# 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_7/inductor_cache/5y/c5yx2tmktzc5brhfqiwvzfmmrhkpiscn4gi6kct3s3fnisnd7xlo.py
# Topologically Sorted Source Nodes: [sub, delta, lt, truediv, add, log, mul, sub_1, losses, sum_1], Original ATen: [aten.sub, aten.abs, aten.lt, aten.div, aten.add, aten.log, aten.mul, aten.where, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   delta => abs_1
#   log => log
#   losses => where
#   lt => lt
#   mul => mul
#   sub => sub
#   sub_1 => sub_1
#   sum_1 => sum_1
#   truediv => div
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %abs_1 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%abs_1, 10.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%abs_1, 2.0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, 1.0), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%log, 10.0), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_1, -7.91759469228055), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %mul, %sub_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%where, [1, 2]), kwargs = {})
triton_per_fused_abs_add_div_log_lt_mul_sub_sum_where_0 = async_compile.triton('triton_per_fused_abs_add_div_log_lt_mul_sub_sum_where_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=[16, 16],
    reduction_hint=ReductionHint.DEFAULT,
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_div_log_lt_mul_sub_sum_where_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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_abs_add_div_log_lt_mul_sub_sum_where_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*r2) + (64*x1)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*r2) + (64*x1)), xmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = 10.0
    tmp5 = tmp3 < tmp4
    tmp6 = 0.5
    tmp7 = tmp3 * tmp6
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp10 * tmp4
    tmp12 = -7.91759469228055
    tmp13 = tmp3 - tmp12
    tmp14 = tl.where(tmp5, tmp11, tmp13)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tl.store(out_ptr0 + (x3), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yi/cyivqxc32riwmi4nsqtijcqefjxyivwafk4qjnscryb6cwpfnqim.py
# Topologically Sorted Source Nodes: [loss, mul_1], Original ATen: [aten.mean, aten.mul]
# Source node to ATen node mapping:
#   loss => mean
#   mul_1 => mul_1
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%sum_1, [0]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 1.0), kwargs = {})
triton_poi_fused_mean_mul_1 = async_compile.triton('triton_poi_fused_mean_mul_1', '''
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.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mean_mul_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 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', 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)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, delta, lt, truediv, add, log, mul, sub_1, losses, sum_1], Original ATen: [aten.sub, aten.abs, aten.lt, aten.div, aten.add, aten.log, aten.mul, aten.where, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_div_log_lt_mul_sub_sum_where_0.run(arg0_1, arg1_1, buf0, 16, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [loss, mul_1], Original ATen: [aten.mean, aten.mul]
        triton_poi_fused_mean_mul_1.run(buf0, buf1, 4, grid=grid(4), stream=stream0)
        del buf0
    return (buf1, )


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 math
import torch
import torch.nn as nn


class WingLoss(nn.Module):
    """Wing Loss. paper ref: 'Wing Loss for Robust Facial Landmark Localisation
    with Convolutional Neural Networks' Feng et al. CVPR'2018.

    Args:
        omega (float): Also referred to as width.
        epsilon (float): Also referred to as curvature.
        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, omega=10.0, epsilon=2.0, use_target_weight=False,
        loss_weight=1.0):
        super().__init__()
        self.omega = omega
        self.epsilon = epsilon
        self.use_target_weight = use_target_weight
        self.loss_weight = loss_weight
        self.C = self.omega * (1.0 - math.log(1.0 + self.omega / self.epsilon))

    def criterion(self, pred, target):
        """Criterion of wingloss.

        Note:
            - batch_size: N
            - num_keypoints: K
            - dimension of keypoints: D (D=2 or D=3)

        Args:
            pred (torch.Tensor[N, K, D]): Output regression.
            target (torch.Tensor[N, K, D]): Target regression.
        """
        delta = (target - pred).abs()
        losses = torch.where(delta < self.omega, self.omega * torch.log(1.0 +
            delta / self.epsilon), delta - self.C)
        return torch.mean(torch.sum(losses, dim=[1, 2]), dim=0)

    def forward(self, output, target, target_weight=None):
        """Forward function.

        Note:
            - batch_size: N
            - num_keypoints: K
            - dimension of keypoints: D (D=2 or D=3)

        Args:
            output (torch.Tensor[N, K, D]): Output regression.
            target (torch.Tensor[N, K, D]): Target regression.
            target_weight (torch.Tensor[N,K,D]):
                Weights across different joint types.
        """
        if self.use_target_weight:
            assert target_weight is not None
            loss = self.criterion(output * target_weight, target *
                target_weight)
        else:
            loss = self.criterion(output, target)
        return loss * self.loss_weight


def get_inputs():
    return [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 math
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_abs_add_div_log_lt_mul_sub_sum_where_0(in_ptr0,
    in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 4
    x1 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * r2 + 64 * x1), xmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = 10.0
    tmp5 = tmp3 < tmp4
    tmp6 = 0.5
    tmp7 = tmp3 * tmp6
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp10 * tmp4
    tmp12 = -7.91759469228055
    tmp13 = tmp3 - tmp12
    tmp14 = tl.where(tmp5, tmp11, tmp13)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp18, xmask)


@triton.jit
def triton_poi_fused_mean_mul_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 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tl.store(out_ptr0 + x0, tmp10, xmask)


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)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_add_div_log_lt_mul_sub_sum_where_0[grid(16)](
            arg0_1, arg1_1, buf0, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mean_mul_1[grid(4)](buf0, buf1, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del buf0
    return buf1,


class WingLossNew(nn.Module):
    """Wing Loss. paper ref: 'Wing Loss for Robust Facial Landmark Localisation
    with Convolutional Neural Networks' Feng et al. CVPR'2018.

    Args:
        omega (float): Also referred to as width.
        epsilon (float): Also referred to as curvature.
        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, omega=10.0, epsilon=2.0, use_target_weight=False,
        loss_weight=1.0):
        super().__init__()
        self.omega = omega
        self.epsilon = epsilon
        self.use_target_weight = use_target_weight
        self.loss_weight = loss_weight
        self.C = self.omega * (1.0 - math.log(1.0 + self.omega / self.epsilon))

    def criterion(self, pred, target):
        """Criterion of wingloss.

        Note:
            - batch_size: N
            - num_keypoints: K
            - dimension of keypoints: D (D=2 or D=3)

        Args:
            pred (torch.Tensor[N, K, D]): Output regression.
            target (torch.Tensor[N, K, D]): Target regression.
        """
        delta = (target - pred).abs()
        losses = torch.where(delta < self.omega, self.omega * torch.log(1.0 +
            delta / self.epsilon), delta - self.C)
        return torch.mean(torch.sum(losses, dim=[1, 2]), dim=0)

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