import torch


def cox_ph_loss_sorted(log_h, event, eps=1e-07):
    """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.
    """
    event = event.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)
    return -log_h.sub(log_cumsum_h).mul(event).sum().div(event.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, events):
        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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@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_div_exp_log_max_mul_neg_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, 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)
    tmp14 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp1, 0))
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp8, = tl.associative_scan((tmp7,), 0, _triton_helper_fn_add0)
    tmp9 = 1e-07
    tmp10 = tmp8 + tmp9
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp11 + tmp3
    tmp13 = tmp0 - tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp19 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = tmp18 / tmp21
    tmp23 = -tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp23, 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        get_raw_stream(0)
        triton_per_fused_add_cumsum_div_exp_log_max_mul_neg_sub_sum_0[grid(1)](
            buf4, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf4,


def cox_ph_loss_sorted(log_h, event, eps=1e-07):
    """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.
    """
    event = event.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)
    return -log_h.sub(log_cumsum_h).mul(event).sum().div(event.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]
