#!/usr/bin/env python3
"""
Script to initialize User States (z_long) from Memory Embeddings.
"""

import sys
import os
import numpy as np
import json
from collections import defaultdict

# Add src to sys.path
sys.path.append(os.path.join(os.path.dirname(__file__), "../src"))

from personalization.user_model.tensor_store import UserTensorStore, UserState
from personalization.retrieval.preference_store.schemas import MemoryCard

def main():
    cards_path = "data/corpora/memory_cards.jsonl"
    item_proj_path = "data/corpora/item_projection.npz"
    user_store_path = "data/users/user_store.npz"
    
    # Ensure user dir
    os.makedirs(os.path.dirname(user_store_path), exist_ok=True)
    
    # 1. Load data
    print("Loading memory cards...")
    cards = []
    if os.path.exists(cards_path):
        with open(cards_path, "r") as f:
            for line in f:
                cards.append(MemoryCard.model_validate_json(line))
    else:
        print("No memory cards found. Exiting.")
        return

    print("Loading item projection V...")
    if not os.path.exists(item_proj_path):
        print("Item projection not found. Run build_item_space.py first.")
        return
        
    proj_data = np.load(item_proj_path)
    V = proj_data["V"] # [M, k]
    
    if len(cards) != V.shape[0]:
        print(f"Warning: Number of cards ({len(cards)}) != V rows ({V.shape[0]}). Mismatch?")
        # If mismatch, we might need to be careful. For now assume aligned.
    
    k = V.shape[1]
    
    # 2. Group by user
    user_indices = defaultdict(list)
    for idx, card in enumerate(cards):
        user_indices[card.user_id].append(idx)
        
    # 3. Initialize Store
    print(f"Initializing UserStore at {user_store_path}...")
    store = UserTensorStore(k=k, path=user_store_path)
    
    # 4. Compute z_long and save
    print(f"Processing {len(user_indices)} users...")
    for uid, indices in user_indices.items():
        if not indices:
            continue
            
        # Get item vectors for this user
        # indices is list of int, V is numpy array
        user_items = V[indices]
        
        # Mean pooling
        z_long = np.mean(user_items, axis=0)
        
        # Get/Create state
        state = store.get_state(uid)
        state.z_long = z_long
        state.z_short = np.zeros(k, dtype=np.float32)
        state.reward_ma = 0.0
        
        store.save_state(state)
        
    store.persist()
    print("Done. User states initialized.")

if __name__ == "__main__":
    main()