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
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 199888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 13
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_convolution_div_hardtanh_mul_1(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 43200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3600 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 3.0
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = 6.0
    tmp8 = triton_helpers.minimum(tmp6, tmp7)
    tmp9 = tmp2 * tmp8
    tmp10 = 0.16666666666666666
    tmp11 = tmp9 * tmp10
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (13, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_3, (13,), (1,))
    assert_size_stride(primals_4, (3, 13, 3, 3), (117, 9, 3, 1))
    assert_size_stride(primals_5, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 13, 62, 62), (49972, 3844, 62, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(199888)](buf1, primals_3, 
            199888, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 3, 60, 60), (10800, 3600, 60, 1))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 3, 60, 60), (10800, 3600, 60, 1),
            torch.float32)
        triton_poi_fused_add_convolution_div_hardtanh_mul_1[grid(43200)](buf3,
            primals_5, buf4, 43200, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
    return buf4, primals_1, primals_2, primals_4, buf1, buf3


class MyActivation(torch.nn.Module):

    def __init__(self):
        super(MyActivation, self).__init__()
        self.relu = torch.nn.ReLU6(inplace=False)

    def forward(self, x):
        return x * self.relu(x + 3) / 6


class testHSwishNew(torch.nn.Module):

    def __init__(self):
        super(testHSwishNew, self).__init__()
        self.quant = torch.quantization.QuantStub()
        self.conv1 = torch.nn.Conv2d(3, 13, 3)
        self.conv2 = torch.nn.Conv2d(13, 3, 3)
        self.hswish = MyActivation()
        self.dequant = torch.quantization.DeQuantStub()

    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: 0.046839