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.utils.data
import torch
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, 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, (1024, 4), (4, 1))
    assert_size_stride(primals_2, (1024,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 1024), (1024, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (1, 1024), (1024, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1024), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(65536)](buf0, primals_2, buf1,
            buf2, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_2
        buf3 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 1024), (1024, 1), 0
            ), reinterpret_tensor(primals_4, (1024, 1024), (1, 1024), 0),
            out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.bool)
        buf5 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_0[grid(65536)](buf3, primals_5, buf4,
            buf5, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del buf3
        del primals_5
        buf6 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 1024), (1024, 1), 0
            ), reinterpret_tensor(primals_6, (1024, 1), (1, 1024), 0), out=buf6
            )
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf6
        triton_poi_fused_sigmoid_1[grid(64)](buf7, primals_7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_7
    return buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf2, (64, 1024), (1024, 1), 0
        ), buf4, reinterpret_tensor(buf5, (64, 1024), (1024, 1), 0
        ), buf7, primals_6, primals_4


class AdversarialNetworkNew(nn.Module):

    def __init__(self, in_feature):
        super(AdversarialNetworkNew, self).__init__()
        self.ad_layer1 = nn.Linear(in_feature, 1024)
        self.ad_layer2 = nn.Linear(1024, 1024)
        self.ad_layer3 = nn.Linear(1024, 1)
        self.ad_layer1.weight.data.normal_(0, 0.01)
        self.ad_layer2.weight.data.normal_(0, 0.01)
        self.ad_layer3.weight.data.normal_(0, 0.3)
        self.ad_layer1.bias.data.fill_(0)
        self.ad_layer2.bias.data.fill_(0)
        self.ad_layer3.bias.data.fill_(0)
        self.relu1 = nn.LeakyReLU()
        self.relu2 = nn.LeakyReLU()
        self.dropout1 = nn.Dropout(0.5)
        self.dropout2 = nn.Dropout(0.5)
        self.sigmoid = nn.Sigmoid()

    def output_num(self):
        return 1

    def forward(self, input_0):
        primals_1 = self.ad_layer1.weight
        primals_2 = self.ad_layer1.bias
        primals_4 = self.ad_layer2.weight
        primals_5 = self.ad_layer2.bias
        primals_6 = self.ad_layer3.weight
        primals_7 = self.ad_layer3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.021741