# 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_8/inductor_cache/2l/c2lumpq6tnnnhy7ek6bzpvqbvoeswddoefom3hhivyzjjsxtatvo.py
# Topologically Sorted Source Nodes: [mean, vx, mean_1, vy, mul, sum_1, pow_1, sum_2, sqrt, pow_2, sum_3, sqrt_1, mul_1, add, rho, mul_2, x_s, mul_3, y_s, mul_4, pow_3, pow_4, add_1, x_m, y_m, sub_2, pow_5, add_2, add_3, ccc, loss, mul_5], Original ATen: [aten.mean, aten.sub, aten.mul, aten.sum, aten.pow, aten.sqrt, aten.add, aten.div, aten.std, aten.rsub]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   ccc => div_1
#   loss => sub_3
#   mean => mean
#   mean_1 => mean_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pow_3 => pow_3
#   pow_4 => pow_4
#   pow_5 => pow_5
#   rho => div
#   sqrt => sqrt
#   sqrt_1 => sqrt_1
#   sub_2 => sub_2
#   sum_1 => sum_1
#   sum_2 => sum_2
#   sum_3 => sum_3
#   vx => sub
#   vy => sub_1
#   x_m => mean_2
#   x_s => sqrt_2, var
#   y_m => mean_3
#   y_s => sqrt_3, var_1
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%arg1_1,), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %mean), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%arg0_1,), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_2,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_2,), kwargs = {})
#   %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_3,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, %sqrt_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1e-10), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %add), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, 2), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%arg1_1,), kwargs = {correction: 1.0})
#   %sqrt_2 : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %sqrt_2), kwargs = {})
#   %var_1 : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%arg0_1,), kwargs = {correction: 1.0})
#   %sqrt_3 : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%var_1,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %sqrt_3), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sqrt_2, 2), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sqrt_3, 2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_3, %pow_4), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%arg1_1,), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%arg0_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean_2, %mean_3), kwargs = {})
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %pow_5), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, 1e-10), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_4, %add_3), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, 1.0), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_rsub_sqrt_std_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_rsub_sqrt_std_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, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_pow_rsub_sqrt_std_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 13, '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_add_div_mean_mul_pow_rsub_sqrt_std_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp4 = tl.load(in_ptr1 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = 256.0
    tmp9 = tmp3 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp7 / tmp8
    tmp12 = tmp4 - tmp11
    tmp13 = tmp10 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = tmp10 * tmp10
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = tmp12 * tmp12
    tmp22 = tl.broadcast_to(tmp21, [RBLOCK])
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp26 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = tl.full([1], 256, tl.int32)
    tmp30 = tmp29.to(tl.float32)
    tmp31 = tmp28 / tmp30
    tmp32 = tmp1 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tl.broadcast_to(tmp33, [RBLOCK])
    tmp36 = triton_helpers.promote_to_tensor(tl.sum(tmp34, 0))
    tmp38 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp40 = triton_helpers.promote_to_tensor(tl.sum(tmp38, 0))
    tmp41 = tmp40 / tmp30
    tmp42 = tmp5 - tmp41
    tmp43 = tmp42 * tmp42
    tmp44 = tl.broadcast_to(tmp43, [RBLOCK])
    tmp46 = triton_helpers.promote_to_tensor(tl.sum(tmp44, 0))
    tmp47 = libdevice.sqrt(tmp20)
    tmp48 = libdevice.sqrt(tmp24)
    tmp49 = tmp47 * tmp48
    tmp50 = 1e-10
    tmp51 = tmp49 + tmp50
    tmp52 = tmp16 / tmp51
    tmp53 = 2.0
    tmp54 = tmp52 * tmp53
    tmp55 = 255.0
    tmp56 = tmp36 / tmp55
    tmp57 = libdevice.sqrt(tmp56)
    tmp58 = tmp54 * tmp57
    tmp59 = tmp46 / tmp55
    tmp60 = libdevice.sqrt(tmp59)
    tmp61 = tmp58 * tmp60
    tmp62 = tmp57 * tmp57
    tmp63 = tmp60 * tmp60
    tmp64 = tmp62 + tmp63
    tmp65 = tmp9 - tmp11
    tmp66 = tmp65 * tmp65
    tmp67 = tmp64 + tmp66
    tmp68 = tmp67 + tmp50
    tmp69 = tmp61 / tmp68
    tmp70 = 1.0
    tmp71 = tmp70 - tmp69
    tmp72 = tmp71 * tmp70
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp72, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf13 = buf11; del buf11  # reuse
        buf14 = buf13; del buf13  # reuse
        # Topologically Sorted Source Nodes: [mean, vx, mean_1, vy, mul, sum_1, pow_1, sum_2, sqrt, pow_2, sum_3, sqrt_1, mul_1, add, rho, mul_2, x_s, mul_3, y_s, mul_4, pow_3, pow_4, add_1, x_m, y_m, sub_2, pow_5, add_2, add_3, ccc, loss, mul_5], Original ATen: [aten.mean, aten.sub, aten.mul, aten.sum, aten.pow, aten.sqrt, aten.add, aten.div, aten.std, aten.rsub]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_pow_rsub_sqrt_std_sub_sum_0.run(buf14, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf14, )


