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


@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, 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)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (64, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (4, 2, 64, 64), (8192, 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, (2, 512, 4, 4), (8192, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 
            2), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 33, 33), (69696, 1089, 33, 1))
        buf1 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.bool)
        buf2 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(278784)](buf0, buf1, buf2, 
            278784, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf0
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 128, 17, 17), (36992, 289, 17, 1))
        buf4 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.bool)
        buf5 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_1[grid(147968)](buf3, buf4, buf5, 
            147968, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf3
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 256, 9, 9), (20736, 81, 9, 1))
        buf7 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.bool
            )
        buf8 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_2[grid(82944)](buf6, buf7, buf8, 82944,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del buf6
        buf9 = extern_kernels.convolution(buf8, primals_5, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 512, 5, 5), (12800, 25, 5, 1))
        buf10 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch
            .bool)
        buf11 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch
            .float32)
        triton_poi_fused_leaky_relu_3[grid(51200)](buf9, buf10, buf11, 
            51200, XBLOCK=512, num_warps=4, num_stages=1)
        del buf9
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 2, 3, 3), (18, 9, 3, 1))
    return (buf12, primals_1, primals_2, primals_3, primals_4, primals_5,
        primals_6, buf1, buf2, buf4, buf5, buf7, buf8, buf10, buf11)


class MaskDiscriminatorNew(nn.Module):

    def __init__(self):
        super(MaskDiscriminatorNew, self).__init__()
        filter_num_list = [64, 128, 256, 512, 2]
        self.conv1 = nn.Conv2d(2, filter_num_list[0], kernel_size=4, stride
            =2, padding=2, bias=False)
        self.conv2 = nn.Conv2d(filter_num_list[0], filter_num_list[1],
            kernel_size=4, stride=2, padding=2, bias=False)
        self.conv3 = nn.Conv2d(filter_num_list[1], filter_num_list[2],
            kernel_size=4, stride=2, padding=2, bias=False)
        self.conv4 = nn.Conv2d(filter_num_list[2], filter_num_list[3],
            kernel_size=4, stride=2, padding=2, bias=False)
        self.conv5 = nn.Conv2d(filter_num_list[3], filter_num_list[4],
            kernel_size=4, stride=2, padding=2, bias=False)
        self.leakyrelu = nn.LeakyReLU(negative_slope=0.2)
        self._initialize_weights()

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                m.weight.data.normal_(0.0, 0.02)
                if m.bias is not None:
                    m.bias.data.zero_()

    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_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.191021