean=False)
+        tss = np.sum(x**2)
+        for i in range(nvar + 1):
+            errors = x - pc.project(i, transform=False, unweight=False)
+            rsquare[i] = 1.0 - np.sum(errors**2) / tss
+        assert_allclose(rsquare, pc.rsquare)
+
+    @pytest.mark.slow
+    def test_missing_dataframe(self):
+        x = self.x.copy()
+        x[::5, ::7] = np.nan
+        pc = PCA(x, ncomp=3, missing="fill-em")
+
+        x = pd.DataFrame(x)
+        pc_df = PCA(x, ncomp=3, missing="fill-em")
+        assert_allclose(pc.coeff, pc_df.coeff)
+        assert_allclose(pc.factors, pc_df.factors)
+
+        pc_df_nomissing = PCA(pd.DataFrame(self.x.copy()), ncomp=3)
+        assert isinstance(pc_df.coeff, type(pc_df_nomissing.coeff))
+        assert isinstance(pc_df.data, type(pc_df_nomissing.data))
+        assert isinstance(pc_df.eigenvals, type(pc_df_nomissing.eigenvals))
+        assert isinstance(pc_df.eigenvecs, type(pc_df_nomissing.eigenvecs))
+
+        x = self.x.copy()
+        x[::5, ::7] = np.nan
+        x_df = pd.DataFrame(x)
+        pc = PCA(x, missing="drop-row")
+        pc_df = PCA(x_df, missing="drop-row")
+        assert_allclose(pc.coeff, pc_df.coeff)
+        assert_allclose(pc.factors, pc_df.factors)
+
+        pc = PCA(x, missing="drop-col")
+        pc_df = PCA(x_df, missing="drop-col")
+        assert_allclose(pc.coeff, pc_df.coeff)
+        assert_allclose(pc.factors, pc_df.factors)
+
+        pc = PCA(x, missing="drop-min")
+        pc_df = PCA(x_df, missing="drop-min")
+        assert_allclose(pc.coeff, pc_df.coeff)
+        assert_allclose(pc.factors, pc_df.factors)
+
+    def test_equivalence(self):
+        x = self.x.copy()
+        assert_allclose(PCA(x).factors, pca(x)[0])
+
+    def test_equivalence_full_matrices(self):
+        x = self.x.copy()
+        pca_true = PCA(x, svd_full_matrices=True)
+        svd_full_matrices_true = pca_true.factors
+        assert pca_true._svd_full_matrices
+        pca_false = PCA(x)
+        svd_full_matrices_false = pca_false.factors
+        assert not pca_false._svd_full_matrices
+        assert_allclose(svd_full_matrices_true, svd_full_matrices_false)
+
+
+def test_missing():
+    data = np.empty((200, 50))
+    data[0, 0] = np.nan
+    with pytest.raises(ValueError, match="data contains non-finite values"):
+        PCA(data)
+
+
+def test_too_many_missing():
+    rs = np.random.RandomState(83429421)
+    data = rs.standard_normal((200, 50))
+    data[0, :-3] = np.nan
+    with pytest.raises(ValueError):
+        PCA(data, ncomp=5, missing="drop-col")
+    p = PCA(data, missing="drop-min")
+    assert max(p.factors.shape) == max(data.shape) - 1
+
+
+def test_prepare_data_all_nan():
+    # GH: _prepare_data's all-NaN branch did
+    # `return np.empty(adj_data.shape[1]).fill(np.nan)`, but ndarray.fill()
+    # mutates in place and returns None, so the branch returned None
+    # instead of a NaN-filled array.
+    rs = np.random.RandomState(0)
+    data = rs.standard_normal((10, 4))
+    p = PCA(data)
+    p._adjusted_data = np.full_like(data, np.nan)
+
+    result = p._prepare_data()
+    assert result is not None
+    assert result.shape == (4,)
+    assert np.all(np.isnan(result))
+
+
+def test_gls_warning():
+    rs = np.random.RandomState(83429421)
+    data = rs.standard_normal((400, 200))
+    data[:, 1:] = data[:, :1] + 0.01 * data[:, 1:]
+    with pytest.warns(EstimationWarning, match="Many series are being down weighted"):
+        factors = PCA(data, ncomp=2, gls=True).factors
+    assert factors.shape == (data.shape[0], 2)

shared/package-files/06/069ed4c1f7c830eee873c527155d75c9819c5d48d26b4361b2e3a041a35132df/blob

@@ -0,0 +1,441 @@
+from sympy.concrete.summations import Sum
+from sympy.core.basic import Basic
+from sympy.core.containers import Tuple
+from sympy.core.function import Lambda
+from sympy.core.numbers import (Rational, nan, oo, pi)
+from sympy.core.relational import Eq
+from sympy.core.singleton import S
+from sympy.core.symbol import (Symbol, symbols)
+from sympy.functions.combinatorial.factorials import (FallingFactorial, binomial)
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.functions.special.delta_functions import DiracDelta
+from sympy.integrals.integrals import integrate
+from sympy.logic.boolalg import (And, Or)
+from sympy.matrices.dense import Matrix
+from sympy.sets.sets import Interval
+from sympy.tensor.indexed import Indexed
+from sympy.stats import (Die, Normal, Exponential, FiniteRV, P, E, H, variance,
+        density, given, independent, dependent, where, pspace, GaussianUnitaryEnsemble,
+        random_symbols, sample, Geometric, factorial_moment, Binomial, Hypergeometric,
+        DiscreteUniform, Poisson, characteristic_function, moment_generating_function,
+        BernoulliProcess, Variance, Expectation, Probability, Covariance, covariance, cmoment,
+        moment, median)
+from sympy.stats.rv import (IndependentProductPSpace, rs_swap, Density, NamedArgsMixin,
+        RandomSymbol, sample_iter, PSpace, is_random, RandomIndexedSymbol, RandomMatrixSymbol)
+from sympy.testing.pytest import raises, skip, XFAIL, warns_deprecated_sympy
+from sympy.external import import_module
+from sympy.core.numbers import comp
+from sympy.stats.frv_types import BernoulliDistribution
+from sympy.core.symbol import Dummy
+from sympy.functions.elementary.piecewise import Piecewise
+
+def test_where():
+    X, Y = Die('X'), Die('Y')
+    Z = Normal('Z', 0, 1)
+
+    assert where(Z**2 <= 1).set == Interval(-1, 1)
+    assert where(Z**2 <= 1).as_boolean() == Interval(-1, 1).as_relational(Z.symbol)
+    assert where(And(X > Y, Y > 4)).as_boolean() == And(
+        Eq(X.symbol, 6), Eq(Y.symbol, 5))
+
+    assert len(where(X < 3).set) == 2
+    assert 1 in where(X < 3).set
+
+    X, Y = Normal('X', 0, 1), Normal('Y', 0, 1)
+    assert where(And(X**2 <= 1, X >= 0)).set == Interval(0, 1)
+    XX = given(X, And(X**2 <= 1, X >= 0))
+    assert XX.pspace.domain.set == Interval(0, 1)
+    assert XX.pspace.domain.as_boolean() == \
+        And(0 <= X.symbol, X.symbol**2 <= 1, -oo < X.symbol, X.symbol < oo)
+
+    with raises(TypeError):
+        XX = given(X, X + 3)
+
+
+def test_random_symbols():
+    X, Y = Normal('X', 0, 1), Normal('Y', 0, 1)
+
+    assert set(random_symbols(2*X + 1)) == {X}
+    assert set(random_symbols(2*X + Y)) == {X, Y}
+    assert set(random_symbols(2*X + Y.symbol)) == {X}
+    assert set(random_symbols(2)) == set()
+
+
+def test_characteristic_function():
+    #  Imports I from sympy
+    from sympy.core.numbers import I
+    X = Normal('X',0,1)
+    Y = DiscreteUniform('Y', [1,2,7])
+    Z = Poisson('Z', 2)
+    t = symbols('_t')
+    P = Lambda(t, exp(-t**2/2))
+    Q = Lambda(t, exp(7*t*I)/3 + exp(2*t*I)/3 + exp(t*I)/3)
+    R = Lambda(t, exp(2 * exp(t*I) - 2))
+
+
+    assert characteristic_function(X).dummy_eq(P)
+    assert characteristic_function(Y).dummy_eq(Q)
+    assert characteristic_function(Z).dummy_eq(R)
+
+
+def test_moment_generating_function():
+
+    X = Normal('X',0,1)
+    Y = DiscreteUniform('Y', [1,2,7])
+    Z = Poisson('Z', 2)
+    t = symbols('_t')
+    P = Lambda(t, exp(t**2/2))
+    Q = Lambda(t, (exp(7*t)/3 + exp(2*t)/3 + exp(t)/3))
+    R = Lambda(t, exp(2 * exp(t) - 2))
+
+
+    assert moment_generating_function(X).dummy_eq(P)
+    assert moment_generating_function(Y).dummy_eq(Q)
+    assert moment_generating_function(Z).dummy_eq(R)
+
+def test_sample_iter():
+
+    X = Normal('X',0,1)
+    Y = DiscreteUniform('Y', [1, 2, 7])
+    Z = Poisson('Z', 2)
+
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests')
+    expr = X**2 + 3
+    iterator = sample_iter(expr)
+
+    expr2 = Y**2 + 5*Y + 4
+    iterator2 = sample_iter(expr2)
+
+    expr3 = Z**3 + 4
+    iterator3 = sample_iter(expr3)
+
+    def is_iterator(obj):
+        if (
+            hasattr(obj, '__iter__') and
+            (hasattr(obj, 'next') or
+            hasattr(obj, '__next__')) and
+            callable(obj.__iter__) and
+            obj.__iter__() is obj
+           ):
+            return True
+        else:
+            return False
+    assert is_iterator(iterator)
+    assert is_iterator(iterator2)
+    assert is_iterator(iterator3)
+
+def test_pspace():
+    X, Y = Normal('X', 0, 1), Normal('Y', 0, 1)
+    x = Symbol('x')
+
+    raises(ValueError, lambda: pspace(5 + 3))
+    raises(ValueError, lambda: pspace(x < 1))
+    assert pspace(X) == X.pspace
+    assert pspace(2*X + 1) == X.pspace
+    assert pspace(2*X + Y) == IndependentProductPSpace(Y.pspace, X.pspace)
+
+def test_rs_swap():
+    X = Normal('x', 0, 1)
+    Y = Exponential('y', 1)
+
+    XX = Normal('x', 0, 2)
+    YY = Normal('y', 0, 3)
+
+    expr = 2*X + Y
+    assert expr.subs(rs_swap((X, Y), (YY, XX))) == 2*XX + YY
+
+
+def test_RandomSymbol():
+
+    X = Normal('x', 0, 1)
+    Y = Normal('x', 0, 2)
+    assert X.symbol == Y.symbol
+    assert X != Y
+
+    assert X.name == X.symbol.name
+
+    X = Normal('lambda', 0, 1) # make sure we can use protected terms
+    X = Normal('Lambda', 0, 1) # make sure we can use SymPy terms
+
+
+def test_RandomSymbol_diff():
+    X = Normal('x', 0, 1)
+    assert (2*X).diff(X)
+
+
+def test_random_symbol_no_pspace():
+    x = RandomSymbol(Symbol('x'))
+    assert x.pspace == PSpace()
+
+def test_overlap():
+    X = Normal('x', 0, 1)
+    Y = Normal('x', 0, 2)
+
+    raises(ValueError, lambda: P(X > Y))
+
+
+def test_IndependentProductPSpace():
+    X = Normal('X', 0, 1)
+    Y = Normal('Y', 0, 1)
+    px = X.pspace
+    py = Y.pspace
+    assert pspace(X + Y) == IndependentProductPSpace(px, py)
+    assert pspace(X + Y) == IndependentProductPSpace(py, px)
+
+
+def test_E():
+    assert E(5) == 5
+
+
+def test_H():
+    X = Normal('X', 0, 1)
+    D = Die('D', sides = 4)
+    G = Geometric('G', 0.5)
+    assert H(X, X > 0) == -log(2)/2 + S.Half + log(pi)/2
+    assert H(D, D > 2) == log(2)
+    assert comp(H(G).evalf().round(2), 1.39)
+
+
+def test_Sample():
+    X = Die('X', 6)
+    Y = Normal('Y', 0, 1)
+    z = Symbol('z', integer=True)
+
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests')
+    assert sample(X) in [1, 2, 3, 4, 5, 6]
+    assert isinstance(sample(X + Y), float)
+
+    assert P(X + Y > 0, Y < 0, numsamples=10).is_number
+    assert E(X + Y, numsamples=10).is_number
+    assert E(X**2 + Y, numsamples=10).is_number
+    assert E((X + Y)**2, numsamples=10).is_number
+    assert variance(X + Y, numsamples=10).is_number
+
+    raises(TypeError, lambda: P(Y > z, numsamples=5))
+
+    assert P(sin(Y) <= 1, numsamples=10) == 1.0
+    assert P(sin(Y) <= 1, cos(Y) < 1, numsamples=10) == 1.0
+
+    assert all(i in range(1, 7) for i in density(X, numsamples=10))
+    assert all(i in range(4, 7) for i in density(X, X>3, numsamples=10))
+
+    numpy = import_module('numpy')
+    if not numpy:
+        skip('Numpy is not installed. Abort tests')
+    #Test Issue #21563: Output of sample must be a float or array
+    assert isinstance(sample(X), (numpy.int32, numpy.int64))
+    assert isinstance(sample(Y), numpy.float64)
+    assert isinstance(sample(X, size=2), numpy.ndarray)
+
+    with warns_deprecated_sympy():
+        sample(X, numsamples=2)
+
+@XFAIL
+def test_samplingE():
+    scipy = import_module('scipy')
+    if not scipy:
+        skip('Scipy is not installed. Abort tests')
+    Y = Normal('Y', 0, 1)
+    z = Symbol('z', integer=True)
+    assert E(Sum(1/z**Y, (z, 1, oo)), Y > 2, numsamples=3).is_number
+
+
+def test_given():
+    X = Normal('X', 0, 1)
+    Y = Normal('Y', 0, 1)
+    A = given(X, True)
+    B = given(X, Y > 2)
+
+    assert X == A == B
+
+
+def test_factorial_moment():
+    X = Poisson('X', 2)
+    Y = Binomial('Y', 2, S.Half)
+    Z = Hypergeometric('Z', 4, 2, 2)
+    assert factorial_moment(X, 2) == 4
+    assert factorial_moment(Y, 2) == S.Half
+    assert factorial_moment(Z, 2) == Rational(1, 3)
+
+    x, y, z, l = symbols('x y z l')
+    Y = Binomial('Y', 2, y)
+    Z = Hypergeometric('Z', 10, 2, 3)
+    assert factorial_moment(Y, l) == y**2*FallingFactorial(
+        2, l) + 2*y*(1 - y)*FallingFactorial(1, l) + (1 - y)**2*\
+            FallingFactorial(0, l)
+    assert factorial_moment(Z, l) == 7*FallingFactorial(0, l)/\
+        15 + 7*FallingFactorial(1, l)/15 + FallingFactorial(2, l)/15
+
+
+def test_dependence():
+    X, Y = Die('X'), Die('Y')
+    assert independent(X, 2*Y)
+    assert not dependent(X, 2*Y)
+
+    X, Y = Normal('X', 0, 1), Normal('Y', 0, 1)
+    assert independent(X, Y)
+    assert dependent(X, 2*X)
+
+    # Create a dependency
+    XX, YY = given(Tuple(X, Y), Eq(X + Y, 3))
+    assert dependent(XX, YY)
+
+def test_dependent_finite():
+    X, Y = Die('X'), Die('Y')
+    # Dependence testing requires symbolic conditions which currently break
+    # finite random variables
+    assert dependent(X, Y + X)
+
+    XX, YY = given(Tuple(X, Y), X + Y > 5)  # Create a dependency
+    assert dependent(XX, YY)
+
+
+def test_normality():
+    X, Y = Normal('X', 0, 1), Normal('Y', 0, 1)
+    x = Symbol('x', real=True)
+    z = Symbol('z', real=True)
+    dens = density(X - Y, Eq(X + Y, z))
+
+    assert integrate(dens(x), (x, -oo, oo)) == 1
+
+
+def test_Density():
+    X = Die('X', 6)
+    d = Density(X)
+    assert d.doit() == density(X)
+
+def test_NamedArgsMixin():
+    class Foo(Basic, NamedArgsMixin):
+        _argnames = 'foo', 'bar'
+
+    a = Foo(S(1), S(2))
+
+    assert a.foo == 1
+    assert a.bar == 2
+
+    raises(AttributeError, lambda: a.baz)
+
+    class Bar(Basic, NamedArgsMixin):
+        pass
+
+    raises(AttributeError, lambda: Bar(S(1), S(2)).foo)
+
+def test_density_constant():
+    assert density(3)(2) == 0
+    assert density(3)(3) == DiracDelta(0)
+
+def test_cmoment_constant():
+    assert variance(3) == 0
+    assert cmoment(3, 3) == 0
+    assert cmoment(3, 4) == 0
+    x = Symbol('x')
+    assert variance(x) == 0
+    assert cmoment(x, 15) == 0
+    assert cmoment(x, 0) == 1
+
+def test_moment_constant():
+    assert moment(3, 0) == 1
+    assert moment(3, 1) == 3
+    assert moment(3, 2) == 9
+    x = Symbol('x')
+    assert moment(x, 2) == x**2
+
+def test_median_constant():
+    assert median(3) == 3
+    x = Symbol('x')
+    assert median(x) == x
+
+def test_real():
+    x = Normal('x', 0, 1)
+    assert x.is_real
+
+
+def test_issue_10052():
+    X = Exponential('X', 3)
+    assert P(X < oo) == 1
+    assert P(X > oo) == 0
+    assert P(X < 2, X > oo) == 0
+    assert P(X < oo, X > oo) == 0
+    assert P(X < oo, X > 2) == 1
+    assert P(X < 3, X == 2) == 0
+    raises(ValueError, lambda: P(1))
+    raises(ValueError, lambda: P(X < 1, 2))
+
+def test_issue_11934():
+    density = {0: .5, 1: .5}
+    X = FiniteRV('X', density)
+    assert E(X) == 0.5
+    assert P( X>= 2) == 0
+
+def test_issue_8129():
+    X = Exponential('X', 4)
+    assert P(X >= X) == 1
+    assert P(X > X) == 0
+    assert P(X > X+1) == 0
+
+def test_issue_12237():
+    X = Normal('X', 0, 1)
+    Y = Normal('Y', 0, 1)
+    U = P(X > 0, X)
+    V = P(Y < 0, X)
+    W = P(X + Y > 0, X)
+    assert W == P(X + Y > 0, X)
+    assert U == BernoulliDistribution(S.Half, S.Zero, S.One)
+    assert V == S.Half
+
+def test_is_random():
+    X = Normal('X', 0, 1)
+    Y = Normal('Y', 0, 1)
+    a, b = symbols('a, b')
+    G = GaussianUnitaryEnsemble('U', 2)
+    B = BernoulliProcess('B', 0.9)
+    assert not is_random(a)
+    assert not is_random(a + b)
+    assert not is_random(a * b)
+    assert not is_random(Matrix([a**2, b**2]))
+    assert is_random(X)
+    assert is_random(X**2 + Y)
+    assert is_random(Y + b**2)
+    assert is_random(Y > 5)
+    assert is_random(B[3] < 1)
+    assert is_random(G)
+    assert is_random(X * Y * B[1])
+    assert is_random(Matrix([[X, B[2]], [G, Y]]))
+    assert is_random(Eq(X, 4))
+
+def test_issue_12283():
+    x = symbols('x')
+    X = RandomSymbol(x)
+    Y = RandomSymbol('Y')
+    Z = RandomMatrixSymbol('Z', 2, 1)
+    W = RandomMatrixSymbol('W', 2, 1)
+    RI = RandomIndexedSymbol(Indexed('RI', 3))
+    assert pspace(Z) == PSpace()
+    assert pspace(RI) == PSpace()
+    assert pspace(X) == PSpace()
+    assert E(X) == Expectation(X)
+    assert P(Y > 3) == Probability(Y > 3)
+    assert variance(X) == Variance(X)
+    assert variance(RI) == Variance(RI)
+    assert covariance(X, Y) == Covariance(X, Y)
+    assert covariance(W, Z) == Covariance(W, Z)
+
+def test_issue_6810():
+    X = Die('X', 6)
+    Y = Normal('Y', 0, 1)
+    assert P(Eq(X, 2)) == S(1)/6
+    assert P(Eq(Y, 0)) == 0
+    assert P(Or(X > 2, X < 3)) == 1
+    assert P(And(X > 3, X > 2)) == S(1)/2
+
+def test_issue_20286():
+    n, p = symbols('n p')
+    B = Binomial('B', n, p)
+    k = Dummy('k', integer = True)
+    eq = Sum(Piecewise((-p**k*(1 - p)**(-k + n)*log(p**k*(1 - p)**(-k + n)*binomial(n, k))*binomial(n, k), (k >= 0) & (k <= n)), (nan, True)), (k, 0, n))
+    assert eq.dummy_eq(H(B))

shared/package-files/06/069f0d6d4cdf0f03e40bf33b01cac2fc3413cce89a528caef3f9c75f4a22ea76/blob

@@ -0,0 +1,97 @@
+#pragma once
+#include <ATen/core/ivalue.h>
+#include <pybind11/pybind11.h>
+#include <torch/csrc/jit/python/pybind_utils.h>
+#include <torch/csrc/python_headers.h>
+#include <torch/csrc/utils/pybind.h>
+
+namespace py = pybind11;
+
+namespace c10::ivalue {
+
+// concrete ivalue Holder that hold a py::object
+struct C10_EXPORT ConcretePyObjectHolder final : PyObjectHolder {
+ public:
+  static c10::intrusive_ptr<PyObjectHolder> create(py::object py_obj) {
+    return c10::make_intrusive<ConcretePyObjectHolder>(std::move(py_obj));
+  }
+
+  static c10::intrusive_ptr<PyObjectHolder> create(const py::handle& handle) {
+    py::gil_scoped_acquire ag;
+    return c10::make_intrusive<ConcretePyObjectHolder>(
+        handle.cast<py::object>());
+  }
+
+  PyObject* getPyObject() override {
+    return py_obj_.ptr();
+  }
+
+  InferredType tryToInferType() override {
+    pybind11::gil_scoped_acquire ag;
+    return torch::jit::tryToInferType(py_obj_);
+  }
+
+  IValue toIValue(const TypePtr& type, c10::optional<int32_t> N = c10::nullopt)
+      override {
+    pybind11::gil_scoped_acquire ag;
+    return torch::jit::toIValue(py_obj_, type, N);
+  }
+
+  std::string toStr() override {
+    pybind11::gil_scoped_acquire ag;
+    return py::str(py_obj_);
+  }
+
+  std::vector<at::Tensor> extractTensors() override {
+    // We could implement this entirely in C++ via pybind11 but it turns out to
+    // be substantially slower. Namely, the total time taken by markCompleted on
+    // a CUDAFuture is 21.5us with this implementation, but goes up to 58.7us
+    // when using C++. The reason is unclear.
+    try {
+      pybind11::gil_scoped_acquire ag;
+      static py::object& extractorFn = *new py::object(
+          py::module::import("torch._jit_internal").attr("_extract_tensors"));
+      return extractorFn(py_obj_).cast<std::vector<at::Tensor>>();
+    } catch (py::error_already_set& e) {
+      auto err = std::runtime_error(
+          c10::str("Cannot extract tensors from value: ", e.what()));
+      {
+        pybind11::gil_scoped_acquire ag;
+        e.restore();
+        PyErr_Clear();
+      }
+      throw err;
+    }
+  }
+
+  // Note [Destructing py::object]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~
+  //
+  // (1) Why py_obj_ = py::none(); does not work. Because we also need to
+  // acquire GIL when destructing py::object of None that de-references None.
+  // https://docs.python.org/3/c-api/none.html#c.Py_RETURN_NONE
+  //
+  // https://stackoverflow.com/questions/15287590/why-should-py-increfpy-none-be-required-before-returning-py-none-in-c
+  //
+  // (2) Why we need to call dec_ref() explicitly. Because py::object of
+  // nullptr, on destruction, effectively does nothing because of it calls
+  // Py_XDECREF(NULL) underlying.
+  // https://docs.python.org/3/c-api/refcounting.html#c.Py_XDECREF
+  ~ConcretePyObjectHolder() override {
+    pybind11::gil_scoped_acquire ag;
+    py_obj_.dec_ref();
+    // explicitly setting PyObject* to nullptr to prevent py::object's dtor to
+    // decref on the PyObject again.
+    py_obj_.ptr() = nullptr;
+  }
+
+  // explicit construction to avoid errornous implicit conversion and
+  // copy-initialization
+  explicit ConcretePyObjectHolder(py::object py_obj)
+      : py_obj_(std::move(py_obj)) {}
+
+ private:
+  py::object py_obj_;
+};
+
+} // namespace c10::ivalue

shared/package-files/06/069ffbf6861f5711899f998744be7a77b7e156d3899cda37f05548160b640ccf/energy_views.py

@@ -0,0 +1,15 @@
+"""Fixed shared TRAIN energy-distribution model; no per-query fitting."""
+from pathlib import Path
+import json
+import numpy as np
+from energy_prior_runtime import predict_energy_nodes
+CONFIG=json.loads(Path(__file__).with_name('energy_prior.json').read_text())
+def eligible(row):return row.get('collision_energy') is None
+def perturb_meta(meta,rows,selection,view):
+ assert meta.dtype==np.float32 and meta.shape==(len(rows),8) and 0<=view<3
+ selection=np.asarray(selection,dtype=bool);assert selection.shape==(len(rows),)
+ assert np.array_equal(selection,np.asarray([eligible(q) for q in rows])) and np.array_equal(meta[:,6],selection.astype(np.float32))
+ out=meta.copy()
+ if selection.any():
+  indices=np.flatnonzero(selection);nodes=predict_energy_nodes([rows[i] for i in indices],CONFIG);out[indices,6]=0.;out[indices,7]=(nodes[:,view]/100.).astype(np.float32)
+ return out

shared/package-files/06/06a1fa254fd8466f82510151513bd68696779988974e6617aaa6992a5a5379bb/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 compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor diag_embed(const at::Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at

shared/package-files/06/06a3179ec5e260fa1a94ccc46a49fa9980f18325b937650f174028ef13ee1956/blob

@@ -0,0 +1 @@
+import xml.parsers as parsers

shared/package-files/06/06a420d1fa970ff4e600124f4960c2f87d9acedec1e7735f384d520ba99b5e22/blob

