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
import numpy as np
from torch import nn
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_poi_fused_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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_ptr1 + x0, xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp1
    tmp5 = tmp0 * tmp4
    tmp6 = tmp3 - tmp0
    tmp7 = tmp6 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)
    tl.store(out_ptr2 + x0, tmp7, xmask)


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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_rsub_0[grid(256)](arg0_1, arg1_1, buf0, buf1,
            buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0, buf1, buf2


def sum_tensor(inp, axes, keepdim=False):
    axes = np.unique(axes).astype(int)
    if keepdim:
        for ax in axes:
            inp = inp.sum(int(ax), keepdim=True)
    else:
        for ax in sorted(axes, reverse=True):
            inp = inp.sum(int(ax))
    return inp


def get_tp_fp_fn(net_output, gt, axes=None, mask=None, square=False):
    """
    net_output must be (b, c, x, y(, z)))
    gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))
    if mask is provided it must have shape (b, 1, x, y(, z)))
    :param net_output:
    :param gt:
    :param axes:
    :param mask: mask must be 1 for valid pixels and 0 for invalid pixels
    :param square: if True then fp, tp and fn will be squared before summation
    :return:
    """
    if axes is None:
        axes = tuple(range(2, len(net_output.size())))
    shp_x = net_output.shape
    shp_y = gt.shape
    with torch.no_grad():
        if len(shp_x) != len(shp_y):
            gt = gt.view((shp_y[0], 1, *shp_y[1:]))
        if all([(i == j) for i, j in zip(net_output.shape, gt.shape)]):
            y_onehot = gt
        else:
            gt = gt.long()
            y_onehot = torch.zeros(shp_x)
            if net_output.device.type == 'cuda':
                y_onehot = y_onehot
            y_onehot.scatter_(1, gt, 1)
    tp = net_output * y_onehot
    fp = net_output * (1 - y_onehot)
    fn = (1 - net_output) * y_onehot
    if mask is not None:
        tp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tp,
            dim=1)), dim=1)
        fp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fp,
            dim=1)), dim=1)
        fn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fn,
            dim=1)), dim=1)
    if square:
        tp = tp ** 2
        fp = fp ** 2
        fn = fn ** 2
    tp = sum_tensor(tp, axes, keepdim=False)
    fp = sum_tensor(fp, axes, keepdim=False)
    fn = sum_tensor(fn, axes, keepdim=False)
    return tp, fp, fn


class SoftDiceLossNew(nn.Module):

    def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True,
        smooth=1.0, square=False):
        """
        """
        super(SoftDiceLossNew, self).__init__()
        self.square = square
        self.do_bg = do_bg
        self.batch_dice = batch_dice
        self.apply_nonlin = apply_nonlin
        self.smooth = smooth

    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: 0.018286