# Copyright 2023 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.
"""Testing suite for the PyTorch Encodec model."""

import copy
import inspect
import os
import tempfile
import unittest

import numpy as np
from datasets import Audio, load_dataset

from transformers import AutoProcessor, EncodecConfig
from transformers.testing_utils import (
    is_torch_available,
    require_torch,
    slow,
    torch_device,
)

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import EncodecFeatureExtractor, EncodecModel


def prepare_inputs_dict(
    config,
    input_ids=None,
    input_values=None,
    decoder_input_ids=None,
    attention_mask=None,
    decoder_attention_mask=None,
    head_mask=None,
    decoder_head_mask=None,
    cross_attn_head_mask=None,
):
    if input_ids is not None:
        encoder_dict = {"input_ids": input_ids}
    else:
        encoder_dict = {"input_values": input_values}

    decoder_dict = {"decoder_input_ids": decoder_input_ids} if decoder_input_ids is not None else {}

    return {**encoder_dict, **decoder_dict}


@require_torch
class EncodecModelTester:
    def __init__(
        self,
        parent,
        # `batch_size` needs to be an even number if the model has some outputs with batch dim != 0.
        batch_size=12,
        num_channels=2,
        is_training=False,
        intermediate_size=40,
        hidden_size=32,
        num_filters=8,
        num_residual_layers=1,
        upsampling_ratios=[8, 4],
        num_lstm_layers=1,
        codebook_size=64,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.intermediate_size = intermediate_size
        self.hidden_size = hidden_size
        self.num_filters = num_filters
        self.num_residual_layers = num_residual_layers
        self.upsampling_ratios = upsampling_ratios
        self.num_lstm_layers = num_lstm_layers
        self.codebook_size = codebook_size

    def prepare_config_and_inputs(self):
        input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0)
        config = self.get_config()
        inputs_dict = {"input_values": input_values}
        return config, inputs_dict

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict

    def prepare_config_and_inputs_for_model_class(self, model_class):
        config, inputs_dict = self.prepare_config_and_inputs()
        inputs_dict["audio_codes"] = ids_tensor([1, self.batch_size, 1, self.num_channels], self.codebook_size).type(
            torch.int32
        )
        inputs_dict["audio_scales"] = [None]

        return config, inputs_dict

    def prepare_config_and_inputs_for_normalization(self):
        input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0)
        config = self.get_config()
        config.normalize = True

        processor = EncodecFeatureExtractor(feature_size=config.audio_channels, sampling_rate=config.sampling_rate)
        input_values = input_values.tolist()
        inputs_dict = processor(
            input_values, sampling_rate=config.sampling_rate, padding=True, return_tensors="pt"
        ).to(torch_device)

        return config, inputs_dict

    def get_config(self):
        return EncodecConfig(
            audio_channels=self.num_channels,
            chunk_in_sec=None,
            hidden_size=self.hidden_size,
            num_filters=self.num_filters,
            num_residual_layers=self.num_residual_layers,
            upsampling_ratios=self.upsampling_ratios,
            num_lstm_layers=self.num_lstm_layers,
            codebook_size=self.codebook_size,
        )

    def create_and_check_model_forward(self, config, inputs_dict):
        model = EncodecModel(config=config).to(torch_device).eval()
        result = model(**inputs_dict)
        self.parent.assertEqual(
            result.audio_values.shape, (self.batch_size, self.num_channels, self.intermediate_size)
        )


@require_torch
class EncodecModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (EncodecModel,) if is_torch_available() else ()
    is_encoder_decoder = True
    test_pruning = False
    test_headmasking = False
    test_resize_embeddings = False
    pipeline_model_mapping = {"feature-extraction": EncodecModel} if is_torch_available() else {}

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        # model does not have attention and does not support returning hidden states
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
        if "output_attentions" in inputs_dict:
            inputs_dict.pop("output_attentions")
        if "output_hidden_states" in inputs_dict:
            inputs_dict.pop("output_hidden_states")
        return inputs_dict

    def setUp(self):
        self.model_tester = EncodecModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=EncodecConfig, hidden_size=37, common_properties=[], has_text_modality=False
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model_forward(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_forward(*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)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["input_values", "padding_mask", "bandwidth"]
            self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)

    @unittest.skip(reason="The EncodecModel is not transformers based, thus it does not have `inputs_embeds` logics")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="The EncodecModel is not transformers based, thus it does not have `inputs_embeds` logics")
    def test_model_get_set_embeddings(self):
        pass

    @unittest.skip(
        reason="The EncodecModel is not transformers based, thus it does not have the usual `attention` logic"
    )
    def test_retain_grad_hidden_states_attentions(self):
        pass

