diff --git a/.gitattributes b/.gitattributes index 611e2cadc8e71f8084d3b8adb502907807e84afa..eb4ccc665c72307de2e3b8474a0decd1af5e872f 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1736,3 +1736,5 @@ vllm/lib/python3.10/site-packages/cupy/_core/raw.cpython-310-x86_64-linux-gnu.so vllm/lib/python3.10/site-packages/cupy/cuda/memory.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text vllm/lib/python3.10/site-packages/cupy/_core/_kernel.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text vllm/lib/python3.10/site-packages/cupy/_core/_routines_math.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +vllm/lib/python3.10/site-packages/cupy/_core/_memory_range.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +vllm/lib/python3.10/site-packages/cupy/_core/_dtype.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text diff --git a/videollama2/lib/python3.10/site-packages/contourpy/_contourpy.pyi b/videollama2/lib/python3.10/site-packages/contourpy/_contourpy.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c79cf8251a60e67f3eb25651cd9a66cb14d677b3 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/_contourpy.pyi @@ -0,0 +1,200 @@ +from typing import ClassVar, NoReturn + +import numpy as np +import numpy.typing as npt +from typing_extensions import TypeAlias + +import contourpy._contourpy as cpy + +# Input numpy array types, the same as in common.h +CoordinateArray: TypeAlias = npt.NDArray[np.float64] +MaskArray: TypeAlias = npt.NDArray[np.bool_] +LevelArray: TypeAlias = npt.ArrayLike + +# Output numpy array types, the same as in common.h +PointArray: TypeAlias = npt.NDArray[np.float64] +CodeArray: TypeAlias = npt.NDArray[np.uint8] +OffsetArray: TypeAlias = npt.NDArray[np.uint32] + +# Types returned from filled() +FillReturn_OuterCode: TypeAlias = tuple[list[PointArray], list[CodeArray]] +FillReturn_OuterOffset: TypeAlias = tuple[list[PointArray], list[OffsetArray]] +FillReturn_ChunkCombinedCode: TypeAlias = tuple[list[PointArray | None], list[CodeArray | None]] +FillReturn_ChunkCombinedOffset: TypeAlias = tuple[list[PointArray | None], list[OffsetArray | None]] +FillReturn_ChunkCombinedCodeOffset: TypeAlias = tuple[list[PointArray | None], list[CodeArray | None], list[OffsetArray | None]] +FillReturn_ChunkCombinedOffsetOffset: TypeAlias = tuple[list[PointArray | None], list[OffsetArray | None], list[OffsetArray | None]] +FillReturn_Chunk: TypeAlias = FillReturn_ChunkCombinedCode | FillReturn_ChunkCombinedOffset | FillReturn_ChunkCombinedCodeOffset | FillReturn_ChunkCombinedOffsetOffset +FillReturn: TypeAlias = FillReturn_OuterCode | FillReturn_OuterOffset | FillReturn_Chunk + +# Types returned from lines() +LineReturn_Separate: TypeAlias = list[PointArray] +LineReturn_SeparateCode: TypeAlias = tuple[list[PointArray], list[CodeArray]] +LineReturn_ChunkCombinedCode: TypeAlias = tuple[list[PointArray | None], list[CodeArray | None]] +LineReturn_ChunkCombinedOffset: TypeAlias = tuple[list[PointArray | None], list[OffsetArray | None]] +LineReturn_ChunkCombinedNan: TypeAlias = tuple[list[PointArray | None]] +LineReturn_Chunk: TypeAlias = LineReturn_ChunkCombinedCode | LineReturn_ChunkCombinedOffset | LineReturn_ChunkCombinedNan +LineReturn: TypeAlias = LineReturn_Separate | LineReturn_SeparateCode | LineReturn_Chunk + + +NDEBUG: int +__version__: str + +class FillType: + ChunkCombinedCode: ClassVar[cpy.FillType] + ChunkCombinedCodeOffset: ClassVar[cpy.FillType] + ChunkCombinedOffset: ClassVar[cpy.FillType] + ChunkCombinedOffsetOffset: ClassVar[cpy.FillType] + OuterCode: ClassVar[cpy.FillType] + OuterOffset: ClassVar[cpy.FillType] + __members__: ClassVar[dict[str, cpy.FillType]] + def __eq__(self, other: object) -> bool: ... + def __getstate__(self) -> int: ... + def __hash__(self) -> int: ... + def __index__(self) -> int: ... + def __init__(self, value: int) -> None: ... + def __int__(self) -> int: ... + def __ne__(self, other: object) -> bool: ... + def __setstate__(self, state: int) -> NoReturn: ... + @property + def name(self) -> str: ... + @property + def value(self) -> int: ... + +class LineType: + ChunkCombinedCode: ClassVar[cpy.LineType] + ChunkCombinedNan: ClassVar[cpy.LineType] + ChunkCombinedOffset: ClassVar[cpy.LineType] + Separate: ClassVar[cpy.LineType] + SeparateCode: ClassVar[cpy.LineType] + __members__: ClassVar[dict[str, cpy.LineType]] + def __eq__(self, other: object) -> bool: ... + def __getstate__(self) -> int: ... + def __hash__(self) -> int: ... + def __index__(self) -> int: ... + def __init__(self, value: int) -> None: ... + def __int__(self) -> int: ... + def __ne__(self, other: object) -> bool: ... + def __setstate__(self, state: int) -> NoReturn: ... + @property + def name(self) -> str: ... + @property + def value(self) -> int: ... + +class ZInterp: + Linear: ClassVar[cpy.ZInterp] + Log: ClassVar[cpy.ZInterp] + __members__: ClassVar[dict[str, cpy.ZInterp]] + def __eq__(self, other: object) -> bool: ... + def __getstate__(self) -> int: ... + def __hash__(self) -> int: ... + def __index__(self) -> int: ... + def __init__(self, value: int) -> None: ... + def __int__(self) -> int: ... + def __ne__(self, other: object) -> bool: ... + def __setstate__(self, state: int) -> NoReturn: ... + @property + def name(self) -> str: ... + @property + def value(self) -> int: ... + +def max_threads() -> int: ... + +class ContourGenerator: + def create_contour(self, level: float) -> LineReturn: ... + def create_filled_contour(self, lower_level: float, upper_level: float) -> FillReturn: ... + def filled(self, lower_level: float, upper_level: float) -> FillReturn: ... + def lines(self, level: float) -> LineReturn: ... + def multi_filled(self, levels: LevelArray) -> list[FillReturn]: ... + def multi_lines(self, levels: LevelArray) -> list[LineReturn]: ... + @staticmethod + def supports_corner_mask() -> bool: ... + @staticmethod + def supports_fill_type(fill_type: FillType) -> bool: ... + @staticmethod + def supports_line_type(line_type: LineType) -> bool: ... + @staticmethod + def supports_quad_as_tri() -> bool: ... + @staticmethod + def supports_threads() -> bool: ... + @staticmethod + def supports_z_interp() -> bool: ... + @property + def chunk_count(self) -> tuple[int, int]: ... + @property + def chunk_size(self) -> tuple[int, int]: ... + @property + def corner_mask(self) -> bool: ... + @property + def fill_type(self) -> FillType: ... + @property + def line_type(self) -> LineType: ... + @property + def quad_as_tri(self) -> bool: ... + @property + def thread_count(self) -> int: ... + @property + def z_interp(self) -> ZInterp: ... + default_fill_type: cpy.FillType + default_line_type: cpy.LineType + +class Mpl2005ContourGenerator(ContourGenerator): + def __init__( + self, + x: CoordinateArray, + y: CoordinateArray, + z: CoordinateArray, + mask: MaskArray, + *, + x_chunk_size: int = 0, + y_chunk_size: int = 0, + ) -> None: ... + +class Mpl2014ContourGenerator(ContourGenerator): + def __init__( + self, + x: CoordinateArray, + y: CoordinateArray, + z: CoordinateArray, + mask: MaskArray, + *, + corner_mask: bool, + x_chunk_size: int = 0, + y_chunk_size: int = 0, + ) -> None: ... + +class SerialContourGenerator(ContourGenerator): + def __init__( + self, + x: CoordinateArray, + y: CoordinateArray, + z: CoordinateArray, + mask: MaskArray, + *, + corner_mask: bool, + line_type: LineType, + fill_type: FillType, + quad_as_tri: bool, + z_interp: ZInterp, + x_chunk_size: int = 0, + y_chunk_size: int = 0, + ) -> None: ... + def _write_cache(self) -> NoReturn: ... + +class ThreadedContourGenerator(ContourGenerator): + def __init__( + self, + x: CoordinateArray, + y: CoordinateArray, + z: CoordinateArray, + mask: MaskArray, + *, + corner_mask: bool, + line_type: LineType, + fill_type: FillType, + quad_as_tri: bool, + z_interp: ZInterp, + x_chunk_size: int = 0, + y_chunk_size: int = 0, + thread_count: int = 0, + ) -> None: ... + def _write_cache(self) -> None: ... diff --git a/videollama2/lib/python3.10/site-packages/contourpy/_version.py b/videollama2/lib/python3.10/site-packages/contourpy/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..67bc602abf06e9bcea675fe21c56a2f3c76bc331 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/_version.py @@ -0,0 +1 @@ +__version__ = "1.3.0" diff --git a/videollama2/lib/python3.10/site-packages/contourpy/chunk.py b/videollama2/lib/python3.10/site-packages/contourpy/chunk.py new file mode 100644 index 0000000000000000000000000000000000000000..94fded1b161d64adb23748b067630370d8fa2f3c --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/chunk.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +import math + + +def calc_chunk_sizes( + chunk_size: int | tuple[int, int] | None, + chunk_count: int | tuple[int, int] | None, + total_chunk_count: int | None, + ny: int, + nx: int, +) -> tuple[int, int]: + """Calculate chunk sizes. + + Args: + chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same + size in both directions if only one is specified. Cannot be negative. + chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the + same count in both directions if only one is specified. If less than 1, set to 1. + total_chunk_count (int, optional): Total number of chunks. If less than 1, set to 1. + ny (int): Number of grid points in y-direction. + nx (int): Number of grid points in x-direction. + + Return: + tuple(int, int): Chunk sizes (y_chunk_size, x_chunk_size). + + Note: + Zero or one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` should be + specified. + """ + if sum([chunk_size is not None, chunk_count is not None, total_chunk_count is not None]) > 1: + raise ValueError("Only one of chunk_size, chunk_count and total_chunk_count should be set") + + if nx < 2 or ny < 2: + raise ValueError(f"(ny, nx) must be at least (2, 2), not ({ny}, {nx})") + + if total_chunk_count is not None: + max_chunk_count = (nx-1)*(ny-1) + total_chunk_count = min(max(total_chunk_count, 1), max_chunk_count) + if total_chunk_count == 1: + chunk_size = 0 + elif total_chunk_count == max_chunk_count: + chunk_size = (1, 1) + else: + factors = two_factors(total_chunk_count) + if ny > nx: + chunk_count = factors + else: + chunk_count = (factors[1], factors[0]) + + if chunk_count is not None: + if isinstance(chunk_count, tuple): + y_chunk_count, x_chunk_count = chunk_count + else: + y_chunk_count = x_chunk_count = chunk_count + x_chunk_count = min(max(x_chunk_count, 1), nx-1) + y_chunk_count = min(max(y_chunk_count, 1), ny-1) + chunk_size = (math.ceil((ny-1) / y_chunk_count), math.ceil((nx-1) / x_chunk_count)) + + if chunk_size is None: + y_chunk_size = x_chunk_size = 0 + elif isinstance(chunk_size, tuple): + y_chunk_size, x_chunk_size = chunk_size + else: + y_chunk_size = x_chunk_size = chunk_size + + if x_chunk_size < 0 or y_chunk_size < 0: + raise ValueError("chunk_size cannot be negative") + + return y_chunk_size, x_chunk_size + + +def two_factors(n: int) -> tuple[int, int]: + """Split an integer into two integer factors. + + The two factors will be as close as possible to the sqrt of n, and are returned in decreasing + order. Worst case returns (n, 1). + + Args: + n (int): The integer to factorize, must be positive. + + Return: + tuple(int, int): The two factors of n, in decreasing order. + """ + if n < 0: + raise ValueError(f"two_factors expects positive integer not {n}") + + i = math.ceil(math.sqrt(n)) + while n % i != 0: + i -= 1 + j = n // i + if i > j: + return i, j + else: + return j, i diff --git a/videollama2/lib/python3.10/site-packages/contourpy/enum_util.py b/videollama2/lib/python3.10/site-packages/contourpy/enum_util.py new file mode 100644 index 0000000000000000000000000000000000000000..14229abebf7e342a2834ffec2a0ad427a727fa5c --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/enum_util.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +from contourpy._contourpy import FillType, LineType, ZInterp + + +def as_fill_type(fill_type: FillType | str) -> FillType: + """Coerce a FillType or string value to a FillType. + + Args: + fill_type (FillType or str): Value to convert. + + Return: + FillType: Converted value. + """ + if isinstance(fill_type, str): + try: + return FillType.__members__[fill_type] + except KeyError as e: + raise ValueError(f"'{fill_type}' is not a valid FillType") from e + else: + return fill_type + + +def as_line_type(line_type: LineType | str) -> LineType: + """Coerce a LineType or string value to a LineType. + + Args: + line_type (LineType or str): Value to convert. + + Return: + LineType: Converted value. + """ + if isinstance(line_type, str): + try: + return LineType.__members__[line_type] + except KeyError as e: + raise ValueError(f"'{line_type}' is not a valid LineType") from e + else: + return line_type + + +def as_z_interp(z_interp: ZInterp | str) -> ZInterp: + """Coerce a ZInterp or string value to a ZInterp. + + Args: + z_interp (ZInterp or str): Value to convert. + + Return: + ZInterp: Converted value. + """ + if isinstance(z_interp, str): + try: + return ZInterp.__members__[z_interp] + except KeyError as e: + raise ValueError(f"'{z_interp}' is not a valid ZInterp") from e + else: + return z_interp diff --git a/videollama2/lib/python3.10/site-packages/contourpy/typecheck.py b/videollama2/lib/python3.10/site-packages/contourpy/typecheck.py new file mode 100644 index 0000000000000000000000000000000000000000..23fbd54856a96f6b6b60b8557c76936f615a9cdc --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/typecheck.py @@ -0,0 +1,203 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, cast + +import numpy as np + +from contourpy import FillType, LineType +from contourpy.enum_util import as_fill_type, as_line_type +from contourpy.types import MOVETO, code_dtype, offset_dtype, point_dtype + +if TYPE_CHECKING: + import contourpy._contourpy as cpy + + +# Minimalist array-checking functions that check dtype, ndims and shape only. +# They do not walk the arrays to check the contents for performance reasons. +def check_code_array(codes: Any) -> None: + if not isinstance(codes, np.ndarray): + raise TypeError(f"Expected numpy array not {type(codes)}") + if codes.dtype != code_dtype: + raise ValueError(f"Expected numpy array of dtype {code_dtype} not {codes.dtype}") + if not (codes.ndim == 1 and len(codes) > 1): + raise ValueError(f"Expected numpy array of shape (?,) not {codes.shape}") + if codes[0] != MOVETO: + raise ValueError(f"First element of code array must be {MOVETO}, not {codes[0]}") + + +def check_offset_array(offsets: Any) -> None: + if not isinstance(offsets, np.ndarray): + raise TypeError(f"Expected numpy array not {type(offsets)}") + if offsets.dtype != offset_dtype: + raise ValueError(f"Expected numpy array of dtype {offset_dtype} not {offsets.dtype}") + if not (offsets.ndim == 1 and len(offsets) > 1): + raise ValueError(f"Expected numpy array of shape (?,) not {offsets.shape}") + if offsets[0] != 0: + raise ValueError(f"First element of offset array must be 0, not {offsets[0]}") + + +def check_point_array(points: Any) -> None: + if not isinstance(points, np.ndarray): + raise TypeError(f"Expected numpy array not {type(points)}") + if points.dtype != point_dtype: + raise ValueError(f"Expected numpy array of dtype {point_dtype} not {points.dtype}") + if not (points.ndim == 2 and points.shape[1] ==2 and points.shape[0] > 1): + raise ValueError(f"Expected numpy array of shape (?, 2) not {points.shape}") + + +def _check_tuple_of_lists_with_same_length( + maybe_tuple: Any, + tuple_length: int, + allow_empty_lists: bool = True, +) -> None: + if not isinstance(maybe_tuple, tuple): + raise TypeError(f"Expected tuple not {type(maybe_tuple)}") + if len(maybe_tuple) != tuple_length: + raise ValueError(f"Expected tuple of length {tuple_length} not {len(maybe_tuple)}") + for maybe_list in maybe_tuple: + if not isinstance(maybe_list, list): + msg = f"Expected tuple to contain {tuple_length} lists but found a {type(maybe_list)}" + raise TypeError(msg) + lengths = [len(item) for item in maybe_tuple] + if len(set(lengths)) != 1: + msg = f"Expected {tuple_length} lists with same length but lengths are {lengths}" + raise ValueError(msg) + if not allow_empty_lists and lengths[0] == 0: + raise ValueError(f"Expected {tuple_length} non-empty lists") + + +def check_filled(filled: cpy.FillReturn, fill_type: FillType | str) -> None: + fill_type = as_fill_type(fill_type) + + if fill_type == FillType.OuterCode: + if TYPE_CHECKING: + filled = cast(cpy.FillReturn_OuterCode, filled) + _check_tuple_of_lists_with_same_length(filled, 2) + for i, (points, codes) in enumerate(zip(*filled)): + check_point_array(points) + check_code_array(codes) + if len(points) != len(codes): + raise ValueError(f"Points and codes have different lengths in polygon {i}") + elif fill_type == FillType.OuterOffset: + if TYPE_CHECKING: + filled = cast(cpy.FillReturn_OuterOffset, filled) + _check_tuple_of_lists_with_same_length(filled, 2) + for i, (points, offsets) in enumerate(zip(*filled)): + check_point_array(points) + check_offset_array(offsets) + if offsets[-1] != len(points): + raise ValueError(f"Inconsistent points and offsets in polygon {i}") + elif fill_type == FillType.ChunkCombinedCode: + if TYPE_CHECKING: + filled = cast(cpy.FillReturn_ChunkCombinedCode, filled) + _check_tuple_of_lists_with_same_length(filled, 2, allow_empty_lists=False) + for chunk, (points_or_none, codes_or_none) in enumerate(zip(*filled)): + if points_or_none is not None and codes_or_none is not None: + check_point_array(points_or_none) + check_code_array(codes_or_none) + if len(points_or_none) != len(codes_or_none): + raise ValueError(f"Points and codes have different lengths in chunk {chunk}") + elif not (points_or_none is None and codes_or_none is None): + raise ValueError(f"Inconsistent Nones in chunk {chunk}") + elif fill_type == FillType.ChunkCombinedOffset: + if TYPE_CHECKING: + filled = cast(cpy.FillReturn_ChunkCombinedOffset, filled) + _check_tuple_of_lists_with_same_length(filled, 2, allow_empty_lists=False) + for chunk, (points_or_none, offsets_or_none) in enumerate(zip(*filled)): + if points_or_none is not None and offsets_or_none is not None: + check_point_array(points_or_none) + check_offset_array(offsets_or_none) + if offsets_or_none[-1] != len(points_or_none): + raise ValueError(f"Inconsistent points and offsets in chunk {chunk}") + elif not (points_or_none is None and offsets_or_none is None): + raise ValueError(f"Inconsistent Nones in chunk {chunk}") + elif fill_type == FillType.ChunkCombinedCodeOffset: + if TYPE_CHECKING: + filled = cast(cpy.FillReturn_ChunkCombinedCodeOffset, filled) + _check_tuple_of_lists_with_same_length(filled, 3, allow_empty_lists=False) + for i, (points_or_none, codes_or_none, outer_offsets_or_none) in enumerate(zip(*filled)): + if (points_or_none is not None and codes_or_none is not None and + outer_offsets_or_none is not None): + check_point_array(points_or_none) + check_code_array(codes_or_none) + check_offset_array(outer_offsets_or_none) + if len(codes_or_none) != len(points_or_none): + raise ValueError(f"Points and codes have different lengths in chunk {i}") + if outer_offsets_or_none[-1] != len(codes_or_none): + raise ValueError(f"Inconsistent codes and outer_offsets in chunk {i}") + elif not (points_or_none is None and codes_or_none is None and + outer_offsets_or_none is None): + raise ValueError(f"Inconsistent Nones in chunk {i}") + elif fill_type == FillType.ChunkCombinedOffsetOffset: + if TYPE_CHECKING: + filled = cast(cpy.FillReturn_ChunkCombinedOffsetOffset, filled) + _check_tuple_of_lists_with_same_length(filled, 3, allow_empty_lists=False) + for i, (points_or_none, offsets_or_none, outer_offsets_or_none) in enumerate(zip(*filled)): + if (points_or_none is not None and offsets_or_none is not None and + outer_offsets_or_none is not None): + check_point_array(points_or_none) + check_offset_array(offsets_or_none) + check_offset_array(outer_offsets_or_none) + if offsets_or_none[-1] != len(points_or_none): + raise ValueError(f"Inconsistent points and offsets in chunk {i}") + if outer_offsets_or_none[-1] != len(offsets_or_none) - 1: + raise ValueError(f"Inconsistent offsets and outer_offsets in chunk {i}") + elif not (points_or_none is None and offsets_or_none is None and + outer_offsets_or_none is None): + raise ValueError(f"Inconsistent Nones in chunk {i}") + else: + raise ValueError(f"Invalid FillType {fill_type}") + + +def check_lines(lines: cpy.LineReturn, line_type: LineType | str) -> None: + line_type = as_line_type(line_type) + + if line_type == LineType.Separate: + if TYPE_CHECKING: + lines = cast(cpy.LineReturn_Separate, lines) + if not isinstance(lines, list): + raise TypeError(f"Expected list not {type(lines)}") + for points in lines: + check_point_array(points) + elif line_type == LineType.SeparateCode: + if TYPE_CHECKING: + lines = cast(cpy.LineReturn_SeparateCode, lines) + _check_tuple_of_lists_with_same_length(lines, 2) + for i, (points, codes) in enumerate(zip(*lines)): + check_point_array(points) + check_code_array(codes) + if len(points) != len(codes): + raise ValueError(f"Points and codes have different lengths in line {i}") + elif line_type == LineType.ChunkCombinedCode: + if TYPE_CHECKING: + lines = cast(cpy.LineReturn_ChunkCombinedCode, lines) + _check_tuple_of_lists_with_same_length(lines, 2, allow_empty_lists=False) + for chunk, (points_or_none, codes_or_none) in enumerate(zip(*lines)): + if points_or_none is not None and codes_or_none is not None: + check_point_array(points_or_none) + check_code_array(codes_or_none) + if len(points_or_none) != len(codes_or_none): + raise ValueError(f"Points and codes have different lengths in chunk {chunk}") + elif not (points_or_none is None and codes_or_none is None): + raise ValueError(f"Inconsistent Nones in chunk {chunk}") + elif line_type == LineType.ChunkCombinedOffset: + if TYPE_CHECKING: + lines = cast(cpy.LineReturn_ChunkCombinedOffset, lines) + _check_tuple_of_lists_with_same_length(lines, 2, allow_empty_lists=False) + for chunk, (points_or_none, offsets_or_none) in enumerate(zip(*lines)): + if points_or_none is not None and offsets_or_none is not None: + check_point_array(points_or_none) + check_offset_array(offsets_or_none) + if offsets_or_none[-1] != len(points_or_none): + raise ValueError(f"Inconsistent points and offsets in chunk {chunk}") + elif not (points_or_none is None and offsets_or_none is None): + raise ValueError(f"Inconsistent Nones in chunk {chunk}") + elif line_type == LineType.ChunkCombinedNan: + if TYPE_CHECKING: + lines = cast(cpy.LineReturn_ChunkCombinedNan, lines) + _check_tuple_of_lists_with_same_length(lines, 1, allow_empty_lists=False) + for _chunk, points_or_none in enumerate(lines[0]): + if points_or_none is not None: + check_point_array(points_or_none) + else: + raise ValueError(f"Invalid LineType {line_type}") diff --git a/videollama2/lib/python3.10/site-packages/contourpy/types.py b/videollama2/lib/python3.10/site-packages/contourpy/types.py new file mode 100644 index 0000000000000000000000000000000000000000..e704b98eac0736d83586c66803db6b79cdfc5967 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/types.py @@ -0,0 +1,13 @@ +from __future__ import annotations + +import numpy as np + +# dtypes of arrays returned by ContourPy. +point_dtype = np.float64 +code_dtype = np.uint8 +offset_dtype = np.uint32 + +# Kind codes used in Matplotlib Paths. +MOVETO = 1 +LINETO = 2 +CLOSEPOLY = 79 diff --git a/videollama2/lib/python3.10/site-packages/contourpy/util/__init__.py b/videollama2/lib/python3.10/site-packages/contourpy/util/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fe33fcef1e18d2a4b92287e434cf6b1257e4274f --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/util/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from contourpy.util._build_config import build_config + +__all__ = ["build_config"] diff --git a/videollama2/lib/python3.10/site-packages/contourpy/util/_build_config.py b/videollama2/lib/python3.10/site-packages/contourpy/util/_build_config.py new file mode 100644 index 0000000000000000000000000000000000000000..f74b97b5644cad04ef13cabe9082f01c0ff1facd --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/util/_build_config.py @@ -0,0 +1,60 @@ +# _build_config.py.in is converted into _build_config.py during the meson build process. + +from __future__ import annotations + + +def build_config() -> dict[str, str]: + """ + Return a dictionary containing build configuration settings. + + All dictionary keys and values are strings, for example ``False`` is + returned as ``"False"``. + + .. versionadded:: 1.1.0 + """ + return dict( + # Python settings + python_version="3.10", + python_install_dir=r"/usr/local/lib/python3.10/site-packages/", + python_path=r"/tmp/build-env-xuiuilzc/bin/python", + + # Package versions + contourpy_version="1.3.0", + meson_version="1.5.1", + mesonpy_version="0.16.0", + pybind11_version="2.13.5", + + # Misc