import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr3 + (4 * x1 + (-12 + x0)), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_2(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp4 * tmp3
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_3(in_out_ptr0, in_out_ptr1, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_out_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp9 = tmp8 * tmp7
    tmp10 = tmp8 * tmp3
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_4(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (8, 16), (16, 1))
    assert_size_stride(primals_6, (8,), (1,))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 8), (8, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 8), (8, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4, 8), (8, 1))
    assert_size_stride(primals_14, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(64)](primals_1, primals_2, primals_3,
            primals_4, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_4
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_5, (16, 8), (1, 
            16), 0), out=buf1)
        del primals_5
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(32)](buf2, primals_6, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del primals_6
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf2, reinterpret_tensor(primals_7,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf3)
        del primals_8
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_9, (8, 4), (1, 8
            ), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_11, (8, 4), (1, 
            8), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_13, (8, 4), (1, 
            8), 0), out=buf6)
        buf7 = buf4
        del buf4
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_2[grid(16)](buf7, primals_10,
            primals_2, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_10
        buf9 = buf6
        del buf6
        buf8 = buf5
        del buf5
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_3[grid(16)](buf9, buf8, primals_14,
            primals_12, primals_3, buf12, buf13, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_12
        del primals_14
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_4[grid(16)](primals_1, buf3, buf10, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
    return (buf10, buf11, buf7, buf12, buf8, buf13, buf9, primals_1,
        primals_2, primals_3, buf0, buf2, buf3, buf7, buf8, buf9,
        primals_13, primals_11, primals_9, primals_7)


class MMTMQuadNew(nn.Module):
    """
    quad modal fusion
    """

    def __init__(self, dim_tab, dim_img, ratio=4):
        """

        Parameters
        ----------
        dim_tab: feature dimension of tabular data
        dim_img: feature dimension of MIL model
        ratio
        """
        super(MMTMQuadNew, self).__init__()
        dim = dim_tab + dim_img * 3
        dim_out = int(2 * dim / ratio)
        self.fc_squeeze = nn.Linear(dim, dim_out)
        self.fc_tab = nn.Linear(dim_out, dim_tab)
        self.fc_img_scale1 = nn.Linear(dim_out, dim_img)
        self.fc_img_scale2 = nn.Linear(dim_out, dim_img)
        self.fc_img_scale3 = nn.Linear(dim_out, dim_img)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_0, input_1, input_2, input_3):
        primals_5 = self.fc_squeeze.weight
        primals_6 = self.fc_squeeze.bias
        primals_7 = self.fc_tab.weight
        primals_8 = self.fc_tab.bias
        primals_9 = self.fc_img_scale1.weight
        primals_10 = self.fc_img_scale1.bias
        primals_11 = self.fc_img_scale2.weight
        primals_12 = self.fc_img_scale2.bias
        primals_13 = self.fc_img_scale3.weight
        primals_14 = self.fc_img_scale3.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        primals_4 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0], output[1], output[2], output[3], output[4], output[5
            ], output[6]


Measured latency_ms: 0.077162