@@ -0,0 +1,1139 @@
+"""
+A module for reading dvi files output by TeX. Several limitations make
+this not (currently) useful as a general-purpose dvi preprocessor, but
+it is currently used by the pdf backend for processing usetex text.
+
+Interface::
+
+  with Dvi(filename, 72) as dvi:
+      # iterate over pages:
+      for page in dvi:
+          w, h, d = page.width, page.height, page.descent
+          for x, y, font, glyph, width in page.text:
+              fontname = font.texname
+              pointsize = font.size
+              ...
+          for x, y, height, width in page.boxes:
+              ...
+"""
+
+from collections import namedtuple
+import enum
+from functools import lru_cache, partial, wraps
+import logging
+import os
+from pathlib import Path
+import re
+import struct
+import subprocess
+import sys
+
+import numpy as np
+
+from matplotlib import _api, cbook
+
+_log = logging.getLogger(__name__)
+
+# Many dvi related files are looked for by external processes, require
+# additional parsing, and are used many times per rendering, which is why they
+# are cached using lru_cache().
+
+# Dvi is a bytecode format documented in
+# https://ctan.org/pkg/dvitype
+# https://texdoc.org/serve/dvitype.pdf/0
+#
+# The file consists of a preamble, some number of pages, a postamble,
+# and a finale. Different opcodes are allowed in different contexts,
+# so the Dvi object has a parser state:
+#
+#   pre:       expecting the preamble
+#   outer:     between pages (followed by a page or the postamble,
+#              also e.g. font definitions are allowed)
+#   page:      processing a page
+#   post_post: state after the postamble (our current implementation
+#              just stops reading)
+#   finale:    the finale (unimplemented in our current implementation)
+
+_dvistate = enum.Enum('DviState', 'pre outer inpage post_post finale')
+
+# The marks on a page consist of text and boxes. A page also has dimensions.
+Page = namedtuple('Page', 'text boxes height width descent')
+Box = namedtuple('Box', 'x y height width')
+
+
+# Also a namedtuple, for backcompat.
+class Text(namedtuple('Text', 'x y font glyph width')):
+    """
+    A glyph in the dvi file.
+
+    The *x* and *y* attributes directly position the glyph.  The *font*,
+    *glyph*, and *width* attributes are kept public for back-compatibility,
+    but users wanting to draw the glyph themselves are encouraged to instead
+    load the font specified by `font_path` at `font_size`, warp it with the
+    effects specified by `font_effects`, and load the glyph specified by
+    `glyph_name_or_index`.
+    """
+
+    def _get_pdftexmap_entry(self):
+        return PsfontsMap(find_tex_file("pdftex.map"))[self.font.texname]
+
+    @property
+    def font_path(self):
+        """The `~pathlib.Path` to the font for this glyph."""
+        psfont = self._get_pdftexmap_entry()
+        if psfont.filename is None:
+            raise ValueError("No usable font file found for {} ({}); "
+                             "the font may lack a Type-1 version"
+                             .format(psfont.psname.decode("ascii"),
+                                     psfont.texname.decode("ascii")))
+        return Path(psfont.filename)
+
+    @property
+    def font_size(self):
+        """The font size."""
+        return self.font.size
+
+    @property
+    def font_effects(self):
+        """
+        The "font effects" dict for this glyph.
+
+        This dict contains the values for this glyph of SlantFont and
+        ExtendFont (if any), read off :file:`pdftex.map`.
+        """
+        return self._get_pdftexmap_entry().effects
+
+    @property
+    def glyph_name_or_index(self):
+        """
+        Either the glyph name or the native charmap glyph index.
+
+        If :file:`pdftex.map` specifies an encoding for this glyph's font, that
+        is a mapping of glyph indices to Adobe glyph names; use it to convert
+        dvi indices to glyph names.  Callers can then convert glyph names to
+        glyph indices (with FT_Get_Name_Index/get_name_index), and load the
+        glyph using FT_Load_Glyph/load_glyph.
+
+        If :file:`pdftex.map` specifies no encoding, the indices directly map
+        to the font's "native" charmap; glyphs should directly load using
+        FT_Load_Char/load_char after selecting the native charmap.
+        """
+        entry = self._get_pdftexmap_entry()
+        return (_parse_enc(entry.encoding)[self.glyph]
+                if entry.encoding is not None else self.glyph)
+
+
+# Opcode argument parsing
+#
+# Each of the following functions takes a Dvi object and delta, which is the
+# difference between the opcode and the minimum opcode with the same meaning.
+# Dvi opcodes often encode the number of argument bytes in this delta.
+_arg_mapping = dict(
+    # raw: Return delta as is.
+    raw=lambda dvi, delta: delta,
+    # u1: Read 1 byte as an unsigned number.
+    u1=lambda dvi, delta: dvi._read_arg(1, signed=False),
+    # u4: Read 4 bytes as an unsigned number.
+    u4=lambda dvi, delta: dvi._read_arg(4, signed=False),
+    # s4: Read 4 bytes as a signed number.
+    s4=lambda dvi, delta: dvi._read_arg(4, signed=True),
+    # slen: Read delta bytes as a signed number, or None if delta is None.
+    slen=lambda dvi, delta: dvi._read_arg(delta, signed=True) if delta else None,
+    # slen1: Read (delta + 1) bytes as a signed number.
+    slen1=lambda dvi, delta: dvi._read_arg(delta + 1, signed=True),
+    # ulen1: Read (delta + 1) bytes as an unsigned number.
+    ulen1=lambda dvi, delta: dvi._read_arg(delta + 1, signed=False),
+    # olen1: Read (delta + 1) bytes as an unsigned number if less than 4 bytes,
+    # as a signed number if 4 bytes.
+    olen1=lambda dvi, delta: dvi._read_arg(delta + 1, signed=(delta == 3)),
+)
+
+
+def _dispatch(table, min, max=None, state=None, args=('raw',)):
+    """
+    Decorator for dispatch by opcode. Sets the values in *table*
+    from *min* to *max* to this method, adds a check that the Dvi state
+    matches *state* if not None, reads arguments from the file according
+    to *args*.
+
+    Parameters
+    ----------
+    table : dict[int, callable]
+        The dispatch table to be filled in.
+
+    min, max : int
+        Range of opcodes that calls the registered function; *max* defaults to
+        *min*.
+
+    state : _dvistate, optional
+        State of the Dvi object in which these opcodes are allowed.
+
+    args : list[str], default: ['raw']
+        Sequence of argument specifications:
+
+        - 'raw': opcode minus minimum
+        - 'u1': read one unsigned byte
+        - 'u4': read four bytes, treat as an unsigned number
+        - 's4': read four bytes, treat as a signed number
+        - 'slen': read (opcode - minimum) bytes, treat as signed
+        - 'slen1': read (opcode - minimum + 1) bytes, treat as signed
+        - 'ulen1': read (opcode - minimum + 1) bytes, treat as unsigned
+        - 'olen1': read (opcode - minimum + 1) bytes, treat as unsigned
+          if under four bytes, signed if four bytes
+    """
+    def decorate(method):
+        get_args = [_arg_mapping[x] for x in args]
+
+        @wraps(method)
+        def wrapper(self, byte):
+            if state is not None and self.state != state:
+                raise ValueError("state precondition failed")
+            return method(self, *[f(self, byte-min) for f in get_args])
+        if max is None:
+            table[min] = wrapper
+        else:
+            for i in range(min, max+1):
+                assert table[i] is None
+                table[i] = wrapper
+        return wrapper
+    return decorate
+
+
+class Dvi:
+    """
+    A reader for a dvi ("device-independent") file, as produced by TeX.
+
+    The current implementation can only iterate through pages in order,
+    and does not even attempt to verify the postamble.
+
+    This class can be used as a context manager to close the underlying
+    file upon exit. Pages can be read via iteration. Here is an overly
+    simple way to extract text without trying to detect whitespace::
+
+        >>> with matplotlib.dviread.Dvi('input.dvi', 72) as dvi:
+        ...     for page in dvi:
+        ...         print(''.join(chr(t.glyph) for t in page.text))
+    """
+    # dispatch table
+    _dtable = [None] * 256
+    _dispatch = partial(_dispatch, _dtable)
+
+    def __init__(self, filename, dpi):
+        """
+        Read the data from the file named *filename* and convert
+        TeX's internal units to units of *dpi* per inch.
+        *dpi* only sets the units and does not limit the resolution.
+        Use None to return TeX's internal units.
+        """
+        _log.debug('Dvi: %s', filename)
+        self.file = open(filename, 'rb')
+        self.dpi = dpi
+        self.fonts = {}
+        self.state = _dvistate.pre
+        self._missing_font = None
+
+    def __enter__(self):
+        """Context manager enter method, does nothing."""
+        return self
+
+    def __exit__(self, etype, evalue, etrace):
+        """
+        Context manager exit method, closes the underlying file if it is open.
+        """
+        self.close()
+
+    def __iter__(self):
+        """
+        Iterate through the pages of the file.
+
+        Yields
+        ------
+        Page
+            Details of all the text and box objects on the page.
+            The Page tuple contains lists of Text and Box tuples and
+            the page dimensions, and the Text and Box tuples contain
+            coordinates transformed into a standard Cartesian
+            coordinate system at the dpi value given when initializing.
+            The coordinates are floating point numbers, but otherwise
+            precision is not lost and coordinate values are not clipped to
+            integers.
+        """
+        while self._read():
+            yield self._output()
+
+    def close(self):
+        """Close the underlying file if it is open."""
+        if not self.file.closed:
+            self.file.close()
+
+    def _output(self):
+        """
+        Output the text and boxes belonging to the most recent page.
+        page = dvi._output()
+        """
+        minx = miny = np.inf
+        maxx = maxy = -np.inf
+        maxy_pure = -np.inf
+        for elt in self.text + self.boxes:
+            if isinstance(elt, Box):
+                x, y, h, w = elt
+                e = 0  # zero depth
+            else:  # glyph
+                x, y, font, g, w = elt
+                h, e = font._height_depth_of(g)
+            minx = min(minx, x)
+            miny = min(miny, y - h)
+            maxx = max(maxx, x + w)
+            maxy = max(maxy, y + e)
+            maxy_pure = max(maxy_pure, y)
+        if self._baseline_v is not None:
+            maxy_pure = self._baseline_v  # This should normally be the case.
+            self._baseline_v = None
+
+        if not self.text and not self.boxes:  # Avoid infs/nans from inf+/-inf.
+            return Page(text=[], boxes=[], width=0, height=0, descent=0)
+
+        if self.dpi is None:
+            # special case for ease of debugging: output raw dvi coordinates
+            return Page(text=self.text, boxes=self.boxes,
+                        width=maxx-minx, height=maxy_pure-miny,
+                        descent=maxy-maxy_pure)
+
+        # convert from TeX's "scaled points" to dpi units
+        d = self.dpi / (72.27 * 2**16)
+        descent = (maxy - maxy_pure) * d
+
+        text = [Text((x-minx)*d, (maxy-y)*d - descent, f, g, w*d)
+                for (x, y, f, g, w) in self.text]
+        boxes = [Box((x-minx)*d, (maxy-y)*d - descent, h*d, w*d)
+                 for (x, y, h, w) in self.boxes]
+
+        return Page(text=text, boxes=boxes, width=(maxx-minx)*d,
+                    height=(maxy_pure-miny)*d, descent=descent)
+
+    def _read(self):
+        """
+        Read one page from the file. Return True if successful,
+        False if there were no more pages.
+        """
+        # Pages appear to start with the sequence
+        #   bop (begin of page)
+        #   xxx comment
+        #   <push, ..., pop>  # if using chemformula
+        #   down
+        #   push
+        #     down
+        #     <push, push, xxx, right, xxx, pop, pop>  # if using xcolor
+        #     down
+        #     push
+        #       down (possibly multiple)
+        #       push  <=  here, v is the baseline position.
+        #         etc.
+        # (dviasm is useful to explore this structure.)
+        # Thus, we use the vertical position at the first time the stack depth
+        # reaches 3, while at least three "downs" have been executed (excluding
+        # those popped out (corresponding to the chemformula preamble)), as the
+        # baseline (the "down" count is necessary to handle xcolor).
+        down_stack = [0]
+        self._baseline_v = None
+        while True:
+            byte = self.file.read(1)[0]
+            self._dtable[byte](self, byte)
+            if self._missing_font:
+                raise self._missing_font.to_exception()
+            name = self._dtable[byte].__name__
+            if name == "_push":
+                down_stack.append(down_stack[-1])
+            elif name == "_pop":
+                down_stack.pop()
+            elif name == "_down":
+                down_stack[-1] += 1
+            if (self._baseline_v is None
+                    and len(getattr(self, "stack", [])) == 3
+                    and down_stack[-1] >= 4):
+                self._baseline_v = self.v
+            if byte == 140:                         # end of page
+                return True
+            if self.state is _dvistate.post_post:   # end of file
+                self.close()
+                return False
+
+    def _read_arg(self, nbytes, signed=False):
+        """
+        Read and return a big-endian integer *nbytes* long.
+        Signedness is determined by the *signed* keyword.
+        """
+        return int.from_bytes(self.file.read(nbytes), "big", signed=signed)
+
+    @_dispatch(min=0, max=127, state=_dvistate.inpage)
+    def _set_char_immediate(self, char):
+        self._put_char_real(char)
+        if isinstance(self.fonts[self.f], cbook._ExceptionInfo):
+            return
+        self.h += self.fonts[self.f]._width_of(char)
+
+    @_dispatch(min=128, max=131, state=_dvistate.inpage, args=('olen1',))
+    def _set_char(self, char):
+        self._put_char_real(char)
+        if isinstance(self.fonts[self.f], cbook._ExceptionInfo):
+            return
+        self.h += self.fonts[self.f]._width_of(char)
+
+    @_dispatch(132, state=_dvistate.inpage, args=('s4', 's4'))
+    def _set_rule(self, a, b):
+        self._put_rule_real(a, b)
+        self.h += b
+
+    @_dispatch(min=133, max=136, state=_dvistate.inpage, args=('olen1',))
+    def _put_char(self, char):
+        self._put_char_real(char)
+
+    def _put_char_real(self, char):
+        font = self.fonts[self.f]
+        if isinstance(font, cbook._ExceptionInfo):
+            self._missing_font = font
+        elif font._vf is None:
+            self.text.append(Text(self.h, self.v, font, char,
+                                  font._width_of(char)))
+        else:
+            scale = font._scale
+            for x, y, f, g, w in font._vf[char].text:
+                newf = DviFont(scale=_mul2012(scale, f._scale),
+                               tfm=f._tfm, texname=f.texname, vf=f._vf)
+                self.text.append(Text(self.h + _mul2012(x, scale),
+                                      self.v + _mul2012(y, scale),
+                                      newf, g, newf._width_of(g)))
+            self.boxes.extend([Box(self.h + _mul2012(x, scale),
+                                   self.v + _mul2012(y, scale),
+                                   _mul2012(a, scale), _mul2012(b, scale))
+                               for x, y, a, b in font._vf[char].boxes])
+
+    @_dispatch(137, state=_dvistate.inpage, args=('s4', 's4'))
+    def _put_rule(self, a, b):
+        self._put_rule_real(a, b)
+
+    def _put_rule_real(self, a, b):
+        if a > 0 and b > 0:
+            self.boxes.append(Box(self.h, self.v, a, b))
+
+    @_dispatch(138)
+    def _nop(self, _):
+        pass
+
+    @_dispatch(139, state=_dvistate.outer, args=('s4',)*11)
+    def _bop(self, c0, c1, c2, c3, c4, c5, c6, c7, c8, c9, p):
+        self.state = _dvistate.inpage
+        self.h = self.v = self.w = self.x = self.y = self.z = 0
+        self.stack = []
+        self.text = []          # list of Text objects
+        self.boxes = []         # list of Box objects
+
+    @_dispatch(140, state=_dvistate.inpage)
+    def _eop(self, _):
+        self.state = _dvistate.outer
+        del self.h, self.v, self.w, self.x, self.y, self.z, self.stack
+
+    @_dispatch(141, state=_dvistate.inpage)
+    def _push(self, _):
+        self.stack.append((self.h, self.v, self.w, self.x, self.y, self.z))
+
+    @_dispatch(142, state=_dvistate.inpage)
+    def _pop(self, _):
+        self.h, self.v, self.w, self.x, self.y, self.z = self.stack.pop()
+
+    @_dispatch(min=143, max=146, state=_dvistate.inpage, args=('slen1',))
+    def _right(self, b):
+        self.h += b
+
+    @_dispatch(min=147, max=151, state=_dvistate.inpage, args=('slen',))
+    def _right_w(self, new_w):
+        if new_w is not None:
+            self.w = new_w
+        self.h += self.w
+
+    @_dispatch(min=152, max=156, state=_dvistate.inpage, args=('slen',))
+    def _right_x(self, new_x):
+        if new_x is not None:
+            self.x = new_x
+        self.h += self.x
+
+    @_dispatch(min=157, max=160, state=_dvistate.inpage, args=('slen1',))
+    def _down(self, a):
+        self.v += a
+
+    @_dispatch(min=161, max=165, state=_dvistate.inpage, args=('slen',))
+    def _down_y(self, new_y):
+        if new_y is not None:
+            self.y = new_y
+        self.v += self.y
+
+    @_dispatch(min=166, max=170, state=_dvistate.inpage, args=('slen',))
+    def _down_z(self, new_z):
+        if new_z is not None:
+            self.z = new_z
+        self.v += self.z
+
+    @_dispatch(min=171, max=234, state=_dvistate.inpage)
+    def _fnt_num_immediate(self, k):
+        self.f = k
+
+    @_dispatch(min=235, max=238, state=_dvistate.inpage, args=('olen1',))
+    def _fnt_num(self, new_f):
+        self.f = new_f
+
+    @_dispatch(min=239, max=242, args=('ulen1',))
+    def _xxx(self, datalen):
+        special = self.file.read(datalen)
+        _log.debug(
+            'Dvi._xxx: encountered special: %s',
+            ''.join([chr(ch) if 32 <= ch < 127 else '<%02x>' % ch
+                     for ch in special]))
+
+    @_dispatch(min=243, max=246, args=('olen1', 'u4', 'u4', 'u4', 'u1', 'u1'))
+    def _fnt_def(self, k, c, s, d, a, l):
+        self._fnt_def_real(k, c, s, d, a, l)
+
+    def _fnt_def_real(self, k, c, s, d, a, l):
+        n = self.file.read(a + l)
+        fontname = n[-l:].decode('ascii')
+        try:
+            tfm = _tfmfile(fontname)
+        except FileNotFoundError as exc:
+            # Explicitly allow defining missing fonts for Vf support; we only
+            # register an error when trying to load a glyph from a missing font
+            # and throw that error in Dvi._read.  For Vf, _finalize_packet
+            # checks whether a missing glyph has been used, and in that case
+            # skips the glyph definition.
+            self.fonts[k] = cbook._ExceptionInfo.from_exception(exc)
+            return
+        if c != 0 and tfm.checksum != 0 and c != tfm.checksum:
+            raise ValueError(f'tfm checksum mismatch: {n}')
+        try:
+            vf = _vffile(fontname)
+        except FileNotFoundError:
+            vf = None
+        self.fonts[k] = DviFont(scale=s, tfm=tfm, texname=n, vf=vf)
+
+    @_dispatch(247, state=_dvistate.pre, args=('u1', 'u4', 'u4', 'u4', 'u1'))
+    def _pre(self, i, num, den, mag, k):
+        self.file.read(k)  # comment in the dvi file
+        if i != 2:
+            raise ValueError(f"Unknown dvi format {i}")
+        if num != 25400000 or den != 7227 * 2**16:
+            raise ValueError("Nonstandard units in dvi file")
+            # meaning: TeX always uses those exact values, so it
+            # should be enough for us to support those
+            # (There are 72.27 pt to an inch so 7227 pt =
+            # 7227 * 2**16 sp to 100 in. The numerator is multiplied
+            # by 10^5 to get units of 10**-7 meters.)
+        if mag != 1000:
+            raise ValueError("Nonstandard magnification in dvi file")
+            # meaning: LaTeX seems to frown on setting \mag, so
+            # I think we can assume this is constant
+        self.state = _dvistate.outer
+
+    @_dispatch(248, state=_dvistate.outer)
+    def _post(self, _):
+        self.state = _dvistate.post_post
+        # TODO: actually read the postamble and finale?
+        # currently post_post just triggers closing the file
+
+    @_dispatch(249)
+    def _post_post(self, _):
+        raise NotImplementedError
+
+    @_dispatch(min=250, max=255)
+    def _malformed(self, offset):
+        raise ValueError(f"unknown command: byte {250 + offset}")
+
+
+class DviFont:
+    """
+    Encapsulation of a font that a DVI file can refer to.
+
+    This class holds a font's texname and size, supports comparison,
+    and knows the widths of glyphs in the same units as the AFM file.
+    There are also internal attributes (for use by dviread.py) that
+    are *not* used for comparison.
+
+    The size is in Adobe points (converted from TeX points).
+
+    Parameters
+    ----------
+    scale : float
+        Factor by which the font is scaled from its natural size.
+    tfm : Tfm
+        TeX font metrics for this font
+    texname : bytes
+       Name of the font as used internally by TeX and friends, as an ASCII
+       bytestring.  This is usually very different from any external font
+       names; `PsfontsMap` can be used to find the external name of the font.
+    vf : Vf
+       A TeX "virtual font" file, or None if this font is not virtual.
+
+    Attributes
+    ----------
+    texname : bytes
+    size : float
+       Size of the font in Adobe points, converted from the slightly
+       smaller TeX points.
+    widths : list
+       Widths of glyphs in glyph-space units, typically 1/1000ths of
+       the point size.
+
+    """
+    __slots__ = ('texname', 'size', 'widths', '_scale', '_vf', '_tfm')
+
+    def __init__(self, scale, tfm, texname, vf):
+        _api.check_isinstance(bytes, texname=texname)
+        self._scale = scale
+        self._tfm = tfm
+        self.texname = texname
+        self._vf = vf
+        self.size = scale * (72.0 / (72.27 * 2**16))
+        try:
+            nchars = max(tfm.width) + 1
+        except ValueError:
+            nchars = 0
+        self.widths = [(1000*tfm.width.get(char, 0)) >> 20
+                       for char in range(nchars)]
+
+    def __eq__(self, other):
+        return (type(self) is type(other)
+                and self.texname == other.texname and self.size == other.size)
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    def __repr__(self):
+        return f"<{type(self).__name__}: {self.texname}>"
+
+    def _width_of(self, char):
+        """Width of char in dvi units."""
+        width = self._tfm.width.get(char, None)
+        if width is not None:
+            return _mul2012(width, self._scale)
+        _log.debug('No width for char %d in font %s.', char, self.texname)
+        return 0
+
+    def _height_depth_of(self, char):
+        """Height and depth of char in dvi units."""
+        result = []
+        for metric, name in ((self._tfm.height, "height"),
+                             (self._tfm.depth, "depth")):
+            value = metric.get(char, None)
+            if value is None:
+                _log.debug('No %s for char %d in font %s',
+                           name, char, self.texname)
+                result.append(0)
+            else:
+                result.append(_mul2012(value, self._scale))
+        # cmsyXX (symbols font) glyph 0 ("minus") has a nonzero descent
+        # so that TeX aligns equations properly
+        # (https://tex.stackexchange.com/q/526103/)
+        # but we actually care about the rasterization depth to align
+        # the dvipng-generated images.
+        if re.match(br'^cmsy\d+$', self.texname) and char == 0:
+            result[-1] = 0
+        return result
+
+
+class Vf(Dvi):
+    r"""
+    A virtual font (\*.vf file) containing subroutines for dvi files.
+
+    Parameters
+    ----------
+    filename : str or path-like
+
+    Notes
+    -----
+    The virtual font format is a derivative of dvi:
+    http://mirrors.ctan.org/info/knuth/virtual-fonts
+    This class reuses some of the machinery of `Dvi`
+    but replaces the `_read` loop and dispatch mechanism.
+
+    Examples
+    --------
+    ::
+
+        vf = Vf(filename)
+        glyph = vf[code]
+        glyph.text, glyph.boxes, glyph.width
+    """
+
+    def __init__(self, filename):
+        super().__init__(filename, 0)
+        try:
+            self._first_font = None
+            self._chars = {}
+            self._read()
+        finally:
+            self.close()
+
+    def __getitem__(self, code):
+        return self._chars[code]
+
+    def _read(self):
+        """
+        Read one page from the file. Return True if successful,
+        False if there were no more pages.
+        """
+        packet_char = packet_ends = None
+        packet_len = packet_width = None
+        while True:
+            byte = self.file.read(1)[0]
+            # If we are in a packet, execute the dvi instructions
+            if self.state is _dvistate.inpage:
+                byte_at = self.file.tell()-1
+                if byte_at == packet_ends:
+                    self._finalize_packet(packet_char, packet_width)
+                    packet_len = packet_char = packet_width = None
+                    # fall through to out-of-packet code
+                elif byte_at > packet_ends:
+                    raise ValueError("Packet length mismatch in vf file")
+                else:
+                    if byte in (139, 140) or byte >= 243:
+                        raise ValueError(f"Inappropriate opcode {byte} in vf file")
+                    Dvi._dtable[byte](self, byte)
+                    continue
+
+            # We are outside a packet
+            if byte < 242:          # a short packet (length given by byte)
+                packet_len = byte
+                packet_char = self._read_arg(1)
+                packet_width = self._read_arg(3)
+                packet_ends = self._init_packet(byte)
+                self.state = _dvistate.inpage
+            elif byte == 242:       # a long packet
+                packet_len = self._read_arg(4)
+                packet_char = self._read_arg(4)
+                packet_width = self._read_arg(4)
+                self._init_packet(packet_len)
+            elif 243 <= byte <= 246:
+                k = self._read_arg(byte - 242, byte == 246)
+                c = self._read_arg(4)
+                s = self._read_arg(4)
+                d = self._read_arg(4)
+                a = self._read_arg(1)
+                l = self._read_arg(1)
+                self._fnt_def_real(k, c, s, d, a, l)
+                if self._first_font is None:
+                    self._first_font = k
+            elif byte == 247:       # preamble
+                i = self._read_arg(1)
+                k = self._read_arg(1)
+                x = self.file.read(k)
+                cs = self._read_arg(4)
+                ds = self._read_arg(4)
+                self._pre(i, x, cs, ds)
+            elif byte == 248:       # postamble (just some number of 248s)
+                break
+            else:
+                raise ValueError(f"Unknown vf opcode {byte}")
+
+    def _init_packet(self, pl):
+        if self.state != _dvistate.outer:
+            raise ValueError("Misplaced packet in vf file")
+        self.h = self.v = self.w = self.x = self.y = self.z = 0
+        self.stack = []
+        self.text = []
+        self.boxes = []
+        self.f = self._first_font
+        self._missing_font = None
+        return self.file.tell() + pl
+
+    def _finalize_packet(self, packet_char, packet_width):
+        if not self._missing_font:  # Otherwise we don't have full glyph definition.
+            self._chars[packet_char] = Page(
+                text=self.text, boxes=self.boxes, width=packet_width,
+                height=None, descent=None)
+        self.state = _dvistate.outer
+
+    def _pre(self, i, x, cs, ds):
+        if self.state is not _dvistate.pre:
+            raise ValueError("pre command in middle of vf file")
+        if i != 202:
+            raise ValueError(f"Unknown vf format {i}")
+        if len(x):
+            _log.debug('vf file comment: %s', x)
+        self.state = _dvistate.outer
+        # cs = checksum, ds = design size
+
+
+def _mul2012(num1, num2):
+    """Multiply two numbers in 20.12 fixed point format."""
+    # Separated into a function because >> has surprising precedence
+    return (num1*num2) >> 20
+
+
+class Tfm:
+    """
+    A TeX Font Metric file.
+
+    This implementation covers only the bare minimum needed by the Dvi class.
+
+    Parameters
+    ----------
+    filename : str or path-like
+
+    Attributes
+    ----------
+    checksum : int
+       Used for verifying against the dvi file.
+    design_size : int
+       Design size of the font (unknown units)
+    width, height, depth : dict
+       Dimensions of each character, need to be scaled by the factor
+       specified in the dvi file. These are dicts because indexing may
+       not start from 0.
+    """
+    __slots__ = ('checksum', 'design_size', 'width', 'height', 'depth')
+
+    def __init__(self, filename):
+        _log.debug('opening tfm file %s', filename)
+        with open(filename, 'rb') as file:
+            header1 = file.read(24)
+            lh, bc, ec, nw, nh, nd = struct.unpack('!6H', header1[2:14])
+            _log.debug('lh=%d, bc=%d, ec=%d, nw=%d, nh=%d, nd=%d',
+                       lh, bc, ec, nw, nh, nd)
+            header2 = file.read(4*lh)
+            self.checksum, self.design_size = struct.unpack('!2I', header2[:8])
+            # there is also encoding information etc.
+            char_info = file.read(4*(ec-bc+1))
+            widths = struct.unpack(f'!{nw}i', file.read(4*nw))
+            heights = struct.unpack(f'!{nh}i', file.read(4*nh))
+            depths = struct.unpack(f'!{nd}i', file.read(4*nd))
+        self.width = {}
+        self.height = {}
+        self.depth = {}
+        for idx, char in enumerate(range(bc, ec+1)):
+            byte0 = char_info[4*idx]
+            byte1 = char_info[4*idx+1]
+            self.width[char] = widths[byte0]
+            self.height[char] = heights[byte1 >> 4]
+            self.depth[char] = depths[byte1 & 0xf]
+
+
+PsFont = namedtuple('PsFont', 'texname psname effects encoding filename')
+
+
+class PsfontsMap:
+    """
+    A psfonts.map formatted file, mapping TeX fonts to PS fonts.
+
+    Parameters
+    ----------
+    filename : str or path-like
+
+    Notes
+    -----
+    For historical reasons, TeX knows many Type-1 fonts by different
+    names than the outside world. (For one thing, the names have to
+    fit in eight characters.) Also, TeX's native fonts are not Type-1
+    but Metafont, which is nontrivial to convert to PostScript except
+    as a bitmap. While high-quality conversions to Type-1 format exist
+    and are shipped with modern TeX distributions, we need to know
+    which Type-1 fonts are the counterparts of which native fonts. For
+    these reasons a mapping is needed from internal font names to font
+    file names.
+
+    A texmf tree typically includes mapping files called e.g.
+    :file:`psfonts.map`, :file:`pdftex.map`, or :file:`dvipdfm.map`.
+    The file :file:`psfonts.map` is used by :program:`dvips`,
+    :file:`pdftex.map` by :program:`pdfTeX`, and :file:`dvipdfm.map`
+    by :program:`dvipdfm`. :file:`psfonts.map` might avoid embedding
+    the 35 PostScript fonts (i.e., have no filename for them, as in
+    the Times-Bold example above), while the pdf-related files perhaps
+    only avoid the "Base 14" pdf fonts. But the user may have
+    configured these files differently.
+
+    Examples
+    --------
+    >>> map = PsfontsMap(find_tex_file('pdftex.map'))
+    >>> entry = map[b'ptmbo8r']
+    >>> entry.texname
+    b'ptmbo8r'
+    >>> entry.psname
+    b'Times-Bold'
+    >>> entry.encoding
+    '/usr/local/texlive/2008/texmf-dist/fonts/enc/dvips/base/8r.enc'
+    >>> entry.effects
+    {'slant': 0.16700000000000001}
+    >>> entry.filename
+    """
+    __slots__ = ('_filename', '_unparsed', '_parsed')
+
+    # Create a filename -> PsfontsMap cache, so that calling
+    # `PsfontsMap(filename)` with the same filename a second time immediately
+    # returns the same object.
+    @lru_cache
+    def __new__(cls, filename):
+        self = object.__new__(cls)
+        self._filename = os.fsdecode(filename)
+        # Some TeX distributions have enormous pdftex.map files which would
+        # take hundreds of milliseconds to parse, but it is easy enough to just
+        # store the unparsed lines (keyed by the first word, which is the
+        # texname) and parse them on-demand.
+        with open(filename, 'rb') as file:
+            self._unparsed = {}
+            for line in file:
+                tfmname = line.split(b' ', 1)[0]
+                self._unparsed.setdefault(tfmname, []).append(line)
+        self._parsed = {}
+        return self
+
+    def __getitem__(self, texname):
+        assert isinstance(texname, bytes)
+        if texname in self._unparsed:
+            for line in self._unparsed.pop(texname):
+                if self._parse_and_cache_line(line):
+                    break
+        try:
+            return self._parsed[texname]
+        except KeyError:
+            raise LookupError(
+                f"An associated PostScript font (required by Matplotlib) "
+                f"could not be found for TeX font {texname.decode('ascii')!r} "
+                f"in {self._filename!r}; this problem can often be solved by "
+                f"installing a suitable PostScript font package in your TeX "
+                f"package manager") from None
+
+    def _parse_and_cache_line(self, line):
+        """
+        Parse a line in the font mapping file.
+
+        The format is (partially) documented at
+        http://mirrors.ctan.org/systems/doc/pdftex/manual/pdftex-a.pdf
+        https://tug.org/texinfohtml/dvips.html#psfonts_002emap
+        Each line can have the following fields:
+
+        - tfmname (first, only required field),
+        - psname (defaults to tfmname, must come immediately after tfmname if
+          present),
+        - fontflags (integer, must come immediately after psname if present,
+          ignored by us),
+        - special (SlantFont and ExtendFont, only field that is double-quoted),
+        - fontfile, encodingfile (optional, prefixed by <, <<, or <[; << always
+          precedes a font, <[ always precedes an encoding, < can precede either
+          but then an encoding file must have extension .enc; < and << also
+          request different font subsetting behaviors but we ignore that; < can
+          be separated from the filename by whitespace).
+
+        special, fontfile, and encodingfile can appear in any order.
+        """
+        # If the map file specifies multiple encodings for a font, we
+        # follow pdfTeX in choosing the last one specified. Such
+        # entries are probably mistakes but they have occurred.
+        # https://tex.stackexchange.com/q/10826/
+
+        if not line or line.startswith((b" ", b"%", b"*", b";", b"#")):
+            return
+        tfmname = basename = special = encodingfile = fontfile = None
+        is_subsetted = is_t1 = is_truetype = False
+        matches = re.finditer(br'"([^"]*)(?:"|$)|(\S+)', line)
+        for match in matches:
+            quoted, unquoted = match.groups()
+            if unquoted:
+                if unquoted.startswith(b"<<"):  # font
+                    fontfile = unquoted[2:]
+                elif unquoted.startswith(b"<["):  # encoding
+                    encodingfile = unquoted[2:]
+                elif unquoted.startswith(b"<"):  # font or encoding
+                    word = (
+                        # <foo => foo
+                        unquoted[1:]
+                        # < by itself => read the next word
+                        or next(filter(None, next(matches).groups())))
+                    if word.endswith(b".enc"):
+                        encodingfile = word
+                    else:
+                        fontfile = word
+                        is_subsetted = True
+                elif tfmname is None:
+                    tfmname = unquoted
+                elif basename is None:
+                    basename = unquoted
+            elif quoted:
+                special = quoted
+        effects = {}
+        if special:
+            words = reversed(special.split())
+            for word in words:
+                if word == b"SlantFont":
+                    effects["slant"] = float(next(words))
+                elif word == b"ExtendFont":
+                    effects["extend"] = float(next(words))
+
+        # Verify some properties of the line that would cause it to be ignored
+        # otherwise.
+        if fontfile is not None:
+            if fontfile.endswith((b".ttf", b".ttc")):
+                is_truetype = True
+            elif not fontfile.endswith(b".otf"):
+                is_t1 = True
+        elif basename is not None:
+            is_t1 = True
+        if is_truetype and is_subsetted and encodingfile is None:
+            return
+        if not is_t1 and ("slant" in effects or "extend" in effects):
+            return
+        if abs(effects.get("slant", 0)) > 1:
+            return
+        if abs(effects.get("extend", 0)) > 2:
+            return
+
+        if basename is None:
+            basename = tfmname
+        if encodingfile is not None:
+            encodingfile = find_tex_file(encodingfile)
+        if fontfile is not None:
+            fontfile = find_tex_file(fontfile)
+        self._parsed[tfmname] = PsFont(
+            texname=tfmname, psname=basename, effects=effects,
+            encoding=encodingfile, filename=fontfile)
+        return True
+
+
+def _parse_enc(path):
+    r"""
+    Parse a \*.enc file referenced from a psfonts.map style file.
+
+    The format supported by this function is a tiny subset of PostScript.
+
+    Parameters
+    ----------
+    path : `os.PathLike`
+
+    Returns
+    -------
+    list
+        The nth entry of the list is the PostScript glyph name of the nth
+        glyph.
+    """
+    no_comments = re.sub("%.*", "", Path(path).read_text(encoding="ascii"))
+    array = re.search(r"(?s)\[(.*)\]", no_comments).group(1)
+    lines = [line for line in array.split() if line]
+    if all(line.startswith("/") for line in lines):
+        return [line[1:] for line in lines]
+    else:
+        raise ValueError(f"Failed to parse {path} as Postscript encoding")
+
+
+class _LuatexKpsewhich:
+    @lru_cache  # A singleton.
+    def __new__(cls):
+        self = object.__new__(cls)
+        self._proc = self._new_proc()
+        return self
+
+    def _new_proc(self):
+        return subprocess.Popen(
+            ["luatex", "--luaonly",
+             str(cbook._get_data_path("kpsewhich.lua"))],
+            stdin=subprocess.PIPE, stdout=subprocess.PIPE)
+
+    def search(self, filename):
+        if self._proc.poll() is not None:  # Dead, restart it.
+            self._proc = self._new_proc()
+        self._proc.stdin.write(os.fsencode(filename) + b"\n")
+        self._proc.stdin.flush()
+        out = self._proc.stdout.readline().rstrip()
+        return None if out == b"nil" else os.fsdecode(out)
+
+
+@lru_cache
+def find_tex_file(filename):
+    """
+    Find a file in the texmf tree using kpathsea_.
+
+    The kpathsea library, provided by most existing TeX distributions, both
+    on Unix-like systems and on Windows (MikTeX), is invoked via a long-lived
+    luatex process if luatex is installed, or via kpsewhich otherwise.
+
+    .. _kpathsea: https://www.tug.org/kpathsea/
+
+    Parameters
+    ----------
+    filename : str or path-like
+
+    Raises
+    ------
+    FileNotFoundError
+        If the file is not found.
+    """
+
+    # we expect these to always be ascii encoded, but use utf-8
+    # out of caution
+    if isinstance(filename, bytes):
+        filename = filename.decode('utf-8', errors='replace')
+
+    try:
+        lk = _LuatexKpsewhich()
+    except FileNotFoundError:
+        lk = None  # Fallback to directly calling kpsewhich, as below.
+
+    if lk:
+        path = lk.search(filename)
+    else:
+        if sys.platform == 'win32':
+            # On Windows only, kpathsea can use utf-8 for cmd args and output.
+            # The `command_line_encoding` environment variable is set to force
+            # it to always use utf-8 encoding.  See Matplotlib issue #11848.
+            kwargs = {'env': {**os.environ, 'command_line_encoding': 'utf-8'},
+                      'encoding': 'utf-8'}
+        else:  # On POSIX, run through the equivalent of os.fsdecode().
+            kwargs = {'encoding': sys.getfilesystemencoding(),
+                      'errors': 'surrogateescape'}
+
+        try:
+            path = (cbook._check_and_log_subprocess(['kpsewhich', filename],
+                                                    _log, **kwargs)
+                    .rstrip('\n'))
+        except (FileNotFoundError, RuntimeError):
+            path = None
+
+    if path:
+        return path
+    else:
+        raise FileNotFoundError(
+            f"Matplotlib's TeX implementation searched for a file named "
+            f"{filename!r} in your texmf tree, but could not find it")
+
+
+@lru_cache
+def _fontfile(cls, suffix, texname):
+    return cls(find_tex_file(texname + suffix))
+
+
+_tfmfile = partial(_fontfile, Tfm, ".tfm")
+_vffile = partial(_fontfile, Vf, ".vf")
+
+
+if __name__ == '__main__':
+    from argparse import ArgumentParser
+    import itertools
+
+    parser = ArgumentParser()
+    parser.add_argument("filename")
+    parser.add_argument("dpi", nargs="?", type=float, default=None)
+    args = parser.parse_args()
+    with Dvi(args.filename, args.dpi) as dvi:
+        fontmap = PsfontsMap(find_tex_file('pdftex.map'))
+        for page in dvi:
+            print(f"=== new page === "
+                  f"(w: {page.width}, h: {page.height}, d: {page.descent})")
+            for font, group in itertools.groupby(
+                    page.text, lambda text: text.font):
+                print(f"font: {font.texname.decode('latin-1')!r}\t"
+                      f"scale: {font._scale / 2 ** 20}")
+                print("x", "y", "glyph", "chr", "w", "(glyphs)", sep="\t")
+                for text in group:
+                    print(text.x, text.y, text.glyph,
+                          chr(text.glyph) if chr(text.glyph).isprintable()
+                          else ".",
+                          text.width, sep="\t")
+            if page.boxes:
+                print("x", "y", "h", "w", "", "(boxes)", sep="\t")
+                for box in page.boxes:
+                    print(box.x, box.y, box.height, box.width, sep="\t")

shared/package-files/06/06a5c5ca12950e815126549205bfe3c4e9163f333632943aec12a2411f39d80f/blob

@@ -0,0 +1,788 @@
+"""Options manager for :class:`~.Poly` and public API functions. """
+
+from __future__ import annotations
+
+__all__ = ["Options"]
+
+from sympy.core import Basic, sympify
+from sympy.polys.polyerrors import GeneratorsError, OptionError, FlagError
+from sympy.utilities import numbered_symbols, topological_sort, public
+from sympy.utilities.iterables import has_dups, is_sequence
+
+import sympy.polys
+
+import re
+
+class Option:
+    """Base class for all kinds of options. """
+
+    option: str | None = None
+
+    is_Flag = False
+
+    requires: list[str] = []
+    excludes: list[str] = []
+
+    after: list[str] = []
+    before: list[str] = []
+
+    @classmethod
+    def default(cls):
+        return None
+
+    @classmethod
+    def preprocess(cls, option):
+        return None
+
+    @classmethod
+    def postprocess(cls, options):
+        pass
+
+
+class Flag(Option):
+    """Base class for all kinds of flags. """
+
+    is_Flag = True
+
+
+class BooleanOption(Option):
+    """An option that must have a boolean value or equivalent assigned. """
+
+    @classmethod
+    def preprocess(cls, value):
+        if value in [True, False]:
+            return bool(value)
+        else:
+            raise OptionError("'%s' must have a boolean value assigned, got %s" % (cls.option, value))
+
+
+class OptionType(type):
+    """Base type for all options that does registers options. """
+
+    def __init__(cls, *args, **kwargs):
+        @property
+        def getter(self):
+            try:
+                return self[cls.option]
+            except KeyError:
+                return cls.default()
+
+        setattr(Options, cls.option, getter)
+        Options.__options__[cls.option] = cls
+
+
+@public
+class Options(dict):
+    """
+    Options manager for polynomial manipulation module.
+
+    Examples
+    ========
+
+    >>> from sympy.polys.polyoptions import Options
+    >>> from sympy.polys.polyoptions import build_options
+
+    >>> from sympy.abc import x, y, z
+
+    >>> Options((x, y, z), {'domain': 'ZZ'})
+    {'auto': False, 'domain': ZZ, 'gens': (x, y, z)}
+
+    >>> build_options((x, y, z), {'domain': 'ZZ'})
+    {'auto': False, 'domain': ZZ, 'gens': (x, y, z)}
+
+    **Options**
+
+    * Expand --- boolean option
+    * Gens --- option
+    * Wrt --- option
+    * Sort --- option
+    * Order --- option
+    * Field --- boolean option
+    * Greedy --- boolean option
+    * Domain --- option
+    * Split --- boolean option
+    * Gaussian --- boolean option
+    * Extension --- option
+    * Modulus --- option
+    * Symmetric --- boolean option
+    * Strict --- boolean option
+
+    **Flags**
+
+    * Auto --- boolean flag
+    * Frac --- boolean flag
+    * Formal --- boolean flag
+    * Polys --- boolean flag
+    * Include --- boolean flag
+    * All --- boolean flag
+    * Gen --- flag
+    * Series --- boolean flag
+
+    """
+
+    __order__ = None
+    __options__: dict[str, type[Option]] = {}
+
+    def __init__(self, gens, args, flags=None, strict=False):
+        dict.__init__(self)
+
+        if gens and args.get('gens', ()):
+            raise OptionError(
+                "both '*gens' and keyword argument 'gens' supplied")
+        elif gens:
+            args = dict(args)
+            args['gens'] = gens
+
+        defaults = args.pop('defaults', {})
+
+        def preprocess_options(args):
+            for option, value in args.items():
+                try:
+                    cls = self.__options__[option]
+                except KeyError:
+                    raise OptionError("'%s' is not a valid option" % option)
+
+                if issubclass(cls, Flag):
+                    if flags is None or option not in flags:
+                        if strict:
+                            raise OptionError("'%s' flag is not allowed in this context" % option)
+
+                if value is not None:
+                    self[option] = cls.preprocess(value)
+
+        preprocess_options(args)
+
+        for key, value in dict(defaults).items():
+            if key in self:
+                del defaults[key]
+            else:
+                for option in self.keys():
+                    cls = self.__options__[option]
+
+                    if key in cls.excludes:
+                        del defaults[key]
+                        break
+
+        preprocess_options(defaults)
+
+        for option in self.keys():
+            cls = self.__options__[option]
+
+            for require_option in cls.requires:
+                if self.get(require_option) is None:
+                    raise OptionError("'%s' option is only allowed together with '%s'" % (option, require_option))
+
+            for exclude_option in cls.excludes:
+                if self.get(exclude_option) is not None:
+                    raise OptionError("'%s' option is not allowed together with '%s'" % (option, exclude_option))
+
+        for option in self.__order__:
+            self.__options__[option].postprocess(self)
+
+    @classmethod
+    def _init_dependencies_order(cls):
+        """Resolve the order of options' processing. """
+        if cls.__order__ is None:
+            vertices, edges = [], set()
+
+            for name, option in cls.__options__.items():
+                vertices.append(name)
+
+                for _name in option.after:
+                    edges.add((_name, name))
+
+                for _name in option.before:
+                    edges.add((name, _name))
+
+            try:
+                cls.__order__ = topological_sort((vertices, list(edges)))
+            except ValueError:
+                raise RuntimeError(
+                    "cycle detected in sympy.polys options framework")
+
+    def clone(self, updates={}):
+        """Clone ``self`` and update specified options. """
+        obj = dict.__new__(self.__class__)
+
+        for option, value in self.items():
+            obj[option] = value
+
+        for option, value in updates.items():
+            obj[option] = value
+
+        return obj
+
+    def __setattr__(self, attr, value):
+        if attr in self.__options__:
+            self[attr] = value
+        else:
+            super().__setattr__(attr, value)
+
+    @property
+    def args(self):
+        args = {}
+
+        for option, value in self.items():
+            if value is not None and option != 'gens':
+                cls = self.__options__[option]
+
+                if not issubclass(cls, Flag):
+                    args[option] = value
+
+        return args
+
+    @property
+    def options(self):
+        options = {}
+
+        for option, cls in self.__options__.items():
+            if not issubclass(cls, Flag):
+                options[option] = getattr(self, option)
+
+        return options
+
+    @property
+    def flags(self):
+        flags = {}
+
+        for option, cls in self.__options__.items():
+            if issubclass(cls, Flag):
+                flags[option] = getattr(self, option)
+
+        return flags
+
+
+class Expand(BooleanOption, metaclass=OptionType):
+    """``expand`` option to polynomial manipulation functions. """
+
+    option = 'expand'
+
+    requires: list[str] = []
+    excludes: list[str] = []
+
+    @classmethod
+    def default(cls):
+        return True
+
+
+class Gens(Option, metaclass=OptionType):
+    """``gens`` option to polynomial manipulation functions. """
+
+    option = 'gens'
+
+    requires: list[str] = []
+    excludes: list[str] = []
+
+    @classmethod
+    def default(cls):
+        return ()
+
+    @classmethod
+    def preprocess(cls, gens):
+        if isinstance(gens, Basic):
+            gens = (gens,)
+        elif len(gens) == 1 and is_sequence(gens[0]):
+            gens = gens[0]
+
+        if gens == (None,):
+            gens = ()
+        elif has_dups(gens):
+            raise GeneratorsError("duplicated generators: %s" % str(gens))
+        elif any(gen.is_commutative is False for gen in gens):
+            raise GeneratorsError("non-commutative generators: %s" % str(gens))
+
+        return tuple(gens)
+
+
+class Wrt(Option, metaclass=OptionType):
+    """``wrt`` option to polynomial manipulation functions. """
+
+    option = 'wrt'
+
+    requires: list[str] = []
+    excludes: list[str] = []
+
+    _re_split = re.compile(r"\s*,\s*|\s+")
+
+    @classmethod
+    def preprocess(cls, wrt):
+        if isinstance(wrt, Basic):
+            return [str(wrt)]
+        elif isinstance(wrt, str):
+            wrt = wrt.strip()
+            if wrt.endswith(','):
+                raise OptionError('Bad input: missing parameter.')
+            if not wrt:
+                return []
+            return list(cls._re_split.split(wrt))
+        elif hasattr(wrt, '__getitem__'):
+            return list(map(str, wrt))
+        else:
+            raise OptionError("invalid argument for 'wrt' option")
+
+
+class Sort(Option, metaclass=OptionType):
+    """``sort`` option to polynomial manipulation functions. """
+
+    option = 'sort'
+
+    requires: list[str] = []
+    excludes: list[str] = []
+
+    @classmethod
+    def default(cls):
+        return []
+
+    @classmethod
+    def preprocess(cls, sort):
+        if isinstance(sort, str):
+            return [ gen.strip() for gen in sort.split('>') ]
+        elif hasattr(sort, '__getitem__'):
+            return list(map(str, sort))
+        else:
+            raise OptionError("invalid argument for 'sort' option")
+
+
+class Order(Option, metaclass=OptionType):
+    """``order`` option to polynomial manipulation functions. """
+
+    option = 'order'
+
+    requires: list[str] = []
+    excludes: list[str] = []
+
+    @classmethod
+    def default(cls):
+        return sympy.polys.orderings.lex
+
+    @classmethod
+    def preprocess(cls, order):
+        return sympy.polys.orderings.monomial_key(order)
+
+
+class Field(BooleanOption, metaclass=OptionType):
+    """``field`` option to polynomial manipulation functions. """
+
+    option = 'field'
+
+    requires: list[str] = []
+    excludes = ['domain', 'split', 'gaussian']
+
+
+class Greedy(BooleanOption, metaclass=OptionType):
+    """``greedy`` option to polynomial manipulation functions. """
+
+    option = 'greedy'
+
+    requires: list[str] = []
+    excludes = ['domain', 'split', 'gaussian', 'extension', 'modulus', 'symmetric']
+
+
+class Composite(BooleanOption, metaclass=OptionType):
+    """``composite`` option to polynomial manipulation functions. """
+
+    option = 'composite'
+
+    @classmethod
+    def default(cls):
+        return None
+
+    requires: list[str] = []
+    excludes = ['domain', 'split', 'gaussian', 'extension', 'modulus', 'symmetric']
+
+
+class Domain(Option, metaclass=OptionType):
+    """``domain`` option to polynomial manipulation functions. """
+
+    option = 'domain'
+
+    requires: list[str] = []
+    excludes = ['field', 'greedy', 'split', 'gaussian', 'extension']
+
+    after = ['gens']
+
+    _re_realfield = re.compile(r"^(R|RR)(_(\d+))?$")
+    _re_complexfield = re.compile(r"^(C|CC)(_(\d+))?$")
+    _re_finitefield = re.compile(r"^(FF|GF)\((\d+)\)$")
+    _re_polynomial = re.compile(r"^(Z|ZZ|Q|QQ|ZZ_I|QQ_I|R|RR|C|CC)\[(.+)\]$")
+    _re_fraction = re.compile(r"^(Z|ZZ|Q|QQ)\((.+)\)$")
+    _re_algebraic = re.compile(r"^(Q|QQ)\<(.+)\>$")
+
+    @classmethod
+    def preprocess(cls, domain):
+        if isinstance(domain, sympy.polys.domains.Domain):
+            return domain
+        elif hasattr(domain, 'to_domain'):
+            return domain.to_domain()
+        elif isinstance(domain, str):
+            if domain in ['Z', 'ZZ']:
+                return sympy.polys.domains.ZZ
+
+            if domain in ['Q', 'QQ']:
+                return sympy.polys.domains.QQ
+
+            if domain == 'ZZ_I':
+                return sympy.polys.domains.ZZ_I
+
+            if domain == 'QQ_I':
+                return sympy.polys.domains.QQ_I
+
+            if domain == 'EX':
+                return sympy.polys.domains.EX
+
+            r = cls._re_realfield.match(domain)
+
+            if r is not None:
+                _, _, prec = r.groups()
+
+                if prec is None:
+                    return sympy.polys.domains.RR
+                else:
+                    return sympy.polys.domains.RealField(int(prec))
+
+            r = cls._re_complexfield.match(domain)
+
+            if r is not None:
+                _, _, prec = r.groups()
+
+                if prec is None:
+                    return sympy.polys.domains.CC
+                else:
+                    return sympy.polys.domains.ComplexField(int(prec))
+
+            r = cls._re_finitefield.match(domain)
+
+            if r is not None:
+                return sympy.polys.domains.FF(int(r.groups()[1]))
+
+            r = cls._re_polynomial.match(domain)
+
+            if r is not None:
+                ground, gens = r.groups()
+
+                gens = list(map(sympify, gens.split(',')))
+
+                if ground in ['Z', 'ZZ']:
+                    return sympy.polys.domains.ZZ.poly_ring(*gens)
+                elif ground in ['Q', 'QQ']:
+                    return sympy.polys.domains.QQ.poly_ring(*gens)
+                elif ground in ['R', 'RR']:
+                    return sympy.polys.domains.RR.poly_ring(*gens)
+                elif ground == 'ZZ_I':
+                    return sympy.polys.domains.ZZ_I.poly_ring(*gens)
+                elif ground == 'QQ_I':
+                    return sympy.polys.domains.QQ_I.poly_ring(*gens)
+                else:
+                    return sympy.polys.domains.CC.poly_ring(*gens)
+
+            r = cls._re_fraction.match(domain)
+
+            if r is not None:
+                ground, gens = r.groups()
+
+                gens = list(map(sympify, gens.split(',')))
+
+                if ground in ['Z', 'ZZ']:
+                    return sympy.polys.domains.ZZ.frac_field(*gens)
+                else:
+                    return sympy.polys.domains.QQ.frac_field(*gens)
+
+            r = cls._re_algebraic.match(domain)
+
+            if r is not None:
+                gens = list(map(sympify, r.groups()[1].split(',')))
+                return sympy.polys.domains.QQ.algebraic_field(*gens)
+
+        raise OptionError('expected a valid domain specification, got %s' % domain)
+
+    @classmethod
+    def postprocess(cls, options):
+        if 'gens' in options and 'domain' in options and options['domain'].is_Composite and \
+                (set(options['domain'].symbols) & set(options['gens'])):
+            raise GeneratorsError(
+                "ground domain and generators interfere together")
+        elif ('gens' not in options or not options['gens']) and \
+                'domain' in options and options['domain'] == sympy.polys.domains.EX:
+            raise GeneratorsError("you have to provide generators because EX domain was requested")
+
+
+class Split(BooleanOption, metaclass=OptionType):
+    """``split`` option to polynomial manipulation functions. """
+
+    option = 'split'
+
+    requires: list[str] = []
+    excludes = ['field', 'greedy', 'domain', 'gaussian', 'extension',
+        'modulus', 'symmetric']
+
+    @classmethod
+    def postprocess(cls, options):
+        if 'split' in options:
+            raise NotImplementedError("'split' option is not implemented yet")
+
+
+class Gaussian(BooleanOption, metaclass=OptionType):
+    """``gaussian`` option to polynomial manipulation functions. """
+
+    option = 'gaussian'
+
+    requires: list[str] = []
+    excludes = ['field', 'greedy', 'domain', 'split', 'extension',
+        'modulus', 'symmetric']
+
+    @classmethod
+    def postprocess(cls, options):
+        if 'gaussian' in options and options['gaussian'] is True:
+            options['domain'] = sympy.polys.domains.QQ_I
+            Extension.postprocess(options)
+
+
+class Extension(Option, metaclass=OptionType):
+    """``extension`` option to polynomial manipulation functions. """
+
+    option = 'extension'
+
+    requires: list[str] = []
+    excludes = ['greedy', 'domain', 'split', 'gaussian', 'modulus',
+        'symmetric']
+
+    @classmethod
+    def preprocess(cls, extension):
+        if extension == 1:
+            return bool(extension)
+        elif extension == 0:
+            raise OptionError("'False' is an invalid argument for 'extension'")
+        else:
+            if not hasattr(extension, '__iter__'):
+                extension = {extension}
+            else:
+                if not extension:
+                    extension = None
+                else:
+                    extension = set(extension)
+
+            return extension
+
+    @classmethod
+    def postprocess(cls, options):
+        if 'extension' in options and options['extension'] is not True:
+            options['domain'] = sympy.polys.domains.QQ.algebraic_field(
+                *options['extension'])
+
+
+class Modulus(Option, metaclass=OptionType):
+    """``modulus`` option to polynomial manipulation functions. """
+
+    option = 'modulus'
+
+    requires: list[str] = []
+    excludes = ['greedy', 'split', 'domain', 'gaussian', 'extension']
+
+    @classmethod
+    def preprocess(cls, modulus):
+        modulus = sympify(modulus)
+
+        if modulus.is_Integer and modulus > 0:
+            return int(modulus)
+        else:
+            raise OptionError(
+                "'modulus' must a positive integer, got %s" % modulus)
+
+    @classmethod
+    def postprocess(cls, options):
+        if 'modulus' in options:
+            modulus = options['modulus']
+            symmetric = options.get('symmetric', True)
+            options['domain'] = sympy.polys.domains.FF(modulus, symmetric)
+
+
+class Symmetric(BooleanOption, metaclass=OptionType):
+    """``symmetric`` option to polynomial manipulation functions. """
+
+    option = 'symmetric'
+
+    requires = ['modulus']
+    excludes = ['greedy', 'domain', 'split', 'gaussian', 'extension']
+
+
+class Strict(BooleanOption, metaclass=OptionType):
+    """``strict`` option to polynomial manipulation functions. """
+
+    option = 'strict'
+
+    @classmethod
+    def default(cls):
+        return True
+
+
+class Auto(BooleanOption, Flag, metaclass=OptionType):
+    """``auto`` flag to polynomial manipulation functions. """
+
+    option = 'auto'
+
+    after = ['field', 'domain', 'extension', 'gaussian']
+
+    @classmethod
+    def default(cls):
+        return True
+
+    @classmethod
+    def postprocess(cls, options):
+        if ('domain' in options or 'field' in options) and 'auto' not in options:
+            options['auto'] = False
+
+
+class Frac(BooleanOption, Flag, metaclass=OptionType):
+    """``auto`` option to polynomial manipulation functions. """
+
+    option = 'frac'
+
+    @classmethod
+    def default(cls):
+        return False
+
+
+class Formal(BooleanOption, Flag, metaclass=OptionType):
+    """``formal`` flag to polynomial manipulation functions. """
+
+    option = 'formal'
+
+    @classmethod
+    def default(cls):
+        return False
+
+
+class Polys(BooleanOption, Flag, metaclass=OptionType):
+    """``polys`` flag to polynomial manipulation functions. """
+
+    option = 'polys'
+
+
+class Include(BooleanOption, Flag, metaclass=OptionType):
+    """``include`` flag to polynomial manipulation functions. """
+
+    option = 'include'
+
+    @classmethod
+    def default(cls):
+        return False
+
+
+class All(BooleanOption, Flag, metaclass=OptionType):
+    """``all`` flag to polynomial manipulation functions. """
+
+    option = 'all'
+
+    @classmethod
+    def default(cls):
+        return False
+
+
+class Gen(Flag, metaclass=OptionType):
+    """``gen`` flag to polynomial manipulation functions. """
+
+    option = 'gen'
+
+    @classmethod
+    def default(cls):
+        return 0
+
+    @classmethod
+    def preprocess(cls, gen):
+        if isinstance(gen, (Basic, int)):
+            return gen
+        else:
+            raise OptionError("invalid argument for 'gen' option")
+
+
+class Series(BooleanOption, Flag, metaclass=OptionType):
+    """``series`` flag to polynomial manipulation functions. """
+
+    option = 'series'
+
+    @classmethod
+    def default(cls):
+        return False
+
+
+class Symbols(Flag, metaclass=OptionType):
+    """``symbols`` flag to polynomial manipulation functions. """
+
+    option = 'symbols'
+
+    @classmethod
+    def default(cls):
+        return numbered_symbols('s', start=1)
+
+    @classmethod
+    def preprocess(cls, symbols):
+        if hasattr(symbols, '__iter__'):
+            return iter(symbols)
+        else:
+            raise OptionError("expected an iterator or iterable container, got %s" % symbols)
+
+
+class Method(Flag, metaclass=OptionType):
+    """``method`` flag to polynomial manipulation functions. """
+
+    option = 'method'
+
+    @classmethod
+    def preprocess(cls, method):
+        if isinstance(method, str):
+            return method.lower()
+        else:
+            raise OptionError("expected a string, got %s" % method)
+
+
+def build_options(gens, args=None):
+    """Construct options from keyword arguments or ... options. """
+    if args is None:
+        gens, args = (), gens
+
+    if len(args) != 1 or 'opt' not in args or gens:
+        return Options(gens, args)
+    else:
+        return args['opt']
+
+
+def allowed_flags(args, flags):
+    """
+    Allow specified flags to be used in the given context.
+
+    Examples
+    ========
+
+    >>> from sympy.polys.polyoptions import allowed_flags
+    >>> from sympy.polys.domains import ZZ
+
+    >>> allowed_flags({'domain': ZZ}, [])
+
+    >>> allowed_flags({'domain': ZZ, 'frac': True}, [])
+    Traceback (most recent call last):
+    ...
+    FlagError: 'frac' flag is not allowed in this context
+
+    >>> allowed_flags({'domain': ZZ, 'frac': True}, ['frac'])
+
+    """
+    flags = set(flags)
+
+    for arg in args.keys():
+        try:
+            if Options.__options__[arg].is_Flag and arg not in flags:
+                raise FlagError(
+                    "'%s' flag is not allowed in this context" % arg)
+        except KeyError:
+            raise OptionError("'%s' is not a valid option" % arg)
+
+
+def set_defaults(options, **defaults):
+    """Update options with default values. """
+    if 'defaults' not in options:
+        options = dict(options)
+        options['defaults'] = defaults
+
+    return options
+
+Options._init_dependencies_order()

