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@@ -527,3 +527,8 @@ mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/distlib/w64.exe filter=l
 mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/distlib/w64-arm.exe filter=lfs diff=lfs merge=lfs -text
 mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/distlib/t64-arm.exe filter=lfs diff=lfs merge=lfs -text
 mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/distlib/t64.exe filter=lfs diff=lfs merge=lfs -text
+moondream/lib/python3.10/site-packages/torch/lib/libtorch.so filter=lfs diff=lfs merge=lfs -text
+moondream/lib/python3.10/site-packages/torch/distributed/__pycache__/distributed_c10d.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+moondream/lib/python3.10/site-packages/torch/testing/_internal/distributed/__pycache__/distributed_test.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+moondream/lib/python3.10/site-packages/torch/_decomp/__pycache__/decompositions.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+moondream/lib/python3.10/site-packages/sympy/printing/pretty/tests/__pycache__/test_pretty.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/_fileno.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from typing import IO, Callable
+
+
+def get_fileno(file_like: IO[str]) -> int | None:
+    """Get fileno() from a file, accounting for poorly implemented file-like objects.
+
+    Args:
+        file_like (IO): A file-like object.
+
+    Returns:
+        int | None: The result of fileno if available, or None if operation failed.
+    """
+    fileno: Callable[[], int] | None = getattr(file_like, "fileno", None)
+    if fileno is not None:
+        try:
+            return fileno()
+        except Exception:
+            # `fileno` is documented as potentially raising a OSError
+            # Alas, from the issues, there are so many poorly implemented file-like objects,
+            # that `fileno()` can raise just about anything.
+            return None
+    return None

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/_windows_renderer.py

@@ -0,0 +1,56 @@
+from typing import Iterable, Sequence, Tuple, cast
+
+from pip._vendor.rich._win32_console import LegacyWindowsTerm, WindowsCoordinates
+from pip._vendor.rich.segment import ControlCode, ControlType, Segment
+
+
+def legacy_windows_render(buffer: Iterable[Segment], term: LegacyWindowsTerm) -> None:
+    """Makes appropriate Windows Console API calls based on the segments in the buffer.
+
+    Args:
+        buffer (Iterable[Segment]): Iterable of Segments to convert to Win32 API calls.
+        term (LegacyWindowsTerm): Used to call the Windows Console API.
+    """
+    for text, style, control in buffer:
+        if not control:
+            if style:
+                term.write_styled(text, style)
+            else:
+                term.write_text(text)
+        else:
+            control_codes: Sequence[ControlCode] = control
+            for control_code in control_codes:
+                control_type = control_code[0]
+                if control_type == ControlType.CURSOR_MOVE_TO:
+                    _, x, y = cast(Tuple[ControlType, int, int], control_code)
+                    term.move_cursor_to(WindowsCoordinates(row=y - 1, col=x - 1))
+                elif control_type == ControlType.CARRIAGE_RETURN:
+                    term.write_text("\r")
+                elif control_type == ControlType.HOME:
+                    term.move_cursor_to(WindowsCoordinates(0, 0))
+                elif control_type == ControlType.CURSOR_UP:
+                    term.move_cursor_up()
+                elif control_type == ControlType.CURSOR_DOWN:
+                    term.move_cursor_down()
+                elif control_type == ControlType.CURSOR_FORWARD:
+                    term.move_cursor_forward()
+                elif control_type == ControlType.CURSOR_BACKWARD:
+                    term.move_cursor_backward()
+                elif control_type == ControlType.CURSOR_MOVE_TO_COLUMN:
+                    _, column = cast(Tuple[ControlType, int], control_code)
+                    term.move_cursor_to_column(column - 1)
+                elif control_type == ControlType.HIDE_CURSOR:
+                    term.hide_cursor()
+                elif control_type == ControlType.SHOW_CURSOR:
+                    term.show_cursor()
+                elif control_type == ControlType.ERASE_IN_LINE:
+                    _, mode = cast(Tuple[ControlType, int], control_code)
+                    if mode == 0:
+                        term.erase_end_of_line()
+                    elif mode == 1:
+                        term.erase_start_of_line()
+                    elif mode == 2:
+                        term.erase_line()
+                elif control_type == ControlType.SET_WINDOW_TITLE:
+                    _, title = cast(Tuple[ControlType, str], control_code)
+                    term.set_title(title)

