# 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/bx/cbxgryyplm5yvgi4frxbrmyhbohjh7xnxxxrbx5zltbiirpvvonm.py
# Topologically Sorted Source Nodes: [mul_4, sub_2, mul_5, at, neg_4, input_1, log, mul_2, sub, sub_1, log_1, mul_3, add_3, cross_entropy, logpt, balanced_cross_entropy, pt, sub_3, pow_1, focal_loss, fc_loss, clamp_1, log_2, mul, intersection, mul_1, add, neg, sum_2, sum_3, add_1, add_2, truediv, dc_loss, clamp_2, log_3, mul_8, loss], Original ATen: [aten.mul, aten.rsub, aten.add, aten.neg, aten.clamp, aten.log, aten.exp, aten.pow, aten.sum, aten.div, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   at => add_4
#   balanced_cross_entropy => mul_6
#   clamp_1 => clamp_min_1
#   clamp_2 => clamp_min_2
#   cross_entropy => neg_2
#   dc_loss => neg_1
#   fc_loss => sum_4
#   focal_loss => mul_7
#   input_1 => clamp_max, clamp_min
#   intersection => sum_1
#   log => log
#   log_1 => log_1
#   log_2 => log_2
#   log_3 => log_3
#   logpt => neg_3
#   loss => sub_4
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_8 => mul_8
#   neg => neg
#   neg_4 => neg_4
#   pow_1 => pow_1
#   pt => exp
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sum_2 => sum_2
#   sum_3 => sum_3
#   truediv => div
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.25), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, 0.75), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_5), kwargs = {})
#   %neg_4 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%add_4,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg0_1, 1e-07), kwargs = {})
#   %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 0.9999999), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_max,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %log), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %clamp_max), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub_1,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %log_1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%add_3,), kwargs = {})
#   %neg_3 : [num_users=2] = call_function[target=torch.ops.aten.neg.default](args = (%neg_2,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg_4, %neg_3), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_3,), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %exp), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %pow_1), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_7,), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_4, 1e-07), kwargs = {})
#   %log_2 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_min_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 2.0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1.0), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%add,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view_1,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, 1.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, %add_2), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%div,), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%neg_1, 1e-07), kwargs = {})
#   %log_3 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_min_2,), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%log_3, 1), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%log_2, %mul_8), kwargs = {})
triton_per_fused_add_clamp_div_exp_log_mul_neg_pow_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_add_clamp_div_exp_log_mul_neg_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, 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_clamp_div_exp_log_mul_neg_pow_rsub_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_clamp_div_exp_log_mul_neg_pow_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)
    tmp9 = tl.load(in_ptr1 + (r0), None)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = 0.75
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp8 = -tmp7
    tmp10 = 1e-07
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 0.9999999
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp0 * tmp14
    tmp16 = tmp3 - tmp13
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp4 * tmp17
    tmp19 = tmp15 + tmp18
    tmp20 = -tmp19
    tmp21 = -tmp20
    tmp22 = tmp8 * tmp21
    tmp23 = tl_math.exp(tmp21)
    tmp24 = tmp3 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tmp22 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0))
    tmp30 = tmp9 * tmp0
    tmp31 = tl.broadcast_to(tmp30, [RBLOCK])
    tmp33 = triton_helpers.promote_to_tensor(tl.sum(tmp31, 0))
    tmp34 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp36 = triton_helpers.promote_to_tensor(tl.sum(tmp34, 0))
    tmp37 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp39 = triton_helpers.promote_to_tensor(tl.sum(tmp37, 0))
    tmp40 = triton_helpers.maximum(tmp29, tmp10)
    tmp41 = tl_math.log(tmp40)
    tmp42 = 2.0
    tmp43 = tmp33 * tmp42
    tmp44 = tmp43 + tmp3
    tmp45 = -tmp44
    tmp46 = tmp36 + tmp39
    tmp47 = tmp46 + tmp3
    tmp48 = tmp45 / tmp47
    tmp49 = -tmp48
    tmp50 = triton_helpers.maximum(tmp49, tmp10)
    tmp51 = tl_math.log(tmp50)
    tmp52 = tmp51 * tmp3
    tmp53 = tmp41 - tmp52
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp53, 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: [mul_4, sub_2, mul_5, at, neg_4, input_1, log, mul_2, sub, sub_1, log_1, mul_3, add_3, cross_entropy, logpt, balanced_cross_entropy, pt, sub_3, pow_1, focal_loss, fc_loss, clamp_1, log_2, mul, intersection, mul_1, add, neg, sum_2, sum_3, add_1, add_2, truediv, dc_loss, clamp_2, log_3, mul_8, loss], Original ATen: [aten.mul, aten.rsub, aten.add, aten.neg, aten.clamp, aten.log, aten.exp, aten.pow, aten.sum, aten.div, aten.sub]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_div_exp_log_mul_neg_pow_rsub_sub_sum_0.run(buf4, arg1_1, arg0_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
import torch.nn as nn