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)
    fn = lambda: call([arg0_1, arg1_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 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
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_mean_mul_pow_rsub_sqrt_std_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp4 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = 256.0
    tmp9 = tmp3 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp7 / tmp8
    tmp12 = tmp4 - tmp11
    tmp13 = tmp10 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = tmp10 * tmp10
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = tmp12 * tmp12
    tmp22 = tl.broadcast_to(tmp21, [RBLOCK])
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp26 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = tl.full([1], 256, tl.int32)
    tmp30 = tmp29.to(tl.float32)
    tmp31 = tmp28 / tmp30
    tmp32 = tmp1 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tl.broadcast_to(tmp33, [RBLOCK])
    tmp36 = triton_helpers.promote_to_tensor(tl.sum(tmp34, 0))
    tmp38 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp40 = triton_helpers.promote_to_tensor(tl.sum(tmp38, 0))
    tmp41 = tmp40 / tmp30
    tmp42 = tmp5 - tmp41
    tmp43 = tmp42 * tmp42
    tmp44 = tl.broadcast_to(tmp43, [RBLOCK])
    tmp46 = triton_helpers.promote_to_tensor(tl.sum(tmp44, 0))
    tmp47 = libdevice.sqrt(tmp20)
    tmp48 = libdevice.sqrt(tmp24)
    tmp49 = tmp47 * tmp48
    tmp50 = 1e-10
    tmp51 = tmp49 + tmp50
    tmp52 = tmp16 / tmp51
    tmp53 = 2.0
    tmp54 = tmp52 * tmp53
    tmp55 = 255.0
    tmp56 = tmp36 / tmp55
    tmp57 = libdevice.sqrt(tmp56)
    tmp58 = tmp54 * tmp57
    tmp59 = tmp46 / tmp55
    tmp60 = libdevice.sqrt(tmp59)
    tmp61 = tmp58 * tmp60
    tmp62 = tmp57 * tmp57
    tmp63 = tmp60 * tmp60
    tmp64 = tmp62 + tmp63
    tmp65 = tmp9 - tmp11
    tmp66 = tmp65 * tmp65
    tmp67 = tmp64 + tmp66
    tmp68 = tmp67 + tmp50
    tmp69 = tmp61 / tmp68
    tmp70 = 1.0
    tmp71 = tmp70 - tmp69
    tmp72 = tmp71 * tmp70
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp72, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf13 = buf11
        del buf11
        buf14 = buf13
        del buf13
        get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_pow_rsub_sqrt_std_sub_sum_0[grid(1)](
            buf14, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf14,


class CCCLossNew(nn.Module):
    """CCC loss for VA regression
    """

    def __init__(self, reduction='mean', loss_weight=1.0):
        super().__init__()
        self.reduction = reduction
        self.loss_weight = loss_weight

    def get_name(self):
        return 'CCC_loss'

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