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 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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


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


@triton.jit
def triton_poi_fused__prelu_kernel_reflection_pad2d_2(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp5 = tmp4 * tmp0
    tmp6 = tl.where(tmp2, tmp0, tmp5)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_convolution_mul_3(in_out_ptr0, in_ptr0, in_ptr1,
    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')
    tmp5 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 + tmp5
    tl.store(in_out_ptr0 + x3, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, (1,), (1,))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576,
            XBLOCK=128, num_warps=4, num_stages=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, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(256)](buf2, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        triton_poi_fused__prelu_kernel_reflection_pad2d_2[grid(576)](buf2,
            primals_4, buf3, 576, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_5, 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 = buf4
        del buf4
        triton_poi_fused_add_convolution_mul_3[grid(256)](buf5, primals_6,
            primals_1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_6
    return buf5, primals_2, primals_4, primals_5, buf0, buf2, buf3


class ConvLayer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super(ConvLayer, self).__init__()
        reflection_padding = kernel_size // 2
        self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride)

    def forward(self, x):
        out = self.reflection_pad(x)
        out = self.conv2d(out)
        return out


class ResidualBlockNew(torch.nn.Module):

    def __init__(self, channels):
        super(ResidualBlockNew, self).__init__()
        self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.relu = nn.PReLU()

    def forward(self, input_0):
        primals_2 = self.conv1.conv2d.weight
        primals_3 = self.conv1.conv2d.bias
        primals_5 = self.conv2.conv2d.weight
        primals_6 = self.conv2.conv2d.bias
        primals_4 = self.relu.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.040714