import torch
import torch.nn as nn
from transformers import AutoModel


class Model(torch.nn.Module):

    def __init__(self, model_dir, dropout=0.2, hidden_dim=768):
        """
        Initialize the model.
        :param model_name: the name of the model
        :param metric_names: the names of the metrics to use
        :param dropout: the dropout rate
        :param hidden_dim: the hidden dimension of the model
        """
        super(Model, self).__init__()
        self.metric_names = ['Happiness', 'Sadness', 'Anger', 'Disgust', 'Fear', 'Pride', 'Valence', 'Arousal']
        self.bert = AutoModel.from_pretrained(model_dir)


        for name in self.metric_names:
            setattr(self, name, nn.Linear(hidden_dim, 1))
            setattr(self, 'l_1_' + name, nn.Linear(hidden_dim, hidden_dim))

        self.layer_norm = nn.LayerNorm(hidden_dim)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(dropout)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_id, mask):
        """
        Forward pass of the model.
        :param args: the inputs
        :return: the outputs
        """
        _, x = self.bert(input_ids = input_id, attention_mask=mask, return_dict=False)
        output = self.rate_embedding(x)
        return output

    def rate_embedding(self, x):
        output_ratings = []
        for name in self.metric_names:
            first_layer = self.relu(self.dropout(self.layer_norm(getattr(self, 'l_1_' + name)(x) + x)))
            second_layer = self.sigmoid(getattr(self, name)(first_layer))
            output_ratings.append(second_layer)

        return output_ratings

    def save_pretrained(self, save_directory):
        self.bert.save_pretrained(save_directory)
        torch.save(self.state_dict(), f'{save_directory}/pytorch_model.bin')

    @classmethod
    def from_pretrained(cls, model_dir, dropout=0.2, hidden_dim=768):
        model = cls(model_dir, dropout, hidden_dim)
        state_dict = torch.load(f'{model_dir}/pytorch_model.bin', map_location=torch.device('cpu'))
        model.load_state_dict(state_dict)
        return model