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.nn import Parameter
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_relu_0(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_relu_threshold_backward_1(in_out_ptr0,
    in_ptr0, in_ptr1, out_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')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = 0.0
    tmp8 = tmp6 <= tmp7
    tl.store(in_out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(256)](buf1, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)](
            buf3, primals_5, primals_1, buf4, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_5
    return buf3, primals_1, primals_2, primals_4, buf1, buf4


def l2normalize(v, eps=1e-12):
    return v / (v.norm() + eps)


class SpectralNorm(nn.Module):

    def __init__(self, module, name='weight', power_iterations=1):
        super(SpectralNorm, self).__init__()
        self.module = module
        self.name = name
        self.power_iterations = power_iterations
        if not self._made_params():
            self._make_params()
        self._update_u_v()

    def _update_u_v(self):
        u = getattr(self.module, self.name + '_u')
        v = getattr(self.module, self.name + '_v')
        w = getattr(self.module, self.name + '_bar')
        height = w.data.shape[0]
        for _ in range(self.power_iterations):
            v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data),
                u.data))
            u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
        sigma = u.dot(w.view(height, -1).mv(v))
        setattr(self.module, self.name, w / sigma.expand_as(w))

    def _made_params(self):
        try:
            getattr(self.module, self.name + '_u')
            getattr(self.module, self.name + '_v')
            getattr(self.module, self.name + '_bar')
            return True
        except AttributeError:
            return False

    def _make_params(self):
        w = getattr(self.module, self.name)
        height = w.data.shape[0]
        width = w.view(height, -1).data.shape[1]
        u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
        v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
        u.data = l2normalize(u.data)
        v.data = l2normalize(v.data)
        w_bar = Parameter(w.data)
        del self.module._parameters[self.name]
        self.module.register_parameter(self.name + '_u', u)
        self.module.register_parameter(self.name + '_v', v)
        self.module.register_parameter(self.name + '_bar', w_bar)

    def forward(self, *args):
        self._update_u_v()
        return self.module.forward(*args)


class ResidualBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels, stride=1, downsample=None,
        norm=None, sn=False):
        super(ResidualBlockNew, self).__init__()
        bias = False if norm == 'BN' else True
        if sn:
            self.conv1 = SpectralNorm(nn.Conv2d(in_channels, out_channels,
                kernel_size=3, stride=stride, padding=1, bias=bias))
        else:
            self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
                stride=stride, padding=1, bias=bias)
        self.norm = norm
        if norm == 'BN':
            self.bn1 = nn.BatchNorm2d(out_channels)
            self.bn2 = nn.BatchNorm2d(out_channels)
        elif norm == 'IN':
            self.bn1 = nn.InstanceNorm2d(out_channels)
            self.bn2 = nn.InstanceNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        if sn:
            self.conv2 = SpectralNorm(nn.Conv2d(out_channels, out_channels,
                kernel_size=3, stride=1, padding=1, bias=bias))
        else:
            self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=
                3, stride=1, padding=1, bias=bias)
        self.downsample = downsample

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


Measured latency_ms: 0.025941