#  ------------------------------------------------------------------------------------------
#  SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models
#  arXiv: https://arxiv.org/abs/2409.00055
#  Copyright (c) 2024 Yang Cao, (c) 2020-present Hugging Face Inc.
#  Licensed under the Apache License, Version 2.0.
#  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.
#  ------------------------------------------------------------------------------------------

"""
The Trainer class, with SORSA implementation.
"""
from .config import SORSATrainingArguments
from .layer import calc_ortho

import contextlib
import copy
import functools
import glob
import importlib.metadata
import inspect
import math
import os
import random
import re
import shutil
import sys
import tempfile
import time
import warnings
from collections.abc import Mapping
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union


# Integrations must be imported before ML frameworks:
# isort: off
from transformers.integrations import (
    get_reporting_integration_callbacks,
    hp_params,
)

# isort: on

import huggingface_hub.utils as hf_hub_utils
import numpy as np
import torch
import torch.distributed as dist
from huggingface_hub import ModelCard, create_repo, upload_folder
from packaging import version
from torch import nn
from torch.utils.data import (
    DataLoader,
    Dataset,
    IterableDataset,
    RandomSampler,
    SequentialSampler,
)

from transformers import __version__
from transformers.configuration_utils import PretrainedConfig
from transformers.data.data_collator import (
    DataCollator,
    DataCollatorWithPadding,
    default_data_collator,
)
from transformers.debug_utils import DebugOption, DebugUnderflowOverflow
from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
from transformers.hyperparameter_search import (
    ALL_HYPERPARAMETER_SEARCH_BACKENDS,
    default_hp_search_backend,
)
from transformers.integrations.deepspeed import (
    deepspeed_init,
    deepspeed_load_checkpoint,
    is_deepspeed_available,
)
from transformers.integrations.tpu import tpu_spmd_dataloader
from transformers.modelcard import TrainingSummary
from transformers.modeling_utils import PreTrainedModel, load_sharded_checkpoint
from transformers.models.auto.modeling_auto import (
    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    MODEL_MAPPING_NAMES,
)
from transformers.optimization import Adafactor, get_scheduler
from transformers.pytorch_utils import (
    ALL_LAYERNORM_LAYERS,
    is_torch_greater_or_equal_than_1_13,
    is_torch_greater_or_equal_than_2_3,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_callback import (
    CallbackHandler,
    DefaultFlowCallback,
    ExportableState,
    PrinterCallback,
    ProgressCallback,
    TrainerCallback,
    TrainerControl,
    TrainerState,
)
from transformers.trainer_pt_utils import (
    DistributedTensorGatherer,
    EvalLoopContainer,
    IterableDatasetShard,
    LabelSmoother,
    LayerWiseDummyOptimizer,
    LengthGroupedSampler,
    SequentialDistributedSampler,
    distributed_broadcast_scalars,
    distributed_concat,
    find_batch_size,
    get_model_param_count,
    get_module_class_from_name,
    get_parameter_names,
    nested_concat,
    nested_detach,
    nested_numpify,
    nested_xla_mesh_reduce,
    reissue_pt_warnings,
    remove_dummy_checkpoint,
)
from transformers.trainer_utils import (
    PREFIX_CHECKPOINT_DIR,
    BestRun,
    EvalLoopOutput,
    EvalPrediction,
    HPSearchBackend,
    HubStrategy,
    IntervalStrategy,
    PredictionOutput,
    RemoveColumnsCollator,
    TrainerMemoryTracker,
    TrainOutput,
    check_target_module_exists,
    default_compute_objective,
    denumpify_detensorize,
    enable_full_determinism,
    find_executable_batch_size,
    get_last_checkpoint,
    has_length,
    neftune_post_forward_hook,
    number_of_arguments,
    seed_worker,
    set_seed,
    speed_metrics,
)
from transformers.training_args import OptimizerNames, ParallelMode, TrainingArguments
from transformers.utils import (
    ADAPTER_CONFIG_NAME,
    ADAPTER_SAFE_WEIGHTS_NAME,
    ADAPTER_WEIGHTS_NAME,
    CONFIG_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    XLA_FSDPV2_MIN_VERSION,
    PushInProgress,
    PushToHubMixin,
    can_return_loss,
    find_labels,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_datasets_available,
    is_galore_torch_available,
    is_in_notebook,
    is_ipex_available,
    is_peft_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_torch_compile_available,
    is_torch_mlu_available,
    is_torch_neuroncore_available,
    is_torch_npu_available,
    is_torch_xla_available,
    logging,
    strtobool,
)
from transformers.utils.quantization_config import QuantizationMethod


DEFAULT_CALLBACKS = [DefaultFlowCallback]
DEFAULT_PROGRESS_CALLBACK = ProgressCallback

if is_in_notebook():
    from transformers.utils.notebook import NotebookProgressCallback

    DEFAULT_PROGRESS_CALLBACK = NotebookProgressCallback

if is_apex_available():
    from apex import amp

if is_datasets_available():
    import datasets

if is_torch_xla_available():
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met
    from torch_xla import __version__ as XLA_VERSION

    IS_XLA_FSDPV2_POST_2_2 = version.parse(XLA_VERSION) >= version.parse(
        XLA_FSDPV2_MIN_VERSION
    )
    if IS_XLA_FSDPV2_POST_2_2:
        import torch_xla.distributed.spmd as xs
        import torch_xla.runtime as xr
else:
    IS_XLA_FSDPV2_POST_2_2 = False


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

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

    from transformers.trainer_pt_utils import (
        smp_forward_backward,
        smp_forward_only,
        smp_gather,
        smp_nested_concat,
    )
else:
    IS_SAGEMAKER_MP_POST_1_10 = False


if is_safetensors_available():
    import safetensors.torch

if is_peft_available():
    from peft import PeftModel


if is_accelerate_available():
    from accelerate import Accelerator, skip_first_batches
    from accelerate import __version__ as accelerate_version
    from accelerate.utils import (
        DistributedDataParallelKwargs,
        DistributedType,
        GradientAccumulationPlugin,
        load_fsdp_model,
        load_fsdp_optimizer,
        save_fsdp_model,
        save_fsdp_optimizer,
    )

    DATA_SAMPLERS = [RandomSampler]
    if version.parse(accelerate_version) > version.parse("0.23.0"):
        from accelerate.data_loader import SeedableRandomSampler

        DATA_SAMPLERS += [SeedableRandomSampler]

    if is_deepspeed_available():
        from accelerate.utils import DeepSpeedSchedulerWrapper

if is_accelerate_available("0.28.0"):
    from accelerate.utils import DataLoaderConfiguration


def _is_peft_model(model):
    if is_peft_available():
        classes_to_check = (PeftModel,) if is_peft_available() else ()
        # Here we also check if the model is an instance of `PeftMixedModel` introduced in peft>=0.7.0: https://github.com/huggingface/transformers/pull/28321
        if version.parse(importlib.metadata.version("peft")) >= version.parse("0.7.0"):
            from peft import PeftMixedModel

            classes_to_check = (*classes_to_check, PeftMixedModel)
        return isinstance(model, classes_to_check)
    return False


def _get_fsdp_ckpt_kwargs():
    # TODO: @AjayP13, @younesbelkada replace this check with version check at the next `accelerate` release
    if is_accelerate_available() and "adapter_only" in list(
        inspect.signature(save_fsdp_model).parameters
    ):
        return {"adapter_only": True}
    else:
        return {}


if TYPE_CHECKING:
    import optuna

    if is_datasets_available():
        import datasets

logger = logging.get_logger(__name__)


# Name of the files used for checkpointing
TRAINING_ARGS_NAME = "training_args.bin"
TRAINER_STATE_NAME = "trainer_state.json"
OPTIMIZER_NAME = "optimizer.pt"
OPTIMIZER_NAME_BIN = "optimizer.bin"
SCHEDULER_NAME = "scheduler.pt"
SCALER_NAME = "scaler.pt"
FSDP_MODEL_NAME = "pytorch_model_fsdp"


class Trainer:
    """
    Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.

    Args:
        model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*):
            The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed.

            <Tip>

            [`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use
            your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers
            models.

            </Tip>

        args ([`TrainingArguments`], *optional*):
            The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the
            `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided.
        data_collator (`DataCollator`, *optional*):
            The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will
            default to [`default_data_collator`] if no `tokenizer` is provided, an instance of
            [`DataCollatorWithPadding`] otherwise.
        train_dataset (Union[`torch.utils.data.Dataset`, `torch.utils.data.IterableDataset`, `datasets.Dataset`], *optional*):
            The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the
            `model.forward()` method are automatically removed.

            Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a
            distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a
            `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will
            manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally
            sets the seed of the RNGs used.
        eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`, `datasets.Dataset`]), *optional*):
             The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the
             `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each
             dataset prepending the dictionary key to the metric name.
        tokenizer ([`PreTrainedTokenizerBase`], *optional*):
            The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the
            maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
            interrupted training or reuse the fine-tuned model.
        model_init (`Callable[[], PreTrainedModel]`, *optional*):
            A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start
            from a new instance of the model as given by this function.

            The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to
            be able to choose different architectures according to hyper parameters (such as layer count, sizes of
            inner layers, dropout probabilities etc).
        compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):
            The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return
            a dictionary string to metric values. *Note* When passing TrainingArgs with `batch_eval_metrics` set to
            `True`, your compute_metrics function must take a boolean `compute_result` argument. This will be triggered
            after the last eval batch to signal that the function needs to calculate and return the global summary
            statistics rather than accumulating the batch-level statistics.
        callbacks (List of [`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[torch.optim.Optimizer, 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 [`AdamW`] on your
            model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
            A function that preprocess the logits right before caching them at each evaluation step. Must take two
            tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
            by this function will be reflected in the predictions received by `compute_metrics`.

            Note that the labels (second parameter) will be `None` if the dataset does not have them.

    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`)

    """

    # Those are used as methods of the Trainer in examples.
    from transformers.trainer_pt_utils import (
        _get_learning_rate,
        log_metrics,
        metrics_format,
        save_metrics,
        save_state,
    )

    def __init__(
        self,
        model: Union[PreTrainedModel, nn.Module] = None,
        args: SORSATrainingArguments = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[
            Union[Dataset, IterableDataset, "datasets.Dataset"]
        ] = None,
        eval_dataset: Optional[
            Union[Dataset, Dict[str, Dataset], "datasets.Dataset"]
        ] = None,
        tokenizer: Optional[PreTrainedTokenizerBase] = None,
        model_init: Optional[Callable[[], PreTrainedModel]] = 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,
    ):
        if args is None:
            output_dir = "tmp_trainer"
            logger.info(
                f"No `TrainingArguments` passed, using `output_dir={output_dir}`."
            )
            args = TrainingArguments(output_dir=output_dir)
        if args.batch_eval_metrics and compute_metrics is not None:
            if (
                "compute_result"
                not in inspect.signature(compute_metrics).parameters.keys()
            ):
                raise ValueError(
                    "When using `batch_eval_metrics`, your `compute_metrics` function must take a `compute_result`"
                    " boolean argument which will be triggered after the last batch of the eval set to signal that the"
                    " summary statistics should be returned by the function."
                )
        self.args = args
        # Seed must be set before instantiating the model when using model
        (
            enable_full_determinism(self.args.seed)
            if self.args.full_determinism
            else set_seed(self.args.seed)
        )
        self.hp_name = None
        self.deepspeed = None
        self.is_in_train = False

        self.create_accelerator_and_postprocess()

        # memory metrics - must set up as early as possible
        self._memory_tracker = TrainerMemoryTracker(self.args.skip_memory_metrics)
        self._memory_tracker.start()

        # set the correct log level depending on the node
        log_level = args.get_process_log_level()
        logging.set_verbosity(log_level)

        # force device and distributed setup init explicitly
        args._setup_devices

        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

        if model.__class__.__name__ in MODEL_MAPPING_NAMES:
            raise ValueError(
                f"The model you have picked ({model.__class__.__name__}) cannot be used as is for training: it only "
                "computes hidden states and does not accept any labels. You should choose a model with a head "
                "suitable for your task like any of the `AutoModelForXxx` listed at "
                "https://huggingface.co/docs/transformers/model_doc/auto"
            )

        if getattr(model, "is_parallelizable", False) and getattr(
            model, "model_parallel", False
        ):
            self.is_model_parallel = True
        else:
            self.is_model_parallel = False

        if getattr(model, "hf_device_map", None) is not None:
            devices = [
                device
                for device in set(model.hf_device_map.values())
                if device not in ["cpu", "disk"]
            ]
            if len(devices) > 1:
                self.is_model_parallel = True
            elif len(devices) == 1:
                self.is_model_parallel = self.args.device != torch.device(devices[0])
            else:
                self.is_model_parallel = False

            # warn users
            if self.is_model_parallel:
                logger.info(
                    "You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set"
                    " to `True` to avoid any unexpected behavior such as device placement mismatching."
                )

        _is_quantized_and_base_model = getattr(
            model, "is_quantized", False
        ) and not getattr(model, "_hf_peft_config_loaded", False)
        _quantization_method_supports_training = (
            getattr(model, "hf_quantizer", None) is not None
            and model.hf_quantizer.is_trainable
        )

        # Filter out quantized + compiled models
        if _is_quantized_and_base_model and hasattr(model, "_orig_mod"):
            raise ValueError(
                "You cannot fine-tune quantized model with `torch.compile()` make sure to pass a non-compiled model when fine-tuning a quantized model with PEFT"
            )

        # At this stage the model is already loaded
        if _is_quantized_and_base_model and not _is_peft_model(model):
            raise ValueError(
                "You cannot perform fine-tuning on purely quantized models. Please attach trainable adapters on top of"
                " the quantized model to correctly perform fine-tuning. Please see: https://huggingface.co/docs/transformers/peft"
                " for more details"
            )
        elif (
            _is_quantized_and_base_model and not _quantization_method_supports_training
        ):
            raise ValueError(
                f"The model you are trying to fine-tune is quantized with {model.hf_quantizer.quantization_config.quant_method}"
                " but that quantization method do not support training. Please open an issue on GitHub: https://github.com/huggingface/transformers"
                f" to request the support for training support for {model.hf_quantizer.quantization_config.quant_method}"
            )

        self.is_fsdp_xla_enabled = args.fsdp_config["xla"]
        if len(args.fsdp) > 0:
            if self.is_deepspeed_enabled:
                raise ValueError(
                    "Using --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags."
                )
            if (
                not args.fsdp_config["xla"]
                and args.parallel_mode != ParallelMode.DISTRIBUTED
            ):
                raise ValueError("Using fsdp only works in distributed training.")

        # one place to sort out whether to place the model on device or not
        # postpone switching model to cuda when:
        # 1. MP - since we are trying to fit a much bigger than 1 gpu model
        # 2. fp16-enabled DeepSpeed loads the model in half the size and it doesn't need .to() anyway,
        #    and we only use deepspeed for training at the moment
        # 3. full bf16 or fp16 eval - since the model needs to be cast to the right dtype first
        # 4. FSDP - same as MP
        self.place_model_on_device = args.place_model_on_device
        if (
            self.is_model_parallel
            or self.is_deepspeed_enabled
            or ((args.fp16_full_eval or args.bf16_full_eval) and not args.do_train)
            or self.is_fsdp_xla_enabled
            or self.is_fsdp_enabled
        ):
            self.place_model_on_device = False

        default_collator = (
            DataCollatorWithPadding(tokenizer)
            if tokenizer is not None
            and isinstance(
                tokenizer, (PreTrainedTokenizerBase, SequenceFeatureExtractor)
            )
            else default_data_collator
        )
        self.data_collator = (
            data_collator if data_collator is not None else default_collator
        )
        self.train_dataset = train_dataset
        self.eval_dataset = eval_dataset
        self.tokenizer = tokenizer

        # Bnb Quantized models doesn't support `.to` operation.
        if (
            self.place_model_on_device
            and not getattr(model, "quantization_method", None)
            == QuantizationMethod.BITS_AND_BYTES
        ):
            self._move_model_to_device(model, args.device)

        # Force n_gpu to 1 to avoid DataParallel as MP will manage the GPUs
        if self.is_model_parallel:
            self.args._n_gpu = 1

        # later use `self.model is self.model_wrapped` to check if it's wrapped or not
        self.model_wrapped = model
        self.model = model

        self.neftune_noise_alpha = args.neftune_noise_alpha

        self.compute_metrics = compute_metrics
        self.preprocess_logits_for_metrics = preprocess_logits_for_metrics
        self.optimizer, self.lr_scheduler = optimizers
        if model_init is not None and (
            self.optimizer is not None or self.lr_scheduler is not None
        ):
            raise RuntimeError(
                "Passing a `model_init` is incompatible with providing the `optimizers` argument. "
                "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
            )
        if is_torch_xla_available() and self.optimizer is not None:
            for param in self.model.parameters():
                model_device = param.device
                break
            for param_group in self.optimizer.param_groups:
                if len(param_group["params"]) > 0:
                    optimizer_device = param_group["params"][0].device
                    break
            if model_device != optimizer_device:
                raise ValueError(
                    "The model and the optimizer parameters are not on the same device, which probably means you"
                    " created an optimizer around your model **before** putting on the device and passing it to the"
                    " `Trainer`. Make sure the lines `import torch_xla.core.xla_model as xm` and"
                    " `model.to(xm.xla_device())` is performed before the optimizer creation in your script."
                )
        if (
            self.is_deepspeed_enabled
            or self.is_fsdp_xla_enabled
            or self.is_fsdp_enabled
        ) and (self.optimizer is not None or self.lr_scheduler is not None):
            raise RuntimeError(
                "Passing `optimizers` is not allowed if Deepspeed or PyTorch FSDP is enabled. "
                "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
            )
        default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(
            self.args.report_to
        )
        callbacks = (
            default_callbacks if callbacks is None else default_callbacks + callbacks
        )
        self.callback_handler = CallbackHandler(
            callbacks, self.model, self.tokenizer, self.optimizer, self.lr_scheduler
        )
        self.add_callback(
            PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK
        )

        # Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
        self._loggers_initialized = False

        # Create distant repo and output directory if needed
        self.hub_model_id = None
        if self.args.push_to_hub:
            self.init_hf_repo()
        if self.args.should_save:
            os.makedirs(self.args.output_dir, exist_ok=True)

        if not callable(self.data_collator) and callable(
            getattr(self.data_collator, "collate_batch", None)
        ):
            raise ValueError(
                "The `data_collator` should be a simple callable (function, class with `__call__`)."
            )

        if args.max_steps > 0 and args.num_train_epochs > 0:
            logger.warning(
                "max_steps is given, it will override any value given in num_train_epochs"
            )

        if (
            train_dataset is not None
            and not has_length(train_dataset)
            and args.max_steps <= 0
        ):
            raise ValueError(
                "The train_dataset does not implement __len__, max_steps has to be specified. "
                "The number of steps needs to be known in advance for the learning rate scheduler."
            )

        if (
            train_dataset is not None
            and isinstance(train_dataset, torch.utils.data.IterableDataset)
            and args.group_by_length
        ):
            raise ValueError(
                "the `--group_by_length` option is only available for `Dataset`, not `IterableDataset"
            )

        self._signature_columns = None

        # Mixed precision setup
        self.use_apex = False
        self.use_cpu_amp = False

        # Mixed precision setup for SageMaker Model Parallel
        if is_sagemaker_mp_enabled():
            # BF16 + model parallelism in SageMaker: currently not supported, raise an error
            if args.bf16:
                raise ValueError(
                    "SageMaker Model Parallelism does not support BF16 yet. Please use FP16 instead "
                )

            if IS_SAGEMAKER_MP_POST_1_10:
                # When there's mismatch between SMP config and trainer argument, use SMP config as truth
                if args.fp16 != smp.state.cfg.fp16:
                    logger.warning(
                        f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16}, "
                        f"but FP16 provided in trainer argument is {args.fp16}, "
                        f"setting to {smp.state.cfg.fp16}"
                    )
                    args.fp16 = smp.state.cfg.fp16
            else:
                # smp < 1.10 does not support fp16 in trainer.
                if hasattr(smp.state.cfg, "fp16"):
                    logger.warning(
                        f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16}, "
                        "but SageMaker Model Parallelism < 1.10 does not support FP16 in trainer."
                    )
        if (args.fp16 or args.bf16) and args.half_precision_backend == "auto":
            if args.device == torch.device("cpu"):
                if args.fp16:
                    if not is_torch_greater_or_equal_than_2_3:
                        raise ValueError(
                            "Tried to use `fp16` but it is not supported on cpu"
                        )
                else:
                    args.half_precision_backend = "cpu_amp"
            logger.info(f"Using {args.half_precision_backend} half precision backend")

        if (args.fp16 or args.bf16) and not (
            self.is_deepspeed_enabled or is_sagemaker_mp_enabled()
        ):
            # deepspeed and SageMaker Model Parallel manage their own half precision
            if args.half_precision_backend == "cpu_amp":
                self.use_cpu_amp = True
                self.amp_dtype = torch.bfloat16
            elif args.half_precision_backend == "apex":
                if not is_apex_available():
                    raise ImportError(
                        "Using FP16 with APEX but APEX is not installed, please refer to"
                        " https://www.github.com/nvidia/apex."
                    )
                self.use_apex = True

