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_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x2 = xindex // 64
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = 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 + x4, tmp9, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_repeat_1(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3, out_ptr4,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 64
    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
    r3 = rindex
    x1 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x0 % 4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (r3 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + x0 % 4, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.full([1, 1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp6, 0)
    tmp9 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp12 / tmp14
    tmp16 = tmp6 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp20 = tl.where(xmask, tmp18, 0)
    tmp21 = tl.sum(tmp20, 1)[:, None]
    tmp22 = tmp5 - tmp15
    tmp23 = 64.0
    tmp24 = tmp21 / tmp23
    tmp25 = 1e-05
    tmp26 = tmp24 + tmp25
    tmp27 = libdevice.rsqrt(tmp26)
    tmp28 = tmp22 * tmp27
    tmp29 = tmp28 * tmp0
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tl.store(in_out_ptr0 + (r3 + 64 * x0), tmp3, xmask)
    tl.store(out_ptr3 + (r3 + 64 * x0), tmp31, xmask)
    tl.store(out_ptr4 + x0, tmp27, xmask)
    tl.store(out_ptr1 + x0, tmp15, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_0[grid(1024)](primals_1, buf0, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), 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))
        buf3 = empty_strided_cuda((16,), (1,), torch.float32)
        buf2 = buf1
        del buf1
        buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf8 = empty_strided_cuda((1, 16, 8, 8), (1024, 64, 8, 1), torch.
            float32)
        buf7 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        triton_per_fused__native_batch_norm_legit_convolution_repeat_1[grid(16)
            ](buf2, primals_4, primals_3, primals_5, buf3, buf4, buf8, buf7,
            16, 64, XBLOCK=8, num_warps=4, num_stages=1)
        del primals_3
        del primals_4
        del primals_5
    return reinterpret_tensor(buf8, (4, 4, 8, 8), (256, 64, 8, 1), 0
        ), primals_2, buf0, buf2, buf3, reinterpret_tensor(buf7, (16,), (1,), 0
        ), reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 1, 1), 0)


class UNetUpsamplingBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels):
        super(UNetUpsamplingBlockNew, self).__init__()
        params = {'kernel_size': 3, 'stride': 1, 'padding': 1, 'bias': True}
        self.conv = nn.Conv2d(in_channels, out_channels, **params)
        self.relu = nn.ReLU(inplace=True)
        self.instance_norm = nn.InstanceNorm2d(out_channels, affine=True)

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_4 = self.instance_norm.weight
        primals_5 = self.instance_norm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.045137