from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Callable, Sequence
from math import log2, ceil

import torch
from numpy.typing import DTypeLike

from .constants import GGML_QUANT_SIZES, GGMLQuantizationType, QK_K
from .lazy import LazyNumpyTensor

import numpy as np


quick_split = lambda x, p: torch.split(x, p + [x.shape[1] - sum(p)], dim=1)


def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]:
    block_size, type_size = GGML_QUANT_SIZES[quant_type]
    if shape[-1] % block_size != 0:
        raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})")
    return (*shape[:-1], shape[-1] // block_size * type_size)


def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]:
    block_size, type_size = GGML_QUANT_SIZES[quant_type]
    if shape[-1] % type_size != 0:
        raise ValueError(f"Quantized tensor bytes per row ({shape[-1]}) is not a multiple of {quant_type.name} type size ({type_size})")
    return (*shape[:-1], shape[-1] // type_size * block_size)


# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
    rows = arr.reshape((-1, arr.shape[-1]))
    osize = 1
    for dim in oshape:
        osize *= dim
    out = np.empty(shape=osize, dtype=otype)
    # compute over groups of 16 rows (arbitrary, but seems good for performance)
    n_groups = (rows.shape[0] // 16) or 1
    np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out)
    return out.reshape(oshape)


# round away from zero
# ref: https://stackoverflow.com/a/59143326/22827863
def np_roundf(n: np.ndarray) -> np.ndarray:
    a = abs(n)
    floored = np.floor(a)
    b = floored + np.floor(2 * (a - floored))
    return np.sign(n) * b


class QuantError(Exception): ...


_type_traits: dict[GGMLQuantizationType, type[__Quant]] = {}


def quantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
    if qtype == GGMLQuantizationType.F32:
        return data.astype(np.float32, copy=False)
    elif qtype == GGMLQuantizationType.F16:
        return data.astype(np.float16, copy=False)
    elif (q := _type_traits.get(qtype)) is not None:
        return q.quantize(data)
    else:
        raise NotImplementedError(f"Quantization for {qtype.name} is not yet implemented")


def dequantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray:
    if qtype == GGMLQuantizationType.F32:
        return data.view(np.float32)
    elif qtype == GGMLQuantizationType.F16:
        return data.view(np.float16).astype(np.float32)
    elif (q := _type_traits.get(qtype)) is not None:
        return q.dequantize(data)
    else:
        raise NotImplementedError(f"Dequantization for {qtype.name} is not yet implemented")


class __Quant(ABC):
    qtype: GGMLQuantizationType
    block_size: int
    type_size: int

    grid: np.ndarray[Any, np.dtype[np.float32]] | None = None
    grid_shape: tuple[int, int] = (0, 0)
    grid_map: tuple[int | float, ...] = ()
    grid_hex: bytes | None = None

    def __init__(self):
        return TypeError("Quant conversion classes can't have instances")

    def __init_subclass__(cls, qtype: GGMLQuantizationType) -> None:
        cls.qtype = qtype
        cls.block_size, cls.type_size = GGML_QUANT_SIZES[qtype]
        cls.__quantize_lazy = LazyNumpyTensor._wrap_fn(
            cls.__quantize_array,
            meta_noop=(np.uint8, cls.__shape_to_bytes)
        )
        cls.__dequantize_lazy = LazyNumpyTensor._wrap_fn(
            cls.__dequantize_array,
            meta_noop=(np.float32, cls.__shape_from_bytes)
        )
        assert qtype not in _type_traits
        _type_traits[qtype] = cls

    @classmethod
    def init_grid(cls):
        if cls.grid is not None or cls.grid_hex is None:
            return

        bits_per_elem = ceil(log2(len(cls.grid_map)))
        assert bits_per_elem != 0, cls.qtype.name
        elems_per_byte = 8 // bits_per_elem

        grid = np.frombuffer(cls.grid_hex, dtype=np.uint8)
        # decode hexadecimal chars from grid
        grid = grid.reshape((-1, 2))
        grid = (np.where(grid > 0x40, grid + 9, grid) & 0x0F) << np.array([4, 0], dtype=np.uint8).reshape((1, 2))
        grid = grid[..., 0] | grid[..., 1]
        # unpack the grid values
        grid = grid.reshape((-1, 1)) >> np.array([i for i in range(0, 8, 8 // elems_per_byte)], dtype=np.uint8).reshape((1, elems_per_byte))
        grid = (grid & ((1 << bits_per_elem) - 1)).reshape((-1, 1))
        grid_map = np.array(cls.grid_map, dtype=np.float32).reshape((1, -1))
        grid = np.take_along_axis(grid_map, grid, axis=-1)
        cls.grid = grid.reshape((1, 1, *cls.grid_shape))

    @classmethod
    def quantize_pytorch(cls, data: torch.Tensor, original_shape) -> torch.Tensor:
        # Copyright Forge 2024, AGPL V3 + CC-BY SA

        original_shape = [x for x in original_shape]
        original_shape[-1] = -1
        original_shape = tuple(original_shape)

        block_size, type_size = GGML_QUANT_SIZES[cls.qtype]
        blocks = data.reshape(-1, block_size)
        blocks = cls.quantize_blocks_pytorch(blocks, block_size, type_size)
        return blocks.reshape(original_shape)

    @classmethod
    def dequantize_pytorch(cls, data: torch.Tensor, original_shape) -> torch.Tensor:
        # (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
        block_size, type_size = GGML_QUANT_SIZES[cls.qtype]
        rows = data.reshape((-1, data.shape[-1])).view(torch.uint8)
        n_blocks = rows.numel() // type_size
        blocks = rows.reshape((n_blocks, type_size))
        blocks = cls.dequantize_blocks_pytorch(blocks, block_size, type_size)
        return blocks.reshape(original_shape)

    @classmethod
    @abstractmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        raise NotImplementedError

    @classmethod
    @abstractmethod
    def quantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        raise NotImplementedError('Low bit LoRA for this data type is not implemented yet. Please select "Automatic (fp16 LoRA)" in "Diffusion in Low Bits" (on the top line of this page) to use this LoRA.')

