import os
import random
from tqdm.auto import tqdm
from glob import glob
import torch
import numpy as np
from PIL import Image
from scipy import linalg
import zipfile
from torch.hub import get_dir
from .utils import *
from .features import build_feature_extractor, get_reference_statistics
from .resize import *


"""
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 Danica 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.
"""


def frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
    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


"""
Compute the KID score given the sets of features
"""


def kernel_distance(feats1, feats2, num_subsets=100, max_subset_size=1000):
    n = feats1.shape[1]
    m = min(min(feats1.shape[0], feats2.shape[0]), max_subset_size)
    t = 0
    for _subset_idx in range(num_subsets):
        x = feats2[np.random.choice(feats2.shape[0], m, replace=False)]
        y = feats1[np.random.choice(feats1.shape[0], m, replace=False)]
        a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3
        b = (x @ y.T / n + 1) ** 3
        t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m
    kid = t / num_subsets / m
    return float(kid)


"""
Compute the inception features for a batch of images
"""


def get_batch_features(batch, model, device):
    with torch.no_grad():
        feat = model(batch.to(device))
    return feat.detach().cpu().numpy()


"""
Compute the inception features for a list of files
"""


def get_files_features(
    l_files,
    model=None,
    num_workers=12,
    batch_size=128,
    device=torch.device("cuda"),
    mode="clean",
    custom_fn_resize=None,
    description="",
    fdir=None,
    verbose=True,
    custom_image_tranform=None,
):
    # wrap the images in a dataloader for parallelizing the resize operation
    dataset = ResizeDataset(l_files, fdir=fdir, mode=mode)
    if custom_image_tranform is not None:
        dataset.custom_image_tranform = custom_image_tranform
    if custom_fn_resize is not None:
        dataset.fn_resize = custom_fn_resize

    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=False,
        drop_last=False,
        num_workers=num_workers,
    )

    # collect all inception features
    l_feats = []
    if verbose:
        pbar = tqdm(dataloader, desc=description)
    else:
        pbar = dataloader

    for batch in pbar:
        l_feats.append(get_batch_features(batch, model, device))
    np_feats = np.concatenate(l_feats)
    return np_feats


"""
Compute the inception features for a folder of image files
"""


def get_folder_features(
    fdir,
    model=None,
    num_workers=12,
    num=None,
    shuffle=False,
    seed=0,
    batch_size=128,
    device=torch.device("cuda"),
    mode="clean",
    custom_fn_resize=None,
    description="",
    verbose=True,
    custom_image_tranform=None,
):
    # get all relevant files in the dataset
    if ".zip" in fdir:
        files = list(set(zipfile.ZipFile(fdir).namelist()))
        # remove the non-image files inside the zip
        files = [x for x in files if os.path.splitext(x)[1].lower()[1:] in EXTENSIONS]
    else:
        files = sorted(
            [
                file
                for ext in EXTENSIONS
                for file in glob(os.path.join(fdir, f"**/*.{ext}"), recursive=True)
            ]
        )
    # use a subset number of files if needed
    if num is not None:
        if shuffle:
            random.seed(seed)
            random.shuffle(files)
        files = files[:num]
    np_feats = get_files_features(
        files,
        model,
        num_workers=num_workers,
        batch_size=batch_size,
        device=device,
        mode=mode,
        custom_fn_resize=custom_fn_resize,
        custom_image_tranform=custom_image_tranform,
        description=description,
        fdir=fdir,
        verbose=verbose,
    )
    return np_feats


"""
Compute the FID score given the inception features stack
"""


def fid_from_feats(feats1, feats2):
    mu1, sig1 = np.mean(feats1, axis=0), np.cov(feats1, rowvar=False)
    mu2, sig2 = np.mean(feats2, axis=0), np.cov(feats2, rowvar=False)
    return frechet_distance(mu1, sig1, mu2, sig2)