shared/package-files/06/06a76dad17702ab297ea355df38eb0d44f9707ac4a0e8d0e73291ee6aa4ab3af/blob

@@ -0,0 +1,21 @@
+#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 bool is_set_to(const at::Tensor & self, const at::Tensor & tensor);
+} // namespace native
+} // namespace at

shared/package-files/06/06afee4a9a5e3b6753f3cc5af979dc409fe959763cb7dedb2c632bc8c21a8bc4/blob

@@ -0,0 +1,960 @@
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+from torch.nn.functional import conv2d, grid_sample, interpolate, pad as torch_pad
+
+
+def _is_tensor_a_torch_image(x: Tensor) -> bool:
+    return x.ndim >= 2
+
+
+def _assert_image_tensor(img: Tensor) -> None:
+    if not _is_tensor_a_torch_image(img):
+        raise TypeError("Tensor is not a torch image.")
+
+
+def get_dimensions(img: Tensor) -> List[int]:
+    _assert_image_tensor(img)
+    channels = 1 if img.ndim == 2 else img.shape[-3]
+    height, width = img.shape[-2:]
+    return [channels, height, width]
+
+
+def get_image_size(img: Tensor) -> List[int]:
+    # Returns (w, h) of tensor image
+    _assert_image_tensor(img)
+    return [img.shape[-1], img.shape[-2]]
+
+
+def get_image_num_channels(img: Tensor) -> int:
+    _assert_image_tensor(img)
+    if img.ndim == 2:
+        return 1
+    elif img.ndim > 2:
+        return img.shape[-3]
+
+    raise TypeError(f"Input ndim should be 2 or more. Got {img.ndim}")
+
+
+def _max_value(dtype: torch.dtype) -> int:
+    if dtype == torch.uint8:
+        return 255
+    elif dtype == torch.int8:
+        return 127
+    elif dtype == torch.int16:
+        return 32767
+    elif dtype == torch.int32:
+        return 2147483647
+    elif dtype == torch.int64:
+        return 9223372036854775807
+    else:
+        # This is only here for completeness. This value is implicitly assumed in a lot of places so changing it is not
+        # easy.
+        return 1
+
+
+def _assert_channels(img: Tensor, permitted: List[int]) -> None:
+    c = get_dimensions(img)[0]
+    if c not in permitted:
+        raise TypeError(f"Input image tensor permitted channel values are {permitted}, but found {c}")
+
+
+def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:
+    if image.dtype == dtype:
+        return image
+
+    if image.is_floating_point():
+
+        # TODO: replace with dtype.is_floating_point when torchscript supports it
+        if torch.tensor(0, dtype=dtype).is_floating_point():
+            return image.to(dtype)
+
+        # float to int
+        if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or (
+            image.dtype == torch.float64 and dtype == torch.int64
+        ):
+            msg = f"The cast from {image.dtype} to {dtype} cannot be performed safely."
+            raise RuntimeError(msg)
+
+        # https://github.com/pytorch/vision/pull/2078#issuecomment-612045321
+        # For data in the range 0-1, (float * 255).to(uint) is only 255
+        # when float is exactly 1.0.
+        # `max + 1 - epsilon` provides more evenly distributed mapping of
+        # ranges of floats to ints.
+        eps = 1e-3
+        max_val = float(_max_value(dtype))
+        result = image.mul(max_val + 1.0 - eps)
+        return result.to(dtype)
+    else:
+        input_max = float(_max_value(image.dtype))
+
+        # int to float
+        # TODO: replace with dtype.is_floating_point when torchscript supports it
+        if torch.tensor(0, dtype=dtype).is_floating_point():
+            image = image.to(dtype)
+            return image / input_max
+
+        output_max = float(_max_value(dtype))
+
+        # int to int
+        if input_max > output_max:
+            # factor should be forced to int for torch jit script
+            # otherwise factor is a float and image // factor can produce different results
+            factor = int((input_max + 1) // (output_max + 1))
+            image = torch.div(image, factor, rounding_mode="floor")
+            return image.to(dtype)
+        else:
+            # factor should be forced to int for torch jit script
+            # otherwise factor is a float and image * factor can produce different results
+            factor = int((output_max + 1) // (input_max + 1))
+            image = image.to(dtype)
+            return image * factor
+
+
+def vflip(img: Tensor) -> Tensor:
+    _assert_image_tensor(img)
+
+    return img.flip(-2)
+
+
+def hflip(img: Tensor) -> Tensor:
+    _assert_image_tensor(img)
+
+    return img.flip(-1)
+
+
+def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:
+    _assert_image_tensor(img)
+
+    _, h, w = get_dimensions(img)
+    right = left + width
+    bottom = top + height
+
+    if left < 0 or top < 0 or right > w or bottom > h:
+        padding_ltrb = [
+            max(-left + min(0, right), 0),
+            max(-top + min(0, bottom), 0),
+            max(right - max(w, left), 0),
+            max(bottom - max(h, top), 0),
+        ]
+        return pad(img[..., max(top, 0) : bottom, max(left, 0) : right], padding_ltrb, fill=0)
+    return img[..., top:bottom, left:right]
+
+
+def rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor:
+    if img.ndim < 3:
+        raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
+    _assert_channels(img, [1, 3])
+
+    if num_output_channels not in (1, 3):
+        raise ValueError("num_output_channels should be either 1 or 3")
+
+    if img.shape[-3] == 3:
+        r, g, b = img.unbind(dim=-3)
+        # This implementation closely follows the TF one:
+        # https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/ops/image_ops_impl.py#L2105-L2138
+        l_img = (0.2989 * r + 0.587 * g + 0.114 * b).to(img.dtype)
+        l_img = l_img.unsqueeze(dim=-3)
+    else:
+        l_img = img.clone()
+
+    if num_output_channels == 3:
+        return l_img.expand(img.shape)
+
+    return l_img
+
+
+def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:
+    if brightness_factor < 0:
+        raise ValueError(f"brightness_factor ({brightness_factor}) is not non-negative.")
+
+    _assert_image_tensor(img)
+
+    _assert_channels(img, [1, 3])
+
+    return _blend(img, torch.zeros_like(img), brightness_factor)
+
+
+def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:
+    if contrast_factor < 0:
+        raise ValueError(f"contrast_factor ({contrast_factor}) is not non-negative.")
+
+    _assert_image_tensor(img)
+
+    _assert_channels(img, [3, 1])
+    c = get_dimensions(img)[0]
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+    if c == 3:
+        mean = torch.mean(rgb_to_grayscale(img).to(dtype), dim=(-3, -2, -1), keepdim=True)
+    else:
+        mean = torch.mean(img.to(dtype), dim=(-3, -2, -1), keepdim=True)
+
+    return _blend(img, mean, contrast_factor)
+
+
+def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
+    if not (-0.5 <= hue_factor <= 0.5):
+        raise ValueError(f"hue_factor ({hue_factor}) is not in [-0.5, 0.5].")
+
+    if not (isinstance(img, torch.Tensor)):
+        raise TypeError("Input img should be Tensor image")
+
+    _assert_image_tensor(img)
+
+    _assert_channels(img, [1, 3])
+    if get_dimensions(img)[0] == 1:  # Match PIL behaviour
+        return img
+
+    orig_dtype = img.dtype
+    img = convert_image_dtype(img, torch.float32)
+
+    img = _rgb2hsv(img)
+    h, s, v = img.unbind(dim=-3)
+    h = (h + hue_factor) % 1.0
+    img = torch.stack((h, s, v), dim=-3)
+    img_hue_adj = _hsv2rgb(img)
+
+    return convert_image_dtype(img_hue_adj, orig_dtype)
+
+
+def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor:
+    if saturation_factor < 0:
+        raise ValueError(f"saturation_factor ({saturation_factor}) is not non-negative.")
+
+    _assert_image_tensor(img)
+
+    _assert_channels(img, [1, 3])
+
+    if get_dimensions(img)[0] == 1:  # Match PIL behaviour
+        return img
+
+    return _blend(img, rgb_to_grayscale(img), saturation_factor)
+
+
+def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
+    if not isinstance(img, torch.Tensor):
+        raise TypeError("Input img should be a Tensor.")
+
+    _assert_channels(img, [1, 3])
+
+    if gamma < 0:
+        raise ValueError("Gamma should be a non-negative real number")
+
+    result = img
+    dtype = img.dtype
+    if not torch.is_floating_point(img):
+        result = convert_image_dtype(result, torch.float32)
+
+    result = (gain * result**gamma).clamp(0, 1)
+
+    result = convert_image_dtype(result, dtype)
+    return result
+
+
+def _blend(img1: Tensor, img2: Tensor, ratio: float) -> Tensor:
+    ratio = float(ratio)
+    bound = _max_value(img1.dtype)
+    return (ratio * img1 + (1.0 - ratio) * img2).clamp(0, bound).to(img1.dtype)
+
+
+def _rgb2hsv(img: Tensor) -> Tensor:
+    r, g, b = img.unbind(dim=-3)
+
+    # Implementation is based on https://github.com/python-pillow/Pillow/blob/4174d4267616897df3746d315d5a2d0f82c656ee/
+    # src/libImaging/Convert.c#L330
+    maxc = torch.max(img, dim=-3).values
+    minc = torch.min(img, dim=-3).values
+
+    # The algorithm erases S and H channel where `maxc = minc`. This avoids NaN
+    # from happening in the results, because
+    #   + S channel has division by `maxc`, which is zero only if `maxc = minc`
+    #   + H channel has division by `(maxc - minc)`.
+    #
+    # Instead of overwriting NaN afterwards, we just prevent it from occurring, so
+    # we don't need to deal with it in case we save the NaN in a buffer in
+    # backprop, if it is ever supported, but it doesn't hurt to do so.
+    eqc = maxc == minc
+
+    cr = maxc - minc
+    # Since `eqc => cr = 0`, replacing denominator with 1 when `eqc` is fine.
+    ones = torch.ones_like(maxc)
+    s = cr / torch.where(eqc, ones, maxc)
+    # Note that `eqc => maxc = minc = r = g = b`. So the following calculation
+    # of `h` would reduce to `bc - gc + 2 + rc - bc + 4 + rc - bc = 6` so it
+    # would not matter what values `rc`, `gc`, and `bc` have here, and thus
+    # replacing denominator with 1 when `eqc` is fine.
+    cr_divisor = torch.where(eqc, ones, cr)
+    rc = (maxc - r) / cr_divisor
+    gc = (maxc - g) / cr_divisor
+    bc = (maxc - b) / cr_divisor
+
+    hr = (maxc == r) * (bc - gc)
+    hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc)
+    hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc)
+    h = hr + hg + hb
+    h = torch.fmod((h / 6.0 + 1.0), 1.0)
+    return torch.stack((h, s, maxc), dim=-3)
+
+
+def _hsv2rgb(img: Tensor) -> Tensor:
+    h, s, v = img.unbind(dim=-3)
+    i = torch.floor(h * 6.0)
+    f = (h * 6.0) - i
+    i = i.to(dtype=torch.int32)
+
+    p = torch.clamp((v * (1.0 - s)), 0.0, 1.0)
+    q = torch.clamp((v * (1.0 - s * f)), 0.0, 1.0)
+    t = torch.clamp((v * (1.0 - s * (1.0 - f))), 0.0, 1.0)
+    i = i % 6
+
+    mask = i.unsqueeze(dim=-3) == torch.arange(6, device=i.device).view(-1, 1, 1)
+
+    a1 = torch.stack((v, q, p, p, t, v), dim=-3)
+    a2 = torch.stack((t, v, v, q, p, p), dim=-3)
+    a3 = torch.stack((p, p, t, v, v, q), dim=-3)
+    a4 = torch.stack((a1, a2, a3), dim=-4)
+
+    return torch.einsum("...ijk, ...xijk -> ...xjk", mask.to(dtype=img.dtype), a4)
+
+
+def _pad_symmetric(img: Tensor, padding: List[int]) -> Tensor:
+    # padding is left, right, top, bottom
+
+    # crop if needed
+    if padding[0] < 0 or padding[1] < 0 or padding[2] < 0 or padding[3] < 0:
+        neg_min_padding = [-min(x, 0) for x in padding]
+        crop_left, crop_right, crop_top, crop_bottom = neg_min_padding
+        img = img[..., crop_top : img.shape[-2] - crop_bottom, crop_left : img.shape[-1] - crop_right]
+        padding = [max(x, 0) for x in padding]
+
+    in_sizes = img.size()
+
+    _x_indices = [i for i in range(in_sizes[-1])]  # [0, 1, 2, 3, ...]
+    left_indices = [i for i in range(padding[0] - 1, -1, -1)]  # e.g. [3, 2, 1, 0]
+    right_indices = [-(i + 1) for i in range(padding[1])]  # e.g. [-1, -2, -3]
+    x_indices = torch.tensor(left_indices + _x_indices + right_indices, device=img.device)
+
+    _y_indices = [i for i in range(in_sizes[-2])]
+    top_indices = [i for i in range(padding[2] - 1, -1, -1)]
+    bottom_indices = [-(i + 1) for i in range(padding[3])]
+    y_indices = torch.tensor(top_indices + _y_indices + bottom_indices, device=img.device)
+
+    ndim = img.ndim
+    if ndim == 3:
+        return img[:, y_indices[:, None], x_indices[None, :]]
+    elif ndim == 4:
+        return img[:, :, y_indices[:, None], x_indices[None, :]]
+    else:
+        raise RuntimeError("Symmetric padding of N-D tensors are not supported yet")
+
+
+def _parse_pad_padding(padding: Union[int, List[int]]) -> List[int]:
+    if isinstance(padding, int):
+        if torch.jit.is_scripting():
+            # This maybe unreachable
+            raise ValueError("padding can't be an int while torchscripting, set it as a list [value, ]")
+        pad_left = pad_right = pad_top = pad_bottom = padding
+    elif len(padding) == 1:
+        pad_left = pad_right = pad_top = pad_bottom = padding[0]
+    elif len(padding) == 2:
+        pad_left = pad_right = padding[0]
+        pad_top = pad_bottom = padding[1]
+    else:
+        pad_left = padding[0]
+        pad_top = padding[1]
+        pad_right = padding[2]
+        pad_bottom = padding[3]
+
+    return [pad_left, pad_right, pad_top, pad_bottom]
+
+
+def pad(
+    img: Tensor, padding: Union[int, List[int]], fill: Optional[Union[int, float]] = 0, padding_mode: str = "constant"
+) -> Tensor:
+    _assert_image_tensor(img)
+
+    if fill is None:
+        fill = 0
+
+    if not isinstance(padding, (int, tuple, list)):
+        raise TypeError("Got inappropriate padding arg")
+    if not isinstance(fill, (int, float)):
+        raise TypeError("Got inappropriate fill arg")
+    if not isinstance(padding_mode, str):
+        raise TypeError("Got inappropriate padding_mode arg")
+
+    if isinstance(padding, tuple):
+        padding = list(padding)
+
+    if isinstance(padding, list):
+        # TODO: Jit is failing on loading this op when scripted and saved
+        # https://github.com/pytorch/pytorch/issues/81100
+        if len(padding) not in [1, 2, 4]:
+            raise ValueError(
+                f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple"
+            )
+
+    if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
+        raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
+
+    p = _parse_pad_padding(padding)
+
+    if padding_mode == "edge":
+        # remap padding_mode str
+        padding_mode = "replicate"
+    elif padding_mode == "symmetric":
+        # route to another implementation
+        return _pad_symmetric(img, p)
+
+    need_squeeze = False
+    if img.ndim < 4:
+        img = img.unsqueeze(dim=0)
+        need_squeeze = True
+
+    out_dtype = img.dtype
+    need_cast = False
+    if (padding_mode != "constant") and img.dtype not in (torch.float32, torch.float64):
+        # Here we temporarily cast input tensor to float
+        # until pytorch issue is resolved :
+        # https://github.com/pytorch/pytorch/issues/40763
+        need_cast = True
+        img = img.to(torch.float32)
+
+    if padding_mode in ("reflect", "replicate"):
+        img = torch_pad(img, p, mode=padding_mode)
+    else:
+        img = torch_pad(img, p, mode=padding_mode, value=float(fill))
+
+    if need_squeeze:
+        img = img.squeeze(dim=0)
+
+    if need_cast:
+        img = img.to(out_dtype)
+
+    return img
+
+
+def resize(
+    img: Tensor,
+    size: List[int],
+    interpolation: str = "bilinear",
+    antialias: Optional[bool] = True,
+) -> Tensor:
+    _assert_image_tensor(img)
+
+    if isinstance(size, tuple):
+        size = list(size)
+
+    if antialias is None:
+        antialias = False
+
+    if antialias and interpolation not in ["bilinear", "bicubic"]:
+        # We manually set it to False to avoid an error downstream in interpolate()
+        # This behaviour is documented: the parameter is irrelevant for modes
+        # that are not bilinear or bicubic. We used to raise an error here, but
+        # now we don't as True is the default.
+        antialias = False
+
+    img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [torch.float32, torch.float64])
+
+    # Define align_corners to avoid warnings
+    align_corners = False if interpolation in ["bilinear", "bicubic"] else None
+
+    img = interpolate(img, size=size, mode=interpolation, align_corners=align_corners, antialias=antialias)
+
+    if interpolation == "bicubic" and out_dtype == torch.uint8:
+        img = img.clamp(min=0, max=255)
+
+    img = _cast_squeeze_out(img, need_cast=need_cast, need_squeeze=need_squeeze, out_dtype=out_dtype)
+
+    return img
+
+
+def _assert_grid_transform_inputs(
+    img: Tensor,
+    matrix: Optional[List[float]],
+    interpolation: str,
+    fill: Optional[Union[int, float, List[float]]],
+    supported_interpolation_modes: List[str],
+    coeffs: Optional[List[float]] = None,
+) -> None:
+
+    if not (isinstance(img, torch.Tensor)):
+        raise TypeError("Input img should be Tensor")
+
+    _assert_image_tensor(img)
+
+    if matrix is not None and not isinstance(matrix, list):
+        raise TypeError("Argument matrix should be a list")
+
+    if matrix is not None and len(matrix) != 6:
+        raise ValueError("Argument matrix should have 6 float values")
+
+    if coeffs is not None and len(coeffs) != 8:
+        raise ValueError("Argument coeffs should have 8 float values")
+
+    if fill is not None and not isinstance(fill, (int, float, tuple, list)):
+        warnings.warn("Argument fill should be either int, float, tuple or list")
+
+    # Check fill
+    num_channels = get_dimensions(img)[0]
+    if fill is not None and isinstance(fill, (tuple, list)) and len(fill) > 1 and len(fill) != num_channels:
+        msg = (
+            "The number of elements in 'fill' cannot broadcast to match the number of "
+            "channels of the image ({} != {})"
+        )
+        raise ValueError(msg.format(len(fill), num_channels))
+
+    if interpolation not in supported_interpolation_modes:
+        raise ValueError(f"Interpolation mode '{interpolation}' is unsupported with Tensor input")
+
+
+def _cast_squeeze_in(img: Tensor, req_dtypes: List[torch.dtype]) -> Tuple[Tensor, bool, bool, torch.dtype]:
+    need_squeeze = False
+    # make image NCHW
+    if img.ndim < 4:
+        img = img.unsqueeze(dim=0)
+        need_squeeze = True
+
+    out_dtype = img.dtype
+    need_cast = False
+    if out_dtype not in req_dtypes:
+        need_cast = True
+        req_dtype = req_dtypes[0]
+        img = img.to(req_dtype)
+    return img, need_cast, need_squeeze, out_dtype
+
+
+def _cast_squeeze_out(img: Tensor, need_cast: bool, need_squeeze: bool, out_dtype: torch.dtype) -> Tensor:
+    if need_squeeze:
+        img = img.squeeze(dim=0)
+
+    if need_cast:
+        if out_dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):
+            # it is better to round before cast
+            img = torch.round(img)
+        img = img.to(out_dtype)
+
+    return img
+
+
+def _apply_grid_transform(
+    img: Tensor, grid: Tensor, mode: str, fill: Optional[Union[int, float, List[float]]]
+) -> Tensor:
+
+    img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [grid.dtype])
+
+    if img.shape[0] > 1:
+        # Apply same grid to a batch of images
+        grid = grid.expand(img.shape[0], grid.shape[1], grid.shape[2], grid.shape[3])
+
+    # Append a dummy mask for customized fill colors, should be faster than grid_sample() twice
+    if fill is not None:
+        mask = torch.ones((img.shape[0], 1, img.shape[2], img.shape[3]), dtype=img.dtype, device=img.device)
+        img = torch.cat((img, mask), dim=1)
+
+    img = grid_sample(img, grid, mode=mode, padding_mode="zeros", align_corners=False)
+
+    # Fill with required color
+    if fill is not None:
+        mask = img[:, -1:, :, :]  # N * 1 * H * W
+        img = img[:, :-1, :, :]  # N * C * H * W
+        mask = mask.expand_as(img)
+        fill_list, len_fill = (fill, len(fill)) if isinstance(fill, (tuple, list)) else ([float(fill)], 1)
+        fill_img = torch.tensor(fill_list, dtype=img.dtype, device=img.device).view(1, len_fill, 1, 1).expand_as(img)
+        if mode == "nearest":
+            mask = mask < 0.5
+            img[mask] = fill_img[mask]
+        else:  # 'bilinear'
+            img = img * mask + (1.0 - mask) * fill_img
+
+    img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype)
+    return img
+
+
+def _gen_affine_grid(
+    theta: Tensor,
+    w: int,
+    h: int,
+    ow: int,
+    oh: int,
+) -> Tensor:
+    # https://github.com/pytorch/pytorch/blob/74b65c32be68b15dc7c9e8bb62459efbfbde33d8/aten/src/ATen/native/
+    # AffineGridGenerator.cpp#L18
+    # Difference with AffineGridGenerator is that:
+    # 1) we normalize grid values after applying theta
+    # 2) we can normalize by other image size, such that it covers "extend" option like in PIL.Image.rotate
+
+    d = 0.5
+    base_grid = torch.empty(1, oh, ow, 3, dtype=theta.dtype, device=theta.device)
+    x_grid = torch.linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, steps=ow, device=theta.device)
+    base_grid[..., 0].copy_(x_grid)
+    y_grid = torch.linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, steps=oh, device=theta.device).unsqueeze_(-1)
+    base_grid[..., 1].copy_(y_grid)
+    base_grid[..., 2].fill_(1)
+
+    rescaled_theta = theta.transpose(1, 2) / torch.tensor([0.5 * w, 0.5 * h], dtype=theta.dtype, device=theta.device)
+    output_grid = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta)
+    return output_grid.view(1, oh, ow, 2)
+
+
+def affine(
+    img: Tensor,
+    matrix: List[float],
+    interpolation: str = "nearest",
+    fill: Optional[Union[int, float, List[float]]] = None,
+) -> Tensor:
+    _assert_grid_transform_inputs(img, matrix, interpolation, fill, ["nearest", "bilinear"])
+
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+    theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3)
+    shape = img.shape
+    # grid will be generated on the same device as theta and img
+    grid = _gen_affine_grid(theta, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2])
+    return _apply_grid_transform(img, grid, interpolation, fill=fill)
+
+
+def _compute_affine_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]:
+
+    # Inspired of PIL implementation:
+    # https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054
+
+    # pts are Top-Left, Top-Right, Bottom-Left, Bottom-Right points.
+    # Points are shifted due to affine matrix torch convention about
+    # the center point. Center is (0, 0) for image center pivot point (w * 0.5, h * 0.5)
+    pts = torch.tensor(
+        [
+            [-0.5 * w, -0.5 * h, 1.0],
+            [-0.5 * w, 0.5 * h, 1.0],
+            [0.5 * w, 0.5 * h, 1.0],
+            [0.5 * w, -0.5 * h, 1.0],
+        ]
+    )
+    theta = torch.tensor(matrix, dtype=torch.float).view(2, 3)
+    new_pts = torch.matmul(pts, theta.T)
+    min_vals, _ = new_pts.min(dim=0)
+    max_vals, _ = new_pts.max(dim=0)
+
+    # shift points to [0, w] and [0, h] interval to match PIL results
+    min_vals += torch.tensor((w * 0.5, h * 0.5))
+    max_vals += torch.tensor((w * 0.5, h * 0.5))
+
+    # Truncate precision to 1e-4 to avoid ceil of Xe-15 to 1.0
+    tol = 1e-4
+    cmax = torch.ceil((max_vals / tol).trunc_() * tol)
+    cmin = torch.floor((min_vals / tol).trunc_() * tol)
+    size = cmax - cmin
+    return int(size[0]), int(size[1])  # w, h
+
+
+def rotate(
+    img: Tensor,
+    matrix: List[float],
+    interpolation: str = "nearest",
+    expand: bool = False,
+    fill: Optional[Union[int, float, List[float]]] = None,
+) -> Tensor:
+    _assert_grid_transform_inputs(img, matrix, interpolation, fill, ["nearest", "bilinear"])
+    w, h = img.shape[-1], img.shape[-2]
+    ow, oh = _compute_affine_output_size(matrix, w, h) if expand else (w, h)
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+    theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3)
+    # grid will be generated on the same device as theta and img
+    grid = _gen_affine_grid(theta, w=w, h=h, ow=ow, oh=oh)
+
+    return _apply_grid_transform(img, grid, interpolation, fill=fill)
+
+
+def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype, device: torch.device) -> Tensor:
+    # https://github.com/python-pillow/Pillow/blob/4634eafe3c695a014267eefdce830b4a825beed7/
+    # src/libImaging/Geometry.c#L394
+
+    #
+    # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1)
+    # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1)
+    #
+    theta1 = torch.tensor(
+        [[[coeffs[0], coeffs[1], coeffs[2]], [coeffs[3], coeffs[4], coeffs[5]]]], dtype=dtype, device=device
+    )
+    theta2 = torch.tensor([[[coeffs[6], coeffs[7], 1.0], [coeffs[6], coeffs[7], 1.0]]], dtype=dtype, device=device)
+
+    d = 0.5
+    base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device)
+    x_grid = torch.linspace(d, ow * 1.0 + d - 1.0, steps=ow, device=device)
+    base_grid[..., 0].copy_(x_grid)
+    y_grid = torch.linspace(d, oh * 1.0 + d - 1.0, steps=oh, device=device).unsqueeze_(-1)
+    base_grid[..., 1].copy_(y_grid)
+    base_grid[..., 2].fill_(1)
+
+    rescaled_theta1 = theta1.transpose(1, 2) / torch.tensor([0.5 * ow, 0.5 * oh], dtype=dtype, device=device)
+    output_grid1 = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta1)
+    output_grid2 = base_grid.view(1, oh * ow, 3).bmm(theta2.transpose(1, 2))
+
+    output_grid = output_grid1 / output_grid2 - 1.0
+    return output_grid.view(1, oh, ow, 2)
+
+
+def perspective(
+    img: Tensor,
+    perspective_coeffs: List[float],
+    interpolation: str = "bilinear",
+    fill: Optional[Union[int, float, List[float]]] = None,
+) -> Tensor:
+    if not (isinstance(img, torch.Tensor)):
+        raise TypeError("Input img should be Tensor.")
+
+    _assert_image_tensor(img)
+
+    _assert_grid_transform_inputs(
+        img,
+        matrix=None,
+        interpolation=interpolation,
+        fill=fill,
+        supported_interpolation_modes=["nearest", "bilinear"],
+        coeffs=perspective_coeffs,
+    )
+
+    ow, oh = img.shape[-1], img.shape[-2]
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+    grid = _perspective_grid(perspective_coeffs, ow=ow, oh=oh, dtype=dtype, device=img.device)
+    return _apply_grid_transform(img, grid, interpolation, fill=fill)
+
+
+def _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Tensor:
+    ksize_half = (kernel_size - 1) * 0.5
+
+    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
+    pdf = torch.exp(-0.5 * (x / sigma).pow(2))
+    kernel1d = pdf / pdf.sum()
+
+    return kernel1d
+
+
+def _get_gaussian_kernel2d(
+    kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device
+) -> Tensor:
+    kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0]).to(device, dtype=dtype)
+    kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1]).to(device, dtype=dtype)
+    kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :])
+    return kernel2d
+
+
+def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Tensor:
+    if not (isinstance(img, torch.Tensor)):
+        raise TypeError(f"img should be Tensor. Got {type(img)}")
+
+    _assert_image_tensor(img)
+
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+    kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype, device=img.device)
+    kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1])
+
+    img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype])
+
+    # padding = (left, right, top, bottom)
+    padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2]
+    img = torch_pad(img, padding, mode="reflect")
+    img = conv2d(img, kernel, groups=img.shape[-3])
+
+    img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype)
+    return img
+
+
+def invert(img: Tensor) -> Tensor:
+
+    _assert_image_tensor(img)
+
+    if img.ndim < 3:
+        raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
+
+    _assert_channels(img, [1, 3])
+
+    return _max_value(img.dtype) - img
+
+
+def posterize(img: Tensor, bits: int) -> Tensor:
+
+    _assert_image_tensor(img)
+
+    if img.ndim < 3:
+        raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
+    if img.dtype != torch.uint8:
+        raise TypeError(f"Only torch.uint8 image tensors are supported, but found {img.dtype}")
+
+    _assert_channels(img, [1, 3])
+    mask = -int(2 ** (8 - bits))  # JIT-friendly for: ~(2 ** (8 - bits) - 1)
+    return img & mask
+
+
+def solarize(img: Tensor, threshold: float) -> Tensor:
+
+    _assert_image_tensor(img)
+
+    if img.ndim < 3:
+        raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
+
+    _assert_channels(img, [1, 3])
+
+    if threshold > _max_value(img.dtype):
+        raise TypeError("Threshold should be less than bound of img.")
+
+    inverted_img = invert(img)
+    return torch.where(img >= threshold, inverted_img, img)
+
+
+def _blurred_degenerate_image(img: Tensor) -> Tensor:
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+
+    kernel = torch.ones((3, 3), dtype=dtype, device=img.device)
+    kernel[1, 1] = 5.0
+    kernel /= kernel.sum()
+    kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1])
+
+    result_tmp, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype])
+    result_tmp = conv2d(result_tmp, kernel, groups=result_tmp.shape[-3])
+    result_tmp = _cast_squeeze_out(result_tmp, need_cast, need_squeeze, out_dtype)
+
+    result = img.clone()
+    result[..., 1:-1, 1:-1] = result_tmp
+
+    return result
+
+
+def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:
+    if sharpness_factor < 0:
+        raise ValueError(f"sharpness_factor ({sharpness_factor}) is not non-negative.")
+
+    _assert_image_tensor(img)
+
+    _assert_channels(img, [1, 3])
+
+    if img.size(-1) <= 2 or img.size(-2) <= 2:
+        return img
+
+    return _blend(img, _blurred_degenerate_image(img), sharpness_factor)
+
+
+def autocontrast(img: Tensor) -> Tensor:
+
+    _assert_image_tensor(img)
+
+    if img.ndim < 3:
+        raise TypeError(f"Input image tensor should have at least 3 dimensions, but found {img.ndim}")
+
+    _assert_channels(img, [1, 3])
+
+    bound = _max_value(img.dtype)
+    dtype = img.dtype if torch.is_floating_point(img) else torch.float32
+
+    minimum = img.amin(dim=(-2, -1), keepdim=True).to(dtype)
+    maximum = img.amax(dim=(-2, -1), keepdim=True).to(dtype)
+    scale = bound / (maximum - minimum)
+    eq_idxs = torch.isfinite(scale).logical_not()
+    minimum[eq_idxs] = 0
+    scale[eq_idxs] = 1
+
+    return ((img - minimum) * scale).clamp(0, bound).to(img.dtype)
+
+
+def _scale_channel(img_chan: Tensor) -> Tensor:
+    # TODO: we should expect bincount to always be faster than histc, but this
+    # isn't always the case. Once
+    # https://github.com/pytorch/pytorch/issues/53194 is fixed, remove the if
+    # block and only use bincount.
+    if img_chan.is_cuda:
+        hist = torch.histc(img_chan.to(torch.float32), bins=256, min=0, max=255)
+    else:
+        hist = torch.bincount(img_chan.reshape(-1), minlength=256)
+
+    nonzero_hist = hist[hist != 0]
+    step = torch.div(nonzero_hist[:-1].sum(), 255, rounding_mode="floor")
+    if step == 0:
+        return img_chan
+
+    lut = torch.div(torch.cumsum(hist, 0) + torch.div(step, 2, rounding_mode="floor"), step, rounding_mode="floor")
+    lut = torch.nn.functional.pad(lut, [1, 0])[:-1].clamp(0, 255)
+
+    return lut[img_chan.to(torch.int64)].to(torch.uint8)
+
+
+def _equalize_single_image(img: Tensor) -> Tensor:
+    return torch.stack([_scale_channel(img[c]) for c in range(img.size(0))])
+
+
+def equalize(img: Tensor) -> Tensor:
+
+    _assert_image_tensor(img)
+
+    if not (3 <= img.ndim <= 4):
+        raise TypeError(f"Input image tensor should have 3 or 4 dimensions, but found {img.ndim}")
+    if img.dtype != torch.uint8:
+        raise TypeError(f"Only torch.uint8 image tensors are supported, but found {img.dtype}")
+
+    _assert_channels(img, [1, 3])
+
+    if img.ndim == 3:
+        return _equalize_single_image(img)
+
+    return torch.stack([_equalize_single_image(x) for x in img])
+
+
+def normalize(tensor: Tensor, mean: List[float], std: List[float], inplace: bool = False) -> Tensor:
+    _assert_image_tensor(tensor)
+
+    if not tensor.is_floating_point():
+        raise TypeError(f"Input tensor should be a float tensor. Got {tensor.dtype}.")
+
+    if tensor.ndim < 3:
+        raise ValueError(
+            f"Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = {tensor.size()}"
+        )
+
+    if not inplace:
+        tensor = tensor.clone()
+
+    dtype = tensor.dtype
+    mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
+    std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
+    if (std == 0).any():
+        raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
+    if mean.ndim == 1:
+        mean = mean.view(-1, 1, 1)
+    if std.ndim == 1:
+        std = std.view(-1, 1, 1)
+    return tensor.sub_(mean).div_(std)
+
+
+def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool = False) -> Tensor:
+    _assert_image_tensor(img)
+
+    if not inplace:
+        img = img.clone()
+
+    img[..., i : i + h, j : j + w] = v
+    return img
+
+
+def _create_identity_grid(size: List[int]) -> Tensor:
+    hw_space = [torch.linspace((-s + 1) / s, (s - 1) / s, s) for s in size]
+    grid_y, grid_x = torch.meshgrid(hw_space, indexing="ij")
+    return torch.stack([grid_x, grid_y], -1).unsqueeze(0)  # 1 x H x W x 2
+
+
+def elastic_transform(
+    img: Tensor,
+    displacement: Tensor,
+    interpolation: str = "bilinear",
+    fill: Optional[Union[int, float, List[float]]] = None,
+) -> Tensor:
+
+    if not (isinstance(img, torch.Tensor)):
+        raise TypeError(f"img should be Tensor. Got {type(img)}")
+
+    size = list(img.shape[-2:])
+    displacement = displacement.to(img.device)
+
+    identity_grid = _create_identity_grid(size)
+    grid = identity_grid.to(img.device) + displacement
+    return _apply_grid_transform(img, grid, interpolation, fill)