    @classmethod
    @abstractmethod
    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        raise NotImplementedError

    @classmethod
    @abstractmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        raise NotImplementedError

    @classmethod
    def quantize_rows(cls, rows: np.ndarray) -> np.ndarray:
        rows = rows.astype(np.float32, copy=False)
        shape = rows.shape
        n_blocks = rows.size // cls.block_size
        blocks = rows.reshape((n_blocks, cls.block_size))
        blocks = cls.quantize_blocks(blocks)
        assert blocks.dtype == np.uint8
        assert blocks.shape[-1] == cls.type_size
        return blocks.reshape(cls.__shape_to_bytes(shape))

    @classmethod
    def dequantize_rows(cls, rows: np.ndarray) -> np.ndarray:
        rows = rows.view(np.uint8)
        shape = rows.shape
        n_blocks = rows.size // cls.type_size
        blocks = rows.reshape((n_blocks, cls.type_size))
        blocks = cls.dequantize_blocks(blocks)
        assert blocks.dtype == np.float32
        assert blocks.shape[-1] == cls.block_size
        return blocks.reshape(cls.__shape_from_bytes(shape))

    @classmethod
    def __shape_to_bytes(cls, shape: Sequence[int]):
        return quant_shape_to_byte_shape(shape, cls.qtype)

    @classmethod
    def __shape_from_bytes(cls, shape: Sequence[int]):
        return quant_shape_from_byte_shape(shape, cls.qtype)

    @classmethod
    def __quantize_array(cls, array: np.ndarray) -> np.ndarray:
        return _apply_over_grouped_rows(cls.quantize_rows, arr=array, otype=np.uint8, oshape=cls.__shape_to_bytes(array.shape))

    @classmethod
    def __dequantize_array(cls, array: np.ndarray) -> np.ndarray:
        cls.init_grid()
        return _apply_over_grouped_rows(cls.dequantize_rows, arr=array, otype=np.float32, oshape=cls.__shape_from_bytes(array.shape))

    @classmethod
    def __quantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any:
        pass

    @classmethod
    def __dequantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any:
        pass

    @classmethod
    def can_quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> bool:
        return tensor.shape[-1] % cls.block_size == 0

    @classmethod
    def quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray:
        if not cls.can_quantize(tensor):
            raise QuantError(f"Can't quantize tensor with shape {tensor.shape} to {cls.qtype.name}")
        if isinstance(tensor, LazyNumpyTensor):
            return cls.__quantize_lazy(tensor)
        else:
            return cls.__quantize_array(tensor)

    @classmethod
    def dequantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray:
        if isinstance(tensor, LazyNumpyTensor):
            return cls.__dequantize_lazy(tensor)
        else:
            return cls.__dequantize_array(tensor)


class BF16(__Quant, qtype=GGMLQuantizationType.BF16):
    @classmethod
    # same as ggml_compute_fp32_to_bf16 in ggml-impl.h
    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n = blocks.view(np.uint32)
        # force nan to quiet
        n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n)
        # round to nearest even
        n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16
        return n.astype(np.uint16).view(np.uint8)

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        return (blocks.view(np.int16).astype(np.int32) << 16).view(np.float32)


class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0):
    @classmethod
    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        imax = abs(blocks).argmax(axis=-1, keepdims=True)
        max = np.take_along_axis(blocks, imax, axis=-1)

        d = max / -8
        with np.errstate(divide="ignore"):
            id = np.where(d == 0, 0, 1 / d)
        # FIXME: Q4_0's reference rounding is cursed and depends on FMA
        qs = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(8.5), dtype=np.float32).astype(np.uint8).clip(0, 15)

        qs = qs.reshape((n_blocks, 2, cls.block_size // 2))
        qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4))

        d = d.astype(np.float16).view(np.uint8)

        return np.concatenate([d, qs], axis=-1)

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, qs = np.hsplit(blocks, [2])

        d = d.view(np.float16).astype(np.float32)

        qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
        qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.int8) - np.int8(8)

        return (d * qs.astype(np.float32))

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        n_blocks = blocks.shape[0]

        d = blocks[:, :2].view(torch.float16)
        qs = blocks[:, 2:]

        qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
        qs = (qs & 0x0F).reshape((n_blocks, -1)).to(torch.int8) - 8
        return d * qs

    @classmethod
    def quantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        # Copyright Forge 2024, AGPL V3 + CC-BY SA

        n_blocks = blocks.shape[0]

        imax = torch.abs(blocks).argmax(dim=-1, keepdim=True)
        max_vals = torch.gather(blocks, -1, imax)

        d = max_vals / -8
        id = torch.where(d == 0, torch.tensor(0.0, device=d.device), 1.0 / d)

        qs = torch.trunc((blocks * id) + 8.5).clip(0, 15).to(torch.uint8)

        qs = qs.reshape((n_blocks, 2, block_size // 2))
        qs = qs[:, 0, :] | (qs[:, 1, :] << 4)

        d = d.to(torch.float16).view(torch.uint8)

        return torch.cat([d, qs], dim=-1)


class Q4_1(__Quant, qtype=GGMLQuantizationType.Q4_1):
    @classmethod
    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        max = blocks.max(axis=-1, keepdims=True)
        min = blocks.min(axis=-1, keepdims=True)

        d = (max - min) / 15
        with np.errstate(divide="ignore"):
            id = np.where(d == 0, 0, 1 / d)
        qs = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 15)

        qs = qs.reshape((n_blocks, 2, cls.block_size // 2))
        qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4))

        d = d.astype(np.float16).view(np.uint8)
        m = min.astype(np.float16).view(np.uint8)

        return np.concatenate([d, m, qs], axis=-1)

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        m, qs = np.hsplit(rest, [2])

        d = d.view(np.float16).astype(np.float32)
        m = m.view(np.float16).astype(np.float32)

        qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
        qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.float32)

        return (d * qs) + m

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        n_blocks = blocks.shape[0]

        d = blocks[:, :2].view(torch.float16)
        m = blocks[:, 2:4].view(torch.float16)
        qs = blocks[:, 4:]

        qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1)
        qs = (qs & 0x0F).reshape(n_blocks, -1)

        return (d * qs) + m


class Q5_0(__Quant, qtype=GGMLQuantizationType.Q5_0):
    @classmethod
    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        imax = abs(blocks).argmax(axis=-1, keepdims=True)
        max = np.take_along_axis(blocks, imax, axis=-1)

        d = max / -16
        with np.errstate(divide="ignore"):
            id = np.where(d == 0, 0, 1 / d)
        # FIXME: Q5_0's reference rounding is cursed and depends on FMA
        q = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(16.5), dtype=np.float32).astype(np.uint8).clip(0, 31)

        qs = q.reshape((n_blocks, 2, cls.block_size // 2))
        qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4))

        qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4)

        d = d.astype(np.float16).view(np.uint8)

        return np.concatenate([d, qh, qs], axis=-1)

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        qh, qs = np.hsplit(rest, [4])

        d = d.view(np.float16).astype(np.float32)
        qh = qh.view(np.uint32)

        qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32))
        ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
        qh = (qh & np.uint32(0x01)).astype(np.uint8)
        ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1))

        qs = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(16)

        return (d * qs.astype(np.float32))

