from typing import Any

import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm

from .base_postprocessor import BasePostprocessor


class ReactPostprocessor(BasePostprocessor):
    def __init__(self, config):
        super(ReactPostprocessor, self).__init__(config)
        self.args = self.config.postprocessor.postprocessor_args
        self.percentile = self.args.percentile
        self.args_dict = self.config.postprocessor.postprocessor_sweep
        self.setup_flag = False

    def setup(self, net: nn.Module, id_loader_dict, ood_loader_dict):
        if not self.setup_flag:
            activation_log = []
            net.eval()
            with torch.no_grad():
                for batch in tqdm(id_loader_dict['train'],
                                  desc='Setup: ',
                                  position=0,
                                  leave=True):
                    data = batch['data'].cuda()
                    data = data.float()

                    _, feature = net(data, return_feature=True)
                    activation_log.append(feature.data.cpu().numpy())

            self.activation_log = np.concatenate(activation_log, axis=0)
            self.setup_flag = True
            self.set_hyperparam()
        else:
            pass

    @torch.no_grad()
    def postprocess(self, net: nn.Module, data: Any):
        output = net.forward_threshold(data, self.threshold)
        score = torch.softmax(output, dim=1)
        _, pred = torch.max(score, dim=1)
        energyconf = torch.logsumexp(output.data.cpu(), dim=1)
        return pred, energyconf

    def set_hyperparam(self):
        self.percentile = 90
        self.threshold = np.percentile(self.activation_log.flatten(),
                                       self.percentile)
        print('Threshold at percentile {:2d} over id data is: {}'.format(
            self.percentile, self.threshold))

    def get_hyperparam(self):
        return self.percentile
