# 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_1/inductor_cache/qu/cquwvkmeubcp6wymk4u4nct43ulebw6jegaozbjmetlib4e3ue5g.py
# Topologically Sorted Source Nodes: [neg, mul_2, pred, sigmoid_x, log, mul_3, sub_1, sub_2, log_1, mul_4, loss, bce, mul, intersection, mul_1, add, sum_2, sum_3, add_1, add_2, truediv, dice, mul_5, loss_1], Original ATen: [aten.neg, aten.mul, aten.sigmoid, aten.clamp, aten.log, aten.rsub, aten.sub, aten.mean, aten.sum, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   bce => mean
#   dice => sub
#   intersection => sum_1
#   log => log
#   log_1 => log_1
#   loss => sub_3
#   loss_1 => add_3
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   neg => neg
#   pred => sigmoid
#   sigmoid_x => clamp_max, clamp_min
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sum_2 => sum_2
#   sum_3 => sum_3
#   truediv => div
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg2_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %arg1_1), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sigmoid, 1e-08), kwargs = {})
#   %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 0.99999999), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_max,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %log), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %sub_2 : [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_2,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %log_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_3, %mul_4), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_3,), 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, 1e-08), 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, 1e-08), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 0.5), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mul_5), kwargs = {})
triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0 = async_compile.triton('triton_per_fused_add_clamp_div_log_mean_mul_neg_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: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_log_mean_mul_neg_rsub_sigmoid_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    tmp2 = tl.load(in_ptr1 + (r0), None)
    tmp4 = tl.load(in_ptr2 + (r0), None)
    tmp1 = -tmp0
    tmp3 = tmp1 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = 1e-08
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = 0.99999999
    tmp9 = triton_helpers.minimum(tmp7, tmp8)
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp3 * tmp10
    tmp12 = 1.0
    tmp13 = tmp12 - tmp2
    tmp14 = tmp12 - tmp9
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp13 * tmp15
    tmp17 = tmp11 - tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = tmp5 * tmp2
    tmp22 = tl.broadcast_to(tmp21, [RBLOCK])
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp25 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp30 = triton_helpers.promote_to_tensor(tl.sum(tmp28, 0))
    tmp31 = 256.0
    tmp32 = tmp20 / tmp31
    tmp33 = 2.0
    tmp34 = tmp24 * tmp33
    tmp35 = tmp34 + tmp6
    tmp36 = tmp27 + tmp30
    tmp37 = tmp36 + tmp6
    tmp38 = tmp35 / tmp37
    tmp39 = tmp12 - tmp38
    tmp40 = 0.5
    tmp41 = tmp39 * tmp40
    tmp42 = tmp32 + tmp41
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp42, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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: [neg, mul_2, pred, sigmoid_x, log, mul_3, sub_1, sub_2, log_1, mul_4, loss, bce, mul, intersection, mul_1, add, sum_2, sum_3, add_1, add_2, truediv, dice, mul_5, loss_1], Original ATen: [aten.neg, aten.mul, aten.sigmoid, aten.clamp, aten.log, aten.rsub, aten.sub, aten.mean, aten.sum, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_div_log_mean_mul_neg_rsub_sigmoid_sub_sum_0.run(buf4, arg2_1, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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
import torch.nn.functional as F


def dice_loss(pred, target, smooth=1e-08):
    iflat = pred.view(-1)
    tflat = target.view(-1)
    intersection = (iflat * tflat).sum()
    return 1 - (2.0 * intersection + smooth) / (iflat.sum() + tflat.sum() +
        smooth)