"""
Computes the FID score for a folder of images for a specific dataset
and a specific resolution
"""


def fid_folder(
    fdir,
    dataset_name,
    dataset_res,
    dataset_split,
    model=None,
    mode="clean",
    model_name="inception_v3",
    num_workers=12,
    batch_size=128,
    device=torch.device("cuda"),
    verbose=True,
    custom_image_tranform=None,
    custom_fn_resize=None,
):
    # Load reference FID statistics (download if needed)
    ref_mu, ref_sigma = get_reference_statistics(
        dataset_name,
        dataset_res,
        mode=mode,
        model_name=model_name,
        seed=0,
        split=dataset_split,
    )
    fbname = os.path.basename(fdir)
    # get all inception features for folder images
    np_feats = get_folder_features(
        fdir,
        model,
        num_workers=num_workers,
        batch_size=batch_size,
        device=device,
        mode=mode,
        description=f"FID {fbname} : ",
        verbose=verbose,
        custom_image_tranform=custom_image_tranform,
        custom_fn_resize=custom_fn_resize,
    )
    mu = np.mean(np_feats, axis=0)
    sigma = np.cov(np_feats, rowvar=False)
    fid = frechet_distance(mu, sigma, ref_mu, ref_sigma)
    return fid


"""
Compute the FID stats from a generator model
"""


def get_model_features(
    G,
    model,
    mode="clean",
    z_dim=512,
    num_gen=50_000,
    batch_size=128,
    device=torch.device("cuda"),
    desc="FID model: ",
    verbose=True,
    return_z=False,
    custom_image_tranform=None,
    custom_fn_resize=None,
):
    if custom_fn_resize is None:
        fn_resize = build_resizer(mode)
    else:
        fn_resize = custom_fn_resize

    # Generate test features
    num_iters = int(np.ceil(num_gen / batch_size))
    l_feats = []
    latents = []
    if verbose:
        pbar = tqdm(range(num_iters), desc=desc)
    else:
        pbar = range(num_iters)
    for idx in pbar:
        with torch.no_grad():
            z_batch = torch.randn((batch_size, z_dim)).to(device)
            if return_z:
                latents.append(z_batch)
            # generated image is in range [0,255]
            img_batch = G(z_batch)
            # split into individual batches for resizing if needed
            if mode != "legacy_tensorflow":
                l_resized_batch = []
                for idx in range(batch_size):
                    curr_img = img_batch[idx]
                    img_np = curr_img.cpu().numpy().transpose((1, 2, 0))
                    if custom_image_tranform is not None:
                        img_np = custom_image_tranform(img_np)
                    img_resize = fn_resize(img_np)
                    l_resized_batch.append(
                        torch.tensor(img_resize.transpose((2, 0, 1))).unsqueeze(0)
                    )
                resized_batch = torch.cat(l_resized_batch, dim=0)
            else:
                resized_batch = img_batch
            feat = get_batch_features(resized_batch, model, device)
        l_feats.append(feat)
    np_feats = np.concatenate(l_feats)[:num_gen]
    if return_z:
        latents = torch.cat(latents, 0)
        return np_feats, latents
    return np_feats


"""
Computes the FID score for a generator model for a specific dataset
and a specific resolution
"""


def fid_model(
    G,
    dataset_name,
    dataset_res,
    dataset_split,
    model=None,
    model_name="inception_v3",
    z_dim=512,
    num_gen=50_000,
    mode="clean",
    num_workers=0,
    batch_size=128,
    device=torch.device("cuda"),
    verbose=True,
    custom_image_tranform=None,
    custom_fn_resize=None,
):
    # Load reference FID statistics (download if needed)
    ref_mu, ref_sigma = get_reference_statistics(
        dataset_name,
        dataset_res,
        mode=mode,
        model_name=model_name,
        seed=0,
        split=dataset_split,
    )
    # Generate features of images generated by the model
    np_feats = get_model_features(
        G,
        model,
        mode=mode,
        z_dim=z_dim,
        num_gen=num_gen,
        batch_size=batch_size,
        device=device,
        verbose=verbose,
        custom_image_tranform=custom_image_tranform,
        custom_fn_resize=custom_fn_resize,
    )
    mu = np.mean(np_feats, axis=0)
    sigma = np.cov(np_feats, rowvar=False)
    fid = frechet_distance(mu, sigma, ref_mu, ref_sigma)
    return fid


