class_docstr: TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
  itself**.
  
  Using [`HfArgumentParser`] we can turn this class into
  [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
  command line.
  
  Parameters:
      output_dir (`str`):
          The output directory where the model predictions and checkpoints will be written.
      overwrite_output_dir (`bool`, *optional*, defaults to `False`):
          If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir`
          points to a checkpoint directory.
      do_train (`bool`, *optional*, defaults to `False`):
          Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used
          by your training/evaluation scripts instead. See the [example
          scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
      do_eval (`bool`, *optional*):
          Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_strategy` is
          different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your
          training/evaluation scripts instead. See the [example
          scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
      do_predict (`bool`, *optional*, defaults to `False`):
          Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's
          intended to be used by your training/evaluation scripts instead. See the [example
          scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
      evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
          The evaluation strategy to adopt during training. Possible values are:
  
              - `"no"`: No evaluation is done during training.
              - `"steps"`: Evaluation is done (and logged) every `eval_steps`.
              - `"epoch"`: Evaluation is done at the end of each epoch.
  
      prediction_loss_only (`bool`, *optional*, defaults to `False`):
          When performing evaluation and generating predictions, only returns the loss.
      per_device_train_batch_size (`int`, *optional*, defaults to 8):
          The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for training.
      per_device_eval_batch_size (`int`, *optional*, defaults to 8):
          The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for evaluation.
      gradient_accumulation_steps (`int`, *optional*, defaults to 1):
          Number of updates steps to accumulate the gradients for, before performing a backward/update pass.
  
          <Tip warning={true}>
  
          When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
          evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples.
  
          </Tip>
  
      eval_accumulation_steps (`int`, *optional*):
          Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
          left unset, the whole predictions are accumulated on GPU/NPU/TPU before being moved to the CPU (faster but
          requires more memory).
      eval_delay (`float`, *optional*):
          Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
          evaluation_strategy.
      learning_rate (`float`, *optional*, defaults to 5e-5):
          The initial learning rate for [`AdamW`] optimizer.
      weight_decay (`float`, *optional*, defaults to 0):
          The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`]
          optimizer.
      adam_beta1 (`float`, *optional*, defaults to 0.9):
          The beta1 hyperparameter for the [`AdamW`] optimizer.
      adam_beta2 (`float`, *optional*, defaults to 0.999):
          The beta2 hyperparameter for the [`AdamW`] optimizer.
      adam_epsilon (`float`, *optional*, defaults to 1e-8):
          The epsilon hyperparameter for the [`AdamW`] optimizer.
      max_grad_norm (`float`, *optional*, defaults to 1.0):
          Maximum gradient norm (for gradient clipping).
      num_train_epochs(`float`, *optional*, defaults to 3.0):
          Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
          the last epoch before stopping training).
      max_steps (`int`, *optional*, defaults to -1):
          If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`.
          For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until
          `max_steps` is reached.
      lr_scheduler_type (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`):
          The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values.
      lr_scheduler_kwargs ('dict', *optional*, defaults to {}):
          The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
      warmup_ratio (`float`, *optional*, defaults to 0.0):
          Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
      warmup_steps (`int`, *optional*, defaults to 0):
          Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
      log_level (`str`, *optional*, defaults to `passive`):
          Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug',
          'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and keeps the
          current log level for the Transformers library (which will be `"warning"` by default).
      log_level_replica (`str`, *optional*, defaults to `"warning"`):
          Logger log level to use on replicas. Same choices as `log_level`"
      log_on_each_node (`bool`, *optional*, defaults to `True`):
          In multinode distributed training, whether to log using `log_level` once per node, or only on the main
          node.
      logging_dir (`str`, *optional*):
          [TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to
          *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.
      logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
          The logging strategy to adopt during training. Possible values are:
  
              - `"no"`: No logging is done during training.
              - `"epoch"`: Logging is done at the end of each epoch.
              - `"steps"`: Logging is done every `logging_steps`.
  
      logging_first_step (`bool`, *optional*, defaults to `False`):
          Whether to log and evaluate the first `global_step` or not.
      logging_steps (`int` or `float`, *optional*, defaults to 500):
          Number of update steps between two logs if `logging_strategy="steps"`. Should be an integer or a float in
          range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
      logging_nan_inf_filter (`bool`, *optional*, defaults to `True`):
          Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan`
          or `inf` is filtered and the average loss of the current logging window is taken instead.
  
