# Original: invokeai.backend.quantization.gguf.utils
# Largely based on https://github.com/city96/ComfyUI-GGUF

from typing import Callable, Optional, Union

import gguf
import torch

TORCH_COMPATIBLE_QTYPES = {None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}

# K Quants #
QK_K = 256
K_SCALE_SIZE = 12


def get_scale_min(scales: torch.Tensor):
    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)))


# Legacy Quants #
def dequantize_blocks_Q8_0(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    d, x = split_block_dims(blocks, 2)
    d = d.view(torch.float16).to(dtype)
    x = x.view(torch.int8)
    return d * x


def dequantize_blocks_Q5_1(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    d, m, qh, qs = split_block_dims(blocks, 2, 2, 4)
    d = d.view(torch.float16).to(dtype)
    m = m.view(torch.float16).to(dtype)
    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


def dequantize_blocks_Q5_0(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    d, qh, qs = split_block_dims(blocks, 2, 4)
    d = d.view(torch.float16).to(dtype)
    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


def dequantize_blocks_Q4_1(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    d, m, qs = split_block_dims(blocks, 2, 2)
    d = d.view(torch.float16).to(dtype)
    m = m.view(torch.float16).to(dtype)

    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


def dequantize_blocks_Q4_0(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    d, qs = split_block_dims(blocks, 2)
    d = d.view(torch.float16).to(dtype)

    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


def dequantize_blocks_BF16(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    return (blocks.view(torch.int16).to(torch.int32) << 16).view(torch.float32)


def dequantize_blocks_Q6_K(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    (
        ql,
        qh,
        scales,
        d,
    ) = split_block_dims(blocks, QK_K // 2, QK_K // 4, QK_K // 16)

    scales = scales.view(torch.int8).to(dtype)
    d = d.view(torch.float16).to(dtype)
    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))


def dequantize_blocks_Q5_K(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    d, dmin, scales, qh, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE, QK_K // 8)

    d = d.view(torch.float16).to(dtype)
    dmin = dmin.view(torch.float16).to(dtype)

    sc, m = get_scale_min(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(list(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))


def dequantize_blocks_Q4_K(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    d, dmin, scales, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE)
    d = d.view(torch.float16).to(dtype)
    dmin = dmin.view(torch.float16).to(dtype)

    sc, m = get_scale_min(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))


def dequantize_blocks_Q3_K(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    hmask, qs, scales, d = split_block_dims(blocks, QK_K // 8, QK_K // 4, 12)
    d = d.view(torch.float16).to(dtype)

    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(list(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))


def dequantize_blocks_Q2_K(
    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
    n_blocks = blocks.shape[0]

    scales, qs, d, dmin = split_block_dims(blocks, QK_K // 16, QK_K // 4, 2)
    d = d.view(torch.float16).to(dtype)
    dmin = dmin.view(torch.float16).to(dtype)

    # (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))


DEQUANTIZE_FUNCTIONS: dict[
    gguf.GGMLQuantizationType, Callable[[torch.Tensor, int, int, Optional[torch.dtype]], torch.Tensor]
] = {
    gguf.GGMLQuantizationType.BF16: dequantize_blocks_BF16,
    gguf.GGMLQuantizationType.Q8_0: dequantize_blocks_Q8_0,
    gguf.GGMLQuantizationType.Q5_1: dequantize_blocks_Q5_1,
    gguf.GGMLQuantizationType.Q5_0: dequantize_blocks_Q5_0,
    gguf.GGMLQuantizationType.Q4_1: dequantize_blocks_Q4_1,
    gguf.GGMLQuantizationType.Q4_0: dequantize_blocks_Q4_0,
    gguf.GGMLQuantizationType.Q6_K: dequantize_blocks_Q6_K,
    gguf.GGMLQuantizationType.Q5_K: dequantize_blocks_Q5_K,
    gguf.GGMLQuantizationType.Q4_K: dequantize_blocks_Q4_K,
    gguf.GGMLQuantizationType.Q3_K: dequantize_blocks_Q3_K,
    gguf.GGMLQuantizationType.Q2_K: dequantize_blocks_Q2_K,
}


def is_torch_compatible(tensor: Optional[torch.Tensor]):
    return getattr(tensor, "tensor_type", None) in TORCH_COMPATIBLE_QTYPES


def is_quantized(tensor: torch.Tensor):
    return not is_torch_compatible(tensor)


def dequantize(
    data: torch.Tensor, qtype: gguf.GGMLQuantizationType, oshape: torch.Size, dtype: Optional[torch.dtype] = None
):
    """
    Dequantize tensor back to usable shape/dtype
    """
    block_size, type_size = gguf.GGML_QUANT_SIZES[qtype]
    dequantize_blocks = DEQUANTIZE_FUNCTIONS[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 = dequantize_blocks(blocks, block_size, type_size, dtype)
    return blocks.reshape(oshape)


def to_uint32(x: torch.Tensor) -> torch.Tensor:
    x = x.view(torch.uint8).to(torch.int32)
    return (x[:, 0] | x[:, 1] << 8 | x[:, 2] << 16 | x[:, 3] << 24).unsqueeze(1)


def split_block_dims(blocks: torch.Tensor, *args):
    n_max = blocks.shape[1]
    dims = list(args) + [n_max - sum(args)]
    return torch.split(blocks, dims, dim=1)


PATCH_TYPES = Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor]]
