# 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_8/inductor_cache/ep/cepnahboi3jdccituqgvonjsmjf3wuszmemtdu3iyxnoshyshlaz.py
# Topologically Sorted Source Nodes: [add, truediv, add_1, truediv_1, smooth_l1_loss, l1_loss, add_2, truediv_2, add_3, truediv_3, smooth_l1_loss_1, mul_1, l1_loss_1, sum_1, sum_2, mul_2, clamp, reg_loss], Original ATen: [aten.add, aten.div, aten.smooth_l1_loss, aten.mul, aten.sum, aten.clamp]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   clamp => clamp_min
#   l1_loss => mul_1
#   l1_loss_1 => add_4
#   mul_1 => mul_3
#   mul_2 => mul_4
#   reg_loss => div_6
#   smooth_l1_loss => abs_1, div_2, lt, mul, pow_1, sub, sub_1, where
#   smooth_l1_loss_1 => abs_2, div_5, lt_1, mul_2, pow_2, sub_2, sub_3, where_1
#   sum_1 => sum_1
#   sum_2 => sum_2
#   truediv => div
#   truediv_1 => div_1
#   truediv_2 => div_3
#   truediv_3 => div_4
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_2, 1e-10), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select_1, %add), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_4, 1e-10), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select_3, %add_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {})
#   %abs_1 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%abs_1, 1.0), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.5), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, 1.0), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_1, 0.5), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %div_2, %sub_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, %where), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_7, 1e-10), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select_6, %add_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_9, 1e-10), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select_8, %add_3), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div_3, %div_4), kwargs = {})
#   %abs_2 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%sub_2,), kwargs = {})
#   %lt_1 : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%abs_2, 1.0), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_2, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, 0.5), kwargs = {})
#   %div_5 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_2, 1.0), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_2, 0.5), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt_1, %div_5, %sub_3), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_5, %where_1), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_3), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%add_4,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%select_10,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 2), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul_4, 1.0), kwargs = {})
#   %div_6 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %clamp_min), kwargs = {})
triton_per_fused_add_clamp_div_mul_smooth_l1_loss_sum_0 = async_compile.triton('triton_per_fused_add_clamp_div_mul_smooth_l1_loss_sum_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_div_mul_smooth_l1_loss_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused_add_clamp_div_mul_smooth_l1_loss_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp2 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp18 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp19 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp3 = 1e-10
    tmp4 = tmp2 + tmp3
    tmp5 = tmp1 / tmp4
    tmp6 = tmp2 / tmp4
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.abs(tmp7)
    tmp9 = 1.0
    tmp10 = tmp8 < tmp9
    tmp11 = tmp8 * tmp8
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tmp13 * tmp9
    tmp15 = tmp8 - tmp12
    tmp16 = tl.where(tmp10, tmp14, tmp15)
    tmp17 = tmp0 * tmp16
    tmp20 = tmp19 + tmp3
    tmp21 = tmp18 / tmp20
    tmp22 = tmp19 / tmp20
    tmp23 = tmp21 - tmp22
    tmp24 = tl_math.abs(tmp23)
    tmp25 = tmp24 < tmp9
    tmp26 = tmp24 * tmp24
    tmp27 = tmp26 * tmp12
    tmp28 = tmp27 * tmp9
    tmp29 = tmp24 - tmp12
    tmp30 = tl.where(tmp25, tmp28, tmp29)
    tmp31 = tmp0 * tmp30
    tmp32 = tmp17 + tmp31
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
    tmp35 = tl.sum(tmp33, 1)[:, None]
    tmp36 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp38 = tl.sum(tmp36, 1)[:, None]
    tmp39 = 2.0
    tmp40 = tmp38 * tmp39
    tmp41 = triton_helpers.maximum(tmp40, tmp9)
    tmp42 = tmp35 / tmp41
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp42, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [add, truediv, add_1, truediv_1, smooth_l1_loss, l1_loss, add_2, truediv_2, add_3, truediv_3, smooth_l1_loss_1, mul_1, l1_loss_1, sum_1, sum_2, mul_2, clamp, reg_loss], Original ATen: [aten.add, aten.div, aten.smooth_l1_loss, aten.mul, aten.sum, aten.clamp]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_div_mul_smooth_l1_loss_sum_0.run(buf3, arg0_1, arg1_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf3, )


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)
    fn = lambda: call([arg0_1, arg1_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


class reg_hw_pos(nn.Module):

    def __init__(self):
        super(reg_hw_pos, self).__init__()
        self.smoothl1 = nn.SmoothL1Loss(reduction='none')

    def forward(self, h_pred, h_label):
        l1_loss = h_label[:, 2, :, :] * self.smoothl1(h_pred[:, 0, :, :] /
            (h_label[:, 0, :, :] + 1e-10), h_label[:, 0, :, :] / (h_label[:,
            0, :, :] + 1e-10))
        l1_loss = l1_loss + h_label[:, 2, :, :] * self.smoothl1(h_pred[:, 1,
            :, :] / (h_label[:, 1, :, :] + 1e-10), h_label[:, 1, :, :] / (
            h_label[:, 1, :, :] + 1e-10))
        reg_loss = torch.sum(l1_loss) / max(1.0, torch.sum(h_label[:, 2, :,
            :]) * 2)
        return reg_loss


def get_inputs():
    return [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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
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


@triton.jit
def triton_per_fused_add_clamp_div_mul_smooth_l1_loss_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp2 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp19 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp3 = 1e-10
    tmp4 = tmp2 + tmp3
    tmp5 = tmp1 / tmp4
    tmp6 = tmp2 / tmp4
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.abs(tmp7)
    tmp9 = 1.0
    tmp10 = tmp8 < tmp9
    tmp11 = tmp8 * tmp8
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tmp13 * tmp9
    tmp15 = tmp8 - tmp12
    tmp16 = tl.where(tmp10, tmp14, tmp15)
    tmp17 = tmp0 * tmp16
    tmp20 = tmp19 + tmp3
    tmp21 = tmp18 / tmp20
    tmp22 = tmp19 / tmp20
    tmp23 = tmp21 - tmp22
    tmp24 = tl_math.abs(tmp23)
    tmp25 = tmp24 < tmp9
    tmp26 = tmp24 * tmp24
    tmp27 = tmp26 * tmp12
    tmp28 = tmp27 * tmp9
    tmp29 = tmp24 - tmp12
    tmp30 = tl.where(tmp25, tmp28, tmp29)
    tmp31 = tmp0 * tmp30
    tmp32 = tmp17 + tmp31
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
    tmp35 = tl.sum(tmp33, 1)[:, None]
    tmp36 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp38 = tl.sum(tmp36, 1)[:, None]
    tmp39 = 2.0
    tmp40 = tmp38 * tmp39
    tmp41 = triton_helpers.maximum(tmp40, tmp9)
    tmp42 = tmp35 / tmp41
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp42, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_clamp_div_mul_smooth_l1_loss_sum_0[grid(1)](buf3,
            arg0_1, arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf3,


class reg_hw_posNew(nn.Module):

    def __init__(self):
        super(reg_hw_posNew, self).__init__()
        self.smoothl1 = nn.SmoothL1Loss(reduction='none')

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
