# pylint: disable=redefined-builtin,no-member,protected-access

from dataclasses import dataclass

import torch

from modules import devices
from .common import dtype_dict, compile_func, use_contiguous_mm, use_tensorwise_fp8_matmul
from .quant_utils import quantize_int_mm, quantize_uint_mm, quantize_fp_mm, rotate_hadamard, get_hadamard
from .packed_int import unpack_int
from .packed_float import unpack_float
from .layers import SDNQLayer


@devices.inference_context()
def dequantize_asymmetric(
    weight: torch.Tensor,
    scale: torch.FloatTensor,
    zero_point: torch.FloatTensor,
    svd_up: torch.FloatTensor | None = None,
    svd_down: torch.FloatTensor | None = None,
    hadamard: torch.FloatTensor | None = None,
    dtype: torch.dtype | None = None,
    result_shape: torch.Size | None = None,
    skip_quantized_matmul: bool = False,
    re_quantize_for_matmul: bool = False,
) -> torch.FloatTensor:
    result = torch.addcmul(zero_point, weight.to(dtype=scale.dtype), scale)
    if skip_quantized_matmul and not re_quantize_for_matmul:
        result.t_()
    if result_shape is not None:
        result = result.view(result_shape)
    is_conv = bool(result.ndim > 2 and weight.ndim > 2)
    if svd_up is not None:
        if skip_quantized_matmul:
            svd_up = svd_up.t().contiguous()
            if use_contiguous_mm:
                svd_down = svd_down.t().contiguous()
            else:
                svd_down = svd_down.contiguous().t()
        if is_conv:
            result = result.add_(torch.mm(svd_up, svd_down).unflatten(-1, (*result.shape[1:],)))
        else:
            result = result.to(dtype=svd_up.dtype).addmm_(svd_up, svd_down)
    if dtype is not None:
        result = result.to(dtype=dtype)
    if hadamard is not None:
        result = rotate_hadamard(result, hadamard=hadamard, is_conv=is_conv)
    return result


@devices.inference_context()
def dequantize_symmetric(
    weight: torch.Tensor,
    scale: torch.FloatTensor,
    svd_up: torch.FloatTensor | None = None,
    svd_down: torch.FloatTensor | None = None,
    hadamard: torch.FloatTensor | None = None,
    dtype: torch.dtype | None = None,
    result_shape: torch.Size | None = None,
    skip_quantized_matmul: bool = False,
    re_quantize_for_matmul: bool = False,
) -> torch.FloatTensor:
    result = weight.to(dtype=scale.dtype).mul_(scale)
    if skip_quantized_matmul and not re_quantize_for_matmul:
        result.t_()
    if result_shape is not None:
        result = result.view(result_shape)
    is_conv = bool(result.ndim > 2 and weight.ndim > 2)
    if svd_up is not None:
        if skip_quantized_matmul:
            svd_up = svd_up.t().contiguous()
            if use_contiguous_mm:
                svd_down = svd_down.t().contiguous()
            else:
                svd_down = svd_down.contiguous().t()
        if is_conv:
            result = result.add_(torch.mm(svd_up, svd_down).unflatten(-1, (*result.shape[1:],)))
        else:
            result = result.to(dtype=svd_up.dtype).addmm_(svd_up, svd_down)
    if dtype is not None:
        result = result.to(dtype=dtype)
    if hadamard is not None:
        result = rotate_hadamard(result, hadamard=hadamard, is_conv=is_conv)
    return result


def dequantize_weight(
    weights_dtype: str,
    weight: torch.Tensor,
    scale: torch.FloatTensor,
    zero_point: torch.FloatTensor | None = None,
    svd_up: torch.FloatTensor | None = None,
    svd_down: torch.FloatTensor | None = None,
    hadamard: torch.FloatTensor | None = None,
    dtype: torch.dtype | None = None,
    result_shape: torch.Size | None = None,
    quantized_weight_shape: torch.Size | None = None,
    skip_quantized_matmul: bool = False,
    re_quantize_for_matmul: bool = False,
) -> torch.FloatTensor:
    if dtype_dict[weights_dtype]["is_packed"]:
        if dtype_dict[weights_dtype]["is_integer"]:
            weight = unpack_int(weight, weights_dtype, quantized_weight_shape, dtype=scale.dtype)
        else:
            weight = unpack_float(weight, weights_dtype, quantized_weight_shape)
    if dtype_dict[weights_dtype]["is_unsigned"]:
        return dequantize_asymmetric(weight, scale, zero_point, svd_up=svd_up, svd_down=svd_down, hadamard=hadamard, dtype=dtype, result_shape=result_shape, skip_quantized_matmul=skip_quantized_matmul, re_quantize_for_matmul=re_quantize_for_matmul)
    else:
        return dequantize_symmetric(weight, scale, svd_up=svd_up, svd_down=svd_down, hadamard=hadamard, dtype=dtype, result_shape=result_shape, skip_quantized_matmul=skip_quantized_matmul, re_quantize_for_matmul=re_quantize_for_matmul)


