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 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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (r1 + 64 * x0), 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 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (32 + r1 + 64 * x0), 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 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (48 + r1 + 64 * x0), 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 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (16 + r1 + 64 * x0), 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 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_rsub_4(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9,
    in_ptr10, in_ptr11, 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)
    tmp4 = tl.load(in_ptr2 + r0, None)
    tmp12 = tl.load(in_ptr3 + r0, None)
    tmp14 = tl.load(in_ptr4 + r0, None)
    tmp15 = tl.load(in_ptr5 + r0, None)
    tmp22 = tl.load(in_ptr6 + r0, None)
    tmp24 = tl.load(in_ptr7 + r0, None)
    tmp25 = tl.load(in_ptr8 + r0, None)
    tmp32 = tl.load(in_ptr9 + r0, None)
    tmp34 = tl.load(in_ptr10 + r0, None)
    tmp35 = tl.load(in_ptr11 + r0, None)
    tmp1 = 2.0
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 / tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp13 = tmp12 * tmp1
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 + tmp6
    tmp18 = tmp13 / tmp17
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tmp23 = tmp22 * tmp1
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 + tmp6
    tmp28 = tmp23 / tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.sum(tmp29, 1)[:, None]
    tmp33 = tmp32 * tmp1
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 + tmp6
    tmp38 = tmp33 / tmp37
    tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41 = tl.sum(tmp39, 1)[:, None]
    tmp42 = 4.0
    tmp43 = tmp11 / tmp42
    tmp44 = tmp6 - tmp43
    tmp45 = 0.0
    tmp46 = tmp44 + tmp45
    tmp47 = tmp21 / tmp42
    tmp48 = tmp6 - tmp47
    tmp49 = tmp46 + tmp48
    tmp50 = tmp31 / tmp42
    tmp51 = tmp6 - tmp50
    tmp52 = tmp49 + tmp51
    tmp53 = tmp41 / tmp42
    tmp54 = tmp6 - tmp53
    tmp55 = tmp52 + tmp54
    tmp56 = 0.25
    tmp57 = tmp55 * tmp56
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp57, 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, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf8 = empty_strided_cuda((4,), (1,), torch.float32)
        buf9 = empty_strided_cuda((4,), (1,), torch.float32)
        buf10 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mul_sum_1[grid(4)](arg0_1, arg1_1, buf8, buf9,
            buf10, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf12 = empty_strided_cuda((4,), (1,), torch.float32)
        buf13 = empty_strided_cuda((4,), (1,), torch.float32)
        buf14 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mul_sum_2[grid(4)](arg0_1, arg1_1, buf12, buf13,
            buf14, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        buf6 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mul_sum_3[grid(4)](arg0_1, arg1_1, buf4, buf5,
            buf6, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf16 = buf11
        del buf11
        triton_per_fused_add_div_mean_mul_rsub_4[grid(1)](buf16, buf0, buf1,
            buf2, buf4, buf5, buf6, buf8, buf9, buf10, buf12, buf13, buf14,
            1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf10
        del buf12
        del buf13
        del buf14
        del buf2
        del buf4
        del buf5
        del buf6
        del buf8
        del buf9
    return buf16,


class BinaryDiceLoss(nn.Module):

    def __init__(self, smooth=1, p=2, reduction='mean'):
        super(BinaryDiceLoss, self).__init__()
        self.smooth = smooth
        self.p = p
        self.reduction = reduction

    def forward(self, predict, target):
        assert predict.shape[0] == target.shape[0
            ], "predict & target batch size don't match"
        predict = predict.contiguous().view(predict.shape[0], -1)
        target = target.contiguous().view(target.shape[0], -1)
        num = torch.sum(torch.mul(predict, target), dim=1)
        den = torch.sum(predict, dim=1) + torch.sum(target, dim=1
            ) + self.smooth
        dice_score = 2 * num / den
        loss_avg = 1 - dice_score.mean()
        return loss_avg


class DiceLoss4BraTSNew(nn.Module):

    def __init__(self, weight=None, ignore_index=None, **kwargs):
        super(DiceLoss4BraTSNew, self).__init__()
        self.kwargs = kwargs
        self.weight = weight
        self.ignore_index = ignore_index

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


Measured latency_ms: 0.087049