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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.autograd import Variable
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_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x3, tmp6, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(256)](buf2, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = extern_kernels.convolution(primals_1, primals_6, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = buf3
        del buf3
        triton_poi_fused_add_convolution_2[grid(256)](buf5, buf4, primals_7,
            primals_5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        del primals_5
        del primals_7
    return buf5, primals_1, primals_2, primals_4, primals_6, buf0, buf2


def spectral_norm(module, name='weight'):
    SpectralNorm.apply(module, name)
    return module


class SpectralNorm:

    def __init__(self, name):
        self.name = name

    def compute_weight(self, module):
        weight = getattr(module, self.name + '_orig')
        u = getattr(module, self.name + '_u')
        size = weight.size()
        weight_mat = weight.contiguous().view(size[0], -1)
        if weight_mat.is_cuda:
            u = u
        v = weight_mat.t() @ u
        v = v / v.norm()
        u = weight_mat @ v
        u = u / u.norm()
        weight_sn = weight_mat / (u.t() @ weight_mat @ v)
        weight_sn = weight_sn.view(*size)
        return weight_sn, Variable(u.data)

    @staticmethod
    def apply(module, name):
        fn = SpectralNorm(name)
        weight = getattr(module, name)
        del module._parameters[name]
        module.register_parameter(name + '_orig', nn.Parameter(weight.data))
        input_size = weight.size(0)
        u = Variable(torch.randn(input_size, 1) * 0.1, requires_grad=False)
        setattr(module, name + '_u', u)
        setattr(module, name, fn.compute_weight(module)[0])
        module.register_forward_pre_hook(fn)
        return fn

    def __call__(self, module, input):
        weight_sn, u = self.compute_weight(module)
        setattr(module, self.name, weight_sn)
        setattr(module, self.name + '_u', u)


class conv2d(nn.Module):

    def __init__(self, in_channels, out_channels, padding, kernel_size=4,
        stride=2, spectral_normed=False):
        super(conv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding=padding)
        if spectral_normed:
            self.conv = spectral_norm(self.conv)

    def forward(self, input):
        out = self.conv(input)
        return out


class Residual_DNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel=3, stride=1,
        spectral_normed=False, down_sampling=False, is_start=False):
        super(Residual_DNew, self).__init__()
        self.down_sampling = down_sampling
        self.is_start = is_start
        self.avgpool_short = nn.AvgPool2d(2, 2, padding=1)
        self.conv_short = conv2d(in_channels, out_channels, kernel_size=1,
            stride=1, padding=0, spectral_normed=False)
        self.conv1 = conv2d(in_channels, out_channels, spectral_normed=
            spectral_normed, kernel_size=kernel, stride=stride, padding=1)
        self.conv2 = conv2d(out_channels, out_channels, spectral_normed=
            spectral_normed, kernel_size=kernel, stride=stride, padding=1)
        self.avgpool2 = nn.AvgPool2d(2, 2, padding=1)
        self.relu = nn.ReLU()

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


Measured latency_ms: 0.021158