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_eq_gt_lt_maximum_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 64
    x3 = xindex % 64
    x1 = xindex // 16 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 128 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x3 + 128 * x2), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp7 = tmp2 == tmp5
    tmp8 = tmp2 > tmp5
    tmp9 = tmp2 < tmp5
    tl.store(out_ptr0 + x4, tmp6, xmask)
    tl.store(out_ptr1 + x4, tmp7, xmask)
    tl.store(out_ptr2 + x4, tmp8, xmask)
    tl.store(out_ptr3 + x4, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (8,), (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=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_gt_lt_maximum_0[grid(256)](buf0, primals_2,
            buf1, buf2, buf3, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return buf1, primals_1, primals_3, buf2, buf3, buf4


class MaxFeatureNew(nn.Module):
    """Conv2d or Linear layer with max feature selector

    Generate feature maps with double channels, split them and select the max
        feature.

    Args:
        in_channels (int): Channel number of inputs.
        out_channels (int): Channel number of outputs.
        kernel_size (int or tuple): Size of the convolving kernel.
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): Zero-padding added to both sides of
            the input. Default: 1
        filter_type (str): Type of filter. Options are 'conv2d' and 'linear'.
            Default: 'conv2d'.
    """

    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, filter_type='conv2d'):
        super().__init__()
        self.out_channels = out_channels
        filter_type = filter_type.lower()
        if filter_type == 'conv2d':
            self.filter = nn.Conv2d(in_channels, 2 * out_channels,
                kernel_size=kernel_size, stride=stride, padding=padding)
        elif filter_type == 'linear':
            self.filter = nn.Linear(in_channels, 2 * out_channels)
        else:
            raise ValueError(
                f"'filter_type' should be 'conv2d' or 'linear', but got {filter_type}"
                )

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


Measured latency_ms: 0.029694