meson settings + meson_backend="ninja", + build_dir=r"/project/.mesonpy-4viy2wlb/lib/contourpy/util", + source_dir=r"/project/lib/contourpy/util", + cross_build="False", + + # Build options + build_options=r"-Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md -Dvsenv=True --native-file=/project/.mesonpy-4viy2wlb/meson-python-native-file.ini", + buildtype="release", + cpp_std="c++17", + debug="False", + optimization="3", + vsenv="True", + b_ndebug="if-release", + b_vscrt="from_buildtype", + + # C++ compiler + compiler_name="gcc", + compiler_version="10.2.1", + linker_id="ld.bfd", + compile_command="c++", + + # Host machine + host_cpu="x86_64", + host_cpu_family="x86_64", + host_cpu_endian="little", + host_cpu_system="linux", + + # Build machine, same as host machine if not a cross_build + build_cpu="x86_64", + build_cpu_family="x86_64", + build_cpu_endian="little", + build_cpu_system="linux", + ) diff --git a/videollama2/lib/python3.10/site-packages/contourpy/util/data.py b/videollama2/lib/python3.10/site-packages/contourpy/util/data.py new file mode 100644 index 0000000000000000000000000000000000000000..5fa75486958c8277430f149daf872e859de8e000 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/contourpy/util/data.py @@ -0,0 +1,78 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +import numpy as np + +if TYPE_CHECKING: + from contourpy._contourpy import CoordinateArray + + +def simple( + shape: tuple[int, int], want_mask: bool = False, +) -> tuple[CoordinateArray, CoordinateArray, CoordinateArray | np.ma.MaskedArray[Any, Any]]: + """Return simple test data consisting of the sum of two gaussians. + + Args: + shape (tuple(int, int)): 2D shape of data to return. + want_mask (bool, optional): Whether test data should be masked or not, default ``False``. + + Return: + Tuple of 3 arrays: ``x``, ``y``, ``z`` test data, ``z`` will be masked if + ``want_mask=True``. + """ + ny, nx = shape + x = np.arange(nx, dtype=np.float64) + y = np.arange(ny, dtype=np.float64) + x, y = np.meshgrid(x, y) + + xscale = nx - 1.0 + yscale = ny - 1.0 + + # z is sum of 2D gaussians. + amp = np.asarray([1.0, -1.0, 0.8, -0.9, 0.7]) + mid = np.asarray([[0.4, 0.2], [0.3, 0.8], [0.9, 0.75], [0.7, 0.3], [0.05, 0.7]]) + width = np.asarray([0.4, 0.2, 0.2, 0.2, 0.1]) + + z = np.zeros_like(x) + for i in range(len(amp)): + z += amp[i]*np.exp(-((x/xscale - mid[i, 0])**2 + (y/yscale - mid[i, 1])**2) / width[i]**2) + + if want_mask: + mask = np.logical_or( + ((x/xscale - 1.0)**2 / 0.2 + (y/yscale - 0.0)**2 / 0.1) < 1.0, + ((x/xscale - 0.2)**2 / 0.02 + (y/yscale - 0.45)**2 / 0.08) < 1.0, + ) + z = np.ma.array(z, mask=mask) # type: ignore[no-untyped-call] + + return x, y, z + + +def random( + shape: tuple[int, int], seed: int = 2187, mask_fraction: float = 0.0, +) -> tuple[CoordinateArray, CoordinateArray, CoordinateArray | np.ma.MaskedArray[Any, Any]]: + """Return random test data in the range 0 to 1. + + Args: + shape (tuple(int, int)): 2D shape of data to return. + seed (int, optional): Seed for random number generator, default 2187. + mask_fraction (float, optional): Fraction of elements to mask, default 0. + + Return: + Tuple of 3 arrays: ``x``, ``y``, ``z`` test data, ``z`` will be masked if + ``mask_fraction`` is greater than zero. + """ + ny, nx = shape + x = np.arange(nx, dtype=np.float64) + y = np.arange(ny, dtype=np.float64) + x, y = np.meshgrid(x, y) + + rng = np.random.default_rng(seed) + z = rng.uniform(size=shape) + + if mask_fraction > 0.0: + mask_fraction = min(mask_fraction, 0.99) + mask = rng.uniform(size=shape) < mask_fraction + z = np.ma.array(z, mask=mask) # type: ignore[no-untyped-call] + + return x, y, z diff --git a/videollama2/lib/python3.10/site-packages/numpy-1.24.4.dist-info/INSTALLER b/videollama2/lib/python3.10/site-packages/numpy-1.24.4.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/numpy-1.24.4.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/COPYING b/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/COPYING new file mode 100644 index 0000000000000000000000000000000000000000..c14f01beb96afe6119f936d08a30ee2069bb0fb2 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/COPYING @@ -0,0 +1,29 @@ +Copyright (c) 2011, Stavros Korokithakis +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +Redistributions of source code must retain the above copyright notice, +this list of conditions and the following disclaimer. + +Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. + +Neither the name of Stochastic Technologies nor the names of its +contributors may be used to endorse or promote products derived from +this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/METADATA b/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..7e6f6095192dbb50ce6d3a6bfbfe7ae17e4d1cc0 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/METADATA @@ -0,0 +1,222 @@ +Metadata-Version: 2.1 +Name: shortuuid +Version: 1.0.13 +Summary: A generator library for concise, unambiguous and URL-safe UUIDs. +Home-page: https://github.com/skorokithakis/shortuuid/ +License: BSD-3-Clause +Author: Stavros Korokithakis +Author-email: hi@stavros.io +Requires-Python: >=3.6 +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Description-Content-Type: text/markdown + +Description +=========== + +`shortuuid` is a simple python library that generates concise, unambiguous, URL-safe +UUIDs. + +Often, one needs to use non-sequential IDs in places where users will see them, but the +IDs must be as concise and easy to use as possible. `shortuuid` solves this problem by +generating uuids using Python's built-in `uuid` module and then translating them to +base57 using lowercase and uppercase letters and digits, and removing similar-looking +characters such as l, 1, I, O and 0. + +[![image](https://travis-ci.org/skorokithakis/shortuuid.svg?branch=master)](https://travis-ci.org/skorokithakis/shortuuid) + + +Installation +------------ + +To install `shortuuid` you need: + +- Python 3.6+ + +If you have the dependencies, you have multiple options of installation: + +- With pip (preferred), do `pip install shortuuid`. +- With setuptools, do `easy_install shortuuid`. +- To install the source, download it from + https://github.com/stochastic-technologies/shortuuid and run `python setup.py + install`. + + +Usage +----- + +To use `shortuuid`, just import it in your project like so: + +```python +>>> import shortuuid +``` + +You can then generate a short UUID: + +```python +>>> shortuuid.uuid() +'vytxeTZskVKR7C7WgdSP3d' +``` + +If you prefer a version 5 UUID, you can pass a name (DNS or URL) to the call and it will +be used as a namespace (`uuid.NAMESPACE_DNS` or `uuid.NAMESPACE_URL`) for the resulting +UUID: + +```python +>>> shortuuid.uuid(name="example.com") +'exu3DTbj2ncsn9tLdLWspw' + +>>> shortuuid.uuid(name="") +'shortuuid.uuid(name="")' +``` + +You can also generate a cryptographically secure random string (using `os.urandom()` +internally) with: + +```python +>>> shortuuid.ShortUUID().random(length=22) +'RaF56o2r58hTKT7AYS9doj' +``` + +To see the alphabet that is being used to generate new UUIDs: + +```python +>>> shortuuid.get_alphabet() +'23456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz' +``` + +If you want to use your own alphabet to generate UUIDs, use `set_alphabet()`: + +```python +>>> shortuuid.set_alphabet("aaaaabcdefgh1230123") +>>> shortuuid.uuid() +'0agee20aa1hehebcagddhedddc0d2chhab3b' +``` + +The default alphabet matches the regex `[2-9A-HJ-NP-Za-km-z]{22}`. + +`shortuuid` will automatically sort and remove duplicates from your alphabet to ensure +consistency: + +```python +>>> shortuuid.get_alphabet() +'0123abcdefgh' +``` + +If the default 22 digits are too long for you, you can get shorter IDs by just +truncating the string to the desired length. The IDs won't be universally unique any +longer, but the probability of a collision will still be very low. + +To serialize existing UUIDs, use `encode()` and `decode()`: + +```python +>>> import uuid +>>> u = uuid.uuid4() +>>> u +UUID('6ca4f0f8-2508-4bac-b8f1-5d1e3da2247a') + +>>> s = shortuuid.encode(u) +>>> s +'MLpZDiEXM4VsUryR9oE8uc' + +>>> shortuuid.decode(s) == u +True + +>>> short = s[:7] +>>> short +'MLpZDiE' + +>>> h = shortuuid.decode(short) +UUID('00000000-0000-0000-0000-009a5b27f8b9') + +>>> shortuuid.decode(shortuuid.encode(h)) == h +True +``` + + +Class-based usage +----------------- + +If you need to have various alphabets per-thread, you can use the `ShortUUID` class, +like so: + +```python +>>> su = shortuuid.ShortUUID(alphabet="01345678") +>>> su.uuid() +'034636353306816784480643806546503818874456' + +>>> su.get_alphabet() +'01345678' + +>>> su.set_alphabet("21345687654123456") +>>> su.get_alphabet() +'12345678' +``` + + +Command-line usage +------------------ + +`shortuuid` provides a simple way to generate a short UUID in a terminal: + +```bash +$ shortuuid +fZpeF6gcskHbSpTgpQCkcJ +``` + + +Django field +------------ + +`shortuuid` includes a Django field that generates random short UUIDs by default, for +your convenience: + +```python +from shortuuid.django_fields import ShortUUIDField + +class MyModel(models.Model): + # A primary key ID of length 16 and a short alphabet. + id = ShortUUIDField( + length=16, + max_length=40, + prefix="id_", + alphabet="abcdefg1234", + primary_key=True, + ) + + # A short UUID of length 22 and the default alphabet. + api_key = ShortUUIDField() +``` + +The field is the same as the `CharField`, with a `length` argument (the length of the +ID), an `alphabet` argument, and the `default` argument removed. Everything else is +exactly the same, e.g. `index`, `help_text`, `max_length`, etc. + + +Compatibility note +------------------ + +Versions of ShortUUID prior to 1.0.0 generated UUIDs with their MSB last, i.e. reversed. +This was later fixed, but if you have some UUIDs stored as a string with the old method, +you need to pass `legacy=True` to `decode()` when converting your strings back to UUIDs. + +That option will go away in the future, so you will want to convert your UUIDs to +strings using the new method. This can be done like so: + +```python +>>> new_uuid_str = encode(decode(old_uuid_str, legacy=True)) +``` + +License +------- + +`shortuuid` is distributed under the BSD license. + diff --git a/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/RECORD b/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..3d55c379f27baf8a642f0c2cf5b754aa60ea33fc --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/RECORD @@ -0,0 +1,20 @@ +../../../bin/shortuuid,sha256=UKWJmVmWAF_gS2Wwle7CPBE-WL_SCAFW-qg4Do2xJJo,227 +COPYING,sha256=ELKwYb5jpsdKluWiyWdCWwZsX7SDt8OIBwM0f80u4ME,1484 +shortuuid-1.0.13.dist-info/COPYING,sha256=ELKwYb5jpsdKluWiyWdCWwZsX7SDt8OIBwM0f80u4ME,1484 +shortuuid-1.0.13.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +shortuuid-1.0.13.dist-info/METADATA,sha256=dvOetBtLs2-ONmgheP9HDTYVPJpJ5tRR2wrp5KgfWBk,5767 +shortuuid-1.0.13.dist-info/RECORD,, +shortuuid-1.0.13.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +shortuuid-1.0.13.dist-info/WHEEL,sha256=d2fvjOD7sXsVzChCqf0Ty0JbHKBaLYwDbGQDwQTnJ50,88 +shortuuid-1.0.13.dist-info/entry_points.txt,sha256=F7-SvJ-aAiTOhrP7VkrJyg45WPWRqM8mQUa8dd7IBj4,47 +shortuuid/__init__.py,sha256=LWYow59sUDM8M609UaraCH-W27sn6RcbFB41tnfa-88,400 +shortuuid/__pycache__/__init__.cpython-310.pyc,, +shortuuid/__pycache__/cli.cpython-310.pyc,, +shortuuid/__pycache__/django_fields.cpython-310.pyc,, +shortuuid/__pycache__/main.cpython-310.pyc,, +shortuuid/__pycache__/test_shortuuid.cpython-310.pyc,, +shortuuid/cli.py,sha256=llBRZMvGb0FHP9yXTrxcqWJxpqyCYnNe_0u2jBHcrSs,1422 +shortuuid/django_fields.py,sha256=JbxaGG4dMPz941pGeBAybYsFNanrLRqvIFlw3wk8ZXo,1349 +shortuuid/main.py,sha256=plbRNcFSCi0Xe_s70kpgCDW5IArLfFLzq8UOTChqgPo,4688 +shortuuid/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +shortuuid/test_shortuuid.py,sha256=MumPW69U-apESWlCs8VR4x1Ici00OVrLB-0Y-4_atGU,7207 diff --git a/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/REQUESTED b/videollama2/lib/python3.10/site-packages/shortuuid-1.0.13.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/INSTALLER b/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/METADATA b/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..3315f09b34b57878a81777dd41671eb10139801f --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/METADATA @@ -0,0 +1,609 @@ +Metadata-Version: 2.1 +Name: timm +Version: 1.0.3 +Summary: PyTorch Image Models +Keywords: pytorch,image-classification +Home-page: https://github.com/huggingface/pytorch-image-models +Author-Email: Ross Wightman +License: Apache-2.0 +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Project-URL: Homepage, https://github.com/huggingface/pytorch-image-models +Project-URL: Documentation, https://huggingface.co/docs/timm/en/index +Project-URL: Repository, https://github.com/huggingface/pytorch-image-models +Requires-Python: >=3.8 +Requires-Dist: torch +Requires-Dist: torchvision +Requires-Dist: pyyaml +Requires-Dist: huggingface_hub +Requires-Dist: safetensors +Description-Content-Type: text/markdown + +# PyTorch Image Models +- [What's New](#whats-new) +- [Introduction](#introduction) +- [Models](#models) +- [Features](#features) +- [Results](#results) +- [Getting Started (Documentation)](#getting-started-documentation) +- [Train, Validation, Inference Scripts](#train-validation-inference-scripts) +- [Awesome PyTorch Resources](#awesome-pytorch-resources) +- [Licenses](#licenses) +- [Citing](#citing) + +## What's New + +❗Updates after Oct 10, 2022 are available in version >= 0.9❗ +* Many changes since the last 0.6.x stable releases. They were previewed in 0.8.x dev releases but not everyone transitioned. +* `timm.models.layers` moved to `timm.layers`: + * `from timm.models.layers import name` will still work via deprecation mapping (but please transition to `timm.layers`). + * `import timm.models.layers.module` or `from timm.models.layers.module import name` needs to be changed now. +* Builder, helper, non-model modules in `timm.models` have a `_` prefix added, ie `timm.models.helpers` -> `timm.models._helpers`, there are temporary deprecation mapping files but those will be removed. +* All models now support `architecture.pretrained_tag` naming (ex `resnet50.rsb_a1`). + * The pretrained_tag is the specific weight variant (different head) for the architecture. + * Using only `architecture` defaults to the first weights in the default_cfgs for that model architecture. + * In adding pretrained tags, many model names that existed to differentiate were renamed to use the tag (ex: `vit_base_patch16_224_in21k` -> `vit_base_patch16_224.augreg_in21k`). There are deprecation mappings for these. +* A number of models had their checkpoints remaped to match architecture changes needed to better support `features_only=True`, there are `checkpoint_filter_fn` methods in any model module that was remapped. These can be passed to `timm.models.load_checkpoint(..., filter_fn=timm.models.swin_transformer_v2.checkpoint_filter_fn)` to remap your existing checkpoint. +* The Hugging Face Hub (https://huggingface.co/timm) is now the primary source for `timm` weights. Model cards include link to papers, original source, license. +* Previous 0.6.x can be cloned from [0.6.x](https://github.com/rwightman/pytorch-image-models/tree/0.6.x) branch or installed via pip with version. + +### May 14, 2024 +* Support loading PaliGemma jax weights into SigLIP ViT models with average pooling. +* Add Hiera models from Meta (https://github.com/facebookresearch/hiera). +* Add `normalize=` flag for transorms, return non-normalized torch.Tensor with original dytpe (for `chug`) +* Version 1.0.3 release + +### May 11, 2024 +* `Searching for Better ViT Baselines (For the GPU Poor)` weights and vit variants released. Exploring model shapes between Tiny and Base. + +| model | top1 | top5 | param_count | img_size | +| -------------------------------------------------- | ------ | ------ | ----------- | -------- | +| [vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k](https://huggingface.co/timm/vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k) | 86.202 | 97.874 | 64.11 | 256 | +| [vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k) | 85.418 | 97.48 | 60.4 | 256 | +| [vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k) | 84.322 | 96.812 | 63.95 | 256 | +| [vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k) | 83.906 | 96.684 | 60.23 | 256 | +| [vit_base_patch16_rope_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_base_patch16_rope_reg1_gap_256.sbb_in1k) | 83.866 | 96.67 | 86.43 | 256 | +| [vit_medium_patch16_rope_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k) | 83.81 | 96.824 | 38.74 | 256 | +| [vit_betwixt_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_256.sbb_in1k) | 83.706 | 96.616 | 60.4 | 256 | +| [vit_betwixt_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg1_gap_256.sbb_in1k) | 83.628 | 96.544 | 60.4 | 256 | +| [vit_medium_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_reg4_gap_256.sbb_in1k) | 83.47 | 96.622 | 38.88 | 256 | +| [vit_medium_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_reg1_gap_256.sbb_in1k) | 83.462 | 96.548 | 38.88 | 256 | +| [vit_little_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_little_patch16_reg4_gap_256.sbb_in1k) | 82.514 | 96.262 | 22.52 | 256 | +| [vit_wee_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_wee_patch16_reg1_gap_256.sbb_in1k) | 80.256 | 95.360 | 13.42 | 256 | +| [vit_pwee_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_pwee_patch16_reg1_gap_256.sbb_in1k) | 80.072 | 95.136 | 15.25 | 256 | +| [vit_mediumd_patch16_reg4_gap_256.sbb_in12k](https://huggingface.co/timm/vit_mediumd_patch16_reg4_gap_256.sbb_in12k) | N/A | N/A | 64.11 | 256 | +| [vit_betwixt_patch16_reg4_gap_256.sbb_in12k](https://huggingface.co/timm/vit_betwixt_patch16_reg4_gap_256.sbb_in12k) | N/A | N/A | 60.4 | 256 | + +* AttentionExtract helper added to extract attention maps from `timm` models. See example in https://github.com/huggingface/pytorch-image-models/discussions/1232#discussioncomment-9320949 +* `forward_intermediates()` API refined and added to more models including some ConvNets that have other extraction methods. +* 1017 of 1047 model architectures support `features_only=True` feature extraction. Remaining 34 architectures can be supported but based on priority requests. +* Remove torch.jit.script annotated functions including old JIT activations. Conflict with dynamo and dynamo does a much better job when used. + +### April 11, 2024 +* Prepping for a long overdue 1.0 release, things have been stable for a while now. +* Significant feature that's been missing for a while, `features_only=True` support for ViT models with flat hidden states or non-std module layouts (so far covering `'vit_*', 'twins_*', 'deit*', 'beit*', 'mvitv2*', 'eva*', 'samvit_*', 'flexivit*'`) +* Above feature support achieved through a new `forward_intermediates()` API that can be used with a feature wrapping module or direclty. +```python +model = timm.create_model('vit_base_patch16_224') +final_feat, intermediates = model.forward_intermediates(input) +output = model.forward_head(final_feat) # pooling + classifier head + +print(final_feat.shape) +torch.Size([2, 197, 768]) + +for f in intermediates: + print(f.shape) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) +torch.Size([2, 768, 14, 14]) + +print(output.shape) +torch.Size([2, 1000]) +``` + +```python +model = timm.create_model('eva02_base_patch16_clip_224', pretrained=True, img_size=512, features_only=True, out_indices=(-3, -2,)) +output = model(torch.randn(2, 3, 512, 512)) + +for o in output: + print(o.shape) +torch.Size([2, 768, 32, 32]) +torch.Size([2, 768, 32, 32]) +``` +* TinyCLIP vision tower weights added, thx [Thien Tran](https://github.com/gau-nernst) + +### Feb 19, 2024 +* Next-ViT models added. Adapted from https://github.com/bytedance/Next-ViT +* HGNet and PP-HGNetV2 models added. Adapted from https://github.com/PaddlePaddle/PaddleClas by [SeeFun](https://github.com/seefun) +* Removed setup.py, moved to pyproject.toml based build supported by PDM +* Add updated model EMA impl using _for_each for less overhead +* Support device args in train script for non GPU devices +* Other misc fixes and small additions +* Min supported Python version increased to 3.8 +* Release 0.9.16 + +### Jan 8, 2024 +Datasets & transform refactoring +* HuggingFace streaming (iterable) dataset support (`--dataset hfids:org/dataset`) +* Webdataset wrapper tweaks for improved split info fetching, can auto fetch splits from supported HF hub webdataset +* Tested HF `datasets` and webdataset wrapper streaming from HF hub with recent `timm` ImageNet uploads to https://huggingface.co/timm +* Make input & target column/field keys consistent across datasets and pass via args +* Full monochrome support when using e:g: `--input-size 1 224 224` or `--in-chans 1`, sets PIL image conversion appropriately in dataset +* Improved several alternate crop & resize transforms (ResizeKeepRatio, RandomCropOrPad, etc) for use in PixParse document AI project +* Add SimCLR style color jitter prob along with grayscale and gaussian blur options to augmentations and args +* Allow train without validation set (`--val-split ''`) in train script +* Add `--bce-sum` (sum over class dim) and `--bce-pos-weight` (positive weighting) args for training as they're common BCE loss tweaks I was often hard coding + +### Nov 23, 2023 +* Added EfficientViT-Large models, thanks [SeeFun](https://github.com/seefun) +* Fix Python 3.7 compat, will be dropping support for it soon +* Other misc fixes +* Release 0.9.12 + +### Nov 20, 2023 +* Added significant flexibility for Hugging Face Hub based timm models via `model_args` config entry. `model_args` will be passed as kwargs through to models on creation. + * See example at https://huggingface.co/gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k/blob/main/config.json + * Usage: https://github.com/huggingface/pytorch-image-models/discussions/2035 +* Updated imagenet eval and test set csv files with latest models +* `vision_transformer.py` typing and doc cleanup by [Laureηt](https://github.com/Laurent2916) +* 0.9.11 release + +### Nov 3, 2023 +* [DFN (Data Filtering Networks)](https://huggingface.co/papers/2309.17425) and [MetaCLIP](https://huggingface.co/papers/2309.16671) ViT weights added +* DINOv2 'register' ViT model weights added (https://huggingface.co/papers/2309.16588, https://huggingface.co/papers/2304.07193) +* Add `quickgelu` ViT variants for OpenAI, DFN, MetaCLIP weights that use it (less efficient) +* Improved typing added to ResNet, MobileNet-v3 thanks to [Aryan](https://github.com/a-r-r-o-w) +* ImageNet-12k fine-tuned (from LAION-2B CLIP) `convnext_xxlarge` +* 0.9.9 release + +### Oct 20, 2023 +* [SigLIP](https://huggingface.co/papers/2303.15343) image tower weights supported in `vision_transformer.py`. + * Great potential for fine-tune and downstream feature use. +* Experimental 'register' support in vit models as per [Vision Transformers Need Registers](https://huggingface.co/papers/2309.16588) +* Updated RepViT with new weight release. Thanks [wangao](https://github.com/jameslahm) +* Add patch resizing support (on pretrained weight load) to Swin models +* 0.9.8 release pending + +### Sep 1, 2023 +* TinyViT added by [SeeFun](https://github.com/seefun) +* Fix EfficientViT (MIT) to use torch.autocast so it works back to PT 1.10 +* 0.9.7 release + +### Aug 28, 2023 +* Add dynamic img size support to models in `vision_transformer.py`, `vision_transformer_hybrid.py`, `deit.py`, and `eva.py` w/o breaking backward compat. + * Add `dynamic_img_size=True` to args at model creation time to allow changing the grid size (interpolate abs and/or ROPE pos embed each forward pass). + * Add `dynamic_img_pad=True` to allow image sizes that aren't divisible by patch size (pad bottom right to patch size each forward pass). + * Enabling either dynamic mode will break FX tracing unless PatchEmbed module added as leaf. + * Existing method of resizing position embedding by passing different `img_size` (interpolate pretrained embed weights once) on creation still works. + * Existing method of changing `patch_size` (resize pretrained patch_embed weights once) on creation still works. + * Example validation cmd `python validate.py /imagenet --model vit_base_patch16_224 --amp --amp-dtype bfloat16 --img-size 255 --crop-pct 1.0 --model-kwargs dynamic_img_size=True dyamic_img_pad=True` + +### Aug 25, 2023 +* Many new models since last release + * FastViT - https://arxiv.org/abs/2303.14189 + * MobileOne - https://arxiv.org/abs/2206.04040 + * InceptionNeXt - https://arxiv.org/abs/2303.16900 + * RepGhostNet - https://arxiv.org/abs/2211.06088 (thanks https://github.com/ChengpengChen) + * GhostNetV2 - https://arxiv.org/abs/2211.12905 (thanks https://github.com/yehuitang) + * EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027 (thanks https://github.com/seefun) + * EfficientViT (MIT) - https://arxiv.org/abs/2205.14756 (thanks https://github.com/seefun) +* Add `--reparam` arg to `benchmark.py`, `onnx_export.py`, and `validate.py` to trigger layer reparameterization / fusion for models with any one of `reparameterize()`, `switch_to_deploy()` or `fuse()` + * Including FastViT, MobileOne, RepGhostNet, EfficientViT (MSRA), RepViT, RepVGG, and LeViT +* Preparing 0.9.6 'back to school' release + +### Aug 11, 2023 +* Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weights +* Example validation cmd to test w/ non-square resize `python validate.py /imagenet --model swin_base_patch4_window7_224.ms_in22k_ft_in1k --amp --amp-dtype bfloat16 --input-size 3 256 320 --model-kwargs window_size=8,10 img_size=256,320` + +### Aug 3, 2023 +* Add GluonCV weights for HRNet w18_small and w18_small_v2. Converted by [SeeFun](https://github.com/seefun) +* Fix `selecsls*` model naming regression +* Patch and position embedding for ViT/EVA works for bfloat16/float16 weights on load (or activations for on-the-fly resize) +* v0.9.5 release prep + +### July 27, 2023 +* Added timm trained `seresnextaa201d_32x8d.sw_in12k_ft_in1k_384` weights (and `.sw_in12k` pretrain) with 87.3% top-1 on ImageNet-1k, best ImageNet ResNet family model I'm aware of. +* RepViT model and weights (https://arxiv.org/abs/2307.09283) added by [wangao](https://github.com/jameslahm) +* I-JEPA ViT feature weights (no classifier) added by [SeeFun](https://github.com/seefun) +* SAM-ViT (segment anything) feature weights (no classifier) added by [SeeFun](https://github.com/seefun) +* Add support for alternative feat extraction methods and -ve indices to EfficientNet +* Add NAdamW optimizer +* Misc fixes + +### May 11, 2023 +* `timm` 0.9 released, transition from 0.8.xdev releases + +### May 10, 2023 +* Hugging Face Hub downloading is now default, 1132 models on https://huggingface.co/timm, 1163 weights in `timm` +* DINOv2 vit feature backbone weights added thanks to [Leng Yue](https://github.com/leng-yue) +* FB MAE vit feature backbone weights added +* OpenCLIP DataComp-XL L/14 feat backbone weights added +* MetaFormer (poolformer-v2, caformer, convformer, updated poolformer (v1)) w/ weights added by [Fredo Guan](https://github.com/fffffgggg54) +* Experimental `get_intermediate_layers` function on vit/deit models for grabbing hidden states (inspired by DINO impl). This is WIP and may change significantly... feedback welcome. +* Model creation throws error if `pretrained=True` and no weights exist (instead of continuing with random initialization) +* Fix regression with inception / nasnet TF sourced weights with 1001 classes in original classifiers +* bitsandbytes (https://github.com/TimDettmers/bitsandbytes) optimizers added to factory, use `bnb` prefix, ie `bnbadam8bit` +* Misc cleanup and fixes +* Final testing before switching to a 0.9 and bringing `timm` out of pre-release state + +### April 27, 2023 +* 97% of `timm` models uploaded to HF Hub and almost all updated to support multi-weight pretrained configs +* Minor cleanup and refactoring of another batch of models as multi-weight added. More fused_attn (F.sdpa) and features_only support, and torchscript fixes. + +### April 21, 2023 +* Gradient accumulation support added to train script and tested (`--grad-accum-steps`), thanks [Taeksang Kim](https://github.com/voidbag) +* More weights on HF Hub (cspnet, cait, volo, xcit, tresnet, hardcorenas, densenet, dpn, vovnet, xception_aligned) +* Added `--head-init-scale` and `--head-init-bias` to train.py to scale classiifer head and set fixed bias for fine-tune +* Remove all InplaceABN (`inplace_abn`) use, replaced use in tresnet with standard BatchNorm (modified weights accordingly). + +### April 12, 2023 +* Add ONNX export script, validate script, helpers that I've had kicking around for along time. Tweak 'same' padding for better export w/ recent ONNX + pytorch. +* Refactor dropout args for vit and vit-like models, separate drop_rate into `drop_rate` (classifier dropout), `proj_drop_rate` (block mlp / out projections), `pos_drop_rate` (position embedding drop), `attn_drop_rate` (attention dropout). Also add patch dropout (FLIP) to vit and eva models. +* fused F.scaled_dot_product_attention support to more vit models, add env var (TIMM_FUSED_ATTN) to control, and config interface to enable/disable +* Add EVA-CLIP backbones w/ image tower weights, all the way up to 4B param 'enormous' model, and 336x336 OpenAI ViT mode that was missed. + +### April 5, 2023 +* ALL ResNet models pushed to Hugging Face Hub with multi-weight support + * All past `timm` trained weights added with recipe based tags to differentiate + * All ResNet strikes back A1/A2/A3 (seed 0) and R50 example B/C1/C2/D weights available + * Add torchvision v2 recipe weights to existing torchvision originals + * See comparison table in https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288#model-comparison +* New ImageNet-12k + ImageNet-1k fine-tunes available for a few anti-aliased ResNet models + * `resnetaa50d.sw_in12k_ft_in1k` - 81.7 @ 224, 82.6 @ 288 + * `resnetaa101d.sw_in12k_ft_in1k` - 83.5 @ 224, 84.1 @ 288 + * `seresnextaa101d_32x8d.sw_in12k_ft_in1k` - 86.0 @ 224, 86.5 @ 288 + * `seresnextaa101d_32x8d.sw_in12k_ft_in1k_288` - 86.5 @ 288, 86.7 @ 320 + +### March 31, 2023 +* Add first ConvNext-XXLarge CLIP -> IN-1k fine-tune and IN-12k intermediate fine-tunes for convnext-base/large CLIP models. + +| model |top1 |top5 |img_size|param_count|gmacs |macts | +|----------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------| +| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45| +| convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384 |88.312|98.578|384 |200.13 |101.11|126.74| +| convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320 |87.968|98.47 |320 |200.13 |70.21 |88.02 | +| convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384 |87.138|98.212|384 |88.59 |45.21 |84.49 | +| convnext_base.clip_laion2b_augreg_ft_in12k_in1k |86.344|97.97 |256 |88.59 |20.09 |37.55 | + +* Add EVA-02 MIM pretrained and fine-tuned weights, push to HF hub and update model cards for all EVA models. First model over 90% top-1 (99% top-5)! Check out the original code & weights at https://github.com/baaivision/EVA for more details on their work blending MIM, CLIP w/ many model, dataset, and train recipe tweaks. + +| model |top1 |top5 |param_count|img_size| +|----------------------------------------------------|------|------|-----------|--------| +| [eva02_large_patch14_448.mim_m38m_ft_in22k_in1k](https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in1k) |90.054|99.042|305.08 |448 | +| eva02_large_patch14_448.mim_in22k_ft_in22k_in1k |89.946|99.01 |305.08 |448 | +| eva_giant_patch14_560.m30m_ft_in22k_in1k |89.792|98.992|1014.45 |560 | +| eva02_large_patch14_448.mim_in22k_ft_in1k |89.626|98.954|305.08 |448 | +| eva02_large_patch14_448.mim_m38m_ft_in1k |89.57 |98.918|305.08 |448 | +| eva_giant_patch14_336.m30m_ft_in22k_in1k |89.56 |98.956|1013.01 |336 | +| eva_giant_patch14_336.clip_ft_in1k |89.466|98.82 |1013.01 |336 | +| eva_large_patch14_336.in22k_ft_in22k_in1k |89.214|98.854|304.53 |336 | +| eva_giant_patch14_224.clip_ft_in1k |88.882|98.678|1012.56 |224 | +| eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12 |448 | +| eva_large_patch14_336.in22k_ft_in1k |88.652|98.722|304.53 |336 | +| eva_large_patch14_196.in22k_ft_in22k_in1k |88.592|98.656|304.14 |196 | +| eva02_base_patch14_448.mim_in22k_ft_in1k |88.23 |98.564|87.12 |448 | +| eva_large_patch14_196.in22k_ft_in1k |87.934|98.504|304.14 |196 | +| eva02_small_patch14_336.mim_in22k_ft_in1k |85.74 |97.614|22.13 |336 | +| eva02_tiny_patch14_336.mim_in22k_ft_in1k |80.658|95.524|5.76 |336 | + +* Multi-weight and HF hub for DeiT and MLP-Mixer based models + +### March 22, 2023 +* More weights pushed to HF hub along with multi-weight support, including: `regnet.py`, `rexnet.py`, `byobnet.py`, `resnetv2.py`, `swin_transformer.py`, `swin_transformer_v2.py`, `swin_transformer_v2_cr.py` +* Swin Transformer models support feature extraction (NCHW feat maps for `swinv2_cr_*`, and NHWC for all others) and spatial embedding outputs. +* FocalNet (from https://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing constraint +* RegNet weights increased with HF hub push, SWAG, SEER, and torchvision v2 weights. SEER is pretty poor wrt to performance for model size, but possibly useful. +* More ImageNet-12k pretrained and 1k fine-tuned `timm` weights: + * `rexnetr_200.sw_in12k_ft_in1k` - 82.6 @ 224, 83.2 @ 288 + * `rexnetr_300.sw_in12k_ft_in1k` - 84.0 @ 224, 84.5 @ 288 + * `regnety_120.sw_in12k_ft_in1k` - 85.0 @ 224, 85.4 @ 288 + * `regnety_160.lion_in12k_ft_in1k` - 85.6 @ 224, 86.0 @ 288 + * `regnety_160.sw_in12k_ft_in1k` - 85.6 @ 224, 86.0 @ 288 (compare to SWAG PT + 1k FT this is same BUT much lower res, blows SEER FT away) +* Model name deprecation + remapping functionality added (a milestone for bringing 0.8.x out of pre-release). Mappings being added... +* Minor bug fixes and improvements. + +### Feb 26, 2023 +* Add ConvNeXt-XXLarge CLIP pretrained image tower weights for fine-tune & features (fine-tuning TBD) -- see [model card](https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup) +* Update `convnext_xxlarge` default LayerNorm eps to 1e-5 (for CLIP weights, improved stability) +* 0.8.15dev0 + +### Feb 20, 2023 +* Add 320x320 `convnext_large_mlp.clip_laion2b_ft_320` and `convnext_lage_mlp.clip_laion2b_ft_soup_320` CLIP image tower weights for features & fine-tune +* 0.8.13dev0 pypi release for latest changes w/ move to huggingface org + +### Feb 16, 2023 +* `safetensor` checkpoint support added +* Add ideas from 'Scaling Vision Transformers to 22 B. Params' (https://arxiv.org/abs/2302.05442) -- qk norm, RmsNorm, parallel block +* Add F.scaled_dot_product_attention support (PyTorch 2.0 only) to `vit_*`, `vit_relpos*`, `coatnet` / `maxxvit` (to start) +* Lion optimizer (w/ multi-tensor option) added (https://arxiv.org/abs/2302.06675) +* gradient checkpointing works with `features_only=True` + +## Introduction + +Py**T**orch **Im**age **M**odels (`timm`) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results. + +The work of many others is present here. I've tried to make sure all source material is acknowledged via links to github, arxiv papers, etc in the README, documentation, and code docstrings. Please let me know if I missed anything. + +## Features + +### Models + +All model architecture families include variants with pretrained weights. There are specific model variants without any weights, it is NOT a bug. Help training new or better weights is always appreciated. + +* Aggregating Nested Transformers - https://arxiv.org/abs/2105.12723 +* BEiT - https://arxiv.org/abs/2106.08254 +* Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370 +* Bottleneck Transformers - https://arxiv.org/abs/2101.11605 +* CaiT (Class-Attention in Image Transformers) - https://arxiv.org/abs/2103.17239 +* CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399 +* CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803 +* ConvNeXt - https://arxiv.org/abs/2201.03545 +* ConvNeXt-V2 - http://arxiv.org/abs/2301.00808 +* ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697 +* CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929 +* DeiT - https://arxiv.org/abs/2012.12877 +* DeiT-III - https://arxiv.org/pdf/2204.07118.pdf +* DenseNet - https://arxiv.org/abs/1608.06993 +* DLA - https://arxiv.org/abs/1707.06484 +* DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629 +* EdgeNeXt - https://arxiv.org/abs/2206.10589 +* EfficientFormer - https://arxiv.org/abs/2206.01191 +* EfficientNet (MBConvNet Family) + * EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252 + * EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665 + * EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946 + * EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html + * EfficientNet V2 - https://arxiv.org/abs/2104.00298 + * FBNet-C - https://arxiv.org/abs/1812.03443 + * MixNet - https://arxiv.org/abs/1907.09595 + * MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626 + * MobileNet-V2 - https://arxiv.org/abs/1801.04381 + * Single-Path NAS - https://arxiv.org/abs/1904.02877 + * TinyNet - https://arxiv.org/abs/2010.14819 +* EfficientViT (MIT) - https://arxiv.org/abs/2205.14756 +* EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027 +* EVA - https://arxiv.org/abs/2211.07636 +* EVA-02 - https://arxiv.org/abs/2303.11331 +* FastViT - https://arxiv.org/abs/2303.14189 +* FlexiViT - https://arxiv.org/abs/2212.08013 +* FocalNet (Focal Modulation Networks) - https://arxiv.org/abs/2203.11926 +* GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959 +* GhostNet - https://arxiv.org/abs/1911.11907 +* GhostNet-V2 - https://arxiv.org/abs/2211.12905 +* gMLP - https://arxiv.org/abs/2105.08050 +* GPU-Efficient Networks - https://arxiv.org/abs/2006.14090 +* Halo Nets - https://arxiv.org/abs/2103.12731 +* HGNet / HGNet-V2 - TBD +* HRNet - https://arxiv.org/abs/1908.07919 +* InceptionNeXt - https://arxiv.org/abs/2303.16900 +* Inception-V3 - https://arxiv.org/abs/1512.00567 +* Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261 +* Lambda Networks - https://arxiv.org/abs/2102.08602 +* LeViT (Vision Transformer in ConvNet's Clothing) - https://arxiv.org/abs/2104.01136 +* MaxViT (Multi-Axis Vision Transformer) - https://arxiv.org/abs/2204.01697 +* MetaFormer (PoolFormer-v2, ConvFormer, CAFormer) - https://arxiv.org/abs/2210.13452 +* MLP-Mixer - https://arxiv.org/abs/2105.01601 +* MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244 + * FBNet-V3 - https://arxiv.org/abs/2006.02049 + * HardCoRe-NAS - https://arxiv.org/abs/2102.11646 + * LCNet - https://arxiv.org/abs/2109.15099 +* MobileOne - https://arxiv.org/abs/2206.04040 +* MobileViT - https://arxiv.org/abs/2110.02178 +* MobileViT-V2 - https://arxiv.org/abs/2206.02680 +* MViT-V2 (Improved Multiscale Vision Transformer) - https://arxiv.org/abs/2112.01526 +* NASNet-A - https://arxiv.org/abs/1707.07012 +* NesT - https://arxiv.org/abs/2105.12723 +* Next-ViT - https://arxiv.org/abs/2207.05501 +* NFNet-F - https://arxiv.org/abs/2102.06171 +* NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692 +* PNasNet - https://arxiv.org/abs/1712.00559 +* PoolFormer (MetaFormer) - https://arxiv.org/abs/2111.11418 +* Pooling-based Vision Transformer (PiT) - https://arxiv.org/abs/2103.16302 +* PVT-V2 (Improved Pyramid Vision Transformer) - https://arxiv.org/abs/2106.13797 +* RegNet - https://arxiv.org/abs/2003.13678 +* RegNetZ - https://arxiv.org/abs/2103.06877 +* RepVGG - https://arxiv.org/abs/2101.03697 +* RepGhostNet - https://arxiv.org/abs/2211.06088 +* RepViT - https://arxiv.org/abs/2307.09283 +* ResMLP - https://arxiv.org/abs/2105.03404 +* ResNet/ResNeXt + * ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385 + * ResNeXt - https://arxiv.org/abs/1611.05431 + * 'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187 + * Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932 + * Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546 + * ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4 + * Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507 + * ResNet-RS - https://arxiv.org/abs/2103.07579 +* Res2Net - https://arxiv.org/abs/1904.01169 +* ResNeSt - https://arxiv.org/abs/2004.08955 +* ReXNet - https://arxiv.org/abs/2007.00992 +* SelecSLS - https://arxiv.org/abs/1907.00837 +* Selective Kernel Networks - https://arxiv.org/abs/1903.06586 +* Sequencer2D - https://arxiv.org/abs/2205.01972 +* Swin S3 (AutoFormerV2) - https://arxiv.org/abs/2111.14725 +* Swin Transformer - https://arxiv.org/abs/2103.14030 +* Swin Transformer V2 - https://arxiv.org/abs/2111.09883 +* Transformer-iN-Transformer (TNT) - https://arxiv.org/abs/2103.00112 +* TResNet - https://arxiv.org/abs/2003.13630 +* Twins (Spatial Attention in Vision Transformers) - https://arxiv.org/pdf/2104.13840.pdf +* Visformer - https://arxiv.org/abs/2104.12533 +* Vision Transformer - https://arxiv.org/abs/2010.11929 +* VOLO (Vision Outlooker) - https://arxiv.org/abs/2106.13112 +* VovNet V2 and V1 - https://arxiv.org/abs/1911.06667 +* Xception - https://arxiv.org/abs/1610.02357 +* Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611 +* Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611 +* XCiT (Cross-Covariance Image Transformers) - https://arxiv.org/abs/2106.09681 + +### Optimizers + +Included optimizers available via `create_optimizer` / `create_optimizer_v2` factory methods: +* `adabelief` an implementation of AdaBelief adapted from https://github.com/juntang-zhuang/Adabelief-Optimizer - https://arxiv.org/abs/2010.07468 +* `adafactor` adapted from [FAIRSeq impl](https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py) - https://arxiv.org/abs/1804.04235 +* `adahessian` by [David Samuel](https://github.com/davda54/ada-hessian) - https://arxiv.org/abs/2006.00719 +* `adamp` and `sgdp` by [Naver ClovAI](https://github.com/clovaai) - https://arxiv.org/abs/2006.08217 +* `adan` an implementation of Adan adapted from https://github.com/sail-sg/Adan - https://arxiv.org/abs/2208.06677 +* `lamb` an implementation of Lamb and LambC (w/ trust-clipping) cleaned up and modified to support use with XLA - https://arxiv.org/abs/1904.00962 +* `lars` an implementation of LARS and LARC (w/ trust-clipping) - https://arxiv.org/abs/1708.03888 +* `lion` and implementation of Lion adapted from https://github.com/google/automl/tree/master/lion - https://arxiv.org/abs/2302.06675 +* `lookahead` adapted from impl by [Liam](https://github.com/alphadl/lookahead.pytorch) - https://arxiv.org/abs/1907.08610 +* `madgrad` - and implementation of MADGRAD adapted from https://github.com/facebookresearch/madgrad - https://arxiv.org/abs/2101.11075 +* `nadam` an implementation of Adam w/ Nesterov momentum +* `nadamw` an impementation of AdamW (Adam w/ decoupled weight-decay) w/ Nesterov momentum. A simplified impl based on https://github.com/mlcommons/algorithmic-efficiency +* `novograd` by [Masashi Kimura](https://github.com/convergence-lab/novograd) - https://arxiv.org/abs/1905.11286 +* `radam` by [Liyuan Liu](https://github.com/LiyuanLucasLiu/RAdam) - https://arxiv.org/abs/1908.03265 +* `rmsprop_tf` adapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour +* `sgdw` and implementation of SGD w/ decoupled weight-decay +* `fused` optimizers by name with [NVIDIA Apex](https://github.com/NVIDIA/apex/tree/master/apex/optimizers) installed +* `bits` optimizers by name with [BitsAndBytes](https://github.com/TimDettmers/bitsandbytes) installed + +### Augmentations +* Random Erasing from [Zhun Zhong](https://github.com/zhunzhong07/Random-Erasing/blob/master/transforms.py) - https://arxiv.org/abs/1708.04896) +* Mixup - https://arxiv.org/abs/1710.09412 +* CutMix - https://arxiv.org/abs/1905.04899 +* AutoAugment (https://arxiv.org/abs/1805.09501) and RandAugment (https://arxiv.org/abs/1909.13719) ImageNet configurations modeled after impl for EfficientNet training (https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py) +* AugMix w/ JSD loss, JSD w/ clean + augmented mixing support works with AutoAugment and RandAugment as well - https://arxiv.org/abs/1912.02781 +* SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data + +### Regularization +* DropPath aka "Stochastic Depth" - https://arxiv.org/abs/1603.09382 +* DropBlock - https://arxiv.org/abs/1810.12890 +* Blur Pooling - https://arxiv.org/abs/1904.11486 + +### Other + +Several (less common) features that I often utilize in my projects are included. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP: + +* All models have a common default configuration interface and API for + * accessing/changing the classifier - `get_classifier` and `reset_classifier` + * doing a forward pass on just the features - `forward_features` (see [documentation](https://huggingface.co/docs/timm/feature_extraction)) + * these makes it easy to write consistent network wrappers that work with any of the models +* All models support multi-scale feature map extraction (feature pyramids) via create_model (see [documentation](https://huggingface.co/docs/timm/feature_extraction)) + * `create_model(name, features_only=True, out_indices=..., output_stride=...)` + * `out_indices` creation arg specifies which feature maps to return, these indices are 0 based and generally correspond to the `C(i + 1)` feature level. + * `output_stride` creation arg controls output stride of the network by using dilated convolutions. Most networks are stride 32 by default. Not all networks support this. + * feature map channel counts, reduction level (stride) can be queried AFTER model creation via the `.feature_info` member +* All models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desired +* High performance [reference training, validation, and inference scripts](https://huggingface.co/docs/timm/training_script) that work in several process/GPU modes: + * NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional) + * PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled) + * PyTorch w/ single GPU single process (AMP optional) +* A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat([average, max]) at model creation. All global pooling is adaptive average by default and compatible with pretrained weights. +* A 'Test Time Pool' wrapper that can wrap any of the included models and usually provides improved performance doing inference with input images larger than the training size. Idea adapted from original DPN implementation when I ported (https://github.com/cypw/DPNs) +* Learning rate schedulers + * Ideas adopted from + * [AllenNLP schedulers](https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers) + * [FAIRseq lr_scheduler](https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler) + * SGDR: Stochastic Gradient Descent with Warm Restarts (https://arxiv.org/abs/1608.03983) + * Schedulers include `step`, `cosine` w/ restarts, `tanh` w/ restarts, `plateau` +* Space-to-Depth by [mrT23](https://github.com/mrT23/TResNet/blob/master/src/models/tresnet/layers/space_to_depth.py) (https://arxiv.org/abs/1801.04590) -- original paper? +* Adaptive Gradient Clipping (https://arxiv.org/abs/2102.06171, https://github.com/deepmind/deepmind-research/tree/master/nfnets) +* An extensive selection of channel and/or spatial attention modules: + * Bottleneck Transformer - https://arxiv.org/abs/2101.11605 + * CBAM - https://arxiv.org/abs/1807.06521 + * Effective Squeeze-Excitation (ESE) - https://arxiv.org/abs/1911.06667 + * Efficient Channel Attention (ECA) - https://arxiv.org/abs/1910.03151 + * Gather-Excite (GE) - https://arxiv.org/abs/1810.12348 + * Global Context (GC) - https://arxiv.org/abs/1904.11492 + * Halo - https://arxiv.org/abs/2103.12731 + * Involution - https://arxiv.org/abs/2103.06255 + * Lambda Layer - https://arxiv.org/abs/2102.08602 + * Non-Local (NL) - https://arxiv.org/abs/1711.07971 + * Squeeze-and-Excitation (SE) - https://arxiv.org/abs/1709.01507 + * Selective Kernel (SK) - (https://arxiv.org/abs/1903.06586 + * Split (SPLAT) - https://arxiv.org/abs/2004.08955 + * Shifted Window (SWIN) - https://arxiv.org/abs/2103.14030 + +## Results + +Model validation results can be found in the [results tables](results/README.md) + +## Getting Started (Documentation) + +The official documentation can be found at https://huggingface.co/docs/hub/timm. Documentation contributions are welcome. + +[Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055) by [Chris Hughes](https://github.com/Chris-hughes10) is an extensive blog post covering many aspects of `timm` in detail. + +[timmdocs](http://timm.fast.ai/) is an alternate set of documentation for `timm`. A big thanks to [Aman Arora](https://github.com/amaarora) for his efforts creating timmdocs. + +[paperswithcode](https://paperswithcode.com/lib/timm) is a good resource for browsing the models within `timm`. + +## Train, Validation, Inference Scripts + +The root folder of the repository contains reference train, validation, and inference scripts that work with the included models and other features of this repository. They are adaptable for other datasets and use cases with a little hacking. See [documentation](https://huggingface.co/docs/timm/training_script). + +## Awesome PyTorch Resources + +One of the greatest assets of PyTorch is the community and their contributions. A few of my favourite resources that pair well with the models and components here are listed below. + +### Object Detection, Instance and Semantic Segmentation +* Detectron2 - https://github.com/facebookresearch/detectron2 +* Segmentation Models (Semantic) - https://github.com/qubvel/segmentation_models.pytorch +* EfficientDet (Obj Det, Semantic soon) - https://github.com/rwightman/efficientdet-pytorch + +### Computer Vision / Image Augmentation +* Albumentations - https://github.com/albumentations-team/albumentations +* Kornia - https://github.com/kornia/kornia + +### Knowledge Distillation +* RepDistiller - https://github.com/HobbitLong/RepDistiller +* torchdistill - https://github.com/yoshitomo-matsubara/torchdistill + +### Metric Learning +* PyTorch Metric Learning - https://github.com/KevinMusgrave/pytorch-metric-learning + +### Training / Frameworks +* fastai - https://github.com/fastai/fastai + +## Licenses + +### Code +The code here is licensed Apache 2.0. I've taken care to make sure any third party code included or adapted has compatible (permissive) licenses such as MIT, BSD, etc. I've made an effort to avoid any GPL / LGPL conflicts. That said, it is your responsibility to ensure you comply with licenses here and conditions of any dependent licenses. Where applicable, I've linked the sources/references for various components in docstrings. If you think I've missed anything please create an issue. + +### Pretrained Weights +So far all of the pretrained weights available here are pretrained on ImageNet with a select few that have some additional pretraining (see extra note below). ImageNet was released for non-commercial research purposes only (https://image-net.org/download). It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product. + +#### Pretrained on more than ImageNet +Several weights included or references here were pretrained with proprietary datasets that I do not have access to. