# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/ht/cht3iys3phvxlbyjckyukc3p63g6utngacelztmjhpv5nwxanb5z.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div, %div_1, %arg3_1], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 11, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    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
    tmp38 = tl.full([1], 12, tl.int64)
    tmp39 = tmp0 < tmp38
    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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg3_1, arg0_1, arg1_1, arg2_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, buf0, 768, grid=grid(768), stream=stream0)
        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, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg4_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg5_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg6_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg7_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg8_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data


class GeometryFeature(nn.Module):

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

    def forward(self, z, vnorm, unorm, h, w, ch, cw, fh, fw):
        x = z * (0.5 * h * (vnorm + 1) - ch) / fh
        y = z * (0.5 * w * (unorm + 1) - cw) / fw
        return torch.cat((x, y, z), 1)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]),
        torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand([4, 
        4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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]
