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]
