chore: import upstream snapshot with attribution

tests/models/xcodec2/test_feature_extraction_xcodec2.py

@@ -0,0 +1,176 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import itertools
+import unittest
+
+import numpy as np
+
+from transformers import Xcodec2FeatureExtractor
+from transformers.testing_utils import require_torch, require_torch_gpu, slow
+from transformers.utils.import_utils import is_torch_available
+
+from ...test_processing_common import floats_list
+from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
+
+
+if is_torch_available():
+    import torch
+
+
+@require_torch
+class Xcodec2FeatureExtractionTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        min_seq_length=400,
+        max_seq_length=2000,
+        feature_size=80,  # number of mel bins
+        sampling_rate=16000,
+        spec_hop_length=160,
+        hop_length=320,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_seq_length = min_seq_length
+        self.max_seq_length = max_seq_length
+        self.spec_hop_length = spec_hop_length
+        self.hop_length = hop_length
+        self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
+        self.feature_size = feature_size
+        self.sampling_rate = sampling_rate
+
+    def prepare_feat_extract_dict(self):
+        return {
+            "feature_size": self.feature_size,
+            "sampling_rate": self.sampling_rate,
+            "hop_length": self.hop_length,
+            "spec_hop_length": self.spec_hop_length,
+            "padding_value": 0.0,
+        }
+
+    # Copied from transformers.tests.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTester.prepare_inputs_for_common
+    def prepare_inputs_for_common(self, equal_length=False, numpify=False):
+        def _flatten(list_of_lists):
+            return list(itertools.chain(*list_of_lists))
+
+        if equal_length:
+            speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
+        else:
+            # make sure that inputs increase in size
+            speech_inputs = [
+                floats_list((x, self.feature_size))
+                for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
+            ]
+        if numpify:
+            speech_inputs = [np.asarray(x) for x in speech_inputs]
+        return speech_inputs
+
+
+@require_torch
+class Xcodec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
+    feature_extraction_class = Xcodec2FeatureExtractor
+
+    def setUp(self):
+        self.feat_extract_tester = Xcodec2FeatureExtractionTester(self)
+
+    def test_call(self):
+        TOL = 1e-6
+
+        # Tests that all call wrap to encode_plus and batch_encode_plus
+        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        # create three inputs of length 800, 1000, and 1200
+        audio_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
+        np_audio_inputs = [np.asarray(audio_input) for audio_input in audio_inputs]
+        torch_audio_inputs = [torch.tensor(audio_input) for audio_input in audio_inputs]
+
+        # Test not batched input
+        sampling_rate = self.feat_extract_tester.sampling_rate
+        encoded_sequences_1 = feat_extract(torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np")
+        encoded_sequences_2 = feat_extract(np_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np")
+        encoded_sequences_3 = feat_extract(
+            torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np", device="cpu"
+        )
+        self.assertTrue(np.allclose(encoded_sequences_1.input_values, encoded_sequences_2.input_values, atol=TOL))
+        self.assertTrue(np.allclose(encoded_sequences_1.input_features, encoded_sequences_2.input_features, atol=TOL))
+        self.assertTrue(np.allclose(encoded_sequences_1.input_features, encoded_sequences_3.input_features, atol=TOL))
+
+        # Test batched
+        encoded_sequences_1 = feat_extract(
+            torch_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np"
+        )
+        encoded_sequences_2 = feat_extract(
+            np_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np"
+        )
+        encoded_sequences_3 = feat_extract(
+            torch_audio_inputs,
+            sampling_rate=sampling_rate,
+            padding=True,
+            return_tensors="np",
+            device="cpu",
+        )
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1.input_values, encoded_sequences_2.input_values):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=TOL))
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1.input_features, encoded_sequences_2.input_features):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=TOL))
+        for enc_seq_1, enc_seq_3 in zip(encoded_sequences_1.input_features, encoded_sequences_3.input_features):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_3, atol=TOL))
+
+    @slow
+    @require_torch_gpu
+    def test_call_gpu(self):
+        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        audio_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
+        torch_audio_inputs = [torch.tensor(audio_input) for audio_input in audio_inputs]
+        sampling_rate = self.feat_extract_tester.sampling_rate
+
+        # Single input: CPU vs GPU output should have same shape and dtype
+        encoded_sequences_cpu = feat_extract(
+            torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np", device="cpu"
+        )
+        encoded_sequences_gpu = feat_extract(
+            torch_audio_inputs[0], sampling_rate=sampling_rate, return_tensors="np", device="cuda"
+        )
+        self.assertEqual(encoded_sequences_cpu.input_values.shape, encoded_sequences_gpu.input_values.shape)
+        self.assertEqual(encoded_sequences_cpu.input_features.shape, encoded_sequences_gpu.input_features.shape)
+        self.assertEqual(encoded_sequences_cpu.input_values.dtype, encoded_sequences_gpu.input_values.dtype)
+        self.assertEqual(encoded_sequences_cpu.input_features.dtype, encoded_sequences_gpu.input_features.dtype)
+
+        # Batched input: CPU vs GPU output should have same shape and dtype
+        encoded_sequences_cpu = feat_extract(
+            torch_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np", device="cpu"
+        )
+        encoded_sequences_gpu = feat_extract(
+            torch_audio_inputs, sampling_rate=sampling_rate, padding=True, return_tensors="np", device="cuda"
+        )
+        self.assertEqual(encoded_sequences_cpu.input_values.shape, encoded_sequences_gpu.input_values.shape)
+        self.assertEqual(encoded_sequences_cpu.input_features.shape, encoded_sequences_gpu.input_features.shape)
+        self.assertEqual(encoded_sequences_cpu.input_values.dtype, encoded_sequences_gpu.input_values.dtype)
+        self.assertEqual(encoded_sequences_cpu.input_features.dtype, encoded_sequences_gpu.input_features.dtype)
+
+    def test_double_precision_pad(self):
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        np_audio_inputs = np.random.rand(100, 32).astype(np.float64)
+        py_audio_inputs = np_audio_inputs.tolist()
+
+        for inputs in [py_audio_inputs, np_audio_inputs]:
+            np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
+            self.assertTrue(np_processed.input_features.dtype == np.float32)
+            pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
+            self.assertTrue(pt_processed.input_features.dtype == torch.float32)
+
+    @unittest.skip("Xcodec2 doesn't support stereo input")
+    def test_integration_stereo(self):
+        pass