Fix Audio Classification Pipeline top_k Documentation Mismatch and Bug #35736 (#35771)

* added condition for top_k Doc mismatch fix

* initilation of test file for top_k changes

* added test for returning all labels

* added test for few labels

* tests/test_audio_classification_top_k.py

* final fix

* ruff fix

---------

Co-authored-by: sambhavnoobcoder <indosambahv@gmail.com>

tests/test_audio_classification_top_k.py

@@ -0,0 +1,60 @@
+import unittest
+
+import numpy as np
+
+from transformers import pipeline
+from transformers.testing_utils import require_torch
+
+
+@require_torch
+class AudioClassificationTopKTest(unittest.TestCase):
+    def test_top_k_none_returns_all_labels(self):
+        model_name = "superb/wav2vec2-base-superb-ks"  # model with more than 5 labels
+        classification_pipeline = pipeline(
+            "audio-classification",
+            model=model_name,
+            top_k=None,
+        )
+
+        # Create dummy input
+        sampling_rate = 16000
+        signal = np.zeros((sampling_rate,), dtype=np.float32)
+
+        result = classification_pipeline(signal)
+        num_labels = classification_pipeline.model.config.num_labels
+
+        self.assertEqual(len(result), num_labels, "Should return all labels when top_k is None")
+
+    def test_top_k_none_with_few_labels(self):
+        model_name = "superb/hubert-base-superb-er"  # model with fewer labels
+        classification_pipeline = pipeline(
+            "audio-classification",
+            model=model_name,
+            top_k=None,
+        )
+
+        # Create dummy input
+        sampling_rate = 16000
+        signal = np.zeros((sampling_rate,), dtype=np.float32)
+
+        result = classification_pipeline(signal)
+        num_labels = classification_pipeline.model.config.num_labels
+
+        self.assertEqual(len(result), num_labels, "Should handle models with fewer labels correctly")
+
+    def test_top_k_greater_than_labels(self):
+        model_name = "superb/hubert-base-superb-er"
+        classification_pipeline = pipeline(
+            "audio-classification",
+            model=model_name,
+            top_k=100,  # intentionally large number
+        )
+
+        # Create dummy input
+        sampling_rate = 16000
+        signal = np.zeros((sampling_rate,), dtype=np.float32)
+
+        result = classification_pipeline(signal)
+        num_labels = classification_pipeline.model.config.num_labels
+
+        self.assertEqual(len(result), num_labels, "Should cap top_k to number of labels")