shared/package-files/06/06b040956a82a017f58a62be3255645dd5d545658338157d2730bc543b8aa64a/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 cuda {
+
+TORCH_API at::Tensor _dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total);
+
+} // namespace cuda
+} // namespace at

shared/package-files/06/06b381caddd5de9c6cedd3e0e350791f7b89745b22a86f514ef65e88772e12aa/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 compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at

shared/package-files/06/06b39b66efb6f772121ab44293451f5d2f1ce5b7ce8c198808f967c889db9977/blob

@@ -0,0 +1,442 @@
+"""
+Created on Sat May 19 15:53:21 2018
+
+Author: Josef Perktold
+License: BSD-3
+"""
+
+from collections import defaultdict
+
+import numpy as np
+
+from statsmodels.base._penalties import SCADSmoothed
+
+
+class ScreeningResults:
+    """Results for Variable Screening
+
+    Note: Indices except for exog_idx and in the iterated case also
+    idx_nonzero_batches are based on the combined [exog_keep, exog] array.
+
+    Attributes
+    ----------
+    results_final : instance
+        Results instance returned by the final fit of the penalized model, i.e.
+        after trimming exog with params below trimming threshold.
+    results_pen : results instance
+        Results instance of the penalized model before trimming. This includes
+        variables from the last forward selection.
+    idx_nonzero : ndarray
+        Index of exog columns in the final selection including exog_keep.
+    idx_exog : ndarray
+        Index of exog columns in the final selection for exog candidates, i.e.
+        without exog_keep.
+    idx_excl : ndarray
+        Index of excluded exog based on combined [exog_keep, exog] array. This
+        is the complement of idx_nonzero.
+    converged : bool
+        True if the iteration has converged and stopped before maxiter has been
+        reached. False if maxiter has been reached.
+    iterations : int
+        Number of iterations in the screening process. Each iteration consists
+        of a forward selection step and a trimming step.
+    history : dict of lists
+        Results collected for each iteration during the screening process.
+        Keys are 'idx_nonzero', 'keep', 'params_keep' and 'idx_added', each
+        holding a list with one entry appended per iteration.
+    screener : instance of VariableScreening
+        The VariableScreening instance that produced these results.
+
+    The ScreeningResults returned by `screen_exog_iterator` has additional
+    attributes:
+
+    idx_nonzero_batches : ndarray, 2-D
+        Two-dimensional array with batch index in the first column and variable
+        index within batch in the second column. They can be used jointly as
+        index for the data in the exog_iterator.
+    exog_final_names : list of str
+        'var<bidx>_<idx>' where `bidx` is the batch index and `idx` is the
+        index of the selected column within batch `bidx`.
+    history_batches : dict of lists
+        This provides information about the selected variables within each
+        batch during the first round screening.
+        'idx_nonzero' is based on the array that includes exog_keep, while
+        'idx_exog' is the index based on the exog of the batch.
+    """
+    def __init__(self, screener, **kwds):
+        self.screener = screener
+        self.__dict__.update(**kwds)
+
+
+class VariableScreening:
+    """Ultra-high, conditional sure independence screening
+
+    This is an adjusted version of Fan's sure independence screening.
+
+    Parameters
+    ----------
+    model : instance of penalizing model
+        Examples: GLMPenalized, PoissonPenalized and LogitPenalized.
+        The attributes of the model instance `pen_weight` and `penal` will be
+        ignored.
+    pen_weight : float, optional
+        Penalization weight use in SCAD penalized MLE.
+    use_weights : bool, optional
+        If True, penalty weights are used so that `exog_keep` is not
+        penalized (weight 0) while candidate exog have weight 1. If False,
+        all penalty weights are set equally.
+    k_add : int, optional
+        Number of exog to add during expansion or forward selection.
+        See Notes section for tie handling.
+    k_max_add : int, optional
+        Maximum number of variables to include during variable addition, i.e.
+        forward selection. Default is 30.
+    threshold_trim : float, optional
+        Threshold for trimming parameters to zero, default is 1e-4.
+    k_max_included : int, optional
+        Maximum total number of variables to include in model.
+    ranking_attr : str, optional
+        This determines the result attribute or model method that is used for
+        the ranking of exog to include. The availability of attributes depends
+        on the model.
+        Default is 'resid_pearson', 'model.score_factor' can be used in GLM.
+    ranking_project : bool, optional
+        If ranking_project is True, then the exog candidates for inclusion are
+        first projected on the already included exog before the computation
+        of the ranking measure. This brings the ranking measure closer to
+        the statistic of a score test for variable addition.
+
+    Notes
+    -----
+    Status: experimental, tested only on a limited set of models and
+    with a limited set of model options.
+
+    Tie handling: If there are ties at the decision threshold, then all those
+    tied exog columns are treated in the same way. During forward selection
+    all exog columns with the same boundary value are included. During
+    elimination, the tied columns are not dropped. Consequently, if ties are
+    present, then the number of included exog can be larger than specified
+    by k_add, k_max_add and k_max_included.
+
+    The screening algorithm works similar to step wise regression. Each
+    iteration of the screening algorithm includes a forward selection step
+    where variables are added to the model, and a backwards selection step
+    where variables are removed. In contrast to step wise regression, we add
+    a fixed number of variables at each forward selection step. The
+    backwards selection step is based on SCAD penalized estimation and
+    trimming of variables with estimated coefficients below a threshold.
+    The tuning parameters can be used to adjust the number of variables to add
+    and to include depending on the size of the dataset.
+
+    There is currently no automatic tuning parameter selection. Candidate
+    explanatory variables should be standardized or should be on a similar
+    scale because penalization and trimming are based on the absolute values
+    of the parameters.
+
+
+    TODOs and current limitations:
+
+    freq_weights are not supported in this. Candidate ranking uses
+    moment condition with resid_pearson or others without freq_weights.
+    pearson_resid: GLM resid_pearson does not include freq_weights.
+
+    variable names: do we keep track of those? currently made-up names
+
+    currently only supports numpy arrays, no exog type check or conversion
+
+    currently only single columns are selected, no terms (multi column exog)
+    """
+
+    def __init__(self, model, pen_weight=None, use_weights=True, k_add=30,
+                 k_max_add=30, threshold_trim=1e-4, k_max_included=20,
+                 ranking_attr="resid_pearson", ranking_project=True):
+
+        self.model = model
+        self.model_class = model.__class__
+        self.init_kwds = model._get_init_kwds()
+        # pen_weight and penal are explicitly included
+        # TODO: check what we want to do here
+        self.init_kwds.pop("pen_weight", None)
+        self.init_kwds.pop("penal", None)
+
+        self.endog = model.endog
+        self.exog_keep = model.exog
+        self.k_keep = model.exog.shape[1]
+        self.nobs = len(self.endog)
+        self.penal = self._get_penal()
+
+        if pen_weight is not None:
+            self.pen_weight = pen_weight
+        else:
+            self.pen_weight = self.nobs * 10
+
+        # option for screening algorithm
+        self.use_weights = use_weights
+        self.k_add = k_add
+        self.k_max_add = k_max_add
+        self.threshold_trim = threshold_trim
+        self.k_max_included = k_max_included
+        self.ranking_attr = ranking_attr
+        self.ranking_project = ranking_project
+
+    def _get_penal(self, weights=None):
+        """Create new Penalty instance"""
+        return SCADSmoothed(0.1, c0=0.0001, weights=weights)
+
+    def ranking_measure(self, res_pen, exog, keep=None):
+        """Compute measure for ranking exog candidates for inclusion"""
+        endog = self.endog
+
+        if self.ranking_project:
+            assert res_pen.model.exog.shape[1] == len(keep)
+            ex_incl = res_pen.model.exog[:, keep]
+            exog = exog - ex_incl.dot(np.linalg.pinv(ex_incl).dot(exog))
+
+        if self.ranking_attr == "predicted_poisson":
+            # I keep this for more experiments
+
+            # TODO: does it really help to change/trim params
+            # we are not reestimating with trimmed model
+            p = res_pen.params.copy()
+            if keep is not None:
+                p[~keep] = 0
+            predicted = res_pen.model.predict(p)
+            # this is currently hardcoded for Poisson
+            resid_factor = (endog - predicted) / np.sqrt(predicted)
+        elif self.ranking_attr[:6] == "model.":
+            # use model method, this is intended for score_factor
+            attr = self.ranking_attr.split(".")[1]
+            resid_factor = getattr(res_pen.model, attr)(res_pen.params)
+            if resid_factor.ndim == 2:
+                # for score_factor when extra params are in model
+                resid_factor = resid_factor[:, 0]
+            mom_cond = np.abs(resid_factor.dot(exog))**2
+        else:
+            # use results attribute
+            resid_factor = getattr(res_pen, self.ranking_attr)
+            mom_cond = np.abs(resid_factor.dot(exog))**2
+        return mom_cond
+
+    def screen_exog(self, exog, endog=None, maxiter=100, method="bfgs",
+                    disp=False, fit_kwds=None):
+        """Screen and select variables (columns) in exog
+
+        Parameters
+        ----------
+        exog : ndarray
+            Candidate explanatory variables that are screened for inclusion in
+            the model.
+        endog : ndarray, optional
+            Use a new endog in the screening model.
+            This is not tested yet, and might not work correctly.
+        maxiter : int, optional
+            Number of screening iterations.
+        method : str, optional
+            Optimization method to use in fit, needs to be only of the gradient
+            optimizers.
+        disp : bool, optional
+            Display option for fit during optimization.
+        fit_kwds : dict-like, optional
+            Additional keyword arguments passed to the model's `fit` method.
+
+        Returns
+        -------
+        res_screen : instance of ScreeningResults
+            The attribute `results_final` contains the results instance
+            with the final model selection.
+            `idx_nonzero` contains the index of the selected exog in the full
+            exog, combined exog that are always kept plus exog_candidates.
+            See ScreeningResults for a full description.
+        """
+        model_class = self.model_class
+        if endog is None:
+            # allow a different endog than used in model
+            endog = self.endog
+        x0 = self.exog_keep
+        k_keep = self.k_keep
+        x1 = exog
+        k_current = x0.shape[1]
+        # TODO: remove the need for x, use x1 separately from x0
+        # needs change to idx to be based on x1 (candidate variables)
+        x = np.column_stack((x0, x1))
+        nobs, k_vars = x.shape
+        fkwds = fit_kwds if fit_kwds is not None else {}
+        fit_kwds = {"maxiter": 200, "disp": False}
+        fit_kwds.update(fkwds)
+
+        history = defaultdict(list)
+        idx_nonzero = np.arange(k_keep, dtype=int)
+        keep = np.ones(k_keep, np.bool_)
+        idx_excl = np.arange(k_keep, k_vars)
+        mod_pen = model_class(endog, x0, **self.init_kwds)
+        # do not penalize initial estimate
+        mod_pen.pen_weight = 0
+        res_pen = mod_pen.fit(**fit_kwds)
+        start_params = res_pen.params
+        converged = False
+        idx_old = []
+        iterations = 0
+        for _ in range(maxiter):
+            iterations += 1
+            # candidates for inclusion in next iteration
+            x1 = x[:, idx_excl]
+            mom_cond = self.ranking_measure(res_pen, x1, keep=keep)
+            assert len(mom_cond) == len(idx_excl)
+            mcs = np.sort(mom_cond)[::-1]
+            idx_thr = min((self.k_max_add, k_current + self.k_add, len(mcs)))
+            threshold = mcs[idx_thr]
+            # indices of exog in current expansion model
+            idx = np.concatenate((idx_nonzero, idx_excl[mom_cond > threshold]))
+            start_params2 = np.zeros(len(idx))
+            start_params2[:len(start_params)] = start_params
+
+            if self.use_weights:
+                weights = np.ones(len(idx))
+                weights[:k_keep] = 0
+                # modify Penalty instance attached to self
+                # damgerous if res_pen is reused
+                self.penal.weights = weights
+            mod_pen = model_class(endog, x[:, idx], penal=self.penal,
+                                  pen_weight=self.pen_weight,
+                                  **self.init_kwds)
+
+            res_pen = mod_pen.fit(method=method,
+                                  start_params=start_params2,
+                                  warn_convergence=False, skip_hessian=True,
+                                  **fit_kwds)
+
+            keep = np.abs(res_pen.params) > self.threshold_trim
+            # use largest params to keep
+            if keep.sum() > self.k_max_included:
+                # TODO we can use now np.partition with partial sort
+                thresh_params = np.sort(np.abs(res_pen.params))[
+                                                        -self.k_max_included]
+                keep2 = np.abs(res_pen.params) > thresh_params
+                keep = np.logical_and(keep, keep2)
+
+            # Note: idx and keep are for current expansion model
+            # idx_nonzero has indices of selected variables in full exog
+            keep[:k_keep] = True  # always keep exog_keep
+            idx_nonzero = idx[keep]
+
+            if disp:
+                print(keep)
+                print(idx_nonzero)
+            # x0 is exog of currently selected model, not used in iteration
+            # x0 = x[:, idx_nonzero]
+            k_current = len(idx_nonzero)
+            start_params = res_pen.params[keep]
+
+            # use mask to get excluded indices
+            mask_excl = np.ones(k_vars, dtype=bool)
+            mask_excl[idx_nonzero] = False
+            idx_excl = np.nonzero(mask_excl)[0]
+            history["idx_nonzero"].append(idx_nonzero)
+            history["keep"].append(keep)
+            history["params_keep"].append(start_params)
+            history["idx_added"].append(idx)
+
+            if (len(idx_nonzero) == len(idx_old) and
+                    (idx_nonzero == idx_old).all()):
+                converged = True
+                break
+            idx_old = idx_nonzero
+
+        # final esimate
+        # check that we still have exog_keep
+        assert np.all(idx_nonzero[:k_keep] == np.arange(k_keep))
+        if self.use_weights:
+            weights = np.ones(len(idx_nonzero))
+            weights[:k_keep] = 0
+            # create new Penalty instance to avoide sharing attached penal
+            penal = self._get_penal(weights=weights)
+        else:
+            penal = self.penal
+        mod_final = model_class(endog, x[:, idx_nonzero],
+                                penal=penal,
+                                pen_weight=self.pen_weight,
+                                **self.init_kwds)
+
+        res_final = mod_final.fit(method=method,
+                                  start_params=start_params,
+                                  warn_convergence=False,
+                                  **fit_kwds)
+        # set exog_names for final model
+        xnames = [f"var{ii:4d}" for ii in idx_nonzero]
+        res_final.model.exog_names[k_keep:] = xnames[k_keep:]
+
+        res = ScreeningResults(self,
+                               results_pen=res_pen,
+                               results_final=res_final,
+                               idx_nonzero=idx_nonzero,
+                               idx_exog=idx_nonzero[k_keep:] - k_keep,
+                               idx_excl=idx_excl,
+                               history=history,
+                               converged=converged,
+                               iterations=iterations
+                               )
+        return res
+
+    def screen_exog_iterator(self, exog_iterator):
+        """
+        Batched version of screen exog
+
+        This screens variables in a two step process:
+
+        In the first step screen_exog is used on each element of the
+        exog_iterator, and the batch winners are collected.
+
+        In the second step all batch winners are combined into a new array
+        of exog candidates and `screen_exog` is used to select a final
+        model.
+
+        Parameters
+        ----------
+        exog_iterator : iterator over ndarrays
+            Batches of candidate explanatory variables that are screened
+            for inclusion in the model.
+
+        Returns
+        -------
+        res_screen_final : instance of ScreeningResults
+            This is the instance returned by the second round call to
+            `screen_exog`. Additional attributes are added to provide
+            more information about the batched selection process.
+            The index of final nonzero variables is
+            `idx_nonzero_batches` which is a 2-dimensional array with batch
+            index in the first column and variable index within batch in the
+            second column. They can be used jointly as index for the data
+            in the exog_iterator.
+            See ScreeningResults for a full description.
+        """
+        k_keep = self.k_keep
+        # res_batches = []
+        res_idx = []
+        exog_winner = []
+        exog_idx = []
+        for ex in exog_iterator:
+            res_screen = self.screen_exog(ex, maxiter=20)
+            # avoid storing res_screen, only for debugging
+            # res_batches.append(res_screen)
+            res_idx.append(res_screen.idx_nonzero)
+            exog_winner.append(ex[:, res_screen.idx_nonzero[k_keep:] - k_keep])
+            exog_idx.append(res_screen.idx_nonzero[k_keep:] - k_keep)
+
+        exog_winner = np.column_stack(exog_winner)
+        res_screen_final = self.screen_exog(exog_winner, maxiter=20)
+
+        exog_winner_names = [f"var{bidx:d}_{idx:d}"
+                             for bidx, batch in enumerate(exog_idx)
+                             for idx in batch]
+
+        idx_full = [(bidx, idx)
+                    for bidx, batch in enumerate(exog_idx)
+                    for idx in batch]
+        ex_final_idx = res_screen_final.idx_nonzero[k_keep:] - k_keep
+        final_names = np.array(exog_winner_names)[ex_final_idx]
+        res_screen_final.idx_nonzero_batches = np.array(idx_full)[ex_final_idx]
+        res_screen_final.exog_final_names = final_names
+        history = {"idx_nonzero": res_idx,
+                   "idx_exog": exog_idx}
+        res_screen_final.history_batches = history
+        return res_screen_final

shared/package-files/06/06b83b8b3586648dc1ad136c0ed25301883cc7e7d7f03b3286981416a15b3057/blob

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API clone {
+  using schema = at::Tensor (const at::Tensor &, c10::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::clone")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API clone_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::clone")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+}} // namespace at::_ops

shared/package-files/06/06b881b824f71e95d72af4ab865de4c35553e791b6d959a125caac61401cc350/blob

@@ -0,0 +1,442 @@
+"""Implementation of JSONEncoder
+"""
+import re
+
+try:
+    from _json import encode_basestring_ascii as c_encode_basestring_ascii
+except ImportError:
+    c_encode_basestring_ascii = None
+try:
+    from _json import encode_basestring as c_encode_basestring
+except ImportError:
+    c_encode_basestring = None
+try:
+    from _json import make_encoder as c_make_encoder
+except ImportError:
+    c_make_encoder = None
+
+ESCAPE = re.compile(r'[\x00-\x1f\\"\b\f\n\r\t]')
+ESCAPE_ASCII = re.compile(r'([\\"]|[^\ -~])')
+HAS_UTF8 = re.compile(b'[\x80-\xff]')
+ESCAPE_DCT = {
+    '\\': '\\\\',
+    '"': '\\"',
+    '\b': '\\b',
+    '\f': '\\f',
+    '\n': '\\n',
+    '\r': '\\r',
+    '\t': '\\t',
+}
+for i in range(0x20):
+    ESCAPE_DCT.setdefault(chr(i), '\\u{0:04x}'.format(i))
+    #ESCAPE_DCT.setdefault(chr(i), '\\u%04x' % (i,))
+
+INFINITY = float('inf')
+
+def py_encode_basestring(s):
+    """Return a JSON representation of a Python string
+
+    """
+    def replace(match):
+        return ESCAPE_DCT[match.group(0)]
+    return '"' + ESCAPE.sub(replace, s) + '"'
+
+
+encode_basestring = (c_encode_basestring or py_encode_basestring)
+
+
+def py_encode_basestring_ascii(s):
+    """Return an ASCII-only JSON representation of a Python string
+
+    """
+    def replace(match):
+        s = match.group(0)
+        try:
+            return ESCAPE_DCT[s]
+        except KeyError:
+            n = ord(s)
+            if n < 0x10000:
+                return '\\u{0:04x}'.format(n)
+                #return '\\u%04x' % (n,)
+            else:
+                # surrogate pair
+                n -= 0x10000
+                s1 = 0xd800 | ((n >> 10) & 0x3ff)
+                s2 = 0xdc00 | (n & 0x3ff)
+                return '\\u{0:04x}\\u{1:04x}'.format(s1, s2)
+    return '"' + ESCAPE_ASCII.sub(replace, s) + '"'
+
+
+encode_basestring_ascii = (
+    c_encode_basestring_ascii or py_encode_basestring_ascii)
+
+class JSONEncoder(object):
+    """Extensible JSON <https://json.org> encoder for Python data structures.
+
+    Supports the following objects and types by default:
+
+    +-------------------+---------------+
+    | Python            | JSON          |
+    +===================+===============+
+    | dict              | object        |
+    +-------------------+---------------+
+    | list, tuple       | array         |
+    +-------------------+---------------+
+    | str               | string        |
+    +-------------------+---------------+
+    | int, float        | number        |
+    +-------------------+---------------+
+    | True              | true          |
+    +-------------------+---------------+
+    | False             | false         |
+    +-------------------+---------------+
+    | None              | null          |
+    +-------------------+---------------+
+
+    To extend this to recognize other objects, subclass and implement a
+    ``.default()`` method with another method that returns a serializable
+    object for ``o`` if possible, otherwise it should call the superclass
+    implementation (to raise ``TypeError``).
+
+    """
+    item_separator = ', '
+    key_separator = ': '
+    def __init__(self, *, skipkeys=False, ensure_ascii=True,
+            check_circular=True, allow_nan=True, sort_keys=False,
+            indent=None, separators=None, default=None):
+        """Constructor for JSONEncoder, with sensible defaults.
+
+        If skipkeys is false, then it is a TypeError to attempt
+        encoding of keys that are not str, int, float or None.  If
+        skipkeys is True, such items are simply skipped.
+
+        If ensure_ascii is true, the output is guaranteed to be str
+        objects with all incoming non-ASCII characters escaped.  If
+        ensure_ascii is false, the output can contain non-ASCII characters.
+
+        If check_circular is true, then lists, dicts, and custom encoded
+        objects will be checked for circular references during encoding to
+        prevent an infinite recursion (which would cause an RecursionError).
+        Otherwise, no such check takes place.
+
+        If allow_nan is true, then NaN, Infinity, and -Infinity will be
+        encoded as such.  This behavior is not JSON specification compliant,
+        but is consistent with most JavaScript based encoders and decoders.
+        Otherwise, it will be a ValueError to encode such floats.
+
+        If sort_keys is true, then the output of dictionaries will be
+        sorted by key; this is useful for regression tests to ensure
+        that JSON serializations can be compared on a day-to-day basis.
+
+        If indent is a non-negative integer, then JSON array
+        elements and object members will be pretty-printed with that
+        indent level.  An indent level of 0 will only insert newlines.
+        None is the most compact representation.
+
+        If specified, separators should be an (item_separator, key_separator)
+        tuple.  The default is (', ', ': ') if *indent* is ``None`` and
+        (',', ': ') otherwise.  To get the most compact JSON representation,
+        you should specify (',', ':') to eliminate whitespace.
+
+        If specified, default is a function that gets called for objects
+        that can't otherwise be serialized.  It should return a JSON encodable
+        version of the object or raise a ``TypeError``.
+
+        """
+
+        self.skipkeys = skipkeys
+        self.ensure_ascii = ensure_ascii
+        self.check_circular = check_circular
+        self.allow_nan = allow_nan
+        self.sort_keys = sort_keys
+        self.indent = indent
+        if separators is not None:
+            self.item_separator, self.key_separator = separators
+        elif indent is not None:
+            self.item_separator = ','
+        if default is not None:
+            self.default = default
+
+    def default(self, o):
+        """Implement this method in a subclass such that it returns
+        a serializable object for ``o``, or calls the base implementation
+        (to raise a ``TypeError``).
+
+        For example, to support arbitrary iterators, you could
+        implement default like this::
+
+            def default(self, o):
+                try:
+                    iterable = iter(o)
+                except TypeError:
+                    pass
+                else:
+                    return list(iterable)
+                # Let the base class default method raise the TypeError
+                return JSONEncoder.default(self, o)
+
+        """
+        raise TypeError(f'Object of type {o.__class__.__name__} '
+                        f'is not JSON serializable')
+
+    def encode(self, o):
+        """Return a JSON string representation of a Python data structure.
+
+        >>> from json.encoder import JSONEncoder
+        >>> JSONEncoder().encode({"foo": ["bar", "baz"]})
+        '{"foo": ["bar", "baz"]}'
+
+        """
+        # This is for extremely simple cases and benchmarks.
+        if isinstance(o, str):
+            if self.ensure_ascii:
+                return encode_basestring_ascii(o)
+            else:
+                return encode_basestring(o)
+        # This doesn't pass the iterator directly to ''.join() because the
+        # exceptions aren't as detailed.  The list call should be roughly
+        # equivalent to the PySequence_Fast that ''.join() would do.
+        chunks = self.iterencode(o, _one_shot=True)
+        if not isinstance(chunks, (list, tuple)):
+            chunks = list(chunks)
+        return ''.join(chunks)
+
+    def iterencode(self, o, _one_shot=False):
+        """Encode the given object and yield each string
+        representation as available.
+
+        For example::
+
+            for chunk in JSONEncoder().iterencode(bigobject):
+                mysocket.write(chunk)
+
+        """
+        if self.check_circular:
+            markers = {}
+        else:
+            markers = None
+        if self.ensure_ascii:
+            _encoder = encode_basestring_ascii
+        else:
+            _encoder = encode_basestring
+
+        def floatstr(o, allow_nan=self.allow_nan,
+                _repr=float.__repr__, _inf=INFINITY, _neginf=-INFINITY):
+            # Check for specials.  Note that this type of test is processor
+            # and/or platform-specific, so do tests which don't depend on the
+            # internals.
+
+            if o != o:
+                text = 'NaN'
+            elif o == _inf:
+                text = 'Infinity'
+            elif o == _neginf:
+                text = '-Infinity'
+            else:
+                return _repr(o)
+
+            if not allow_nan:
+                raise ValueError(
+                    "Out of range float values are not JSON compliant: " +
+                    repr(o))
+
+            return text
+
+
+        if (_one_shot and c_make_encoder is not None
+                and self.indent is None):
+            _iterencode = c_make_encoder(
+                markers, self.default, _encoder, self.indent,
+                self.key_separator, self.item_separator, self.sort_keys,
+                self.skipkeys, self.allow_nan)
+        else:
+            _iterencode = _make_iterencode(
+                markers, self.default, _encoder, self.indent, floatstr,
+                self.key_separator, self.item_separator, self.sort_keys,
+                self.skipkeys, _one_shot)
+        return _iterencode(o, 0)
+
+def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,
+        _key_separator, _item_separator, _sort_keys, _skipkeys, _one_shot,
+        ## HACK: hand-optimized bytecode; turn globals into locals
+        ValueError=ValueError,
+        dict=dict,
+        float=float,
+        id=id,
+        int=int,
+        isinstance=isinstance,
+        list=list,
+        str=str,
+        tuple=tuple,
+        _intstr=int.__repr__,
+    ):
+
+    if _indent is not None and not isinstance(_indent, str):
+        _indent = ' ' * _indent
+
+    def _iterencode_list(lst, _current_indent_level):
+        if not lst:
+            yield '[]'
+            return
+        if markers is not None:
+            markerid = id(lst)
+            if markerid in markers:
+                raise ValueError("Circular reference detected")
+            markers[markerid] = lst
+        buf = '['
+        if _indent is not None:
+            _current_indent_level += 1
+            newline_indent = '\n' + _indent * _current_indent_level
+            separator = _item_separator + newline_indent
+            buf += newline_indent
+        else:
+            newline_indent = None
+            separator = _item_separator
+        first = True
+        for value in lst:
+            if first:
+                first = False
+            else:
+                buf = separator
+            if isinstance(value, str):
+                yield buf + _encoder(value)
+            elif value is None:
+                yield buf + 'null'
+            elif value is True:
+                yield buf + 'true'
+            elif value is False:
+                yield buf + 'false'
+            elif isinstance(value, int):
+                # Subclasses of int/float may override __repr__, but we still
+                # want to encode them as integers/floats in JSON. One example
+                # within the standard library is IntEnum.
+                yield buf + _intstr(value)
+            elif isinstance(value, float):
+                # see comment above for int
+                yield buf + _floatstr(value)
+            else:
+                yield buf
+                if isinstance(value, (list, tuple)):
+                    chunks = _iterencode_list(value, _current_indent_level)
+                elif isinstance(value, dict):
+                    chunks = _iterencode_dict(value, _current_indent_level)
+                else:
+                    chunks = _iterencode(value, _current_indent_level)
+                yield from chunks
+        if newline_indent is not None:
+            _current_indent_level -= 1
+            yield '\n' + _indent * _current_indent_level
+        yield ']'
+        if markers is not None:
+            del markers[markerid]
+
+    def _iterencode_dict(dct, _current_indent_level):
+        if not dct:
+            yield '{}'
+            return
+        if markers is not None:
+            markerid = id(dct)
+            if markerid in markers:
+                raise ValueError("Circular reference detected")
+            markers[markerid] = dct
+        yield '{'
+        if _indent is not None:
+            _current_indent_level += 1
+            newline_indent = '\n' + _indent * _current_indent_level
+            item_separator = _item_separator + newline_indent
+            yield newline_indent
+        else:
+            newline_indent = None
+            item_separator = _item_separator
+        first = True
+        if _sort_keys:
+            items = sorted(dct.items())
+        else:
+            items = dct.items()
+        for key, value in items:
+            if isinstance(key, str):
+                pass
+            # JavaScript is weakly typed for these, so it makes sense to
+            # also allow them.  Many encoders seem to do something like this.
+            elif isinstance(key, float):
+                # see comment for int/float in _make_iterencode
+                key = _floatstr(key)
+            elif key is True:
+                key = 'true'
+            elif key is False:
+                key = 'false'
+            elif key is None:
+                key = 'null'
+            elif isinstance(key, int):
+                # see comment for int/float in _make_iterencode
+                key = _intstr(key)
+            elif _skipkeys:
+                continue
+            else:
+                raise TypeError(f'keys must be str, int, float, bool or None, '
+                                f'not {key.__class__.__name__}')
+            if first:
+                first = False
+            else:
+                yield item_separator
+            yield _encoder(key)
+            yield _key_separator
+            if isinstance(value, str):
+                yield _encoder(value)
+            elif value is None:
+                yield 'null'
+            elif value is True:
+                yield 'true'
+            elif value is False:
+                yield 'false'
+            elif isinstance(value, int):
+                # see comment for int/float in _make_iterencode
+                yield _intstr(value)
+            elif isinstance(value, float):
+                # see comment for int/float in _make_iterencode
+                yield _floatstr(value)
+            else:
+                if isinstance(value, (list, tuple)):
+                    chunks = _iterencode_list(value, _current_indent_level)
+                elif isinstance(value, dict):
+                    chunks = _iterencode_dict(value, _current_indent_level)
+                else:
+                    chunks = _iterencode(value, _current_indent_level)
+                yield from chunks
+        if newline_indent is not None:
+            _current_indent_level -= 1
+            yield '\n' + _indent * _current_indent_level
+        yield '}'
+        if markers is not None:
+            del markers[markerid]
+
+    def _iterencode(o, _current_indent_level):
+        if isinstance(o, str):
+            yield _encoder(o)
+        elif o is None:
+            yield 'null'
+        elif o is True:
+            yield 'true'
+        elif o is False:
+            yield 'false'
+        elif isinstance(o, int):
+            # see comment for int/float in _make_iterencode
+            yield _intstr(o)
+        elif isinstance(o, float):
+            # see comment for int/float in _make_iterencode
+            yield _floatstr(o)
+        elif isinstance(o, (list, tuple)):
+            yield from _iterencode_list(o, _current_indent_level)
+        elif isinstance(o, dict):
+            yield from _iterencode_dict(o, _current_indent_level)
+        else:
+            if markers is not None:
+                markerid = id(o)
+                if markerid in markers:
+                    raise ValueError("Circular reference detected")
+                markers[markerid] = o
+            o = _default(o)
+            yield from _iterencode(o, _current_indent_level)
+            if markers is not None:
+                del markers[markerid]
+    return _iterencode

shared/package-files/06/06b8b7396bb8964915e9818b68b518b5d03de58f1e84ae4ca1b313843a024edd/blob

@@ -0,0 +1,62 @@
+import operator
+from functools import reduce
+
+
+def maybe_view(tensor, size, check_same_size=True):
+    if check_same_size and tensor.size() == size:
+        return tensor
+    return tensor.contiguous().view(size)
+
+
+def maybe_unexpand(tensor, old_size, check_same_size=True):
+    if check_same_size and tensor.size() == old_size:
+        return tensor
+    num_unsqueezed = tensor.dim() - len(old_size)
+    expanded_dims = [
+        dim
+        for dim, (expanded, original) in enumerate(
+            zip(tensor.size()[num_unsqueezed:], old_size)
+        )
+        if expanded != original
+    ]
+
+    for _ in range(num_unsqueezed):
+        tensor = tensor.sum(0, keepdim=False)
+    for dim in expanded_dims:
+        tensor = tensor.sum(dim, keepdim=True)
+    return tensor
+
+
+# Check whether the op enable broadcasting, and whether it is supported by ONNX.
+# If dims1 and dims2 are different, then broadcast is True.
+# We always assume the combination of dims1 and dims2 is broadcastable.
+# The following types of broadcasting are supported in ONNX:
+#     1) Only one element in dims2, such as dims2 = [1, 1]
+#     2) dims2 is suffix of dims1, such as dims1 = [2, 3, 4], and dims2 = [3, 4]
+# Details can be found here: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm
+def check_onnx_broadcast(dims1, dims2):
+    broadcast = False
+    supported = True
+    len1 = len(dims1)
+    len2 = len(dims2)
+    numel1 = reduce(operator.mul, dims1)
+    numel2 = reduce(operator.mul, dims2)
+    if len1 < len2:
+        broadcast = True
+        if numel2 != 1:
+            supported = False
+    elif len1 > len2:
+        broadcast = True
+        if numel2 != 1 and dims1[len1 - len2 :] != dims2:
+            supported = False
+    else:
+        if dims1 != dims2:
+            broadcast = True
+            if numel2 != 1:
+                supported = False
+
+    if not supported:
+        raise ValueError(
+            f"Numpy style broadcasting is not supported in ONNX. Input dims are: {dims1}, {dims2}"
+        )
+    return broadcast

shared/package-files/06/06bcfff317bfc97d78441e81f29564e8169c33a1e2cea914088439bdcf54fbf2/blob

