#!/usr/bin/env python3
"""
Sheikh-2.5-Coder Training Script
================================

This script handles the training pipeline for Sheikh-2.5-Coder model.
"""

import os
import torch
import argparse
from typing import Optional
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TrainingArguments,
    Trainer,
    DataCollatorForSeq2Seq,
    get_linear_schedule_with_warmup,
)
from datasets import load_dataset, Dataset
import yaml
from model import SheikhModel, SheikhConfig, setup_training_args

def load_config(config_path: str) -> dict:
    """Load training configuration from YAML file."""
    with open(config_path, 'r') as f:
        return yaml.safe_load(f)

def prepare_training_data(data_config: dict) -> Dataset:
    """Prepare training dataset."""
    # This would be implemented based on your specific data sources
    # For now, return a placeholder
    print("Loading training data...")
    
    # Example data preparation logic would go here
    # This might involve loading from Hugging Face datasets
    # or custom data sources
    
    # Placeholder: return empty dataset for now
    train_dataset = Dataset.from_dict({
        'input_ids': [[1, 2, 3, 4, 5]],
        'attention_mask': [[1, 1, 1, 1, 1]],
        'labels': [[2, 3, 4, 5, 6]]
    })
    
    return train_dataset

def setup_model_and_tokenizer(config: dict) -> tuple:
    """Setup model and tokenizer."""
    print("Initializing model and tokenizer...")
    
    # Load tokenizer (would be from a base model for continued training)
    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B")
    
    # Create model configuration
    model_config = SheikhConfig(
        vocab_size=tokenizer.vocab_size,
        hidden_size=config['model']['hidden_size'],
        num_attention_heads=config['model']['num_attention_heads'],
        num_key_value_heads=config['model']['num_key_value_heads'],
        num_hidden_layers=config['model']['num_hidden_layers'],
        intermediate_size=config['model']['intermediate_size'],
        max_position_embeddings=config['model']['context_length'],
    )
    
    # Initialize model
    model = SheikhModel(model_config)
    
    # Resize token embeddings if needed
    model.resize_token_embeddings(len(tokenizer))
    
    # Ensure tokenizer has proper padding token
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    
    return model, tokenizer

def train_model(
    model,
    tokenizer,
    train_dataset,
    eval_dataset,
    config: dict,
    output_dir: str,
):
    """Train the Sheikh-2.5-Coder model."""
    
    # Setup training arguments
    training_config = config['training']
    args = setup_training_args(
        output_dir=output_dir,
        learning_rate=training_config['learning_rate']
    )
    
    # Data collator
    data_collator = DataCollatorForSeq2Seq(
        tokenizer=tokenizer,
        model=model,
        padding=True,
        return_tensors="pt",
    )
    
    # Initialize trainer
    trainer = Trainer(
        model=model,
        args=args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        data_collator=data_collator,
        tokenizer=tokenizer,
    )
    
    # Start training
    print("Starting training...")
    trainer.train()
    
    # Save final model
    trainer.save_model(output_dir)
    tokenizer.save_pretrained(output_dir)
    
    print(f"Training completed! Model saved to {output_dir}")

def main():
    parser = argparse.ArgumentParser(description="Train Sheikh-2.5-Coder model")
    parser.add_argument(
        "--config",
        type=str,
        default="training_config.yaml",
        help="Path to training configuration file"
    )
    parser.add_argument(
        "--output-dir",
        type=str,
        default="./sheikh-2.5-coder-output",
        help="Directory to save the trained model"
    )
    parser.add_argument(
        "--resume-from-checkpoint",
        type=str,
        default=None,
        help="Path to checkpoint to resume from"
    )
    
    args = parser.parse_args()
    
    # Load configuration
    config = load_config(args.config)
    
    # Setup model and tokenizer
    model, tokenizer = setup_model_and_tokenizer(config)
    
    # Prepare training data
    train_dataset = prepare_training_data(config['data'])
    eval_dataset = prepare_training_data(config['data'])  # Placeholder
    
    # Create output directory
    os.makedirs(args.output_dir, exist_ok=True)
    
    # Train model
    train_model(
        model=model,
        tokenizer=tokenizer,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        config=config,
        output_dir=args.output_dir
    )

if __name__ == "__main__":
    main()