@devices.inference_context()
def re_quantize_int_mm(weight: torch.FloatTensor, matmul_dtype: str = "int8") -> tuple[torch.Tensor, torch.FloatTensor]:
    if weight.ndim > 2: # convs
        weight = weight.flatten(1,-1)
    if use_contiguous_mm:
        weight, scale = quantize_int_mm(weight.t().contiguous(), dim=0, matmul_dtype=matmul_dtype)
    else:
        weight, scale = quantize_int_mm(weight.contiguous(), dim=-1, matmul_dtype=matmul_dtype)
        weight, scale = weight.t_(), scale.t_()
    return weight, scale


@devices.inference_context()
def re_quantize_uint_mm(weight: torch.FloatTensor, matmul_dtype: str = "uint8") -> tuple[torch.Tensor, torch.FloatTensor, torch.FloatTensor]:
    if weight.ndim > 2: # convs
        weight = weight.flatten(1,-1)
    if use_contiguous_mm:
        weight, scale, zero_point = quantize_uint_mm(weight.t().contiguous(), dim=0, matmul_dtype=matmul_dtype)
    else:
        weight, scale, zero_point = quantize_uint_mm(weight.contiguous(), dim=-1, matmul_dtype=matmul_dtype)
        weight, scale, zero_point = weight.t_(), scale.t_(), zero_point.t_()
    return weight, scale, zero_point


@devices.inference_context()
def re_quantize_fp_mm(weight: torch.FloatTensor, matmul_dtype: str = "float8_e4m3fn") -> tuple[torch.Tensor, torch.FloatTensor]:
    if weight.ndim > 2: # convs
        weight = weight.flatten(1,-1)
    if use_contiguous_mm and matmul_dtype in {"fp16", "float16"}:
        weight, scale = quantize_fp_mm(weight.t().contiguous(), dim=0, matmul_dtype=matmul_dtype)
    else:
        weight, scale = quantize_fp_mm(weight.contiguous(), dim=-1, matmul_dtype=matmul_dtype)
        weight, scale = weight.t_(), scale.t_()
    if not use_tensorwise_fp8_matmul and dtype_dict[matmul_dtype]["num_bits"] == 8:
        scale = scale.to(dtype=torch.float32)
    return weight, scale


def re_quantize_matmul(
    weights_dtype: str,
    weight: torch.Tensor,
    scale: torch.FloatTensor,
    zero_point: torch.FloatTensor | None = None,
    svd_up: torch.FloatTensor | None = None,
    svd_down: torch.FloatTensor | None = None,
    hadamard: torch.FloatTensor | None = None,
    matmul_dtype: str = "int8",
    result_shape: torch.Size | None = None,
    quantized_weight_shape: torch.Size | None = None,
) -> tuple[torch.Tensor, torch.FloatTensor] | tuple[torch.Tensor, torch.FloatTensor, torch.FloatTensor]:
    if dtype_dict[weights_dtype]["is_packed"]:
        if dtype_dict[weights_dtype]["is_integer"]:
            weight = unpack_int(weight, weights_dtype, quantized_weight_shape, dtype=scale.dtype)
        else:
            weight = unpack_float(weight, weights_dtype, quantized_weight_shape)
    if dtype_dict[weights_dtype]["is_unsigned"]:
        weight = dequantize_asymmetric(weight, scale, zero_point, svd_up=svd_up, svd_down=svd_down, hadamard=hadamard, dtype=scale.dtype, result_shape=result_shape)
    else:
        weight = dequantize_symmetric(weight, scale, svd_up=svd_up, svd_down=svd_down, hadamard=hadamard, dtype=scale.dtype, result_shape=result_shape)
    if dtype_dict[matmul_dtype]["is_integer"]:
        if dtype_dict[matmul_dtype]["is_unsigned"]:
            return re_quantize_uint_mm(weight, matmul_dtype=matmul_dtype)
        else:
            return re_quantize_int_mm(weight, matmul_dtype=matmul_dtype)
    else:
        return re_quantize_fp_mm(weight, matmul_dtype=matmul_dtype)


@devices.inference_context()
def dequantize_sdnq_module(model: torch.nn.Module) -> torch.nn.Module:
    if isinstance(model, SDNQLayer):
        model = model.dequantize()
    has_children = list(model.children())
    if not has_children:
        return model
    for module_name, module in model.named_children():
        if isinstance(module, SDNQLayer):
            setattr(model, module_name, module.dequantize())
        else:
            setattr(model, module_name, dequantize_sdnq_model(module))
    return model


@devices.inference_context()
def dequantize_sdnq_model(model: torch.nn.Module) -> torch.nn.Module:
    model = dequantize_sdnq_module(model)
    if hasattr(model, "quantization_method"):
        del model.quantization_method
    if hasattr(model, "quantization_config"):
        del model.quantization_config
    if hasattr(model, "config"):
        try:
            if hasattr(model.config, "quantization_config"):
                del model.config.quantization_config
        except Exception:
            pass
        try:
            if hasattr(model.config, "pop"):
                model.config.pop("quantization_config", None)
        except Exception:
            pass
    return model