"""
Computes the FID score between the two given folders
"""


def compare_folders(
    fdir1,
    fdir2,
    feat_model,
    mode,
    num_workers=0,
    batch_size=8,
    device=torch.device("cuda"),
    verbose=True,
    custom_image_tranform=None,
    custom_fn_resize=None,
):
    # get all inception features for the first folder
    fbname1 = os.path.basename(fdir1)
    np_feats1 = get_folder_features(
        fdir1,
        feat_model,
        num_workers=num_workers,
        batch_size=batch_size,
        device=device,
        mode=mode,
        description=f"FID {fbname1} : ",
        verbose=verbose,
        custom_image_tranform=custom_image_tranform,
        custom_fn_resize=custom_fn_resize,
    )
    mu1 = np.mean(np_feats1, axis=0)
    sigma1 = np.cov(np_feats1, rowvar=False)
    # get all inception features for the second folder
    fbname2 = os.path.basename(fdir2)
    np_feats2 = get_folder_features(
        fdir2,
        feat_model,
        num_workers=num_workers,
        batch_size=batch_size,
        device=device,
        mode=mode,
        description=f"FID {fbname2} : ",
        verbose=verbose,
        custom_image_tranform=custom_image_tranform,
        custom_fn_resize=custom_fn_resize,
    )
    mu2 = np.mean(np_feats2, axis=0)
    sigma2 = np.cov(np_feats2, rowvar=False)
    fid = frechet_distance(mu1, sigma1, mu2, sigma2)
    return fid


"""
Test if a custom statistic exists
"""


def test_stats_exists(name, mode, model_name="inception_v3", metric="FID"):
    stats_folder = os.path.join(get_dir(), "fid_stats")
    split, res = "custom", "na"
    if model_name == "inception_v3":
        model_modifier = ""
    else:
        model_modifier = "_" + model_name
    if metric == "FID":
        fname = f"{name}_{mode}{model_modifier}_{split}_{res}.npz"
    elif metric == "KID":
        fname = f"{name}_{mode}{model_modifier}_{split}_{res}_kid.npz"
    fpath = os.path.join(stats_folder, fname)
    return os.path.exists(fpath)


"""
Remove the custom FID features from the stats folder
"""


def remove_custom_stats(name, mode="clean", model_name="inception_v3"):
    stats_folder = os.path.join(get_dir(), "fid_stats")
    # remove the FID stats
    split, res = "custom", "na"
    if model_name == "inception_v3":
        model_modifier = ""
    else:
        model_modifier = "_" + model_name
    outf = os.path.join(
        stats_folder, f"{name}_{mode}{model_modifier}_{split}_{res}.npz"
    )
    if not os.path.exists(outf):
        msg = f"The stats file {name} does not exist."
        raise Exception(msg)
    os.remove(outf)
    # remove the KID stats
    outf = os.path.join(
        stats_folder, f"{name}_{mode}{model_modifier}_{split}_{res}_kid.npz"
    )
    if not os.path.exists(outf):
        msg = f"The stats file {name} does not exist."
        raise Exception(msg)
    os.remove(outf)


"""
Cache a custom dataset statistics file
"""


def make_custom_stats(
    name,
    fdir,
    num=None,
    mode="clean",
    model_name="inception_v3",
    num_workers=0,
    batch_size=64,
    device=torch.device("cuda"),
    verbose=True,
):
    stats_folder = os.path.join(get_dir(), "fid_stats")
    os.makedirs(stats_folder, exist_ok=True)
    split, res = "custom", "na"
    if model_name == "inception_v3":
        model_modifier = ""
    else:
        model_modifier = "_" + model_name
    outf = os.path.join(
        stats_folder, f"{name}_{mode}{model_modifier}_{split}_{res}.npz"
    )
    # if the custom stat file already exists
    if os.path.exists(outf):
        msg = f"The statistics file {name} already exists. "
        msg += "Use remove_custom_stats function to delete it first."
        raise Exception(msg)
    if model_name == "inception_v3":
        feat_model = build_feature_extractor(mode, device)
        custom_fn_resize = None
        custom_image_tranform = None
    elif model_name == "clip_vit_b_32":
        from .clip_features import CLIP_fx, img_preprocess_clip

        clip_fx = CLIP_fx("ViT-B/32")
        feat_model = clip_fx
        custom_fn_resize = img_preprocess_clip
        custom_image_tranform = None
    else:
        raise ValueError(f"The entered model name - {model_name} was not recognized.")

