# 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/xg/cxguni5jf72tehrxe5n2crdrwr65ntkgntgu2myyuohydixgduga.py
# Topologically Sorted Source Nodes: [outputs, mul, intersection, sum_2, sum_3, union, eq, mul_1, add_1, sub, add_2, iou_score, iou, bce, loss], Original ATen: [aten.sigmoid, aten.mul, aten.sum, aten.add, aten.eq, aten.sub, aten.div, aten.rsub, aten.binary_cross_entropy_with_logits]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   bce => abs_1, exp, full_default, log1p, mean, minimum, mul_2, neg, sub_2, sub_3, sub_4
#   eq => eq
#   intersection => sum_1
#   iou => sub_1
#   iou_score => div
#   loss => add_3
#   mul => mul
#   mul_1 => mul_1
#   outputs => sigmoid
#   sub => sub
#   sum_2 => sum_2
#   sum_3 => sum_3
#   union => add
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %sigmoid), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%arg1_1,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sigmoid,), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%add, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%eq, 1e-07), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %mul_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %sum_1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 1e-07), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, %add_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %arg0_1), 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, %arg0_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), 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_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %sub_3), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_4,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, %mean), kwargs = {})
triton_per_fused_add_binary_cross_entropy_with_logits_div_eq_mul_rsub_sigmoid_sub_sum_0 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_div_eq_mul_rsub_sigmoid_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_add_binary_cross_entropy_with_logits_div_eq_mul_rsub_sigmoid_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 4, '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_div_eq_mul_rsub_sigmoid_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)
    tmp5 = tl.load(in_ptr1 + (r0), None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
    tmp6 = tmp5 * tmp1
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = 1.0
    tmp11 = tmp10 - tmp5
    tmp12 = tmp11 * tmp0
    tmp13 = 0.0
    tmp14 = triton_helpers.minimum(tmp13, tmp0)
    tmp15 = tl_math.abs(tmp0)
    tmp16 = -tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = libdevice.log1p(tmp17)
    tmp19 = tmp14 - tmp18
    tmp20 = tmp12 - tmp19
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tmp24 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp26 = triton_helpers.promote_to_tensor(tl.sum(tmp24, 0))
    tmp27 = tmp26 + tmp4
    tmp28 = tmp27 == tmp13
    tmp29 = tmp28.to(tl.float32)
    tmp30 = 1e-07
    tmp31 = tmp29 * tmp30
    tmp32 = tmp9 + tmp31
    tmp33 = tmp27 - tmp9
    tmp34 = tmp33 + tmp30
    tmp35 = tmp32 / tmp34
    tmp36 = tmp10 - tmp35
    tmp37 = 256.0
    tmp38 = tmp23 / tmp37
    tmp39 = tmp36 + tmp38
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp39, 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)
        buf4 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [outputs, mul, intersection, sum_2, sum_3, union, eq, mul_1, add_1, sub, add_2, iou_score, iou, bce, loss], Original ATen: [aten.sigmoid, aten.mul, aten.sum, aten.add, aten.eq, aten.sub, aten.div, aten.rsub, aten.binary_cross_entropy_with_logits]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_div_eq_mul_rsub_sigmoid_sub_sum_0.run(buf4, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf4, )


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
from typing import Callable
from functools import partial
from torch import nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends


def get_activation_fn(activation: 'str'=None):
    """Returns the activation function from ``torch.nn`` by its name."""
    if activation is None or activation.lower() == 'none':

        def activation_fn(x):
            return x
    else:
        activation_fn = torch.nn.__dict__[activation]()
    return activation_fn


def wrap_metric_fn_with_activation(metric_fn: 'Callable', activation: 'str'
    =None):
    """Wraps model outputs for ``metric_fn` with specified ``activation``.

    Args:
        metric_fn: metric function to compute
        activation: activation name to use

    Returns:
        wrapped metric function with wrapped model outputs

    .. note::
        Works only with ``metric_fn`` like
        ``metric_fn(outputs, targets, *args, **kwargs)``.
    """
    activation_fn = get_activation_fn(activation)

    def wrapped_metric_fn(outputs: 'torch.Tensor', targets: 'torch.Tensor',
        *args, **kwargs):
        outputs = activation_fn(outputs)
        output = metric_fn(outputs, targets, *args, **kwargs)
        return output
    return wrapped_metric_fn


def iou(outputs: 'torch.Tensor', targets: 'torch.Tensor', eps: 'float'=
    1e-07, threshold: 'float'=None) ->torch.Tensor:
    """Computes the dice score.

