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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 262144 % 2
    x0 = xindex % 262144
    x2 = xindex // 524288
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 524288 * x2), tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (262144 + x0 + 524288 * x2), tmp4,
        eviction_policy='evict_last', other=0.0)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp13 = tl.load(in_ptr0 + (x0 + 524288 * x2), tmp10, eviction_policy=
        'evict_last', other=0.0)
    tmp14 = tl.load(in_ptr0 + (262144 + x0 + 524288 * x2), tmp10,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = 2.0
    tmp17 = tmp15 / tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tmp20 = tl.where(tmp4, tmp9, tmp19)
    tl.store(out_ptr0 + x3, tmp20, None)


@triton.jit
def triton_poi_fused_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_red_fused__native_batch_norm_legit_2(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 8192 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4 = tmp4_tmp[:, None]
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp3, xmask)
    tl.store(out_ptr2 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_3(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 32
    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_ptr0 + (r1 + 32 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 32 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1 + 32 * x0), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp12[:, None]
    tmp16 = 262144.0
    tmp17 = tmp14 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused_sigmoid_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x1 = xindex // 262144
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tl.store(out_ptr0 + x2, tmp9, None)


@triton.jit
def triton_poi_fused_mul_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 262144
    x2 = xindex // 524288
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (x0 + 262144 * x2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2, 64, 64, 64), (524288, 262144, 4096,
        64, 1))
    assert_size_stride(primals_2, (1, 2, 3, 3, 3), (54, 27, 9, 3, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 64, 64, 64), (524288, 262144, 4096,
            64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(2097152)](primals_1, buf0, 2097152,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, 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(buf1, (4, 1, 64, 64, 64), (262144, 262144, 4096,
            64, 1))
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_repeat_1[grid(4)](primals_3, buf2, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_repeat_1[grid(4)](primals_4, buf3, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_4
        buf4 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 
            128, 1), torch.float32)
        buf5 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 
            128, 1), torch.float32)
        buf6 = empty_strided_cuda((1, 4, 1, 1, 1, 32), (128, 32, 128, 128, 
            128, 1), torch.float32)
        triton_red_fused__native_batch_norm_legit_2[grid(128)](buf1, buf4,
            buf5, buf6, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf7 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 1, 1, 1), torch.
            float32)
        buf8 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.
            float32)
        buf10 = reinterpret_tensor(buf8, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1), 0)
        del buf8
        triton_per_fused__native_batch_norm_legit_3[grid(4)](buf10, buf4,
            buf5, buf6, buf7, 4, 32, XBLOCK=1, num_warps=2, num_stages=1)
        del buf4
        del buf5
        del buf6
        buf11 = empty_strided_cuda((4, 1, 64, 64, 64), (262144, 1048576, 
            4096, 64, 1), torch.float32)
        triton_poi_fused_sigmoid_4[grid(1048576)](buf1, buf7, buf10, buf2,
            buf3, buf11, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        buf12 = empty_strided_cuda((4, 2, 64, 64, 64), (524288, 262144, 
            4096, 64, 1), torch.float32)
        triton_poi_fused_mul_5[grid(2097152)](primals_1, buf11, buf12, 
            2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf11
    return buf12, primals_1, primals_2, buf0, buf1, buf2, buf3, buf7, buf10


class BasicConv3D(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
        super(BasicConv3D, self).__init__()
        self.out_channels = out_planes
        self.conv = nn.Conv3d(in_planes, out_planes, kernel_size=
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            groups=groups, bias=bias)
        self.bn = nn.InstanceNorm3d(out_planes, eps=1e-05, momentum=0.01,
            affine=True)
        self.relu = nn.LeakyReLU() if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x


class ChannelPool3D(nn.Module):

    def forward(self, x):
        return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
            .unsqueeze(1)), dim=1)


class SpatialGate3DNew(nn.Module):

    def __init__(self):
        super(SpatialGate3DNew, self).__init__()
        kernel_size = 3
        self.compress = ChannelPool3D()
        self.spatial = BasicConv3D(2, 1, kernel_size, stride=1, padding=(
            kernel_size - 1) // 2, relu=False)

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


Measured latency_ms: 1.99596