import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.onnx.operators


def get_activation_fn(activation):
    """
    Get activation function by name

    Args:
        activation: activation function name

    Returns:
        - activation function
    """
    if activation == 'relu':
        return F.relu
    elif activation == 'gelu':
        return F.gelu
    else:
        raise KeyError


class HuggingfaceClassifier(nn.Module):
    """
    Classifier implemented in HuggingfaceClassificationHead style.

    Args:
        d_model: feature dimensionality
        labels: number of classes
        inner_dim: dimensionality in the inner vector space.
        activation: activation function used in the feed-forward network
        dropout: dropout rate
    """

    def __init__(self, d_model, labels, inner_dim=None, activation='relu',
        dropout=0.0):
        super().__init__()
        inner_dim = inner_dim or d_model * 2
        self._fc1 = nn.Linear(d_model, inner_dim)
        self._dropout = nn.Dropout(dropout)
        self._fc2 = nn.Linear(inner_dim, labels)
        self._activation = get_activation_fn(activation)

    def forward(self, x):
        """
        Args:
            x: feature to predict labels
                :math:`(*, D)`, where D is the feature dimension

        Returns:
            - log probability of each classes
                :math: `(*, L)`, where L is the number of classes
        """
        x = self._dropout(x)
        x = self._fc1(x)
        x = self._activation(x)
        x = self._dropout(x)
        x = self._fc2(x)
        return x


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


def get_init_inputs():
    return [[], {'d_model': 4, 'labels': 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 import triton_helpers
import torch.nn.functional as F
import torch.nn as nn
import torch.onnx.operators
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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, (8, 4), (4, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (4, 8), (8, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 8), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 8), (128, 32, 8, 1), 0)
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(512)](buf1,
            primals_3, buf3, 512, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 8), (
            8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 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_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 8), (8, 1), 0), primals_4, buf3


def get_activation_fn(activation):
    """
    Get activation function by name

    Args:
        activation: activation function name

    Returns:
        - activation function
    """
    if activation == 'relu':
        return F.relu
    elif activation == 'gelu':
        return F.gelu
    else:
        raise KeyError


class HuggingfaceClassifierNew(nn.Module):
    """
    Classifier implemented in HuggingfaceClassificationHead style.

    Args:
        d_model: feature dimensionality
        labels: number of classes
        inner_dim: dimensionality in the inner vector space.
        activation: activation function used in the feed-forward network
        dropout: dropout rate
    """

    def __init__(self, d_model, labels, inner_dim=None, activation='relu',
        dropout=0.0):
        super().__init__()
        inner_dim = inner_dim or d_model * 2
        self._fc1 = nn.Linear(d_model, inner_dim)
        self._dropout = nn.Dropout(dropout)
        self._fc2 = nn.Linear(inner_dim, labels)
        self._activation = get_activation_fn(activation)

    def forward(self, input_0):
        primals_2 = self._fc1.weight
        primals_3 = self._fc1.bias
        primals_4 = self._fc2.weight
        primals_5 = self._fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