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        def to_uint32(x):
            # pytorch uint32 by City96 - Apache-2.0
            x = x.view(torch.uint8).to(torch.int32)
            return (x[:, 0] | x[:, 1] << 8 | x[:, 2] << 16 | x[:, 3] << 24).unsqueeze(1)

        n_blocks = blocks.shape[0]

        d = blocks[:, :2]
        qh = blocks[:, 2:6]
        qs = blocks[:, 6:]

        d = d.view(torch.float16).to(torch.float32)
        qh = to_uint32(qh)

        qh = qh.reshape(n_blocks, 1) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32)
        ql = qs.reshape(n_blocks, -1, 1, block_size // 2) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1)

        qh = (qh & 1).to(torch.uint8)
        ql = (ql & 0x0F).reshape(n_blocks, -1)

        qs = (ql | (qh << 4)).to(torch.int8) - 16
        return d * qs

    @classmethod
    def quantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        # Copyright Forge 2024, AGPL V3 + CC-BY SA

        n_blocks = blocks.shape[0]

        imax = torch.abs(blocks).argmax(dim=-1, keepdim=True)
        max_val = torch.gather(blocks, dim=-1, index=imax)

        d = max_val / -16
        id = torch.where(d == 0, torch.tensor(0.0, device=d.device), 1.0 / d)

        q = torch.trunc((blocks.float() * id.float()) + 16.5).clamp(0, 31).to(torch.uint8)

        qs = q.view(n_blocks, 2, block_size // 2)
        qs = (qs[..., 0, :] & 0x0F) | (qs[..., 1, :] << 4)

        qh = q.view(n_blocks, 32)
        qh_packed = torch.zeros((n_blocks, 4), dtype=torch.uint8, device=qh.device)

        for i in range(4):
            qh_packed[:, i] = (
                    (qh[:, i * 8 + 0] >> 4) |
                    (qh[:, i * 8 + 1] >> 3 & 0x02) |
                    (qh[:, i * 8 + 2] >> 2 & 0x04) |
                    (qh[:, i * 8 + 3] >> 1 & 0x08) |
                    (qh[:, i * 8 + 4] << 0 & 0x10) |
                    (qh[:, i * 8 + 5] << 1 & 0x20) |
                    (qh[:, i * 8 + 6] << 2 & 0x40) |
                    (qh[:, i * 8 + 7] << 3 & 0x80)
            )

        d = d.to(torch.float16).view(torch.uint8)

        return torch.cat([d, qh_packed, qs], dim=-1)


class Q5_1(__Quant, qtype=GGMLQuantizationType.Q5_1):
    @classmethod
    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        max = blocks.max(axis=-1, keepdims=True)
        min = blocks.min(axis=-1, keepdims=True)

        d = (max - min) / 31
        with np.errstate(divide="ignore"):
            id = np.where(d == 0, 0, 1 / d)
        q = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 31)

        qs = q.reshape((n_blocks, 2, cls.block_size // 2))
        qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4))

        qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4)

        d = d.astype(np.float16).view(np.uint8)
        m = min.astype(np.float16).view(np.uint8)

        return np.concatenate([d, m, qh, qs], axis=-1)

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        m, rest = np.hsplit(rest, [2])
        qh, qs = np.hsplit(rest, [4])

        d = d.view(np.float16).astype(np.float32)
        m = m.view(np.float16).astype(np.float32)
        qh = qh.view(np.uint32)

        qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32))
        ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
        qh = (qh & np.uint32(0x01)).astype(np.uint8)
        ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1))

        qs = (ql | (qh << np.uint8(4))).astype(np.float32)

        return (d * qs) + m

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        def to_uint32(x):
            # pytorch uint32 by City96 - Apache-2.0
            x = x.view(torch.uint8).to(torch.int32)
            return (x[:, 0] | x[:, 1] << 8 | x[:, 2] << 16 | x[:, 3] << 24).unsqueeze(1)

        n_blocks = blocks.shape[0]

        d = blocks[:, :2].view(torch.float16)
        m = blocks[:, 2:4].view(torch.float16)
        qh = blocks[:, 4:8]
        qs = blocks[:, 8:]

        qh = to_uint32(qh)

        qh = qh.reshape((n_blocks, 1)) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32)
        ql = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1)
        qh = (qh & 1).to(torch.uint8)
        ql = (ql & 0x0F).reshape((n_blocks, -1))

        qs = (ql | (qh << 4))
        return (d * qs) + m


class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0):
    @classmethod
    # Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c
    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:

        d = abs(blocks).max(axis=1, keepdims=True) / 127
        with np.errstate(divide="ignore"):
            id = np.where(d == 0, 0, 1 / d)
        qs = np_roundf(blocks * id)

        # (n_blocks, 2)
        d = d.astype(np.float16).view(np.uint8)
        # (n_blocks, block_size)
        qs = qs.astype(np.int8).view(np.uint8)

        return np.concatenate([d, qs], axis=1)

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        d, x = np.split(blocks, [2], axis=1)
        d = d.view(np.float16).astype(np.float32)
        x = x.view(np.int8).astype(np.float32)

        return (x * d)

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        d = blocks[:, :2].view(torch.float16)
        x = blocks[:, 2:].view(torch.int8).to(torch.float16)
        return x * d

    @classmethod
    def quantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        # Copyright Forge 2024, AGPL V3 + CC-BY SA
        d = torch.abs(blocks).max(dim=1, keepdim=True).values / 127
        ids = torch.where(d == 0, torch.zeros_like(d), 1 / d)
        qs = torch.round(blocks * ids)
        d = d.to(torch.float16).view(torch.uint8)
        qs = qs.to(torch.int8).view(torch.uint8)
        return torch.cat([d, qs], dim=1)


class Q2_K(__Quant, qtype=GGMLQuantizationType.Q2_K):
    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        scales, rest = np.hsplit(blocks, [QK_K // 16])
        qs, rest = np.hsplit(rest, [QK_K // 4])
        d, dmin = np.hsplit(rest, [2])

        d = d.view(np.float16).astype(np.float32)
        dmin = dmin.view(np.float16).astype(np.float32)

        # (n_blocks, 16, 1)
        dl = (d * (scales & 0xF).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1))
        ml = (dmin * (scales >> 4).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1))

        shift = np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))

        qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & np.uint8(3)

        qs = qs.reshape((n_blocks, QK_K // 16, 16)).astype(np.float32)

        qs = dl * qs - ml

        return qs.reshape((n_blocks, -1))

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        # (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
        n_blocks = blocks.shape[0]
        scales, qs, d, dmin = quick_split(blocks, [QK_K // 16, QK_K // 4, 2])
        d = d.view(torch.float16)
        dmin = dmin.view(torch.float16)
        # (n_blocks, 16, 1)
        dl = (d * (scales & 0xF)).reshape((n_blocks, QK_K // 16, 1))
        ml = (dmin * (scales >> 4)).reshape((n_blocks, QK_K // 16, 1))
        shift = torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1))
        qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & 3
        qs = qs.reshape((n_blocks, QK_K // 16, 16))
        qs = dl * qs - ml
        return qs.reshape((n_blocks, -1))


class Q3_K(__Quant, qtype=GGMLQuantizationType.Q3_K):
    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        hmask, rest = np.hsplit(blocks, [QK_K // 8])
        qs, rest = np.hsplit(rest, [QK_K // 4])
        scales, d = np.hsplit(rest, [12])

        d = d.view(np.float16).astype(np.float32)

