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.functional as F
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 = 16
    RBLOCK: tl.constexpr = 16
    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 + 16 * 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 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tl.store(in_out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_convolution_div_hardtanh_mul_2(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 16
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = 1.2
    tmp5 = tmp3 * tmp4
    tmp6 = 3.0
    tmp7 = tmp5 + tmp6
    tmp8 = 0.0
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp10 = 6.0
    tmp11 = triton_helpers.minimum(tmp9, tmp10)
    tmp12 = 0.16666666666666666
    tmp13 = tmp11 * tmp12
    tmp14 = tmp0 * tmp13
    tl.store(out_ptr0 + x3, tmp14, xmask)


@triton.jit
def triton_poi_fused_add_convolution_hardtanh_backward_mul_3(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.2
    tmp4 = tmp2 * tmp3
    tmp5 = 3.0
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = tmp6 <= tmp7
    tmp9 = 6.0
    tmp10 = tmp6 >= tmp9
    tmp11 = tmp8 | tmp10
    tl.store(out_ptr0 + x2, tmp11, 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, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 1, 1, 1), (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, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 1, 1), (1, 1, 1, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(4)](buf3, primals_3, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        del primals_3
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 1, 1), (4, 1, 1, 1))
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_convolution_div_hardtanh_mul_2[grid(256)](
            primals_1, buf4, primals_5, buf5, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        triton_poi_fused_add_convolution_hardtanh_backward_mul_3[grid(16)](buf4
            , primals_5, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf4
        del primals_5
    return buf5, primals_1, primals_2, primals_4, buf1, buf3, buf6


class Hswish(nn.Module):

    def __init__(self, inplace=True):
        super(Hswish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0


class Hsigmoid(nn.Module):

    def __init__(self, inplace=True):
        super(Hsigmoid, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        return F.relu6(1.2 * x + 3.0, inplace=self.inplace) / 6.0


class Activation(nn.Module):

    def __init__(self, act_type, inplace=True):
        super(Activation, self).__init__()
        act_type = act_type.lower()
        if act_type == 'relu':
            self.act = nn.ReLU(inplace=inplace)
        elif act_type == 'relu6':
            self.act = nn.ReLU6(inplace=inplace)
        elif act_type == 'sigmoid':
            raise NotImplementedError
        elif act_type == 'hard_sigmoid':
            self.act = Hsigmoid(inplace)
        elif act_type == 'hard_swish':
            self.act = Hswish(inplace=inplace)
        elif act_type == 'leakyrelu':
            self.act = nn.LeakyReLU(inplace=inplace)
        else:
            raise NotImplementedError

    def forward(self, inputs):
        return self.act(inputs)


class SEModuleNew(nn.Module):

    def __init__(self, in_channels, reduction=4, name=''):
        super(SEModuleNew, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=
            in_channels // reduction, kernel_size=1, stride=1, padding=0,
            bias=True)
        self.relu1 = Activation(act_type='relu', inplace=True)
        self.conv2 = nn.Conv2d(in_channels=in_channels // reduction,
            out_channels=in_channels, kernel_size=1, stride=1, padding=0,
            bias=True)
        self.hard_sigmoid = Activation(act_type='hard_sigmoid', inplace=True)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.072941