# 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 os
import re
import textwrap
import warnings
from contextlib import nullcontext
from functools import wraps
from pathlib import Path
from typing import Any, Callable, Optional, Union

import jinja2
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from accelerate import logging
from accelerate.utils import broadcast_object_list, gather_object, is_peft_model
from datasets import Dataset
from packaging import version
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.utils.data import DataLoader, IterableDataset
from transformers import (
    AutoModelForCausalLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollator,
    GenerationConfig,
    PreTrainedModel,
    PreTrainedTokenizerBase,
    ProcessorMixin,
    Trainer,
    TrainerCallback,
)
from transformers.models.auto.modeling_auto import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES
from transformers.trainer_utils import EvalPrediction, seed_worker
from transformers.training_args import OptimizerNames
from transformers.utils import (
    is_flash_attn_2_available,
    is_peft_available,
    is_sagemaker_mp_enabled,
)

from ..data_utils import apply_chat_template, is_conversational, maybe_apply_chat_template
from ..extras.profiling import profiling_context
from ..extras.vllm_client import VLLMClient
from ..import_utils import is_vllm_available
from ..models import (
    create_reference_model,
    prepare_deepspeed,
    prepare_fsdp,
    prepare_peft_model,
    unwrap_model_for_generation,
)
from .base_trainer import BaseTrainer
from .judges import BasePairwiseJudge
from .online_dpo_config import OnlineDPOConfig
from .utils import (
    SIMPLE_CHAT_TEMPLATE,
    DPODataCollatorWithPadding,
    disable_dropout_in_model,
    empty_cache,
    ensure_master_addr_port,
    pad,
    truncate_right,
)


if is_peft_available():
    from peft import PeftConfig, PeftModel


if is_sagemaker_mp_enabled():
    from smdistributed.modelparallel import __version__ as SMP_VERSION

    IS_SAGEMAKER_MP_POST_1_10 = version.parse(SMP_VERSION) >= version.parse("1.10")

else:
    IS_SAGEMAKER_MP_POST_1_10 = False


if is_vllm_available():
    from vllm import LLM, SamplingParams
    from vllm.sampling_params import GuidedDecodingParams


logger = logging.get_logger(__name__)

# What we call a reward function is a callable that takes a list of prompts and completions and returns a list of
# rewards. When it's a string, it's a model ID, so it's loaded as a pretrained model.
RewardFunc = Union[str, PreTrainedModel, Callable[[list, list], list[float]]]


class OnlineDPOTrainer(BaseTrainer):
    r"""
    Initialize OnlineDPOTrainer.

    Args:
        model (`Union[str, nn.Module, PreTrainedModel]`):
            Model to be trained. Can be either:

            - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a
              path to a *directory* containing model weights saved using
              [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
              using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in
              `args.model_init_kwargs`.
            - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported.
        ref_model ([`~transformers.PreTrainedModel`] or `torch.nn.Module` or `None`):
            The reference model to use for training. If None is specified, the reference model will be created from the
            model.
        judge ([`BasePairwiseJudge`]):
            The judge to use for pairwise comparison of model completions.
        reward_funcs (`Union[RewardFunc, list[RewardFunc]]`, *optional*):
            Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward
            functions with the prompts and completions and sum the rewards. Can be either:

            - A single reward function: Can be a string (path to model), a [`~transformers.PreTrainedModel`], or a
              custom callable function.
            - A list of reward functions: Must all be of compatible types.

            Note: Only one of `judge`, or `reward_funcs` should be provided.
        args ([`OnlineDPOConfig`]):
            The online DPO config arguments to use for training.
        data_collator ([`~transformers.DataCollator`]):
            The data collator to use for training. If None is specified, the default data collator
            ([`DPODataCollatorWithPadding`]) will be used which will pad the sequences to the maximum length of the
            sequences in the batch, given a dataset of paired sequences.
        train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
            The dataset to use for training.
        eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`):
            The dataset to use for evaluation.
        processing_class ([`~transformers.PreTrainedTokenizerBase`] or [`~transformers.ProcessorMixin`], *optional*):
            Processing class used to process the data. If provided, will be used to automatically process the inputs
            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
            reuse the fine-tuned model.
        reward_processing_classes ([`~transformers.PreTrainedTokenizerBase`] or `list[PreTrainedTokenizerBase]`, *optional*):
            Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either:

            - A single processing class: Used when `reward_funcs` contains only one reward function.
            - A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`.

            If set to `None`, the tokenizer for each model-based reward function is automatically loaded using
            [`~transformers.AutoTokenizer.from_pretrained`].
        peft_config ([`~peft.PeftConfig`], *optional*):
            PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
            The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to
            metric values.
        callbacks (`list[transformers.TrainerCallback]`):
            The callbacks to use for training.
        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
            The optimizer and scheduler to use for training.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
            The function to use to preprocess the logits before computing the metrics.

        reward_model:

            <Deprecated version="0.22.0">

            This parameter is deprecated and will be removed in version 0.25.0. Use `reward_funcs` instead.

            </Deprecated>
    """

    _tag_names = ["trl", "online-dpo"]
    _name = "Online DPO"
    _paper = {
        "title": "Direct Language Model Alignment from Online AI Feedback",
        "id": "2402.04792",
        # docstyle-ignore
        "citation": textwrap.dedent("""\
            @article{guo2024direct,
                title        = {{Direct Language Model Alignment from Online AI Feedback}},
                author       = {Shangmin Guo and Biao Zhang and Tianlin Liu and Tianqi Liu and Misha Khalman and Felipe Llinares and Alexandre Ram{\'{e}} and Thomas Mesnard and Yao Zhao and Bilal Piot and Johan Ferret and Mathieu Blondel},
                year         = 2024,
                eprint       = {arXiv:2402.04792}
            }"""),
    }

    def __init__(
        self,
        model: Union[PreTrainedModel, nn.Module, str],
        ref_model: Union[PreTrainedModel, nn.Module, None] = None,
        reward_funcs: Optional[Union[RewardFunc, list[RewardFunc]]] = None,
        judge: Optional[BasePairwiseJudge] = None,
        args: Optional[OnlineDPOConfig] = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
        eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None,
        processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None,
        reward_processing_classes: Optional[Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]] = None,
        peft_config: Optional["PeftConfig"] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
        callbacks: Optional[list[TrainerCallback]] = None,
        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        # Deprecated parameters
        reward_model: Optional[Union[PreTrainedModel, nn.Module]] = None,
        reward_processing_class: Optional[PreTrainedTokenizerBase] = None,
    ) -> None:
        if not os.environ.get("TRL_EXPERIMENTAL_SILENCE"):
            warnings.warn(
                "This trainer will soon be moved to trl.experimental and is a candidate for removal. If you rely on "
                "it and want it to remain, please share your comments here: "
                "https://github.com/huggingface/trl/issues/4223. Silence this warning by setting environment variable "
                "TRL_EXPERIMENTAL_SILENCE=1."
            )
        if ref_model is model:
            raise ValueError(
                "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the "
                "same as `model`, either omit the `ref_model` argument or pass `None`."
            )

        self.ref_model = ref_model

        # Handle deprecated parameters for backward compatibility
        if reward_model is not None:
            warnings.warn(
                "The `reward_model` parameter is deprecated and will be removed in version 0.25.0. "
                "Please use `reward_funcs` instead. For example, change `reward_model=model` to `reward_funcs=model`.",
            )
            # Convert old reward_model to new reward_funcs format
            if reward_funcs is None:
                reward_funcs = reward_model
            else:
                warnings.warn(
                    "Both `reward_model` and `reward_funcs` are provided. Using `reward_funcs` and ignoring "
                    "`reward_model`.",
                )

        if reward_processing_class is not None:
            warnings.warn(
                "The `reward_processing_class` parameter is deprecated and will be removed in version 0.25.0. "
                "Please use `reward_processing_classes` instead. For example, change "
                "`reward_processing_class=tokenizer` to `reward_processing_classes=tokenizer`.",
            )
            # Convert old reward_processing_class to new reward_processing_classes format
            if reward_processing_classes is None:
                reward_processing_classes = reward_processing_class
            else:
                warnings.warn(
                    "Both `reward_processing_class` and `reward_processing_classes` are provided. Using "
                    "`reward_processing_classes` and ignoring `reward_processing_class`.",
                )

