# 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_4/inductor_cache/nx/cnxzajv6akmfdny3r4ud7m5las64tc7zew2ydgeuizr3yk7g7cfn.py
# Topologically Sorted Source Nodes: [sub, cos, sub_1, weight_angle, sub_2, pow_1, l2_distant, mul, mean, mean_1], Original ATen: [aten.sub, aten.cos, aten.rsub, aten.sum, aten.pow, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   cos => cos
#   l2_distant => sum_2
#   mean => mean
#   mean_1 => mean_1
#   mul => mul
#   pow_1 => pow_1
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   weight_angle => sum_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %cos : [num_users=1] = call_function[target=torch.ops.aten.cos.default](args = (%sub,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %cos), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_1, [1]), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg2_1, %arg3_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {})
#   %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, %sum_2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul,), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_per_fused_cos_mean_mul_pow_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_cos_mean_mul_pow_rsub_sub_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: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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': {6: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=(6,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_cos_mean_mul_pow_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 16, '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_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):
    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 + (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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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  # reuse
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [sub, cos, sub_1, weight_angle, sub_2, pow_1, l2_distant, mul, mean, mean_1], Original ATen: [aten.sub, aten.cos, aten.rsub, aten.sum, aten.pow, aten.mul, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_cos_mean_mul_pow_rsub_sub_sum_0.run(buf4, buf5, arg2_1, arg3_1, arg0_1, arg1_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return (buf4, buf5, )


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)
    arg3_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, arg3_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.nn.parallel
import torch.optim
import torch.utils.data
from typing import *


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

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

    def forward(self, landmark_gt, euler_angle_gt, angle, landmarks):
        """
        Calculate weighted L2 loss for PFLD.

        Parameters
        ----------
        landmark_gt : tensor
            the ground truth of landmarks
        euler_angle_gt : tensor
            the ground truth of pose angle
        angle : tensor
            the predicted pose angle
        landmarks : float32
            the predicted landmarks

        Returns
        -------
        output: tensor
            the weighted L2 loss
        output: tensor
            the normal L2 loss
        """
        weight_angle = torch.sum(1 - torch.cos(angle - euler_angle_gt), axis=1)
        l2_distant = torch.sum((landmark_gt - landmarks) ** 2, axis=1)
        return torch.mean(weight_angle * l2_distant), torch.mean(l2_distant)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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.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]
