import torch
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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 12
    x0 = xindex % 16
    x2 = xindex // 192
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp7 = 0.5
    tmp8 = tmp6 * tmp7
    tmp9 = tl.load(in_ptr2 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp10 = 1.0
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 * tmp11
    tmp13 = tl.load(in_ptr3 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0
        )
    tmp14 = tmp12 - tmp13
    tmp15 = tmp5 * tmp14
    tmp16 = tl.load(in_ptr4 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0
        )
    tmp17 = tmp15 / tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp4, tmp17, tmp18)
    tmp20 = tmp0 >= tmp3
    tmp21 = tl.full([1], 8, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr5 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp26 = tmp25 * tmp7
    tmp27 = tl.load(in_ptr6 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp28 = tmp27 + tmp10
    tmp29 = tmp26 * tmp28
    tmp30 = tl.load(in_ptr7 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp31 = tmp29 - tmp30
    tmp32 = tmp24 * tmp31
    tmp33 = tl.load(in_ptr8 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp34 = tmp32 / tmp33
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp23, tmp34, tmp35)
    tmp37 = tmp0 >= tmp21
    tl.full([1], 12, tl.int64)
    tmp40 = tl.load(in_ptr0 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp37 &
        xmask, other=0.0)
    tmp41 = tl.where(tmp23, tmp36, tmp40)
    tmp42 = tl.where(tmp4, tmp19, tmp41)
    tl.store(out_ptr0 + x3, tmp42, xmask)


def call(args):
    (arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1
        ) = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg5_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg6_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg7_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg8_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(768)](arg3_1, arg0_1, arg1_1, arg2_1,
            arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, buf0, 768, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
        del arg5_1
        del arg6_1
        del arg7_1
        del arg8_1
    return buf0,


class GeometryFeatureNew(nn.Module):

    def __init__(self):
        super(GeometryFeatureNew, self).__init__()

    def forward(self, input_0, input_1, input_2, input_3, input_4, input_5,
        input_6, input_7, input_8):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        arg4_1 = input_4
        arg5_1 = input_5
        arg6_1 = input_6
        arg7_1 = input_7
        arg8_1 = input_8
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1,
            arg6_1, arg7_1, arg8_1])
        return output[0]


Measured latency_ms: 0.017565