#!/usr/bin/env python3
"""
Working training script for Elizabeth with clean data
"""

import os
os.environ['HF_HOME'] = '/home/x/.cache/huggingface'

import torch
from transformers import (
    AutoModelForCausalLM, 
    AutoTokenizer, 
    TrainingArguments, 
    Trainer,
    DataCollatorForLanguageModeling
)
from datasets import Dataset
import json

# Configuration
MODEL_NAME = "Qwen/Qwen3-8B"
TRAIN_DATA_PATH = "/home/x/adaptai/aiml/e-train-1/clean_training_data.jsonl"
OUTPUT_DIR = "/home/x/adaptai/experiments/qwen3-8b-elizabeth-working"

class WorkingTrainer:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        
    def setup_model(self):
        """Load model"""
        print("🚀 Loading Qwen3-8B...")
        
        # Load tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(
            MODEL_NAME,
            trust_remote_code=True
        )
        
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token
        
        # Load model
        self.model = AutoModelForCausalLM.from_pretrained(
            MODEL_NAME,
            torch_dtype=torch.bfloat16,
            device_map="auto",
            trust_remote_code=True
        )
        
        print(f"✅ Model loaded")
        
    def load_dataset(self):
        """Load clean dataset"""
        print("📊 Loading clean data...")
        
        # Load data
        texts = []
        with open(TRAIN_DATA_PATH, 'r') as f:
            for line in f:
                data = json.loads(line)
                messages = data.get('messages', [])
                
                # Convert to text format
                text = ""
                for msg in messages:
                    text += f"{msg['role']}: {msg['content']}\n"
                texts.append(text)
        
        print(f"✅ Loaded {len(texts)} examples")
        
        # Create dataset
        dataset = Dataset.from_dict({"text": texts})
        return dataset
    
    def tokenize_function(self, examples):
        """Tokenize text"""
        return self.tokenizer(
            examples["text"],
            truncation=True,
            padding=False,
            max_length=2048,
        )
    
    def train(self):
        """Start training"""
        self.setup_model()
        dataset = self.load_dataset()
        
        # Tokenize
        tokenized_dataset = dataset.map(
            self.tokenize_function,
            batched=True,
            remove_columns=dataset.column_names
        )
        
        # Training arguments
        training_args = TrainingArguments(
            output_dir=OUTPUT_DIR,
            num_train_epochs=3,
            per_device_train_batch_size=1,
            gradient_accumulation_steps=8,
            learning_rate=5e-5,
            warmup_ratio=0.1,
            logging_steps=5,
            save_steps=50,
            bf16=True,
            remove_unused_columns=False,
            report_to=[],
        )
        
        # Data collator
        data_collator = DataCollatorForLanguageModeling(
            tokenizer=self.tokenizer,
            mlm=False,
        )
        
        # Trainer
        trainer = Trainer(
            model=self.model,
            args=training_args,
            train_dataset=tokenized_dataset,
            data_collator=data_collator,
        )
        
        print("🎯 Starting training...")
        print("⏰ This will take approximately 1-2 hours...")
        
        # Start training
        trainer.train()
        
        # Save final model
        trainer.save_model()
        
        print(f"✅ Training completed!")
        print(f"💾 Model saved to: {OUTPUT_DIR}")

if __name__ == "__main__":
    trainer = WorkingTrainer()
    trainer.train()