class DiceLoss(nn.Module):
    """DiceLoss.

    .. seealso::
        Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for
        volumetric medical image segmentation." 2016 fourth international conference on 3D vision (3DV). IEEE, 2016.

    Args:
        smooth (float): Value to avoid division by zero when images and predictions are empty.

    Attributes:
        smooth (float): Value to avoid division by zero when images and predictions are empty.
    """

    def __init__(self, smooth=1.0):
        super(DiceLoss, self).__init__()
        self.smooth = smooth

    def forward(self, prediction, target):
        iflat = prediction.reshape(-1)
        tflat = target.reshape(-1)
        intersection = (iflat * tflat).sum()
        return -(2.0 * intersection + self.smooth) / (iflat.sum() + tflat.
            sum() + self.smooth)


class FocalLoss(nn.Module):
    """FocalLoss.

    .. seealso::
        Lin, Tsung-Yi, et al. "Focal loss for dense object detection."
        Proceedings of the IEEE international conference on computer vision. 2017.

    Args:
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
        eps (float): Epsilon to avoid division by zero.

    Attributes:
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
        eps (float): Epsilon to avoid division by zero.
    """

    def __init__(self, gamma=2, alpha=0.25, eps=1e-07):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.eps = eps

    def forward(self, input, target):
        input = input.clamp(self.eps, 1.0 - self.eps)
        cross_entropy = -(target * torch.log(input) + (1 - target) * torch.
            log(1 - input))
        logpt = -cross_entropy
        pt = torch.exp(logpt)
        at = self.alpha * target + (1 - self.alpha) * (1 - target)
        balanced_cross_entropy = -at * logpt
        focal_loss = balanced_cross_entropy * (1 - pt) ** self.gamma
        return focal_loss.sum()


class FocalDiceLoss(nn.Module):
    """FocalDiceLoss.

    .. seealso::
        Wong, Ken CL, et al. "3D segmentation with exponential logarithmic loss for highly unbalanced object sizes."
        International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018.

    Args:
        beta (float): Value from 0 to 1, indicating the weight of the dice loss.
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.

    Attributes:
        beta (float): Value from 0 to 1, indicating the weight of the dice loss.
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
    """

    def __init__(self, beta=1, gamma=2, alpha=0.25):
        super().__init__()
        self.beta = beta
        self.focal = FocalLoss(gamma, alpha)
        self.dice = DiceLoss()

