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 libdevice, math as tl_math
from torch import nn as 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_binary_cross_entropy_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 - tmp1
    tmp4 = -tmp3
    tmp5 = libdevice.log1p(tmp4)
    tmp6 = -100.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tl_math.log(tmp3)
    tmp10 = triton_helpers.maximum(tmp9, tmp6)
    tmp11 = tmp0 * tmp10
    tmp12 = tmp8 - tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp15, None)


@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 = 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)
    tmp1 = tl.load(in_ptr1 + (16 * x0 + 64 * (r1 // 16) + r1 % 16), xmask,
        other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tmp0 * tmp0
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tmp1 * tmp1
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp16, xmask)


@triton.jit
def triton_per_fused_add_binary_cross_entropy_clamp_div_mean_mul_rsub_2(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tl.load(in_ptr2 + r0, None)
    tmp12 = tl.load(in_out_ptr0 + 0)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, 1])
    tmp3 = tmp1 + tmp2
    tmp4 = 1e-06
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 / tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 4.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp11 / tmp16
    tmp19 = 1.0
    tmp20 = tmp19 - tmp18
    tmp21 = tmp20 * tmp16
    tmp22 = tmp17 + tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp22, 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((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_binary_cross_entropy_0[grid(1)](arg0_1, arg1_1,
            buf0, 1, 256, num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mul_sum_1[grid(4)](arg1_1, arg0_1, buf1, buf2,
            buf3, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf5 = buf0
        del buf0
        triton_per_fused_add_binary_cross_entropy_clamp_div_mean_mul_rsub_2[
            grid(1)](buf5, buf1, buf2, buf3, 1, 4, XBLOCK=1, num_warps=2,
            num_stages=1)
        del buf1
        del buf2
        del buf3
    return buf5,


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-06, weight=None):
    """
    Computes DiceCoefficient as defined in https://arxiv.org/abs/1606.04797 given  a multi channel input and target.
    Assumes the input is a normalized probability, e.g. a result of Sigmoid or Softmax function.

    Args:
         input (torch.Tensor): NxCxSpatial input tensor
         target (torch.Tensor): NxCxSpatial target tensor
         epsilon (float): prevents division by zero
         weight (torch.Tensor): Cx1 tensor of weight per channel/class
    """
    assert input.size() == target.size(
        ), "'input' and 'target' must have the same shape"
    input = flatten(input)
    target = flatten(target)
    target = target.float()
    intersect = (input * target).sum(-1)
    if weight is not None:
        intersect = weight * intersect
    denominator = (input * input).sum(-1) + (target * target).sum(-1)
    return 2 * (intersect / denominator.clamp(min=epsilon))


class _AbstractDiceLoss(nn.Module):
    """
    Base class for different implementations of Dice loss.
    """

    def __init__(self, weight=None, normalization='sigmoid'):
        super(_AbstractDiceLoss, self).__init__()
        self.register_buffer('weight', weight)
        assert normalization in ['sigmoid', 'softmax', 'none']
        if normalization == 'sigmoid':
            self.normalization = nn.Sigmoid()
        elif normalization == 'softmax':
            self.normalization = nn.Softmax(dim=1)
        else:
            self.normalization = lambda x: x

    def dice(self, input, target, weight):
        raise NotImplementedError

    def forward(self, input, target):
        input = self.normalization(input)
        per_channel_dice = self.dice(input, target, weight=self.weight)
        return 1.0 - torch.mean(per_channel_dice)


class DiceLoss(_AbstractDiceLoss):
    """Computes Dice Loss according to https://arxiv.org/abs/1606.04797.
    For multi-class segmentation `weight` parameter can be used to assign different weights per class.
    The input to the loss function is assumed to be a logit and will be normalized by the Sigmoid function.
    """

    def __init__(self, weight=None, normalization='sigmoid'):
        super().__init__(weight, normalization)

    def dice(self, input, target, weight):
        return compute_per_channel_dice(input, target, weight=self.weight)


class BCEDiceProbLossNew(nn.Module):
    """ Same as BCEDiceLoss but applied to probabilities instead of logits """

    def __init__(self, alpha, beta):
        super(BCEDiceProbLossNew, self).__init__()
        self.alpha = alpha
        self.bce = nn.BCELoss()
        self.beta = beta
        self.dice = DiceLoss(normalization='none')

    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: 0.043858