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.cuda
import torch.distributed
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 = 16
    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 = 16
    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)


@triton.jit
def triton_poi_fused_mean_2(in_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
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 / tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 1, 4), (0, 0, 
            1), 0), reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0),
            out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 1, 4), (4, 4, 1), 0)
        del buf1
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 1, 4), (4, 0, 1), 0
            ), primals_1, out=buf3)
        del buf2
        buf4 = reinterpret_tensor(buf3, (4, 4), (4, 1), 0)
        del buf3
        triton_poi_fused_mean_2[grid(16)](buf4, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return buf4, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), buf0


def aeq(*args):
    """
    Assert all arguments have the same value
    """
    arguments = (arg for arg in args)
    first = next(arguments)
    assert all(arg == first for arg in arguments
        ), 'Not all arguments have the same value: ' + str(args)


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, device=lengths.device).type_as(lengths
        ).repeat(batch_size, 1).lt(lengths.unsqueeze(1))


class GlobalAttentionContextNew(nn.Module):
    """
    Global attention takes a matrix and a query vector. It
    then computes a parameterized convex combination of the matrix
    based on the input query.

    Constructs a unit mapping a query `q` of size `dim`
    and a source matrix `H` of size `n x dim`, to an output
    of size `dim`.


    .. mermaid::

       graph BT
          A[Query]
          subgraph RNN
            C[H 1]
            D[H 2]
            E[H N]
          end
          F[Attn]
          G[Output]
          A --> F
          C --> F
          D --> F
          E --> F
          C -.-> G
          D -.-> G
          E -.-> G
          F --> G

    All models compute the output as
    :math:`c = \\sum_{j=1}^{\\text{SeqLength}} a_j H_j` where
    :math:`a_j` is the softmax of a score function.
    Then then apply a projection layer to [q, c].

    However they
    differ on how they compute the attention score.

    * Luong Attention (dot, general):
       * dot: :math:`\\text{score}(H_j,q) = H_j^T q`
       * general: :math:`\\text{score}(H_j, q) = H_j^T W_a q`


    * Bahdanau Attention (mlp):
       * :math:`\\text{score}(H_j, q) = v_a^T \\text{tanh}(W_a q + U_a h_j)`


    Args:
       dim (int): dimensionality of query and key
       coverage (bool): use coverage term
       attn_type (str): type of attention to use, options [dot,general,mlp]
       attn_func (str): attention function to use, options [softmax,sparsemax]

    """

    def __init__(self, dim, coverage=False, attn_type='dot', attn_func=
        'softmax'):
        super(GlobalAttentionContextNew, self).__init__()
        self.dim = dim
        assert attn_type in ['dot', 'general', 'mlp'
            ], 'Please select a valid attention type (got {:s}).'.format(
            attn_type)
        self.attn_type = attn_type
        assert attn_func in ['softmax', 'sparsemax'
            ], 'Please select a valid attention function.'
        self.attn_func = attn_func
        self.source = nn.Parameter(torch.Tensor(1, dim))
        if self.attn_type == 'general':
            self.linear_in = nn.Linear(dim, dim, bias=False)
        elif self.attn_type == 'mlp':
            self.linear_context = nn.Linear(dim, dim, bias=False)
            self.linear_query = nn.Linear(dim, dim, bias=True)
            self.v = nn.Linear(dim, 1, bias=False)
        if coverage:
            self.linear_cover = nn.Linear(1, dim, bias=False)

    def score(self, h_t, h_s):
        """
        Args:
          h_t (FloatTensor): sequence of queries ``(batch, tgt_len, dim)``
          h_s (FloatTensor): sequence of sources ``(batch, src_len, dim``

        Returns:
          FloatTensor: raw attention scores (unnormalized) for each src index
            ``(batch, tgt_len, src_len)``
        """
        src_batch, src_len, src_dim = h_s.size()
        tgt_batch, tgt_len, tgt_dim = h_t.size()
        aeq(src_batch, tgt_batch)
        aeq(src_dim, tgt_dim)
        aeq(self.dim, src_dim)
        if self.attn_type in ['general', 'dot']:
            if self.attn_type == 'general':
                h_t_ = h_t.view(tgt_batch * tgt_len, tgt_dim)
                h_t_ = self.linear_in(h_t_)
                h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim)
            h_s_ = h_s.transpose(1, 2)
            return torch.bmm(h_t, h_s_)
        else:
            dim = self.dim
            wq = self.linear_query(h_t.view(-1, dim))
            wq = wq.view(tgt_batch, tgt_len, 1, dim)
            wq = wq.expand(tgt_batch, tgt_len, src_len, dim)
            uh = self.linear_context(h_s.contiguous().view(-1, dim))
            uh = uh.view(src_batch, 1, src_len, dim)
            uh = uh.expand(src_batch, tgt_len, src_len, dim)
            wquh = torch.tanh(wq + uh)
            return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len)

    def forward(self, input_0):
        primals_2 = self.source
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.033274