import random
import os
import torch
import numpy as np


class Model:
    def __init__(self, config, device):
        self.provider = config["model_info"]["provider"]
        self.name = config["model_info"]["name"]
        self.seed = int(config["params"]["seed"])
        self.temperature = float(config["params"]["temperature"])
        self.gpus = [str(gpu) for gpu in config["params"]["gpus"]]
        self.device = device

    def print_model_info(self):
        print(f"{'-'*len(f'| Model name: {self.name}')}\n| Provider: {self.provider}\n| Model name: {self.name}\n{'-'*len(f'| Model name: {self.name}')}")

    def set_API_key(self):
        raise NotImplementedError("ERROR: Interface doesn't have the implementation for set_API_key")
    
    def query(self):
        raise NotImplementedError("ERROR: Interface doesn't have the implementation for query")
    
    def initialize_seed(self):
        random.seed(self.seed)
        os.environ['PYTHONHASHSEED'] = str(self.seed)
        np.random.seed(self.seed)
        torch.manual_seed(self.seed)
        torch.cuda.manual_seed(self.seed)
        torch.backends.cudnn.benchmark = False
        torch.backends.cudnn.deterministic = True
        # if you are using multi-GPU.
        if len(self.gpus) > 1:
            torch.cuda.manual_seed_all(self.seed)
    
    def initialize_gpus(self):
        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(self.gpus)