Copied from for test files (#26713)

* copied statement for test files

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/clap/test_feature_extraction_clap.py

@@ -19,6 +19,7 @@ import random
 import unittest
 
 import numpy as np
+from datasets import load_dataset
 
 from transformers import ClapFeatureExtractor
 from transformers.testing_utils import require_torch, require_torchaudio
@@ -110,10 +111,10 @@ class ClapFeatureExtractionTester(unittest.TestCase):
 
 @require_torch
 @require_torchaudio
-# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest with Whisper->Clap
 class ClapFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
     feature_extraction_class = ClapFeatureExtractor
 
+    # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.setUp with Whisper->Clap
     def setUp(self):
         self.feat_extract_tester = ClapFeatureExtractionTester(self)
 
@@ -147,6 +148,7 @@ class ClapFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.Tes
         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))
 
+    # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad
     def test_double_precision_pad(self):
         import torch
 
@@ -160,9 +162,8 @@ class ClapFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.Tes
             pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
             self.assertTrue(pt_processed.input_features.dtype == torch.float32)
 
+    # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest._load_datasamples
     def _load_datasamples(self, num_samples):
-        from datasets import load_dataset
-
         ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         # automatic decoding with librispeech
         speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]