# 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