# 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_7/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 = (%arg1_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_7/inductor_cache/bu/cbuvn7q5hcxxos2llx7iyucnrqp5rxu3phitjjgtdiexbwfpfsls.py
# Topologically Sorted Source Nodes: [gamma], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   gamma => max_1
# Graph fragment:
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.default](args = (%view_1,), kwargs = {})
triton_red_fused_max_1 = async_compile.triton('triton_red_fused_max_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.reduction(
    size_hints=[2, 8192],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*i16', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_max_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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_red_fused_max_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 2
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp9 = tl.full([XBLOCK, RBLOCK], float("-inf"), tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + ((128*x0) + (r1 // 64)), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0.to(tl.int64)
        tmp2 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
        tmp3 = tmp1 + tmp2
        tmp4 = tmp1 < 0
        tmp5 = tl.where(tmp4, tmp3, tmp1)
        tl.device_assert(((0 <= tmp5) & (tmp5 < 4)) | ~(rmask & xmask), "index out of bounds: 0 <= tmp5 < 4")
        tmp7 = tl.load(in_ptr1 + ((64*tmp5) + (r1 % 64)), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
        tmp10 = triton_helpers.maximum(_tmp9, tmp8)
        _tmp9 = tl.where(rmask & xmask, tmp10, _tmp9)
    tmp9 = triton_helpers.max2(_tmp9, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4s/c4svd7ast6nrnrq3ddyyzhoyhw3iwxlnxt4jkl7u57tggu4fsrqh.py
# Topologically Sorted Source Nodes: [gamma], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   gamma => max_1
# Graph fragment:
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.default](args = (%view_1,), kwargs = {})
triton_per_fused_max_2 = async_compile.triton('triton_per_fused_max_2', '''
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=[1, 2],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_max_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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_max_2(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 2
    RBLOCK: tl.constexpr = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp3, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/gn/cgns6funurqslgwtbqh733vpcpjwcpxezudfgmk2gyx6mbrt5dj4.py
# Topologically Sorted Source Nodes: [sub, exp, cumsum], Original ATen: [aten.sub, aten.exp, aten.cumsum]
# Source node to ATen node mapping:
#   cumsum => cumsum
#   exp => exp
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %max_1), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %cumsum : [num_users=1] = call_function[target=torch.ops.aten.cumsum.default](args = (%exp, 0), kwargs = {})
triton_spl_fused_cumsum_exp_sub_3 = async_compile.triton('triton_spl_fused_cumsum_exp_sub_3', '''
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.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0

