import math

import numpy as np
import torch
import transformers
from auto_gptq.nn_modules.triton_utils.mixin import TritonModuleMixin
from torch import nn


def weight_quant(weight, num_bits=1):
    dtype = weight.dtype
    weight = weight.float()
    s = 1 / weight.abs().mean().clamp(min=1e-5)
    result = (weight * s).round().clamp(-1, 1) / s
    return result.type(dtype)


def weight_quant2(weight, num_bits=1):
    dtype = weight.dtype
    weight = weight.float()
    s = 1 / weight.abs().mean().clamp(min=1e-5)
    result = (weight * s).round().clamp(-1, 1)
    return result.type(dtype), 1 / s


def activation_quant(x, num_bits=8):
    dtype = x.dtype
    x = x.float()
    Qn = -(2 ** (num_bits - 1))
    Qp = 2 ** (num_bits - 1) - 1
    s = Qp / x.abs().max(dim=-1, keepdim=True).values.clamp(min=1e-5)
    result = (x * s).round().clamp(Qn, Qp) / s
    return result.type(dtype)


def optimized_linear(quant_input, weight, scale):
    # 重み行列のスパース性を利用して、乗算の代わりに加算と減算を行う
    pos_mask = weight == 1
    neg_mask = weight == -1

    # 加算と減算を行い、結果を集約する
    pos_sum = torch.matmul(quant_input, pos_mask.to(quant_input.dtype))
    neg_sum = torch.matmul(quant_input, neg_mask.to(quant_input.dtype))
    result = pos_sum - neg_sum

    # scaleをかける
    result *= scale

    return result


class BitLinear(nn.Linear):
    def __init__(self, *kargs, weight_bits=1, input_bits=8, **kwargs):
        super(BitLinear, self).__init__(*kargs, **kwargs)
        """
        RMSNorm is placed outside BitLinear
        """
        self.weight_bits = weight_bits
        self.input_bits = input_bits
        self.quant_initialized = False
        self.quant_scale = None

    def quantize(self):
        if not self.quant_initialized:
            quant_weight, quant_scale = weight_quant2(self.weight, self.weight_bits)
            quant_weight = self.weight + (quant_weight - self.weight).detach()
            # print(f'Quantized weight: {quant_weight}')
            # print(f'Quantized scale: {quant_scale}')
            self.weight.data = quant_weight
            self.quant_scale = quant_scale
            self.quant_initialized = True

    def forward(self, input):
        if not self.quant_initialized:
            self.quantize()

        quant_input = (
            input + (activation_quant(input, self.input_bits) - input).detach()
        )

        out = nn.functional.linear(quant_input, self.weight) * self.quant_scale
        if self.bias is not None:
            out += self.bias.view(1, -1).expand_as(out)

        return out



# Original code from https://github.com/AutoGPTQ/AutoGPTQ
# MIT License

try:
    from auto_gptq.nn_modules.triton_utils.kernels import (
        QuantLinearFunction,
        QuantLinearInferenceOnlyFunction,
        quant_matmul_248,
        quant_matmul_inference_only_248,
        transpose_quant_matmul_248,
    )
except ImportError as e:
    triton_import_exception = e

    def error_raiser_triton(*args, **kwargs):
        raise ValueError(
            f"Trying to use the triton backend, but could not import triton dependencies with the following error: {triton_import_exception}"
        )

    class FakeTriton:
        def __getattr__(self, name):
            raise ImportError(
                f"Trying to use the triton backend, but could not import triton dependencies with the following error: {triton_import_exception}"
            )

    quant_matmul_248 = error_raiser_triton
    transpose_quant_matmul_248 = error_raiser_triton
    quant_matmul_inference_only_248 = error_raiser_triton
    QuantLinearFunction = FakeTriton
    QuantLinearInferenceOnlyFunction = FakeTriton


class QuantizedBitLinear(nn.Module, TritonModuleMixin):
    QUANT_TYPE = "triton"

