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
from torch.nn.utils import spectral_norm as spectral_norm_fn
from torch.nn.utils import weight_norm as weight_norm_fn
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_convolution_leaky_relu_0(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
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, 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)
    tl.store(in_out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    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)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(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
    x2 = xindex
    x0 = xindex % 16
    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)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_3(in_out_ptr0,
    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
    x0 = xindex % 16
    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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 5, 5), (100, 25, 5, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (8, 4, 5, 5), (100, 25, 5, 1))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (16, 8, 5, 5), (200, 25, 5, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (16, 16, 5, 5), (400, 25, 5, 1))
    assert_size_stride(primals_9, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(2, 
            2), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(64)](buf1, primals_3,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 1, 1), (8, 1, 1, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_leaky_relu_1[grid(32)](buf3, primals_5,
            32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 16, 1, 1), (16, 1, 1, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_leaky_relu_2[grid(64)](buf5, primals_7,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 16, 1, 1), (16, 1, 1, 1))
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 1, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_3[grid(64)
            ](buf7, primals_9, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
    return (buf7, primals_1, primals_2, primals_4, primals_6, primals_8,
        buf1, buf3, buf5, buf8)


def dis_conv(input_dim, output_dim, kernel_size=5, stride=2, padding=0,
    rate=1, activation='lrelu'):
    return Conv2dBlock(input_dim, output_dim, kernel_size, stride,
        conv_padding=padding, dilation=rate, activation=activation)


class Conv2dBlock(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, stride, padding=
        0, conv_padding=0, dilation=1, weight_norm='none', norm='none',
        activation='relu', pad_type='zero', transpose=False):
        super(Conv2dBlock, self).__init__()
        self.use_bias = True
        if pad_type == 'reflect':
            self.pad = nn.ReflectionPad2d(padding)
        elif pad_type == 'replicate':
            self.pad = nn.ReplicationPad2d(padding)
        elif pad_type == 'zero':
            self.pad = nn.ZeroPad2d(padding)
        elif pad_type == 'none':
            self.pad = None
        else:
            assert 0, 'Unsupported padding type: {}'.format(pad_type)
        norm_dim = output_dim
        if norm == 'bn':
            self.norm = nn.BatchNorm2d(norm_dim)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(norm_dim)
        elif norm == 'none':
            self.norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(norm)
        if weight_norm == 'sn':
            self.weight_norm = spectral_norm_fn
        elif weight_norm == 'wn':
            self.weight_norm = weight_norm_fn
        elif weight_norm == 'none':
            self.weight_norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(weight_norm)
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'elu':
            self.activation = nn.ELU(inplace=True)
        elif activation == 'lrelu':
            self.activation = nn.LeakyReLU(0.2, inplace=True)
        elif activation == 'prelu':
            self.activation = nn.PReLU()
        elif activation == 'selu':
            self.activation = nn.SELU(inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if transpose:
            self.conv = nn.ConvTranspose2d(input_dim, output_dim,
                kernel_size, stride, padding=conv_padding, output_padding=
                conv_padding, dilation=dilation, bias=self.use_bias)
        else:
            self.conv = nn.Conv2d(input_dim, output_dim, kernel_size,
                stride, padding=conv_padding, dilation=dilation, bias=self.
                use_bias)
        if self.weight_norm:
            self.conv = self.weight_norm(self.conv)

    def forward(self, x):
        if self.pad:
            x = self.conv(self.pad(x))
        else:
            x = self.conv(x)
        if self.norm:
            x = self.norm(x)
        if self.activation:
            x = self.activation(x)
        return x


class DisConvModuleNew(nn.Module):

    def __init__(self, input_dim, cnum, use_cuda=True, device_ids=None):
        super(DisConvModuleNew, self).__init__()
        self.use_cuda = use_cuda
        self.device_ids = device_ids
        self.conv1 = dis_conv(input_dim, cnum, 5, 2, 2)
        self.conv2 = dis_conv(cnum, cnum * 2, 5, 2, 2)
        self.conv3 = dis_conv(cnum * 2, cnum * 4, 5, 2, 2)
        self.conv4 = dis_conv(cnum * 4, cnum * 4, 5, 2, 2)

    def forward(self, input_0):
        primals_2 = self.conv1.conv.weight
        primals_3 = self.conv1.conv.bias
        primals_4 = self.conv2.conv.weight
        primals_5 = self.conv2.conv.bias
        primals_6 = self.conv3.conv.weight
        primals_7 = self.conv3.conv.bias
        primals_8 = self.conv4.conv.weight
        primals_9 = self.conv4.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.084452