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
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_per_fused__native_batch_norm_legit_relu_0(in_ptr0, out_ptr2,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 + (r1 + 16 * x0), 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 = tmp0 - tmp10
    tmp18 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp25, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_relu_1(in_ptr0, out_ptr0,
    out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    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 + (r1 + 16 * x0), 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 = tmp0 - tmp10
    tmp18 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp25, xmask)
    tl.store(out_ptr3 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_relu_2(in_ptr0, out_ptr0,
    out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (r1 + 16 * x0), 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 = tmp0 - tmp10
    tmp18 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tl.full([1, 1], 0, tl.int32)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp25, xmask)
    tl.store(out_ptr3 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp17 = tl.load(in_ptr3 + x3, xmask)
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 32 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 3, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * x2), tmp9 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 4, tl.int64)
    tmp14 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tmp18 = tmp16 + tmp17
    tl.store(out_ptr0 + x3, tmp18, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (1, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_4, (1, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_relu_0[grid(16)](primals_1,
            buf3, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 2, 4, 4), (32, 16, 4, 1))
        buf5 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        buf9 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        triton_per_fused__native_batch_norm_legit_relu_1[grid(8)](buf4,
            buf5, buf9, buf8, 8, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1, 4, 4), (16, 16, 4, 1))
        buf11 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        buf15 = empty_strided_cuda((4, 1, 4, 4), (16, 1, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        triton_per_fused__native_batch_norm_legit_relu_2[grid(4)](buf10,
            buf11, buf15, buf14, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf16 = extern_kernels.convolution(reinterpret_tensor(buf15, (4, 1,
            4, 4), (16, 0, 4, 1), 0), primals_4, stride=(1, 1), padding=(1,
            1), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf16, (4, 1, 4, 4), (16, 16, 4, 1))
        buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_cat_3[grid(256)](buf4, buf10, buf16, primals_1,
            buf17, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf16
        del primals_1
    return (buf17, primals_2, primals_3, primals_4, buf3, buf4,
        reinterpret_tensor(buf8, (8,), (1,), 0), buf9, buf10,
        reinterpret_tensor(buf14, (4,), (1,), 0), reinterpret_tensor(buf15,
        (4, 1, 4, 4), (16, 16, 4, 1), 0), reinterpret_tensor(buf11, (1, 4, 
        1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(buf5, (1, 8, 1, 1), (8,
        1, 1, 1), 0))


def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd,
        padding=padding, bias=bias)


class ConvBlockNew(nn.Module):

    def __init__(self, in_planes, out_planes, use_instance_norm):
        super(ConvBlockNew, self).__init__()
        self.bn1 = nn.InstanceNorm2d(in_planes
            ) if use_instance_norm else nn.BatchNorm2d(in_planes)
        self.conv1 = conv3x3(in_planes, int(out_planes // 2))
        self.bn2 = nn.InstanceNorm2d(int(out_planes / 2)
            ) if use_instance_norm else nn.BatchNorm2d(int(out_planes / 2))
        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
        self.bn3 = nn.InstanceNorm2d(int(out_planes / 4)
            ) if use_instance_norm else nn.BatchNorm2d(int(out_planes / 4))
        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
        if in_planes != out_planes:
            self.downsample = nn.Sequential(nn.InstanceNorm2d(in_planes) if
                use_instance_norm else nn.BatchNorm2d(in_planes), nn.ReLU(
                True), nn.Conv2d(in_planes, out_planes, kernel_size=1,
                stride=1, bias=False))
        else:
            self.downsample = None

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_4 = self.conv3.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.092858