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/align.py

@@ -0,0 +1,312 @@
+import sys
+from itertools import chain
+from typing import TYPE_CHECKING, Iterable, Optional
+
+if sys.version_info >= (3, 8):
+    from typing import Literal
+else:
+    from pip._vendor.typing_extensions import Literal  # pragma: no cover
+
+from .constrain import Constrain
+from .jupyter import JupyterMixin
+from .measure import Measurement
+from .segment import Segment
+from .style import StyleType
+
+if TYPE_CHECKING:
+    from .console import Console, ConsoleOptions, RenderableType, RenderResult
+
+AlignMethod = Literal["left", "center", "right"]
+VerticalAlignMethod = Literal["top", "middle", "bottom"]
+
+
+class Align(JupyterMixin):
+    """Align a renderable by adding spaces if necessary.
+
+    Args:
+        renderable (RenderableType): A console renderable.
+        align (AlignMethod): One of "left", "center", or "right""
+        style (StyleType, optional): An optional style to apply to the background.
+        vertical (Optional[VerticalAlignMethod], optional): Optional vertical align, one of "top", "middle", or "bottom". Defaults to None.
+        pad (bool, optional): Pad the right with spaces. Defaults to True.
+        width (int, optional): Restrict contents to given width, or None to use default width. Defaults to None.
+        height (int, optional): Set height of align renderable, or None to fit to contents. Defaults to None.
+
+    Raises:
+        ValueError: if ``align`` is not one of the expected values.
+    """
+
+    def __init__(
+        self,
+        renderable: "RenderableType",
+        align: AlignMethod = "left",
+        style: Optional[StyleType] = None,
+        *,
+        vertical: Optional[VerticalAlignMethod] = None,
+        pad: bool = True,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+    ) -> None:
+        if align not in ("left", "center", "right"):
+            raise ValueError(
+                f'invalid value for align, expected "left", "center", or "right" (not {align!r})'
+            )
+        if vertical is not None and vertical not in ("top", "middle", "bottom"):
+            raise ValueError(
+                f'invalid value for vertical, expected "top", "middle", or "bottom" (not {vertical!r})'
+            )
+        self.renderable = renderable
+        self.align = align
+        self.style = style
+        self.vertical = vertical
+        self.pad = pad
+        self.width = width
+        self.height = height
+
+    def __repr__(self) -> str:
+        return f"Align({self.renderable!r}, {self.align!r})"
+
+    @classmethod
+    def left(
+        cls,
+        renderable: "RenderableType",
+        style: Optional[StyleType] = None,
+        *,
+        vertical: Optional[VerticalAlignMethod] = None,
+        pad: bool = True,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+    ) -> "Align":
+        """Align a renderable to the left."""
+        return cls(
+            renderable,
+            "left",
+            style=style,
+            vertical=vertical,
+            pad=pad,
+            width=width,
+            height=height,
+        )
+
+    @classmethod
+    def center(
+        cls,
+        renderable: "RenderableType",
+        style: Optional[StyleType] = None,
+        *,
+        vertical: Optional[VerticalAlignMethod] = None,
+        pad: bool = True,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+    ) -> "Align":
+        """Align a renderable to the center."""
+        return cls(
+            renderable,
+            "center",
+            style=style,
+            vertical=vertical,
+            pad=pad,
+            width=width,
+            height=height,
+        )
+
+    @classmethod
+    def right(
+        cls,
+        renderable: "RenderableType",
+        style: Optional[StyleType] = None,
+        *,
+        vertical: Optional[VerticalAlignMethod] = None,
+        pad: bool = True,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+    ) -> "Align":
+        """Align a renderable to the right."""
+        return cls(
+            renderable,
+            "right",
+            style=style,
+            vertical=vertical,
+            pad=pad,
+            width=width,
+            height=height,
+        )
+
+    def __rich_console__(
+        self, console: "Console", options: "ConsoleOptions"
+    ) -> "RenderResult":
+        align = self.align
+        width = console.measure(self.renderable, options=options).maximum
+        rendered = console.render(
+            Constrain(
+                self.renderable, width if self.width is None else min(width, self.width)
+            ),
+            options.update(height=None),
+        )
+        lines = list(Segment.split_lines(rendered))
+        width, height = Segment.get_shape(lines)
+        lines = Segment.set_shape(lines, width, height)
+        new_line = Segment.line()
+        excess_space = options.max_width - width
+        style = console.get_style(self.style) if self.style is not None else None
+
+        def generate_segments() -> Iterable[Segment]:
+            if excess_space <= 0:
+                # Exact fit
+                for line in lines:
+                    yield from line
+                    yield new_line
+
+            elif align == "left":
+                # Pad on the right
+                pad = Segment(" " * excess_space, style) if self.pad else None
+                for line in lines:
+                    yield from line
+                    if pad:
+                        yield pad
+                    yield new_line
+
+            elif align == "center":
+                # Pad left and right
+                left = excess_space // 2
+                pad = Segment(" " * left, style)
+                pad_right = (
+                    Segment(" " * (excess_space - left), style) if self.pad else None
+                )
+                for line in lines:
+                    if left:
+                        yield pad
+                    yield from line
+                    if pad_right:
+                        yield pad_right
+                    yield new_line
+
+            elif align == "right":
+                # Padding on left
+                pad = Segment(" " * excess_space, style)
+                for line in lines:
+                    yield pad
+                    yield from line
+                    yield new_line
+
+        blank_line = (
+            Segment(f"{' ' * (self.width or options.max_width)}\n", style)
+            if self.pad
+            else Segment("\n")
+        )
+
+        def blank_lines(count: int) -> Iterable[Segment]:
+            if count > 0:
+                for _ in range(count):
+                    yield blank_line
+
+        vertical_height = self.height or options.height
+        iter_segments: Iterable[Segment]
+        if self.vertical and vertical_height is not None:
+            if self.vertical == "top":
+                bottom_space = vertical_height - height
+                iter_segments = chain(generate_segments(), blank_lines(bottom_space))
+            elif self.vertical == "middle":
+                top_space = (vertical_height - height) // 2
+                bottom_space = vertical_height - top_space - height
+                iter_segments = chain(
+                    blank_lines(top_space),
+                    generate_segments(),
+                    blank_lines(bottom_space),
+                )
+            else:  #  self.vertical == "bottom":
+                top_space = vertical_height - height
+                iter_segments = chain(blank_lines(top_space), generate_segments())
+        else:
+            iter_segments = generate_segments()
+        if self.style:
+            style = console.get_style(self.style)
+            iter_segments = Segment.apply_style(iter_segments, style)
+        yield from iter_segments
+
+    def __rich_measure__(
+        self, console: "Console", options: "ConsoleOptions"
+    ) -> Measurement:
+        measurement = Measurement.get(console, options, self.renderable)
+        return measurement
+
+
+class VerticalCenter(JupyterMixin):
+    """Vertically aligns a renderable.
+
+    Warn:
+        This class is deprecated and may be removed in a future version. Use Align class with
+        `vertical="middle"`.
+
+    Args:
+        renderable (RenderableType): A renderable object.
+        style (StyleType, optional): An optional style to apply to the background. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        renderable: "RenderableType",
+        style: Optional[StyleType] = None,
+    ) -> None:
+        self.renderable = renderable
+        self.style = style
+
+    def __repr__(self) -> str:
+        return f"VerticalCenter({self.renderable!r})"
+
+    def __rich_console__(
+        self, console: "Console", options: "ConsoleOptions"
+    ) -> "RenderResult":
+        style = console.get_style(self.style) if self.style is not None else None
+        lines = console.render_lines(
+            self.renderable, options.update(height=None), pad=False
+        )
+        width, _height = Segment.get_shape(lines)
+        new_line = Segment.line()
+        height = options.height or options.size.height
+        top_space = (height - len(lines)) // 2
+        bottom_space = height - top_space - len(lines)
+        blank_line = Segment(f"{' ' * width}", style)
+
+        def blank_lines(count: int) -> Iterable[Segment]:
+            for _ in range(count):
+                yield blank_line
+                yield new_line
+
+        if top_space > 0:
+            yield from blank_lines(top_space)
+        for line in lines:
+            yield from line
+            yield new_line
+        if bottom_space > 0:
+            yield from blank_lines(bottom_space)
+
+    def __rich_measure__(
+        self, console: "Console", options: "ConsoleOptions"
+    ) -> Measurement:
+        measurement = Measurement.get(console, options, self.renderable)
+        return measurement
+
+
+if __name__ == "__main__":  # pragma: no cover
+    from pip._vendor.rich.console import Console, Group
+    from pip._vendor.rich.highlighter import ReprHighlighter
+    from pip._vendor.rich.panel import Panel
+
+    highlighter = ReprHighlighter()
+    console = Console()
+
+    panel = Panel(
+        Group(
+            Align.left(highlighter("align='left'")),
+            Align.center(highlighter("align='center'")),
+            Align.right(highlighter("align='right'")),
+        ),
+        width=60,
+        style="on dark_blue",
+        title="Align",
+    )
+
+    console.print(
+        Align.center(panel, vertical="middle", style="on red", height=console.height)
+    )

