# 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_1/inductor_cache/pi/cpichfxibui6mq4pmlhdz47ncspmnhv33wdmfin5ccqwxaz6gllg.py
# Topologically Sorted Source Nodes: [isnan, target, diff, n, lt, pow_1, mul, truediv, sub_1, loss], Original ATen: [aten.isnan, aten.where, aten.sub, aten.abs, aten.lt, aten.pow, aten.mul, aten.div]
# Source node to ATen node mapping:
#   diff => sub
#   isnan => isnan
#   loss => where_1
#   lt => lt
#   mul => mul
#   n => abs_1
#   pow_1 => pow_1
#   sub_1 => sub_1
#   target => where
#   truediv => div
# Graph fragment:
#   %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%arg0_1,), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan, %arg1_1, %arg0_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %where), 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, 0.1111111111111111), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, 0.1111111111111111), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_1, 0.05555555555555555), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %div, %sub_1), kwargs = {})
triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0 = async_compile.triton('triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_abs_div_isnan_lt_mul_pow_sub_where_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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_ptr1 + (x0), xmask)
    tmp2 = libdevice.isnan(tmp1).to(tl.int1)
    tmp3 = tl.where(tmp2, tmp0, tmp1)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.abs(tmp4)
    tmp6 = 0.1111111111111111
    tmp7 = tmp5 < tmp6
    tmp8 = tmp5 * tmp5
    tmp9 = 0.5
    tmp10 = tmp8 * tmp9
    tmp11 = 9.0
    tmp12 = tmp10 * tmp11
    tmp13 = 0.05555555555555555
    tmp14 = tmp5 - tmp13
    tmp15 = tl.where(tmp7, tmp12, tmp14)
    tl.store(out_ptr0 + (x0), tmp15, 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, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [isnan, target, diff, n, lt, pow_1, mul, truediv, sub_1, loss], Original ATen: [aten.isnan, aten.where, aten.sub, aten.abs, aten.lt, aten.pow, aten.mul, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0.run(arg1_1, arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


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 numpy as np
import torch.nn as nn


class WeightedSmoothL1Loss(nn.Module):
    """
    Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
    https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
                  | 0.5 * x ** 2 / beta   if abs(x) < beta
    smoothl1(x) = |
                  | abs(x) - 0.5 * beta   otherwise,
    where x = input - target.
    """

    def __init__(self, beta: 'float'=1.0 / 9.0, code_weights: 'list'=None):
        """
        Args:
            beta: Scalar float.
                L1 to L2 change point.
                For beta values < 1e-5, L1 loss is computed.
            code_weights: (#codes) float list if not None.
                Code-wise weights.
        """
        super(WeightedSmoothL1Loss, self).__init__()
        self.beta = beta
        if code_weights is not None:
            self.code_weights = np.array(code_weights, dtype=np.float32)
            self.code_weights = torch.from_numpy(self.code_weights)
        else:
            self.code_weights = None

    @staticmethod
    def smooth_l1_loss(diff, beta):
        if beta < 1e-05:
            loss = torch.abs(diff)
        else:
            n = torch.abs(diff)
            loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
        return loss

    def forward(self, input: 'torch.Tensor', target: 'torch.Tensor',
        weights: 'torch.Tensor'=None):
        """
        Args:
            input: (B, #anchors, #codes) float tensor.
                Ecoded predicted locations of objects.
            target: (B, #anchors, #codes) float tensor.
                Regression targets.
            weights: (B, #anchors) float tensor if not None.

        Returns:
            loss: (B, #anchors) float tensor.
                Weighted smooth l1 loss without reduction.
        """
        target = torch.where(torch.isnan(target), input, target)
        diff = input - target
        if self.code_weights is not None:
            diff = diff * self.code_weights.view(1, 1, -1)
        loss = self.smooth_l1_loss(diff, self.beta)
        if weights is not None:
            assert weights.shape[0] == loss.shape[0] and weights.shape[1
                ] == loss.shape[1]
            loss = loss * weights.unsqueeze(-1)
        return loss


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 libdevice, math as tl_math
import numpy as np
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_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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_ptr1 + x0, xmask)
    tmp2 = libdevice.isnan(tmp1).to(tl.int1)
    tmp3 = tl.where(tmp2, tmp0, tmp1)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.abs(tmp4)
    tmp6 = 0.1111111111111111
    tmp7 = tmp5 < tmp6
    tmp8 = tmp5 * tmp5
    tmp9 = 0.5
    tmp10 = tmp8 * tmp9
    tmp11 = 9.0
    tmp12 = tmp10 * tmp11
    tmp13 = 0.05555555555555555
    tmp14 = tmp5 - tmp13
    tmp15 = tl.where(tmp7, tmp12, tmp14)
    tl.store(out_ptr0 + x0, tmp15, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0[grid(256)](arg1_1
            , arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class WeightedSmoothL1LossNew(nn.Module):
    """
    Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
    https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
                  | 0.5 * x ** 2 / beta   if abs(x) < beta
    smoothl1(x) = |
                  | abs(x) - 0.5 * beta   otherwise,
    where x = input - target.
    """

    def __init__(self, beta: 'float'=1.0 / 9.0, code_weights: 'list'=None):
        """
        Args:
            beta: Scalar float.
                L1 to L2 change point.
                For beta values < 1e-5, L1 loss is computed.
            code_weights: (#codes) float list if not None.
                Code-wise weights.
        """
        super(WeightedSmoothL1LossNew, self).__init__()
        self.beta = beta
        if code_weights is not None:
            self.code_weights = np.array(code_weights, dtype=np.float32)
            self.code_weights = torch.from_numpy(self.code_weights)
        else:
            self.code_weights = None

    @staticmethod
    def smooth_l1_loss(diff, beta):
        if beta < 1e-05:
            loss = torch.abs(diff)
        else:
            n = torch.abs(diff)
            loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
        return 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]
