#!/usr/bin/env python3
"""Calculates the Frechet Inception Distance (FID) and The Kernel Inception Distance (KID) to evalulate GANs
##usage
cd /auto/data2/odalmaz/CVproject/FID_KID/
python3 fid_kid.py --fake_path /auto/data2/odalmaz/CVproject/pytorch-CycleGAN-and-pix2pix/results/cityscapes_boundary/test_latest/images/fake_B/ --real_path /auto/data2/odalmaz/CVproject/pytorch-CycleGAN-and-pix2pix/results/cityscapes_boundary/test_latest/images/real_B/

The FID and KID is calculated by assuming that X_1 and X_2 are the activations of
the pool_3 layer of the inception net for generated samples and real world
samples respectively.

Code apapted from https://github.com/bioinf-jku/TTUR and  https://github.com/mbinkowski/MMD-GAN
"""
import os
import pathlib
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter

import numpy as np
import torch
from scipy import linalg
from scipy.misc import imread
from torch.nn.functional import adaptive_avg_pool2d
from sklearn.metrics.pairwise import polynomial_kernel

import os.path, sys, tarfile

from PIL import Image

try:
    from tqdm import tqdm
except ImportError:
    # If not tqdm is not available, provide a mock version of it
    def tqdm(x): return x

# try:
#     from .inception import InceptionV3
# except ImportError:
from inception import InceptionV3

parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--fake_path', type=str,default='/auto/data2/odalmaz/CVproject/CEN/image2image_translation/ckpt/pix2pix_boundary/results/best/fake2/',
                    help=('Path to the generated images or '
                          'to .npz statistic files'))
parser.add_argument('--real_path', type=str,default='/auto/data2/odalmaz/CVproject/CEN/image2image_translation/ckpt/pix2pix_boundary/results/real/',
                    help=('Path to the generated images or '
                          'to .npz statistic files'))
# parser.add_argument('--batch-size', type=int, default=50,
#                     help='Batch size to use')
parser.add_argument('--dims', type=int, default=2048,
                    choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),
                    help=('Dimensionality of Inception features to use. '
                          'By default, uses pool3 features'))
# parser.add_argument('-c', '--gpu', default='', type=str,
#                     help='GPU to use (leave blank for CPU only)')
parser.add_argument('--splits', type=int, default=10)
parser.add_argument('--split-method', choices=['openai', 'bootstrap'],
                    default='bootstrap')
parser.add_argument('--mmd-degree', type=int, default=3)
parser.add_argument('--mmd-gamma', type=float, default=None)
parser.add_argument('--mmd-coef0', type=float, default=1)

parser.add_argument('--mmd-subsets', type=int, default=50)
parser.add_argument('--mmd-subset-size', type=int, default=1000)
parser.add_argument('--mmd-var', action='store_true', default=False)

splits = 10
split_method = 'bootstrap'
mmd_degree = 3
mmd_gamma = None
mmd_coef0 = 1
mmd_subsets = 50
mmd_subset_size = 1000
mmd_var = False

def get_activations(files, model, batch_size=50, dims=2048,
                    cuda=False, verbose=False):
    """Calculates the activations of the pool_3 layer for all images.
    Params:
    -- files       : List of image files paths
    -- model       : Instance of inception model
    -- batch_size  : Batch size of images for the model to process at once.
                     Make sure that the number of samples is a multiple of
                     the batch size, otherwise some samples are ignored. This
                     behavior is retained to match the original FID score
                     implementation.
    -- dims        : Dimensionality of features returned by Inception
    -- cuda        : If set to True, use GPU
    -- verbose     : If set to True and parameter out_step is given, the number
                     of calculated batches is reported.
    Returns:
    -- A numpy array of dimension (num images, dims) that contains the
       activations of the given tensor when feeding inception with the
       query tensor.
    """
    model.eval()

    if batch_size > len(files):
        print(('Warning: batch size is bigger than the data size. '
               'Setting batch size to data size'))
        batch_size = len(files)
    print(batch_size)
    pred_arr = np.empty((len(files), dims))
    print(batch_size)
    for i in range(0, len(files), batch_size):
        if verbose:
            print('\rPropagating batch %d/%d' % (i + 1, n_batches),
                  end='', flush=True)
        start = i
        end = i + batch_size

        # img = []

        # for f in files[start:end]:
        #     print(f)
        #     ii = imread(str(f)).astype(np.float32)
        #     print(ii.shape)

        images = np.array([imread(str(f)).astype(np.float32)
                    for f in files[start:end]])

        # Reshape to (n_images, 3, height, width)
        images = images.transpose((0, 3, 1, 2))
        images /= 255

        batch = torch.from_numpy(images).type(torch.FloatTensor)[:, :3]
        if cuda:
            batch = batch.cuda()

        pred = model(batch)[0]

