#!/usr/bin/env python3

import json
import glob
import joblib
import os
import torch
import numpy as np  # Required for array operations
from transformers import AutoModel

# Set proxy (replace with your proxy address and port)
# os.environ['HTTP_PROXY'] = 'http://localhost:1080'
# os.environ['HTTPS_PROXY'] = 'http://localhost:1080'

# --- Configuration ---
# Assuming your data directory structure is as follows:
# ./data/
# ├── san-francisco/
# │   ├── fast_general_..._forecast_2017.json
# │   └── ...
# ├── san-diego/
# │   ├── fast_general_..._forecast_2017.json
# │   └── ...
# └── id_info_imputed.json

DATA_DIR = "./weather/weather_report"  # Your main data directory
EMBEDDING_MODEL = "jinaai/jina-embeddings-v3"
TRUNCATE_DIM = 256
BATCH_SIZE = 1500 # Adjust based on your hardware

# --- New Configuration: Define the cities ---
BOROUGHS = ['formal_report']
BASE_BOROUGH = 'formal_report' # We will use this city's files as the baseline for finding corresponding files

def initialize_model():
    """
    Initializes and loads the embedding model, prioritizing GPU usage.
    """
    print("Initializing embedding model...")
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    print(f"Using device: {device}")
    
    model = AutoModel.from_pretrained(
        EMBEDDING_MODEL, 
        trust_remote_code=True
    ).to(device)
    
    print("Model loaded successfully.")
    return model

def process_dynamic_data_for_borough(model, borough):
    """
    Processes all dynamic forecast JSON files for a specific city,
    generating and saving their embeddings.
    """
    print(f"\n--- Starting processing for city: {borough} ---")
    borough_path = os.path.join(DATA_DIR, borough)
    json_files = glob.glob(os.path.join(borough_path, "wm_messages_??.json"))
    
    if not json_files:
        print(f"No dynamic forecast JSON files found for {borough}. Skipping.")
        return

    for file_path in json_files:
        print(f"\nProcessing file: {file_path}")
        with open(file_path, "r") as f:
            data = json.load(f)
        
        timestamps = list(data.keys())
        if not timestamps:
            print(f"File {file_path} is empty. Skipping.")
            continue
        print(f"Loaded {len(data)} records from {file_path}")
            
        emb_dict = {}
        num_batches = (len(timestamps) + BATCH_SIZE - 1) // BATCH_SIZE
        for i in range(0, len(timestamps), BATCH_SIZE):
            batch_timestamps = timestamps[i:i + BATCH_SIZE]
            print(f"  Processing batch {i // BATCH_SIZE + 1}/{num_batches}")
            
            batch_texts, len_list = [], [0]
            for ts in batch_timestamps:
                # Preprocess text uniformly, adding city information
                texts = [f'{borough.replace("-", " ").title()}: {text}' for text in data[ts].values()]
                batch_texts.extend(texts)
                len_list.append(len(batch_texts))

            embeddings = model.encode(batch_texts, truncate_dim=TRUNCATE_DIM)
            
            for j, ts in enumerate(batch_timestamps):
                start_idx, end_idx = len_list[j], len_list[j+1]
                emb_dict[ts] = embeddings[start_idx:end_idx, :]
        
        output_file_name = os.path.basename(file_path).replace("forecast", "embeddings").replace(".json", ".pkl")
        output_path = os.path.join(borough_path, output_file_name)

        with open(output_path, "wb") as f:
            joblib.dump(emb_dict, f)
        print(f"Saved city-specific embeddings to {output_path}")

def merge_borough_embeddings():
    """
    Merges the embedding files from all specified cities.
    """
    print("\n--- Starting merging of city embeddings ---")
    base_path = os.path.join(DATA_DIR, 'weather', BASE_BOROUGH)
    base_embedding_files = glob.glob(os.path.join(base_path, "fast_general_*_embeddings_*.pkl"))

    if not base_embedding_files:
        print("No base embedding files found to merge. Skipping.")
        return
        
    for base_file in base_embedding_files:
        print(f"\nMerging based on: {base_file}")
        
        # Load the corresponding files for all cities
        data_files = {}
        try:
            for borough in BOROUGHS:
                target_file = base_file.replace(BASE_BOROUGH, borough)
                with open(target_file, "rb") as f:
                    data_files[borough] = joblib.load(f)
        except FileNotFoundError as e:
            print(f"Could not find a corresponding file for {e.filename}. Skipping this year.")
            continue
            
        merged_embeddings = {}
        # Iterate through the timestamps of the base file
        for timestamp in data_files[BASE_BOROUGH].keys():
            embeddings_to_merge = []
            # Ensure each city has data for this timestamp
            if all(timestamp in data_files[b] for b in BOROUGHS):
                for borough in BOROUGHS:
                    embeddings_to_merge.append(data_files[borough][timestamp])
                
                # Concatenate the embeddings using numpy
                merged_embeddings[timestamp] = np.concatenate(embeddings_to_merge, axis=0)
        
        output_filename = os.path.basename(base_file)
        final_output_path = os.path.join(DATA_DIR, 'weather', output_filename)
        joblib.dump(merged_embeddings, final_output_path)
        print(f"Saved final merged embeddings to {final_output_path}")

def main():
    """
    Main execution function
    """
    model = initialize_model()
    
    # 1. Generate embeddings for each city individually
    for borough in BOROUGHS:
        process_dynamic_data_for_borough(model, borough)
        
    # 2. Merge the embeddings from all cities
    merge_borough_embeddings()
    
    print("\nAll processing complete.")

if __name__ == "__main__":
    main()