from abc import ABCMeta
import torch
from transformers.pytorch_utils import nn
import torch.nn.functional as F
from transformers import AlbertModel, AlbertForSequenceClassification, PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers import AlbertConfig
from transformers import PretrainedConfig

class AlbertABSAConfig(PretrainedConfig):
    model_type = "albertCNNForSequenceClassification"

    def __init__(self,
                 num_classes=2,
                 embed_dim=768,
                 conv_out_channels=256,  # New parameter for Conv1d
                 conv_kernel_size=3,
                 fc_hidden=128,           # New parameter for FC layer
                 dropout_rate=0.1,
                 num_layers=12,
                 **kwargs):
        super().__init__(**kwargs)
        self.num_classes = num_classes
        self.embed_dim = embed_dim
        self.conv_out_channels = conv_out_channels  # Assign Conv1d output channels
        self.conv_kernel_size = conv_kernel_size    # Assign Conv1d kernel size
        self.fc_hidden = fc_hidden                  # Assign FC layer hidden units
        self.dropout_rate = dropout_rate
        self.num_layers = num_layers
        self.id2label = {
            0: "fake",
            1: "true",
        }
        self.label2id = {
            "fake": 0,
            "true": 1,
        }
                     

class AlbertCNNForSequenceClassification(PreTrainedModel, metaclass=ABCMeta):
    config_class = AlbertABSAConfig

    def __init__(self, config):
        super(AlbertCNNForSequenceClassification, self).__init__(config)
        self.num_classes = config.num_classes
        self.embed_dim = config.embed_dim
        self.num_layers = config.num_layers
        self.conv_out_channels = config.conv_out_channels
        self.conv_kernel_size = config.conv_kernel_size
        self.dropout = nn.Dropout(config.dropout_rate)
        self.albert = AlbertModel.from_pretrained('albert-base-v2',
                                                  output_hidden_states=True,
                                                  output_attentions=False)
        print("ALBERT Model Loaded")
        self.conv1d = nn.Conv1d(in_channels=self.embed_dim, out_channels=self.conv_out_channels, kernel_size=self.conv_kernel_size)
        self.fc = nn.Linear(self.conv_out_channels, self.num_classes)

    def forward(self, input_ids, attention_mask, token_type_ids, labels=None):
        albert_output = self.albert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
        hidden_states = albert_output["hidden_states"]

        hidden_states = torch.stack([hidden_states[layer_i][:, 0].squeeze()
                                     for layer_i in range(0, self.num_layers)], dim=-1)  # noqa
        hidden_states = hidden_states.view(-1, self.num_layers, self.embed_dim)
        hidden_states = hidden_states.permute(0, 2, 1)  # Permute to match Conv1d input shape
        conv_output = self.conv1d(hidden_states)
        conv_output = F.relu(conv_output)
        conv_output = F.max_pool1d(conv_output, kernel_size=conv_output.size(2))  # Global Max Pooling
        conv_output = conv_output.squeeze(-1)
        conv_output = self.dropout(conv_output)
        logits = self.fc(conv_output)
        loss = None
        if labels is not None:
            loss = F.cross_entropy(logits, labels)
        out = SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=albert_output.hidden_states,
            attentions=albert_output.attentions,
        )
        return out