import torch
from torch import nn


class DiceCoefMultilabelLoss(nn.Module):

    def __init__(self, cuda=True):
        super().__init__()
        self.one = torch.tensor(1.0, dtype=torch.float32)
        self.activation = torch.nn.Softmax2d()

    def dice_loss(self, predict, target):
        predict = predict.contiguous().view(-1)
        target = target.contiguous().view(-1)
        intersection = predict * target.float()
        score = (intersection.sum() * 2.0 + 1.0) / (predict.sum() + target.
            sum() + 1.0)
        return 1.0 - score

    def forward(self, predict, target, numLabels=3, channel='channel_first'):
        assert channel == 'channel_first' or channel == 'channel_last', "channel has to be either 'channel_first' or 'channel_last'"
        dice = 0
        predict = self.activation(predict)
        if channel == 'channel_first':
            for index in range(numLabels):
                temp = self.dice_loss(predict[:, index, :, :], target[:,
                    index, :, :])
                dice += temp
        else:
            for index in range(numLabels):
                temp = self.dice_loss(predict[:, :, :, index], target[:, :,
                    :, index])
                dice += temp
        dice = dice / numLabels
        return 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._inductor.runtime.triton_helpers import math as tl_math
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_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_per_fused_add_div_mul_rsub_sum_2(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
    tmp0 = tl.load(in_ptr0 + (64 * (r0 // 16) + r0 % 16), None)
    tmp1 = tl.load(in_ptr1 + (64 * (r0 // 16) + r0 % 16), None)
    tmp12 = tl.load(in_ptr0 + (16 + 64 * (r0 // 16) + r0 % 16), None)
    tmp13 = tl.load(in_ptr1 + (16 + 64 * (r0 // 16) + r0 % 16), None)
    tmp24 = tl.load(in_ptr0 + (32 + 64 * (r0 // 16) + r0 % 16), None)
    tmp25 = tl.load(in_ptr1 + (32 + 64 * (r0 // 16) + r0 % 16), None)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tmp9 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp14 = tmp12 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.sum(tmp15, 1)[:, None]
    tmp18 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp20 = tl.sum(tmp18, 1)[:, None]
    tmp21 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp23 = tl.sum(tmp21, 1)[:, None]
    tmp26 = tmp24 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp32 = tl.sum(tmp30, 1)[:, None]
    tmp33 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
    tmp35 = tl.sum(tmp33, 1)[:, None]
    tmp36 = 2.0
    tmp37 = tmp5 * tmp36
    tmp38 = 1.0
    tmp39 = tmp37 + tmp38
    tmp40 = tmp8 + tmp11
    tmp41 = tmp40 + tmp38
    tmp42 = tmp39 / tmp41
    tmp43 = tmp38 - tmp42
    tmp44 = 0.0
    tmp45 = tmp43 + tmp44
    tmp46 = tmp17 * tmp36
    tmp47 = tmp46 + tmp38
    tmp48 = tmp20 + tmp23
    tmp49 = tmp48 + tmp38
    tmp50 = tmp47 / tmp49
    tmp51 = tmp38 - tmp50
    tmp52 = tmp45 + tmp51
    tmp53 = tmp29 * tmp36
    tmp54 = tmp53 + tmp38
    tmp55 = tmp32 + tmp35
    tmp56 = tmp55 + tmp38
    tmp57 = tmp54 / tmp56
    tmp58 = tmp38 - tmp57
    tmp59 = tmp52 + tmp58
    tmp60 = 0.3333333333333333
    tmp61 = tmp59 * tmp60
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp61, 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf0, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
        buf10 = empty_strided_cuda((), (), torch.float32)
        buf11 = buf10
        del buf10
        buf12 = buf11
        del buf11
        triton_per_fused_add_div_mul_rsub_sum_2[grid(1)](buf12, buf1,
            arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf1
    return buf12,


class DiceCoefMultilabelLossNew(nn.Module):

    def __init__(self, cuda=True):
        super().__init__()
        self.one = torch.tensor(1.0, dtype=torch.float32)
        self.activation = torch.nn.Softmax2d()

    def dice_loss(self, predict, target):
        predict = predict.contiguous().view(-1)
        target = target.contiguous().view(-1)
        intersection = predict * target.float()
        score = (intersection.sum() * 2.0 + 1.0) / (predict.sum() + target.
            sum() + 1.0)
        return 1.0 - score

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