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
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_hardtanh_hardtanh_backward_0(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 6.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 <= tmp3
    tmp8 = tmp2 >= tmp5
    tmp9 = tmp7 | tmp8
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_hardtanh_hardtanh_backward_0[grid(256)](
            buf0, primals_2, buf1, buf5, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        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, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf0
        del buf0
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_hardtanh_hardtanh_backward_0[grid(256)](
            buf2, primals_5, buf3, buf4, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf2
        del primals_5
    return buf3, primals_1, primals_3, primals_4, buf1, buf4, buf5


def _get_padding(kernel_size, stride, dilation):
    padding = (stride - 1 + dilation * (kernel_size - 1)) // 2
    return padding


class SeperableConvNew(nn.Module):

    def __init__(self, inp, outp, k=3, stride=1, dilation=1):
        super(SeperableConvNew, self).__init__()
        self.depthwise = nn.Conv2d(inp, inp, k, stride, padding=
            _get_padding(k, stride, dilation), dilation=dilation, groups=inp)
        self.pointwise = nn.Conv2d(inp, outp, 1, 1)

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


Measured latency_ms: 0.024792