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_tanh_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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused__softmax_add_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 + tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tmp2 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp5 - tmp14
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp9 - tmp14
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp13 - tmp14
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tl.store(out_ptr0 + x2, tmp14, xmask)
    tl.store(out_ptr1 + x2, tmp25, xmask)


@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex % 64
    x3 = xindex // 64
    x5 = xindex // 4 % 16
    x2 = xindex // 16 % 4
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr4 + (x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr0 + (64 + x4), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (16 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr2 + (16 + x5), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr3 + (4 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr4 + (4 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (128 + x4), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (32 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr2 + (32 + x5), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + (8 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr4 + (8 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (192 + x4), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr1 + (48 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp34 = tl.load(in_ptr2 + (48 + x5), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr3 + (12 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp39 = tl.load(in_ptr4 + (12 + x2 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 * tmp8
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp18 = tmp16 / tmp17
    tmp19 = tmp10 * tmp18
    tmp20 = tmp9 + tmp19
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 - tmp25
    tmp27 = tl_math.exp(tmp26)
    tmp29 = tmp27 / tmp28
    tmp30 = tmp21 * tmp29
    tmp31 = tmp20 + tmp30
    tmp35 = tmp33 + tmp34
    tmp37 = tmp35 - tmp36
    tmp38 = tl_math.exp(tmp37)
    tmp40 = tmp38 / tmp39
    tmp41 = tmp32 * tmp40
    tmp42 = tmp31 + tmp41
    tl.store(out_ptr0 + x7, tmp42, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (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_tanh_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_add_1[grid(64)](primals_4, buf2, buf3,
            buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sum_2[grid(256)](buf0, primals_4, buf2, buf3,
            buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del buf4
    return buf5, primals_4, buf0, buf2


class SelfAttentionPooling(nn.Module):
    """
    Implementation of SelfAttentionPooling 
    Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
    https://arxiv.org/pdf/2008.01077v1.pdf
    """

    def __init__(self, input_dim):
        super(SelfAttentionPooling, self).__init__()
        self.W = nn.Linear(input_dim, 1)
        self.softmax = nn.functional.softmax

    def forward(self, batch_rep, att_mask):
        """
        input:
        batch_rep : size (N, T, H), N: batch size, T: sequence length, H: Hidden dimension
        
        attention_weight:
        att_w : size (N, T, 1)
        
        return:
        utter_rep: size (N, H)
        """
        batch_rep.shape[1]
        att_logits = self.W(batch_rep).squeeze(-1)
        att_logits = att_mask + att_logits
        att_w = self.softmax(att_logits, dim=-1).unsqueeze(-1)
        utter_rep = torch.sum(batch_rep * att_w, dim=1)
        return utter_rep


class SAPNew(nn.Module):
    """ Self Attention Pooling module incoporate attention mask"""

    def __init__(self, out_dim):
        super(SAPNew, self).__init__()
        self.act_fn = nn.Tanh()
        self.sap_layer = SelfAttentionPooling(out_dim)

    def forward(self, input_0, input_1):
        primals_2 = self.sap_layer.W.weight
        primals_3 = self.sap_layer.W.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.023869