Deployment in notebook(Kaggle)  Transformers can't Integration DeepSpeed 
Author: ohand007
Pull request: no

### System Info

transformers==4.35.0
deepspeed==0.12.2
pytorch==2.0.0

### Who can help?

_No response_

### Information

- [X] The official example scripts
- [x] My own modified scripts

### Tasks

- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

```
import transformers
from transformers import (
    Seq2SeqTrainingArguments
)

 Seq2SeqTrainingArguments(do_train=True,do_eval=True,
                          output_dir='/kaggle/working/',
                          per_device_train_batch_size=8,                          
                         per_device_eval_batch_size=8,overwrite_output_dir=True,predict_with_generate=True,
                        save_total_limit=6,num_train_epochs = 1,
                        deepspeed='/workspace/transformerTest/transformers/examples/pytorch/translation/ds_config_zero31.json',)


```
Here is my error:
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[109], line 3
      1 import os
----> 3 Seq2SeqTrainingArguments(do_train=True,do_eval=True,
      4                          output_dir='/kaggle/working/',
      5                          per_device_train_batch_size=8,
      6                          per_device_eval_batch_size=8,overwrite_output_dir=True,predict_with_generate=True,
      7                         save_total_limit=6,num_train_epochs = 1,
      8                          deepspeed='/workspace/transformerTest/transformers/examples/pytorch/translation/ds_config_zero31.json',)

File <string>:122, 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_cpu, 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, ddp_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, 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, ddp_broadcast_buffers, dataloader_pin_memory, skip_memory_metrics, use_legacy_prediction_loop, push_to_hub, resume_from_checkpoint, hub_model_id, hub_strategy, hub_token, hub_private_repo, hub_always_push, gradient_checkpointing, gradient_checkpointing_kwargs, include_inputs_for_metrics, fp16_backend, push_to_hub_model_id, push_to_hub_organization, push_to_hub_token, mp_parameters, auto_find_batch_size, full_determinism, torchdynamo, ray_scope, ddp_timeout, torch_compile, torch_compile_backend, torch_compile_mode, dispatch_batches, split_batches, include_tokens_per_second, neftune_noise_alpha, sortish_sampler, predict_with_generate, generation_max_length, generation_num_beams, generation_config)

File /opt/conda/lib/python3.10/site-packages/transformers/training_args.py:1679, in TrainingArguments.__post_init__(self)
   1675 from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
   1677 # will be used later by the Trainer
   1678 # note: leave self.deepspeed unmodified in case a user relies on it not to be modified)
-> 1679 self.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.deepspeed)
   1680 self.hf_deepspeed_config.trainer_config_process(self)
   1682 # Accelerate DeepSpeed Plugin

File /opt/conda/lib/python3.10/site-packages/transformers/integrations/deepspeed.py:88, in HfTrainerDeepSpeedConfig.__init__(self, config_file_or_dict)
     87 def __init__(self, config_file_or_dict):
---> 88     super().__init__(config_file_or_dict)
     89     self._dtype = None
     90     self.mismatches = []

File /opt/conda/lib/python3.10/site-packages/transformers/integrations/deepspeed.py:78, in HfDeepSpeedConfig.__init__(self, config_file_or_dict)
     76 dep_version_check("accelerate")
     77 dep_version_check("deepspeed")
---> 78 super().__init__(config_file_or_dict)

TypeError: object.__init__() takes exactly one argument (the instance to initialize)

![error](https://github.com/huggingface/transformers/assets/66542185/aa328572-1379-489a-9212-0607f34a3f36)



### Expected behavior

The code does not report errors.

Comments:
cc @pacman100 and @muellerzr 

99% sure you can't use DeepSpeed in a notebook. @pacman100 

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