@triton_heuristics.split_scan(
    size_hints=[1, 16384],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*i16', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i8', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_spl_fused_cumsum_exp_sub_3', 'mutated_arg_names': ['ws_ptr'], 'no_x_dim': True, 'num_load': 2, '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_spl_fused_cumsum_exp_sub_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ws_ptr, xnumel, rnumel, RBLOCK : tl.constexpr):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 16384
    xoffset = tl.program_id(1) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    roffset = tl.program_id(0) * RBLOCK
    rindex = roffset + tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + ((r0 // 64)), rmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tl.load(in_ptr2 + (0))
    tmp9 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp12 = tl.num_programs(0)
    tmp13 = ws_ptr.to(tl.pointer_type(tl.uint64)) + xoffset * 1 * tmp12
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([RBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert(((0 <= tmp5) & (tmp5 < 4)) | ~(rmask), "index out of bounds: 0 <= tmp5 < 4")
    tmp7 = tl.load(in_ptr1 + ((64*tmp5) + (r0 % 64)), rmask, other=0.0)
    tmp10 = tmp7 - tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp14 = tmp11.to(tl.float32)
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp16 = tl.reduce(tmp15, 0, _triton_helper_fn_add0)
    tmp17 = triton_helpers.exclusive_scan_decoupled_lookback(
        tmp13,
        tmp16,
        tl.program_id(0),
        _triton_helper_fn_add0,
        DTYPE_VALUE_AS_UINT=tl.uint32,
        DTYPE_PACK=tl.uint64,
    )
    tmp18 = tl.associative_scan(tmp15, 0, _triton_helper_fn_add0)
    tmp19 = _triton_helper_fn_add0(tmp17, tmp18)
    tmp20 = tl.where(roffset == 0, tmp18, tmp19)
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [RBLOCK])), tmp20, rmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sr/csraps56wurb43vdz2i3p323w5in3o6s6evt3gnybxlxhm7k6x7m.py
# Topologically Sorted Source Nodes: [add, log, log_cumsum_h, sub_1, mul, sum_1, sum_2], Original ATen: [aten.add, aten.log, aten.sub, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   log => log
#   log_cumsum_h => add_1
#   mul => mul
#   sub_1 => sub_1
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%cumsum, 1e-07), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, %max_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %add_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %view), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view,), kwargs = {})
triton_red_fused_add_log_mul_sub_sum_4 = async_compile.triton('triton_red_fused_add_log_mul_sub_sum_4', '''
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.reduction(
    size_hints=[2, 8192],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*i16', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: '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, 6, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_add_log_mul_sub_sum_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, '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_red_fused_add_log_mul_sub_sum_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 2
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp12 = tl.load(in_ptr3 + (0))
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    _tmp19 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    _tmp22 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + ((128*x0) + (r1 // 64)), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp8 = tl.load(in_ptr2 + (r1 + (8192*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tmp0.to(tl.int64)
        tmp2 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
        tmp3 = tmp1 + tmp2
        tmp4 = tmp1 < 0
        tmp5 = tl.where(tmp4, tmp3, tmp1)
        tl.device_assert(((0 <= tmp5) & (tmp5 < 4)) | ~(rmask & xmask), "index out of bounds: 0 <= tmp5 < 4")
        tmp7 = tl.load(in_ptr1 + ((64*tmp5) + (r1 % 64)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp9 = 1e-07
        tmp10 = tmp8 + tmp9
        tmp11 = tl_math.log(tmp10)
        tmp14 = tmp11 + tmp13
        tmp15 = tmp7 - tmp14
        tmp16 = tl.load(in_ptr4 + ((64*tmp5) + (r1 % 64)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp17 = tmp15 * tmp16
        tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
        tmp20 = _tmp19 + tmp18
        _tmp19 = tl.where(rmask & xmask, tmp20, _tmp19)
        tmp21 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
        tmp23 = _tmp22 + tmp21
        _tmp22 = tl.where(rmask & xmask, tmp23, _tmp22)
    tmp19 = tl.sum(_tmp19, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp19, xmask)
    tmp22 = tl.sum(_tmp22, 1)[:, None]
    tl.store(out_ptr1 + (x0), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ws/cws2kvw5nbabxumdolhguy43oogrbdvxc52jldvpi4mb3jfcgkrz.py
# Topologically Sorted Source Nodes: [add, log, log_cumsum_h, sub_1, mul, sum_1, sum_2, div, neg], Original ATen: [aten.add, aten.log, aten.sub, aten.mul, aten.sum, aten.div, aten.neg]
# Source node to ATen node mapping:
#   add => add
#   div => div
#   log => log
#   log_cumsum_h => add_1
#   mul => mul
#   neg => neg
#   sub_1 => sub_1
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%cumsum, 1e-07), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, %max_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %add_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %view), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %sum_2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%div,), kwargs = {})
triton_per_fused_add_div_log_mul_neg_sub_sum_5 = async_compile.triton('triton_per_fused_add_div_log_mul_neg_sub_sum_5', '''
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=[1, 2],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_log_mul_neg_sub_sum_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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_add_div_log_mul_neg_sub_sum_5(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 2
    RBLOCK: tl.constexpr = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, 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, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tmp8 = tmp3 / tmp7
    tmp9 = -tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp9, 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(arg1_1, buf1, 64, 4, grid=grid(64), stream=stream0)
        del arg1_1
        buf2 = empty_strided_cuda((2, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [gamma], Original ATen: [aten.max]
        triton_red_fused_max_1.run(buf1, arg0_1, buf2, 2, 8192, grid=grid(2), stream=stream0)
        buf3 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [gamma], Original ATen: [aten.max]
        triton_per_fused_max_2.run(buf2, buf3, 1, 2, grid=grid(1), stream=stream0)
        buf4 = empty_strided_cuda((16384, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [sub, exp, cumsum], Original ATen: [aten.sub, aten.exp, aten.cumsum]
        workspace = empty_strided_cuda((512, ), (1, ), torch.uint8)
        workspace.zero_()
        triton_spl_fused_cumsum_exp_sub_3.run(buf1, arg0_1, buf3, buf4, workspace, 1, 16384, grid=split_scan_grid(1, 16384), stream=stream0)
        del workspace
        buf5 = buf2; del buf2  # reuse
        buf7 = empty_strided_cuda((2, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [add, log, log_cumsum_h, sub_1, mul, sum_1, sum_2], Original ATen: [aten.add, aten.log, aten.sub, aten.mul, aten.sum]
        triton_red_fused_add_log_mul_sub_sum_4.run(buf1, arg0_1, buf4, buf3, arg2_1, buf5, buf7, 2, 8192, grid=grid(2), stream=stream0)
        del arg0_1
        del arg2_1
        del buf1
        del buf4
        buf6 = buf3; del buf3  # reuse
        buf9 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [add, log, log_cumsum_h, sub_1, mul, sum_1, sum_2, div, neg], Original ATen: [aten.add, aten.log, aten.sub, aten.mul, aten.sum, aten.div, aten.neg]
        triton_per_fused_add_div_log_mul_neg_sub_sum_5.run(buf9, buf5, buf7, 1, 2, grid=grid(1), stream=stream0)
        del buf5
        del buf7
    return (buf9, )