          <Tip>
  
          `logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the
          gradient is computed or applied to the model.
  
          </Tip>
  
      save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
          The checkpoint save strategy to adopt during training. Possible values are:
  
              - `"no"`: No save is done during training.
              - `"epoch"`: Save is done at the end of each epoch.
              - `"steps"`: Save is done every `save_steps`.
      save_steps (`int` or `float`, *optional*, defaults to 500):
          Number of updates steps before two checkpoint saves if `save_strategy="steps"`. Should be an integer or a
          float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
      save_total_limit (`int`, *optional*):
          If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
          `output_dir`. When `load_best_model_at_end` is enabled, the "best" checkpoint according to
          `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for
          `save_total_limit=5` and `load_best_model_at_end`, the four last checkpoints will always be retained
          alongside the best model. When `save_total_limit=1` and `load_best_model_at_end`, it is possible that two
          checkpoints are saved: the last one and the best one (if they are different).
      save_safetensors (`bool`, *optional*, defaults to `True`):
          Use [safetensors](https://huggingface.co/docs/safetensors) saving and loading for state dicts instead of
          default `torch.load` and `torch.save`.
      save_on_each_node (`bool`, *optional*, defaults to `False`):
          When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
          the main one.
  
          This should not be activated when the different nodes use the same storage as the files will be saved with
          the same names for each node.
      save_only_model (`bool`, *optional*, defaults to `False`):
          When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state.
          Note that when this is true, you won't be able to resume training from checkpoint.
          This enables you to save storage by not storing the optimizer, scheduler & rng state.
          You can only load the model using `from_pretrained` with this option set to `True`.
      use_cpu (`bool`, *optional*, defaults to `False`):
          Whether or not to use cpu. If set to False, we will use cuda or mps device if available.
      seed (`int`, *optional*, defaults to 42):
          Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
          [`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters.
      data_seed (`int`, *optional*):
          Random seed to be used with data samplers. If not set, random generators for data sampling will use the
          same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model
          seed.
      jit_mode_eval (`bool`, *optional*, defaults to `False`):
          Whether or not to use PyTorch jit trace for inference.
      use_ipex (`bool`, *optional*, defaults to `False`):
          Use Intel extension for PyTorch when it is available. [IPEX
          installation](https://github.com/intel/intel-extension-for-pytorch).
      bf16 (`bool`, *optional*, defaults to `False`):
          Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
          NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change.
      fp16 (`bool`, *optional*, defaults to `False`):
          Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
      fp16_opt_level (`str`, *optional*, defaults to 'O1'):
          For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on
          the [Apex documentation](https://nvidia.github.io/apex/amp).
      fp16_backend (`str`, *optional*, defaults to `"auto"`):
          This argument is deprecated. Use `half_precision_backend` instead.
      half_precision_backend (`str`, *optional*, defaults to `"auto"`):
          The backend to use for mixed precision training. Must be one of `"auto", "apex", "cpu_amp"`. `"auto"` will
          use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the
          requested backend.
      bf16_full_eval (`bool`, *optional*, defaults to `False`):
          Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm
          metric values. This is an experimental API and it may change.
      fp16_full_eval (`bool`, *optional*, defaults to `False`):
          Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm
          metric values.
      tf32 (`bool`, *optional*):
          Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends
          on PyTorch's version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to
          the [TF32](https://huggingface.co/docs/transformers/performance#tf32) documentation. This is an
          experimental API and it may change.
      local_rank (`int`, *optional*, defaults to -1):
          Rank of the process during distributed training.
      ddp_backend (`str`, *optional*):
          The backend to use for distributed training. Must be one of `"nccl"`, `"mpi"`, `"ccl"`, `"gloo"`, `"hccl"`.
      tpu_num_cores (`int`, *optional*):
          When training on TPU, the number of TPU cores (automatically passed by launcher script).
      dataloader_drop_last (`bool`, *optional*, defaults to `False`):
          Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
          or not.
      eval_steps (`int` or `float`, *optional*):
          Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same
          value as `logging_steps` if not set. Should be an integer or a float in range `[0,1)`. If smaller than 1,
          will be interpreted as ratio of total training steps.
      dataloader_num_workers (`int`, *optional*, defaults to 0):
          Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
          main process.
      past_index (`int`, *optional*, defaults to -1):
          Some models like [TransformerXL](../model_doc/transformerxl) or [XLNet](../model_doc/xlnet) can make use of
          the past hidden states for their predictions. If this argument is set to a positive int, the `Trainer` will
          use the corresponding output (usually index 2) as the past state and feed it to the model at the next
          training step under the keyword argument `mems`.
      run_name (`str`, *optional*):
          A descriptor for the run. Typically used for [wandb](https://www.wandb.com/) and
          [mlflow](https://www.mlflow.org/) logging.
      disable_tqdm (`bool`, *optional*):
          Whether or not to disable the tqdm progress bars and table of metrics produced by
          [`~notebook.NotebookTrainingTracker`] in Jupyter Notebooks. Will default to `True` if the logging level is
          set to warn or lower (default), `False` otherwise.
      remove_unused_columns (`bool`, *optional*, defaults to `True`):
          Whether or not to automatically remove the columns unused by the model forward method.
      label_names (`List[str]`, *optional*):
          The list of keys in your dictionary of inputs that correspond to the labels.
  
