import math
import torch
import torch.nn as nn
import torch.cuda
import torch.distributed


def get_activation_fn(activation):
    """Return an activation function Module according to its name."""
    if activation == 'gelu':
        fn = GELU()
    elif activation == 'relu':
        fn = nn.ReLU()
    elif activation == 'tanh':
        fn = nn.Tanh()
    else:
        raise ValueError(
            'Please pass a valid                           activation function'
            )
    return fn


class GELU(nn.Module):
    """ Implementation of the gelu activation function
        :cite:`DBLP:journals/corr/HendrycksG16`

        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))))

        Examples::
        >>> m = GELU()
        >>> inputs = torch.randn(2)
        >>> outputs = m(inputs)
    """

    def forward(self, x):
        gelu = x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
        return gelu


class BertPredictionTransform(nn.Module):
    """{Linear(h,h), Activation, LN} block."""

    def __init__(self, hidden_size):
        """
        Args:
            hidden_size (int): BERT model hidden layer size.
        """
        super(BertPredictionTransform, self).__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.activation = get_activation_fn('gelu')
        self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-12)

    def forward(self, hidden_states):
        """
        Args:
            hidden_states (Tensor): BERT encoder output ``(B, S, H)``
        """
        hidden_states = self.layer_norm(self.activation(self.dense(
            hidden_states)))
        return hidden_states


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


def get_init_inputs():
    return [[], {'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
import torch.cuda
import torch.distributed
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_native_layer_norm_0(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    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
    tmp10 = tmp9 * tmp1
    tmp11 = tmp9 * tmp3
    tmp12 = libdevice.erf(tmp11)
    tmp13 = tmp12 + tmp6
    tmp14 = tmp10 * tmp13
    tmp15 = tmp8 + tmp14
    tmp17 = tmp16 * tmp1
    tmp18 = tmp16 * tmp3
    tmp19 = libdevice.erf(tmp18)
    tmp20 = tmp19 + tmp6
    tmp21 = tmp17 * tmp20
    tmp22 = tmp15 + tmp21
    tmp24 = tmp23 * tmp1
    tmp25 = tmp23 * tmp3
    tmp26 = libdevice.erf(tmp25)
    tmp27 = tmp26 + tmp6
    tmp28 = tmp24 * tmp27
    tmp29 = tmp22 + tmp28
    tmp30 = 4.0
    tmp31 = tmp29 / tmp30
    tmp32 = tmp8 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp14 - tmp31
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp21 - tmp31
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp28 - tmp31
    tmp41 = tmp40 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42 / tmp30
    tl.store(out_ptr0 + x0, tmp31, xmask)
    tl.store(out_ptr1 + x0, tmp43, xmask)


@triton.jit
def triton_poi_fused_add_div_erf_mul_native_layer_norm_1(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp9 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    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
    tmp10 = tmp8 - tmp9
    tmp12 = 1e-12
    tmp13 = tmp11 + tmp12
    tmp14 = libdevice.rsqrt(tmp13)
    tmp15 = tmp10 * tmp14
    tmp17 = tmp15 * tmp16
    tmp19 = tmp17 + tmp18
    tl.store(out_ptr0 + x2, tmp19, 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,), (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, 1), (16, 4, 1, 64), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_erf_mul_native_layer_norm_0[grid(64)](buf0,
            buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_erf_mul_native_layer_norm_1[grid(256)](buf0,
            buf1, buf2, primals_4, primals_5, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        del buf2
        del primals_5
    return buf3, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0


def get_activation_fn(activation):
    """Return an activation function Module according to its name."""
    if activation == 'gelu':
        fn = GELU()
    elif activation == 'relu':
        fn = nn.ReLU()
    elif activation == 'tanh':
        fn = nn.Tanh()
    else:
        raise ValueError(
            'Please pass a valid                           activation function'
            )
    return fn


class GELU(nn.Module):
    """ Implementation of the gelu activation function
        :cite:`DBLP:journals/corr/HendrycksG16`

        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))))

        Examples::
        >>> m = GELU()
        >>> inputs = torch.randn(2)
        >>> outputs = m(inputs)
    """

    def forward(self, x):
        gelu = x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
        return gelu


class BertPredictionTransformNew(nn.Module):
    """{Linear(h,h), Activation, LN} block."""

    def __init__(self, hidden_size):
        """
        Args:
            hidden_size (int): BERT model hidden layer size.
        """
        super(BertPredictionTransformNew, self).__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.activation = get_activation_fn('gelu')
        self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-12)

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