# SDNQDequantizer has to be a dataclass for torch.compile
@dataclass
class SDNQDequantizer:
    result_dtype: torch.dtype
    result_shape: torch.Size
    original_shape: torch.Size
    original_stride: list[int]
    quantized_weight_shape: torch.Size
    weights_dtype: str
    quantized_matmul_dtype: str
    hadamard_group_size: int
    group_size: int
    svd_rank: int
    svd_steps: int
    use_quantized_matmul: bool
    re_quantize_for_matmul: bool
    use_stochastic_rounding: bool
    layer_class_name: str
    is_packed: bool
    is_unsigned: bool
    is_integer: bool
    is_integer_matmul: bool

    def __init__(
        self,
        result_dtype: torch.dtype,
        result_shape: torch.Size,
        original_shape: torch.Size,
        original_stride: list[int],
        quantized_weight_shape: torch.Size,
        weights_dtype: str,
        quantized_matmul_dtype: str,
        hadamard_group_size: int,
        group_size: int,
        svd_rank: int,
        svd_steps: int,
        use_quantized_matmul: bool,
        re_quantize_for_matmul: bool,
        use_stochastic_rounding: bool,
        use_hadamard: bool,
        layer_class_name: str,
    ):
        self.result_dtype = result_dtype
        self.result_shape = result_shape
        self.original_shape = original_shape
        self.original_stride = original_stride
        self.quantized_weight_shape = quantized_weight_shape
        self.weights_dtype = weights_dtype
        self.quantized_matmul_dtype = quantized_matmul_dtype
        self.hadamard_group_size = hadamard_group_size
        self.group_size = group_size
        self.svd_rank = svd_rank
        self.svd_steps = svd_steps
        self.use_quantized_matmul = use_quantized_matmul
        self.re_quantize_for_matmul = re_quantize_for_matmul
        self.use_stochastic_rounding = use_stochastic_rounding
        self.use_hadamard = use_hadamard
        self.layer_class_name = layer_class_name
        self.is_packed = dtype_dict[weights_dtype]["is_packed"]
        self.is_unsigned = dtype_dict[weights_dtype]["is_unsigned"]
        self.is_integer = dtype_dict[weights_dtype]["is_integer"]
        self.is_integer_matmul = dtype_dict[quantized_matmul_dtype]["is_integer"]

    @devices.inference_context()
    def re_quantize_matmul(
        self,
        weight: torch.Tensor,
        scale: torch.FloatTensor,
        zero_point: torch.FloatTensor | None = None,
        svd_up: torch.FloatTensor | None = None,
        svd_down: torch.FloatTensor | None = None,
        hadamard: torch.FloatTensor | None = None,
        non_hadamard: bool = True,
    ) -> tuple[torch.Tensor, torch.FloatTensor]: # pylint: disable=unused-argument
        if hadamard is None and self.use_hadamard and not non_hadamard:
            hadamard = get_hadamard(self.hadamard_group_size, dtype=self.result_dtype, device=weight.device)
        return re_quantize_matmul_compiled(
            self.weights_dtype,
            weight,
            scale,
            zero_point=zero_point,
            svd_up=svd_up,
            svd_down=svd_down,
            hadamard=hadamard,
            matmul_dtype=self.quantized_matmul_dtype,
            result_shape=self.result_shape,
            quantized_weight_shape=self.quantized_weight_shape,
        )

    @devices.inference_context()
    def __call__(
        self,
        weight: torch.Tensor,
        scale: torch.FloatTensor,
        zero_point: torch.FloatTensor | None = None,
        svd_up: torch.FloatTensor | None = None,
        svd_down: torch.FloatTensor | None = None,
        hadamard: torch.FloatTensor | None = None,
        skip_quantized_matmul: bool = False,
        non_hadamard: bool = False,
        skip_compile: bool = False,
        dtype: torch.dtype | None = None,
    ) -> torch.FloatTensor: # pylint: disable=unused-argument
        if dtype is None:
            dtype = self.result_dtype
        if hadamard is None and self.use_hadamard and not non_hadamard:
            hadamard = get_hadamard(self.hadamard_group_size, dtype=dtype, device=weight.device)
        re_quantize_for_matmul = self.re_quantize_for_matmul or self.is_packed
        dequantize_weight_func = dequantize_weight if skip_compile else dequantize_weight_compiled
        return dequantize_weight_func(
            self.weights_dtype,
            weight,
            scale,
            zero_point=zero_point,
            svd_up=svd_up,
            svd_down=svd_down,
            hadamard=hadamard,
            dtype=dtype,
            result_shape=self.result_shape,
            quantized_weight_shape=self.quantized_weight_shape,
            skip_quantized_matmul=skip_quantized_matmul,
            re_quantize_for_matmul=re_quantize_for_matmul,
        )


dequantize_asymmetric_compiled = compile_func(dequantize_asymmetric)
dequantize_symmetric_compiled = compile_func(dequantize_symmetric)
dequantize_weight_compiled = compile_func(dequantize_weight)
re_quantize_matmul_compiled = compile_func(re_quantize_matmul)

torch.serialization.add_safe_globals([SDNQDequantizer])