        # Label smoothing
        if self.args.label_smoothing_factor != 0:
            self.label_smoother = LabelSmoother(
                epsilon=self.args.label_smoothing_factor
            )
        else:
            self.label_smoother = None

        self.control = TrainerControl()

        self.state = TrainerState(
            is_local_process_zero=self.is_local_process_zero(),
            is_world_process_zero=self.is_world_process_zero(),
            stateful_callbacks=[
                cb
                for cb in self.callback_handler.callbacks + [self.control]
                if isinstance(cb, ExportableState)
            ],
        )
        # Internal variable to count flos in each process, will be accumulated in `self.state.total_flos` then
        # returned to 0 every time flos need to be logged
        self.current_flos = 0
        self.hp_search_backend = None
        default_label_names = find_labels(self.model.__class__)
        self.label_names = (
            default_label_names
            if self.args.label_names is None
            else self.args.label_names
        )
        self.can_return_loss = can_return_loss(self.model.__class__)
        self.control = self.callback_handler.on_init_end(
            self.args, self.state, self.control
        )

        # Internal variables to help with automatic batch size reduction
        self._train_batch_size = args.train_batch_size
        self._created_lr_scheduler = False

        # very last
        self._memory_tracker.stop_and_update_metrics()

        # torch.compile
        if args.torch_compile and not is_torch_compile_available():
            raise RuntimeError("Using torch.compile requires PyTorch 2.0 or higher.")

        self.is_fsdp_xla_v2_enabled = args.fsdp_config.get("xla_fsdp_v2", False)
        if self.is_fsdp_xla_v2_enabled:
            if not IS_XLA_FSDPV2_POST_2_2:
                raise ValueError("FSDPv2 requires `torch_xla` 2.2 or higher.")
            # Prepare the SPMD mesh that is going to be used by the data loader and the FSDPv2 wrapper.
            # Tensor axis is just a placeholder where it will not be used in FSDPv2.
            num_devices = xr.global_runtime_device_count()
            xs.set_global_mesh(
                xs.Mesh(
                    np.array(range(num_devices)),
                    (num_devices, 1),
                    axis_names=("fsdp", "tensor"),
                )
            )

    def _activate_neftune(self, model):
        r"""
        Activates the neftune as presented in this code: https://github.com/neelsjain/NEFTune and paper:
        https://arxiv.org/abs/2310.05914
        """
        unwrapped_model = self.accelerator.unwrap_model(model)

        if _is_peft_model(unwrapped_model):
            embeddings = unwrapped_model.base_model.model.get_input_embeddings()
        else:
            embeddings = unwrapped_model.get_input_embeddings()

        del unwrapped_model

        embeddings.neftune_noise_alpha = self.neftune_noise_alpha
        hook_handle = embeddings.register_forward_hook(neftune_post_forward_hook)
        self.neftune_hook_handle = hook_handle
        return model

    def _deactivate_neftune(self, model):
        """
        Deactivates the neftune method. Make sure to call `_activate_neftune` first.
        """
        if not hasattr(self, "neftune_hook_handle"):
            raise ValueError(
                "Neftune is not activated make sure to call `trainer._activate_neftune()` first"
            )

        unwrapped_model = self.accelerator.unwrap_model(model)

        if _is_peft_model(unwrapped_model):
            embeddings = unwrapped_model.base_model.model.get_input_embeddings()
        else:
            embeddings = unwrapped_model.get_input_embeddings()

        self.neftune_hook_handle.remove()
        del embeddings.neftune_noise_alpha, unwrapped_model

    def add_callback(self, callback):
        """
        Add a callback to the current list of [`~transformers.TrainerCallback`].

        Args:
           callback (`type` or [`~transformers.TrainerCallback`]):
               A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
               first case, will instantiate a member of that class.
        """
        self.callback_handler.add_callback(callback)

    def pop_callback(self, callback):
        """
        Remove a callback from the current list of [`~transformers.TrainerCallback`] and returns it.

        If the callback is not found, returns `None` (and no error is raised).

        Args:
           callback (`type` or [`~transformers.TrainerCallback`]):
               A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
               first case, will pop the first member of that class found in the list of callbacks.

        Returns:
            [`~transformers.TrainerCallback`]: The callback removed, if found.
        """
        return self.callback_handler.pop_callback(callback)

    def remove_callback(self, callback):
        """
        Remove a callback from the current list of [`~transformers.TrainerCallback`].

        Args:
           callback (`type` or [`~transformers.TrainerCallback`]):
               A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
               first case, will remove the first member of that class found in the list of callbacks.
        """
        self.callback_handler.remove_callback(callback)

    def _move_model_to_device(self, model, device):
        model = model.to(device)
        # Moving a model to an XLA device disconnects the tied weights, so we have to retie them.
        if self.args.parallel_mode == ParallelMode.TPU and hasattr(
            model, "tie_weights"
        ):
            model.tie_weights()

    def _set_signature_columns_if_needed(self):
        if self._signature_columns is None:
            # Inspect model forward signature to keep only the arguments it accepts.
            model_to_inspect = self.model
            if _is_peft_model(self.model):
                if hasattr(self.model, "get_base_model"):
                    model_to_inspect = self.model.get_base_model()
                else:
                    # PeftMixedModel do not provide a `get_base_model` method
                    model_to_inspect = self.model.base_model.model
            signature = inspect.signature(model_to_inspect.forward)
            self._signature_columns = list(signature.parameters.keys())
            # Labels may be named label or label_ids, the default data collator handles that.
            self._signature_columns += list(
                set(["label", "label_ids"] + self.label_names)
            )

    def _remove_unused_columns(
        self, dataset: "datasets.Dataset", description: Optional[str] = None
    ):
        if not self.args.remove_unused_columns:
            return dataset
        self._set_signature_columns_if_needed()
        signature_columns = self._signature_columns

        ignored_columns = list(set(dataset.column_names) - set(signature_columns))
        if len(ignored_columns) > 0:
            dset_description = (
                "" if description is None else f"in the {description} set"
            )
            logger.info(
                f"The following columns {dset_description} don't have a corresponding argument in "
                f"`{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}."
                f" If {', '.join(ignored_columns)} are not expected by `{self.model.__class__.__name__}.forward`, "
                " you can safely ignore this message."
            )

        columns = [k for k in signature_columns if k in dataset.column_names]

        if version.parse(datasets.__version__) < version.parse("1.4.0"):
            dataset.set_format(
                type=dataset.format["type"],
                columns=columns,
                format_kwargs=dataset.format["format_kwargs"],
            )
            return dataset
        else:
            return dataset.remove_columns(ignored_columns)

    def _get_collator_with_removed_columns(
        self, data_collator: Callable, description: Optional[str] = None
    ) -> Callable:
        """Wrap the data collator in a callable removing unused columns."""
        if not self.args.remove_unused_columns:
            return data_collator
        self._set_signature_columns_if_needed()
        signature_columns = self._signature_columns

        remove_columns_collator = RemoveColumnsCollator(
            data_collator=data_collator,
            signature_columns=signature_columns,
            logger=logger,
            description=description,
            model_name=self.model.__class__.__name__,
        )
        return remove_columns_collator

    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
        if self.train_dataset is None or not has_length(self.train_dataset):
            return None

        # Build the sampler.
        if self.args.group_by_length:
            if is_datasets_available() and isinstance(
                self.train_dataset, datasets.Dataset
            ):
                lengths = (
                    self.train_dataset[self.args.length_column_name]
                    if self.args.length_column_name in self.train_dataset.column_names
                    else None
                )
            else:
                lengths = None
            model_input_name = (
                self.tokenizer.model_input_names[0]
                if self.tokenizer is not None
                else None
            )
            return LengthGroupedSampler(
                self.args.train_batch_size * self.args.gradient_accumulation_steps,
                dataset=self.train_dataset,
                lengths=lengths,
                model_input_name=model_input_name,
            )

        else:
            return RandomSampler(self.train_dataset)

    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
        if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
            train_dataset = self._remove_unused_columns(
                train_dataset, description="training"
            )
        else:
            data_collator = self._get_collator_with_removed_columns(
                data_collator, description="training"
            )

        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))

    def _get_eval_sampler(
        self, eval_dataset: Dataset
    ) -> Optional[torch.utils.data.Sampler]:
        # Deprecated code
        if self.args.use_legacy_prediction_loop:
            if is_torch_xla_available():
                return SequentialDistributedSampler(
                    eval_dataset,
                    num_replicas=xm.xrt_world_size(),
                    rank=xm.get_ordinal(),
                )
            elif is_sagemaker_mp_enabled():
                return SequentialDistributedSampler(
                    eval_dataset,
                    num_replicas=smp.dp_size(),
                    rank=smp.dp_rank(),
                    batch_size=self.args.per_device_eval_batch_size,
                )
            else:
                return SequentialSampler(eval_dataset)

        if self.args.world_size <= 1:
            return SequentialSampler(eval_dataset)
        else:
            return None

    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = 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:
            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
        if (
            hasattr(self, "_eval_dataloader")
            and self.args.dataloader_persistent_workers
        ):
            return self.accelerator.prepare(self._eval_dataloader)
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
        data_collator = self.data_collator

        if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
            eval_dataset = self._remove_unused_columns(
                eval_dataset, description="evaluation"
            )
        else:
            data_collator = self._get_collator_with_removed_columns(
                data_collator, description="evaluation"
            )

        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:
            self._eval_dataloader = eval_dataloader

        return self.accelerator.prepare(eval_dataloader)

    def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
        """
        Returns the test [`~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

        if is_datasets_available() and isinstance(test_dataset, datasets.Dataset):
            test_dataset = self._remove_unused_columns(test_dataset, description="test")
        else:
            data_collator = self._get_collator_with_removed_columns(
                data_collator, description="test"
            )

        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(test_dataset, torch.utils.data.IterableDataset):
            dataloader_params["sampler"] = self._get_eval_sampler(test_dataset)
            dataloader_params["drop_last"] = self.args.dataloader_drop_last
            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor

        # We use the same batch_size as for eval.
        return self.accelerator.prepare(DataLoader(test_dataset, **dataloader_params))

    def create_optimizer_and_scheduler(self, num_training_steps: int):
        """
        Setup the optimizer and the learning rate scheduler.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or
        `create_scheduler`) in a subclass.
        """
        self.create_optimizer()
        if IS_SAGEMAKER_MP_POST_1_10 and smp.state.cfg.fp16:
            # If smp >= 1.10 and fp16 is enabled, we unwrap the optimizer
            optimizer = self.optimizer.optimizer
        else:
            optimizer = self.optimizer
        self.create_scheduler(
            num_training_steps=num_training_steps, optimizer=optimizer
        )

    def get_decay_parameter_names(self, model) -> List[str]:
        """
        Get all parameter names that weight decay will be applied to

        Note that some models implement their own layernorm instead of calling nn.LayerNorm, weight decay could still
        apply to those modules since this function only filter out instance of nn.LayerNorm
        """
        decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS)
        decay_parameters = [name for name in decay_parameters if "bias" not in name]
        return decay_parameters

    def create_optimizer(self):
        """
        Setup the optimizer.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through `optimizers`, or subclass and override this method in a subclass.
        """
        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model

        if self.optimizer is None:
            decay_parameters = self.get_decay_parameter_names(opt_model)
            optimizer_grouped_parameters = [
                {
                    "params": [
                        p
                        for n, p in opt_model.named_parameters()
                        if (n in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [
                        p
                        for n, p in opt_model.named_parameters()
                        if (n not in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": 0.0,
                },
            ]

            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
                self.args, opt_model
            )

            # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
            # e.g. for GaLore optimizer.
            if "params" in optimizer_kwargs:
                optimizer_grouped_parameters = optimizer_kwargs.pop("params")

            # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
            # to avoid arguments conflicts.
            if "optimizer_dict" in optimizer_kwargs:
                optimizer_grouped_parameters = optimizer_kwargs.pop("optimizer_dict")

            self.optimizer = optimizer_cls(
                optimizer_grouped_parameters, **optimizer_kwargs
            )
            if optimizer_cls.__name__ == "Adam8bit":
                import bitsandbytes

                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()

                skipped = 0
                for module in opt_model.modules():
                    if isinstance(module, nn.Embedding):
                        skipped += sum(
                            {
                                p.data_ptr(): p.numel() for p in module.parameters()
                            }.values()
                        )
                        logger.info(f"skipped {module}: {skipped/2**20}M params")
                        manager.register_module_override(
                            module, "weight", {"optim_bits": 32}
                        )
                        logger.debug(f"bitsandbytes: will optimize {module} in fp32")
                logger.info(f"skipped: {skipped/2**20}M params")

        if is_sagemaker_mp_enabled():
            self.optimizer = smp.DistributedOptimizer(self.optimizer)

        return self.optimizer

    def get_num_trainable_parameters(self):
        """
        Get the number of trainable parameters.
        """
        return sum(p.numel() for p in self.model.parameters() if p.requires_grad)

    def get_learning_rates(self):
        """
        Returns the learning rate of each parameter from self.optimizer.
        """
        if self.optimizer is None:
            raise ValueError(
                "Trainer optimizer is None, please make sure you have setup the optimizer before."
            )
        return [group["lr"] for group in self.optimizer.param_groups]

    def get_optimizer_group(
        self, param: Optional[Union[str, torch.nn.parameter.Parameter]] = None
    ):
        """
        Returns optimizer group for a parameter if given, else returns all optimizer groups for params.

        Args:
            param (`str` or `torch.nn.parameter.Parameter`, *optional*):
                The parameter for which optimizer group needs to be returned.
        """
        if self.optimizer is None:
            raise ValueError(
                "Trainer optimizer is None, please make sure you have setup the optimizer before."
            )
        if param is not None:
            for group in self.optimizer.param_groups:
                if param in group["params"]:
                    return group
        return [group["params"] for group in self.optimizer.param_groups]

    @staticmethod
    def get_optimizer_cls_and_kwargs(
        args: TrainingArguments, model: Optional[PreTrainedModel] = None
    ) -> Tuple[Any, Any]:
        """
        Returns the optimizer class and optimizer parameters based on the training arguments.

        Args:
            args (`transformers.training_args.TrainingArguments`):
                The training arguments for the training session.

        """

        # parse args.optim_args
        optim_args = {}
        if args.optim_args:
            for mapping in args.optim_args.replace(" ", "").split(","):
                key, value = mapping.split("=")
                optim_args[key] = value

        optimizer_kwargs = {"lr": args.learning_rate}

        adam_kwargs = {
            "betas": (args.adam_beta1, args.adam_beta2),
            "eps": args.adam_epsilon,
        }
        if args.optim == OptimizerNames.ADAFACTOR:
            optimizer_cls = Adafactor
            optimizer_kwargs.update({"scale_parameter": False, "relative_step": False})
        elif args.optim == OptimizerNames.ADAMW_HF:
            from transformers.optimization import AdamW

            optimizer_cls = AdamW
            optimizer_kwargs.update(adam_kwargs)
        elif args.optim in [
            OptimizerNames.ADAMW_TORCH,
            OptimizerNames.ADAMW_TORCH_FUSED,
        ]:
            from torch.optim import AdamW

            optimizer_cls = AdamW
            optimizer_kwargs.update(adam_kwargs)
            if args.optim == OptimizerNames.ADAMW_TORCH_FUSED:
                optimizer_kwargs.update({"fused": True})
        elif args.optim == OptimizerNames.ADAMW_TORCH_XLA:
            try:
                from torch_xla.amp.syncfree import AdamW

                optimizer_cls = AdamW
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError(
                    "Trainer failed to import syncfree AdamW from torch_xla."
                )
        elif args.optim == OptimizerNames.ADAMW_TORCH_NPU_FUSED:
            try:
                from torch_npu.optim import NpuFusedAdamW

                optimizer_cls = NpuFusedAdamW
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError("Trainer failed to import FusedAdamW from torch_npu.")
        elif args.optim == OptimizerNames.ADAMW_APEX_FUSED:
            try:
                from apex.optimizers import FusedAdam

                optimizer_cls = FusedAdam
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError(
                    "Trainer tried to instantiate apex FusedAdam but apex is not installed!"
                )
        elif args.optim in [
            OptimizerNames.ADAMW_BNB,
            OptimizerNames.ADAMW_8BIT,
            OptimizerNames.PAGED_ADAMW,
            OptimizerNames.PAGED_ADAMW_8BIT,
            OptimizerNames.LION,
            OptimizerNames.LION_8BIT,
            OptimizerNames.PAGED_LION,
            OptimizerNames.PAGED_LION_8BIT,
            OptimizerNames.RMSPROP_BNB,
            OptimizerNames.RMSPROP_8BIT,
            OptimizerNames.RMSPROP_32BIT,
        ]:
            try:
                from bitsandbytes.optim import AdamW, Lion, RMSprop

                is_paged = False
                optim_bits = 32
                optimizer_cls = None
                additional_optim_kwargs = adam_kwargs
                if "paged" in args.optim:
                    is_paged = True
                if "8bit" in args.optim:
                    optim_bits = 8
                if "adam" in args.optim:
                    optimizer_cls = AdamW
                elif "lion" in args.optim:
                    optimizer_cls = Lion
                    additional_optim_kwargs = {
                        "betas": (args.adam_beta1, args.adam_beta2)
                    }
                elif "rmsprop" in args.optim:
                    optimizer_cls = RMSprop
                    # Above we pass all `adam_kwargs` to the optimizer, here
                    # we only pass `optim_args` which can be passed by the user.
                    additional_optim_kwargs = optim_args

                bnb_kwargs = {"optim_bits": optim_bits}
                if "rmsprop" not in args.optim:
                    bnb_kwargs["is_paged"] = is_paged

                optimizer_kwargs.update(additional_optim_kwargs)
                optimizer_kwargs.update(bnb_kwargs)
            except ImportError:
                raise ValueError(
                    "Trainer tried to instantiate bnb optimizer but bnb is not installed!"
                )
            if is_bitsandbytes_available() and version.parse(
                importlib.metadata.version("bitsandbytes")
            ) < version.parse("0.41.1"):
                logger.warning(
                    "You are using 8-bit optimizers with a version of `bitsandbytes` < 0.41.1. "
                    "It is recommended to update your version as a major bug has been fixed in 8-bit optimizers."
                )
        elif args.optim == OptimizerNames.ADAMW_ANYPRECISION:
            try:
                from torchdistx.optimizers import AnyPrecisionAdamW

                optimizer_cls = AnyPrecisionAdamW
                optimizer_kwargs.update(adam_kwargs)

                # TODO Change dtypes back to M=FP32, Var = BF16, Kahan = False once they can be cast together in torchdistx.
                optimizer_kwargs.update(
                    {
                        "use_kahan_summation": strtobool(
                            optim_args.get("use_kahan_summation", "False")
                        ),
                        "momentum_dtype": getattr(
                            torch, optim_args.get("momentum_dtype", "float32")
                        ),
                        "variance_dtype": getattr(
                            torch, optim_args.get("variance_dtype", "float32")
                        ),
                        "compensation_buffer_dtype": getattr(
                            torch,
                            optim_args.get("compensation_buffer_dtype", "bfloat16"),
                        ),
                    }
                )
            except ImportError:
                raise ValueError("Please install https://github.com/pytorch/torchdistx")
        elif args.optim == OptimizerNames.SGD:
            optimizer_cls = torch.optim.SGD
        elif args.optim == OptimizerNames.ADAGRAD:
            optimizer_cls = torch.optim.Adagrad
        elif args.optim == OptimizerNames.RMSPROP:
            optimizer_cls = torch.optim.RMSprop
        elif args.optim in [
            OptimizerNames.GALORE_ADAMW,
            OptimizerNames.GALORE_ADAMW_8BIT,
            OptimizerNames.GALORE_ADAFACTOR,
            OptimizerNames.GALORE_ADAMW_LAYERWISE,
            OptimizerNames.GALORE_ADAMW_8BIT_LAYERWISE,
            OptimizerNames.GALORE_ADAFACTOR_LAYERWISE,
        ]:
            if not is_galore_torch_available():
                raise ImportError(
                    "You need to install `galore_torch` in order to use GaLore optimizers"
                    " install it with `pip install git+https://github.com/jiaweizzhao/GaLore`"
                )
            from galore_torch import GaLoreAdafactor, GaLoreAdamW, GaLoreAdamW8bit

            is_layerwise = args.optim.lower().endswith("layerwise")
            if is_layerwise and args.parallel_mode == ParallelMode.DISTRIBUTED:
                raise NotImplementedError(
                    "Layer-wise GaLore does not support DDP at this time"
                )

            optimizer_mapping = {
                OptimizerNames.GALORE_ADAMW: GaLoreAdamW,
                OptimizerNames.GALORE_ADAMW_8BIT: GaLoreAdamW8bit,
                OptimizerNames.GALORE_ADAFACTOR: GaLoreAdafactor,
                OptimizerNames.GALORE_ADAMW_LAYERWISE: GaLoreAdamW,
                OptimizerNames.GALORE_ADAMW_8BIT_LAYERWISE: GaLoreAdamW8bit,
                OptimizerNames.GALORE_ADAFACTOR_LAYERWISE: GaLoreAdafactor,
            }

            optimizer_cls = optimizer_mapping[args.optim]

            if args.optim_target_modules is None:
                raise ValueError(
                    "You need to define a `optim_target_modules` in order to properly use GaLore optimizers"
                )

            if not isinstance(args.optim_target_modules, (list, str)):
                raise ValueError(
                    f"`optim_target_modules` has to be a list of strings, a string corresponding to a regex, or a specific module or 'all-linear', you passed {args.optim_target_modules}"
                )

            if model is None:
                raise ValueError(
                    "You need to pass a model in order to correctly initialize a GaLore optimizer."
                )

            logger.warning(
                "Activated GaLoRE fine-tuning, depending on your model size and hardware, the training might take a while before starting. Please be patient !"
            )

            all_linear = (
                isinstance(args.optim_target_modules, str)
                and args.optim_target_modules.replace("_", "-") == "all-linear"
            )

            galore_params = []
            galore_params_names = []
            for module_name, module in model.named_modules():
                target_module_exists, is_regex = check_target_module_exists(
                    args.optim_target_modules, module_name, return_is_regex=True
                )

                if not isinstance(module, nn.Linear):
                    # Warn in case we match but it's not a linear layer
                    if target_module_exists and not is_regex:
                        logger.warning(
                            f"{module_name} has been matched but ignored as GaLore only supports linear layers. Please double check your `optim_target_modules`!"
                        )

                    continue

                if not target_module_exists and not all_linear:
                    continue

                galore_params.append(module.weight)
                galore_params_names.append(module_name + ".weight")

            if len(galore_params) == 0:
                raise ValueError(
                    f"None of the target modules were found! ({args.optim_target_modules}). Please make sure to pass a valid `target_modules`."
                )

            non_galore_params = [
                p for n, p in model.named_parameters() if n not in galore_params_names
            ]

            galore_optim_kwargs = {
                "rank": int(optim_args.pop("rank", 128)),
                "update_proj_gap": int(optim_args.pop("update_proj_gap", 200)),
                "scale": float(optim_args.pop("scale", 0.25)),
                "proj_type": optim_args.pop("proj_type", "std"),
            }