    # get all inception features for folder images
    np_feats = get_folder_features(
        fdir,
        feat_model,
        num_workers=num_workers,
        num=num,
        batch_size=batch_size,
        device=device,
        verbose=verbose,
        mode=mode,
        description=f"custom stats: {os.path.basename(fdir)} : ",
        custom_image_tranform=custom_image_tranform,
        custom_fn_resize=custom_fn_resize,
    )

    mu = np.mean(np_feats, axis=0)
    sigma = np.cov(np_feats, rowvar=False)
    # print(f"saving custom FID stats to {outf}")
    np.savez_compressed(outf, mu=mu, sigma=sigma)

    # KID stats
    outf = os.path.join(
        stats_folder, f"{name}_{mode}{model_modifier}_{split}_{res}_kid.npz"
    )
    # print(f"saving custom KID stats to {outf}")
    np.savez_compressed(outf, feats=np_feats)


def compute_kid(
    fdir1=None,
    fdir2=None,
    gen=None,
    mode="clean",
    num_workers=12,
    batch_size=32,
    device=torch.device("cuda"),
    dataset_name="FFHQ",
    dataset_res=1024,
    dataset_split="train",
    num_gen=50_000,
    z_dim=512,
    verbose=True,
    use_dataparallel=True,
):
    # build the feature extractor based on the mode
    feat_model = build_feature_extractor(
        mode, device, use_dataparallel=use_dataparallel
    )

    # if both dirs are specified, compute KID between folders
    if fdir1 is not None and fdir2 is not None:
        # get all inception features for the first folder
        fbname1 = os.path.basename(fdir1)
        np_feats1 = get_folder_features(
            fdir1,
            feat_model,
            num_workers=num_workers,
            batch_size=batch_size,
            device=device,
            mode=mode,
            description=f"KID {fbname1} : ",
            verbose=verbose,
        )
        # get all inception features for the second folder
        fbname2 = os.path.basename(fdir2)
        np_feats2 = get_folder_features(
            fdir2,
            feat_model,
            num_workers=num_workers,
            batch_size=batch_size,
            device=device,
            mode=mode,
            description=f"KID {fbname2} : ",
            verbose=verbose,
        )
        score = kernel_distance(np_feats1, np_feats2)
        return score

    # compute kid of a folder
    elif fdir1 is not None and fdir2 is None:
        if verbose:
            print(f"compute KID of a folder with {dataset_name} statistics")
        ref_feats = get_reference_statistics(
            dataset_name,
            dataset_res,
            mode=mode,
            seed=0,
            split=dataset_split,
            metric="KID",
        )
        fbname = os.path.basename(fdir1)
        # get all inception features for folder images
        np_feats = get_folder_features(
            fdir1,
            feat_model,
            num_workers=num_workers,
            batch_size=batch_size,
            device=device,
            mode=mode,
            description=f"KID {fbname} : ",
            verbose=verbose,
        )
        score = kernel_distance(ref_feats, np_feats)
        return score

    # compute kid for a generator, using images in fdir2
    elif gen is not None and fdir2 is not None:
        if verbose:
            print(f"compute KID of a model, using references in fdir2")
        # get all inception features for the second folder
        fbname2 = os.path.basename(fdir2)
        ref_feats = get_folder_features(
            fdir2,
            feat_model,
            num_workers=num_workers,
            batch_size=batch_size,
            device=device,
            mode=mode,
            description=f"KID {fbname2} : ",
        )
        # Generate test features
        np_feats = get_model_features(
            gen,
            feat_model,
            mode=mode,
            z_dim=z_dim,
            num_gen=num_gen,
            desc="KID model: ",
            batch_size=batch_size,
            device=device,
        )
        score = kernel_distance(ref_feats, np_feats)
        return score

    # compute fid for a generator, using reference statistics
    elif gen is not None:
        if verbose:
            print(
                f"compute KID of a model with {dataset_name}-{dataset_res} statistics"
            )
        ref_feats = get_reference_statistics(
            dataset_name,
            dataset_res,
            mode=mode,
            seed=0,
            split=dataset_split,
            metric="KID",
        )
        # Generate test features
        np_feats = get_model_features(
            gen,
            feat_model,
            mode=mode,
            z_dim=z_dim,
            num_gen=num_gen,
            desc="KID model: ",
            batch_size=batch_size,
            device=device,
            verbose=verbose,
        )
        score = kernel_distance(ref_feats, np_feats)
        return score

    else:
        raise ValueError("invalid combination of directories and models entered")


