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.nn.parallel
import torch.optim
import torch.utils.data
from typing import *
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_cos_mean_mul_pow_rsub_sub_sum_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp9 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp14 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp19 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp20 = tl.load(in_ptr3 + (r0 + 64 * r1), None)
    tmp25 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp26 = tl.load(in_ptr3 + (16 + r0 + 64 * r1), None)
    tmp31 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr3 + (32 + r0 + 64 * r1), None)
    tmp37 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    tmp38 = tl.load(in_ptr3 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp21 = tmp19 - tmp20
    tmp22 = tl_math.cos(tmp21)
    tmp23 = 1.0
    tmp24 = tmp23 - tmp22
    tmp27 = tmp25 - tmp26
    tmp28 = tl_math.cos(tmp27)
    tmp29 = tmp23 - tmp28
    tmp30 = tmp24 + tmp29
    tmp33 = tmp31 - tmp32
    tmp34 = tl_math.cos(tmp33)
    tmp35 = tmp23 - tmp34
    tmp36 = tmp30 + tmp35
    tmp39 = tmp37 - tmp38
    tmp40 = tl_math.cos(tmp39)
    tmp41 = tmp23 - tmp40
    tmp42 = tmp36 + tmp41
    tmp43 = tmp42 * tmp18
    tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
    tmp46 = tl.sum(tmp44, 1)[:, None]
    tmp47 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp49 = tl.sum(tmp47, 1)[:, None]
    tmp50 = 64.0
    tmp51 = tmp46 / tmp50
    tmp52 = tmp49 / tmp50
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp51, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp52, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        buf5 = buf3
        del buf3
        get_raw_stream(0)
        triton_per_fused_cos_mean_mul_pow_rsub_sub_sum_0[grid(1)](buf4,
            buf5, arg2_1, arg3_1, arg0_1, arg1_1, 1, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf4, buf5


class PFLDLossNew(nn.Module):
    """Weighted loss of L2 distance with the pose angle for PFLD."""

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

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0], output[1]


Measured latency_ms: 0.018208