            # The default args are from the official repository: https://github.com/jiaweizzhao/GaLore
            param_groups = [
                {"params": non_galore_params},
                {"params": galore_params, **galore_optim_kwargs},
            ]

            if is_layerwise:
                # For layer-wise optimizers, the optimization step is done through post accumulation
                # gradient hooks. The trick is to first attach these hooks to the model parameters then
                # create a dummy optimizer that will perform no-ops in the Trainer.
                # See the original implementation or the nice implementation from @hiyouga
                # here: https://github.com/hiyouga/LLaMA-Factory/commit/8664262cde3919e10eaecbd66e8c5d356856362e#diff-ebe08ab14496dfb9e06075f0fdd36799ef6d1535cc4dd4715b74c4e3e06fe3ba
                if args.gradient_accumulation_steps != 1:
                    raise ValueError(
                        "Layerwise GaLoRE optimizer do not support gradient accumulation !"
                    )

                optimizer_dict = {}
                for param in non_galore_params:
                    param_groups = [{"params": [param]}]
                    optimizer_dict[param] = optimizer_cls(
                        param_groups, **optimizer_kwargs
                    )
                for param in galore_params:
                    param_groups = [{"params": [param], **galore_optim_kwargs}]
                    optimizer_dict[param] = optimizer_cls(
                        param_groups, **optimizer_kwargs
                    )

                def optimizer_hook(param):
                    if param.grad is not None:
                        optimizer_dict[param].step()
                        optimizer_dict[param].zero_grad()

                for param in model.parameters():
                    if param.requires_grad:
                        param.register_post_accumulate_grad_hook(optimizer_hook)

                optimizer_cls = LayerWiseDummyOptimizer
                optimizer_kwargs.update({"optimizer_dict": optimizer_dict})

            optimizer_kwargs.update({"params": param_groups})

            if args.optim == OptimizerNames.GALORE_ADAFACTOR:
                optimizer_kwargs.update(
                    {"scale_parameter": False, "relative_step": False}
                )
        else:
            raise ValueError(
                f"Trainer cannot instantiate unsupported optimizer: {args.optim}"
            )
        return optimizer_cls, optimizer_kwargs

    def create_scheduler(
        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
    ):
        """
        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
        passed as an argument.

        Args:
            num_training_steps (int): The number of training steps to do.
        """
        if self.lr_scheduler is None:
            self.lr_scheduler = get_scheduler(
                self.args.lr_scheduler_type,
                optimizer=self.optimizer if optimizer is None else optimizer,
                num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                num_training_steps=num_training_steps,
                scheduler_specific_kwargs=self.args.lr_scheduler_kwargs,
            )
            self._created_lr_scheduler = True
        return self.lr_scheduler

    def num_examples(self, dataloader: DataLoader) -> int:
        """
        Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When
        dataloader.dataset does not exist or has no length, estimates as best it can
        """
        try:
            dataset = dataloader.dataset
            # Special case for IterableDatasetShard, we need to dig deeper
            if isinstance(dataset, IterableDatasetShard):
                return len(dataloader.dataset.dataset)
            return len(dataloader.dataset)
        except (
            NameError,
            AttributeError,
            TypeError,
        ):  # no dataset or length, estimate by length of dataloader
            return len(dataloader) * self.args.per_device_train_batch_size

    def num_tokens(self, train_dl: DataLoader, max_steps: Optional[int] = None) -> int:
        """
        Helper to get number of tokens in a [`~torch.utils.data.DataLoader`] by enumerating dataloader.
        """
        train_tokens = 0
        try:
            for step, batch in enumerate(train_dl):
                tokens = batch["input_ids"].numel()
                if max_steps is not None:
                    return tokens * max_steps
                train_tokens += tokens
            return train_tokens
        except KeyError:
            logger.warning("Cannot get num_tokens from dataloader")
            return train_tokens

    def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
        """HP search setup code"""
        self._trial = trial

        if self.hp_search_backend is None or trial is None:
            return
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            params = self.hp_space(trial)
        elif self.hp_search_backend == HPSearchBackend.RAY:
            params = trial
            params.pop("wandb", None)
        elif self.hp_search_backend == HPSearchBackend.SIGOPT:
            params = {
                k: int(v) if isinstance(v, str) else v
                for k, v in trial.assignments.items()
            }
        elif self.hp_search_backend == HPSearchBackend.WANDB:
            params = trial

        for key, value in params.items():
            if not hasattr(self.args, key):
                logger.warning(
                    f"Trying to set {key} in the hyperparameter search but there is no corresponding field in"
                    " `TrainingArguments`."
                )
                continue
            old_attr = getattr(self.args, key, None)
            # Casting value to the proper type
            if old_attr is not None:
                value = type(old_attr)(value)

            setattr(self.args, key, value)
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            logger.info(f"Trial: {trial.params}")
        if self.hp_search_backend == HPSearchBackend.SIGOPT:
            logger.info(f"SigOpt Assignments: {trial.assignments}")
        if self.hp_search_backend == HPSearchBackend.WANDB:
            logger.info(f"W&B Sweep parameters: {trial}")
        if self.is_deepspeed_enabled:
            if self.args.deepspeed is None:
                raise ValueError(
                    "For sweeps with deepspeed, `args.deepspeed` must be set"
                )
            # Rebuild the deepspeed config to reflect the updated training parameters
            from accelerate.utils import DeepSpeedPlugin

            from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig

            self.args.hf_deepspeed_config = HfTrainerDeepSpeedConfig(
                self.args.deepspeed
            )
            self.args.hf_deepspeed_config.trainer_config_process(self.args)
            self.args.deepspeed_plugin = DeepSpeedPlugin(
                hf_ds_config=self.args.hf_deepspeed_config
            )

        self.create_accelerator_and_postprocess()

    def _report_to_hp_search(
        self,
        trial: Union["optuna.Trial", Dict[str, Any]],
        step: int,
        metrics: Dict[str, float],
    ):
        if self.hp_search_backend is None or trial is None:
            return
        metrics = metrics.copy()
        self.objective = self.compute_objective(metrics)
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            import optuna

            if not trial.study._is_multi_objective():
                trial.report(self.objective, step)
                if trial.should_prune():
                    self.callback_handler.on_train_end(
                        self.args, self.state, self.control
                    )
                    raise optuna.TrialPruned()
        elif self.hp_search_backend == HPSearchBackend.RAY:
            import ray.train

            with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
                checkpoint = None
                if self.control.should_save:
                    self._tune_save_checkpoint(checkpoint_dir=temp_checkpoint_dir)
                    checkpoint = ray.train.Checkpoint.from_directory(
                        temp_checkpoint_dir
                    )
                metrics["objective"] = self.objective
                ray.train.report(metrics, checkpoint=checkpoint)

    def _tune_save_checkpoint(self, checkpoint_dir: str):
        output_dir = os.path.join(
            checkpoint_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
        )
        self.save_model(output_dir, _internal_call=True)
        if self.args.should_save:
            # Update the `TrainerControl` state to where we are currently
            self.state.stateful_callbacks["TrainerControl"] = self.control.state()
            self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
            torch.save(
                self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME)
            )
            torch.save(
                self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME)
            )

    def call_model_init(self, trial=None):
        model_init_argcount = number_of_arguments(self.model_init)
        if model_init_argcount == 0:
            model = self.model_init()
        elif model_init_argcount == 1:
            model = self.model_init(trial)
        else:
            raise RuntimeError("model_init should have 0 or 1 argument.")

        if model is None:
            raise RuntimeError("model_init should not return None.")

        return model

    def torch_jit_model_eval(self, model, dataloader, training=False):
        if not training:
            if dataloader is None:
                logger.warning(
                    "failed to use PyTorch jit mode due to current dataloader is none."
                )
                return model
            example_batch = next(iter(dataloader))
            example_batch = self._prepare_inputs(example_batch)
            try:
                jit_model = copy.copy(model)
                jit_model.eval()
                original_forward = jit_model.__dict__.pop("_original_forward", None)
                # remove mixed precision hooks from the model
                if original_forward:
                    jit_model.forward = original_forward
                with self.accelerator.autocast(cache_enabled=False), torch.no_grad():
                    if version.parse(
                        version.parse(torch.__version__).base_version
                    ) >= version.parse("2.0.0"):
                        if isinstance(example_batch, dict):
                            jit_model = torch.jit.trace(
                                jit_model,
                                example_kwarg_inputs=example_batch,
                                strict=False,
                            )
                        else:
                            jit_model = torch.jit.trace(
                                jit_model,
                                example_kwarg_inputs={
                                    key: example_batch[key] for key in example_batch
                                },
                                strict=False,
                            )
                    else:
                        jit_inputs = []
                        for key in example_batch:
                            example_tensor = torch.ones_like(example_batch[key])
                            jit_inputs.append(example_tensor)
                        jit_inputs = tuple(jit_inputs)
                        jit_model = torch.jit.trace(jit_model, jit_inputs, strict=False)
                jit_model = torch.jit.freeze(jit_model)
                with torch.no_grad():
                    jit_model(**example_batch)
                    jit_model(**example_batch)
                model = jit_model
                self.use_cpu_amp = False
            except (RuntimeError, TypeError, ValueError, NameError, IndexError) as e:
                logger.warning(f"failed to use PyTorch jit mode due to: {e}.")

        return model

    def ipex_optimize_model(self, model, training=False, dtype=torch.float32):
        if not is_ipex_available():
            raise ImportError(
                "Using IPEX but IPEX is not installed or IPEX's version does not match current PyTorch, please refer"
                " to https://github.com/intel/intel-extension-for-pytorch."
            )

        import intel_extension_for_pytorch as ipex

        if not training:
            model.eval()
            dtype = (
                torch.bfloat16
                if not self.is_in_train and self.args.bf16_full_eval
                else dtype
            )
            # conv_bn_folding is disabled as it fails in symbolic tracing, resulting in ipex warnings
            model = ipex.optimize(
                model,
                dtype=dtype,
                level="O1",
                conv_bn_folding=False,
                inplace=not self.is_in_train,
            )
        else:
            if not model.training:
                model.train()
            model, self.optimizer = ipex.optimize(
                model, dtype=dtype, optimizer=self.optimizer, inplace=True, level="O1"
            )

        return model

    def compare_trainer_and_checkpoint_args(self, training_args, trainer_state):
        attributes_map = {
            "logging_steps": "logging_steps",
            "eval_steps": "eval_steps",
            "save_steps": "save_steps",
        }

        has_warning = False
        warning_str = "Warning: The following arguments do not match the ones in the `trainer_state.json` within the checkpoint directory: "
        for arg_attr, state_attr in attributes_map.items():
            arg_value = getattr(training_args, arg_attr, None)
            state_value = getattr(trainer_state, state_attr, None)

            if (
                arg_value is not None
                and state_value is not None
                and arg_value != state_value
            ):
                warning_str += f"\n\t{arg_attr}: {arg_value} (from args) != {state_value} (from trainer_state.json)"
                has_warning = True

        # train bs is special as we need to account for multi-GPU
        train_bs_args = training_args.per_device_train_batch_size
        train_bs_state = trainer_state.train_batch_size // max(1, training_args.n_gpu)

        if train_bs_args != train_bs_state:
            warning_str += f"\n\tper_device_train_batch_size: {train_bs_args} (from args) != {train_bs_state} (from trainer_state.json)"
            has_warning = True

        if has_warning:
            logger.warning_once(warning_str)

    def _wrap_model(self, model, training=True, dataloader=None):
        if self.args.use_ipex:
            dtype = torch.bfloat16 if self.use_cpu_amp else torch.float32
            model = self.ipex_optimize_model(model, training, dtype=dtype)

        if is_sagemaker_mp_enabled():
            # Wrapping the base model twice in a DistributedModel will raise an error.
            if isinstance(self.model_wrapped, smp.model.DistributedModel):
                return self.model_wrapped
            return smp.DistributedModel(
                model, backward_passes_per_step=self.args.gradient_accumulation_steps
            )

        # train/eval could be run multiple-times - if already wrapped, don't re-wrap it again
        if self.accelerator.unwrap_model(model) is not model:
            return model

        # Mixed precision training with apex (torch < 1.6)
        if self.use_apex and training:
            model, self.optimizer = amp.initialize(
                model, self.optimizer, opt_level=self.args.fp16_opt_level
            )

        # Multi-gpu training (should be after apex fp16 initialization) / 8bit models does not support DDP
        if self.args.n_gpu > 1 and not getattr(model, "is_loaded_in_8bit", False):
            model = nn.DataParallel(model)

        if self.args.jit_mode_eval:
            start_time = time.time()
            model = self.torch_jit_model_eval(model, dataloader, training)
            self.jit_compilation_time = round(time.time() - start_time, 4)

        # Note: in torch.distributed mode, there's no point in wrapping the model
        # inside a DistributedDataParallel as we'll be under `no_grad` anyways.
        if not training:
            return model

        # Distributed training (should be after apex fp16 initialization)
        # Distributed training using PyTorch FSDP
        if self.is_fsdp_xla_enabled:
            try:
                from torch_xla.distributed.fsdp import (
                    XlaFullyShardedDataParallel as FSDP,
                )
                from torch_xla.distributed.fsdp import checkpoint_module
                from torch_xla.distributed.fsdp.wrap import (
                    size_based_auto_wrap_policy,
                    transformer_auto_wrap_policy,
                )

                if self.is_fsdp_xla_v2_enabled:
                    from torch_xla.experimental.spmd_fully_sharded_data_parallel import (
                        SpmdFullyShardedDataParallel as FSDPv2,
                    )
            except ImportError:
                raise ImportError(
                    "Missing XLA FSDP related module; please make sure to use torch-xla >= 2.0."
                )
            auto_wrap_policy = None
            auto_wrapper_callable = None
            default_transformer_cls_names_to_wrap = getattr(
                model, "_no_split_modules", None
            )
            fsdp_transformer_layer_cls_to_wrap = self.args.fsdp_config.get(
                "transformer_layer_cls_to_wrap", default_transformer_cls_names_to_wrap
            )

            if self.args.fsdp_config["min_num_params"] > 0:
                auto_wrap_policy = functools.partial(
                    size_based_auto_wrap_policy,
                    min_num_params=self.args.fsdp_config["min_num_params"],
                )
            elif fsdp_transformer_layer_cls_to_wrap is not None:
                transformer_cls_to_wrap = set()
                for layer_class in fsdp_transformer_layer_cls_to_wrap:
                    transformer_cls = get_module_class_from_name(model, layer_class)
                    if transformer_cls is None:
                        raise Exception(
                            "Could not find the transformer layer class to wrap in the model."
                        )
                    else:
                        transformer_cls_to_wrap.add(transformer_cls)

                auto_wrap_policy = functools.partial(
                    transformer_auto_wrap_policy,
                    # Transformer layer class to wrap
                    transformer_layer_cls=transformer_cls_to_wrap,
                )
            fsdp_kwargs = self.args.xla_fsdp_config
            if self.args.fsdp_config["xla_fsdp_grad_ckpt"]:
                if model.config.use_cache:
                    logger.warning_once(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
                    )
                    model.config.use_cache = False

                # Apply gradient checkpointing to auto-wrapped sub-modules if specified
                def auto_wrapper_callable(m, *args, **kwargs):
                    target_cls = FSDP if not self.is_fsdp_xla_v2_enabled else FSDPv2
                    return target_cls(checkpoint_module(m), *args, **kwargs)

            # Wrap the base model with an outer FSDP wrapper
            if self.is_fsdp_xla_v2_enabled:

                def shard_output(output, mesh):
                    from transformers.modeling_outputs import CausalLMOutputWithPast

                    real_output = None
                    if isinstance(output, torch.Tensor):
                        real_output = output
                    elif isinstance(output, tuple):
                        real_output = output[0]
                    elif isinstance(output, CausalLMOutputWithPast):
                        real_output = output.logits

                    if real_output is None:
                        raise ValueError(
                            "Something went wrong, the output of the model shouldn't be `None`"
                        )
                    xs.mark_sharding(real_output, mesh, ("fsdp", None, None))

                self.model = model = FSDPv2(
                    model,
                    shard_output=shard_output,
                    auto_wrap_policy=auto_wrap_policy,
                    auto_wrapper_callable=auto_wrapper_callable,
                )
            else:
                self.model = model = FSDP(
                    model,
                    auto_wrap_policy=auto_wrap_policy,
                    auto_wrapper_callable=auto_wrapper_callable,
                    **fsdp_kwargs,
                )

            # Patch `xm.optimizer_step` should not reduce gradients in this case,
            # as FSDP does not need gradient reduction over sharded parameters.
            def patched_optimizer_step(optimizer, barrier=False, optimizer_args={}):
                loss = optimizer.step(**optimizer_args)
                if barrier:
                    xm.mark_step()
                return loss

            xm.optimizer_step = patched_optimizer_step
        elif is_sagemaker_dp_enabled():
            model = nn.parallel.DistributedDataParallel(
                model, device_ids=[int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"))]
            )
        elif self.args.parallel_mode == ParallelMode.DISTRIBUTED:
            if is_torch_neuroncore_available():
                return model
            kwargs = {}
            if self.args.ddp_find_unused_parameters is not None:
                kwargs["find_unused_parameters"] = self.args.ddp_find_unused_parameters
            elif isinstance(model, PreTrainedModel):
                # find_unused_parameters breaks checkpointing as per
                # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
                kwargs["find_unused_parameters"] = not model.is_gradient_checkpointing
            else:
                kwargs["find_unused_parameters"] = True

            if self.args.ddp_bucket_cap_mb is not None:
                kwargs["bucket_cap_mb"] = self.args.ddp_bucket_cap_mb

            if self.args.ddp_broadcast_buffers is not None:
                kwargs["broadcast_buffers"] = self.args.ddp_broadcast_buffers

            self.accelerator.ddp_handler = DistributedDataParallelKwargs(**kwargs)

        return model

    def train(
        self,
        resume_from_checkpoint: Optional[Union[str, bool]] = None,
        trial: Union["optuna.Trial", Dict[str, Any]] = None,
        ignore_keys_for_eval: Optional[List[str]] = None,
        **kwargs,
    ):
        """
        Main training entry point.

