Repository: huggingface/transformers
Pull request: Update: add type hints to check_tokenizers.py

chore(typing): add type hints to scripts/check_tokenizers.py and use PreTrainedTokenizerBase

# What does this PR do?
Adds lightweight type hints to `scripts/check_tokenizers.py`:

- Annotate params/returns for helper functions: `check_diff`, `check_LTR_mark`, `check_details`, `test_string`, `test_tokenizer`
- Uses `transformers.tokenization_utils_base.PreTrainedTokenizerBase` for `slow`/`fast` instead of `Any`
- Make `check_LTR_mark` return `bool` explicitly (no behavior change)

This improves readability, IDE support, and static analysis (mypy/pyright) without changing runtime behavior.

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Fixes # (issue)


## Before submitting
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Models:

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- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
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Library:

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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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- tokenizers: @ArthurZucker
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Changed files:
filename: scripts/check_tokenizers.py
patch: @@ -5,6 +5,7 @@
 import transformers
 from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
 from transformers.utils import logging
+from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 
 
 logging.set_verbosity_info()
@@ -21,7 +22,7 @@
 wrong = 0
 
 
-def check_diff(spm_diff, tok_diff, slow, fast):
+def check_diff(spm_diff: list[int], tok_diff: list[int], slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase) -> bool:
     if spm_diff == list(reversed(tok_diff)):
         # AAA -> AA+A vs A+AA case.
         return True
@@ -42,17 +43,18 @@ def check_diff(spm_diff, tok_diff, slow, fast):
     return False
 
 
-def check_LTR_mark(line, idx, fast):
+def check_LTR_mark(line: str, idx: int, fast: PreTrainedTokenizerBase) -> bool:
     enc = fast.encode_plus(line)[0]
     offsets = enc.offsets
     curr, prev = offsets[idx], offsets[idx - 1]
     if curr is not None and line[curr[0] : curr[1]] == "\u200f":
         return True
     if prev is not None and line[prev[0] : prev[1]] == "\u200f":
         return True
+    return False
 
 
-def check_details(line, spm_ids, tok_ids, slow, fast):
+def check_details(line: str, spm_ids: list[int], tok_ids: list[int], slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase) -> bool:
     # Encoding can be the same with same result AAA -> A + AA vs AA + A
     # We can check that we use at least exactly the same number of tokens.
     for i, (spm_id, tok_id) in enumerate(zip(spm_ids, tok_ids)):
@@ -111,7 +113,7 @@ def check_details(line, spm_ids, tok_ids, slow, fast):
     return False
 
 
-def test_string(slow, fast, text):
+def test_string(slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase, text: str) -> None:
     global perfect
     global imperfect
     global wrong
@@ -143,7 +145,7 @@ def test_string(slow, fast, text):
     ), f"line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(text)}\n{fast.tokenize(text)}"
 
 
-def test_tokenizer(slow, fast):
+def test_tokenizer(slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase) -> None:
     global batch_total
     for i in range(len(dataset)):
         # premise, all languages