# /// script
# dependencies = [
#     "trl>=0.12.0",
#     "peft>=0.7.0",
#     "transformers>=4.36.0",
#     "accelerate>=0.24.0",
#     "datasets>=2.14.0",
#     "trackio",
#     "torch",
#     "bitsandbytes",
# ]
# ///

import os
import trackio
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
from transformers import AutoTokenizer
from huggingface_hub import login

# Login with HF token
hf_token = os.environ.get("HF_TOKEN")
if hf_token:
    login(token=hf_token)
    print("Logged in to Hugging Face Hub")
else:
    print("Warning: HF_TOKEN not found in environment")

# Load dataset - using the solutions configuration with messages format
print("Loading open-r1/codeforces-cots dataset...")
dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train")
print(f"Full dataset loaded: {len(dataset)} examples")

# Take 1000 examples for demo
dataset = dataset.select(range(min(1000, len(dataset))))
print(f"Using {len(dataset)} examples for demo training")

# The dataset has both 'prompt' (string) and 'messages' (chat format) columns
# TRL gets confused with both present. Keep only 'messages' for chat-based SFT.
print("Preparing dataset for chat-based SFT...")

# Filter for valid messages and keep only the messages column
def filter_valid_messages(example):
    """Filter out samples with empty or invalid messages"""
    messages = example.get("messages", [])
    if not messages or len(messages) < 2:
        return False
    for msg in messages:
        if not msg.get("content"):
            return False
    return True

dataset = dataset.filter(filter_valid_messages)
print(f"After filtering: {len(dataset)} examples")

# Remove all columns except 'messages' to avoid confusion
columns_to_remove = [col for col in dataset.column_names if col != "messages"]
dataset = dataset.remove_columns(columns_to_remove)
print(f"Dataset columns: {dataset.column_names}")

# Create train/eval split
print("Creating train/eval split...")
dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
train_dataset = dataset_split["train"]
eval_dataset = dataset_split["test"]
print(f"   Train: {len(train_dataset)} examples")
print(f"   Eval: {len(eval_dataset)} examples")

# Load tokenizer for chat template
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# Training configuration
config = SFTConfig(
    # CRITICAL: Hub settings
    output_dir="qwen3-0.6b-codeforces-sft",
    push_to_hub=True,
    hub_model_id="Godsonntungi2/qwen3-0.6b-codeforces-sft",
    hub_strategy="every_save",
    hub_token=hf_token,  # Explicitly pass token

    # Training parameters
    num_train_epochs=3,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=1,  # Smaller eval batch to prevent OOM
    gradient_accumulation_steps=8,
    learning_rate=2e-5,
    max_length=1024,  # Reduced from 2048 to save memory

    # Logging & checkpointing
    logging_steps=10,
    save_strategy="steps",
    save_steps=100,
    save_total_limit=2,

    # Evaluation - disable to save memory and time
    eval_strategy="no",

    # Optimization
    warmup_ratio=0.1,
    lr_scheduler_type="cosine",
    gradient_checkpointing=True,
    bf16=True,

    # Monitoring
    report_to="trackio",
    project="qwen3-codeforces-sft",
    run_name="demo-1k-v2",
)

# LoRA configuration for efficient training
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
)

# Initialize and train
print("Initializing trainer with Qwen/Qwen3-0.6B...")
trainer = SFTTrainer(
    model="Qwen/Qwen3-0.6B",
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    processing_class=tokenizer,
    args=config,
    peft_config=peft_config,
)

print("Starting training...")
trainer.train()

print("Pushing to Hub...")
trainer.push_to_hub()

print("Complete! Model at: https://huggingface.co/Godsonntungi2/qwen3-0.6b-codeforces-sft")
print("View metrics at: https://huggingface.co/spaces/Godsonntungi2/trackio")