          Will eventually default to the list of argument names accepted by the model that contain the word "label",
          except if the model used is one of the `XxxForQuestionAnswering` in which case it will also include the
          `["start_positions", "end_positions"]` keys.
      load_best_model_at_end (`bool`, *optional*, defaults to `False`):
          Whether or not to load the best model found during training at the end of training. When this option is
          enabled, the best checkpoint will always be saved. See
          [`save_total_limit`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments.save_total_limit)
          for more.
  
          <Tip>
  
          When set to `True`, the parameters `save_strategy` needs to be the same as `evaluation_strategy`, and in
          the case it is "steps", `save_steps` must be a round multiple of `eval_steps`.
  
          </Tip>
  
      metric_for_best_model (`str`, *optional*):
          Use in conjunction with `load_best_model_at_end` to specify the metric to use to compare two different
          models. Must be the name of a metric returned by the evaluation with or without the prefix `"eval_"`. Will
          default to `"loss"` if unspecified and `load_best_model_at_end=True` (to use the evaluation loss).
  
          If you set this value, `greater_is_better` will default to `True`. Don't forget to set it to `False` if
          your metric is better when lower.
      greater_is_better (`bool`, *optional*):
          Use in conjunction with `load_best_model_at_end` and `metric_for_best_model` to specify if better models
          should have a greater metric or not. Will default to:
  
          - `True` if `metric_for_best_model` is set to a value that isn't `"loss"` or `"eval_loss"`.
          - `False` if `metric_for_best_model` is not set, or set to `"loss"` or `"eval_loss"`.
      ignore_data_skip (`bool`, *optional*, defaults to `False`):
          When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
          stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step
          can take a long time) but will not yield the same results as the interrupted training would have.
      fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `''`):
          Use PyTorch Distributed Parallel Training (in distributed training only).
  
          A list of options along the following:
  
          - `"full_shard"`: Shard parameters, gradients and optimizer states.
          - `"shard_grad_op"`: Shard optimizer states and gradients.
          - `"hybrid_shard"`: Apply `FULL_SHARD` within a node, and replicate parameters across nodes.
          - `"hybrid_shard_zero2"`: Apply `SHARD_GRAD_OP` within a node, and replicate parameters across nodes.
          - `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and
            `"shard_grad_op"`).
          - `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`.
      fsdp_config (`str` or `dict`, *optional*):
          Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of
          fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`.
  
          A List of config and its options:
              - min_num_params (`int`, *optional*, defaults to `0`):
                  FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is
                  passed).
              - transformer_layer_cls_to_wrap (`List[str]`, *optional*):
                  List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`,
                  `T5Block` .... (useful only when `fsdp` flag is passed).
              - backward_prefetch (`str`, *optional*)
                  FSDP's backward prefetch mode. Controls when to prefetch next set of parameters (useful only when
                  `fsdp` field is passed).
  
