# 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_4/inductor_cache/c3/cc35rxagqvvcixjawxcetgbs56htfzstazykjhyczpil5zctftss.py
# Topologically Sorted Source Nodes: [mul, intersect, add, denominator], Original ATen: [aten.mul, aten.sum, aten.add]
# Source node to ATen node mapping:
#   add => add
#   denominator => sum_2
#   intersect => sum_1
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_4, %slice_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_4, %slice_2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add, [-1]), kwargs = {})
triton_per_fused_add_mul_sum_0 = async_compile.triton('triton_per_fused_add_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, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 15
    RBLOCK: tl.constexpr = 16
    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 = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + r1 + (16*x0)), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (1 + r1 + (16*x0)), rmask & xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tmp1 + tmp2
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(rmask & xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp7, xmask)
    tl.store(out_ptr1 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5i/c5igbunkwt5rtqc5m4objcih3pcqb6hetejxaqtxaz6ll2xxrvjb.py
# Topologically Sorted Source Nodes: [mul_1, clamp, per_channel_dice, sub, mean], Original ATen: [aten.mul, aten.clamp, aten.div, aten.rsub, aten.mean]
# Source node to ATen node mapping:
#   clamp => clamp_min
#   mean => mean
#   mul_1 => mul_1
#   per_channel_dice => div
#   sub => sub
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 2.0), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_2, 1e-05), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %clamp_min), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %div), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub,), kwargs = {})
triton_per_fused_clamp_div_mean_mul_rsub_1 = async_compile.triton('triton_per_fused_clamp_div_mean_mul_rsub_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: '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), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_clamp_div_mean_mul_rsub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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_clamp_div_mean_mul_rsub_1(in_out_ptr0, in_ptr0, in_ptr1, 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)
    tmp3 = tl.load(in_ptr1 + (r0), None)
    tmp1 = 2.0
    tmp2 = tmp0 * tmp1
    tmp4 = 1e-05
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp2 / tmp5
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp13, 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)
        # Topologically Sorted Source Nodes: [mul, intersect, add, denominator], Original ATen: [aten.mul, aten.sum, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mul_sum_0.run(arg0_1, arg1_1, buf0, buf1, 4, 15, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [mul_1, clamp, per_channel_dice, sub, mean], Original ATen: [aten.mul, aten.clamp, aten.div, aten.rsub, aten.mean]
        triton_per_fused_clamp_div_mean_mul_rsub_1.run(buf3, buf0, buf1, 1, 4, grid=grid(1), stream=stream0)
        del buf0
        del buf1
    return (buf3, )


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
from torch.autograd import Variable


def compute_per_channel_dice(input, target, epsilon=1e-05, ignore_index=
    None, weight=None):
    assert input.size() == target.size(
        ), "'input' and 'target' must have the same shape"
    if ignore_index is not None:
        mask = target.clone().ne_(ignore_index)
        mask.requires_grad = False
        input = input * mask
        target = target * mask
    seg_pred = torch.reshape(input[0], [4, -1])
    seg_true = torch.reshape(target[0], [4, -1])
    seg_true = seg_true[:, 1:]
    seg_pred = seg_pred[:, 1:]
    intersect = (seg_pred * seg_true).sum(-1)
    if weight is not None:
        intersect = weight * intersect
    denominator = (seg_pred + seg_true).sum(-1)
    return 2.0 * intersect / denominator.clamp(min=epsilon)


class DiceLoss(nn.Module):
    """Computes Dice Loss, which just 1 - DiceCoefficient described above.
    Additionally allows per-class weights to be provided.
    """

    def __init__(self, epsilon=1e-05, weight=None, ignore_index=None,
        sigmoid_normalization=True, skip_last_target=False):
        super(DiceLoss, self).__init__()
        self.epsilon = epsilon
        self.register_buffer('weight', weight)
        self.ignore_index = ignore_index
        if sigmoid_normalization:
            self.normalization = nn.Sigmoid()
        else:
            self.normalization = nn.Softmax(dim=1)
        self.skip_last_target = skip_last_target

    def forward(self, input, target):
        input = self.normalization(input)
        if self.weight is not None:
            weight = Variable(self.weight, requires_grad=False)
        else:
            weight = None
        if self.skip_last_target:
            target = target[:, :-1, ...]
        per_channel_dice = compute_per_channel_dice(input, target, epsilon=
            self.epsilon, ignore_index=self.ignore_index, weight=weight)
        return torch.mean(1.0 - per_channel_dice)


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 import 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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 15
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (1 + r1 + 16 * x0), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (1 + r1 + 16 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tmp1 + tmp2
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(rmask & xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp12, xmask)


@triton.jit
def triton_per_fused_clamp_div_mean_mul_rsub_1(in_out_ptr0, in_ptr0,
    in_ptr1, 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)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp1 = 2.0
    tmp2 = tmp0 * tmp1
    tmp4 = 1e-05
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp2 / tmp5
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp13, 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)
        get_raw_stream(0)
        triton_per_fused_add_mul_sum_0[grid(4)](arg0_1, arg1_1, buf0, buf1,
            4, 15, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_clamp_div_mean_mul_rsub_1[grid(1)](buf3, buf0,
            buf1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf3,


def compute_per_channel_dice(input, target, epsilon=1e-05, ignore_index=
    None, weight=None):
    assert input.size() == target.size(
        ), "'input' and 'target' must have the same shape"
    if ignore_index is not None:
        mask = target.clone().ne_(ignore_index)
        mask.requires_grad = False
        input = input * mask
        target = target * mask
    seg_pred = torch.reshape(input[0], [4, -1])
    seg_true = torch.reshape(target[0], [4, -1])
    seg_true = seg_true[:, 1:]
    seg_pred = seg_pred[:, 1:]
    intersect = (seg_pred * seg_true).sum(-1)
    if weight is not None:
        intersect = weight * intersect
    denominator = (seg_pred + seg_true).sum(-1)
    return 2.0 * intersect / denominator.clamp(min=epsilon)


class DiceLossNew(nn.Module):
    """Computes Dice Loss, which just 1 - DiceCoefficient described above.
    Additionally allows per-class weights to be provided.
    """

    def __init__(self, epsilon=1e-05, weight=None, ignore_index=None,
        sigmoid_normalization=True, skip_last_target=False):
        super(DiceLossNew, self).__init__()
        self.epsilon = epsilon
        self.register_buffer('weight', weight)
        self.ignore_index = ignore_index
        if sigmoid_normalization:
            self.normalization = nn.Sigmoid()
        else:
            self.normalization = nn.Softmax(dim=1)
        self.skip_last_target = skip_last_target

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