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. The Facebook models have an explicit non-commercial license (CC-BY-NC 4.0, https://github.com/facebookresearch/semi-supervised-ImageNet1K-models, https://github.com/facebookresearch/WSL-Images). The Google models do not appear to have any restriction beyond the Apache 2.0 license (and ImageNet concerns). In either case, you should contact Facebook or Google with any questions. + +## Citing + +### BibTeX + +```bibtex +@misc{rw2019timm, + author = {Ross Wightman}, + title = {PyTorch Image Models}, + year = {2019}, + publisher = {GitHub}, + journal = {GitHub repository}, + doi = {10.5281/zenodo.4414861}, + howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} +} +``` + +### Latest DOI + +[![DOI](https://zenodo.org/badge/168799526.svg)](https://zenodo.org/badge/latestdoi/168799526) diff --git a/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/RECORD b/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..037686fd67568244ef23b70b0faa49c5716d1964 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/timm-1.0.3.dist-info/RECORD @@ -0,0 +1,510 @@ +timm-1.0.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2019 Ross Wightman + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/crc_alt.h b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/crc_alt.h new file mode 100644 index 0000000000000000000000000000000000000000..9d1c4f1dc7ddc8997f7cc1297ef20d74de67afe0 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/crc_alt.h @@ -0,0 +1,1343 @@ +#pragma once + +// ////////////////////////////////////////////////////////// +// Crc32.h +// Copyright (c) 2011-2019 Stephan Brumme. All rights reserved. +// Slicing-by-16 contributed by Bulat Ziganshin +// Tableless bytewise CRC contributed by Hagai Gold +// see http://create.stephan-brumme.com/disclaimer.html +// + +// if running on an embedded system, you might consider shrinking the +// big Crc32Lookup table by undefining these lines: +#define CRC32_USE_LOOKUP_TABLE_BYTE +#define CRC32_USE_LOOKUP_TABLE_SLICING_BY_4 +#define CRC32_USE_LOOKUP_TABLE_SLICING_BY_8 +#define CRC32_USE_LOOKUP_TABLE_SLICING_BY_16 +// - crc32_bitwise doesn't need it at all +// - crc32_halfbyte has its own small lookup table +// - crc32_1byte_tableless and crc32_1byte_tableless2 don't need it at all +// - crc32_1byte needs only Crc32Lookup[0] +// - crc32_4bytes needs only Crc32Lookup[0..3] +// - crc32_8bytes needs only Crc32Lookup[0..7] +// - crc32_4x8bytes needs only Crc32Lookup[0..7] +// - crc32_16bytes needs all of Crc32Lookup +// using the aforementioned #defines the table is automatically fitted to your needs + +// uint8_t, uint32_t, int32_t +#include +// size_t +#include + +// crc32_fast selects the fastest algorithm depending on flags (CRC32_USE_LOOKUP_...) +/// compute CRC32 using the fastest algorithm for large datasets on modern CPUs +uint32_t crc32_fast (const void* data, size_t length, uint32_t previousCrc32 = 0); + +/// merge two CRC32 such that result = crc32(dataB, lengthB, crc32(dataA, lengthA)) +uint32_t crc32_combine (uint32_t crcA, uint32_t crcB, size_t lengthB); + +/// compute CRC32 (bitwise algorithm) +uint32_t crc32_bitwise (const void* data, size_t length, uint32_t previousCrc32 = 0); +/// compute CRC32 (half-byte algoritm) +uint32_t crc32_halfbyte(const void* data, size_t length, uint32_t previousCrc32 = 0); + +#ifdef CRC32_USE_LOOKUP_TABLE_BYTE +/// compute CRC32 (standard algorithm) +uint32_t crc32_1byte (const void* data, size_t length, uint32_t previousCrc32 = 0); +#endif + +/// compute CRC32 (byte algorithm) without lookup tables +uint32_t crc32_1byte_tableless (const void* data, size_t length, uint32_t previousCrc32 = 0); +/// compute CRC32 (byte algorithm) without lookup tables +uint32_t crc32_1byte_tableless2(const void* data, size_t length, uint32_t previousCrc32 = 0); + +#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_4 +/// compute CRC32 (Slicing-by-4 algorithm) +uint32_t crc32_4bytes (const void* data, size_t length, uint32_t previousCrc32 = 0); +#endif + +#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_8 +/// compute CRC32 (Slicing-by-8 algorithm) +uint32_t crc32_8bytes (const void* data, size_t length, uint32_t previousCrc32 = 0); +/// compute CRC32 (Slicing-by-8 algorithm), unroll inner loop 4 times +uint32_t crc32_4x8bytes(const void* data, size_t length, uint32_t previousCrc32 = 0); +#endif + +#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16 +/// compute CRC32 (Slicing-by-16 algorithm) +uint32_t crc32_16bytes (const void* data, size_t length, uint32_t previousCrc32 = 0); +/// compute CRC32 (Slicing-by-16 algorithm, prefetch upcoming data blocks) +uint32_t crc32_16bytes_prefetch(const void* data, size_t length, uint32_t previousCrc32 = 0, size_t prefetchAhead = 256); +#endif + +// ////////////////////////////////////////////////////////// +// Crc32.cpp +// Copyright (c) 2011-2019 Stephan Brumme. All rights reserved. +// Slicing-by-16 contributed by Bulat Ziganshin +// Tableless bytewise CRC contributed by Hagai Gold +// see http://create.stephan-brumme.com/disclaimer.html +// + +// if running on an embedded system, you might consider shrinking the +// big Crc32Lookup table: +// - crc32_bitwise doesn't need it at all +// - crc32_halfbyte has its own small lookup table +// - crc32_1byte needs only Crc32Lookup[0] +// - crc32_4bytes needs only Crc32Lookup[0..3] +// - crc32_8bytes needs only Crc32Lookup[0..7] +// - crc32_4x8bytes needs only Crc32Lookup[0..7] +// - crc32_16bytes needs all of Crc32Lookup + + +#ifndef __LITTLE_ENDIAN + #define __LITTLE_ENDIAN 1234 +#endif +#ifndef __BIG_ENDIAN + #define __BIG_ENDIAN 4321 +#endif + +// define endianess and some integer data types +#if defined(_MSC_VER) || defined(__MINGW32__) + // Windows always little endian + #define __BYTE_ORDER __LITTLE_ENDIAN + + // intrinsics / prefetching + #if defined(_M_ARM64) + #include + #else + #include + #endif + + #ifdef __MINGW32__ + #define PREFETCH(location) __builtin_prefetch(location) + #else + #if defined(_M_ARM64) + #define PREFETCH(location) __prefetch(location) + #else + #define PREFETCH(location) _mm_prefetch(location, _MM_HINT_T0) + #endif + #endif +#elif defined(__APPLE__) + #include + #if TARGET_IPHONE_SIMULATOR + #define __BYTE_ORDER __LITTLE_ENDIAN + #elif TARGET_OS_IPHONE + #define __BYTE_ORDER __LITTLE_ENDIAN + #elif TARGET_OS_MAC + #include + #if defined(__BIG_ENDIAN__) + #define __BYTE_ORDER __BIG_ENDIAN + #endif + #if defined(__LITTLE_ENDIAN__) + #define __BYTE_ORDER __LITTLE_ENDIAN + #endif + #else + # error "Unknown Apple platform" + #endif +#elif defined(__ARMEB__) + #define __BYTE_ORDER __BIG_ENDIAN +#elif (defined(__BYTE_ORDER__) and !defined(__BYTE_ORDER)) + #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ + #define __BYTE_ORDER __BIG_ENDIAN + #else + #define __BYTE_ORDER __LITTLE_ENDIAN + #endif +#else + // defines __BYTE_ORDER as __LITTLE_ENDIAN or __BIG_ENDIAN + #include +#endif + +// intrinsics / prefetching +#ifdef __GNUC__ + #define PREFETCH(location) __builtin_prefetch(location) +#else +#ifndef PREFETCH + // no prefetching + #define PREFETCH(location) ; +#endif +#endif + +// abort if byte order is undefined +#ifndef __BYTE_ORDER +#error undefined byte order, compile with -D__BYTE_ORDER=1234 (if little endian) or -D__BYTE_ORDER=4321 (big endian) +#endif + + +namespace +{ + /// zlib's CRC32 polynomial + const uint32_t Polynomial = 0xEDB88320; + + /// swap endianess + static inline uint32_t swap(uint32_t x) + { + #if defined(__GNUC__) || defined(__clang__) + return __builtin_bswap32(x); + #else + return (x >> 24) | + ((x >> 8) & 0x0000FF00) | + ((x << 8) & 0x00FF0000) | + (x << 24); + #endif + } + + /// Slicing-By-16 + #ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16 + const size_t MaxSlice = 16; + #elif defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_8) + const size_t MaxSlice = 8; + #elif defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_4) + const size_t MaxSlice = 4; + #elif defined(CRC32_USE_LOOKUP_TABLE_BYTE) + const size_t MaxSlice = 1; + #else + #define NO_LUT // don't need Crc32Lookup at all + #endif + +} // anonymous namespace + +#ifndef NO_LUT +/// forward declaration, table is at the end of this file +extern const uint32_t Crc32Lookup[MaxSlice][256]; // extern is needed to keep compiler happy +#endif + + +/// compute CRC32 (bitwise algorithm) +uint32_t crc32_bitwise(const void* data, size_t length, uint32_t previousCrc32) +{ + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint8_t* current = (const uint8_t*) data; + + while (length-- != 0) + { + crc ^= *current++; + + for (int j = 0; j < 8; j++) + { + // branch-free + crc = (crc >> 1) ^ (-int32_t(crc & 1) & Polynomial); + + // branching, much slower: + //if (crc & 1) + // crc = (crc >> 1) ^ Polynomial; + //else + // crc = crc >> 1; + } + } + + return ~crc; // same as crc ^ 0xFFFFFFFF +} + + +/// compute CRC32 (half-byte algoritm) +uint32_t crc32_halfbyte(const void* data, size_t length, uint32_t previousCrc32) +{ + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint8_t* current = (const uint8_t*) data; + + /// look-up table for half-byte, same as crc32Lookup[0][16*i] + static const uint32_t Crc32Lookup16[16] = + { + 0x00000000,0x1DB71064,0x3B6E20C8,0x26D930AC,0x76DC4190,0x6B6B51F4,0x4DB26158,0x5005713C, + 0xEDB88320,0xF00F9344,0xD6D6A3E8,0xCB61B38C,0x9B64C2B0,0x86D3D2D4,0xA00AE278,0xBDBDF21C + }; + + while (length-- != 0) + { + crc = Crc32Lookup16[(crc ^ *current ) & 0x0F] ^ (crc >> 4); + crc = Crc32Lookup16[(crc ^ (*current >> 4)) & 0x0F] ^ (crc >> 4); + current++; + } + + return ~crc; // same as crc ^ 0xFFFFFFFF +} + + +#ifdef CRC32_USE_LOOKUP_TABLE_BYTE +/// compute CRC32 (standard algorithm) +uint32_t crc32_1byte(const void* data, size_t length, uint32_t previousCrc32) +{ + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint8_t* current = (const uint8_t*) data; + + while (length-- != 0) + crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *current++]; + + return ~crc; // same as crc ^ 0xFFFFFFFF +} +#endif + + +/// compute CRC32 (byte algorithm) without lookup tables +uint32_t crc32_1byte_tableless(const void* data, size_t length, uint32_t previousCrc32) +{ + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint8_t* current = (const uint8_t*) data; + + while (length-- != 0) + { + uint8_t s = uint8_t(crc) ^ *current++; + + // Hagai Gold made me aware of this table-less algorithm and send me code + + // polynomial 0xEDB88320 can be written in binary as 11101101101110001000001100100000b + // reverse the bits (or just assume bit 0 is the first one) + // and we have bits set at position 0, 1, 2, 4, 5, 7, 8, 10, 11, 12, 16, 22, 23, 26 + // => those are the shift offsets: + //crc = (crc >> 8) ^ + // t ^ + // (t >> 1) ^ (t >> 2) ^ (t >> 4) ^ (t >> 5) ^ // == y + // (t >> 7) ^ (t >> 8) ^ (t >> 10) ^ (t >> 11) ^ // == y >> 6 + // (t >> 12) ^ (t >> 16) ^ // == z + // (t >> 22) ^ (t >> 26) ^ // == z >> 10 + // (t >> 23); + + // the fastest I can come up with: + uint32_t low = (s ^ (s << 6)) & 0xFF; + uint32_t a = (low * ((1 << 23) + (1 << 14) + (1 << 2))); + crc = (crc >> 8) ^ + (low * ((1 << 24) + (1 << 16) + (1 << 8))) ^ + a ^ + (a >> 1) ^ + (low * ((1 << 20) + (1 << 12) )) ^ + (low << 19) ^ + (low << 17) ^ + (low >> 2); + + // Hagai's code: + /*uint32_t t = (s ^ (s << 6)) << 24; + // some temporaries to optimize XOR + uint32_t x = (t >> 1) ^ (t >> 2); + uint32_t y = x ^ (x >> 3); + uint32_t z = (t >> 12) ^ (t >> 16); + crc = (crc >> 8) ^ + t ^ (t >> 23) ^ + y ^ (y >> 6) ^ + z ^ (z >> 10);*/ + } + + return ~crc; // same as crc ^ 0xFFFFFFFF +} + + +/// compute CRC32 (byte algorithm) without lookup tables +uint32_t crc32_1byte_tableless2(const void* data, size_t length, uint32_t previousCrc32) +{ + int32_t crc = ~previousCrc32; // note: signed integer, right shift distributes sign bit into lower bits + const uint8_t* current = (const uint8_t*) data; + + while (length-- != 0) + { + crc = crc ^ *current++; + + uint32_t c = (((crc << 31) >> 31) & ((Polynomial >> 7) ^ (Polynomial >> 1))) ^ + (((crc << 30) >> 31) & ((Polynomial >> 6) ^ Polynomial)) ^ + (((crc << 29) >> 31) & (Polynomial >> 5)) ^ + (((crc << 28) >> 31) & (Polynomial >> 4)) ^ + (((crc << 27) >> 31) & (Polynomial >> 3)) ^ + (((crc << 26) >> 31) & (Polynomial >> 2)) ^ + (((crc << 25) >> 31) & (Polynomial >> 1)) ^ + (((crc << 24) >> 31) & Polynomial); + + crc = ((uint32_t)crc >> 8) ^ c; // convert to unsigned integer before right shift + } + + return ~crc; // same as crc ^ 0xFFFFFFFF +} + + +#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_4 +/// compute CRC32 (Slicing-by-4 algorithm) +uint32_t crc32_4bytes(const void* data, size_t length, uint32_t previousCrc32) +{ + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint32_t* current = (const uint32_t*) data; + + // process four bytes at once (Slicing-by-4) + while (length >= 4) + { +#if __BYTE_ORDER == __BIG_ENDIAN + uint32_t one = *current++ ^ swap(crc); + crc = Crc32Lookup[0][ one & 0xFF] ^ + Crc32Lookup[1][(one>> 8) & 0xFF] ^ + Crc32Lookup[2][(one>>16) & 0xFF] ^ + Crc32Lookup[3][(one>>24) & 0xFF]; +#else + uint32_t one = *current++ ^ crc; + crc = Crc32Lookup[0][(one>>24) & 0xFF] ^ + Crc32Lookup[1][(one>>16) & 0xFF] ^ + Crc32Lookup[2][(one>> 8) & 0xFF] ^ + Crc32Lookup[3][ one & 0xFF]; +#endif + + length -= 4; + } + + const uint8_t* currentChar = (const uint8_t*) current; + // remaining 1 to 3 bytes (standard algorithm) + while (length-- != 0) + crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++]; + + return ~crc; // same as crc ^ 0xFFFFFFFF +} +#endif + + +#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_8 +/// compute CRC32 (Slicing-by-8 algorithm) +uint32_t crc32_8bytes(const void* data, size_t length, uint32_t previousCrc32) +{ + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint32_t* current = (const uint32_t*) data; + + // process eight bytes at once (Slicing-by-8) + while (length >= 8) + { +#if __BYTE_ORDER == __BIG_ENDIAN + uint32_t one = *current++ ^ swap(crc); + uint32_t two = *current++; + crc = Crc32Lookup[0][ two & 0xFF] ^ + Crc32Lookup[1][(two>> 8) & 0xFF] ^ + Crc32Lookup[2][(two>>16) & 0xFF] ^ + Crc32Lookup[3][(two>>24) & 0xFF] ^ + Crc32Lookup[4][ one & 0xFF] ^ + Crc32Lookup[5][(one>> 8) & 0xFF] ^ + Crc32Lookup[6][(one>>16) & 0xFF] ^ + Crc32Lookup[7][(one>>24) & 0xFF]; +#else + uint32_t one = *current++ ^ crc; + uint32_t two = *current++; + crc = Crc32Lookup[0][(two>>24) & 0xFF] ^ + Crc32Lookup[1][(two>>16) & 0xFF] ^ + Crc32Lookup[2][(two>> 8) & 0xFF] ^ + Crc32Lookup[3][ two & 0xFF] ^ + Crc32Lookup[4][(one>>24) & 0xFF] ^ + Crc32Lookup[5][(one>>16) & 0xFF] ^ + Crc32Lookup[6][(one>> 8) & 0xFF] ^ + Crc32Lookup[7][ one & 0xFF]; +#endif + + length -= 8; + } + + const uint8_t* currentChar = (const uint8_t*) current; + // remaining 1 to 7 bytes (standard algorithm) + while (length-- != 0) + crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++]; + + return ~crc; // same as crc ^ 0xFFFFFFFF +} + + +/// compute CRC32 (Slicing-by-8 algorithm), unroll inner loop 4 times +uint32_t crc32_4x8bytes(const void* data, size_t length, uint32_t previousCrc32) +{ + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint32_t* current = (const uint32_t*) data; + + // enabling optimization (at least -O2) automatically unrolls the inner for-loop + const size_t Unroll = 4; + const size_t BytesAtOnce = 8 * Unroll; + + // process 4x eight bytes at once (Slicing-by-8) + while (length >= BytesAtOnce) + { + for (size_t unrolling = 0; unrolling < Unroll; unrolling++) + { +#if __BYTE_ORDER == __BIG_ENDIAN + uint32_t one = *current++ ^ swap(crc); + uint32_t two = *current++; + crc = Crc32Lookup[0][ two & 0xFF] ^ + Crc32Lookup[1][(two>> 8) & 0xFF] ^ + Crc32Lookup[2][(two>>16) & 0xFF] ^ + Crc32Lookup[3][(two>>24) & 0xFF] ^ + Crc32Lookup[4][ one & 0xFF] ^ + Crc32Lookup[5][(one>> 8) & 0xFF] ^ + Crc32Lookup[6][(one>>16) & 0xFF] ^ + Crc32Lookup[7][(one>>24) & 0xFF]; +#else + uint32_t one = *current++ ^ crc; + uint32_t two = *current++; + crc = Crc32Lookup[0][(two>>24) & 0xFF] ^ + Crc32Lookup[1][(two>>16) & 0xFF] ^ + Crc32Lookup[2][(two>> 8) & 0xFF] ^ + Crc32Lookup[3][ two & 0xFF] ^ + Crc32Lookup[4][(one>>24) & 0xFF] ^ + Crc32Lookup[5][(one>>16) & 0xFF] ^ + Crc32Lookup[6][(one>> 8) & 0xFF] ^ + Crc32Lookup[7][ one & 0xFF]; +#endif + + } + + length -= BytesAtOnce; + } + + const uint8_t* currentChar = (const uint8_t*) current; + // remaining 1 to 31 bytes (standard algorithm) + while (length-- != 0) + crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++]; + + return ~crc; // same as crc ^ 0xFFFFFFFF +} +#endif // CRC32_USE_LOOKUP_TABLE_SLICING_BY_8 + + +#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16 +/// compute CRC32 (Slicing-by-16 algorithm) +uint32_t crc32_16bytes(const void* data, size_t length, uint32_t previousCrc32) +{ + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint32_t* current = (const uint32_t*) data; + + // enabling optimization (at least -O2) automatically unrolls the inner for-loop + const size_t Unroll = 4; + const size_t BytesAtOnce = 16 * Unroll; + + while (length >= BytesAtOnce) + { + for (size_t unrolling = 0; unrolling < Unroll; unrolling++) + { +#if __BYTE_ORDER == __BIG_ENDIAN + uint32_t one = *current++ ^ swap(crc); + uint32_t two = *current++; + uint32_t three = *current++; + uint32_t four = *current++; + crc = Crc32Lookup[ 0][ four & 0xFF] ^ + Crc32Lookup[ 1][(four >> 8) & 0xFF] ^ + Crc32Lookup[ 2][(four >> 16) & 0xFF] ^ + Crc32Lookup[ 3][(four >> 24) & 0xFF] ^ + Crc32Lookup[ 4][ three & 0xFF] ^ + Crc32Lookup[ 5][(three >> 8) & 0xFF] ^ + Crc32Lookup[ 6][(three >> 16) & 0xFF] ^ + Crc32Lookup[ 7][(three >> 24) & 0xFF] ^ + Crc32Lookup[ 8][ two & 0xFF] ^ + Crc32Lookup[ 9][(two >> 8) & 0xFF] ^ + Crc32Lookup[10][(two >> 16) & 0xFF] ^ + Crc32Lookup[11][(two >> 24) & 0xFF] ^ + Crc32Lookup[12][ one & 0xFF] ^ + Crc32Lookup[13][(one >> 8) & 0xFF] ^ + Crc32Lookup[14][(one >> 16) & 0xFF] ^ + Crc32Lookup[15][(one >> 24) & 0xFF]; +#else + uint32_t one = *current++ ^ crc; + uint32_t two = *current++; + uint32_t three = *current++; + uint32_t four = *current++; + crc = Crc32Lookup[ 0][(four >> 24) & 0xFF] ^ + Crc32Lookup[ 1][(four >> 16) & 0xFF] ^ + Crc32Lookup[ 2][(four >> 8) & 0xFF] ^ + Crc32Lookup[ 3][ four & 0xFF] ^ + Crc32Lookup[ 4][(three >> 24) & 0xFF] ^ + Crc32Lookup[ 5][(three >> 16) & 0xFF] ^ + Crc32Lookup[ 6][(three >> 8) & 0xFF] ^ + Crc32Lookup[ 7][ three & 0xFF] ^ + Crc32Lookup[ 8][(two >> 24) & 0xFF] ^ + Crc32Lookup[ 9][(two >> 16) & 0xFF] ^ + Crc32Lookup[10][(two >> 8) & 0xFF] ^ + Crc32Lookup[11][ two & 0xFF] ^ + Crc32Lookup[12][(one >> 24) & 0xFF] ^ + Crc32Lookup[13][(one >> 16) & 0xFF] ^ + Crc32Lookup[14][(one >> 8) & 0xFF] ^ + Crc32Lookup[15][ one & 0xFF]; +#endif + } + + length -= BytesAtOnce; + } + + const uint8_t* currentChar = (const uint8_t*) current; + // remaining 1 to 63 bytes (standard algorithm) + while (length-- != 0) + crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++]; + + return ~crc; // same as crc ^ 0xFFFFFFFF +} + + +/// compute CRC32 (Slicing-by-16 algorithm, prefetch upcoming data blocks) +uint32_t crc32_16bytes_prefetch(const void* data, size_t length, uint32_t previousCrc32, size_t prefetchAhead) +{ + // CRC code is identical to crc32_16bytes (including unrolling), only added prefetching + // 256 bytes look-ahead seems to be the sweet spot on Core i7 CPUs + + uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF + const uint32_t* current = (const uint32_t*) data; + + // enabling optimization (at least -O2) automatically unrolls the for-loop + const size_t Unroll = 4; + const size_t BytesAtOnce = 16 * Unroll; + + while (length >= BytesAtOnce + prefetchAhead) + { + PREFETCH(((const char*) current) + prefetchAhead); + + for (size_t unrolling = 0; unrolling < Unroll; unrolling++) + { +#if __BYTE_ORDER == __BIG_ENDIAN + uint32_t one = *current++ ^ swap(crc); + uint32_t two = *current++; + uint32_t three = *current++; + uint32_t four = *current++; + crc = Crc32Lookup[ 0][ four & 0xFF] ^ + Crc32Lookup[ 1][(four >> 8) & 0xFF] ^ + Crc32Lookup[ 2][(four >> 16) & 0xFF] ^ + Crc32Lookup[ 3][(four >> 24) & 0xFF] ^ + Crc32Lookup[ 4][ three & 0xFF] ^ + Crc32Lookup[ 5][(three >> 8) & 0xFF] ^ + Crc32Lookup[ 6][(three >> 16) & 0xFF] ^ + Crc32Lookup[ 7][(three >> 24) & 0xFF] ^ + Crc32Lookup[ 8][ two & 0xFF] ^ + Crc32Lookup[ 9][(two >> 8) & 0xFF] ^ + Crc32Lookup[10][(two >> 16) & 0xFF] ^ + Crc32Lookup[11][(two >> 24) & 0xFF] ^ + Crc32Lookup[12][ one & 0xFF] ^ + Crc32Lookup[13][(one >> 8) & 0xFF] ^ + Crc32Lookup[14][(one >> 16) & 0xFF] ^ + Crc32Lookup[15][(one >> 24) & 0xFF]; +#else + uint32_t one = *current++ ^ crc; + uint32_t two = *current++; + uint32_t three = *current++; + uint32_t four = *current++; + crc = Crc32Lookup[ 0][(four >> 24) & 0xFF] ^ + Crc32Lookup[ 1][(four >> 16) & 0xFF] ^ + Crc32Lookup[ 2][(four >> 8) & 0xFF] ^ + Crc32Lookup[ 3][ four & 0xFF] ^ + Crc32Lookup[ 4][(three >> 24) & 0xFF] ^ + Crc32Lookup[ 5][(three >> 16) & 0xFF] ^ + Crc32Lookup[ 6][(three >> 8) & 0xFF] ^ + Crc32Lookup[ 7][ three & 0xFF] ^ + Crc32Lookup[ 8][(two >> 24) & 0xFF] ^ + Crc32Lookup[ 9][(two >> 16) & 0xFF] ^ + Crc32Lookup[10][(two >> 8) & 0xFF] ^ + Crc32Lookup[11][ two & 0xFF] ^ + Crc32Lookup[12][(one >> 24) & 0xFF] ^ + Crc32Lookup[13][(one >> 16) & 0xFF] ^ + Crc32Lookup[14][(one >> 8) & 0xFF] ^ + Crc32Lookup[15][ one & 0xFF]; +#endif + } + + length -= BytesAtOnce; + } + + const uint8_t* currentChar = (const uint8_t*) current; + // remaining 1 to 63 bytes (standard algorithm) + while (length-- != 0) + crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++]; + + return ~crc; // same as crc ^ 0xFFFFFFFF +} +#endif + + +/// compute CRC32 using the fastest algorithm for large datasets on modern CPUs +uint32_t crc32_fast(const void* data, size_t length, uint32_t previousCrc32) +{ +#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16 + return crc32_16bytes (data, length, previousCrc32); +#elif defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_8) + return crc32_8bytes (data, length, previousCrc32); +#elif defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_4) + return crc32_4bytes (data, length, previousCrc32); +#elif defined(CRC32_USE_LOOKUP_TABLE_BYTE) + return crc32_1byte (data, length, previousCrc32); +#else + return crc32_halfbyte(data, length, previousCrc32); +#endif +} + + +/// merge two CRC32 such that result = crc32(dataB, lengthB, crc32(dataA, lengthA)) +uint32_t crc32_combine(uint32_t crcA, uint32_t crcB, size_t lengthB) +{ + // based on Mark Adler's crc_combine from + // https://github.com/madler/pigz/blob/master/pigz.c + + // main idea: + // - if you have two equally-sized blocks A and B, + // then you can create a block C = A ^ B + // which has the property crc(C) = crc(A) ^ crc(B) + // - if you append length(B) zeros to A and call it A' (think of it as AAAA000) + // and prepend length(A) zeros to B and call it B' (think of it as 0000BBB) + // then exists a C' = A' ^ B' + // - remember: if you XOR someting with zero, it remains unchanged: X ^ 0 = X + // - that means C' = A concat B so that crc(A concat B) = crc(C') = crc(A') ^ crc(B') + // - the trick is to compute crc(A') based on crc(A) + // and crc(B') based on crc(B) + // - since