                  A list of options along the following:
  
                  - `"backward_pre"` : Prefetches the next set of parameters before the current set of parameter's
                    gradient
                      computation.
                  - `"backward_post"` : This prefetches the next set of parameters after the current set of
                    parameter’s
                      gradient computation.
              - forward_prefetch (`bool`, *optional*, defaults to `False`)
                  FSDP's forward prefetch mode (useful only when `fsdp` field is passed).
                   If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the
                   forward pass.
              - limit_all_gathers (`bool`, *optional*, defaults to `False`)
                  FSDP's limit_all_gathers (useful only when `fsdp` field is passed).
                   If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight
                   all-gathers.
              - use_orig_params (`bool`, *optional*, defaults to `True`)
                  If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed
                  frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please
                  refer this
                  [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019
              - sync_module_states (`bool`, *optional*, defaults to `True`)
                  If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to
                  ensure they are the same across all ranks after initialization
              - activation_checkpointing (`bool`, *optional*, defaults to `False`):
                  If `"True"`, activation checkpointing is a technique to reduce memory usage by clearing activations of
                  certain layers and recomputing them during a backward pass. Effectively, this trades extra
                  computation time for reduced memory usage.
              - xla (`bool`, *optional*, defaults to `False`):
                  Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature
                  and its API may evolve in the future.
              - xla_fsdp_settings (`dict`, *optional*)
                  The value is a dictionary which stores the XLA FSDP wrapping parameters.
  
                  For a complete list of options, please see [here](
                  https://github.com/pytorch/xla/blob/master/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py).
              - xla_fsdp_grad_ckpt (`bool`, *optional*, defaults to `False`):
                  Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be
                  used when the xla flag is set to true, and an auto wrapping policy is specified through
                  fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.
  
      deepspeed (`str` or `dict`, *optional*):
          Use [Deepspeed](https://github.com/microsoft/deepspeed). This is an experimental feature and its API may
          evolve in the future. The value is either the location of DeepSpeed json config file (e.g.,
          `ds_config.json`) or an already loaded json file as a `dict`"
      label_smoothing_factor (`float`, *optional*, defaults to 0.0):
          The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
          labels are changed from 0s and 1s to `label_smoothing_factor/num_labels` and `1 - label_smoothing_factor +
          label_smoothing_factor/num_labels` respectively.
      debug (`str` or list of [`~debug_utils.DebugOption`], *optional*, defaults to `""`):
          Enable one or more debug features. This is an experimental feature.
  
          Possible options are:
  
          - `"underflow_overflow"`: detects overflow in model's input/outputs and reports the last frames that led to
            the event
          - `"tpu_metrics_debug"`: print debug metrics on TPU
  
          The options should be separated by whitespaces.
      optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch"`):
          The optimizer to use: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or
          adafactor.
      optim_args (`str`, *optional*):
          Optional arguments that are supplied to AnyPrecisionAdamW.
      group_by_length (`bool`, *optional*, defaults to `False`):
          Whether or not to group together samples of roughly the same length in the training dataset (to minimize
          padding applied and be more efficient). Only useful if applying dynamic padding.
      length_column_name (`str`, *optional*, defaults to `"length"`):
          Column name for precomputed lengths. If the column exists, grouping by length will use these values rather
          than computing them on train startup. Ignored unless `group_by_length` is `True` and the dataset is an
          instance of `Dataset`.
      report_to (`str` or `List[str]`, *optional*, defaults to `"all"`):
          The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,
          `"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"dvclive"`, `"flyte"`, `"mlflow"`, `"neptune"`,
          `"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no
          integrations.
      ddp_find_unused_parameters (`bool`, *optional*):
          When using distributed training, the value of the flag `find_unused_parameters` passed to
          `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
      ddp_bucket_cap_mb (`int`, *optional*):
          When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`.
      ddp_broadcast_buffers (`bool`, *optional*):
          When using distributed training, the value of the flag `broadcast_buffers` passed to
          `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
      dataloader_pin_memory (`bool`, *optional*, defaults to `True`):
          Whether you want to pin memory in data loaders or not. Will default to `True`.
      dataloader_persistent_workers (`bool`, *optional*, defaults to `False`):
          If True, the data loader will not shut down the worker processes after a dataset has been consumed once.
          This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will
          increase RAM usage. Will default to `False`.
      skip_memory_metrics (`bool`, *optional*, defaults to `True`):
          Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
          down the training and evaluation speed.
      push_to_hub (`bool`, *optional*, defaults to `False`):
          Whether or not to push the model to the Hub every time the model is saved. If this is activated,
          `output_dir` will begin a git directory synced with the repo (determined by `hub_model_id`) and the content
          will be pushed each time a save is triggered (depending on your `save_strategy`). Calling
          [`~Trainer.save_model`] will also trigger a push.
  
