import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted

from ...modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
from ...util import log_txt_as_img, default, ismap, instantiate_from_config

__models__ = {
    'class_label': EncoderUNetModel,
    'segmentation': UNetModel
}


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


class NoisyLatentImageClassifier(pl.LightningModule):

    def __init__(self,
                 diffusion_path,
                 num_classes,
                 ckpt_path=None,
                 pool='attention',
                 label_key=None,
                 diffusion_ckpt_path=None,
                 scheduler_config=None,
                 weight_decay=1.e-2,
                 log_steps=10,
                 monitor='val/loss',
                 *args,
                 **kwargs):
        super().__init__(*args, **kwargs)
        self.num_classes = num_classes
        # get latest config of diffusion model
        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
        self.diffusion_config = OmegaConf.load(diffusion_config).model
        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
        self.load_diffusion()

        self.monitor = monitor
        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
        self.log_steps = log_steps

        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
            else self.diffusion_model.cond_stage_key

        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'

        if self.label_key not in __models__:
            raise NotImplementedError()

        self.load_classifier(ckpt_path, pool)

        self.scheduler_config = scheduler_config
        self.use_scheduler = self.scheduler_config is not None
        self.weight_decay = weight_decay

    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
        sd = torch.load(path, map_location="cpu")
        if "state_dict" in list(sd.keys()):
            sd = sd["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
            sd, strict=False)
        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
        if len(missing) > 0:
            print(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            print(f"Unexpected Keys: {unexpected}")

    def load_diffusion(self):
        model = instantiate_from_config(self.diffusion_config)
        self.diffusion_model = model.eval()
        self.diffusion_model.train = disabled_train
        for param in self.diffusion_model.parameters():
            param.requires_grad = False

    def load_classifier(self, ckpt_path, pool):
        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
        model_config.out_channels = self.num_classes
        if self.label_key == 'class_label':
            model_config.pool = pool

        self.model = __models__[self.label_key](**model_config)
        if ckpt_path is not None:
            print('#####################################################################')
            print(f'load from ckpt "{ckpt_path}"')
            print('#####################################################################')
            self.init_from_ckpt(ckpt_path)

    @torch.no_grad()
    def get_x_noisy(self, x, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x))
        continuous_sqrt_alpha_cumprod = None
        if self.diffusion_model.use_continuous_noise:
            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
            # todo: make sure t+1 is correct here

        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)

    def forward(self, x_noisy, t, *args, **kwargs):
        return self.model(x_noisy, t)

    @torch.no_grad()
    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = rearrange(x, 'b h w c -> b c h w')
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    @torch.no_grad()
    def get_conditioning(self, batch, k=None):
        if k is None:
            k = self.label_key
        assert k is not None, 'Needs to provide label key'

        targets = batch[k].to(self.device)

        if self.label_key == 'segmentation':
            targets = rearrange(targets, 'b h w c -> b c h w')
            for down in range(self.numd):
                h, w = targets.shape[-2:]
                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')

            # targets = rearrange(targets,'b c h w -> b h w c')

        return targets

    def compute_top_k(self, logits, labels, k, reduction="mean"):
        _, top_ks = torch.topk(logits, k, dim=1)
        if reduction == "mean":
            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
        elif reduction == "none":
            return (top_ks == labels[:, None]).float().sum(dim=-1)

    def on_train_epoch_start(self):
        # save some memory
        self.diffusion_model.model.to('cpu')

    @torch.no_grad()
    def write_logs(self, loss, logits, targets):
        log_prefix = 'train' if self.training else 'val'
        log = {}
        log[f"{log_prefix}/loss"] = loss.mean()
        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
            logits, targets, k=1, reduction="mean"
        )
        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
            logits, targets, k=5, reduction="mean"
        )

        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
        lr = self.optimizers().param_groups[0]['lr']
        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)

    def shared_step(self, batch, t=None):
        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
        targets = self.get_conditioning(batch)
        if targets.dim() == 4:
            targets = targets.argmax(dim=1)
        if t is None:
            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
        else:
            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
        x_noisy = self.get_x_noisy(x, t)
        logits = self(x_noisy, t)

        loss = F.cross_entropy(logits, targets, reduction='none')

        self.write_logs(loss.detach(), logits.detach(), targets.detach())

        loss = loss.mean()
        return loss, logits, x_noisy, targets

    def training_step(self, batch, batch_idx):
        loss, *_ = self.shared_step(batch)
        return loss

    def reset_noise_accs(self):
        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}

    def on_validation_start(self):
        self.reset_noise_accs()

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        loss, *_ = self.shared_step(batch)

        for t in self.noisy_acc:
            _, logits, _, targets = self.shared_step(batch, t)
            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))

        return loss

    def configure_optimizers(self):
        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)

        if self.use_scheduler:
            scheduler = instantiate_from_config(self.scheduler_config)

            print("Setting up LambdaLR scheduler...")
            scheduler = [
                {
                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
                    'interval': 'step',
                    'frequency': 1
                }]
            return [optimizer], scheduler

        return optimizer

    @torch.no_grad()
    def log_images(self, batch, N=8, *args, **kwargs):
        log = dict()
        x = self.get_input(batch, self.diffusion_model.first_stage_key)
        log['inputs'] = x

        y = self.get_conditioning(batch)

        if self.label_key == 'class_label':
            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
            log['labels'] = y

        if ismap(y):
            log['labels'] = self.diffusion_model.to_rgb(y)

            for step in range(self.log_steps):
                current_time = step * self.log_time_interval

                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)

                log[f'inputs@t{current_time}'] = x_noisy

                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
                pred = rearrange(pred, 'b h w c -> b c h w')

                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)

        for key in log:
            log[key] = log[key][:N]

        return log