@@ -0,0 +1,51 @@
+from datetime import datetime, tzinfo
+from typing import IO, Any, Dict, List, Mapping, Optional, Text, Tuple, Union
+
+_FileOrStr = Union[bytes, Text, IO[str], IO[Any]]
+
+class parserinfo(object):
+    JUMP: List[str]
+    WEEKDAYS: List[Tuple[str, str]]
+    MONTHS: List[Tuple[str, str]]
+    HMS: List[Tuple[str, str, str]]
+    AMPM: List[Tuple[str, str]]
+    UTCZONE: List[str]
+    PERTAIN: List[str]
+    TZOFFSET: Dict[str, int]
+    def __init__(self, dayfirst: bool = ..., yearfirst: bool = ...) -> None: ...
+    def jump(self, name: Text) -> bool: ...
+    def weekday(self, name: Text) -> Optional[int]: ...
+    def month(self, name: Text) -> Optional[int]: ...
+    def hms(self, name: Text) -> Optional[int]: ...
+    def ampm(self, name: Text) -> Optional[int]: ...
+    def pertain(self, name: Text) -> bool: ...
+    def utczone(self, name: Text) -> bool: ...
+    def tzoffset(self, name: Text) -> Optional[int]: ...
+    def convertyear(self, year: int) -> int: ...
+    def validate(self, res: datetime) -> bool: ...
+
+class parser(object):
+    def __init__(self, info: Optional[parserinfo] = ...) -> None: ...
+    def parse(
+        self,
+        timestr: _FileOrStr,
+        default: Optional[datetime] = ...,
+        ignoretz: bool = ...,
+        tzinfos: Optional[Mapping[Text, tzinfo]] = ...,
+        **kwargs: Any,
+    ) -> datetime: ...
+
+def isoparse(dt_str: Union[str, bytes, IO[str], IO[bytes]]) -> datetime: ...
+
+DEFAULTPARSER: parser
+
+def parse(timestr: _FileOrStr, parserinfo: Optional[parserinfo] = ..., **kwargs: Any) -> datetime: ...
+
+class _tzparser: ...
+
+DEFAULTTZPARSER: _tzparser
+
+class InvalidDatetimeError(ValueError): ...
+class InvalidDateError(InvalidDatetimeError): ...
+class InvalidTimeError(InvalidDatetimeError): ...
+class ParserError(ValueError): ...

shared/package-files/06/06bd03cb0ab219a5701da59b81b21664b9d0610ed2a5ad38678794f13ac7cfef/blob

@@ -0,0 +1,309 @@
+import sys
+from array import array
+from typing import (
+    Any,
+    Callable,
+    ClassVar,
+    Generic,
+    Iterable,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    Sequence,
+    Text,
+    Tuple,
+    Type,
+    TypeVar,
+    Union as _UnionT,
+    overload,
+)
+
+if sys.version_info >= (3, 9):
+    from types import GenericAlias
+
+_T = TypeVar("_T")
+_DLLT = TypeVar("_DLLT", bound=CDLL)
+_CT = TypeVar("_CT", bound=_CData)
+
+RTLD_GLOBAL: int = ...
+RTLD_LOCAL: int = ...
+DEFAULT_MODE: int = ...
+
+class CDLL(object):
+    _func_flags_: ClassVar[int] = ...
+    _func_restype_: ClassVar[_CData] = ...
+    _name: str = ...
+    _handle: int = ...
+    _FuncPtr: Type[_FuncPointer] = ...
+    def __init__(
+        self,
+        name: Optional[str],
+        mode: int = ...,
+        handle: Optional[int] = ...,
+        use_errno: bool = ...,
+        use_last_error: bool = ...,
+        winmode: Optional[int] = ...,
+    ) -> None: ...
+    def __getattr__(self, name: str) -> _NamedFuncPointer: ...
+    def __getitem__(self, name: str) -> _NamedFuncPointer: ...
+
+if sys.platform == "win32":
+    class OleDLL(CDLL): ...
+    class WinDLL(CDLL): ...
+
+class PyDLL(CDLL): ...
+
+class LibraryLoader(Generic[_DLLT]):
+    def __init__(self, dlltype: Type[_DLLT]) -> None: ...
+    def __getattr__(self, name: str) -> _DLLT: ...
+    def __getitem__(self, name: str) -> _DLLT: ...
+    def LoadLibrary(self, name: str) -> _DLLT: ...
+    if sys.version_info >= (3, 9):
+        def __class_getitem__(cls, item: Any) -> GenericAlias: ...
+
+cdll: LibraryLoader[CDLL] = ...
+if sys.platform == "win32":
+    windll: LibraryLoader[WinDLL] = ...
+    oledll: LibraryLoader[OleDLL] = ...
+pydll: LibraryLoader[PyDLL] = ...
+pythonapi: PyDLL = ...
+
+# Anything that implements the read-write buffer interface.
+# The buffer interface is defined purely on the C level, so we cannot define a normal Protocol
+# for it. Instead we have to list the most common stdlib buffer classes in a Union.
+_WritableBuffer = _UnionT[bytearray, memoryview, array, _CData]
+# Same as _WritableBuffer, but also includes read-only buffer types (like bytes).
+_ReadOnlyBuffer = _UnionT[_WritableBuffer, bytes]
+
+class _CDataMeta(type):
+    # By default mypy complains about the following two methods, because strictly speaking cls
+    # might not be a Type[_CT]. However this can never actually happen, because the only class that
+    # uses _CDataMeta as its metaclass is _CData. So it's safe to ignore the errors here.
+    def __mul__(cls: Type[_CT], other: int) -> Type[Array[_CT]]: ...  # type: ignore
+    def __rmul__(cls: Type[_CT], other: int) -> Type[Array[_CT]]: ...  # type: ignore
+
+class _CData(metaclass=_CDataMeta):
+    _b_base: int = ...
+    _b_needsfree_: bool = ...
+    _objects: Optional[Mapping[Any, int]] = ...
+    @classmethod
+    def from_buffer(cls: Type[_CT], source: _WritableBuffer, offset: int = ...) -> _CT: ...
+    @classmethod
+    def from_buffer_copy(cls: Type[_CT], source: _ReadOnlyBuffer, offset: int = ...) -> _CT: ...
+    @classmethod
+    def from_address(cls: Type[_CT], address: int) -> _CT: ...
+    @classmethod
+    def from_param(cls: Type[_CT], obj: Any) -> _UnionT[_CT, _CArgObject]: ...
+    @classmethod
+    def in_dll(cls: Type[_CT], library: CDLL, name: str) -> _CT: ...
+
+class _CanCastTo(_CData): ...
+class _PointerLike(_CanCastTo): ...
+
+_ECT = Callable[[Optional[Type[_CData]], _FuncPointer, Tuple[_CData, ...]], _CData]
+_PF = _UnionT[Tuple[int], Tuple[int, str], Tuple[int, str, Any]]
+
+class _FuncPointer(_PointerLike, _CData):
+    restype: _UnionT[Type[_CData], Callable[[int], None], None] = ...
+    argtypes: Sequence[Type[_CData]] = ...
+    errcheck: _ECT = ...
+    @overload
+    def __init__(self, address: int) -> None: ...
+    @overload
+    def __init__(self, callable: Callable[..., Any]) -> None: ...
+    @overload
+    def __init__(self, func_spec: Tuple[_UnionT[str, int], CDLL], paramflags: Tuple[_PF, ...] = ...) -> None: ...
+    @overload
+    def __init__(self, vtlb_index: int, name: str, paramflags: Tuple[_PF, ...] = ..., iid: pointer[c_int] = ...) -> None: ...
+    def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
+
+class _NamedFuncPointer(_FuncPointer):
+    __name__: str
+
+class ArgumentError(Exception): ...
+
+def CFUNCTYPE(
+    restype: Optional[Type[_CData]], *argtypes: Type[_CData], use_errno: bool = ..., use_last_error: bool = ...
+) -> Type[_FuncPointer]: ...
+
+if sys.platform == "win32":
+    def WINFUNCTYPE(
+        restype: Optional[Type[_CData]], *argtypes: Type[_CData], use_errno: bool = ..., use_last_error: bool = ...
+    ) -> Type[_FuncPointer]: ...
+
+def PYFUNCTYPE(restype: Optional[Type[_CData]], *argtypes: Type[_CData]) -> Type[_FuncPointer]: ...
+
+class _CArgObject: ...
+
+# Any type that can be implicitly converted to c_void_p when passed as a C function argument.
+# (bytes is not included here, see below.)
+_CVoidPLike = _UnionT[_PointerLike, Array[Any], _CArgObject, int]
+# Same as above, but including types known to be read-only (i. e. bytes).
+# This distinction is not strictly necessary (ctypes doesn't differentiate between const
+# and non-const pointers), but it catches errors like memmove(b'foo', buf, 4)
+# when memmove(buf, b'foo', 4) was intended.
+_CVoidConstPLike = _UnionT[_CVoidPLike, bytes]
+
+def addressof(obj: _CData) -> int: ...
+def alignment(obj_or_type: _UnionT[_CData, Type[_CData]]) -> int: ...
+def byref(obj: _CData, offset: int = ...) -> _CArgObject: ...
+
+_CastT = TypeVar("_CastT", bound=_CanCastTo)
+
+def cast(obj: _UnionT[_CData, _CArgObject, int], type: Type[_CastT]) -> _CastT: ...
+def create_string_buffer(init: _UnionT[int, bytes], size: Optional[int] = ...) -> Array[c_char]: ...
+
+c_buffer = create_string_buffer
+
+def create_unicode_buffer(init: _UnionT[int, Text], size: Optional[int] = ...) -> Array[c_wchar]: ...
+
+if sys.platform == "win32":
+    def DllCanUnloadNow() -> int: ...
+    def DllGetClassObject(rclsid: Any, riid: Any, ppv: Any) -> int: ...  # TODO not documented
+    def FormatError(code: int) -> str: ...
+    def GetLastError() -> int: ...
+
+def get_errno() -> int: ...
+
+if sys.platform == "win32":
+    def get_last_error() -> int: ...
+
+def memmove(dst: _CVoidPLike, src: _CVoidConstPLike, count: int) -> None: ...
+def memset(dst: _CVoidPLike, c: int, count: int) -> None: ...
+def POINTER(type: Type[_CT]) -> Type[pointer[_CT]]: ...
+
+# The real ctypes.pointer is a function, not a class. The stub version of pointer behaves like
+# ctypes._Pointer in that it is the base class for all pointer types. Unlike the real _Pointer,
+# it can be instantiated directly (to mimic the behavior of the real pointer function).
+class pointer(Generic[_CT], _PointerLike, _CData):
+    _type_: ClassVar[Type[_CT]] = ...
+    contents: _CT = ...
+    def __init__(self, arg: _CT = ...) -> None: ...
+    @overload
+    def __getitem__(self, i: int) -> _CT: ...
+    @overload
+    def __getitem__(self, s: slice) -> List[_CT]: ...
+    @overload
+    def __setitem__(self, i: int, o: _CT) -> None: ...
+    @overload
+    def __setitem__(self, s: slice, o: Iterable[_CT]) -> None: ...
+
+def resize(obj: _CData, size: int) -> None: ...
+
+if sys.version_info < (3,):
+    def set_conversion_mode(encoding: str, errors: str) -> Tuple[str, str]: ...
+
+def set_errno(value: int) -> int: ...
+
+if sys.platform == "win32":
+    def set_last_error(value: int) -> int: ...
+
+def sizeof(obj_or_type: _UnionT[_CData, Type[_CData]]) -> int: ...
+def string_at(address: _CVoidConstPLike, size: int = ...) -> bytes: ...
+
+if sys.platform == "win32":
+    def WinError(code: Optional[int] = ..., descr: Optional[str] = ...) -> OSError: ...
+
+def wstring_at(address: _CVoidConstPLike, size: int = ...) -> str: ...
+
+class _SimpleCData(Generic[_T], _CData):
+    value: _T = ...
+    def __init__(self, value: _T = ...) -> None: ...
+
+class c_byte(_SimpleCData[int]): ...
+
+class c_char(_SimpleCData[bytes]):
+    def __init__(self, value: _UnionT[int, bytes] = ...) -> None: ...
+
+class c_char_p(_PointerLike, _SimpleCData[Optional[bytes]]):
+    def __init__(self, value: Optional[_UnionT[int, bytes]] = ...) -> None: ...
+
+class c_double(_SimpleCData[float]): ...
+class c_longdouble(_SimpleCData[float]): ...
+class c_float(_SimpleCData[float]): ...
+class c_int(_SimpleCData[int]): ...
+class c_int8(_SimpleCData[int]): ...
+class c_int16(_SimpleCData[int]): ...
+class c_int32(_SimpleCData[int]): ...
+class c_int64(_SimpleCData[int]): ...
+class c_long(_SimpleCData[int]): ...
+class c_longlong(_SimpleCData[int]): ...
+class c_short(_SimpleCData[int]): ...
+class c_size_t(_SimpleCData[int]): ...
+class c_ssize_t(_SimpleCData[int]): ...
+class c_ubyte(_SimpleCData[int]): ...
+class c_uint(_SimpleCData[int]): ...
+class c_uint8(_SimpleCData[int]): ...
+class c_uint16(_SimpleCData[int]): ...
+class c_uint32(_SimpleCData[int]): ...
+class c_uint64(_SimpleCData[int]): ...
+class c_ulong(_SimpleCData[int]): ...
+class c_ulonglong(_SimpleCData[int]): ...
+class c_ushort(_SimpleCData[int]): ...
+class c_void_p(_PointerLike, _SimpleCData[Optional[int]]): ...
+class c_wchar(_SimpleCData[Text]): ...
+
+class c_wchar_p(_PointerLike, _SimpleCData[Optional[Text]]):
+    def __init__(self, value: Optional[_UnionT[int, Text]] = ...) -> None: ...
+
+class c_bool(_SimpleCData[bool]):
+    def __init__(self, value: bool = ...) -> None: ...
+
+if sys.platform == "win32":
+    class HRESULT(_SimpleCData[int]): ...  # TODO undocumented
+
+class py_object(_CanCastTo, _SimpleCData[_T]): ...
+
+class _CField:
+    offset: int = ...
+    size: int = ...
+
+class _StructUnionMeta(_CDataMeta):
+    _fields_: Sequence[_UnionT[Tuple[str, Type[_CData]], Tuple[str, Type[_CData], int]]] = ...
+    _pack_: int = ...
+    _anonymous_: Sequence[str] = ...
+    def __getattr__(self, name: str) -> _CField: ...
+
+class _StructUnionBase(_CData, metaclass=_StructUnionMeta):
+    def __init__(self, *args: Any, **kw: Any) -> None: ...
+    def __getattr__(self, name: str) -> Any: ...
+    def __setattr__(self, name: str, value: Any) -> None: ...
+
+class Union(_StructUnionBase): ...
+class Structure(_StructUnionBase): ...
+class BigEndianStructure(Structure): ...
+class LittleEndianStructure(Structure): ...
+
+class Array(Generic[_CT], _CData):
+    _length_: ClassVar[int] = ...
+    _type_: ClassVar[Type[_CT]] = ...
+    raw: bytes = ...  # Note: only available if _CT == c_char
+    value: Any = ...  # Note: bytes if _CT == c_char, Text if _CT == c_wchar, unavailable otherwise
+    # TODO These methods cannot be annotated correctly at the moment.
+    # All of these "Any"s stand for the array's element type, but it's not possible to use _CT
+    # here, because of a special feature of ctypes.
+    # By default, when accessing an element of an Array[_CT], the returned object has type _CT.
+    # However, when _CT is a "simple type" like c_int, ctypes automatically "unboxes" the object
+    # and converts it to the corresponding Python primitive. For example, when accessing an element
+    # of an Array[c_int], a Python int object is returned, not a c_int.
+    # This behavior does *not* apply to subclasses of "simple types".
+    # If MyInt is a subclass of c_int, then accessing an element of an Array[MyInt] returns
+    # a MyInt, not an int.
+    # This special behavior is not easy to model in a stub, so for now all places where
+    # the array element type would belong are annotated with Any instead.
+    def __init__(self, *args: Any) -> None: ...
+    @overload
+    def __getitem__(self, i: int) -> Any: ...
+    @overload
+    def __getitem__(self, s: slice) -> List[Any]: ...
+    @overload
+    def __setitem__(self, i: int, o: Any) -> None: ...
+    @overload
+    def __setitem__(self, s: slice, o: Iterable[Any]) -> None: ...
+    def __iter__(self) -> Iterator[Any]: ...
+    # Can't inherit from Sized because the metaclass conflict between
+    # Sized and _CData prevents using _CDataMeta.
+    def __len__(self) -> int: ...
+    if sys.version_info >= (3, 9):
+        def __class_getitem__(cls, item: Any) -> GenericAlias: ...

shared/package-files/06/06befcb259155c51e608af2dade2b9489b0fa76c41343c193b5e073f142b203e/blob

@@ -0,0 +1,36 @@
+
+####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
+####### Any changes to this file will be overwritten by the next CMake run ####
+####### The input file was reproc-config.cmake.in                            ########
+
+get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+
+macro(set_and_check _var _file)
+  set(${_var} "${_file}")
+  if(NOT EXISTS "${_file}")
+    message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !")
+  endif()
+endmacro()
+
+macro(check_required_components _NAME)
+  foreach(comp ${${_NAME}_FIND_COMPONENTS})
+    if(NOT ${_NAME}_${comp}_FOUND)
+      if(${_NAME}_FIND_REQUIRED_${comp})
+        set(${_NAME}_FOUND FALSE)
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+####################################################################################
+
+set(REPROC_MULTITHREADED ON)
+
+include(CMakeFindDependencyMacro)
+
+if(REPROC_MULTITHREADED)
+  set(THREADS_PREFER_PTHREAD_FLAG ON)
+  find_dependency(Threads)
+endif()
+
+include(${CMAKE_CURRENT_LIST_DIR}/reproc-targets.cmake)

shared/package-files/06/06bf5768b6f7ada260d4f4009d5e66799ddfb647e56d702c17a66d0cd7ce88cb/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 arctanh {
+  using schema = 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::arctanh")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arctanh(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API arctanh_ {
+  using schema = 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::arctanh_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arctanh_(Tensor(a!) self) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API arctanh_out {
+  using schema = 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::arctanh")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops

shared/package-files/06/06c51c6abfc2d276733c657669c92b27d0a9554f3fd16fe91d82b0a5d9d675d3/blob

@@ -0,0 +1 @@
+jsonpointer

shared/package-files/06/06c640bb2cf3469d4affb4fc801035db3b053729306a5faa542e9a35104932ed/blob

@@ -0,0 +1,23 @@
+#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 ::std::tuple<at::Tensor &,at::Tensor &> _native_multi_head_attention_out(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask, bool need_weights, bool average_attn_weights, c10::optional<int64_t> mask_type, at::Tensor & out0, at::Tensor & out1);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> native_multi_head_attention_cpu(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask={}, bool need_weights=true, bool average_attn_weights=true, c10::optional<int64_t> mask_type=c10::nullopt);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> native_multi_head_attention_cuda(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask={}, bool need_weights=true, bool average_attn_weights=true, c10::optional<int64_t> mask_type=c10::nullopt);
+} // namespace native
+} // namespace at

shared/package-files/06/06c8ab6557752e63d64f8b41233ea5a193e365c227a10ac819f0bbaefcea989c/blob

@@ -0,0 +1,3 @@
+# Tableau colorblind 10 palette
+axes.prop_cycle: cycler('color', ['006BA4', 'FF800E', 'ABABAB', '595959', '5F9ED1', 'C85200', '898989', 'A2C8EC', 'FFBC79', 'CFCFCF'])
+patch.facecolor: 006BA4

shared/package-files/06/06cbdf88bccd27374d0fe0b31052b363769d1faaa425d3910e6ec2d9968b9433/blob

@@ -0,0 +1,61 @@
+#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 eye {
+  using schema = at::Tensor (c10::SymInt, c10::optional<at::ScalarType>, c10::optional<at::Layout>, c10::optional<at::Device>, c10::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::eye")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor")
+  static at::Tensor call(c10::SymInt n, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+};
+
+struct TORCH_API eye_m {
+  using schema = at::Tensor (c10::SymInt, c10::SymInt, c10::optional<at::ScalarType>, c10::optional<at::Layout>, c10::optional<at::Device>, c10::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::eye")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "m")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor")
+  static at::Tensor call(c10::SymInt n, c10::SymInt m, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::SymInt m, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+};
+
+struct TORCH_API eye_out {
+  using schema = at::Tensor & (c10::SymInt, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::eye")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(c10::SymInt n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, at::Tensor & out);
+};
+
+struct TORCH_API eye_m_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymInt, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::eye")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "m_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(c10::SymInt n, c10::SymInt m, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::SymInt m, at::Tensor & out);
+};
+
+}} // namespace at::_ops

shared/package-files/06/06cdac7a4743272c3fc2b5291bbbd94061f4cb44b80b801bc464a9794ffc0b6b/blob

@@ -0,0 +1,184 @@
+from sympy.vector.vector import Vector
+from sympy.vector.coordsysrect import CoordSys3D
+from sympy.vector.functions import express, matrix_to_vector, orthogonalize
+from sympy.core.numbers import Rational
+from sympy.core.singleton import S
+from sympy.core.symbol import symbols
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.matrices.immutable import ImmutableDenseMatrix as Matrix
+from sympy.testing.pytest import raises
+
+N = CoordSys3D('N')
+q1, q2, q3, q4, q5 = symbols('q1 q2 q3 q4 q5')
+A = N.orient_new_axis('A', q1, N.k)  # type: ignore
+B = A.orient_new_axis('B', q2, A.i)
+C = B.orient_new_axis('C', q3, B.j)
+
+
+def test_express():
+    assert express(Vector.zero, N) == Vector.zero
+    assert express(S.Zero, N) is S.Zero
+    assert express(A.i, C) == cos(q3)*C.i + sin(q3)*C.k
+    assert express(A.j, C) == sin(q2)*sin(q3)*C.i + cos(q2)*C.j - \
+        sin(q2)*cos(q3)*C.k
+    assert express(A.k, C) == -sin(q3)*cos(q2)*C.i + sin(q2)*C.j + \
+        cos(q2)*cos(q3)*C.k
+    assert express(A.i, N) == cos(q1)*N.i + sin(q1)*N.j
+    assert express(A.j, N) == -sin(q1)*N.i + cos(q1)*N.j
+    assert express(A.k, N) == N.k
+    assert express(A.i, A) == A.i
+    assert express(A.j, A) == A.j
+    assert express(A.k, A) == A.k
+    assert express(A.i, B) == B.i
+    assert express(A.j, B) == cos(q2)*B.j - sin(q2)*B.k
+    assert express(A.k, B) == sin(q2)*B.j + cos(q2)*B.k
+    assert express(A.i, C) == cos(q3)*C.i + sin(q3)*C.k
+    assert express(A.j, C) == sin(q2)*sin(q3)*C.i + cos(q2)*C.j - \
+        sin(q2)*cos(q3)*C.k
+    assert express(A.k, C) == -sin(q3)*cos(q2)*C.i + sin(q2)*C.j + \
+        cos(q2)*cos(q3)*C.k
+    # Check to make sure UnitVectors get converted properly
+    assert express(N.i, N) == N.i
+    assert express(N.j, N) == N.j
+    assert express(N.k, N) == N.k
+    assert express(N.i, A) == (cos(q1)*A.i - sin(q1)*A.j)
+    assert express(N.j, A) == (sin(q1)*A.i + cos(q1)*A.j)
+    assert express(N.k, A) == A.k
+    assert express(N.i, B) == (cos(q1)*B.i - sin(q1)*cos(q2)*B.j +
+            sin(q1)*sin(q2)*B.k)
+    assert express(N.j, B) == (sin(q1)*B.i + cos(q1)*cos(q2)*B.j -
+            sin(q2)*cos(q1)*B.k)
+    assert express(N.k, B) == (sin(q2)*B.j + cos(q2)*B.k)
+    assert express(N.i, C) == (
+        (cos(q1)*cos(q3) - sin(q1)*sin(q2)*sin(q3))*C.i -
+        sin(q1)*cos(q2)*C.j +
+        (sin(q3)*cos(q1) + sin(q1)*sin(q2)*cos(q3))*C.k)
+    assert express(N.j, C) == (
+        (sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1))*C.i +
+        cos(q1)*cos(q2)*C.j +
+        (sin(q1)*sin(q3) - sin(q2)*cos(q1)*cos(q3))*C.k)
+    assert express(N.k, C) == (-sin(q3)*cos(q2)*C.i + sin(q2)*C.j +
+            cos(q2)*cos(q3)*C.k)
+
+    assert express(A.i, N) == (cos(q1)*N.i + sin(q1)*N.j)
+    assert express(A.j, N) == (-sin(q1)*N.i + cos(q1)*N.j)
+    assert express(A.k, N) == N.k
+    assert express(A.i, A) == A.i
+    assert express(A.j, A) == A.j
+    assert express(A.k, A) == A.k
+    assert express(A.i, B) == B.i
+    assert express(A.j, B) == (cos(q2)*B.j - sin(q2)*B.k)
+    assert express(A.k, B) == (sin(q2)*B.j + cos(q2)*B.k)
+    assert express(A.i, C) == (cos(q3)*C.i + sin(q3)*C.k)
+    assert express(A.j, C) == (sin(q2)*sin(q3)*C.i + cos(q2)*C.j -
+            sin(q2)*cos(q3)*C.k)
+    assert express(A.k, C) == (-sin(q3)*cos(q2)*C.i + sin(q2)*C.j +
+            cos(q2)*cos(q3)*C.k)
+
+    assert express(B.i, N) == (cos(q1)*N.i + sin(q1)*N.j)
+    assert express(B.j, N) == (-sin(q1)*cos(q2)*N.i +
+            cos(q1)*cos(q2)*N.j + sin(q2)*N.k)
+    assert express(B.k, N) == (sin(q1)*sin(q2)*N.i -
+            sin(q2)*cos(q1)*N.j + cos(q2)*N.k)
+    assert express(B.i, A) == A.i
+    assert express(B.j, A) == (cos(q2)*A.j + sin(q2)*A.k)
+    assert express(B.k, A) == (-sin(q2)*A.j + cos(q2)*A.k)
+    assert express(B.i, B) == B.i
+    assert express(B.j, B) == B.j
+    assert express(B.k, B) == B.k
+    assert express(B.i, C) == (cos(q3)*C.i + sin(q3)*C.k)
+    assert express(B.j, C) == C.j
+    assert express(B.k, C) == (-sin(q3)*C.i + cos(q3)*C.k)
+
+    assert express(C.i, N) == (
+        (cos(q1)*cos(q3) - sin(q1)*sin(q2)*sin(q3))*N.i +
+        (sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1))*N.j -
+        sin(q3)*cos(q2)*N.k)
+    assert express(C.j, N) == (
+        -sin(q1)*cos(q2)*N.i + cos(q1)*cos(q2)*N.j + sin(q2)*N.k)
+    assert express(C.k, N) == (
+        (sin(q3)*cos(q1) + sin(q1)*sin(q2)*cos(q3))*N.i +
+        (sin(q1)*sin(q3) - sin(q2)*cos(q1)*cos(q3))*N.j +
+        cos(q2)*cos(q3)*N.k)
+    assert express(C.i, A) == (cos(q3)*A.i + sin(q2)*sin(q3)*A.j -
+            sin(q3)*cos(q2)*A.k)
+    assert express(C.j, A) == (cos(q2)*A.j + sin(q2)*A.k)
+    assert express(C.k, A) == (sin(q3)*A.i - sin(q2)*cos(q3)*A.j +
+            cos(q2)*cos(q3)*A.k)
+    assert express(C.i, B) == (cos(q3)*B.i - sin(q3)*B.k)
+    assert express(C.j, B) == B.j
+    assert express(C.k, B) == (sin(q3)*B.i + cos(q3)*B.k)
+    assert express(C.i, C) == C.i
+    assert express(C.j, C) == C.j
+    assert express(C.k, C) == C.k == (C.k)
+
+    #  Check to make sure Vectors get converted back to UnitVectors
+    assert N.i == express((cos(q1)*A.i - sin(q1)*A.j), N).simplify()
+    assert N.j == express((sin(q1)*A.i + cos(q1)*A.j), N).simplify()
+    assert N.i == express((cos(q1)*B.i - sin(q1)*cos(q2)*B.j +
+            sin(q1)*sin(q2)*B.k), N).simplify()
+    assert N.j == express((sin(q1)*B.i + cos(q1)*cos(q2)*B.j -
+        sin(q2)*cos(q1)*B.k), N).simplify()
+    assert N.k == express((sin(q2)*B.j + cos(q2)*B.k), N).simplify()
+
+
+    assert A.i == express((cos(q1)*N.i + sin(q1)*N.j), A).simplify()
+    assert A.j == express((-sin(q1)*N.i + cos(q1)*N.j), A).simplify()
+
+    assert A.j == express((cos(q2)*B.j - sin(q2)*B.k), A).simplify()
+    assert A.k == express((sin(q2)*B.j + cos(q2)*B.k), A).simplify()
+
+    assert A.i == express((cos(q3)*C.i + sin(q3)*C.k), A).simplify()
+    assert A.j == express((sin(q2)*sin(q3)*C.i + cos(q2)*C.j -
+            sin(q2)*cos(q3)*C.k), A).simplify()
+
+    assert A.k == express((-sin(q3)*cos(q2)*C.i + sin(q2)*C.j +
+            cos(q2)*cos(q3)*C.k), A).simplify()
+    assert B.i == express((cos(q1)*N.i + sin(q1)*N.j), B).simplify()
+    assert B.j == express((-sin(q1)*cos(q2)*N.i +
+            cos(q1)*cos(q2)*N.j + sin(q2)*N.k), B).simplify()
+
+    assert B.k == express((sin(q1)*sin(q2)*N.i -
+            sin(q2)*cos(q1)*N.j + cos(q2)*N.k), B).simplify()
+
+    assert B.j == express((cos(q2)*A.j + sin(q2)*A.k), B).simplify()
+    assert B.k == express((-sin(q2)*A.j + cos(q2)*A.k), B).simplify()
+    assert B.i == express((cos(q3)*C.i + sin(q3)*C.k), B).simplify()
+    assert B.k == express((-sin(q3)*C.i + cos(q3)*C.k), B).simplify()
+    assert C.i == express((cos(q3)*A.i + sin(q2)*sin(q3)*A.j -
+            sin(q3)*cos(q2)*A.k), C).simplify()
+    assert C.j == express((cos(q2)*A.j + sin(q2)*A.k), C).simplify()
+    assert C.k == express((sin(q3)*A.i - sin(q2)*cos(q3)*A.j +
+            cos(q2)*cos(q3)*A.k), C).simplify()
+    assert C.i == express((cos(q3)*B.i - sin(q3)*B.k), C).simplify()
+    assert C.k == express((sin(q3)*B.i + cos(q3)*B.k), C).simplify()
+
+
+def test_matrix_to_vector():
+    m = Matrix([[1], [2], [3]])
+    assert matrix_to_vector(m, C) == C.i + 2*C.j + 3*C.k
+    m = Matrix([[0], [0], [0]])
+    assert matrix_to_vector(m, N) == matrix_to_vector(m, C) == \
+           Vector.zero
+    m = Matrix([[q1], [q2], [q3]])
+    assert matrix_to_vector(m, N) == q1*N.i + q2*N.j + q3*N.k
+
+
+def test_orthogonalize():
+    C = CoordSys3D('C')
+    a, b = symbols('a b', integer=True)
+    i, j, k = C.base_vectors()
+    v1 = i + 2*j
+    v2 = 2*i + 3*j
+    v3 = 3*i + 5*j
+    v4 = 3*i + j
+    v5 = 2*i + 2*j
+    v6 = a*i + b*j
+    v7 = 4*a*i + 4*b*j
+    assert orthogonalize(v1, v2) == [C.i + 2*C.j, C.i*Rational(2, 5) + -C.j/5]
+    # from wikipedia
+    assert orthogonalize(v4, v5, orthonormal=True) == \
+        [(3*sqrt(10))*C.i/10 + (sqrt(10))*C.j/10, (-sqrt(10))*C.i/10 + (3*sqrt(10))*C.j/10]
+    raises(ValueError, lambda: orthogonalize(v1, v2, v3))
+    raises(ValueError, lambda: orthogonalize(v6, v7))

shared/package-files/06/06ce13e038f1e1764e6ddd251af78123ab55b15bcf19ebf9476dc6b1a8a8768b/blob

