# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's TRL library.
# https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/dpo_trainer.py
#
# 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.

from typing import TYPE_CHECKING

import torch
from ktransformers.sft.lora import KTrainer  # type: ignore
from typing_extensions import override

from ..trainer_utils import get_batch_logps, nested_detach
from .trainer import CustomDPOTrainer


if TYPE_CHECKING:
    from transformers import PreTrainedModel


class KDPOTrainer(KTrainer, CustomDPOTrainer):
    @override
    def concatenated_forward(
        self, model: "PreTrainedModel", batch: dict[str, "torch.Tensor"], is_ref_model: bool = False
    ) -> tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
        r"""Compute the sum log probabilities of the labels under given logits if loss_type is not IPO, ORPO or SimPO.

        Otherwise the average log probabilities.
        """
        if self.finetuning_args.use_ref_model:
            batch = nested_detach(batch, clone=True)  # avoid error

        labels = batch.pop("labels")  # dpo do not need compute loss in forward
        all_logits: torch.Tensor = model(**batch, return_dict=True, use_cache=False).logits.to(torch.float32)
        all_logits = all_logits.to("cpu")
        labels = labels.to(all_logits.device)
        all_logps, valid_length = get_batch_logps(
            logits=all_logits, labels=labels, ld_alpha=(self.ld_alpha if not is_ref_model else None)
        )
        if self.loss_type in ["ipo", "orpo", "simpo"]:
            all_logps = all_logps / valid_length

        batch_size = batch["input_ids"].size(0) // 2
        chosen_logps, rejected_logps = all_logps.split(batch_size, dim=0)
        chosen_logits, rejected_logits = all_logits.split(batch_size, dim=0)
        chosen_length, _ = valid_length.split(batch_size, dim=0)

        if self.loss_type in ["ipo", "orpo", "simpo"]:
            return chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_logps
        else:
            return chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_logps / chosen_length