# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/ti/ctiljzl4dc6aw3rse3i33dz7ntgejsdixtq5q2it3jzqtvyozmu5.py
# Topologically Sorted Source Nodes: [sort], Original ATen: [aten.sort]
# Source node to ATen node mapping:
#   sort => sort
# Graph fragment:
#   %sort : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%arg0_1, -1, True), kwargs = {})
triton_per_fused_sort_0 = async_compile.triton('triton_per_fused_sort_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[64, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i16', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_sort_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_sort_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (4*x0)), xmask, other=0.0)
    tmp1 = r1
    tmp2 = tmp1.to(tl.int16)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5, tmp6, = triton_helpers.sort_with_index(tmp3, tmp4, None, 1, stable=False, descending=True)
    tl.store(out_ptr0 + (r1 + (4*x0)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gg/cggkjovpe63xchmrn34rr7bv4e2p23h45zxuj24fhmlp4aar5qaj.py
# Topologically Sorted Source Nodes: [log_h, events], Original ATen: [aten.index]
# Source node to ATen node mapping:
#   events => index
#   log_h => index_1
# Graph fragment:
#   %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg2_1, [%getitem_1]), kwargs = {})
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg1_1, [%getitem_1]), kwargs = {})
triton_poi_fused_index_1 = async_compile.triton('triton_poi_fused_index_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*i16', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_index_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64)
    x0 = xindex % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 4), "index out of bounds: 0 <= tmp5 < 4")
    tmp7 = tl.load(in_ptr1 + (x0 + (64*tmp5)), None)
    tmp8 = tl.load(in_ptr2 + (x0 + (64*tmp5)), None)
    tl.store(out_ptr0 + (x2), tmp7, None)
    tl.store(out_ptr1 + (x2), tmp8, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.int16)
        # Topologically Sorted Source Nodes: [sort], Original ATen: [aten.sort]
        stream0 = get_raw_stream(0)
        triton_per_fused_sort_0.run(arg0_1, buf1, 64, 4, grid=grid(64), stream=stream0)
        del arg0_1
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4, 4, 4), (4096, 1024, 256, 64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 4, 4, 4), (4096, 1024, 256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_h, events], Original ATen: [aten.index]
        triton_poi_fused_index_1.run(buf1, arg2_1, arg1_1, buf2, buf3, 16384, grid=grid(16384), stream=stream0)
        del arg1_1
        del arg2_1
        del buf1
    return (buf2, buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

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())


def cox_ph_loss(log_h: 'Tensor', durations: 'Tensor', events: 'Tensor', eps:
    'float'=1e-07) ->Tensor:
    """Loss for CoxPH model. If data is sorted by descending duration, see `cox_ph_loss_sorted`.
    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.
    """
    idx = durations.sort(descending=True)[1]
    events = events[idx]
    log_h = log_h[idx]
    return cox_ph_loss_sorted(log_h, events, eps)


class CoxPHLoss(torch.nn.Module):
    """Loss for CoxPH model. If data is sorted by descending duration, see `cox_ph_loss_sorted`.
    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 forward(self, log_h: 'Tensor', durations: 'Tensor', events: 'Tensor'
        ) ->Tensor:
        return cox_ph_loss(log_h, durations, events)


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


@triton.jit
def triton_per_fused_sort_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 4
    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 + (r1 + 4 * x0), xmask, other=0.0)
    tmp1 = r1
    tmp2 = tmp1.to(tl.int16)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    _tmp5, tmp6 = triton_helpers.sort_with_index(tmp3, tmp4, None, 1,
        stable=False, descending=True)
    tl.store(out_ptr0 + (r1 + 4 * x0), tmp6, xmask)


@triton.jit
def triton_poi_fused_index_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 64
    x0 = xindex % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 4),
        'index out of bounds: 0 <= tmp5 < 4')
    tmp7 = tl.load(in_ptr1 + (x0 + 64 * tmp5), None)
    tmp8 = tl.load(in_ptr2 + (x0 + 64 * tmp5), None)
    tl.store(out_ptr0 + x2, tmp7, None)
    tl.store(out_ptr1 + x2, tmp8, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.int16)
        get_raw_stream(0)
        triton_per_fused_sort_0[grid(64)](arg0_1, buf1, 64, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4, 4, 4), (4096, 1024, 256, 
            64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 4, 4, 4), (4096, 1024, 256, 
            64, 16, 4, 1), torch.float32)
        triton_poi_fused_index_1[grid(16384)](buf1, arg2_1, arg1_1, buf2,
            buf3, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        del arg2_1
        del buf1
    return buf2, buf3


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())


def cox_ph_loss(log_h: 'Tensor', durations: 'Tensor', events: 'Tensor', eps:
    'float'=1e-07) ->Tensor:
    """Loss for CoxPH model. If data is sorted by descending duration, see `cox_ph_loss_sorted`.
    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.
    """
    idx = durations.sort(descending=True)[1]
    events = events[idx]
    log_h = log_h[idx]
    return cox_ph_loss_sorted(log_h, events, eps)


class CoxPHLossNew(torch.nn.Module):
    """Loss for CoxPH model. If data is sorted by descending duration, see `cox_ph_loss_sorted`.
    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 forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
