2023 the huggingface team 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 orglicenseslicense2 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 create noise on cpu for reproducibility univnetmodel currently cannot be traced with torch jit trace the univnetmodel is not a transformer and does not use any attention mechanisms so skip transformerattention related tests univnetmodel is not a sequence classification model univnetmodel does not have a basemodelprefix attribute univnetmodel does not implement a parallelize method signature parameters is an ordereddict so argnames order is deterministic automatic decoding with librispeech note hardcode modelinchannels 64 explicity generate noisesequence on cpu for consistency put noisesequence on the desired device note hardcode nummelchannels 100 note spectrogram should already be on torchdevice permute to match diffusers implementation load sample checkpoint from tortoise tts get batched noise and spectrogram inputs load sample checkpoint from tortoise tts get unbatched noise and spectrogram inputs fmt off fmt on 2023 the huggingface team 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 create noise on cpu for reproducibility univnetmodel currently cannot be traced with torch jit trace the univnetmodel is not a transformer and does not use any attention mechanisms so skip transformer attention related tests univnetmodel is not a sequence classification model univnetmodel does not have a base_model_prefix attribute univnetmodel does not implement a parallelize method signature parameters is an ordereddict so arg_names order is deterministic automatic decoding with librispeech note hardcode model_in_channels 64 explicity generate noise_sequence on cpu for consistency put noise_sequence on the desired device note hardcode num_mel_channels 100 note spectrogram should already be on torch_device permute to match diffusers implementation load sample checkpoint from tortoise tts get batched noise and spectrogram inputs load sample checkpoint from tortoise tts get unbatched noise and spectrogram inputs fmt off fmt on
import gc
import inspect
import random
import unittest

from datasets import Audio, load_dataset

from transformers import UnivNetConfig, UnivNetFeatureExtractor
from transformers.testing_utils import (
    is_torch_available,
    require_torch,
    require_torch_gpu,
    slow,
    torch_device,
)

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    floats_tensor,
)


if is_torch_available():
    import torch

    from transformers import UnivNetModel


class UnivNetModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        seq_length=7,
        in_channels=8,
        hidden_channels=8,
        num_mel_bins=20,
        kernel_predictor_hidden_channels=8,
        seed=0,
        is_training=False,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.in_channels = in_channels
        self.hidden_channels = hidden_channels
        self.num_mel_bins = num_mel_bins
        self.kernel_predictor_hidden_channels = kernel_predictor_hidden_channels
        self.seed = seed
        self.is_training = is_training

    def prepare_noise_sequence(self):
        generator = torch.manual_seed(self.seed)
        noise_shape = (self.seq_length, self.in_channels)
        
        noise_sequence = torch.randn(noise_shape, generator=generator, dtype=torch.float)
        return noise_sequence

    def prepare_config_and_inputs(self):
        spectrogram = floats_tensor([self.seq_length, self.num_mel_bins], scale=1.0)
        noise_sequence = self.prepare_noise_sequence()
        noise_sequence = noise_sequence.to(spectrogram.device)
        config = self.get_config()
        return config, spectrogram, noise_sequence

    def get_config(self):
        return UnivNetConfig(
            model_in_channels=self.in_channels,
            model_hidden_channels=self.hidden_channels,
            num_mel_bins=self.num_mel_bins,
            kernel_predictor_hidden_channels=self.kernel_predictor_hidden_channels,
        )

    def create_and_check_model(self, config, spectrogram, noise_sequence):
        model = UnivNetModel(config=config).to(torch_device).eval()
        result = model(spectrogram, noise_sequence)[0]
        self.parent.assertEqual(result.shape, (1, self.seq_length * 256))

    def prepare_config_and_inputs_for_common(self):
        config, spectrogram, noise_sequence = self.prepare_config_and_inputs()
        inputs_dict = {"input_features": spectrogram, "noise_sequence": noise_sequence}
        return config, inputs_dict


