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)
    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(arg0_1, reinterpret_tensor(arg1_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf0)
        del arg0_1
        buf1 = empty_strided_cuda((16, 4), (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 = reinterpret_tensor(buf0, (16, 4), (4, 1), 0)
        del buf0
        triton_poi_fused__softmax_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 
            0), arg1_1, out=buf3)
        del arg1_1
    return buf3, reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)


class AttentionNew(nn.Module):
    """
    Applies an attention mechanism on the query features from the decoder.

    .. math::
            \\begin{array}{ll}
            x = context*query \\\\
            attn_scores = exp(x_i) / sum_j exp(x_j) \\\\
            attn_out = attn * context
            \\end{array}

    Args:
        dim(int): The number of expected features in the query

    Inputs: query, context
        - **query** (batch, query_len, dimensions): tensor containing the query features from the decoder.
        - **context** (batch, input_len, dimensions): tensor containing features of the encoded input sequence.

    Outputs: query, attn
        - **query** (batch, query_len, dimensions): tensor containing the attended query features from the decoder.
        - **attn** (batch, query_len, input_len): tensor containing attention weights.

    Attributes:
        mask (torch.Tensor, optional): applies a :math:`-inf` to the indices specified in the `Tensor`.

    """

    def __init__(self):
        super(AttentionNew, self).__init__()
        self.mask = None

    def set_mask(self, mask):
        """
        Sets indices to be masked

        Args:
            mask (torch.Tensor): tensor containing indices to be masked
        """
        self.mask = mask
    """
        - query   (batch, query_len, dimensions): tensor containing the query features from the decoder.
        - context (batch, input_len, dimensions): tensor containing features of the encoded input sequence.
    """

    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: 0.039069