        # The scales are packed at 6-bit each in this pattern:
        #  0: IIIIAAAA
        #  1: JJJJBBBB
        #  2: KKKKCCCC
        #  3: LLLLDDDD
        #  4: MMMMEEEE
        #  5: NNNNFFFF
        #  6: OOOOGGGG
        #  7: PPPPHHHH
        #  8: MMIIEEAA
        #  9: NNJJFFBB
        # 10: OOKKGGCC
        # 11: PPLLHHDD
        lscales, hscales = np.hsplit(scales, [8])
        lscales = lscales.reshape((n_blocks, 1, 8)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 2, 1))
        lscales = lscales.reshape((n_blocks, 16))
        hscales = hscales.reshape((n_blocks, 1, 4)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 4, 1))
        hscales = hscales.reshape((n_blocks, 16))
        scales = (lscales & np.uint8(0x0F)) | ((hscales & np.uint8(0x03)) << np.uint8(4))
        scales = (scales.astype(np.int8) - np.int8(32)).astype(np.float32)

        dl = (d * scales).reshape((n_blocks, 16, 1))

        ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
        qh = hmask.reshape(n_blocks, -1, 1, 32) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1))
        ql = ql.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(3)
        qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(1))
        qh = qh ^ np.uint8(1)  # strangely, the offset is zero when the bitmask is 1
        q = (ql.astype(np.int8) - (qh << np.uint8(2)).astype(np.int8)).astype(np.float32)

        return (dl * q).reshape((n_blocks, QK_K))

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        # (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
        n_blocks = blocks.shape[0]
        hmask, qs, scales, d = quick_split(blocks, [QK_K // 8, QK_K // 4, 12])
        d = d.view(torch.float16)
        lscales, hscales = scales[:, :8], scales[:, 8:]
        lscales = lscales.reshape((n_blocks, 1, 8)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 2, 1))
        lscales = lscales.reshape((n_blocks, 16))
        hscales = hscales.reshape((n_blocks, 1, 4)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 4, 1))
        hscales = hscales.reshape((n_blocks, 16))
        scales = (lscales & 0x0F) | ((hscales & 0x03) << 4)
        scales = (scales.to(torch.int8) - 32)
        dl = (d * scales).reshape((n_blocks, 16, 1))
        ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1))
        qh = hmask.reshape(n_blocks, -1, 1, 32) >> torch.tensor([i for i in range(8)], device=d.device, dtype=torch.uint8).reshape((1, 1, 8, 1))
        ql = ql.reshape((n_blocks, 16, QK_K // 16)) & 3
        qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & 1) ^ 1
        q = (ql.to(torch.int8) - (qh << 2).to(torch.int8))
        return (dl * q).reshape((n_blocks, QK_K))


class Q4_K(__Quant, qtype=GGMLQuantizationType.Q4_K):
    K_SCALE_SIZE = 12

    @staticmethod
    def get_scale_min(scales: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
        n_blocks = scales.shape[0]
        scales = scales.view(np.uint8)
        ### Unpacking the following: ###
        #  0 EEAAAAAA
        #  1 FFBBBBBB
        #  2 GGCCCCCC
        #  3 HHDDDDDD
        #  4 eeaaaaaa
        #  5 ffbbbbbb
        #  6 ggcccccc
        #  7 hhdddddd
        #  8 eeeeEEEE
        #  9 ffffFFFF
        # 10 ggggGGGG
        # 11 hhhhHHHH
        scales = scales.reshape((n_blocks, 3, 4))
        d, m, m_d = np.split(scales, 3, axis=-2)

        sc = np.concatenate([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], axis=-1)
        min = np.concatenate([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], axis=-1)

        return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8)))

    @staticmethod
    def get_scale_min_pytorch(scales):
        n_blocks = scales.shape[0]
        scales = scales.view(torch.uint8)
        scales = scales.reshape((n_blocks, 3, 4))
        d, m, m_d = torch.split(scales, scales.shape[-2] // 3, dim=-2)
        sc = torch.cat([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], dim=-1)
        min = torch.cat([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], dim=-1)
        return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8)))

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        dmin, rest = np.hsplit(rest, [2])
        scales, qs = np.hsplit(rest, [cls.K_SCALE_SIZE])

        d = d.view(np.float16).astype(np.float32)
        dmin = dmin.view(np.float16).astype(np.float32)

        sc, m = Q4_K.get_scale_min(scales)

        d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1))
        dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1))

        qs = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
        qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 32)).astype(np.float32)

        return (d * qs - dm).reshape((n_blocks, QK_K))

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        # (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
        QK_K = 256
        K_SCALE_SIZE = 12
        n_blocks = blocks.shape[0]
        d, dmin, scales, qs = quick_split(blocks, [2, 2, K_SCALE_SIZE])
        d = d.view(torch.float16)
        dmin = dmin.view(torch.float16)
        sc, m = Q4_K.get_scale_min_pytorch(scales)
        d = (d * sc).reshape((n_blocks, -1, 1))
        dm = (dmin * m).reshape((n_blocks, -1, 1))
        qs = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
        qs = (qs & 0x0F).reshape((n_blocks, -1, 32))
        return (d * qs - dm).reshape((n_blocks, QK_K))


class Q5_K(__Quant, qtype=GGMLQuantizationType.Q5_K):
    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        dmin, rest = np.hsplit(rest, [2])
        scales, rest = np.hsplit(rest, [Q4_K.K_SCALE_SIZE])
        qh, qs = np.hsplit(rest, [QK_K // 8])

        d = d.view(np.float16).astype(np.float32)
        dmin = dmin.view(np.float16).astype(np.float32)

        sc, m = Q4_K.get_scale_min(scales)

        d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1))
        dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1))

        ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
        qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1))
        ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32))
        qh = (qh & np.uint8(0x01)).reshape((n_blocks, -1, 32))
        q = (ql | (qh << np.uint8(4))).astype(np.float32)

        return (d * q - dm).reshape((n_blocks, QK_K))

