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_ptr0,
    out_ptr2, out_ptr3, 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)
    tl.store(out_ptr3 + x0, tmp22, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_relu_threshold_backward_1(
    in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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)
    tmp22 = tl.load(in_ptr1 + (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 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp23 = tmp0 - tmp10
    tmp24 = tmp23 * tmp21
    tmp25 = tl.full([1, 1], 0, tl.int32)
    tmp26 = triton_helpers.maximum(tmp25, tmp24)
    tmp27 = tmp22 + tmp26
    tmp28 = triton_helpers.maximum(tmp25, tmp27)
    tmp29 = 0.0
    tmp30 = tmp28 <= tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr1 + (r1 + 16 * x0), tmp28, xmask)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp30, xmask)
    tl.store(out_ptr0 + x0, tmp10, 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, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, 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 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_relu_0[grid(16)](buf0,
            buf1, buf5, buf4, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf8 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf10 = reinterpret_tensor(buf8, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf8
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_per_fused__native_batch_norm_legit_add_relu_threshold_backward_1[
            grid(16)](buf10, buf6, primals_1, buf7, buf11, buf12, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
    return buf11, primals_1, primals_2, primals_3, buf0, reinterpret_tensor(
        buf4, (16,), (1,), 0
        ), buf5, buf6, buf7, buf10, buf12, reinterpret_tensor(buf1, (1, 16,
        1, 1), (16, 1, 1, 1), 0)


class ResidualBlockNew(nn.Module):

    def __init__(self, in_planes, planes, norm_layer=nn.InstanceNorm2d,
        stride=1, dilation=1):
        super(ResidualBlockNew, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, dilation=
            dilation, padding=dilation, stride=stride, bias=False)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, dilation=
            dilation, padding=dilation, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.norm1 = norm_layer(planes)
        self.norm2 = norm_layer(planes)
        if not stride == 1 or in_planes != planes:
            self.norm3 = norm_layer(planes)
        if stride == 1 and in_planes == planes:
            self.downsample = None
        else:
            self.downsample = nn.Sequential(nn.Conv2d(in_planes, planes,
                kernel_size=1, stride=stride), self.norm3)

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


Measured latency_ms: 0.057211