# 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/wk/cwkf64p2awlt4uhkbzwctplgk7lo7cwoikrsk4av6eqcge5xdaff.py
# Topologically Sorted Source Nodes: [sub_4, pow_1, ge, sub, sub_1, sub_2, st, sub_3, factor, mul, loss, mean], Original ATen: [aten.sub, aten.pow, aten.ge, aten.rsub, aten.where, aten.abs, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   factor => abs_1
#   ge => ge
#   loss => mul_1
#   mean => mean
#   mul => mul
#   pow_1 => pow_1
#   st => where
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sub_4 => sub_4
# Graph fragment:
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_4, 2), kwargs = {})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%arg1_1, 0.01), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, 0.1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, 0.02), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ge, %sub, %sub_2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %where), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_3,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %abs_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %arg2_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul_1, [3]), kwargs = {})
triton_poi_fused_abs_ge_mean_mul_pow_rsub_sub_where_0 = async_compile.triton('triton_poi_fused_abs_ge_mean_mul_pow_rsub_sub_where_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_ge_mean_mul_pow_rsub_sub_where_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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_abs_ge_mean_mul_pow_rsub_sub_where_0(in_ptr0, in_ptr1, in_ptr2, 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_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp49 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp60 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = 0.01
    tmp5 = tmp1 >= tmp4
    tmp6 = 0.1
    tmp7 = tmp0 - tmp6
    tmp8 = 1.0
    tmp9 = tmp8 - tmp0
    tmp10 = 0.02
    tmp11 = tmp9 - tmp10
    tmp12 = tl.where(tmp5, tmp7, tmp11)
    tmp13 = tmp8 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp3 * tmp14
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 - tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 >= tmp4
    tmp23 = tmp18 - tmp6
    tmp24 = tmp8 - tmp18
    tmp25 = tmp24 - tmp10
    tmp26 = tl.where(tmp22, tmp23, tmp25)
    tmp27 = tmp8 - tmp26
    tmp28 = tl_math.abs(tmp27)
    tmp29 = tmp21 * tmp28
    tmp31 = tmp29 * tmp30
    tmp32 = tmp17 + tmp31
    tmp35 = tmp33 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 >= tmp4
    tmp38 = tmp33 - tmp6
    tmp39 = tmp8 - tmp33
    tmp40 = tmp39 - tmp10
    tmp41 = tl.where(tmp37, tmp38, tmp40)
    tmp42 = tmp8 - tmp41
    tmp43 = tl_math.abs(tmp42)
    tmp44 = tmp36 * tmp43
    tmp46 = tmp44 * tmp45
    tmp47 = tmp32 + tmp46
    tmp50 = tmp48 - tmp49
    tmp51 = tmp50 * tmp50
    tmp52 = tmp49 >= tmp4
    tmp53 = tmp48 - tmp6
    tmp54 = tmp8 - tmp48
    tmp55 = tmp54 - tmp10
    tmp56 = tl.where(tmp52, tmp53, tmp55)
    tmp57 = tmp8 - tmp56
    tmp58 = tl_math.abs(tmp57)
    tmp59 = tmp51 * tmp58
    tmp61 = tmp59 * tmp60
    tmp62 = tmp47 + tmp61
    tmp63 = 4.0
    tmp64 = tmp62 / tmp63
    tl.store(out_ptr0 + (x0), tmp64, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/52/c523pn66wra4rfjkrgcnorjvvivoimaibk37xqhluh2ynu4fjzq7.py
# Topologically Sorted Source Nodes: [mean_1, loss_1], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   loss_1 => mean_2
#   mean_1 => mean_1
# Graph fragment:
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean, [2]), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean_1, [1]), kwargs = {})
triton_poi_fused_mean_1 = async_compile.triton('triton_poi_fused_mean_1', '''
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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tl.store(out_ptr0 + (x0), tmp36, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub_4, pow_1, ge, sub, sub_1, sub_2, st, sub_3, factor, mul, loss, mean], Original ATen: [aten.sub, aten.pow, aten.ge, aten.rsub, aten.where, aten.abs, aten.mul, aten.mean]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_ge_mean_mul_pow_rsub_sub_where_0.run(arg0_1, arg1_1, arg2_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mean_1, loss_1], Original ATen: [aten.mean]
        triton_poi_fused_mean_1.run(buf0, buf1, 4, grid=grid(4), stream=stream0)
        del buf0
    return (buf1, )


