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.parallel
import torch.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_leaky_relu_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(in_out_ptr0 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(in_out_ptr0 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(in_out_ptr0 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(in_out_ptr0 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_4(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(in_out_ptr0 + x0, tmp5, None)


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, (64, 1024, 4, 4), (16384, 16, 4, 1))
    assert_size_stride(primals_2, (4, 1024, 64, 64), (4194304, 4096, 64, 1))
    assert_size_stride(primals_3, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_4, (256, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_5, (512, 256, 4, 4), (4096, 16, 4, 1))
    assert_size_stride(primals_6, (1024, 512, 4, 4), (8192, 16, 4, 1))
    assert_size_stride(primals_7, (1, 1024, 2, 2), (4096, 4, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(262144)](buf1, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_leaky_relu_1[grid(131072)](buf3, 131072, XBLOCK=
            1024, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_leaky_relu_2[grid(65536)](buf5, 65536, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_5, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_leaky_relu_3[grid(32768)](buf7, 32768, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 1024, 2, 2), (4096, 4, 2, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_leaky_relu_4[grid(16384)](buf9, 16384, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_7, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1, 1, 1), (1, 1, 1, 1))
    return (buf10, primals_1, primals_2, primals_3, primals_4, primals_5,
        primals_6, primals_7, buf1, buf3, buf5, buf7, buf9)


class DomainClassifierNew(nn.Module):

    def __init__(self, input_dim=1024, ndf=64, with_bias=False):
        super(DomainClassifierNew, self).__init__()
        self.conv1 = nn.Conv2d(input_dim, ndf, kernel_size=4, stride=2,
            padding=1, bias=with_bias)
        self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2,
            padding=1, bias=with_bias)
        self.conv3 = nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2,
            padding=1, bias=with_bias)
        self.conv4 = nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2,
            padding=1, bias=with_bias)
        self.conv5 = nn.Conv2d(ndf * 8, ndf * 16, kernel_size=4, stride=2,
            padding=1, bias=with_bias)
        self.conv6 = nn.Conv2d(ndf * 16, 1, kernel_size=2, stride=1, bias=
            with_bias)
        self.leaky_relu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, mean=0, std=0.001)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_4 = self.conv3.weight
        primals_5 = self.conv4.weight
        primals_6 = self.conv5.weight
        primals_7 = self.conv6.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.895593