@@ -0,0 +1,372 @@
+#pragma once
+
+#include <torch/detail/static.h>
+#include <torch/nn/module.h>
+#include <torch/nn/modules/container/any_module_holder.h>
+#include <torch/nn/modules/container/any_value.h>
+#include <torch/nn/pimpl.h>
+#include <torch/types.h>
+
+#include <torch/csrc/autograd/variable.h>
+#include <torch/csrc/utils/variadic.h>
+
+#include <ATen/Device.h>
+
+#include <memory>
+#include <type_traits>
+#include <typeinfo>
+#include <utility>
+#include <vector>
+
+namespace torch {
+namespace nn {
+
+/// Stores a type erased `Module`.
+///
+/// The PyTorch C++ API does not impose an interface on the signature of
+/// `forward()` in `Module` subclasses. This gives you complete freedom to
+/// design your `forward()` methods to your liking. However, this also means
+/// there is no unified base type you could store in order to call `forward()`
+/// polymorphically for any module. This is where the `AnyModule` comes in.
+/// Instead of inheritance, it relies on type erasure for polymorphism.
+///
+/// An `AnyModule` can store any `nn::Module` subclass that provides a
+/// `forward()` method. This `forward()` may accept any types and return any
+/// type. Once stored in an `AnyModule`, you can invoke the underlying module's
+/// `forward()` by calling `AnyModule::forward()` with the arguments you would
+/// supply to the stored module (though see one important limitation below).
+/// Example:
+///
+/// \rst
+/// .. code-block:: cpp
+///
+///   struct GenericTrainer {
+///     torch::nn::AnyModule module;
+///
+///     void train(torch::Tensor input) {
+///       module.forward(input);
+///     }
+///   };
+///
+///   GenericTrainer trainer1{torch::nn::Linear(3, 4)};
+///   GenericTrainer trainer2{torch::nn::Conv2d(3, 4, 2)};
+/// \endrst
+///
+/// As `AnyModule` erases the static type of the stored module (and its
+/// `forward()` method) to achieve polymorphism, type checking of arguments is
+/// moved to runtime. That is, passing an argument with an incorrect type to an
+/// `AnyModule` will compile, but throw an exception at runtime:
+///
+/// \rst
+/// .. code-block:: cpp
+///
+///   torch::nn::AnyModule module(torch::nn::Linear(3, 4));
+///   // Linear takes a tensor as input, but we are passing an integer.
+///   // This will compile, but throw a `torch::Error` exception at runtime.
+///   module.forward(123);
+/// \endrst
+///
+/// \rst
+/// .. attention::
+///   One noteworthy limitation of `AnyModule` is that its `forward()` method
+///   does not support implicit conversion of argument types. For example, if
+///   the stored module's `forward()` method accepts a `float` and you call
+///   `any_module.forward(3.4)` (where `3.4` is a `double`), this will throw
+///   an exception.
+/// \endrst
+///
+/// The return type of the `AnyModule`'s `forward()` method is controlled via
+/// the first template argument to `AnyModule::forward()`. It defaults to
+/// `torch::Tensor`. To change it, you can write `any_module.forward<int>()`,
+/// for example.
+///
+/// \rst
+/// .. code-block:: cpp
+///
+///   torch::nn::AnyModule module(torch::nn::Linear(3, 4));
+///   auto output = module.forward(torch::ones({2, 3}));
+///
+///   struct IntModule {
+///     int forward(int x) { return x; }
+///   };
+///   torch::nn::AnyModule module(IntModule{});
+///   int output = module.forward<int>(5);
+/// \endrst
+///
+/// The only other method an `AnyModule` provides access to on the stored
+/// module is `clone()`. However, you may acquire a handle on the module via
+/// `.ptr()`, which returns a `shared_ptr<nn::Module>`. Further, if you know
+/// the concrete type of the stored module, you can get a concrete handle to it
+/// using `.get<T>()` where `T` is the concrete module type.
+///
+/// \rst
+/// .. code-block:: cpp
+///
+///   torch::nn::AnyModule module(torch::nn::Linear(3, 4));
+///   std::shared_ptr<nn::Module> ptr = module.ptr();
+///   torch::nn::Linear linear(module.get<torch::nn::Linear>());
+/// \endrst
+class AnyModule {
+ public:
+  /// A default-constructed `AnyModule` is in an empty state.
+  AnyModule() = default;
+
+  /// Constructs an `AnyModule` from a `shared_ptr` to concrete module object.
+  template <typename ModuleType>
+  explicit AnyModule(std::shared_ptr<ModuleType> module);
+
+  /// Constructs an `AnyModule` from a concrete module object.
+  template <
+      typename ModuleType,
+      typename = torch::detail::enable_if_module_t<ModuleType>>
+  explicit AnyModule(ModuleType&& module);
+
+  /// Constructs an `AnyModule` from a module holder.
+  template <typename ModuleType>
+  explicit AnyModule(const ModuleHolder<ModuleType>& module_holder);
+
+  /// Move construction and assignment is allowed, and follows the default
+  /// behavior of move for `std::unique_ptr`.
+  AnyModule(AnyModule&&) = default;
+  AnyModule& operator=(AnyModule&&) = default;
+
+  /// Creates a shallow copy of an `AnyModule`.
+  AnyModule(const AnyModule& other);
+  AnyModule& operator=(const AnyModule& other);
+
+  /// Creates a deep copy of an `AnyModule` if it contains a module, else an
+  /// empty `AnyModule` if it is empty.
+  AnyModule clone(optional<Device> device = nullopt) const;
+
+  /// Assigns a module to the `AnyModule` (to circumvent the explicit
+  /// constructor).
+  template <typename ModuleType>
+  AnyModule& operator=(std::shared_ptr<ModuleType> module);
+
+  /// Invokes `forward()` on the contained module with the given arguments, and
+  /// returns the return value as an `AnyValue`. Use this method when chaining
+  /// `AnyModule`s in a loop.
+  template <typename... ArgumentTypes>
+  AnyValue any_forward(ArgumentTypes&&... arguments);
+
+  /// Invokes `forward()` on the contained module with the given arguments, and
+  /// casts the returned `AnyValue` to the supplied `ReturnType` (which defaults
+  /// to `torch::Tensor`).
+  template <typename ReturnType = torch::Tensor, typename... ArgumentTypes>
+  ReturnType forward(ArgumentTypes&&... arguments);
+
+  /// Attempts to cast the underlying module to the given module type. Throws an
+  /// exception if the types do not match.
+  template <typename T, typename = torch::detail::enable_if_module_t<T>>
+  T& get();
+
+  /// Attempts to cast the underlying module to the given module type. Throws an
+  /// exception if the types do not match.
+  template <typename T, typename = torch::detail::enable_if_module_t<T>>
+  const T& get() const;
+
+  /// Returns the contained module in a `nn::ModuleHolder` subclass if possible
+  /// (i.e. if `T` has a constructor for the underlying module type).
+  template <typename T, typename ContainedType = typename T::ContainedType>
+  T get() const;
+
+  /// Returns a `std::shared_ptr` whose dynamic type is that of the underlying
+  /// module.
+  std::shared_ptr<Module> ptr() const;
+
+  /// Like `ptr()`, but casts the pointer to the given type.
+  template <typename T, typename = torch::detail::enable_if_module_t<T>>
+  std::shared_ptr<T> ptr() const;
+
+  /// Returns the `type_info` object of the contained value.
+  const std::type_info& type_info() const;
+
+  /// Returns true if the `AnyModule` does not contain a module.
+  bool is_empty() const noexcept;
+
+ private:
+  /// Creates a `unique_ptr<AnyModulePlaceholder>` pointing to a
+  /// `AnyModuleHolder` of the correct type. This method is used to deduce the
+  /// arguments of the module's `forward()` method.
+  template <
+      typename ModuleType,
+      typename Class,
+      typename ReturnType,
+      typename... ArgumentTypes>
+  std::unique_ptr<AnyModulePlaceholder> make_holder(
+      std::shared_ptr<ModuleType>&& module,
+      ReturnType (Class::*)(ArgumentTypes...));
+
+  /// Helper method invoked by const and non-const `get()`.
+  template <typename ModuleType, typename ReturnType, typename... ArgumentTypes>
+  ModuleType& get_(ReturnType (ModuleType::*)(ArgumentTypes...)) const;
+
+  /// Helper method invoked by const and non-const `get()`.
+  template <typename ModuleType>
+  ModuleType& get_() const;
+
+  /// The type erased module.
+  std::unique_ptr<AnyModulePlaceholder> content_;
+};
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AnyModule ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+template <typename ModuleType>
+AnyModule::AnyModule(std::shared_ptr<ModuleType> module)
+    : content_(make_holder(
+          std::move(module),
+          &std::remove_reference<ModuleType>::type::forward)) {
+  // `AnyModule` can only store an `nn::Module` subclass object that provides
+  // a `forward()` method that has a non-templatized return type.
+  // (e.g. `AnyModule` cannot store `nn::Sequential`, because `nn::Sequential`'s
+  // `forward()` method has a templatized return type.)
+  static_assert(
+      torch::detail::is_module<ModuleType>::value,
+      "Can only store object derived from nn::Module into AnyModule");
+  static_assert(
+      torch::detail::has_forward<ModuleType>::value,
+      "Can only store module with a forward() method that has a non-templatized"
+      " argument type and return type into AnyModule (e.g. we cannot store nn::Sequential"
+      "into AnyModule, because its forward() method's argument type and return type are templatized."
+      " If you need to use nn::Sequentials inside each other you can subclass "
+      "nn::Sequential and write a non-templatized forward function for it. You can checkout "
+      "https://github.com/pytorch/vision/blob/2f46070f3cb1ea894d82578f3dc5677f82f34958/torchvision/csrc/models/mnasnet.cpp#L59 "
+      "for an example on how to do this.).");
+}
+
+template <typename ModuleType, typename>
+AnyModule::AnyModule(ModuleType&& module)
+    : AnyModule(
+          std::make_shared<ModuleType>(std::forward<ModuleType>(module))) {}
+
+template <typename ModuleType>
+AnyModule::AnyModule(const ModuleHolder<ModuleType>& module_holder)
+    : AnyModule(module_holder.ptr()) {}
+
+inline AnyModule::AnyModule(const AnyModule& other)
+    : content_(other.content_ ? other.content_->copy() : nullptr) {}
+
+inline AnyModule& AnyModule::operator=(const AnyModule& other) {
+  if (this != &other) {
+    content_ = other.content_ ? other.content_->copy() : nullptr;
+  }
+  return *this;
+}
+
+inline AnyModule AnyModule::clone(optional<Device> device) const {
+  AnyModule clone;
+  clone.content_ = content_ ? content_->clone_module(device) : nullptr;
+  return clone;
+}
+
+template <typename ModuleType>
+AnyModule& AnyModule::operator=(std::shared_ptr<ModuleType> module) {
+  // NOLINTNEXTLINE(cppcoreguidelines-c-copy-assignment-signature)
+  return (*this = AnyModule(std::move(module)));
+}
+
+template <typename... ArgumentTypes>
+AnyValue AnyModule::any_forward(ArgumentTypes&&... arguments) {
+  TORCH_CHECK(!is_empty(), "Cannot call forward() on an empty AnyModule");
+  std::vector<AnyValue> values;
+  values.reserve(sizeof...(ArgumentTypes));
+  torch::apply(
+      [&values](AnyValue&& value) { values.push_back(std::move(value)); },
+      AnyValue(std::forward<ArgumentTypes>(arguments))...);
+  return content_->forward(std::move(values));
+}
+
+template <typename ReturnType, typename... ArgumentTypes>
+ReturnType AnyModule::forward(ArgumentTypes&&... arguments) {
+  return any_forward(std::forward<ArgumentTypes>(arguments)...)
+      .template get<ReturnType>();
+}
+
+template <typename T, typename>
+T& AnyModule::get() {
+  TORCH_CHECK(!is_empty(), "Cannot call get() on an empty AnyModule");
+  return get_<T>();
+}
+
+template <typename T, typename>
+const T& AnyModule::get() const {
+  TORCH_CHECK(!is_empty(), "Cannot call get() on an empty AnyModule");
+  return get_<T>();
+}
+
+template <typename T, typename ContainedType>
+T AnyModule::get() const {
+  return T(ptr<ContainedType>());
+}
+
+inline std::shared_ptr<Module> AnyModule::ptr() const {
+  TORCH_CHECK(!is_empty(), "Cannot call ptr() on an empty AnyModule");
+  return content_->ptr();
+}
+
+template <typename T, typename>
+std::shared_ptr<T> AnyModule::ptr() const {
+  TORCH_CHECK(!is_empty(), "Cannot call ptr() on an empty AnyModule");
+  // Call get() but discard the value, just to do the type checking.
+  get_<T>();
+  return std::dynamic_pointer_cast<T>(ptr());
+}
+
+inline const std::type_info& AnyModule::type_info() const {
+  TORCH_CHECK(!is_empty(), "Cannot call type_info() on an empty AnyModule");
+  return content_->type_info;
+}
+
+inline bool AnyModule::is_empty() const noexcept {
+  return content_ == nullptr;
+}
+
+// Private Methods
+
+template <
+    typename ModuleType,
+    typename Class,
+    typename ReturnType,
+    typename... ArgumentTypes>
+std::unique_ptr<AnyModulePlaceholder> AnyModule::make_holder(
+    std::shared_ptr<ModuleType>&& module,
+    ReturnType (Class::*)(ArgumentTypes...)) {
+  static_assert(
+      torch::detail::check_not_lvalue_references<ArgumentTypes...>(),
+      "Modules stored inside AnyModule must not take references. "
+      "Use pointers instead.");
+  static_assert(
+      !std::is_void<ReturnType>::value,
+      "AnyModule cannot store modules that return void "
+      "(you can return a dummy value).");
+  return std::make_unique<
+      AnyModuleHolder<decay_t<ModuleType>, ArgumentTypes...>>(
+      std::move(module));
+}
+
+template <typename ModuleType>
+ModuleType& AnyModule::get_() const {
+  using M = typename std::remove_reference<ModuleType>::type;
+  static_assert(
+      torch::detail::has_forward<M>::value,
+      "Can only call AnyModule::get<T> with a type T that has a forward method");
+  return get_(&M::forward);
+}
+
+template <typename ModuleType, typename ReturnType, typename... ArgumentTypes>
+ModuleType& AnyModule::get_(
+    ReturnType (ModuleType::*)(ArgumentTypes...)) const {
+  if (typeid(ModuleType).hash_code() == type_info().hash_code()) {
+    return *static_cast<AnyModuleHolder<ModuleType, ArgumentTypes...>&>(
+                *content_)
+                .module;
+  }
+  AT_ERROR(
+      "Attempted to cast module of type ",
+      c10::demangle(type_info().name()),
+      " to type ",
+      c10::demangle(typeid(ModuleType).name()));
+}
+
+} // namespace nn
+} // namespace torch

shared/package-files/06/06d043d88fb3f0ae88e46420f5080735f8995747a72a2e483128b14fee094525/blob

@@ -0,0 +1,237 @@
+import math
+
+import torch
+from torch.distributions import constraints
+from torch.distributions.distribution import Distribution
+from torch.distributions.multivariate_normal import _batch_mahalanobis, _batch_mv
+from torch.distributions.utils import _standard_normal, lazy_property
+
+__all__ = ["LowRankMultivariateNormal"]
+
+
+def _batch_capacitance_tril(W, D):
+    r"""
+    Computes Cholesky of :math:`I + W.T @ inv(D) @ W` for a batch of matrices :math:`W`
+    and a batch of vectors :math:`D`.
+    """
+    m = W.size(-1)
+    Wt_Dinv = W.mT / D.unsqueeze(-2)
+    K = torch.matmul(Wt_Dinv, W).contiguous()
+    K.view(-1, m * m)[:, :: m + 1] += 1  # add identity matrix to K
+    return torch.linalg.cholesky(K)
+
+
+def _batch_lowrank_logdet(W, D, capacitance_tril):
+    r"""
+    Uses "matrix determinant lemma"::
+        log|W @ W.T + D| = log|C| + log|D|,
+    where :math:`C` is the capacitance matrix :math:`I + W.T @ inv(D) @ W`, to compute
+    the log determinant.
+    """
+    return 2 * capacitance_tril.diagonal(dim1=-2, dim2=-1).log().sum(-1) + D.log().sum(
+        -1
+    )
+
+
+def _batch_lowrank_mahalanobis(W, D, x, capacitance_tril):
+    r"""
+    Uses "Woodbury matrix identity"::
+        inv(W @ W.T + D) = inv(D) - inv(D) @ W @ inv(C) @ W.T @ inv(D),
+    where :math:`C` is the capacitance matrix :math:`I + W.T @ inv(D) @ W`, to compute the squared
+    Mahalanobis distance :math:`x.T @ inv(W @ W.T + D) @ x`.
+    """
+    Wt_Dinv = W.mT / D.unsqueeze(-2)
+    Wt_Dinv_x = _batch_mv(Wt_Dinv, x)
+    mahalanobis_term1 = (x.pow(2) / D).sum(-1)
+    mahalanobis_term2 = _batch_mahalanobis(capacitance_tril, Wt_Dinv_x)
+    return mahalanobis_term1 - mahalanobis_term2
+
+
+class LowRankMultivariateNormal(Distribution):
+    r"""
+    Creates a multivariate normal distribution with covariance matrix having a low-rank form
+    parameterized by :attr:`cov_factor` and :attr:`cov_diag`::
+
+        covariance_matrix = cov_factor @ cov_factor.T + cov_diag
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = LowRankMultivariateNormal(torch.zeros(2), torch.tensor([[1.], [0.]]), torch.ones(2))
+        >>> m.sample()  # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]`
+        tensor([-0.2102, -0.5429])
+
+    Args:
+        loc (Tensor): mean of the distribution with shape `batch_shape + event_shape`
+        cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape
+            `batch_shape + event_shape + (rank,)`
+        cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape
+            `batch_shape + event_shape`
+
+    Note:
+        The computation for determinant and inverse of covariance matrix is avoided when
+        `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity
+        <https://en.wikipedia.org/wiki/Woodbury_matrix_identity>`_ and
+        `matrix determinant lemma <https://en.wikipedia.org/wiki/Matrix_determinant_lemma>`_.
+        Thanks to these formulas, we just need to compute the determinant and inverse of
+        the small size "capacitance" matrix::
+
+            capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor
+    """
+    arg_constraints = {
+        "loc": constraints.real_vector,
+        "cov_factor": constraints.independent(constraints.real, 2),
+        "cov_diag": constraints.independent(constraints.positive, 1),
+    }
+    support = constraints.real_vector
+    has_rsample = True
+
+    def __init__(self, loc, cov_factor, cov_diag, validate_args=None):
+        if loc.dim() < 1:
+            raise ValueError("loc must be at least one-dimensional.")
+        event_shape = loc.shape[-1:]
+        if cov_factor.dim() < 2:
+            raise ValueError(
+                "cov_factor must be at least two-dimensional, "
+                "with optional leading batch dimensions"
+            )
+        if cov_factor.shape[-2:-1] != event_shape:
+            raise ValueError(
+                f"cov_factor must be a batch of matrices with shape {event_shape[0]} x m"
+            )
+        if cov_diag.shape[-1:] != event_shape:
+            raise ValueError(
+                f"cov_diag must be a batch of vectors with shape {event_shape}"
+            )
+
+        loc_ = loc.unsqueeze(-1)
+        cov_diag_ = cov_diag.unsqueeze(-1)
+        try:
+            loc_, self.cov_factor, cov_diag_ = torch.broadcast_tensors(
+                loc_, cov_factor, cov_diag_
+            )
+        except RuntimeError as e:
+            raise ValueError(
+                f"Incompatible batch shapes: loc {loc.shape}, cov_factor {cov_factor.shape}, cov_diag {cov_diag.shape}"
+            ) from e
+        self.loc = loc_[..., 0]
+        self.cov_diag = cov_diag_[..., 0]
+        batch_shape = self.loc.shape[:-1]
+
+        self._unbroadcasted_cov_factor = cov_factor
+        self._unbroadcasted_cov_diag = cov_diag
+        self._capacitance_tril = _batch_capacitance_tril(cov_factor, cov_diag)
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(LowRankMultivariateNormal, _instance)
+        batch_shape = torch.Size(batch_shape)
+        loc_shape = batch_shape + self.event_shape
+        new.loc = self.loc.expand(loc_shape)
+        new.cov_diag = self.cov_diag.expand(loc_shape)
+        new.cov_factor = self.cov_factor.expand(loc_shape + self.cov_factor.shape[-1:])
+        new._unbroadcasted_cov_factor = self._unbroadcasted_cov_factor
+        new._unbroadcasted_cov_diag = self._unbroadcasted_cov_diag
+        new._capacitance_tril = self._capacitance_tril
+        super(LowRankMultivariateNormal, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    @property
+    def mean(self):
+        return self.loc
+
+    @property
+    def mode(self):
+        return self.loc
+
+    @lazy_property
+    def variance(self):
+        return (
+            self._unbroadcasted_cov_factor.pow(2).sum(-1) + self._unbroadcasted_cov_diag
+        ).expand(self._batch_shape + self._event_shape)
+
+    @lazy_property
+    def scale_tril(self):
+        # The following identity is used to increase the numerically computation stability
+        # for Cholesky decomposition (see http://www.gaussianprocess.org/gpml/, Section 3.4.3):
+        #     W @ W.T + D = D1/2 @ (I + D-1/2 @ W @ W.T @ D-1/2) @ D1/2
+        # The matrix "I + D-1/2 @ W @ W.T @ D-1/2" has eigenvalues bounded from below by 1,
+        # hence it is well-conditioned and safe to take Cholesky decomposition.
+        n = self._event_shape[0]
+        cov_diag_sqrt_unsqueeze = self._unbroadcasted_cov_diag.sqrt().unsqueeze(-1)
+        Dinvsqrt_W = self._unbroadcasted_cov_factor / cov_diag_sqrt_unsqueeze
+        K = torch.matmul(Dinvsqrt_W, Dinvsqrt_W.mT).contiguous()
+        K.view(-1, n * n)[:, :: n + 1] += 1  # add identity matrix to K
+        scale_tril = cov_diag_sqrt_unsqueeze * torch.linalg.cholesky(K)
+        return scale_tril.expand(
+            self._batch_shape + self._event_shape + self._event_shape
+        )
+
+    @lazy_property
+    def covariance_matrix(self):
+        covariance_matrix = torch.matmul(
+            self._unbroadcasted_cov_factor, self._unbroadcasted_cov_factor.mT
+        ) + torch.diag_embed(self._unbroadcasted_cov_diag)
+        return covariance_matrix.expand(
+            self._batch_shape + self._event_shape + self._event_shape
+        )
+
+    @lazy_property
+    def precision_matrix(self):
+        # We use "Woodbury matrix identity" to take advantage of low rank form::
+        #     inv(W @ W.T + D) = inv(D) - inv(D) @ W @ inv(C) @ W.T @ inv(D)
+        # where :math:`C` is the capacitance matrix.
+        Wt_Dinv = (
+            self._unbroadcasted_cov_factor.mT
+            / self._unbroadcasted_cov_diag.unsqueeze(-2)
+        )
+        A = torch.linalg.solve_triangular(self._capacitance_tril, Wt_Dinv, upper=False)
+        precision_matrix = (
+            torch.diag_embed(self._unbroadcasted_cov_diag.reciprocal()) - A.mT @ A
+        )
+        return precision_matrix.expand(
+            self._batch_shape + self._event_shape + self._event_shape
+        )
+
+    def rsample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        W_shape = shape[:-1] + self.cov_factor.shape[-1:]
+        eps_W = _standard_normal(W_shape, dtype=self.loc.dtype, device=self.loc.device)
+        eps_D = _standard_normal(shape, dtype=self.loc.dtype, device=self.loc.device)
+        return (
+            self.loc
+            + _batch_mv(self._unbroadcasted_cov_factor, eps_W)
+            + self._unbroadcasted_cov_diag.sqrt() * eps_D
+        )
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        diff = value - self.loc
+        M = _batch_lowrank_mahalanobis(
+            self._unbroadcasted_cov_factor,
+            self._unbroadcasted_cov_diag,
+            diff,
+            self._capacitance_tril,
+        )
+        log_det = _batch_lowrank_logdet(
+            self._unbroadcasted_cov_factor,
+            self._unbroadcasted_cov_diag,
+            self._capacitance_tril,
+        )
+        return -0.5 * (self._event_shape[0] * math.log(2 * math.pi) + log_det + M)
+
+    def entropy(self):
+        log_det = _batch_lowrank_logdet(
+            self._unbroadcasted_cov_factor,
+            self._unbroadcasted_cov_diag,
+            self._capacitance_tril,
+        )
+        H = 0.5 * (self._event_shape[0] * (1.0 + math.log(2 * math.pi)) + log_det)
+        if len(self._batch_shape) == 0:
+            return H
+        else:
+            return H.expand(self._batch_shape)

shared/package-files/06/06d090ae1a5cf3f42e004c937acfa25d9bcd1830a4e0c7218eaf4e0ee7a40025/blob

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sparse_csc_tensor_ccol_row_value_size {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::IntArrayRef, c10::optional<at::ScalarType>, c10::optional<at::Layout>, c10::optional<at::Device>, c10::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::sparse_csc_tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "ccol_row_value_size")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "sparse_csc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor")
+  static at::Tensor call(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+};
+
+struct TORCH_API sparse_csc_tensor_ccol_row_value {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, c10::optional<at::ScalarType>, c10::optional<at::Layout>, c10::optional<at::Device>, c10::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::sparse_csc_tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "ccol_row_value")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "sparse_csc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor")
+  static at::Tensor call(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory);
+};
+
+}} // namespace at::_ops

shared/package-files/06/06d0d1944c1296f3a1c68d38997407a73cada15f8523f2b9c95b344405fc7812/blob