"""
custom_image_tranform:
    function that takes an np_array image as input [0,255] and 
    applies a custom transform such as cropping
"""


def compute_fid(
    fdir1=None,
    fdir2=None,
    gen=None,
    mode="clean",
    model_name="inception_v3",
    num_workers=12,
    batch_size=32,
    device=torch.device("cuda"),
    dataset_name="FFHQ",
    dataset_res=1024,
    dataset_split="train",
    num_gen=50_000,
    z_dim=512,
    custom_feat_extractor=None,
    verbose=True,
    custom_image_tranform=None,
    custom_fn_resize=None,
    use_dataparallel=True,
):
    # build the feature extractor based on the mode and the model to be used
    if custom_feat_extractor is None and model_name == "inception_v3":
        feat_model = build_feature_extractor(
            mode, device, use_dataparallel=use_dataparallel
        )
    elif custom_feat_extractor is None and model_name == "clip_vit_b_32":
        from .clip_features import CLIP_fx, img_preprocess_clip

        clip_fx = CLIP_fx("ViT-B/32", device=device)
        feat_model = clip_fx
        custom_fn_resize = img_preprocess_clip
    else:
        feat_model = custom_feat_extractor

    # if both dirs are specified, compute FID between folders
    if fdir1 is not None and fdir2 is not None:
        score = compare_folders(
            fdir1,
            fdir2,
            feat_model,
            mode=mode,
            batch_size=batch_size,
            num_workers=num_workers,
            device=device,
            custom_image_tranform=custom_image_tranform,
            custom_fn_resize=custom_fn_resize,
            verbose=verbose,
        )
        return score

    # compute fid of a folder
    elif fdir1 is not None and fdir2 is None:
        if verbose:
            print(f"compute FID of a folder with {dataset_name} statistics")
        score = fid_folder(
            fdir1,
            dataset_name,
            dataset_res,
            dataset_split,
            model=feat_model,
            mode=mode,
            model_name=model_name,
            custom_fn_resize=custom_fn_resize,
            custom_image_tranform=custom_image_tranform,
            num_workers=num_workers,
            batch_size=batch_size,
            device=device,
            verbose=verbose,
        )
        return score

    # compute fid for a generator, using images in fdir2
    elif gen is not None and fdir2 is not None:
        if verbose:
            print(f"compute FID of a model, using references in fdir2")
        # get all inception features for the second folder
        fbname2 = os.path.basename(fdir2)
        np_feats2 = get_folder_features(
            fdir2,
            feat_model,
            num_workers=num_workers,
            batch_size=batch_size,
            device=device,
            mode=mode,
            description=f"FID {fbname2} : ",
            verbose=verbose,
            custom_fn_resize=custom_fn_resize,
            custom_image_tranform=custom_image_tranform,
        )
        mu2 = np.mean(np_feats2, axis=0)
        sigma2 = np.cov(np_feats2, rowvar=False)
        # Generate test features
        np_feats = get_model_features(
            gen,
            feat_model,
            mode=mode,
            z_dim=z_dim,
            num_gen=num_gen,
            custom_fn_resize=custom_fn_resize,
            custom_image_tranform=custom_image_tranform,
            batch_size=batch_size,
            device=device,
            verbose=verbose,
        )

        mu = np.mean(np_feats, axis=0)
        sigma = np.cov(np_feats, rowvar=False)
        fid = frechet_distance(mu, sigma, mu2, sigma2)
        return fid

    # compute fid for a generator, using reference statistics
    elif gen is not None:
        if verbose:
            print(
                f"compute FID of a model with {dataset_name}-{dataset_res} statistics"
            )
        score = fid_model(
            gen,
            dataset_name,
            dataset_res,
            dataset_split,
            model=feat_model,
            model_name=model_name,
            z_dim=z_dim,
            num_gen=num_gen,
            mode=mode,
            num_workers=num_workers,
            batch_size=batch_size,
            custom_image_tranform=custom_image_tranform,
            custom_fn_resize=custom_fn_resize,
            device=device,
            verbose=verbose,
        )
        return score

    else:
        raise ValueError("invalid combination of directories and models entered")