# 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/c5/cc545zhrt3tyhef5kaivf4deqpdpayhp73nzjodse5cfp5eoxu4z.py
# Topologically Sorted Source Nodes: [add_1, a, e_true, Sss, Sss_1, add_2, e_res, Snn, Snn_1, add_3, truediv_1, log10, mul_1], Original ATen: [aten.add, aten.div, aten.mul, aten.pow, aten.sum, aten.sub, aten.log10]
# Source node to ATen node mapping:
#   Snn => pow_2
#   Snn_1 => sum_2
#   Sss => pow_1
#   Sss_1 => sum_1
#   a => div
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   e_res => sub
#   e_true => mul
#   log10 => log10
#   mul_1 => mul_1
#   truediv_1 => div_1
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, 1.1920928955078125e-07), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%unsqueeze_1, %add_1), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mul, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1]), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1.1920928955078125e-07), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mul), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [-1]), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 1.1920928955078125e-07), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_2, %add_3), kwargs = {})
#   %log10 : [num_users=1] = call_function[target=torch.ops.aten.log10.default](args = (%div_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%log10, 10), kwargs = {})
triton_poi_fused_add_div_log10_mul_pow_sub_sum_0 = async_compile.triton('triton_poi_fused_add_div_log10_mul_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_log10_mul_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 10, '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_div_log10_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask)
    tmp7 = tl.load(in_ptr3 + (4*x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr3 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr3 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = 1.1920928955078125e-07
    tmp3 = tmp1 + tmp2
    tmp5 = tmp4 + tmp2
    tmp6 = tmp3 / tmp5
    tmp8 = tmp6 * tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp13 = tmp6 * tmp12
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp19 = tmp6 * tmp18
    tmp20 = tmp17 - tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp25 = tmp6 * tmp24
    tmp26 = tmp23 - tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = tmp22 + tmp27
    tmp29 = tmp8 * tmp8
    tmp30 = tmp13 * tmp13
    tmp31 = tmp29 + tmp30
    tmp32 = tmp19 * tmp19
    tmp33 = tmp31 + tmp32
    tmp34 = tmp25 * tmp25
    tmp35 = tmp33 + tmp34
    tmp36 = tmp35 + tmp2
    tmp37 = tmp28 + tmp2
    tmp38 = tmp36 / tmp37
    tmp39 = libdevice.log10(tmp38)
    tmp40 = 10.0
    tmp41 = tmp39 * tmp40
    tl.store(in_out_ptr0 + (x0), tmp41, 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, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1), 0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1), 0), reinterpret_tensor(arg1_1, (4, 4, 1), (4, 1, 1), 0), out=buf1)
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [add_1, a, e_true, Sss, Sss_1, add_2, e_res, Snn, Snn_1, add_3, truediv_1, log10, mul_1], Original ATen: [aten.add, aten.div, aten.mul, aten.pow, aten.sum, aten.sub, aten.log10]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_log10_mul_pow_sub_sum_0.run(buf3, arg0_1, buf0, buf1, arg1_1, 4, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
    return (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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (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 torch import Tensor
from torch import nn


class SiSdr(nn.Module):

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

    def forward(self, input: 'Tensor', target: 'Tensor'):
        eps = torch.finfo(input.dtype).eps
        Rss: 'Tensor' = torch.einsum('bi,bi->b', target, target).unsqueeze(-1)
        a: 'Tensor' = torch.einsum('bi,bi->b', target, input).add(eps
            ).unsqueeze(-1) / Rss.add(eps)
        e_true = a * target
        e_res = input - e_true
        Sss = e_true.square()
        Snn = e_res.square()
        Sss = Sss.sum(-1)
        Snn = Snn.sum(-1)
        return 10 * torch.log10(Sss.add(eps) / Snn.add(eps))


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


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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 libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_div_log10_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp7 = tl.load(in_ptr3 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr3 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr3 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr3 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = 1.1920928955078125e-07
    tmp3 = tmp1 + tmp2
    tmp5 = tmp4 + tmp2
    tmp6 = tmp3 / tmp5
    tmp8 = tmp6 * tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp13 = tmp6 * tmp12
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp19 = tmp6 * tmp18
    tmp20 = tmp17 - tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp25 = tmp6 * tmp24
    tmp26 = tmp23 - tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = tmp22 + tmp27
    tmp29 = tmp8 * tmp8
    tmp30 = tmp13 * tmp13
    tmp31 = tmp29 + tmp30
    tmp32 = tmp19 * tmp19
    tmp33 = tmp31 + tmp32
    tmp34 = tmp25 * tmp25
    tmp35 = tmp33 + tmp34
    tmp36 = tmp35 + tmp2
    tmp37 = tmp28 + tmp2
    tmp38 = tmp36 / tmp37
    tmp39 = libdevice.log10(tmp38)
    tmp40 = 10.0
    tmp41 = tmp39 * tmp40
    tl.store(in_out_ptr0 + x0, tmp41, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1),
            0), reinterpret_tensor(arg0_1, (4, 4, 1), (4, 1, 1), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 1, 4), (4, 4, 1),
            0), reinterpret_tensor(arg1_1, (4, 4, 1), (4, 1, 1), 0), out=buf1)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        buf3 = buf2
        del buf2
        get_raw_stream(0)
        triton_poi_fused_add_div_log10_mul_pow_sub_sum_0[grid(4)](buf3,
            arg0_1, buf0, buf1, arg1_1, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
    return buf3,


class SiSdrNew(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]