def weighted_binary_cross_entropy(sigmoid_x, targets, pos_weight, weight=
    None, size_average=True, reduce=True):
    """
    Args:
        sigmoid_x: predicted probability of size [N,C], N sample and C Class. Eg. Must be in range of [0,1], i.e. Output from Sigmoid.
        targets: true value, one-hot-like vector of size [N,C]
        pos_weight: Weight for postive sample
    """
    if not targets.size() == sigmoid_x.size():
        raise ValueError('Target size ({}) must be the same as input size ({})'
            .format(targets.size(), sigmoid_x.size()))
    sigmoid_x = sigmoid_x.clamp(min=1e-08, max=1 - 1e-08)
    loss = -pos_weight * targets * sigmoid_x.log() - (1 - targets) * (1 -
        sigmoid_x).log()
    if weight is not None:
        loss = loss * weight
    if not reduce:
        return loss
    elif size_average:
        return loss.mean()
    else:
        return loss.sum()


class WBCEDiceLoss(nn.Module):

    def __init__(self, alpha=0.5):
        super(WBCEDiceLoss, self).__init__()
        self.alpha = alpha

    def forward(self, pred, target, weight):
        pred = F.sigmoid(pred)
        dice = dice_loss(pred, target)
        bce = weighted_binary_cross_entropy(pred, target, weight)
        loss = bce + dice * self.alpha
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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_log_mean_mul_neg_rsub_sigmoid_sub_sum_0(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    tmp2 = tl.load(in_ptr1 + r0, None)
    tmp4 = tl.load(in_ptr2 + r0, None)
    tmp1 = -tmp0
    tmp3 = tmp1 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = 1e-08
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = 0.99999999
    tmp9 = triton_helpers.minimum(tmp7, tmp8)
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp3 * tmp10
    tmp12 = 1.0
    tmp13 = tmp12 - tmp2
    tmp14 = tmp12 - tmp9
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp13 * tmp15
    tmp17 = tmp11 - tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = tmp5 * tmp2
    tmp22 = tl.broadcast_to(tmp21, [RBLOCK])
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp25 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp30 = triton_helpers.promote_to_tensor(tl.sum(tmp28, 0))
    tmp31 = 256.0
    tmp32 = tmp20 / tmp31
    tmp33 = 2.0
    tmp34 = tmp24 * tmp33
    tmp35 = tmp34 + tmp6
    tmp36 = tmp27 + tmp30
    tmp37 = tmp36 + tmp6
    tmp38 = tmp35 / tmp37
    tmp39 = tmp12 - tmp38
    tmp40 = 0.5
    tmp41 = tmp39 * tmp40
    tmp42 = tmp32 + tmp41
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp42, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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_log_mean_mul_neg_rsub_sigmoid_sub_sum_0[
            grid(1)](buf4, arg2_1, arg1_1, arg0_1, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf4,


def dice_loss(pred, target, smooth=1e-08):
    iflat = pred.view(-1)
    tflat = target.view(-1)
    intersection = (iflat * tflat).sum()
    return 1 - (2.0 * intersection + smooth) / (iflat.sum() + tflat.sum() +
        smooth)


def weighted_binary_cross_entropy(sigmoid_x, targets, pos_weight, weight=
    None, size_average=True, reduce=True):
    """
    Args:
        sigmoid_x: predicted probability of size [N,C], N sample and C Class. Eg. Must be in range of [0,1], i.e. Output from Sigmoid.
        targets: true value, one-hot-like vector of size [N,C]
        pos_weight: Weight for postive sample
    """
    if not targets.size() == sigmoid_x.size():
        raise ValueError('Target size ({}) must be the same as input size ({})'
            .format(targets.size(), sigmoid_x.size()))
    sigmoid_x = sigmoid_x.clamp(min=1e-08, max=1 - 1e-08)
    loss = -pos_weight * targets * sigmoid_x.log() - (1 - targets) * (1 -
        sigmoid_x).log()
    if weight is not None:
        loss = loss * weight
    if not reduce:
        return loss
    elif size_average:
        return loss.mean()
    else:
        return loss.sum()


class WBCEDiceLossNew(nn.Module):

    def __init__(self, alpha=0.5):
        super(WBCEDiceLossNew, self).__init__()
        self.alpha = alpha

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
