trainer.py

from __future__ import annotations

import logging
import os
import warnings
from collections import OrderedDict
from contextlib import nullcontext
from typing import TYPE_CHECKING, Any, Callable

import torch
from torch import nn
from torch.utils.data import BatchSampler, ConcatDataset, DataLoader, SubsetRandomSampler
from transformers import EvalPrediction, PreTrainedTokenizerBase, Trainer, TrainerCallback
from transformers.data.data_collator import DataCollator
from transformers.integrations import WandbCallback
from transformers.trainer import TRAINING_ARGS_NAME
from transformers.trainer_utils import EvalLoopOutput

from sentence_transformers.data_collator import SentenceTransformerDataCollator
from sentence_transformers.evaluation import SentenceEvaluator, SequentialEvaluator
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.model_card import ModelCardCallback
from sentence_transformers.models.Transformer import Transformer
from sentence_transformers.sampler import (
    DefaultBatchSampler,
    GroupByLabelBatchSampler,
    NoDuplicatesBatchSampler,
    ProportionalBatchSampler,
    RoundRobinBatchSampler,
)
from sentence_transformers.training_args import (
    BatchSamplers,
    MultiDatasetBatchSamplers,
    SentenceTransformerTrainingArguments,
)
from sentence_transformers.util import disable_logging, is_datasets_available, is_training_available

if is_datasets_available():
    from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict, Value

logger = logging.getLogger(__name__)

if TYPE_CHECKING:
    from sentence_transformers.SentenceTransformer import SentenceTransformer


class SentenceTransformerTrainer(Trainer):
    """
    SentenceTransformerTrainer is a simple but feature-complete training and eval loop for PyTorch
    based on the 🤗 Transformers :class:`~transformers.Trainer`.

    This trainer integrates support for various :class:`transformers.TrainerCallback` subclasses, such as:

    - :class:`~transformers.integrations.WandbCallback` to automatically log training metrics to W&B if `wandb` is installed
    - :class:`~transformers.integrations.TensorBoardCallback` to log training metrics to TensorBoard if `tensorboard` is accessible.
    - :class:`~transformers.integrations.CodeCarbonCallback` to track the carbon emissions of your model during training if `codecarbon` is installed.

        - Note: These carbon emissions will be included in your automatically generated model card.

    See the Transformers `Callbacks <https://huggingface.co/docs/transformers/main/en/main_classes/callback>`_
    documentation for more information on the integrated callbacks and how to write your own callbacks.

    Args:
        model (:class:`~sentence_transformers.SentenceTransformer`, *optional*):
            The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed.
        args (:class:`~sentence_transformers.training_args.SentenceTransformerTrainingArguments`, *optional*):
            The arguments to tweak for training. Will default to a basic instance of
            :class:`~sentence_transformers.training_args.SentenceTransformerTrainingArguments` with the
            `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided.
        train_dataset (Union[:class:`datasets.Dataset`, :class:`datasets.DatasetDict`, :class:`datasets.IterableDataset`, Dict[str, :class:`datasets.Dataset`]], *optional*):
            The dataset to use for training. Must have a format accepted by your loss function, see
            `Training Overview > Dataset Format <../../../docs/sentence_transformer/training_overview.html#dataset-format>`_.
        eval_dataset (Union[:class:`datasets.Dataset`, :class:`datasets.DatasetDict`, :class:`datasets.IterableDataset`, Dict[str, :class:`datasets.Dataset`]], *optional*):
            The dataset to use for evaluation. Must have a format accepted by your loss function, see
            `Training Overview > Dataset Format <../../../docs/sentence_transformer/training_overview.html#dataset-format>`_.
        loss (Optional[Union[:class:`torch.nn.Module`, Dict[str, :class:`torch.nn.Module`],\
            Callable[[:class:`~sentence_transformers.SentenceTransformer`], :class:`torch.nn.Module`],\
            Dict[str, Callable[[:class:`~sentence_transformers.SentenceTransformer`]]]], *optional*):
            The loss function to use for training. Can either be a loss class instance, a dictionary mapping dataset names to
            loss class instances, a function that returns a loss class instance given a model, or a dictionary mapping
            dataset names to functions that return a loss class instance given a model. In practice, the latter two
            are primarily used for hyper-parameter optimization. Will default to
            :class:`~sentence_transformers.losses.CoSENTLoss` if no ``loss`` is provided.
        evaluator (Union[:class:`~sentence_transformers.evaluation.SentenceEvaluator`,\
            List[:class:`~sentence_transformers.evaluation.SentenceEvaluator`]], *optional*):
            The evaluator instance for useful evaluation metrics during training. You can use an ``evaluator`` with
            or without an ``eval_dataset``, and vice versa. Generally, the metrics that an ``evaluator`` returns
            are more useful than the loss value returned from the ``eval_dataset``. A list of evaluators will be
            wrapped in a :class:`~sentence_transformers.evaluation.SequentialEvaluator` to run them sequentially.
        callbacks (List of [:class:`transformers.TrainerCallback`], *optional*):
            A list of callbacks to customize the training loop. Will add those to the list of default callbacks
            detailed in [here](callback).

