Introduce `PartialState` as the device handler in the `Trainer`
# What does this PR do?

This PR is the start of the `Trainer` integration of [Accelerate](https://github.com/huggingface/accelerate)

The integration will follow multiple stages to ensure small changes happen iteratively. This first one simply changes the device handler/setter to be the `PartialState` in `Accelerate` and nothing more. In a follow-up PR, I will start to include more utilities utilizing it.


## Before submitting
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- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@sgugger @pacman100 

_The documentation is not available anymore as the PR was closed or merged._<|||||>@pacman100 I'm starting small with what is minimally needed with the API. E.g. the AcceleratorState isn't needed until we get into parts such as mixed precision. The device handling can be done separately altogether as it doesn't need to rely on an accelerator object the user passes in

This may eventually be the `AcceleratorState`, but for now just starting with the `PartialState` seemed okay to me. <|||||>PR is good for final review, tested on single + multi gpu + deepspeed and seemed to work fine. Ignore the `examples_tensorflow` failure, as that's actually a good failure (if one could exist)<|||||>@muellerzr 

I don't know if here is a right place to report but

I think this merge causing error in Seq2SeqTrainingArguments.

before this merge, I was able to run this code

```
from transformers import Seq2SeqTrainingArguments

training_args = Seq2SeqTrainingArguments(
    output_dir="./train_test",  # change to a repo name of your choice
    per_device_train_batch_size=8,
    gradient_accumulation_steps=2,  # increase by 2x for every 2x decrease in batch size
    learning_rate=1e-5,
    warmup_steps=5,
    max_steps=40,
    gradient_checkpointing=True,
    fp16=True,
    group_by_length=True,
    evaluation_strategy="steps",
    per_device_eval_batch_size=8,
    predict_with_generate=True,
    generation_max_length=225,
    save_steps=10,
    eval_steps=10,
    logging_steps=25,
    report_to=["tensorboard"],
    load_best_model_at_end=True,
    metric_for_best_model="wer",
    greater_is_better=False,
    push_to_hub=False,
)
```

Error messages

```
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
[<ipython-input-25-7693116680cc>](https://localhost:8080/#) in <cell line: 3>()
      1 from transformers import Seq2SeqTrainingArguments
      2 
----> 3 training_args = Seq2SeqTrainingArguments(
      4     output_dir="./whisper-large-ja-7",  # change to a repo name of your choice
      5     per_device_train_batch_size=8,

usr/local/lib/python3.9/dist-packages/transformers/training_args_seq2seq.py in __init__(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, xpu_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, sharded_ddp, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, dataloader_pin_memory, skip_mem...

[/usr/local/lib/python3.9/dist-packages/transformers/training_args.py](https://localhost:8080/#) in __post_init__(self)
   1253             self.framework == "pt"
   1254             and is_torch_available()
-> 1255             and (self.device.type != "cuda")
   1256             and (get_xla_device_type(self.device) != "GPU")
   1257             and (self.fp16 or self.fp16_full_eval)

[/usr/local/lib/python3.9/dist-packages/transformers/training_args.py](https://localhost:8080/#) in device(self)
   1613         """
   1614         requires_backends(self, ["torch"])
-> 1615         return self._setup_devices
   1616 
   1617     @property

[/usr/local/lib/python3.9/dist-packages/transformers/utils/generic.py](https://localhost:8080/#) in __get__(self, obj, objtype)
     52         cached = getattr(obj, attr, None)
     53         if cached is None:
---> 54             cached = self.fget(obj)
     55             setattr(obj, attr, cached)
     56         return cached

[/usr/local/lib/python3.9/dist-packages/transformers/training_args.py](https://localhost:8080/#) in _setup_devices(self)
   1547             device = self.distributed_state.device
   1548         else:
-> 1549             self.distributed_state = PartialState(backend=self.xpu_backend)
   1550             device = self.distributed_state.device
   1551             self._n_gpu = 1

NameError: name 'PartialState' is not defined
```<|||||>@rennn2002 Could you try updating your version of accelerate i.e. `pip install --upgrade accelerate`? 