# Copyright (c) Alibaba, Inc. and its affiliates.
from typing import Dict

import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy import linalg
from torchvision import models

from modelscope.metainfo import Metrics
from modelscope.models.cv.image_inpainting.modules.inception import InceptionV3
from modelscope.utils.registry import default_group
from modelscope.utils.tensor_utils import (torch_nested_detach,
                                           torch_nested_numpify)
from .base import Metric
from .builder import METRICS, MetricKeys
from .image_denoise_metric import calculate_psnr
from .image_inpainting_metric import FIDScore


@METRICS.register_module(
    group_key=default_group, module_name=Metrics.image_colorization_metric)
class ImageColorizationMetric(Metric):
    """The metric computation class for image colorization.
    """

    def __init__(self):
        self.preds = []
        self.targets = []
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'

    def add(self, outputs: Dict, inputs: Dict):
        ground_truths = outputs['preds']
        eval_results = outputs['targets']
        self.preds.append(eval_results)
        self.targets.append(ground_truths)

    def evaluate(self):
        psnr_list = []
        cf_list = []

        fid = calculate_fid(self.preds, self.targets, device=self.device)
        for (pred, target) in zip(self.preds, self.targets):
            # shape of pred: [8, 3, 256, 256]
            cf_list.append(calculate_colorfulness(pred))
            psnr_list.append(calculate_psnr(target[0], pred[0], crop_border=0))

        return {
            MetricKeys.PSNR: np.mean(psnr_list),
            MetricKeys.FID: fid,
            MetricKeys.Colorfulness: np.mean(cf_list)
        }

    def merge(self, other: 'ImageColorizationMetric'):
        self.preds.extend(other.preds)
        self.targets.extend(other.targets)

    def __getstate__(self):
        return self.preds, self.targets

    def __setstate__(self, state):
        self.__init__()
        self.preds, self.targets = state


def image_colorfulness(image):
    image = image * 255.0
    (R, G, B) = (image[0], image[1], image[2])
    rg = np.absolute(R - G)
    yb = np.absolute(0.5 * (R + G) - B)
    (rbMean, rbStd) = (np.mean(rg), np.std(rg))
    (ybMean, ybStd) = (np.mean(yb), np.std(yb))
    stdRoot = np.sqrt((rbStd**2) + (ybStd**2))
    meanRoot = np.sqrt((rbMean**2) + (ybMean**2))
    return stdRoot + (0.3 * meanRoot)


def calculate_colorfulness(pred):
    total_colorfulness = 0
    for img_tensor in pred:
        img_np = img_tensor.cpu().numpy()

        C = image_colorfulness(img_np)
        total_colorfulness += C

    colorfulness = total_colorfulness / len(pred)
    return colorfulness


class INCEPTION_V3_FID(nn.Module):
    """pretrained InceptionV3 network returning feature maps"""
    # Index of default block of inception to return,
    # corresponds to output of final average pooling
    DEFAULT_BLOCK_INDEX = 3

    # Maps feature dimensionality to their output blocks indices
    BLOCK_INDEX_BY_DIM = {
        64: 0,  # First max pooling features
        192: 1,  # Second max pooling featurs
        768: 2,  # Pre-aux classifier features
        2048: 3  # Final average pooling features
    }

    def __init__(self,
                 incep_state_dict,
                 output_blocks=[DEFAULT_BLOCK_INDEX],
                 resize_input=True):
        """Build pretrained InceptionV3
        Args:
            output_blocks (list of int):
                Indices of blocks to return features of. Possible values are:
                - 0: corresponds to output of first max pooling
                - 1: corresponds to output of second max pooling
                - 2: corresponds to output which is fed to aux classifier
                - 3: corresponds to output of final average pooling
            resize_input (bool):
                If true, bilinearly resizes input to width and height 299 before
                feeding input to model. As the network without fully connected
                layers is fully convolutional, it should be able to handle inputs
                of arbitrary size, so resizing might not be strictly needed
            normalize_input (bool):
                If true, normalizes the input to the statistics the pretrained
                Inception network expects
        """
        super(INCEPTION_V3_FID, self).__init__()

        self.resize_input = resize_input
        self.output_blocks = sorted(output_blocks)
        self.last_needed_block = max(output_blocks)

        assert self.last_needed_block <= 3, \
            'Last possible output block index is 3'

        self.blocks = nn.ModuleList()

        inception = models.inception_v3()
        inception.load_state_dict(incep_state_dict)
        for param in inception.parameters():
            param.requires_grad = False

        # Block 0: input to maxpool1
        block0 = [
            inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3,
            inception.Conv2d_2b_3x3,
            nn.MaxPool2d(kernel_size=3, stride=2)
        ]
        self.blocks.append(nn.Sequential(*block0))