            If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.
        optimizers (`Tuple[:class:`torch.optim.Optimizer`, :class:`torch.optim.lr_scheduler.LambdaLR`]`, *optional*, defaults to `(None, None)`):
            A tuple containing the optimizer and the scheduler to use. Will default to an instance of :class:`torch.optim.AdamW`
            on your model and a scheduler given by :func:`transformers.get_linear_schedule_with_warmup` controlled by `args`.

    Important attributes:

        - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`]
          subclass.
        - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the
          original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`,
          the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner
          model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`.
        - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from
          data parallelism, this means some of the model layers are split on different GPUs).
        - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set
          to `False` if model parallel or deepspeed is used, or if the default
          `TrainingArguments.place_model_on_device` is overridden to return `False` .
        - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while
          in `train`)

    """

    def __init__(
        self,
        model: SentenceTransformer | None = None,
        args: SentenceTransformerTrainingArguments = None,
        train_dataset: Dataset | DatasetDict | IterableDataset | dict[str, Dataset] | None = None,
        eval_dataset: Dataset | DatasetDict | IterableDataset | dict[str, Dataset] | None = None,
        loss: nn.Module
        | dict[str, nn.Module]
        | Callable[[SentenceTransformer], torch.nn.Module]
        | dict[str, Callable[[SentenceTransformer], torch.nn.Module]]
        | None = None,
        evaluator: SentenceEvaluator | list[SentenceEvaluator] | None = None,
        data_collator: DataCollator | None = None,
        tokenizer: PreTrainedTokenizerBase | Callable | None = None,
        model_init: Callable[[], SentenceTransformer] | None = None,
        compute_metrics: Callable[[EvalPrediction], dict] | None = None,
        callbacks: list[TrainerCallback] | None = None,
        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None,
    ) -> None:
        if not is_training_available():
            raise RuntimeError(
                "To train a SentenceTransformer model, you need to install the `accelerate` and `datasets` modules. "
                "You can do so with the `train` extra:\n"
                'pip install -U "sentence-transformers[train]"'
            )

        if args is None:
            output_dir = "tmp_trainer"
            logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.")
            args = SentenceTransformerTrainingArguments(output_dir=output_dir)
        elif not isinstance(args, SentenceTransformerTrainingArguments):
            raise ValueError("Please use `TrainingArguments` imported from `sentence_transformers`.")

        if model is None:
            if model_init is not None:
                self.model_init = model_init
                model = self.call_model_init()
            else:
                raise RuntimeError("`Trainer` requires either a `model` or `model_init` argument")
        else:
            if model_init is not None:
                warnings.warn(
                    "`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will"
                    " overwrite your model when calling the `train` method. This will become a fatal error in the next"
                    " release.",
                    FutureWarning,
                )
            self.model_init = model_init

        # Get a dictionary of the default training arguments, so we can determine which arguments have been changed
        # for the model card
        default_args_dict = SentenceTransformerTrainingArguments(output_dir="unused").to_dict()

        # If the model ID is set via the SentenceTransformerTrainingArguments, but not via the SentenceTransformerModelCardData,
        # then we can set it here for the model card regardless
        if args.hub_model_id and not model.model_card_data.model_id:
            model.model_card_data.set_model_id(args.hub_model_id)

        if tokenizer is None and isinstance(model.tokenizer, PreTrainedTokenizerBase):
            tokenizer = model.tokenizer

        if data_collator is None:
            data_collator = SentenceTransformerDataCollator(tokenize_fn=model.tokenize)

        for dataset_name, dataset in zip(["train", "eval"], [train_dataset, eval_dataset]):
            if isinstance(dataset, IterableDataset) and dataset.column_names is None:
                sample = next(iter(dataset))
                naive_type_mapping = {str: "string", int: "int64", float: "float32", bool: "bool"}
                example_features = {
                    key: Value(naive_type_mapping.get(type(value), "null")) for key, value in sample.items()
                }
                raise ValueError(
                    f"The provided `{dataset_name}_dataset` must have Features. Specify them with e.g.:\n"
                    f"{dataset_name}_dataset = {dataset_name}_dataset.cast(Features({example_features}))\n"
                    "or by providing the Features to the IterableDataset initialization method. See the Datasets "
                    "documentation for more information on dataset Features: "
                    "https://huggingface.co/docs/datasets/en/about_dataset_features"
                )

        if isinstance(train_dataset, dict) and not isinstance(train_dataset, DatasetDict):
            train_dataset = DatasetDict(train_dataset)
        if isinstance(eval_dataset, dict) and not isinstance(eval_dataset, DatasetDict):
            eval_dataset = DatasetDict(eval_dataset)
        super().__init__(
            model=None if self.model_init else model,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            tokenizer=tokenizer,
            model_init=model_init,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )
        # Every Sentence Transformer model can always return a loss, so we set this to True
        # to avoid having to specify it in the data collator or model's forward
        self.can_return_loss = True

        self.model: SentenceTransformer
        self.args: SentenceTransformerTrainingArguments
        self.data_collator: SentenceTransformerDataCollator
        # Set the W&B project via environment variables if it's not already set
        if any([isinstance(callback, WandbCallback) for callback in self.callback_handler.callbacks]):
            os.environ.setdefault("WANDB_PROJECT", "sentence-transformers")

        if loss is None:
            logger.info("No `loss` passed, using `losses.CoSENTLoss` as a default option.")
            loss = CoSENTLoss(self.model)

        if isinstance(loss, dict):
            self.loss = {dataset_name: self.prepare_loss(loss_fn, model) for dataset_name, loss_fn in loss.items()}
            for dataset_name, dataset in zip(["train", "eval"], [train_dataset, eval_dataset]):
                if dataset is None:
                    continue
                if not isinstance(dataset, dict):
                    raise ValueError(
                        f"If the provided `loss` is a dict, then the `{dataset_name}_dataset` must be a `DatasetDict`."
                    )
                if missing := set(dataset.keys()) - set(loss.keys()):
                    raise ValueError(
                        f"If the provided `loss` is a dict, then all keys from the `{dataset_name}_dataset` dictionary must occur in `loss` also. "
                        f"Currently, {sorted(missing)} occur{'s' if len(missing) == 1 else ''} in `{dataset_name}_dataset` but not in `loss`."
                    )
        else:
            self.loss = self.prepare_loss(loss, model)
        # If evaluator is a list, we wrap it in a SequentialEvaluator
        if evaluator is not None and not isinstance(evaluator, SentenceEvaluator):
            evaluator = SequentialEvaluator(evaluator)
        self.evaluator = evaluator

        # Add a callback responsible for automatically tracking data required for the automatic model card generation
        model_card_callback = ModelCardCallback(self, default_args_dict)
        self.add_callback(model_card_callback)
        model_card_callback.on_init_end(self.args, self.state, self.control, self.model)

    def call_model_init(self, trial=None) -> SentenceTransformer:
        model = super().call_model_init(trial=trial)
        # If the Trainer already has a loss, then we'll want to override the model in the loss function
        if not hasattr(self, "loss"):
            return model

        # Multi-loss training:
        if isinstance(self.loss, dict):
            for key, loss_fn in self.loss.items():
                # If a loss function is not yet initialized, we initialize it here
                if not isinstance(loss_fn, torch.nn.Module):
                    self.loss[key] = loss_fn(model)
                # Otherwise, we override the original model with the updated model in the loss function
                elif hasattr(loss_fn, "model"):
                    self.loss = self.override_model_in_loss(self.loss, model)

        # Loss is a function accepting a model as an argument
        elif not isinstance(self.loss, torch.nn.Module):
            self.loss = self.loss(model)