@require_torch
class UnivNetModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (UnivNetModel,) if is_torch_available() else ()
    
    test_torchscript = False
    
    
    test_pruning = False
    test_resize_embeddings = False
    test_resize_position_embeddings = False
    test_head_masking = False
    
    test_mismatched_shapes = False
    
    test_missing_keys = False
    
    test_model_parallel = False
    is_encoder_decoder = False
    has_attentions = False

    input_name = "input_features"

    def setUp(self):
        self.model_tester = UnivNetModelTester(self)
        self.config_tester = ConfigTester(self, config_class=UnivNetConfig)

    def test_config(self):
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_from_and_save_pretrained_subfolder()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = [
                "input_features",
            ]
            self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)

    @unittest.skip(reason="UnivNetModel does not output hidden_states.")
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="UnivNetModel.forward does not accept an inputs_embeds argument.")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="UnivNetModel does not use input embeddings and thus has no get_input_embeddings method.")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="UnivNetModel does not support all arguments tested, such as output_hidden_states.")
    def test_model_outputs_equivalence(self):
        pass

    @unittest.skip(reason="UnivNetModel does not output hidden_states.")
    def test_retain_grad_hidden_states_attentions(self):
        pass

    def test_batched_inputs_outputs(self):
        config, inputs = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            batched_spectrogram = inputs["input_features"].unsqueeze(0).repeat(2, 1, 1)
            batched_noise_sequence = inputs["noise_sequence"].unsqueeze(0).repeat(2, 1, 1)
            with torch.no_grad():
                batched_outputs = model(
                    batched_spectrogram.to(torch_device),
                    batched_noise_sequence.to(torch_device),
                )[0]

            self.assertEqual(
                batched_spectrogram.shape[0],
                batched_outputs.shape[0],
                msg="Got different batch dims for input and output",
            )

    def test_unbatched_inputs_outputs(self):
        config, inputs = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(inputs["input_features"].to(torch_device), inputs["noise_sequence"].to(torch_device))[
                    0
                ]
            self.assertTrue(outputs.shape[0] == 1, msg="Unbatched input should create batched output with bsz = 1")

    def test_unbatched_batched_outputs_consistency(self):
        config, inputs = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            unbatched_spectrogram = inputs["input_features"].detach().clone()
            unbatched_noise_sequence = inputs["noise_sequence"].detach().clone()
            batched_spectrogram = inputs["input_features"].unsqueeze(0)
            batched_noise_sequence = inputs["noise_sequence"].unsqueeze(0)

            with torch.no_grad():
                unbatched_outputs = model(
                    unbatched_spectrogram.to(torch_device),
                    unbatched_noise_sequence.to(torch_device),
                )[0]

                batched_outputs = model(
                    batched_spectrogram.to(torch_device),
                    batched_noise_sequence.to(torch_device),
                )[0]

            torch.testing.assert_close(unbatched_outputs, batched_outputs)


@require_torch_gpu
@slow
class UnivNetModelIntegrationTests(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def _load_datasamples(self, num_samples, sampling_rate=24000):
        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        ds = ds.cast_column("audio", Audio(sampling_rate=sampling_rate))
        
        speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]

        return [x["array"] for x in speech_samples], [x["sampling_rate"] for x in speech_samples]

    def get_inputs(self, device, num_samples: int = 3, noise_length: int = 10, seed: int = 0):
        generator = torch.manual_seed(seed)
        
        if num_samples == 1:
            noise_sequence_shape = (64, noise_length)
        else:
            noise_sequence_shape = (num_samples, 64, noise_length)
        
        noise_sequence = torch.randn(noise_sequence_shape, generator=generator, dtype=torch.float32, device="cpu")
        
        noise_sequence = noise_sequence.to(device)

        
        if num_samples == 1:
            spectrogram_shape = [100, noise_length]
        else:
            spectrogram_shape = [num_samples, 100, noise_length]
        spectrogram = floats_tensor(spectrogram_shape, scale=1.0, rng=random.Random(seed))
        

        
        if num_samples == 1:
            noise_sequence = noise_sequence.transpose(1, 0)
            spectrogram = spectrogram.transpose(1, 0)
        else:
            noise_sequence = noise_sequence.transpose(2, 1)
            spectrogram = spectrogram.transpose(2, 1)

        inputs = {
            "input_features": spectrogram,
            "noise_sequence": noise_sequence,
            "generator": generator,
        }

        return inputs

    def test_model_inference_batched(self):
        
        model = UnivNetModel.from_pretrained("dg845/univnet-dev")
        model.eval().to(torch_device)

        
        input_speech = self.get_inputs(torch_device, num_samples=3)

        with torch.no_grad():
            waveform = model(**input_speech)[0]
        waveform = waveform.cpu()

        waveform_mean = torch.mean(waveform)
        waveform_stddev = torch.std(waveform)
        waveform_slice = waveform[-1, -9:].flatten()

