from itertools import chain

import ignite.distributed as idist
import torch

from engine.base.i2i import EngineKernel, run_kernel
from engine.util.build import build_model
from engine.util.container import GANImageBuffer, LossContainer
from engine.util.loss import pixel_loss, gan_loss, feature_match_loss
from loss.I2I.edge_loss import EdgeLoss
from loss.I2I.minimal_geometry_distortion_constraint_loss import MGCLoss
from model.weight_init import generation_init_weights


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

        self.gan_loss = gan_loss(config.loss.gan)
        self.cycle_loss = LossContainer(config.loss.cycle.weight, pixel_loss(config.loss.cycle.level))
        self.id_loss = LossContainer(config.loss.id.weight, pixel_loss(config.loss.id.level))
        self.mgc_loss = LossContainer(config.loss.mgc.weight, MGCLoss("opposite"))
        self.fm_loss = LossContainer(config.loss.fm.weight, feature_match_loss(1, "same"))
        self.edge_loss = LossContainer(config.loss.edge.weight, EdgeLoss(
            "HED", hed_pretrained_model_path=config.loss.edge.hed_pretrained_model_path).to(idist.device()))
        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.id_loss.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 ph in "ab":
            loss[f"cycle_{ph}"] = self.cycle_loss(generated["a2b2a" if ph == "a" else "b2a2b"], batch[ph])
            loss[f"id_{ph}"] = self.id_loss(generated[f"{ph}2{ph}"], batch[ph])
            loss[f"mgc_{ph}"] = self.mgc_loss(generated["a2b" if ph == "a" else "b2a"], batch[ph])
            prediction_fake = self.discriminators[ph](generated["a2b" if ph == "b" else "b2a"])
            loss[f"gan_{ph}"] = self.config.loss.gan.weight * self.gan_loss(prediction_fake, True)
            if self.fm_loss.weight > 0:
                prediction_real = self.discriminators[ph](batch[ph])
                loss[f"feature_match_{ph}"] = self.fm_loss(prediction_fake, prediction_real)
            loss[f"edge_{ph}"] = self.edge_loss(generated["a2b" if ph == "a" else "b2a"], batch[ph], gt_is_edge=False)
        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 = CycleGANEngineKernel(config)
    run_kernel(task, config, kernel)