"""
Calculate Frechet Audio Distance betweeen two audio directories.

Frechet distance implementation adapted from: https://github.com/mseitzer/pytorch-fid

VGGish adapted from: https://github.com/harritaylor/torchvggish
"""
import os
import numpy as np
import torch
from torch import nn
from scipy import linalg
from tqdm import tqdm
import soundfile as sf
import resampy
from multiprocessing.dummy import Pool as ThreadPool

SAMPLE_RATE = 16000


def load_audio_task(fname):
    try:
        wav_data, sr = sf.read(fname, dtype="int16")
    except Exception as e:
        print(e)
        wav_data = np.zeros(160000)
        sr = 16000
    assert wav_data.dtype == np.int16, "Bad sample type: %r" % wav_data.dtype
    wav_data = wav_data / 32768.0  # Convert to [-1.0, +1.0]

    # Convert to mono
    if len(wav_data.shape) > 1:
        wav_data = np.mean(wav_data, axis=1)

    if sr != SAMPLE_RATE:
        if SAMPLE_RATE == 16000 and sr == 32000:
            wav_data = wav_data[::2]
        else:
            wav_data = resampy.resample(wav_data, sr, SAMPLE_RATE)

    return wav_data, SAMPLE_RATE


class FrechetAudioDistance:
    def __init__(
        self, use_pca=False, use_activation=False, verbose=False, audio_load_worker=8
    ):
        self.__get_model(use_pca=use_pca, use_activation=use_activation)
        self.verbose = verbose
        self.audio_load_worker = audio_load_worker

    def __get_model(self, use_pca=False, use_activation=False):
        """
        Params:
        -- x   : Either
            (i) a string which is the directory of a set of audio files, or
            (ii) a np.ndarray of shape (num_samples, sample_length)
        """
        self.model = torch.hub.load("harritaylor/torchvggish", "vggish")
        if not use_pca:
            self.model.postprocess = False
        if not use_activation:
            self.model.embeddings = nn.Sequential(
                *list(self.model.embeddings.children())[:-1]
            )
        self.model.eval()

    def get_embeddings(self, x, sr=16000, limit_num=None):
        """
        Get embeddings using VGGish model.
        Params:
        -- x    : Either
            (i) a string which is the directory of a set of audio files, or
            (ii) a list of np.ndarray audio samples
        -- sr   : Sampling rate, if x is a list of audio samples. Default value is 16000.
        """
        embd_lst = []
        if isinstance(x, list):
            try:
                for audio, sr in tqdm(x, disable=(not self.verbose)):
                    embd = self.model.forward(audio, sr)
                    if self.model.device == torch.device("cuda"):
                        embd = embd.cpu()
                    embd = embd.detach().numpy()
                    embd_lst.append(embd)
            except Exception as e:
                print(
                    "[Frechet Audio Distance] get_embeddings throw an exception: {}".format(
                        str(e)
                    )
                )
        elif isinstance(x, str):
            if self.verbose:
                print("Calculating the embedding of the audio files inside %s" % x)
            try:
                for i, fname in tqdm(
                    enumerate(os.listdir(x)), disable=(not self.verbose)
                ):
                    if fname.endswith(".wav"):
                        if limit_num is not None and i > limit_num:
                            break
                        try:
                            audio, sr = load_audio_task(os.path.join(x, fname))
                            embd = self.model.forward(audio, sr)
                            if self.model.device == torch.device("cuda"):
                                embd = embd.cpu()
                            embd = embd.detach().numpy()
                            embd_lst.append(embd)
                        except Exception as e:
                            print(e, fname)
                            continue
            except Exception as e:
                print(
                    "[Frechet Audio Distance] get_embeddings throw an exception: {}".format(
                        str(e)
                    )
                )
        else:
            raise AttributeError

        return np.concatenate(embd_lst, axis=0)

    def calculate_embd_statistics(self, embd_lst):
        if isinstance(embd_lst, list):
            embd_lst = np.array(embd_lst)
        mu = np.mean(embd_lst, axis=0)
        sigma = np.cov(embd_lst, rowvar=False)
        return mu, sigma

    def calculate_frechet_distance(self, mu1, sigma1, mu2, sigma2, eps=1e-6):
        """
        Adapted from: https://github.com/mseitzer/pytorch-fid/blob/master/src/pytorch_fid/fid_score.py