    @unittest.skip(
        reason="The EncodecModel is not transformers based, thus it does not have the usual `attention` logic"
    )
    def test_torchscript_output_attentions(self):
        pass

    @unittest.skip(
        reason="The EncodecModel is not transformers based, thus it does not have the usual `hidden_states` logic"
    )
    def test_torchscript_output_hidden_state(self):
        pass

    def _create_and_check_torchscript(self, config, inputs_dict):
        if not self.test_torchscript:
            self.skipTest(reason="test_torchscript is set to False")

        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.torchscript = True
        configs_no_init.return_dict = False
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
            inputs = self._prepare_for_class(inputs_dict, model_class)

            main_input_name = model_class.main_input_name

            try:
                main_input = inputs[main_input_name]
                model(main_input)
                traced_model = torch.jit.trace(model, main_input)
            except RuntimeError:
                self.fail("Couldn't trace module.")

            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")

                try:
                    torch.jit.save(traced_model, pt_file_name)
                except Exception:
                    self.fail("Couldn't save module.")

                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")

            model.to(torch_device)
            model.eval()

            loaded_model.to(torch_device)
            loaded_model.eval()

            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()

            non_persistent_buffers = {}
            for key in loaded_model_state_dict.keys():
                if key not in model_state_dict.keys():
                    non_persistent_buffers[key] = loaded_model_state_dict[key]

            loaded_model_state_dict = {
                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
            }

            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))

            model_buffers = list(model.buffers())
            for non_persistent_buffer in non_persistent_buffers.values():
                found_buffer = False
                for i, model_buffer in enumerate(model_buffers):
                    if torch.equal(non_persistent_buffer, model_buffer):
                        found_buffer = True
                        break

                self.assertTrue(found_buffer)
                model_buffers.pop(i)

            model_buffers = list(model.buffers())
            for non_persistent_buffer in non_persistent_buffers.values():
                found_buffer = False
                for i, model_buffer in enumerate(model_buffers):
                    if torch.equal(non_persistent_buffer, model_buffer):
                        found_buffer = True
                        break

                self.assertTrue(found_buffer)
                model_buffers.pop(i)

            models_equal = True
            for layer_name, p1 in model_state_dict.items():
                if layer_name in loaded_model_state_dict:
                    p2 = loaded_model_state_dict[layer_name]
                    if p1.data.ne(p2.data).sum() > 0:
                        models_equal = False

            self.assertTrue(models_equal)

            # Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
            # (Even with this call, there are still memory leak by ~0.04MB)
            self.clear_torch_jit_class_registry()

    @unittest.skip(
        reason="The EncodecModel is not transformers based, thus it does not have the usual `attention` logic"
    )
    def test_attention_outputs(self):
        pass

    def test_feed_forward_chunking(self):
        (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            torch.manual_seed(0)
            config = copy.deepcopy(original_config)
            config.chunk_length_s = None
            config.overlap = None
            config.sampling_rate = 10

            model = model_class(config)
            model.to(torch_device)
            model.eval()
            inputs = self._prepare_for_class(inputs_dict, model_class)
            inputs["input_values"] = inputs["input_values"].repeat(1, 1, 10)

            hidden_states_no_chunk = model(**inputs)[1]

            torch.manual_seed(0)
            config.chunk_length_s = 1
            config.overlap = 0
            config.sampling_rate = 10

            model = model_class(config)
            model.to(torch_device)
            model.eval()

            hidden_states_with_chunk = model(**inputs)[1]
            torch.testing.assert_close(hidden_states_no_chunk, hidden_states_with_chunk, rtol=1e-1, atol=1e-2)

    @unittest.skip(
        reason="The EncodecModel is not transformers based, thus it does not have the usual `hidden_states` logic"
    )
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="No support for low_cpu_mem_usage=True.")
    def test_save_load_low_cpu_mem_usage(self):
        pass

    @unittest.skip(reason="No support for low_cpu_mem_usage=True.")
    def test_save_load_low_cpu_mem_usage_checkpoints(self):
        pass

    @unittest.skip(reason="No support for low_cpu_mem_usage=True.")
    def test_save_load_low_cpu_mem_usage_no_safetensors(self):
        pass

    def test_determinism(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def check_determinism(first, second):
            # outputs are not tensors but list (since each sequence don't have the same frame_length)
            out_1 = first.cpu().numpy()
            out_2 = second.cpu().numpy()
            out_1 = out_1[~np.isnan(out_1)]
            out_2 = out_2[~np.isnan(out_2)]
            max_diff = np.amax(np.abs(out_1 - out_2))
            self.assertLessEqual(max_diff, 1e-5)

