Fix error in M4T feature extractor (#28340)

* fix M4T FE error when no attention mask

* modify logic

* add test

* go back to initial test situation + add other tests

tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py

@@ -171,6 +171,42 @@ class SeamlessM4TFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
         for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
             self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
 
+    def test_call_without_attention_mask(self):
+        feature_extractor_args = self.feat_extract_tester.prepare_feat_extract_dict()
+        feature_extractor = self.feature_extraction_class(**feature_extractor_args)
+
+        # create three inputs of length 800, 1000, and 1200
+        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
+        np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
+
+        # Test attention mask when passing no attention mask to forward call
+        output = feature_extractor(np_speech_inputs, padding=True, return_tensors="np", return_attention_mask=False)
+        self.assertTrue("attention_mask" not in output)
+
+        # Test attention mask when no attention mask by default
+        feature_extractor_args["return_attention_mask"] = False
+        feature_extractor = self.feature_extraction_class(**feature_extractor_args)
+        output = feature_extractor(np_speech_inputs, padding=True, return_tensors="np", return_attention_mask=False)
+        self.assertTrue("attention_mask" not in output)
+
+    def test_attention_mask(self):
+        # test attention mask has the right output shape
+        feature_extractor_args = self.feat_extract_tester.prepare_feat_extract_dict()
+
+        feature_extractor = self.feature_extraction_class(**feature_extractor_args)
+        # create three inputs of length 800, 1000, and 1200
+        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
+        np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
+
+        # Test attention mask when passing it to forward call
+        output = feature_extractor(np_speech_inputs, padding=True, return_tensors="np")
+        input_features = output.input_features
+
+        attention_mask = output.attention_mask
+        self.assertTrue(attention_mask.ndim == 2)
+        self.assertTrue(attention_mask.shape[0] == 3)
+        self.assertTrue(attention_mask.shape[-1] == input_features.shape[1])
+
     @require_torch
     def test_call_torch(self):
         import torch