from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn


def gelu(x):
    """Implementation of the gelu activation function.
        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


class BERTLowRank(nn.Module):

    def __init__(self, config, extra_dim=None):
        super(BERTLowRank, self).__init__()
        if config.extra_dim:
            self.aug_dense = nn.Linear(config.hidden_size, config.extra_dim)
            self.aug_dense2 = nn.Linear(config.extra_dim, config.hidden_size)
        else:
            self.aug_dense = nn.Linear(config.hidden_size, config.
                hidden_size_aug)
            self.aug_dense2 = nn.Linear(config.hidden_size_aug, config.
                hidden_size)
        self.config = config
        self.hidden_act_fn = gelu

    def forward(self, hidden_states, attention_mask=None):
        hidden_states_aug = self.aug_dense(hidden_states)
        hidden_states_aug = self.hidden_act_fn(hidden_states_aug)
        hidden_states = self.aug_dense2(hidden_states_aug)
        return hidden_states


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'config': _mock_config(extra_dim=4, hidden_size=4)}]

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.triton_helpers import libdevice
import 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_add_div_erf_mul_0(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865475
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_erf_mul_0[grid(256)](buf0, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), primals_4


def gelu(x):
    """Implementation of the gelu activation function.
        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


class BERTLowRankNew(nn.Module):

    def __init__(self, config, extra_dim=None):
        super(BERTLowRankNew, self).__init__()
        if config.extra_dim:
            self.aug_dense = nn.Linear(config.hidden_size, config.extra_dim)
            self.aug_dense2 = nn.Linear(config.extra_dim, config.hidden_size)
        else:
            self.aug_dense = nn.Linear(config.hidden_size, config.
                hidden_size_aug)
            self.aug_dense2 = nn.Linear(config.hidden_size_aug, config.
                hidden_size)
        self.config = config
        self.hidden_act_fn = gelu

    def forward(self, input_0):
        primals_1 = self.aug_dense.weight
        primals_2 = self.aug_dense.bias
        primals_4 = self.aug_dense2.weight
        primals_5 = self.aug_dense2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