        # Validate reward configuration - must have exactly one of: judge, or reward_funcs
        reward_configs = sum(x is not None for x in [judge, reward_funcs])
        if reward_configs == 0:
            raise ValueError("One of `judge` or `reward_funcs` must be provided.")
        elif reward_configs > 1:
            if judge is not None:
                logger.warning(
                    "Both `judge` and `reward_funcs` are provided. Using `judge` and ignoring `reward_funcs`.",
                    UserWarning,
                )
                reward_funcs = None
        self.judge = judge

        # Handle reward_funcs
        if reward_funcs is not None:
            if not isinstance(reward_funcs, list):
                reward_funcs = [reward_funcs]
            self.reward_func_names = []

            # Process reward functions (convert strings to models, collect names)
            model_init_kwargs = args.model_init_kwargs or {}
            for i, reward_func in enumerate(reward_funcs):
                if isinstance(reward_func, str):
                    # Load model from string path
                    reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained(
                        reward_func, num_labels=1, **model_init_kwargs
                    )
                if isinstance(reward_funcs[i], nn.Module):
                    self.reward_func_names.append(reward_funcs[i].config._name_or_path.split("/")[-1])
                else:
                    self.reward_func_names.append(reward_funcs[i].__name__)
            self.reward_funcs = reward_funcs

            # Handle reward processing classes for reward_funcs
            if reward_processing_classes is None:
                reward_processing_classes = [None] * len(reward_funcs)
            elif not isinstance(reward_processing_classes, list):
                reward_processing_classes = [reward_processing_classes]
            else:
                if len(reward_processing_classes) != len(reward_funcs):
                    raise ValueError(
                        "The number of reward processing classes must match the number of reward functions."
                    )

            self.reward_processing_classes = []
            for reward_processing_class_i, reward_func in zip(reward_processing_classes, reward_funcs):
                if isinstance(reward_func, PreTrainedModel):
                    if reward_processing_class_i is None:
                        reward_processing_class_i = AutoTokenizer.from_pretrained(reward_func.config._name_or_path)
                    if reward_processing_class_i.pad_token_id is None:
                        reward_processing_class_i.pad_token = reward_processing_class_i.eos_token
                    # Set pad token ID on reward model config
                    reward_func.config.pad_token_id = reward_processing_class_i.pad_token_id
                self.reward_processing_classes.append(reward_processing_class_i)
        else:
            self.reward_funcs = None
            self.reward_func_names = []
            self.reward_processing_classes = []

        # Handle reward_weights
        if reward_funcs is not None:
            if args.reward_weights is not None:
                if len(args.reward_weights) != len(self.reward_funcs):
                    raise ValueError(
                        f"Number of reward weights ({len(args.reward_weights)}) must match number of reward "
                        f"functions ({len(self.reward_funcs)})"
                    )
                self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32)
            else:
                self.reward_weights = torch.ones(len(self.reward_funcs), dtype=torch.float32)
        else:
            self.reward_weights = None

        if args.missing_eos_penalty is not None and reward_funcs is None and judge is None:
            # Check if this is the old reward_model case
            if reward_model is not None:
                logger.warning(
                    "The `missing_eos_penalty` parameter is deprecated when used with the deprecated `reward_model` parameter. "
                    "Please use `reward_funcs` instead of `reward_model` to continue using this feature.",
                    FutureWarning,
                    stacklevel=2,
                )
            else:
                raise ValueError("`missing_eos_penalty` is only supported when `reward_funcs` is provided.")

        if args is None:
            raise ValueError("`args` must be provided.")

        # Check that the processing_class is provided
        if processing_class is None:
            raise ValueError("`processing_class` must be provided.")

        model_init_kwargs = args.model_init_kwargs or {}
        if isinstance(model, str):
            model_id = model

            # Handle dtype in model_init_kwargs
            dtype = model_init_kwargs.get("dtype")
            if isinstance(dtype, torch.dtype) or dtype == "auto" or dtype is None:
                pass
            elif isinstance(dtype, str):
                dtype = getattr(torch, dtype)
                model_init_kwargs["dtype"] = dtype
            else:
                raise ValueError(
                    "Invalid `dtype` passed to `OnlineDPOConfig`. Expected either 'auto' or a string "
                    f"representing a `torch.dtype` (e.g., 'float32'), but got {dtype}."
                )

            model = AutoModelForCausalLM.from_pretrained(model_id, **model_init_kwargs)
        else:
            if args.model_init_kwargs is not None:
                raise ValueError(
                    "You passed `model_init_kwargs` to the `OnlineDPOConfig`, but your model is already instantiated. "
                    "This argument can only be used when the `model` argument is a string."
                )
        self.is_encoder_decoder = model.config.is_encoder_decoder
        self.is_vision_model = model.config.model_type in MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES.keys()

        if peft_config is not None or (is_peft_available() and isinstance(model, PeftModel)):
            model = prepare_peft_model(model, peft_config, args)

        # Enable gradient checkpointing if requested
        if args.gradient_checkpointing:
            model = self._enable_gradient_checkpointing(model, args)

        # Disable dropout in the model and reference model
        if args.disable_dropout:
            disable_dropout_in_model(model)
            if self.ref_model is not None:
                disable_dropout_in_model(self.ref_model)

        # Handle the ref_model
        # Usually, the user wants the ref model to be the initial version of the model. When using PEFT, it's easy to
        # get the ref model, as it's just the model with a disabled adapter. When not using PEFT, we need to create
        # the ref model from the model by copying it and disable the gradients and set it in evaluation mode.
        if ref_model is None:  # No ref model provided, the most common case
            if peft_config is None:
                self.ref_model = create_reference_model(model)  # copy, disable gradients, set eval mode
            else:
                self.ref_model = None  # we don't need a ref model here, we can just disable the adapter.
        else:  # rare case, the user provided a ref model
            self.ref_model = ref_model
            self.ref_model.eval()

        # Disable the gradient and set the reward model in eval mode
        if reward_funcs is not None:
            for reward_func in reward_funcs:
                if isinstance(reward_func, PreTrainedModel):
                    reward_func.eval()

        self.max_length = args.max_length

        self.stats = {
            "objective/kl": [],
            "objective/entropy": [],
            "objective/non_score_reward": [],
            "rewards/chosen": [],
            "rewards/rejected": [],
            "rewards/accuracies": [],
            "rewards/margins": [],
            "logps/chosen": [],
            "logps/rejected": [],
            "val/contain_eos_token": [],
            "beta": [],
        }
        if self.reward_funcs is not None:
            self.stats["objective/rlhf_reward"] = []
            self.stats["objective/scores_margin"] = []
            self.stats["objective/scores"] = []

