Improve naming of token and vocab transition keys in regex.py

outlines/fsm/regex.py

@@ -414,7 +414,7 @@ def _walk_fsm(
     fsm_transitions: Dict[Tuple[int, int], int],
     fsm_initial: int,
     fsm_finals: Set[int],
-    token_trans_key_seq: Sequence[int],
+    token_transition_keys: Sequence[int],
     start_state: int,
     full_match: bool = True,
 ) -> List[int]:
@@ -424,7 +424,7 @@ def _walk_fsm(
 
     # Iterate over token transition key sequence. The transition key
     # sequence represents the FSM traversal rules of the tokens symbols.
-    for i, trans_key in enumerate(token_trans_key_seq):
+    for i, trans_key in enumerate(token_transition_keys):
         new_state = fsm_transitions.get((state, trans_key))
 
         if new_state is None:
@@ -448,7 +448,7 @@ def _walk_fsm(
 
 def walk_fsm(
     fsm: BetterFSM,
-    token_trans_key_seq: Sequence[int],
+    token_transition_keys: Sequence[int],
     start_state: int,
     full_match: bool = True,
 ) -> List[int]:
@@ -462,7 +462,7 @@ def walk_fsm(
 
     # Iterate over token transition key sequence. The transition key
     # sequence represents the FSM traversal rules of the tokens symbols.
-    for i, trans_key in enumerate(token_trans_key_seq):
+    for i, trans_key in enumerate(token_transition_keys):
         new_state = fsm_transitions.get((state, trans_key))
 
         if new_state is None:
@@ -703,10 +703,10 @@ def get_token_transition_keys(
             alphabet_symbol_mapping.get(symbol, alphabet_anything_value)
         )
 
-    tok_trans_array = np.empty(len(token_transition_keys), dtype=np.int64)
+    token_transition_keys_array = np.empty(len(token_transition_keys), dtype=np.int64)
     for j in range(len(token_transition_keys)):
-        tok_trans_array[j] = token_transition_keys[j]
-    return tok_trans_array
+        token_transition_keys_array[j] = token_transition_keys[j]
+    return token_transition_keys_array
 
 
 @numba.njit(cache=True, nogil=True)
@@ -718,14 +718,14 @@ def get_vocabulary_transition_keys(
     """
     Calculate the sequence transition keys for each token str within a vocabulary
     """
-    tokens_trans_keys = numba.typed.List.empty_list(numba.int64[:])
+    vocab_transition_keys = numba.typed.List.empty_list(numba.int64[:])
     for token_str, _ in vocabulary:
-        trans_key_seq_array = get_token_transition_keys(
+        token_transition_keys = get_token_transition_keys(
             alphabet_symbol_mapping, alphabet_anything_value, token_str
         )
-        tokens_trans_keys.append(trans_key_seq_array)
+        vocab_transition_keys.append(token_transition_keys)
 
-    return tokens_trans_keys
+    return vocab_transition_keys
 
 
 def create_fsm_index_end_to_end(