        Args:
            resume_from_checkpoint (`str` or `bool`, *optional*):
                If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a
                `bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance
                of [`Trainer`]. If present, training will resume from the model/optimizer/scheduler states loaded here.
            trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):
                The trial run or the hyperparameter dictionary for hyperparameter search.
            ignore_keys_for_eval (`List[str]`, *optional*)
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions for evaluation during the training.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments used to hide deprecated arguments
        """
        if resume_from_checkpoint is False:
            resume_from_checkpoint = None

        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        args = self.args

        self.is_in_train = True

        # Attach NEFTune hooks if necessary
        if self.neftune_noise_alpha is not None:
            self.model = self._activate_neftune(self.model)

        # do_train is not a reliable argument, as it might not be set and .train() still called, so
        # the following is a workaround:
        if (args.fp16_full_eval or args.bf16_full_eval) and not args.do_train:
            self._move_model_to_device(self.model, args.device)

        if "model_path" in kwargs:
            resume_from_checkpoint = kwargs.pop("model_path")
            warnings.warn(
                "`model_path` is deprecated and will be removed in a future version. Use `resume_from_checkpoint` "
                "instead.",
                FutureWarning,
            )
        if len(kwargs) > 0:
            raise TypeError(
                f"train() received got unexpected keyword arguments: {', '.join(list(kwargs.keys()))}."
            )
        # This might change the seed so needs to run first.
        self._hp_search_setup(trial)
        self._train_batch_size = self.args.train_batch_size

        # Model re-init
        model_reloaded = False
        if self.model_init is not None:
            # Seed must be set before instantiating the model when using model_init.
            (
                enable_full_determinism(self.args.seed)
                if self.args.full_determinism
                else set_seed(self.args.seed)
            )
            self.model = self.call_model_init(trial)
            model_reloaded = True
            # Reinitializes optimizer and scheduler
            self.optimizer, self.lr_scheduler = None, None

        # Load potential model checkpoint
        if isinstance(resume_from_checkpoint, bool) and resume_from_checkpoint:
            resume_from_checkpoint = get_last_checkpoint(args.output_dir)
            if resume_from_checkpoint is None:
                raise ValueError(
                    f"No valid checkpoint found in output directory ({args.output_dir})"
                )

        if resume_from_checkpoint is not None:
            if (
                not is_sagemaker_mp_enabled()
                and not self.is_deepspeed_enabled
                and not self.is_fsdp_enabled
            ):
                self._load_from_checkpoint(resume_from_checkpoint)
            # In case of repeating the find_executable_batch_size, set `self._train_batch_size` properly
            state = TrainerState.load_from_json(
                os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
            )
            if state.train_batch_size is not None:
                self._train_batch_size = state.train_batch_size

        # If model was re-initialized, put it on the right device and update self.model_wrapped
        if model_reloaded:
            if self.place_model_on_device:
                self._move_model_to_device(self.model, args.device)
            self.model_wrapped = self.model

        inner_training_loop = find_executable_batch_size(
            self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
        )
        if args.push_to_hub:
            try:
                # Disable progress bars when uploading models during checkpoints to avoid polluting stdout
                hf_hub_utils.disable_progress_bars()
                return inner_training_loop(
                    args=args,
                    resume_from_checkpoint=resume_from_checkpoint,
                    trial=trial,
                    ignore_keys_for_eval=ignore_keys_for_eval,
                )
            finally:
                hf_hub_utils.enable_progress_bars()
        else:
            return inner_training_loop(
                args=args,
                resume_from_checkpoint=resume_from_checkpoint,
                trial=trial,
                ignore_keys_for_eval=ignore_keys_for_eval,
            )

    def _inner_training_loop(
        self,
        batch_size=None,
        args=None,
        resume_from_checkpoint=None,
        trial=None,
        ignore_keys_for_eval=None,
    ):
        self.accelerator.free_memory()
        self._train_batch_size = batch_size
        if self.args.auto_find_batch_size:
            if self.state.train_batch_size != self._train_batch_size:
                from accelerate.utils import release_memory

                (self.model_wrapped,) = release_memory(self.model_wrapped)
                self.model_wrapped = self.model

                # Check for DeepSpeed *after* the intial pass and modify the config
                if self.is_deepspeed_enabled:
                    # Temporarily unset `self.args.train_batch_size`
                    original_bs = self.args.per_device_train_batch_size
                    self.args.per_device_train_batch_size = (
                        self._train_batch_size // max(1, self.args.n_gpu)
                    )
                    self.propagate_args_to_deepspeed(True)
                    self.args.per_device_train_batch_size = original_bs
            self.state.train_batch_size = self._train_batch_size
        logger.debug(
            f"Currently training with a batch size of: {self._train_batch_size}"
        )
        # Data loader and number of training steps
        train_dataloader = self.get_train_dataloader()
        if self.is_fsdp_xla_v2_enabled:
            train_dataloader = tpu_spmd_dataloader(train_dataloader)

        # Setting up training control variables:
        # number of training epochs: num_train_epochs
        # number of training steps per epoch: num_update_steps_per_epoch
        # total number of training steps to execute: max_steps
        total_train_batch_size = (
            self._train_batch_size * args.gradient_accumulation_steps * args.world_size
        )

        len_dataloader = None
        num_train_tokens = None
        if has_length(train_dataloader):
            len_dataloader = len(train_dataloader)
            num_update_steps_per_epoch = (
                len_dataloader // args.gradient_accumulation_steps
            )
            num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
            num_examples = self.num_examples(train_dataloader)
            if args.max_steps > 0:
                max_steps = args.max_steps
                num_train_epochs = args.max_steps // num_update_steps_per_epoch + int(
                    args.max_steps % num_update_steps_per_epoch > 0
                )
                # May be slightly incorrect if the last batch in the training dataloader has a smaller size but it's
                # the best we can do.
                num_train_samples = args.max_steps * total_train_batch_size
                if args.include_tokens_per_second:
                    num_train_tokens = (
                        self.num_tokens(train_dataloader, args.max_steps)
                        * args.gradient_accumulation_steps
                    )
            else:
                max_steps = math.ceil(
                    args.num_train_epochs * num_update_steps_per_epoch
                )
                num_train_epochs = math.ceil(args.num_train_epochs)
                num_train_samples = (
                    self.num_examples(train_dataloader) * args.num_train_epochs
                )
                if args.include_tokens_per_second:
                    num_train_tokens = (
                        self.num_tokens(train_dataloader) * args.num_train_epochs
                    )
        elif (
            args.max_steps > 0
        ):  # Rely on max_steps when dataloader does not have a working size
            max_steps = args.max_steps
            # Setting a very large number of epochs so we go as many times as necessary over the iterator.
            num_train_epochs = sys.maxsize
            num_update_steps_per_epoch = max_steps
            num_examples = total_train_batch_size * args.max_steps
            num_train_samples = args.max_steps * total_train_batch_size
            if args.include_tokens_per_second:
                num_train_tokens = (
                    self.num_tokens(train_dataloader, args.max_steps)
                    * args.gradient_accumulation_steps
                )
        else:
            raise ValueError(
                "args.max_steps must be set to a positive value if dataloader does not have a length, was"
                f" {args.max_steps}"
            )

        if DebugOption.UNDERFLOW_OVERFLOW in self.args.debug:
            if self.args.n_gpu > 1:
                # nn.DataParallel(model) replicates the model, creating new variables and module
                # references registered here no longer work on other gpus, breaking the module
                raise ValueError(
                    "Currently --debug underflow_overflow is not supported under DP. Please use DDP"
                    " (torchrun or torch.distributed.launch (deprecated))."
                )
            else:
                debug_overflow = DebugUnderflowOverflow(self.model)  # noqa

        delay_optimizer_creation = (
            is_sagemaker_mp_enabled()
            or self.is_fsdp_xla_enabled
            or self.is_fsdp_enabled
        )

        # We need to reset the scheduler, as its parameters may be different on subsequent calls
        if self._created_lr_scheduler:
            self.lr_scheduler = None
            self._created_lr_scheduler = False

        if self.is_deepspeed_enabled:
            self.optimizer, self.lr_scheduler = deepspeed_init(
                self, num_training_steps=max_steps
            )

        if not delay_optimizer_creation:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        self.state = TrainerState(
            stateful_callbacks=[
                cb
                for cb in self.callback_handler.callbacks + [self.control]
                if isinstance(cb, ExportableState)
            ]
        )
        self.state.is_hyper_param_search = trial is not None
        self.state.train_batch_size = self._train_batch_size

        # Compute absolute values for logging, eval, and save if given as ratio
        if args.logging_steps is not None:
            if args.logging_steps < 1:
                self.state.logging_steps = math.ceil(max_steps * args.logging_steps)
            else:
                self.state.logging_steps = args.logging_steps
        if args.eval_steps is not None:
            if args.eval_steps < 1:
                self.state.eval_steps = math.ceil(max_steps * args.eval_steps)
            else:
                self.state.eval_steps = args.eval_steps
        if args.save_steps is not None:
            if args.save_steps < 1:
                self.state.save_steps = math.ceil(max_steps * args.save_steps)
            else:
                self.state.save_steps = args.save_steps

        # Activate gradient checkpointing if needed
        if args.gradient_checkpointing:
            if args.gradient_checkpointing_kwargs is None:
                gradient_checkpointing_kwargs = {}
            else:
                gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs

            self.model.gradient_checkpointing_enable(
                gradient_checkpointing_kwargs=gradient_checkpointing_kwargs
            )

        model = self._wrap_model(self.model_wrapped)

        # as the model is wrapped, don't use `accelerator.prepare`
        # this is for unhandled cases such as
        # FSDP-XLA, SageMaker MP/DP, DataParallel, IPEX
        use_accelerator_prepare = True if model is self.model else False

        if delay_optimizer_creation:
            if use_accelerator_prepare:
                self._fsdp_qlora_plugin_updates()
                self.model = self.accelerator.prepare(self.model)
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        # prepare using `accelerator` prepare
        if use_accelerator_prepare:
            self.model.train()
            if hasattr(self.lr_scheduler, "step"):
                if self.use_apex:
                    model = self.accelerator.prepare(self.model)
                else:
                    model, self.optimizer = self.accelerator.prepare(
                        self.model, self.optimizer
                    )
            else:
                # to handle cases wherein we pass "DummyScheduler" such as when it is specified in DeepSpeed config.
                model, self.optimizer, self.lr_scheduler = self.accelerator.prepare(
                    self.model, self.optimizer, self.lr_scheduler
                )

        if self.is_fsdp_enabled:
            self.model = self.model_wrapped = model

        # for the rest of this function `model` is the outside model, whether it was wrapped or not
        if model is not self.model:
            self.model_wrapped = model

        # backward compatibility
        if self.is_deepspeed_enabled:
            self.deepspeed = self.model_wrapped

        # ckpt loading
        if resume_from_checkpoint is not None:
            if self.is_deepspeed_enabled:
                deepspeed_load_checkpoint(
                    self.model_wrapped,
                    resume_from_checkpoint,
                    load_module_strict=not _is_peft_model(self.model),
                )
            elif is_sagemaker_mp_enabled() or self.is_fsdp_enabled:
                self._load_from_checkpoint(resume_from_checkpoint, self.model_wrapped)

        # Check if saved optimizer or scheduler states exist
        self._load_optimizer_and_scheduler(resume_from_checkpoint)

        # important: at this point:
        # self.model         is the Transformers Model
        # self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model),
        # FSDP(Transformers Model), Dynamo Optimized Module(Transformers Model) etc.

        # Train!
        logger.info("***** Running training *****")
        logger.info(f"  Num examples = {num_examples:,}")
        logger.info(f"  Num Epochs = {num_train_epochs:,}")
        logger.info(
            f"  Instantaneous batch size per device = {self.args.per_device_train_batch_size:,}"
        )
        if self.args.per_device_train_batch_size != self._train_batch_size:
            logger.info(
                f"  Training with DataParallel so batch size has been adjusted to: {self._train_batch_size:,}"
            )
        logger.info(
            f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size:,}"
        )
        logger.info(
            f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}"
        )
        logger.info(f"  Total optimization steps = {max_steps:,}")
        logger.info(
            f"  Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}"
        )

        self.state.epoch = 0
        start_time = time.time()
        epochs_trained = 0
        steps_trained_in_current_epoch = 0
        steps_trained_progress_bar = None

        # Check if continuing training from a checkpoint
        if resume_from_checkpoint is not None and os.path.isfile(
            os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
        ):
            self.state = TrainerState.load_from_json(
                os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
            )
            self.compare_trainer_and_checkpoint_args(self.args, self.state)
            self._load_callback_state()
            epochs_trained = self.state.global_step // num_update_steps_per_epoch
            if not args.ignore_data_skip:
                steps_trained_in_current_epoch = self.state.global_step % (
                    num_update_steps_per_epoch
                )
                steps_trained_in_current_epoch *= args.gradient_accumulation_steps
            else:
                steps_trained_in_current_epoch = 0

            logger.info(
                "  Continuing training from checkpoint, will skip to saved global_step"
            )
            logger.info(f"  Continuing training from epoch {epochs_trained}")
            logger.info(
                f"  Continuing training from global step {self.state.global_step}"
            )
            if not args.ignore_data_skip:
                logger.info(
                    f"  Will skip the first {epochs_trained} epochs then the first"
                    f" {steps_trained_in_current_epoch} batches in the first epoch."
                )

        # Update the references
        self.callback_handler.model = self.model
        self.callback_handler.optimizer = self.optimizer
        self.callback_handler.lr_scheduler = self.lr_scheduler
        self.callback_handler.train_dataloader = train_dataloader
        if self.hp_name is not None and self._trial is not None:
            # use self._trial because the SigOpt/Optuna hpo only call `_hp_search_setup(trial)` instead of passing trial
            # parameter to Train when using DDP.
            self.state.trial_name = self.hp_name(self._trial)
        if trial is not None:
            assignments = (
                trial.assignments
                if self.hp_search_backend == HPSearchBackend.SIGOPT
                else trial
            )
            self.state.trial_params = hp_params(assignments)
        else:
            self.state.trial_params = None
        # This should be the same if the state has been saved but in case the training arguments changed, it's safer
        # to set this after the load.
        self.state.max_steps = max_steps
        self.state.num_train_epochs = num_train_epochs
        self.state.is_local_process_zero = self.is_local_process_zero()
        self.state.is_world_process_zero = self.is_world_process_zero()

        # tr_loss is a tensor to avoid synchronization of TPUs through .item()
        tr_loss = torch.tensor(0.0).to(args.device)
        # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses
        self._total_loss_scalar = 0.0
        self._globalstep_last_logged = self.state.global_step
        model.zero_grad()
        grad_norm: Optional[float] = None

        self.control = self.callback_handler.on_train_begin(
            args, self.state, self.control
        )

        total_batched_samples = 0
        for epoch in range(epochs_trained, num_train_epochs):
            epoch_iterator = train_dataloader
            if hasattr(epoch_iterator, "set_epoch"):
                epoch_iterator.set_epoch(epoch)

            # Reset the past mems state at the beginning of each epoch if necessary.
            if args.past_index >= 0:
                self._past = None

            steps_in_epoch = (
                len(epoch_iterator)
                if len_dataloader is not None
                else args.max_steps * args.gradient_accumulation_steps
            )
            self.control = self.callback_handler.on_epoch_begin(
                args, self.state, self.control
            )

            if (
                epoch == epochs_trained
                and resume_from_checkpoint is not None
                and steps_trained_in_current_epoch == 0
            ):
                self._load_rng_state(resume_from_checkpoint)

            rng_to_sync = False
            steps_skipped = 0
            if steps_trained_in_current_epoch > 0:
                epoch_iterator = skip_first_batches(
                    epoch_iterator, steps_trained_in_current_epoch
                )
                steps_skipped = steps_trained_in_current_epoch
                steps_trained_in_current_epoch = 0
                rng_to_sync = True

            step = -1
            for step, inputs in enumerate(epoch_iterator):
                total_batched_samples += 1

                if self.args.include_num_input_tokens_seen:
                    main_input_name = getattr(
                        self.model, "main_input_name", "input_ids"
                    )
                    if main_input_name not in inputs:
                        logger.warning(
                            "Tried to track the number of tokens seen, however the current model is "
                            "not configured properly to know what item is the input. To fix this, add "
                            "a `main_input_name` attribute to the model class you are using."
                        )
                    else:
                        input_device = inputs[main_input_name].device
                        self.state.num_input_tokens_seen += torch.sum(
                            self.accelerator.gather(
                                torch.tensor(
                                    inputs[main_input_name].numel(),
                                    device=input_device,
                                    dtype=torch.int64,
                                )
                            )
                        ).item()
                if rng_to_sync:
                    self._load_rng_state(resume_from_checkpoint)
                    rng_to_sync = False

                # Skip past any already trained steps if resuming training
                if steps_trained_in_current_epoch > 0:
                    steps_trained_in_current_epoch -= 1
                    if steps_trained_progress_bar is not None:
                        steps_trained_progress_bar.update(1)
                    if steps_trained_in_current_epoch == 0:
                        self._load_rng_state(resume_from_checkpoint)
                    continue
                elif steps_trained_progress_bar is not None:
                    steps_trained_progress_bar.close()
                    steps_trained_progress_bar = None

                if step % args.gradient_accumulation_steps == 0:
                    self.control = self.callback_handler.on_step_begin(
                        args, self.state, self.control
                    )

                with self.accelerator.accumulate(model):
                    tr_loss_step = self.training_step(model, inputs)

                if (
                    args.logging_nan_inf_filter
                    and not is_torch_xla_available()
                    and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
                ):
                    # if loss is nan or inf simply add the average of previous logged losses
                    tr_loss += tr_loss / (
                        1 + self.state.global_step - self._globalstep_last_logged
                    )
                else:
                    if tr_loss.device != tr_loss_step.device:
                        raise ValueError(
                            f"Calculated loss must be on the original device: {tr_loss.device} but device in use is {tr_loss_step.device}"
                        )
                    tr_loss += tr_loss_step

                self.current_flos += float(self.floating_point_ops(inputs))

                is_last_step_and_steps_less_than_grad_acc = (
                    steps_in_epoch <= args.gradient_accumulation_steps
                    and (step + 1) == steps_in_epoch
                )

                if (
                    total_batched_samples % args.gradient_accumulation_steps == 0
                    or
                    # last step in epoch but step is always smaller than gradient_accumulation_steps
                    is_last_step_and_steps_less_than_grad_acc
                ):
                    # the `or` condition of `is_last_step_and_steps_less_than_grad_acc` is not covered
                    # in accelerate. So, explicitly enable sync gradients to True in that case.
                    if is_last_step_and_steps_less_than_grad_acc:
                        self.accelerator.gradient_state._set_sync_gradients(True)

                    # Gradient clipping
                    if args.max_grad_norm is not None and args.max_grad_norm > 0:
                        # deepspeed does its own clipping

                        if is_sagemaker_mp_enabled() and args.fp16:
                            _grad_norm = self.optimizer.clip_master_grads(
                                args.max_grad_norm
                            )
                        elif self.use_apex:
                            # Revert to normal clipping otherwise, handling Apex or full precision
                            _grad_norm = nn.utils.clip_grad_norm_(
                                amp.master_params(self.optimizer),
                                args.max_grad_norm,
                            )
                        else:
                            _grad_norm = self.accelerator.clip_grad_norm_(
                                model.parameters(),
                                args.max_grad_norm,
                            )

                        if (
                            is_accelerate_available()
                            and self.accelerator.distributed_type
                            == DistributedType.DEEPSPEED
                        ):
                            grad_norm = model.get_global_grad_norm()
                            # In some cases the grad norm may not return a float
                            if hasattr(grad_norm, "item"):
                                grad_norm = grad_norm.item()
                        else:
                            grad_norm = _grad_norm

                    # SORSA maintain orthonormality
                    with self.compute_loss_context_manager():
                        ortho_loss = calc_ortho(model)
                        if ortho_loss is not None:
                            s_gamma = self.args.gamma / self.args.learning_rate
                            self.accelerator.backward(s_gamma * ortho_loss)

                    # Optimizer step
                    self.optimizer.step()
                    optimizer_was_run = not self.accelerator.optimizer_step_was_skipped
                    if optimizer_was_run:
                        # Delay optimizer scheduling until metrics are generated
                        if not isinstance(
                            self.lr_scheduler,
                            torch.optim.lr_scheduler.ReduceLROnPlateau,
                        ):
                            self.lr_scheduler.step()

                    model.zero_grad()
                    self.state.global_step += 1
                    self.state.epoch = (
                        epoch + (step + 1 + steps_skipped) / steps_in_epoch
                    )
                    self.control = self.callback_handler.on_step_end(
                        args, self.state, self.control
                    )

                    self._maybe_log_save_evaluate(
                        tr_loss,
                        ortho_loss,
                        grad_norm,
                        model,
                        trial,
                        epoch,
                        ignore_keys_for_eval,
                    )
                else:
                    self.control = self.callback_handler.on_substep_end(
                        args, self.state, self.control
                    )

                if self.control.should_epoch_stop or self.control.should_training_stop:
                    # PyTorch/XLA relies on the data loader to insert the mark_step for
                    # each step. Since we are breaking the loop early, we need to manually
                    # insert the mark_step here.
                    if is_torch_xla_available():
                        xm.mark_step()
                    break
            if step < 0:
                logger.warning(
                    "There seems to be not a single sample in your epoch_iterator, stopping training at step"
                    f" {self.state.global_step}! This is expected if you're using an IterableDataset and set"
                    f" num_steps ({max_steps}) higher than the number of available samples."
                )
                self.control.should_training_stop = True

            self.control = self.callback_handler.on_epoch_end(
                args, self.state, self.control
            )
            self._maybe_log_save_evaluate(
                tr_loss,
                ortho_loss,
                grad_norm,
                model,
                trial,
                epoch,
                ignore_keys_for_eval,
            )

            if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
                if is_torch_xla_available():
                    # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                    xm.master_print(met.metrics_report())
                else:
                    logger.warning(
                        "You enabled PyTorch/XLA debug metrics but you don't have a TPU "
                        "configured. Check your training configuration if this is unexpected."
                    )
            if self.control.should_training_stop:
                break

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of training
            delattr(self, "_past")

        logger.info(
            "\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n"
        )
        if args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
            # Wait for everyone to get here so we are sure the model has been saved by process 0.
            if is_torch_xla_available():
                xm.rendezvous("load_best_model_at_end")
            elif args.parallel_mode == ParallelMode.DISTRIBUTED:
                dist.barrier()
            elif is_sagemaker_mp_enabled():
                smp.barrier()

            self._load_best_model()

