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
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, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    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)
    tmp11 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + x2, tmp10, xmask)
    tl.store(out_ptr1 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_out_ptr0, in_out_ptr1, in_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')
    tmp8 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp8 + tmp1
    tmp10 = tmp9 > tmp3
    tmp11 = tmp9 * tmp5
    tmp12 = tl.where(tmp10, tmp9, tmp11)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(in_out_ptr1 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_2(in_out_ptr0, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = 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, 8), (8, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, primals_3,
            buf0, buf1, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (8, 4), (1, 8
            ), 0), out=buf2)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (8, 4), (1, 8
            ), 0), out=buf4)
        del primals_4
        buf3 = buf2
        del buf2
        buf5 = buf4
        del buf4
        triton_poi_fused_leaky_relu_1[grid(16)](buf3, buf5, primals_5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (4, 4), (1, 4
            ), 0), out=buf6)
        buf7 = buf6
        del buf6
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_2[grid(16)](buf7,
            primals_7, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_7
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (4, 4), (1, 4
            ), 0), out=buf8)
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_2[grid(16)](buf9,
            primals_9, buf10, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_9
    return (buf7, buf9, buf0, buf1, buf3, buf5, buf10, primals_8, buf11,
        primals_6)


def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Linear') != -1:
        m.weight.data.normal_(0.0, 0.02)
        m.bias.data.fill_(0)
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)


class MLP_gNew(nn.Module):

    def __init__(self, opt):
        super(MLP_gNew, self).__init__()
        self.SharedFC = nn.Linear(opt.attSize + opt.nz, opt.ngh)
        self.fc1 = nn.Linear(opt.ngh, opt.resSize)
        self.fc2 = nn.Linear(opt.ngh, opt.resSize)
        self.lrelu = nn.LeakyReLU(0.2, True)
        self.relu = nn.ReLU(True)
        self.apply(weights_init)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.SharedFC.weight
        primals_5 = self.SharedFC.bias
        primals_1 = self.fc1.weight
        primals_7 = self.fc1.bias
        primals_2 = self.fc2.weight
        primals_9 = self.fc2.bias
        primals_3 = input_0
        primals_6 = input_1
        primals_8 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]


Measured latency_ms: 0.042202