from typing import TYPE_CHECKING, Optional

import torch

from sglang.srt.hardware_backend.npu.utils import npu_format_cast
from sglang.srt.layers.quantization.base_config import LinearMethodBase

if TYPE_CHECKING:
    from sglang.srt.layers.quantization.base_config import QuantizationConfig


class _NPULinearMethodBase(LinearMethodBase):

    def __init__(
        self,
        quant_config: Optional["QuantizationConfig"] = None,
    ):
        self.quant_config = quant_config


class NPUW8A8Int8LinearMethod(_NPULinearMethodBase):

    def process_weights_after_loading(self, layer: torch.nn.Module):
        layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
        layer.weight.data = npu_format_cast(layer.weight.data)

        layer.weight_scale.data = layer.weight_scale.data.flatten()
        # Compressed-tensors format doesn't have this field
        if hasattr(layer, "weight_offset"):
            layer.weight_offset.data = layer.weight_offset.data.flatten()

        expanding_factor = layer.weight.data.shape[0]
        layer.aclnn_input_scale = torch.nn.Parameter(
            layer.input_scale.data.repeat(expanding_factor).to(device="npu"),
            requires_grad=False,
        )
        layer.aclnn_input_scale_reciprocal = 1 / torch.nn.Parameter(
            layer.input_scale.data.repeat(expanding_factor).to(device="npu"),
            requires_grad=False,
        )
        layer.aclnn_input_offset = torch.nn.Parameter(
            layer.input_offset.data.repeat(expanding_factor).to(device="npu"),
            requires_grad=False,
        )

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        from sglang.srt.layers.linear import RowParallelLinear

        original_dtype = x.dtype
        if original_dtype != torch.int8:
            x = torch.ops.npu.npu_quantize(
                x,
                layer.aclnn_input_scale_reciprocal,
                layer.aclnn_input_offset,
                torch.qint8,
                -1,
                False,
            )
        # Only fuse bias add into GEMM for rank 0 (this ensures that
        # bias will not get added more than once in Attention TP>1 case)
        if isinstance(layer, RowParallelLinear) and layer.tp_rank > 0:
            quant_bias = None
        else:
            quant_bias = layer.quant_bias
        return torch.ops.npu.npu_quant_matmul(
            x,
            layer.weight,
            layer.deq_scale,
            bias=quant_bias,
            output_dtype=original_dtype,
        )


class NPUW8A8Int8DynamicLinearMethod(_NPULinearMethodBase):

    def process_weights_after_loading(self, layer: torch.nn.Module):
        layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
        layer.weight.data = npu_format_cast(layer.weight.data)

        layer.weight_scale.data = layer.weight_scale.data.flatten()
        # Compressed-tensors format doesn't have this field
        if hasattr(layer, "weight_offset"):
            layer.weight_offset.data = layer.weight_offset.data.flatten()

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:

        if isinstance(x, tuple):
            """dynamic_scale is calculated in malprolog kernel"""
            original_dtype = torch.bfloat16
            quant_out, dynamic_scale = x
        else:
            original_dtype = x.dtype
            quant_out, dynamic_scale = torch.ops.npu.npu_dynamic_quant(x)
        return torch.ops.npu.npu_quant_matmul(
            quant_out,
            layer.weight,
            layer.weight_scale,
            pertoken_scale=dynamic_scale.flatten(),
            bias=bias,
            output_dtype=original_dtype,
        )


class NPU_W4A4DynamicLinearMethod(_NPULinearMethodBase):

    def process_weights_after_loading(self, layer):
        layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
        layer.weight_scale.data = layer.weight_scale.data.flatten()
        layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32)
        layer.weight_offset.data = layer.weight_offset.data.flatten()
        layer.weight.data = torch.ops.npu.npu_convert_weight_to_int4pack(
            layer.weight.data.to(torch.int32)
        )

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
        tp_rank: Optional[int] = 0,
    ) -> torch.Tensor:
        original_dtype = x.dtype
        quant_out, dynamic_scale = torch.ops.npu.npu_dynamic_quant(
            x, dst_type=torch.quint4x2
        )
        return torch.ops.npu.npu_quant_matmul(
            quant_out,
            layer.weight,
            layer.weight_scale,
            pertoken_scale=dynamic_scale.flatten(),
            bias=bias,
            output_dtype=original_dtype,
        )
