Merge pull request #1270 from PyThaiNLP/copilot/verify-type-hints-annotations

Align docstrings with type hints across codebase

Author: bact

pythainlp/augment/wordnet.py

@@ -133,7 +133,7 @@ def find_synonyms(
         :param Optional[str] pos: part-of-speech type. Default is None.
         :param str postag_corpus: name of POS tag corpus
         :return: list of synonyms
-        :rtype: List[str]
+        :rtype: list[str]
         """
         self.synonyms = []
         if pos is None:
@@ -171,7 +171,7 @@ def augment(
         :param str postag_corpus: name of POS tag corpus
 
         :return: list of synonyms
-        :rtype: List[Tuple[str]]
+        :rtype: list[list[str]]
 
         :Example:
         ::

pythainlp/benchmarks/word_tokenization.py

@@ -5,6 +5,7 @@
 
 import re
 import sys
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -40,7 +41,7 @@ def _f1(precision: float, recall: float) -> float:
     return 2 * precision * recall / (precision + recall)
 
 
-def _flatten_result(my_dict: dict, sep: str = ":") -> dict:
+def _flatten_result(my_dict: dict, sep: str = ":") -> dict[str, Any]:
     """Flatten two-dimension dictionary.
 
     Use keys in the first dimension as a prefix for keys in the second dimension.
@@ -54,7 +55,7 @@ def _flatten_result(my_dict: dict, sep: str = ":") -> dict:
     :param str sep: separator between the two keys (default: ":")
 
     :return: a one-dimension dictionary with keys combined
-    :rtype: dict[str, Union[float, str]]
+    :rtype: dict[str, Any]
     """
     return {
         f"{k1}{sep}{k2}": v
@@ -129,7 +130,7 @@ def preprocessing(txt: str, remove_space: bool = True) -> str:
     return txt
 
 
-def compute_stats(ref_sample: str, raw_sample: str) -> dict:
+def compute_stats(ref_sample: str, raw_sample: str) -> dict[str, Any]:
     """Compute statistics for tokenization quality
 
     These statistics include:
@@ -146,7 +147,7 @@ def compute_stats(ref_sample: str, raw_sample: str) -> dict:
     :param str samples: samples that we want to evaluate
 
     :return: metrics at character- and word-level and indicators of correctly tokenized words
-    :rtype: dict[str, Union[float, str]]
+    :rtype: dict[str, Any]
     """
     ref_sample_arr = _binary_representation(ref_sample)
     sample_arr = _binary_representation(raw_sample)
@@ -222,7 +223,11 @@ def _binary_representation(txt: str, verbose: bool = False) -> np.ndarray:
     sample_wo_seps = list(txt.replace(SEPARATOR, ""))
 
     # sanity check
-    assert len(sample_wo_seps) == len(bin_rept)
+    if len(sample_wo_seps) != len(bin_rept):
+        raise ValueError(
+            f"Length mismatch: sample_wo_seps={len(sample_wo_seps)}, "
+            f"bin_rept={len(bin_rept)}"
+        )
 
     if verbose:
         for c, m in zip(sample_wo_seps, bin_rept):
@@ -231,13 +236,13 @@ def _binary_representation(txt: str, verbose: bool = False) -> np.ndarray:
     return bin_rept
 
 
-def _find_word_boundaries(bin_reps) -> list:
+def _find_word_boundaries(bin_reps) -> list[tuple[int, int]]:
     """Find the starting and ending location of each word.
 
     :param str bin_reps: binary representation of a text
 
     :return: list of tuples (start, end)
-    :rtype: list[tuple(int, int)]
+    :rtype: list[tuple[int, int]]
     """
     boundary = np.argwhere(bin_reps == 1).reshape(-1)
     start_idx = boundary
@@ -252,8 +257,8 @@ def _find_words_correctly_tokenised(
 ) -> tuple[int, ...]:
     """Find whether each word is correctly tokenized.
 
-    :param list[tuple(int, int)] ref_boundaries: word boundaries of reference tokenization
-    :param list[tuple(int, int)] predicted_boundaries: word boundareies of predicted tokenization
+    :param list[tuple[int, int]] ref_boundaries: word boundaries of reference tokenization
+    :param list[tuple[int, int]] predicted_boundaries: word boundaries of predicted tokenization
 
     :return: binary sequence where 1 indicates the corresponding word is tokenized correctly
     :rtype: tuple[int, ...]

pythainlp/cli/benchmark.py

@@ -81,9 +81,10 @@ def __init__(self, name: str, argv: Sequence[str]) -> None:
         actual = _read_file(args.input_file)
         expected = _read_file(args.test_file)
 
-        assert len(actual) == len(expected), (
-            "Input and test files do not have the same number of samples"
-        )
+        if len(actual) != len(expected):
+            raise ValueError(
+                "Input and test files do not have the same number of samples"
+            )
 
         safe_print(
             "Benchmarking %s against %s with %d samples in total"

pythainlp/coref/core.py

@@ -15,12 +15,12 @@ def coreference_resolution(
 ) -> list[dict]:
     """Coreference Resolution
 
-    :param List[str] texts: list of texts to apply coreference resolution to
+    :param Union[str, list[str]] texts: list of texts to apply coreference resolution to
     :param str model_name: coreference resolution model
     :param str device: device for running coreference resolution model on\
         ("cpu", "cuda", and others)
     :return: List of texts with coreference resolution
-    :rtype: List[dict]
+    :rtype: list[dict]
 
     :Options for model_name:
         * *han-coref-v1.0* - (default) Han-Coref: Thai coreference resolution\

pythainlp/el/core.py

@@ -42,9 +42,9 @@ def get_el(
     ) -> Union[list[dict], str]:
         """Get Entity Linking from Thai Text
 
-        :param str Union[List[str], str]: list of Thai text or text
+        :param str Union[list[str], str]: list of Thai text or text
         :return: list of entity linking
-        :rtype: Union[List[dict], str]
+        :rtype: Union[list[dict], str]
 
         :Example:
         ::

pythainlp/generate/core.py

@@ -58,7 +58,7 @@ def gen_sentence(
         :param bool duplicate: allow duplicate words in sentence
 
         :return: list of words or a word string
-        :rtype: List[str], str
+        :rtype: list[str], str
 
         :Example:
         ::
@@ -153,7 +153,7 @@ def gen_sentence(
         :param bool duplicate: allow duplicate words in sentence
 
         :return: list of words or a word string
-        :rtype: List[str], str
+        :rtype: list[str], str
 
         :Example:
         ::
@@ -244,7 +244,7 @@ def gen_sentence(
         :param bool duplicate: allow duplicate words in sentence
 
         :return: list of words or a word string
-        :rtype: List[str], str
+        :rtype: list[str], str
 
         :Example:
         ::

pythainlp/generate/thai2fit.py

@@ -120,7 +120,7 @@ def gen_sentence(
     :param bool duplicate: allow duplicate words in sentence
 
     :return: list words or str words
-    :rtype: List[str], str
+    :rtype: list[str], str
 
     :Example:
     ::