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 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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, 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 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.1
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(16)](buf0, primals_2,
            buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del primals_2
    return buf2, primals_1, primals_3, buf1


class Conv2dNew(nn.Module):
    """docstring for Conv2d

    Attributes
    ----------
    bn : TYPE
        Description
    conv : TYPE
        Description
    relu : TYPE
        Description
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        relu=True, same_padding=False, bn=False):
        """Summary

        Parameters
        ----------
        in_channels : TYPE
            Description
        out_channels : TYPE
            Description
        kernel_size : TYPE
            Description
        stride : int, optional
            Description
        relu : bool, optional
            Description
        same_padding : bool, optional
            Description
        bn : bool, optional
            Description
        """
        super(Conv2dNew, self).__init__()
        padding = int((kernel_size - 1) / 2) if same_padding else 0
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding=padding, bias=not bn)
        nn.init.xavier_normal_(self.conv.weight)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001, affine=True
            ) if bn else None
        self.relu = nn.LeakyReLU(negative_slope=0.1) if relu else None

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


Measured latency_ms: 0.024562