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_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr1 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
        buf1 = buf0
        del buf0
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(64)](buf1, buf5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 1, 2), (8, 2, 2, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 4, 1, 2), (8, 2, 2, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(32)](buf1, buf2,
            buf3, 32, XBLOCK=32, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf3, (4, 8), (8,
            1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), alpha
            =1, beta=1, out=buf4)
        del primals_4
    return buf4, primals_2, primals_1, reinterpret_tensor(buf1, (4, 4, 1, 4
        ), (16, 4, 4, 1), 0), buf2, reinterpret_tensor(buf3, (4, 8), (8, 1), 0
        ), primals_3, buf5


class TwoLayerCNNNew(nn.Module):

    def __init__(self, C, M, embedding, channel, mtc_input, *args, **kwargs):
        super(TwoLayerCNNNew, self).__init__()
        self.C = C
        self.M = M
        self.embedding = embedding
        self.mtc_input = C if mtc_input else 1
        self.conv1 = nn.Conv1d(self.mtc_input, channel, 3, 1, padding=1,
            bias=False)
        self.flat_size = M // 2 * C // self.mtc_input * channel
        self.fc1 = nn.Linear(self.flat_size, embedding)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.fc1.weight
        primals_4 = self.fc1.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.050314