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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__adaptive_avg_pool2d_0(in_ptr0, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 512
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 128
    y1 = yindex // 128
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 128 * x2 + 2048 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (128, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (128,), (1,))
    assert_size_stride(primals_4, (1024, 2048), (2048, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (4, 1024), (1024, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__adaptive_avg_pool2d_0[grid(16, 16)](primals_1,
            buf0, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf2 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.
            float32)
        triton_poi_fused_convolution_1[grid(512, 16)](buf1, primals_3, buf2,
            512, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del buf1
        del primals_3
        buf3 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (4, 2048), (2048, 1), 0),
            reinterpret_tensor(primals_4, (2048, 1024), (1, 2048), 0), out=buf3
            )
        buf4 = buf3
        del buf3
        triton_poi_fused_relu_2[grid(4096)](buf4, primals_5, 4096, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf4, reinterpret_tensor(primals_6,
            (1024, 4), (1, 1024), 0), alpha=1, beta=1, out=buf5)
        del primals_7
    return buf5, primals_2, buf0, reinterpret_tensor(buf2, (4, 2048), (2048,
        1), 0), buf4, primals_6, primals_4


class AuxiliaryNew(nn.Module):

    def __init__(self, input_channels, n_classes):
        super(AuxiliaryNew, self).__init__()
        self.Conv2 = nn.Conv2d(input_channels, 128, kernel_size=1)
        self.FC1 = nn.Linear(2048, 1024)
        self.FC2 = nn.Linear(1024, n_classes)

    def forward(self, input_0):
        primals_2 = self.Conv2.weight
        primals_3 = self.Conv2.bias
        primals_4 = self.FC1.weight
        primals_5 = self.FC1.bias
        primals_6 = self.FC2.weight
        primals_7 = self.FC2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.073662