        # If model output is not scalar, apply global spatial average pooling.
        # This happens if you choose a dimensionality not equal 2048.
        if pred.size(2) != 1 or pred.size(3) != 1:
            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))

        pred_arr[start:end] = pred.cpu().data.numpy().reshape(pred.size(0), -1)

    if verbose:
        print(' done')

    return pred_arr


def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
    """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 calculate_activation_statistics(files, model, batch_size=50,
                                    dims=2048, cuda=False, verbose=False):
    """Calculation of the statistics used by the FID.
    Params:
    -- files       : List of image files paths
    -- model       : Instance of inception model
    -- batch_size  : The images numpy array is split into batches with
                     batch size batch_size. A reasonable batch size
                     depends on the hardware.
    -- dims        : Dimensionality of features returned by Inception
    -- cuda        : If set to True, use GPU
    -- verbose     : If set to True and parameter out_step is given, the
                     number of calculated batches is reported.
    Returns:
    -- mu    : The mean over samples of the activations of the pool_3 layer of
               the inception model.
    -- sigma : The covariance matrix of the activations of the pool_3 layer of
               the inception model.
    """
    act = get_activations(files, model, batch_size, dims, cuda, verbose)
    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)
    return act, mu, sigma


def _compute_statistics_of_path(path, model, batch_size, dims, cuda):
    if path.endswith('.npz'):
        f = np.load(path)
        m, s = f['mu'][:], f['sigma'][:]
        f.close()
        act = m
    else:
        path = pathlib.Path(path)
        files = list(path.glob('*.jpg')) + list(path.glob('*.png'))
        act, m, s = calculate_activation_statistics(files, model, batch_size,
                                               dims, cuda)

    return act,m, s


def polynomial_mmd_averages(codes_g, codes_r, n_subsets=50, subset_size=1000,
                            ret_var=True, output=sys.stdout, **kernel_args):
    m = min(codes_g.shape[0], codes_r.shape[0])
    mmds = np.zeros(n_subsets)
    if ret_var:
        vars = np.zeros(n_subsets)
    choice = np.random.choice
    if subset_size > len(codes_g):
        # print(('Warning: subset_size is bigger than len(codes_g). '))
        subset_size = len(codes_g)
    if subset_size > len(codes_r):
        # print(('Warning: subset_size is bigger than len(codes_g). '))
        subset_size = len(codes_r)
    # with tqdm(range(n_subsets), desc='MMD', file=output) as bar:
    #     for i in bar:
    for i in range(n_subsets):
        g = codes_g[choice(len(codes_g), subset_size, replace=False)]
        r = codes_r[choice(len(codes_r), subset_size, replace=False)]
        # print(subset_size)
        # print(g.shape)
        # print(r.shape)
        o = polynomial_mmd(g, r, **kernel_args, var_at_m=m, ret_var=ret_var)
        if ret_var:
            mmds[i], vars[i] = o
        else:
            mmds[i] = o
            # bar.set_postfix({'mean': mmds[:i+1].mean()})
    return (mmds, vars) if ret_var else mmds


def polynomial_mmd(codes_g, codes_r, degree=3, gamma=None, coef0=1,
                   var_at_m=None, ret_var=True):
    # use  k(x, y) = (gamma <x, y> + coef0)^degree
    # default gamma is 1 / dim
    X = codes_g
    Y = codes_r

    K_XX = polynomial_kernel(X, degree=degree, gamma=gamma, coef0=coef0)
    K_YY = polynomial_kernel(Y, degree=degree, gamma=gamma, coef0=coef0)
    K_XY = polynomial_kernel(X, Y, degree=degree, gamma=gamma, coef0=coef0)

    return _mmd2_and_variance(K_XX, K_XY, K_YY,
                              var_at_m=var_at_m, ret_var=ret_var)


def _sqn(arr):
    flat = np.ravel(arr)
    return flat.dot(flat)


def _mmd2_and_variance(K_XX, K_XY, K_YY, unit_diagonal=False,
                       mmd_est='unbiased', block_size=1024,
                       var_at_m=None, ret_var=True):
    # based on
    # https://github.com/dougalsutherland/opt-mmd/blob/master/two_sample/mmd.py
    # but changed to not compute the full kernel matrix at once
    m = K_XX.shape[0]
    assert K_XX.shape == (m, m)
    assert K_XY.shape == (m, m)
    assert K_YY.shape == (m, m)
    if var_at_m is None:
        var_at_m = m

    # Get the various sums of kernels that we'll use
    # Kts drop the diagonal, but we don't need to compute them explicitly
    if unit_diagonal:
        diag_X = diag_Y = 1
        sum_diag_X = sum_diag_Y = m
        sum_diag2_X = sum_diag2_Y = m
    else:
        diag_X = np.diagonal(K_XX)
        diag_Y = np.diagonal(K_YY)

        sum_diag_X = diag_X.sum()
        sum_diag_Y = diag_Y.sum()

        sum_diag2_X = _sqn(diag_X)
        sum_diag2_Y = _sqn(diag_Y)

