import torch
from torch import Tensor
from torch import nn as nn


def cox_ph_loss_sorted(log_h: 'Tensor', events: 'Tensor', eps: 'float'=1e-07
    ) ->Tensor:
    """Requires the input to be sorted by descending duration time.
    See DatasetDurationSorted.
    We calculate the negative log of $(rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
    where h = exp(log_h) are the hazards and R is the risk set, and d is event.
    We just compute a cumulative sum, and not the true Risk sets. This is a
    limitation, but simple and fast.
    """
    events = events.float()
    events = events.view(-1)
    log_h = log_h.view(-1)
    gamma = log_h.max()
    log_cumsum_h = log_h.sub(gamma).exp().cumsum(0).add(eps).log().add(gamma)
    loss = -log_h.sub(log_cumsum_h).mul(events).sum()
    if events.sum().item() == 0:
        return loss
    return loss.div(events.sum())


class CoxPHLossSorted(torch.nn.Module):
    """Loss for CoxPH.
    Requires the input to be sorted by descending duration time.
    See DatasetDurationSorted.
    We calculate the negative log of $(rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
    where h = exp(log_h) are the hazards and R is the risk set, and d is event.
    We just compute a cumulative sum, and not the true Risk sets. This is a
    limitation, but simple and fast.
    """

    def __init__(self):
        super().__init__()

    def forward(self, log_h: 'Tensor', events: 'Tensor') ->Tensor:
        return cox_ph_loss_sorted(log_h, events)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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 math as tl_math
from torch import Tensor
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused_add_cumsum_exp_log_max_mul_neg_sub_sum_0(in_out_ptr0,
    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)
    tmp4 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp5, 0))
    tmp8 = tmp4 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp12, = tl.associative_scan((tmp11,), 0, _triton_helper_fn_add0)
    tmp13 = 1e-07
    tmp14 = tmp12 + tmp13
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp15 + tmp7
    tmp17 = tmp4 - tmp16
    tmp18 = tmp17 * tmp0
    tmp19 = tl.broadcast_to(tmp18, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = -tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp22, None)
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, 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)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        get_raw_stream(0)
        triton_per_fused_add_cumsum_exp_log_max_mul_neg_sub_sum_0[grid(1)](buf4
            , arg0_1, arg1_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg1_1
    return buf0, reinterpret_tensor(arg0_1, (256,), (1,), 0), buf4


def cox_ph_loss_sorted(log_h: 'Tensor', events: 'Tensor', eps: 'float'=1e-07
    ) ->Tensor:
    """Requires the input to be sorted by descending duration time.
    See DatasetDurationSorted.
    We calculate the negative log of $(rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
    where h = exp(log_h) are the hazards and R is the risk set, and d is event.
    We just compute a cumulative sum, and not the true Risk sets. This is a
    limitation, but simple and fast.
    """
    events = events.float()
    events = events.view(-1)
    log_h = log_h.view(-1)
    gamma = log_h.max()
    log_cumsum_h = log_h.sub(gamma).exp().cumsum(0).add(eps).log().add(gamma)
    loss = -log_h.sub(log_cumsum_h).mul(events).sum()
    if events.sum().item() == 0:
        return loss
    return loss.div(events.sum())


class CoxPHLossSortedNew(torch.nn.Module):
    """Loss for CoxPH.
    Requires the input to be sorted by descending duration time.
    See DatasetDurationSorted.
    We calculate the negative log of $(rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
    where h = exp(log_h) are the hazards and R is the risk set, and d is event.
    We just compute a cumulative sum, and not the true Risk sets. This is a
    limitation, but simple and fast.
    """

    def __init__(self):
        super().__init__()

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