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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x4 = xindex // 48
    x1 = xindex // 12 % 4
    x3 = xindex // 192
    x5 = 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 * x4 + 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_ptr0 + (4 * x1 + 16 * x3 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr0 + (4 * x4 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr0 + (4 * x1 + 16 * x3 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp14 * tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp11, tmp16, tmp17)
    tmp19 = tl.where(tmp9, tmp10, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tl.store(out_ptr0 + x5, tmp20, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    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
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_4(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp7 = libdevice.tanh(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tmp3 + tmp8
    tl.store(out_ptr0 + x0, tmp9, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 12), (12, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(768)](primals_1, buf0, 768, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 12), (12, 1), 0),
            reinterpret_tensor(primals_2, (12, 1), (1, 12), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        triton_poi_fused__softmax_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = buf2
        del buf2
        extern_kernels.bmm(buf3, primals_1, out=buf4)
        buf5 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_3[grid(128)](primals_1, buf4, buf5, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (16, 4), (4, 1), 0)
        del buf4
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf5, (16, 8), (
            8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf6)
        del primals_4
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf5, (16, 8), (
            8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf7)
        del primals_6
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf5, (16, 8), (
            8, 1), 0), reinterpret_tensor(primals_7, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf8)
        del primals_8
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_tanh_4[grid(64)](buf7, primals_1,
            buf8, buf6, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf9, primals_1, reinterpret_tensor(buf0, (64, 12), (12, 1), 0
        ), buf3, reinterpret_tensor(buf5, (16, 8), (8, 1), 0
        ), buf6, buf7, buf8, primals_7, primals_5, primals_3


class SemanticCompositeNew(nn.Module):
    """
    SemanticComposite module.

    Apply a self-attention layer and a semantic composite fuse gate to compute the
    encoding result of one tensor.

    :param in_features: Feature size of input.
    :param dropout_rate: The dropout rate.

    Examples:
        >>> import torch
        >>> module = SemanticComposite(in_features=10)
        >>> x = torch.randn(4, 5, 10)
        >>> x.shape
        torch.Size([4, 5, 10])
        >>> module(x).shape
        torch.Size([4, 5, 10])

    """

    def __init__(self, in_features, dropout_rate: 'float'=0.0):
        """Init."""
        super().__init__()
        self.att_linear = nn.Linear(3 * in_features, 1, False)
        self.z_gate = nn.Linear(2 * in_features, in_features, True)
        self.r_gate = nn.Linear(2 * in_features, in_features, True)
        self.f_gate = nn.Linear(2 * in_features, in_features, True)
        self.dropout = nn.Dropout(p=dropout_rate)

    def forward(self, input_0):
        primals_2 = self.att_linear.weight
        primals_3 = self.z_gate.weight
        primals_4 = self.z_gate.bias
        primals_5 = self.r_gate.weight
        primals_6 = self.r_gate.bias
        primals_7 = self.f_gate.weight
        primals_8 = self.f_gate.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.02835