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


@triton.jit
def triton_poi_fused_convolution_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
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, 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_0[grid(256)](buf1, primals_2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), 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))
    return buf2, primals_1, primals_3, primals_4, buf1


class ConvBNReLU(nn.Module):

    def __init__(self, in_chan, out_chan, ks=1, stride=1, padding=0,
        norm_layer=None, bias=True, *args, **kwargs):
        super(ConvBNReLU, self).__init__()
        self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=
            stride, padding=padding, bias=bias)
        self.norm_layer = norm_layer
        if norm_layer is not None:
            self.bn = norm_layer(out_chan, activation='leaky_relu')
        self.init_weight()

    def forward(self, x):
        x = self.conv(x)
        if self.norm_layer is not None:
            x = self.bn(x)
        return x

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if ly.bias is not None:
                    nn.init.constant_(ly.bias, 0)


class FPNOutputNew(nn.Module):

    def __init__(self, in_chan, mid_chan, n_classes, norm_layer=None, *args,
        **kwargs):
        super(FPNOutputNew, self).__init__()
        self.norm_layer = norm_layer
        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1,
            norm_layer=norm_layer)
        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=
            False)
        self.init_weight()

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if ly.bias is not None:
                    nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                wd_params.append(module.weight)
                if module.bias is not None:
                    nowd_params.append(module.bias)
            elif isinstance(module, self.norm_layer):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params

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


Measured latency_ms: 0.03663