    def __init__(
        self,
        infeatures,
        outfeatures,
        bias,
        weight_bits=1,
        input_bits=8,
        quant_bits=2,
        group_size=128,
        trainable=False,
        **kwargs,
    ):
        super().__init__()
        if quant_bits not in [2, 4, 8]:
            raise NotImplementedError("Only 2,4,8 bits are supported.")
        if infeatures % 32 != 0 or outfeatures % 32 != 0:
            raise NotImplementedError(
                "in_feature and out_feature must be divisible by 32."
            )
        self.infeatures = infeatures
        self.outfeatures = outfeatures
        self.weight_bits = weight_bits
        self.input_bits = input_bits
        self.quant_bits = quant_bits
        self.group_size = group_size if group_size != -1 else infeatures
        self.maxq = 2**self.quant_bits - 1

        self.register_buffer(
            "qweight",
            torch.zeros(
                (infeatures // 32 * self.quant_bits, outfeatures), dtype=torch.int32
            ),
        )
        self.register_buffer(
            "qzeros",
            torch.zeros(
                (
                    math.ceil(infeatures / self.group_size),
                    outfeatures // 32 * self.quant_bits,
                ),
                dtype=torch.int32,
            ),
        )
        self.register_buffer(
            "scales",
            torch.zeros(
                (math.ceil(infeatures / self.group_size), outfeatures),
                dtype=torch.float16,
            ),
        )
        self.register_buffer(
            "g_idx",
            torch.tensor(
                [i // self.group_size for i in range(infeatures)], dtype=torch.int32
            ),
        )
        if bias:
            self.register_buffer(
                "bias", torch.zeros((outfeatures), dtype=torch.float16)
            )
        else:
            self.bias = None

        self.register_buffer("scale", torch.tensor(1.0, dtype=torch.float16))

        self.trainable = trainable

    def post_init(self):
        pass

    def pack(self, bitlinear: BitLinear):
        device = bitlinear.weight.device
        bitlinear = bitlinear.cpu()

        W = bitlinear.weight.data.clone()
        if isinstance(bitlinear, nn.Conv2d):
            W = W.flatten(1)
        if isinstance(bitlinear, transformers.pytorch_utils.Conv1D):
            W = W.t()

        self.scale = torch.tensor(bitlinear.quant_scale, dtype=torch.float16)
        # self.scales.fill_(self.scale).half()
        # self.scales.fill_(1).half()

        scales = torch.ones(
            self.outfeatures,
            math.ceil(self.infeatures / self.group_size),
        )
        zero = 1
        zeros = torch.zeros(
            self.outfeatures,
            math.ceil(self.infeatures / self.group_size),
        )
        zeros.fill_(zero)

        scales = scales.t().contiguous()
        zeros = zeros.t().contiguous()
        scale_zeros = zeros * scales
        self.scales = scales.clone().half()
        if bitlinear.bias is not None:
            self.bias = bitlinear.bias.clone().half()

        intweight = []
        for idx in range(self.infeatures):
            intweight.append(
                torch.round(
                    (W[:, idx] + scale_zeros[self.g_idx[idx]])
                    / self.scales[self.g_idx[idx]]
                ).to(torch.int)[:, None]
            )

        intweight = torch.cat(intweight, dim=1)
        intweight = intweight.t().contiguous()
        intweight = intweight.numpy().astype(np.uint32)

        print(f"Int weight: {intweight}")

        i = 0
        row = 0
        qweight = np.zeros(
            (intweight.shape[0] // 32 * self.quant_bits, intweight.shape[1]),
            dtype=np.uint32,
        )
        while row < qweight.shape[0]:
            if self.quant_bits in [2, 4, 8]:
                for j in range(i, i + (32 // self.quant_bits)):
                    qweight[row] |= intweight[j] << (self.quant_bits * (j - i))
                i += 32 // self.quant_bits
                row += 1
            else:
                raise NotImplementedError("Only 2,4,8 bits are supported.")

