# Inspired by: https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py

from typing import TYPE_CHECKING, List, Optional

from ...data import get_dataset, split_dataset
from ...extras.constants import IGNORE_INDEX
from ...extras.ploting import plot_loss
from ...hparams import ModelArguments
from ...model import load_model, load_tokenizer
from ..utils import create_custom_optimzer, create_modelcard_and_push, create_ref_model
from .collator import DPODataCollatorWithPadding
from .trainer import CustomDPOTrainer


if TYPE_CHECKING:
    from transformers import Seq2SeqTrainingArguments, TrainerCallback

    from ...hparams import DataArguments, FinetuningArguments


def run_dpo(
    model_args: "ModelArguments",
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
    callbacks: Optional[List["TrainerCallback"]] = None,
):
    tokenizer = load_tokenizer(model_args)
    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
    data_collator = DPODataCollatorWithPadding(
        tokenizer=tokenizer,
        pad_to_multiple_of=8,
        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
    )

    # Create reference model
    if finetuning_args.ref_model is None and (not training_args.do_train):  # use the model itself
        ref_model = model
    else:
        ref_model = create_ref_model(model_args, finetuning_args)

    # Update arguments
    training_args.remove_unused_columns = False  # important for pairwise dataset

    # Initialize our Trainer
    optimizer = create_custom_optimzer(model, dataset, training_args, finetuning_args)
    trainer = CustomDPOTrainer(
        beta=finetuning_args.dpo_beta,
        loss_type=finetuning_args.dpo_loss,
        ftx_gamma=finetuning_args.dpo_ftx,
        model=model,
        ref_model=ref_model,
        args=training_args,
        tokenizer=tokenizer,
        data_collator=data_collator,
        callbacks=callbacks,
        optimizers=(optimizer, None),
        **split_dataset(dataset, data_args, training_args),
    )

    # Training
    if training_args.do_train:
        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
        trainer.save_model()
        trainer.log_metrics("train", train_result.metrics)
        trainer.save_metrics("train", train_result.metrics)
        trainer.save_state()
        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
            plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])

    # Evaluation
    if training_args.do_eval:
        metrics = trainer.evaluate(metric_key_prefix="eval")
        if id(model) == id(ref_model):  # unable to compute rewards without a reference model
            remove_keys = [key for key in metrics.keys() if "rewards" in key]
            for key in remove_keys:
                metrics.pop(key)
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    # Create model card
    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)