@@ -0,0 +1,1603 @@
+# Copyright (C) 2009, Pauli Virtanen <pav@iki.fi>
+# Distributed under the same license as SciPy.
+
+import inspect
+import sys
+import warnings
+
+import numpy as np
+from numpy import asarray, dot, vdot
+
+from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
+import scipy.sparse.linalg
+import scipy.sparse
+from scipy.linalg import get_blas_funcs
+from scipy._lib._util import copy_if_needed
+from scipy._lib._util import getfullargspec_no_self as _getfullargspec
+from ._linesearch import scalar_search_wolfe1, scalar_search_armijo
+
+
+__all__ = [
+    'broyden1', 'broyden2', 'anderson', 'linearmixing',
+    'diagbroyden', 'excitingmixing', 'newton_krylov',
+    'BroydenFirst', 'KrylovJacobian', 'InverseJacobian', 'NoConvergence']
+
+#------------------------------------------------------------------------------
+# Utility functions
+#------------------------------------------------------------------------------
+
+
+class NoConvergence(Exception):
+    """Exception raised when nonlinear solver fails to converge within the specified
+    `maxiter`."""
+    pass
+
+
+def maxnorm(x):
+    return np.absolute(x).max()
+
+
+def _as_inexact(x):
+    """Return `x` as an array, of either floats or complex floats"""
+    x = asarray(x)
+    if not np.issubdtype(x.dtype, np.inexact):
+        return asarray(x, dtype=np.float64)
+    return x
+
+
+def _array_like(x, x0):
+    """Return ndarray `x` as same array subclass and shape as `x0`"""
+    x = np.reshape(x, np.shape(x0))
+    wrap = getattr(x0, '__array_wrap__', x.__array_wrap__)
+    return wrap(x)
+
+
+def _safe_norm(v):
+    if not np.isfinite(v).all():
+        return np.array(np.inf)
+    return norm(v)
+
+#------------------------------------------------------------------------------
+# Generic nonlinear solver machinery
+#------------------------------------------------------------------------------
+
+
+_doc_parts = dict(
+    params_basic="""
+    F : function(x) -> f
+        Function whose root to find; should take and return an array-like
+        object.
+    xin : array_like
+        Initial guess for the solution
+    """.strip(),
+    params_extra="""
+    iter : int, optional
+        Number of iterations to make. If omitted (default), make as many
+        as required to meet tolerances.
+    verbose : bool, optional
+        Print status to stdout on every iteration.
+    maxiter : int, optional
+        Maximum number of iterations to make. If more are needed to
+        meet convergence, `NoConvergence` is raised.
+    f_tol : float, optional
+        Absolute tolerance (in max-norm) for the residual.
+        If omitted, default is 6e-6.
+    f_rtol : float, optional
+        Relative tolerance for the residual. If omitted, not used.
+    x_tol : float, optional
+        Absolute minimum step size, as determined from the Jacobian
+        approximation. If the step size is smaller than this, optimization
+        is terminated as successful. If omitted, not used.
+    x_rtol : float, optional
+        Relative minimum step size. If omitted, not used.
+    tol_norm : function(vector) -> scalar, optional
+        Norm to use in convergence check. Default is the maximum norm.
+    line_search : {None, 'armijo' (default), 'wolfe'}, optional
+        Which type of a line search to use to determine the step size in the
+        direction given by the Jacobian approximation. Defaults to 'armijo'.
+    callback : function, optional
+        Optional callback function. It is called on every iteration as
+        ``callback(x, f)`` where `x` is the current solution and `f`
+        the corresponding residual.
+
+    Returns
+    -------
+    sol : ndarray
+        An array (of similar array type as `x0`) containing the final solution.
+
+    Raises
+    ------
+    NoConvergence
+        When a solution was not found.
+
+    """.strip()
+)
+
+
+def _set_doc(obj):
+    if obj.__doc__:
+        obj.__doc__ = obj.__doc__ % _doc_parts
+
+
+def nonlin_solve(F, x0, jacobian='krylov', iter=None, verbose=False,
+                 maxiter=None, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None,
+                 tol_norm=None, line_search='armijo', callback=None,
+                 full_output=False, raise_exception=True):
+    """
+    Find a root of a function, in a way suitable for large-scale problems.
+
+    Parameters
+    ----------
+    %(params_basic)s
+    jacobian : Jacobian
+        A Jacobian approximation: `Jacobian` object or something that
+        `asjacobian` can transform to one. Alternatively, a string specifying
+        which of the builtin Jacobian approximations to use:
+
+            krylov, broyden1, broyden2, anderson
+            diagbroyden, linearmixing, excitingmixing
+
+    %(params_extra)s
+    full_output : bool
+        If true, returns a dictionary `info` containing convergence
+        information.
+    raise_exception : bool
+        If True, a `NoConvergence` exception is raise if no solution is found.
+
+    See Also
+    --------
+    asjacobian, Jacobian
+
+    Notes
+    -----
+    This algorithm implements the inexact Newton method, with
+    backtracking or full line searches. Several Jacobian
+    approximations are available, including Krylov and Quasi-Newton
+    methods.
+
+    References
+    ----------
+    .. [KIM] C. T. Kelley, \"Iterative Methods for Linear and Nonlinear
+       Equations\". Society for Industrial and Applied Mathematics. (1995)
+       https://archive.siam.org/books/kelley/fr16/
+
+    """
+    # Can't use default parameters because it's being explicitly passed as None
+    # from the calling function, so we need to set it here.
+    tol_norm = maxnorm if tol_norm is None else tol_norm
+    condition = TerminationCondition(f_tol=f_tol, f_rtol=f_rtol,
+                                     x_tol=x_tol, x_rtol=x_rtol,
+                                     iter=iter, norm=tol_norm)
+
+    x0 = _as_inexact(x0)
+    def func(z):
+        return _as_inexact(F(_array_like(z, x0))).flatten()
+    x = x0.flatten()
+
+    dx = np.full_like(x, np.inf)
+    Fx = func(x)
+    Fx_norm = norm(Fx)
+
+    jacobian = asjacobian(jacobian)
+    jacobian.setup(x.copy(), Fx, func)
+
+    if maxiter is None:
+        if iter is not None:
+            maxiter = iter + 1
+        else:
+            maxiter = 100*(x.size+1)
+
+    if line_search is True:
+        line_search = 'armijo'
+    elif line_search is False:
+        line_search = None
+
+    if line_search not in (None, 'armijo', 'wolfe'):
+        raise ValueError("Invalid line search")
+
+    # Solver tolerance selection
+    gamma = 0.9
+    eta_max = 0.9999
+    eta_treshold = 0.1
+    eta = 1e-3
+
+    for n in range(maxiter):
+        status = condition.check(Fx, x, dx)
+        if status:
+            break
+
+        # The tolerance, as computed for scipy.sparse.linalg.* routines
+        tol = min(eta, eta*Fx_norm)
+        dx = -jacobian.solve(Fx, tol=tol)
+
+        if norm(dx) == 0:
+            raise ValueError("Jacobian inversion yielded zero vector. "
+                             "This indicates a bug in the Jacobian "
+                             "approximation.")
+
+        # Line search, or Newton step
+        if line_search:
+            s, x, Fx, Fx_norm_new = _nonlin_line_search(func, x, Fx, dx,
+                                                        line_search)
+        else:
+            s = 1.0
+            x = x + dx
+            Fx = func(x)
+            Fx_norm_new = norm(Fx)
+
+        jacobian.update(x.copy(), Fx)
+
+        if callback:
+            callback(x, Fx)
+
+        # Adjust forcing parameters for inexact methods
+        eta_A = gamma * Fx_norm_new**2 / Fx_norm**2
+        if gamma * eta**2 < eta_treshold:
+            eta = min(eta_max, eta_A)
+        else:
+            eta = min(eta_max, max(eta_A, gamma*eta**2))
+
+        Fx_norm = Fx_norm_new
+
+        # Print status
+        if verbose:
+            sys.stdout.write("%d:  |F(x)| = %g; step %g\n" % (
+                n, tol_norm(Fx), s))
+            sys.stdout.flush()
+    else:
+        if raise_exception:
+            raise NoConvergence(_array_like(x, x0))
+        else:
+            status = 2
+
+    if full_output:
+        info = {'nit': condition.iteration,
+                'fun': Fx,
+                'status': status,
+                'success': status == 1,
+                'message': {1: 'A solution was found at the specified '
+                               'tolerance.',
+                            2: 'The maximum number of iterations allowed '
+                               'has been reached.'
+                            }[status]
+                }
+        return _array_like(x, x0), info
+    else:
+        return _array_like(x, x0)
+
+
+_set_doc(nonlin_solve)
+
+
+def _nonlin_line_search(func, x, Fx, dx, search_type='armijo', rdiff=1e-8,
+                        smin=1e-2):
+    tmp_s = [0]
+    tmp_Fx = [Fx]
+    tmp_phi = [norm(Fx)**2]
+    s_norm = norm(x) / norm(dx)
+
+    def phi(s, store=True):
+        if s == tmp_s[0]:
+            return tmp_phi[0]
+        xt = x + s*dx
+        v = func(xt)
+        p = _safe_norm(v)**2
+        if store:
+            tmp_s[0] = s
+            tmp_phi[0] = p
+            tmp_Fx[0] = v
+        return p
+
+    def derphi(s):
+        ds = (abs(s) + s_norm + 1) * rdiff
+        return (phi(s+ds, store=False) - phi(s)) / ds
+
+    if search_type == 'wolfe':
+        s, phi1, phi0 = scalar_search_wolfe1(phi, derphi, tmp_phi[0],
+                                             xtol=1e-2, amin=smin)
+    elif search_type == 'armijo':
+        s, phi1 = scalar_search_armijo(phi, tmp_phi[0], -tmp_phi[0],
+                                       amin=smin)
+
+    if s is None:
+        # XXX: No suitable step length found. Take the full Newton step,
+        #      and hope for the best.
+        s = 1.0
+
+    x = x + s*dx
+    if s == tmp_s[0]:
+        Fx = tmp_Fx[0]
+    else:
+        Fx = func(x)
+    Fx_norm = norm(Fx)
+
+    return s, x, Fx, Fx_norm
+
+
+class TerminationCondition:
+    """
+    Termination condition for an iteration. It is terminated if
+
+    - |F| < f_rtol*|F_0|, AND
+    - |F| < f_tol
+
+    AND
+
+    - |dx| < x_rtol*|x|, AND
+    - |dx| < x_tol
+
+    """
+    def __init__(self, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None,
+                 iter=None, norm=maxnorm):
+
+        if f_tol is None:
+            f_tol = np.finfo(np.float64).eps ** (1./3)
+        if f_rtol is None:
+            f_rtol = np.inf
+        if x_tol is None:
+            x_tol = np.inf
+        if x_rtol is None:
+            x_rtol = np.inf
+
+        self.x_tol = x_tol
+        self.x_rtol = x_rtol
+        self.f_tol = f_tol
+        self.f_rtol = f_rtol
+
+        self.norm = norm
+
+        self.iter = iter
+
+        self.f0_norm = None
+        self.iteration = 0
+
+    def check(self, f, x, dx):
+        self.iteration += 1
+        f_norm = self.norm(f)
+        x_norm = self.norm(x)
+        dx_norm = self.norm(dx)
+
+        if self.f0_norm is None:
+            self.f0_norm = f_norm
+
+        if f_norm == 0:
+            return 1
+
+        if self.iter is not None:
+            # backwards compatibility with SciPy 0.6.0
+            return 2 * (self.iteration > self.iter)
+
+        # NB: condition must succeed for rtol=inf even if norm == 0
+        return int((f_norm <= self.f_tol
+                    and f_norm/self.f_rtol <= self.f0_norm)
+                   and (dx_norm <= self.x_tol
+                        and dx_norm/self.x_rtol <= x_norm))
+
+
+#------------------------------------------------------------------------------
+# Generic Jacobian approximation
+#------------------------------------------------------------------------------
+
+class Jacobian:
+    """
+    Common interface for Jacobians or Jacobian approximations.
+
+    The optional methods come useful when implementing trust region
+    etc., algorithms that often require evaluating transposes of the
+    Jacobian.
+
+    Methods
+    -------
+    solve
+        Returns J^-1 * v
+    update
+        Updates Jacobian to point `x` (where the function has residual `Fx`)
+
+    matvec : optional
+        Returns J * v
+    rmatvec : optional
+        Returns A^H * v
+    rsolve : optional
+        Returns A^-H * v
+    matmat : optional
+        Returns A * V, where V is a dense matrix with dimensions (N,K).
+    todense : optional
+        Form the dense Jacobian matrix. Necessary for dense trust region
+        algorithms, and useful for testing.
+
+    Attributes
+    ----------
+    shape
+        Matrix dimensions (M, N)
+    dtype
+        Data type of the matrix.
+    func : callable, optional
+        Function the Jacobian corresponds to
+
+    """
+
+    def __init__(self, **kw):
+        names = ["solve", "update", "matvec", "rmatvec", "rsolve",
+                 "matmat", "todense", "shape", "dtype"]
+        for name, value in kw.items():
+            if name not in names:
+                raise ValueError(f"Unknown keyword argument {name}")
+            if value is not None:
+                setattr(self, name, kw[name])
+
+
+        if hasattr(self, "todense"):
+            def __array__(self, dtype=None, copy=None):
+                if dtype is not None:
+                    raise ValueError(f"`dtype` must be None, was {dtype}")
+                return self.todense()
+
+    def aspreconditioner(self):
+        return InverseJacobian(self)
+
+    def solve(self, v, tol=0):
+        raise NotImplementedError
+
+    def update(self, x, F):
+        pass
+
+    def setup(self, x, F, func):
+        self.func = func
+        self.shape = (F.size, x.size)
+        self.dtype = F.dtype
+        if self.__class__.setup is Jacobian.setup:
+            # Call on the first point unless overridden
+            self.update(x, F)
+
+
+class InverseJacobian:
+    """
+    A simple wrapper that inverts the Jacobian using the `solve` method.
+
+    .. legacy:: class
+
+        See the newer, more consistent interfaces in :mod:`scipy.optimize`.
+
+    Parameters
+    ----------
+    jacobian : Jacobian
+        The Jacobian to invert.
+    
+    Attributes
+    ----------
+    shape
+        Matrix dimensions (M, N)
+    dtype
+        Data type of the matrix.
+
+    """
+    def __init__(self, jacobian):
+        self.jacobian = jacobian
+        self.matvec = jacobian.solve
+        self.update = jacobian.update
+        if hasattr(jacobian, 'setup'):
+            self.setup = jacobian.setup
+        if hasattr(jacobian, 'rsolve'):
+            self.rmatvec = jacobian.rsolve
+
+    @property
+    def shape(self):
+        return self.jacobian.shape
+
+    @property
+    def dtype(self):
+        return self.jacobian.dtype
+
+
+def asjacobian(J):
+    """
+    Convert given object to one suitable for use as a Jacobian.
+    """
+    spsolve = scipy.sparse.linalg.spsolve
+    if isinstance(J, Jacobian):
+        return J
+    elif inspect.isclass(J) and issubclass(J, Jacobian):
+        return J()
+    elif isinstance(J, np.ndarray):
+        if J.ndim > 2:
+            raise ValueError('array must have rank <= 2')
+        J = np.atleast_2d(np.asarray(J))
+        if J.shape[0] != J.shape[1]:
+            raise ValueError('array must be square')
+
+        return Jacobian(matvec=lambda v: dot(J, v),
+                        rmatvec=lambda v: dot(J.conj().T, v),
+                        solve=lambda v, tol=0: solve(J, v),
+                        rsolve=lambda v, tol=0: solve(J.conj().T, v),
+                        dtype=J.dtype, shape=J.shape)
+    elif scipy.sparse.issparse(J):
+        if J.shape[0] != J.shape[1]:
+            raise ValueError('matrix must be square')
+        return Jacobian(matvec=lambda v: J @ v,
+                        rmatvec=lambda v: J.conj().T @ v,
+                        solve=lambda v, tol=0: spsolve(J, v),
+                        rsolve=lambda v, tol=0: spsolve(J.conj().T, v),
+                        dtype=J.dtype, shape=J.shape)
+    elif hasattr(J, 'shape') and hasattr(J, 'dtype') and hasattr(J, 'solve'):
+        return Jacobian(matvec=getattr(J, 'matvec'),
+                        rmatvec=getattr(J, 'rmatvec'),
+                        solve=J.solve,
+                        rsolve=getattr(J, 'rsolve'),
+                        update=getattr(J, 'update'),
+                        setup=getattr(J, 'setup'),
+                        dtype=J.dtype,
+                        shape=J.shape)
+    elif callable(J):
+        # Assume it's a function J(x) that returns the Jacobian
+        class Jac(Jacobian):
+            def update(self, x, F):
+                self.x = x
+
+            def solve(self, v, tol=0):
+                m = J(self.x)
+                if isinstance(m, np.ndarray):
+                    return solve(m, v)
+                elif scipy.sparse.issparse(m):
+                    return spsolve(m, v)
+                else:
+                    raise ValueError("Unknown matrix type")
+
+            def matvec(self, v):
+                m = J(self.x)
+                if isinstance(m, np.ndarray):
+                    return dot(m, v)
+                elif scipy.sparse.issparse(m):
+                    return m @ v
+                else:
+                    raise ValueError("Unknown matrix type")
+
+            def rsolve(self, v, tol=0):
+                m = J(self.x)
+                if isinstance(m, np.ndarray):
+                    return solve(m.conj().T, v)
+                elif scipy.sparse.issparse(m):
+                    return spsolve(m.conj().T, v)
+                else:
+                    raise ValueError("Unknown matrix type")
+
+            def rmatvec(self, v):
+                m = J(self.x)
+                if isinstance(m, np.ndarray):
+                    return dot(m.conj().T, v)
+                elif scipy.sparse.issparse(m):
+                    return m.conj().T @ v
+                else:
+                    raise ValueError("Unknown matrix type")
+        return Jac()
+    elif isinstance(J, str):
+        return dict(broyden1=BroydenFirst,
+                    broyden2=BroydenSecond,
+                    anderson=Anderson,
+                    diagbroyden=DiagBroyden,
+                    linearmixing=LinearMixing,
+                    excitingmixing=ExcitingMixing,
+                    krylov=KrylovJacobian)[J]()
+    else:
+        raise TypeError('Cannot convert object to a Jacobian')
+
+
+#------------------------------------------------------------------------------
+# Broyden
+#------------------------------------------------------------------------------
+
+class GenericBroyden(Jacobian):
+    def setup(self, x0, f0, func):
+        Jacobian.setup(self, x0, f0, func)
+        self.last_f = f0
+        self.last_x = x0
+
+        if hasattr(self, 'alpha') and self.alpha is None:
+            # Autoscale the initial Jacobian parameter
+            # unless we have already guessed the solution.
+            normf0 = norm(f0)
+            if normf0:
+                self.alpha = 0.5*max(norm(x0), 1) / normf0
+            else:
+                self.alpha = 1.0
+
+    def _update(self, x, f, dx, df, dx_norm, df_norm):
+        raise NotImplementedError
+
+    def update(self, x, f):
+        df = f - self.last_f
+        dx = x - self.last_x
+        self._update(x, f, dx, df, norm(dx), norm(df))
+        self.last_f = f
+        self.last_x = x
+
+
+class LowRankMatrix:
+    r"""
+    A matrix represented as
+
+    .. math:: \alpha I + \sum_{n=0}^{n=M} c_n d_n^\dagger
+
+    However, if the rank of the matrix reaches the dimension of the vectors,
+    full matrix representation will be used thereon.
+
+    """
+
+    def __init__(self, alpha, n, dtype):
+        self.alpha = alpha
+        self.cs = []
+        self.ds = []
+        self.n = n
+        self.dtype = dtype
+        self.collapsed = None
+
+    @staticmethod
+    def _matvec(v, alpha, cs, ds):
+        axpy, scal, dotc = get_blas_funcs(['axpy', 'scal', 'dotc'],
+                                          cs[:1] + [v])
+        w = alpha * v
+        for c, d in zip(cs, ds):
+            a = dotc(d, v)
+            w = axpy(c, w, w.size, a)
+        return w
+
+    @staticmethod
+    def _solve(v, alpha, cs, ds):
+        """Evaluate w = M^-1 v"""
+        if len(cs) == 0:
+            return v/alpha
+
+        # (B + C D^H)^-1 = B^-1 - B^-1 C (I + D^H B^-1 C)^-1 D^H B^-1
+
+        axpy, dotc = get_blas_funcs(['axpy', 'dotc'], cs[:1] + [v])
+
+        c0 = cs[0]
+        A = alpha * np.identity(len(cs), dtype=c0.dtype)
+        for i, d in enumerate(ds):
+            for j, c in enumerate(cs):
+                A[i,j] += dotc(d, c)
+
+        q = np.zeros(len(cs), dtype=c0.dtype)
+        for j, d in enumerate(ds):
+            q[j] = dotc(d, v)
+        q /= alpha
+        q = solve(A, q)
+
+        w = v/alpha
+        for c, qc in zip(cs, q):
+            w = axpy(c, w, w.size, -qc)
+
+        return w
+
+    def matvec(self, v):
+        """Evaluate w = M v"""
+        if self.collapsed is not None:
+            return np.dot(self.collapsed, v)
+        return LowRankMatrix._matvec(v, self.alpha, self.cs, self.ds)
+
+    def rmatvec(self, v):
+        """Evaluate w = M^H v"""
+        if self.collapsed is not None:
+            return np.dot(self.collapsed.T.conj(), v)
+        return LowRankMatrix._matvec(v, np.conj(self.alpha), self.ds, self.cs)
+
+    def solve(self, v, tol=0):
+        """Evaluate w = M^-1 v"""
+        if self.collapsed is not None:
+            return solve(self.collapsed, v)
+        return LowRankMatrix._solve(v, self.alpha, self.cs, self.ds)
+
+    def rsolve(self, v, tol=0):
+        """Evaluate w = M^-H v"""
+        if self.collapsed is not None:
+            return solve(self.collapsed.T.conj(), v)
+        return LowRankMatrix._solve(v, np.conj(self.alpha), self.ds, self.cs)
+
+    def append(self, c, d):
+        if self.collapsed is not None:
+            self.collapsed += c[:,None] * d[None,:].conj()
+            return
+
+        self.cs.append(c)
+        self.ds.append(d)
+
+        if len(self.cs) > c.size:
+            self.collapse()
+
+    def __array__(self, dtype=None, copy=None):
+        if dtype is not None:
+            warnings.warn("LowRankMatrix is scipy-internal code, `dtype` "
+                          f"should only be None but was {dtype} (not handled)",
+                          stacklevel=3)
+        if copy is not None:
+            warnings.warn("LowRankMatrix is scipy-internal code, `copy` "
+                          f"should only be None but was {copy} (not handled)",
+                          stacklevel=3)
+        if self.collapsed is not None:
+            return self.collapsed
+
+        Gm = self.alpha*np.identity(self.n, dtype=self.dtype)
+        for c, d in zip(self.cs, self.ds):
+            Gm += c[:,None]*d[None,:].conj()
+        return Gm
+
+    def collapse(self):
+        """Collapse the low-rank matrix to a full-rank one."""
+        self.collapsed = np.array(self, copy=copy_if_needed)
+        self.cs = None
+        self.ds = None
+        self.alpha = None
+
+    def restart_reduce(self, rank):
+        """
+        Reduce the rank of the matrix by dropping all vectors.
+        """
+        if self.collapsed is not None:
+            return
+        assert rank > 0
+        if len(self.cs) > rank:
+            del self.cs[:]
+            del self.ds[:]
+
+    def simple_reduce(self, rank):
+        """
+        Reduce the rank of the matrix by dropping oldest vectors.
+        """
+        if self.collapsed is not None:
+            return
+        assert rank > 0
+        while len(self.cs) > rank:
+            del self.cs[0]
+            del self.ds[0]
+
+    def svd_reduce(self, max_rank, to_retain=None):
+        """
+        Reduce the rank of the matrix by retaining some SVD components.
+
+        This corresponds to the \"Broyden Rank Reduction Inverse\"
+        algorithm described in [1]_.
+
+        Note that the SVD decomposition can be done by solving only a
+        problem whose size is the effective rank of this matrix, which
+        is viable even for large problems.
+
+        Parameters
+        ----------
+        max_rank : int
+            Maximum rank of this matrix after reduction.
+        to_retain : int, optional
+            Number of SVD components to retain when reduction is done
+            (ie. rank > max_rank). Default is ``max_rank - 2``.
+
+        References
+        ----------
+        .. [1] B.A. van der Rotten, PhD thesis,
+           \"A limited memory Broyden method to solve high-dimensional
+           systems of nonlinear equations\". Mathematisch Instituut,
+           Universiteit Leiden, The Netherlands (2003).
+
+           https://web.archive.org/web/20161022015821/http://www.math.leidenuniv.nl/scripties/Rotten.pdf
+
+        """
+        if self.collapsed is not None:
+            return
+
+        p = max_rank
+        if to_retain is not None:
+            q = to_retain
+        else:
+            q = p - 2
+
+        if self.cs:
+            p = min(p, len(self.cs[0]))
+        q = max(0, min(q, p-1))
+
+        m = len(self.cs)
+        if m < p:
+            # nothing to do
+            return
+
+        C = np.array(self.cs).T
+        D = np.array(self.ds).T
+
+        D, R = qr(D, mode='economic')
+        C = dot(C, R.T.conj())
+
+        U, S, WH = svd(C, full_matrices=False)
+
+        C = dot(C, inv(WH))
+        D = dot(D, WH.T.conj())
+
+        for k in range(q):
+            self.cs[k] = C[:,k].copy()
+            self.ds[k] = D[:,k].copy()
+
+        del self.cs[q:]
+        del self.ds[q:]
+
+
+_doc_parts['broyden_params'] = """
+    alpha : float, optional
+        Initial guess for the Jacobian is ``(-1/alpha)``.
+    reduction_method : str or tuple, optional
+        Method used in ensuring that the rank of the Broyden matrix
+        stays low. Can either be a string giving the name of the method,
+        or a tuple of the form ``(method, param1, param2, ...)``
+        that gives the name of the method and values for additional parameters.
+
+        Methods available:
+
+        - ``restart``: drop all matrix columns. Has no extra parameters.
+        - ``simple``: drop oldest matrix column. Has no extra parameters.
+        - ``svd``: keep only the most significant SVD components.
+          Takes an extra parameter, ``to_retain``, which determines the
+          number of SVD components to retain when rank reduction is done.
+          Default is ``max_rank - 2``.
+
+    max_rank : int, optional
+        Maximum rank for the Broyden matrix.
+        Default is infinity (i.e., no rank reduction).
+    """.strip()
+
+
+class BroydenFirst(GenericBroyden):
+    """
+    Find a root of a function, using Broyden's first Jacobian approximation.
+
+    This method is also known as "Broyden's good method".
+
+    Parameters
+    ----------
+    %(params_basic)s
+    %(broyden_params)s
+    %(params_extra)s
+
+    See Also
+    --------
+    root : Interface to root finding algorithms for multivariate
+           functions. See ``method='broyden1'`` in particular.
+
+    Notes
+    -----
+    This algorithm implements the inverse Jacobian Quasi-Newton update
+
+    .. math:: H_+ = H + (dx - H df) dx^\\dagger H / ( dx^\\dagger H df)
+
+    which corresponds to Broyden's first Jacobian update
+
+    .. math:: J_+ = J + (df - J dx) dx^\\dagger / dx^\\dagger dx
+
+
+    References
+    ----------
+    .. [1] B.A. van der Rotten, PhD thesis,
+       "A limited memory Broyden method to solve high-dimensional
+       systems of nonlinear equations". Mathematisch Instituut,
+       Universiteit Leiden, The Netherlands (2003).
+       https://math.leidenuniv.nl/scripties/Rotten.pdf
+
+    Examples
+    --------
+    The following functions define a system of nonlinear equations
+
+    >>> def fun(x):
+    ...     return [x[0]  + 0.5 * (x[0] - x[1])**3 - 1.0,
+    ...             0.5 * (x[1] - x[0])**3 + x[1]]
+
+    A solution can be obtained as follows.
+
+    >>> from scipy import optimize
+    >>> sol = optimize.broyden1(fun, [0, 0])
+    >>> sol
+    array([0.84116396, 0.15883641])
+
+    """
+
+    def __init__(self, alpha=None, reduction_method='restart', max_rank=None):
+        GenericBroyden.__init__(self)
+        self.alpha = alpha
+        self.Gm = None
+
+        if max_rank is None:
+            max_rank = np.inf
+        self.max_rank = max_rank
+
+        if isinstance(reduction_method, str):
+            reduce_params = ()
+        else:
+            reduce_params = reduction_method[1:]
+            reduction_method = reduction_method[0]
+        reduce_params = (max_rank - 1,) + reduce_params
+
+        if reduction_method == 'svd':
+            self._reduce = lambda: self.Gm.svd_reduce(*reduce_params)
+        elif reduction_method == 'simple':
+            self._reduce = lambda: self.Gm.simple_reduce(*reduce_params)
+        elif reduction_method == 'restart':
+            self._reduce = lambda: self.Gm.restart_reduce(*reduce_params)
+        else:
+            raise ValueError(f"Unknown rank reduction method '{reduction_method}'")
+
+    def setup(self, x, F, func):
+        GenericBroyden.setup(self, x, F, func)
+        self.Gm = LowRankMatrix(-self.alpha, self.shape[0], self.dtype)
+
+    def todense(self):
+        return inv(self.Gm)
+
+    def solve(self, f, tol=0):
+        r = self.Gm.matvec(f)
+        if not np.isfinite(r).all():
+            # singular; reset the Jacobian approximation
+            self.setup(self.last_x, self.last_f, self.func)
+            return self.Gm.matvec(f)
+        return r
+
+    def matvec(self, f):
+        return self.Gm.solve(f)
+
+    def rsolve(self, f, tol=0):
+        return self.Gm.rmatvec(f)
+
+    def rmatvec(self, f):
+        return self.Gm.rsolve(f)
+
+    def _update(self, x, f, dx, df, dx_norm, df_norm):
+        self._reduce()  # reduce first to preserve secant condition
+
+        v = self.Gm.rmatvec(dx)
+        c = dx - self.Gm.matvec(df)
+        d = v / vdot(df, v)
+
+        self.Gm.append(c, d)
+
+
+class BroydenSecond(BroydenFirst):
+    """
+    Find a root of a function, using Broyden\'s second Jacobian approximation.
+
+    This method is also known as \"Broyden's bad method\".
+
+    Parameters
+    ----------
+    %(params_basic)s
+    %(broyden_params)s
+    %(params_extra)s
+
+    See Also
+    --------
+    root : Interface to root finding algorithms for multivariate
+           functions. See ``method='broyden2'`` in particular.
+
+    Notes
+    -----
+    This algorithm implements the inverse Jacobian Quasi-Newton update
+
+    .. math:: H_+ = H + (dx - H df) df^\\dagger / ( df^\\dagger df)
+
+    corresponding to Broyden's second method.
+
+    References
+    ----------
+    .. [1] B.A. van der Rotten, PhD thesis,
+       \"A limited memory Broyden method to solve high-dimensional
+       systems of nonlinear equations\". Mathematisch Instituut,
+       Universiteit Leiden, The Netherlands (2003).
+
+       https://web.archive.org/web/20161022015821/http://www.math.leidenuniv.nl/scripties/Rotten.pdf
+
+    Examples
+    --------
+    The following functions define a system of nonlinear equations
+
+    >>> def fun(x):
+    ...     return [x[0]  + 0.5 * (x[0] - x[1])**3 - 1.0,
+    ...             0.5 * (x[1] - x[0])**3 + x[1]]
+
+    A solution can be obtained as follows.
+
+    >>> from scipy import optimize
+    >>> sol = optimize.broyden2(fun, [0, 0])
+    >>> sol
+    array([0.84116365, 0.15883529])
+
+    """
+
+    def _update(self, x, f, dx, df, dx_norm, df_norm):
+        self._reduce()  # reduce first to preserve secant condition
+
+        v = df
+        c = dx - self.Gm.matvec(df)
+        d = v / df_norm**2
+        self.Gm.append(c, d)
+
+
+#------------------------------------------------------------------------------
+# Broyden-like (restricted memory)
+#------------------------------------------------------------------------------
+
+class Anderson(GenericBroyden):
+    """
+    Find a root of a function, using (extended) Anderson mixing.
+
+    The Jacobian is formed by for a 'best' solution in the space
+    spanned by last `M` vectors. As a result, only a MxM matrix
+    inversions and MxN multiplications are required. [Ey]_
+
+    Parameters
+    ----------
+    %(params_basic)s
+    alpha : float, optional
+        Initial guess for the Jacobian is (-1/alpha).
+    M : float, optional
+        Number of previous vectors to retain. Defaults to 5.
+    w0 : float, optional
+        Regularization parameter for numerical stability.
+        Compared to unity, good values of the order of 0.01.
+    %(params_extra)s
+
+    See Also
+    --------
+    root : Interface to root finding algorithms for multivariate
+           functions. See ``method='anderson'`` in particular.
+
+    References
+    ----------
+    .. [Ey] V. Eyert, J. Comp. Phys., 124, 271 (1996).
+
+    Examples
+    --------
+    The following functions define a system of nonlinear equations
+
+    >>> def fun(x):
+    ...     return [x[0]  + 0.5 * (x[0] - x[1])**3 - 1.0,
+    ...             0.5 * (x[1] - x[0])**3 + x[1]]
+
+    A solution can be obtained as follows.
+
+    >>> from scipy import optimize
+    >>> sol = optimize.anderson(fun, [0, 0])
+    >>> sol
+    array([0.84116588, 0.15883789])
+
+    """
+
+    # Note:
+    #
+    # Anderson method maintains a rank M approximation of the inverse Jacobian,
+    #
+    #     J^-1 v ~ -v*alpha + (dX + alpha dF) A^-1 dF^H v
+    #     A      = W + dF^H dF
+    #     W      = w0^2 diag(dF^H dF)
+    #
+    # so that for w0 = 0 the secant condition applies for last M iterates, i.e.,
+    #
+    #     J^-1 df_j = dx_j
+    #
+    # for all j = 0 ... M-1.
+    #
+    # Moreover, (from Sherman-Morrison-Woodbury formula)
+    #
+    #    J v ~ [ b I - b^2 C (I + b dF^H A^-1 C)^-1 dF^H ] v
+    #    C   = (dX + alpha dF) A^-1
+    #    b   = -1/alpha
+    #
+    # and after simplification
+    #
+    #    J v ~ -v/alpha + (dX/alpha + dF) (dF^H dX - alpha W)^-1 dF^H v
+    #
+
+    def __init__(self, alpha=None, w0=0.01, M=5):
+        GenericBroyden.__init__(self)
+        self.alpha = alpha
+        self.M = M
+        self.dx = []
+        self.df = []
+        self.gamma = None
+        self.w0 = w0
+
+    def solve(self, f, tol=0):
+        dx = -self.alpha*f
+
+        n = len(self.dx)
+        if n == 0:
+            return dx
+
+        df_f = np.empty(n, dtype=f.dtype)
+        for k in range(n):
+            df_f[k] = vdot(self.df[k], f)
+
+        try:
+            gamma = solve(self.a, df_f)
+        except LinAlgError:
+            # singular; reset the Jacobian approximation
+            del self.dx[:]
+            del self.df[:]
+            return dx
+
+        for m in range(n):
+            dx += gamma[m]*(self.dx[m] + self.alpha*self.df[m])
+        return dx
+
+    def matvec(self, f):
+        dx = -f/self.alpha
+
+        n = len(self.dx)
+        if n == 0:
+            return dx
+
+        df_f = np.empty(n, dtype=f.dtype)
+        for k in range(n):
+            df_f[k] = vdot(self.df[k], f)
+
+        b = np.empty((n, n), dtype=f.dtype)
+        for i in range(n):
+            for j in range(n):
+                b[i,j] = vdot(self.df[i], self.dx[j])
+                if i == j and self.w0 != 0:
+                    b[i,j] -= vdot(self.df[i], self.df[i])*self.w0**2*self.alpha
+        gamma = solve(b, df_f)
+
+        for m in range(n):
+            dx += gamma[m]*(self.df[m] + self.dx[m]/self.alpha)
+        return dx
+
+    def _update(self, x, f, dx, df, dx_norm, df_norm):
+        if self.M == 0:
+            return
+
+        self.dx.append(dx)
+        self.df.append(df)
+
+        while len(self.dx) > self.M:
+            self.dx.pop(0)
+            self.df.pop(0)
+
+        n = len(self.dx)
+        a = np.zeros((n, n), dtype=f.dtype)
+
+        for i in range(n):
+            for j in range(i, n):
+                if i == j:
+                    wd = self.w0**2
+                else:
+                    wd = 0
+                a[i,j] = (1+wd)*vdot(self.df[i], self.df[j])
+
+        a += np.triu(a, 1).T.conj()
+        self.a = a
+
+#------------------------------------------------------------------------------
+# Simple iterations
+#------------------------------------------------------------------------------
+
+
+class DiagBroyden(GenericBroyden):
+    """
+    Find a root of a function, using diagonal Broyden Jacobian approximation.
+
+    The Jacobian approximation is derived from previous iterations, by
+    retaining only the diagonal of Broyden matrices.
+
+    .. warning::
+
+       This algorithm may be useful for specific problems, but whether
+       it will work may depend strongly on the problem.
+
+    Parameters
+    ----------
+    %(params_basic)s
+    alpha : float, optional
+        Initial guess for the Jacobian is (-1/alpha).
+    %(params_extra)s
+
+    See Also
+    --------
+    root : Interface to root finding algorithms for multivariate
+           functions. See ``method='diagbroyden'`` in particular.
+
+    Examples
+    --------
+    The following functions define a system of nonlinear equations
+
+    >>> def fun(x):
+    ...     return [x[0]  + 0.5 * (x[0] - x[1])**3 - 1.0,
+    ...             0.5 * (x[1] - x[0])**3 + x[1]]
+
+    A solution can be obtained as follows.
+
+    >>> from scipy import optimize
+    >>> sol = optimize.diagbroyden(fun, [0, 0])
+    >>> sol
+    array([0.84116403, 0.15883384])
+
+    """
+
+    def __init__(self, alpha=None):
+        GenericBroyden.__init__(self)
+        self.alpha = alpha
+
+    def setup(self, x, F, func):
+        GenericBroyden.setup(self, x, F, func)
+        self.d = np.full((self.shape[0],), 1 / self.alpha, dtype=self.dtype)
+
+    def solve(self, f, tol=0):
+        return -f / self.d
+
+    def matvec(self, f):
+        return -f * self.d
+
+    def rsolve(self, f, tol=0):
+        return -f / self.d.conj()
+
+    def rmatvec(self, f):
+        return -f * self.d.conj()
+
+    def todense(self):
+        return np.diag(-self.d)
+
+    def _update(self, x, f, dx, df, dx_norm, df_norm):
+        self.d -= (df + self.d*dx)*dx/dx_norm**2
+
+
+class LinearMixing(GenericBroyden):
+    """
+    Find a root of a function, using a scalar Jacobian approximation.
+
+    .. warning::
+
+       This algorithm may be useful for specific problems, but whether
+       it will work may depend strongly on the problem.
+
+    Parameters
+    ----------
+    %(params_basic)s
+    alpha : float, optional
+        The Jacobian approximation is (-1/alpha).
+    %(params_extra)s
+
+    See Also
+    --------
+    root : Interface to root finding algorithms for multivariate
+           functions. See ``method='linearmixing'`` in particular.
+
+    """
+
+    def __init__(self, alpha=None):
+        GenericBroyden.__init__(self)
+        self.alpha = alpha
+
+    def solve(self, f, tol=0):
+        return -f*self.alpha
+
+    def matvec(self, f):
+        return -f/self.alpha
+
+    def rsolve(self, f, tol=0):
+        return -f*np.conj(self.alpha)
+
+    def rmatvec(self, f):
+        return -f/np.conj(self.alpha)
+
+    def todense(self):
+        return np.diag(np.full(self.shape[0], -1/self.alpha))
+
+    def _update(self, x, f, dx, df, dx_norm, df_norm):
+        pass
+
+
+class ExcitingMixing(GenericBroyden):
+    """
+    Find a root of a function, using a tuned diagonal Jacobian approximation.
+
+    The Jacobian matrix is diagonal and is tuned on each iteration.
+
+    .. warning::
+
+       This algorithm may be useful for specific problems, but whether
+       it will work may depend strongly on the problem.
+
+    See Also
+    --------
+    root : Interface to root finding algorithms for multivariate
+           functions. See ``method='excitingmixing'`` in particular.
+
+    Parameters
+    ----------
+    %(params_basic)s
+    alpha : float, optional
+        Initial Jacobian approximation is (-1/alpha).
+    alphamax : float, optional
+        The entries of the diagonal Jacobian are kept in the range
+        ``[alpha, alphamax]``.
+    %(params_extra)s
+    """
+
+    def __init__(self, alpha=None, alphamax=1.0):
+        GenericBroyden.__init__(self)
+        self.alpha = alpha
+        self.alphamax = alphamax
+        self.beta = None
+
+    def setup(self, x, F, func):
+        GenericBroyden.setup(self, x, F, func)
+        self.beta = np.full((self.shape[0],), self.alpha, dtype=self.dtype)
+
+    def solve(self, f, tol=0):
+        return -f*self.beta
+
+    def matvec(self, f):
+        return -f/self.beta
+
+    def rsolve(self, f, tol=0):
+        return -f*self.beta.conj()
+
+    def rmatvec(self, f):
+        return -f/self.beta.conj()
+
+    def todense(self):
+        return np.diag(-1/self.beta)
+
+    def _update(self, x, f, dx, df, dx_norm, df_norm):
+        incr = f*self.last_f > 0
+        self.beta[incr] += self.alpha
+        self.beta[~incr] = self.alpha
+        np.clip(self.beta, 0, self.alphamax, out=self.beta)
+
+
+#------------------------------------------------------------------------------
+# Iterative/Krylov approximated Jacobians
+#------------------------------------------------------------------------------
+
+class KrylovJacobian(Jacobian):
+    """
+    Find a root of a function, using Krylov approximation for inverse Jacobian.
+
+    This method is suitable for solving large-scale problems.
+
+    Parameters
+    ----------
+    %(params_basic)s
+    rdiff : float, optional
+        Relative step size to use in numerical differentiation.
+    method : str or callable, optional
+        Krylov method to use to approximate the Jacobian.  Can be a string,
+        or a function implementing the same interface as the iterative
+        solvers in `scipy.sparse.linalg`. If a string, needs to be one of:
+        ``'lgmres'``, ``'gmres'``, ``'bicgstab'``, ``'cgs'``, ``'minres'``,
+        ``'tfqmr'``.
+
+        The default is `scipy.sparse.linalg.lgmres`.
+    inner_maxiter : int, optional
+        Parameter to pass to the "inner" Krylov solver: maximum number of
+        iterations. Iteration will stop after maxiter steps even if the
+        specified tolerance has not been achieved.
+    inner_M : LinearOperator or InverseJacobian
+        Preconditioner for the inner Krylov iteration.
+        Note that you can use also inverse Jacobians as (adaptive)
+        preconditioners. For example,
+
+        >>> from scipy.optimize import BroydenFirst, KrylovJacobian
+        >>> from scipy.optimize import InverseJacobian
+        >>> jac = BroydenFirst()
+        >>> kjac = KrylovJacobian(inner_M=InverseJacobian(jac))
+
+        If the preconditioner has a method named 'update', it will be called
+        as ``update(x, f)`` after each nonlinear step, with ``x`` giving
+        the current point, and ``f`` the current function value.
+    outer_k : int, optional
+        Size of the subspace kept across LGMRES nonlinear iterations.
+        See `scipy.sparse.linalg.lgmres` for details.
+    inner_kwargs : kwargs
+        Keyword parameters for the "inner" Krylov solver
+        (defined with `method`). Parameter names must start with
+        the `inner_` prefix which will be stripped before passing on
+        the inner method. See, e.g., `scipy.sparse.linalg.gmres` for details.
+    %(params_extra)s
+
+    See Also
+    --------
+    root : Interface to root finding algorithms for multivariate
+           functions. See ``method='krylov'`` in particular.
+    scipy.sparse.linalg.gmres
+    scipy.sparse.linalg.lgmres
+
+    Notes
+    -----
+    This function implements a Newton-Krylov solver. The basic idea is
+    to compute the inverse of the Jacobian with an iterative Krylov
+    method. These methods require only evaluating the Jacobian-vector
+    products, which are conveniently approximated by a finite difference:
+
+    .. math:: J v \\approx (f(x + \\omega*v/|v|) - f(x)) / \\omega
+
+    Due to the use of iterative matrix inverses, these methods can
+    deal with large nonlinear problems.
+
+    SciPy's `scipy.sparse.linalg` module offers a selection of Krylov
+    solvers to choose from. The default here is `lgmres`, which is a
+    variant of restarted GMRES iteration that reuses some of the
+    information obtained in the previous Newton steps to invert
+    Jacobians in subsequent steps.
+
+    For a review on Newton-Krylov methods, see for example [1]_,
+    and for the LGMRES sparse inverse method, see [2]_.
+
+    References
+    ----------
+    .. [1] C. T. Kelley, Solving Nonlinear Equations with Newton's Method,
+           SIAM, pp.57-83, 2003.
+           :doi:`10.1137/1.9780898718898.ch3`
+    .. [2] D.A. Knoll and D.E. Keyes, J. Comp. Phys. 193, 357 (2004).
+           :doi:`10.1016/j.jcp.2003.08.010`
+    .. [3] A.H. Baker and E.R. Jessup and T. Manteuffel,
+           SIAM J. Matrix Anal. Appl. 26, 962 (2005).
+           :doi:`10.1137/S0895479803422014`
+
+    Examples
+    --------
+    The following functions define a system of nonlinear equations
+
+    >>> def fun(x):
+    ...     return [x[0] + 0.5 * x[1] - 1.0,
+    ...             0.5 * (x[1] - x[0]) ** 2]
+
+    A solution can be obtained as follows.
+
+    >>> from scipy import optimize
+    >>> sol = optimize.newton_krylov(fun, [0, 0])
+    >>> sol
+    array([0.66731771, 0.66536458])
+
+    """
+
+    def __init__(self, rdiff=None, method='lgmres', inner_maxiter=20,
+                 inner_M=None, outer_k=10, **kw):
+        self.preconditioner = inner_M
+        self.rdiff = rdiff
+        # Note that this retrieves one of the named functions, or otherwise
+        # uses `method` as is (i.e., for a user-provided callable).
+        self.method = dict(
+            bicgstab=scipy.sparse.linalg.bicgstab,
+            gmres=scipy.sparse.linalg.gmres,
+            lgmres=scipy.sparse.linalg.lgmres,
+            cgs=scipy.sparse.linalg.cgs,
+            minres=scipy.sparse.linalg.minres,
+            tfqmr=scipy.sparse.linalg.tfqmr,
+            ).get(method, method)
+
+        self.method_kw = dict(maxiter=inner_maxiter, M=self.preconditioner)
+
+        if self.method is scipy.sparse.linalg.gmres:
+            # Replace GMRES's outer iteration with Newton steps
+            self.method_kw['restart'] = inner_maxiter
+            self.method_kw['maxiter'] = 1
+            self.method_kw.setdefault('atol', 0)
+        elif self.method in (scipy.sparse.linalg.gcrotmk,
+                             scipy.sparse.linalg.bicgstab,
+                             scipy.sparse.linalg.cgs):
+            self.method_kw.setdefault('atol', 0)
+        elif self.method is scipy.sparse.linalg.lgmres:
+            self.method_kw['outer_k'] = outer_k
+            # Replace LGMRES's outer iteration with Newton steps
+            self.method_kw['maxiter'] = 1
+            # Carry LGMRES's `outer_v` vectors across nonlinear iterations
+            self.method_kw.setdefault('outer_v', [])
+            self.method_kw.setdefault('prepend_outer_v', True)
+            # But don't carry the corresponding Jacobian*v products, in case
+            # the Jacobian changes a lot in the nonlinear step
+            #
+            # XXX: some trust-region inspired ideas might be more efficient...
+            #      See e.g., Brown & Saad. But needs to be implemented separately
+            #      since it's not an inexact Newton method.
+            self.method_kw.setdefault('store_outer_Av', False)
+            self.method_kw.setdefault('atol', 0)
+
+        for key, value in kw.items():
+            if not key.startswith('inner_'):
+                raise ValueError(f"Unknown parameter {key}")
+            self.method_kw[key[6:]] = value
+
+    def _update_diff_step(self):
+        mx = abs(self.x0).max()
+        mf = abs(self.f0).max()
+        self.omega = self.rdiff * max(1, mx) / max(1, mf)
+
+    def matvec(self, v):
+        nv = norm(v)
+        if nv == 0:
+            return 0*v
+        sc = self.omega / nv
+        r = (self.func(self.x0 + sc*v) - self.f0) / sc
+        if not np.all(np.isfinite(r)) and np.all(np.isfinite(v)):
+            raise ValueError('Function returned non-finite results')
+        return r
+
+    def solve(self, rhs, tol=0):
+        if 'rtol' in self.method_kw:
+            sol, info = self.method(self.op, rhs, **self.method_kw)
+        else:
+            sol, info = self.method(self.op, rhs, rtol=tol, **self.method_kw)
+        return sol
+
+    def update(self, x, f):
+        self.x0 = x
+        self.f0 = f
+        self._update_diff_step()
+
+        # Update also the preconditioner, if possible
+        if self.preconditioner is not None:
+            if hasattr(self.preconditioner, 'update'):
+                self.preconditioner.update(x, f)
+
+    def setup(self, x, f, func):
+        Jacobian.setup(self, x, f, func)
+        self.x0 = x
+        self.f0 = f
+        self.op = scipy.sparse.linalg.aslinearoperator(self)
+
+        if self.rdiff is None:
+            self.rdiff = np.finfo(x.dtype).eps ** (1./2)
+
+        self._update_diff_step()
+
+        # Setup also the preconditioner, if possible
+        if self.preconditioner is not None:
+            if hasattr(self.preconditioner, 'setup'):
+                self.preconditioner.setup(x, f, func)
+
+
+#------------------------------------------------------------------------------
+# Wrapper functions
+#------------------------------------------------------------------------------
+
+def _nonlin_wrapper(name, jac):
+    """
+    Construct a solver wrapper with given name and Jacobian approx.
+
+    It inspects the keyword arguments of ``jac.__init__``, and allows to
+    use the same arguments in the wrapper function, in addition to the
+    keyword arguments of `nonlin_solve`
+
+    """
+    signature = _getfullargspec(jac.__init__)
+    args, varargs, varkw, defaults, kwonlyargs, kwdefaults, _ = signature
+    kwargs = list(zip(args[-len(defaults):], defaults))
+    kw_str = ", ".join([f"{k}={v!r}" for k, v in kwargs])
+    if kw_str:
+        kw_str = ", " + kw_str
+    kwkw_str = ", ".join([f"{k}={k}" for k, v in kwargs])
+    if kwkw_str:
+        kwkw_str = kwkw_str + ", "
+    if kwonlyargs:
+        raise ValueError(f'Unexpected signature {signature}')
+
+    # Construct the wrapper function so that its keyword arguments
+    # are visible in pydoc.help etc.
+    wrapper = """
+def %(name)s(F, xin, iter=None %(kw)s, verbose=False, maxiter=None,
+             f_tol=None, f_rtol=None, x_tol=None, x_rtol=None,
+             tol_norm=None, line_search='armijo', callback=None, **kw):
+    jac = %(jac)s(%(kwkw)s **kw)
+    return nonlin_solve(F, xin, jac, iter, verbose, maxiter,
+                        f_tol, f_rtol, x_tol, x_rtol, tol_norm, line_search,
+                        callback)
+"""
+
+    wrapper = wrapper % dict(name=name, kw=kw_str, jac=jac.__name__,
+                             kwkw=kwkw_str)
+    ns = {}
+    ns.update(globals())
+    exec(wrapper, ns)
+    func = ns[name]
+    func.__doc__ = jac.__doc__
+    _set_doc(func)
+    return func
+
+
+broyden1 = _nonlin_wrapper('broyden1', BroydenFirst)
+broyden2 = _nonlin_wrapper('broyden2', BroydenSecond)
+anderson = _nonlin_wrapper('anderson', Anderson)
+linearmixing = _nonlin_wrapper('linearmixing', LinearMixing)
+diagbroyden = _nonlin_wrapper('diagbroyden', DiagBroyden)
+excitingmixing = _nonlin_wrapper('excitingmixing', ExcitingMixing)
+newton_krylov = _nonlin_wrapper('newton_krylov', KrylovJacobian)

shared/package-files/06/06d3b49e34f00c5ad736bde730aabfa7c3c384fcc7f517f30ab9cb909c73e5fc/blob

@@ -0,0 +1,364 @@
+"""
+test_indexing tests the following Index methods:
+    __getitem__
+    get_loc
+    get_value
+    __contains__
+    take
+    where
+    get_indexer
+    get_indexer_for
+    slice_locs
+    asof_locs
+
+The corresponding tests.indexes.[index_type].test_indexing files
+contain tests for the corresponding methods specific to those Index subclasses.
+"""
+import numpy as np
+import pytest
+
+from pandas.compat import PY314
+from pandas.errors import InvalidIndexError
+
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_scalar,
+)
+
+from pandas import (
+    NA,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    PeriodIndex,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+
+
+class TestTake:
+    def test_take_invalid_kwargs(self, index):
+        indices = [1, 2]
+
+        msg = r"take\(\) got an unexpected keyword argument 'foo'"
+        with pytest.raises(TypeError, match=msg):
+            index.take(indices, foo=2)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            index.take(indices, out=indices)
+
+        msg = "the 'mode' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            index.take(indices, mode="clip")
+
+    def test_take(self, index):
+        indexer = [4, 3, 0, 2]
+        if len(index) < 5:
+            pytest.skip("Test doesn't make sense since not enough elements")
+
+        result = index.take(indexer)
+        expected = index[indexer]
+        assert result.equals(expected)
+
+        if not isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
+            # GH 10791
+            msg = r"'(.*Index)' object has no attribute 'freq'"
+            with pytest.raises(AttributeError, match=msg):
+                index.freq
+
+    def test_take_indexer_type(self):
+        # GH#42875
+        integer_index = Index([0, 1, 2, 3])
+        scalar_index = 1
+        msg = "Expected indices to be array-like"
+        with pytest.raises(TypeError, match=msg):
+            integer_index.take(scalar_index)
+
+    def test_take_minus1_without_fill(self, index):
+        # -1 does not get treated as NA unless allow_fill=True is passed
+        if len(index) == 0:
+            # Test is not applicable
+            pytest.skip("Test doesn't make sense for empty index")
+
+        result = index.take([0, 0, -1])
+
+        expected = index.take([0, 0, len(index) - 1])
+        tm.assert_index_equal(result, expected)
+
+
+class TestContains:
+    @pytest.mark.parametrize(
+        "index,val",
+        [
+            (Index([0, 1, 2]), 2),
+            (Index([0, 1, "2"]), "2"),
+            (Index([0, 1, 2, np.inf, 4]), 4),
+            (Index([0, 1, 2, np.nan, 4]), 4),
+            (Index([0, 1, 2, np.inf]), np.inf),
+            (Index([0, 1, 2, np.nan]), np.nan),
+        ],
+    )
+    def test_index_contains(self, index, val):
+        assert val in index
+
+    @pytest.mark.parametrize(
+        "index,val",
+        [
+            (Index([0, 1, 2]), "2"),
+            (Index([0, 1, "2"]), 2),
+            (Index([0, 1, 2, np.inf]), 4),
+            (Index([0, 1, 2, np.nan]), 4),
+            (Index([0, 1, 2, np.inf]), np.nan),
+            (Index([0, 1, 2, np.nan]), np.inf),
+            # Checking if np.inf in int64 Index should not cause an OverflowError
+            # Related to GH 16957
+            (Index([0, 1, 2], dtype=np.int64), np.inf),
+            (Index([0, 1, 2], dtype=np.int64), np.nan),
+            (Index([0, 1, 2], dtype=np.uint64), np.inf),
+            (Index([0, 1, 2], dtype=np.uint64), np.nan),
+        ],
+    )
+    def test_index_not_contains(self, index, val):
+        assert val not in index
+
+    @pytest.mark.parametrize(
+        "index,val", [(Index([0, 1, "2"]), 0), (Index([0, 1, "2"]), "2")]
+    )
+    def test_mixed_index_contains(self, index, val):
+        # GH#19860
+        assert val in index
+
+    @pytest.mark.parametrize(
+        "index,val", [(Index([0, 1, "2"]), "1"), (Index([0, 1, "2"]), 2)]
+    )
+    def test_mixed_index_not_contains(self, index, val):
+        # GH#19860
+        assert val not in index
+
+    def test_contains_with_float_index(self, any_real_numpy_dtype):
+        # GH#22085
+        dtype = any_real_numpy_dtype
+        data = [0, 1, 2, 3] if not is_float_dtype(dtype) else [0.1, 1.1, 2.2, 3.3]
+        index = Index(data, dtype=dtype)
+
+        if not is_float_dtype(index.dtype):
+            assert 1.1 not in index
+            assert 1.0 in index
+            assert 1 in index
+        else:
+            assert 1.1 in index
+            assert 1.0 not in index
+            assert 1 not in index
+
+    def test_contains_requires_hashable_raises(self, index):
+        if isinstance(index, MultiIndex):
+            return  # TODO: do we want this to raise?
+
+        msg = "unhashable type: 'list'"
+        with pytest.raises(TypeError, match=msg):
+            [] in index
+
+        if PY314:
+            container_or_iterable = "a container or iterable"
+        else:
+            container_or_iterable = "iterable"
+
+        msg = "|".join(
+            [
+                r"unhashable type: 'dict'",
+                r"must be real number, not dict",
+                r"an integer is required",
+                r"\{\}",
+                r"pandas\._libs\.interval\.IntervalTree' is not "
+                f"{container_or_iterable}",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            {} in index._engine
+
+
+class TestGetLoc:
+    def test_get_loc_non_hashable(self, index):
+        with pytest.raises(InvalidIndexError, match="[0, 1]"):
+            index.get_loc([0, 1])
+
+    def test_get_loc_non_scalar_hashable(self, index):
+        # GH52877
+        from enum import Enum
+
+        class E(Enum):
+            X1 = "x1"
+
+        assert not is_scalar(E.X1)
+
+        exc = KeyError
+        msg = "<E.X1: 'x1'>"
+        if isinstance(
+            index,
+            (
+                DatetimeIndex,
+                TimedeltaIndex,
+                PeriodIndex,
+                IntervalIndex,
+            ),
+        ):
+            # TODO: make these more consistent?
+            exc = InvalidIndexError
+            msg = "E.X1"
+        with pytest.raises(exc, match=msg):
+            index.get_loc(E.X1)
+
+    def test_get_loc_generator(self, index):
+        exc = KeyError
+        if isinstance(
+            index,
+            (
+                DatetimeIndex,
+                TimedeltaIndex,
+                PeriodIndex,
+                IntervalIndex,
+                MultiIndex,
+            ),
+        ):
+            # TODO: make these more consistent?
+            exc = InvalidIndexError
+        with pytest.raises(exc, match="generator object"):
+            # MultiIndex specifically checks for generator; others for scalar
+            index.get_loc(x for x in range(5))
+
+    def test_get_loc_masked_duplicated_na(self):
+        # GH#48411
+        idx = Index([1, 2, NA, NA], dtype="Int64")
+        result = idx.get_loc(NA)
+        expected = np.array([False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestGetIndexer:
+    def test_get_indexer_base(self, index):
+        if index._index_as_unique:
+            expected = np.arange(index.size, dtype=np.intp)
+            actual = index.get_indexer(index)
+            tm.assert_numpy_array_equal(expected, actual)
+        else:
+            msg = "Reindexing only valid with uniquely valued Index objects"
+            with pytest.raises(InvalidIndexError, match=msg):
+                index.get_indexer(index)
+
+        with pytest.raises(ValueError, match="Invalid fill method"):
+            index.get_indexer(index, method="invalid")
+
+    def test_get_indexer_consistency(self, index):
+        # See GH#16819
+
+        if index._index_as_unique:
+            indexer = index.get_indexer(index[0:2])
+            assert isinstance(indexer, np.ndarray)
+            assert indexer.dtype == np.intp
+        else:
+            msg = "Reindexing only valid with uniquely valued Index objects"
+            with pytest.raises(InvalidIndexError, match=msg):
+                index.get_indexer(index[0:2])
+
+        indexer, _ = index.get_indexer_non_unique(index[0:2])
+        assert isinstance(indexer, np.ndarray)
+        assert indexer.dtype == np.intp
+
+    def test_get_indexer_masked_duplicated_na(self):
+        # GH#48411
+        idx = Index([1, 2, NA, NA], dtype="Int64")
+        result = idx.get_indexer_for(Index([1, NA], dtype="Int64"))
+        expected = np.array([0, 2, 3], dtype=result.dtype)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestConvertSliceIndexer:
+    def test_convert_almost_null_slice(self, index):
+        # slice with None at both ends, but not step
+
+        key = slice(None, None, "foo")
+
+        if isinstance(index, IntervalIndex):
+            msg = "label-based slicing with step!=1 is not supported for IntervalIndex"
+            with pytest.raises(ValueError, match=msg):
+                index._convert_slice_indexer(key, "loc")
+        else:
+            msg = "'>=' not supported between instances of 'str' and 'int'"
+            with pytest.raises(TypeError, match=msg):
+                index._convert_slice_indexer(key, "loc")
+
+
+class TestPutmask:
+    def test_putmask_with_wrong_mask(self, index):
+        # GH#18368
+        if not len(index):
+            pytest.skip("Test doesn't make sense for empty index")
+
+        fill = index[0]
+
+        msg = "putmask: mask and data must be the same size"
+        with pytest.raises(ValueError, match=msg):
+            index.putmask(np.ones(len(index) + 1, np.bool_), fill)
+
+        with pytest.raises(ValueError, match=msg):
+            index.putmask(np.ones(len(index) - 1, np.bool_), fill)
+
+        with pytest.raises(ValueError, match=msg):
+            index.putmask("foo", fill)
+
+
+@pytest.mark.parametrize(
+    "idx", [Index([1, 2, 3]), Index([0.1, 0.2, 0.3]), Index(["a", "b", "c"])]
+)
+def test_getitem_deprecated_float(idx):
+    # https://github.com/pandas-dev/pandas/issues/34191
+
+    msg = "Indexing with a float is no longer supported"
+    with pytest.raises(IndexError, match=msg):
+        idx[1.0]
+
+
+@pytest.mark.parametrize(
+    "idx,target,expected",
+    [
+        ([np.nan, "var1", np.nan], [np.nan], np.array([0, 2], dtype=np.intp)),
+        (
+            [np.nan, "var1", np.nan],
+            [np.nan, "var1"],
+            np.array([0, 2, 1], dtype=np.intp),
+        ),
+        (
+            np.array([np.nan, "var1", np.nan], dtype=object),
+            [np.nan],
+            np.array([0, 2], dtype=np.intp),
+        ),
+        (
+            DatetimeIndex(["2020-08-05", NaT, NaT]),
+            [NaT],
+            np.array([1, 2], dtype=np.intp),
+        ),
+        (["a", "b", "a", np.nan], [np.nan], np.array([3], dtype=np.intp)),
+        (
+            np.array(["b", np.nan, float("NaN"), "b"], dtype=object),
+            Index([np.nan], dtype=object),
+            np.array([1, 2], dtype=np.intp),
+        ),
+    ],
+)
+def test_get_indexer_non_unique_multiple_nans(idx, target, expected):
+    # GH 35392
+    axis = Index(idx)
+    actual = axis.get_indexer_for(target)
+    tm.assert_numpy_array_equal(actual, expected)
+
+
+def test_get_indexer_non_unique_nans_in_object_dtype_target(nulls_fixture):
+    idx = Index([1.0, 2.0])
+    target = Index([1, nulls_fixture], dtype="object")
+
+    result_idx, result_missing = idx.get_indexer_non_unique(target)
+    tm.assert_numpy_array_equal(result_idx, np.array([0, -1], dtype=np.intp))
+    tm.assert_numpy_array_equal(result_missing, np.array([1], dtype=np.intp))

shared/package-files/06/06d495c7e6c9b6c8ada94a07c3e31a624c551d6fad4e2051f9728d29aeb1bb1c/score_glacier_predictions_serial.py