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        # (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
        QK_K = 256
        K_SCALE_SIZE = 12
        n_blocks = blocks.shape[0]
        d, dmin, scales, qh, qs = quick_split(blocks, [2, 2, K_SCALE_SIZE, QK_K // 8])
        d = d.view(torch.float16)
        dmin = dmin.view(torch.float16)
        sc, m = Q4_K.get_scale_min_pytorch(scales)
        d = (d * sc).reshape((n_blocks, -1, 1))
        dm = (dmin * m).reshape((n_blocks, -1, 1))
        ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
        qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([i for i in range(8)], device=d.device, dtype=torch.uint8).reshape((1, 1, 8, 1))
        ql = (ql & 0x0F).reshape((n_blocks, -1, 32))
        qh = (qh & 0x01).reshape((n_blocks, -1, 32))
        q = (ql | (qh << 4))
        return (d * q - dm).reshape((n_blocks, QK_K))


class Q6_K(__Quant, qtype=GGMLQuantizationType.Q6_K):
    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        ql, rest = np.hsplit(blocks, [QK_K // 2])
        qh, rest = np.hsplit(rest, [QK_K // 4])
        scales, d = np.hsplit(rest, [QK_K // 16])

        scales = scales.view(np.int8).astype(np.float32)
        d = d.view(np.float16).astype(np.float32)
        d = (d * scales).reshape((n_blocks, QK_K // 16, 1))

        ql = ql.reshape((n_blocks, -1, 1, 64)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
        ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32))
        qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
        qh = (qh & np.uint8(0x03)).reshape((n_blocks, -1, 32))
        q = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(32)
        q = q.reshape((n_blocks, QK_K // 16, -1)).astype(np.float32)

        return (d * q).reshape((n_blocks, QK_K))

    @classmethod
    def dequantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
        # Written by ChatGPT
        n_blocks = blocks.shape[0]
        ql, qh, scales, d, = quick_split(blocks, [QK_K // 2, QK_K // 4, QK_K // 16])
        scales = scales.view(torch.int8)
        d = d.view(torch.float16)
        d = (d * scales).reshape((n_blocks, QK_K // 16, 1))
        ql = ql.reshape((n_blocks, -1, 1, 64)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1))
        ql = (ql & 0x0F).reshape((n_blocks, -1, 32))
        qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1))
        qh = (qh & 0x03).reshape((n_blocks, -1, 32))
        q = (ql | (qh << 4)).to(torch.int8) - 32
        q = q.reshape((n_blocks, QK_K // 16, -1))
        return (d * q).reshape((n_blocks, QK_K))


class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS):
    ksigns: bytes = (
        b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f"
        b"\x90\x11\x12\x93\x14\x95\x96\x17\x18\x99\x9a\x1b\x9c\x1d\x1e\x9f"
        b"\xa0\x21\x22\xa3\x24\xa5\xa6\x27\x28\xa9\xaa\x2b\xac\x2d\x2e\xaf"
        b"\x30\xb1\xb2\x33\xb4\x35\x36\xb7\xb8\x39\x3a\xbb\x3c\xbd\xbe\x3f"
        b"\xc0\x41\x42\xc3\x44\xc5\xc6\x47\x48\xc9\xca\x4b\xcc\x4d\x4e\xcf"
        b"\x50\xd1\xd2\x53\xd4\x55\x56\xd7\xd8\x59\x5a\xdb\x5c\xdd\xde\x5f"
        b"\x60\xe1\xe2\x63\xe4\x65\x66\xe7\xe8\x69\x6a\xeb\x6c\xed\xee\x6f"
        b"\xf0\x71\x72\xf3\x74\xf5\xf6\x77\x78\xf9\xfa\x7b\xfc\x7d\x7e\xff"
    )

    # iq2xxs_grid, but with each byte of the original packed in 2 bits,
    # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
    grid_shape = (256, 8)
    grid_map = (0x08, 0x19, 0x2b)
    grid_hex = (
        b"00000200050008000a00110014002000220028002a0041004400500058006100"
        b"6400800082008a00a20001010401100115014001840198010002020222028202"
        b"010404041004210424044004420448046004810484049004a404000502050805"
        b"200546056905800591050906100640068406a406000805080808140828084108"
        b"440850085208880804094009020a140a01100410101021104010601084109010"
        b"951000110811201150115a118011241245120014081420142514491480141815"
        b"6215001616160118041810184018811800190519a019511a002002200a204420"
        b"6120802082202921482100220222012404241024402456240025412564259026"
        b"082820289428442a014004401040184021402440404048405640604081408440"
        b"9040004120416141804185410142104248425642684200440844204480449944"
        b"124524450046014804481048404845480049584961498249454a904a00500850"
        b"1150195020508050885004514251a4519152905492540a550156545600581158"
        b"195864584059085a046010604060686000615561186260620064056410651265"
        b"84654268008002800a8041808280048118814081118201840484108415844084"
        b"608400854685948509864086608602880489118a0490109024904090a1901691"
        b"8091459200942294449451958198209902a050a085a009a100a218a450a804a9"
    )

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, qs = np.hsplit(blocks, [2])

        d = d.view(np.float16).astype(np.float32)

        qs = qs.view(np.uint32).reshape(n_blocks, -1, 2)

        db = d * (np.float32(0.5) + (qs[..., 1] >> 28).astype(np.float32)) * np.float32(0.25)
        db = db.reshape((n_blocks, -1, 1, 1))

        # get the sign indices and unpack the bits
        signs = qs[..., 1].reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4))
        ksigns = np.frombuffer(cls.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128))
        signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1))
        signs = np.take_along_axis(ksigns, signs, axis=-1)
        signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8))
        signs = signs & np.uint8(0x01)
        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
        signs = signs.reshape((n_blocks, -1, 4, 8))

        assert cls.grid is not None
        grid = np.take_along_axis(cls.grid, qs[..., 0].copy().view(np.uint8).reshape((n_blocks, -1, 1, 1)), axis=-2)
        grid = grid.reshape((n_blocks, -1, 4, 8))

        return (db * grid * signs).reshape((n_blocks, -1))


class IQ2_XS(__Quant, qtype=GGMLQuantizationType.IQ2_XS):
    # iq2xs_grid, but with each byte of the original packed in 2 bits,
    # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
    grid_shape = (512, 8)
    grid_map = (0x08, 0x19, 0x2b)
    grid_hex = (
        b"00000200050008000a0011001400160019002000220025002800410044004600"
        b"49005000520055005800610064008000820085008800910094009900a0000101"
        b"04010601090110011201150118011a0121012401400142014501480151015401"
        b"6001680181018401900100020202050208021102140220024102440250025502"
        b"80028a0201040404060409041004120415041804210424044004420445044804"
        b"5104540456046004810484049004000502050505080511051405200541054405"
        b"500561058005010604061006260640064206840600080208050808080a081108"
        b"14082008250841084408500858088008a008aa08010904091009400981098909"
        b"000a200a280a960aa00a01100410061009101010121015101810211024104010"
        b"4210451048105110541060106a10811084109010001102110511081111111411"
        b"2011411144115011801194119611011204120612101240126012001402140514"
        b"0814111414142014411444144914501464148014011504151015401500161416"
        b"49160118041810181218401854188618001905196619511aa91a002002200520"
        b"08200a201120142020204120442050208020a020012104211021402148216521"
        b"002222228022a82201240424102429244024002541255225992501261a26a626"
        b"002808280a28202855288828a22868299029082a202a822a882a8a2a01400440"
        b"0640094010401240154018402140244040404240454048404a40514054406040"
        b"6540814084409040004102410541084111411441204141414441504180418541"
        b"a241014204421042124229424042004402440544084411441444194420444144"
        b"4444504480449444014504451045244540459a4500460a464446504601480448"
        b"1048404845485448624800491149444950496949044a00500250055008501150"
        b"145020502850415044505050805001510451105115514051425100524452aa52"
        b"0154045410542154405460548154a154005508558055885521566856a1560058"
        b"14584158505899581a5940594259855a0160046010604060546062608660a960"
        b"006124624a62926200641664106540654565a46501686a682569066a546a626a"
        b"00800280058008801180148020802a8041804480508080808280a880aa800181"
        b"0481068110814081518159810082208280828282a082a8820184048410841284"
        b"158440846084898400854485a58518866a860088088825885a8880888288a888"
        b"0689228a808a888a968aa88a0190049010904090569084900091229164915692"
        b"89920094059444945094589429959095929541965198a6984999159a609a00a0"
        b"02a008a00aa020a02aa0a0a051a159a1a6a100a202a208a22aa280a2a0a240a4"
        b"95a465a698a60aa820a822a828a8a0a8a8a804a984a986a928aa2aaa91aaaaaa"
    )

