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_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    xnumel = 3
    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 % 3
    y1 = yindex // 3
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 3 * x2 + 9 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 3 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_3(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)
    x3 = xindex
    x1 = xindex // 3 % 1024
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_max_relu_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 3 * x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 3 * x0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 3 * x0), None, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = triton_helpers.maximum(tmp1, tmp6)
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tl.store(out_ptr0 + x0, tmp8, None)


@triton.jit
def triton_poi_fused_relu_5(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 % 512
    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)


@triton.jit
def triton_poi_fused_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, 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, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 3), (9, 3, 1))
    assert_size_stride(primals_2, (64, 3, 1), (3, 1, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (128, 64, 1), (64, 1, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (1024, 128, 1), (128, 1, 1))
    assert_size_stride(primals_7, (1024,), (1,))
    assert_size_stride(primals_8, (512, 1024), (1024, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (256, 512), (512, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (4, 256), (256, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 3), (9, 3, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(12, 3)](primals_1, buf0, 12, 3,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 64, 3), (192, 3, 1))
        del buf0
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(768)](buf2, primals_3, 768,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 128, 3), (384, 3, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_2[grid(1536)](buf4, primals_5, 
            1536, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1024, 3), (3072, 3, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_3[grid(12288)](buf6, primals_7, 12288,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf7 = empty_strided_cuda((4, 1024, 1), (1024, 1, 1), torch.float32)
        triton_poi_fused_max_relu_4[grid(4096)](buf6, buf7, 4096, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 1024), (1024, 1), 0),
            reinterpret_tensor(primals_8, (1024, 512), (1, 1024), 0), out=buf8)
        del buf7
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_5[grid(2048)](buf9, primals_9, 2048, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_10, (512, 256),
            (1, 512), 0), out=buf10)
        buf11 = buf10
        del buf10
        triton_poi_fused_relu_6[grid(1024)](buf11, primals_11, 1024, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_11
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf11, reinterpret_tensor(
            primals_12, (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf12)
        del primals_13
    return buf12, primals_2, primals_4, primals_6, reinterpret_tensor(primals_1
        , (4, 3, 3), (9, 1, 3), 0
        ), buf2, buf4, buf6, buf9, buf11, primals_12, primals_10, primals_8


class point_modelNew(nn.Module):

    def __init__(self, num_classes):
        super(point_modelNew, self).__init__()
        self.mlp1 = nn.Conv1d(3, 64, 1)
        self.mlp2 = nn.Conv1d(64, 128, 1)
        self.mlp3 = nn.Conv1d(128, 1024, 1)
        self.fc1 = nn.Linear(1024, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, num_classes)

    def forward(self, input_0):
        primals_2 = self.mlp1.weight
        primals_3 = self.mlp1.bias
        primals_4 = self.mlp2.weight
        primals_5 = self.mlp2.bias
        primals_6 = self.mlp3.weight
        primals_7 = self.mlp3.bias
        primals_8 = self.fc1.weight
        primals_9 = self.fc1.bias
        primals_10 = self.fc2.weight
        primals_11 = self.fc2.bias
        primals_12 = self.fc3.weight
        primals_13 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]


Measured latency_ms: 0.159252