        # Loss is an initialized torch.nn.Module
        elif hasattr(self.loss, "model"):
            self.loss = self.override_model_in_loss(self.loss, model)
        return model

    def override_model_in_loss(self, loss: torch.nn.Module, model: SentenceTransformer) -> torch.nn.Module:
        from sentence_transformers import SentenceTransformer

        for name, child in loss.named_children():
            if name == "model" and isinstance(child, SentenceTransformer):
                loss.model = model
            elif isinstance(child, torch.nn.Module):
                setattr(loss, name, self.override_model_in_loss(child, model))
        return loss

    def prepare_loss(
        self,
        loss: Callable[[SentenceTransformer], torch.nn.Module] | torch.nn.Module,
        model: SentenceTransformer,
    ) -> torch.nn.Module:
        if isinstance(loss, torch.nn.Module):
            return loss.to(model.device)
        return loss(model).to(model.device)

    def add_dataset_name_column(self, dataset_dict: DatasetDict) -> DatasetDict:
        for key, dataset in dataset_dict.items():
            if "dataset_name" not in dataset.column_names:
                dataset_dict[key] = dataset.add_column("dataset_name", [key] * len(dataset))
        return dataset_dict

    def compute_loss(
        self,
        model: SentenceTransformer,
        inputs: dict[str, torch.Tensor | Any],
        return_outputs: bool = False,
    ) -> torch.Tensor | tuple[torch.Tensor, dict[str, Any]]:
        """
        Computes the loss for the SentenceTransformer model.

        It uses ``self.loss`` to compute the loss, which can be a single loss function or a dictionary of loss functions
        for different datasets. If the loss is a dictionary, the dataset name is expected to be passed in the inputs
        under the key "dataset_name". This is done automatically in the ``add_dataset_name_column`` method.
        Note that even if ``return_outputs = True``, the outputs will be empty, as the SentenceTransformers losses do not
        return outputs.

        Args:
            model (SentenceTransformer): The SentenceTransformer model.
            inputs (Dict[str, Union[torch.Tensor, Any]]): The input data for the model.
            return_outputs (bool, optional): Whether to return the outputs along with the loss. Defaults to False.

        Returns:
            Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, Any]]]: The computed loss. If `return_outputs` is True, returns a tuple of loss and outputs. Otherwise, returns only the loss.
        """
        dataset_name = inputs.pop("dataset_name", None)
        features, labels = self.collect_features(inputs)
        loss_fn = self.loss

        if isinstance(loss_fn, dict) and dataset_name:
            loss_fn = loss_fn[dataset_name]

        # Insert the wrapped (e.g. distributed or compiled) model into the loss function,
        # if the loss stores the model. Only called once per process
        if (
            model == self.model_wrapped
            and model != self.model  # Only if the model is wrapped
            and hasattr(loss_fn, "model")  # Only if the loss stores the model
            and loss_fn.model != model  # Only if the wrapped model is not already stored
        ):
            loss_fn = self.override_model_in_loss(loss_fn, model)
        loss = loss_fn(features, labels)
        if return_outputs:
            # During prediction/evaluation, `compute_loss` will be called with `return_outputs=True`.
            # However, Sentence Transformer losses do not return outputs, so we return an empty dictionary.
            # This does not result in any problems, as the SentenceTransformerTrainingArguments sets
            # `prediction_loss_only=True` which means that the output is not used.
            return loss, {}
        return loss

    def collect_features(
        self, inputs: dict[str, torch.Tensor | Any]
    ) -> tuple[list[dict[str, torch.Tensor]], torch.Tensor | None]:
        """Turn the inputs from the dataloader into the separate model inputs & the labels.

        Example::

            >>> list(inputs.keys())
            ['return_loss', 'label', 'sentence_0_input_ids', 'sentence_0_token_type_ids', 'sentence_0_attention_mask', 'sentence_1_input_ids', 'sentence_1_token_type_ids', 'sentence_1_attention_mask']
            >>> features, labels = self.collect_features(inputs)
            >>> len(features)
            2
            >>> list(features[0].keys())
            ['input_ids', 'token_type_ids', 'attention_mask']
            >>> list(features[1].keys())
            ['input_ids', 'token_type_ids', 'attention_mask']
            >>> torch.equal(labels, inputs["label"])
            True
        """
        # All inputs ending with `_input_ids` (Transformers), `_sentence_embedding` (BoW), `_pixel_values` (CLIPModel)
        # are considered to correspond to a feature
        features = []
        for column in inputs:
            if column.endswith("_input_ids"):
                prefix = column[: -len("input_ids")]
            elif column.endswith("_sentence_embedding"):
                prefix = column[: -len("sentence_embedding")]
            elif column.endswith("_pixel_values"):
                prefix = column[: -len("pixel_values")]
            else:
                continue
            features.append({key[len(prefix) :]: value for key, value in inputs.items() if key.startswith(prefix)})
        labels = inputs.get("label", None)
        return features, labels

    def evaluate(
        self,
        eval_dataset: Dataset | dict[str, Dataset] | None = None,
        ignore_keys: list[str] | None = None,
        metric_key_prefix: str = "eval",
    ) -> dict[str, float]:
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
        if isinstance(eval_dataset, DatasetDict) and isinstance(self.loss, dict):
            eval_dataset = self.add_dataset_name_column(eval_dataset)
        return super().evaluate(eval_dataset, ignore_keys, metric_key_prefix)

    def evaluation_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: bool | None = None,
        ignore_keys: list[str] | None = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        output = super().evaluation_loop(
            dataloader=dataloader,
            description=description,
            prediction_loss_only=prediction_loss_only,
            ignore_keys=ignore_keys,
            metric_key_prefix=metric_key_prefix,
        )

        # If the evaluator is not defined, we can just return the output
        if self.evaluator is None:
            return output

        # If we are training and eval_dataset is a DatasetDict, then we should
        # 1) only run the evaluator for the first dataset
        # 2) prefix that only run as "eval", rather than e.g. "eval_multi_nli"
        if self.is_in_train and isinstance(self.eval_dataset, dict) and metric_key_prefix.startswith("eval_"):
            if metric_key_prefix[5:] == list(self.eval_dataset.keys())[0]:
                metric_key_prefix = "eval"
            else:
                return output

        with nullcontext() if self.is_local_process_zero() else disable_logging(logging.INFO):
            evaluator_metrics = self.evaluator(self.model)
        if not isinstance(evaluator_metrics, dict):
            evaluator_metrics = {"evaluator": evaluator_metrics}

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(evaluator_metrics.keys()):
            if not key.startswith(f"{metric_key_prefix}_"):
                evaluator_metrics[f"{metric_key_prefix}_{key}"] = evaluator_metrics.pop(key)

        output.metrics.update(evaluator_metrics)

        return output

    def _load_best_model(self) -> None:
        # We want to ensure that this does not fail, and it may change if transformers updates how checkpoints are saved
        # Loading the best model is only supported for `transformers`-based models
        if not isinstance(self.model[0], Transformer):
            logger.info("Could not load best model, as the model is not a `transformers`-based model.")
            return

        try:
            if checkpoint := self.state.best_model_checkpoint:
                step = checkpoint.rsplit("-", 1)[-1]
                self.model.model_card_data.set_best_model_step(int(step))
        except Exception:
            pass

        # Override the model with the `transformers`-based auto_model, and restore the original SentenceTransformers
        # model with the loaded `transformers` model
        full_model = self.model
        self.model = self.model[0].auto_model
        try:
            return super()._load_best_model()
        finally:
            loaded_auto_model = self.model
            self.model = full_model
            self.model[0].auto_model = loaded_auto_model

    def validate_column_names(self, dataset: Dataset, dataset_name: str | None = None) -> bool:
        if overlap := set(dataset.column_names) & {"return_loss", "dataset_name"}:
            raise ValueError(
                f"The following column names are invalid in your {dataset_name + ' ' if dataset_name else ''}dataset: {list(overlap)}."
                " Avoid using these column names, as they are reserved for internal use."
            )

    def get_batch_sampler(
        self,
        dataset: Dataset,
        batch_size: int,
        drop_last: bool,
        valid_label_columns: list[str] | None = None,
        generator: torch.Generator | None = None,
    ) -> BatchSampler | None:
        """
        Returns the appropriate batch sampler based on the ``batch_sampler`` argument in ``self.args``.
        This batch sampler class supports ``__len__`` and ``__iter__`` methods, and is used as the ``batch_sampler``
        to create the :class:`torch.utils.data.DataLoader`.

        .. note::
            Override this method to provide a custom batch sampler.

        Args:
            dataset (Dataset): The dataset to sample from.
            batch_size (int): Number of samples per batch.
            drop_last (bool): If True, drop the last incomplete batch if the dataset size
                is not divisible by the batch size.
            valid_label_columns (List[str]): List of column names to check for labels.
                The first column name from ``valid_label_columns`` found in the dataset will
                be used as the label column.
            generator (torch.Generator, optional): Optional random number generator for shuffling
                the indices.
        """
        if isinstance(dataset, IterableDataset):
            if self.args.batch_sampler != BatchSamplers.BATCH_SAMPLER:
                logger.warning("When using an IterableDataset, you cannot specify a batch sampler.")
            return None

        if self.args.batch_sampler == BatchSamplers.NO_DUPLICATES:
            return NoDuplicatesBatchSampler(
                dataset=dataset,
                batch_size=batch_size,
                drop_last=drop_last,
                valid_label_columns=valid_label_columns,
                generator=generator,
            )

        if self.args.batch_sampler == BatchSamplers.GROUP_BY_LABEL:
            return GroupByLabelBatchSampler(
                dataset=dataset,
                batch_size=batch_size,
                drop_last=drop_last,
                valid_label_columns=valid_label_columns,
            )

        if self.args.batch_sampler == BatchSamplers.BATCH_SAMPLER:
            return DefaultBatchSampler(
                SubsetRandomSampler(range(len(dataset)), generator=generator),
                batch_size=batch_size,
                drop_last=drop_last,
            )

    def get_multi_dataset_batch_sampler(
        self,
        dataset: ConcatDataset,
        batch_samplers: list[BatchSampler],
        generator: torch.Generator | None = None,
        seed: int | None = 0,
    ) -> BatchSampler:
        """
        Returns the appropriate multi-dataset batch sampler based on the ``multi_dataset_batch_sampler`` argument
        in ``self.args``. This batch sampler class supports ``__len__`` and ``__iter__`` methods, and is used as the
        ``batch_sampler`` to create the :class:`torch.utils.data.DataLoader`.

        .. note::
            Override this method to provide a custom multi-dataset batch sampler.

        Args:
            dataset (ConcatDataset): The concatenation of all datasets.
            batch_samplers (List[BatchSampler]): List of batch samplers for each dataset in the concatenated dataset.
            generator (torch.Generator, optional): Optional random number generator for shuffling the indices.
            seed (int, optional): Optional seed for the random number generator
        """
        if self.args.multi_dataset_batch_sampler == MultiDatasetBatchSamplers.ROUND_ROBIN:
            return RoundRobinBatchSampler(
                dataset=dataset,
                batch_samplers=batch_samplers,
                generator=generator,
                seed=seed,
            )

        if self.args.multi_dataset_batch_sampler == MultiDatasetBatchSamplers.PROPORTIONAL:
            return ProportionalBatchSampler(
                dataset=dataset,
                batch_samplers=batch_samplers,
                generator=generator,
                seed=seed,
            )

    def get_train_dataloader(self) -> DataLoader:
        """
        Returns the training [`~torch.utils.data.DataLoader`].

        Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
        training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.
        """
        if self.train_dataset is None:
            raise ValueError("Trainer: training requires a train_dataset.")

        train_dataset = self.train_dataset
        data_collator = self.data_collator

        generator = torch.Generator()
        if self.args.seed:
            generator.manual_seed(self.args.seed)

        dataloader_params = {
            "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,
            "prefetch_factor": self.args.dataloader_prefetch_factor,
        }

        if isinstance(train_dataset, IterableDataset):
            dataloader_params.update(
                {
                    "batch_size": self.args.train_batch_size,
                    "drop_last": self.args.dataloader_drop_last,
                }
            )

        elif isinstance(train_dataset, IterableDatasetDict):
            raise ValueError(
                "Sentence Transformers is not compatible with IterableDatasetDict. Please use a DatasetDict instead."
            )

        elif isinstance(train_dataset, DatasetDict):
            for dataset_name, dataset in train_dataset.items():
                self.validate_column_names(dataset, dataset_name=dataset_name)
                if isinstance(dataset, IterableDataset):
                    raise ValueError(
                        "Sentence Transformers is not compatible with a DatasetDict containing an IterableDataset."
                    )
            if isinstance(self.loss, dict):
                train_dataset = self.add_dataset_name_column(train_dataset)
            batch_samplers = [
                self.get_batch_sampler(
                    dataset,
                    batch_size=self.args.train_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    valid_label_columns=data_collator.valid_label_columns,
                    generator=generator,
                )
                for dataset in train_dataset.values()
            ]

            train_dataset = ConcatDataset(train_dataset.values())
            batch_sampler = self.get_multi_dataset_batch_sampler(
                dataset=train_dataset,
                batch_samplers=batch_samplers,
                generator=generator,
                seed=self.args.seed,
            )
            dataloader_params["batch_sampler"] = batch_sampler

        elif isinstance(train_dataset, Dataset):
            self.validate_column_names(train_dataset)

            batch_sampler = self.get_batch_sampler(
                train_dataset,
                batch_size=self.args.train_batch_size,
                drop_last=self.args.dataloader_drop_last,
                valid_label_columns=data_collator.valid_label_columns,
                generator=generator,
            )
            dataloader_params["batch_sampler"] = batch_sampler
        else:
            raise ValueError(
                "Unsupported `train_dataset` type. Use a Dataset, DatasetDict, or IterableDataset for training."
            )

        # If 'even_batches' is True, it will use the initial few samples to pad out the last sample. This can
        # cause issues with multi-dataset training, so we want to set this to False.
        # For evaluation, setting 'even_batches' to False results in hanging, so we keep it as True there.
        self.accelerator.even_batches = False
        self._train_dataloader = self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
        return self._train_dataloader

    def get_eval_dataloader(self, eval_dataset: Dataset | None = None) -> DataLoader:
        """
        Returns the evaluation [`~torch.utils.data.DataLoader`].