B' starts with many zeros, the crc of those initial zeros is still zero + // - that means crc(B') = crc(B) + // - unfortunately the trailing zeros of A' change the crc, so usually crc(A') != crc(A) + // - the following code is a fast algorithm to compute crc(A') + // - starting with crc(A) and appending length(B) zeros, needing just log2(length(B)) iterations + // - the details are explained by the original author at + // https://stackoverflow.com/questions/23122312/crc-calculation-of-a-mostly-static-data-stream/23126768 + // + // notes: + // - I squeezed everything into one function to keep global namespace clean (original code two helper functions) + // - most original comments are still in place, I added comments where these helper functions where made inline code + // - performance-wise there isn't any differenze to the original zlib/pigz code + + // degenerated case + if (lengthB == 0) + return crcA; + + /// CRC32 => 32 bits + const uint32_t CrcBits = 32; + + uint32_t odd [CrcBits]; // odd-power-of-two zeros operator + uint32_t even[CrcBits]; // even-power-of-two zeros operator + + // put operator for one zero bit in odd + odd[0] = Polynomial; // CRC-32 polynomial + for (uint32_t i = 1; i < CrcBits; i++) + odd[i] = 1 << (i - 1); + + // put operator for two zero bits in even + // same as gf2_matrix_square(even, odd); + for (uint32_t i = 0; i < CrcBits; i++) + { + uint32_t vec = odd[i]; + even[i] = 0; + for (int j = 0; vec != 0; j++, vec >>= 1) + if (vec & 1) + even[i] ^= odd[j]; + } + // put operator for four zero bits in odd + // same as gf2_matrix_square(odd, even); + for (uint32_t i = 0; i < CrcBits; i++) + { + uint32_t vec = even[i]; + odd[i] = 0; + for (int j = 0; vec != 0; j++, vec >>= 1) + if (vec & 1) + odd[i] ^= even[j]; + } + + // the following loop becomes much shorter if I keep swapping even and odd + uint32_t* a = even; + uint32_t* b = odd; + // apply secondLength zeros to firstCrc32 + for (; lengthB > 0; lengthB >>= 1) + { + // same as gf2_matrix_square(a, b); + for (uint32_t i = 0; i < CrcBits; i++) + { + uint32_t vec = b[i]; + a[i] = 0; + for (int j = 0; vec != 0; j++, vec >>= 1) + if (vec & 1) + a[i] ^= b[j]; + } + + // apply zeros operator for this bit + if (lengthB & 1) + { + // same as firstCrc32 = gf2_matrix_times(a, firstCrc32); + uint32_t sum = 0; + for (int i = 0; crcA != 0; i++, crcA >>= 1) + if (crcA & 1) + sum ^= a[i]; + crcA = sum; + } + + // switch even and odd + uint32_t* t = a; a = b; b = t; + } + + // return combined crc + return crcA ^ crcB; +} + + +// ////////////////////////////////////////////////////////// +// constants + + +#ifndef NO_LUT +/// look-up table, already declared above +const uint32_t Crc32Lookup[MaxSlice][256] = +{ + //// same algorithm as crc32_bitwise + //for (int i = 0; i <= 0xFF; i++) + //{ + // uint32_t crc = i; + // for (int j = 0; j < 8; j++) + // crc = (crc >> 1) ^ ((crc & 1) * Polynomial); + // Crc32Lookup[0][i] = crc; + //} + //// ... and the following slicing-by-8 algorithm (from Intel): + //// http://www.intel.com/technology/comms/perfnet/download/CRC_generators.pdf + //// http://sourceforge.net/projects/slicing-by-8/ + //for (int slice = 1; slice < MaxSlice; slice++) + // Crc32Lookup[slice][i] = (Crc32Lookup[slice - 1][i] >> 8) ^ Crc32Lookup[0][Crc32Lookup[slice - 1][i] & 0xFF]; + { + // note: the first number of every second row corresponds to the half-byte look-up table ! + 0x00000000,0x77073096,0xEE0E612C,0x990951BA,0x076DC419,0x706AF48F,0xE963A535,0x9E6495A3, + 0x0EDB8832,0x79DCB8A4,0xE0D5E91E,0x97D2D988,0x09B64C2B,0x7EB17CBD,0xE7B82D07,0x90BF1D91, + 0x1DB71064,0x6AB020F2,0xF3B97148,0x84BE41DE,0x1ADAD47D,0x6DDDE4EB,0xF4D4B551,0x83D385C7, + 0x136C9856,0x646BA8C0,0xFD62F97A,0x8A65C9EC,0x14015C4F,0x63066CD9,0xFA0F3D63,0x8D080DF5, + 0x3B6E20C8,0x4C69105E,0xD56041E4,0xA2677172,0x3C03E4D1,0x4B04D447,0xD20D85FD,0xA50AB56B, + 0x35B5A8FA,0x42B2986C,0xDBBBC9D6,0xACBCF940,0x32D86CE3,0x45DF5C75,0xDCD60DCF,0xABD13D59, + 0x26D930AC,0x51DE003A,0xC8D75180,0xBFD06116,0x21B4F4B5,0x56B3C423,0xCFBA9599,0xB8BDA50F, + 0x2802B89E,0x5F058808,0xC60CD9B2,0xB10BE924,0x2F6F7C87,0x58684C11,0xC1611DAB,0xB6662D3D, + 0x76DC4190,0x01DB7106,0x98D220BC,0xEFD5102A,0x71B18589,0x06B6B51F,0x9FBFE4A5,0xE8B8D433, + 0x7807C9A2,0x0F00F934,0x9609A88E,0xE10E9818,0x7F6A0DBB,0x086D3D2D,0x91646C97,0xE6635C01, + 0x6B6B51F4,0x1C6C6162,0x856530D8,0xF262004E,0x6C0695ED,0x1B01A57B,0x8208F4C1,0xF50FC457, + 0x65B0D9C6,0x12B7E950,0x8BBEB8EA,0xFCB9887C,0x62DD1DDF,0x15DA2D49,0x8CD37CF3,0xFBD44C65, + 0x4DB26158,0x3AB551CE,0xA3BC0074,0xD4BB30E2,0x4ADFA541,0x3DD895D7,0xA4D1C46D,0xD3D6F4FB, + 0x4369E96A,0x346ED9FC,0xAD678846,0xDA60B8D0,0x44042D73,0x33031DE5,0xAA0A4C5F,0xDD0D7CC9, + 0x5005713C,0x270241AA,0xBE0B1010,0xC90C2086,0x5768B525,0x206F85B3,0xB966D409,0xCE61E49F, + 0x5EDEF90E,0x29D9C998,0xB0D09822,0xC7D7A8B4,0x59B33D17,0x2EB40D81,0xB7BD5C3B,0xC0BA6CAD, + 0xEDB88320,0x9ABFB3B6,0x03B6E20C,0x74B1D29A,0xEAD54739,0x9DD277AF,0x04DB2615,0x73DC1683, + 0xE3630B12,0x94643B84,0x0D6D6A3E,0x7A6A5AA8,0xE40ECF0B,0x9309FF9D,0x0A00AE27,0x7D079EB1, + 0xF00F9344,0x8708A3D2,0x1E01F268,0x6906C2FE,0xF762575D,0x806567CB,0x196C3671,0x6E6B06E7, + 0xFED41B76,0x89D32BE0,0x10DA7A5A,0x67DD4ACC,0xF9B9DF6F,0x8EBEEFF9,0x17B7BE43,0x60B08ED5, + 0xD6D6A3E8,0xA1D1937E,0x38D8C2C4,0x4FDFF252,0xD1BB67F1,0xA6BC5767,0x3FB506DD,0x48B2364B, + 0xD80D2BDA,0xAF0A1B4C,0x36034AF6,0x41047A60,0xDF60EFC3,0xA867DF55,0x316E8EEF,0x4669BE79, + 0xCB61B38C,0xBC66831A,0x256FD2A0,0x5268E236,0xCC0C7795,0xBB0B4703,0x220216B9,0x5505262F, + 0xC5BA3BBE,0xB2BD0B28,0x2BB45A92,0x5CB36A04,0xC2D7FFA7,0xB5D0CF31,0x2CD99E8B,0x5BDEAE1D, + 0x9B64C2B0,0xEC63F226,0x756AA39C,0x026D930A,0x9C0906A9,0xEB0E363F,0x72076785,0x05005713, + 0x95BF4A82,0xE2B87A14,0x7BB12BAE,0x0CB61B38,0x92D28E9B,0xE5D5BE0D,0x7CDCEFB7,0x0BDBDF21, + 0x86D3D2D4,0xF1D4E242,0x68DDB3F8,0x1FDA836E,0x81BE16CD,0xF6B9265B,0x6FB077E1,0x18B74777, + 0x88085AE6,0xFF0F6A70,0x66063BCA,0x11010B5C,0x8F659EFF,0xF862AE69,0x616BFFD3,0x166CCF45, + 0xA00AE278,0xD70DD2EE,0x4E048354,0x3903B3C2,0xA7672661,0xD06016F7,0x4969474D,0x3E6E77DB, + 0xAED16A4A,0xD9D65ADC,0x40DF0B66,0x37D83BF0,0xA9BCAE53,0xDEBB9EC5,0x47B2CF7F,0x30B5FFE9, + 0xBDBDF21C,0xCABAC28A,0x53B39330,0x24B4A3A6,0xBAD03605,0xCDD70693,0x54DE5729,0x23D967BF, + 0xB3667A2E,0xC4614AB8,0x5D681B02,0x2A6F2B94,0xB40BBE37,0xC30C8EA1,0x5A05DF1B,0x2D02EF8D, + } + +#if defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_4) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_8) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_16) + // beyond this point only relevant for Slicing-by-4, Slicing-by-8 and Slicing-by-16 + ,{ + 0x00000000,0x191B3141,0x32366282,0x2B2D53C3,0x646CC504,0x7D77F445,0x565AA786,0x4F4196C7, + 0xC8D98A08,0xD1C2BB49,0xFAEFE88A,0xE3F4D9CB,0xACB54F0C,0xB5AE7E4D,0x9E832D8E,0x87981CCF, + 0x4AC21251,0x53D92310,0x78F470D3,0x61EF4192,0x2EAED755,0x37B5E614,0x1C98B5D7,0x05838496, + 0x821B9859,0x9B00A918,0xB02DFADB,0xA936CB9A,0xE6775D5D,0xFF6C6C1C,0xD4413FDF,0xCD5A0E9E, + 0x958424A2,0x8C9F15E3,0xA7B24620,0xBEA97761,0xF1E8E1A6,0xE8F3D0E7,0xC3DE8324,0xDAC5B265, + 0x5D5DAEAA,0x44469FEB,0x6F6BCC28,0x7670FD69,0x39316BAE,0x202A5AEF,0x0B07092C,0x121C386D, + 0xDF4636F3,0xC65D07B2,0xED705471,0xF46B6530,0xBB2AF3F7,0xA231C2B6,0x891C9175,0x9007A034, + 0x179FBCFB,0x0E848DBA,0x25A9DE79,0x3CB2EF38,0x73F379FF,0x6AE848BE,0x41C51B7D,0x58DE2A3C, + 0xF0794F05,0xE9627E44,0xC24F2D87,0xDB541CC6,0x94158A01,0x8D0EBB40,0xA623E883,0xBF38D9C2, + 0x38A0C50D,0x21BBF44C,0x0A96A78F,0x138D96CE,0x5CCC0009,0x45D73148,0x6EFA628B,0x77E153CA, + 0xBABB5D54,0xA3A06C15,0x888D3FD6,0x91960E97,0xDED79850,0xC7CCA911,0xECE1FAD2,0xF5FACB93, + 0x7262D75C,0x6B79E61D,0x4054B5DE,0x594F849F,0x160E1258,0x0F152319,0x243870DA,0x3D23419B, + 0x65FD6BA7,0x7CE65AE6,0x57CB0925,0x4ED03864,0x0191AEA3,0x188A9FE2,0x33A7CC21,0x2ABCFD60, + 0xAD24E1AF,0xB43FD0EE,0x9F12832D,0x8609B26C,0xC94824AB,0xD05315EA,0xFB7E4629,0xE2657768, + 0x2F3F79F6,0x362448B7,0x1D091B74,0x04122A35,0x4B53BCF2,0x52488DB3,0x7965DE70,0x607EEF31, + 0xE7E6F3FE,0xFEFDC2BF,0xD5D0917C,0xCCCBA03D,0x838A36FA,0x9A9107BB,0xB1BC5478,0xA8A76539, + 0x3B83984B,0x2298A90A,0x09B5FAC9,0x10AECB88,0x5FEF5D4F,0x46F46C0E,0x6DD93FCD,0x74C20E8C, + 0xF35A1243,0xEA412302,0xC16C70C1,0xD8774180,0x9736D747,0x8E2DE606,0xA500B5C5,0xBC1B8484, + 0x71418A1A,0x685ABB5B,0x4377E898,0x5A6CD9D9,0x152D4F1E,0x0C367E5F,0x271B2D9C,0x3E001CDD, + 0xB9980012,0xA0833153,0x8BAE6290,0x92B553D1,0xDDF4C516,0xC4EFF457,0xEFC2A794,0xF6D996D5, + 0xAE07BCE9,0xB71C8DA8,0x9C31DE6B,0x852AEF2A,0xCA6B79ED,0xD37048AC,0xF85D1B6F,0xE1462A2E, + 0x66DE36E1,0x7FC507A0,0x54E85463,0x4DF36522,0x02B2F3E5,0x1BA9C2A4,0x30849167,0x299FA026, + 0xE4C5AEB8,0xFDDE9FF9,0xD6F3CC3A,0xCFE8FD7B,0x80A96BBC,0x99B25AFD,0xB29F093E,0xAB84387F, + 0x2C1C24B0,0x350715F1,0x1E2A4632,0x07317773,0x4870E1B4,0x516BD0F5,0x7A468336,0x635DB277, + 0xCBFAD74E,0xD2E1E60F,0xF9CCB5CC,0xE0D7848D,0xAF96124A,0xB68D230B,0x9DA070C8,0x84BB4189, + 0x03235D46,0x1A386C07,0x31153FC4,0x280E0E85,0x674F9842,0x7E54A903,0x5579FAC0,0x4C62CB81, + 0x8138C51F,0x9823F45E,0xB30EA79D,0xAA1596DC,0xE554001B,0xFC4F315A,0xD7626299,0xCE7953D8, + 0x49E14F17,0x50FA7E56,0x7BD72D95,0x62CC1CD4,0x2D8D8A13,0x3496BB52,0x1FBBE891,0x06A0D9D0, + 0x5E7EF3EC,0x4765C2AD,0x6C48916E,0x7553A02F,0x3A1236E8,0x230907A9,0x0824546A,0x113F652B, + 0x96A779E4,0x8FBC48A5,0xA4911B66,0xBD8A2A27,0xF2CBBCE0,0xEBD08DA1,0xC0FDDE62,0xD9E6EF23, + 0x14BCE1BD,0x0DA7D0FC,0x268A833F,0x3F91B27E,0x70D024B9,0x69CB15F8,0x42E6463B,0x5BFD777A, + 0xDC656BB5,0xC57E5AF4,0xEE530937,0xF7483876,0xB809AEB1,0xA1129FF0,0x8A3FCC33,0x9324FD72, + }, + + { + 0x00000000,0x01C26A37,0x0384D46E,0x0246BE59,0x0709A8DC,0x06CBC2EB,0x048D7CB2,0x054F1685, + 0x0E1351B8,0x0FD13B8F,0x0D9785D6,0x0C55EFE1,0x091AF964,0x08D89353,0x0A9E2D0A,0x0B5C473D, + 0x1C26A370,0x1DE4C947,0x1FA2771E,0x1E601D29,0x1B2F0BAC,0x1AED619B,0x18ABDFC2,0x1969B5F5, + 0x1235F2C8,0x13F798FF,0x11B126A6,0x10734C91,0x153C5A14,0x14FE3023,0x16B88E7A,0x177AE44D, + 0x384D46E0,0x398F2CD7,0x3BC9928E,0x3A0BF8B9,0x3F44EE3C,0x3E86840B,0x3CC03A52,0x3D025065, + 0x365E1758,0x379C7D6F,0x35DAC336,0x3418A901,0x3157BF84,0x3095D5B3,0x32D36BEA,0x331101DD, + 0x246BE590,0x25A98FA7,0x27EF31FE,0x262D5BC9,0x23624D4C,0x22A0277B,0x20E69922,0x2124F315, + 0x2A78B428,0x2BBADE1F,0x29FC6046,0x283E0A71,0x2D711CF4,0x2CB376C3,0x2EF5C89A,0x2F37A2AD, + 0x709A8DC0,0x7158E7F7,0x731E59AE,0x72DC3399,0x7793251C,0x76514F2B,0x7417F172,0x75D59B45, + 0x7E89DC78,0x7F4BB64F,0x7D0D0816,0x7CCF6221,0x798074A4,0x78421E93,0x7A04A0CA,0x7BC6CAFD, + 0x6CBC2EB0,0x6D7E4487,0x6F38FADE,0x6EFA90E9,0x6BB5866C,0x6A77EC5B,0x68315202,0x69F33835, + 0x62AF7F08,0x636D153F,0x612BAB66,0x60E9C151,0x65A6D7D4,0x6464BDE3,0x662203BA,0x67E0698D, + 0x48D7CB20,0x4915A117,0x4B531F4E,0x4A917579,0x4FDE63FC,0x4E1C09CB,0x4C5AB792,0x4D98DDA5, + 0x46C49A98,0x4706F0AF,0x45404EF6,0x448224C1,0x41CD3244,0x400F5873,0x4249E62A,0x438B8C1D, + 0x54F16850,0x55330267,0x5775BC3E,0x56B7D609,0x53F8C08C,0x523AAABB,0x507C14E2,0x51BE7ED5, + 0x5AE239E8,0x5B2053DF,0x5966ED86,0x58A487B1,0x5DEB9134,0x5C29FB03,0x5E6F455A,0x5FAD2F6D, + 0xE1351B80,0xE0F771B7,0xE2B1CFEE,0xE373A5D9,0xE63CB35C,0xE7FED96B,0xE5B86732,0xE47A0D05, + 0xEF264A38,0xEEE4200F,0xECA29E56,0xED60F461,0xE82FE2E4,0xE9ED88D3,0xEBAB368A,0xEA695CBD, + 0xFD13B8F0,0xFCD1D2C7,0xFE976C9E,0xFF5506A9,0xFA1A102C,0xFBD87A1B,0xF99EC442,0xF85CAE75, + 0xF300E948,0xF2C2837F,0xF0843D26,0xF1465711,0xF4094194,0xF5CB2BA3,0xF78D95FA,0xF64FFFCD, + 0xD9785D60,0xD8BA3757,0xDAFC890E,0xDB3EE339,0xDE71F5BC,0xDFB39F8B,0xDDF521D2,0xDC374BE5, + 0xD76B0CD8,0xD6A966EF,0xD4EFD8B6,0xD52DB281,0xD062A404,0xD1A0CE33,0xD3E6706A,0xD2241A5D, + 0xC55EFE10,0xC49C9427,0xC6DA2A7E,0xC7184049,0xC25756CC,0xC3953CFB,0xC1D382A2,0xC011E895, + 0xCB4DAFA8,0xCA8FC59F,0xC8C97BC6,0xC90B11F1,0xCC440774,0xCD866D43,0xCFC0D31A,0xCE02B92D, + 0x91AF9640,0x906DFC77,0x922B422E,0x93E92819,0x96A63E9C,0x976454AB,0x9522EAF2,0x94E080C5, + 0x9FBCC7F8,0x9E7EADCF,0x9C381396,0x9DFA79A1,0x98B56F24,0x99770513,0x9B31BB4A,0x9AF3D17D, + 0x8D893530,0x8C4B5F07,0x8E0DE15E,0x8FCF8B69,0x8A809DEC,0x8B42F7DB,0x89044982,0x88C623B5, + 0x839A6488,0x82580EBF,0x801EB0E6,0x81DCDAD1,0x8493CC54,0x8551A663,0x8717183A,0x86D5720D, + 0xA9E2D0A0,0xA820BA97,0xAA6604CE,0xABA46EF9,0xAEEB787C,0xAF29124B,0xAD6FAC12,0xACADC625, + 0xA7F18118,0xA633EB2F,0xA4755576,0xA5B73F41,0xA0F829C4,0xA13A43F3,0xA37CFDAA,0xA2BE979D, + 0xB5C473D0,0xB40619E7,0xB640A7BE,0xB782CD89,0xB2CDDB0C,0xB30FB13B,0xB1490F62,0xB08B6555, + 0xBBD72268,0xBA15485F,0xB853F606,0xB9919C31,0xBCDE8AB4,0xBD1CE083,0xBF5A5EDA,0xBE9834ED, + }, + + { + 0x00000000,0xB8BC6765,0xAA09C88B,0x12B5AFEE,0x8F629757,0x37DEF032,0x256B5FDC,0x9DD738B9, + 0xC5B428EF,0x7D084F8A,0x6FBDE064,0xD7018701,0x4AD6BFB8,0xF26AD8DD,0xE0DF7733,0x58631056, + 0x5019579F,0xE8A530FA,0xFA109F14,0x42ACF871,0xDF7BC0C8,0x67C7A7AD,0x75720843,0xCDCE6F26, + 0x95AD7F70,0x2D111815,0x3FA4B7FB,0x8718D09E,0x1ACFE827,0xA2738F42,0xB0C620AC,0x087A47C9, + 0xA032AF3E,0x188EC85B,0x0A3B67B5,0xB28700D0,0x2F503869,0x97EC5F0C,0x8559F0E2,0x3DE59787, + 0x658687D1,0xDD3AE0B4,0xCF8F4F5A,0x7733283F,0xEAE41086,0x525877E3,0x40EDD80D,0xF851BF68, + 0xF02BF8A1,0x48979FC4,0x5A22302A,0xE29E574F,0x7F496FF6,0xC7F50893,0xD540A77D,0x6DFCC018, + 0x359FD04E,0x8D23B72B,0x9F9618C5,0x272A7FA0,0xBAFD4719,0x0241207C,0x10F48F92,0xA848E8F7, + 0x9B14583D,0x23A83F58,0x311D90B6,0x89A1F7D3,0x1476CF6A,0xACCAA80F,0xBE7F07E1,0x06C36084, + 0x5EA070D2,0xE61C17B7,0xF4A9B859,0x4C15DF3C,0xD1C2E785,0x697E80E0,0x7BCB2F0E,0xC377486B, + 0xCB0D0FA2,0x73B168C7,0x6104C729,0xD9B8A04C,0x446F98F5,0xFCD3FF90,0xEE66507E,0x56DA371B, + 0x0EB9274D,0xB6054028,0xA4B0EFC6,0x1C0C88A3,0x81DBB01A,0x3967D77F,0x2BD27891,0x936E1FF4, + 0x3B26F703,0x839A9066,0x912F3F88,0x299358ED,0xB4446054,0x0CF80731,0x1E4DA8DF,0xA6F1CFBA, + 0xFE92DFEC,0x462EB889,0x549B1767,0xEC277002,0x71F048BB,0xC94C2FDE,0xDBF98030,0x6345E755, + 0x6B3FA09C,0xD383C7F9,0xC1366817,0x798A0F72,0xE45D37CB,0x5CE150AE,0x4E54FF40,0xF6E89825, + 0xAE8B8873,0x1637EF16,0x048240F8,0xBC3E279D,0x21E91F24,0x99557841,0x8BE0D7AF,0x335CB0CA, + 0xED59B63B,0x55E5D15E,0x47507EB0,0xFFEC19D5,0x623B216C,0xDA874609,0xC832E9E7,0x708E8E82, + 0x28ED9ED4,0x9051F9B1,0x82E4565F,0x3A58313A,0xA78F0983,0x1F336EE6,0x0D86C108,0xB53AA66D, + 0xBD40E1A4,0x05FC86C1,0x1749292F,0xAFF54E4A,0x322276F3,0x8A9E1196,0x982BBE78,0x2097D91D, + 0x78F4C94B,0xC048AE2E,0xD2FD01C0,0x6A4166A5,0xF7965E1C,0x4F2A3979,0x5D9F9697,0xE523F1F2, + 0x4D6B1905,0xF5D77E60,0xE762D18E,0x5FDEB6EB,0xC2098E52,0x7AB5E937,0x680046D9,0xD0BC21BC, + 0x88DF31EA,0x3063568F,0x22D6F961,0x9A6A9E04,0x07BDA6BD,0xBF01C1D8,0xADB46E36,0x15080953, + 0x1D724E9A,0xA5CE29FF,0xB77B8611,0x0FC7E174,0x9210D9CD,0x2AACBEA8,0x38191146,0x80A57623, + 0xD8C66675,0x607A0110,0x72CFAEFE,0xCA73C99B,0x57A4F122,0xEF189647,0xFDAD39A9,0x45115ECC, + 0x764DEE06,0xCEF18963,0xDC44268D,0x64F841E8,0xF92F7951,0x41931E34,0x5326B1DA,0xEB9AD6BF, + 0xB3F9C6E9,0x0B45A18C,0x19F00E62,0xA14C6907,0x3C9B51BE,0x842736DB,0x96929935,0x2E2EFE50, + 0x2654B999,0x9EE8DEFC,0x8C5D7112,0x34E11677,0xA9362ECE,0x118A49AB,0x033FE645,0xBB838120, + 0xE3E09176,0x5B5CF613,0x49E959FD,0xF1553E98,0x6C820621,0xD43E6144,0xC68BCEAA,0x7E37A9CF, + 0xD67F4138,0x6EC3265D,0x7C7689B3,0xC4CAEED6,0x591DD66F,0xE1A1B10A,0xF3141EE4,0x4BA87981, + 0x13CB69D7,0xAB770EB2,0xB9C2A15C,0x017EC639,0x9CA9FE80,0x241599E5,0x36A0360B,0x8E1C516E, + 0x866616A7,0x3EDA71C2,0x2C6FDE2C,0x94D3B949,0x090481F0,0xB1B8E695,0xA30D497B,0x1BB12E1E, + 0x43D23E48,0xFB6E592D,0xE9DBF6C3,0x516791A6,0xCCB0A91F,0x740CCE7A,0x66B96194,0xDE0506F1, + } +#endif // defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_4) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_8) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_16) +#if defined (CRC32_USE_LOOKUP_TABLE_SLICING_BY_8) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_16) + // beyond this point only relevant for Slicing-by-8 and Slicing-by-16 + ,{ + 0x00000000,0x3D6029B0,0x7AC05360,0x47A07AD0,0xF580A6C0,0xC8E08F70,0x8F40F5A0,0xB220DC10, + 0x30704BC1,0x0D106271,0x4AB018A1,0x77D03111,0xC5F0ED01,0xF890C4B1,0xBF30BE61,0x825097D1, + 0x60E09782,0x5D80BE32,0x1A20C4E2,0x2740ED52,0x95603142,0xA80018F2,0xEFA06222,0xD2C04B92, + 0x5090DC43,0x6DF0F5F3,0x2A508F23,0x1730A693,0xA5107A83,0x98705333,0xDFD029E3,0xE2B00053, + 0xC1C12F04,0xFCA106B4,0xBB017C64,0x866155D4,0x344189C4,0x0921A074,0x4E81DAA4,0x73E1F314, + 0xF1B164C5,0xCCD14D75,0x8B7137A5,0xB6111E15,0x0431C205,0x3951EBB5,0x7EF19165,0x4391B8D5, + 0xA121B886,0x9C419136,0xDBE1EBE6,0xE681C256,0x54A11E46,0x69C137F6,0x2E614D26,0x13016496, + 0x9151F347,0xAC31DAF7,0xEB91A027,0xD6F18997,0x64D15587,0x59B17C37,0x1E1106E7,0x23712F57, + 0x58F35849,0x659371F9,0x22330B29,0x1F532299,0xAD73FE89,0x9013D739,0xD7B3ADE9,0xEAD38459, + 0x68831388,0x55E33A38,0x124340E8,0x2F236958,0x9D03B548,0xA0639CF8,0xE7C3E628,0xDAA3CF98, + 0x3813CFCB,0x0573E67B,0x42D39CAB,0x7FB3B51B,0xCD93690B,0xF0F340BB,0xB7533A6B,0x8A3313DB, + 0x0863840A,0x3503ADBA,0x72A3D76A,0x4FC3FEDA,0xFDE322CA,0xC0830B7A,0x872371AA,0xBA43581A, + 0x9932774D,0xA4525EFD,0xE3F2242D,0xDE920D9D,0x6CB2D18D,0x51D2F83D,0x167282ED,0x2B12AB5D, + 0xA9423C8C,0x9422153C,0xD3826FEC,0xEEE2465C,0x5CC29A4C,0x61A2B3FC,0x2602C92C,0x1B62E09C, + 0xF9D2E0CF,0xC4B2C97F,0x8312B3AF,0xBE729A1F,0x0C52460F,0x31326FBF,0x7692156F,0x4BF23CDF, + 0xC9A2AB0E,0xF4C282BE,0xB362F86E,0x8E02D1DE,0x3C220DCE,0x0142247E,0x46E25EAE,0x7B82771E, + 0xB1E6B092,0x8C869922,0xCB26E3F2,0xF646CA42,0x44661652,0x79063FE2,0x3EA64532,0x03C66C82, + 0x8196FB53,0xBCF6D2E3,0xFB56A833,0xC6368183,0x74165D93,0x49767423,0x0ED60EF3,0x33B62743, + 0xD1062710,0xEC660EA0,0xABC67470,0x96A65DC0,0x248681D0,0x19E6A860,0x5E46D2B0,0x6326FB00, + 0xE1766CD1,0xDC164561,0x9BB63FB1,0xA6D61601,0x14F6CA11,0x2996E3A1,0x6E369971,0x5356B0C1, + 0x70279F96,0x4D47B626,0x0AE7CCF6,0x3787E546,0x85A73956,0xB8C710E6,0xFF676A36,0xC2074386, + 0x4057D457,0x7D37FDE7,0x3A978737,0x07F7AE87,0xB5D77297,0x88B75B27,0xCF1721F7,0xF2770847, + 0x10C70814,0x2DA721A4,0x6A075B74,0x576772C4,0xE547AED4,0xD8278764,0x9F87FDB4,0xA2E7D404, + 0x20B743D5,0x1DD76A65,0x5A7710B5,0x67173905,0xD537E515,0xE857CCA5,0xAFF7B675,0x92979FC5, + 0xE915E8DB,0xD475C16B,0x93D5BBBB,0xAEB5920B,0x1C954E1B,0x21F567AB,0x66551D7B,0x5B3534CB, + 0xD965A31A,0xE4058AAA,0xA3A5F07A,0x9EC5D9CA,0x2CE505DA,0x11852C6A,0x562556BA,0x6B457F0A, + 0x89F57F59,0xB49556E9,0xF3352C39,0xCE550589,0x7C75D999,0x4115F029,0x06B58AF9,0x3BD5A349, + 0xB9853498,0x84E51D28,0xC34567F8,0xFE254E48,0x4C059258,0x7165BBE8,0x36C5C138,0x0BA5E888, + 0x28D4C7DF,0x15B4EE6F,0x521494BF,0x6F74BD0F,0xDD54611F,0xE03448AF,0xA794327F,0x9AF41BCF, + 0x18A48C1E,0x25C4A5AE,0x6264DF7E,0x5F04F6CE,0xED242ADE,0xD044036E,0x97E479BE,0xAA84500E, + 0x4834505D,0x755479ED,0x32F4033D,0x0F942A8D,0xBDB4F69D,0x80D4DF2D,0xC774A5FD,0xFA148C4D, + 0x78441B9C,0x4524322C,0x028448FC,0x3FE4614C,0x8DC4BD5C,0xB0A494EC,0xF704EE3C,0xCA64C78C, + }, + + { + 0x00000000,0xCB5CD3A5,0x4DC8A10B,0x869472AE,0x9B914216,0x50CD91B3,0xD659E31D,0x1D0530B8, + 0xEC53826D,0x270F51C8,0xA19B2366,0x6AC7F0C3,0x77C2C07B,0xBC9E13DE,0x3A0A6170,0xF156B2D5, + 0x03D6029B,0xC88AD13E,0x4E1EA390,0x85427035,0x9847408D,0x531B9328,0xD58FE186,0x1ED33223, + 0xEF8580F6,0x24D95353,0xA24D21FD,0x6911F258,0x7414C2E0,0xBF481145,0x39DC63EB,0xF280B04E, + 0x07AC0536,0xCCF0D693,0x4A64A43D,0x81387798,0x9C3D4720,0x57619485,0xD1F5E62B,0x1AA9358E, + 0xEBFF875B,0x20A354FE,0xA6372650,0x6D6BF5F5,0x706EC54D,0xBB3216E8,0x3DA66446,0xF6FAB7E3, + 0x047A07AD,0xCF26D408,0x49B2A6A6,0x82EE7503,0x9FEB45BB,0x54B7961E,0xD223E4B0,0x197F3715, + 0xE82985C0,0x23755665,0xA5E124CB,0x6EBDF76E,0x73B8C7D6,0xB8E41473,0x3E7066DD,0xF52CB578, + 0x0F580A6C,0xC404D9C9,0x4290AB67,0x89CC78C2,0x94C9487A,0x5F959BDF,0xD901E971,0x125D3AD4, + 0xE30B8801,0x28575BA4,0xAEC3290A,0x659FFAAF,0x789ACA17,0xB3C619B2,0x35526B1C,0xFE0EB8B9, + 0x0C8E08F7,0xC7D2DB52,0x4146A9FC,0x8A1A7A59,0x971F4AE1,0x5C439944,0xDAD7EBEA,0x118B384F, + 0xE0DD8A9A,0x2B81593F,0xAD152B91,0x6649F834,0x7B4CC88C,0xB0101B29,0x36846987,0xFDD8BA22, + 0x08F40F5A,0xC3A8DCFF,0x453CAE51,0x8E607DF4,0x93654D4C,0x58399EE9,0xDEADEC47,0x15F13FE2, + 0xE4A78D37,0x2FFB5E92,0xA96F2C3C,0x6233FF99,0x7F36CF21,0xB46A1C84,0x32FE6E2A,0xF9A2BD8F, + 0x0B220DC1,0xC07EDE64,0x46EAACCA,0x8DB67F6F,0x90B34FD7,0x5BEF9C72,0xDD7BEEDC,0x16273D79, + 0xE7718FAC,0x2C2D5C09,0xAAB92EA7,0x61E5FD02,0x7CE0CDBA,0xB7BC1E1F,0x31286CB1,0xFA74BF14, + 0x1EB014D8,0xD5ECC77D,0x5378B5D3,0x98246676,0x852156CE,0x4E7D856B,0xC8E9F7C5,0x03B52460, + 0xF2E396B5,0x39BF4510,0xBF2B37BE,0x7477E41B,0x6972D4A3,0xA22E0706,0x24BA75A8,0xEFE6A60D, + 0x1D661643,0xD63AC5E6,0x50AEB748,0x9BF264ED,0x86F75455,0x4DAB87F0,0xCB3FF55E,0x006326FB, + 0xF135942E,0x3A69478B,0xBCFD3525,0x77A1E680,0x6AA4D638,0xA1F8059D,0x276C7733,0xEC30A496, + 0x191C11EE,0xD240C24B,0x54D4B0E5,0x9F886340,0x828D53F8,0x49D1805D,0xCF45F2F3,0x04192156, + 0xF54F9383,0x3E134026,0xB8873288,0x73DBE12D,0x6EDED195,0xA5820230,0x2316709E,0xE84AA33B, + 0x1ACA1375,0xD196C0D0,0x5702B27E,0x9C5E61DB,0x815B5163,0x4A0782C6,0xCC93F068,0x07CF23CD, + 0xF6999118,0x3DC542BD,0xBB513013,0x700DE3B6,0x6D08D30E,0xA65400AB,0x20C07205,0xEB9CA1A0, + 0x11E81EB4,0xDAB4CD11,0x5C20BFBF,0x977C6C1A,0x8A795CA2,0x41258F07,0xC7B1FDA9,0x0CED2E0C, + 0xFDBB9CD9,0x36E74F7C,0xB0733DD2,0x7B2FEE77,0x662ADECF,0xAD760D6A,0x2BE27FC4,0xE0BEAC61, + 0x123E1C2F,0xD962CF8A,0x5FF6BD24,0x94AA6E81,0x89AF5E39,0x42F38D9C,0xC467FF32,0x0F3B2C97, + 0xFE6D9E42,0x35314DE7,0xB3A53F49,0x78F9ECEC,0x65FCDC54,0xAEA00FF1,0x28347D5F,0xE368AEFA, + 0x16441B82,0xDD18C827,0x5B8CBA89,0x90D0692C,0x8DD55994,0x46898A31,0xC01DF89F,0x0B412B3A, + 0xFA1799EF,0x314B4A4A,0xB7DF38E4,0x7C83EB41,0x6186DBF9,0xAADA085C,0x2C4E7AF2,0xE712A957, + 0x15921919,0xDECECABC,0x585AB812,0x93066BB7,0x8E035B0F,0x455F88AA,0xC3CBFA04,0x089729A1, + 0xF9C19B74,0x329D48D1,0xB4093A7F,0x7F55E9DA,0x6250D962,0xA90C0AC7,0x2F987869,0xE4C4ABCC, + }, + + { + 0x00000000,0xA6770BB4,0x979F1129,0x31E81A9D,0xF44F2413,0x52382FA7,0x63D0353A,0xC5A73E8E, + 0x33EF4E67,0x959845D3,0xA4705F4E,0x020754FA,0xC7A06A74,0x61D761C0,0x503F7B5D,0xF64870E9, + 0x67DE9CCE,0xC1A9977A,0xF0418DE7,0x56368653,0x9391B8DD,0x35E6B369,0x040EA9F4,0xA279A240, + 0x5431D2A9,0xF246D91D,0xC3AEC380,0x65D9C834,0xA07EF6BA,0x0609FD0E,0x37E1E793,0x9196EC27, + 0xCFBD399C,0x69CA3228,0x582228B5,0xFE552301,0x3BF21D8F,0x9D85163B,0xAC6D0CA6,0x0A1A0712, + 0xFC5277FB,0x5A257C4F,0x6BCD66D2,0xCDBA6D66,0x081D53E8,0xAE6A585C,0x9F8242C1,0x39F54975, + 0xA863A552,0x0E14AEE6,0x3FFCB47B,0x998BBFCF,0x5C2C8141,0xFA5B8AF5,0xCBB39068,0x6DC49BDC, + 0x9B8CEB35,0x3DFBE081,0x0C13FA1C,0xAA64F1A8,0x6FC3CF26,0xC9B4C492,0xF85CDE0F,0x5E2BD5BB, + 0x440B7579,0xE27C7ECD,0xD3946450,0x75E36FE4,0xB044516A,0x16335ADE,0x27DB4043,0x81AC4BF7, + 0x77E43B1E,0xD19330AA,0xE07B2A37,0x460C2183,0x83AB1F0D,0x25DC14B9,0x14340E24,0xB2430590, + 0x23D5E9B7,0x85A2E203,0xB44AF89E,0x123DF32A,0xD79ACDA4,0x71EDC610,0x4005DC8D,0xE672D739, + 0x103AA7D0,0xB64DAC64,0x87A5B6F9,0x21D2BD4D,0xE47583C3,0x42028877,0x73EA92EA,0xD59D995E, + 0x8BB64CE5,0x2DC14751,0x1C295DCC,0xBA5E5678,0x7FF968F6,0xD98E6342,0xE86679DF,0x4E11726B, + 0xB8590282,0x1E2E0936,0x2FC613AB,0x89B1181F,0x4C162691,0xEA612D25,0xDB8937B8,0x7DFE3C0C, + 0xEC68D02B,0x4A1FDB9F,0x7BF7C102,0xDD80CAB6,0x1827F438,0xBE50FF8C,0x8FB8E511,0x29CFEEA5, + 0xDF879E4C,0x79F095F8,0x48188F65,0xEE6F84D1,0x2BC8BA5F,0x8DBFB1EB,0xBC57AB76,0x1A20A0C2, + 0x8816EAF2,0x2E61E146,0x1F89FBDB,0xB9FEF06F,0x7C59CEE1,0xDA2EC555,0xEBC6DFC8,0x4DB1D47C, + 0xBBF9A495,0x1D8EAF21,0x2C66B5BC,0x8A11BE08,0x4FB68086,0xE9C18B32,0xD82991AF,0x7E5E9A1B, + 0xEFC8763C,0x49BF7D88,0x78576715,0xDE206CA1,0x1B87522F,0xBDF0599B,0x8C184306,0x2A6F48B2, + 0xDC27385B,0x7A5033EF,0x4BB82972,0xEDCF22C6,0x28681C48,0x8E1F17FC,0xBFF70D61,0x198006D5, + 0x47ABD36E,0xE1DCD8DA,0xD034C247,0x7643C9F3,0xB3E4F77D,0x1593FCC9,0x247BE654,0x820CEDE0, + 0x74449D09,0xD23396BD,0xE3DB8C20,0x45AC8794,0x800BB91A,0x267CB2AE,0x1794A833,0xB1E3A387, + 0x20754FA0,0x86024414,0xB7EA5E89,0x119D553D,0xD43A6BB3,0x724D6007,0x43A57A9A,0xE5D2712E, + 0x139A01C7,0xB5ED0A73,0x840510EE,0x22721B5A,0xE7D525D4,0x41A22E60,0x704A34FD,0xD63D3F49, + 0xCC1D9F8B,0x6A6A943F,0x5B828EA2,0xFDF58516,0x3852BB98,0x9E25B02C,0xAFCDAAB1,0x09BAA105, + 0xFFF2D1EC,0x5985DA58,0x686DC0C5,0xCE1ACB71,0x0BBDF5FF,0xADCAFE4B,0x9C22E4D6,0x3A55EF62, + 0xABC30345,0x0DB408F1,0x3C5C126C,0x9A2B19D8,0x5F8C2756,0xF9FB2CE2,0xC813367F,0x6E643DCB, + 0x982C4D22,0x3E5B4696,0x0FB35C0B,0xA9C457BF,0x6C636931,0xCA146285,0xFBFC7818,0x5D8B73AC, + 0x03A0A617,0xA5D7ADA3,0x943FB73E,0x3248BC8A,0xF7EF8204,0x519889B0,0x6070932D,0xC6079899, + 0x304FE870,0x9638E3C4,0xA7D0F959,0x01A7F2ED,0xC400CC63,0x6277C7D7,0x539FDD4A,0xF5E8D6FE, + 0x647E3AD9,0xC209316D,0xF3E12BF0,0x55962044,0x90311ECA,0x3646157E,0x07AE0FE3,0xA1D90457, + 0x579174BE,0xF1E67F0A,0xC00E6597,0x66796E23,0xA3DE50AD,0x05A95B19,0x34414184,0x92364A30, + }, + + { + 0x00000000,0xCCAA009E,0x4225077D,0x8E8F07E3,0x844A0EFA,0x48E00E64,0xC66F0987,0x0AC50919, + 0xD3E51BB5,0x1F4F1B2B,0x91C01CC8,0x5D6A1C56,0x57AF154F,0x9B0515D1,0x158A1232,0xD92012AC, + 0x7CBB312B,0xB01131B5,0x3E9E3656,0xF23436C8,0xF8F13FD1,0x345B3F4F,0xBAD438AC,0x767E3832, + 0xAF5E2A9E,0x63F42A00,0xED7B2DE3,0x21D12D7D,0x2B142464,0xE7BE24FA,0x69312319,0xA59B2387, + 