          <Tip warning={true}>
  
          If `output_dir` exists, it needs to be a local clone of the repository to which the [`Trainer`] will be
          pushed.
  
          </Tip>
  
      resume_from_checkpoint (`str`, *optional*):
          The path to a folder with a valid checkpoint for your model. This argument is not directly used by
          [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example
          scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
      hub_model_id (`str`, *optional*):
          The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in
          which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
          for instance `"user_name/model"`, which allows you to push to an organization you are a member of with
          `"organization_name/model"`. Will default to `user_name/output_dir_name` with *output_dir_name* being the
          name of `output_dir`.
  
          Will default to the name of `output_dir`.
      hub_strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`):
          Defines the scope of what is pushed to the Hub and when. Possible values are:
  
          - `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a
            draft of a model card when the [`~Trainer.save_model`] method is called.
          - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and
            a draft of a model card each time there is a model save. The pushes are asynchronous to not block
            training, and in case the save are very frequent, a new push is only attempted if the previous one is
            finished. A last push is made with the final model at the end of training.
          - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named
            last-checkpoint, allowing you to resume training easily with
            `trainer.train(resume_from_checkpoint="last-checkpoint")`.
          - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output
            folder (so you will get one checkpoint folder per folder in your final repository)
  
      hub_token (`str`, *optional*):
          The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
          `huggingface-cli login`.
      hub_private_repo (`bool`, *optional*, defaults to `False`):
          If True, the Hub repo will be set to private.
      hub_always_push (`bool`, *optional*, defaults to `False`):
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      gradient_checkpointing (`bool`, *optional*, defaults to `False`):
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      include_inputs_for_metrics (`bool`, *optional*, defaults to `False`):
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      auto_find_batch_size (`bool`, *optional*, defaults to `False`)
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      full_determinism (`bool`, *optional*, defaults to `False`)
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          The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will
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      ddp_timeout (`int`, *optional*, defaults to 1800):
          The timeout for `torch.distributed.init_process_group` calls, used to avoid GPU socket timeouts when
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      use_mps_device (`bool`, *optional*, defaults to `False`):
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          You can customize the defaults with the argument `torch_compile_backend` and `torch_compile_mode` but we
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          This flag and the whole compile API is experimental and subject to change in future releases.
      torch_compile_backend (`str`, *optional*):
          The backend to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.
  
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          This flag is experimental and subject to change in future releases.
      torch_compile_mode (`str`, *optional*):
          The mode to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.
  
