import abc
import torch
import torch.nn as nn
from torch.nn.functional import *
import torch.utils.data.dataset


class BaseHead(nn.Module, metaclass=abc.ABCMeta):
    """Absract class for task heads"""

    @abc.abstractmethod
    def __init__(self):
        super().__init__()


class RegressionHead(BaseHead):

    def __init__(self, task, hidden_size, hidden_dropout_prob, **kwargs):
        """From RobertaClassificationHead"""
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.dropout = nn.Dropout(hidden_dropout_prob)
        self.out_proj = nn.Linear(hidden_size, 1)

    def forward(self, pooled):
        x = self.dropout(pooled)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        scores = self.out_proj(x)
        return scores


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


def get_init_inputs():
    return [[], {'task': 4, 'hidden_size': 4, 'hidden_dropout_prob': 0.5}]

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 abc
import torch.nn as nn
from torch.nn.functional import *
import torch.utils.data.dataset
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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    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)
    tl.store(in_out_ptr0 + x2, tmp3, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(256)](buf1, primals_3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
    return reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, primals_4


class BaseHead(nn.Module, metaclass=abc.ABCMeta):
    """Absract class for task heads"""

    @abc.abstractmethod
    def __init__(self):
        super().__init__()


class RegressionHeadNew(BaseHead):

    def __init__(self, task, hidden_size, hidden_dropout_prob, **kwargs):
        """From RobertaClassificationHead"""
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.dropout = nn.Dropout(hidden_dropout_prob)
        self.out_proj = nn.Linear(hidden_size, 1)

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