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/ansi.py

@@ -0,0 +1,241 @@
+import re
+import sys
+from contextlib import suppress
+from typing import Iterable, NamedTuple, Optional
+
+from .color import Color
+from .style import Style
+from .text import Text
+
+re_ansi = re.compile(
+    r"""
+(?:\x1b[0-?])|
+(?:\x1b\](.*?)\x1b\\)|
+(?:\x1b([(@-Z\\-_]|\[[0-?]*[ -/]*[@-~]))
+""",
+    re.VERBOSE,
+)
+
+
+class _AnsiToken(NamedTuple):
+    """Result of ansi tokenized string."""
+
+    plain: str = ""
+    sgr: Optional[str] = ""
+    osc: Optional[str] = ""
+
+
+def _ansi_tokenize(ansi_text: str) -> Iterable[_AnsiToken]:
+    """Tokenize a string in to plain text and ANSI codes.
+
+    Args:
+        ansi_text (str): A String containing ANSI codes.
+
+    Yields:
+        AnsiToken: A named tuple of (plain, sgr, osc)
+    """
+
+    position = 0
+    sgr: Optional[str]
+    osc: Optional[str]
+    for match in re_ansi.finditer(ansi_text):
+        start, end = match.span(0)
+        osc, sgr = match.groups()
+        if start > position:
+            yield _AnsiToken(ansi_text[position:start])
+        if sgr:
+            if sgr == "(":
+                position = end + 1
+                continue
+            if sgr.endswith("m"):
+                yield _AnsiToken("", sgr[1:-1], osc)
+        else:
+            yield _AnsiToken("", sgr, osc)
+        position = end
+    if position < len(ansi_text):
+        yield _AnsiToken(ansi_text[position:])
+
+
+SGR_STYLE_MAP = {
+    1: "bold",
+    2: "dim",
+    3: "italic",
+    4: "underline",
+    5: "blink",
+    6: "blink2",
+    7: "reverse",
+    8: "conceal",
+    9: "strike",
+    21: "underline2",
+    22: "not dim not bold",
+    23: "not italic",
+    24: "not underline",
+    25: "not blink",
+    26: "not blink2",
+    27: "not reverse",
+    28: "not conceal",
+    29: "not strike",
+    30: "color(0)",
+    31: "color(1)",
+    32: "color(2)",
+    33: "color(3)",
+    34: "color(4)",
+    35: "color(5)",
+    36: "color(6)",
+    37: "color(7)",
+    39: "default",
+    40: "on color(0)",
+    41: "on color(1)",
+    42: "on color(2)",
+    43: "on color(3)",
+    44: "on color(4)",
+    45: "on color(5)",
+    46: "on color(6)",
+    47: "on color(7)",
+    49: "on default",
+    51: "frame",
+    52: "encircle",
+    53: "overline",
+    54: "not frame not encircle",
+    55: "not overline",
+    90: "color(8)",
+    91: "color(9)",
+    92: "color(10)",
+    93: "color(11)",
+    94: "color(12)",
+    95: "color(13)",
+    96: "color(14)",
+    97: "color(15)",
+    100: "on color(8)",
+    101: "on color(9)",
+    102: "on color(10)",
+    103: "on color(11)",
+    104: "on color(12)",
+    105: "on color(13)",
+    106: "on color(14)",
+    107: "on color(15)",
+}
+
+
+class AnsiDecoder:
+    """Translate ANSI code in to styled Text."""
+
+    def __init__(self) -> None:
+        self.style = Style.null()
+
+    def decode(self, terminal_text: str) -> Iterable[Text]:
+        """Decode ANSI codes in an iterable of lines.
+
+        Args:
+            lines (Iterable[str]): An iterable of lines of terminal output.
+
+        Yields:
+            Text: Marked up Text.
+        """
+        for line in terminal_text.splitlines():
+            yield self.decode_line(line)
+
+    def decode_line(self, line: str) -> Text:
+        """Decode a line containing ansi codes.
+
+        Args:
+            line (str): A line of terminal output.
+
+        Returns:
+            Text: A Text instance marked up according to ansi codes.
+        """
+        from_ansi = Color.from_ansi
+        from_rgb = Color.from_rgb
+        _Style = Style
+        text = Text()
+        append = text.append
+        line = line.rsplit("\r", 1)[-1]
+        for plain_text, sgr, osc in _ansi_tokenize(line):
+            if plain_text:
+                append(plain_text, self.style or None)
+            elif osc is not None:
+                if osc.startswith("8;"):
+                    _params, semicolon, link = osc[2:].partition(";")
+                    if semicolon:
+                        self.style = self.style.update_link(link or None)
+            elif sgr is not None:
+                # Translate in to semi-colon separated codes
+                # Ignore invalid codes, because we want to be lenient
+                codes = [
+                    min(255, int(_code) if _code else 0)
+                    for _code in sgr.split(";")
+                    if _code.isdigit() or _code == ""
+                ]
+                iter_codes = iter(codes)
+                for code in iter_codes:
+                    if code == 0:
+                        # reset
+                        self.style = _Style.null()
+                    elif code in SGR_STYLE_MAP:
+                        # styles
+                        self.style += _Style.parse(SGR_STYLE_MAP[code])
+                    elif code == 38:
+                        #  Foreground
+                        with suppress(StopIteration):
+                            color_type = next(iter_codes)
+                            if color_type == 5:
+                                self.style += _Style.from_color(
+                                    from_ansi(next(iter_codes))
+                                )
+                            elif color_type == 2:
+                                self.style += _Style.from_color(
+                                    from_rgb(
+                                        next(iter_codes),
+                                        next(iter_codes),
+                                        next(iter_codes),
+                                    )
+                                )
+                    elif code == 48:
+                        # Background
+                        with suppress(StopIteration):
+                            color_type = next(iter_codes)
+                            if color_type == 5:
+                                self.style += _Style.from_color(
+                                    None, from_ansi(next(iter_codes))
+                                )
+                            elif color_type == 2:
+                                self.style += _Style.from_color(
+                                    None,
+                                    from_rgb(
+                                        next(iter_codes),
+                                        next(iter_codes),
+                                        next(iter_codes),
+                                    ),
+                                )
+
+        return text
+
+
+if sys.platform != "win32" and __name__ == "__main__":  # pragma: no cover
+    import io
+    import os
+    import pty
+    import sys
+
+    decoder = AnsiDecoder()
+
+    stdout = io.BytesIO()
+
+    def read(fd: int) -> bytes:
+        data = os.read(fd, 1024)
+        stdout.write(data)
+        return data
+
+    pty.spawn(sys.argv[1:], read)
+
+    from .console import Console
+
+    console = Console(record=True)
+
+    stdout_result = stdout.getvalue().decode("utf-8")
+    print(stdout_result)
+
+    for line in decoder.decode(stdout_result):
+        console.print(line)
+
+    console.save_html("stdout.html")

