import torch
import torch.nn as nn
import torch.optim as optim
from .model import ScratchMultimodalLLM, SimpleTokenizer

def train():
    print("--- Starting Training Loop (Skeleton) ---")
    
    # 1. Hyperparameters
    embed_dim = 256
    learning_rate = 3e-4
    epochs = 1 # Just for demo
    
    # 2. Initialize Model
    tokenizer = SimpleTokenizer()
    model = ScratchMultimodalLLM(vocab_size=tokenizer.vocab_size, embed_dim=embed_dim)
    
    # 3. Optimization
    optimizer = optim.AdamW(model.parameters(), lr=learning_rate)
    criterion = nn.CrossEntropyLoss()
    
    # 4. Dummy Dataset (Batch Size = 2)
    # Real training requires a dataset of (Screenshot, ErrorLog, ExpertAnalysis)
    # 4. Load Dataset from JSON
    import json
    import os
    
    data_path = os.path.join(os.path.dirname(__file__), "training_data.json")
    if os.path.exists(data_path):
        with open(data_path, "r") as f:
            dataset = json.load(f)
            print(f"Loaded {len(dataset)} examples from {data_path}")
    else:
        print("Warning: training_data.json not found. Using empty set.")
        dataset = []

    # Prepare batch
    dummy_logs = [item["error"] for item in dataset]
    target_analyses = [item["analysis"] for item in dataset]
    
    # Generate dummy images matches the count
    count = len(dataset)
    if count > 0:
        dummy_images = torch.randn(count, 3, 256, 256)
    else:
        print("No data to train on!")
        return
    
    print(f"Model Architecture Created: {sum(p.numel() for p in model.parameters())} parameters")

    for epoch in range(epochs):
        model.train()
        total_loss = 0
        
        for i, (log, target) in enumerate(zip(dummy_logs, target_analyses)):
            # Prepare inputs
            input_ids = tokenizer.encode(log).unsqueeze(0) # (1, Seq)
            target_ids = tokenizer.encode(target).unsqueeze(0)
            
            # Forward Pass
            # In a real casual LM training, inputs and targets are shifted
            logits = model(dummy_images[i].unsqueeze(0), input_ids) 
            
            # Simple loss calculation (predicting the next token based on current)
            # Reshaping for CrossEntropy: (Batch*Seq, Vocab)
            # Note: This is simplified. Real training uses masking.
            output_seq_len = min(logits.shape[1], target_ids.shape[1])
            loss = criterion(logits[:, :output_seq_len, :].reshape(-1, tokenizer.vocab_size), 
                           target_ids[:, :output_seq_len].reshape(-1))
            
            # Backward Pass
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
            
        print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss:.4f}")

    print("\nTraining complete! (Model weights updated in memory)")
    print("To make this model smart, you need 100,000+ examples.")
    
    # Save the model
    torch.save(model.state_dict(), "custom_llm_checkpoint.pth")
    print("Saved checkpoint to 'custom_llm_checkpoint.pth'")

if __name__ == "__main__":
    train()
