# Adaptive Information Transfer Multi-task
import torch
import corerec.torch_nn as nn
# from cr_utility.dataloader import DataLoader
from torch.utils.data import DataLoader

from corerec.engines.contentFilterEngine.multi_modal_cross_domain_methods import (
    MUL_MULTI_MODAL, MUL_CROSS_DOMAIN, MUL_CROSS_LINGUAL
)
from corerec.engines.contentFilterEngine.learning_paradigms.transfer_learning import TransferLearningLearner

# Define SourceModel and TargetModel
class SourceModel(nn.Module):
    def __init__(self, input_dim, feature_dim):
        super(SourceModel, self).__init__()
        self.fc = nn.Linear(input_dim, feature_dim)
    
    def forward(self, x):
        return torch.relu(self.fc(x))

class TargetModel(nn.Module):
    def __init__(self, feature_dim, output_dim):
        super(TargetModel, self).__init__()
        self.fc = nn.Linear(feature_dim, output_dim)
    
    def forward(self, x):
        return self.fc(x)

class MultilingualModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(MultilingualModel, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x):
        out = self.relu(self.fc1(x))
        return self.fc2(out)
    
    def translate(self, text_input, source_lang, target_lang):
        # Placeholder translation logic
        # This could be a simple transformation or a call to an external translation service
        return text_input  # For now, just return the input as-is

class AITM:
    def __init__(self, input_dim, feature_dim, output_dim):
        self.source_model = SourceModel(input_dim, feature_dim)
        self.target_model = TargetModel(feature_dim, output_dim)
        self.multilingual_model = MultilingualModel(input_dim, feature_dim, output_dim)
        
        # Pass the appropriate models to MUL_MULTI_MODAL
        self.multi_modal_model = MUL_MULTI_MODAL(self.source_model, self.target_model)
        
        self.cross_domain_model = MUL_CROSS_DOMAIN(self.source_model, self.target_model)
        self.cross_lingual_model = MUL_CROSS_LINGUAL(self.multilingual_model)

    def parameters(self):
        # Aggregate parameters from all component models
        return list(self.source_model.parameters()) + \
               list(self.target_model.parameters()) + \
               list(self.multilingual_model.parameters())

    def train(self, data_loader, criterion, optimizer, num_epochs):
        self.source_model.train()
        self.target_model.train()
        for epoch in range(num_epochs):
            for X, y in data_loader:
                optimizer.zero_grad()
                source_output = self.source_model(X)
                target_output = self.target_model(source_output)
                loss = criterion(target_output, y)
                loss.backward()
                optimizer.step()
            print(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}")

    def evaluate(self, data_loader, criterion):
        self.source_model.eval()
        self.target_model.eval()
        total_loss = 0
        with torch.no_grad():
            for X, y in data_loader:
                source_output = self.source_model(X)
                target_output = self.target_model(source_output)
                loss = criterion(target_output, y)
                total_loss += loss.item()
        print(f"Evaluation Loss: {total_loss / len(data_loader)}")

    def transfer_knowledge(self, data_loader, criterion, optimizer, num_epochs):
        transfer_learner = TransferLearningLearner(self.target_model, data_loader, criterion, optimizer, num_epochs)
        transfer_learner.train()

    def translate_and_recommend(self, text_input, source_lang, target_lang):
        translated_text = self.cross_lingual_model.translate(text_input, source_lang, target_lang)
        # Further recommendation logic can be added here
        return translated_text