        # Block 1: maxpool1 to maxpool2
        if self.last_needed_block >= 1:
            block1 = [
                inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3,
                nn.MaxPool2d(kernel_size=3, stride=2)
            ]
            self.blocks.append(nn.Sequential(*block1))

        # Block 2: maxpool2 to aux classifier
        if self.last_needed_block >= 2:
            block2 = [
                inception.Mixed_5b,
                inception.Mixed_5c,
                inception.Mixed_5d,
                inception.Mixed_6a,
                inception.Mixed_6b,
                inception.Mixed_6c,
                inception.Mixed_6d,
                inception.Mixed_6e,
            ]
            self.blocks.append(nn.Sequential(*block2))

        # Block 3: aux classifier to final avgpool
        if self.last_needed_block >= 3:
            block3 = [
                inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c,
                nn.AdaptiveAvgPool2d(output_size=(1, 1))
            ]
            self.blocks.append(nn.Sequential(*block3))

    def forward(self, inp):
        """Get Inception feature maps
        Args:
            inp (torch.tensor):
                Input tensor of shape Bx3xHxW. Values are expected to be in
                range (0, 1)
        Returns:
            List of torch.tensor corresponding to the selected output
                block, sorted ascending by index
        """
        outp = []
        x = inp

        if self.resize_input:
            x = F.interpolate(x, size=(299, 299), mode='bilinear')

        x = x.clone()
        # [-1.0, 1.0] --> [0, 1.0]
        x = x * 0.5 + 0.5
        x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
        x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
        x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5

        for idx, block in enumerate(self.blocks):
            x = block(x)
            if idx in self.output_blocks:
                outp.append(x)

            if idx == self.last_needed_block:
                break

        return outp


def get_activations(images, model, batch_size, verbose=False):
    """Calculates the activations of the pool_3 layer for all images.
    Args:
        images: Numpy array of dimension (n_images, 3, hi, wi). The values
            must lie between 0 and 1.
        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.
        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()

    d0 = int(images.size(0))
    if batch_size > d0:
        print(('Warning: batch size is bigger than the data size. '
               'Setting batch size to data size'))
        batch_size = d0

    n_batches = d0 // batch_size
    n_used_imgs = n_batches * batch_size

    pred_arr = np.empty((n_used_imgs, 2048))
    for i in range(n_batches):
        if verbose:
            print(
                '\rPropagating batch %d/%d' % (i + 1, n_batches),
                end='',
                flush=True)
        start = i * batch_size
        end = start + batch_size
        '''batch = torch.from_numpy(images[start:end]).type(torch.FloatTensor)
        batch = Variable(batch, volatile=True)
        if cfg.CUDA:
            batch = batch.cuda()'''
        batch = images[start:end]

        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.shape[2] != 1 or pred.shape[3] != 1:
            pred = F.adaptive_avg_pool2d(pred, output_size=(1, 1))

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

    if verbose:
        print(' done')

    return pred_arr


def calculate_activation_statistics(act):
    """Calculation of the statistics used by the FID.
    Args:
        act: Numpy array of dimension (n_images, dim (e.g. 2048)).
    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.
    """
    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)
    return mu, sigma


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.
    Args:
        mu1: Numpy array containing the activations of a layer of the
            nception net (like returned by the function 'get_predictions')
            or generated samples.
        mu2: The sample mean over activations, precalculated on an
            representive data set.
        sigma1: The covariance matrix over activations for generated samples.
        sigma2: The covariance matrix over activations, precalculated on an
            epresentive 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_fid(preds, targets, device):
    incep_url = 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'
    try:
        from torchvision.models.utils import load_state_dict_from_url
    except ImportError:
        from torch.utils.model_zoo import load_url as load_state_dict_from_url
    incep_state_dict = load_state_dict_from_url(incep_url, progress=True)

    block_idx = INCEPTION_V3_FID.BLOCK_INDEX_BY_DIM[2048]
    inception_model_fid = INCEPTION_V3_FID(incep_state_dict, [block_idx])
    inception_model_fid.to(device)
    inception_model_fid.eval()

    fake_acts_set, acts_set = [], []
    with torch.no_grad():
        for (pred, gt) in zip(preds, targets):
            pred, gt = pred.to(device), gt.to(device)
            fake_act = get_activations(pred, inception_model_fid,
                                       pred.shape[0])
            real_act = get_activations(gt, inception_model_fid, gt.shape[0])
            fake_acts_set.append(fake_act)
            acts_set.append(real_act)
            # break
        acts_set = np.concatenate(acts_set, 0)
        fake_acts_set = np.concatenate(fake_acts_set, 0)

        real_mu, real_sigma = calculate_activation_statistics(acts_set)
        fake_mu, fake_sigma = calculate_activation_statistics(fake_acts_set)
        fid_score = calculate_frechet_distance(real_mu, real_sigma, fake_mu,
                                               fake_sigma)
    return fid_score