    Args:
        outputs: A list of predicted elements
        targets:  A list of elements that are to be predicted
        eps: epsilon to avoid zero division
        threshold: threshold for outputs binarization

    Returns:
        IoU (Jaccard) score

    Examples:
        >>> iou(
        >>>     outputs=torch.tensor([
        >>>         [1, 0, 0],
        >>>         [0, 1, 0],
        >>>         [0, 0, 1],
        >>>     ]),
        >>>     targets=torch.tensor([
        >>>         [1, 0, 0],
        >>>         [0, 1, 0],
        >>>         [0, 0, 1],
        >>>     ]),
        >>>     threshold=0.5,
        >>> )
        tensor(1.0)
        >>> iou(
        >>>     outputs=torch.tensor([
        >>>         [1, 0, 0],
        >>>         [0, 1, 0],
        >>>         [0, 0, 1],
        >>>     ]),
        >>>     targets=torch.tensor([
        >>>         [1, 0, 0],
        >>>         [0, 1, 0],
        >>>         [0, 0, 0],
        >>>     ]),
        >>>     threshold=0.5,
        >>> )
        tensor(0.6667)
    """
    if threshold is not None:
        outputs = (outputs > threshold).float()
    intersection = torch.sum(targets * outputs)
    union = torch.sum(targets) + torch.sum(outputs)
    iou_score = (intersection + eps * (union == 0)) / (union - intersection +
        eps)
    return iou_score


class IoULoss(nn.Module):
    """The intersection over union (Jaccard) loss.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, eps: 'float'=1e-07, threshold: 'float'=None,
        activation: 'str'='Sigmoid'):
        """
        Args:
            eps: epsilon to avoid zero division
            threshold: threshold for outputs binarization
            activation: An torch.nn activation applied to the outputs.
                Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
        """
        super().__init__()
        metric_fn = wrap_metric_fn_with_activation(metric_fn=iou,
            activation=activation)
        self.loss_fn = partial(metric_fn, eps=eps, threshold=threshold)

    def forward(self, outputs, targets):
        """@TODO: Docs. Contribution is welcome."""
        iou = self.loss_fn(outputs, targets)
        return 1 - iou


class BCEIoULoss(nn.Module):
    """The Intersection over union (Jaccard) with BCE loss.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, eps: 'float'=1e-07, threshold: 'float'=None,
        activation: 'str'='Sigmoid', reduction: 'str'='mean'):
        """
        Args:
            eps: epsilon to avoid zero division
            threshold: threshold for outputs binarization
            activation: An torch.nn activation applied to the outputs.
                Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
            reduction: Specifies the reduction to apply
                to the output of BCE
        """
        super().__init__()
        self.bce_loss = nn.BCEWithLogitsLoss(reduction=reduction)
        self.iou_loss = IoULoss(eps, threshold, activation)

    def forward(self, outputs, targets):
        """@TODO: Docs. Contribution is welcome."""
        iou = self.iou_loss.forward(outputs, targets)
        bce = self.bce_loss(outputs, targets)
        loss = iou + bce
        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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from typing import Callable
from functools import partial
from torch import nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
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_div_eq_mul_rsub_sigmoid_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)
    tmp5 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
    tmp6 = tmp5 * tmp1
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = 1.0
    tmp11 = tmp10 - tmp5
    tmp12 = tmp11 * tmp0
    tmp13 = 0.0
    tmp14 = triton_helpers.minimum(tmp13, tmp0)
    tmp15 = tl_math.abs(tmp0)
    tmp16 = -tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = libdevice.log1p(tmp17)
    tmp19 = tmp14 - tmp18
    tmp20 = tmp12 - tmp19
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tmp24 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp26 = triton_helpers.promote_to_tensor(tl.sum(tmp24, 0))
    tmp27 = tmp26 + tmp4
    tmp28 = tmp27 == tmp13
    tmp29 = tmp28.to(tl.float32)
    tmp30 = 1e-07
    tmp31 = tmp29 * tmp30
    tmp32 = tmp9 + tmp31
    tmp33 = tmp27 - tmp9
    tmp34 = tmp33 + tmp30
    tmp35 = tmp32 / tmp34
    tmp36 = tmp10 - tmp35
    tmp37 = 256.0
    tmp38 = tmp23 / tmp37
    tmp39 = tmp36 + tmp38
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp39, 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)
        buf4 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_div_eq_mul_rsub_sigmoid_sub_sum_0[
            grid(1)](buf4, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf4,


