# code was heavily based on https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
import os
import paddle
import numpy as np
from collections import OrderedDict
from abc import ABC, abstractmethod

from ..solver.lr_scheduler import build_lr_scheduler


class BaseModel(ABC):
    """This class is an abstract base class (ABC) for models.
    To create a subclass, you need to implement the following five functions:
        -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
        -- <set_input>:                     unpack data from dataset and apply preprocessing.
        -- <forward>:                       produce intermediate results.
        -- <optimize_parameters>:           calculate losses, gradients, and update network weights.
        -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
    """
    def __init__(self, opt):
        """Initialize the BaseModel class.

        Parameters:
            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions

        When creating your custom class, you need to implement your own initialization.
        In this function, you should first call <BaseModel.__init__(self, opt)>
        Then, you need to define four lists:
            -- self.loss_names (str list):          specify the training losses that you want to plot and save.
            -- self.model_names (str list):         define networks used in our training.
            -- self.visual_names (str list):        specify the images that you want to display and save.
            -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
        """
        self.opt = opt
        self.isTrain = opt.isTrain
        self.save_dir = os.path.join(
            opt.output_dir,
            opt.model.name)  # save all the checkpoints to save_dir

        self.loss_names = []
        self.model_names = []
        self.visual_names = []
        self.optimizers = []
        self.optimizer_names = []
        self.image_paths = []
        self.metric = 0  # used for learning rate policy 'plateau'

    @staticmethod
    def modify_commandline_options(parser, is_train):
        """Add new model-specific options, and rewrite default values for existing options.

        Parameters:
            parser          -- original option parser
            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.

        Returns:
            the modified parser.
        """
        return parser

    @abstractmethod
    def set_input(self, input):
        """Unpack input data from the dataloader and perform necessary pre-processing steps.

        Parameters:
            input (dict): includes the data itself and its metadata information.
        """
        pass

    @abstractmethod
    def forward(self):
        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
        pass

    @abstractmethod
    def optimize_parameters(self):
        """Calculate losses, gradients, and update network weights; called in every training iteration"""
        pass

    def build_lr_scheduler(self):
        self.lr_scheduler = build_lr_scheduler(self.opt.lr_scheduler)

    def eval(self):
        """Make models eval mode during test time"""
        for name in self.model_names:
            if isinstance(name, str):
                net = getattr(self, 'net' + name)
                net.eval()

    def test(self):
        """Forward function used in test time.

        This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop
        It also calls <compute_visuals> to produce additional visualization results
        """
        with paddle.imperative.no_grad():
            self.forward()
            self.compute_visuals()

    def compute_visuals(self):
        """Calculate additional output images for visdom and HTML visualization"""
        pass

    def get_image_paths(self):
        """ Return image paths that are used to load current data"""
        return self.image_paths

    def get_current_visuals(self):
        """Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
        visual_ret = OrderedDict()
        for name in self.visual_names:
            if isinstance(name, str) and hasattr(self, name):
                visual_ret[name] = getattr(self, name)
        return visual_ret

    def get_current_losses(self):
        """Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
        errors_ret = OrderedDict()
        for name in self.loss_names:
            if isinstance(name, str):
                errors_ret[name] = float(
                    getattr(self, 'loss_' + name)
                )  # float(...) works for both scalar tensor and float number
        return errors_ret

    def set_requires_grad(self, nets, requires_grad=False):
        """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
        Parameters:
            nets (network list)   -- a list of networks
            requires_grad (bool)  -- whether the networks require gradients or not
        """
        if not isinstance(nets, list):
            nets = [nets]
        for net in nets:
            if net is not None:
                for param in net.parameters():
                    # print('trainable:', param.trainable)
                    param.trainable = requires_grad
                    # param.stop_gradient = not requires_grad