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 numpy as np
import torch.nn as nn
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__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):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf0, (4, 4, 4), (
            16, 1, 4), 0), out=buf1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused__softmax_1[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = buf2
        del buf2
        extern_kernels.bmm(buf3, primals_3, out=buf4)
    return buf4, primals_3, buf3


class BaseSelfAttention(nn.Module):

    def __init__(self):
        super(BaseSelfAttention, self).__init__()

    def init_linear(self, input_linear):
        """Initialize linear transformation"""
        bias = np.sqrt(6.0 / (input_linear.weight.size(0) + input_linear.
            weight.size(1)))
        nn.init.uniform_(input_linear.weight, -bias, bias)
        if input_linear.bias is not None:
            input_linear.bias.data.zero_()

    def initialize_layers(self):
        raise NotImplementedError

    def forward(self, X):
        raise NotImplementedError

    def score(self, a, b):
        raise NotImplementedError


class SelfAttentionNew(BaseSelfAttention):

    def __init__(self, hidden_dim, scoring='general'):
        super(SelfAttentionNew, self).__init__()
        self.scoring = scoring
        self.hidden_dim = hidden_dim
        self.initialize_layers()

    def initialize_layers(self):
        if self.scoring == 'general':
            self.W = nn.Linear(self.hidden_dim, self.hidden_dim)
            self.init_linear(self.W)
        elif self.scoring == 'concat':
            self.W = nn.Linear(2 * self.hidden_dim, self.hidden_dim)
            self.v = nn.Linear(self.hidden_dim, 1)
            self.init_linear(self.W)
            self.init_linear(self.v)
        elif self.scoring == 'dot':
            pass
        else:
            raise RuntimeError('Unrecognized attention scoring method: %s' %
                self.scoring)

    def score(self, hidden_outputs):
        if self.scoring == 'dot':
            H = hidden_outputs.transpose(1, 2)
            attention_energies = hidden_outputs.bmm(H)
            scores = F.softmax(attention_energies, dim=2)
            return scores
        elif self.scoring == 'general':
            H = self.W(hidden_outputs)
            H = H.transpose(1, 2)
            attention_energies = hidden_outputs.bmm(H)
            scores = F.softmax(attention_energies, dim=2)
            return scores
        elif self.scoring == 'concat':
            H = hidden_outputs.transpose(1, 2)
            scores = []
            batch_size, doc_maxlen, hidden_dim = hidden_outputs.shape
            for doc_idx in range(H.shape[-1]):
                h_t = hidden_outputs[:, doc_idx, :]
                h_t = h_t.unsqueeze(1)
                h_t = h_t.repeat(1, doc_maxlen, 1)
                H_t = torch.cat((h_t, hidden_outputs), dim=2)
                H_t = self.W(H_t)
                H_t = torch.nn.functional.tanh(H_t)
                H_t = self.v(H_t)
                H_t = H_t.view(batch_size, doc_maxlen)
                scores.append(H_t)
            scores = torch.stack(scores)
            scores = scores.transpose(0, 1)
            scores = scores / torch.sqrt(torch.Tensor([hidden_dim]))
            scores = F.softmax(scores, dim=2)
            return scores
        else:
            raise RuntimeError('Unrecognized scoring method: %s' % self.scoring
                )

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


Measured latency_ms: 0.044654