# 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/tt/ctthaisn2h6qlimrjmp65k5b5meevfl4by5fxrswxdbne2bmkie2.py
# Topologically Sorted Source Nodes: [gt, pmask, sum_1, num_positive, ratio, clamp_1, ratio_1, coef_1, mul_2, add, log, mul_3, mul, sub, coef_0, sub_1, mul_4, sub_2, add_1, log_1, mul_5, loss, mean, loss_1], Original ATen: [aten.gt, aten._to_copy, aten.sum, aten.clamp, aten.reciprocal, aten.mul, aten.add, aten.log, aten.sub, aten.div, aten.rsub, aten.mean, aten.neg]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   clamp_1 => clamp_min_1
#   coef_0 => div
#   coef_1 => mul_2
#   gt => gt
#   log => log
#   log_1 => log_1
#   loss => add_2
#   loss_1 => neg
#   mean => mean
#   mul => mul_1
#   mul_2 => mul_3
#   mul_3 => mul_4
#   mul_4 => mul_5
#   mul_5 => mul_6
#   num_positive => clamp_min
#   pmask => convert_element_type
#   ratio => mul, reciprocal
#   ratio_1 => clamp_max
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sum_1 => sum_1
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view, 0.5), kwargs = {})
#   %convert_element_type : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt, torch.float32), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_1, 1), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%clamp_min,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 256), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul, 1.05), kwargs = {})
#   %clamp_max : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 21), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, 0.5), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %convert_element_type), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, 1e-05), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %log), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, 0.5), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_max, 1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %sub), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %convert_element_type), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %sub_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %view_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, 1e-05), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_5, %log_1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_6), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add_2,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean,), kwargs = {})
triton_per_fused__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_rsub_sub_sum_0 = async_compile.triton('triton_per_fused__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_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, 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__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, '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__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_rsub_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)
    tmp19 = tl.load(in_ptr1 + (r0), None)
    tmp1 = 0.5
    tmp2 = tmp0 > tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = 1.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 256.0
    tmp12 = tmp10 * tmp11
    tmp13 = 1.05
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = 21.0
    tmp16 = triton_helpers.minimum(tmp14, tmp15)
    tmp17 = tmp16 * tmp1
    tmp18 = tmp17 * tmp3
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = tl_math.log(tmp21)
    tmp23 = tmp18 * tmp22
    tmp24 = tmp16 - tmp7
    tmp25 = tmp17 / tmp24
    tmp26 = tmp7 - tmp3
    tmp27 = tmp25 * tmp26
    tmp28 = tmp7 - tmp19
    tmp29 = tmp28 + tmp20
    tmp30 = tl_math.log(tmp29)
    tmp31 = tmp27 * tmp30
    tmp32 = tmp23 + tmp31
    tmp33 = tl.broadcast_to(tmp32, [RBLOCK])
    tmp35 = triton_helpers.promote_to_tensor(tl.sum(tmp33, 0))
    tmp36 = tmp35 / tmp11
    tmp37 = -tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp37, 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [gt, pmask, sum_1, num_positive, ratio, clamp_1, ratio_1, coef_1, mul_2, add, log, mul_3, mul, sub, coef_0, sub_1, mul_4, sub_2, add_1, log_1, mul_5, loss, mean, loss_1], Original ATen: [aten.gt, aten._to_copy, aten.sum, aten.clamp, aten.reciprocal, aten.mul, aten.add, aten.log, aten.sub, aten.div, aten.rsub, aten.mean, aten.neg]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_rsub_sub_sum_0.run(buf3, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf3, )


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


def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
    ratio_range=(1.05, 21), eps=1e-05):
    """Binary Logistic Regression Loss."""
    label = label.view(-1)
    reg_score = reg_score.contiguous().view(-1)
    pmask = (label > threshold).float()
    num_positive = max(torch.sum(pmask), 1)
    num_entries = len(label)
    ratio = num_entries / num_positive
    ratio = min(max(ratio, ratio_range[0]), ratio_range[1])
    coef_0 = 0.5 * ratio / (ratio - 1)
    coef_1 = 0.5 * ratio
    loss = coef_1 * pmask * torch.log(reg_score + eps) + coef_0 * (1.0 - pmask
        ) * torch.log(1.0 - reg_score + eps)
    loss = -torch.mean(loss)
    return loss


class BinaryLogisticRegressionLoss(nn.Module):
    """Binary Logistic Regression Loss.

    It will calculate binary logistic regression loss given reg_score and
    label.
    """

    def forward(self, reg_score, label, threshold=0.5, ratio_range=(1.05, 
        21), eps=1e-05):
        """Calculate Binary Logistic Regression Loss.

        Args:
                reg_score (torch.Tensor): Predicted score by model.
                label (torch.Tensor): Groundtruth labels.
                threshold (float): Threshold for positive instances.
                    Default: 0.5.
                ratio_range (tuple): Lower bound and upper bound for ratio.
                    Default: (1.05, 21)
                eps (float): Epsilon for small value. Default: 1e-5.

        Returns:
                torch.Tensor: Returned binary logistic loss.
        """
        return binary_logistic_regression_loss(reg_score, label, threshold,
            ratio_range, eps)


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 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__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_rsub_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)
    tmp19 = tl.load(in_ptr1 + r0, None)
    tmp1 = 0.5
    tmp2 = tmp0 > tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = 1.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tl.full([1], 1, tl.int32)
    tmp10 = tmp9 / tmp8
    tmp11 = 256.0
    tmp12 = tmp10 * tmp11
    tmp13 = 1.05
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = 21.0
    tmp16 = triton_helpers.minimum(tmp14, tmp15)
    tmp17 = tmp16 * tmp1
    tmp18 = tmp17 * tmp3
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = tl_math.log(tmp21)
    tmp23 = tmp18 * tmp22
    tmp24 = tmp16 - tmp7
    tmp25 = tmp17 / tmp24
    tmp26 = tmp7 - tmp3
    tmp27 = tmp25 * tmp26
    tmp28 = tmp7 - tmp19
    tmp29 = tmp28 + tmp20
    tmp30 = tl_math.log(tmp29)
    tmp31 = tmp27 * tmp30
    tmp32 = tmp23 + tmp31
    tmp33 = tl.broadcast_to(tmp32, [RBLOCK])
    tmp35 = triton_helpers.promote_to_tensor(tl.sum(tmp33, 0))
    tmp36 = tmp35 / tmp11
    tmp37 = -tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp37, 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        get_raw_stream(0)
        triton_per_fused__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_rsub_sub_sum_0[
            grid(1)](buf3, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf3,


def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
    ratio_range=(1.05, 21), eps=1e-05):
    """Binary Logistic Regression Loss."""
    label = label.view(-1)
    reg_score = reg_score.contiguous().view(-1)
    pmask = (label > threshold).float()
    num_positive = max(torch.sum(pmask), 1)
    num_entries = len(label)
    ratio = num_entries / num_positive
    ratio = min(max(ratio, ratio_range[0]), ratio_range[1])
    coef_0 = 0.5 * ratio / (ratio - 1)
    coef_1 = 0.5 * ratio
    loss = coef_1 * pmask * torch.log(reg_score + eps) + coef_0 * (1.0 - pmask
        ) * torch.log(1.0 - reg_score + eps)
    loss = -torch.mean(loss)
    return loss


class BinaryLogisticRegressionLossNew(nn.Module):
    """Binary Logistic Regression Loss.

    It will calculate binary logistic regression loss given reg_score and
    label.
    """

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