        # add remaining tr_loss
        self._total_loss_scalar += tr_loss.item()
        effective_global_step = max(
            self.state.global_step, 0.001
        )  # Avoid ZeroDivisionError
        train_loss = self._total_loss_scalar / effective_global_step

        metrics = speed_metrics(
            "train",
            start_time,
            num_samples=num_train_samples,
            num_steps=self.state.max_steps,
            num_tokens=num_train_tokens,
        )
        self.store_flos()
        metrics["total_flos"] = self.state.total_flos
        metrics["train_loss"] = train_loss

        self.is_in_train = False

        self._memory_tracker.stop_and_update_metrics(metrics)

        self.log(metrics)

        run_dir = self._get_output_dir(trial)
        checkpoints_sorted = self._sorted_checkpoints(
            use_mtime=False, output_dir=run_dir
        )

        # Delete the last checkpoint when save_total_limit=1 if it's different from the best checkpoint and process allowed to save.
        if (
            self.args.should_save
            and self.state.best_model_checkpoint is not None
            and self.args.save_total_limit == 1
        ):
            for checkpoint in checkpoints_sorted:
                if not os.path.samefile(checkpoint, self.state.best_model_checkpoint):
                    logger.info(
                        f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit"
                    )
                    shutil.rmtree(checkpoint)

        self.control = self.callback_handler.on_train_end(
            args, self.state, self.control
        )

        # Wait for the checkpoint to be uploaded.
        self._finish_current_push()

        # After training we make sure to retrieve back the original forward pass method
        # for the embedding layer by removing the forward post hook.
        if self.neftune_noise_alpha is not None:
            self._deactivate_neftune(self.model)

        return TrainOutput(self.state.global_step, train_loss, metrics)

    def _get_output_dir(self, trial):
        if self.hp_search_backend is not None and trial is not None:
            if self.hp_search_backend == HPSearchBackend.OPTUNA:
                run_id = trial.number
            elif self.hp_search_backend == HPSearchBackend.RAY:
                import ray.train

                run_id = ray.train.get_context().get_trial_id()
            elif self.hp_search_backend == HPSearchBackend.SIGOPT:
                run_id = trial.id
            elif self.hp_search_backend == HPSearchBackend.WANDB:
                import wandb

                run_id = wandb.run.id
            run_name = (
                self.hp_name(trial) if self.hp_name is not None else f"run-{run_id}"
            )
            run_dir = os.path.join(self.args.output_dir, run_name)
        else:
            run_dir = self.args.output_dir
        return run_dir

    def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
        if model is None:
            model = self.model

        config_file = os.path.join(resume_from_checkpoint, CONFIG_NAME)
        adapter_weights_file = os.path.join(
            resume_from_checkpoint, ADAPTER_WEIGHTS_NAME
        )
        adapter_safe_weights_file = os.path.join(
            resume_from_checkpoint, ADAPTER_SAFE_WEIGHTS_NAME
        )
        weights_file = os.path.join(resume_from_checkpoint, WEIGHTS_NAME)
        weights_index_file = os.path.join(resume_from_checkpoint, WEIGHTS_INDEX_NAME)
        safe_weights_file = os.path.join(resume_from_checkpoint, SAFE_WEIGHTS_NAME)
        safe_weights_index_file = os.path.join(
            resume_from_checkpoint, SAFE_WEIGHTS_INDEX_NAME
        )
        is_fsdp_ckpt = os.path.isdir(resume_from_checkpoint) and (
            # this checks the FSDP state dict when `SHARDED_STATE_DICT` is used
            any(
                FSDP_MODEL_NAME in folder_name
                for folder_name in os.listdir(resume_from_checkpoint)
                if os.path.isdir(os.path.join(resume_from_checkpoint, folder_name))
            )
            # this checks the FSDP state dict when `FULL_STATE_DICT` is used
            or os.path.isfile(
                os.path.join(resume_from_checkpoint, f"{FSDP_MODEL_NAME}.bin")
            )
        )
        # if multiple adapters exist, they get saved in sub directories
        adapter_subdirs = (
            [
                folder_name
                for folder_name in os.listdir(resume_from_checkpoint)
                if os.path.isdir(os.path.join(resume_from_checkpoint, folder_name))
                and (
                    os.path.isfile(
                        os.path.join(
                            resume_from_checkpoint, folder_name, ADAPTER_WEIGHTS_NAME
                        )
                    )
                    or os.path.isfile(
                        os.path.join(
                            resume_from_checkpoint,
                            folder_name,
                            ADAPTER_SAFE_WEIGHTS_NAME,
                        )
                    )
                )
            ]
            if os.path.isdir(resume_from_checkpoint)
            else []
        )

        if is_fsdp_ckpt and not self.is_fsdp_enabled:
            raise ValueError(
                f"Checkpoint found at {resume_from_checkpoint} is only supported when using PyTorch FSDP"
            )

        if not (
            any(
                os.path.isfile(f)
                for f in [
                    weights_file,
                    safe_weights_file,
                    weights_index_file,
                    safe_weights_index_file,
                    adapter_weights_file,
                    adapter_safe_weights_file,
                ]
            )
            or is_fsdp_ckpt
            or adapter_subdirs
        ):
            raise ValueError(
                f"Can't find a valid checkpoint at {resume_from_checkpoint}"
            )

        logger.info(f"Loading model from {resume_from_checkpoint}.")

        if os.path.isfile(config_file):
            config = PretrainedConfig.from_json_file(config_file)
            checkpoint_version = config.transformers_version
            if checkpoint_version is not None and checkpoint_version != __version__:
                logger.warning(
                    f"You are resuming training from a checkpoint trained with {checkpoint_version} of "
                    f"Transformers but your current version is {__version__}. This is not recommended and could "
                    "yield to errors or unwanted behaviors."
                )

        if (
            os.path.isfile(weights_file)
            or os.path.isfile(safe_weights_file)
            or is_fsdp_ckpt
        ):
            weights_only_kwarg = (
                {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
            )
            # If the model is on the GPU, it still works!
            if is_sagemaker_mp_enabled():
                if os.path.isfile(
                    os.path.join(resume_from_checkpoint, "user_content.pt")
                ):
                    # If the 'user_content.pt' file exists, load with the new smp api.
                    # Checkpoint must have been saved with the new smp api.
                    smp.resume_from_checkpoint(
                        path=resume_from_checkpoint,
                        tag=WEIGHTS_NAME,
                        partial=False,
                        load_optimizer=False,
                    )
                else:
                    # If the 'user_content.pt' file does NOT exist, load with the old smp api.
                    # Checkpoint must have been saved with the old smp api.
                    if hasattr(self.args, "fp16") and self.args.fp16 is True:
                        logger.warning(
                            "Enabling FP16 and loading from smp < 1.10 checkpoint together is not suppported."
                        )
                    state_dict = torch.load(
                        weights_file,
                        map_location="cpu",
                        **weights_only_kwarg,
                    )
                    # Required for smp to not auto-translate state_dict from hf to smp (is already smp).
                    state_dict["_smp_is_partial"] = False
                    load_result = model.load_state_dict(state_dict, strict=True)
                    # release memory
                    del state_dict
            elif self.is_fsdp_enabled:
                load_fsdp_model(
                    self.accelerator.state.fsdp_plugin,
                    self.accelerator,
                    model,
                    resume_from_checkpoint,
                    **_get_fsdp_ckpt_kwargs(),
                )
            else:
                # We load the model state dict on the CPU to avoid an OOM error.
                if self.args.save_safetensors and os.path.isfile(safe_weights_file):
                    state_dict = safetensors.torch.load_file(
                        safe_weights_file, device="cpu"
                    )
                else:
                    state_dict = torch.load(
                        weights_file,
                        map_location="cpu",
                        **weights_only_kwarg,
                    )

                # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
                # which takes *args instead of **kwargs
                load_result = model.load_state_dict(state_dict, False)
                # release memory
                del state_dict
                self._issue_warnings_after_load(load_result)

        # Load adapters following PR # 24096
        elif _is_peft_model(model):
            # If train a model using PEFT & LoRA, assume that adapter have been saved properly.
            # TODO: in the future support only specific min PEFT versions
            if (
                hasattr(model, "active_adapter") or hasattr(model, "active_adapters")
            ) and hasattr(model, "load_adapter"):
                if os.path.exists(resume_from_checkpoint):
                    # For BC for older PEFT versions
                    if hasattr(model, "active_adapters"):
                        active_adapters = model.active_adapters
                        if len(active_adapters) > 1:
                            logger.warning(
                                "Multiple active adapters detected will only consider the first adapter"
                            )
                        active_adapter = active_adapters[0]
                    else:
                        active_adapter = model.active_adapter

                    if adapter_subdirs:
                        for subdir_name in adapter_subdirs:
                            peft_id = os.path.join(resume_from_checkpoint, subdir_name)
                            model.load_adapter(
                                peft_id,
                                subdir_name,
                                is_trainable=(subdir_name == active_adapter),
                            )
                        model.set_adapter(active_adapter)
                    else:
                        model.load_adapter(
                            resume_from_checkpoint, active_adapter, is_trainable=True
                        )
                else:
                    logger.warning(
                        "The intermediate checkpoints of PEFT may not be saved correctly, "
                        f"consider using a custom callback to save {ADAPTER_WEIGHTS_NAME} in corresponding saving folders. "
                        "Check some examples here: https://github.com/huggingface/peft/issues/96"
                    )
            else:
                logger.warning(
                    "Could not load adapter model, make sure to have `peft>=0.3.0` installed"
                )
        else:
            # We load the sharded checkpoint
            load_result = load_sharded_checkpoint(
                model,
                resume_from_checkpoint,
                strict=is_sagemaker_mp_enabled(),
                prefer_safe=self.args.save_safetensors,
            )
            if not is_sagemaker_mp_enabled():
                self._issue_warnings_after_load(load_result)

    def _load_best_model(self):
        logger.info(
            f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
        )
        best_model_path = os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME)
        best_safe_model_path = os.path.join(
            self.state.best_model_checkpoint, SAFE_WEIGHTS_NAME
        )
        best_adapter_model_path = os.path.join(
            self.state.best_model_checkpoint, ADAPTER_WEIGHTS_NAME
        )
        best_safe_adapter_model_path = os.path.join(
            self.state.best_model_checkpoint, ADAPTER_SAFE_WEIGHTS_NAME
        )

        model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
        if self.is_deepspeed_enabled:
            deepspeed_load_checkpoint(
                self.model_wrapped,
                self.state.best_model_checkpoint,
                load_module_strict=not _is_peft_model(self.model),
            )
        elif self.is_fsdp_enabled:
            load_result = load_fsdp_model(
                self.accelerator.state.fsdp_plugin,
                self.accelerator,
                model,
                self.state.best_model_checkpoint,
                **_get_fsdp_ckpt_kwargs(),
            )
        elif (
            os.path.exists(best_model_path)
            or os.path.exists(best_safe_model_path)
            or os.path.exists(best_adapter_model_path)
            or os.path.exists(best_safe_adapter_model_path)
        ):
            has_been_loaded = True
            weights_only_kwarg = (
                {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
            )
            if is_sagemaker_mp_enabled():
                if os.path.isfile(
                    os.path.join(self.state.best_model_checkpoint, "user_content.pt")
                ):
                    # If the 'user_content.pt' file exists, load with the new smp api.
                    # Checkpoint must have been saved with the new smp api.
                    smp.resume_from_checkpoint(
                        path=self.state.best_model_checkpoint,
                        tag=WEIGHTS_NAME,
                        partial=False,
                        load_optimizer=False,
                    )
                else:
                    # If the 'user_content.pt' file does NOT exist, load with the old smp api.
                    # Checkpoint must have been saved with the old smp api.
                    if self.args.save_safetensors and os.path.isfile(
                        best_safe_model_path
                    ):
                        state_dict = safetensors.torch.load_file(
                            best_safe_model_path, device="cpu"
                        )
                    else:
                        state_dict = torch.load(
                            best_model_path,
                            map_location="cpu",
                            **weights_only_kwarg,
                        )

                    state_dict["_smp_is_partial"] = False
                    load_result = model.load_state_dict(state_dict, strict=True)
            else:
                if _is_peft_model(model):
                    # If train a model using PEFT & LoRA, assume that adapter have been saved properly.
                    # TODO: in the future support only specific min PEFT versions
                    if (
                        hasattr(model, "active_adapter")
                        or hasattr(model, "active_adapters")
                    ) and hasattr(model, "load_adapter"):
                        # For BC for older PEFT versions
                        if hasattr(model, "active_adapters"):
                            active_adapter = model.active_adapters[0]
                            if len(model.active_adapters) > 1:
                                logger.warning(
                                    "Detected multiple active adapters, will only consider the first one"
                                )
                        else:
                            active_adapter = model.active_adapter

                        if os.path.exists(best_adapter_model_path) or os.path.exists(
                            best_safe_adapter_model_path
                        ):
                            model.load_adapter(
                                self.state.best_model_checkpoint, active_adapter
                            )
                            # Load_adapter has no return value present, modify it when appropriate.
                            from torch.nn.modules.module import _IncompatibleKeys

                            load_result = _IncompatibleKeys([], [])
                        else:
                            logger.warning(
                                "The intermediate checkpoints of PEFT may not be saved correctly, "
                                f"consider using a custom callback to save {ADAPTER_WEIGHTS_NAME} in corresponding saving folders. "
                                "Check some examples here: https://github.com/huggingface/peft/issues/96"
                            )
                            has_been_loaded = False
                    else:
                        logger.warning(
                            "Could not load adapter model, make sure to have `peft>=0.3.0` installed"
                        )
                        has_been_loaded = False
                else:
                    # We load the model state dict on the CPU to avoid an OOM error.
                    if self.args.save_safetensors and os.path.isfile(
                        best_safe_model_path
                    ):
                        state_dict = safetensors.torch.load_file(
                            best_safe_model_path, device="cpu"
                        )
                    else:
                        state_dict = torch.load(
                            best_model_path,
                            map_location="cpu",
                            **weights_only_kwarg,
                        )

                    # If the model is on the GPU, it still works!
                    # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
                    # which takes *args instead of **kwargs
                    load_result = model.load_state_dict(state_dict, False)
                if not is_sagemaker_mp_enabled() and has_been_loaded:
                    self._issue_warnings_after_load(load_result)
        elif os.path.exists(
            os.path.join(self.state.best_model_checkpoint, SAFE_WEIGHTS_INDEX_NAME)
        ) or os.path.exists(
            os.path.join(self.state.best_model_checkpoint, WEIGHTS_INDEX_NAME)
        ):
            load_result = load_sharded_checkpoint(
                model,
                self.state.best_model_checkpoint,
                strict=is_sagemaker_mp_enabled(),
            )
            if not is_sagemaker_mp_enabled():
                self._issue_warnings_after_load(load_result)
        else:
            logger.warning(
                f"Could not locate the best model at {best_model_path}, if you are running a distributed training "
                "on multiple nodes, you should activate `--save_on_each_node`."
            )

    def _issue_warnings_after_load(self, load_result):
        if len(load_result.missing_keys) != 0:
            if self.model._keys_to_ignore_on_save is not None and set(
                load_result.missing_keys
            ) == set(self.model._keys_to_ignore_on_save):
                self.model.tie_weights()
            else:
                logger.warning(
                    f"There were missing keys in the checkpoint model loaded: {load_result.missing_keys}."
                )
        if len(load_result.unexpected_keys) != 0:
            logger.warning(
                f"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}."
            )

    def _maybe_log_save_evaluate(
        self, tr_loss, ortho_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval
    ):
        if (
            self.control.should_log
            and self.state.global_step > self._globalstep_last_logged
        ):
            if is_torch_xla_available():
                xm.mark_step()

            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 ortho_loss is not None:
                logs["orthonormality"] = ortho_loss.item()
            else:
                logs["orthonormality"] = 0
            if grad_norm is not None:
                logs["grad_norm"] = (
                    grad_norm.detach().item()
                    if isinstance(grad_norm, torch.Tensor)
                    else grad_norm
                )
            logs["learning_rate"] = self._get_learning_rate()

            self._total_loss_scalar += tr_loss_scalar
            self._globalstep_last_logged = self.state.global_step
            self.store_flos()

            self.log(logs)

        metrics = None
        if self.control.should_evaluate:
            metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
            self._report_to_hp_search(trial, self.state.global_step, metrics)

            # Run delayed LR scheduler now that metrics are populated
            if isinstance(
                self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau
            ):
                metric_to_check = self.args.metric_for_best_model
                if not metric_to_check.startswith("eval_"):
                    metric_to_check = f"eval_{metric_to_check}"
                self.lr_scheduler.step(metrics[metric_to_check])

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

    def _load_rng_state(self, checkpoint):
        # Load RNG states from `checkpoint`
        if checkpoint is None:
            return

        if self.args.world_size > 1:
            process_index = self.args.process_index
            rng_file = os.path.join(checkpoint, f"rng_state_{process_index}.pth")
            if not os.path.isfile(rng_file):
                logger.info(
                    f"Didn't find an RNG file for process {process_index}, if you are resuming a training that "
                    "wasn't launched in a distributed fashion, reproducibility is not guaranteed."
                )
                return
        else:
            rng_file = os.path.join(checkpoint, "rng_state.pth")
            if not os.path.isfile(rng_file):
                logger.info(
                    "Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
                    "fashion, reproducibility is not guaranteed."
                )
                return

        checkpoint_rng_state = torch.load(rng_file)
        random.setstate(checkpoint_rng_state["python"])
        np.random.set_state(checkpoint_rng_state["numpy"])
        torch.random.set_rng_state(checkpoint_rng_state["cpu"])
        if torch.cuda.is_available():
            if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
                torch.cuda.random.set_rng_state_all(checkpoint_rng_state["cuda"])
            else:
                try:
                    torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"])
                except Exception as e:
                    logger.info(
                        f"Didn't manage to set back the RNG states of the GPU because of the following error:\n {e}"
                        "\nThis won't yield the same results as if the training had not been interrupted."
                    )
        if is_torch_xla_available():
            xm.set_rng_state(checkpoint_rng_state["xla"])
        if is_torch_npu_available():
            if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
                torch.npu.random.set_rng_state_all(checkpoint_rng_state["npu"])
            else:
                try:
                    torch.npu.random.set_rng_state(checkpoint_rng_state["npu"])
                except Exception as e:
                    logger.info(
                        f"Didn't manage to set back the RNG states of the NPU because of the following error:\n {e}"
                        "\nThis won't yield the same results as if the training had not been interrupted."
                    )
        if is_torch_mlu_available():
            if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
                torch.mlu.random.set_rng_state_all(checkpoint_rng_state["mlu"])
            else:
                try:
                    torch.mlu.random.set_rng_state(checkpoint_rng_state["mlu"])
                except Exception as e:
                    logger.info(
                        f"Didn't manage to set back the RNG states of the MLU because of the following error:\n {e}"
                        "\nThis won't yield the same results as if the training had not been interrupted."
                    )

    def _save_checkpoint(self, model, trial, metrics=None):
        # In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we
        # want to save except FullyShardedDDP.
        # assert unwrap_model(model) is self.model, "internal model should be a reference to self.model"

        # Save model checkpoint
        checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"

        if self.hp_search_backend is None and trial is None:
            self.store_flos()

        run_dir = self._get_output_dir(trial=trial)
        output_dir = os.path.join(run_dir, checkpoint_folder)
        self.save_model(output_dir, _internal_call=True)

        if not self.args.save_only_model:
            # Save optimizer and scheduler
            self._save_optimizer_and_scheduler(output_dir)
            # Save RNG state
            self._save_rng_state(output_dir)

        # Determine the new best metric / best model checkpoint
        if metrics is not None and self.args.metric_for_best_model is not None:
            metric_to_check = self.args.metric_for_best_model
            if not metric_to_check.startswith("eval_"):
                metric_to_check = f"eval_{metric_to_check}"
            metric_value = metrics[metric_to_check]

            operator = np.greater if self.args.greater_is_better else np.less
            if (
                self.state.best_metric is None
                or self.state.best_model_checkpoint is None
                or operator(metric_value, self.state.best_metric)
            ):
                self.state.best_metric = metric_value
                self.state.best_model_checkpoint = output_dir

        # Save the Trainer state
        if self.args.should_save:
            # Update the `TrainerControl` state to where we are currently
            self.state.stateful_callbacks["TrainerControl"] = self.control.state()
            self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))

        if self.args.push_to_hub:
            self._push_from_checkpoint(output_dir)

        # Maybe delete some older checkpoints.
        if self.args.should_save:
            # Solely rely on numerical checkpoint id for rotation.
            # mtime is not reliable especially on some fuse fs in cloud environments.
            self._rotate_checkpoints(use_mtime=False, output_dir=run_dir)

    def _save_rng_state(self, output_dir):
        # Save RNG state in non-distributed training
        rng_states = {
            "python": random.getstate(),
            "numpy": np.random.get_state(),
            "cpu": torch.random.get_rng_state(),
        }
        if torch.cuda.is_available():
            if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
                # In non distributed, we save the global CUDA RNG state (will take care of DataParallel)
                rng_states["cuda"] = torch.cuda.random.get_rng_state_all()
            else:
                rng_states["cuda"] = torch.cuda.random.get_rng_state()

        if is_torch_xla_available():
            rng_states["xla"] = xm.get_rng_state()

        if is_torch_npu_available():
            if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
                rng_states["npu"] = torch.npu.random.get_rng_state_all()
            else:
                rng_states["npu"] = torch.npu.random.get_rng_state()

        if is_torch_mlu_available():
            if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
                rng_states["mlu"] = torch.mlu.random.get_rng_state_all()
            else:
                rng_states["mlu"] = torch.mlu.random.get_rng_state()

