import os
import numpy as np
import random
from transformers import GPT2Tokenizer

# --- Settings ---
BIN_PATH = "data_19b.bin"
NUM_SAMPLES = 10
SAMPLE_LEN = 200 # Number of tokens to decode per sample

def main():
    if not os.path.exists(BIN_PATH):
        print(f"Error: {BIN_PATH} not found.")
        return

    # 1. Load the tokenizer
    print("Loading GPT-2 Tokenizer...")
    tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neox-20b")

    # 2. Map the data (instantly points to the 40GB file)
    data = np.memmap(BIN_PATH, dtype=np.uint16, mode='r')
    total_tokens = len(data)
    print(f"File loaded. Total tokens: {total_tokens:,}")

    # 3. Pick random spots and decode
    print(f"\n--- Decoding {NUM_SAMPLES} Random Samples ---\n")
    
    for i in range(NUM_SAMPLES):
        # Pick a random starting index
        start_idx = random.randint(0, total_tokens - SAMPLE_LEN)
        end_idx = start_idx + SAMPLE_LEN
        
        # Pull the uint16 tokens and convert to standard Python list
        token_ids = data[start_idx:end_idx].tolist()
        
        # Decode to text
        decoded_text = tokenizer.decode(token_ids, skip_special_tokens=True)
        
        print(f"Sample {i+1} (Index {start_idx:,}):")
        print("-" * 50)
        print(decoded_text)
        print("-" * 50 + "\n")

if __name__ == "__main__":
    main()