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]


Measured latency_ms: 0.027809