from NeuroFlex.config import get_cfg
import os
import logging
import torch

# Mock implementations for DefaultTrainer and DefaultPredictor
class DefaultTrainer:
    def __init__(self, cfg):
        self.cfg = cfg

    def resume_or_load(self, resume=False):
        pass

    def train(self):
        pass

class DefaultPredictor:
    def __init__(self, cfg):
        self.cfg = cfg

    def __call__(self, image):
        return {"mock_output": "This is a mock prediction"}

class Detectron2Integration:

    def __init__(self):
        self.cfg = get_cfg()
        self.logger = logging.getLogger(__name__)

    def setup_config(self, config_file, num_classes, output_dir):
        try:
            self.cfg.merge_from_file(config_file)
            self.cfg.DATASETS.TRAIN = ("my_dataset_train",)  # Example dataset entry
            self.cfg.DATASETS.TEST = ("my_dataset_val",)  # Example dataset entry
            self.cfg.DATALOADER.NUM_WORKERS = 2
            self.cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"  # Default model
            self.cfg.SOLVER.IMS_PER_BATCH = 2
            self.cfg.SOLVER.BASE_LR = 0.00025
            self.cfg.SOLVER.MAX_ITER = 1000  # Example number of iterations
            self.cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
            self.cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes
            self.cfg.OUTPUT_DIR = output_dir
        except Exception as e:
            self.logger.error(f"Error in setup_config: {str(e)}")
            raise

    def train_model(self, dataset, config_file, num_classes, output_dir):
        try:
            self.setup_config(config_file, num_classes, output_dir)
            os.makedirs(output_dir, exist_ok=True)
            trainer = DefaultTrainer(self.cfg)
            trainer.resume_or_load(resume=False)
            trainer.train()
        except Exception as e:
            self.logger.error(f"Error in train_model: {str(e)}")
            raise

    def get_predictor(self, model_weights):
        try:
            self.cfg.MODEL.WEIGHTS = model_weights
            return DefaultPredictor(self.cfg)
        except Exception as e:
            self.logger.error(f"Error in get_predictor: {str(e)}")
            raise

    def inference(self, image, model_weights):
        try:
            predictor = self.get_predictor(model_weights)
            with torch.no_grad():
                outputs = predictor(image)
            return outputs
        except Exception as e:
            self.logger.error(f"Error in inference: {str(e)}")
            raise
