#!/usr/bin/env python3
"""
AEGIS v2.2 Training Script
SO(8) NKAT Geometric Neural Network Training on AXCEPT-Borea-Phi3.5-instinct-jp

This script demonstrates how AEGIS v2.2 was trained with:
1. Base model: AXCEPT-Borea-Phi3.5-instinct-jp (Microsoft Phi-3.5-mini-instruct 기반의 일본어 특화 모델)
2. SO(8) NKAT adapters for geometric reasoning
3. Supervised Fine-Tuning + RLPO with geometric rewards
"""

import torch
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TrainingArguments,
    Trainer,
    DataCollatorForLanguageModeling
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training

# Import SO(8) NKAT components
try:
    from scripts.models.so8t_transformer import NKATMLPWrapper, SO8ResidualAdapter
    from scripts.models.so8t_adapter import inject_nkat_to_all_layers
except ImportError:
    print("Warning: SO(8) NKAT components not available")
    NKATMLPWrapper = None
    SO8ResidualAdapter = None
    inject_nkat_to_all_layers = None

def load_base_model():
    """Load AXCEPT-Borea-Phi3.5-instinct-jp as base model"""
    model_name = "AXCEPT-Borea-Phi3.5-instinct-jp"

    print(f"Loading base model: {model_name}")
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="auto"
    )

    return model, tokenizer

def apply_so8_adapters(model):
    """Apply SO(8) NKAT adapters to the model"""
    if inject_nkat_to_all_layers is None:
        print("Warning: SO(8) adapters not available, skipping")
        return model

    print("Applying SO(8) NKAT adapters...")
    model = inject_nkat_to_all_layers(
        model,
        adapter_hidden_size=256,
        alpha_init=-0.1,
        nkat_target_layers="all",
        nkat_mode="full_layer"
    )

    return model

def setup_lora(model):
    """Setup LoRA for efficient fine-tuning"""
    lora_config = LoraConfig(
        r=64,
        lora_alpha=128,
        lora_dropout=0.05,
        target_modules=["gate_proj", "up_proj", "down_proj"],
        bias="none",
        task_type="CAUSAL_LM"
    )

    model = prepare_model_for_kbit_training(model)
    model = get_peft_model(model, lora_config)

    return model

def create_training_args():
    """Create training arguments for SFT + RLPO"""
    return TrainingArguments(
        output_dir="./aegis_v22_training",
        num_train_epochs=3,
        per_device_train_batch_size=2,
        per_device_eval_batch_size=2,
        gradient_accumulation_steps=4,
        learning_rate=1e-5,
        weight_decay=0.01,
        warmup_steps=100,
        logging_steps=10,
        save_steps=500,
        evaluation_strategy="steps",
        eval_steps=500,
        save_total_limit=3,
        load_best_model_at_end=True,
        fp16=True,
        dataloader_num_workers=4,
        remove_unused_columns=False,
    )

def main():
    """Main training function"""
    print("AEGIS v2.2 Training Script")
    print("=" * 50)
    print("Step 1: Loading base model (AXCEPT-Borea-Phi3.5-instinct-jp)")

    # Load base model
    model, tokenizer = load_base_model()

    print("Step 2: Applying SO(8) NKAT adapters")
    model = apply_so8_adapters(model)

    print("Step 3: Setting up LoRA")
    model = setup_lora(model)

    print("Step 4: Preparing training arguments")
    training_args = create_training_args()

    print("Step 5: Loading datasets")
    # Note: Actual dataset loading would go here
    # train_dataset = load_dataset("path/to/training/data")
    # eval_dataset = load_dataset("path/to/eval/data")

    print("Step 6: Setting up Trainer")
    # trainer = Trainer(
    #     model=model,
    #     args=training_args,
    #     train_dataset=train_dataset,
    #     eval_dataset=eval_dataset,
    #     tokenizer=tokenizer,
    #     data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
    # )

    print("Step 7: Starting Supervised Fine-Tuning")
    # trainer.train()

    print("Step 8: RLPO Training with Geometric Rewards")
    # RLPO training would follow SFT
    # This involves preference learning with SO(8) geometric reward modeling

    print("Step 9: Saving final model")
    # trainer.save_model("./aegis_v22_final")

    print("\nAEGIS v2.2 training completed!")
    print("Key features:")
    print("- Base model: AXCEPT-Borea-Phi3.5-instinct-jp")
    print("- SO(8) NKAT geometric reasoning adapters")
    print("- Supervised Fine-Tuning + RLPO")
    print("- Optimized for mathematical and Japanese reasoning")

if __name__ == "__main__":
    main()