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


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.
    """
    if events.dtype is torch.bool:
        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)
    return -log_h.sub(log_cumsum_h).mul(events).sum().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, split_scan_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
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_red_fused_max_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 2
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp9 = tl.full([XBLOCK, RBLOCK], float('-inf'), tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (128 * x0 + r1 // 64), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0.to(tl.int64)
        tmp2 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
        tmp3 = tmp1 + tmp2
        tmp4 = tmp1 < 0
        tmp5 = tl.where(tmp4, tmp3, tmp1)
        tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~(rmask & xmask),
            'index out of bounds: 0 <= tmp5 < 4')
        tmp7 = tl.load(in_ptr1 + (64 * tmp5 + r1 % 64), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
        tmp10 = triton_helpers.maximum(_tmp9, tmp8)
        _tmp9 = tl.where(rmask & xmask, tmp10, _tmp9)
    tmp9 = triton_helpers.max2(_tmp9, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_per_fused_max_2(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    RBLOCK: tl.constexpr = 2
    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.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, None)


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


@triton.jit
def triton_spl_fused_cumsum_exp_sub_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    ws_ptr, xnumel, rnumel, RBLOCK: tl.constexpr):
    XBLOCK: tl.constexpr = 1
    rnumel = 16384
    xoffset = tl.program_id(1) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    roffset = tl.program_id(0) * RBLOCK
    rindex = roffset + tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0 // 64, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp8 = tl.load(in_ptr2 + 0)
    tmp9 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp12 = tl.num_programs(0)
    tmp13 = ws_ptr.to(tl.pointer_type(tl.uint64)) + xoffset * 1 * tmp12
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([RBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~rmask,
        'index out of bounds: 0 <= tmp5 < 4')
    tmp7 = tl.load(in_ptr1 + (64 * tmp5 + r0 % 64), rmask, other=0.0)
    tmp10 = tmp7 - tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp14 = tmp11.to(tl.float32)
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp16 = tl.reduce(tmp15, 0, _triton_helper_fn_add0)
    tmp17 = triton_helpers.exclusive_scan_decoupled_lookback(tmp13, tmp16,
        tl.program_id(0), _triton_helper_fn_add0, DTYPE_VALUE_AS_UINT=tl.
        uint32, DTYPE_PACK=tl.uint64)
    tmp18 = tl.associative_scan(tmp15, 0, _triton_helper_fn_add0)
    tmp19 = _triton_helper_fn_add0(tmp17, tmp18)
    tmp20 = tl.where(roffset == 0, tmp18, tmp19)
    tl.store(out_ptr0 + tl.broadcast_to(r0, [RBLOCK]), tmp20, rmask)