        Numpy implementation of the Frechet Distance.
        The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
        and X_2 ~ N(mu_2, C_2) is
                d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
        Stable version by Dougal J. Sutherland.
        Params:
        -- mu1   : Numpy array containing the activations of a layer of the
                inception net (like returned by the function 'get_predictions')
                for generated samples.
        -- mu2   : The sample mean over activations, precalculated on an
                representative data set.
        -- sigma1: The covariance matrix over activations for generated samples.
        -- sigma2: The covariance matrix over activations, precalculated on an
                representative data set.
        Returns:
        --   : The Frechet Distance.
        """

        mu1 = np.atleast_1d(mu1)
        mu2 = np.atleast_1d(mu2)

        sigma1 = np.atleast_2d(sigma1)
        sigma2 = np.atleast_2d(sigma2)

        assert (
            mu1.shape == mu2.shape
        ), "Training and test mean vectors have different lengths"
        assert (
            sigma1.shape == sigma2.shape
        ), "Training and test covariances have different dimensions"

        diff = mu1 - mu2

        # Product might be almost singular
        covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
        if not np.isfinite(covmean).all():
            msg = (
                "fid calculation produces singular product; "
                "adding %s to diagonal of cov estimates"
            ) % eps
            print(msg)
            offset = np.eye(sigma1.shape[0]) * eps
            covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))

        # Numerical error might give slight imaginary component
        if np.iscomplexobj(covmean):
            if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
                m = np.max(np.abs(covmean.imag))
                raise ValueError("Imaginary component {}".format(m))
            covmean = covmean.real

        tr_covmean = np.trace(covmean)

        return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean

    def __load_audio_files(self, dir):
        task_results = []

        pool = ThreadPool(self.audio_load_worker)
        pbar = tqdm(total=len(os.listdir(dir)), disable=(not self.verbose))

        def update(*a):
            pbar.update()

        if self.verbose:
            print("[Frechet Audio Distance] Loading audio from {}...".format(dir))
        for fname in os.listdir(dir):
            res = pool.apply_async(
                load_audio_task, args=(os.path.join(dir, fname),), callback=update
            )

            task_results.append(res)
        pool.close()
        pool.join()

        return [k.get() for k in task_results]

    def score(self, background_dir, eval_dir, store_embds=False, limit_num=None):
        # background_dir: generated samples
        # eval_dir: groundtruth samples
        try:
            # audio_background = self.__load_audio_files(background_dir)
            # audio_eval = self.__load_audio_files(eval_dir)
            embds_background = self.get_embeddings(background_dir, limit_num=limit_num)
            embds_eval = self.get_embeddings(eval_dir, limit_num=limit_num)

            if store_embds:
                np.save("embds_background.npy", embds_background)
                np.save("embds_eval.npy", embds_eval)

            if len(embds_background) == 0:
                print(
                    "[Frechet Audio Distance] background set dir is empty, exitting..."
                )
                return -1

            if len(embds_eval) == 0:
                print("[Frechet Audio Distance] eval set dir is empty, exitting...")
                return -1

            mu_background, sigma_background = self.calculate_embd_statistics(
                embds_background
            )
            mu_eval, sigma_eval = self.calculate_embd_statistics(embds_eval)

            fad_score = self.calculate_frechet_distance(
                mu_background, sigma_background, mu_eval, sigma_eval
            )

            return {"frechet_audio_distance": fad_score}

        except Exception as e:
            print("[Frechet Audio Distance] exception thrown, {}".format(str(e)))
            return -1