from dataclasses import dataclass
from typing import Optional, Tuple
import torch
import torch.nn as nn
from transformers.models.wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2PreTrainedModel,
    Wav2Vec2Model
)

@dataclass
class AudioClassifierOutput:
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None

class Wav2Vec2ForAudioClassification(Wav2Vec2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.wav2vec2 = Wav2Vec2Model(config)
        self.classifier = nn.Sequential(
            nn.Linear(config.hidden_size, config.classifier_proj_size),
            nn.GELU(),
            nn.Dropout(config.final_dropout),
            nn.Linear(config.classifier_proj_size, config.num_labels)
        )
        self.init_weights()

    def freeze_feature_encoder(self):
        self.wav2vec2.feature_extractor._freeze_parameters()

    def forward(
        self,
        input_values,
        attention_mask=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        outputs = self.wav2vec2(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]
        pooled_output = torch.mean(hidden_states, dim=1)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))

        return AudioClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )