Different results when using `__call__` and `encode` and `encode_plus` of (fast/slow) bert tokenizers
### System Info

- `transformers` version: 4.30.0.dev0
- Platform: Linux-5.15.0-1033-aws-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.15.1
- Safetensors version: 0.3.1
- PyTorch version (GPU?): 2.0.1+cu117 (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?

@ArthurZucker @younesbelkada

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

Steps to reproduce the differences:

[hf_github_issue.zip](https://github.com/huggingface/transformers/files/11691030/hf_github_issue.zip) contains all the input data used to reproduce the issue. Please download and unzip.

1. I got my customized token2id mapping and stored the three files in one folder let's say "hf_github_issue".
    1. `vocab.txt`: my own vocabulary
    2. `special_tokens_map.json`
    3. `tokenizer_config.json`
2. `raw_transformed_uncased.csv`: each row in this csv is a **pre-tokenized** sequence (i.e., each cell is a word that does not need further tokenization)
    ```
    import pandas as pd
    from datasets import Dataset
    
    raw_transformed_df = pd.read_csv("hf_github_issue/raw_transformed.csv")
    training_dataset = Dataset.from_pandas(raw_transformed_df, preserve_index=False)
    ```
3. Load as a fast BertTokenizer:
    ```
    from transformers import AutoTokenizer
    
    tokenizer = AutoTokenizer.from_pretrained("hf_github_issue", use_fast=True)
    
    # method 1
    print(tokenizer.encode(list(training_dataset[0].values()), is_split_into_words=True))
    
    # method 2
    print(tokenizer.encode_plus(list(training_dataset[0].values()), is_split_into_words=True))
    
    # method 3
    print(tokenizer(list(training_dataset[0].values()), is_split_into_words=True))
    ```
4. Load as a slow BertTokenizer
    ```
    tokenizer = AutoTokenizer.from_pretrained("hf_github_issue", use_fast=False)
    
    # method 4
    print(tokenizer.encode(list(training_dataset[0].values()), is_split_into_words=True))
    
    # method 5
    print(tokenizer.encode_plus(list(training_dataset[0].values()), is_split_into_words=True))
    
    # method 6
    print(tokenizer(list(training_dataset[0].values()), is_split_into_words=True))
    
    # method 7
    print(tokenizer.encode(list(training_dataset[0].values())))
   ```

### Expected behavior

I doubt this is not a bug but more like my misunderstanding from either creating the custom tokenizer or choosing the right API(s). Any help is appreciated.

I thought all seven methods here will give the same output. However, only `method 7` gives my expected output:
```
[3, 8, 16, 29, 32, 48, 55, 61, 77, 84, 90, 103, 111, 115, 131, 139, 145, 161, 167, 174, 187, 194, 201, 215, 225, 234, 244, 255, 263, 276, 285, 288, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 307, 317, 327, 338, 343, 354, 359, 369, 370, 371, 374, 384, 391, 404, 412, 418, 429, 430, 431, 440, 448, 453, 461, 470, 479, 493, 4]
```

Method 1-6 gives the same (unexpected) output:
```
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```

In particular, I would like to get the `__call__` function work as expected so that I may use the example [run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py)
It seems that the misunderstanding of `is_split_into_words` from #8217 is the main issue here.

But the fix in #24121 should also be helpful to align the expected outputs of slow tokenizer when input is a list of list of pretokenized strings.<|||||>cc @ArthurZucker :D<|||||>Pr reviewed 😉 <|||||>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.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.