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
from engine.util.build import build_model
from loss.gan import GANLoss
from model.GAN.base import GANImageBuffer
from model.weight_init import generation_init_weights


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

        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.cycle_loss = nn.L1Loss() if config.loss.cycle.level == 1 else nn.MSELoss()
        self.id_loss = nn.L1Loss() if config.loss.id.level == 1 else nn.MSELoss()
        self.image_buffers = {k: GANImageBuffer(config.data.train.buffer_size or 50) for k in
                              self.discriminators.keys()}

    def build_models(self) -> (dict, dict):
        generators = dict(
            a2b=build_model(self.config.model.generator),
            b2a=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["a2b"])

        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:
        images = dict()
        with torch.set_grad_enabled(not inference):
            images["a2b"] = self.generators["a2b"](batch["a"])
            images["b2a"] = self.generators["b2a"](batch["b"])
            images["a2b2a"] = self.generators["b2a"](images["a2b"])
            images["b2a2b"] = self.generators["a2b"](images["b2a"])
            if self.config.loss.id.weight > 0:
                images["a2a"] = self.generators["b2a"](batch["a"])
                images["b2b"] = self.generators["a2b"](batch["b"])
        return images

    def criterion_generators(self, batch, generated) -> dict:
        loss = dict()
        for phase in ["a2b", "b2a"]:
            loss[f"cycle_{phase[0]}"] = self.config.loss.cycle.weight * self.cycle_loss(
                generated[f"{phase}2{phase[0]}"], batch[phase[0]])
            loss[f"gan_{phase}"] = self.config.loss.gan.weight * self.gan_loss(
                self.discriminators[phase[-1]](generated[phase]), True)
            if self.config.loss.id.weight > 0:
                loss[f"id_{phase[0]}"] = self.config.loss.id.weight * self.id_loss(
                    generated[f"{phase[0]}2{phase[0]}"], batch[phase[0]])
        return loss

    def criterion_discriminators(self, batch, generated) -> dict:
        loss = dict()
        for phase in "ab":
            generated_image = self.image_buffers[phase].query(generated["b2a" if phase == "a" else "a2b"].detach())
            loss[f"gan_{phase}"] = (self.gan_loss(self.discriminators[phase](generated_image), False,
                                                  is_discriminator=True) +
                                    self.gan_loss(self.discriminators[phase](batch[phase]), 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["a"].detach(), generated["a2b"].detach(), generated["a2b2a"].detach()],
            b=[batch["b"].detach(), generated["b2a"].detach(), generated["b2a2b"].detach()],
        )


def run(task, config, _):
    kernel = TAFGEngineKernel(config)
    run_kernel(task, config, kernel)