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
import torch.nn.parallel
import torch.optim
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_native_group_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 4
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_native_group_norm_relu_threshold_backward_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 4
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 * tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = tmp0 + tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp13 = 0.0
    tmp14 = tmp12 <= tmp13
    tl.store(out_ptr0 + x3, tmp12, xmask)
    tl.store(out_ptr1 + x3, tmp14, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 8, 4, 4), (128, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.max_pool3d_with_indices.default(primals_1, [2,
            2, 2], [2, 2, 2])
        del primals_1
        buf1 = buf0[0]
        del buf0
        buf3 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 4,
            2, 2), (64, 16, 4, 2, 1), 0), primals_2, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1))
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_group_norm_0[grid(16)](buf3, buf4, buf5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_native_group_norm_1[grid(64)](buf3, buf4, buf5,
            primals_3, primals_4, buf6, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_4
        buf7 = extern_kernels.convolution(reinterpret_tensor(buf6, (1, 4, 4,
            2, 2), (64, 16, 4, 2, 1), 0), primals_8, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1))
        buf8 = buf5
        del buf5
        buf9 = buf4
        del buf4
        triton_poi_fused_native_group_norm_0[grid(16)](buf7, buf8, buf9, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.bool)
        triton_poi_fused_add_native_group_norm_relu_threshold_backward_2[grid
            (64)](buf6, buf7, buf8, buf9, primals_9, primals_10, buf10,
            buf11, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf8
        del buf9
        del primals_10
    return (buf10, primals_2, primals_3, primals_8, primals_9,
        reinterpret_tensor(buf1, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1), 0),
        buf3, reinterpret_tensor(buf6, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1), 
        0), buf7, buf11)


def normalization(planes, norm='gn'):
    if norm == 'bn':
        m = nn.BatchNorm3d(planes)
    elif norm == 'gn':
        m = nn.GroupNorm(4, planes)
    elif norm == 'in':
        m = nn.InstanceNorm3d(planes)
    else:
        raise ValueError('normalization type {} is not supported'.format(norm))
    return m


class ConvDNew(nn.Module):

    def __init__(self, inplanes, planes, dropout=0.0, norm='gn', first=False):
        super(ConvDNew, self).__init__()
        self.first = first
        self.maxpool = nn.MaxPool3d(2, 2)
        self.dropout = dropout
        self.relu = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv3d(inplanes, planes, 3, 1, 1, bias=False)
        self.bn1 = normalization(planes, norm)
        self.conv2 = nn.Conv3d(planes, planes, 3, 1, 1, bias=False)
        self.bn2 = normalization(planes, norm)
        self.conv3 = nn.Conv3d(planes, planes, 3, 1, 1, bias=False)
        self.bn3 = normalization(planes, norm)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.bn1.weight
        primals_4 = self.bn1.bias
        primals_5 = self.conv2.weight
        primals_6 = self.bn2.weight
        primals_7 = self.bn2.bias
        primals_8 = self.conv3.weight
        primals_9 = self.bn3.weight
        primals_10 = self.bn3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.117146