def get_activation_fn(activation: 'str'=None):
    """Returns the activation function from ``torch.nn`` by its name."""
    if activation is None or activation.lower() == 'none':

        def activation_fn(x):
            return x
    else:
        activation_fn = torch.nn.__dict__[activation]()
    return activation_fn


def wrap_metric_fn_with_activation(metric_fn: 'Callable', activation: 'str'
    =None):
    """Wraps model outputs for ``metric_fn` with specified ``activation``.

    Args:
        metric_fn: metric function to compute
        activation: activation name to use

    Returns:
        wrapped metric function with wrapped model outputs

    .. note::
        Works only with ``metric_fn`` like
        ``metric_fn(outputs, targets, *args, **kwargs)``.
    """
    activation_fn = get_activation_fn(activation)

    def wrapped_metric_fn(outputs: 'torch.Tensor', targets: 'torch.Tensor',
        *args, **kwargs):
        outputs = activation_fn(outputs)
        output = metric_fn(outputs, targets, *args, **kwargs)
        return output
    return wrapped_metric_fn


def iou(outputs: 'torch.Tensor', targets: 'torch.Tensor', eps: 'float'=
    1e-07, threshold: 'float'=None) ->torch.Tensor:
    """Computes the dice score.

    Args:
        outputs: A list of predicted elements
        targets:  A list of elements that are to be predicted
        eps: epsilon to avoid zero division
        threshold: threshold for outputs binarization

    Returns:
        IoU (Jaccard) score

    Examples:
        >>> iou(
        >>>     outputs=torch.tensor([
        >>>         [1, 0, 0],
        >>>         [0, 1, 0],
        >>>         [0, 0, 1],
        >>>     ]),
        >>>     targets=torch.tensor([
        >>>         [1, 0, 0],
        >>>         [0, 1, 0],
        >>>         [0, 0, 1],
        >>>     ]),
        >>>     threshold=0.5,
        >>> )
        tensor(1.0)
        >>> iou(
        >>>     outputs=torch.tensor([
        >>>         [1, 0, 0],
        >>>         [0, 1, 0],
        >>>         [0, 0, 1],
        >>>     ]),
        >>>     targets=torch.tensor([
        >>>         [1, 0, 0],
        >>>         [0, 1, 0],
        >>>         [0, 0, 0],
        >>>     ]),
        >>>     threshold=0.5,
        >>> )
        tensor(0.6667)
    """
    if threshold is not None:
        outputs = (outputs > threshold).float()
    intersection = torch.sum(targets * outputs)
    union = torch.sum(targets) + torch.sum(outputs)
    iou_score = (intersection + eps * (union == 0)) / (union - intersection +
        eps)
    return iou_score


class IoULoss(nn.Module):
    """The intersection over union (Jaccard) loss.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, eps: 'float'=1e-07, threshold: 'float'=None,
        activation: 'str'='Sigmoid'):
        """
        Args:
            eps: epsilon to avoid zero division
            threshold: threshold for outputs binarization
            activation: An torch.nn activation applied to the outputs.
                Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
        """
        super().__init__()
        metric_fn = wrap_metric_fn_with_activation(metric_fn=iou,
            activation=activation)
        self.loss_fn = partial(metric_fn, eps=eps, threshold=threshold)

    def forward(self, outputs, targets):
        """@TODO: Docs. Contribution is welcome."""
        iou = self.loss_fn(outputs, targets)
        return 1 - iou


class BCEIoULossNew(nn.Module):
    """The Intersection over union (Jaccard) with BCE loss.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, eps: 'float'=1e-07, threshold: 'float'=None,
        activation: 'str'='Sigmoid', reduction: 'str'='mean'):
        """
        Args:
            eps: epsilon to avoid zero division
            threshold: threshold for outputs binarization
            activation: An torch.nn activation applied to the outputs.
                Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
            reduction: Specifies the reduction to apply
                to the output of BCE
        """
        super().__init__()
        self.bce_loss = nn.BCEWithLogitsLoss(reduction=reduction)
        self.iou_loss = IoULoss(eps, threshold, activation)

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