# 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.

import gc
import itertools

import pytest
import torch
from accelerate.utils.memory import release_memory
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from transformers.testing_utils import backend_empty_cache, require_torch_accelerator, torch_device
from transformers.utils import is_peft_available

from trl import DPOConfig, DPOTrainer

from ..testing_utils import TrlTestCase, require_bitsandbytes, require_peft
from .testing_constants import DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS, GRADIENT_CHECKPOINTING_KWARGS, MODELS_TO_TEST


if is_peft_available():
    from peft import LoraConfig, PeftModel


@pytest.mark.slow
@require_torch_accelerator
@require_peft
class TestDPOTrainerSlow(TrlTestCase):
    def setup_method(self):
        self.dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
        self.peft_config = LoraConfig(
            lora_alpha=16,
            lora_dropout=0.1,
            r=8,
            bias="none",
            task_type="CAUSAL_LM",
        )
        self.max_length = 128

    def teardown_method(self):
        gc.collect()
        backend_empty_cache(torch_device)
        gc.collect()

    @parameterized.expand(list(itertools.product(MODELS_TO_TEST, DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS)))
    def test_dpo_bare_model(self, model_id, loss_type, pre_compute_logits):
        """
        A test that tests the simple usage of `DPOTrainer` using a bare model in full precision.
        """
        model = AutoModelForCausalLM.from_pretrained(model_id)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token

        training_args = DPOConfig(
            output_dir=self.tmp_dir,
            per_device_train_batch_size=2,
            max_steps=2,
            remove_unused_columns=False,
            gradient_accumulation_steps=2,
            learning_rate=9e-1,
            eval_strategy="steps",
            fp16=True,
            logging_strategy="no",
            report_to="none",
            beta=0.1,
            loss_type=loss_type,
            precompute_ref_log_probs=pre_compute_logits,
            max_length=self.max_length,
        )

        # dpo train lora model
        trainer = DPOTrainer(
            model=model,
            ref_model=None,
            args=training_args,
            train_dataset=self.dataset["train"],
            eval_dataset=self.dataset["test"],
            processing_class=tokenizer,
        )

        # train the model
        trainer.train()

        # save trained model or adapter
        trainer.save_model()

        release_memory(model, trainer)

    @parameterized.expand(
        list(
            itertools.product(
                MODELS_TO_TEST,
                DPO_LOSS_TYPES,
                DPO_PRECOMPUTE_LOGITS,
                GRADIENT_CHECKPOINTING_KWARGS,
            )
        )
    )
    @require_peft
    def test_dpo_peft_model(self, model_id, loss_type, pre_compute_logits, gradient_checkpointing_kwargs):
        """
        A test that tests the simple usage of `DPOTrainer` using a peft model in full precision + different scenarios
        of gradient checkpointing.
        """
        model = AutoModelForCausalLM.from_pretrained(model_id)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token

        training_args = DPOConfig(
            output_dir=self.tmp_dir,
            per_device_train_batch_size=2,
            max_steps=2,
            remove_unused_columns=False,
            gradient_accumulation_steps=2,
            learning_rate=9e-1,
            eval_strategy="steps",
            fp16=True,
            logging_strategy="no",
            report_to="none",
            gradient_checkpointing=True,
            gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
            generate_during_eval=False,
            loss_type=loss_type,
            precompute_ref_log_probs=pre_compute_logits,
            beta=0.1,
            max_length=self.max_length,
        )

        # dpo train lora model
        trainer = DPOTrainer(
            model=model,
            ref_model=None,
            args=training_args,
            train_dataset=self.dataset["train"],
            eval_dataset=self.dataset["test"],
            processing_class=tokenizer,
            peft_config=self.peft_config,
        )

        assert isinstance(trainer.model, PeftModel)
        assert trainer.ref_model is None

        # train the model
        trainer.train()

        # save trained model or adapter
        trainer.save_model()

        release_memory(model, trainer)

    @parameterized.expand(
        list(
            itertools.product(
                MODELS_TO_TEST,
                DPO_LOSS_TYPES,
                DPO_PRECOMPUTE_LOGITS,
                GRADIENT_CHECKPOINTING_KWARGS,
            )
        )
    )
    @require_bitsandbytes
    @require_peft
    def test_dpo_peft_model_qlora(self, model_id, loss_type, pre_compute_logits, gradient_checkpointing_kwargs):
        """
        A test that tests the simple usage of `DPOTrainer` using QLoRA + different scenarios of gradient checkpointing.
        """
        quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)

        model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token

        training_args = DPOConfig(
            output_dir=self.tmp_dir,
            per_device_train_batch_size=2,
            max_steps=2,
            remove_unused_columns=False,
            gradient_accumulation_steps=2,
            learning_rate=9e-1,
            eval_strategy="steps",
            fp16=True,
            logging_strategy="no",
            report_to="none",
            gradient_checkpointing=True,
            gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
            beta=0.1,
            generate_during_eval=False,
            loss_type=loss_type,
            precompute_ref_log_probs=pre_compute_logits,
            max_length=self.max_length,
        )

        # dpo train lora model
        trainer = DPOTrainer(
            model=model,
            ref_model=None,
            args=training_args,
            train_dataset=self.dataset["train"],
            eval_dataset=self.dataset["test"],
            processing_class=tokenizer,
            peft_config=self.peft_config,
        )

        assert isinstance(trainer.model, PeftModel)
        assert trainer.ref_model is None

        # train the model
        trainer.train()

        # save trained model or adapter
        trainer.save_model()

        release_memory(model, trainer)