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      split_batches (`bool`, *optional*):
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      include_tokens_per_second (`bool`, *optional*):
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          If not `None`, this will activate NEFTune noise embeddings. This can drastically improve model performance
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code: def _setup_devices(self) -> "torch.device":
      requires_backends(self, ["torch"])
      logger.info("PyTorch: setting up devices")
      if not is_sagemaker_mp_enabled():
          if not is_accelerate_available():
              raise ImportError(
                  f"Using the `Trainer` with `PyTorch` requires `accelerate>={ACCELERATE_MIN_VERSION}`: "
                  "Please run `pip install transformers[torch]` or `pip install accelerate -U`"
              )
          AcceleratorState._reset_state(reset_partial_state=True)
      self.distributed_state = None
      if not self.use_ipex and "ACCELERATE_USE_IPEX" not in os.environ:
          os.environ["ACCELERATE_USE_IPEX"] = "false"
      if self.use_cpu or strtobool(
          os.environ.get("ACCELERATE_USE_CPU", "False")
      ):
          self.distributed_state = PartialState(
              cpu=True, backend=self.ddp_backend
          )
          self._n_gpu = 0
      elif is_sagemaker_mp_enabled():
          local_rank = smp.local_rank()
          device = torch.device("cuda", local_rank)
          self._n_gpu = 1
          torch.cuda.set_device(device)
      elif (
          is_torch_xpu_available()
          and "ACCELERATE_USE_XPU" not in os.environ
      ):
          os.environ["ACCELERATE_USE_XPU"] = "true"
          self.distributed_state = PartialState(
              timeout=timedelta(seconds=self.ddp_timeout)
          )
          device = torch.device("xpu:0")
          self._n_gpu = 1
      elif is_sagemaker_dp_enabled():
          self.distributed_state = PartialState(_use_sagemaker_dp=True)
          self._n_gpu = 1
      elif self.deepspeed:
          # Need to do similar for Accelerator init
          os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
          self.distributed_state = PartialState(
              timeout=timedelta(seconds=self.ddp_timeout)
          )
          del os.environ["ACCELERATE_USE_DEEPSPEED"]
          self._n_gpu = 1
      else:
          self.distributed_state = PartialState(
              backend=self.ddp_backend,
              timeout=timedelta(seconds=self.ddp_timeout),
          )
          self._n_gpu = 1
      if not is_sagemaker_mp_enabled():
          device = self.distributed_state.device
          self.local_rank = self.distributed_state.local_process_index
      if (
          dist.is_available()
          and dist.is_initialized()
          and self.parallel_mode != ParallelMode.DISTRIBUTED
      ):
          logger.warning(
              "torch.distributed process group is initialized, but parallel_mode != ParallelMode.DISTRIBUTED. "
              "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"
          )
      if is_torch_tpu_available():
          device = self.distributed_state.device
          self._n_gpu = 0
      elif is_sagemaker_dp_enabled() or is_sagemaker_mp_enabled():
          # Already set _n_gpu
          pass
      elif (
          self.distributed_state.distributed_type
          == DistributedType.MULTI_XPU
      ):
          if "ACCELERATE_USE_XPU" not in os.environ:
              os.environ["ACCELERATE_USE_XPU"] = "true"
          self._n_gpu = 1
          device = torch.device("xpu:0")
          torch.xpu.set_device(device)
      elif (
          self.distributed_state.distributed_type == DistributedType.NO
      ):
          if self.use_mps_device:
              warnings.warn(
                  "`use_mps_device` is deprecated and will be removed in version 5.0 of 🤗 Transformers. "
                  "`mps` device will be used by default if available similar to the way `cuda` device is used."
                  "Therefore, no action from user is required. "
              )
              if device.type != "mps":
                  raise ValueError(
                      "Either you do not have an MPS-enabled device on this machine or MacOS version is not 12.3+ "
                      "or current PyTorch install was not built with MPS enabled."
                  )
          if device.type == "mps":
              self._n_gpu = 1
          elif self.use_cpu:
              device = torch.device("cpu")
              self._n_gpu = 0
          elif is_torch_xpu_available():
              device = torch.device("xpu:0")
              torch.xpu.set_device(device)
              self._n_gpu = 1
          elif is_torch_npu_available():
              device = torch.device("npu:0")
              torch.npu.set_device(device)
              self._n_gpu = 1
          else:
              # if n_gpu is > 1 we'll use nn.DataParallel.
              # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
              # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
              # trigger an error that a device index is missing. Index 0 takes into account the
              # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
              # will use the first GPU in that env, i.e. GPU#1
              device = torch.device(
                  "cuda:0" if torch.cuda.is_available() else "cpu"
              )
              # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
              # the default value.
              self._n_gpu = torch.cuda.device_count()
              if device.type == "cuda":
                  torch.cuda.set_device(device)
      return device
  
imports: import contextlib
  import io
  import json
  import math
  import os
  import warnings
  from dataclasses import asdict, dataclass, field, fields
  from datetime import timedelta
  from enum import Enum
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Union
  from huggingface_hub import get_full_repo_name
  from packaging import version
  from .debug_utils import DebugOption
  from .trainer_utils import EvaluationStrategy, FSDPOption, HubStrategy, IntervalStrategy, SchedulerType
  from .utils import ACCELERATE_MIN_VERSION, ExplicitEnum, cached_property, is_accelerate_available, is_safetensors_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_torch_available, is_torch_bf16_cpu_available, is_torch_bf16_gpu_available, is_torch_neuroncore_available, is_torch_npu_available, is_torch_tf32_available, is_torch_tpu_available, is_torch_xpu_available, logging, requires_backends
  from .utils.generic import strtobool
  from .utils.import_utils import is_optimum_neuron_available
label_desc: Use image node_id 6 for calling the TrainingArguments obj's underlying member method code with example usage: obj._setup_devices() and returns: device