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 libdevice
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_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_per_fused_add_div_log10_mean_mul_neg_pow_sub_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp1 * tmp1
    tmp16 = tmp4 * tmp4
    tmp17 = tmp15 + tmp16
    tmp18 = tmp8 * tmp8
    tmp19 = tmp17 + tmp18
    tmp20 = tmp12 * tmp12
    tmp21 = tmp19 + tmp20
    tmp22 = 1e-08
    tmp23 = tmp21 + tmp22
    tmp24 = tmp14 / tmp23
    tmp25 = tmp1 * tmp24
    tmp26 = tmp0 - tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = tmp4 * tmp24
    tmp29 = tmp3 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp27 + tmp30
    tmp32 = tmp8 * tmp24
    tmp33 = tmp7 - tmp32
    tmp34 = tmp33 * tmp33
    tmp35 = tmp31 + tmp34
    tmp36 = tmp12 * tmp24
    tmp37 = tmp11 - tmp36
    tmp38 = tmp37 * tmp37
    tmp39 = tmp35 + tmp38
    tmp40 = tmp25 * tmp25
    tmp41 = tmp28 * tmp28
    tmp42 = tmp40 + tmp41
    tmp43 = tmp32 * tmp32
    tmp44 = tmp42 + tmp43
    tmp45 = tmp36 * tmp36
    tmp46 = tmp44 + tmp45
    tmp47 = tmp39 + tmp22
    tmp48 = tmp46 / tmp47
    tmp49 = tmp48 + tmp22
    tmp50 = libdevice.log10(tmp49)
    tmp51 = 10.0
    tmp52 = tmp50 * tmp51
    tmp53 = tl.broadcast_to(tmp52, [XBLOCK, RBLOCK])
    tmp55 = tl.sum(tmp53, 1)[:, None]
    tmp56 = 64.0
    tmp57 = tmp55 / tmp56
    tmp58 = -tmp57
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp58, 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_sub_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mean_sub_0[grid(256)](arg1_1, buf1, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg1_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_add_div_log10_mean_mul_neg_pow_sub_sum_1[grid(1)](buf5
            , buf0, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf5,


def apply_reduction(losses, reduction='none'):
    """Apply reduction to collection of losses."""
    if reduction == 'mean':
        losses = losses.mean()
    elif reduction == 'sum':
        losses = losses.sum()
    return losses


class SISDRLossNew(torch.nn.Module):
    """Scale-invariant signal-to-distortion ratio loss module.

    Note that this returns the negative of the SI-SDR loss.

    See [Le Roux et al., 2018](https://arxiv.org/abs/1811.02508)

    Args:
        zero_mean (bool, optional) Remove any DC offset in the inputs. Default: ``True``
        eps (float, optional): Small epsilon value for stablity. Default: 1e-8
        reduction (string, optional): Specifies the reduction to apply to the output:
            'none': no reduction will be applied,
            'mean': the sum of the output will be divided by the number of elements in the output,
            'sum': the output will be summed. Default: 'mean'
    Shape:
        - input : :math:`(batch, nchs, ...)`.
        - target: :math:`(batch, nchs, ...)`.
    """

    def __init__(self, zero_mean=True, eps=1e-08, reduction='mean'):
        super(SISDRLossNew, self).__init__()
        self.zero_mean = zero_mean
        self.eps = eps
        self.reduction = reduction

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]


Measured latency_ms: 0.015149