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
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)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, 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):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), primals_3, out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((1, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (1, 16, 4), (64, 4,
            1), 0), primals_4, out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = buf2
        del buf2
        triton_poi_fused__softmax_1[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        extern_kernels.bmm(buf4, reinterpret_tensor(buf0, (4, 4, 4), (16, 4,
            1), 0), out=buf5)
    return buf5, reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0
        ), buf4, reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_1, (1, 4, 16), (64, 1, 4), 0
        ), reinterpret_tensor(primals_2, (1, 4, 16), (64, 1, 4), 0)


class AttentionNew(nn.Module):

    def __init__(self, embed_dim, hidden_dim=None, n_head=1, score_function
        ='scaled_dot_product'):
        super(AttentionNew, self).__init__()
        if hidden_dim is None:
            hidden_dim = embed_dim
        self.embed_dim = embed_dim
        self.hidden_dim = hidden_dim
        self.n_head = n_head
        self.score_function = score_function
        self.w_kx = nn.Parameter(torch.FloatTensor(n_head, embed_dim,
            hidden_dim))
        self.w_qx = nn.Parameter(torch.FloatTensor(n_head, embed_dim,
            hidden_dim))
        if score_function == 'mlp':
            self.weight = nn.Parameter(torch.Tensor(hidden_dim * 2, 1))
        elif self.score_function == 'bi_linear':
            self.weight = nn.Parameter(torch.Tensor(hidden_dim, hidden_dim))
        elif self.score_function == 'mlp_sum':
            self.weight = nn.Parameter(torch.Tensor(hidden_dim, 1))
            self.b1 = nn.Parameter(torch.Tensor(hidden_dim))
        else:
            self.register_parameter('weight', None)
    """ TRY JONATAN WAY
   score =  W_1 tanh ( W_m    m_i  +  W_a  + b  )
    (1)    (dx1)      (dxd)   (d)    (dxd)  (d) 
    """

    def forward(self, input_0, input_1):
        primals_3 = self.w_kx
        primals_4 = self.w_qx
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.053416