        # Store generation parameters for later use
        self.use_vllm = args.use_vllm
        self.num_generations = 2  # Generate 2 completions per prompt for Online DPO
        self.temperature = args.temperature
        self.top_p = args.top_p
        self.top_k = args.top_k
        self.min_p = args.min_p
        self.repetition_penalty = args.repetition_penalty
        self.use_transformers_paged = args.use_transformers_paged
        self.vllm_mode = args.vllm_mode if args.use_vllm else None
        self.vllm_gpu_memory_utilization = args.vllm_gpu_memory_utilization
        self.vllm_tensor_parallel_size = args.vllm_tensor_parallel_size
        self.vllm_model_impl = args.vllm_model_impl

        # Handle pad token for processors or tokenizers
        if isinstance(processing_class, ProcessorMixin):
            tokenizer = processing_class.tokenizer
        elif isinstance(processing_class, PreTrainedTokenizerBase):
            tokenizer = processing_class
        else:
            raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`")

        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        self.pad_token = tokenizer.pad_token
        self.pad_token_id = tokenizer.pad_token_id
        self.eos_token_id = tokenizer.eos_token_id

        # Vision tokens for VLM support
        self.image_token_id = getattr(processing_class, "image_token_id", None)
        self.vision_start_token_id = getattr(processing_class, "vision_start_token_id", None)
        self.vision_end_token_id = getattr(processing_class, "vision_end_token_id", None)
        # Get the image token string for token collapsing
        self.image_token = None
        if self.image_token_id is not None:
            self.image_token = tokenizer.decode([self.image_token_id])

        # Define the collator if not provided
        if data_collator is None:
            data_collator = DPODataCollatorWithPadding(pad_token_id=self.pad_token_id)

        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
        # input tensor associated with the key "input_ids". However, in Online DPO, the sampled data does not include
        # the "input_ids" key. As a result, the trainer issues the warning: "Could not estimate the number of tokens
        # of the input, floating-point operations will not be computed." To suppress this warning, we set the
        # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate
        # that the warning has already been issued.
        model.warnings_issued["estimate_tokens"] = True

        super().__init__(
            model=model,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )

        # Add tags for models that have been loaded with the correct transformers version
        if hasattr(self.model, "add_model_tags"):
            self.model.add_model_tags(self._tag_names)

        self._beta = args.beta

        # Set up generation configuration and vLLM after super().__init__
        if self.use_vllm:
            if not is_vllm_available():
                raise ImportError(
                    "vLLM is not available and `use_vllm` is set to True. Please install vLLM with "
                    "`pip install trl[vllm]` to use it."
                )

            if self.vllm_mode == "server":
                if self.accelerator.is_main_process:
                    if args.vllm_server_base_url is not None:
                        base_url = args.vllm_server_base_url
                    else:
                        base_url = f"http://{args.vllm_server_host}:{args.vllm_server_port}"
                    self.vllm_client = VLLMClient(base_url=base_url, connection_timeout=args.vllm_server_timeout)
                    self.vllm_client.init_communicator(device=torch.cuda.current_device())
                else:
                    self.vllm_client = None
            elif self.vllm_mode == "colocate":
                # vLLM dynamically adjusts the size of the key-value cache based on available GPU memory at instantiation.
                # A larger cache size improves speed, so we would expect gpu_memory_utilization=1.
                # However, at this stage, the optimizer's weights are not yet loaded onto the GPU; they will be loaded
                # after the first optimizer step and remain in GPU memory throughout training. So we must reserve enough
                # space for them.
                # Configure vLLM parameters
                vllm_kwargs = {
                    "model": model.name_or_path,
                    "tensor_parallel_size": self.vllm_tensor_parallel_size,
                    "gpu_memory_utilization": self.vllm_gpu_memory_utilization,
                    "model_impl": self.vllm_model_impl,
                    "max_num_seqs": self.args.per_device_train_batch_size * self.vllm_tensor_parallel_size,
                    "max_model_len": args.max_length + args.max_new_tokens,  # max_length includes prompt + completion
                    "distributed_executor_backend": "external_launcher",
                    # Feed identical seed for tp groups to ensure sampling results are the same across workers
                    "seed": self.accelerator.process_index // self.vllm_tensor_parallel_size,
                    # Latest vLLM v1 memory profiler is misled by the high default value (i.e., 32768)
                    "max_num_batched_tokens": 4096,
                }

                # vLLM requires the environment variables to be set for distributed training.
                os.environ["RANK"] = str(self.accelerator.process_index)
                os.environ["LOCAL_RANK"] = str(self.accelerator.local_process_index)
                os.environ["WORLD_SIZE"] = str(self.accelerator.num_processes)
                # Ensure distributed rendezvous variables are set without colliding across concurrent runs
                ensure_master_addr_port()

                self.llm = LLM(**vllm_kwargs)
            else:
                raise ValueError(f"vllm_mode must be either 'server' or 'colocate', got '{self.vllm_mode}'.")
            # vLLM specific sampling arguments
            self.guided_decoding_regex = args.vllm_guided_decoding_regex
            self._last_loaded_step = -1  # tag to avoid useless loading during grad accumulation

            # Set up vLLM generation config
            generation_params = {
                "n": 2,  # 2 generations per prompt for Online DPO
                "repetition_penalty": self.repetition_penalty,
                "temperature": self.temperature,
                "top_p": self.top_p,
                "top_k": -1 if self.top_k is None else self.top_k,
                "min_p": 0.0 if self.min_p is None else self.min_p,
                "max_tokens": args.max_new_tokens,
                "detokenize": False,  # to avoid vllm to decode (we don't need it)
            }
            if args.generation_kwargs is not None:
                generation_params.update(args.generation_kwargs)
            if self.guided_decoding_regex:
                generation_params["guided_decoding"] = GuidedDecodingParams(regex=self.guided_decoding_regex)
            self.generation_config = SamplingParams(**generation_params)

            # When using vLLM, the main process is responsible for loading the model weights. This can cause process
            # desynchronization and seems to lead to DeepSpeed hanging during initialization. To prevent this, we
            # synchronize all processes after vLLM has been fully initialized.
            self.accelerator.wait_for_everyone()
        else:
            # Set up transformers generation config
            generation_kwargs = {
                "max_new_tokens": args.max_new_tokens,
                "do_sample": True,
                "pad_token_id": self.pad_token_id,
                "bos_token_id": tokenizer.bos_token_id,
                "eos_token_id": self.eos_token_id,
                "temperature": self.temperature,
                "top_k": self.top_k,
                "top_p": self.top_p,
                "repetition_penalty": self.repetition_penalty,
                "use_cache": True if not self.args.gradient_checkpointing else False,
            }
            # Add min_p if supported
            if self.min_p is not None:
                generation_kwargs["min_p"] = self.min_p
            if args.generation_kwargs is not None:
                generation_kwargs.update(args.generation_kwargs)
            # Remove None values
            generation_kwargs = {k: v for k, v in generation_kwargs.items() if v is not None}
            self.generation_config = GenerationConfig(**generation_kwargs)

        if self.ref_model is not None:
            if self.is_deepspeed_enabled:
                self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
            elif self.is_fsdp_enabled:
                self.ref_model = prepare_fsdp(self.ref_model, self.accelerator)
            else:
                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
        if self.reward_funcs is not None:
            for i, reward_func in enumerate(self.reward_funcs):
                if isinstance(reward_func, PreTrainedModel):
                    if self.is_deepspeed_enabled:
                        self.reward_funcs[i] = prepare_deepspeed(reward_func, self.accelerator)
                    else:
                        # set device placement to True to make `prepare_model` move `reward_func` to device when using fsdp
                        self.reward_funcs[i] = self.accelerator.prepare_model(
                            reward_func, evaluation_mode=True, device_placement=True
                        )

