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 math as tl_math
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    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 = 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 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    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 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(arg1_1, reinterpret_tensor(arg0_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf0)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf0, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf2 = buf0
        del buf0
        triton_poi_fused__softmax_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        extern_kernels.bmm(buf2, arg0_1, out=buf3)
        del arg0_1
    return buf3, buf2


def sequence_mask(lengths, max_len=None):
    """
    Creates a boolean mask from sequence lengths.
    """
    batch_size = lengths.numel()
    max_len = max_len or lengths.max()
    return torch.arange(0, max_len).type_as(lengths).repeat(batch_size, 1).lt(
        lengths.unsqueeze(1))


class AttnScore(nn.Module):

    def __init__(self, input_size, activation=nn.Tanh(), method='dot'):
        super(AttnScore, self).__init__()
        self.activation = activation
        self.input_size = input_size
        self.method = method
        if method == 'general':
            self.linear = nn.Linear(input_size, input_size)
            init.uniform(self.linear.weight.data, -0.005, 0.005)
        elif method == 'concat':
            self.linear_1 = nn.Linear(input_size * 2, input_size)
            self.linear_2 = nn.Linear(input_size, 1)
            init.uniform(self.linear_1.weight.data, -0.005, 0.005)
            init.uniform(self.linear_2.weight.data, -0.005, 0.005)
        elif method == 'tri_concat':
            self.linear = nn.Linear(input_size * 3, 1)
            init.uniform(self.linear.weight.data, -0.005, 0.005)

    def forward(self, h1, h2, h1_lens=None, h2_lens=None, normalize=True):
        """
        :param h1: b x m x d
        :param h2: b x n x d
        :return: attn_weights: b x 1 x m
        """
        _bsize, seq_l1, _dim = h1.size()
        _bsize, seq_l2, _dim = h2.size()
        assert h1.size(-1) == self.input_size
        assert h2.size(-1) == self.input_size
        if self.method == 'dot':
            align = h2.bmm(h1.transpose(1, 2))
        elif self.method == 'general':
            align = h2.bmm(self.linear(h1).transpose(1, 2))
        elif self.method == 'concat':
            h1 = h1.unsqueeze(1).repeat(1, seq_l2, 1, 1)
            h2 = h2.unsqueeze(2).repeat(1, 1, seq_l1, 1)
            align = self.linear_2(self.activation(self.linear_1(torch.cat([
                h1, h2], dim=3)))).squeeze(-1)
            align = F.softmax(align, dim=2)
        elif self.method == 'tri_concat':
            h1 = h1.unsqueeze(1).repeat(1, seq_l2, 1, 1)
            h2 = h2.unsqueeze(2).repeat(1, 1, seq_l1, 1)
            align = self.linear(torch.cat([h1, h2, h1 * h2], dim=3)).squeeze(-1
                )
        if h1_lens is not None:
            mask = sequence_mask(h1_lens, max_len=seq_l1).unsqueeze(1)
            align.data.masked_fill_(1 - mask, -100000000.0)
        if normalize:
            attn_weights = F.softmax(align, dim=2)
        else:
            attn_weights = F.softmax(align, dim=2)
        return attn_weights


class AttentionNew(nn.Module):

    def __init__(self, input_size, activation=nn.Tanh(), method='dot'):
        super(AttentionNew, self).__init__()
        self.attn_score = AttnScore(input_size=input_size, activation=
            activation, method=method)

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1]


Measured latency_ms: 0.031052