# 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/zw/czwkdr5kj5kvov6jgdxr47eylapl5isggzw76mkedkqzt26xbniw.py
# Topologically Sorted Source Nodes: [mul, numerator, mul_1, sum_2, mul_2, sum_3], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   numerator => sum_1
#   sum_2 => sum_2
#   sum_3 => sum_3
# Graph fragment:
#   %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.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1]), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %view_1), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [-1]), kwargs = {})
triton_per_fused_mul_sum_0 = async_compile.triton('triton_per_fused_mul_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=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 3, '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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + ((16*x0) + (64*(r1 // 16)) + (r1 % 16)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + ((16*x0) + (64*(r1 // 16)) + (r1 % 16)), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tmp1 * tmp1
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = tmp2 * tmp2
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.sum(tmp16, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp7, xmask)
    tl.store(out_ptr1 + (x0), tmp12, xmask)
    tl.store(out_ptr2 + (x0), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ow/cow45uy76wdortu6qm2wtsqlpzlmjty77mvzrpxh2y7u3t4lgfoy.py
# Topologically Sorted Source Nodes: [denominator, clamp, truediv, channelwise_score, channelwise_score_1, score], Original ATen: [aten.add, aten.clamp, aten.div, aten.mul, aten.rsub, aten.sum]
# Source node to ATen node mapping:
#   channelwise_score => mul_3
#   channelwise_score_1 => sub
#   clamp => clamp_min
#   denominator => add
#   score => sum_4
#   truediv => div
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 1e-07), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %clamp_min), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %mul_3), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub,), kwargs = {})
triton_per_fused_add_clamp_div_mul_rsub_sum_1 = async_compile.triton('triton_per_fused_add_clamp_div_mul_rsub_sum_1', '''
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, 4],
    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), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_div_mul_rsub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 1, '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_mul_rsub_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp2 = tl.load(in_ptr2 + (r0), None)
    tmp3 = tmp1 + tmp2
    tmp4 = 1e-07
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 / tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = 1.0
    tmp10 = tmp9 - tmp8
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/pj/cpj665hzyutddfso57lgimdbgfbtaxy53oaowlpwtxp7qhdjcp5o.py
# Topologically Sorted Source Nodes: [mul_4, loss_bce, mul_5, add_1], Original ATen: [aten.mul, aten.binary_cross_entropy_with_logits, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_1
#   loss_bce => abs_1, exp, full_default, log1p, mean, minimum, mul_4, neg, sub_1, sub_2, sub_3
#   mul_4 => mul_5
#   mul_5 => mul_6
# Graph fragment:
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_4, 1.0), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %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_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_4, %sub_2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_3,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 1.0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %mul_6), kwargs = {})
triton_per_fused_add_binary_cross_entropy_with_logits_mul_2 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_mul_2', '''
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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 1, '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_binary_cross_entropy_with_logits_mul_2(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)
    tmp3 = tl.load(in_ptr1 + (r0), None)
    tmp16 = tl.load(in_out_ptr0 + (0))
    tmp17 = tl.broadcast_to(tmp16, [1])
    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, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp18 = tmp17 * tmp1
    tmp19 = 256.0
    tmp20 = tmp15 / tmp19
    tmp21 = tmp20 * tmp1
    tmp22 = tmp18 + tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp22, 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((4, ), (1, ), torch.float32)
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mul, numerator, mul_1, sum_2, mul_2, sum_3], Original ATen: [aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_mul_sum_0.run(arg0_1, arg1_1, buf0, buf1, buf2, 4, 64, grid=grid(4), stream=stream0)
        buf3 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [denominator, clamp, truediv, channelwise_score, channelwise_score_1, score], Original ATen: [aten.add, aten.clamp, aten.div, aten.mul, aten.rsub, aten.sum]
        triton_per_fused_add_clamp_div_mul_rsub_sum_1.run(buf0, buf1, buf2, buf3, 1, 4, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf2
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [mul_4, loss_bce, mul_5, add_1], Original ATen: [aten.mul, aten.binary_cross_entropy_with_logits, aten.add]
        triton_per_fused_add_binary_cross_entropy_with_logits_mul_2.run(buf5, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf5, )


