#!/usr/bin/env python3
"""
Bitcoin AI Fine-Tuning Script
CPU-optimized QLoRA fine-tuning for Bitcoin operations assistant.
"""

import os
import json
import torch
from pathlib import Path
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    TrainingArguments,
)
from peft import (
    LoraConfig,
    get_peft_model,
    prepare_model_for_kbit_training,
)
from datasets import Dataset

# Configuration
MODEL_NAME = "Qwen/Qwen2.5-7B-Instruct"
OUTPUT_DIR = Path(__file__).parent.parent / "output"
DATA_DIR = Path(__file__).parent.parent / "data" / "bitcoin_finetuning.json"
MAX_STEPS = 1000  # Adjust based on dataset size and time available

def load_model_cpu():
    """Load model optimized for CPU inference/training."""
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.float32,
        bnb_4bit_use_double_quant=True,
    )
    
    print("Loading base model (this may take a while on CPU)...")
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        quantization_config=bnb_config,
        device_map="cpu",
        torch_dtype=torch.float32,
    )
    
    model = prepare_model_for_kbit_training(model)
    return model

def add_lora(model):
    """Add LoRA adapters for efficient fine-tuning."""
    lora_config = LoraConfig(
        r=16,
        lora_alpha=32,
        target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
        lora_dropout=0.05,
        bias="none",
        task_type="CAUSAL_LM",
    )
    
    return get_peft_model(model, lora_config)

def prepare_dataset():
    """Load and format training data."""
    if not DATA_DIR.exists():
        raise FileNotFoundError(f"Training data not found: {DATA_DIR}")
    
    with open(DATA_DIR, 'r') as f:
        data = json.load(f)
    
    # Convert to HuggingFace Dataset format
    dataset = Dataset.from_list(data)
    return dataset

def train():
    """Run the fine-tuning process."""
    print("Starting Bitcoin AI fine-tuning...")
    print(f"Base model: {MODEL_NAME}")
    print(f"Training steps: {MAX_STEPS}")
    
    # Load components
    model = load_model_cpu()
    model = add_lora(model)
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    dataset = prepare_dataset()
    
    # Training arguments optimized for CPU
    training_args = TrainingArguments(
        output_dir=str(OUTPUT_DIR),
        num_train_epochs=1,
        max_steps=MAX_STEPS,
        per_device_train_batch_size=1,
        gradient_accumulation_steps=4,
        learning_rate=2e-4,
        fp16=False,
        bf16=False,
        logging_steps=10,
        save_steps=100,
        save_total_limit=3,
        optim="adamw_torch",
        warmup_ratio=0.1,
        logging_dir=str(OUTPUT_DIR / "logs"),
        report_to="none",
    )
    
    print("Configuration ready. Starting training...")
    print("This will run on CPU and take ~7-14 days")
    
    # TODO: Implement actual training loop with Trainer
    # This is the skeleton - will be fleshed out with proper tokenization
    
if __name__ == "__main__":
    train()