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
                second = model(**self._prepare_for_class(inputs_dict, model_class))[0]

            if isinstance(first, tuple) and isinstance(second, tuple):
                for tensor1, tensor2 in zip(first, second):
                    check_determinism(tensor1, tensor2)
            else:
                check_determinism(first, second)

    def test_model_outputs_equivalence(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def set_nan_tensor_to_zero(t):
            t[t != t] = 0
            return t

        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            with torch.no_grad():
                tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
                dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs)

                self.assertTrue(isinstance(tuple_output, tuple))
                self.assertTrue(isinstance(dict_output, dict))

                for tuple_value, dict_value in zip(tuple_output, dict_output.values()):
                    self.assertTrue(
                        torch.allclose(
                            set_nan_tensor_to_zero(tuple_value), set_nan_tensor_to_zero(dict_value), atol=1e-5
                        ),
                        msg=(
                            "Tuple and dict output are not equal. Difference:"
                            f" {torch.max(torch.abs(tuple_value - dict_value))}. Tuple has `nan`:"
                            f" {torch.isnan(tuple_value).any()} and `inf`: {torch.isinf(tuple_value)}. Dict has"
                            f" `nan`: {torch.isnan(dict_value).any()} and `inf`: {torch.isinf(dict_value)}."
                        ),
                    )

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

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs)

    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                uniform_init_parms = ["conv"]
                ignore_init = ["lstm"]
                if param.requires_grad:
                    if any(x in name for x in uniform_init_parms):
                        self.assertTrue(
                            -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    elif not any(x in name for x in ignore_init):
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    def test_identity_shortcut(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        config.use_conv_shortcut = False
        self.model_tester.create_and_check_model_forward(config, inputs_dict)

    def test_model_forward_with_normalization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_normalization()
        self.model_tester.create_and_check_model_forward(config, inputs_dict)


def normalize(arr):
    norm = np.linalg.norm(arr)
    normalized_arr = arr / norm
    return normalized_arr


def compute_rmse(arr1, arr2):
    arr1_normalized = normalize(arr1)
    arr2_normalized = normalize(arr2)
    return np.sqrt(((arr1_normalized - arr2_normalized) ** 2).mean())


@slow
@require_torch
class EncodecIntegrationTest(unittest.TestCase):
    def test_integration_24kHz(self):
        expected_rmse = {
            "1.5": 0.0025,
            "24.0": 0.0015,
        }
        expected_codesums = {
            "1.5": [371955],
            "24.0": [6659962],
        }
        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        model_id = "facebook/encodec_24khz"

        model = EncodecModel.from_pretrained(model_id).to(torch_device)
        processor = AutoProcessor.from_pretrained(model_id)

        librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
        audio_sample = librispeech_dummy[-1]["audio"]["array"]

        inputs = processor(
            raw_audio=audio_sample,
            sampling_rate=processor.sampling_rate,
            return_tensors="pt",
        ).to(torch_device)

        for bandwidth, expected_rmse in expected_rmse.items():
            with torch.no_grad():
                # use max bandwidth for best possible reconstruction
                encoder_outputs = model.encode(inputs["input_values"], bandwidth=float(bandwidth))

                audio_code_sums = [a[0].sum().item() for a in encoder_outputs[0]]

                # make sure audio encoded codes are correct
                self.assertListEqual(audio_code_sums, expected_codesums[bandwidth])

                audio_codes, scales = encoder_outputs.to_tuple()
                input_values_dec = model.decode(audio_codes, scales, inputs["padding_mask"])[0]
                input_values_enc_dec = model(
                    inputs["input_values"], inputs["padding_mask"], bandwidth=float(bandwidth)
                )[-1]

            # make sure forward and decode gives same result
            torch.testing.assert_close(input_values_dec, input_values_enc_dec, rtol=1e-3, atol=1e-3)

            # make sure shape matches
            self.assertTrue(inputs["input_values"].shape == input_values_enc_dec.shape)

            arr = inputs["input_values"][0].cpu().numpy()
            arr_enc_dec = input_values_enc_dec[0].cpu().numpy()

            # make sure audios are more or less equal
            # the RMSE of two random gaussian noise vectors with ~N(0, 1) is around 1.0
            rmse = compute_rmse(arr, arr_enc_dec)
            self.assertTrue(rmse < expected_rmse)

    def test_integration_48kHz(self):
        expected_rmse = {
            "3.0": 0.001,
            "24.0": 0.0005,
        }
        expected_codesums = {
            "3.0": [144259, 146765, 156435, 176871, 161971],
            "24.0": [1568553, 1294948, 1306190, 1464747, 1663150],
        }
        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        model_id = "facebook/encodec_48khz"

        model = EncodecModel.from_pretrained(model_id).to(torch_device)
        model = model.eval()
        processor = AutoProcessor.from_pretrained(model_id)

        librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
        audio_sample = librispeech_dummy[-1]["audio"]["array"]