        # A process can arrive here before the process 0 has a chance to save the model, in which case output_dir may
        # not yet exist.
        os.makedirs(output_dir, exist_ok=True)

        if self.args.world_size <= 1:
            torch.save(rng_states, os.path.join(output_dir, "rng_state.pth"))
        else:
            torch.save(
                rng_states,
                os.path.join(output_dir, f"rng_state_{self.args.process_index}.pth"),
            )

    def _save_optimizer_and_scheduler(self, output_dir):
        if is_torch_xla_available():
            xm.rendezvous("saving_optimizer_states")
            xm.save(
                self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME)
            )
            with warnings.catch_warnings(record=True) as caught_warnings:
                xm.save(
                    self.lr_scheduler.state_dict(),
                    os.path.join(output_dir, SCHEDULER_NAME),
                )
                reissue_pt_warnings(caught_warnings)
        elif is_sagemaker_mp_enabled():
            opt_state_dict = self.optimizer.local_state_dict(gather_if_shard=False)
            smp.barrier()
            if smp.rdp_rank() == 0 or smp.state.cfg.shard_optimizer_state:
                smp.save(
                    opt_state_dict,
                    os.path.join(output_dir, OPTIMIZER_NAME),
                    partial=True,
                    v3=smp.state.cfg.shard_optimizer_state,
                )
        elif self.is_deepspeed_enabled:
            # under zero3 model file itself doesn't get saved since it's bogus! Unless deepspeed
            # config `stage3_gather_16bit_weights_on_model_save` is True
            accept_exclude_frozen_parameters = "exclude_frozen_parameters" in set(
                inspect.signature(self.model_wrapped.save_checkpoint).parameters.keys()
            )
            if accept_exclude_frozen_parameters and _is_peft_model(self.model):
                self.model_wrapped.save_checkpoint(
                    output_dir, exclude_frozen_parameters=True
                )
            else:
                self.model_wrapped.save_checkpoint(output_dir)
        elif self.is_fsdp_enabled:
            # save fsdp specific ckpt for resuming from ckpt
            save_fsdp_model(
                self.accelerator.state.fsdp_plugin,
                self.accelerator,
                self.model,
                output_dir,
                **_get_fsdp_ckpt_kwargs(),
            )
            save_fsdp_optimizer(
                self.accelerator.state.fsdp_plugin,
                self.accelerator,
                self.optimizer,
                self.model,
                output_dir,
            )
        elif self.args.should_save:
            # deepspeed.save_checkpoint above saves model/optim/sched
            torch.save(
                self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME)
            )

        # Save SCHEDULER & SCALER
        is_deepspeed_custom_scheduler = self.is_deepspeed_enabled and not isinstance(
            self.lr_scheduler, DeepSpeedSchedulerWrapper
        )
        if (
            self.args.should_save
            and (not self.is_deepspeed_enabled or is_deepspeed_custom_scheduler)
            and not is_torch_xla_available()
        ):
            with warnings.catch_warnings(record=True) as caught_warnings:
                torch.save(
                    self.lr_scheduler.state_dict(),
                    os.path.join(output_dir, SCHEDULER_NAME),
                )
            reissue_pt_warnings(caught_warnings)

    def _load_optimizer_and_scheduler(self, checkpoint):
        """If optimizer and scheduler states exist, load them."""
        if checkpoint is None:
            return

        if self.is_deepspeed_enabled:
            # deepspeed loads optimizer/lr_scheduler together with the model in deepspeed_init
            if not isinstance(self.lr_scheduler, DeepSpeedSchedulerWrapper):
                with warnings.catch_warnings(record=True) as caught_warnings:
                    self.lr_scheduler.load_state_dict(
                        torch.load(os.path.join(checkpoint, SCHEDULER_NAME))
                    )
                reissue_pt_warnings(caught_warnings)
            return

        checkpoint_file_exists = (
            glob.glob(os.path.join(checkpoint, OPTIMIZER_NAME) + "_*")
            if is_sagemaker_mp_enabled()
            else (
                os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME))
                or os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME_BIN))
                or (
                    os.path.isdir(checkpoint)
                    and any(
                        OPTIMIZER_NAME_BIN.split(".")[0] in folder_name
                        for folder_name in os.listdir(checkpoint)
                        if os.path.isdir(os.path.join(checkpoint, folder_name))
                    )
                )
            )
        )
        if checkpoint_file_exists and os.path.isfile(
            os.path.join(checkpoint, SCHEDULER_NAME)
        ):
            # Load in optimizer and scheduler states
            if is_torch_xla_available():
                # On TPU we have to take some extra precautions to properly load the states on the right device.
                optimizer_state = torch.load(
                    os.path.join(checkpoint, OPTIMIZER_NAME), map_location="cpu"
                )
                with warnings.catch_warnings(record=True) as caught_warnings:
                    lr_scheduler_state = torch.load(
                        os.path.join(checkpoint, SCHEDULER_NAME), map_location="cpu"
                    )
                reissue_pt_warnings(caught_warnings)

                xm.send_cpu_data_to_device(optimizer_state, self.args.device)
                xm.send_cpu_data_to_device(lr_scheduler_state, self.args.device)

                self.optimizer.load_state_dict(optimizer_state)
                self.lr_scheduler.load_state_dict(lr_scheduler_state)
            else:
                if is_sagemaker_mp_enabled():
                    if os.path.isfile(os.path.join(checkpoint, "user_content.pt")):
                        # Optimizer checkpoint was saved with smp >= 1.10
                        def opt_load_hook(mod, opt):
                            opt.load_state_dict(
                                smp.load(
                                    os.path.join(checkpoint, OPTIMIZER_NAME),
                                    partial=True,
                                )
                            )

                    else:
                        # Optimizer checkpoint was saved with smp < 1.10
                        def opt_load_hook(mod, opt):
                            if IS_SAGEMAKER_MP_POST_1_10:
                                opt.load_state_dict(
                                    smp.load(
                                        os.path.join(checkpoint, OPTIMIZER_NAME),
                                        partial=True,
                                        back_compat=True,
                                    )
                                )
                            else:
                                opt.load_state_dict(
                                    smp.load(
                                        os.path.join(checkpoint, OPTIMIZER_NAME),
                                        partial=True,
                                    )
                                )

                    self.model_wrapped.register_post_step_hook(opt_load_hook)
                else:
                    # We use the CPU when training on one GPU to avoid OOM for GPU RAM when training big models.
                    # In distributed training however, we load directly on each GPU and risk the GPU OOM as it's more
                    # likely to get OOM on CPU (since we load num_gpu times the optimizer state
                    map_location = (
                        self.args.device if self.args.world_size > 1 else "cpu"
                    )
                    if self.is_fsdp_enabled:
                        load_fsdp_optimizer(
                            self.accelerator.state.fsdp_plugin,
                            self.accelerator,
                            self.optimizer,
                            self.model,
                            checkpoint,
                            **_get_fsdp_ckpt_kwargs(),
                        )
                    else:
                        self.optimizer.load_state_dict(
                            torch.load(
                                os.path.join(checkpoint, OPTIMIZER_NAME),
                                map_location=map_location,
                            )
                        )
                with warnings.catch_warnings(record=True) as caught_warnings:
                    self.lr_scheduler.load_state_dict(
                        torch.load(os.path.join(checkpoint, SCHEDULER_NAME))
                    )
                reissue_pt_warnings(caught_warnings)

    def _load_callback_state(self):
        """If callback states exist and were passed in, restore their states if enabled"""
        if not self.args.restore_callback_states_from_checkpoint:
            return
        # Callback states are stored in stateful_callbacks
        not_found = []
        new_callbacks = []
        original_callbacks = self.callback_handler.callbacks + [self.control]
        for stored_callback, data in self.state.stateful_callbacks.items():
            if not isinstance(data, list):
                data = [data]
            if any(
                callback.__class__.__name__ == stored_callback
                for callback in original_callbacks
            ):
                # We can load/restore from multiple callbacks of the same type.
                duplicates = [
                    callback
                    for callback in original_callbacks
                    if callback.__class__.__name__ == stored_callback
                ]
                for callback, callback_data in zip(duplicates, data):
                    args = callback_data.get("args", {})
                    attributes = callback_data.get("attributes", {})
                    new_callback = type(callback)(**args)
                    for attribute, value in attributes.items():
                        setattr(new_callback, attribute, value)
                    if isinstance(callback, TrainerControl):
                        # Specifically for restoring the `control` state
                        self.control = new_callback
                    else:
                        new_callbacks.append(new_callback)
                    # We remove the existing callback and add it to the list of new callbacks
                    self.callback_handler.remove_callback(type(new_callback))
                logger.info(
                    "Continuing training from checkpoint, restoring any callbacks that were passed in"
                )
            else:
                not_found.append(stored_callback)
        if len(not_found) > 0:
            logger.warning(
                f"Checkpoint included callbacks not included in current configuration. Ignoring. ({', '.join(not_found)})"
            )
        for callback in new_callbacks:
            self.callback_handler.add_callback(callback)

    def hyperparameter_search(
        self,
        hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
        compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
        n_trials: int = 20,
        direction: Union[str, List[str]] = "minimize",
        backend: Optional[Union["str", HPSearchBackend]] = None,
        hp_name: Optional[Callable[["optuna.Trial"], str]] = None,
        **kwargs,
    ) -> Union[BestRun, List[BestRun]]:
        """
        Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined
        by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,
        the sum of all metrics otherwise.

        <Tip warning={true}>

        To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to
        reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to
        subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom
        optimizer/scheduler.

        </Tip>

        Args:
            hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*):
                A function that defines the hyperparameter search space. Will default to
                [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or
                [`~trainer_utils.default_hp_space_sigopt`] depending on your backend.
            compute_objective (`Callable[[Dict[str, float]], float]`, *optional*):
                A function computing the objective to minimize or maximize from the metrics returned by the `evaluate`
                method. Will default to [`~trainer_utils.default_compute_objective`].
            n_trials (`int`, *optional*, defaults to 100):
                The number of trial runs to test.
            direction (`str` or `List[str]`, *optional*, defaults to `"minimize"`):
                If it's single objective optimization, direction is `str`, can be `"minimize"` or `"maximize"`, you
                should pick `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or
                several metrics. If it's multi objectives optimization, direction is `List[str]`, can be List of
                `"minimize"` and `"maximize"`, you should pick `"minimize"` when optimizing the validation loss,
                `"maximize"` when optimizing one or several metrics.
            backend (`str` or [`~training_utils.HPSearchBackend`], *optional*):
                The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending
                on which one is installed. If all are installed, will default to optuna.
            hp_name (`Callable[["optuna.Trial"], str]]`, *optional*):
                A function that defines the trial/run name. Will default to None.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more
                information see:

                - the documentation of
                  [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)
                - the documentation of [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run)
                - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create)

        Returns:
            [`trainer_utils.BestRun` or `List[trainer_utils.BestRun]`]: All the information about the best run or best
            runs for multi-objective optimization. Experiment summary can be found in `run_summary` attribute for Ray
            backend.
        """
        if backend is None:
            backend = default_hp_search_backend()
        backend = HPSearchBackend(backend)
        backend_obj = ALL_HYPERPARAMETER_SEARCH_BACKENDS[backend]()
        backend_obj.ensure_available()
        self.hp_search_backend = backend
        if self.model_init is None:
            raise RuntimeError(
                "To use hyperparameter search, you need to pass your model through a model_init function."
            )

        self.hp_space = backend_obj.default_hp_space if hp_space is None else hp_space
        self.hp_name = hp_name
        self.compute_objective = (
            default_compute_objective
            if compute_objective is None
            else compute_objective
        )

        best_run = backend_obj.run(self, n_trials, direction, **kwargs)

        self.hp_search_backend = None
        return best_run

    def log(self, logs: Dict[str, float]) -> None:
        """
        Log `logs` on the various objects watching training.

        Subclass and override this method to inject custom behavior.

        Args:
            logs (`Dict[str, float]`):
                The values to log.
        """
        if self.state.epoch is not None:
            logs["epoch"] = self.state.epoch
        if self.args.include_num_input_tokens_seen:
            logs["num_input_tokens_seen"] = self.state.num_input_tokens_seen

        output = {**logs, **{"step": self.state.global_step}}
        self.state.log_history.append(output)
        self.control = self.callback_handler.on_log(
            self.args, self.state, self.control, logs
        )

    def _prepare_input(
        self, data: Union[torch.Tensor, Any]
    ) -> Union[torch.Tensor, Any]:
        """
        Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
        """
        if isinstance(data, Mapping):
            return type(data)({k: self._prepare_input(v) for k, v in data.items()})
        elif isinstance(data, (tuple, list)):
            return type(data)(self._prepare_input(v) for v in data)
        elif isinstance(data, torch.Tensor):
            kwargs = {"device": self.args.device}
            if self.is_deepspeed_enabled and (
                torch.is_floating_point(data) or torch.is_complex(data)
            ):
                # NLP models inputs are int/uint and those get adjusted to the right dtype of the
                # embedding. Other models such as wav2vec2's inputs are already float and thus
                # may need special handling to match the dtypes of the model
                kwargs.update(
                    {
                        "dtype": self.accelerator.state.deepspeed_plugin.hf_ds_config.dtype()
                    }
                )
            return data.to(**kwargs)
        return data

    def _prepare_inputs(
        self, inputs: Dict[str, Union[torch.Tensor, Any]]
    ) -> Dict[str, Union[torch.Tensor, Any]]:
        """
        Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and
        handling potential state.
        """
        inputs = self._prepare_input(inputs)
        if len(inputs) == 0:
            raise ValueError(
                "The batch received was empty, your model won't be able to train on it. Double-check that your "
                f"training dataset contains keys expected by the model: {','.join(self._signature_columns)}."
            )
        if self.args.past_index >= 0 and self._past is not None:
            inputs["mems"] = self._past

        return inputs

    def compute_loss_context_manager(self):
        """
        A helper wrapper to group together context managers.
        """
        return self.autocast_smart_context_manager()

    def autocast_smart_context_manager(self, cache_enabled: Optional[bool] = True):
        """
        A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired
        arguments, depending on the situation.
        """
        if self.use_cpu_amp:
            ctx_manager = torch.cpu.amp.autocast(
                cache_enabled=cache_enabled, dtype=self.amp_dtype
            )
        else:
            ctx_manager = contextlib.nullcontext()

        return ctx_manager

    def training_step(
        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
    ) -> torch.Tensor:
        """
        Perform a training step on a batch of inputs.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to train.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.

        Return:
            `torch.Tensor`: The tensor with training loss on this batch.
        """
        model.train()
        inputs = self._prepare_inputs(inputs)

        if is_sagemaker_mp_enabled():
            loss_mb = smp_forward_backward(
                model, inputs, self.args.gradient_accumulation_steps
            )
            return loss_mb.reduce_mean().detach().to(self.args.device)

        with self.compute_loss_context_manager():
            loss = self.compute_loss(model, inputs)

        del inputs
        torch.cuda.empty_cache()

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

        if self.use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            self.accelerator.backward(loss)

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

    def compute_loss(self, model, inputs, return_outputs=False):
        """
        How the loss is computed by Trainer. By default, all models return the loss in the first element.

        Subclass and override for custom behavior.
        """
        if self.label_smoother is not None and "labels" in inputs:
            labels = inputs.pop("labels")
        else:
            labels = None
        outputs = model(**inputs)
        # Save past state if it exists
        # TODO: this needs to be fixed and made cleaner later.
        if self.args.past_index >= 0:
            self._past = outputs[self.args.past_index]

        if labels is not None:
            unwrapped_model = self.accelerator.unwrap_model(model)
            if _is_peft_model(unwrapped_model):
                model_name = unwrapped_model.base_model.model._get_name()
            else:
                model_name = unwrapped_model._get_name()
            if model_name in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():
                loss = self.label_smoother(outputs, labels, shift_labels=True)
            else:
                loss = self.label_smoother(outputs, labels)
        else:
            if isinstance(outputs, dict) and "loss" not in outputs:
                raise ValueError(
                    "The model did not return a loss from the inputs, only the following keys: "
                    f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}."
                )
            # We don't use .loss here since the model may return tuples instead of ModelOutput.
            loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

        return (loss, outputs) if return_outputs else loss

    def is_local_process_zero(self) -> bool:
        """
        Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
        machines) main process.
        """
        return self.args.local_process_index == 0

    def is_world_process_zero(self) -> bool:
        """
        Whether or not this process is the global main process (when training in a distributed fashion on several
        machines, this is only going to be `True` for one process).
        """
        # Special case for SageMaker ModelParallel since there process_index is dp_process_index, not the global
        # process index.
        if is_sagemaker_mp_enabled():
            return smp.rank() == 0
        else:
            return self.args.process_index == 0

    def save_model(
        self, output_dir: Optional[str] = None, _internal_call: bool = False
    ):
        """
        Will save the model, so you can reload it using `from_pretrained()`.

        Will only save from the main process.
        """

        if output_dir is None:
            output_dir = self.args.output_dir

        if is_torch_xla_available():
            self._save_tpu(output_dir)
        elif is_sagemaker_mp_enabled():
            # Calling the state_dict needs to be done on the wrapped model and on all processes.
            os.makedirs(output_dir, exist_ok=True)
            state_dict = self.model_wrapped.state_dict()
            if self.args.should_save:
                self._save(output_dir, state_dict=state_dict)
            if IS_SAGEMAKER_MP_POST_1_10:
                # 'user_content.pt' indicates model state_dict saved with smp >= 1.10
                Path(os.path.join(output_dir, "user_content.pt")).touch()
        elif self.is_fsdp_enabled:
            if (
                "FULL_STATE_DICT"
                in str(self.accelerator.state.fsdp_plugin.state_dict_type)
            ) and (version.parse(accelerate_version) > version.parse("0.24.1")):
                state_dict = self.accelerator.get_state_dict(self.model)
                if self.args.should_save:
                    self._save(output_dir, state_dict=state_dict)
        elif self.is_deepspeed_enabled:
            try:
                state_dict = self.accelerator.get_state_dict(self.deepspeed)
                if self.args.should_save:
                    self._save(output_dir, state_dict=state_dict)
            except ValueError:
                logger.warning(
                    " stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead, use"
                    " zero_to_fp32.py to recover weights"
                )
                if self.args.should_save:
                    self._save(output_dir, state_dict={})
                # remove the dummy state_dict
                remove_dummy_checkpoint(
                    self.args.should_save, output_dir, [WEIGHTS_NAME, SAFE_WEIGHTS_NAME]
                )
                self.model_wrapped.save_checkpoint(output_dir)

        elif self.args.should_save:
            self._save(output_dir)

        # Push to the Hub when `save_model` is called by the user.
        if self.args.push_to_hub and not _internal_call:
            self.push_to_hub(commit_message="Model save")

    def _save_tpu(self, output_dir: Optional[str] = None):
        output_dir = output_dir if output_dir is not None else self.args.output_dir

        logger.info(f"Saving model checkpoint to {output_dir}")
        model = self.model
        xm.mark_step()
        if self.args.save_safetensors:
            model.to("cpu")

        if xm.is_master_ordinal():
            os.makedirs(output_dir, exist_ok=True)
            torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        supported_classes = (PushToHubMixin,)
        xm.rendezvous("saving_checkpoint")
        if not isinstance(model, supported_classes):
            if isinstance(self.accelerator.unwrap_model(model), supported_classes):
                self.accelerator.unwrap_model(model).save_pretrained(
                    output_dir,
                    is_main_process=self.args.should_save,
                    state_dict=model.state_dict(),
                    save_function=xm.save,
                    safe_serialization=self.args.save_safetensors,
                )
            else:
                logger.info(
                    "Trainer.model is not a `PreTrainedModel`, only saving its state dict."
                )
                state_dict = model.state_dict()
                xm.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
        else:
            model.save_pretrained(
                output_dir,
                is_main_process=self.args.should_save,
                save_function=xm.save,
                safe_serialization=self.args.save_safetensors,
            )
        if self.tokenizer is not None and self.args.should_save:
            self.tokenizer.save_pretrained(output_dir)

        # We moved the model from TPU -> CPU for saving the weights.
        # Now we should move it back to subsequent compute still works.
        if self.args.save_safetensors:
            model.to(self.args.device)

    def _save(self, output_dir: Optional[str] = None, state_dict=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}")

        supported_classes = (
            (PreTrainedModel,)
            if not is_peft_available()
            else (PreTrainedModel, PeftModel)
        )
        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        if not isinstance(self.model, supported_classes):
            if state_dict is None:
                state_dict = self.model.state_dict()

            if isinstance(self.accelerator.unwrap_model(self.model), supported_classes):
                self.accelerator.unwrap_model(self.model).save_pretrained(
                    output_dir,
                    state_dict=state_dict,
                    safe_serialization=self.args.save_safetensors,
                )
            else:
                logger.info(
                    "Trainer.model is not a `PreTrainedModel`, only saving its state dict."
                )
                if self.args.save_safetensors:
                    safetensors.torch.save_file(
                        state_dict,
                        os.path.join(output_dir, SAFE_WEIGHTS_NAME),
                        metadata={"format": "pt"},
                    )
                else:
                    torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
        else:
            self.model.save_pretrained(
                output_dir,
                state_dict=state_dict,
                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 store_flos(self):
        # Storing the number of floating-point operations that went into the model
        if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
            self.state.total_flos += (
                distributed_broadcast_scalars(
                    [self.current_flos], device=self.args.device
                )
                .sum()
                .item()
            )
            self.current_flos = 0
        else:
            self.state.total_flos += self.current_flos
            self.current_flos = 0

    def _sorted_checkpoints(
        self, output_dir=None, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False
    ) -> List[str]:
        ordering_and_checkpoint_path = []

        glob_checkpoints = [
            str(x)
            for x in Path(output_dir).glob(f"{checkpoint_prefix}-*")
            if os.path.isdir(x)
        ]

        for path in glob_checkpoints:
            if use_mtime:
                ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
            else:
                regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
                if regex_match is not None and regex_match.groups() is not None:
                    ordering_and_checkpoint_path.append(
                        (int(regex_match.groups()[0]), path)
                    )

        checkpoints_sorted = sorted(ordering_and_checkpoint_path)
        checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
        # Make sure we don't delete the best model.
        if (
            self.state.best_model_checkpoint is not None
            and str(Path(self.state.best_model_checkpoint)) in checkpoints_sorted
        ):
            best_model_index = checkpoints_sorted.index(
                str(Path(self.state.best_model_checkpoint))
            )
            for i in range(best_model_index, len(checkpoints_sorted) - 2):
                checkpoints_sorted[i], checkpoints_sorted[i + 1] = (
                    checkpoints_sorted[i + 1],
                    checkpoints_sorted[i],
                )
        return checkpoints_sorted

    def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:
        if self.args.save_total_limit is None or self.args.save_total_limit <= 0:
            return

        # Check if we should delete older checkpoint(s)
        checkpoints_sorted = self._sorted_checkpoints(
            use_mtime=use_mtime, output_dir=output_dir
        )
        if len(checkpoints_sorted) <= self.args.save_total_limit:
            return

        # If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which
        # we don't do to allow resuming.
        save_total_limit = self.args.save_total_limit
        if (
            self.state.best_model_checkpoint is not None
            and self.args.save_total_limit == 1
            and checkpoints_sorted[-1] != self.state.best_model_checkpoint
        ):
            save_total_limit = 2

        number_of_checkpoints_to_delete = max(
            0, len(checkpoints_sorted) - save_total_limit
        )
        checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
        for checkpoint in checkpoints_to_be_deleted:
            logger.info(
                f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit"
            )
            shutil.rmtree(checkpoint, ignore_errors=True)

    def evaluate(
        self,
        eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> Dict[str, float]:
        """
        Run evaluation and returns metrics.

