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__unsafe_view_clone_0(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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * (x1 // 4) + 16 * (x1 % 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused__softmax_leaky_relu_1(in_ptr0, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 4.0
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp7 = tmp6 > tmp1
    tmp8 = tmp6 * tmp3
    tmp9 = tl.where(tmp7, tmp6, tmp8)
    tmp11 = tmp10 > tmp1
    tmp12 = tmp10 * tmp3
    tmp13 = tl.where(tmp11, tmp10, tmp12)
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 > tmp1
    tmp17 = tmp15 * tmp3
    tmp18 = tl.where(tmp16, tmp15, tmp17)
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = tmp20 > tmp1
    tmp22 = tmp20 * tmp3
    tmp23 = tl.where(tmp21, tmp20, tmp22)
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tl.store(out_ptr0 + x2, tmp26, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, 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
    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, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + x0, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + x0, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 4, tl.int64)
    tmp19 = tl.load(in_ptr3 + 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)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_view_clone_0[grid(64)](primals_1, buf0, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf0, primals_2, out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_leaky_relu_1[grid(16)](buf1, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf2
        buf4 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (1, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (16, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (1, 4), (4, 1), 4),
            reinterpret_tensor(primals_1, (4, 4), (16, 1), 4), out=buf5)
        buf6 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (1, 4), (4, 1), 8),
            reinterpret_tensor(primals_1, (4, 4), (16, 1), 8), out=buf6)
        buf7 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (1, 4), (4, 1), 12),
            reinterpret_tensor(primals_1, (4, 4), (16, 1), 12), out=buf7)
        buf8 = buf3
        del buf3
        triton_poi_fused_cat_3[grid(16)](buf4, buf5, buf6, buf7, buf8, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf4
        del buf5
        del buf6
        del buf7
    return buf8, buf1, reinterpret_tensor(primals_1, (4, 4), (1, 16), 12
        ), reinterpret_tensor(primals_1, (4, 4), (1, 16), 8
        ), reinterpret_tensor(primals_1, (4, 4), (1, 16), 4
        ), reinterpret_tensor(primals_1, (4, 4), (1, 16), 0
        ), reinterpret_tensor(buf0, (4, 16), (1, 4), 0)


class MetapathAggrLayerNew(nn.Module):
    """
    metapath attention layer.
    """

    def __init__(self, in_features, nmeta, dropout, alpha):
        super(MetapathAggrLayerNew, self).__init__()
        self.dropout = dropout
        self.in_features = in_features
        self.alpha = alpha
        self.n_meta = nmeta
        self.a = nn.Parameter(torch.zeros(size=(in_features, 1)))
        nn.init.xavier_uniform_(self.a.data, gain=1.414)
        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'

    def forward(self, input_0):
        primals_2 = self.a
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.090657