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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clamp_mul_reciprocal_sum_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = 1e-05
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp10 = tl.full([1], 1, tl.int32)
    tmp11 = tmp10 / tmp9
    tmp12 = 1.0
    tmp13 = tmp11 * tmp12
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_mul_reciprocal_sum_0[grid(4)](arg1_1, buf0, 
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_sigmoid_1[grid(16)](arg0_1, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
    return buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0
        ), reinterpret_tensor(arg1_1, (4, 4), (1, 4), 0)


def expand_as_one_hot(input, C, ignore_index=None):
    """
    Converts NxDxHxW label image to NxCxDxHxW, where each label is stored in a separate channel
    :param input: 4D input image (NxDxHxW)
    :param C: number of channels/labels
    :param ignore_index: ignore index to be kept during the expansion
    :return: 5D output image (NxCxDxHxW)
    """
    assert input.dim() == 4
    shape = input.size()
    shape = list(shape)
    shape.insert(1, C)
    shape = tuple(shape)
    src = input.unsqueeze(0)
    if ignore_index is not None:
        expanded_src = src.expand(shape)
        mask = expanded_src == ignore_index
        src = src.clone()
        src[src == ignore_index] = 0
        result = torch.zeros(shape).scatter_(1, src, 1)
        result[mask] = ignore_index
        return result
    else:
        return torch.zeros(shape).scatter(1, src.type(torch.LongTensor), 1)


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.view(C, -1)


class GeneralizedDiceLossNew(nn.Module):
    """Computes Generalized Dice Loss (GDL) as described in https://arxiv.org/pdf/1707.03237.pdf
    """

    def __init__(self, epsilon=1e-05, weight=None, ignore_index=None,
        sigmoid_normalization=True):
        super(GeneralizedDiceLossNew, 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)

    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: 0.01387