# 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/cz/ccz3mqay2w4lq4ij6e3tbcm2zs2h4dw3raeu4rxxshf35zxkmmzh.py
# Topologically Sorted Source Nodes: [max_1, differences_2, exp, exps, sum_1], Original ATen: [aten.max, aten.sub, aten.exp, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   differences_2 => sub_1
#   exp => exp
#   exps => mul
#   max_1 => max_1
#   sum_1 => sum_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%view, 1, True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %view_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
triton_per_fused_exp_max_mul_sub_sum_0 = async_compile.triton('triton_per_fused_exp_max_mul_sub_sum_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=[4, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_exp_max_mul_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 4, '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_exp_max_mul_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 4
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + ((64*x0) + (r1 % 64)), None)
    tmp1 = tl.load(in_ptr0 + ((16*(r1 // 64)) + (64*x0) + (r1 % 16)), None)
    tmp8 = tl.load(in_ptr1 + ((64*x0) + (r1 % 64)), None)
    tmp9 = tl.load(in_ptr1 + ((16*(r1 // 64)) + (64*x0) + (r1 % 16)), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tmp6 = tmp2 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp10 = tmp8 < tmp9
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp7 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tl.store(out_ptr0 + (x0), tmp5, None)
    tl.store(out_ptr1 + (x0), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/o7/co755tglhy3eavlx5pegfdmy36akzbmekxaszx5knjrxhhqk72o7.py
# Topologically Sorted Source Nodes: [neg, exp_1, add, log, lsep, mean], Original ATen: [aten.neg, aten.exp, aten.add, aten.log, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   exp_1 => exp_1
#   log => log
#   lsep => add_1
#   mean => mean
#   neg => neg
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%getitem,), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp_1, %sum_1), 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 = (%getitem, %log), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add_1,), kwargs = {})
triton_per_fused_add_exp_log_mean_neg_1 = async_compile.triton('triton_per_fused_add_exp_log_mean_neg_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.persistent_reduction(
    size_hints=[1, 16],
    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, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_exp_log_mean_neg_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_add_exp_log_mean_neg_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r1 = (rindex // 4)
    r0 = rindex % 4
    tmp0 = tl.load(in_ptr0 + (r1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (r0), None, eviction_policy='evict_last')
    tmp1 = -tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 + tmp3
    tmp5 = tl_math.log(tmp4)
    tmp6 = tmp0 + tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 16.0
    tmp11 = tmp9 / tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp11, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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((4, 1), (1, 4), torch.float32)
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [max_1, differences_2, exp, exps, sum_1], Original ATen: [aten.max, aten.sub, aten.exp, aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_exp_max_mul_sub_sum_0.run(arg0_1, arg1_1, buf0, buf2, 4, 256, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [neg, exp_1, add, log, lsep, mean], Original ATen: [aten.neg, aten.exp, aten.add, aten.log, aten.mean]
        triton_per_fused_add_exp_log_mean_neg_1.run(buf4, buf0, buf2, 1, 16, grid=grid(1), stream=stream0)
        del buf0
        del buf2
    return (buf4, )


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)
    fn = lambda: call([arg0_1, arg1_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
import torch.nn as nn


def lsep_loss_stable(input, target, average=True):
    n = input.size(0)
    differences = input.unsqueeze(1) - input.unsqueeze(2)
    where_lower = (target.unsqueeze(1) < target.unsqueeze(2)).float()
    differences = differences.view(n, -1)
    where_lower = where_lower.view(n, -1)
    max_difference, _index = torch.max(differences, dim=1, keepdim=True)
    differences = differences - max_difference
    exps = differences.exp() * where_lower
    lsep = max_difference + torch.log(torch.exp(-max_difference) + exps.sum(-1)
        )
    if average:
        return lsep.mean()
    else:
        return lsep


class LSEPLoss(nn.Module):

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

    def forward(self, preds, targets):
        return lsep_loss_stable(preds, targets)


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
import torch.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_exp_max_mul_sub_sum_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (64 * x0 + r1 % 64), None)
    tmp1 = tl.load(in_ptr0 + (16 * (r1 // 64) + 64 * x0 + r1 % 16), None)
    tmp8 = tl.load(in_ptr1 + (64 * x0 + r1 % 64), None)
    tmp9 = tl.load(in_ptr1 + (16 * (r1 // 64) + 64 * x0 + r1 % 16), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tmp6 = tmp2 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp10 = tmp8 < tmp9
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp7 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tl.store(out_ptr0 + x0, tmp5, None)
    tl.store(out_ptr1 + x0, tmp15, None)


@triton.jit
def triton_per_fused_add_exp_log_mean_neg_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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)
    r1 = rindex // 4
    r0 = rindex % 4
    tmp0 = tl.load(in_ptr0 + r1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp1 = -tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 + tmp3
    tmp5 = tl_math.log(tmp4)
    tmp6 = tmp0 + tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 16.0
    tmp11 = tmp9 / tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp11, 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((4, 1), (1, 4), torch.float32)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_exp_max_mul_sub_sum_0[grid(4)](arg0_1, arg1_1,
            buf0, buf2, 4, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused_add_exp_log_mean_neg_1[grid(1)](buf4, buf0, buf2, 
            1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf2
    return buf4,


def lsep_loss_stable(input, target, average=True):
    n = input.size(0)
    differences = input.unsqueeze(1) - input.unsqueeze(2)
    where_lower = (target.unsqueeze(1) < target.unsqueeze(2)).float()
    differences = differences.view(n, -1)
    where_lower = where_lower.view(n, -1)
    max_difference, _index = torch.max(differences, dim=1, keepdim=True)
    differences = differences - max_difference
    exps = differences.exp() * where_lower
    lsep = max_difference + torch.log(torch.exp(-max_difference) + exps.sum(-1)
        )
    if average:
        return lsep.mean()
    else:
        return lsep


class LSEPLossNew(nn.Module):

    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]