    @property
    def beta(self):
        if isinstance(self._beta, list):
            epoch = self.state.epoch
            return self._beta[epoch] if epoch < len(self._beta) else self._beta[-1]
        else:
            return self._beta

    @staticmethod
    def tokenize_row(feature, is_encoder_decoder: bool, tokenizer: PreTrainedTokenizerBase) -> dict[str, Any]:
        """Tokenize a single row from a DPO specific dataset."""
        if not is_encoder_decoder:
            batch = tokenizer(feature["prompt"], add_special_tokens=False)
            # Add BOS token to head of prompt. Avoid adding if it's already there
            if tokenizer.bos_token_id is not None:
                prompt_len_input_ids = len(batch["input_ids"])
                if prompt_len_input_ids == 0 or tokenizer.bos_token_id != batch["input_ids"][0]:
                    batch["input_ids"] = [tokenizer.bos_token_id] + batch["input_ids"]
                    batch["attention_mask"] = [1] + batch["attention_mask"]
        else:
            batch = tokenizer(feature["prompt"], add_special_tokens=True)
        batch = {f"prompt_{key}": value for key, value in batch.items()}
        return batch

    # Same as Trainer.get_train_dataloader but skip the "remove_unused_columns".
    @wraps(Trainer.get_train_dataloader)
    def get_train_dataloader(self) -> DataLoader:
        if self.train_dataset is None:
            raise ValueError("Trainer: training requires a train_dataset.")

        train_dataset = self.train_dataset
        data_collator = self.data_collator
        dataloader_params = {
            "batch_size": self._train_batch_size,
            "collate_fn": data_collator,
            "num_workers": self.args.dataloader_num_workers,
            "pin_memory": self.args.dataloader_pin_memory,
            "persistent_workers": self.args.dataloader_persistent_workers,
        }

        if not isinstance(train_dataset, torch.utils.data.IterableDataset):
            dataloader_params["sampler"] = self._get_train_sampler()
            dataloader_params["drop_last"] = self.args.dataloader_drop_last
            dataloader_params["worker_init_fn"] = seed_worker
            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor

        return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))

    # Same as Trainer.get_eval_dataloader but skip the "remove_unused_columns".
    @wraps(Trainer.get_eval_dataloader)
    def get_eval_dataloader(self, eval_dataset: Optional[Union[str, Dataset]] = None) -> DataLoader:
        if eval_dataset is None and self.eval_dataset is None:
            raise ValueError("Trainer: evaluation requires an eval_dataset.")

        # If we have persistent workers, don't do a fork bomb especially as eval datasets
        # don't change during training
        dataloader_key = eval_dataset if isinstance(eval_dataset, str) else "eval"
        if (
            hasattr(self, "_eval_dataloaders")
            and dataloader_key in self._eval_dataloaders
            and self.args.dataloader_persistent_workers
        ):
            return self.accelerator.prepare(self._eval_dataloaders[dataloader_key])

        eval_dataset = (
            self.eval_dataset[eval_dataset]
            if isinstance(eval_dataset, str)
            else eval_dataset
            if eval_dataset is not None
            else self.eval_dataset
        )
        data_collator = self.data_collator

        dataloader_params = {
            "batch_size": self.args.eval_batch_size,
            "collate_fn": data_collator,
            "num_workers": self.args.dataloader_num_workers,
            "pin_memory": self.args.dataloader_pin_memory,
            "persistent_workers": self.args.dataloader_persistent_workers,
        }

        if not isinstance(eval_dataset, torch.utils.data.IterableDataset):
            dataloader_params["sampler"] = self._get_eval_sampler(eval_dataset)
            dataloader_params["drop_last"] = self.args.dataloader_drop_last
            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor

        # accelerator.free_memory() will destroy the references, so
        # we need to store the non-prepared version
        eval_dataloader = DataLoader(eval_dataset, **dataloader_params)
        if self.args.dataloader_persistent_workers:
            if hasattr(self, "_eval_dataloaders"):
                self._eval_dataloaders[dataloader_key] = eval_dataloader
            else:
                self._eval_dataloaders = {dataloader_key: eval_dataloader}

        return self.accelerator.prepare(eval_dataloader)

    def _enable_gradient_checkpointing(self, model: PreTrainedModel, args: OnlineDPOConfig) -> PreTrainedModel:
        """Enables gradient checkpointing for the model."""
        # Ensure use_cache is disabled
        model.config.use_cache = False

        # Enable gradient checkpointing on the base model for PEFT
        if is_peft_model(model):
            model.base_model.gradient_checkpointing_enable()
        # Enable gradient checkpointing for non-PEFT models
        else:
            model.gradient_checkpointing_enable()

        gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
        use_reentrant = (
            "use_reentrant" not in gradient_checkpointing_kwargs or gradient_checkpointing_kwargs["use_reentrant"]
        )

        if use_reentrant:
            model.enable_input_require_grads()

        return model

    def _generate_vllm(self, prompts, images=None):
        eos_token_id = self.eos_token_id
        pad_token_id = self.pad_token_id

        # Generate completion_ids and prompt_ids based on mode
        if self.vllm_mode == "server":
            completion_ids, prompt_ids = self._generate_vllm_server(prompts, images)
        elif self.vllm_mode == "colocate":
            completion_ids, prompt_ids = self._generate_vllm_colocate(prompts, images)

        # Shared padding, masking, and tensor conversion logic
        max_prompt_length = max(len(ids) for ids in prompt_ids)
        prompt_mask = [[0] * (max_prompt_length - len(ids)) + [1] * len(ids) for ids in prompt_ids]
        prompt_ids = [[pad_token_id] * (max_prompt_length - len(ids)) + ids for ids in prompt_ids]
        max_tokens = self.generation_config.max_tokens
        completion_mask = [[1] * len(ids) + [0] * (max_tokens - len(ids)) for ids in completion_ids]
        completion_ids = [
            ids + [eos_token_id] if ids[-1] != eos_token_id and len(ids) < max_tokens else ids
            for ids in completion_ids
        ]
        completion_ids = [ids + [pad_token_id] * (max_tokens - len(ids)) for ids in completion_ids]

        # Convert to tensors
        prompt_ids = torch.tensor(prompt_ids, device=self.accelerator.device)
        prompt_mask = torch.tensor(prompt_mask, device=self.accelerator.device)
        completion_ids = torch.tensor(completion_ids, device=self.accelerator.device)
        completion_mask = torch.tensor(completion_mask, device=self.accelerator.device)

        return prompt_ids, prompt_mask, completion_ids, completion_mask

    def _generate_vllm_server(self, prompts, images=None):
        """Generate completions using vLLM server mode"""
        has_images = images is not None

        # Update vLLM server weights if needed
        if hasattr(self, "_last_loaded_step") and self.state.global_step != self._last_loaded_step:
            self._move_model_to_vllm()
            self._last_loaded_step = self.state.global_step
        elif not hasattr(self, "_last_loaded_step"):
            self._move_model_to_vllm()
            self._last_loaded_step = self.state.global_step

