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)


@triton.jit
def triton_poi_fused_eq_gt_lt_maximum_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 324
    x3 = xindex % 324
    x1 = xindex // 81 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 648 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (324 + x3 + 648 * 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, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (8, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (8,), (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, 8, 4, 4), (128, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf9 = 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, buf7, buf8, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 9, 9), (648, 81, 9, 1))
        buf3 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        buf6 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        triton_poi_fused_eq_gt_lt_maximum_1[grid(1296)](buf2, primals_5,
            buf3, buf4, buf5, buf6, 1296, XBLOCK=128, num_warps=4, num_stages=1
            )
        del buf2
        del primals_5
    return (buf3, primals_1, primals_3, primals_4, buf1, buf4, buf5, buf6,
        buf7, buf8, buf9)


class Mfm(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, f_type=1):
        super(Mfm, self).__init__()
        self.out_channels = out_channels
        if f_type == 1:
            self.filter = nn.Conv2d(in_channels, 2 * out_channels,
                kernel_size=kernel_size, stride=stride, padding=padding)
        else:
            self.filter = nn.Linear(in_channels, 2 * out_channels)

    def forward(self, x):
        x = self.filter(x)
        out = torch.split(x, self.out_channels, 1)
        return torch.max(out[0], out[1])


class GroupNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding
        ):
        super(GroupNew, self).__init__()
        self.conv_a = Mfm(in_channels, in_channels, 1, 1, 0)
        self.conv = Mfm(in_channels, out_channels, kernel_size, stride, padding
            )

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


Measured latency_ms: 0.029915