        The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
        (pass it to the init `compute_metrics` argument).

        You can also subclass and override this method to inject custom behavior.

        Args:
            eval_dataset (Union[`Dataset`, Dict[str, `Dataset`]), *optional*):
                Pass a dataset if you wish to override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns
                not accepted by the `model.forward()` method are automatically removed. If it is a dictionary, it will
                evaluate on each dataset, prepending the dictionary key to the metric name. Datasets must implement the
                `__len__` method.

                <Tip>

                If you pass a dictionary with names of datasets as keys and datasets as values, evaluate will run
                separate evaluations on each dataset. This can be useful to monitor how training affects other
                datasets or simply to get a more fine-grained evaluation.
                When used with `load_best_model_at_end`, make sure `metric_for_best_model` references exactly one
                of the datasets. If you, for example, pass in `{"data1": data1, "data2": data2}` for two datasets
                `data1` and `data2`, you could specify `metric_for_best_model="eval_data1_loss"` for using the
                loss on `data1` and `metric_for_best_model="eval_data1_loss"` for the loss on `data2`.

                </Tip>

            ignore_keys (`List[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"eval"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "eval_bleu" if the prefix is "eval" (default)

        Returns:
            A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
            dictionary also contains the epoch number which comes from the training state.
        """
        # handle multipe eval datasets
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
        if isinstance(eval_dataset, dict):
            metrics = {}
            for eval_dataset_name, _eval_dataset in eval_dataset.items():
                dataset_metrics = self.evaluate(
                    eval_dataset=_eval_dataset,
                    ignore_keys=ignore_keys,
                    metric_key_prefix=f"{metric_key_prefix}_{eval_dataset_name}",
                )
                metrics.update(dataset_metrics)
            return metrics

        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        eval_dataloader = self.get_eval_dataloader(eval_dataset)
        if self.is_fsdp_xla_v2_enabled:
            eval_dataloader = tpu_spmd_dataloader(eval_dataloader)

        start_time = time.time()

        eval_loop = (
            self.prediction_loop
            if self.args.use_legacy_prediction_loop
            else self.evaluation_loop
        )
        output = eval_loop(
            eval_dataloader,
            description="Evaluation",
            # No point gathering the predictions if there are no metrics, otherwise we defer to
            # self.args.prediction_loss_only
            prediction_loss_only=True if self.compute_metrics is None else None,
            ignore_keys=ignore_keys,
            metric_key_prefix=metric_key_prefix,
        )

        total_batch_size = self.args.eval_batch_size * self.args.world_size
        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
        output.metrics.update(
            speed_metrics(
                metric_key_prefix,
                start_time,
                num_samples=output.num_samples,
                num_steps=math.ceil(output.num_samples / total_batch_size),
            )
        )

        self.log(output.metrics)

        if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report())

        self.control = self.callback_handler.on_evaluate(
            self.args, self.state, self.control, output.metrics
        )

        self._memory_tracker.stop_and_update_metrics(output.metrics)

        return output.metrics

    def predict(
        self,
        test_dataset: Dataset,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "test",
    ) -> PredictionOutput:
        """
        Run prediction and returns predictions and potential metrics.

        Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
        will also return metrics, like in `evaluate()`.

        Args:
            test_dataset (`Dataset`):
                Dataset to run the predictions on. If it is an `datasets.Dataset`, columns not accepted by the
                `model.forward()` method are automatically removed. Has to implement the method `__len__`
            ignore_keys (`List[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"test"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "test_bleu" if the prefix is "test" (default)

        <Tip>

        If your predictions or labels have different sequence length (for instance because you're doing dynamic padding
        in a token classification task) the predictions will be padded (on the right) to allow for concatenation into
        one array. The padding index is -100.

        </Tip>

        Returns: *NamedTuple* A namedtuple with the following keys:

            - predictions (`np.ndarray`): The predictions on `test_dataset`.
            - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some).
            - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained
              labels).
        """
        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        test_dataloader = self.get_test_dataloader(test_dataset)
        start_time = time.time()

        eval_loop = (
            self.prediction_loop
            if self.args.use_legacy_prediction_loop
            else self.evaluation_loop
        )
        output = eval_loop(
            test_dataloader,
            description="Prediction",
            ignore_keys=ignore_keys,
            metric_key_prefix=metric_key_prefix,
        )
        total_batch_size = self.args.eval_batch_size * self.args.world_size
        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
        output.metrics.update(
            speed_metrics(
                metric_key_prefix,
                start_time,
                num_samples=output.num_samples,
                num_steps=math.ceil(output.num_samples / total_batch_size),
            )
        )

        self.control = self.callback_handler.on_predict(
            self.args, self.state, self.control, output.metrics
        )
        self._memory_tracker.stop_and_update_metrics(output.metrics)

        return PredictionOutput(
            predictions=output.predictions,
            label_ids=output.label_ids,
            metrics=output.metrics,
        )

    def evaluation_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """
        args = self.args

        prediction_loss_only = (
            prediction_loss_only
            if prediction_loss_only is not None
            else args.prediction_loss_only
        )

        # if eval is called w/o train, handle model prep here
        if self.is_deepspeed_enabled and self.deepspeed is None:
            _, _ = deepspeed_init(self, num_training_steps=0, inference=True)

        model = self._wrap_model(self.model, training=False, dataloader=dataloader)

        if len(self.accelerator._models) == 0 and model is self.model:
            model = (
                self.accelerator.prepare(model)
                if self.is_deepspeed_enabled
                else self.accelerator.prepare_model(model, evaluation_mode=True)
            )

            if self.is_fsdp_enabled:
                self.model = model

            # for the rest of this function `model` is the outside model, whether it was wrapped or not
            if model is not self.model:
                self.model_wrapped = model

            # backward compatibility
            if self.is_deepspeed_enabled:
                self.deepspeed = self.model_wrapped

        # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
        # while ``train`` is running, cast it to the right dtype first and then put on device
        if not self.is_in_train:
            if args.fp16_full_eval:
                model = model.to(dtype=torch.float16, device=args.device)
            elif args.bf16_full_eval:
                model = model.to(dtype=torch.bfloat16, device=args.device)

        batch_size = self.args.eval_batch_size

        logger.info(f"***** Running {description} *****")
        if has_length(dataloader):
            logger.info(f"  Num examples = {self.num_examples(dataloader)}")
        else:
            logger.info("  Num examples: Unknown")
        logger.info(f"  Batch size = {batch_size}")

        model.eval()

        self.callback_handler.eval_dataloader = dataloader
        # Do this before wrapping.
        eval_dataset = getattr(dataloader, "dataset", None)

        if args.past_index >= 0:
            self._past = None

        # Initialize containers
        all_losses = EvalLoopContainer(
            self.args.eval_do_concat_batches, padding_index=-100
        )
        all_preds = EvalLoopContainer(
            self.args.eval_do_concat_batches, padding_index=-100
        )
        all_labels = EvalLoopContainer(
            self.args.eval_do_concat_batches, padding_index=-100
        )
        all_inputs = EvalLoopContainer(
            self.args.eval_do_concat_batches, padding_index=-100
        )

        metrics = None

        # Will be useful when we have an iterable dataset so don't know its length.
        observed_num_examples = 0

        # Main evaluation loop
        for step, inputs in enumerate(dataloader):
            # Update the observed num examples
            observed_batch_size = find_batch_size(inputs)
            if observed_batch_size is not None:
                observed_num_examples += observed_batch_size
                # For batch samplers, batch_size is not known by the dataloader in advance.
                if batch_size is None:
                    batch_size = observed_batch_size

            # Prediction step
            loss, logits, labels = self.prediction_step(
                model, inputs, prediction_loss_only, ignore_keys=ignore_keys
            )
            main_input_name = getattr(self.model, "main_input_name", "input_ids")
            inputs_decode = (
                self._prepare_input(inputs[main_input_name])
                if args.include_inputs_for_metrics
                else None
            )

            if is_torch_xla_available():
                xm.mark_step()

            # Update containers
            if loss is not None:
                losses = self.gather_function((loss.repeat(batch_size)))
                all_losses.add(losses)
            if inputs_decode is not None:
                inputs_decode = self.accelerator.pad_across_processes(
                    inputs_decode, dim=1, pad_index=-100
                )
                inputs_decode = self.gather_function((inputs_decode))
                if not self.args.batch_eval_metrics or description == "Prediction":
                    all_inputs.add(inputs_decode)
            if logits is not None:
                logits = self.accelerator.pad_across_processes(
                    logits, dim=1, pad_index=-100
                )
                if self.preprocess_logits_for_metrics is not None:
                    logits = self.preprocess_logits_for_metrics(logits, labels)
                logits = self.gather_function((logits))
                if not self.args.batch_eval_metrics or description == "Prediction":
                    all_preds.add(logits)
            if labels is not None:
                labels = self.accelerator.pad_across_processes(
                    labels, dim=1, pad_index=-100
                )
                labels = self.gather_function((labels))
                if not self.args.batch_eval_metrics or description == "Prediction":
                    all_labels.add(labels)

            self.control = self.callback_handler.on_prediction_step(
                args, self.state, self.control
            )

            if self.args.batch_eval_metrics:
                if (
                    self.compute_metrics is not None
                    and logits is not None
                    and labels is not None
                ):
                    is_last_step = self.accelerator.gradient_state.end_of_dataloader
                    if args.include_inputs_for_metrics:
                        metrics = self.compute_metrics(
                            EvalPrediction(
                                predictions=logits, label_ids=labels, inputs=inputs
                            ),
                            compute_result=is_last_step,
                        )
                    else:
                        metrics = self.compute_metrics(
                            EvalPrediction(predictions=logits, label_ids=labels),
                            compute_result=is_last_step,
                        )

                del losses, logits, labels, inputs
                torch.cuda.empty_cache()

            # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
            elif (
                args.eval_accumulation_steps is not None
                and (step + 1) % args.eval_accumulation_steps == 0
            ):
                all_losses.to_cpu_and_numpy()
                all_preds.to_cpu_and_numpy()
                all_labels.to_cpu_and_numpy()
                all_inputs.to_cpu_and_numpy()

                del losses, logits, labels, inputs
                torch.cuda.empty_cache()

        # After all calls to `.gather_function`, reset to `gather_for_metrics`:
        self.gather_function = self.accelerator.gather_for_metrics
        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of the evaluation loop
            delattr(self, "_past")

        # Gather all remaining tensors and put them back on the CPU
        all_losses = all_losses.get_arrays()
        all_preds = all_preds.get_arrays()
        all_labels = all_labels.get_arrays()
        all_inputs = all_inputs.get_arrays()

        # Number of samples
        if has_length(eval_dataset):
            num_samples = len(eval_dataset)
        # The instance check is weird and does not actually check for the type, but whether the dataset has the right
        # methods. Therefore we need to make sure it also has the attribute.
        elif (
            isinstance(eval_dataset, IterableDatasetShard)
            and getattr(eval_dataset, "num_examples", 0) > 0
        ):
            num_samples = eval_dataset.num_examples
        else:
            if has_length(dataloader):
                num_samples = self.num_examples(dataloader)
            else:  # both len(dataloader.dataset) and len(dataloader) fail
                num_samples = observed_num_examples
        if num_samples == 0 and observed_num_examples > 0:
            num_samples = observed_num_examples

        # Metrics!
        if (
            self.compute_metrics is not None
            and all_preds is not None
            and all_labels is not None
            and not self.args.batch_eval_metrics
        ):
            if args.include_inputs_for_metrics:
                metrics = self.compute_metrics(
                    EvalPrediction(
                        predictions=all_preds, label_ids=all_labels, inputs=all_inputs
                    )
                )
            else:
                metrics = self.compute_metrics(
                    EvalPrediction(predictions=all_preds, label_ids=all_labels)
                )
        elif metrics is None:
            metrics = {}

        # To be JSON-serializable, we need to remove numpy types or zero-d tensors
        metrics = denumpify_detensorize(metrics)

        if isinstance(all_losses, list) and all_losses:
            metrics[f"{metric_key_prefix}_loss"] = (
                np.concatenate(all_losses).mean().item()
            )
        elif isinstance(all_losses, np.ndarray):
            metrics[f"{metric_key_prefix}_loss"] = all_losses.mean().item()
        if hasattr(self, "jit_compilation_time"):
            metrics[f"{metric_key_prefix}_jit_compilation_time"] = (
                self.jit_compilation_time
            )

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

        return EvalLoopOutput(
            predictions=all_preds,
            label_ids=all_labels,
            metrics=metrics,
            num_samples=num_samples,
        )

    def _nested_gather(self, tensors, name=None):
        """
        Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
        concatenating them to `gathered`
        """
        if tensors is None:
            return
        if is_torch_xla_available():
            if name is None:
                name = "nested_gather"
            tensors = nested_xla_mesh_reduce(tensors, name)
        elif is_sagemaker_mp_enabled():
            tensors = smp_gather(tensors)
        elif (
            self.args.distributed_state is not None
            and self.args.distributed_state.distributed_type != "NO"
        ) or (self.args.distributed_state is None and self.args.local_rank != -1):
            tensors = distributed_concat(tensors)
        return tensors

    def prediction_step(
        self,
        model: nn.Module,
        inputs: Dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[List[str]] = None,
    ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on `model` using `inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to evaluate.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (`bool`):
                Whether or not to return the loss only.
            ignore_keys (`List[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.

        Return:
            Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss,
            logits and labels (each being optional).
        """
        has_labels = (
            False
            if len(self.label_names) == 0
            else all(inputs.get(k) is not None for k in self.label_names)
        )
        # For CLIP-like models capable of returning loss values.
        # If `return_loss` is not specified or being `None` in `inputs`, we check if the default value of `return_loss`
        # is `True` in `model.forward`.
        return_loss = inputs.get("return_loss", None)
        if return_loss is None:
            return_loss = self.can_return_loss
        loss_without_labels = (
            True if len(self.label_names) == 0 and return_loss else False
        )

        inputs = self._prepare_inputs(inputs)
        if ignore_keys is None:
            if hasattr(self.model, "config"):
                ignore_keys = getattr(
                    self.model.config, "keys_to_ignore_at_inference", []
                )
            else:
                ignore_keys = []

        # labels may be popped when computing the loss (label smoothing for instance) so we grab them first.
        if has_labels or loss_without_labels:
            labels = nested_detach(tuple(inputs.get(name) for name in self.label_names))
            if len(labels) == 1:
                labels = labels[0]
        else:
            labels = None

        with torch.no_grad():
            if is_sagemaker_mp_enabled():
                raw_outputs = smp_forward_only(model, inputs)
                if has_labels or loss_without_labels:
                    if isinstance(raw_outputs, dict):
                        loss_mb = raw_outputs["loss"]
                        logits_mb = tuple(
                            v
                            for k, v in raw_outputs.items()
                            if k not in ignore_keys + ["loss"]
                        )
                    else:
                        loss_mb = raw_outputs[0]
                        logits_mb = raw_outputs[1:]

                    loss = loss_mb.reduce_mean().detach().cpu()
                    logits = smp_nested_concat(logits_mb)
                else:
                    loss = None
                    if isinstance(raw_outputs, dict):
                        logits_mb = tuple(
                            v for k, v in raw_outputs.items() if k not in ignore_keys
                        )
                    else:
                        logits_mb = raw_outputs
                    logits = smp_nested_concat(logits_mb)
            else:
                if has_labels or loss_without_labels:
                    with self.compute_loss_context_manager():
                        loss, outputs = self.compute_loss(
                            model, inputs, return_outputs=True
                        )
                    loss = loss.mean().detach()

                    if isinstance(outputs, dict):
                        logits = tuple(
                            v
                            for k, v in outputs.items()
                            if k not in ignore_keys + ["loss"]
                        )
                    else:
                        logits = outputs[1:]
                else:
                    loss = None
                    with self.compute_loss_context_manager():
                        outputs = model(**inputs)
                    if isinstance(outputs, dict):
                        logits = tuple(
                            v for k, v in outputs.items() if k not in ignore_keys
                        )
                    else:
                        logits = outputs
                    # TODO: this needs to be fixed and made cleaner later.
                    if self.args.past_index >= 0:
                        self._past = outputs[self.args.past_index - 1]

        if prediction_loss_only:
            return (loss, None, None)

        logits = nested_detach(logits)
        if len(logits) == 1:
            logits = logits[0]

        return (loss, logits, labels)

    def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):
        """
        For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
        operations for every backward + forward pass. If using another model, either implement such a method in the
        model or subclass and override this method.

        Args:
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

        Returns:
            `int`: The number of floating-point operations.
        """
        if hasattr(self.model, "floating_point_ops"):
            return self.model.floating_point_ops(inputs)
        else:
            return 0

    def init_hf_repo(self, token: Optional[str] = None):
        """
        Initializes a git repo in `self.args.hub_model_id`.
        """
        # Only on process zero
        if not self.is_world_process_zero():
            return

        if self.args.hub_model_id is None:
            repo_name = Path(self.args.output_dir).absolute().name
        else:
            repo_name = self.args.hub_model_id

        token = token if token is not None else self.args.hub_token
        repo_url = create_repo(
            repo_name, token=token, private=self.args.hub_private_repo, exist_ok=True
        )
        self.hub_model_id = repo_url.repo_id
        self.push_in_progress = None

    def create_model_card(
        self,
        language: Optional[str] = None,
        license: Optional[str] = None,
        tags: Union[str, List[str], None] = None,
        model_name: Optional[str] = None,
        finetuned_from: Optional[str] = None,
        tasks: Union[str, List[str], None] = None,
        dataset_tags: Union[str, List[str], None] = None,
        dataset: Union[str, List[str], None] = None,
        dataset_args: Union[str, List[str], None] = None,
    ):
        """
        Creates a draft of a model card using the information available to the `Trainer`.