        # Apply chat template if conversational
        if is_conversational({"prompt": prompts[0]}):
            prompts_text = [apply_chat_template({"prompt": p}, self.processing_class)["prompt"] for p in prompts]
        else:
            prompts_text = prompts
        # Gather all prompts to main process
        all_prompts = gather_object(prompts_text)
        if has_images:
            all_images = gather_object(images)

        if self.accelerator.is_main_process:
            # Since 'prompts' contains 'num_generations' duplicates, we first take unique prompts, and generate
            # num_generations outputs for each one. This is faster than generating outputs for each duplicate
            # prompt individually.
            ordered_set_of_prompts = all_prompts[:: self.num_generations]
            if has_images:
                ordered_set_of_images = all_images[:: self.num_generations]
            else:
                ordered_set_of_images = None
            completion_ids = self.vllm_client.generate(
                prompts=ordered_set_of_prompts,
                images=ordered_set_of_images,
                n=self.num_generations,
                repetition_penalty=self.repetition_penalty,
                temperature=self.temperature,
                top_p=self.top_p,
                top_k=-1 if self.top_k is None else self.top_k,
                min_p=0.0 if self.min_p is None else self.min_p,
                max_tokens=self.generation_config.max_tokens,
                guided_decoding_regex=self.guided_decoding_regex if hasattr(self, "guided_decoding_regex") else None,
                generation_kwargs=self.args.generation_kwargs,
            )
            # Flatten: each prompt generates 2 completions
            completion_ids = [[comp_id] for prompt_completions in completion_ids for comp_id in prompt_completions]
        else:
            completion_ids = [None] * (len(all_prompts) * 2)

        # Broadcast completions to all processes
        completion_ids = broadcast_object_list(completion_ids, from_process=0)

        # Each process takes its slice
        process_slice = slice(
            self.accelerator.process_index * len(prompts) * 2,
            (self.accelerator.process_index + 1) * len(prompts) * 2,
        )
        completion_ids = completion_ids[process_slice]

        # Create prompt_ids by tokenizing locally
        prompt_inputs = self.processing_class(
            text=prompts_text,
            return_tensors="pt",
            padding=True,
            padding_side="left",
            add_special_tokens=False,
        )
        prompt_ids = []
        for prompt_tokens in prompt_inputs["input_ids"]:
            prompt_ids.extend([prompt_tokens.tolist(), prompt_tokens.tolist()])  # 2 copies for 2 completions
        return completion_ids, prompt_ids

    def _generate_vllm_colocate(self, prompts, images=None):
        """Generate completions using vLLM colocate mode"""
        # Update model weights if needed - only after gradient accumulation completes
        if self.state.global_step != self._last_loaded_step:
            self._move_model_to_vllm()
            self._last_loaded_step = self.state.global_step

        # Apply chat template if conversational
        if is_conversational({"prompt": prompts[0]}):
            prompts_text = [apply_chat_template({"prompt": p}, self.processing_class)["prompt"] for p in prompts]
        else:
            prompts_text = prompts

        # Prepare vLLM inputs with images if available
        if images is not None:
            vllm_inputs = []
            for prompt, image in zip(prompts_text, images):
                if image is not None:
                    vllm_inputs.append({"prompt": prompt, "multi_modal_data": {"image": image}})
                else:
                    vllm_inputs.append(prompt)
        else:
            vllm_inputs = prompts_text

        outputs = self.llm.generate(vllm_inputs, self.generation_config, use_tqdm=False)

        completion_ids = [list(output.outputs[i].token_ids) for i in range(2) for output in outputs]
        prompt_ids = [list(output.prompt_token_ids) for _ in range(2) for output in outputs]

        return completion_ids, prompt_ids

    def _move_model_to_vllm(self):
        """Synchronize model weights to vLLM server with support for PEFT, DeepSpeed, and FSDP"""
        # For DeepSpeed ZeRO-3 and FSDP, we need to gather all parameters before operations
        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
        zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3
        if zero_stage_3:
            import deepspeed

            gather_if_zero3 = deepspeed.zero.GatheredParameters
        else:
            gather_if_zero3 = nullcontext

        if is_peft_model(self.model):
            # With PEFT and FSDP/DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as
            # merging adapters in a sharded manner is not supported.
            # TODO: does this work with FSDP?
            with gather_if_zero3(list(self.model.parameters())):
                self.model.merge_adapter()

                # Update vLLM weights while parameters are gathered
                if self.is_fsdp_enabled:  # note if using FSDP, gather_if_zero3 is nullcontext
                    # Update vLLM weights while parameters are gathered
                    # For PEFT with FSDP we need to use the memory efficient post-order traversal
                    fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None)
                    fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1
                    if fsdp_version == 1:
                        # use memory-efficient post-order traversal for FSDP
                        self._sync_fsdp1_params_to_vllm(self.model)
                    elif fsdp_version == 2:
                        self._sync_fsdp2_params_to_vllm(self.model)
                else:
                    # DeepSpeed ZeRO-3 with PEFT
                    for name, param in self.model.named_parameters():
                        # When using PEFT, we need to recover the original parameter name and discard some parameters
                        name = name.removeprefix("base_model.model.").replace(".base_layer", "")
                        if self.model.prefix in name:
                            continue
                        # When module to save, remove its prefix and discard the original module
                        if "original_module" in name:
                            continue
                        name = self._fix_param_name_to_vllm(name, extra_prefixes=["modules_to_save.default."])

                        if self.vllm_mode == "server" and self.accelerator.is_main_process:
                            self.vllm_client.update_named_param(name, param.data)
                        elif self.vllm_mode == "colocate":
                            llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
                            llm_model.load_weights([(name, param.data)])
                # Unmerge adapters while parameters are still gathered
                self.model.unmerge_adapter()
                # Parameters will automatically be repartitioned when exiting the context
        else:
            # For non-PEFT models, simply gather (if needed) and update each parameter individually.
            if self.is_fsdp_enabled:
                fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None)
                fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1
                if fsdp_version == 1:
                    self._sync_fsdp1_params_to_vllm(self.model)  # use memory-efficient post-order traversal for FSDP
                elif fsdp_version == 2:
                    self._sync_fsdp2_params_to_vllm(self.model)
            else:
                for name, param in self.model.named_parameters():
                    name = self._fix_param_name_to_vllm(name)
                    with gather_if_zero3([param]):
                        if self.vllm_mode == "server" and self.accelerator.is_main_process:
                            self.vllm_client.update_named_param(name, param.data)
                        elif self.vllm_mode == "colocate":
                            llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
                            llm_model.load_weights([(name, param.data)])

        # Reset cache on vLLM
        if self.vllm_mode == "server" and self.accelerator.is_main_process:
            self.vllm_client.reset_prefix_cache()
        elif self.vllm_mode == "colocate":
            self.llm.reset_prefix_cache()

    def _sync_fsdp1_params_to_vllm(self, module: nn.Module, prefix: str = "", visited=None):
        """Memory-efficient post-order traversal of FSDP modules to extract full parameters and sync with vLLM."""
        # For FSDP1, we need to recurse into children and also use summon_full_params
        if visited is None:
            visited = set()
        for child_name, child_module in module.named_children():
            child_prefix = f"{prefix}.{child_name}" if prefix else child_name
            self._sync_fsdp1_params_to_vllm(
                child_module, prefix=child_prefix, visited=visited
            )  # recurse into the child

        if isinstance(module, FSDP):
            with FSDP.summon_full_params(module, recurse=False, writeback=False):
                for param_name, param in module.named_parameters():
                    full_name = f"{prefix}.{param_name}" if prefix else param_name
                    full_name = self._fix_param_name_to_vllm(full_name, extra_prefixes=["_fsdp_wrapped_module."])

