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
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_div_mse_loss_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 - tmp4
    tmp6 = tmp5 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp11 = tmp4 * tmp10
    tmp13 = tmp4 * tmp12
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.sum(tmp16, 1)[:, None]
    tmp20 = tmp4 * tmp19
    tmp22 = tmp4 * tmp21
    tmp23 = tmp20 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    tmp28 = 4.0
    tmp29 = tmp9 / tmp28
    tmp30 = 0.5
    tmp31 = tmp29 * tmp30
    tmp32 = 0.0
    tmp33 = tmp31 + tmp32
    tmp34 = tmp18 / tmp28
    tmp35 = tmp34 * tmp30
    tmp36 = tmp33 + tmp35
    tmp37 = tmp27 / tmp28
    tmp38 = tmp37 * tmp30
    tmp39 = tmp36 + tmp38
    tmp40 = 1.0
    tmp41 = tmp39 * tmp40
    tmp42 = tmp41 * tmp40
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp42, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_div_mse_loss_mul_0[grid(1)](buf3, arg0_1,
            arg2_1, arg1_1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf3,


class CombinedTargetMSELossNew(nn.Module):
    """MSE loss for combined target.
        CombinedTarget: The combination of classification target
        (response map) and regression target (offset map).
        Paper ref: Huang et al. The Devil is in the Details: Delving into
        Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

    Args:
        use_target_weight (bool): Option to use weighted MSE loss.
            Different joint types may have different target weights.
        loss_weight (float): Weight of the loss. Default: 1.0.
    """

    def __init__(self, use_target_weight, loss_weight=1.0):
        super().__init__()
        self.criterion = nn.MSELoss(reduction='mean')
        self.use_target_weight = use_target_weight
        self.loss_weight = loss_weight

    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: 0.01544