        qweight = qweight.astype(np.int32)
        self.qweight = torch.from_numpy(qweight)

        print(f"Quantized weight: {self.qweight}")

        zeros -= 1
        zeros = zeros.numpy().astype(np.uint32)
        qzeros = np.zeros(
            (zeros.shape[0], zeros.shape[1] // 32 * self.quant_bits), dtype=np.uint32
        )  # math.ceil(infeatures / self.group_size), outfeatures // 32 * self.quant_bits,
        i = 0
        col = 0
        while col < qzeros.shape[1]:
            if self.quant_bits in [2, 4, 8]:
                for j in range(i, i + (32 // self.quant_bits)):
                    qzeros[:, col] |= zeros[:, j] << (self.quant_bits * (j - i))
                i += 32 // self.quant_bits
                col += 1
            else:
                raise NotImplementedError("Only 2,4,8 bits are supported.")

        qzeros = qzeros.astype(np.int32)
        self.qzeros = torch.from_numpy(qzeros)

        self.to(device)

        # zeros -= 1
        # zeros = zeros.numpy().astype(np.uint32)
        # qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.quant_bits), dtype=np.uint32)
        # i = 0
        # col = 0
        # while col < qzeros.shape[1]:
        #     if self.quant_bits in [2, 4, 8]:
        #         for j in range(i, i + (32 // self.quant_bits)):
        #             qzeros[:, col] |= zeros[:, j] << (self.quant_bits * (j - i))
        #         i += 32 // self.quant_bits
        #         col += 1
        #     else:
        #         raise NotImplementedError("Only 2,4,8 bits are supported.")

        # qzeros = qzeros.astype(np.int32)
        # self.qzeros = torch.from_numpy(qzeros)

    def forward(self, x):
        # out_shape = x.shape[:-1] + (self.outfeatures,)
        # quant_linear_fn = QuantLinearFunction if self.trainable else QuantLinearInferenceOnlyFunction
        # out = quant_linear_fn.apply(
        #     x.reshape(-1, x.shape[-1]),
        #     self.qweight,
        #     self.scales,
        #     self.qzeros,
        #     self.g_idx,
        #     self.quant_bits,
        #     self.maxq,
        # )
        # out = out.half().reshape(out_shape)
        # out = out + self.bias if self.bias is not None else out
        # return out

        x = x + (activation_quant(x, self.input_bits) - x).detach()
        out_shape = x.shape[:-1] + (self.outfeatures,)

        quant_linear_fn = (
            QuantLinearFunction if self.trainable else QuantLinearInferenceOnlyFunction
        )
        out = quant_linear_fn.apply(
            x.reshape(-1, x.shape[-1]),
            self.qweight,
            self.scales,
            self.qzeros,
            self.g_idx,
            self.quant_bits,
            self.maxq,
        )

        out *= self.scale

        out = out.half().reshape(out_shape)
        out = out + self.bias if self.bias is not None else out

        return out

    @classmethod
    def warmup(cls, model, transpose=False, seqlen=2048):
        """
        Pre-tunes the quantized kernel
        """
        from tqdm import tqdm

        kn_values = {}

        for _, m in model.named_modules():
            if not isinstance(m, cls):
                continue

            k = m.infeatures
            n = m.outfeatures

            if (k, n) not in kn_values:
                kn_values[(k, n)] = (
                    m.qweight,
                    m.scales,
                    m.qzeros,
                    m.g_idx,
                    m.bits,
                    m.maxq,
                )

        # logger.info(f"Found {len(kn_values)} unique KN Linear values.")
        # logger.info("Warming up autotune cache ...")
        with torch.no_grad():
            for m in tqdm(range(0, math.ceil(math.log2(seqlen)) + 1)):
                m = 2**m
                for (k, n), (
                    qweight,
                    scales,
                    qzeros,
                    g_idx,
                    bits,
                    maxq,
                ) in kn_values.items():
                    if transpose:
                        a = torch.randn(m, k, dtype=torch.float16, device=model.device)
                        quant_matmul_248(a, qweight, scales, qzeros, g_idx, bits, maxq)
                        a = torch.randn(m, n, dtype=torch.float16, device=model.device)
                        transpose_quant_matmul_248(
                            a, qweight, scales, qzeros, g_idx, bits, maxq
                        )
                    else:
                        a = torch.randn(m, k, dtype=torch.float16, device=model.device)
                        quant_matmul_inference_only_248(
                            a, qweight, scales, qzeros, g_idx, bits, maxq
                        )
        del kn_values