@@ -0,0 +1,318 @@
+from contextlib import nullcontext
+#!/usr/bin/env python3
+"""Score upstream GLACIER PredSpecDB output under the benchmark contract."""
+
+import argparse
+from collections import Counter
+from functools import lru_cache
+import json
+import math
+from pathlib import Path
+
+import numpy as np
+from mass_tolerant_entropy import observed_peaks, tolerant_entropy, precursor_removed_entropy, adduct_conditioned_entropy
+from rdkit import Chem
+
+from glacier_contract import condition_key
+import ms_pred.common as common
+from ms_pred.common import chem_utils
+from ms_pred.retrieval.retrieval_benchmark import _cosine_sparse, _entropy_sparse
+
+
+def read_jsonl(path: Path):
+    with path.open(encoding="utf-8") as stream:
+        for line_number, line in enumerate(stream, 1):
+            try:
+                value = json.loads(line)
+            except json.JSONDecodeError as error:
+                raise ValueError(f"invalid JSON at {path}:{line_number}: {error}") from error
+            if not isinstance(value, dict):
+                raise ValueError(f"expected object at {path}:{line_number}")
+            yield value
+
+
+def load_pools(path: Path) -> dict:
+    pools = {}
+    for row in read_jsonl(path):
+        pool_id = row["candidate_pool_id"]
+        if pool_id in pools:
+            raise ValueError(f"duplicate candidate pool {pool_id}")
+        pools[pool_id] = row["candidates"]
+    return pools
+
+
+@lru_cache(maxsize=262144)
+def prediction_key_and_support(smiles: str) -> tuple[str, str]:
+    """Mirror GLACIER's public inference-domain structure checks."""
+    try:
+        molecule = Chem.MolFromSmiles(smiles)
+        if molecule is None:
+            return "", "rdkit_invalid"
+        if any(atom.GetAtomicNum() == 0 for atom in molecule.GetAtoms()):
+            return "", "wildcard_atom"
+        if any(
+            atom.GetAtomicNum() not in chem_utils.VALID_ATOM_NUM
+            for atom in molecule.GetAtoms()
+        ):
+            return Chem.MolToInchiKey(molecule), "unsupported_atom"
+        molecule = Chem.RemoveHs(molecule)
+        if molecule.GetNumAtoms() > 100:
+            return Chem.MolToInchiKey(molecule), "more_than_100_heavy_atoms"
+        return Chem.MolToInchiKey(molecule), "supported"
+    except Exception:
+        return "", "structure_exception"
+
+
+def sparse_observed(
+    query: dict,
+    num_bins: int,
+    upper_limit: int,
+    max_peaks: int,
+    intensity_threshold: float,
+):
+    mzs = np.asarray(query["mzs"], dtype=np.float32)
+    intensities = np.asarray(query["intensities"], dtype=np.float32)
+    keep = (mzs <= float(query["precursor_mz"]) + 1) & (intensities > 0)
+    mzs = mzs[keep]
+    intensities = intensities[keep]
+    if intensities.size == 0:
+        raise ValueError(f"query {query['query_id']} has no usable intensity")
+    maximum = float(intensities.max())
+    if maximum <= 0:
+        raise ValueError(f"query {query['query_id']} has no positive intensity")
+    # This reproduces process_spec_file() followed by max_inten_spec() in the
+    # official no_subform MassSpecGym preprocessing: max-normalize, square
+    # root, retain the strongest 50 peaks, then apply the 0.001 threshold.
+    intensities = np.sqrt(intensities / maximum)
+    strongest = np.argsort(intensities)[::-1][:max_peaks]
+    mzs = mzs[strongest]
+    intensities = intensities[strongest]
+    above_threshold = intensities > intensity_threshold
+    peaks = np.column_stack([mzs[above_threshold], intensities[above_threshold]])
+    dense = common.bin_spectra([peaks], num_bins=num_bins, upper_limit=upper_limit)[0]
+    indices = np.flatnonzero(dense > 0).astype(np.uint32)
+    return indices, dense[indices].astype(np.float32, copy=False)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input-root", type=Path, required=True)
+    parser.add_argument(
+        "--pred-file",
+        type=Path,
+        required=True,
+        nargs="+",
+        help="One or more GLACIER PredSpecDB shards.",
+    )
+    parser.add_argument("--output-root", type=Path, required=True)
+    parser.add_argument("--distance", choices=("entropy", "cos", "mass_tolerant_entropy"), default="entropy")
+    parser.add_argument(
+        "--tie-break",
+        choices=("candidate_id", "input_order"),
+        default="candidate_id",
+        help=(
+            "Use content-derived IDs for the benchmark. input_order exists only "
+            "to audit answer-keyed upstream candidate tables."
+        ),
+    )
+    parser.add_argument("--num-bins", type=int, default=150000)
+    parser.add_argument("--upper-limit", type=int, default=1500)
+    parser.add_argument("--max-observed-peaks", type=int, default=50)
+    parser.add_argument("--observed-intensity-threshold", type=float, default=0.001)
+    parser.add_argument("--max-queries", type=int)
+    parser.add_argument(
+        "--query-id-file",
+        type=Path,
+        help="Optional newline-delimited allowlist for query-disjoint scoring shards.",
+    )
+    parser.add_argument(
+        "--exclude-ranking",
+        type=Path,
+        action="append",
+        default=[],
+        help=(
+            "Skip query IDs already present in a partial ranking file. This is "
+            "an answer-free recovery option; merge the disjoint outputs with "
+            "merge_partial_rankings.py."
+        ),
+    )
+    parser.add_argument("--scores-output", type=Path)
+    args = parser.parse_args()
+
+    input_root = args.input_root.resolve()
+    output_root = args.output_root.resolve()
+    if output_root.exists():
+        raise FileExistsError(f"refusing to overwrite {output_root}")
+    output_root.mkdir(parents=True)
+    pools = load_pools(input_root / "candidate_pools.jsonl")
+    pool_prediction_keys = {}
+    predictions = [
+        common.PredSpecDB(h5_path=path.resolve(), mode="r")
+        for path in args.pred_file
+    ]
+    selected_queries = None
+    if args.query_id_file is not None:
+        selected_queries = {
+            line.strip()
+            for line in args.query_id_file.read_text(encoding="utf-8").splitlines()
+            if line.strip()
+        }
+        if not selected_queries:
+            raise ValueError("query ID allowlist is empty")
+    excluded_queries = {
+        row["query_id"]
+        for ranking_path in args.exclude_ranking
+        for row in read_jsonl(ranking_path.resolve())
+    }
+
+    ranked_queries = 0
+    candidate_rows = 0
+    missing_predictions = 0
+    missing_supported_predictions = 0
+    unsupported_missing = Counter()
+    with (output_root / "rankings.jsonl").open("w", encoding="utf-8") as stream, (args.scores_output.open("w") if args.scores_output else nullcontext(None)) as score_stream:
+        for query in read_jsonl(input_root / "queries.jsonl"):
+            if (
+                selected_queries is not None
+                and query["query_id"] not in selected_queries
+            ):
+                continue
+            if args.max_queries is not None and ranked_queries >= args.max_queries:
+                break
+            if query["query_id"] in excluded_queries:
+                continue
+            collision_energy = query.get("collision_energy")
+            if collision_energy is None:collision_energy='nan'
+            pred_name = 'pred_'+condition_key(query)
+            shard_specs = []
+            for prediction_db in predictions:
+                spec_dict, present = prediction_db.read_from_name(pred_name)
+                if present:
+                    shard_specs.append(spec_dict)
+            if not shard_specs:
+                continue
+            by_ikey = {}
+            raw_by_ikey = {}
+            for spec_dict in shard_specs:
+                for remark, ce_dict in spec_dict.items():
+                    normalized = str(remark).removeprefix("ikey ").strip()
+                    normalized_specs = by_ikey.setdefault(normalized, {})
+                    if args.distance == "mass_tolerant_entropy":
+                        raw_by_ikey.setdefault(normalized, {}).update({
+                            common.get_collision_energy(ce): spectrum.spec
+                            for ce, spectrum in ce_dict.items()
+                        })
+                    if args.distance != "mass_tolerant_entropy":
+                        normalized_specs.update({
+                            common.get_collision_energy(ce): spectrum.binned_spec_sparse
+                            if spectrum.has_matching_binned_spec(
+                                mass_upper_limit=args.upper_limit,
+                                num_bins=args.num_bins,
+                                pool_fn="add",
+                            )
+                            else (
+                                spectrum.ensure_binned_spectrum(
+                                    mass_upper_limit=args.upper_limit,
+                                    num_bins=args.num_bins,
+                                    pool_fn="add",
+                                )
+                                or spectrum.binned_spec_sparse
+                            )
+                            for ce, spectrum in ce_dict.items()
+                        })
+
+            pool_id = query["candidate_pool_id"]
+            candidates = pools[pool_id]
+            candidate_keys = pool_prediction_keys.get(pool_id)
+            if candidate_keys is None:
+                candidate_keys = [
+                    prediction_key_and_support(candidate["smiles"])
+                    for candidate in candidates
+                ]
+                pool_prediction_keys[pool_id] = candidate_keys
+            observed = sparse_observed(
+                query,
+                args.num_bins,
+                args.upper_limit,
+                args.max_observed_peaks,
+                args.observed_intensity_threshold,
+            )
+            ce_key = common.get_collision_energy(collision_energy)
+            candidate_predictions = []
+            for candidate_ikey, support in candidate_keys:
+                pred = by_ikey.get(candidate_ikey)
+                if pred is None:
+                    missing_predictions += 1
+                    if support == "supported":
+                        missing_supported_predictions += 1
+                    else:
+                        unsupported_missing[support] += 1
+                    pred = {}
+                candidate_predictions.append(pred)
+            if args.distance == "mass_tolerant_entropy":
+                raw_observed = observed_peaks(query)
+                distances = np.asarray([
+                    adduct_conditioned_entropy(raw_by_ikey.get(key, {}).get(ce_key, []), raw_observed, float(query['precursor_mz']), query['adduct'])
+                    for key, support in candidate_keys
+                ], dtype=np.float64)
+            else:
+                distance_fn = _entropy_sparse if args.distance == "entropy" else _cosine_sparse
+                distances = distance_fn(candidate_predictions, observed, ce_key, sqrt_inten=True)
+            # GLACIER emits identical predictions for candidates that collapse to
+            # the same InChIKey. Break exact score ties by the content-derived
+            # candidate ID, never by the public candidate-file order.
+            candidate_ids = np.asarray(
+                [candidate["candidate_id"] for candidate in candidates]
+            )
+            if args.tie_break == "candidate_id":
+                order = np.lexsort((candidate_ids, distances))
+            else:
+                order = np.argsort(distances, kind="stable")
+            ranking = [candidates[int(index)]["candidate_id"] for index in order]
+            stream.write(
+                json.dumps(
+                    {"query_id": query["query_id"], "ranking": ranking},
+                    sort_keys=True,
+                    separators=(",", ":"),
+                )
+                + "\n"
+            )
+            if score_stream is not None:
+                score_stream.write(json.dumps({"query_id":query["query_id"],"candidate_ids":candidate_ids.tolist(),"distances":distances.tolist()},separators=(",",":"))+"\n")
+            ranked_queries += 1
+            candidate_rows += len(candidates)
+
+    for prediction_db in predictions:
+        prediction_db.close()
+    if selected_queries is not None and ranked_queries != len(selected_queries):
+        raise ValueError(
+            "query ID allowlist was not fully ranked: "
+            f"{ranked_queries}/{len(selected_queries)}"
+        )
+    report = {
+        "schema": "glacier-contract-score/v2",
+        "distance": args.distance,
+        "tie_break": args.tie_break,
+        "ranked_queries": ranked_queries,
+        "candidate_rows": candidate_rows,
+        "missing_candidate_predictions": missing_predictions,
+        "missing_supported_candidate_predictions": missing_supported_predictions,
+        "model_unsupported_candidate_rows": dict(sorted(unsupported_missing.items())),
+        "num_bins": args.num_bins,
+        "upper_limit": args.upper_limit,
+        "prediction_shards": len(predictions),
+        "max_observed_peaks": args.max_observed_peaks,
+        "observed_intensity_threshold": args.observed_intensity_threshold,
+        "excluded_queries": len(excluded_queries),
+        "selected_queries": (
+            None if selected_queries is None else len(selected_queries)
+        ),
+    }
+    (output_root / "runtime.json").write_text(
+        json.dumps(report, sort_keys=True, indent=2) + "\n", encoding="utf-8"
+    )
+    print(json.dumps(report, sort_keys=True))
+
+
+if __name__ == "__main__":
+    main()

shared/package-files/06/06dd7572626df5cb0a8d3affbac9bb74cb12469076836d66fd19ae5b5fab42c7/blob

@@ -0,0 +1,91 @@
+namespace eval ::tk {
+    ::msgcat::mcset de "&Abort" "&Abbruch"
+    ::msgcat::mcset de "&About..." "&\u00dcber..."
+    ::msgcat::mcset de "All Files" "Alle Dateien"
+    ::msgcat::mcset de "Application Error" "Applikationsfehler"
+    ::msgcat::mcset de "&Apply" "&Anwenden"
+    ::msgcat::mcset de "Bold" "Fett"
+    ::msgcat::mcset de "Bold Italic" "Fett kursiv"
+    ::msgcat::mcset de "&Blue" "&Blau"
+    ::msgcat::mcset de "Cancel" "Abbruch"
+    ::msgcat::mcset de "&Cancel" "&Abbruch"
+    ::msgcat::mcset de "Cannot change to the directory \"%1\$s\".\nPermission denied." "Kann nicht in das Verzeichnis \"%1\$s\" wechseln.\nKeine Rechte vorhanden."
+    ::msgcat::mcset de "Choose Directory" "W\u00e4hle Verzeichnis"
+    ::msgcat::mcset de "Cl&ear" "&R\u00fccksetzen"
+    ::msgcat::mcset de "&Clear Console" "&Konsole l\u00f6schen"
+    ::msgcat::mcset de "Color" "Farbe"
+    ::msgcat::mcset de "Console" "Konsole"
+    ::msgcat::mcset de "&Copy" "&Kopieren"
+    ::msgcat::mcset de "Cu&t" "Aus&schneiden"
+    ::msgcat::mcset de "&Delete" "&L\u00f6schen"
+    ::msgcat::mcset de "Details >>"
+    ::msgcat::mcset de "Directory \"%1\$s\" does not exist." "Das Verzeichnis \"%1\$s\" existiert nicht."
+    ::msgcat::mcset de "&Directory:" "&Verzeichnis:"
+    ::msgcat::mcset de "&Edit" "&Bearbeiten"
+    ::msgcat::mcset de "Effects" "Effekte"
+    ::msgcat::mcset de "Error: %1\$s" "Fehler: %1\$s"
+    ::msgcat::mcset de "E&xit" "&Ende"
+    ::msgcat::mcset de "&File" "&Datei"
+    ::msgcat::mcset de "File \"%1\$s\" already exists.\nDo you want to overwrite it?" "Die Datei \"%1\$s\" ist bereits vorhanden.\nWollen sie diese Datei \u00fcberschreiben ?"
+    ::msgcat::mcset de "File \"%1\$s\" already exists.\n\n" "Die Datei \"%1\$s\" ist bereits vorhanden.\n\n"
+    ::msgcat::mcset de "File \"%1\$s\" does not exist." "Die Datei \"%1\$s\" existiert nicht."
+    ::msgcat::mcset de "File &name:" "Datei&name:"
+    ::msgcat::mcset de "File &names:" "Datei&namen:"
+    ::msgcat::mcset de "Files of &type:" "Dateien des &Typs:"
+    ::msgcat::mcset de "Fi&les:" "Dat&eien:"
+    ::msgcat::mcset de "&Filter"
+    ::msgcat::mcset de "Fil&ter:"
+    ::msgcat::mcset de "Font" "Schriftart"
+    ::msgcat::mcset de "&Font:" "Schriftart:"
+    ::msgcat::mcset de "Font st&yle:" "Schriftschnitt:"
+    ::msgcat::mcset de "&Green" "&Gr\u00fcn"
+    ::msgcat::mcset de "&Help" "&Hilfe"
+    ::msgcat::mcset de "Hi" "Hallo"
+    ::msgcat::mcset de "&Hide Console" "&Konsole unsichtbar machen"
+    ::msgcat::mcset de "&Ignore" "&Ignorieren"
+    ::msgcat::mcset de "Invalid file name \"%1\$s\"." "Ung\u00fcltiger Dateiname \"%1\$s\"."
+    ::msgcat::mcset de "Italic" "Kursiv"
+    ::msgcat::mcset de "Log Files" "Protokolldatei"
+    ::msgcat::mcset de "&No" "&Nein"
+    ::msgcat::mcset de "&OK"
+    ::msgcat::mcset de "OK"
+    ::msgcat::mcset de "Ok"
+    ::msgcat::mcset de "Open" "\u00d6ffnen"
+    ::msgcat::mcset de "&Open" "\u00d6&ffnen"
+    ::msgcat::mcset de "Open Multiple Files" "Mehrere Dateien \u00F6ffnen"
+    ::msgcat::mcset de "P&aste" "E&inf\u00fcgen"
+    ::msgcat::mcset de "&Quit" "&Beenden"
+    ::msgcat::mcset de "&Red" "&Rot"
+    ::msgcat::mcset de "Regular" "Standard"
+    ::msgcat::mcset de "Replace existing file?" "Existierende Datei ersetzen?"
+    ::msgcat::mcset de "&Retry" "&Wiederholen"
+    ::msgcat::mcset de "Sample" "Beispiel"
+    ::msgcat::mcset de "&Save" "&Speichern"
+    ::msgcat::mcset de "Save As" "Speichern unter"
+    ::msgcat::mcset de "Save To Log" "In Protokoll speichern"
+    ::msgcat::mcset de "Select Log File" "Protokolldatei ausw\u00e4hlen"
+    ::msgcat::mcset de "Select a file to source" "Auszuf\u00fchrende Datei ausw\u00e4hlen"
+    ::msgcat::mcset de "&Selection:" "Auswah&l:"
+    ::msgcat::mcset de "&Size:" "Schriftgrad:"
+    ::msgcat::mcset de "Show &Hidden Directories" "Zeige versteckte Dateien"
+    ::msgcat::mcset de "Show &Hidden Files and Directories" "Zeige versteckte Dateien und Verzeichnisse"
+    ::msgcat::mcset de "Skip Messages" "Weitere Nachrichten \u00fcberspringen"
+    ::msgcat::mcset de "&Source..." "&Ausf\u00fchren..."
+    ::msgcat::mcset de "Stri&keout" "&Durchgestrichen"
+    ::msgcat::mcset de "Tcl Scripts" "Tcl-Skripte"
+    ::msgcat::mcset de "Tcl for Windows" "Tcl f\u00fcr Windows"
+    ::msgcat::mcset de "Text Files" "Textdateien"
+    ::msgcat::mcset de "&Underline" "&Unterstrichen"
+    ::msgcat::mcset de "&Yes" "&Ja"
+    ::msgcat::mcset de "abort" "abbrechen"
+    ::msgcat::mcset de "blue" "blau"
+    ::msgcat::mcset de "cancel" "abbrechen"
+    ::msgcat::mcset de "extension" "Erweiterung"
+    ::msgcat::mcset de "extensions" "Erweiterungen"
+    ::msgcat::mcset de "green" "gr\u00fcn"
+    ::msgcat::mcset de "ignore" "ignorieren"
+    ::msgcat::mcset de "ok"
+    ::msgcat::mcset de "red" "rot"
+    ::msgcat::mcset de "retry" "wiederholen"
+    ::msgcat::mcset de "yes" "ja"
+}

shared/package-files/06/06e073b6b3d045ab149956615ebdbf36026e8dd513361370779f696591e4de50/blob

@@ -0,0 +1,77 @@
+"""
+DType classes and utility (:mod:`numpy.dtypes`)
+===============================================
+
+This module is home to specific dtypes related functionality and their classes.
+For more general information about dtypes, also see `numpy.dtype` and
+:ref:`arrays.dtypes`.
+
+Similar to the builtin ``types`` module, this submodule defines types (classes)
+that are not widely used directly.
+
+.. versionadded:: NumPy 1.25
+
+    The dtypes module is new in NumPy 1.25.  Previously DType classes were
+    only accessible indirectly.
+
+
+DType classes
+-------------
+
+The following are the classes of the corresponding NumPy dtype instances and
+NumPy scalar types.  The classes can be used in ``isinstance`` checks and can
+also be instantiated or used directly.  Direct use of these classes is not
+typical, since their scalar counterparts (e.g. ``np.float64``) or strings
+like ``"float64"`` can be used.
+
+.. list-table::
+    :header-rows: 1
+
+    * - Group
+      - DType class
+
+    * - Boolean
+      - ``BoolDType``
+
+    * - Bit-sized integers
+      - ``Int8DType``, ``UInt8DType``, ``Int16DType``, ``UInt16DType``,
+        ``Int32DType``, ``UInt32DType``, ``Int64DType``, ``UInt64DType``
+
+    * - C-named integers (may be aliases)
+      - ``ByteDType``, ``UByteDType``, ``ShortDType``, ``UShortDType``,
+        ``IntDType``, ``UIntDType``, ``LongDType``, ``ULongDType``,
+        ``LongLongDType``, ``ULongLongDType``
+
+    * - Floating point
+      - ``Float16DType``, ``Float32DType``, ``Float64DType``,
+        ``LongDoubleDType``
+
+    * - Complex
+      - ``Complex64DType``, ``Complex128DType``, ``CLongDoubleDType``
+
+    * - Strings
+      - ``BytesDType``, ``BytesDType``
+
+    * - Times
+      - ``DateTime64DType``, ``TimeDelta64DType``
+
+    * - Others
+      - ``ObjectDType``, ``VoidDType``
+
+"""
+
+__all__ = []
+
+
+def _add_dtype_helper(DType, alias):
+    # Function to add DTypes a bit more conveniently without channeling them
+    # through `numpy.core._multiarray_umath` namespace or similar.
+    from numpy import dtypes
+
+    setattr(dtypes, DType.__name__, DType)
+    __all__.append(DType.__name__)
+
+    if alias:
+        alias = alias.removeprefix("numpy.dtypes.")
+        setattr(dtypes, alias, DType)
+        __all__.append(alias)

shared/package-files/06/06e09e6fc73ad2e0c7a626e83a4281976baedd3f84121ad841cb5ea2a23186a4/blob

@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: setuptools (84.0.0)
+Root-Is-Purelib: false
+Tag: cp310-cp310-manylinux_2_12_x86_64
+Tag: cp310-cp310-manylinux2010_x86_64
+

shared/package-files/06/06e1e22eec6eeae11f64568f256dd13e93dac5a55cb44400de47522520cbd61c/blob

@@ -0,0 +1,9 @@
+from warnings import warn
+
+from .notebook import *  # NOQA
+from .notebook import __all__  # NOQA
+from .std import TqdmDeprecationWarning
+
+warn("This function will be removed in tqdm==5.0.0\n"
+     "Please use `tqdm.notebook.*` instead of `tqdm._tqdm_notebook.*`",
+     TqdmDeprecationWarning, stacklevel=2)

shared/package-files/06/06e2ecaf4175380e08780f35e81cee2c5892346a693e800ba62795dbff0d7e0f/blob

@@ -0,0 +1,303 @@
+""" test scalar indexing, including at and iat """
+from datetime import (
+    datetime,
+    timedelta,
+)
+import itertools
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+def generate_indices(f, values=False):
+    """
+    generate the indices
+    if values is True , use the axis values
+    is False, use the range
+    """
+    axes = f.axes
+    if values:
+        axes = (list(range(len(ax))) for ax in axes)
+
+    return itertools.product(*axes)
+
+
+class TestScalar:
+    @pytest.mark.parametrize("kind", ["series", "frame"])
+    @pytest.mark.parametrize("col", ["ints", "uints"])
+    def test_iat_set_ints(self, kind, col, request):
+        f = request.getfixturevalue(f"{kind}_{col}")
+        indices = generate_indices(f, True)
+        for i in indices:
+            f.iat[i] = 1
+            expected = f.values[i]
+            tm.assert_almost_equal(expected, 1)
+
+    @pytest.mark.parametrize("kind", ["series", "frame"])
+    @pytest.mark.parametrize("col", ["labels", "ts", "floats"])
+    def test_iat_set_other(self, kind, col, request):
+        f = request.getfixturevalue(f"{kind}_{col}")
+        msg = "iAt based indexing can only have integer indexers"
+        with pytest.raises(ValueError, match=msg):
+            idx = next(generate_indices(f, False))
+            f.iat[idx] = 1
+
+    @pytest.mark.parametrize("kind", ["series", "frame"])
+    @pytest.mark.parametrize("col", ["ints", "uints", "labels", "ts", "floats"])
+    def test_at_set_ints_other(self, kind, col, request):
+        f = request.getfixturevalue(f"{kind}_{col}")
+        indices = generate_indices(f, False)
+        for i in indices:
+            f.at[i] = 1
+            expected = f.loc[i]
+            tm.assert_almost_equal(expected, 1)
+
+
+class TestAtAndiAT:
+    # at and iat tests that don't need Base class
+
+    def test_float_index_at_iat(self):
+        ser = Series([1, 2, 3], index=[0.1, 0.2, 0.3])
+        for el, item in ser.items():
+            assert ser.at[el] == item
+        for i in range(len(ser)):
+            assert ser.iat[i] == i + 1
+
+    def test_at_iat_coercion(self):
+        # as timestamp is not a tuple!
+        dates = date_range("1/1/2000", periods=8)
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((8, 4)),
+            index=dates,
+            columns=["A", "B", "C", "D"],
+        )
+        s = df["A"]
+
+        result = s.at[dates[5]]
+        xp = s.values[5]
+        assert result == xp
+
+    @pytest.mark.parametrize(
+        "ser, expected",
+        [
+            [
+                Series(["2014-01-01", "2014-02-02"], dtype="datetime64[ns]"),
+                Timestamp("2014-02-02"),
+            ],
+            [
+                Series(["1 days", "2 days"], dtype="timedelta64[ns]"),
+                Timedelta("2 days"),
+            ],
+        ],
+    )
+    def test_iloc_iat_coercion_datelike(self, indexer_ial, ser, expected):
+        # GH 7729
+        # make sure we are boxing the returns
+        result = indexer_ial(ser)[1]
+        assert result == expected
+
+    def test_imethods_with_dups(self):
+        # GH6493
+        # iat/iloc with dups
+
+        s = Series(range(5), index=[1, 1, 2, 2, 3], dtype="int64")
+        result = s.iloc[2]
+        assert result == 2
+        result = s.iat[2]
+        assert result == 2
+
+        msg = "index 10 is out of bounds for axis 0 with size 5"
+        with pytest.raises(IndexError, match=msg):
+            s.iat[10]
+        msg = "index -10 is out of bounds for axis 0 with size 5"
+        with pytest.raises(IndexError, match=msg):
+            s.iat[-10]
+
+        result = s.iloc[[2, 3]]
+        expected = Series([2, 3], [2, 2], dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+        df = s.to_frame()
+        result = df.iloc[2]
+        expected = Series(2, index=[0], name=2)
+        tm.assert_series_equal(result, expected)
+
+        result = df.iat[2, 0]
+        assert result == 2
+
+    def test_frame_at_with_duplicate_axes(self):
+        # GH#33041
+        arr = np.random.default_rng(2).standard_normal(6).reshape(3, 2)
+        df = DataFrame(arr, columns=["A", "A"])
+
+        result = df.at[0, "A"]
+        expected = df.iloc[0].copy()
+
+        tm.assert_series_equal(result, expected)
+
+        result = df.T.at["A", 0]
+        tm.assert_series_equal(result, expected)
+
+        # setter
+        df.at[1, "A"] = 2
+        expected = Series([2.0, 2.0], index=["A", "A"], name=1)
+        tm.assert_series_equal(df.iloc[1], expected)
+
+    def test_at_getitem_dt64tz_values(self):
+        # gh-15822
+        df = DataFrame(
+            {
+                "name": ["John", "Anderson"],
+                "date": [
+                    Timestamp(2017, 3, 13, 13, 32, 56),
+                    Timestamp(2017, 2, 16, 12, 10, 3),
+                ],
+            }
+        )
+        df["date"] = df["date"].dt.tz_localize("Asia/Shanghai")
+
+        expected = Timestamp("2017-03-13 13:32:56+0800", tz="Asia/Shanghai")
+
+        result = df.loc[0, "date"]
+        assert result == expected
+
+        result = df.at[0, "date"]
+        assert result == expected
+
+    def test_mixed_index_at_iat_loc_iloc_series(self):
+        # GH 19860
+        s = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2])
+        for el, item in s.items():
+            assert s.at[el] == s.loc[el] == item
+        for i in range(len(s)):
+            assert s.iat[i] == s.iloc[i] == i + 1
+
+        with pytest.raises(KeyError, match="^4$"):
+            s.at[4]
+        with pytest.raises(KeyError, match="^4$"):
+            s.loc[4]
+
+    def test_mixed_index_at_iat_loc_iloc_dataframe(self):
+        # GH 19860
+        df = DataFrame(
+            [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], columns=["a", "b", "c", 1, 2]
+        )
+        for rowIdx, row in df.iterrows():
+            for el, item in row.items():
+                assert df.at[rowIdx, el] == df.loc[rowIdx, el] == item
+
+        for row in range(2):
+            for i in range(5):
+                assert df.iat[row, i] == df.iloc[row, i] == row * 5 + i
+
+        with pytest.raises(KeyError, match="^3$"):
+            df.at[0, 3]
+        with pytest.raises(KeyError, match="^3$"):
+            df.loc[0, 3]
+
+    def test_iat_setter_incompatible_assignment(self):
+        # GH 23236
+        result = DataFrame({"a": [0.0, 1.0], "b": [4, 5]})
+        result.iat[0, 0] = None
+        expected = DataFrame({"a": [None, 1], "b": [4, 5]})
+        tm.assert_frame_equal(result, expected)
+
+
+def test_iat_dont_wrap_object_datetimelike():
+    # GH#32809 .iat calls go through DataFrame._get_value, should not
+    #  call maybe_box_datetimelike
+    dti = date_range("2016-01-01", periods=3)
+    tdi = dti - dti
+    ser = Series(dti.to_pydatetime(), dtype=object)
+    ser2 = Series(tdi.to_pytimedelta(), dtype=object)
+    df = DataFrame({"A": ser, "B": ser2})
+    assert (df.dtypes == object).all()
+
+    for result in [df.at[0, "A"], df.iat[0, 0], df.loc[0, "A"], df.iloc[0, 0]]:
+        assert result is ser[0]
+        assert isinstance(result, datetime)
+        assert not isinstance(result, Timestamp)
+
+    for result in [df.at[1, "B"], df.iat[1, 1], df.loc[1, "B"], df.iloc[1, 1]]:
+        assert result is ser2[1]
+        assert isinstance(result, timedelta)
+        assert not isinstance(result, Timedelta)
+
+
+def test_at_with_tuple_index_get():
+    # GH 26989
+    # DataFrame.at getter works with Index of tuples
+    df = DataFrame({"a": [1, 2]}, index=[(1, 2), (3, 4)])
+    assert df.index.nlevels == 1
+    assert df.at[(1, 2), "a"] == 1
+
+    # Series.at getter works with Index of tuples
+    series = df["a"]
+    assert series.index.nlevels == 1
+    assert series.at[(1, 2)] == 1
+
+
+@pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
+def test_at_with_tuple_index_set():
+    # GH 26989
+    # DataFrame.at setter works with Index of tuples
+    df = DataFrame({"a": [1, 2]}, index=[(1, 2), (3, 4)])
+    assert df.index.nlevels == 1
+    df.at[(1, 2), "a"] = 2
+    assert df.at[(1, 2), "a"] == 2
+
+    # Series.at setter works with Index of tuples
+    series = df["a"]
+    assert series.index.nlevels == 1
+    series.at[1, 2] = 3
+    assert series.at[1, 2] == 3
+
+
+class TestMultiIndexScalar:
+    def test_multiindex_at_get(self):
+        # GH 26989
+        # DataFrame.at and DataFrame.loc getter works with MultiIndex
+        df = DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]])
+        assert df.index.nlevels == 2
+        assert df.at[(1, 3), "a"] == 1
+        assert df.loc[(1, 3), "a"] == 1
+
+        # Series.at and Series.loc getter works with MultiIndex
+        series = df["a"]
+        assert series.index.nlevels == 2
+        assert series.at[1, 3] == 1
+        assert series.loc[1, 3] == 1
+
+    @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
+    def test_multiindex_at_set(self):
+        # GH 26989
+        # DataFrame.at and DataFrame.loc setter works with MultiIndex
+        df = DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]])
+        assert df.index.nlevels == 2
+        df.at[(1, 3), "a"] = 3
+        assert df.at[(1, 3), "a"] == 3
+        df.loc[(1, 3), "a"] = 4
+        assert df.loc[(1, 3), "a"] == 4
+
+        # Series.at and Series.loc setter works with MultiIndex
+        series = df["a"]
+        assert series.index.nlevels == 2
+        series.at[1, 3] = 5
+        assert series.at[1, 3] == 5
+        series.loc[1, 3] = 6
+        assert series.loc[1, 3] == 6
+
+    def test_multiindex_at_get_one_level(self):
+        # GH#38053
+        s2 = Series((0, 1), index=[[False, True]])
+        result = s2.at[False]
+        assert result == 0

shared/package-files/06/06e4113c5d11f4cadb2f31b2338e39475c67e2f79bdca96cd05181611f54afda/blob

@@ -0,0 +1,22 @@
+#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 at::Tensor linalg_tensorinv(const at::Tensor & self, int64_t ind=2);
+TORCH_API at::Tensor & linalg_tensorinv_out(const at::Tensor & self, int64_t ind, at::Tensor & out);
+} // namespace native
+} // namespace at