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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clone_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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_3(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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_clamp_mul_pow_sqrt_sub_sum_4(in_ptr0, in_ptr1,
    out_ptr0, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x4), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x0 + 16 * x4), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp8 = tl.load(in_ptr1 + (8 + x0 + 16 * x4), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp12 = tl.load(in_ptr1 + (12 + x0 + 16 * x4), xmask)
    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 = tmp0 * tmp15
    tmp17 = tmp4 * tmp4
    tmp18 = tmp3 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp8 * tmp8
    tmp21 = tmp7 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = tmp12 * tmp12
    tmp24 = tmp11 * tmp23
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 * tmp14
    tmp27 = tmp25 - tmp26
    tmp28 = 1e-06
    tmp29 = triton_helpers.maximum(tmp27, tmp28)
    tmp30 = libdevice.sqrt(tmp29)
    tl.store(out_ptr0 + (x3 + 32 * x2), tmp14, xmask)
    tl.store(out_ptr2 + (x3 + 32 * x2), tmp30, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (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_clone_0[grid(256)](primals_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_add_tanh_1[grid(256)](buf2, primals_3, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 4, 16, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf3, buf4, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 4, 16, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf4, buf5, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf4
        buf9 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        buf6 = reinterpret_tensor(buf9, (4, 4, 4), (32, 4, 1), 0)
        buf8 = reinterpret_tensor(buf9, (4, 4, 4), (32, 4, 1), 16)
        triton_poi_fused_clamp_mul_pow_sqrt_sub_sum_4[grid(64)](buf5,
            primals_1, buf6, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf5
    return buf9, primals_1, reinterpret_tensor(buf0, (64, 4), (4, 1), 0
        ), buf2, buf3, primals_4


class AttentiveStatsPoolingNew(nn.Module):
    """
    The attentive statistics pooling layer uses an attention
    mechanism to give different weights to different frames and
    generates not only weighted means but also weighted variances,
    to form utterance-level features from frame-level features

    "Attentive Statistics Pooling for Deep Speaker Embedding",
    Okabe et al., https://arxiv.org/abs/1803.10963
    """

    def __init__(self, input_size, hidden_size, eps=1e-06):
        super(AttentiveStatsPoolingNew, self).__init__()
        self.eps = eps
        self.in_linear = nn.Linear(input_size, hidden_size)
        self.out_linear = nn.Linear(hidden_size, input_size)

    def forward(self, input_0):
        primals_2 = self.in_linear.weight
        primals_3 = self.in_linear.bias
        primals_4 = self.out_linear.weight
        primals_5 = self.out_linear.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.083929