    def forward(self, input, target):
        dc_loss = -self.dice(input, target)
        fc_loss = self.focal(input, target)
        loss = torch.log(torch.clamp(fc_loss, 1e-07)) - self.beta * torch.log(
            torch.clamp(dc_loss, 1e-07))
        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 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_clamp_div_exp_log_mul_neg_pow_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)
    tmp9 = tl.load(in_ptr1 + r0, None)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = 0.75
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp8 = -tmp7
    tmp10 = 1e-07
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 0.9999999
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp0 * tmp14
    tmp16 = tmp3 - tmp13
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp4 * tmp17
    tmp19 = tmp15 + tmp18
    tmp20 = -tmp19
    tmp21 = -tmp20
    tmp22 = tmp8 * tmp21
    tmp23 = tl_math.exp(tmp21)
    tmp24 = tmp3 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tmp22 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0))
    tmp30 = tmp9 * tmp0
    tmp31 = tl.broadcast_to(tmp30, [RBLOCK])
    tmp33 = triton_helpers.promote_to_tensor(tl.sum(tmp31, 0))
    tmp34 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp36 = triton_helpers.promote_to_tensor(tl.sum(tmp34, 0))
    tmp37 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp39 = triton_helpers.promote_to_tensor(tl.sum(tmp37, 0))
    tmp40 = triton_helpers.maximum(tmp29, tmp10)
    tmp41 = tl_math.log(tmp40)
    tmp42 = 2.0
    tmp43 = tmp33 * tmp42
    tmp44 = tmp43 + tmp3
    tmp45 = -tmp44
    tmp46 = tmp36 + tmp39
    tmp47 = tmp46 + tmp3
    tmp48 = tmp45 / tmp47
    tmp49 = -tmp48
    tmp50 = triton_helpers.maximum(tmp49, tmp10)
    tmp51 = tl_math.log(tmp50)
    tmp52 = tmp51 * tmp3
    tmp53 = tmp41 - tmp52
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp53, 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_clamp_div_exp_log_mul_neg_pow_rsub_sub_sum_0[grid
            (1)](buf4, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf4,


class DiceLoss(nn.Module):
    """DiceLoss.

    .. seealso::
        Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for
        volumetric medical image segmentation." 2016 fourth international conference on 3D vision (3DV). IEEE, 2016.

    Args:
        smooth (float): Value to avoid division by zero when images and predictions are empty.

    Attributes:
        smooth (float): Value to avoid division by zero when images and predictions are empty.
    """

    def __init__(self, smooth=1.0):
        super(DiceLoss, self).__init__()
        self.smooth = smooth

    def forward(self, prediction, target):
        iflat = prediction.reshape(-1)
        tflat = target.reshape(-1)
        intersection = (iflat * tflat).sum()
        return -(2.0 * intersection + self.smooth) / (iflat.sum() + tflat.
            sum() + self.smooth)


class FocalLoss(nn.Module):
    """FocalLoss.

    .. seealso::
        Lin, Tsung-Yi, et al. "Focal loss for dense object detection."
        Proceedings of the IEEE international conference on computer vision. 2017.

    Args:
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
        eps (float): Epsilon to avoid division by zero.

    Attributes:
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
        eps (float): Epsilon to avoid division by zero.
    """

    def __init__(self, gamma=2, alpha=0.25, eps=1e-07):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.eps = eps

    def forward(self, input, target):
        input = input.clamp(self.eps, 1.0 - self.eps)
        cross_entropy = -(target * torch.log(input) + (1 - target) * torch.
            log(1 - input))
        logpt = -cross_entropy
        pt = torch.exp(logpt)
        at = self.alpha * target + (1 - self.alpha) * (1 - target)
        balanced_cross_entropy = -at * logpt
        focal_loss = balanced_cross_entropy * (1 - pt) ** self.gamma
        return focal_loss.sum()


class FocalDiceLossNew(nn.Module):
    """FocalDiceLoss.

    .. seealso::
        Wong, Ken CL, et al. "3D segmentation with exponential logarithmic loss for highly unbalanced object sizes."
        International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018.

    Args:
        beta (float): Value from 0 to 1, indicating the weight of the dice loss.
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.

    Attributes:
        beta (float): Value from 0 to 1, indicating the weight of the dice loss.
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
    """

    def __init__(self, beta=1, gamma=2, alpha=0.25):
        super().__init__()
        self.beta = beta
        self.focal = FocalLoss(gamma, alpha)
        self.dice = DiceLoss()

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