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 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_div_sum_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
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, 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
    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 = 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)


@triton.jit
def triton_poi_fused__log_softmax_2(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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_3(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')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x2, tmp13, 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, (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((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_sum_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(64)](buf2,
            primals_3, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax_2[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        triton_poi_fused__log_softmax_3[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf4
    return buf5, buf0, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (16, 4), (4, 1), 0), buf5, primals_4, buf6


def transformer_weights_init(module, std_init_range=0.02, xavier=True):
    """
    Initialize different weights in Transformer model.
    Args:
        module: torch.nn.Module to be initialized
        std_init_range: standard deviation of normal initializer
        xavier: if True, xavier initializer will be used in Linear layers
            as was proposed in AIAYN paper, otherwise normal initializer
            will be used (like in BERT paper)
    """
    if isinstance(module, nn.Linear):
        if xavier:
            nn.init.xavier_uniform_(module.weight)
        else:
            nn.init.normal_(module.weight, mean=0.0, std=std_init_range)
        if module.bias is not None:
            nn.init.constant_(module.bias, 0.0)
    elif isinstance(module, nn.Embedding):
        nn.init.normal_(module.weight, mean=0.0, std=std_init_range)
    elif isinstance(module, nn.LayerNorm):
        nn.init.constant_(module.weight, 1.0)
        nn.init.constant_(module.bias, 0.0)


class SelfAttention(nn.Module):

    def __init__(self, hidden_size, batch_first=True):
        super(SelfAttention, self).__init__()
        self.hidden_size = hidden_size
        self.batch_first = batch_first
        self.register_parameter('att_weights', nn.Parameter(torch.Tensor(1,
            hidden_size), requires_grad=True))
        nn.init.xavier_uniform_(self.att_weights.data)

    def get_mask(self):
        pass

    def forward(self, hidden_states, attention_mask=None):
        if self.batch_first:
            batch_size, _max_len = hidden_states.size()[:2]
        else:
            _max_len, batch_size = hidden_states.size()[:2]
        weights = torch.bmm(hidden_states, self.att_weights.permute(1, 0).
            unsqueeze(0).repeat(batch_size, 1, 1))
        attentions = F.softmax(torch.tanh(weights.squeeze()), dim=-1)
        masked = attentions * attention_mask
        if len(attentions.shape) == 1:
            attentions = attentions.unsqueeze(0)
        _sums = masked.sum(-1, keepdim=True).expand(attentions.shape)
        attentions = masked.div(_sums)
        weighted = torch.mul(hidden_states, attentions.unsqueeze(-1).
            expand_as(hidden_states))
        representations = weighted.sum(1).squeeze(dim=1)
        return representations, attentions


class MultiLayerPerceptron(torch.nn.Module):
    """
    A simple MLP that can either be used independently or put on top
    of pretrained models (such as BERT) and act as a classifier.
    Args:
        hidden_size (int): the size of each layer
        num_classes (int): number of output classes
        num_layers (int): number of layers
        activation (str): type of activations for layers in between
        log_softmax (bool): whether to add a log_softmax layer before output
    """

    def __init__(self, hidden_size: 'int', num_classes: 'int', num_layers:
        'int'=2, activation: 'str'='relu', log_softmax: 'bool'=True):
        super().__init__()
        self.layers = 0
        activations = {'relu': nn.ReLU(), 'gelu': nn.GELU(), 'sigmoid': nn.
            Sigmoid(), 'tanh': nn.Tanh()}
        for _ in range(num_layers - 1):
            layer = torch.nn.Linear(hidden_size, hidden_size)
            setattr(self, f'layer{self.layers}', layer)
            setattr(self, f'layer{self.layers + 1}', activations[activation])
            self.layers += 2
        layer = torch.nn.Linear(hidden_size, num_classes)
        setattr(self, f'layer{self.layers}', layer)
        self.layers += 1
        self.log_softmax = log_softmax

    @property
    def last_linear_layer(self):
        return getattr(self, f'layer{self.layers - 1}')

    def forward(self, hidden_states):
        output_states = hidden_states[:]
        for i in range(self.layers):
            output_states = getattr(self, f'layer{i}')(output_states)
        if self.log_softmax:
            output_states = torch.log_softmax(output_states, dim=-1)
        else:
            output_states = torch.softmax(output_states, dim=-1)
        return output_states


class SequenceClassifierNew(nn.Module):

    def __init__(self, hidden_size: 'int', num_classes: 'int', num_layers:
        'int'=2, activation: 'str'='relu', log_softmax: 'bool'=True,
        dropout: 'float'=0.0, use_transformer_init: 'bool'=True, pooling:
        'str'='mean', idx_conditioned_on: 'int'=None):
        """
        Initializes the SequenceClassifier module.
        Args:
            hidden_size: the hidden size of the mlp head on the top of the encoder
            num_classes: number of the classes to predict
            num_layers: number of the linear layers of the mlp head on the top of the encoder
            activation: type of activations between layers of the mlp head
            log_softmax: applies the log softmax on the output
            dropout: the dropout used for the mlp head
            use_transformer_init: initializes the weights with the same approach used in Transformer
            idx_conditioned_on: index of the token to use as the sequence representation for the classification task, default is the first token
        """
        super().__init__()
        self.log_softmax = log_softmax
        self._idx_conditioned_on = idx_conditioned_on
        self.pooling = pooling
        self.mlp = MultiLayerPerceptron(hidden_size=hidden_size * 2 if 
            pooling == 'mean_max' else hidden_size, num_classes=num_classes,
            num_layers=num_layers, activation=activation, log_softmax=
            log_softmax)
        self.dropout = nn.Dropout(dropout)
        if use_transformer_init:
            self.apply(lambda module: transformer_weights_init(module,
                xavier=False))
        if pooling == 'attention':
            self.attention = SelfAttention(hidden_size)

    def forward(self, input_0):
        primals_2 = self.mlp.layer0.weight
        primals_3 = self.mlp.layer0.bias
        primals_4 = self.mlp.layer2.weight
        primals_5 = self.mlp.layer2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]


Measured latency_ms: 0.070112