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, math as tl_math
from torch.nn import Module
from torch.nn import Linear
from typing import Optional
from torch.nn import Tanh
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_out_ptr0, in_ptr0, 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 % 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_exp_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, 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')
    tmp2 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tl.store(out_ptr0 + x0, tmp18, xmask)


@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp9 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp17 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr2 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp25 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp27 = tl.load(in_ptr2 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 / tmp5
    tmp7 = tmp0 * tmp6
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp12 / tmp5
    tmp14 = tmp8 * tmp13
    tmp15 = tmp7 + tmp14
    tmp18 = tl_math.exp(tmp17)
    tmp20 = tmp18 * tmp19
    tmp21 = tmp20 / tmp5
    tmp22 = tmp16 * tmp21
    tmp23 = tmp15 + tmp22
    tmp26 = tl_math.exp(tmp25)
    tmp28 = tmp26 * tmp27
    tmp29 = tmp28 / tmp5
    tmp30 = tmp24 * tmp29
    tmp31 = tmp23 + tmp30
    tl.store(out_ptr0 + x2, tmp31, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(64)](buf1, primals_4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_exp_mul_sum_1[grid(4)](buf2, primals_2, buf3, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sum_2[grid(16)](primals_1, buf2, primals_2,
            buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf3
    return buf4, primals_1, primals_2, buf1, buf2, primals_5


def masked_softmax(vector: 'torch.FloatTensor', mask: 'torch.ByteTensor'):
    """
    计算带有 masked 的 softmax
    :param vector: shape: (B, seq_len)
    :param mask: shape: (B, seq_len),
    :return: (B, seq_len)
    """
    exp_vector = vector.exp()
    masked_vector = exp_vector * mask.float()
    return masked_vector / torch.sum(masked_vector, dim=-1, keepdim=True)


class AttentionSeq2VecNew(Module):
    """
    基于 attention 将 seq2vec. 具体操作如下:

    1. sequence: (B, seq_len, input_size)
    2. K = WkSeqeunce 将 sequence 进行变换, K shape: (B, seq_len, query_hidden_size)
    3. Q = Shape: (query_hidden_size)
    4. attention = softmax(KQ), shape: (B, seq_len)
    5. V = WvSequence, shape: (B, seq_len, value_hidden_size); 如果 value_hidden_size is None,
    shape: (B, seq_len, input_size)
    6. sum(V*attention, dim=-1), shape: (B, input_size)
    """

    def __init__(self, input_size: 'int', query_hidden_size: 'int',
        value_hidden_size: 'Optional[int]'=None):
        """
        初始化。遵循 Q K V，计算 attention 方式。
        :param input_size: 输入的 sequence token 的 embedding dim
        :param query_hidden_size: 将 seqence 变成 Q 的时候，变换后的 token embedding dim.
        :param value_hidden_size: 将 seqence 变成 V 的时候, 变换后的 token embedding dim.
        如果 value_hidden_size is None, 那么，该模型就与 2016-Hierarchical Attention Networks for Document Classification
        是一致的, 最后的输出结果 shape (B, seq_len, input_size);
        如果 value_hidden_size 被设置了, 那么，就与 Attention is All your Need 中 变换是一致的, 最后的输出结果
        shape (B, seq_len, value_hidden_size)
        """
        super().__init__()
        self.wk = Linear(in_features=input_size, out_features=
            query_hidden_size, bias=True)
        self.key_activation = Tanh()
        self.attention = Linear(in_features=query_hidden_size, out_features
            =1, bias=False)
        self.wv = None
        if value_hidden_size is not None:
            self.wv = Linear(in_features=input_size, out_features=
                value_hidden_size, bias=True)
        self.reset_parameters()

    def reset_parameters(self):
        pass

    def forward(self, input_0, input_1):
        primals_2 = self.wk.weight
        primals_4 = self.wk.bias
        primals_5 = self.attention.weight
        primals_1 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.057374