reduce len() calls

src/rapidfuzz/fuzz_py.py

@@ -316,17 +316,18 @@ def partial_ratio_alignment(
 
     if not s1 and not s2:
         return ScoreAlignment(100.0, 0, 0, 0, 0)
-
     s1, s2 = conv_sequences(s1, s2)
-    if len(s1) <= len(s2):
+    len1 = len(s1)
+    len2 = len(s2)
+    if len1 <= len2:
         shorter = s1
         longer = s2
     else:
         shorter = s2
         longer = s1
 
     res = _partial_ratio_impl(shorter, longer, score_cutoff / 100)
-    if res.score != 100 and len(s1) == len(s2):
+    if res.score != 100 and len1 == len2:
         score_cutoff = max(score_cutoff, res.score)
         res2 = _partial_ratio_impl(longer, shorter, score_cutoff / 100)
         if res2.score > res.score:
@@ -335,7 +336,7 @@ def partial_ratio_alignment(
     if res.score < score_cutoff:
         return None
 
-    if len(s1) <= len(s2):
+    if len1 <= len2:
         return res
 
     return ScoreAlignment(res.score, res.dest_start, res.dest_end, res.src_start, res.src_end)

src/rapidfuzz/process_cpp_impl.pyx

@@ -1208,6 +1208,7 @@ def extract(query, choices, *, scorer=WRatio, processor=None, limit=5, score_cut
     cdef RF_Scorer* scorer_context = NULL
     cdef RF_ScorerFlags scorer_flags
     cdef int64_t c_limit
+    cdef int64_t choices_len
     scorer_kwargs = scorer_kwargs.copy() if scorer_kwargs else {}
 
     setupPandas()
@@ -1216,16 +1217,17 @@ def extract(query, choices, *, scorer=WRatio, processor=None, limit=5, score_cut
         return []
 
     try:
-        if limit is None or limit > len(choices):
-            limit = len(choices)
+        choices_len = <int64_t>len(choices)
     except TypeError:
         # handle generators. In Theory we could retrieve the length later on while
         # preprocessing the choices, but this is good enough for now
         choices = list(choices)
-        if limit is None or limit > len(choices):
-            limit = len(choices)
+        choices_len = <int64_t>len(choices)
+
+    c_limit = choices_len
+    if limit is not None:
+        c_limit = min(c_limit, <int64_t>limit)
 
-    c_limit = limit
     if c_limit == 1:
         res = extractOne(
             query,

src/rapidfuzz/process_py.py

@@ -643,14 +643,17 @@ def cpdist(
     """
     import numpy as np
 
-    if len(queries) != len(choices):
+    len_queries = len(queries)
+    len_choices = len(choices)
+
+    if len_queries != len_choices:
         error_message = "Length of queries and choices must be the same!"
         raise ValueError(error_message)
 
     _ = workers, score_hint
     scorer_kwargs = scorer_kwargs or {}
     dtype = _dtype_to_type_num(dtype, scorer, scorer_kwargs)
-    results = np.zeros((len(queries),), dtype=dtype)
+    results = np.zeros((len_queries,), dtype=dtype)
 
     setupPandas()