# 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/te/cteo5ara6efwi62vihroqlhdapoikm3sxobflsefdoski5cxzrd7.py
# Topologically Sorted Source Nodes: [bce_loss, total_loss, bce_loss_1, total_loss_1, bce_loss_2, total_loss_2, bce_loss_3, total_loss_3, mean], Original ATen: [aten.binary_cross_entropy_with_logits, aten.add, aten.mean]
# Source node to ATen node mapping:
#   bce_loss => abs_1, exp, full_default, log1p, mean, minimum, mul, neg, sub, sub_1, sub_2
#   bce_loss_1 => abs_2, exp_1, full_default_1, log1p_1, mean_1, minimum_1, mul_1, neg_1, sub_3, sub_4, sub_5
#   bce_loss_2 => abs_3, exp_2, full_default_2, log1p_2, mean_2, minimum_2, mul_2, neg_2, sub_6, sub_7, sub_8
#   bce_loss_3 => abs_4, exp_3, full_default_3, log1p_3, mean_3, minimum_3, mul_3, neg_3, sub_10, sub_11, sub_9
#   mean => mean_4
#   total_loss => add
#   total_loss_1 => add_1
#   total_loss_2 => add_2
#   total_loss_3 => add_3
# Graph fragment:
#   %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 = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_2,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 0), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %select_3), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %select_2), kwargs = {})
#   %full_default_1 : [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_1 : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default_1, %select_2), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%select_2,), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_2,), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {})
#   %log1p_1 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp_1,), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum_1, %log1p_1), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, %sub_4), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_5,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mean_1), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %select_5), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %select_4), kwargs = {})
#   %full_default_2 : [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_2 : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default_2, %select_4), kwargs = {})
#   %abs_3 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%select_4,), kwargs = {})
#   %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_3,), kwargs = {})
#   %exp_2 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_2,), kwargs = {})
#   %log1p_2 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp_2,), kwargs = {})
#   %sub_7 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum_2, %log1p_2), kwargs = {})
#   %sub_8 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %sub_7), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_8,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mean_2), kwargs = {})
#   %sub_9 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %select_7), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_9, %select_6), kwargs = {})
#   %full_default_3 : [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_3 : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default_3, %select_6), kwargs = {})
#   %abs_4 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%select_6,), kwargs = {})
#   %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_4,), kwargs = {})
#   %exp_3 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_3,), kwargs = {})
#   %log1p_3 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp_3,), kwargs = {})
#   %sub_10 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum_3, %log1p_3), kwargs = {})
#   %sub_11 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_3, %sub_10), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_11,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mean_3), kwargs = {})
#   %mean_4 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add_3,), kwargs = {})
triton_per_fused_add_binary_cross_entropy_with_logits_mean_0 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_mean_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_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_mean_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 + (r0 + (64*r1)), None)
    tmp3 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp16 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp18 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp30 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp32 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp44 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp46 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.minimum(tmp5, tmp3)
    tmp7 = tl_math.abs(tmp3)
    tmp8 = -tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = libdevice.log1p(tmp9)
    tmp11 = tmp6 - tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp17 = tmp1 - tmp16
    tmp19 = tmp17 * tmp18
    tmp20 = triton_helpers.minimum(tmp5, tmp18)
    tmp21 = tl_math.abs(tmp18)
    tmp22 = -tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = libdevice.log1p(tmp23)
    tmp25 = tmp20 - tmp24
    tmp26 = tmp19 - tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp31 = tmp1 - tmp30
    tmp33 = tmp31 * tmp32
    tmp34 = triton_helpers.minimum(tmp5, tmp32)
    tmp35 = tl_math.abs(tmp32)
    tmp36 = -tmp35
    tmp37 = tl_math.exp(tmp36)
    tmp38 = libdevice.log1p(tmp37)
    tmp39 = tmp34 - tmp38
    tmp40 = tmp33 - tmp39
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp45 = tmp1 - tmp44
    tmp47 = tmp45 * tmp46
    tmp48 = triton_helpers.minimum(tmp5, tmp46)
    tmp49 = tl_math.abs(tmp46)
    tmp50 = -tmp49
    tmp51 = tl_math.exp(tmp50)
    tmp52 = libdevice.log1p(tmp51)
    tmp53 = tmp48 - tmp52
    tmp54 = tmp47 - tmp53
    tmp55 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp57 = tl.sum(tmp55, 1)[:, None]
    tmp58 = 64.0
    tmp59 = tmp15 / tmp58
    tmp60 = tmp59 + tmp5
    tmp61 = tmp29 / tmp58
    tmp62 = tmp60 + tmp61
    tmp63 = tmp43 / tmp58
    tmp64 = tmp62 + tmp63
    tmp65 = tmp57 / tmp58
    tmp66 = tmp64 + tmp65
    tmp67 = tmp66 / tmp1
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp67, 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: [bce_loss, total_loss, bce_loss_1, total_loss_1, bce_loss_2, total_loss_2, bce_loss_3, total_loss_3, mean], Original ATen: [aten.binary_cross_entropy_with_logits, aten.add, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_mean_0.run(buf4, arg1_1, arg0_1, 1, 64, 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 torch import nn
import torch.jit
import torch.nn.functional