@triton.jit
def triton_red_fused_add_log_mul_sub_sum_4(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.
    constexpr, RBLOCK: tl.constexpr):
    xnumel = 2
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp12 = tl.load(in_ptr3 + 0)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    _tmp19 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    _tmp22 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (128 * x0 + r1 // 64), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp8 = tl.load(in_ptr2 + (r1 + 8192 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tmp0.to(tl.int64)
        tmp2 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
        tmp3 = tmp1 + tmp2
        tmp4 = tmp1 < 0
        tmp5 = tl.where(tmp4, tmp3, tmp1)
        tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~(rmask & xmask),
            'index out of bounds: 0 <= tmp5 < 4')
        tmp7 = tl.load(in_ptr1 + (64 * tmp5 + r1 % 64), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp9 = 1e-07
        tmp10 = tmp8 + tmp9
        tmp11 = tl_math.log(tmp10)
        tmp14 = tmp11 + tmp13
        tmp15 = tmp7 - tmp14
        tmp16 = tl.load(in_ptr4 + (64 * tmp5 + r1 % 64), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp17 = tmp15 * tmp16
        tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
        tmp20 = _tmp19 + tmp18
        _tmp19 = tl.where(rmask & xmask, tmp20, _tmp19)
        tmp21 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
        tmp23 = _tmp22 + tmp21
        _tmp22 = tl.where(rmask & xmask, tmp23, _tmp22)
    tmp19 = tl.sum(_tmp19, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp19, xmask)
    tmp22 = tl.sum(_tmp22, 1)[:, None]
    tl.store(out_ptr1 + x0, tmp22, xmask)


@triton.jit
def triton_per_fused_add_div_log_mul_neg_sub_sum_5(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 2
    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)
    tmp4 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tmp8 = tmp3 / tmp7
    tmp9 = -tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp9, 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)](arg1_1, buf1, 64, 4, XBLOCK=8,
            num_warps=2, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((2,), (1,), torch.float32)
        triton_red_fused_max_1[grid(2)](buf1, arg0_1, buf2, 2, 8192, XBLOCK
            =1, RBLOCK=2048, num_warps=16, num_stages=1)
        buf3 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_max_2[grid(1)](buf2, buf3, 1, 2, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf4 = empty_strided_cuda((16384,), (1,), torch.float32)
        workspace = empty_strided_cuda((512,), (1,), torch.uint8)
        workspace.zero_()
        triton_spl_fused_cumsum_exp_sub_3[split_scan_grid(1, 16384)](buf1,
            arg0_1, buf3, buf4, workspace, 1, 16384, RBLOCK=2048, num_warps
            =16, num_stages=1)
        del workspace
        buf5 = buf2
        del buf2
        buf7 = empty_strided_cuda((2,), (1,), torch.float32)
        triton_red_fused_add_log_mul_sub_sum_4[grid(2)](buf1, arg0_1, buf4,
            buf3, arg2_1, buf5, buf7, 2, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        del arg0_1
        del arg2_1
        del buf1
        del buf4
        buf6 = buf3
        del buf3
        buf9 = buf6
        del buf6
        triton_per_fused_add_div_log_mul_neg_sub_sum_5[grid(1)](buf9, buf5,
            buf7, 1, 2, XBLOCK=1, num_warps=2, num_stages=1)
        del buf5
        del buf7
    return buf9,


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.
    """
    if events.dtype is torch.bool:
        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)
    return -log_h.sub(log_cumsum_h).mul(events).sum().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]
