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
import torch.onnx
import torch.nn.functional as F
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_clone_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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * (x1 // 4)), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 4 * (x1 // 16) + 16 * (x1 % 4)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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_3(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_4(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 % 4
    x2 = xindex // 32
    x3 = xindex // 8
    x4 = 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 * x2 + 16 * 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 * x3 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x4, tmp10, xmask)


@triton.jit
def triton_poi_fused_tanh_tanh_backward_5(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, primals_5, primals_6,
        primals_7, primals_8) = 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, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (1,), (1,))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused_tanh_1[grid(256)](buf1, primals_4, primals_1, buf2,
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf4 = reinterpret_tensor(buf1, (64, 1), (1, 1), 0)
        del buf1
        extern_kernels.addmm(primals_6, buf2, reinterpret_tensor(primals_5,
            (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_6
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
        del buf4
        triton_poi_fused__softmax_3[grid(64)](buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = buf5
        del buf5
        extern_kernels.bmm(buf6, reinterpret_tensor(primals_1, (4, 4, 4), (
            4, 16, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_4[grid(128)](primals_2, buf7, buf8, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
        del buf7
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_7, (8, 4), (1, 8), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0)
        del buf9
        buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_tanh_tanh_backward_5[grid(64)](buf10, primals_8,
            buf11, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_8
    return reinterpret_tensor(buf10, (4, 4, 4), (4, 16, 1), 0
        ), reinterpret_tensor(buf6, (4, 4, 4), (4, 16, 1), 0
        ), reinterpret_tensor(buf0, (16, 4), (4, 1), 0
        ), buf2, buf6, reinterpret_tensor(buf8, (16, 8), (8, 1), 0
        ), buf11, primals_7, reinterpret_tensor(primals_1, (4, 4, 4), (4, 1,
        16), 0), primals_5


class AttentionLayerNew(nn.Module):
    """
    Attention layer according to https://arxiv.org/abs/1409.0473.

    Params:
      num_units: Number of units used in the attention layer
    """

    def __init__(self, query_size, key_size, value_size=None, mode=
        'bahdanau', normalize=False, dropout=0, batch_first=False,
        weight_norm=False, bias=True, query_transform=True,
        output_transform=True, output_nonlinearity='tanh', output_size=None):
        super(AttentionLayerNew, self).__init__()
        assert mode == 'bahdanau' or mode == 'dot_prod'
        value_size = value_size or key_size
        self.mode = mode
        self.query_size = query_size
        self.key_size = key_size
        self.value_size = value_size
        self.normalize = normalize
        wn_func = wn if weight_norm else lambda x: x
        if mode == 'bahdanau':
            self.linear_att = nn.Linear(key_size, 1, bias=bias)
            if normalize:
                self.linear_att = nn.utils.weight_norm(self.linear_att)
        elif normalize:
            self.scale = nn.Parameter(torch.Tensor([1]))
        if output_transform:
            output_size = output_size or query_size
            self.linear_out = wn_func(nn.Linear(query_size + value_size,
                output_size, bias=bias))
            self.output_size = output_size
        else:
            self.output_size = value_size
        if query_transform:
            self.linear_q = wn_func(nn.Linear(query_size, key_size, bias=bias))
        self.dropout = nn.Dropout(dropout)
        self.batch_first = batch_first
        self.output_nonlinearity = output_nonlinearity
        self.mask = None

    def set_mask(self, mask):
        self.mask = mask
        if mask is not None and not self.batch_first:
            self.mask = self.mask.t()

    def calc_score(self, att_query, att_keys):
        """
        att_query is: b x t_q x n
        att_keys is b x t_k x n
        return b x t_q x t_k scores
        """
        b, t_k, n = list(att_keys.size())
        t_q = att_query.size(1)
        if self.mode == 'bahdanau':
            att_query = att_query.unsqueeze(2).expand(b, t_q, t_k, n)
            att_keys = att_keys.unsqueeze(1).expand(b, t_q, t_k, n)
            sum_qk = att_query + att_keys
            sum_qk = sum_qk.view(b * t_k * t_q, n)
            out = self.linear_att(F.tanh(sum_qk)).view(b, t_q, t_k)
        elif self.mode == 'dot_prod':
            out = torch.bmm(att_query, att_keys.transpose(1, 2))
            if hasattr(self, 'scale'):
                out = out * self.scale
        return out

    def forward(self, input_0, input_1):
        primals_5 = self.linear_att.weight
        primals_6 = self.linear_att.bias
        primals_7 = self.linear_out.weight
        primals_4 = self.linear_out.bias
        primals_3 = self.linear_q.weight
        primals_8 = self.linear_q.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0], output[1]


Measured latency_ms: 0.023563