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_repeat_0(
    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 = 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
    r3 = rindex
    x1 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x0 % 4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (r3 + 16 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp4, 0)
    tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp10 / tmp12
    tmp14 = tmp4 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tmp20 = tmp3 - tmp13
    tmp21 = 16.0
    tmp22 = tmp19 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tmp26 = tmp20 * tmp25
    tmp27 = tmp26 * tmp0
    tmp29 = tmp27 + tmp28
    tmp30 = tl.full([1, 1], 0, tl.int32)
    tmp31 = triton_helpers.maximum(tmp30, tmp29)
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tl.store(in_out_ptr0 + (r3 + 16 * x0), tmp3, xmask)
    tl.store(out_ptr3 + (r3 + 16 * x0), tmp31, xmask)
    tl.store(out_ptr4 + x0, tmp25, xmask)
    tl.store(out_ptr1 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 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 = extern_kernels.convolution(primals_3, primals_1, 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))
        buf2 = empty_strided_cuda((16,), (1,), torch.float32)
        buf1 = buf0
        del buf0
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_0[
            grid(16)](buf1, primals_4, primals_2, primals_5, buf2, buf3,
            buf7, buf6, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_2
        del primals_4
        del primals_5
        buf8 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_avg_pool2d_1[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf8, primals_1, primals_3, buf1, buf2, reinterpret_tensor(buf6,
        (16,), (1,), 0), buf7, reinterpret_tensor(buf3, (1, 16, 1, 1), (16,
        1, 1, 1), 0)


class DownBlock2dNew(nn.Module):
    """
    Downsampling block for use in encoder.
    """

    def __init__(self, in_features, out_features, kernel_size=3, padding=1,
        groups=1):
        super(DownBlock2dNew, self).__init__()
        self.conv = nn.Conv2d(in_channels=in_features, out_channels=
            out_features, kernel_size=kernel_size, padding=padding, groups=
            groups)
        self.norm = nn.InstanceNorm2d(out_features, affine=True)
        self.pool = nn.AvgPool2d(kernel_size=(2, 2))

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


Measured latency_ms: 0.045792