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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_1(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 512
    xnumel = 36
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex % 6
    x3 = xindex // 6
    y4 = yindex
    x5 = xindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x2)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x3)) + 16 * y4), 
        xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + 128 * x5 + 4608 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_poi_fused_repeat_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0 % 128, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_4(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (128 * r1 + 2048 * (x0 // 128) + x0 % 128),
        xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 128
    x1 = xindex // 128 % 6
    x2 = xindex // 768 % 6
    x3 = xindex // 4608
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (1920 + x0 + -512 * tl_math.abs(-3 + tl_math.
        abs(-1 + x2)) + -128 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 
        2048 * x3), None)
    tmp1 = tl.load(in_ptr1 + (x0 + 128 * x3), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 128 * x3), None, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr3 + (x0 + 128 * x3), None, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr4 + (x0 + 128 * x3), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x5, tmp10, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_repeat_6(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr3, out_ptr4, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    x0 = xindex
    r1 = rindex
    x2 = xindex % 128
    x3 = xindex // 128
    tmp0 = tl.load(in_ptr0 + x0 % 128, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (128 * r1 + 2048 * (x0 // 128) + x0 % 128),
        xmask, other=0.0)
    tmp23 = tl.load(in_ptr1 + (x2 + 128 * r1 + 2048 * x3), xmask, other=0.0)
    tmp27 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr3 + (r1 + 16 * x0), xmask, other=0.0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp2, 0)
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp5, 0)
    tmp8 = tl.sum(tmp7, 1)[:, None]
    tmp9 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp8 / tmp10
    tmp12 = tmp2 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.sum(tmp16, 1)[:, None]
    tmp18 = 16.0
    tmp19 = tmp17 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp24 = tmp23 - tmp11
    tmp25 = tmp24 * tmp22
    tmp26 = tmp25 * tmp0
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tl.store(out_ptr3 + x0, tmp22, xmask)
    tl.store(out_ptr4 + (r1 + 16 * x0), tmp30, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_2, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_3, (128,), (1,))
    assert_size_stride(primals_4, (128,), (1,))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128,), (1,))
    assert_size_stride(primals_9, (128,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(16384, 9)](primals_2, buf0, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
            torch.float32)
        triton_poi_fused_0[grid(16384, 9)](primals_6, buf1, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf2 = empty_strided_cuda((4, 128, 6, 6), (4608, 1, 768, 128),
            torch.float32)
        triton_poi_fused_reflection_pad2d_1[grid(512, 36)](primals_1, buf2,
            512, 36, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_2[grid(8192)](buf4, primals_3, 8192,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf5 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_3[grid(512)](primals_4, buf5, 512, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_4
        buf6 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_3[grid(512)](primals_5, buf6, 512, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch
            .float32)
        buf8 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch
            .float32)
        buf10 = buf8
        del buf8
        triton_per_fused__native_batch_norm_legit_4[grid(512)](buf10, buf4,
            buf7, 512, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf11 = empty_strided_cuda((4, 128, 6, 6), (4608, 1, 768, 128),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_5[grid(18432)](buf4, buf7,
            buf10, buf5, buf6, buf11, 18432, XBLOCK=256, num_warps=4,
            num_stages=1)
        buf12 = extern_kernels.convolution(buf11, buf1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_2[grid(8192)](buf13, primals_7, 8192,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf14 = empty_strided_cuda((512,), (1,), torch.float32)
        buf15 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf18 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf19 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_add_repeat_6[grid(512)](
            primals_8, buf13, primals_9, primals_1, buf14, buf15, buf18,
            buf19, 512, 16, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_1
        del primals_8
        del primals_9
    return (buf19, buf0, buf1, buf2, buf4, buf5, buf6, buf7, buf10, buf11,
        buf13, buf14, reinterpret_tensor(buf18, (512,), (1,), 0),
        reinterpret_tensor(buf15, (1, 512, 1, 1), (512, 1, 1, 1), 0))


class ConvLayer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, norm
        ='instance'):
        super(ConvLayer, self).__init__()
        padding_size = kernel_size // 2
        self.reflection_pad = nn.ReflectionPad2d(padding_size)
        self.conv_layer = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride)
        self.norm_type = norm
        if norm == 'instance':
            self.norm_layer = nn.InstanceNorm2d(out_channels, affine=True)
        elif norm == 'batch':
            self.norm_layer = nn.BatchNorm2d(out_channels, affine=True)

    def forward(self, x):
        x = self.reflection_pad(x)
        x = self.conv_layer(x)
        if self.norm_type == 'None':
            out = x
        else:
            out = self.norm_layer(x)
        return out


class ResidualLayerNew(nn.Module):

    def __init__(self, channels=128, kernel_size=3):
        super(ResidualLayerNew, self).__init__()
        self.conv1 = ConvLayer(channels, channels, kernel_size, stride=1)
        self.relu = nn.ReLU()
        self.conv2 = ConvLayer(channels, channels, kernel_size, stride=1)

    def forward(self, input_0):
        primals_2 = self.conv1.conv_layer.weight
        primals_3 = self.conv1.conv_layer.bias
        primals_4 = self.conv1.norm_layer.weight
        primals_5 = self.conv1.norm_layer.bias
        primals_6 = self.conv2.conv_layer.weight
        primals_7 = self.conv2.conv_layer.bias
        primals_8 = self.conv2.norm_layer.weight
        primals_9 = self.conv2.norm_layer.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.193007