from itertools import chain

import ignite.distributed as idist
import torch
import torch.nn as nn
from omegaconf import OmegaConf

from engine.base.i2i import EngineKernel, run_kernel, TestEngineKernel
from engine.util.build import build_model
from loss.I2I.perceptual_loss import PerceptualLoss
from loss.gan import GANLoss
from model.weight_init import generation_init_weights


class TSITEngineKernel(EngineKernel):
    def __init__(self, config):
        super().__init__(config)
        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()

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

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

        return generators, discriminators

    def setup_after_g(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:
        with torch.set_grad_enabled(not inference):
            fake = dict(
                b=self.generators["main"](content_img=batch["a"], style_img=batch["b"])
            )
        return fake

    def criterion_generators(self, batch, generated) -> dict:
        loss = dict()
        loss_perceptual, _ = self.perceptual_loss(generated["b"], batch["a"])
        loss["perceptual"] = loss_perceptual * self.config.loss.perceptual.weight
        for phase in "b":
            pred_fake = self.discriminators[phase](generated[phase])
            loss[f"gan_{phase}"] = 0
            for sub_pred_fake in pred_fake:
                # last output is actual prediction
                loss[f"gan_{phase}"] += self.config.loss.gan.weight * 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
        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(
            b=[batch["a"].detach(), batch["b"].detach(), generated["b"].detach()]
        )


class TSITTestEngineKernel(TestEngineKernel):
    def __init__(self, config):
        super().__init__(config)

    def build_generators(self) -> dict:
        generators = dict(
            main=build_model(self.config.model.generator)
        )
        return generators

    def to_load(self):
        return {f"generator_{k}": self.generators[k] for k in self.generators}

    def inference(self, batch):
        with torch.no_grad():
            fake = self.generators["main"](content_img=batch["a"][0], style_img=batch["b"][0])
            return {"a": fake.detach()}


def run(task, config, _):
    if task == "train":
        kernel = TSITEngineKernel(config)
        run_kernel(task, config, kernel)
    elif task == "test":
        kernel = TSITTestEngineKernel(config)
        run_kernel(task, config, kernel)
    else:
        raise NotImplemented