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 import Tensor
import torch.nn.functional as F
from typing import Optional
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_mean_0(in_out_ptr0, in_ptr0, 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_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 64.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_sigmoid_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_3(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + x2, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (8, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (4, 8, 1, 1, 1), (8, 1, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(4)](buf1, primals_1, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 1,
            1, 1), (0, 1, 0, 0, 0), 0), primals_2, 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(buf2, (1, 8, 1, 1, 1), (8, 1, 1, 1, 1))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((8, 1, 1, 1), (1, 8, 8, 8), torch.float32)
        triton_poi_fused_convolution_mul_sigmoid_1[grid(8)](buf3, primals_3,
            buf4, 8, XBLOCK=8, num_warps=1, num_stages=1)
        del primals_3
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (1, 8, 1,
            1, 1), (0, 1, 0, 0, 0), 0), primals_4, 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(buf5, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_2[grid(4)](buf6, primals_5, 4, XBLOCK=
            4, num_warps=1, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_3[grid(256)](buf6, primals_1, buf7, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf7, primals_1, primals_2, primals_4, reinterpret_tensor(buf1,
        (1, 4, 1, 1, 1), (4, 1, 1, 1, 1), 0), buf3, reinterpret_tensor(buf4,
        (1, 8, 1, 1, 1), (8, 1, 1, 1, 1), 0), buf6


def _make_divisible(v: 'float', divisor: 'int', min_value: 'Optional[int]'=None
    ) ->int:
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class swish(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x: 'Tensor') ->Tensor:
        return x * torch.sigmoid(x)


class SqueezeExcitationNew(nn.Module):

    def __init__(self, input_channels: 'int', squeeze_factor: 'int'=4):
        super().__init__()
        squeeze_channels = _make_divisible(input_channels // squeeze_factor, 8)
        self.fc1 = nn.Conv3d(input_channels, squeeze_channels, (1, 1, 1))
        self.swish = swish()
        self.fc2 = nn.Conv3d(squeeze_channels, input_channels, (1, 1, 1))

    def _scale(self, input: 'Tensor') ->Tensor:
        scale = F.adaptive_avg_pool3d(input, 1)
        scale = self.fc1(scale)
        scale = self.swish(scale)
        scale = self.fc2(scale)
        return torch.sigmoid(scale)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.074942