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        qs, scales = np.hsplit(rest, [2 * QK_K // 8])

        d = d.view(np.float16).astype(np.float32)
        qs = qs.view(np.uint16)

        scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
        scales = (scales & 0x0F).reshape((n_blocks, -1))
        db = d * (np.float32(0.5) + scales) * np.float32(0.25)
        db = db.reshape((n_blocks, -1, 1, 1))

        # get the sign indices and unpack the bits
        signs = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape(1, 1, 128)
        signs = np.take_along_axis(signs, (qs >> 9).reshape((n_blocks, -1, 1)), axis=-1)
        signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
        signs = signs & np.uint8(0x01)
        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
        signs = signs.reshape((n_blocks, -1, 2, 8))

        assert cls.grid is not None
        grid = np.take_along_axis(cls.grid, (qs & np.uint16(511)).reshape((n_blocks, -1, 1, 1)), axis=-2)
        grid = grid.reshape((n_blocks, -1, 2, 8))

        return (db * grid * signs).reshape((n_blocks, -1))


class IQ2_S(__Quant, qtype=GGMLQuantizationType.IQ2_S):
    # iq2s_grid, but with each byte of the original packed in 2 bits,
    # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2.
    grid_shape = (1024, 8)
    grid_map = (0x08, 0x19, 0x2b)
    grid_hex = (
        b"00000200050008000a0011001400160019002000220025002800410044004600"
        b"490050005200550058006100640066006900800082008500880091009400a000"
        b"a500aa0001010401060109011001120115011801210124014001420145014801"
        b"510154015601590160016501680181018401900192019501a101a40100020202"
        b"050208021102140220022a02410244024602490250025502800285028a029402"
        b"a202010404040604090410041204150418042104240426042904400442044504"
        b"48044a0451045404560459046004620465048104840486048904900495049804"
        b"a104a40400050205050508050a05110514051605190520052505280541054405"
        b"46054905500552055505580561056405800582058505880591059405a0050106"
        b"0406060609061006150640064506480651065406600681068406900600080208"
        b"050808081108140816081908200825082a084108440846084908500852085508"
        b"580861086408800885089408aa08010904091009120915091809210940094509"
        b"480951095409600981099009000a110a140a220a280a2a0a500a990a01100410"
        b"0610091010101210151018102110241026104010421045104810511054105610"
        b"59106010621065106810811084108610901095109810a110a410001102110511"
        b"08110a1111111411161119112011221125112811411144114611491150115211"
        b"5511581161116411801182118511881191119411011204120912101215122112"
        b"2412401245125112541281128412901200140214051408141114141416141914"
        b"2014251428144114441446144914501452145514581461146414801482148514"
        b"881491149414a014011504150615091510151215151518152115241540154215"
        b"4515481551155415601581158415901500160516081611161416201641164416"
        b"50168016aa160118041806180918101815181818211840184218451848185118"
        b"541860188118841800190219051908191119141920194119441950196919a219"
        b"041a101a401a561a00200220052008201120142016201920202025202a204120"
        b"4420502052205520642080208a209420aa200121042110211221152121214021"
        b"4221452151215421602181218421902100220a22222228222a22442250228822"
        b"8a22a82201240424062409241024152418242124242440244224452448245124"
        b"5424602481248424902400250525082511251425202541254425502566258025"
        b"0126042610264026592600280528112814284128442850288a28aa2801290429"
        b"102995290a2a222a642a882a8a2a014004400640094010401240154018401a40"
        b"21402440264040404240454048404a4051405440564059406040624065408140"
        b"8440904095409840a140a4400041024105410841114114411641194120412241"
        b"2541414144414641494150415241554158416141644180418241854188419141"
        b"9441a04101420442104212421542184224424042454248425142544260428142"
        b"844200440244054408440a441144144416441944204422442544284441444444"
        b"46444944504452445544584461446444804482448544884491449444a0440145"
        b"0445064509451045124515451845214524454045424545454845514554456045"
        b"6a4581458445904500460246054608461146144620464146444650468046a546"
        b"0148044809481048124815481848214824484048424845484848514854486048"
        b"84489048004902490549084911491449204941494449504980499649014a044a"
        b"104a404a00500250055008501150145016501950205022502550285041504450"
        b"4650495050505250555058506150645080508250855088509150945001510451"
        b"0651095110511251155118512151245140514251455148515151545160518151"
        b"8451905100520552085211521452205241524452505269528052015404540654"
        b"0954105412541554185421542454405442544554485451545454605481548454"
        b"9054005502550555085511551455205541554455505580550156045610562656"
        b"405600580258055808581158145820584158445850585a588058015904591059"
        b"4059005a195a855aa85a01600460066010601260156018602160246040604560"
        b"4860516054606060846090600061026105610861116114612061416144615061"
        b"806199610462106240625662a162006405640864116414642064416444645064"
        b"806401650465106540654a656865926500669466016804681068656898680069"
        b"2a69426aa16a0080028005800880118014801980208025804180448050805280"
        b"5580588061808080858091809480018104810981108112811581188121812481"
        b"408142814581488151815481818184819081a981008205820a82118214824182"
        b"4482508201840484068409841084128415841884218440844284458448845184"
        b"5484608481848484908400850285058508851185148520854185448550858085"
        b"8a85018604861086298640860088058811881488418844885088a28801890489"
        b"40896589228a588a5a8a828aa28a019004900990109012901590189024904090"
        b"4290459048905190549060908190849090900091059111911491419144915091"
        b"5a910192049210924092a6920094029405940894119414942094419444945094"
        b"8094969401950495109540959895a19500964696649601980498109826984098"
        b"a998009949995299909a00a005a00aa014a022a02aa041a044a050a0a2a0aaa0"
        b"40a165a102a20aa222a228a22aa282a288a28aa2a8a201a404a410a440a489a4"
        b"a4a400a519a551a60aa828a8a2a854a986a908aa0aaa20aa22aa28aa88aaaaaa"
    )