        EXPECTED_MEAN = torch.tensor(-0.19989729)
        EXPECTED_STDDEV = torch.tensor(0.35230172)
        EXPECTED_SLICE = torch.tensor([-0.3408, -0.6045, -0.5052, 0.1160, -0.1556, -0.0405, -0.3024, -0.5290, -0.5019])

        torch.testing.assert_close(waveform_mean, EXPECTED_MEAN, atol=1e-4, rtol=1e-5)
        torch.testing.assert_close(waveform_stddev, EXPECTED_STDDEV, atol=1e-4, rtol=1e-5)
        torch.testing.assert_close(waveform_slice, EXPECTED_SLICE, atol=5e-4, rtol=1e-5)

    def test_model_inference_unbatched(self):
        
        model = UnivNetModel.from_pretrained("dg845/univnet-dev")
        model.eval().to(torch_device)

        
        input_speech = self.get_inputs(torch_device, num_samples=1)

        with torch.no_grad():
            waveform = model(**input_speech)[0]
        waveform = waveform.cpu()

        waveform_mean = torch.mean(waveform)
        waveform_stddev = torch.std(waveform)
        waveform_slice = waveform[-1, -9:].flatten()

        EXPECTED_MEAN = torch.tensor(-0.22895093)
        EXPECTED_STDDEV = torch.tensor(0.33986747)
        EXPECTED_SLICE = torch.tensor([-0.3276, -0.5504, -0.3484, 0.3574, -0.0373, -0.1826, -0.4880, -0.6431, -0.5162])

        torch.testing.assert_close(waveform_mean, EXPECTED_MEAN, atol=1e-4, rtol=1e-5)
        torch.testing.assert_close(waveform_stddev, EXPECTED_STDDEV, atol=1e-4, rtol=1e-5)
        torch.testing.assert_close(waveform_slice, EXPECTED_SLICE, atol=1e-3, rtol=1e-5)

    def test_integration(self):
        feature_extractor = UnivNetFeatureExtractor.from_pretrained("dg845/univnet-dev")
        model = UnivNetModel.from_pretrained("dg845/univnet-dev")
        model.eval().to(torch_device)

        audio, sr = self._load_datasamples(1, sampling_rate=feature_extractor.sampling_rate)

        input_features = feature_extractor(audio, sampling_rate=sr[0], return_tensors="pt").input_features
        input_features = input_features.to(device=torch_device)

        input_speech = self.get_inputs(torch_device, num_samples=1, noise_length=input_features.shape[1])
        input_speech["input_features"] = input_features

        with torch.no_grad():
            waveform = model(**input_speech)[0]
        waveform = waveform.cpu()

        waveform_mean = torch.mean(waveform)
        waveform_stddev = torch.std(waveform)
        waveform_slice = waveform[-1, -9:].flatten()

        EXPECTED_MEAN = torch.tensor(0.00051374)
        EXPECTED_STDDEV = torch.tensor(0.058105603)
        
        EXPECTED_SLICE = torch.tensor([-4.3934e-04, -1.8203e-04, -3.3033e-04, -3.8716e-04, -1.6125e-04, 3.5389e-06, -3.3149e-04, -3.7613e-04, -2.3331e-04])
        

        torch.testing.assert_close(waveform_mean, EXPECTED_MEAN, atol=5e-6, rtol=1e-5)
        torch.testing.assert_close(waveform_stddev, EXPECTED_STDDEV, atol=1e-4, rtol=1e-5)
        torch.testing.assert_close(waveform_slice, EXPECTED_SLICE, atol=5e-6, rtol=1e-5)