from itertools import chain
from math import ceil

from omegaconf import read_write, OmegaConf

import torch
import torch.nn as nn
import torch.nn.functional as F
import ignite.distributed as idist

import data
from engine.base.i2i import get_trainer, EngineKernel, build_model
from model.weight_init import generation_init_weights

from loss.I2I.perceptual_loss import PerceptualLoss
from loss.gan import GANLoss


class TAFGEngineKernel(EngineKernel):
    def __init__(self, config, logger):
        super().__init__(config, logger)
        perceptual_loss_cfg = OmegaConf.to_container(config.loss.perceptual)
        perceptual_loss_cfg.pop("weight")
        self.perceptual_loss = PerceptualLoss(**perceptual_loss_cfg).to(idist.device())

        gan_loss_cfg = OmegaConf.to_container(config.loss.gan)
        gan_loss_cfg.pop("weight")
        self.gan_loss = GANLoss(**gan_loss_cfg).to(idist.device())

        self.fm_loss = nn.L1Loss() if config.loss.fm.level == 1 else nn.MSELoss()
        self.recon_loss = nn.L1Loss() if config.loss.recon.level == 1 else nn.MSELoss()

    def build_models(self) -> (dict, dict):
        generators = dict(
            main=build_model(self.config.model.generator)
        )
        discriminators = dict(
            a=build_model(self.config.model.discriminator),
            b=build_model(self.config.model.discriminator)
        )
        self.logger.debug(discriminators["a"])
        self.logger.debug(generators["main"])

        for m in chain(generators.values(), discriminators.values()):
            generation_init_weights(m)

        return generators, discriminators

    def setup_before_d(self):
        for discriminator in self.discriminators.values():
            discriminator.requires_grad_(True)

    def setup_before_g(self):
        for discriminator in self.discriminators.values():
            discriminator.requires_grad_(False)

    def forward(self, batch, inference=False) -> dict:
        generator = self.generators["main"]
        with torch.set_grad_enabled(not inference):
            fake = dict(
                a=generator(content_img=batch["edge_a"], style_img=batch["a"], which_decoder="a"),
                b=generator(content_img=batch["edge_a"], style_img=batch["b"], which_decoder="b"),
            )
        return fake

    def criterion_generators(self, batch, generated) -> dict:
        loss = dict()
        loss["perceptual"], _, = self.perceptual_loss(generated["b"], batch["b"]) * self.config.loss.perceptual.weight
        for phase in "ab":
            pred_fake = self.discriminators[phase](generated[phase])
            for i, sub_pred_fake in enumerate(pred_fake):
                # last output is actual prediction
                loss[f"gan_{phase}_sub_{i}"] = self.gan_loss(sub_pred_fake[-1], True)

            if self.config.loss.fm.weight > 0 and phase == "b":
                pred_real = self.discriminators[phase](batch[phase])
                loss_fm = 0
                num_scale_discriminator = len(pred_fake)
                for i in range(num_scale_discriminator):
                    # last output is the final prediction, so we exclude it
                    num_intermediate_outputs = len(pred_fake[i]) - 1
                    for j in range(num_intermediate_outputs):
                        loss_fm += self.fm_loss(pred_fake[i][j], pred_real[i][j].detach()) / num_scale_discriminator
                loss[f"fm_{phase}"] = self.config.loss.fm.weight * loss_fm
        loss["recon"] = self.recon_loss(generated["a"], batch["a"]) * self.config.loss.recon.weight
        return loss

    def criterion_discriminators(self, batch, generated) -> dict:
        loss = dict()
        for phase in self.discriminators.keys():
            pred_real = self.discriminators[phase](batch[phase])
            pred_fake = self.discriminators[phase](generated[phase].detach())
            loss[f"gan_{phase}"] = 0
            for i in range(len(pred_fake)):
                loss[f"gan_{phase}"] += (self.gan_loss(pred_fake[i][-1], False, is_discriminator=True)
                                         + self.gan_loss(pred_real[i][-1], True, is_discriminator=True)) / 2
        return loss

    def intermediate_images(self, batch, generated) -> dict:
        """
        returned dict must be like: {"a": [img1, img2, ...], "b": [img3, img4, ...]}
        :param batch:
        :param generated: dict of images
        :return: dict like: {"a": [img1, img2, ...], "b": [img3, img4, ...]}
        """
        return dict(
            a=[batch[f"edge_a"].expand(-1, 3, -1, -1).detach(), batch["a"].detach(), generated["a"].detach()],
            b=[batch["b"].detach(), generated["b"].detach()]
        )


def run(task, config, logger):
    assert torch.backends.cudnn.enabled
    torch.backends.cudnn.benchmark = True
    logger.info(f"start task {task}")
    with read_write(config):
        config.max_iteration = ceil(config.max_pairs / config.data.train.dataloader.batch_size)

    if task == "train":
        train_dataset = data.DATASET.build_with(config.data.train.dataset)
        logger.info(f"train with dataset:\n{train_dataset}")
        train_data_loader = idist.auto_dataloader(train_dataset, **config.data.train.dataloader)
        trainer = get_trainer(config, TAFGEngineKernel(config, logger), len(train_data_loader))
        if idist.get_rank() == 0:
            test_dataset = data.DATASET.build_with(config.data.test.dataset)
            trainer.state.test_dataset = test_dataset
        try:
            trainer.run(train_data_loader, max_epochs=ceil(config.max_iteration / len(train_data_loader)))
        except Exception:
            import traceback
            print(traceback.format_exc())
    else:
        return NotImplemented(f"invalid task: {task}")