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/bar.py

@@ -0,0 +1,93 @@
+from typing import Optional, Union
+
+from .color import Color
+from .console import Console, ConsoleOptions, RenderResult
+from .jupyter import JupyterMixin
+from .measure import Measurement
+from .segment import Segment
+from .style import Style
+
+# There are left-aligned characters for 1/8 to 7/8, but
+# the right-aligned characters exist only for 1/8 and 4/8.
+BEGIN_BLOCK_ELEMENTS = ["█", "█", "█", "▐", "▐", "▐", "▕", "▕"]
+END_BLOCK_ELEMENTS = [" ", "▏", "▎", "▍", "▌", "▋", "▊", "▉"]
+FULL_BLOCK = "█"
+
+
+class Bar(JupyterMixin):
+    """Renders a solid block bar.
+
+    Args:
+        size (float): Value for the end of the bar.
+        begin (float): Begin point (between 0 and size, inclusive).
+        end (float): End point (between 0 and size, inclusive).
+        width (int, optional): Width of the bar, or ``None`` for maximum width. Defaults to None.
+        color (Union[Color, str], optional): Color of the bar. Defaults to "default".
+        bgcolor (Union[Color, str], optional): Color of bar background. Defaults to "default".
+    """
+
+    def __init__(
+        self,
+        size: float,
+        begin: float,
+        end: float,
+        *,
+        width: Optional[int] = None,
+        color: Union[Color, str] = "default",
+        bgcolor: Union[Color, str] = "default",
+    ):
+        self.size = size
+        self.begin = max(begin, 0)
+        self.end = min(end, size)
+        self.width = width
+        self.style = Style(color=color, bgcolor=bgcolor)
+
+    def __repr__(self) -> str:
+        return f"Bar({self.size}, {self.begin}, {self.end})"
+
+    def __rich_console__(
+        self, console: Console, options: ConsoleOptions
+    ) -> RenderResult:
+        width = min(
+            self.width if self.width is not None else options.max_width,
+            options.max_width,
+        )
+
+        if self.begin >= self.end:
+            yield Segment(" " * width, self.style)
+            yield Segment.line()
+            return
+
+        prefix_complete_eights = int(width * 8 * self.begin / self.size)
+        prefix_bar_count = prefix_complete_eights // 8
+        prefix_eights_count = prefix_complete_eights % 8
+
+        body_complete_eights = int(width * 8 * self.end / self.size)
+        body_bar_count = body_complete_eights // 8
+        body_eights_count = body_complete_eights % 8
+
+        # When start and end fall into the same cell, we ideally should render
+        # a symbol that's "center-aligned", but there is no good symbol in Unicode.
+        # In this case, we fall back to right-aligned block symbol for simplicity.
+
+        prefix = " " * prefix_bar_count
+        if prefix_eights_count:
+            prefix += BEGIN_BLOCK_ELEMENTS[prefix_eights_count]
+
+        body = FULL_BLOCK * body_bar_count
+        if body_eights_count:
+            body += END_BLOCK_ELEMENTS[body_eights_count]
+
+        suffix = " " * (width - len(body))
+
+        yield Segment(prefix + body[len(prefix) :] + suffix, self.style)
+        yield Segment.line()
+
+    def __rich_measure__(
+        self, console: Console, options: ConsoleOptions
+    ) -> Measurement:
+        return (
+            Measurement(self.width, self.width)
+            if self.width is not None
+            else Measurement(4, options.max_width)
+        )

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/control.py

@@ -0,0 +1,225 @@
+import sys
+import time
+from typing import TYPE_CHECKING, Callable, Dict, Iterable, List, Union
+
+if sys.version_info >= (3, 8):
+    from typing import Final
+else:
+    from pip._vendor.typing_extensions import Final  # pragma: no cover
+
+from .segment import ControlCode, ControlType, Segment
+
+if TYPE_CHECKING:
+    from .console import Console, ConsoleOptions, RenderResult
+
+STRIP_CONTROL_CODES: Final = [
+    7,  # Bell
+    8,  # Backspace
+    11,  # Vertical tab
+    12,  # Form feed
+    13,  # Carriage return
+]
+_CONTROL_STRIP_TRANSLATE: Final = {
+    _codepoint: None for _codepoint in STRIP_CONTROL_CODES
+}
+
+CONTROL_ESCAPE: Final = {
+    7: "\\a",
+    8: "\\b",
+    11: "\\v",
+    12: "\\f",
+    13: "\\r",
+}
+
+CONTROL_CODES_FORMAT: Dict[int, Callable[..., str]] = {
+    ControlType.BELL: lambda: "\x07",
+    ControlType.CARRIAGE_RETURN: lambda: "\r",
+    ControlType.HOME: lambda: "\x1b[H",
+    ControlType.CLEAR: lambda: "\x1b[2J",
+    ControlType.ENABLE_ALT_SCREEN: lambda: "\x1b[?1049h",
+    ControlType.DISABLE_ALT_SCREEN: lambda: "\x1b[?1049l",
+    ControlType.SHOW_CURSOR: lambda: "\x1b[?25h",
+    ControlType.HIDE_CURSOR: lambda: "\x1b[?25l",
+    ControlType.CURSOR_UP: lambda param: f"\x1b[{param}A",
+    ControlType.CURSOR_DOWN: lambda param: f"\x1b[{param}B",
+    ControlType.CURSOR_FORWARD: lambda param: f"\x1b[{param}C",
+    ControlType.CURSOR_BACKWARD: lambda param: f"\x1b[{param}D",
+    ControlType.CURSOR_MOVE_TO_COLUMN: lambda param: f"\x1b[{param+1}G",
+    ControlType.ERASE_IN_LINE: lambda param: f"\x1b[{param}K",
+    ControlType.CURSOR_MOVE_TO: lambda x, y: f"\x1b[{y+1};{x+1}H",
+    ControlType.SET_WINDOW_TITLE: lambda title: f"\x1b]0;{title}\x07",
+}
+
+
+class Control:
+    """A renderable that inserts a control code (non printable but may move cursor).
+
+    Args:
+        *codes (str): Positional arguments are either a :class:`~rich.segment.ControlType` enum or a
+            tuple of ControlType and an integer parameter
+    """
+
+    __slots__ = ["segment"]
+
+    def __init__(self, *codes: Union[ControlType, ControlCode]) -> None:
+        control_codes: List[ControlCode] = [
+            (code,) if isinstance(code, ControlType) else code for code in codes
+        ]
+        _format_map = CONTROL_CODES_FORMAT
+        rendered_codes = "".join(
+            _format_map[code](*parameters) for code, *parameters in control_codes
+        )
+        self.segment = Segment(rendered_codes, None, control_codes)
+
+    @classmethod
+    def bell(cls) -> "Control":
+        """Ring the 'bell'."""
+        return cls(ControlType.BELL)
+
+    @classmethod
+    def home(cls) -> "Control":
+        """Move cursor to 'home' position."""
+        return cls(ControlType.HOME)
+
+    @classmethod
+    def move(cls, x: int = 0, y: int = 0) -> "Control":
+        """Move cursor relative to current position.
+
+        Args:
+            x (int): X offset.
+            y (int): Y offset.
+
+        Returns:
+            ~Control: Control object.
+
+        """
+
+        def get_codes() -> Iterable[ControlCode]:
+            control = ControlType
+            if x:
+                yield (
+                    control.CURSOR_FORWARD if x > 0 else control.CURSOR_BACKWARD,
+                    abs(x),
+                )
+            if y:
+                yield (
+                    control.CURSOR_DOWN if y > 0 else control.CURSOR_UP,
+                    abs(y),
+                )
+
+        control = cls(*get_codes())
+        return control
+
+    @classmethod
+    def move_to_column(cls, x: int, y: int = 0) -> "Control":
+        """Move to the given column, optionally add offset to row.
+
+        Returns:
+            x (int): absolute x (column)
+            y (int): optional y offset (row)
+
+        Returns:
+            ~Control: Control object.
+        """
+
+        return (
+            cls(
+                (ControlType.CURSOR_MOVE_TO_COLUMN, x),
+                (
+                    ControlType.CURSOR_DOWN if y > 0 else ControlType.CURSOR_UP,
+                    abs(y),
+                ),
+            )
+            if y
+            else cls((ControlType.CURSOR_MOVE_TO_COLUMN, x))
+        )
+
+    @classmethod
+    def move_to(cls, x: int, y: int) -> "Control":
+        """Move cursor to absolute position.
+
+        Args:
+            x (int): x offset (column)
+            y (int): y offset (row)
+
+        Returns:
+            ~Control: Control object.
+        """
+        return cls((ControlType.CURSOR_MOVE_TO, x, y))
+
+    @classmethod
+    def clear(cls) -> "Control":
+        """Clear the screen."""
+        return cls(ControlType.CLEAR)
+
+    @classmethod
+    def show_cursor(cls, show: bool) -> "Control":
+        """Show or hide the cursor."""
+        return cls(ControlType.SHOW_CURSOR if show else ControlType.HIDE_CURSOR)
+
+    @classmethod
+    def alt_screen(cls, enable: bool) -> "Control":
+        """Enable or disable alt screen."""
+        if enable:
+            return cls(ControlType.ENABLE_ALT_SCREEN, ControlType.HOME)
+        else:
+            return cls(ControlType.DISABLE_ALT_SCREEN)
+
+    @classmethod
+    def title(cls, title: str) -> "Control":
+        """Set the terminal window title
+
+        Args:
+            title (str): The new terminal window title
+        """
+        return cls((ControlType.SET_WINDOW_TITLE, title))
+
+    def __str__(self) -> str:
+        return self.segment.text
+
+    def __rich_console__(
+        self, console: "Console", options: "ConsoleOptions"
+    ) -> "RenderResult":
+        if self.segment.text:
+            yield self.segment
+
+
+def strip_control_codes(
+    text: str, _translate_table: Dict[int, None] = _CONTROL_STRIP_TRANSLATE
+) -> str:
+    """Remove control codes from text.
+
+    Args:
+        text (str): A string possibly contain control codes.
+
+    Returns:
+        str: String with control codes removed.
+    """
+    return text.translate(_translate_table)
+
+
+def escape_control_codes(
+    text: str,
+    _translate_table: Dict[int, str] = CONTROL_ESCAPE,
+) -> str:
+    """Replace control codes with their "escaped" equivalent in the given text.
+    (e.g. "\b" becomes "\\b")
+
+    Args:
+        text (str): A string possibly containing control codes.
+
+    Returns:
+        str: String with control codes replaced with their escaped version.
+    """
+    return text.translate(_translate_table)
+
+
+if __name__ == "__main__":  # pragma: no cover
+    from pip._vendor.rich.console import Console
+
+    console = Console()
+    console.print("Look at the title of your terminal window ^")
+    # console.print(Control((ControlType.SET_WINDOW_TITLE, "Hello, world!")))
+    for i in range(10):
+        console.set_window_title("🚀 Loading" + "." * i)
+        time.sleep(0.5)

