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 math as tl_math
from torch import 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, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + x1, 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_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + x2, xmask)
    tmp12 = tl.load(in_ptr2 + x2, xmask)
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp0 - tmp2
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp1 - tmp2
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 + tmp6
    tmp8 = tmp4 / tmp7
    tmp10 = tmp8 * tmp9
    tmp11 = tmp6 / tmp7
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 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), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 1, 4), (4, 4, 1), 
            0), reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0), out=buf3)
        buf4 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 1, 4), (4, 4, 1), 
            0), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 2, 1), (2, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](buf3, buf4, buf5, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del buf3
        del buf4
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf6)
        del primals_6
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf7)
        del primals_7
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_1[grid(64)](buf5, buf6, buf7, buf8, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
    return buf8, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
        ), buf5, buf6, buf7, reinterpret_tensor(buf0, (16, 4, 1), (4, 1, 4), 0
        ), reinterpret_tensor(buf2, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0)


class GatingNew(nn.Module):

    def __init__(self, in0_size: 'int', in1_size: 'int', query_size: 'int',
        hidden_size: 'int'):
        super(GatingNew, self).__init__()
        self.q = nn.Linear(in0_size, query_size, bias=False)
        self.w_k0 = nn.Linear(in0_size, query_size, bias=False)
        self.w_k1 = nn.Linear(in1_size, query_size, bias=False)
        self.w_f0 = nn.Linear(in0_size, hidden_size, bias=False)
        self.w_f1 = nn.Linear(in1_size, hidden_size, bias=False)

    def forward(self, input_0, input_1):
        primals_2 = self.q.weight
        primals_3 = self.w_k0.weight
        primals_5 = self.w_k1.weight
        primals_6 = self.w_f0.weight
        primals_7 = self.w_f1.weight
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.014728