0xF9766256,0x35DC62C8,0xBB53652B,0x77F965B5,0x7D3C6CAC,0xB1966C32,0x3F196BD1,0xF3B36B4F, + 0x2A9379E3,0xE639797D,0x68B67E9E,0xA41C7E00,0xAED97719,0x62737787,0xECFC7064,0x205670FA, + 0x85CD537D,0x496753E3,0xC7E85400,0x0B42549E,0x01875D87,0xCD2D5D19,0x43A25AFA,0x8F085A64, + 0x562848C8,0x9A824856,0x140D4FB5,0xD8A74F2B,0xD2624632,0x1EC846AC,0x9047414F,0x5CED41D1, + 0x299DC2ED,0xE537C273,0x6BB8C590,0xA712C50E,0xADD7CC17,0x617DCC89,0xEFF2CB6A,0x2358CBF4, + 0xFA78D958,0x36D2D9C6,0xB85DDE25,0x74F7DEBB,0x7E32D7A2,0xB298D73C,0x3C17D0DF,0xF0BDD041, + 0x5526F3C6,0x998CF358,0x1703F4BB,0xDBA9F425,0xD16CFD3C,0x1DC6FDA2,0x9349FA41,0x5FE3FADF, + 0x86C3E873,0x4A69E8ED,0xC4E6EF0E,0x084CEF90,0x0289E689,0xCE23E617,0x40ACE1F4,0x8C06E16A, + 0xD0EBA0BB,0x1C41A025,0x92CEA7C6,0x5E64A758,0x54A1AE41,0x980BAEDF,0x1684A93C,0xDA2EA9A2, + 0x030EBB0E,0xCFA4BB90,0x412BBC73,0x8D81BCED,0x8744B5F4,0x4BEEB56A,0xC561B289,0x09CBB217, + 0xAC509190,0x60FA910E,0xEE7596ED,0x22DF9673,0x281A9F6A,0xE4B09FF4,0x6A3F9817,0xA6959889, + 0x7FB58A25,0xB31F8ABB,0x3D908D58,0xF13A8DC6,0xFBFF84DF,0x37558441,0xB9DA83A2,0x7570833C, + 0x533B85DA,0x9F918544,0x111E82A7,0xDDB48239,0xD7718B20,0x1BDB8BBE,0x95548C5D,0x59FE8CC3, + 0x80DE9E6F,0x4C749EF1,0xC2FB9912,0x0E51998C,0x04949095,0xC83E900B,0x46B197E8,0x8A1B9776, + 0x2F80B4F1,0xE32AB46F,0x6DA5B38C,0xA10FB312,0xABCABA0B,0x6760BA95,0xE9EFBD76,0x2545BDE8, + 0xFC65AF44,0x30CFAFDA,0xBE40A839,0x72EAA8A7,0x782FA1BE,0xB485A120,0x3A0AA6C3,0xF6A0A65D, + 0xAA4DE78C,0x66E7E712,0xE868E0F1,0x24C2E06F,0x2E07E976,0xE2ADE9E8,0x6C22EE0B,0xA088EE95, + 0x79A8FC39,0xB502FCA7,0x3B8DFB44,0xF727FBDA,0xFDE2F2C3,0x3148F25D,0xBFC7F5BE,0x736DF520, + 0xD6F6D6A7,0x1A5CD639,0x94D3D1DA,0x5879D144,0x52BCD85D,0x9E16D8C3,0x1099DF20,0xDC33DFBE, + 0x0513CD12,0xC9B9CD8C,0x4736CA6F,0x8B9CCAF1,0x8159C3E8,0x4DF3C376,0xC37CC495,0x0FD6C40B, + 0x7AA64737,0xB60C47A9,0x3883404A,0xF42940D4,0xFEEC49CD,0x32464953,0xBCC94EB0,0x70634E2E, + 0xA9435C82,0x65E95C1C,0xEB665BFF,0x27CC5B61,0x2D095278,0xE1A352E6,0x6F2C5505,0xA386559B, + 0x061D761C,0xCAB77682,0x44387161,0x889271FF,0x825778E6,0x4EFD7878,0xC0727F9B,0x0CD87F05, + 0xD5F86DA9,0x19526D37,0x97DD6AD4,0x5B776A4A,0x51B26353,0x9D1863CD,0x1397642E,0xDF3D64B0, + 0x83D02561,0x4F7A25FF,0xC1F5221C,0x0D5F2282,0x079A2B9B,0xCB302B05,0x45BF2CE6,0x89152C78, + 0x50353ED4,0x9C9F3E4A,0x121039A9,0xDEBA3937,0xD47F302E,0x18D530B0,0x965A3753,0x5AF037CD, + 0xFF6B144A,0x33C114D4,0xBD4E1337,0x71E413A9,0x7B211AB0,0xB78B1A2E,0x39041DCD,0xF5AE1D53, + 0x2C8E0FFF,0xE0240F61,0x6EAB0882,0xA201081C,0xA8C40105,0x646E019B,0xEAE10678,0x264B06E6, + } +#endif // CRC32_USE_LOOKUP_TABLE_SLICING_BY_8 || CRC32_USE_LOOKUP_TABLE_SLICING_BY_16 +#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16 + // beyond this point only relevant for Slicing-by-16 + ,{ + 0x00000000,0x177B1443,0x2EF62886,0x398D3CC5,0x5DEC510C,0x4A97454F,0x731A798A,0x64616DC9, + 0xBBD8A218,0xACA3B65B,0x952E8A9E,0x82559EDD,0xE634F314,0xF14FE757,0xC8C2DB92,0xDFB9CFD1, + 0xACC04271,0xBBBB5632,0x82366AF7,0x954D7EB4,0xF12C137D,0xE657073E,0xDFDA3BFB,0xC8A12FB8, + 0x1718E069,0x0063F42A,0x39EEC8EF,0x2E95DCAC,0x4AF4B165,0x5D8FA526,0x640299E3,0x73798DA0, + 0x82F182A3,0x958A96E0,0xAC07AA25,0xBB7CBE66,0xDF1DD3AF,0xC866C7EC,0xF1EBFB29,0xE690EF6A, + 0x392920BB,0x2E5234F8,0x17DF083D,0x00A41C7E,0x64C571B7,0x73BE65F4,0x4A335931,0x5D484D72, + 0x2E31C0D2,0x394AD491,0x00C7E854,0x17BCFC17,0x73DD91DE,0x64A6859D,0x5D2BB958,0x4A50AD1B, + 0x95E962CA,0x82927689,0xBB1F4A4C,0xAC645E0F,0xC80533C6,0xDF7E2785,0xE6F31B40,0xF1880F03, + 0xDE920307,0xC9E91744,0xF0642B81,0xE71F3FC2,0x837E520B,0x94054648,0xAD887A8D,0xBAF36ECE, + 0x654AA11F,0x7231B55C,0x4BBC8999,0x5CC79DDA,0x38A6F013,0x2FDDE450,0x1650D895,0x012BCCD6, + 0x72524176,0x65295535,0x5CA469F0,0x4BDF7DB3,0x2FBE107A,0x38C50439,0x014838FC,0x16332CBF, + 0xC98AE36E,0xDEF1F72D,0xE77CCBE8,0xF007DFAB,0x9466B262,0x831DA621,0xBA909AE4,0xADEB8EA7, + 0x5C6381A4,0x4B1895E7,0x7295A922,0x65EEBD61,0x018FD0A8,0x16F4C4EB,0x2F79F82E,0x3802EC6D, + 0xE7BB23BC,0xF0C037FF,0xC94D0B3A,0xDE361F79,0xBA5772B0,0xAD2C66F3,0x94A15A36,0x83DA4E75, + 0xF0A3C3D5,0xE7D8D796,0xDE55EB53,0xC92EFF10,0xAD4F92D9,0xBA34869A,0x83B9BA5F,0x94C2AE1C, + 0x4B7B61CD,0x5C00758E,0x658D494B,0x72F65D08,0x169730C1,0x01EC2482,0x38611847,0x2F1A0C04, + 0x6655004F,0x712E140C,0x48A328C9,0x5FD83C8A,0x3BB95143,0x2CC24500,0x154F79C5,0x02346D86, + 0xDD8DA257,0xCAF6B614,0xF37B8AD1,0xE4009E92,0x8061F35B,0x971AE718,0xAE97DBDD,0xB9ECCF9E, + 0xCA95423E,0xDDEE567D,0xE4636AB8,0xF3187EFB,0x97791332,0x80020771,0xB98F3BB4,0xAEF42FF7, + 0x714DE026,0x6636F465,0x5FBBC8A0,0x48C0DCE3,0x2CA1B12A,0x3BDAA569,0x025799AC,0x152C8DEF, + 0xE4A482EC,0xF3DF96AF,0xCA52AA6A,0xDD29BE29,0xB948D3E0,0xAE33C7A3,0x97BEFB66,0x80C5EF25, + 0x5F7C20F4,0x480734B7,0x718A0872,0x66F11C31,0x029071F8,0x15EB65BB,0x2C66597E,0x3B1D4D3D, + 0x4864C09D,0x5F1FD4DE,0x6692E81B,0x71E9FC58,0x15889191,0x02F385D2,0x3B7EB917,0x2C05AD54, + 0xF3BC6285,0xE4C776C6,0xDD4A4A03,0xCA315E40,0xAE503389,0xB92B27CA,0x80A61B0F,0x97DD0F4C, + 0xB8C70348,0xAFBC170B,0x96312BCE,0x814A3F8D,0xE52B5244,0xF2504607,0xCBDD7AC2,0xDCA66E81, + 0x031FA150,0x1464B513,0x2DE989D6,0x3A929D95,0x5EF3F05C,0x4988E41F,0x7005D8DA,0x677ECC99, + 0x14074139,0x037C557A,0x3AF169BF,0x2D8A7DFC,0x49EB1035,0x5E900476,0x671D38B3,0x70662CF0, + 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0x642CE432,0xCA4475A3,0xE38CC151,0x4DE450C0,0xB01DA8B5,0x1E753924,0x37BD8DD6,0x99D51C47, + 0xF11845E3,0x5F70D472,0x76B86080,0xD8D0F111,0x25290964,0x8B4198F5,0xA2892C07,0x0CE1BD96, + 0x820BDAAC,0x2C634B3D,0x05ABFFCF,0xABC36E5E,0x563A962B,0xF85207BA,0xD19AB348,0x7FF222D9, + 0x2E7EF6FA,0x8016676B,0xA9DED399,0x07B64208,0xFA4FBA7D,0x54272BEC,0x7DEF9F1E,0xD3870E8F, + 0x5D6D69B5,0xF305F824,0xDACD4CD6,0x74A5DD47,0x895C2532,0x2734B4A3,0x0EFC0051,0xA09491C0, + 0xC859C864,0x663159F5,0x4FF9ED07,0xE1917C96,0x1C6884E3,0xB2001572,0x9BC8A180,0x35A03011, + 0xBB4A572B,0x1522C6BA,0x3CEA7248,0x9282E3D9,0x6F7B1BAC,0xC1138A3D,0xE8DB3ECF,0x46B3AF5E, + 0x39418D87,0x97291C16,0xBEE1A8E4,0x10893975,0xED70C100,0x43185091,0x6AD0E463,0xC4B875F2, + 0x4A5212C8,0xE43A8359,0xCDF237AB,0x639AA63A,0x9E635E4F,0x300BCFDE,0x19C37B2C,0xB7ABEABD, + 0xDF66B319,0x710E2288,0x58C6967A,0xF6AE07EB,0x0B57FF9E,0xA53F6E0F,0x8CF7DAFD,0x229F4B6C, + 0xAC752C56,0x021DBDC7,0x2BD50935,0x85BD98A4,0x784460D1,0xD62CF140,0xFFE445B2,0x518CD423, + 0x5CFDEDF4,0xF2957C65,0xDB5DC897,0x75355906,0x88CCA173,0x26A430E2,0x0F6C8410,0xA1041581, + 0x2FEE72BB,0x8186E32A,0xA84E57D8,0x0626C649,0xFBDF3E3C,0x55B7AFAD,0x7C7F1B5F,0xD2178ACE, + 0xBADAD36A,0x14B242FB,0x3D7AF609,0x93126798,0x6EEB9FED,0xC0830E7C,0xE94BBA8E,0x47232B1F, + 0xC9C94C25,0x67A1DDB4,0x4E696946,0xE001F8D7,0x1DF800A2,0xB3909133,0x9A5825C1,0x3430B450, + 0x4BC29689,0xE5AA0718,0xCC62B3EA,0x620A227B,0x9FF3DA0E,0x319B4B9F,0x1853FF6D,0xB63B6EFC, + 0x38D109C6,0x96B99857,0xBF712CA5,0x1119BD34,0xECE04541,0x4288D4D0,0x6B406022,0xC528F1B3, + 0xADE5A817,0x038D3986,0x2A458D74,0x842D1CE5,0x79D4E490,0xD7BC7501,0xFE74C1F3,0x501C5062, + 0xDEF63758,0x709EA6C9,0x5956123B,0xF73E83AA,0x0AC77BDF,0xA4AFEA4E,0x8D675EBC,0x230FCF2D, + 0x72831B0E,0xDCEB8A9F,0xF5233E6D,0x5B4BAFFC,0xA6B25789,0x08DAC618,0x211272EA,0x8F7AE37B, + 0x01908441,0xAFF815D0,0x8630A122,0x285830B3,0xD5A1C8C6,0x7BC95957,0x5201EDA5,0xFC697C34, + 0x94A42590,0x3ACCB401,0x130400F3,0xBD6C9162,0x40956917,0xEEFDF886,0xC7354C74,0x695DDDE5, + 0xE7B7BADF,0x49DF2B4E,0x60179FBC,0xCE7F0E2D,0x3386F658,0x9DEE67C9,0xB426D33B,0x1A4E42AA, + 0x65BC6073,0xCBD4F1E2,0xE21C4510,0x4C74D481,0xB18D2CF4,0x1FE5BD65,0x362D0997,0x98459806, + 0x16AFFF3C,0xB8C76EAD,0x910FDA5F,0x3F674BCE,0xC29EB3BB,0x6CF6222A,0x453E96D8,0xEB560749, + 0x839B5EED,0x2DF3CF7C,0x043B7B8E,0xAA53EA1F,0x57AA126A,0xF9C283FB,0xD00A3709,0x7E62A698, + 0xF088C1A2,0x5EE05033,0x7728E4C1,0xD9407550,0x24B98D25,0x8AD11CB4,0xA319A846,0x0D7139D7, + } +#endif // CRC32_USE_LOOKUP_TABLE_SLICING_BY_16 +}; +#endif diff --git a/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/file_adapter.h b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/file_adapter.h new file mode 100644 index 0000000000000000000000000000000000000000..36bc5de2710034ccaa4ccc9468520dab31cc9786 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/file_adapter.h @@ -0,0 +1,36 @@ +#pragma once + +#include +#include +#include + +#include "caffe2/serialize/istream_adapter.h" +#include "caffe2/serialize/read_adapter_interface.h" + +namespace caffe2 { +namespace serialize { + +class TORCH_API FileAdapter final : public ReadAdapterInterface { + public: + C10_DISABLE_COPY_AND_ASSIGN(FileAdapter); + explicit FileAdapter(const std::string& file_name); + size_t size() const override; + size_t read(uint64_t pos, void* buf, size_t n, const char* what = "") + const override; + ~FileAdapter() override; + + private: + // An RAII Wrapper for a FILE pointer. Closes on destruction. + struct RAIIFile { + FILE* fp_; + explicit RAIIFile(const std::string& file_name); + ~RAIIFile(); + }; + + RAIIFile file_; + // The size of the opened file in bytes + uint64_t size_; +}; + +} // namespace serialize +} // namespace caffe2 diff --git a/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/in_memory_adapter.h b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/in_memory_adapter.h new file mode 100644 index 0000000000000000000000000000000000000000..817f3f5d677493aa3a1e739df8c3b5dc8a8219ef --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/in_memory_adapter.h @@ -0,0 +1,32 @@ +#pragma once +#include +#include + + +namespace caffe2 { +namespace serialize { + +class MemoryReadAdapter final : public caffe2::serialize::ReadAdapterInterface { + public: + explicit MemoryReadAdapter(const void* data, off_t size) + : data_(data), size_(size) {} + + size_t size() const override { + return size_; + } + + size_t read(uint64_t pos, void* buf, size_t n, const char* what = "") + const override { + (void) what; + memcpy(buf, (int8_t*)(data_) + pos, n); + return n; + } + + private: + const void* data_; + off_t size_; +}; + + +} // namespace serialize +} // namespace caffe2 diff --git a/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/inline_container.h b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/inline_container.h new file mode 100644 index 0000000000000000000000000000000000000000..aa0cb8e04329085b818042ac8e16d27a427d971c --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/inline_container.h @@ -0,0 +1,277 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "caffe2/serialize/istream_adapter.h" +#include "caffe2/serialize/read_adapter_interface.h" +#include "caffe2/serialize/versions.h" + + +extern "C" { +typedef struct mz_zip_archive mz_zip_archive; +} + +// PyTorch containers are a special zip archive with the following layout +// archive_name.zip contains: +// archive_name/ +// version # a file with a single decimal number written in ascii, +// # used to establish the version of the archive format +// model.json # overall model description, this is a json output of +// # ModelDef from torch.proto +// # the following names are by convention only, model.json will +// # refer to these files by full names +// tensors/ +// 0 # flat storage for tensor data, meta-data about shapes, etc. is +// # in model.json +// 1 +// ... +// # code entries will only exist for modules that have methods attached +// code/ +// archive_name.py # serialized torch script code (python syntax, using +// PythonPrint) archive_name_my_submodule.py # submodules have separate +// files +// +// The PyTorchStreamWriter also ensures additional useful properties for these +// files +// 1. All files are stored uncompressed. +// 2. All files in the archive are aligned to 64 byte boundaries such that +// it is possible to mmap the entire file and get an aligned pointer to +// tensor data. +// 3. We universally write in ZIP64 format for consistency. + +// The PyTorchStreamReader also provides additional properties: +// 1. It can read zip files that are created with common +// zip tools. This means that even though our writer doesn't compress files, +// the reader can still read files that were compressed. +// 2. It provides a getRecordOffset function which returns the offset into the +// raw file where file data lives. If the file was written with +// PyTorchStreamWriter it is guaranteed to be 64 byte aligned. + +// PyTorchReader/Writer handle checking the version number on the archive format +// and ensure that all files are written to a archive_name directory so they +// unzip cleanly. + +// When developing this format we want to pay particular attention to the +// following use cases: +// +// -- Reading -- +// 1) Reading with full random access +// a) Reading with file api's such as fread() +// b) mmaping the file and jumping around the mapped region +// 2) Reading with 1-pass sequential access +// -> A reader will need to build up a data structure of parsed structures +// as it reads +// +// -- Writing -- +// 1) Writing with full random access +// 2) Writing with 1-pass sequential access +// -> We must take care not to require updating values that have already +// been written. We place the variable-length index at the end and do +// not put any indicies into the header to fulfill this constraint. + +// The model.json, which contains all the metadata information, +// should be written as the last file. One reason is that the size of tensor +// data is usually stable. As long as the shape and type of the tensor do not +// change, the size of the data won't change. On the other sied, the size of the +// serialized model is likely to change, so we store it as the last record, and +// we don't need to move previous records when updating the model data. + +// The zip format is sufficiently flexible to handle the above use-case. +// it puts its central directory at the end of the archive and we write +// model.json as the last file when writing after we have accumulated all +// other information. + +namespace caffe2 { +namespace serialize { + +static constexpr const char* kSerializationIdRecordName = ".data/serialization_id"; + +struct MzZipReaderIterWrapper; + +class TORCH_API ChunkRecordIterator { + public: + ~ChunkRecordIterator(); + + // Read at most `chunkSize` into `buf`. Return the number of actual bytes read. + size_t next(void* buf); + + private: + ChunkRecordIterator( + size_t recordSize, + size_t chunkSize, + std::unique_ptr iter); + + const size_t recordSize_; + const size_t chunkSize_; + size_t offset_; + std::unique_ptr iter_; + + friend class PyTorchStreamReader; +}; + +class TORCH_API PyTorchStreamReader final { + public: + explicit PyTorchStreamReader(const std::string& file_name); + explicit PyTorchStreamReader(std::istream* in); + explicit PyTorchStreamReader(std::shared_ptr in); + + // return dataptr, size + std::tuple getRecord(const std::string& name); + // multi-thread getRecord + std::tuple getRecord(const std::string& name, std::vector>& additionalReaders); + // inplace memory writing + size_t getRecord(const std::string& name, void* dst, size_t n); + // inplace memory writing, multi-threads. + // When additionalReaders is empty, the default behavior is call getRecord(name, dst, n) with default reader + // This approach can be used for reading large tensors. + size_t getRecord(const std::string& name, void* dst, size_t n, + std::vector>& additionalReaders); + size_t getRecord( + const std::string& name, + void* dst, + size_t n, + size_t chunk_size, + void* buf, + const std::function& memcpy_func = nullptr); + + // Concurrent reading records with multiple readers. + // additionalReaders are additional clients to access the underlying record at different offsets + // and write to different trunks of buffers. + // If the overall size of the tensor is 10, and size of additionalReader is 2. + // The default thread will read [0,4), the additional reader will read [4,8). + // The default reader will read [8,10). + // The default reader will write to buffer[0,4), the additional reader will write to buffer[4,8), + // the additional reader will write to buffer[8,10). + // When additionalReaders is empty, the default behavior is call getRecord(name) with default reader + // This approach can be used for reading large tensors. + size_t getRecordMultiReaders(const std::string& name, + std::vector>& additionalReaders, + void *dst, size_t n); + + size_t getRecordSize(const std::string& name); + + size_t getRecordOffset(const std::string& name); + bool hasRecord(const std::string& name); + std::vector getAllRecords(); + + ChunkRecordIterator createChunkReaderIter( + const std::string& name, + const size_t recordSize, + const size_t chunkSize); + + ~PyTorchStreamReader(); + uint64_t version() const { + return version_; + } + const std::string& serializationId() { + return serialization_id_; + } + + void setShouldLoadDebugSymbol(bool should_load_debug_symbol) { + load_debug_symbol_ = should_load_debug_symbol; + } + void setAdditionalReaderSizeThreshold(const size_t& size){ + additional_reader_size_threshold_ = size; + } + private: + void init(); + size_t read(uint64_t pos, char* buf, size_t n); + void valid(const char* what, const char* info = ""); + size_t getRecordID(const std::string& name); + + friend size_t + istream_read_func(void* pOpaque, uint64_t file_ofs, void* pBuf, size_t n); + std::unique_ptr ar_; + std::string archive_name_; + std::string archive_name_plus_slash_; + std::shared_ptr in_; + int64_t version_; + std::mutex reader_lock_; + bool load_debug_symbol_ = true; + std::string serialization_id_; + size_t additional_reader_size_threshold_; +}; + +class TORCH_API PyTorchStreamWriter final { + public: + explicit PyTorchStreamWriter(const std::string& archive_name); + explicit PyTorchStreamWriter( + const std::function writer_func); + + void setMinVersion(const uint64_t version); + + void writeRecord( + const std::string& name, + const void* data, + size_t size, + bool compress = false); + void writeEndOfFile(); + + const std::unordered_set& getAllWrittenRecords(); + + bool finalized() const { + return finalized_; + } + + const std::string& archiveName() { + return archive_name_; + } + + const std::string& serializationId() { + return serialization_id_; + } + + ~PyTorchStreamWriter(); + + private: + void setup(const std::string& file_name); + void valid(const char* what, const char* info = ""); + void writeSerializationId(); + size_t current_pos_ = 0; + std::unordered_set files_written_; + std::unique_ptr ar_; + std::string archive_name_; + std::string archive_name_plus_slash_; + std::string padding_; + std::ofstream file_stream_; + std::function writer_func_; + uint64_t combined_uncomp_crc32_ = 0; + std::string serialization_id_; + + // This number will be updated when the model has operators + // that have valid upgraders. + uint64_t version_ = kMinProducedFileFormatVersion; + bool finalized_ = false; + bool err_seen_ = false; + friend size_t ostream_write_func( + void* pOpaque, + uint64_t file_ofs, + const void* pBuf, + size_t n); +}; + +namespace detail { +// Writer-specific constants +constexpr uint64_t kFieldAlignment = 64; + +// Returns a record to be appended to the local user extra data entry in order +// to make data beginning aligned at kFieldAlignment bytes boundary. +size_t getPadding( + size_t cursor, + size_t filename_size, + size_t size, + std::string& padding_buf); +} // namespace detail + +} // namespace serialize +} // namespace caffe2 diff --git a/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/istream_adapter.h b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/istream_adapter.h new file mode 100644 index 0000000000000000000000000000000000000000..680c288a15f2e89d5a3b8b51d8aaddf44f727d19 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/istream_adapter.h @@ -0,0 +1,27 @@ +#pragma once + +#include + +#include "c10/macros/Macros.h" +#include "caffe2/serialize/read_adapter_interface.h" + +namespace caffe2 { +namespace serialize { + +// this is a reader implemented by std::istream +class TORCH_API IStreamAdapter final : public ReadAdapterInterface { + public: + C10_DISABLE_COPY_AND_ASSIGN(IStreamAdapter); + explicit IStreamAdapter(std::istream* istream); + size_t size() const override; + size_t read(uint64_t pos, void* buf, size_t n, const char* what = "") + const override; + ~IStreamAdapter() override; + + private: + std::istream* istream_; + void validate(const char* what) const; +}; + +} // namespace serialize +} // namespace caffe2 diff --git a/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/read_adapter_interface.h b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/read_adapter_interface.h new file mode 100644 index 0000000000000000000000000000000000000000..0a6b5b74a762e7c90b64a6f93717802f658dd164 --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/read_adapter_interface.h @@ -0,0 +1,23 @@ +#pragma once + +#include +#include + +#include "c10/macros/Macros.h" + +namespace caffe2 { +namespace serialize { + +// this is the interface for the (file/stream/memory) reader in +// PyTorchStreamReader. with this interface, we can extend the support +// besides standard istream +class TORCH_API ReadAdapterInterface { + public: + virtual size_t size() const = 0; + virtual size_t read(uint64_t pos, void* buf, size_t n, const char* what = "") + const = 0; + virtual ~ReadAdapterInterface(); +}; + +} // namespace serialize +} // namespace caffe2 diff --git a/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/versions.h b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/versions.h new file mode 100644 index 0000000000000000000000000000000000000000..6e2c27adc8fae8fdb07dcd9dafe72c503c243c2b --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/caffe2/serialize/versions.h @@ -0,0 +1,133 @@ +#pragma once +#include + +namespace caffe2 { +namespace serialize { + +constexpr uint64_t kMinSupportedFileFormatVersion = 0x1L; + +constexpr uint64_t kMaxSupportedFileFormatVersion = 0xAL; + +// Versions (i.e. why was the version number bumped?) + +// Note [Dynamic Versions and torch.jit.save vs. torch.save] +// +// Our versioning scheme has a "produced file format version" which +// describes how an archive is to be read. The version written in an archive +// is at least this current produced file format version, but may be greater +// if it includes certain symbols. We refer to these conditional versions +// as "dynamic," since they are identified at runtime. +// +// Dynamic versioning is useful when an operator's semantics are updated. +// When using torch.jit.save we want those semantics to be preserved. If +// we bumped the produced file format version on every change, however, +// then older versions of PyTorch couldn't read even simple archives, like +// a single tensor, from newer versions of PyTorch. Instead, we +// assign dynamic versions to these changes that override the +// produced file format version as needed. That is, when the semantics +// of torch.div changed it was assigned dynamic version 4, and when +// torch.jit.saving modules that use torch.div those archives also have +// (at least) version 4. This prevents earlier versions of PyTorch +// from accidentally performing the wrong kind of division. Modules +// that don't use torch.div or other operators with dynamic versions +// can write the produced file format version, and these programs will +// run as expected on earlier versions of PyTorch. +// +// While torch.jit.save attempts to preserve operator semantics, +// torch.save does not. torch.save is analogous to pickling Python, so +// a function that uses torch.div will have different behavior if torch.saved +// and torch.loaded across PyTorch versions. From a technical perspective, +// torch.save ignores dynamic versioning. + +// 1. Initial version +// 2. Removed op_version_set version numbers +// 3. Added type tags to pickle serialization of container types +// 4. (Dynamic) Stopped integer division using torch.div +// (a versioned symbol preserves the historic behavior of versions 1--3) +// 5. (Dynamic) Stops torch.full inferring a floating point dtype +// when given bool or integer fill values. +// 6. Write version string to `./data/version` instead of `version`. + +// [12/15/2021] +// kProducedFileFormatVersion is set to 7 from 3 due to a different +// interpretation of what file format version is. +// Whenever there is new upgrader introduced, +// this number should be bumped. +// The reasons that version is bumped in the past: +// 1. aten::div is changed at version 4 +// 2. aten::full is changed at version 5 +// 3. torch.package uses version 6 +// 4. Introduce new upgrader design and set the version number to 7 +// mark this change +// -------------------------------------------------- +// We describe new operator version bump reasons here: +// 1) [01/24/2022] +// We bump the version number to 8 to update aten::linspace +// and aten::linspace.out to error out when steps is not +// provided. (see: https://github.com/pytorch/pytorch/issues/55951) +// 2) [01/30/2022] +// Bump the version number