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            eval_dataset (`torch.utils.data.Dataset`, *optional*):
                If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
                by the `model.forward()` method are automatically removed. It must implement `__len__`.
        """
        if eval_dataset is None and self.eval_dataset is None:
            # Prevent errors if the evaluator is set but no eval_dataset is provided
            if self.evaluator is not None:
                return DataLoader([])
            raise ValueError("Trainer: evaluation requires an eval_dataset.")
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
        data_collator = self.data_collator

        generator = torch.Generator()
        if self.args.seed:
            generator.manual_seed(self.args.seed)

        dataloader_params = {
            "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,
            "prefetch_factor": self.args.dataloader_prefetch_factor,
        }
        if isinstance(eval_dataset, IterableDataset):
            dataloader_params.update(
                {
                    "batch_size": self.args.eval_batch_size,
                    "drop_last": self.args.dataloader_drop_last,
                }
            )

        elif isinstance(eval_dataset, IterableDatasetDict):
            raise ValueError(
                "Sentence Transformers is not compatible with IterableDatasetDict. Please use a DatasetDict instead."
            )

        elif isinstance(eval_dataset, DatasetDict):
            for dataset in eval_dataset.values():
                if isinstance(dataset, IterableDataset):
                    raise ValueError(
                        "Sentence Transformers is not compatible with a DatasetDict containing an IterableDataset."
                    )
            if isinstance(self.loss, dict):
                eval_dataset = self.add_dataset_name_column(eval_dataset)
            batch_samplers = [
                self.get_batch_sampler(
                    dataset,
                    batch_size=self.args.eval_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    valid_label_columns=data_collator.valid_label_columns,
                    generator=generator,
                )
                for dataset in eval_dataset.values()
            ]

            eval_dataset = ConcatDataset(eval_dataset.values())
            batch_sampler = self.get_multi_dataset_batch_sampler(
                dataset=eval_dataset,
                batch_samplers=batch_samplers,
                generator=generator,
                seed=self.args.seed,
            )
            dataloader_params["batch_sampler"] = batch_sampler

        elif isinstance(eval_dataset, Dataset):
            batch_sampler = self.get_batch_sampler(
                eval_dataset,
                batch_size=self.args.eval_batch_size,
                drop_last=self.args.dataloader_drop_last,
                valid_label_columns=data_collator.valid_label_columns,
                generator=generator,
            )
            dataloader_params["batch_sampler"] = batch_sampler

        else:
            raise ValueError(
                "Unsupported `eval_dataset` type. Use a Dataset, DatasetDict, or IterableDataset for evaluation."
            )

        # If 'even_batches' is True, it will use the initial few samples to pad out the last sample. This can
        # cause issues with multi-dataset training, so we want to set this to False during training.
        # For evaluation, setting 'even_batches' to False results in hanging, so we keep it as True here.
        self.accelerator.even_batches = True
        return self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params))

    def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
        """
        Returns the training [`~torch.utils.data.DataLoader`].

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            test_dataset (`torch.utils.data.Dataset`, *optional*):
                The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the
                `model.forward()` method are automatically removed. It must implement `__len__`.
        """
        data_collator = self.data_collator

        generator = torch.Generator()
        if self.args.seed:
            generator.manual_seed(self.args.seed)

        dataloader_params = {
            "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,
            "prefetch_factor": self.args.dataloader_prefetch_factor,
        }

        if isinstance(test_dataset, IterableDataset):
            dataloader_params.update(
                {
                    "batch_size": self.args.eval_batch_size,
                    "drop_last": self.args.dataloader_drop_last,
                }
            )

        elif isinstance(test_dataset, IterableDatasetDict):
            raise ValueError(
                "Sentence Transformers is not compatible with IterableDatasetDict. Please use a DatasetDict instead."
            )

