# Copyright 2021 Tencent
# SPDX-License-Identifier: BSD-3-Clause

import torch
import torch.nn as nn
import torch.nn.functional as F
from packaging import version

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

        self.attention_0_0 = nn.MultiheadAttention(embed_dim=64, num_heads=4)
        self.attention_0_1 = nn.MultiheadAttention(embed_dim=64, num_heads=8, bias=False, add_bias_kv=False, add_zero_attn=False)
        self.attention_0_2 = nn.MultiheadAttention(embed_dim=64, num_heads=16, bias=True, add_bias_kv=True, add_zero_attn=True)

        self.attention_0_3 = nn.MultiheadAttention(embed_dim=32, num_heads=8, bias=True)

        self.attention_0_4 = nn.MultiheadAttention(embed_dim=40, num_heads=4, kdim=30, vdim=20)
        self.attention_0_5 = nn.MultiheadAttention(embed_dim=40, num_heads=8, kdim=30, vdim=20, bias=False, add_bias_kv=False, add_zero_attn=False)
        self.attention_0_6 = nn.MultiheadAttention(embed_dim=40, num_heads=10, kdim=30, vdim=20, bias=True, add_bias_kv=True, add_zero_attn=True)

        if version.parse(torch.__version__) >= version.parse('1.9'):
            self.attention_1_0 = nn.MultiheadAttention(embed_dim=64, num_heads=4, batch_first=True)
            self.attention_1_1 = nn.MultiheadAttention(embed_dim=64, num_heads=8, bias=False, add_bias_kv=False, add_zero_attn=False, batch_first=True)
            self.attention_1_2 = nn.MultiheadAttention(embed_dim=64, num_heads=16, bias=True, add_bias_kv=True, add_zero_attn=True, batch_first=True)

            self.attention_1_3 = nn.MultiheadAttention(embed_dim=32, num_heads=8, bias=True, batch_first=True)

            self.attention_1_4 = nn.MultiheadAttention(embed_dim=40, num_heads=4, kdim=30, vdim=20, batch_first=True)
            self.attention_1_5 = nn.MultiheadAttention(embed_dim=40, num_heads=8, kdim=30, vdim=20, bias=False, add_bias_kv=False, add_zero_attn=False, batch_first=True)
            self.attention_1_6 = nn.MultiheadAttention(embed_dim=40, num_heads=10, kdim=30, vdim=20, bias=True, add_bias_kv=True, add_zero_attn=True, batch_first=True)

    def forward(self, xq, xk, xv, z, zmask, yq, yk, yv, ymask, ymask2):
        x0, x0w = self.attention_0_0(xq, xk, xv)
        x1, x1w = self.attention_0_1(xq, xk, xv)
        x2, x2w = self.attention_0_2(xq, xk, xk)

        x3, _ = self.attention_0_3(z, z, z, need_weights=False)
        x33, _ = self.attention_0_3(z, z, z, attn_mask=zmask)

        x4, x4w = self.attention_0_4(yq, yk, yv)
        x5, x5w = self.attention_0_5(yq, yk, yv, attn_mask=ymask)
        x6, x6w = self.attention_0_6(yq, yk, yv, attn_mask=ymask2)

        if version.parse(torch.__version__) < version.parse('1.9'):
            return x0, x0w, x1, x1w, x2, x2w, x3, x33, x4, x4w, x5, x5w, x6, x6w

        xq = xq.transpose(0, 1)
        xk = xk.transpose(0, 1)
        xv = xv.transpose(0, 1)
        z = z.transpose(0, 1)
        yq = yq.transpose(0, 1)
        yk = yk.transpose(0, 1)
        yv = yv.transpose(0, 1)

        y0, y0w = self.attention_1_0(xq, xk, xv)
        y1, y1w = self.attention_1_1(xq, xk, xv)
        y2, y2w = self.attention_1_2(xq, xk, xk)

        y3, _ = self.attention_1_3(z, z, z)
        if version.parse(torch.__version__) >= version.parse('1.12') and version.parse(torch.__version__) < version.parse('1.13'):
            # HACK pytorch 1.12 breaks 2-dim zmask
            # https://github.com/pytorch/pytorch/issues/97409
            # zmask2 = zmask.reshape(1, 1, 30, 30).expand(1, 8, 30, 30)
            # y33, _ = self.attention_1_3(z, z, z, attn_mask=zmask2)
            # but it produce all nan then, skip test :(
            y33 = y3
        elif version.parse(torch.__version__) >= version.parse('2.0') and version.parse(torch.__version__) < version.parse('2.1'):
            # HACK pytorch 2.0 produce all nan, skip test :(
            y33 = y3
        else:
            y33, _ = self.attention_1_3(z, z, z, attn_mask=zmask)

        y4, y4w = self.attention_1_4(yq, yk, yv)
        y5, y5w = self.attention_1_5(yq, yk, yv, attn_mask=ymask)
        y6, y6w = self.attention_1_6(yq, yk, yv, attn_mask=ymask2)

        return x0, x0w, x1, x1w, x2, x2w, x3, x33, x4, x4w, x5, x5w, x6, x6w, y0, y0w, y1, y1w, y2, y2w, y3, y33, y4, y4w, y5, y5w, y6, y6w

def test():
    torch.set_grad_enabled(False)

    net = Model()
    net.eval()

    torch.manual_seed(0)
    xq = torch.rand(20, 1, 64)
    xk = torch.rand(20, 1, 64)
    xv = torch.rand(20, 1, 64)
    z = torch.rand(30, 1, 32)
    zmask = torch.rand(30, 30)
    yq = torch.rand(15, 1, 40)
    yk = torch.rand(24, 1, 30)
    yv = torch.rand(24, 1, 20)
    ymask = torch.rand(15, 24)
    ymask2 = torch.rand(10, 15, 24)

    a = net(xq, xk, xv, z, zmask, yq, yk, yv, ymask, ymask2)

    # export torchscript
    print(torch.__version__)
    if version.parse(torch.__version__) >= version.parse('1.12.0'):
        mod = torch.jit.trace(net, (xq, xk, xv, z, zmask, yq, yk, yv, ymask, ymask2), check_trace=False)
    else:
        mod = torch.jit.trace(net, (xq, xk, xv, z, zmask, yq, yk, yv, ymask, ymask2))
    mod.save("test_nn_MultiheadAttention.pt")

    # torchscript to pnnx
    import os
    os.system("../src/pnnx test_nn_MultiheadAttention.pt inputshape=[20,1,64],[20,1,64],[20,1,64],[30,1,32],[30,30],[15,1,40],[24,1,30],[24,1,20],[15,24],[10,15,24]")

    # pnnx inference
    import test_nn_MultiheadAttention_pnnx
    b = test_nn_MultiheadAttention_pnnx.test_inference()

    for a0, b0 in zip(a, b):
        if not torch.allclose(a0, b0, 1e-4, 1e-4):
            return False
    return True

if __name__ == "__main__":
    if test():
        exit(0)
    else:
        exit(1)
