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 libdevice, 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)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_tanh_tanh_backward_3(in_out_ptr0, 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
    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)
    tmp4 = tmp3 * tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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, (4, 8), (8, 1))
    assert_size_stride(primals_4, (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(primals_1, reinterpret_tensor(primals_2, (4, 4, 
            4), (16, 1, 4), 0), out=buf0)
        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), primals_2, out=buf3)
        del primals_2
        buf4 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_2[grid(128)](buf3, primals_1, buf4, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf5 = reinterpret_tensor(buf3, (16, 4), (4, 1), 0)
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf4, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf5)
        del primals_3
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        triton_poi_fused_tanh_tanh_backward_3[grid(64)](buf6, primals_4,
            buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_4
    return reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf4, (16, 8), (8, 1), 0), buf7


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

    .. math::
            \\begin{array}{ll}
            x = context*output \\\\
            attn = exp(x_i) / sum_j exp(x_j) \\\\
            output = \\tanh(w * (attn * context) + b * output)
            \\end{array}

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

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

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

    Attributes:
        linear_out (torch.nn.Linear): applies a linear transformation to the incoming data: :math:`y = Ax + b`.
        mask (torch.Tensor, optional): applies a :math:`-inf` to the indices specified in the `Tensor`.

    Examples::

         >>> attention = seq2seq.models.Attention(256)
         >>> context = Variable(torch.randn(5, 3, 256))
         >>> output = Variable(torch.randn(5, 5, 256))
         >>> output, attn = attention(output, context)

    """

    def __init__(self, dim):
        super(AttentionNew, self).__init__()
        self.linear_out = nn.Linear(dim * 2, dim)
        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

    def forward(self, input_0, input_1):
        primals_3 = self.linear_out.weight
        primals_4 = self.linear_out.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]


Measured latency_ms: 0.080225