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/json.py

@@ -0,0 +1,139 @@
+from pathlib import Path
+from json import loads, dumps
+from typing import Any, Callable, Optional, Union
+
+from .text import Text
+from .highlighter import JSONHighlighter, NullHighlighter
+
+
+class JSON:
+    """A renderable which pretty prints JSON.
+
+    Args:
+        json (str): JSON encoded data.
+        indent (Union[None, int, str], optional): Number of characters to indent by. Defaults to 2.
+        highlight (bool, optional): Enable highlighting. Defaults to True.
+        skip_keys (bool, optional): Skip keys not of a basic type. Defaults to False.
+        ensure_ascii (bool, optional): Escape all non-ascii characters. Defaults to False.
+        check_circular (bool, optional): Check for circular references. Defaults to True.
+        allow_nan (bool, optional): Allow NaN and Infinity values. Defaults to True.
+        default (Callable, optional): A callable that converts values that can not be encoded
+            in to something that can be JSON encoded. Defaults to None.
+        sort_keys (bool, optional): Sort dictionary keys. Defaults to False.
+    """
+
+    def __init__(
+        self,
+        json: str,
+        indent: Union[None, int, str] = 2,
+        highlight: bool = True,
+        skip_keys: bool = False,
+        ensure_ascii: bool = False,
+        check_circular: bool = True,
+        allow_nan: bool = True,
+        default: Optional[Callable[[Any], Any]] = None,
+        sort_keys: bool = False,
+    ) -> None:
+        data = loads(json)
+        json = dumps(
+            data,
+            indent=indent,
+            skipkeys=skip_keys,
+            ensure_ascii=ensure_ascii,
+            check_circular=check_circular,
+            allow_nan=allow_nan,
+            default=default,
+            sort_keys=sort_keys,
+        )
+        highlighter = JSONHighlighter() if highlight else NullHighlighter()
+        self.text = highlighter(json)
+        self.text.no_wrap = True
+        self.text.overflow = None
+
+    @classmethod
+    def from_data(
+        cls,
+        data: Any,
+        indent: Union[None, int, str] = 2,
+        highlight: bool = True,
+        skip_keys: bool = False,
+        ensure_ascii: bool = False,
+        check_circular: bool = True,
+        allow_nan: bool = True,
+        default: Optional[Callable[[Any], Any]] = None,
+        sort_keys: bool = False,
+    ) -> "JSON":
+        """Encodes a JSON object from arbitrary data.
+
+        Args:
+            data (Any): An object that may be encoded in to JSON
+            indent (Union[None, int, str], optional): Number of characters to indent by. Defaults to 2.
+            highlight (bool, optional): Enable highlighting. Defaults to True.
+            default (Callable, optional): Optional callable which will be called for objects that cannot be serialized. Defaults to None.
+            skip_keys (bool, optional): Skip keys not of a basic type. Defaults to False.
+            ensure_ascii (bool, optional): Escape all non-ascii characters. Defaults to False.
+            check_circular (bool, optional): Check for circular references. Defaults to True.
+            allow_nan (bool, optional): Allow NaN and Infinity values. Defaults to True.
+            default (Callable, optional): A callable that converts values that can not be encoded
+                in to something that can be JSON encoded. Defaults to None.
+            sort_keys (bool, optional): Sort dictionary keys. Defaults to False.
+
+        Returns:
+            JSON: New JSON object from the given data.
+        """
+        json_instance: "JSON" = cls.__new__(cls)
+        json = dumps(
+            data,
+            indent=indent,
+            skipkeys=skip_keys,
+            ensure_ascii=ensure_ascii,
+            check_circular=check_circular,
+            allow_nan=allow_nan,
+            default=default,
+            sort_keys=sort_keys,
+        )
+        highlighter = JSONHighlighter() if highlight else NullHighlighter()
+        json_instance.text = highlighter(json)
+        json_instance.text.no_wrap = True
+        json_instance.text.overflow = None
+        return json_instance
+
+    def __rich__(self) -> Text:
+        return self.text
+
+
+if __name__ == "__main__":
+    import argparse
+    import sys
+
+    parser = argparse.ArgumentParser(description="Pretty print json")
+    parser.add_argument(
+        "path",
+        metavar="PATH",
+        help="path to file, or - for stdin",
+    )
+    parser.add_argument(
+        "-i",
+        "--indent",
+        metavar="SPACES",
+        type=int,
+        help="Number of spaces in an indent",
+        default=2,
+    )
+    args = parser.parse_args()
+
+    from pip._vendor.rich.console import Console
+
+    console = Console()
+    error_console = Console(stderr=True)
+
+    try:
+        if args.path == "-":
+            json_data = sys.stdin.read()
+        else:
+            json_data = Path(args.path).read_text()
+    except Exception as error:
+        error_console.print(f"Unable to read {args.path!r}; {error}")
+        sys.exit(-1)
+
+    console.print(JSON(json_data, indent=args.indent), soft_wrap=True)