                    if full_name in visited:
                        continue  # skip FSDP subtrees already traversed
                    visited.add(full_name)

                    if self.vllm_mode == "server" and self.accelerator.is_main_process:
                        self.vllm_client.update_named_param(full_name, param.data)
                    elif self.vllm_mode == "colocate":
                        llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
                        llm_model.load_weights([(full_name, param.data)])

    def _sync_fsdp2_params_to_vllm(self, module: nn.Module):
        # For FSDP2, module.state_dict() already covers all parameters, so no need for recursion
        for name, param in module.state_dict().items():
            if param.is_cpu:
                param = param.to(torch.device("cuda"))
            param = param.full_tensor()

            if self.vllm_mode == "server" and self.accelerator.is_main_process:
                self.vllm_client.update_named_param(name, param)
            elif self.vllm_mode == "colocate":
                llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
                llm_model.load_weights([(name, param)])

    def _fix_param_name_to_vllm(self, name, extra_prefixes: Optional[list[str]] = None):
        """Clean parameter names for vLLM compatibility"""
        extra_prefixes = extra_prefixes or []
        prefixes = ["_checkpoint_wrapped_module."] + extra_prefixes
        for prefix in prefixes:
            name = name.replace(prefix, "")
        return name

    def process_vision_row(
        self, features: dict[str, Union[list, torch.Tensor]], processing_class=None
    ) -> dict[str, list[int]]:
        """
        Process a vision row for VLM models (adapted from DPO trainer)
        """
        processor = processing_class or self.processing_class
        processed_features = processor(images=[features["image"]], text=features["prompt"], add_special_tokens=False)

        prompt_input_ids = processed_features["input_ids"][0]

        # Create the output dict with required fields
        output = {
            "prompt_input_ids": prompt_input_ids,
            "prompt_attention_mask": processed_features["attention_mask"][0],
        }

        # Add vision-specific fields
        if "pixel_values" in processed_features:
            output["pixel_values"] = processed_features["pixel_values"][0]
        if "pixel_attention_mask" in processed_features:
            output["pixel_attention_mask"] = processed_features["pixel_attention_mask"][0]
        if "image_sizes" in processed_features:
            output["image_sizes"] = processed_features["image_sizes"][0]

        return output

    def _generate(self, model, prompts, images=None):
        """Generate completions using the model"""
        device = next(model.parameters()).device
        eos_token_id = self.eos_token_id
        pad_token_id = self.pad_token_id

        # Apply chat template and tokenize the input
        inputs = [{"prompt": prompt} for prompt in prompts]

        # Add images if provided (VLM support)
        if images is not None:
            for i, image in enumerate(images):
                inputs[i]["image"] = image

        # Apply chat template to get text prompts
        prompts_text = [maybe_apply_chat_template(x, self.processing_class)["prompt"] for x in inputs]

        # Handle image token collapsing/removal
        # The chat template sometimes inserts a single image token into the prompt text. However, when this text is
        # later tokenized, the single image token string is expanded into multiple image token IDs, depending on the
        # image size. We need to handle this properly.
        if self.image_token is not None and images is not None:
            escaped_img_token = re.escape(self.image_token)
            # Search for the image token in the chat template
            if hasattr(self.processing_class, "chat_template") and self.processing_class.chat_template:
                if re.search(escaped_img_token, self.processing_class.chat_template):
                    # Collapse repeated image tokens back into a single token
                    prompts_text = [
                        re.sub(rf"({escaped_img_token})+", self.image_token, text) for text in prompts_text
                    ]
                else:
                    # If the chat template doesn't use the image token, remove all instances
                    if self.vision_end_token_id is not None:
                        escaped_eoi_token = re.escape(
                            self.processing_class.tokenizer.decode([self.vision_end_token_id])
                        )
                        prompts_text = [
                            re.sub(rf"({escaped_img_token})+{escaped_eoi_token}", "", text) for text in prompts_text
                        ]
                    else:
                        # If vision_end_token_id is None, just remove the image tokens
                        prompts_text = [re.sub(rf"({escaped_img_token})+", "", text) for text in prompts_text]

        # Prepare kwargs for processing class
        kwargs = {}
        if images is not None:
            kwargs = {"images": [[img] for img in images]}

        # Process inputs using the processing class (handles both VLM and LLM)
        prompt_inputs = self.processing_class(
            text=prompts_text,
            return_tensors="pt",
            padding=True,
            padding_side="left",
            add_special_tokens=False,
            **kwargs,
        )

        prompt_inputs = {k: v.to(device) for k, v in prompt_inputs.items()}
        # Convert vision inputs to model's dtype for proper computation
        if "pixel_values" in prompt_inputs:
            # Handle DataParallel wrapped models
            model_dtype = getattr(model, "dtype", None)
            if model_dtype is None and hasattr(model, "module"):
                model_dtype = model.module.dtype
            if model_dtype is not None:
                prompt_inputs["pixel_values"] = prompt_inputs["pixel_values"].to(model_dtype)

        # Sample 2 completions per prompt of size `max_new_tokens` from the model
        prompt_ids = prompt_inputs["input_ids"].repeat(2, 1)
        prompt_mask = prompt_inputs["attention_mask"].repeat(2, 1)

        # Prepare vision inputs if available
        vision_generation_kwargs = {}
        if self.is_vision_model and images is not None:
            if "pixel_values" in prompt_inputs:
                vision_generation_kwargs["pixel_values"] = prompt_inputs["pixel_values"].repeat(2, 1, 1, 1)
            if "pixel_attention_mask" in prompt_inputs:
                vision_generation_kwargs["pixel_attention_mask"] = prompt_inputs["pixel_attention_mask"].repeat(2, 1)
            if "image_sizes" in prompt_inputs:
                vision_generation_kwargs["image_sizes"] = prompt_inputs["image_sizes"].repeat(2, 1)
            if "image_grid_thw" in prompt_inputs:
                vision_generation_kwargs["image_grid_thw"] = prompt_inputs["image_grid_thw"].repeat(2, 1)

        if self.use_transformers_paged:
            previous_attn = self.model_wrapped.config._attn_implementation

            if is_flash_attn_2_available():
                self.model_wrapped.config._attn_implementation = "paged_attention"
            else:
                self.model_wrapped.config._attn_implementation = "sdpa_paged"
            with (
                profiling_context(self, "transformers.generate_batch"),
                unwrap_model_for_generation(
                    model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
                ) as unwrapped_model,
                torch.no_grad(),
                FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(),
            ):
                # Cast to the appropriate dtype based on training configuration
                if self.args.bf16:
                    unwrapped_model.to(torch.bfloat16)
                elif self.args.fp16:
                    unwrapped_model.to(torch.float16)
                with torch.inference_mode():
                    all_outputs = unwrapped_model.generate_batch(
                        prompt_ids.tolist(),
                        generation_config=self.generation_config,
                        progress_bar=False,
                    )
                    unwrapped_model.train()  # restore training mode, as generate_batch forces eval mode
            completion_ids = [output.generated_tokens for output in all_outputs.values()]
            completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids]
            completion_ids = pad(completion_ids, padding_value=self.pad_token_id, padding_side="right")
            prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)
            # Restore the original attention implementation, training mode
            self.model_wrapped.config._attn_implementation = previous_attn

            # Extract completion_ids and create completion_mask
            prompt_length = prompt_ids.size(1)
            completion_ids = prompt_completion_ids[:, prompt_length:]
            completion_ids, completion_mask = truncate_right(completion_ids, eos_token_id, pad_token_id)

            return prompt_ids, prompt_mask, completion_ids, completion_mask
        else:
            # Regular generation path
            with (
                profiling_context(self, "transformers.generate"),
                unwrap_model_for_generation(
                    model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
                ) as unwrapped_model,
                torch.no_grad(),
                FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(),
            ):
                # Setup cache implementation if specified
                if self.args.cache_implementation is not None:
                    unwrapped_model.generation_config.cache_implementation = self.args.cache_implementation

