# 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/p2/cp2wtrg56vvgsi7rjvlyqzepkq4fqkjs7kwovok5vip4fsiujc5e.py
# Topologically Sorted Source Nodes: [sub, pow_1, err, add, log, mean], Original ATen: [aten.sub, aten.pow, aten.sum, aten.add, aten.log, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   err => sum_1
#   log => log
#   mean => mean
#   pow_1 => pow_1
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%log, [-1]), kwargs = {})
triton_poi_fused_add_log_mean_pow_sub_sum_0 = async_compile.triton('triton_poi_fused_add_log_mean_pow_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_log_mean_pow_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 32, '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_add_log_mean_pow_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (16*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr1 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr1 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr1 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp49 = tl.load(in_ptr1 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp53 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp54 = tl.load(in_ptr1 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp59 = tl.load(in_ptr1 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp66 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp67 = tl.load(in_ptr1 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp70 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr1 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp75 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp76 = tl.load(in_ptr1 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp80 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp81 = tl.load(in_ptr1 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = 1.0
    tmp20 = tmp18 + tmp19
    tmp21 = tl_math.log(tmp20)
    tmp24 = tmp22 - tmp23
    tmp25 = tmp24 * tmp24
    tmp28 = tmp26 - tmp27
    tmp29 = tmp28 * tmp28
    tmp30 = tmp25 + tmp29
    tmp33 = tmp31 - tmp32
    tmp34 = tmp33 * tmp33
    tmp35 = tmp30 + tmp34
    tmp38 = tmp36 - tmp37
    tmp39 = tmp38 * tmp38
    tmp40 = tmp35 + tmp39
    tmp41 = tmp40 + tmp19
    tmp42 = tl_math.log(tmp41)
    tmp43 = tmp21 + tmp42
    tmp46 = tmp44 - tmp45
    tmp47 = tmp46 * tmp46
    tmp50 = tmp48 - tmp49
    tmp51 = tmp50 * tmp50
    tmp52 = tmp47 + tmp51
    tmp55 = tmp53 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tmp52 + tmp56
    tmp60 = tmp58 - tmp59
    tmp61 = tmp60 * tmp60
    tmp62 = tmp57 + tmp61
    tmp63 = tmp62 + tmp19
    tmp64 = tl_math.log(tmp63)
    tmp65 = tmp43 + tmp64
    tmp68 = tmp66 - tmp67
    tmp69 = tmp68 * tmp68
    tmp72 = tmp70 - tmp71
    tmp73 = tmp72 * tmp72
    tmp74 = tmp69 + tmp73
    tmp77 = tmp75 - tmp76
    tmp78 = tmp77 * tmp77
    tmp79 = tmp74 + tmp78
    tmp82 = tmp80 - tmp81
    tmp83 = tmp82 * tmp82
    tmp84 = tmp79 + tmp83
    tmp85 = tmp84 + tmp19
    tmp86 = tl_math.log(tmp85)
    tmp87 = tmp65 + tmp86
    tmp88 = 4.0
    tmp89 = tmp87 / tmp88
    tl.store(out_ptr0 + (x0), tmp89, xmask)
''', 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, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, pow_1, err, add, log, mean], Original ATen: [aten.sub, aten.pow, aten.sum, aten.add, aten.log, aten.mean]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_log_mean_pow_sub_sum_0.run(arg0_1, arg1_1, buf0, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


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
from typing import *
import torch.nn as nn


class CauchyLoss(nn.Module):

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

    def forward(self, x, y):
        err = torch.sum(torch.pow(x - y, 2), dim=-1)
        return torch.mean(torch.log(1 + err), dim=-1)


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.triton_helpers import math as tl_math
from typing import *
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_poi_fused_add_log_mean_pow_sub_sum_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr1 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr1 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr1 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp26 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr1 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp31 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr1 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp36 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp37 = tl.load(in_ptr1 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp44 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp45 = tl.load(in_ptr1 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp48 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp49 = tl.load(in_ptr1 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp53 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp54 = tl.load(in_ptr1 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp58 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp59 = tl.load(in_ptr1 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp66 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp67 = tl.load(in_ptr1 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp70 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp71 = tl.load(in_ptr1 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp75 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp76 = tl.load(in_ptr1 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp80 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp81 = tl.load(in_ptr1 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = 1.0
    tmp20 = tmp18 + tmp19
    tmp21 = tl_math.log(tmp20)
    tmp24 = tmp22 - tmp23
    tmp25 = tmp24 * tmp24
    tmp28 = tmp26 - tmp27
    tmp29 = tmp28 * tmp28
    tmp30 = tmp25 + tmp29
    tmp33 = tmp31 - tmp32
    tmp34 = tmp33 * tmp33
    tmp35 = tmp30 + tmp34
    tmp38 = tmp36 - tmp37
    tmp39 = tmp38 * tmp38
    tmp40 = tmp35 + tmp39
    tmp41 = tmp40 + tmp19
    tmp42 = tl_math.log(tmp41)
    tmp43 = tmp21 + tmp42
    tmp46 = tmp44 - tmp45
    tmp47 = tmp46 * tmp46
    tmp50 = tmp48 - tmp49
    tmp51 = tmp50 * tmp50
    tmp52 = tmp47 + tmp51
    tmp55 = tmp53 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tmp52 + tmp56
    tmp60 = tmp58 - tmp59
    tmp61 = tmp60 * tmp60
    tmp62 = tmp57 + tmp61
    tmp63 = tmp62 + tmp19
    tmp64 = tl_math.log(tmp63)
    tmp65 = tmp43 + tmp64
    tmp68 = tmp66 - tmp67
    tmp69 = tmp68 * tmp68
    tmp72 = tmp70 - tmp71
    tmp73 = tmp72 * tmp72
    tmp74 = tmp69 + tmp73
    tmp77 = tmp75 - tmp76
    tmp78 = tmp77 * tmp77
    tmp79 = tmp74 + tmp78
    tmp82 = tmp80 - tmp81
    tmp83 = tmp82 * tmp82
    tmp84 = tmp79 + tmp83
    tmp85 = tmp84 + tmp19
    tmp86 = tl_math.log(tmp85)
    tmp87 = tmp65 + tmp86
    tmp88 = 4.0
    tmp89 = tmp87 / tmp88
    tl.store(out_ptr0 + x0, tmp89, xmask)


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, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_log_mean_pow_sub_sum_0[grid(16)](arg0_1,
            arg1_1, buf0, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class CauchyLossNew(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]