to 9 to update aten::logspace and +// and aten::logspace.out to error out when steps is not +// provided. (see: https://github.com/pytorch/pytorch/issues/55951) +// 3) [02/11/2022] +// Bump the version number to 10 to update aten::gelu and +// and aten::gelu.out to support the new approximate kwarg. +// (see: https://github.com/pytorch/pytorch/pull/61439) +constexpr uint64_t kProducedFileFormatVersion = 0xAL; + +// Absolute minimum version we will write packages. This +// means that every package from now on will always be +// greater than this number. +constexpr uint64_t kMinProducedFileFormatVersion = 0x3L; + +// The version we write when the archive contains bytecode. +// It must be higher or eq to kProducedFileFormatVersion. +// Because torchscript changes is likely introduce bytecode change. +// If kProducedFileFormatVersion is increased, kProducedBytecodeVersion +// should be increased too. The relationship is: +// kMaxSupportedFileFormatVersion >= (most likely ==) kProducedBytecodeVersion +// >= kProducedFileFormatVersion +// If a format change is forward compatible (still readable by older +// executables), we will not increment the version number, to minimize the +// risk of breaking existing clients. TODO: A better way would be to allow +// the caller that creates a model to specify a maximum version that its +// clients can accept. +// Versions: +// 0x1L: Initial version +// 0x2L: (Comment missing) +// 0x3L: (Comment missing) +// 0x4L: (update) Added schema to function tuple. Forward-compatible change. +// 0x5L: (update) Update bytecode is sharing constant tensor files from +// torchscript, and only serialize extra tensors that are not in the +// torchscript constant table. Also update tensor storage schema adapting to +// the unify format, the root key of tensor storage is updated from {index} to +// {the_pointer_value_the_tensor.storage}, for example: +// `140245072983168.storage` Forward-compatibility change. +// 0x6L: Implicit opereator versioning using number of specified argument. +// Refer to the summary of https://github.com/pytorch/pytorch/pull/56845 for +// details. +// 0x7L: Enable support for operators with default arguments plus out +// arguments. Refer. See https://github.com/pytorch/pytorch/pull/63651 for +// details. +// 0x8L: Emit promoted operators as instructions. See +// https://github.com/pytorch/pytorch/pull/71662 for details. +// 0x9L: Change serialization format from pickle to format This version is to +// serve migration. v8 pickle and v9 flatbuffer are the same. Refer to the +// summary of https://github.com/pytorch/pytorch/pull/75201 for more details. +constexpr uint64_t kProducedBytecodeVersion = 0x8L; + +// static_assert( +// kProducedBytecodeVersion >= kProducedFileFormatVersion, +// "kProducedBytecodeVersion must be higher or equal to +// kProducedFileFormatVersion."); + +// Introduce kMinSupportedBytecodeVersion and kMaxSupportedBytecodeVersion +// for limited backward/forward compatibility support of bytecode. If +// kMinSupportedBytecodeVersion <= model_version <= kMaxSupportedBytecodeVersion +// (in loader), we should support this model_version. For example, we provide a +// wrapper to handle an updated operator. +constexpr uint64_t kMinSupportedBytecodeVersion = 0x4L; +constexpr uint64_t kMaxSupportedBytecodeVersion = 0x9L; + +} // namespace serialize +} // namespace caffe2 diff --git a/videollama2/lib/python3.10/site-packages/torch/include/pybind11/attr.h b/videollama2/lib/python3.10/site-packages/torch/include/pybind11/attr.h new file mode 100644 index 0000000000000000000000000000000000000000..1044db94d906ac5fcf6faab6ac7668187314598f --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/pybind11/attr.h @@ -0,0 +1,690 @@ +/* + pybind11/attr.h: Infrastructure for processing custom + type and function attributes + + Copyright (c) 2016 Wenzel Jakob + + All rights reserved. Use of this source code is governed by a + BSD-style license that can be found in the LICENSE file. +*/ + +#pragma once + +#include "detail/common.h" +#include "cast.h" + +#include + +PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) + +/// \addtogroup annotations +/// @{ + +/// Annotation for methods +struct is_method { + handle class_; + explicit is_method(const handle &c) : class_(c) {} +}; + +/// Annotation for setters +struct is_setter {}; + +/// Annotation for operators +struct is_operator {}; + +/// Annotation for classes that cannot be subclassed +struct is_final {}; + +/// Annotation for parent scope +struct scope { + handle value; + explicit scope(const handle &s) : value(s) {} +}; + +/// Annotation for documentation +struct doc { + const char *value; + explicit doc(const char *value) : value(value) {} +}; + +/// Annotation for function names +struct name { + const char *value; + explicit name(const char *value) : value(value) {} +}; + +/// Annotation indicating that a function is an overload associated with a given "sibling" +struct sibling { + handle value; + explicit sibling(const handle &value) : value(value.ptr()) {} +}; + +/// Annotation indicating that a class derives from another given type +template +struct base { + + PYBIND11_DEPRECATED( + "base() was deprecated in favor of specifying 'T' as a template argument to class_") + base() = default; +}; + +/// Keep patient alive while nurse lives +template +struct keep_alive {}; + +/// Annotation indicating that a class is involved in a multiple inheritance relationship +struct multiple_inheritance {}; + +/// Annotation which enables dynamic attributes, i.e. adds `__dict__` to a class +struct dynamic_attr {}; + +/// Annotation which enables the buffer protocol for a type +struct buffer_protocol {}; + +/// Annotation which requests that a special metaclass is created for a type +struct metaclass { + handle value; + + PYBIND11_DEPRECATED("py::metaclass() is no longer required. It's turned on by default now.") + metaclass() = default; + + /// Override pybind11's default metaclass + explicit metaclass(handle value) : value(value) {} +}; + +/// Specifies a custom callback with signature `void (PyHeapTypeObject*)` that +/// may be used to customize the Python type. +/// +/// The callback is invoked immediately before `PyType_Ready`. +/// +/// Note: This is an advanced interface, and uses of it may require changes to +/// work with later versions of pybind11. You may wish to consult the +/// implementation of `make_new_python_type` in `detail/classes.h` to understand +/// the context in which the callback will be run. +struct custom_type_setup { + using callback = std::function; + + explicit custom_type_setup(callback value) : value(std::move(value)) {} + + callback value; +}; + +/// Annotation that marks a class as local to the module: +struct module_local { + const bool value; + constexpr explicit module_local(bool v = true) : value(v) {} +}; + +/// Annotation to mark enums as an arithmetic type +struct arithmetic {}; + +/// Mark a function for addition at the beginning of the existing overload chain instead of the end +struct prepend {}; + +/** \rst + A call policy which places one or more guard variables (``Ts...``) around the function call. + + For example, this definition: + + .. code-block:: cpp + + m.def("foo", foo, py::call_guard()); + + is equivalent to the following pseudocode: + + .. code-block:: cpp + + m.def("foo", [](args...) { + T scope_guard; + return foo(args...); // forwarded arguments + }); + \endrst */ +template +struct call_guard; + +template <> +struct call_guard<> { + using type = detail::void_type; +}; + +template +struct call_guard { + static_assert(std::is_default_constructible::value, + "The guard type must be default constructible"); + + using type = T; +}; + +template +struct call_guard { + struct type { + T guard{}; // Compose multiple guard types with left-to-right default-constructor order + typename call_guard::type next{}; + }; +}; + +/// @} annotations + +PYBIND11_NAMESPACE_BEGIN(detail) +/* Forward declarations */ +enum op_id : int; +enum op_type : int; +struct undefined_t; +template +struct op_; +void keep_alive_impl(size_t Nurse, size_t Patient, function_call &call, handle ret); + +/// Internal data structure which holds metadata about a keyword argument +struct argument_record { + const char *name; ///< Argument name + const char *descr; ///< Human-readable version of the argument value + handle value; ///< Associated Python object + bool convert : 1; ///< True if the argument is allowed to convert when loading + bool none : 1; ///< True if None is allowed when loading + + argument_record(const char *name, const char *descr, handle value, bool convert, bool none) + : name(name), descr(descr), value(value), convert(convert), none(none) {} +}; + +/// Internal data structure which holds metadata about a bound function (signature, overloads, +/// etc.) +struct function_record { + function_record() + : is_constructor(false), is_new_style_constructor(false), is_stateless(false), + is_operator(false), is_method(false), is_setter(false), has_args(false), + has_kwargs(false), prepend(false) {} + + /// Function name + char *name = nullptr; /* why no C++ strings? They generate heavier code.. */ + + // User-specified documentation string + char *doc = nullptr; + + /// Human-readable version of the function signature + char *signature = nullptr; + + /// List of registered keyword arguments + std::vector args; + + /// Pointer to lambda function which converts arguments and performs the actual call + handle (*impl)(function_call &) = nullptr; + + /// Storage for the wrapped function pointer and captured data, if any + void *data[3] = {}; + + /// Pointer to custom destructor for 'data' (if needed) + void (*free_data)(function_record *ptr) = nullptr; + + /// Return value policy associated with this function + return_value_policy policy = return_value_policy::automatic; + + /// True if name == '__init__' + bool is_constructor : 1; + + /// True if this is a new-style `__init__` defined in `detail/init.h` + bool is_new_style_constructor : 1; + + /// True if this is a stateless function pointer + bool is_stateless : 1; + + /// True if this is an operator (__add__), etc. + bool is_operator : 1; + + /// True if this is a method + bool is_method : 1; + + /// True if this is a setter + bool is_setter : 1; + + /// True if the function has a '*args' argument + bool has_args : 1; + + /// True if the function has a '**kwargs' argument + bool has_kwargs : 1; + + /// True if this function is to be inserted at the beginning of the overload resolution chain + bool prepend : 1; + + /// Number of arguments (including py::args and/or py::kwargs, if present) + std::uint16_t nargs; + + /// Number of leading positional arguments, which are terminated by a py::args or py::kwargs + /// argument or by a py::kw_only annotation. + std::uint16_t nargs_pos = 0; + + /// Number of leading arguments (counted in `nargs`) that are positional-only + std::uint16_t nargs_pos_only = 0; + + /// Python method object + PyMethodDef *def = nullptr; + + /// Python handle to the parent scope (a class or a module) + handle scope; + + /// Python handle to the sibling function representing an overload chain + handle sibling; + + /// Pointer to next overload + function_record *next = nullptr; +}; + +/// Special data structure which (temporarily) holds metadata about a bound class +struct type_record { + PYBIND11_NOINLINE type_record() + : multiple_inheritance(false), dynamic_attr(false), buffer_protocol(false), + default_holder(true), module_local(false), is_final(false) {} + + /// Handle to the parent scope + handle scope; + + /// Name of the class + const char *name = nullptr; + + // Pointer to RTTI type_info data structure + const std::type_info *type = nullptr; + + /// How large is the underlying C++ type? + size_t type_size = 0; + + /// What is the alignment of the underlying C++ type? + size_t type_align = 0; + + /// How large is the type's holder? + size_t holder_size = 0; + + /// The global operator new can be overridden with a class-specific variant + void *(*operator_new)(size_t) = nullptr; + + /// Function pointer to class_<..>::init_instance + void (*init_instance)(instance *, const void *) = nullptr; + + /// Function pointer to class_<..>::dealloc + void (*dealloc)(detail::value_and_holder &) = nullptr; + + /// List of base classes of the newly created type + list bases; + + /// Optional docstring + const char *doc = nullptr; + + /// Custom metaclass (optional) + handle metaclass; + + /// Custom type setup. + custom_type_setup::callback custom_type_setup_callback; + + /// Multiple inheritance marker + bool multiple_inheritance : 1; + + /// Does the class manage a __dict__? + bool dynamic_attr : 1; + + /// Does the class implement the buffer protocol? + bool buffer_protocol : 1; + + /// Is the default (unique_ptr) holder type used? + bool default_holder : 1; + + /// Is the class definition local to the module shared object? + bool module_local : 1; + + /// Is the class inheritable from python classes? + bool is_final : 1; + + PYBIND11_NOINLINE void add_base(const std::type_info &base, void *(*caster)(void *) ) { + auto *base_info = detail::get_type_info(base, false); + if (!base_info) { + std::string tname(base.name()); + detail::clean_type_id(tname); + pybind11_fail("generic_type: type \"" + std::string(name) + + "\" referenced unknown base type \"" + tname + "\""); + } + + if (default_holder != base_info->default_holder) { + std::string tname(base.name()); + detail::clean_type_id(tname); + pybind11_fail("generic_type: type \"" + std::string(name) + "\" " + + (default_holder ? "does not have" : "has") + + " a non-default holder type while its base \"" + tname + "\" " + + (base_info->default_holder ? "does not" : "does")); + } + + bases.append((PyObject *) base_info->type); + +#if PY_VERSION_HEX < 0x030B0000 + dynamic_attr |= base_info->type->tp_dictoffset != 0; +#else + dynamic_attr |= (base_info->type->tp_flags & Py_TPFLAGS_MANAGED_DICT) != 0; +#endif + + if (caster) { + base_info->implicit_casts.emplace_back(type, caster); + } + } +}; + +inline function_call::function_call(const function_record &f, handle p) : func(f), parent(p) { + args.reserve(f.nargs); + args_convert.reserve(f.nargs); +} + +/// Tag for a new-style `__init__` defined in `detail/init.h` +struct is_new_style_constructor {}; + +/** + * Partial template specializations to process custom attributes provided to + * cpp_function_ and class_. These are either used to initialize the respective + * fields in the type_record and function_record data structures or executed at + * runtime to deal with custom call policies (e.g. keep_alive). + */ +template +struct process_attribute; + +template +struct process_attribute_default { + /// Default implementation: do nothing + static void init(const T &, function_record *) {} + static void init(const T &, type_record *) {} + static void precall(function_call &) {} + static void postcall(function_call &, handle) {} +}; + +/// Process an attribute specifying the function's name +template <> +struct process_attribute : process_attribute_default { + static void init(const name &n, function_record *r) { r->name = const_cast(n.value); } +}; + +/// Process an attribute specifying the function's docstring +template <> +struct process_attribute : process_attribute_default { + static void init(const doc &n, function_record *r) { r->doc = const_cast(n.value); } +}; + +/// Process an attribute specifying the function's docstring (provided as a C-style string) +template <> +struct process_attribute : process_attribute_default { + static void init(const char *d, function_record *r) { r->doc = const_cast(d); } + static void init(const char *d, type_record *r) { r->doc = d; } +}; +template <> +struct process_attribute : process_attribute {}; + +/// Process an attribute indicating the function's return value policy +template <> +struct process_attribute : process_attribute_default { + static void init(const return_value_policy &p, function_record *r) { r->policy = p; } +}; + +/// Process an attribute which indicates that this is an overloaded function associated with a +/// given sibling +template <> +struct process_attribute : process_attribute_default { + static void init(const sibling &s, function_record *r) { r->sibling = s.value; } +}; + +/// Process an attribute which indicates that this function is a method +template <> +struct process_attribute : process_attribute_default { + static void init(const is_method &s, function_record *r) { + r->is_method = true; + r->scope = s.class_; + } +}; + +/// Process an attribute which indicates that this function is a setter +template <> +struct process_attribute : process_attribute_default { + static void init(const is_setter &, function_record *r) { r->is_setter = true; } +}; + +/// Process an attribute which indicates the parent scope of a method +template <> +struct process_attribute : process_attribute_default { + static void init(const scope &s, function_record *r) { r->scope = s.value; } +}; + +/// Process an attribute which indicates that this function is an operator +template <> +struct process_attribute : process_attribute_default { + static void init(const is_operator &, function_record *r) { r->is_operator = true; } +}; + +template <> +struct process_attribute + : process_attribute_default { + static void init(const is_new_style_constructor &, function_record *r) { + r->is_new_style_constructor = true; + } +}; + +inline void check_kw_only_arg(const arg &a, function_record *r) { + if (r->args.size() > r->nargs_pos && (!a.name || a.name[0] == '\0')) { + pybind11_fail("arg(): cannot specify an unnamed argument after a kw_only() annotation or " + "args() argument"); + } +} + +inline void append_self_arg_if_needed(function_record *r) { + if (r->is_method && r->args.empty()) { + r->args.emplace_back("self", nullptr, handle(), /*convert=*/true, /*none=*/false); + } +} + +/// Process a keyword argument attribute (*without* a default value) +template <> +struct process_attribute : process_attribute_default { + static void init(const arg &a, function_record *r) { + append_self_arg_if_needed(r); + r->args.emplace_back(a.name, nullptr, handle(), !a.flag_noconvert, a.flag_none); + + check_kw_only_arg(a, r); + } +}; + +/// Process a keyword argument attribute (*with* a default value) +template <> +struct process_attribute : process_attribute_default { + static void init(const arg_v &a, function_record *r) { + if (r->is_method && r->args.empty()) { + r->args.emplace_back( + "self", /*descr=*/nullptr, /*parent=*/handle(), /*convert=*/true, /*none=*/false); + } + + if (!a.value) { +#if defined(PYBIND11_DETAILED_ERROR_MESSAGES) + std::string descr("'"); + if (a.name) { + descr += std::string(a.name) + ": "; + } + descr += a.type + "'"; + if (r->is_method) { + if (r->name) { + descr += " in method '" + (std::string) str(r->scope) + "." + + (std::string) r->name + "'"; + } else { + descr += " in method of '" + (std::string) str(r->scope) + "'"; + } + } else if (r->name) { + descr += " in function '" + (std::string) r->name + "'"; + } + pybind11_fail("arg(): could not convert default argument " + descr + + " into a Python object (type not registered yet?)"); +#else + pybind11_fail("arg(): could not convert default argument " + "into a Python object (type not registered yet?). " + "#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for " + "more information."); +#endif + } + r->args.emplace_back(a.name, a.descr, a.value.inc_ref(), !a.flag_noconvert, a.flag_none); + + check_kw_only_arg(a, r); + } +}; + +/// Process a keyword-only-arguments-follow pseudo argument +template <> +struct process_attribute : process_attribute_default { + static void init(const kw_only &, function_record *r) { + append_self_arg_if_needed(r); + if (r->has_args && r->nargs_pos != static_cast(r->args.size())) { + pybind11_fail("Mismatched args() and kw_only(): they must occur at the same relative " + "argument location (or omit kw_only() entirely)"); + } + r->nargs_pos = static_cast(r->args.size()); + } +}; + +/// Process a positional-only-argument maker +template <> +struct process_attribute : process_attribute_default { + static void init(const pos_only &, function_record *r) { + append_self_arg_if_needed(r); + r->nargs_pos_only = static_cast(r->args.size()); + if (r->nargs_pos_only > r->nargs_pos) { + pybind11_fail("pos_only(): cannot follow a py::args() argument"); + } + // It also can't follow a kw_only, but a static_assert in pybind11.h checks that + } +}; + +/// Process a parent class attribute. Single inheritance only (class_ itself already guarantees +/// that) +template +struct process_attribute::value>> + : process_attribute_default { + static void init(const handle &h, type_record *r) { r->bases.append(h); } +}; + +/// Process a parent class attribute (deprecated, does not support multiple inheritance) +template +struct process_attribute> : process_attribute_default> { + static void init(const base &, type_record *r) { r->add_base(typeid(T), nullptr); } +}; + +/// Process a multiple inheritance attribute +template <> +struct process_attribute : process_attribute_default { + static void init(const multiple_inheritance &, type_record *r) { + r->multiple_inheritance = true; + } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const dynamic_attr &, type_record *r) { r->dynamic_attr = true; } +}; + +template <> +struct process_attribute { + static void init(const custom_type_setup &value, type_record *r) { + r->custom_type_setup_callback = value.value; + } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const is_final &, type_record *r) { r->is_final = true; } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const buffer_protocol &, type_record *r) { r->buffer_protocol = true; } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const metaclass &m, type_record *r) { r->metaclass = m.value; } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const module_local &l, type_record *r) { r->module_local = l.value; } +}; + +/// Process a 'prepend' attribute, putting this at the beginning of the overload chain +template <> +struct process_attribute : process_attribute_default { + static void init(const prepend &, function_record *r) { r->prepend = true; } +}; + +/// Process an 'arithmetic' attribute for enums (does nothing here) +template <> +struct process_attribute : process_attribute_default {}; + +template +struct process_attribute> : process_attribute_default> {}; + +/** + * Process a keep_alive call policy -- invokes keep_alive_impl during the + * pre-call handler if both Nurse, Patient != 0 and use the post-call handler + * otherwise + */ +template +struct process_attribute> + : public process_attribute_default> { + template = 0> + static void precall(function_call &call) { + keep_alive_impl(Nurse, Patient, call, handle()); + } + template = 0> + static void postcall(function_call &, handle) {} + template = 0> + static void precall(function_call &) {} + template = 0> + static void postcall(function_call &call, handle ret) { + keep_alive_impl(Nurse, Patient, call, ret); + } +}; + +/// Recursively iterate over variadic template arguments +template +struct process_attributes { + static void init(const Args &...args, function_record *r) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r); + PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r); + using expander = int[]; + (void) expander{ + 0, ((void) process_attribute::type>::init(args, r), 0)...}; + } + static void init(const Args &...args, type_record *r) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r); + PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r); + using expander = int[]; + (void) expander{0, + (process_attribute::type>::init(args, r), 0)...}; + } + static void precall(function_call &call) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call); + using expander = int[]; + (void) expander{0, + (process_attribute::type>::precall(call), 0)...}; + } + static void postcall(function_call &call, handle fn_ret) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call, fn_ret); + PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(fn_ret); + using expander = int[]; + (void) expander{ + 0, (process_attribute::type>::postcall(call, fn_ret), 0)...}; + } +}; + +template +using is_call_guard = is_instantiation; + +/// Extract the ``type`` from the first `call_guard` in `Extras...` (or `void_type` if none found) +template +using extract_guard_t = typename exactly_one_t, Extra...>::type; + +/// Check the number of named arguments at compile time +template ::value...), + size_t self = constexpr_sum(std::is_same::value...)