        Args:
            language (`str`, *optional*):
                The language of the model (if applicable)
            license (`str`, *optional*):
                The license of the model. Will default to the license of the pretrained model used, if the original
                model given to the `Trainer` comes from a repo on the Hub.
            tags (`str` or `List[str]`, *optional*):
                Some tags to be included in the metadata of the model card.
            model_name (`str`, *optional*):
                The name of the model.
            finetuned_from (`str`, *optional*):
                The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo
                of the original model given to the `Trainer` (if it comes from the Hub).
            tasks (`str` or `List[str]`, *optional*):
                One or several task identifiers, to be included in the metadata of the model card.
            dataset_tags (`str` or `List[str]`, *optional*):
                One or several dataset tags, to be included in the metadata of the model card.
            dataset (`str` or `List[str]`, *optional*):
                One or several dataset identifiers, to be included in the metadata of the model card.
            dataset_args (`str` or `List[str]`, *optional*):
               One or several dataset arguments, to be included in the metadata of the model card.
        """
        if not self.is_world_process_zero():
            return

        model_card_filepath = os.path.join(self.args.output_dir, "README.md")
        is_peft_library = False
        if os.path.exists(model_card_filepath):
            library_name = ModelCard.load(model_card_filepath).data.get("library_name")
            is_peft_library = library_name == "peft"

            # Append existing tags in `tags`
            existing_tags = ModelCard.load(model_card_filepath).data.tags
            if tags is not None and existing_tags is not None:
                if isinstance(tags, str):
                    tags = [tags]
                for tag in existing_tags:
                    if tag not in tags:
                        tags.append(tag)

        training_summary = TrainingSummary.from_trainer(
            self,
            language=language,
            license=license,
            tags=tags,
            model_name=model_name,
            finetuned_from=finetuned_from,
            tasks=tasks,
            dataset_tags=dataset_tags,
            dataset=dataset,
            dataset_args=dataset_args,
        )
        model_card = training_summary.to_model_card()
        with open(model_card_filepath, "w") as f:
            f.write(model_card)

        if is_peft_library:
            self.accelerator.unwrap_model(self.model).create_or_update_model_card(
                self.args.output_dir
            )

    def _push_from_checkpoint(self, checkpoint_folder):
        # Only push from one node.
        if (
            not self.is_world_process_zero()
            or self.args.hub_strategy == HubStrategy.END
        ):
            return
        # If we haven't finished the last push, we don't do this one unless args.hub_always_push=True.
        if (
            not self.args.hub_always_push
            and self.push_in_progress is not None
            and not self.push_in_progress.is_done()
        ):
            return

        output_dir = self.args.output_dir
        # To avoid a new synchronization of all model weights, we just copy the file from the checkpoint folder
        modeling_files = [CONFIG_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_NAME]
        if is_peft_available():
            modeling_files.extend(
                [ADAPTER_CONFIG_NAME, ADAPTER_WEIGHTS_NAME, ADAPTER_SAFE_WEIGHTS_NAME]
            )
        for modeling_file in modeling_files:
            if os.path.isfile(os.path.join(checkpoint_folder, modeling_file)):
                shutil.copy(
                    os.path.join(checkpoint_folder, modeling_file),
                    os.path.join(output_dir, modeling_file),
                )
        # Saving the tokenizer is fast and we don't know how many files it may have spawned, so we resave it to be sure.
        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(output_dir)
        # Same for the training arguments
        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

        if self.args.save_strategy == IntervalStrategy.STEPS:
            commit_message = f"Training in progress, step {self.state.global_step}"
        else:
            commit_message = f"Training in progress, epoch {int(self.state.epoch)}"

        model_push_job = upload_folder(
            repo_id=self.hub_model_id,
            folder_path=output_dir,
            commit_message=commit_message,
            token=self.args.hub_token,
            run_as_future=True,
            ignore_patterns=["_*", f"{PREFIX_CHECKPOINT_DIR}-*"],
        )

        push_jobs = [model_push_job]

        if self.args.hub_strategy in [
            HubStrategy.CHECKPOINT,
            HubStrategy.ALL_CHECKPOINTS,
        ]:
            path_in_repo = (
                "last-checkpoint"
                if self.args.hub_strategy == HubStrategy.CHECKPOINT
                else Path(checkpoint_folder).name
            )
            checkpoint_push = upload_folder(
                repo_id=self.hub_model_id,
                folder_path=checkpoint_folder,
                path_in_repo=path_in_repo,
                commit_message=commit_message + ", checkpoint",
                token=self.args.hub_token,
                run_as_future=True,
            )
            push_jobs.append(checkpoint_push)

        if self.push_in_progress is None or self.push_in_progress.is_done():
            self.push_in_progress = PushInProgress(push_jobs)
        else:
            self.push_in_progress.jobs.extend(push_jobs)

    def _finish_current_push(self):
        if not hasattr(self, "push_in_progress"):
            return
        if self.push_in_progress is not None and not self.push_in_progress.is_done():
            logger.info(
                "Waiting for the current checkpoint push to be finished, this might take a couple of minutes."
            )
            self.push_in_progress.wait_until_done()

    def push_to_hub(
        self,
        commit_message: Optional[str] = "End of training",
        blocking: bool = True,
        token: Optional[str] = None,
        **kwargs,
    ) -> str:
        """
        Upload `self.model` and `self.tokenizer` to the 🤗 model hub on the repo `self.args.hub_model_id`.

        Parameters:
            commit_message (`str`, *optional*, defaults to `"End of training"`):
                Message to commit while pushing.
            blocking (`bool`, *optional*, defaults to `True`):
                Whether the function should return only when the `git push` has finished.
            token (`str`, *optional*, defaults to `None`):
                Token with write permission to overwrite Trainer's original args.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to [`~Trainer.create_model_card`].

        Returns:
            The URL of the repository where the model was pushed if `blocking=False`, or a `Future` object tracking the
            progress of the commit if `blocking=True`.
        """
        model_name = kwargs.pop("model_name", None)
        if model_name is None and self.args.should_save:
            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]
        token = token if token is not None else self.args.hub_token

        # In case the user calls this method with args.push_to_hub = False
        if self.hub_model_id is None:
            self.init_hf_repo(token=token)

        # Needs to be executed on all processes for TPU training, but will only save on the processed determined by
        # self.args.should_save.
        self.save_model(_internal_call=True)

        # Only push from one node.
        if not self.is_world_process_zero():
            return

        # Add additional tags in the case the model has already some tags and users pass
        # "tags" argument to `push_to_hub` so that trainer automatically handles internal tags
        # from all models since Trainer does not call `model.push_to_hub`.
        if getattr(self.model, "model_tags", None) is not None:
            if "tags" not in kwargs:
                kwargs["tags"] = []

            # If it is a string, convert it to a list
            if isinstance(kwargs["tags"], str):
                kwargs["tags"] = [kwargs["tags"]]

            for model_tag in self.model.model_tags:
                if model_tag not in kwargs["tags"]:
                    kwargs["tags"].append(model_tag)

        self.create_model_card(model_name=model_name, **kwargs)

        # Wait for the current upload to be finished.
        self._finish_current_push()
        return upload_folder(
            repo_id=self.hub_model_id,
            folder_path=self.args.output_dir,
            commit_message=commit_message,
            token=token,
            run_as_future=not blocking,
            ignore_patterns=["_*", f"{PREFIX_CHECKPOINT_DIR}-*"],
        )

    #
    # Deprecated code
    #

    def prediction_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """
        args = self.args

        if not has_length(dataloader):
            raise ValueError("dataloader must implement a working __len__")

        prediction_loss_only = (
            prediction_loss_only
            if prediction_loss_only is not None
            else args.prediction_loss_only
        )

        # if eval is called w/o train, handle model prep here
        if self.is_deepspeed_enabled and self.deepspeed is None:
            _, _ = deepspeed_init(self, num_training_steps=0, inference=True)

        model = self._wrap_model(self.model, training=False, dataloader=dataloader)

        if len(self.accelerator._models) == 0 and model is self.model:
            model = (
                self.accelerator.prepare(model)
                if self.is_deepspeed_enabled
                else self.accelerator.prepare_model(model, evaluation_mode=True)
            )

            if self.is_fsdp_enabled:
                self.model = model

            # for the rest of this function `model` is the outside model, whether it was wrapped or not
            if model is not self.model:
                self.model_wrapped = model

            # backward compatibility
            if self.is_deepspeed_enabled:
                self.deepspeed = self.model_wrapped

        # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
        # while ``train`` is running, cast it to the right dtype first and then put on device
        if not self.is_in_train:
            if args.fp16_full_eval:
                model = model.to(dtype=torch.float16, device=args.device)
            elif args.bf16_full_eval:
                model = model.to(dtype=torch.bfloat16, device=args.device)

        batch_size = dataloader.batch_size
        num_examples = self.num_examples(dataloader)
        logger.info(f"***** Running {description} *****")
        logger.info(f"  Num examples = {num_examples}")
        logger.info(f"  Batch size = {batch_size}")

        losses_host: torch.Tensor = None
        preds_host: Union[torch.Tensor, List[torch.Tensor]] = None
        labels_host: Union[torch.Tensor, List[torch.Tensor]] = None
        inputs_host: Union[torch.Tensor, List[torch.Tensor]] = None
        metrics: Optional[dict] = None

        world_size = max(1, args.world_size)

        eval_losses_gatherer = DistributedTensorGatherer(
            world_size, num_examples, make_multiple_of=batch_size
        )
        if not prediction_loss_only:
            # The actual number of eval_sample can be greater than num_examples in distributed settings (when we pass
            # a batch size to the sampler)
            make_multiple_of = None
            if hasattr(dataloader, "sampler") and isinstance(
                dataloader.sampler, SequentialDistributedSampler
            ):
                make_multiple_of = dataloader.sampler.batch_size
            preds_gatherer = DistributedTensorGatherer(
                world_size, num_examples, make_multiple_of=make_multiple_of
            )
            labels_gatherer = DistributedTensorGatherer(
                world_size, num_examples, make_multiple_of=make_multiple_of
            )
            inputs_gatherer = DistributedTensorGatherer(
                world_size, num_examples, make_multiple_of=make_multiple_of
            )

        model.eval()

        if args.past_index >= 0:
            self._past = None

        self.callback_handler.eval_dataloader = dataloader

        for step, inputs in enumerate(dataloader):
            loss, logits, labels = self.prediction_step(
                model, inputs, prediction_loss_only, ignore_keys=ignore_keys
            )
            main_input_name = getattr(self.model, "main_input_name", "input_ids")
            inputs_decode = (
                self._prepare_input(inputs[main_input_name])
                if args.include_inputs_for_metrics
                else None
            )

            if loss is not None:
                losses = loss.repeat(batch_size)
                losses_host = (
                    losses
                    if losses_host is None
                    else torch.cat((losses_host, losses), dim=0)
                )
            if logits is not None:
                preds_host = (
                    logits
                    if preds_host is None
                    else nested_concat(preds_host, logits, padding_index=-100)
                )
            if labels is not None:
                labels_host = (
                    labels
                    if labels_host is None
                    else nested_concat(labels_host, labels, padding_index=-100)
                )
            if inputs_decode is not None:
                inputs_host = (
                    inputs_decode
                    if inputs_host is None
                    else nested_concat(inputs_host, inputs_decode, padding_index=-100)
                )
            self.control = self.callback_handler.on_prediction_step(
                args, self.state, self.control
            )

            if self.args.batch_eval_metrics:
                if (
                    self.compute_metrics is not None
                    and preds_host is not None
                    and labels_host is not None
                ):
                    is_last_step = self.accelerator.gradient_state.end_of_dataloader
                    if args.include_inputs_for_metrics:
                        metrics = self.compute_metrics(
                            EvalPrediction(
                                predictions=preds_host,
                                label_ids=labels_host,
                                inputs=inputs_host,
                            ),
                            compute_result=is_last_step,
                        )
                    else:
                        metrics = self.compute_metrics(
                            EvalPrediction(
                                predictions=preds_host, label_ids=labels_host
                            ),
                            compute_result=is_last_step,
                        )

            if self.args.batch_eval_metrics or (
                args.eval_accumulation_steps is not None
                and (step + 1) % args.eval_accumulation_steps == 0
            ):
                # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
                eval_losses_gatherer.add_arrays(
                    self._gather_and_numpify(losses_host, "eval_losses")
                )
                if not prediction_loss_only:
                    preds_gatherer.add_arrays(
                        self._gather_and_numpify(preds_host, "eval_preds")
                    )
                    labels_gatherer.add_arrays(
                        self._gather_and_numpify(labels_host, "eval_label_ids")
                    )
                    inputs_gatherer.add_arrays(
                        self._gather_and_numpify(inputs_host, "eval_inputs_ids")
                    )

                # Set back to None to begin a new accumulation
                del losses_host, preds_host, labels_host, inputs_host
                torch.cuda.empty_cache()
                losses_host, preds_host, labels_host, inputs_host = (
                    None,
                    None,
                    None,
                    None,
                )

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of the evaluation loop
            delattr(self, "_past")

        # Gather all remaining tensors and put them back on the CPU
        eval_losses_gatherer.add_arrays(
            self._gather_and_numpify(losses_host, "eval_losses")
        )
        if not prediction_loss_only:
            preds_gatherer.add_arrays(
                self._gather_and_numpify(preds_host, "eval_preds")
            )
            labels_gatherer.add_arrays(
                self._gather_and_numpify(labels_host, "eval_label_ids")
            )
            inputs_gatherer.add_arrays(
                self._gather_and_numpify(inputs_host, "eval_inputs_ids")
            )

        eval_loss = eval_losses_gatherer.finalize()
        preds = preds_gatherer.finalize() if not prediction_loss_only else None
        label_ids = labels_gatherer.finalize() if not prediction_loss_only else None
        inputs_ids = inputs_gatherer.finalize() if not prediction_loss_only else None

        if (
            self.compute_metrics is not None
            and preds is not None
            and label_ids is not None
            and not self.args.batch_eval_metrics
        ):
            if args.include_inputs_for_metrics:
                metrics = self.compute_metrics(
                    EvalPrediction(
                        predictions=preds, label_ids=label_ids, inputs=inputs_ids
                    )
                )
            else:
                metrics = self.compute_metrics(
                    EvalPrediction(predictions=preds, label_ids=label_ids)
                )
        elif metrics is None:
            metrics = {}

        # To be JSON-serializable, we need to remove numpy types or zero-d tensors
        metrics = denumpify_detensorize(metrics)

        if eval_loss is not None:
            metrics[f"{metric_key_prefix}_loss"] = eval_loss.mean().item()

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

        return EvalLoopOutput(
            predictions=preds,
            label_ids=label_ids,
            metrics=metrics,
            num_samples=num_examples,
        )

    def _gather_and_numpify(self, tensors, name):
        """
        Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
        concatenating them to `gathered`
        """
        if tensors is None:
            return
        if is_torch_xla_available():
            tensors = nested_xla_mesh_reduce(tensors, name)
        elif is_sagemaker_mp_enabled():
            tensors = smp_gather(tensors)
        elif self.args.parallel_mode == ParallelMode.DISTRIBUTED:
            tensors = distributed_concat(tensors)

        return nested_numpify(tensors)

    def _add_sm_patterns_to_gitignore(self) -> None:
        """Add SageMaker Checkpointing patterns to .gitignore file."""
        # Make sure we only do this on the main process
        if not self.is_world_process_zero():
            return

        patterns = ["*.sagemaker-uploading", "*.sagemaker-uploaded"]

        # Get current .gitignore content
        if os.path.exists(os.path.join(self.repo.local_dir, ".gitignore")):
            with open(os.path.join(self.repo.local_dir, ".gitignore"), "r") as f:
                current_content = f.read()
        else:
            current_content = ""

        # Add the patterns to .gitignore
        content = current_content
        for pattern in patterns:
            if pattern not in content:
                if content.endswith("\n"):
                    content += pattern
                else:
                    content += f"\n{pattern}"

        # Write the .gitignore file if it has changed
        if content != current_content:
            with open(os.path.join(self.repo.local_dir, ".gitignore"), "w") as f:
                logger.debug(f"Writing .gitignore file. Content: {content}")
                f.write(content)

        self.repo.git_add(".gitignore")

        # avoid race condition with git status
        time.sleep(0.5)

        if not self.repo.is_repo_clean():
            self.repo.git_commit("Add *.sagemaker patterns to .gitignore.")
            self.repo.git_push()

    def create_accelerator_and_postprocess(self):
        grad_acc_kwargs = {}
        if (
            is_accelerate_available("0.28.0")
            and self.args.accelerator_config.gradient_accumulation_kwargs is not None
        ):
            grad_acc_kwargs = self.args.accelerator_config.gradient_accumulation_kwargs

        # check if num_steps is attempted to be passed in gradient_accumulation_kwargs
        if "num_steps" in grad_acc_kwargs and self.args.gradient_accumulation_steps > 1:
            # raise because we do not know which setting is intended.
            raise ValueError(
                "The `AcceleratorConfig`'s `num_steps` is set but `gradient_accumulation_steps` is greater than 1 in the passed `TrainingArguments`"
                "If using the passed `AcceleratorConfig` is desired, do not set the `TrainingArguments` `gradient_accumulation_steps`."
            )
        elif "num_steps" not in grad_acc_kwargs:
            # take the gradient_accumulation_steps setting from TrainingArguments.
            grad_acc_kwargs["num_steps"] = self.args.gradient_accumulation_steps

        grad_acc_kwargs["sync_with_dataloader"] = False

        gradient_accumulation_plugin = GradientAccumulationPlugin(**grad_acc_kwargs)

        accelerator_config = self.args.accelerator_config.to_dict()

        if is_accelerate_available("0.28.0"):
            dataloader_config = DataLoaderConfiguration(
                split_batches=accelerator_config.pop("split_batches"),
                dispatch_batches=accelerator_config.pop("dispatch_batches"),
                even_batches=accelerator_config.pop("even_batches"),
                use_seedable_sampler=accelerator_config.pop("use_seedable_sampler"),
            )
        non_blocking = accelerator_config.pop("non_blocking")
        if not is_accelerate_available("0.30.0"):
            if non_blocking:
                raise ImportError(
                    "`non_blocking` is only supported in accelerate v0.30.0 and above. Please upgrade accelerate to use this feature."
                )
        else:
            if non_blocking and not self.args.dataloader_pin_memory:
                logger.warning(
                    "`non_blocking` is enabled but `dataloader_pin_memory` is not. For the best performance, it's recommended to enable both."
                )
            dataloader_config.non_blocking = non_blocking
        # this would have been updated above, no need for it anymore
        accelerator_config.pop("gradient_accumulation_kwargs")

        args = {
            "deepspeed_plugin": self.args.deepspeed_plugin,
            "gradient_accumulation_plugin": gradient_accumulation_plugin,
        }
        if is_accelerate_available("0.28.0"):
            args["dataloader_config"] = dataloader_config
        else:
            args.update(accelerator_config)

        # create accelerator object
        self.accelerator = Accelerator(**args)
        # some Trainer classes need to use `gather` instead of `gather_for_metrics`, thus we store a flag
        self.gather_function = self.accelerator.gather_for_metrics

        # deepspeed and accelerate flags covering both trainer args and accelerate launcher
        self.is_deepspeed_enabled = (
            getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
        )
        self.is_fsdp_enabled = (
            getattr(self.accelerator.state, "fsdp_plugin", None) is not None
        )

        # post accelerator creation setup
        if self.is_fsdp_enabled:
            fsdp_plugin = self.accelerator.state.fsdp_plugin
            fsdp_plugin.limit_all_gathers = self.args.fsdp_config.get(
                "limit_all_gathers", fsdp_plugin.limit_all_gathers
            )
            if is_accelerate_available("0.23.0"):
                fsdp_plugin.activation_checkpointing = self.args.fsdp_config.get(
                    "activation_checkpointing", fsdp_plugin.activation_checkpointing
                )
                if (
                    fsdp_plugin.activation_checkpointing
                    and self.args.gradient_checkpointing
                ):
                    raise ValueError(
                        "The activation_checkpointing in FSDP config and the gradient_checkpointing in training arg "
                        "can't be set to True simultaneously. Please use FSDP's activation_checkpointing logic "
                        "when using FSDP."
                    )

        if (
            self.is_deepspeed_enabled
            and getattr(self.args, "hf_deepspeed_config", None) is None
        ):
            self.propagate_args_to_deepspeed()

        # `save_only_model` can't be used with DeepSpeed/FSDP along with `load_best_model_at_end`
        if (
            self.args.save_only_model
            and (self.is_deepspeed_enabled or self.is_fsdp_enabled)
            and self.args.load_best_model_at_end
        ):
            wrapper = "DeepSpeed" if self.is_deepspeed_enabled else "FSDP"
            raise ValueError(
                f"{wrapper} can't be used with `save_only_model` along with `load_best_model_at_end`."
            )

        # `auto_find_batch_size` isn't yet supported with DeepSpeed/FSDP
        if (
            self.is_deepspeed_enabled or self.is_fsdp_enabled
        ) and self.args.auto_find_batch_size:
            wrapper = "DeepSpeed" if self.is_deepspeed_enabled else "FSDP"
            raise NotImplementedError(
                f"`{wrapper}` doesn't support `auto_find_batch_size`."
            )

    def propagate_args_to_deepspeed(self, auto_find_batch_size=False):
        """
        Sets values in the deepspeed plugin based on the Trainer args
        """
        from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig

        ds_plugin = self.accelerator.state.deepspeed_plugin

        ds_plugin.hf_ds_config = HfTrainerDeepSpeedConfig(ds_plugin.hf_ds_config.config)
        ds_plugin.deepspeed_config = ds_plugin.hf_ds_config.config
        ds_plugin.hf_ds_config.trainer_config_process(self.args, auto_find_batch_size)

    def _fsdp_qlora_plugin_updates(self):
        if self.is_fsdp_enabled and _is_peft_model(self.model):
            from peft import LoraConfig
            from peft.utils.other import fsdp_auto_wrap_policy

            if isinstance(self.model.active_peft_config, LoraConfig):
                fsdp_plugin = self.accelerator.state.fsdp_plugin
                fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(self.model)
            if (
                getattr(self.model, "quantization_method", None)
                == QuantizationMethod.BITS_AND_BYTES
                and self.model.hf_quantizer.quantization_config.bnb_4bit_quant_storage.is_floating_point
                and version.parse(accelerate_version) > version.parse("0.27.0")
            ):
                fsdp_plugin.set_mixed_precision(
                    self.model.hf_quantizer.quantization_config.bnb_4bit_quant_storage,
                    override=True,
                )
