# coding=utf-8
# 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
from typing import Dict, List, Tuple

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,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import 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,
        num_hidden_layers=4,
        intermediate_size=40,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.is_training = is_training

        self.num_hidden_layers = num_hidden_layers
        self.intermediate_size = intermediate_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 get_config(self):
        return EncodecConfig(audio_channels=self.num_channels, chunk_in_sec=None)

    def create_and_check_model_forward(self, config, inputs_dict):
        model = EncodecModel(config=config).to(torch_device).eval()

        input_values = inputs_dict["input_values"]
        result = model(input_values)
        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 {}
    input_name = "input_values"

    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("The EncodecModel is not transformers based, thus it does not have `inputs_embeds` logics")
    def test_inputs_embeds(self):
        pass

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

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

    @unittest.skip("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:
            return

        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("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)[0]

            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)[0]
            self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))

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

                def recursive_check(tuple_object, dict_object):
                    if isinstance(tuple_object, (List, Tuple)):
                        for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif isinstance(tuple_object, Dict):
                        for tuple_iterable_value, dict_iterable_value in zip(
                            tuple_object.values(), dict_object.values()
                        ):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif tuple_object is None:
                        return
                    else:
                        self.assertTrue(
                            torch.allclose(
                                set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
                            ),
                            msg=(
                                "Tuple and dict output are not equal. Difference:"
                                f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
                                f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
                                f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
                            ),
                        )

                recursive_check(tuple_output, dict_output)

        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",
                        )

    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
    def test_model_is_small(self):
        pass

    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 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 bandwith for best possible reconstruction
                encoder_outputs = model.encode(inputs["input_values"], bandwidth=float(bandwidth))

                audio_code_sums = [a[0].sum().cpu().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
            self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, 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 bandwith 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().cpu().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
            self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, 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 bandwith for best possible reconstruction
                encoder_outputs = model.encode(input_values, bandwidth=float(bandwidth), return_dict=False)
                audio_code_sums_0 = [a[0][0].sum().cpu().item() for a in encoder_outputs[0]]
                audio_code_sums_1 = [a[0][1].sum().cpu().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
            self.assertTrue(torch.allclose(input_values_dec, input_values_enc_dec, 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)
