# 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/mh/cmhhxeom2jdylxria4u4fu5r5o5ad6fq574sqwsxlru7ylbf46gu.py
# Topologically Sorted Source Nodes: [sub_1, pow_1, fore_weight, negatives, sub_2, pow_2, mul_1, pow_3, back_weight, focal_weight, log_loss, mul_3, sum_2, assigned_box, clamp, cls_loss], Original ATen: [aten.rsub, aten.pow, aten.mul, aten.sub, aten.add, aten.binary_cross_entropy_with_logits, aten.sum, aten.clamp, aten.div]
# Source node to ATen node mapping:
#   assigned_box => sum_1
#   back_weight => mul_3
#   clamp => clamp_min
#   cls_loss => div
#   focal_weight => add
#   fore_weight => mul_1
#   log_loss => abs_1, exp, full_default, log1p, minimum, mul, neg, sub, sub_1, sub_2
#   mul_1 => mul_2
#   mul_3 => mul_4
#   negatives => sub_3
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pow_3 => pow_3
#   sub_1 => sub_4
#   sub_2 => sub_5
#   sum_2 => sum_2
# Graph fragment:
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %select_5), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_4, 2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_2, %pow_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_3, %select_4), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %select_6), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_5, 4.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %pow_2), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select_7, 2.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %pow_3), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_3), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %select_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %select), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default, %select), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%select,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %sub_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %sub_2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_4,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%select_8,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_1, 1.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, %clamp_min), kwargs = {})
triton_per_fused_add_binary_cross_entropy_with_logits_clamp_div_mul_pow_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_clamp_div_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: '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_binary_cross_entropy_with_logits_clamp_div_mul_pow_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_add_binary_cross_entropy_with_logits_clamp_div_mul_pow_rsub_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, 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)
    tmp0 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp7 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp9 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = 1.0
    tmp4 = tmp3 - tmp2
    tmp5 = tmp4 * tmp4
    tmp6 = tmp0 * tmp5
    tmp8 = tmp7 - tmp0
    tmp10 = tmp3 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 * tmp12
    tmp14 = tmp2 * tmp2
    tmp15 = tmp13 * tmp14
    tmp16 = tmp6 + tmp15
    tmp17 = tmp3 - tmp0
    tmp18 = tmp17 * tmp1
    tmp19 = 0.0
    tmp20 = triton_helpers.minimum(tmp19, tmp1)
    tmp21 = tl_math.abs(tmp1)
    tmp22 = -tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = libdevice.log1p(tmp23)
    tmp25 = tmp20 - tmp24
    tmp26 = tmp18 - tmp25
    tmp27 = tmp16 * tmp26
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tmp31 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp33 = tl.sum(tmp31, 1)[:, None]
    tmp34 = triton_helpers.maximum(tmp33, tmp3)
    tmp35 = tmp30 / tmp34
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp35, 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)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [sub_1, pow_1, fore_weight, negatives, sub_2, pow_2, mul_1, pow_3, back_weight, focal_weight, log_loss, mul_3, sum_2, assigned_box, clamp, cls_loss], Original ATen: [aten.rsub, aten.pow, aten.mul, aten.sub, aten.add, aten.binary_cross_entropy_with_logits, aten.sum, aten.clamp, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_clamp_div_mul_pow_rsub_sub_sum_0.run(buf2, arg1_1, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, )


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 torch.nn as nn


class cls_pos(nn.Module):

    def __init__(self):
        super(cls_pos, self).__init__()
        self.bce = nn.BCEWithLogitsLoss(reduction='none')

    def forward(self, pos_pred, pos_label):
        log_loss = self.bce(pos_pred[:, 0, :, :], pos_label[:, 2, :, :])
        pos_pred = pos_pred.sigmoid()
        positives = pos_label[:, 2, :, :]
        negatives = pos_label[:, 1, :, :] - pos_label[:, 2, :, :]
        fore_weight = positives * (1.0 - pos_pred[:, 0, :, :]) ** 2
        back_weight = negatives * (1.0 - pos_label[:, 0, :, :]
            ) ** 4.0 * pos_pred[:, 0, :, :] ** 2.0
        focal_weight = fore_weight + back_weight
        assigned_box = torch.sum(pos_label[:, 2, :, :])
        cls_loss = torch.sum(focal_weight * log_loss) / max(1.0, assigned_box)
        return cls_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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_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_add_binary_cross_entropy_with_logits_clamp_div_mul_pow_rsub_sub_sum_0(
    in_out_ptr0, in_ptr0, in_ptr1, 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 + (32 + r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp7 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp9 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = 1.0
    tmp4 = tmp3 - tmp2
    tmp5 = tmp4 * tmp4
    tmp6 = tmp0 * tmp5
    tmp8 = tmp7 - tmp0
    tmp10 = tmp3 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 * tmp12
    tmp14 = tmp2 * tmp2
    tmp15 = tmp13 * tmp14
    tmp16 = tmp6 + tmp15
    tmp17 = tmp3 - tmp0
    tmp18 = tmp17 * tmp1
    tmp19 = 0.0
    tmp20 = triton_helpers.minimum(tmp19, tmp1)
    tmp21 = tl_math.abs(tmp1)
    tmp22 = -tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = libdevice.log1p(tmp23)
    tmp25 = tmp20 - tmp24
    tmp26 = tmp18 - tmp25
    tmp27 = tmp16 * tmp26
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tmp31 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp33 = tl.sum(tmp31, 1)[:, None]
    tmp34 = triton_helpers.maximum(tmp33, tmp3)
    tmp35 = tmp30 / tmp34
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp35, 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)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_clamp_div_mul_pow_rsub_sub_sum_0[
            grid(1)](buf2, arg1_1, arg0_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class cls_posNew(nn.Module):

    def __init__(self):
        super(cls_posNew, self).__init__()
        self.bce = nn.BCEWithLogitsLoss(reduction='none')

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