class BCELoss4BraTS(nn.Module):

    def __init__(self, ignore_index=None, **kwargs):
        super(BCELoss4BraTS, self).__init__()
        self.kwargs = kwargs
        self.ignore_index = ignore_index
        self.criterion = nn.BCEWithLogitsLoss()

    def weighted_BCE_cross_entropy(self, output, target, weights=None):
        if weights is not None:
            assert len(weights) == 2
            output = torch.clamp(output, min=1e-07, max=1 - 1e-07)
            bce = weights[1] * (target * torch.log(output)) + weights[0] * ((
                1 - target) * torch.log(1 - output))
        else:
            output = torch.clamp(output, min=0.001, max=1 - 0.001)
            bce = target * torch.log(output) + (1 - target) * torch.log(1 -
                output)
        return torch.neg(torch.mean(bce))

    def forward(self, predict, target):
        assert predict.shape == target.shape, 'predict & target shape do not match'
        total_loss = 0
        for i in range(target.shape[1]):
            if i != self.ignore_index:
                bce_loss = self.criterion(predict[:, i], target[:, i])
                total_loss += bce_loss
        return total_loss.mean()


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 torch import nn
import torch.jit
import torch.nn.functional
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_mean_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 + (r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp30 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp44 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp46 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.minimum(tmp5, tmp3)
    tmp7 = tl_math.abs(tmp3)
    tmp8 = -tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = libdevice.log1p(tmp9)
    tmp11 = tmp6 - tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp17 = tmp1 - tmp16
    tmp19 = tmp17 * tmp18
    tmp20 = triton_helpers.minimum(tmp5, tmp18)
    tmp21 = tl_math.abs(tmp18)
    tmp22 = -tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = libdevice.log1p(tmp23)
    tmp25 = tmp20 - tmp24
    tmp26 = tmp19 - tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp31 = tmp1 - tmp30
    tmp33 = tmp31 * tmp32
    tmp34 = triton_helpers.minimum(tmp5, tmp32)
    tmp35 = tl_math.abs(tmp32)
    tmp36 = -tmp35
    tmp37 = tl_math.exp(tmp36)
    tmp38 = libdevice.log1p(tmp37)
    tmp39 = tmp34 - tmp38
    tmp40 = tmp33 - tmp39
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp45 = tmp1 - tmp44
    tmp47 = tmp45 * tmp46
    tmp48 = triton_helpers.minimum(tmp5, tmp46)
    tmp49 = tl_math.abs(tmp46)
    tmp50 = -tmp49
    tmp51 = tl_math.exp(tmp50)
    tmp52 = libdevice.log1p(tmp51)
    tmp53 = tmp48 - tmp52
    tmp54 = tmp47 - tmp53
    tmp55 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp57 = tl.sum(tmp55, 1)[:, None]
    tmp58 = 64.0
    tmp59 = tmp15 / tmp58
    tmp60 = tmp59 + tmp5
    tmp61 = tmp29 / tmp58
    tmp62 = tmp60 + tmp61
    tmp63 = tmp43 / tmp58
    tmp64 = tmp62 + tmp63
    tmp65 = tmp57 / tmp58
    tmp66 = tmp64 + tmp65
    tmp67 = tmp66 / tmp1
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp67, 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_mean_0[grid(1)](
            buf4, arg1_1, arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf4,


class BCELoss4BraTSNew(nn.Module):

    def __init__(self, ignore_index=None, **kwargs):
        super(BCELoss4BraTSNew, self).__init__()
        self.kwargs = kwargs
        self.ignore_index = ignore_index
        self.criterion = nn.BCEWithLogitsLoss()

    def weighted_BCE_cross_entropy(self, output, target, weights=None):
        if weights is not None:
            assert len(weights) == 2
            output = torch.clamp(output, min=1e-07, max=1 - 1e-07)
            bce = weights[1] * (target * torch.log(output)) + weights[0] * ((
                1 - target) * torch.log(1 - output))
        else:
            output = torch.clamp(output, min=0.001, max=1 - 0.001)
            bce = target * torch.log(output) + (1 - target) * torch.log(1 -
                output)
        return torch.neg(torch.mean(bce))

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