Repository: huggingface/transformers (Python)
Task: Bug Fix (2022-01-14 12:19:38+00:00)

Issue:
the `tokenize_chinese_chars` argument is not always taken into account with the fast version of the bert tokenizer
## Environment info
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     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.16.0.dev0
- Platform: Linux-5.11.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): 1.10.1+cu102 (False)
- Tensorflow version (GPU?): 2.7.0 (False)
- Flax version (CPU?/GPU?/TPU?): 0.3.6 (cpu)
- Jax version: 0.2.26
- JaxLib version: 0.1.75
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

### Who can help
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Models:

- ALBERT, BERT, XLM, DeBERTa, DeBERTa-v2, ELECTRA, MobileBert, SqueezeBert: @LysandreJik
- T5, BART, Marian, Pegasus, EncoderDecoder: @patrickvonplaten
- Blenderbot, MBART: @patil-suraj
- Longformer, Reformer, TransfoXL, XLNet, FNet, BigBird: @patrickvonplaten
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- TAPAS, LayoutLM, LayoutLMv2, LUKE, ViT, BEiT, DEiT, DETR, CANINE: @NielsRogge
- GPT-Neo, GPT-J, CLIP: @patil-suraj
- Wav2Vec2, HuBERT, SpeechEncoderDecoder, UniSpeech, UniSpeechSAT, SEW, SEW-D, Speech2Text: @patrickvonplaten, @anton-l

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Library:

- Benchmarks: @patrickvonplaten
- Deepspeed: @stas00
- Ray/raytune: @richardliaw, @amogkam
- Text generation: @patrickvonplaten @narsil
- Tokenizers: @SaulLu
- Trainer: @sgugger
- Pipelines: @Narsil
- Speech: @patrickvonplaten, @anton-l
- Vision: @NielsRogge, @sgugger

Documentation: @sgugger

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- for issues with a model, report at https://discuss.huggingface.co/ and tag the model's creator.

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
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## Information

Model I am using (Bert, XLNet ...):

The problem arises when using:
* [x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

```python
from transformers import BertTokenizer, BertTokenizerFast

list_of_commun_chinese_char = ["的", "人", "有"]
text = "".join(list_of_commun_chinese_char)
print(text)
# 的人有

model_name = "bert-base-uncased"

tokenizer_slow = BertTokenizer.from_pretrained(model_name, tokenize_chinese_chars=False)
tokenizer_slow.tokenize(text)
# ['的', '##人', '##有']

tokenizer_slow = BertTokenizer.from_pretrained(model_name, tokenize_chinese_chars=True)
tokenizer_slow.tokenize(text)
# ['的', '人', '有']

tokenizer_fast = BertTokenizerFast.from_pretrained(model_name, tokenize_chinese_chars=False)
tokenizer_fast.tokenize(text)
# ['的', '人', '有']


tokenizer_fast = BertTokenizerFast.from_pretrained(model_name, tokenize_chinese_chars=True)
tokenizer_fast.tokenize(text)
# ['的', '人', '有']
```

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## Expected behavior
If the user indicates `tokenize_chinese_chars=False` when he initializes a fast bert tokenizer, we expect that this characteristic is reflected on the tokenizer. In other words, in the previous example, we expect that:

```python
tokenizer_fast = BertTokenizerFast.from_pretrained(model_name, tokenize_chinese_chars=False)
tokenizer_fast.tokenize(text)
# ['的', '##人', '##有']
```


Discussion:

Patch:
diff --git a/src/transformers/models/bert/tokenization_bert_fast.py b/src/transformers/models/bert/tokenization_bert_fast.py
--- a/src/transformers/models/bert/tokenization_bert_fast.py
+++ b/src/transformers/models/bert/tokenization_bert_fast.py
@@ -188,15 +188,17 @@ def __init__(
             **kwargs,
         )
 
-        pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
+        normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
         if (
-            pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
-            or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
+            normalizer_state.get("lowercase", do_lower_case) != do_lower_case
+            or normalizer_state.get("strip_accents", strip_accents) != strip_accents
+            or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
         ):
-            pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
-            pre_tok_state["lowercase"] = do_lower_case
-            pre_tok_state["strip_accents"] = strip_accents
-            self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
+            normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
+            normalizer_state["lowercase"] = do_lower_case
+            normalizer_state["strip_accents"] = strip_accents
+            normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
+            self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
 
         self.do_lower_case = do_lower_case
 


Test patch:
diff --git a/tests/test_tokenization_bert.py b/tests/test_tokenization_bert.py
--- a/tests/test_tokenization_bert.py
+++ b/tests/test_tokenization_bert.py
@@ -299,3 +299,40 @@ def test_offsets_with_special_characters(self):
                     [e[1] for e in expected_results], tokenizer_r.convert_ids_to_tokens(tokens["input_ids"])
                 )
                 self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"])
+
+    def test_change_tokenize_chinese_chars(self):
+        list_of_commun_chinese_char = ["的", "人", "有"]
+        text_with_chinese_char = "".join(list_of_commun_chinese_char)
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+
+                kwargs["tokenize_chinese_chars"] = True
+                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+
+                ids_without_spe_char_p = tokenizer_p.encode(text_with_chinese_char, add_special_tokens=False)
+                ids_without_spe_char_r = tokenizer_r.encode(text_with_chinese_char, add_special_tokens=False)
+
+                tokens_without_spe_char_r = tokenizer_r.convert_ids_to_tokens(ids_without_spe_char_r)
+                tokens_without_spe_char_p = tokenizer_p.convert_ids_to_tokens(ids_without_spe_char_p)
+
+                # it is expected that each Chinese character is not preceded by "##"
+                self.assertListEqual(tokens_without_spe_char_p, list_of_commun_chinese_char)
+                self.assertListEqual(tokens_without_spe_char_r, list_of_commun_chinese_char)
+
+                kwargs["tokenize_chinese_chars"] = False
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+
+                ids_without_spe_char_r = tokenizer_r.encode(text_with_chinese_char, add_special_tokens=False)
+                ids_without_spe_char_p = tokenizer_p.encode(text_with_chinese_char, add_special_tokens=False)
+
+                tokens_without_spe_char_r = tokenizer_r.convert_ids_to_tokens(ids_without_spe_char_r)
+                tokens_without_spe_char_p = tokenizer_p.convert_ids_to_tokens(ids_without_spe_char_p)
+
+                # it is expected that only the first Chinese character is not preceded by "##".
+                expected_tokens = [
+                    f"##{token}" if idx != 0 else token for idx, token in enumerate(list_of_commun_chinese_char)
+                ]
+                self.assertListEqual(tokens_without_spe_char_p, expected_tokens)
+                self.assertListEqual(tokens_without_spe_char_r, expected_tokens)