fix mypy error

forte/data/converter/converter.py

@@ -283,7 +283,7 @@ def convert(
             masks_tensor_list: List[torch.Tensor] = []
             for batch_masks_dim_i in masks_list:
                 masks_tensor_list.append(
-                    self._to_tensor_type(batch_masks_dim_i, np.bool)
+                    self._to_tensor_type(batch_masks_dim_i, np.bool_)
                 )
 
             return data_tensor, masks_tensor_list
@@ -293,7 +293,7 @@ def convert(
             masks_np_list: List[np.ndarray] = []
             for batch_masks_dim_i in masks_list:
                 masks_np_list.append(
-                    self._to_numpy_type(batch_masks_dim_i, np.bool)
+                    self._to_numpy_type(batch_masks_dim_i, np.bool_)
                 )
             return data_np, masks_np_list
 

forte/data/data_pack.py

@@ -386,7 +386,7 @@ def get_span_text(self, begin: int, end: int) -> str:
         """
         return self._text[begin:end]
 
-    def get_span_audio(self, begin: int, end: int) -> str:
+    def get_span_audio(self, begin: int, end: int) -> np.ndarray:
         r"""Get the audio in the data pack contained in the span.
         `begin` and `end` represent the starting and ending indices of the span
         in audio payload respectively. Each index corresponds to one sample in

forte/data/ontology/core.py

@@ -610,7 +610,7 @@ class FNdArray:
     """
 
     def __init__(
-        self, dtype: Optional[str] = None, shape: Optional[Iterable[int]] = None
+        self, dtype: Optional[str] = None, shape: Optional[List[int]] = None
     ):
         super().__init__()
         self._dtype: Optional[np.dtype] = (

forte/data/types.py

@@ -15,6 +15,7 @@
 from typing import Dict, List, Tuple, Type, Union
 
 import torch
+import numpy as np
 
 from forte.data.ontology.core import Entry
 from forte.data.span import Span
@@ -25,4 +26,4 @@
 
 DataRequest = Dict[Type[Entry], Union[Dict, List]]
 
-MatrixLike = Union[torch.TensorType, List]
+MatrixLike = Union[torch.TensorType, np.ndarray, List]

forte/processors/base/batch_processor.py

@@ -208,7 +208,7 @@ def _process_batch(
         pred = self.predict(batched_data)
         self.pack_all(packs, contexts, pred)
 
-    def predict(self, data_batch: Dict) -> Dict[str, List[Any]]:
+    def predict(self, data_batch: Dict) -> Dict[str, Dict[str, List[Any]]]:
         r"""The function that task processors should implement. Make
         predictions for the input ``data_batch``.
 

forte/processors/ir/bert_based_query_creator.py

@@ -108,7 +108,7 @@ def _build_query(self, text: str) -> np.ndarray:
 
     def _process_query(
         self, input_pack: MultiPack
-    ) -> Tuple[DataPack, Dict[str, Any]]:
+    ) -> Tuple[DataPack, np.ndarray[Any, Any]]:
         query_pack: DataPack = input_pack.get_pack(self.config.query_pack_name)
         context = [query_pack.text]
 

forte/processors/nlp/ner_predictor.py

@@ -124,7 +124,7 @@ def load_model(path):
     @torch.no_grad()
     def predict(
         self, data_batch: Dict[str, Dict[str, List[str]]]
-    ) -> Dict[str, Dict[str, List[np.array]]]:
+    ) -> Dict[str, Dict[str, List[np.ndarray]]]:
         tokens = data_batch["Token"]
 
         instances = []