                # Standard generation
                output = unwrapped_model.generate(
                    input_ids=prompt_ids,
                    attention_mask=prompt_mask,
                    generation_config=self.generation_config,
                    **vision_generation_kwargs,
                )

            completion_ids = output[:, prompt_ids.size(1) :]
            completion_ids, completion_mask = truncate_right(completion_ids, eos_token_id, pad_token_id)

            return prompt_ids, prompt_mask, completion_ids, completion_mask

    def _calculate_rewards_from_functions(self, prompts, completions, completion_ids_list, **reward_kwargs):
        """
        Calculate rewards using reward functions
        """
        device = self.accelerator.device
        rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device)

        # Add trainer state to reward kwargs for dynamic reward shaping
        reward_kwargs["trainer_state"] = self.state

        for i, (reward_func, reward_processing_class) in enumerate(
            zip(self.reward_funcs, self.reward_processing_classes)
        ):
            if isinstance(reward_func, nn.Module):  # Model-based reward function
                # Handle conversational vs text input
                if is_conversational({"prompt": prompts[0]}):
                    messages = [{"messages": p + c} for p, c in zip(prompts, completions)]
                    texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages]
                else:
                    texts = [p + c for p, c in zip(prompts, completions)]

                # Tokenize and get reward scores
                reward_inputs = reward_processing_class(
                    text=texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False
                )
                reward_inputs = {k: v.to(device) for k, v in reward_inputs.items()}

                with torch.inference_mode():
                    rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0]  # Shape (B*G,)
            else:
                # Custom reward function
                output_reward_func = reward_func(
                    prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs
                )
                # Convert None values to NaN
                output_reward_func = [reward if reward is not None else torch.nan for reward in output_reward_func]
                rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device)

        # Weight and sum across all reward functions
        if self.reward_weights is not None:
            total_rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1)
        else:
            total_rewards = rewards_per_func.nansum(dim=1)

        return total_rewards

    def _forward(self, model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs=None):
        # Get the number of tokens to truncate from prompt
        num_tokens_to_truncate = max(prompt_ids.size(1) + completion_ids.size(1) - self.max_length, 0)

        # Truncate left to avoid oom
        prompt_ids = prompt_ids[:, num_tokens_to_truncate:]
        prompt_mask = prompt_mask[:, num_tokens_to_truncate:]

        # Concat the prompt and completion
        prompt_completion_ids = torch.cat((prompt_ids, completion_ids), dim=1)
        prompt_completion_mask = torch.cat((prompt_mask, completion_mask), dim=1)

        # Prepare model kwargs with vision inputs if available
        model_kwargs = {"attention_mask": prompt_completion_mask}
        if vision_inputs is not None:
            if "pixel_values" in vision_inputs:
                model_kwargs["pixel_values"] = vision_inputs["pixel_values"]
            if "pixel_attention_mask" in vision_inputs:
                model_kwargs["pixel_attention_mask"] = vision_inputs["pixel_attention_mask"]
            if "image_sizes" in vision_inputs:
                model_kwargs["image_sizes"] = vision_inputs["image_sizes"]
            if "image_grid_thw" in vision_inputs:
                model_kwargs["image_grid_thw"] = vision_inputs["image_grid_thw"]

        # Get the logprobs of the completions from the model
        output = model(prompt_completion_ids, **model_kwargs)

        # There is 1 offset, because the model predicts the next token
        prompt_len = prompt_ids.size(1)
        start_idx = prompt_len - 1 if prompt_len > 0 else 0
        # Only slice off the last logit when we have a prompt, otherwise we need all logits
        end_idx = -1 if prompt_len > 0 else None
        logits = output.logits[:, start_idx:end_idx]

        # Take the completion tokens logprob
        logprobs = torch.take_along_dim(logits.log_softmax(dim=-1), completion_ids.unsqueeze(-1), dim=2).squeeze(-1)
        return logprobs

    def training_step(
        self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None
    ) -> torch.Tensor:
        model.train()

        prompts = inputs["prompt"]
        batch_size = len(prompts)

        # Handle images for VLM support
        has_images = "image" in inputs
        images = None
        if has_images:
            images = inputs["image"]
            # Convert conversational prompts to include image tokens
            for prompt in prompts:
                if isinstance(prompt, list):
                    for message in prompt:
                        if not isinstance(message, dict):
                            continue
                        content = message.get("content")
                        role = message.get("role")
                        if isinstance(content, str):
                            if role == "user":
                                message["content"] = [{"type": "image"}, {"type": "text", "text": content}]
                            elif role == "system":
                                message["content"] = [{"type": "text", "text": content}]

        if self.args.use_vllm:
            prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate_vllm(prompts, images)
        else:
            prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate(model, prompts, images)

        contain_eos_token = torch.any(completion_ids == self.eos_token_id, dim=-1)

        # Extract vision inputs if available for VLM support
        vision_inputs = None
        if has_images and self.is_vision_model and not self.args.use_vllm:
            # For vision models with transformers generation, we need to prepare vision inputs
            # Process the images to get vision inputs that can be passed through the forward pass
            vision_inputs = {}
            kwargs = {"images": [[img] for img in images]}
            processed = self.processing_class(
                text=[""] * len(images),  # Dummy text for vision processing
                return_tensors="pt",
                **kwargs,
            )
            # Handle DataParallel wrapped models
            model_device = getattr(model, "device", None)
            model_dtype = getattr(model, "dtype", None)
            if model_device is None and hasattr(model, "module"):
                model_device = model.module.device
                model_dtype = model.module.dtype
            # Move vision tensors to device and convert to model dtype
            # Need to duplicate for 2 completions per prompt
            if "pixel_values" in processed:
                vision_inputs["pixel_values"] = (
                    processed["pixel_values"].to(model_device, dtype=model_dtype).repeat(2, 1, 1, 1)
                )
            if "pixel_attention_mask" in processed:
                vision_inputs["pixel_attention_mask"] = processed["pixel_attention_mask"].to(model_device).repeat(2, 1)
            if "image_sizes" in processed:
                vision_inputs["image_sizes"] = processed["image_sizes"].to(model_device).repeat(2, 1)
            if "image_grid_thw" in processed:
                vision_inputs["image_grid_thw"] = processed["image_grid_thw"].to(model_device).repeat(2, 1)

        logprobs = self._forward(model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs)
        with torch.no_grad():
            if self.ref_model is not None:
                ref_logprobs = self._forward(
                    self.ref_model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs
                )
            else:  # peft case: we just need to disable the adapter
                with self.model.disable_adapter():
                    ref_logprobs = self._forward(
                        self.model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs
                    )

        # Decode the completions, and format them if the input is conversational
        device = logprobs.device
        completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)
        if is_conversational({"prompt": prompts[0]}):
            completions = [[{"role": "assistant", "content": completion}] for completion in completions]

        # Get the reward from reward functions, judge, or deprecated reward_model
        if self.reward_funcs is not None:
            # First create completion_ids_list for custom reward functions
            completion_ids_list = [completion_ids[i].tolist() for i in range(completion_ids.shape[0])]

            # Extract additional fields from inputs for reward functions
            reward_kwargs = {}
            keys = [key for key in inputs if key not in ["prompt"]]
            for key in keys:
                if isinstance(inputs[key], (list, tuple)):
                    # Repeat input fields to match number of completions (2 per prompt)
                    reward_kwargs[key] = inputs[key] * 2
                else:
                    reward_kwargs[key] = inputs[key]

            # Calculate rewards using reward functions
            rewards = self._calculate_rewards_from_functions(
                prompts=2 * prompts, completions=completions, completion_ids_list=completion_ids_list, **reward_kwargs
            )

