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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@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_sigmoid_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, None)


@triton.jit
def triton_poi_fused_mul_2(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 = 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, 7, 7), (294, 147, 49, 7, 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, 3, 3), 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 = buf1
        del buf1
        triton_poi_fused_sigmoid_1[grid(1048576)](buf2, 1048576, XBLOCK=
            1024, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 2, 64, 64, 64), (524288, 262144, 4096,
            64, 1), torch.float32)
        triton_poi_fused_mul_2[grid(2097152)](primals_1, buf2, buf3, 
            2097152, XBLOCK=1024, num_warps=4, num_stages=1)
    return buf3, buf2, primals_1, primals_2, buf0, buf2


class Conv3DSimple(nn.Conv3d):

    def __init__(self, in_planes, out_planes, stride=1, kernel_size=3):
        padding = (kernel_size - 1) // 2
        super(Conv3DSimple, self).__init__(in_channels=in_planes,
            out_channels=out_planes, kernel_size=(3, kernel_size,
            kernel_size), stride=(1, stride, stride), padding=(1, padding,
            padding), bias=False)


class ChannelPool(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 SpatialGateNew(nn.Module):

    def __init__(self, conv_builder=Conv3DSimple):
        super(SpatialGateNew, self).__init__()
        self.compress = ChannelPool()
        self.spatial = conv_builder(2, 1, kernel_size=7)

    def forward(self, input_0):
        primals_2 = self.spatial.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]


Measured latency_ms: 0.01557