Fix new typing

Signed-off-by: Beat Buesser <beat.buesser@ibm.com>

art/defences/preprocessor/mp3_compression.py

@@ -150,7 +150,7 @@ def wav_to_mp3(x, sample_rate):
         if x.dtype != object and self.channels_first:
             x_mp3 = np.swapaxes(x_mp3, 1, 2)
 
-        if x_orig_type is not object and x.dtype is object and x.ndim == 2:
+        if x_orig_type != object and x.dtype == object and x.ndim == 2:
             x_mp3 = x_mp3.astype(x_orig_type)
 
         return x_mp3, y

art/defences/preprocessor/mp3_compression_pytorch.py

@@ -24,7 +24,7 @@
 from __future__ import absolute_import, division, print_function, unicode_literals, annotations
 
 import logging
-from typing import Tuple, TYPE_CHECKING
+from typing import TYPE_CHECKING
 
 from art.defences.preprocessor.mp3_compression import Mp3Compression
 from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
@@ -106,7 +106,7 @@ def backward(ctx, grad_output):
 
     def forward(
         self, x: "torch.Tensor", y: "torch.Tensor" | None = None
-    ) -> Tuple["torch.Tensor", "torch.Tensor" | None]:
+    ) -> tuple["torch.Tensor", "torch.Tensor" | None]:
         """
         Apply MP3 compression to sample `x`.
 

art/estimators/pytorch.py

@@ -289,7 +289,7 @@ def _set_layer(self, train: bool, layerinfo: list["torch.nn.modules.Module"]) ->
         Set all layers that are an instance of `layerinfo` into training or evaluation mode.
 
         :param train: False for evaluation mode.
-        :param layerinfo: list of module types.
+        :param layerinfo: List of module types.
         """
         import torch
 

art/estimators/speech_recognition/pytorch_deep_speech.py

@@ -24,7 +24,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Tuple, TYPE_CHECKING
+from typing import TYPE_CHECKING
 
 import numpy as np
 
@@ -34,7 +34,6 @@
 from art.utils import get_file
 
 if TYPE_CHECKING:
-
     import torch
     from deepspeech_pytorch.model import DeepSpeech
 
@@ -333,7 +332,7 @@ def __init__(
                 loss_scale=1.0,
             )
 
-    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> Tuple[np.ndarray, np.ndarray] | np.ndarray:
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> tuple[np.ndarray, np.ndarray] | np.ndarray:
         """
         Perform prediction for a batch of inputs.
 
@@ -658,7 +657,7 @@ def _preprocess_transform_model_input(
         x: "torch.Tensor",
         y: np.ndarray,
         real_lengths: np.ndarray,
-    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", list]:
+    ) -> tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", list]:
         """
         Apply preprocessing and then transform the user input space into the model input space. This function is used
         by the ASR attack to attack into the PyTorchDeepSpeech estimator whose defences are called with the
@@ -704,7 +703,7 @@ def _transform_model_input(
         compute_gradient: bool = False,
         tensor_input: bool = False,
         real_lengths: np.ndarray | None = None,
-    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", list]:
+    ) -> tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", list]:
         """
         Transform the user input space into the model input space.