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)
    fn = lambda: call([arg0_1, arg1_1, arg2_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.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.multiprocessing


class FocalL2Loss(nn.Module):
    """
    Compute focal l2 loss between predict and groundtruth
    :param thre: the threshold to distinguish between the foreground
                 heatmap pixels and the background heatmap pixels
    :param alpha beta: compensation factors to reduce the punishment of easy
                 samples (both easy foreground pixels and easy background pixels) 
    """

    def __init__(self, thre=0.01, alpha=0.1, beta=0.02):
        super().__init__()
        self.thre = thre
        self.alpha = alpha
        self.beta = beta

    def forward(self, pred, gt, mask):
        assert pred.size() == gt.size()
        st = torch.where(torch.ge(gt, self.thre), pred - self.alpha, 1 -
            pred - self.beta)
        factor = torch.abs(1.0 - st)
        loss = (pred - gt) ** 2 * factor * mask
        loss = loss.mean(dim=3).mean(dim=2).mean(dim=1)
        return loss


def get_inputs():
    return [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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torch.multiprocessing
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_abs_ge_mean_mul_pow_rsub_sub_where_0(in_ptr0, in_ptr1,
    in_ptr2, 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_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp30 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp34 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp45 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp48 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp49 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp60 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = 0.01
    tmp5 = tmp1 >= tmp4
    tmp6 = 0.1
    tmp7 = tmp0 - tmp6
    tmp8 = 1.0
    tmp9 = tmp8 - tmp0
    tmp10 = 0.02
    tmp11 = tmp9 - tmp10
    tmp12 = tl.where(tmp5, tmp7, tmp11)
    tmp13 = tmp8 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp3 * tmp14
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 - tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 >= tmp4
    tmp23 = tmp18 - tmp6
    tmp24 = tmp8 - tmp18
    tmp25 = tmp24 - tmp10
    tmp26 = tl.where(tmp22, tmp23, tmp25)
    tmp27 = tmp8 - tmp26
    tmp28 = tl_math.abs(tmp27)
    tmp29 = tmp21 * tmp28
    tmp31 = tmp29 * tmp30
    tmp32 = tmp17 + tmp31
    tmp35 = tmp33 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 >= tmp4
    tmp38 = tmp33 - tmp6
    tmp39 = tmp8 - tmp33
    tmp40 = tmp39 - tmp10
    tmp41 = tl.where(tmp37, tmp38, tmp40)
    tmp42 = tmp8 - tmp41
    tmp43 = tl_math.abs(tmp42)
    tmp44 = tmp36 * tmp43
    tmp46 = tmp44 * tmp45
    tmp47 = tmp32 + tmp46
    tmp50 = tmp48 - tmp49
    tmp51 = tmp50 * tmp50
    tmp52 = tmp49 >= tmp4
    tmp53 = tmp48 - tmp6
    tmp54 = tmp8 - tmp48
    tmp55 = tmp54 - tmp10
    tmp56 = tl.where(tmp52, tmp53, tmp55)
    tmp57 = tmp8 - tmp56
    tmp58 = tl_math.abs(tmp57)
    tmp59 = tmp51 * tmp58
    tmp61 = tmp59 * tmp60
    tmp62 = tmp47 + tmp61
    tmp63 = 4.0
    tmp64 = tmp62 / tmp63
    tl.store(out_ptr0 + x0, tmp64, xmask)


@triton.jit
def triton_poi_fused_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tl.store(out_ptr0 + x0, tmp36, xmask)


def call(args):
    arg0_1, arg1_1, arg2_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_ge_mean_mul_pow_rsub_sub_where_0[grid(64)](arg0_1,
            arg1_1, arg2_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mean_1[grid(4)](buf0, buf1, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        del buf0
    return buf1,


class FocalL2LossNew(nn.Module):
    """
    Compute focal l2 loss between predict and groundtruth
    :param thre: the threshold to distinguish between the foreground
                 heatmap pixels and the background heatmap pixels
    :param alpha beta: compensation factors to reduce the punishment of easy
                 samples (both easy foreground pixels and easy background pixels) 
    """

    def __init__(self, thre=0.01, alpha=0.1, beta=0.02):
        super().__init__()
        self.thre = thre
        self.alpha = alpha
        self.beta = beta

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