            # Apply missing EOS penalty if configured
            if self.args.missing_eos_penalty is not None:
                rewards[~contain_eos_token] -= self.args.missing_eos_penalty

            # Split rewards into chosen/rejected pairs
            first_half, second_half = rewards.split(batch_size)
            mask = first_half >= second_half
        elif self.judge is not None:
            # Once formatted, conversational data may contain special tokens (such as <|im_start|>) that are not
            # directly understandable by the judge and could alter its judgment. To avoid this and make the judge
            # independent of the model's chat template, we use the raw conversation data, and apply our own chat
            # template to it.
            if is_conversational({"prompt": prompts[0]}):
                environment = jinja2.Environment()
                template = environment.from_string(SIMPLE_CHAT_TEMPLATE)
                prompts = [template.render(messages=prompt) for prompt in prompts]
                completions = [template.render(messages=completion) for completion in completions]

            ranks_of_first_completion = self.judge.judge(
                prompts, list(zip(completions[:batch_size], completions[batch_size:]))
            )

            # convert ranks to a True/False mask:
            # when rank == 0, it means the first completion is the best
            # when rank == 1, it means the second completion is the best
            mask = torch.tensor([rank == 0 for rank in ranks_of_first_completion], device=device)

        batch_range = torch.arange(batch_size, device=device)
        chosen_indices = batch_range + (~mask * batch_size)
        rejected_indices = batch_range + (mask * batch_size)

        # Build tensor so that the first half is the chosen examples and the second half the rejected examples
        cr_indices = torch.cat((chosen_indices, rejected_indices), dim=0)  # cr = chosen and rejected
        cr_logprobs = logprobs[cr_indices]
        cr_ref_logprobs = ref_logprobs[cr_indices]

        # mask out the padding tokens
        padding_mask = ~completion_mask.bool()
        cr_padding_mask = padding_mask[cr_indices]

        cr_logprobs_sum = (cr_logprobs * ~cr_padding_mask).sum(1)
        cr_ref_logprobs_sum = (cr_ref_logprobs * ~cr_padding_mask).sum(1)

        # Split the chosen and rejected examples
        chosen_logprobs_sum, rejected_logprobs_sum = torch.split(cr_logprobs_sum, batch_size)
        chosen_ref_logprobs_sum, rejected_ref_logprobs_sum = torch.split(cr_ref_logprobs_sum, batch_size)
        pi_logratios = chosen_logprobs_sum - rejected_logprobs_sum
        ref_logratios = chosen_ref_logprobs_sum - rejected_ref_logprobs_sum

        logits = pi_logratios - ref_logratios

        if self.args.loss_type == "sigmoid":
            losses = -F.logsigmoid(self.beta * logits)
        elif self.args.loss_type == "ipo":
            losses = (logits - 1 / (2 * self.beta)) ** 2
        else:
            raise NotImplementedError(f"invalid loss type {self.loss_type}")

        loss = losses.mean()

        # Log everything
        if self.reward_funcs is not None:
            # When using reward_funcs, we have rewards instead of scores
            scores_margin = rewards[chosen_indices] - rewards[rejected_indices]
            self.stats["objective/scores_margin"].append(
                self.accelerator.gather_for_metrics(scores_margin.mean()).mean().item()
            )
            self.stats["objective/scores"].append(self.accelerator.gather_for_metrics(rewards.mean()).mean().item())
        self.stats["val/contain_eos_token"].append(contain_eos_token.float().mean().item())
        self.stats["logps/chosen"].append(self.accelerator.gather_for_metrics(chosen_logprobs_sum).mean().item())
        self.stats["logps/rejected"].append(self.accelerator.gather_for_metrics(rejected_logprobs_sum).mean().item())

        kl = logprobs - ref_logprobs
        mean_kl = kl.sum(1).mean()
        self.stats["objective/kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item())
        non_score_reward = (-self.beta * kl).sum(1)
        mean_non_score_reward = non_score_reward.mean()
        self.stats["objective/non_score_reward"].append(
            self.accelerator.gather_for_metrics(mean_non_score_reward).mean().item()
        )
        if self.reward_funcs is not None:
            # Calculate RLHF reward by combining rewards with non_score_reward
            rlhf_reward = rewards + non_score_reward
            self.stats["objective/rlhf_reward"].append(self.accelerator.gather_for_metrics(rlhf_reward).mean().item())

        mean_entropy = -logprobs.sum(1).mean()
        self.stats["objective/entropy"].append(self.accelerator.gather_for_metrics(mean_entropy).mean().item())
        chosen_rewards = self.beta * (chosen_logprobs_sum - chosen_ref_logprobs_sum)
        gathered_chosen_rewards = self.accelerator.gather_for_metrics(chosen_rewards)
        self.stats["rewards/chosen"].append(gathered_chosen_rewards.mean().item())
        rejected_rewards = self.beta * (rejected_logprobs_sum - rejected_ref_logprobs_sum)
        gathered_rejected_rewards = self.accelerator.gather_for_metrics(rejected_rewards)
        self.stats["rewards/rejected"].append(gathered_rejected_rewards.mean().item())
        margin = gathered_chosen_rewards - gathered_rejected_rewards
        self.stats["rewards/margins"].append(margin.mean().item())
        accuracy = margin > 0
        self.stats["rewards/accuracies"].append(accuracy.float().mean().item())
        self.stats["beta"].append(self.beta)

        if (
            self.args.torch_empty_cache_steps is not None
            and self.state.global_step % self.args.torch_empty_cache_steps == 0
        ):
            empty_cache()

        kwargs = {}

        # For LOMO optimizers you need to explicitly use the learning rate
        if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]:
            kwargs["learning_rate"] = self._get_learning_rate()

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training

        self.accelerator.backward(loss, **kwargs)

        return loss.detach() / self.args.gradient_accumulation_steps

    # Same as Trainer._maybe_log_save_evaluate but log our metrics
    def _maybe_log_save_evaluate(
        self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time, learning_rate=None
    ):
        if self.control.should_log and self.state.global_step > self._globalstep_last_logged:
            logs: dict[str, float] = {}

            # all_gather + mean() to get average loss over all processes
            tr_loss_scalar = self._nested_gather(tr_loss).mean().item()

            # reset tr_loss to zero
            tr_loss -= tr_loss

            logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
            if grad_norm is not None:
                logs["grad_norm"] = grad_norm.detach().item() if isinstance(grad_norm, torch.Tensor) else grad_norm
            if learning_rate is not None:
                logs["learning_rate"] = learning_rate
            else:
                logs["learning_rate"] = self._get_learning_rate()

            # Add our metrics
            for key, val in self.stats.items():
                logs[key] = sum(val) / len(val)
            self.stats = {key: [] for key in self.stats}  # reset stats

            self._total_loss_scalar += tr_loss_scalar
            self._globalstep_last_logged = self.state.global_step
            self.store_flos()
            self.log(logs, start_time)

        metrics = None
        if self.control.should_evaluate:
            metrics = self._evaluate(trial, ignore_keys_for_eval)
            is_new_best_metric = self._determine_best_metric(metrics=metrics, trial=trial)

            if self.args.save_strategy == "best":
                self.control.should_save = is_new_best_metric

        if self.control.should_save:
            self._save_checkpoint(model, trial)
            self.control = self.callback_handler.on_save(self.args, self.state, self.control)

    # Ensure the model card is saved along with the checkpoint
    def _save_checkpoint(self, model, trial):
        if self.args.hub_model_id is None:
            model_name = Path(self.args.output_dir).name
        else:
            model_name = self.args.hub_model_id.split("/")[-1]
        self.create_model_card(model_name=model_name)
        super()._save_checkpoint(model, trial)
