import os
import numpy as np
import tiktoken
from datasets import load_dataset
from tqdm import tqdm

OUTPUT_DIR = "data/apex-1.6-sft"
TOKENIZER_NAME = "gpt2" 
SEED = 1337

FINEWEB_SAMPLES = 100000
MAX_TOKENS_PER_SAMPLE = 1000

enc = tiktoken.get_encoding(TOKENIZER_NAME)
EOS_TOKEN = "<|endoftext|>"

def format_prompt_with_mask(instruction, input_text, output):
    """
    Formatiert den Prompt und erstellt die Loss-Maske.
    Format:
    Instruction: ...
    Input: ... (optional)
    Response: ... <|endoftext|>
    """
    if input_text and input_text.strip():
        prompt_text = f"Instruction:\n{instruction}\n\nInput:\n{input_text}\n\nResponse:\n"
    else:
        prompt_text = f"Instruction:\n{instruction}\n\nResponse:\n"
    
    completion_text = f"{output}{EOS_TOKEN}"
    
    prompt_ids = enc.encode(prompt_text, allowed_special={'<|endoftext|>'})
    completion_ids = enc.encode(completion_text, allowed_special={'<|endoftext|>'})
    
    full_ids = prompt_ids + completion_ids
    
    mask = [0] * len(prompt_ids) + [1] * len(completion_ids)
    
    return full_ids, mask

def main():
    np.random.seed(SEED)
    print(f"🚀 Starting prepare-script for Apex 1.6 (350M SFT)...")
    print(f"📚 Tokenizer: {TOKENIZER_NAME}")
    
    os.makedirs(OUTPUT_DIR, exist_ok=True)

    print("📥 Loading 'yahma/alpaca-cleaned' (Chat-Instructions)...")
    alpaca = load_dataset("yahma/alpaca-cleaned", split='train')
    
    print(f"📥 Loading 'HuggingFaceFW/fineweb-edu' (Sample-10BT) for {FINEWEB_SAMPLES} Samples...")
    fineweb = load_dataset("HuggingFaceFW/fineweb-edu", name="sample-10BT", split='train', streaming=True)
    
    all_tokens = []
    all_masks = []
    
    print("⚙️  Processing Alpaca...")
    for ex in tqdm(alpaca, desc="Alpaca"):
        ids, mask = format_prompt_with_mask(ex['instruction'], ex['input'], ex['output'])
        all_tokens.extend(ids)
        all_masks.extend(mask)
        
    alpaca_len = len(all_tokens)
    print(f"   -> Alpaca Tokens: {alpaca_len:,}")

    print("⚙️  Processing FineWeb (Knowledge in Chat-Format!)...")
    fw_iter = iter(fineweb)
    fw_count = 0
    fw_tokens_count = 0
    
    for _ in tqdm(range(FINEWEB_SAMPLES), desc="FineWeb Chat-Injection"):
        try:
            ex = next(fw_iter)
            raw_text = ex['text']
            
            words = raw_text.split()
            if len(words) < 50:
                continue
                
            words = words[:MAX_TOKENS_PER_SAMPLE]
            
            split_idx = int(len(words) * 0.3)
            context_text = " ".join(words[:split_idx])
            response_text = " ".join(words[split_idx:])
            
            instruction = "Continue the following educational text factually and coherently."
            
            ids, mask = format_prompt_with_mask(
                instruction=instruction, 
                input_text=context_text, 
                output=response_text
            )
            
            all_tokens.extend(ids)
            all_masks.extend(mask)
            
            fw_tokens_count += len(ids)
            fw_count += 1
            
        except StopIteration:
            break
            
    print(f"   -> FineWeb Tokens (formatted as chat): {fw_tokens_count:,} (from {fw_count} documents)")

    total_tokens = len(all_tokens)
    print(f"\n💾 Saving {total_tokens:,} tokens to '{OUTPUT_DIR}'...")
    
    token_arr = np.array(all_tokens, dtype=np.uint16)
    token_arr.tofile(os.path.join(OUTPUT_DIR, "train.bin"))
    
    mask_arr = np.array(all_masks, dtype=np.uint8)
    mask_arr.tofile(os.path.join(OUTPUT_DIR, "train_mask.bin"))
    
    print("\n🔍 --- SANITY CHECK ---")
    print("Decoding the first 50 tokens to check if everything is fine")
    print("Green (TRAIN) = What the model learns. Grey (IGNORE) = What the model reads")
    
    check_len = 100
    sample_ids = all_tokens[:check_len]
    sample_mask = all_masks[:check_len]
    
    decoded_parts = []
    for t_id, m_val in zip(sample_ids, sample_mask):
        token_str = enc.decode([t_id])
        if m_val == 1:
            decoded_parts.append(f"\033[92m{token_str}\033[0m")
        else:
            decoded_parts.append(f"\033[90m{token_str}\033[0m")
            
    print("".join(decoded_parts))
    print("\n(Legend: \033[Grey=Prompt/Ignored\033[0m, \033[Green=Response/Learned\033[0m)")
    
    if len(token_arr) != len(mask_arr):
        print("\n❌ ERROR!")
    else:
        print("\n✅ Check - everything is fine!")

if __name__ == "__main__":
    main()