Incorrect learning rate when using 'cosine_with_restarts' scheduler type
### System Info

- `transformers` version: 4.20.1
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.8.1
- PyTorch version (GPU?): 1.12.0+cu116 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no

### Who can help?

@sgugger

### Information

- [X] The official example scripts
- [ ] 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

Start training a Roberta model for masked LM using the following parameters.
All parameters are shared for the sake of context, but the parameter that fails is "lr_scheduler_type".
The learning rate remains fixed at a default value.

```py
training_args = TrainingArguments(
    output_dir='./roberta',
    overwrite_output_dir=True,
    evaluation_strategy = 'steps',
    num_train_epochs=100,
    lr_scheduler_type='cosine_with_restarts',
    warmup_steps=100,
    weight_decay=0.01,
    per_device_train_batch_size=20,
    per_device_eval_batch_size=20,
    save_steps=2048,
    eval_steps=2048,
    save_total_limit=3,
    report_to="wandb",
    ignore_data_skip=True,
    gradient_accumulation_steps=4, 
    gradient_checkpointing=True,
    fp16=True
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    #prediction_loss_only=True,
)
```

### Expected behavior

The learning rate should behave like configured and like documented for 'cosine_with_restarts', instead of remaining fixed through the epochs.
Could you explain to us how you are seeing the learning rate being constant? Just tried your code and adding a print statement for the learning rate at every step, and I see something that changes.<|||||>I'm using weight and biases at wandb.ai to keep track of my metrics
![image](https://user-images.githubusercontent.com/5707158/182159636-2c728deb-6f23-43eb-836c-dd6ae5041a7b.png)
<|||||>training_args = TrainingArguments(
    output_dir='./roberta',
    overwrite_output_dir=True,
    evaluation_strategy = 'steps',
    num_train_epochs=100,
    learning_rate=1e-4,
    weight_decay=0.01,
    per_device_train_batch_size=20,
    per_device_eval_batch_size=20,
    save_steps=2048,
    eval_steps=2048,
    save_total_limit=3,
    report_to="wandb",
    ignore_data_skip=True,
    gradient_accumulation_steps=4, 
    gradient_checkpointing=True,
    fp16=True
)

Results in 
![image](https://user-images.githubusercontent.com/5707158/182160682-3c14ecab-57f2-4526-acb4-a24e1d127e7c.png)
<|||||>So this only shows the learning rates at steps that are multiple of 500 since that is the default logging step I wouldn't trust a curve with 6 points on it.<|||||>could you please share how you add print statements for each step?<|||||>By changing the source code of the Trainer. YOu can also log more frequently by changing the `loggin_steps` argument.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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