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
import torch.utils.data


def flatten_samples(input_):
    """
    Flattens a tensor or a variable such that the channel axis is first and the sample axis
    is second. The shapes are transformed as follows:
        (N, C, H, W) --> (C, N * H * W)
        (N, C, D, H, W) --> (C, N * D * H * W)
        (N, C) --> (C, N)
    The input must be atleast 2d.
    """
    num_channels = input_.size(1)
    permute_axes = list(range(input_.dim()))
    permute_axes[0], permute_axes[1] = permute_axes[1], permute_axes[0]
    permuted = input_.permute(*permute_axes).contiguous()
    flattened = permuted.view(num_channels, -1)
    return flattened


def dice_score(input_, target, invert=False, channelwise=True, eps=1e-07):
    if channelwise:
        input_ = flatten_samples(input_)
        target = flatten_samples(target)
        numerator = (input_ * target).sum(-1)
        denominator = (input_ * input_).sum(-1) + (target * target).sum(-1)
        channelwise_score = 2 * (numerator / denominator.clamp(min=eps))
        if invert:
            channelwise_score = 1.0 - channelwise_score
        score = channelwise_score.sum()
    else:
        numerator = (input_ * target).sum()
        denominator = (input_ * input_).sum() + (target * target).sum()
        score = 2.0 * (numerator / denominator.clamp(min=eps))
        if invert:
            score = 1.0 - score
    return score


class BCEDiceLossWithLogits(nn.Module):

    def __init__(self, alpha=1.0, beta=1.0, channelwise=True, eps=1e-07):
        super().__init__()
        self.alpha = alpha
        self.beta = beta
        self.channelwise = channelwise
        self.eps = eps
        self.init_kwargs = {'alpha': alpha, 'beta': beta, 'channelwise':
            channelwise, 'eps': self.eps}

    def forward(self, input_, target):
        loss_dice = dice_score(nn.functional.sigmoid(input_), target,
            invert=True, channelwise=self.channelwise, eps=self.eps)
        loss_bce = nn.functional.binary_cross_entropy_with_logits(input_,
            target)
        return self.alpha * loss_dice + self.beta * loss_bce


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
import torch.nn as nn
import torch.utils.data
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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (16 * x0 + 64 * (r1 // 16) + r1 % 16), xmask,
        other=0.0)
    tmp2 = tl.load(in_ptr1 + (16 * x0 + 64 * (r1 // 16) + r1 % 16), xmask,
        other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tmp1 * tmp1
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = tmp2 * tmp2
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.sum(tmp16, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp12, xmask)
    tl.store(out_ptr2 + x0, tmp17, xmask)


@triton.jit
def triton_per_fused_add_clamp_div_mul_rsub_sum_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tl.load(in_ptr2 + r0, None)
    tmp3 = tmp1 + tmp2
    tmp4 = 1e-07
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 / tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = 1.0
    tmp10 = tmp9 - tmp8
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp13, None)


@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_mul_2(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)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp16 = tl.load(in_out_ptr0 + 0)
    tmp17 = tl.broadcast_to(tmp16, [1])
    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, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp18 = tmp17 * tmp1
    tmp19 = 256.0
    tmp20 = tmp15 / tmp19
    tmp21 = tmp20 * tmp1
    tmp22 = tmp18 + tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp22, 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((4,), (1,), torch.float32)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mul_sum_0[grid(4)](arg0_1, arg1_1, buf0, buf1,
            buf2, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_add_clamp_div_mul_rsub_sum_1[grid(1)](buf0, buf1,
            buf2, buf3, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf2
        buf5 = buf3
        del buf3
        triton_per_fused_add_binary_cross_entropy_with_logits_mul_2[grid(1)](
            buf5, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf5,


def flatten_samples(input_):
    """
    Flattens a tensor or a variable such that the channel axis is first and the sample axis
    is second. The shapes are transformed as follows:
        (N, C, H, W) --> (C, N * H * W)
        (N, C, D, H, W) --> (C, N * D * H * W)
        (N, C) --> (C, N)
    The input must be atleast 2d.
    """
    num_channels = input_.size(1)
    permute_axes = list(range(input_.dim()))
    permute_axes[0], permute_axes[1] = permute_axes[1], permute_axes[0]
    permuted = input_.permute(*permute_axes).contiguous()
    flattened = permuted.view(num_channels, -1)
    return flattened


def dice_score(input_, target, invert=False, channelwise=True, eps=1e-07):
    if channelwise:
        input_ = flatten_samples(input_)
        target = flatten_samples(target)
        numerator = (input_ * target).sum(-1)
        denominator = (input_ * input_).sum(-1) + (target * target).sum(-1)
        channelwise_score = 2 * (numerator / denominator.clamp(min=eps))
        if invert:
            channelwise_score = 1.0 - channelwise_score
        score = channelwise_score.sum()
    else:
        numerator = (input_ * target).sum()
        denominator = (input_ * input_).sum() + (target * target).sum()
        score = 2.0 * (numerator / denominator.clamp(min=eps))
        if invert:
            score = 1.0 - score
    return score


class BCEDiceLossWithLogitsNew(nn.Module):

    def __init__(self, alpha=1.0, beta=1.0, channelwise=True, eps=1e-07):
        super().__init__()
        self.alpha = alpha
        self.beta = beta
        self.channelwise = channelwise
        self.eps = eps
        self.init_kwargs = {'alpha': alpha, 'beta': beta, 'channelwise':
            channelwise, 'eps': self.eps}

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