# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# /// script
# dependencies = [
#     "trl @ git+https://github.com/huggingface/trl.git",
#     "peft",
# ]
# ///

"""
Run the CPO training script with the following command with some example arguments.
In general, the optimal configuration for CPO will be similar to that of DPO:

# regular:
python examples/scripts/cpo.py \
    --dataset_name trl-lib/ultrafeedback_binarized \
    --model_name_or_path=gpt2 \
    --per_device_train_batch_size 4 \
    --max_steps 1000 \
    --learning_rate 8e-6 \
    --gradient_accumulation_steps 1 \
    --eval_steps 500 \
    --output_dir="gpt2-aligned-cpo" \
    --warmup_steps 150 \
    --report_to wandb \
    --bf16 \
    --logging_first_step \
    --no_remove_unused_columns

# peft:
python examples/scripts/cpo.py \
    --dataset_name trl-lib/ultrafeedback_binarized \
    --model_name_or_path=gpt2 \
    --per_device_train_batch_size 4 \
    --max_steps 1000 \
    --learning_rate 8e-5 \
    --gradient_accumulation_steps 1 \
    --eval_steps 500 \
    --output_dir="gpt2-lora-aligned-cpo" \
    --optim rmsprop \
    --warmup_steps 150 \
    --report_to wandb \
    --bf16 \
    --logging_first_step \
    --no_remove_unused_columns \
    --use_peft \
    --lora_r=16 \
    --lora_alpha=16
"""

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser

from trl import CPOConfig, CPOTrainer, ModelConfig, ScriptArguments, get_peft_config
from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE


if __name__ == "__main__":
    parser = HfArgumentParser((ScriptArguments, CPOConfig, ModelConfig))
    script_args, training_args, model_args = parser.parse_args_into_dataclasses()

    ################
    # Model & Tokenizer
    ################
    model = AutoModelForCausalLM.from_pretrained(
        model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
    )
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    ################
    # Dataset
    ################
    dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
    if tokenizer.chat_template is None:
        tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE

    ################
    # Training
    ################
    trainer = CPOTrainer(
        model,
        args=training_args,
        train_dataset=dataset[script_args.dataset_train_split],
        eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
        processing_class=tokenizer,
        peft_config=get_peft_config(model_args),
    )

    # train and save the model
    trainer.train()

    # Save and push to hub
    trainer.save_model(training_args.output_dir)
    if training_args.push_to_hub:
        trainer.push_to_hub(dataset_name=script_args.dataset_name)