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
from torch import 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_convolution_relu_threshold_backward_0(
    in_out_ptr0, in_ptr0, out_ptr0, out_ptr2, out_ptr3, out_ptr4, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    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)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 2
    tmp0 = tl.load(in_out_ptr0 + (r2 + 64 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tmp2 - tmp12
    tmp20 = 64.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tmp26 = tl.full([1, 1], 0, tl.int32)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tmp28 = 0.0
    tmp29 = tmp27 <= tmp28
    tl.store(in_out_ptr0 + (r2 + 64 * x3), tmp2, xmask)
    tl.store(out_ptr2 + (r2 + 64 * x3), tmp27, xmask)
    tl.store(out_ptr3 + (r2 + 64 * x3), tmp29, xmask)
    tl.store(out_ptr4 + x3, tmp24, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_2, (2,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 8, 8), (128, 64, 8, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        buf6 = empty_strided_cuda((4, 2, 8, 8), (128, 64, 8, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 2, 8, 8), (128, 64, 8, 1), torch.bool)
        buf5 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_0[
            grid(8)](buf1, primals_2, buf2, buf6, buf7, buf5, 8, 64, XBLOCK
            =8, num_warps=4, num_stages=1)
        del primals_2
    return buf6, primals_1, primals_3, buf1, reinterpret_tensor(buf5, (8,),
        (1,), 0), buf7, reinterpret_tensor(buf2, (1, 8, 1, 1), (8, 1, 1, 1), 0)


class ExpandingBlockNew(nn.Module):

    def __init__(self, input_channels, use_bn=True):
        super(ExpandingBlockNew, self).__init__()
        self.conv1 = nn.ConvTranspose2d(input_channels, input_channels // 2,
            kernel_size=3, stride=2, padding=1, output_padding=1)
        if use_bn:
            self.instancenorm = nn.InstanceNorm2d(input_channels // 2)
        self.use_bn = use_bn
        self.activation = nn.ReLU()

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


Measured latency_ms: 0.083002