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        qs, rest = np.hsplit(rest, [QK_K // 8])
        signs, rest = np.hsplit(rest, [QK_K // 8])
        qh, scales = np.hsplit(rest, [QK_K // 32])

        d = d.view(np.float16).astype(np.float32)

        scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
        scales = (scales & 0x0F).reshape((n_blocks, -1))
        db = d * (np.float32(0.5) + scales) * np.float32(0.25)
        db = db.reshape((n_blocks, -1, 1, 1))

        # unpack the sign bits
        signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
        signs = signs & np.uint8(0x01)
        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
        signs = signs.reshape((n_blocks, -1, 2, 8))

        qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4))
        qs = qs.astype(np.uint16) | ((qh & 0x03).astype(np.uint16) << 8).reshape((n_blocks, -1))

        assert cls.grid is not None
        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
        grid = grid.reshape((n_blocks, -1, 2, 8))

        return (db * grid * signs).reshape((n_blocks, -1))


class IQ3_XXS(__Quant, qtype=GGMLQuantizationType.IQ3_XXS):
    grid_shape = (256, 4)
    grid_map = (0x04, 0x0c, 0x14, 0x1c, 0x24, 0x2c, 0x34, 0x3e)
    grid_hex = (
        b"0000020004001100130017002000220031004200730075000101030110011201"
        b"2101250130013201410154017001000202020402110220022202310233023702"
        b"5102570275020103070310031203250370031304370444045704730475040105"
        b"0705320552053506640610071407160743076107011003101010121021102310"
        b"3010321034104710501000110211111120112211011203121012121221123012"
        b"7212001302132013311346136613011405145014201524154615711505162217"
        b"4017002002201120132020202220262031204220012103210521102112212121"
        b"3021632167217021002202221122172220222222372240225522012310231423"
        b"7023742335245324032527254125742501270327162745270130103012302130"
        b"2330503065307230003102312031313144314631013203321032253252327232"
        b"1133333330344734723400350635223555351436363663363337603704401740"
        b"3540374053405740744120423742404260426642074345430444514464442545"
        b"4345704505471047124730471250415070500051065126515551145232527252"
        b"0253535310542354275472540255315550562457425724604460466064602161"
        b"6161176264623063366344640565526533660367216703700570077010703270"
        b"5270267140711272457252720073157333736073217441740075027524753076"
    )

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        qs, scales = np.hsplit(rest, [QK_K // 4])

        d = d.view(np.float16).astype(np.float32)
        scales = scales.view(np.uint32)

        db = d * (np.float32(0.5) + (scales >> 28).astype(np.float32)) * np.float32(0.5)
        db = db.reshape((n_blocks, -1, 1, 1))

        # get the sign indices and unpack the bits
        signs = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4))
        ksigns = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128))
        signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1))
        signs = np.take_along_axis(ksigns, signs, axis=-1)
        signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8))
        signs = signs & np.uint8(0x01)
        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
        signs = signs.reshape((n_blocks, -1, 4, 8))

        assert cls.grid is not None
        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
        grid = grid.reshape((n_blocks, -1, 4, 8))

        return (db * grid * signs).reshape((n_blocks, -1))


class IQ3_S(__Quant, qtype=GGMLQuantizationType.IQ3_S):
    grid_shape = (512, 4)
    grid_map = (0x01, 0x03, 0x05, 0x07, 0x09, 0x0b, 0x0d, 0x0f)
    grid_hex = (
        b"0000010002000500070010001100120014001600200021002500330040004200"
        b"4500470051005300600062007100740077000001010102010401100111011501"
        b"2001230127013101350144016101650172010002010205020702100213021602"
        b"2102250230023402420245024702510253027002730203031103150320032203"
        b"3103330336034403500352036703710375030004130417042104240432044004"
        b"4304510470040205040520052205260533054105450547056605730506061106"
        b"1306310652067106000702070407200722072607330750075407001001100210"
        b"0410101011101310151017102010221031103410361054105610611072100011"
        b"0111031106111011141121113011331141115011521170117611001212121512"
        b"1712201224123212401243125512601272120113041307131013131321132713"
        b"3013341341136213701303140514121414143114331442144614501454140115"
        b"1015131521153015321551152016241627164416461601170317101712172117"
        b"3517411762177017002001200320052007201020122014201620212023202720"
        b"3020322041204320452050205220672070207320752000210221102113211721"
        b"2221252131213421422151210122042207222122232230223722412253225722"
        b"7122742200230223052311232223242331233323422350236623012407242024"
        b"2324322435244124722475240425112522253725402553257025002602260726"
        b"2126552661260527112726273027432750270230113013301530173022303130"
        b"3330353042304430473051306330713001310331053114312131233140316031"
        b"7231763100321232203232323432503201331033143321332333273330334133"
        b"4333473355337333033411341634223431345234603464340135103512352535"
        b"3235443556357335163641360137033720372237353700400440124020402440"
        b"2740324041405040704002410741114113412241304135414341514155410142"
        b"0342104215422142334240425742624270420443114313432043224331433543"
        b"0044024424443744404471440545074521456245134634466046104715473047"
        b"4347514702501050145022504050445047505250665074500151035105511251"
        b"2151325172510052115223523052365253520253075310532753445351536553"
        b"7353015404542054325446541255265551555355425602570457225711601360"
        b"1560316033606060006120612761646112623462426255626262706200631463"
        b"2163406325644364626400650365346560650566406611671367007004700770"
        b"2070227036704070547062700271117124714371457101720472107216722172"
        b"3072517202733273357353730174057413742074507422754275027631760077"
    )

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        qs, rest = np.hsplit(rest, [QK_K // 4])
        qh, rest = np.hsplit(rest, [QK_K // 32])
        signs, scales = np.hsplit(rest, [QK_K // 8])

        d = d.view(np.float16).astype(np.float32)

        scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
        scales = (scales & 0x0F).reshape((n_blocks, -1))
        db = d * (1 + 2 * scales)
        db = db.reshape((n_blocks, -1, 1, 1))

