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
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_add_repeat_0(in_ptr0, in_ptr1, 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
    x0 = xindex % 16
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x2 // 4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 * x1 + x0 % 4), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_leaky_relu_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)
    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 = 0.2
    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 = 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)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_repeat_0[grid(64)](buf0, buf1, buf2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_leaky_relu_1[grid(64)](buf2, buf3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        extern_kernels.bmm(buf4, primals_1, out=buf5)
        del buf4
    return reinterpret_tensor(buf5, (4, 4, 4), (16, 1, 4), 0
        ), primals_1, reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)


class GATNew(nn.Module):

    def __init__(self, num_feats):
        super(GATNew, self).__init__()
        self.num_feats = num_feats
        self.weight_key = nn.Parameter(torch.zeros(size=(self.num_feats, 1)))
        self.weight_query = nn.Parameter(torch.zeros(size=(self.num_feats, 1)))
        nn.init.xavier_uniform_(self.weight_key, gain=1.414)
        nn.init.xavier_uniform_(self.weight_query, gain=1.414)
        self.dropout = nn.Dropout(0.5)

    def forward(self, input_0):
        primals_2 = self.weight_key
        primals_3 = self.weight_query
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.060054