import os
import sys

sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))

import time

import numpy as np
import torch
from PIL import Image

from models.base_model import BaseModelMainModel
from utils import configs
from utils.functional import image_augmentations, active_learning_uncertainty

from .lightning_module import ImageClassificationLightningModule


class DeepLearningModel(BaseModelMainModel):
    def __init__(
        self,
        name_model: str,
        freeze_model: bool,
        pretrained_model: bool,
        support_set_method: str,
    ):
        super().__init__(name_model, freeze_model, pretrained_model, support_set_method)
        self.init_model()

    def init_model(self):
        self.model = ImageClassificationLightningModule.load_from_checkpoint(
            os.path.join(
                configs.WEIGHTS_PATH,
                self.name_model,
                self.support_set_method,
                "best.ckpt",
            ),
            name_model=self.name_model,
            freeze_model=self.freeze_model,
            pretrained_model=self.pretrained_model,
        )
        self.model = self.model.model
        for layer in self.model.children():
            if hasattr(layer, "reset_parameters") and not self.pretrained_model:
                layer.reset_parameters()
        for param in self.model.parameters():
            param.required_grad = False if not self.freeze_model else True
        self.model.to(self.device)
        self.model.eval()

    def predict(self, image: np.ndarray) -> dict:
        image_input = image_augmentations()(image=image)["image"]
        image_input = image_input.unsqueeze(axis=0).to(self.device)
        with torch.no_grad():
            start_time = time.perf_counter()
            result = self.model(image_input)
            end_time = time.perf_counter() - start_time
            result = torch.softmax(result, dim=1)
            result = result.detach().cpu().numpy()
        result_index = np.argmax(result)
        confidence = result[0][result_index]
        uncertainty_score = active_learning_uncertainty(result[0])
        uncertainty_score = uncertainty_score if uncertainty_score > 0 else 0
        if (
            uncertainty_score
            > configs.NAME_MODELS[self.name_model][
                "deep_learning_out_of_distribution_threshold"
            ][self.support_set_method]
        ):
            return {
                "character": configs.CLASS_CHARACTERS[-1],
                "confidence": confidence,
                "inference_time": end_time,
            }
        return {
            "character": configs.CLASS_CHARACTERS[result_index],
            "confidence": confidence,
            "inference_time": end_time,
        }


if __name__ == "__main__":
    model = DeepLearningModel("resnet50", True, True, "1_shot")
    image = np.array(
        Image.open(
            "../../assets/example_images/gon/306e5d35-b301-4299-8022-0c89dc0b7690.png"
        ).convert("RGB")
    )
    result = model.predict(image)
    print(result)