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
    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 + tmp2
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(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, 64, 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 flatten(tensor):
    """Flattens a given tensor such that the channel axis is first.
    The shapes are transformed as follows:
       (N, C, D, H, W) -> (C, N * D * H * W)
    """
    C = tensor.size(1)
    axis_order = (1, 0) + tuple(range(2, tensor.dim()))
    transposed = tensor.permute(axis_order)
    return transposed.contiguous().view(C, -1)


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"
    target = target.float()
    if ignore_index is not None:
        mask = target.clone().ne_(ignore_index)
        mask.requires_grad = False
        input = input * mask
        target = target * mask
    input = flatten(input)
    target = flatten(target)
    intersect = (input * target).sum(-1)
    if weight is not None:
        intersect = weight * intersect
    denominator = (input + target).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]


Measured latency_ms: 0.02979