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/palette.py

@@ -0,0 +1,100 @@
+from math import sqrt
+from functools import lru_cache
+from typing import Sequence, Tuple, TYPE_CHECKING
+
+from .color_triplet import ColorTriplet
+
+if TYPE_CHECKING:
+    from pip._vendor.rich.table import Table
+
+
+class Palette:
+    """A palette of available colors."""
+
+    def __init__(self, colors: Sequence[Tuple[int, int, int]]):
+        self._colors = colors
+
+    def __getitem__(self, number: int) -> ColorTriplet:
+        return ColorTriplet(*self._colors[number])
+
+    def __rich__(self) -> "Table":
+        from pip._vendor.rich.color import Color
+        from pip._vendor.rich.style import Style
+        from pip._vendor.rich.text import Text
+        from pip._vendor.rich.table import Table
+
+        table = Table(
+            "index",
+            "RGB",
+            "Color",
+            title="Palette",
+            caption=f"{len(self._colors)} colors",
+            highlight=True,
+            caption_justify="right",
+        )
+        for index, color in enumerate(self._colors):
+            table.add_row(
+                str(index),
+                repr(color),
+                Text(" " * 16, style=Style(bgcolor=Color.from_rgb(*color))),
+            )
+        return table
+
+    # This is somewhat inefficient and needs caching
+    @lru_cache(maxsize=1024)
+    def match(self, color: Tuple[int, int, int]) -> int:
+        """Find a color from a palette that most closely matches a given color.
+
+        Args:
+            color (Tuple[int, int, int]): RGB components in range 0 > 255.
+
+        Returns:
+            int: Index of closes matching color.
+        """
+        red1, green1, blue1 = color
+        _sqrt = sqrt
+        get_color = self._colors.__getitem__
+
+        def get_color_distance(index: int) -> float:
+            """Get the distance to a color."""
+            red2, green2, blue2 = get_color(index)
+            red_mean = (red1 + red2) // 2
+            red = red1 - red2
+            green = green1 - green2
+            blue = blue1 - blue2
+            return _sqrt(
+                (((512 + red_mean) * red * red) >> 8)
+                + 4 * green * green
+                + (((767 - red_mean) * blue * blue) >> 8)
+            )
+
+        min_index = min(range(len(self._colors)), key=get_color_distance)
+        return min_index
+
+
+if __name__ == "__main__":  # pragma: no cover
+    import colorsys
+    from typing import Iterable
+    from pip._vendor.rich.color import Color
+    from pip._vendor.rich.console import Console, ConsoleOptions
+    from pip._vendor.rich.segment import Segment
+    from pip._vendor.rich.style import Style
+
+    class ColorBox:
+        def __rich_console__(
+            self, console: Console, options: ConsoleOptions
+        ) -> Iterable[Segment]:
+            height = console.size.height - 3
+            for y in range(0, height):
+                for x in range(options.max_width):
+                    h = x / options.max_width
+                    l = y / (height + 1)
+                    r1, g1, b1 = colorsys.hls_to_rgb(h, l, 1.0)
+                    r2, g2, b2 = colorsys.hls_to_rgb(h, l + (1 / height / 2), 1.0)
+                    bgcolor = Color.from_rgb(r1 * 255, g1 * 255, b1 * 255)
+                    color = Color.from_rgb(r2 * 255, g2 * 255, b2 * 255)
+                    yield Segment("▄", Style(color=color, bgcolor=bgcolor))
+                yield Segment.line()
+
+    console = Console()
+    console.print(ColorBox())

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/progress_bar.py

@@ -0,0 +1,223 @@
+import math
+from functools import lru_cache
+from time import monotonic
+from typing import Iterable, List, Optional
+
+from .color import Color, blend_rgb
+from .color_triplet import ColorTriplet
+from .console import Console, ConsoleOptions, RenderResult
+from .jupyter import JupyterMixin
+from .measure import Measurement
+from .segment import Segment
+from .style import Style, StyleType
+
+# Number of characters before 'pulse' animation repeats
+PULSE_SIZE = 20
+
+
+class ProgressBar(JupyterMixin):
+    """Renders a (progress) bar. Used by rich.progress.
+
+    Args:
+        total (float, optional): Number of steps in the bar. Defaults to 100. Set to None to render a pulsing animation.
+        completed (float, optional): Number of steps completed. Defaults to 0.
+        width (int, optional): Width of the bar, or ``None`` for maximum width. Defaults to None.
+        pulse (bool, optional): Enable pulse effect. Defaults to False. Will pulse if a None total was passed.
+        style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
+        complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
+        finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
+        pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
+        animation_time (Optional[float], optional): Time in seconds to use for animation, or None to use system time.
+    """
+
+    def __init__(
+        self,
+        total: Optional[float] = 100.0,
+        completed: float = 0,
+        width: Optional[int] = None,
+        pulse: bool = False,
+        style: StyleType = "bar.back",
+        complete_style: StyleType = "bar.complete",
+        finished_style: StyleType = "bar.finished",
+        pulse_style: StyleType = "bar.pulse",
+        animation_time: Optional[float] = None,
+    ):
+        self.total = total
+        self.completed = completed
+        self.width = width
+        self.pulse = pulse
+        self.style = style
+        self.complete_style = complete_style
+        self.finished_style = finished_style
+        self.pulse_style = pulse_style
+        self.animation_time = animation_time
+
+        self._pulse_segments: Optional[List[Segment]] = None
+
+    def __repr__(self) -> str:
+        return f"<Bar {self.completed!r} of {self.total!r}>"
+
+    @property
+    def percentage_completed(self) -> Optional[float]:
+        """Calculate percentage complete."""
+        if self.total is None:
+            return None
+        completed = (self.completed / self.total) * 100.0
+        completed = min(100, max(0.0, completed))
+        return completed
+
+    @lru_cache(maxsize=16)
+    def _get_pulse_segments(
+        self,
+        fore_style: Style,
+        back_style: Style,
+        color_system: str,
+        no_color: bool,
+        ascii: bool = False,
+    ) -> List[Segment]:
+        """Get a list of segments to render a pulse animation.
+
+        Returns:
+            List[Segment]: A list of segments, one segment per character.
+        """
+        bar = "-" if ascii else "━"
+        segments: List[Segment] = []
+        if color_system not in ("standard", "eight_bit", "truecolor") or no_color:
+            segments += [Segment(bar, fore_style)] * (PULSE_SIZE // 2)
+            segments += [Segment(" " if no_color else bar, back_style)] * (
+                PULSE_SIZE - (PULSE_SIZE // 2)
+            )
+            return segments
+
+        append = segments.append
+        fore_color = (
+            fore_style.color.get_truecolor()
+            if fore_style.color
+            else ColorTriplet(255, 0, 255)
+        )
+        back_color = (
+            back_style.color.get_truecolor()
+            if back_style.color
+            else ColorTriplet(0, 0, 0)
+        )
+        cos = math.cos
+        pi = math.pi
+        _Segment = Segment
+        _Style = Style
+        from_triplet = Color.from_triplet
+
+        for index in range(PULSE_SIZE):
+            position = index / PULSE_SIZE
+            fade = 0.5 + cos(position * pi * 2) / 2.0
+            color = blend_rgb(fore_color, back_color, cross_fade=fade)
+            append(_Segment(bar, _Style(color=from_triplet(color))))
+        return segments
+
+    def update(self, completed: float, total: Optional[float] = None) -> None:
+        """Update progress with new values.
+
+        Args:
+            completed (float): Number of steps completed.
+            total (float, optional): Total number of steps, or ``None`` to not change. Defaults to None.
+        """
+        self.completed = completed
+        self.total = total if total is not None else self.total
+
+    def _render_pulse(
+        self, console: Console, width: int, ascii: bool = False
+    ) -> Iterable[Segment]:
+        """Renders the pulse animation.
+
+        Args:
+            console (Console): Console instance.
+            width (int): Width in characters of pulse animation.
+
+        Returns:
+            RenderResult: [description]
+
+        Yields:
+            Iterator[Segment]: Segments to render pulse
+        """
+        fore_style = console.get_style(self.pulse_style, default="white")
+        back_style = console.get_style(self.style, default="black")
+
+        pulse_segments = self._get_pulse_segments(
+            fore_style, back_style, console.color_system, console.no_color, ascii=ascii
+        )
+        segment_count = len(pulse_segments)
+        current_time = (
+            monotonic() if self.animation_time is None else self.animation_time
+        )
+        segments = pulse_segments * (int(width / segment_count) + 2)
+        offset = int(-current_time * 15) % segment_count
+        segments = segments[offset : offset + width]
+        yield from segments
+
+    def __rich_console__(
+        self, console: Console, options: ConsoleOptions
+    ) -> RenderResult:
+        width = min(self.width or options.max_width, options.max_width)
+        ascii = options.legacy_windows or options.ascii_only
+        should_pulse = self.pulse or self.total is None
+        if should_pulse:
+            yield from self._render_pulse(console, width, ascii=ascii)
+            return
+
+        completed: Optional[float] = (
+            min(self.total, max(0, self.completed)) if self.total is not None else None
+        )
+
+        bar = "-" if ascii else "━"
+        half_bar_right = " " if ascii else "╸"
+        half_bar_left = " " if ascii else "╺"
+        complete_halves = (
+            int(width * 2 * completed / self.total)
+            if self.total and completed is not None
+            else width * 2
+        )
+        bar_count = complete_halves // 2
+        half_bar_count = complete_halves % 2
+        style = console.get_style(self.style)
+        is_finished = self.total is None or self.completed >= self.total
+        complete_style = console.get_style(
+            self.finished_style if is_finished else self.complete_style
+        )
+        _Segment = Segment
+        if bar_count:
+            yield _Segment(bar * bar_count, complete_style)
+        if half_bar_count:
+            yield _Segment(half_bar_right * half_bar_count, complete_style)
+
+        if not console.no_color:
+            remaining_bars = width - bar_count - half_bar_count
+            if remaining_bars and console.color_system is not None:
+                if not half_bar_count and bar_count:
+                    yield _Segment(half_bar_left, style)
+                    remaining_bars -= 1
+                if remaining_bars:
+                    yield _Segment(bar * remaining_bars, style)
+
+    def __rich_measure__(
+        self, console: Console, options: ConsoleOptions
+    ) -> Measurement:
+        return (
+            Measurement(self.width, self.width)
+            if self.width is not None
+            else Measurement(4, options.max_width)
+        )
+
+
+if __name__ == "__main__":  # pragma: no cover
+    console = Console()
+    bar = ProgressBar(width=50, total=100)
+
+    import time
+
+    console.show_cursor(False)
+    for n in range(0, 101, 1):
+        bar.update(n)
+        console.print(bar)
+        console.file.write("\r")
+        time.sleep(0.05)
+    console.show_cursor(True)
+    console.print()

mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/rich/repr.py

@@ -0,0 +1,149 @@
+import inspect
+from functools import partial
+from typing import (
+    Any,
+    Callable,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    overload,
+)
+
+T = TypeVar("T")
+
+
+Result = Iterable[Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]]
+RichReprResult = Result
+
+
+class ReprError(Exception):
+    """An error occurred when attempting to build a repr."""
+
+
+@overload
+def auto(cls: Optional[Type[T]]) -> Type[T]:
+    ...
+
+
+@overload
+def auto(*, angular: bool = False) -> Callable[[Type[T]], Type[T]]:
+    ...
+
+
+def auto(
+    cls: Optional[Type[T]] = None, *, angular: Optional[bool] = None
+) -> Union[Type[T], Callable[[Type[T]], Type[T]]]:
+    """Class decorator to create __repr__ from __rich_repr__"""
+
+    def do_replace(cls: Type[T], angular: Optional[bool] = None) -> Type[T]:
+        def auto_repr(self: T) -> str:
+            """Create repr string from __rich_repr__"""
+            repr_str: List[str] = []
+            append = repr_str.append
+
+            angular: bool = getattr(self.__rich_repr__, "angular", False)  # type: ignore[attr-defined]
+            for arg in self.__rich_repr__():  # type: ignore[attr-defined]
+                if isinstance(arg, tuple):
+                    if len(arg) == 1:
+                        append(repr(arg[0]))
+                    else:
+                        key, value, *default = arg
+                        if key is None:
+                            append(repr(value))
+                        else:
+                            if default and default[0] == value:
+                                continue
+                            append(f"{key}={value!r}")
+                else:
+                    append(repr(arg))
+            if angular:
+                return f"<{self.__class__.__name__} {' '.join(repr_str)}>"
+            else:
+                return f"{self.__class__.__name__}({', '.join(repr_str)})"
+
+        def auto_rich_repr(self: Type[T]) -> Result:
+            """Auto generate __rich_rep__ from signature of __init__"""
+            try:
+                signature = inspect.signature(self.__init__)
+                for name, param in signature.parameters.items():
+                    if param.kind == param.POSITIONAL_ONLY:
+                        yield getattr(self, name)
+                    elif param.kind in (
+                        param.POSITIONAL_OR_KEYWORD,
+                        param.KEYWORD_ONLY,
+                    ):
+                        if param.default is param.empty:
+                            yield getattr(self, param.name)
+                        else:
+                            yield param.name, getattr(self, param.name), param.default
+            except Exception as error:
+                raise ReprError(
+                    f"Failed to auto generate __rich_repr__; {error}"
+                ) from None
+
+        if not hasattr(cls, "__rich_repr__"):
+            auto_rich_repr.__doc__ = "Build a rich repr"
+            cls.__rich_repr__ = auto_rich_repr  # type: ignore[attr-defined]
+
+        auto_repr.__doc__ = "Return repr(self)"
+        cls.__repr__ = auto_repr  # type: ignore[assignment]
+        if angular is not None:
+            cls.__rich_repr__.angular = angular  # type: ignore[attr-defined]
+        return cls
+
+    if cls is None:
+        return partial(do_replace, angular=angular)
+    else:
+        return do_replace(cls, angular=angular)
+
+
+@overload
+def rich_repr(cls: Optional[Type[T]]) -> Type[T]:
+    ...
+
+
+@overload
+def rich_repr(*, angular: bool = False) -> Callable[[Type[T]], Type[T]]:
+    ...
+
+
+def rich_repr(
+    cls: Optional[Type[T]] = None, *, angular: bool = False
+) -> Union[Type[T], Callable[[Type[T]], Type[T]]]:
+    if cls is None:
+        return auto(angular=angular)
+    else:
+        return auto(cls)
+
+
+if __name__ == "__main__":
+
+    @auto
+    class Foo:
+        def __rich_repr__(self) -> Result:
+            yield "foo"
+            yield "bar", {"shopping": ["eggs", "ham", "pineapple"]}
+            yield "buy", "hand sanitizer"
+
+    foo = Foo()
+    from pip._vendor.rich.console import Console
+
+    console = Console()
+
+    console.rule("Standard repr")
+    console.print(foo)
+
+    console.print(foo, width=60)
+    console.print(foo, width=30)
+
+    console.rule("Angular repr")
+    Foo.__rich_repr__.angular = True  # type: ignore[attr-defined]
+
+    console.print(foo)
+
+    console.print(foo, width=60)
+    console.print(foo, width=30)

moondream/lib/python3.10/site-packages/sympy/printing/pretty/tests/__pycache__/test_pretty.cpython-310.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f2bdedabfaa94cb60038eb16b2342c3c87909a238dbdc4928f4d4994fc7a971
+size 154766

moondream/lib/python3.10/site-packages/torch/_decomp/__pycache__/decompositions.cpython-310.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e3c3fe84db82e9590615c7dffaccd80972e5844d084f9e2db795fe8ae83e588
+size 109721

moondream/lib/python3.10/site-packages/torch/distributed/__pycache__/distributed_c10d.cpython-310.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7653865eee05fc57a16a333bf03f5a20d108d21ea64fbf5f5318b4b67668498
+size 133059