        elif isinstance(test_dataset, DatasetDict):
            for dataset_name, dataset in test_dataset.items():
                self.validate_column_names(dataset, dataset_name=dataset_name)
                if isinstance(dataset, IterableDataset):
                    raise ValueError(
                        "Sentence Transformers is not compatible with a DatasetDict containing an IterableDataset."
                    )
            if isinstance(self.loss, dict):
                test_dataset = self.add_dataset_name_column(test_dataset)
            batch_samplers = [
                self.get_batch_sampler(
                    dataset,
                    batch_size=self.args.eval_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    valid_label_columns=data_collator.valid_label_columns,
                    generator=generator,
                )
                for dataset in test_dataset.values()
            ]

            test_dataset = ConcatDataset(test_dataset.values())
            batch_sampler = self.get_multi_dataset_batch_sampler(
                dataset=test_dataset,
                batch_samplers=batch_samplers,
                generator=generator,
                seed=self.args.seed,
            )
            dataloader_params["batch_sampler"] = batch_sampler

        elif isinstance(test_dataset, Dataset):
            self.validate_column_names(test_dataset)

            batch_sampler = self.get_batch_sampler(
                test_dataset,
                batch_size=self.args.eval_batch_size,
                drop_last=self.args.dataloader_drop_last,
                valid_label_columns=data_collator.valid_label_columns,
                generator=generator,
            )
            dataloader_params["batch_sampler"] = batch_sampler

        else:
            raise ValueError(
                "Unsupported `test_dataset` type. Use a Dataset, DatasetDict, or IterableDataset for testing."
            )

        # If 'even_batches' is True, it will use the initial few samples to pad out the last sample. This can
        # cause issues with multi-dataset training, so we want to set this to False.
        # For evaluation, setting 'even_batches' to False results in hanging, so we keep it as True there.
        self.accelerator.even_batches = False
        self._train_dataloader = self.accelerator.prepare(DataLoader(test_dataset, **dataloader_params))
        return self._train_dataloader

    def _save(self, output_dir: str | None = None, state_dict=None) -> None:
        # If we are executing this function, we are the process zero, so we don't check for that.
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        os.makedirs(output_dir, exist_ok=True)
        logger.info(f"Saving model checkpoint to {output_dir}")

        self.model.save_pretrained(output_dir, safe_serialization=self.args.save_safetensors)

        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(output_dir)

        # Good practice: save your training arguments together with the trained model
        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

    def _load_from_checkpoint(self, checkpoint_path: str) -> None:
        from sentence_transformers import SentenceTransformer

        loaded_model = SentenceTransformer(checkpoint_path, trust_remote_code=self.model.trust_remote_code)
        self.model.load_state_dict(loaded_model.state_dict())

    def create_model_card(
        self,
        language: str | None = None,
        license: str | None = None,
        tags: str | list[str] | None = None,
        model_name: str | None = None,
        finetuned_from: str | None = None,
        tasks: str | list[str] | None = None,
        dataset_tags: str | list[str] | None = None,
        dataset: str | list[str] | None = None,
        dataset_args: str | list[str] | None = None,
        **kwargs,
    ) -> None:
        if not self.is_world_process_zero():
            return

        if language:
            self.model.model_card_data.set_language(language)
        if license:
            self.model.model_card_data.set_license(license)
        if tags:
            self.model.model_card_data.add_tags(tags)

        self.model._create_model_card(self.args.output_dir, model_name=model_name)

    def get_optimizer_cls_and_kwargs(
        self, args: SentenceTransformerTrainingArguments, model: SentenceTransformer | None = None
    ) -> tuple[Any, Any]:
        """
        We have to override the optimizer_grouped_parameters because the Trainer superclass bases it on the `model`
        itself, but the SentenceTransformer losses can have weights that should be updated as well, e.g.
        SoftmaxLoss (see #2872).

        This method requires `transformers` >= 4.43.0.
        """

        if isinstance(self.loss, dict):
            loss_model = nn.Sequential(OrderedDict(self.loss))
        else:
            loss_model = self.loss
        optimizer_cls, optimizer_kwargs = super().get_optimizer_cls_and_kwargs(args, loss_model)

        # If the kwargs were not overridden by the super() call, then we should override them here so that the potential
        # weights in the loss(es) can also be updated.
        if not {"params", "model", "optimizer_dict"} & set(optimizer_kwargs.keys()):
            decay_parameters = self.get_decay_parameter_names(loss_model)
            optimizer_kwargs["optimizer_dict"] = [
                {
                    "params": [
                        p for n, p in loss_model.named_parameters() if (n in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [
                        p for n, p in loss_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": 0.0,
                },
            ]

        return optimizer_cls, optimizer_kwargs