Different results when using `__call__` and `encode` and `encode_plus` of (fast/slow) bert tokenizers
Author: sniperyyc
Pull request: no

### 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:
```
{'input_ids': [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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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)

Comments:
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.

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