        # transform mono to stereo
        audio_sample = np.array([audio_sample, audio_sample])

        inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt").to(
            torch_device
        )

        for bandwidth, expected_rmse in expected_rmse.items():
            with torch.no_grad():
                # use max bandwidth for best possible reconstruction
                encoder_outputs = model.encode(
                    inputs["input_values"], inputs["padding_mask"], bandwidth=float(bandwidth), return_dict=False
                )
                audio_code_sums = [a[0].sum().item() for a in encoder_outputs[0]]

                # make sure audio encoded codes are correct
                self.assertListEqual(audio_code_sums, expected_codesums[bandwidth])
                audio_codes, scales = encoder_outputs
                input_values_dec = model.decode(audio_codes, scales, inputs["padding_mask"])[0]
                input_values_enc_dec = model(
                    inputs["input_values"], inputs["padding_mask"], bandwidth=float(bandwidth)
                )[-1]

            # make sure forward and decode gives same result
            torch.testing.assert_close(input_values_dec, input_values_enc_dec, rtol=1e-3, atol=1e-3)

            # make sure shape matches
            self.assertTrue(inputs["input_values"].shape == input_values_enc_dec.shape)

            arr = inputs["input_values"][0].cpu().numpy()
            arr_enc_dec = input_values_enc_dec[0].cpu().numpy()

            # make sure audios are more or less equal
            # the RMSE of two random gaussian noise vectors with ~N(0, 1) is around 1.0
            rmse = compute_rmse(arr, arr_enc_dec)
            self.assertTrue(rmse < expected_rmse)

    def test_batch_48kHz(self):
        expected_rmse = {
            "3.0": 0.001,
            "24.0": 0.0005,
        }
        expected_codesums = {
            "3.0": [
                [72410, 79137, 76694, 90854, 73023, 82980, 72707, 54842],
                [85561, 81870, 76953, 48967, 79315, 85442, 81479, 107241],
            ],
            "24.0": [
                [72410, 79137, 76694, 90854, 73023, 82980, 72707, 54842],
                [85561, 81870, 76953, 48967, 79315, 85442, 81479, 107241],
            ],
        }
        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        model_id = "facebook/encodec_48khz"

        model = EncodecModel.from_pretrained(model_id).to(torch_device)
        processor = AutoProcessor.from_pretrained(model_id, chunk_length_s=1, overlap=0.01)

        librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))

        audio_samples = [
            np.array([audio_sample["array"], audio_sample["array"]])
            for audio_sample in librispeech_dummy[-2:]["audio"]
        ]

        inputs = processor(raw_audio=audio_samples, sampling_rate=processor.sampling_rate, return_tensors="pt")
        input_values = inputs["input_values"].to(torch_device)
        for bandwidth, expected_rmse in expected_rmse.items():
            with torch.no_grad():
                # use max bandwidth for best possible reconstruction
                encoder_outputs = model.encode(input_values, bandwidth=float(bandwidth), return_dict=False)
                audio_code_sums_0 = [a[0][0].sum().item() for a in encoder_outputs[0]]
                audio_code_sums_1 = [a[0][1].sum().item() for a in encoder_outputs[0]]

                # make sure audio encoded codes are correct
                self.assertListEqual(audio_code_sums_0, expected_codesums[bandwidth][0])
                self.assertListEqual(audio_code_sums_1, expected_codesums[bandwidth][1])

                audio_codes, scales = encoder_outputs
                input_values_dec = model.decode(audio_codes, scales)[0]
                input_values_enc_dec = model(input_values, bandwidth=float(bandwidth))[-1]

            # make sure forward and decode gives same result
            torch.testing.assert_close(input_values_dec, input_values_enc_dec, rtol=1e-3, atol=1e-3)

            # make sure shape matches
            self.assertTrue(input_values.shape == input_values_enc_dec.shape)

            arr = input_values[0].cpu().numpy()
            arr_enc_dec = input_values_enc_dec[0].cpu().numpy()

            # make sure audios are more or less equal
            # the RMSE of two random gaussian noise vectors with ~N(0, 1) is around 1.0
            rmse = compute_rmse(arr, arr_enc_dec)
            self.assertTrue(rmse < expected_rmse)