    Kt_XX_sums = K_XX.sum(axis=1) - diag_X
    Kt_YY_sums = K_YY.sum(axis=1) - diag_Y
    K_XY_sums_0 = K_XY.sum(axis=0)
    K_XY_sums_1 = K_XY.sum(axis=1)

    Kt_XX_sum = Kt_XX_sums.sum()
    Kt_YY_sum = Kt_YY_sums.sum()
    K_XY_sum = K_XY_sums_0.sum()

    if mmd_est == 'biased':
        mmd2 = ((Kt_XX_sum + sum_diag_X) / (m * m)
                + (Kt_YY_sum + sum_diag_Y) / (m * m)
                - 2 * K_XY_sum / (m * m))
    else:
        assert mmd_est in {'unbiased', 'u-statistic'}
        mmd2 = (Kt_XX_sum + Kt_YY_sum) / (m * (m-1))
        if mmd_est == 'unbiased':
            mmd2 -= 2 * K_XY_sum / (m * m)
        else:
            mmd2 -= 2 * (K_XY_sum - np.trace(K_XY)) / (m * (m-1))

    if not ret_var:
        return mmd2

    Kt_XX_2_sum = _sqn(K_XX) - sum_diag2_X
    Kt_YY_2_sum = _sqn(K_YY) - sum_diag2_Y
    K_XY_2_sum = _sqn(K_XY)

    dot_XX_XY = Kt_XX_sums.dot(K_XY_sums_1)
    dot_YY_YX = Kt_YY_sums.dot(K_XY_sums_0)

    m1 = m - 1
    m2 = m - 2
    zeta1_est = (
        1 / (m * m1 * m2) * (
            _sqn(Kt_XX_sums) - Kt_XX_2_sum + _sqn(Kt_YY_sums) - Kt_YY_2_sum)
        - 1 / (m * m1)**2 * (Kt_XX_sum**2 + Kt_YY_sum**2)
        + 1 / (m * m * m1) * (
            _sqn(K_XY_sums_1) + _sqn(K_XY_sums_0) - 2 * K_XY_2_sum)
        - 2 / m**4 * K_XY_sum**2
        - 2 / (m * m * m1) * (dot_XX_XY + dot_YY_YX)
        + 2 / (m**3 * m1) * (Kt_XX_sum + Kt_YY_sum) * K_XY_sum
    )
    zeta2_est = (
        1 / (m * m1) * (Kt_XX_2_sum + Kt_YY_2_sum)
        - 1 / (m * m1)**2 * (Kt_XX_sum**2 + Kt_YY_sum**2)
        + 2 / (m * m) * K_XY_2_sum
        - 2 / m**4 * K_XY_sum**2
        - 4 / (m * m * m1) * (dot_XX_XY + dot_YY_YX)
        + 4 / (m**3 * m1) * (Kt_XX_sum + Kt_YY_sum) * K_XY_sum
    )
    var_est = (4 * (var_at_m - 2) / (var_at_m * (var_at_m - 1)) * zeta1_est
               + 2 / (var_at_m * (var_at_m - 1)) * zeta2_est)

    return mmd2, var_est


def calculate_given_paths(paths, batch_size, cuda, dims):
    """Calculates the FID of two paths"""
    for p in paths:
        if not os.path.exists(p):
            raise RuntimeError('Invalid path: %s' % p)

    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]

    model = InceptionV3([block_idx])
    if cuda:
        model.cuda()

    act1,m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size,
                                         dims, cuda)
    act2,m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size,
                                         dims, cuda)
    fid_value = calculate_frechet_distance(m1, s1, m2, s2)

    ret = polynomial_mmd_averages(
            act1, act2, degree=mmd_degree, gamma=mmd_gamma,
            coef0=mmd_coef0, ret_var=mmd_var,
            n_subsets=mmd_subsets, subset_size=mmd_subset_size)
    if mmd_var:
        mmd2s, vars = ret
    else:
        mmd2s = ret
    return fid_value, mmd2s.mean() * 100

    print('FID: ', fid_value)
    print("mean MMD^2 estimate:", mmd2s.mean()*100)
    print("std MMD^2 estimate:", mmd2s.std())
    # print("MMD^2 estimates:", mmd2s, sep='\n')
    print()
    if mmd_var:
        print("mean Var[MMD^2] estimate:", vars.mean()*100)
        print("std Var[MMD^2] estimate:", vars.std())
        # print("Var[MMD^2] estimates:", vars, sep='\n')
        print()


if __name__ == '__main__':
    args = parser.parse_args()
    # os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
    paths = [os.path.join(args.fake_path ), os.path.join(args.real_path)]
    fid, kid = calculate_given_paths(paths, batch_size=50, cuda=True, dims=2048)
    print('fid: %.3f ,  kid: %.3f'  % (fid, kid))
    # fids[l], kids[l] = fid, kid
    # calculate_given_paths(args.path,
    #                         args.batch_size,
    #                         args.gpu != '',
    #                         args.dims)
