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_convolution_relu_threshold_backward_0(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3,
    out_ptr4, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + 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
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.full([1, 1], 0, tl.int32)
    tmp31 = triton_helpers.maximum(tmp30, tmp29)
    tmp32 = 0.0
    tmp33 = tmp31 <= tmp32
    tl.store(in_out_ptr0 + (r1 + 64 * x0), tmp2, xmask)
    tl.store(out_ptr2 + (r1 + 64 * x0), tmp31, xmask)
    tl.store(out_ptr3 + (r1 + 64 * x0), tmp33, xmask)
    tl.store(out_ptr4 + x0, tmp24, xmask)
    tl.store(out_ptr0 + x0, tmp12, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1, 1), (4, 1, 1, 1, 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,))
    assert_size_stride(primals_6, (4, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 
            1), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.
            float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf5 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.
            float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_0[
            grid(4)](buf1, primals_2, primals_4, primals_5, buf2, buf6,
            buf15, buf5, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_2
        del primals_5
        buf7 = extern_kernels.convolution(reinterpret_tensor(buf6, (1, 4, 4,
            4, 4), (0, 64, 16, 4, 1), 0), primals_6, stride=(1, 1, 1),
            padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf8 = buf7
        del buf7
        buf9 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.
            float32)
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf12 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_0[
            grid(4)](buf8, primals_7, primals_8, primals_9, buf9, buf13,
            buf14, buf12, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_7
        del primals_9
    return (buf13, primals_1, primals_4, primals_6, primals_8,
        reinterpret_tensor(primals_3, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1),
        0), buf1, reinterpret_tensor(buf5, (4,), (1,), 0),
        reinterpret_tensor(buf6, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0),
        buf8, reinterpret_tensor(buf12, (4,), (1,), 0), buf14,
        reinterpret_tensor(buf9, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1), 0),
        buf15, reinterpret_tensor(buf2, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1), 0))


class Conv(nn.Module):

    def __init__(self, conv, in_channels, out_channels):
        super().__init__()
        self.conv_type = conv
        self.relu = nn.ReLU(inplace=True)
        if self.conv_type == 'conv2d':
            self.conv2d = nn.Conv3d(in_channels, out_channels, stride=1,
                kernel_size=(3, 3, 1), padding=(1, 1, 0))
            self.bn2d = nn.InstanceNorm3d(out_channels, affine=True)
        elif self.conv_type == 'conv3d':
            self.conv3d = nn.Conv3d(in_channels, out_channels, kernel_size=
                3, stride=1, padding=1)
            self.bn3d = nn.InstanceNorm3d(out_channels, affine=True)
        elif self.conv_type == 'convp3d':
            self.convp3d1 = nn.Conv3d(in_channels, out_channels, stride=1,
                kernel_size=(3, 3, 1), padding=(1, 1, 0))
            self.p3dbn1 = nn.InstanceNorm3d(out_channels, affine=True)
            self.convp3d2 = nn.Conv3d(out_channels, out_channels, stride=1,
                kernel_size=(1, 1, 3), padding=(0, 0, 1))
            self.p3dbn2 = nn.InstanceNorm3d(out_channels, affine=True)

    def forward(self, x):
        if self.conv_type == 'conv2d':
            x = self.conv2d(x)
            x = self.bn2d(x)
            x = self.relu(x)
        elif self.conv_type == 'conv3d':
            x = self.conv3d(x)
            x = self.bn3d(x)
            x = self.relu(x)
        elif self.conv_type == 'convp3d':
            x = self.convp3d1(x)
            x = self.p3dbn1(x)
            x = self.convp3d2(x)
            x = self.p3dbn2(x)
            x = self.relu(x)
        return x


class CellNew(nn.Module):

    def __init__(self, conv, in_channels, out_channels, double=False):
        super().__init__()
        self.conv_type = conv
        self.double = double
        self.conv_i1 = nn.Conv3d(in_channels, in_channels, kernel_size=1,
            stride=1)
        self.bni1 = nn.InstanceNorm3d(in_channels, affine=True)
        self.relu = nn.ReLU(inplace=True)
        self.conv1 = Conv(self.conv_type, in_channels, out_channels)
        if self.double:
            self.conv_i2 = nn.Conv3d(in_channels, in_channels, kernel_size=
                1, stride=1)
            self.bni2 = nn.InstanceNorm3d(in_channels, affine=True)
            self.conv2 = Conv(self.conv_type, in_channels, out_channels)
        self.conv_f = nn.Conv3d(out_channels, out_channels, kernel_size=1,
            stride=1)
        self.bnf = nn.InstanceNorm3d(out_channels, affine=True)

    def forward(self, input_0):
        primals_1 = self.conv_i1.weight
        primals_2 = self.conv_i1.bias
        primals_4 = self.bni1.weight
        primals_5 = self.bni1.bias
        primals_6 = self.conv_f.weight
        primals_7 = self.conv_f.bias
        primals_8 = self.bnf.weight
        primals_9 = self.bnf.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.049466