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_convert_indices_from_coo_to_csr_cuda_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor _convert_indices_from_coo_to_csr(const at::Tensor & self, int64_t size, bool out_int32=false);
+TORCH_API at::Tensor & _convert_indices_from_coo_to_csr_out(at::Tensor & out, const at::Tensor & self, int64_t size, bool out_int32=false);
+TORCH_API at::Tensor & _convert_indices_from_coo_to_csr_outf(const at::Tensor & self, int64_t size, bool out_int32, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_clamp_max_cuda_dispatch.h

@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, const at::Scalar & scalar);
+TORCH_API void _foreach_clamp_max_(at::TensorList self, const at::Scalar & scalar);
+TORCH_API ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, at::TensorList other);
+TORCH_API void _foreach_clamp_max_(at::TensorList self, at::TensorList other);
+TORCH_API ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, at::ArrayRef<at::Scalar> scalars);
+TORCH_API void _foreach_clamp_max_(at::TensorList self, at::ArrayRef<at::Scalar> scalars);
+
+} // namespace cuda
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_log2.h

@@ -0,0 +1,44 @@
+#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/_foreach_log2_ops.h>
+
+namespace at {
+
+
+// aten::_foreach_log2(Tensor[] self) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_log2(at::TensorList self) {
+    return at::_ops::_foreach_log2::call(self);
+}
+
+// aten::_foreach_log2_(Tensor(a!)[] self) -> ()
+inline void _foreach_log2_(at::TensorList self) {
+    return at::_ops::_foreach_log2_::call(self);
+}
+
+// aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
+inline void _foreach_log2_out(at::TensorList out, at::TensorList self) {
+    return at::_ops::_foreach_log2_out::call(self, out);
+}
+// aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
+inline void _foreach_log2_outf(at::TensorList self, at::TensorList out) {
+    return at::_ops::_foreach_log2_out::call(self, out);
+}
+
+}

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_lazy_clone_ops.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _lazy_clone {
+  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::_lazy_clone")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_lazy_clone(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_mps_convolution_transpose_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _mps_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups);
+TORCH_API at::Tensor & _mps_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out);
+TORCH_API at::Tensor & _mps_convolution_transpose_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups);
+TORCH_API at::Tensor & _mps_convolution_transpose_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_reshape_alias_ops.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _reshape_alias {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_reshape_alias")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride);
+};
+
+}} // namespace at::_ops

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_compressed_tensor_unsafe.h

@@ -0,0 +1,69 @@
+#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/_sparse_compressed_tensor_unsafe_ops.h>
+
+namespace at {
+
+
+// aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_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) {
+    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_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) {
+    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor _sparse_compressed_tensor_unsafe_symint(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor _sparse_compressed_tensor_unsafe_symint(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
+    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory);
+  }
+}
+
+}

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_softmax_backward_data_native.h

@@ -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 at::Tensor & _sparse_softmax_backward_data_out(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor softmax_backward_sparse_cpu(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self);
+TORCH_API at::Tensor softmax_backward_sparse_cuda(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self);
+} // namespace native
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_to_copy_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor _to_copy(const at::Tensor & self, at::TensorOptions options={}, bool non_blocking=false, c10::optional<at::MemoryFormat> memory_format=c10::nullopt);
+TORCH_API at::Tensor _to_copy(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, bool non_blocking, c10::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor & _to_copy_out(at::Tensor & out, const at::Tensor & self, bool non_blocking=false, c10::optional<at::MemoryFormat> memory_format=c10::nullopt);
+TORCH_API at::Tensor & _to_copy_outf(const at::Tensor & self, bool non_blocking, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_upsample_bilinear2d_aa_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.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/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured__upsample_bilinear2d_aa : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, c10::optional<double> scales_h, c10::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/_validate_sparse_csr_tensor_args_ops.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _validate_sparse_csr_tensor_args {
+  using schema = void (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_validate_sparse_csr_tensor_args")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> ()")
+  static void call(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size);
+};
+
+}} // namespace at::_ops

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_max_pool2d_backward_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.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/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_adaptive_max_pool2d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices);
+};
+
+} // namespace native
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/align_to_native.h

@@ -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 align_to(const at::Tensor & self, at::DimnameList names);
+TORCH_API at::Tensor align_to(const at::Tensor & self, at::DimnameList order, int64_t ellipsis_idx);
+} // namespace native
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/align_to_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API align_to {
+  using schema = at::Tensor (const at::Tensor &, at::DimnameList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::align_to")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "align_to(Tensor(a) self, Dimname[] names) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, at::DimnameList names);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList names);
+};
+
+struct TORCH_API align_to_ellipsis_idx {
+  using schema = at::Tensor (const at::Tensor &, at::DimnameList, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::align_to")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "ellipsis_idx")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "align_to.ellipsis_idx(Tensor(a) self, Dimname[] order, int ellipsis_idx) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, at::DimnameList order, int64_t ellipsis_idx);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList order, int64_t ellipsis_idx);
+};
+
+}} // namespace at::_ops

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/arccosh_ops.h

@@ -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 arccosh {
+  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::arccosh")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arccosh(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 arccosh_ {
+  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::arccosh_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arccosh_(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 arccosh_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::arccosh")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arccosh.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

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/atan2_meta_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor atan2(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & atan2_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & atan2_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & atan2_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace meta
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/batch_norm_gather_stats_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_outf(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional<at::Tensor> & running_mean, const c10::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count, at::Tensor & out0, at::Tensor & out1);
+
+} // namespace compositeexplicitautograd
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/col_indices_copy_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & col_indices_copy_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & col_indices_copy_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/col_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h

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

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/cudnn_grid_sampler_ops.h

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

moondream/lib/python3.10/site-packages/torch/include/ATen/ops/diff_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor diff(const at::Tensor & self, int64_t n=1, int64_t dim=-1, const c10::optional<at::Tensor> & prepend={}, const c10::optional<at::Tensor> & append={});
+TORCH_API at::Tensor & diff_out(at::Tensor & out, const at::Tensor & self, int64_t n=1, int64_t dim=-1, const c10::optional<at::Tensor> & prepend={}, const c10::optional<at::Tensor> & append={});
+TORCH_API at::Tensor & diff_outf(const at::Tensor & self, int64_t n, int64_t dim, const c10::optional<at::Tensor> & prepend, const c10::optional<at::Tensor> & append, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at