        # unpack the sign bits
        signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8))
        signs = signs & np.uint8(0x01)
        signs = np.where(signs == 0, np.float32(1), np.float32(-1))
        signs = signs.reshape((n_blocks, -1, 4, 8))

        qh = qh.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8)
        qh = (qh & 0x01).astype(np.uint16).reshape((n_blocks, -1))
        qs = qs.astype(np.uint16) | (qh << 8)

        assert cls.grid is not None
        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
        grid = grid.reshape((n_blocks, -1, 4, 8))

        return (db * grid * signs).reshape((n_blocks, -1))


class IQ1_S(__Quant, qtype=GGMLQuantizationType.IQ1_S):
    # iq1s_grid, with each byte packed into 2 bits
    # -1, 0, 1 <=> 0, 1, 2
    grid_shape = (2048, 8)
    grid_map = (-1, 0, 1)
    grid_hex = (
        b"00000200050008000a00110015002000220028002a0045005100540056006500"
        b"8000820088008a009500a000a200a800aa000401050111011401160119011a01"
        b"2501410146014901520155015a0161016401660168018501910194019601a501"
        b"0002020208020a0215022002220228022a024502510259026402690280028202"
        b"88028a02910295029902a002a202a802aa021104140416042504410449045504"
        b"5a046404650491049904a5040105040505050605150518051a05290540054505"
        b"4a0550055105540555055605590560056205650568056a058105910595059805"
        b"9a05a105a405a505a605a9051406190641064406500652065506580660066106"
        b"6606690685069106940699060008020808080a0815082008220828082a084508"
        b"5108560865088008820888088a089508a008a208a808aa080509110914091909"
        b"2409250941095009510955096109640969099109940996099909a509000a020a"
        b"080a0a0a150a200a220a280a2a0a450a510a590a610a650a800a820a850a880a"
        b"8a0a950aa00aa20aa80aaa0a1010111014101910241025104110441050105510"
        b"58106110641065106910911094109610a110a510011104110611091110111211"
        b"1511181121112411291145114a11501151115211541155115611591160116511"
        b"841192119511a111a41111121412161225124012461249125212551258125a12"
        b"641266128512911294129612a512011406140914141415141814191421142614"
        b"41144514461448144a1451145414551456145914621465146814841489149014"
        b"94149514981499149a14a114a414a514a914021505150a151115141515151615"
        b"191520152215251528152a154115441545154615511552155415551556155915"
        b"5a1561156415651566156915801582158415851588158a159015911594159515"
        b"961599159a15a015a215a51501160416051606161516161618161a1621162616"
        b"401642164416451648164a165116551656165816591661166416651668166916"
        b"6a1686168a1692169516a416a916111816182518411844184618491850185518"
        b"58185a1860186118641866186918851891189418a5181019121915191a192119"
        b"25194219441945194819511954195519561959195a19601965196a1989199119"
        b"921995199819a119a619a919091a161a241a261a441a461a491a501a521a551a"
        b"581a611a661a691a851a911a961a9a1a0020022008200a201520202022202520"
        b"28202a20452051205920612065208020822088208a209520a020a220a520a820"
        b"aa2005211121142119212521422144214921552158215a216121642165216621"
        b"8521902196219921a521012208220a22112215222022222228222a2245225122"
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    )

    delta = np.float32(0.125)

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        qs, qh = np.hsplit(rest, [QK_K // 8])

        d = d.view(np.float16).astype(np.float32)
        qh = qh.view(np.uint16)

        dl = d * (2 * ((qh >> 12) & 7) + 1)
        dl = dl.reshape((n_blocks, -1, 1, 1))
        delta = np.where((qh & np.uint16(0x8000)) == 0, cls.delta, -cls.delta)
        delta = delta.reshape((n_blocks, -1, 1, 1))

        qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4))
        qs = qs.astype(np.uint16) | ((qh & 7) << 8).reshape((n_blocks, -1))

        assert cls.grid is not None
        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
        grid = grid.reshape((n_blocks, -1, 4, 8))

        return (dl * (grid + delta)).reshape((n_blocks, -1))


class IQ1_M(__Quant, qtype=GGMLQuantizationType.IQ1_M):
    grid_shape = IQ1_S.grid_shape
    grid_map = IQ1_S.grid_map
    grid_hex = IQ1_S.grid_hex

    delta = IQ1_S.delta

    # Okay *this* type is weird. It's the only one which stores the f16 scales in multiple parts.
    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        qs, rest = np.hsplit(blocks, [QK_K // 8])
        qh, scales = np.hsplit(rest, [QK_K // 16])

        # The f16 scale is packed across multiple bytes
        scales = scales.view(np.uint16)
        d = (scales.reshape((n_blocks, 4)) & np.uint16(0xF000)) >> np.array([12, 8, 4, 0], dtype=np.uint16).reshape((1, 4))
        d = d[..., 0] | d[..., 1] | d[..., 2] | d[..., 3]
        d = d.view(np.float16).astype(np.float32).reshape((n_blocks, 1))

        scales = scales.reshape(n_blocks, -1, 1) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4))
        scales = (scales & 0x07).reshape((n_blocks, -1))
        dl = d * (2 * scales + 1)
        dl = dl.reshape((n_blocks, -1, 2, 1, 1))

        qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
        qs = qs.astype(np.uint16) | ((qh & 0x07).astype(np.uint16) << 8).reshape((n_blocks, -1))

        delta = np.where(qh & 0x08 == 0, cls.delta, -cls.delta)
        delta = delta.reshape((n_blocks, -1, 2, 2, 1))

        assert cls.grid is not None
        grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2)
        grid = grid.reshape((n_blocks, -1, 2, 2, 8))

        return (dl * (grid + delta)).reshape((n_blocks, -1))


class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL):
    kvalues = (-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113)

    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, qs = np.hsplit(blocks, [2])

        d = d.view(np.float16).astype(np.float32)

        qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))

        qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 1))

        kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16)
        qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1))

        return (d * qs)


class IQ4_XS(__Quant, qtype=GGMLQuantizationType.IQ4_XS):
    @classmethod
    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
        n_blocks = blocks.shape[0]

        d, rest = np.hsplit(blocks, [2])
        scales_h, rest = np.hsplit(rest, [2])
        scales_l, qs = np.hsplit(rest, [QK_K // 64])

        d = d.view(np.float16).astype(np.float32)
        scales_h = scales_h.view(np.uint16)

        scales_l = scales_l.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2))
        scales_h = scales_h.reshape((n_blocks, 1, -1)) >> np.array([2 * i for i in range(QK_K // 32)], dtype=np.uint16).reshape((1, -1, 1))
        scales_l = scales_l.reshape((n_blocks, -1)) & np.uint8(0x0F)
        scales_h = scales_h.reshape((n_blocks, -1)).astype(np.uint8) & np.uint8(0x03)

        scales = (scales_l | (scales_h << np.uint8(4))).astype(np.int8) - np.int8(32)
        dl = (d * scales.astype(np.float32)).reshape((n_blocks, -1, 1))

        qs = qs.reshape((n_blocks, -1, 1, 16)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1))
        qs = qs.reshape((n_blocks, -1, 32, 1)) & np.uint8(0x0F)

        kvalues = np.array(IQ4_NL.kvalues, dtype=np.int8).reshape((1, 1, 1, -1))
        qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1, 32))

        return (dl * qs).reshape((n_blocks, -1))