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.triton_helpers import libdevice, math as tl_math
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__unsafe_index_reflection_pad2d_0(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 10 % 10
    x0 = xindex % 10
    x2 = xindex // 100
    x5 = xindex
    tmp0 = 7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1))
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = 7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0))
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x5, tmp9, xmask)


@triton.jit
def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 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 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = tmp7 * tmp5
    tmp9 = tl.where(tmp4, tmp6, tmp8)
    tl.store(in_out_ptr0 + x3, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_reflection_pad2d_0[grid(1600)](primals_1
            , buf0, 1600, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_elu_1[grid(1024)](buf2, primals_3, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf2, primals_2, buf0, buf2


class Conv3x3(nn.Module):

    def __init__(self, in_channels, out_channels, use_refl=True):
        super(Conv3x3, self).__init__()
        if use_refl:
            self.pad = nn.ReflectionPad2d(1)
        else:
            self.pad = nn.ZeroPad2d(1)
        self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3)

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


class UpConvNew(nn.Module):

    def __init__(self, in_channels, out_channels):
        super(UpConvNew, self).__init__()
        self.upsamp = nn.UpsamplingNearest2d(scale_factor=2)
        self.conv = Conv3x3(in_channels=in_channels, out_channels=out_channels)
        self.elu = nn.ELU(inplace=True)

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


Measured latency_ms: 0.033327