> +constexpr bool expected_num_args(size_t nargs, bool has_args, bool has_kwargs) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(nargs, has_args, has_kwargs); + return named == 0 || (self + named + size_t(has_args) + size_t(has_kwargs)) == nargs; +} + +PYBIND11_NAMESPACE_END(detail) +PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) diff --git a/videollama2/lib/python3.10/site-packages/torch/include/pybind11/buffer_info.h b/videollama2/lib/python3.10/site-packages/torch/include/pybind11/buffer_info.h new file mode 100644 index 0000000000000000000000000000000000000000..b99ee8bef422a6dc65d27e364eded545615e9ccc --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/pybind11/buffer_info.h @@ -0,0 +1,208 @@ +/* + pybind11/buffer_info.h: Python buffer object interface + + Copyright (c) 2016 Wenzel Jakob + + All rights reserved. Use of this source code is governed by a + BSD-style license that can be found in the LICENSE file. +*/ + +#pragma once + +#include "detail/common.h" + +PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) + +PYBIND11_NAMESPACE_BEGIN(detail) + +// Default, C-style strides +inline std::vector c_strides(const std::vector &shape, ssize_t itemsize) { + auto ndim = shape.size(); + std::vector strides(ndim, itemsize); + if (ndim > 0) { + for (size_t i = ndim - 1; i > 0; --i) { + strides[i - 1] = strides[i] * shape[i]; + } + } + return strides; +} + +// F-style strides; default when constructing an array_t with `ExtraFlags & f_style` +inline std::vector f_strides(const std::vector &shape, ssize_t itemsize) { + auto ndim = shape.size(); + std::vector strides(ndim, itemsize); + for (size_t i = 1; i < ndim; ++i) { + strides[i] = strides[i - 1] * shape[i - 1]; + } + return strides; +} + +template +struct compare_buffer_info; + +PYBIND11_NAMESPACE_END(detail) + +/// Information record describing a Python buffer object +struct buffer_info { + void *ptr = nullptr; // Pointer to the underlying storage + ssize_t itemsize = 0; // Size of individual items in bytes + ssize_t size = 0; // Total number of entries + std::string format; // For homogeneous buffers, this should be set to + // format_descriptor::format() + ssize_t ndim = 0; // Number of dimensions + std::vector shape; // Shape of the tensor (1 entry per dimension) + std::vector strides; // Number of bytes between adjacent entries + // (for each per dimension) + bool readonly = false; // flag to indicate if the underlying storage may be written to + + buffer_info() = default; + + buffer_info(void *ptr, + ssize_t itemsize, + const std::string &format, + ssize_t ndim, + detail::any_container shape_in, + detail::any_container strides_in, + bool readonly = false) + : ptr(ptr), itemsize(itemsize), size(1), format(format), ndim(ndim), + shape(std::move(shape_in)), strides(std::move(strides_in)), readonly(readonly) { + if (ndim != (ssize_t) shape.size() || ndim != (ssize_t) strides.size()) { + pybind11_fail("buffer_info: ndim doesn't match shape and/or strides length"); + } + for (size_t i = 0; i < (size_t) ndim; ++i) { + size *= shape[i]; + } + } + + template + buffer_info(T *ptr, + detail::any_container shape_in, + detail::any_container strides_in, + bool readonly = false) + : buffer_info(private_ctr_tag(), + ptr, + sizeof(T), + format_descriptor::format(), + static_cast(shape_in->size()), + std::move(shape_in), + std::move(strides_in), + readonly) {} + + buffer_info(void *ptr, + ssize_t itemsize, + const std::string &format, + ssize_t size, + bool readonly = false) + : buffer_info(ptr, itemsize, format, 1, {size}, {itemsize}, readonly) {} + + template + buffer_info(T *ptr, ssize_t size, bool readonly = false) + : buffer_info(ptr, sizeof(T), format_descriptor::format(), size, readonly) {} + + template + buffer_info(const T *ptr, ssize_t size, bool readonly = true) + : buffer_info( + const_cast(ptr), sizeof(T), format_descriptor::format(), size, readonly) {} + + explicit buffer_info(Py_buffer *view, bool ownview = true) + : buffer_info( + view->buf, + view->itemsize, + view->format, + view->ndim, + {view->shape, view->shape + view->ndim}, + /* Though buffer::request() requests PyBUF_STRIDES, ctypes objects + * ignore this flag and return a view with NULL strides. + * When strides are NULL, build them manually. */ + view->strides + ? std::vector(view->strides, view->strides + view->ndim) + : detail::c_strides({view->shape, view->shape + view->ndim}, view->itemsize), + (view->readonly != 0)) { + // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer) + this->m_view = view; + // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer) + this->ownview = ownview; + } + + buffer_info(const buffer_info &) = delete; + buffer_info &operator=(const buffer_info &) = delete; + + buffer_info(buffer_info &&other) noexcept { (*this) = std::move(other); } + + buffer_info &operator=(buffer_info &&rhs) noexcept { + ptr = rhs.ptr; + itemsize = rhs.itemsize; + size = rhs.size; + format = std::move(rhs.format); + ndim = rhs.ndim; + shape = std::move(rhs.shape); + strides = std::move(rhs.strides); + std::swap(m_view, rhs.m_view); + std::swap(ownview, rhs.ownview); + readonly = rhs.readonly; + return *this; + } + + ~buffer_info() { + if (m_view && ownview) { + PyBuffer_Release(m_view); + delete m_view; + } + } + + Py_buffer *view() const { return m_view; } + Py_buffer *&view() { return m_view; } + + /* True if the buffer item type is equivalent to `T`. */ + // To define "equivalent" by example: + // `buffer_info::item_type_is_equivalent_to(b)` and + // `buffer_info::item_type_is_equivalent_to(b)` may both be true + // on some platforms, but `int` and `unsigned` will never be equivalent. + // For the ground truth, please inspect `detail::compare_buffer_info<>`. + template + bool item_type_is_equivalent_to() const { + return detail::compare_buffer_info::compare(*this); + } + +private: + struct private_ctr_tag {}; + + buffer_info(private_ctr_tag, + void *ptr, + ssize_t itemsize, + const std::string &format, + ssize_t ndim, + detail::any_container &&shape_in, + detail::any_container &&strides_in, + bool readonly) + : buffer_info( + ptr, itemsize, format, ndim, std::move(shape_in), std::move(strides_in), readonly) {} + + Py_buffer *m_view = nullptr; + bool ownview = false; +}; + +PYBIND11_NAMESPACE_BEGIN(detail) + +template +struct compare_buffer_info { + static bool compare(const buffer_info &b) { + // NOLINTNEXTLINE(bugprone-sizeof-expression) Needed for `PyObject *` + return b.format == format_descriptor::format() && b.itemsize == (ssize_t) sizeof(T); + } +}; + +template +struct compare_buffer_info::value>> { + static bool compare(const buffer_info &b) { + return (size_t) b.itemsize == sizeof(T) + && (b.format == format_descriptor::value + || ((sizeof(T) == sizeof(long)) + && b.format == (std::is_unsigned::value ? "L" : "l")) + || ((sizeof(T) == sizeof(size_t)) + && b.format == (std::is_unsigned::value ? "N" : "n"))); + } +}; + +PYBIND11_NAMESPACE_END(detail) +PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) diff --git a/videollama2/lib/python3.10/site-packages/torch/include/pybind11/cast.h b/videollama2/lib/python3.10/site-packages/torch/include/pybind11/cast.h new file mode 100644 index 0000000000000000000000000000000000000000..db39341180e3e231e8da7d4c74049a5d931246ea --- /dev/null +++ b/videollama2/lib/python3.10/site-packages/torch/include/pybind11/cast.h @@ -0,0 +1,1704 @@ +/* + pybind11/cast.h: Partial template specializations to cast between + C++ and Python types + + Copyright (c) 2016 Wenzel Jakob + + All rights reserved. Use of this source code is governed by a + BSD-style license that can be found in the LICENSE file. +*/ + +#pragma once + +#include "detail/common.h" +#include "detail/descr.h" +#include "detail/type_caster_base.h" +#include "detail/typeid.h" +#include "pytypes.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) + +PYBIND11_WARNING_DISABLE_MSVC(4127) + +PYBIND11_NAMESPACE_BEGIN(detail) + +template +class type_caster : public type_caster_base {}; +template +using make_caster = type_caster>; + +// Shortcut for calling a caster's `cast_op_type` cast operator for casting a type_caster to a T +template +typename make_caster::template cast_op_type cast_op(make_caster &caster) { + return caster.operator typename make_caster::template cast_op_type(); +} +template +typename make_caster::template cast_op_type::type> +cast_op(make_caster &&caster) { + return std::move(caster).operator typename make_caster:: + template cast_op_type::type>(); +} + +template +class type_caster> { +private: + using caster_t = make_caster; + caster_t subcaster; + using reference_t = type &; + using subcaster_cast_op_type = typename caster_t::template cast_op_type; + + static_assert( + std::is_same::type &, subcaster_cast_op_type>::value + || std::is_same::value, + "std::reference_wrapper caster requires T to have a caster with an " + "`operator T &()` or `operator const T &()`"); + +public: + bool load(handle src, bool convert) { return subcaster.load(src, convert); } + static constexpr auto name = caster_t::name; + static handle + cast(const std::reference_wrapper &src, return_value_policy policy, handle parent) { + // It is definitely wrong to take ownership of this pointer, so mask that rvp + if (policy == return_value_policy::take_ownership + || policy == return_value_policy::automatic) { + policy = return_value_policy::automatic_reference; + } + return caster_t::cast(&src.get(), policy, parent); + } + template + using cast_op_type = std::reference_wrapper; + explicit operator std::reference_wrapper() { return cast_op(subcaster); } +}; + +#define PYBIND11_TYPE_CASTER(type, py_name) \ +protected: \ + type value; \ + \ +public: \ + static constexpr auto name = py_name; \ + template >::value, \ + int> \ + = 0> \ + static ::pybind11::handle cast( \ + T_ *src, ::pybind11::return_value_policy policy, ::pybind11::handle parent) { \ + if (!src) \ + return ::pybind11::none().release(); \ + if (policy == ::pybind11::return_value_policy::take_ownership) { \ + auto h = cast(std::move(*src), policy, parent); \ + delete src; \ + return h; \ + } \ + return cast(*src, policy, parent); \ + } \ + operator type *() { return &value; } /* NOLINT(bugprone-macro-parentheses) */ \ + operator type &() { return value; } /* NOLINT(bugprone-macro-parentheses) */ \ + operator type &&() && { return std::move(value); } /* NOLINT(bugprone-macro-parentheses) */ \ + template \ + using cast_op_type = ::pybind11::detail::movable_cast_op_type + +template +using is_std_char_type = any_of, /* std::string */ +#if defined(PYBIND11_HAS_U8STRING) + std::is_same, /* std::u8string */ +#endif + std::is_same, /* std::u16string */ + std::is_same, /* std::u32string */ + std::is_same /* std::wstring */ + >; + +template +struct type_caster::value && !is_std_char_type::value>> { + using _py_type_0 = conditional_t; + using _py_type_1 = conditional_t::value, + _py_type_0, + typename std::make_unsigned<_py_type_0>::type>; + using py_type = conditional_t::value, double, _py_type_1>; + +public: + bool load(handle src, bool convert) { + py_type py_value; + + if (!src) { + return false; + } + +#if !defined(PYPY_VERSION) + auto index_check = [](PyObject *o) { return PyIndex_Check(o); }; +#else + // In PyPy 7.3.3, `PyIndex_Check` is implemented by calling `__index__`, + // while CPython only considers the existence of `nb_index`/`__index__`. + auto index_check = [](PyObject *o) { return hasattr(o, "__index__"); }; +#endif + + if (std::is_floating_point::value) { + if (convert || PyFloat_Check(src.ptr())) { + py_value = (py_type) PyFloat_AsDouble(src.ptr()); + } else { + return false; + } + } else if (PyFloat_Check(src.ptr()) + || (!convert && !PYBIND11_LONG_CHECK(src.ptr()) && !index_check(src.ptr()))) { + return false; + } else { + handle src_or_index = src; + // PyPy: 7.3.7's 3.8 does not implement PyLong_*'s __index__ calls. +#if PY_VERSION_HEX < 0x03080000 || defined(PYPY_VERSION) + object index; + if (!PYBIND11_LONG_CHECK(src.ptr())) { // So: index_check(src.ptr()) + index = reinterpret_steal(PyNumber_Index(src.ptr())); + if (!index) { + PyErr_Clear(); + if (!convert) + return false; + } else { + src_or_index = index; + } + } +#endif + if (std::is_unsigned::value) { + py_value = as_unsigned(src_or_index.ptr()); + } else { // signed integer: + py_value = sizeof(T) <= sizeof(long) + ? (py_type) PyLong_AsLong(src_or_index.ptr()) + : (py_type) PYBIND11_LONG_AS_LONGLONG(src_or_index.ptr()); + } + } + + // Python API reported an error + bool py_err = py_value == (py_type) -1 && PyErr_Occurred(); + + // Check to see if the conversion is valid (integers should match exactly) + // Signed/unsigned checks happen elsewhere + if (py_err + || (std::is_integral::value && sizeof(py_type) != sizeof(T) + && py_value != (py_type) (T) py_value)) { + PyErr_Clear(); + if (py_err && convert && (PyNumber_Check(src.ptr()) != 0)) { + auto tmp = reinterpret_steal(std::is_floating_point::value + ? PyNumber_Float(src.ptr()) + : PyNumber_Long(src.ptr())); + PyErr_Clear(); + return load(tmp, false); + } + return false; + } + + value = (T) py_value; + return true; + } + + template + static typename std::enable_if::value, handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PyFloat_FromDouble((double) src); + } + + template + static typename std::enable_if::value && std::is_signed::value + && (sizeof(U) <= sizeof(long)), + handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PYBIND11_LONG_FROM_SIGNED((long) src); + } + + template + static typename std::enable_if::value && std::is_unsigned::value + && (sizeof(U) <= sizeof(unsigned long)), + handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PYBIND11_LONG_FROM_UNSIGNED((unsigned long) src); + } + + template + static typename std::enable_if::value && std::is_signed::value + && (sizeof(U) > sizeof(long)), + handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PyLong_FromLongLong((long long) src); + } + + template + static typename std::enable_if::value && std::is_unsigned::value + && (sizeof(U) > sizeof(unsigned long)), + handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PyLong_FromUnsignedLongLong((unsigned long long) src); + } + + PYBIND11_TYPE_CASTER(T, const_name::value>("int", "float")); +}; + +template +struct void_caster { +public: + bool load(handle src, bool) { + if (src && src.is_none()) { + return true; + } + return false; + } + static handle cast(T, return_value_policy /* policy */, handle /* parent */) { + return none().release(); + } + PYBIND11_TYPE_CASTER(T, const_name("None")); +}; + +template <> +class type_caster : public void_caster {}; + +template <> +class type_caster : public type_caster { +public: + using type_caster::cast; + + bool load(handle h, bool) { + if (!h) { + return false; + } + if (h.is_none()) { + value = nullptr; + return true; + } + + /* Check if this is a capsule */ + if (isinstance(h)) { + value = reinterpret_borrow(h); + return true; + } + + /* Check if this is a C++ type */ + const auto &bases = all_type_info((PyTypeObject *) type::handle_of(h).ptr()); + if (bases.size() == 1) { // Only allowing loading from a single-value type + value = values_and_holders(reinterpret_cast(h.ptr())).begin()->value_ptr(); + return true; + } + + /* Fail */ + return false; + } + + static handle cast(const void *ptr, return_value_policy /* policy */, handle /* parent */) { + if (ptr) { + return capsule(ptr).release(); + } + return none().release(); + } + + template + using cast_op_type = void *&; + explicit operator void *&() { return value; } + static constexpr auto name = const_name("capsule"); + +private: + void *value = nullptr; +}; + +template <> +class type_caster : public void_caster {}; + +template <> +class type_caster { +public: + bool load(handle src, bool convert) { + if (!src) { + return false; + } + if (src.ptr() == Py_True) { + value = true; + return true; + } + if (src.ptr() == Py_False) { + value = false; + return true; + } + if (convert || (std::strcmp("numpy.bool_", Py_TYPE(src.ptr())->tp_name) == 0)) { + // (allow non-implicit conversion for numpy booleans) + + Py_ssize_t res = -1; + if (src.is_none()) { + res = 0; // None is implicitly converted to False + } +#if defined(PYPY_VERSION) + // On PyPy, check that "__bool__" attr exists + else if (hasattr(src, PYBIND11_BOOL_ATTR)) { + res = PyObject_IsTrue(src.ptr()); + } +#else + // Alternate approach for CPython: this does the same as the above, but optimized + // using the CPython API so as to avoid an unneeded attribute lookup. + else if (auto *tp_as_number = src.ptr()->ob_type->tp_as_number) { + if (PYBIND11_NB_BOOL(tp_as_number)) { + res = (*PYBIND11_NB_BOOL(tp_as_number))(src.ptr()); + } + } +#endif + if (res == 0 || res == 1) { + value = (res != 0); + return true; + } + PyErr_Clear(); + } + return false; + } + static handle cast(bool src, return_value_policy /* policy */, handle /* parent */) { + return handle(src ? Py_True : Py_False).inc_ref(); + } + PYBIND11_TYPE_CASTER(bool, const_name("bool")); +}; + +// Helper class for UTF-{8,16,32} C++ stl strings: +template +struct string_caster { + using CharT = typename StringType::value_type; + + // Simplify life by being able to assume standard char sizes (the standard only guarantees + // minimums, but Python requires exact sizes) + static_assert(!std::is_same::value || sizeof(CharT) == 1, + "Unsupported char size != 1"); +#if defined(PYBIND11_HAS_U8STRING) + static_assert(!std::is_same::value || sizeof(CharT) == 1, + "Unsupported char8_t size != 1"); +#endif + static_assert(!std::is_same::value || sizeof(CharT) == 2, + "Unsupported char16_t size != 2"); + static_assert(!std::is_same::value || sizeof(CharT) == 4, + "Unsupported char32_t size != 4"); + // wchar_t can be either 16 bits (Windows) or 32 (everywhere else) + static_assert(!std::is_same::value || sizeof(CharT) == 2 || sizeof(CharT) == 4, + "Unsupported wchar_t size != 2/4"); + static constexpr size_t UTF_N = 8 * sizeof(CharT); + + bool load(handle src, bool) { + handle load_src = src; + if (!src) { + return false; + } + if (!PyUnicode_Check(load_src.ptr())) { + return load_raw(load_src); + } + + // For UTF-8 we avoid the need for a temporary `bytes` object by using + // `PyUnicode_AsUTF8AndSize`. + if (UTF_N == 8) { + Py_ssize_t size = -1; + const auto *buffer + = reinterpret_cast(PyUnicode_AsUTF8AndSize(load_src.ptr(), &size)); + if (!buffer) { + PyErr_Clear(); + return false; + } + value = StringType(buffer, static_cast(size)); + return true; + } + + auto utfNbytes + = reinterpret_steal(PyUnicode_AsEncodedString(load_src.ptr(), + UTF_N == 8 ? "utf-8" + : UTF_N == 16 ? "utf-16" + : "utf-32", + nullptr)); + if (!utfNbytes) { + PyErr_Clear(); + return false; + } + + const auto *buffer + = reinterpret_cast(PYBIND11_BYTES_AS_STRING(utfNbytes.ptr())); + size_t length = (size_t) PYBIND11_BYTES_SIZE(utfNbytes.ptr()) / sizeof(CharT); + // Skip BOM for UTF-16/32 + if (UTF_N > 8) { + buffer++; + length--; + } + value = StringType(buffer, length); + + // If we're loading a string_view we need to keep the encoded Python object alive: + if (IsView) { + loader_life_support::add_patient(utfNbytes); + } + + return true; + } + + static handle + cast(const StringType &src, return_value_policy /* policy */, handle /* parent */) { + const char *buffer = reinterpret_cast(src.data()); + auto nbytes = ssize_t(src.size() * sizeof(CharT)); + handle s = decode_utfN(buffer, nbytes); + if (!s) { + throw error_already_set(); + } + return s; + } + + PYBIND11_TYPE_CASTER(StringType, const_name(PYBIND11_STRING_NAME)); + +private: + static handle decode_utfN(const char *buffer, ssize_t nbytes) { +#if !defined(PYPY_VERSION) + return UTF_N == 8 ? PyUnicode_DecodeUTF8(buffer, nbytes, nullptr) + : UTF_N == 16 ? PyUnicode_DecodeUTF16(buffer, nbytes, nullptr, nullptr) + : PyUnicode_DecodeUTF32(buffer, nbytes, nullptr, nullptr); +#else + // PyPy segfaults when on PyUnicode_DecodeUTF16 (and possibly on PyUnicode_DecodeUTF32 as + // well), so bypass the whole thing by just passing the encoding as a string value, which + // works properly: + return PyUnicode_Decode(buffer, + nbytes, + UTF_N == 8 ? "utf-8" + : UTF_N == 16 ? "utf-16" + : "utf-32", + nullptr); +#endif + } + + // When loading into a std::string or char*, accept a bytes/bytearray object as-is (i.e. + // without any encoding/decoding attempt). For other C++ char sizes this is a no-op. + // which supports loading a unicode from a str, doesn't take this path. + template + bool load_raw(enable_if_t::value, handle> src) { + if (PYBIND11_BYTES_CHECK(src.ptr())) { + // We were passed raw bytes; accept it into a std::string or char* + // without any encoding attempt. + const char *bytes = PYBIND11_BYTES_AS_STRING(src.ptr()); + if (!bytes) { + pybind11_fail("Unexpected PYBIND11_BYTES_AS_STRING() failure."); + } + value = StringType(bytes, (size_t) PYBIND11_BYTES_SIZE(src.ptr())); + return true; + } + if (PyByteArray_Check(src.ptr())) { + // We were passed a bytearray; accept it into a std::string or char* + // without any encoding attempt. + const char *bytearray = PyByteArray_AsString(src.ptr()); + if (!bytearray) { + pybind11_fail("Unexpected PyByteArray_AsString() failure."); + } + value = StringType(bytearray, (size_t) PyByteArray_Size(src.ptr())); + return true; + } + + return false; + } + + template + bool load_raw(enable_if_t::value, handle>) { + return false; + } +}; + +template +struct type_caster, + enable_if_t::value>> + : string_caster> {}; + +#ifdef PYBIND11_HAS_STRING_VIEW +template +struct type_caster, + enable_if_t::value>> + : string_caster, true> {}; +#endif + +// Type caster for C-style strings. We basically use a std::string type caster, but also add the +// ability to use None as a nullptr char* (which the string caster doesn't allow). +template +struct type_caster::value>> { + using StringType = std::basic_string; + using StringCaster = make_caster; + StringCaster str_caster; + bool none = false; + CharT one_char = 0; + +public: + bool load(handle src, bool convert) { + if (!src) { + return false; + } + if (src.is_none()) { + // Defer accepting None to other overloads (if we aren't in convert mode): + if (!convert) { + return false; + } + none = true; + return true; + } + return str_caster.load(src, convert); + } + + static handle cast(const CharT *src, return_value_policy policy, handle parent) { + if (src == nullptr) { + return pybind11::none().release(); + } + return StringCaster::cast(StringType(src), policy, parent); + } + + static handle cast(CharT src, return_value_policy policy, handle parent) { + if (std::is_same::value) { + handle s = PyUnicode_DecodeLatin1((const char *) &src, 1, nullptr); + if (!s) { + throw error_already_set(); + } + return s; + } + return StringCaster::cast(StringType(1, src), policy, parent); + } + + explicit operator CharT *() { + return none ? nullptr : const_cast(static_cast(str_caster).c_str()); + } + explicit operator CharT &() { + if (none) { + throw value_error("Cannot convert None to a character"); + } + + auto &value = static_cast(str_caster); + size_t str_len = value.size(); + if (str_len == 0) { + throw value_error("Cannot convert empty string to a character"); + } + + // If we're in UTF-8 mode, we have two possible failures: one for a unicode character that + // is too high, and one for multiple unicode characters (caught later), so we need to + // figure out how long the first encoded character is in bytes to distinguish between these + // two errors. We also allow want to allow unicode characters U+0080 through U+00FF, as + // those can fit into a single char value. + if (StringCaster::UTF_N == 8 && str_len > 1 && str_len <= 4) { + auto v0 = static_cast(value[0]); + // low bits only: 0-127 + // 0b110xxxxx - start of 2-byte sequence + // 0b1110xxxx - start of 3-byte sequence + // 0b11110xxx - start of 4-byte sequence + size_t char0_bytes = (v0 & 0x80) == 0 ? 1 + : (v0 & 0xE0) == 0xC0 ? 2 + : (v0 & 0xF0) == 0xE0 ? 3 + : 4; + + if (char0_bytes == str_len) { + // If we have a 128-255 value, we can decode it into a single char: + if (char0_bytes == 2 && (v0 & 0xFC) == 0xC0) { // 0x110000xx 0x10xxxxxx + one_char = static_cast(((v0 & 3) << 6) + + (static_cast(value[1]) & 0x3F)); + return one_char; + } + // Otherwise we have a single character, but it's > U+00FF + throw value_error("Character code point not in range(0x100)"); + } + } + + // UTF-16 is much easier: we can only have a surrogate pair for values above U+FFFF, thus a + // surrogate pair with total length 2 instantly indicates a range error (but not a "your + // string was too long" error). + else if (StringCaster::UTF_N == 16 && str_len == 2) { + one_char = static_cast(value[0]); + if (one_char >= 0xD800 && one_char < 0xE000) { + throw value_error("Character code point not in range(0x10000)"); + } + } + + if (str_len != 1) { + throw value_error("Expected a character, but multi-character string found"); + } + + one_char = value[0]; + return one_char; + } + + static constexpr auto name = const_name(PYBIND11_STRING_NAME); + template + using cast_op_type = pybind11::detail::cast_op_type<_T>; +}; + +// Base implementation for std::tuple and std::pair +template