import argparse
import sys
import torch
from multiprocessing import cpu_count

class Config:
    def __init__(self):
        self.device = "cuda:0"
        self.is_half = True
        self.n_cpu = 0
        self.gpu_name = None
        self.gpu_mem = None
        self.python_cmd, self.listen_port, self.iscolab, self.noparallel, self.noautoopen = self.arg_parse()
        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()

    @staticmethod
    def arg_parse() -> tuple:
        exe = sys.executable or "python"
        parser = argparse.ArgumentParser()
        parser.add_argument("--port", type=int, default=7865, help="Listen port")
        parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
        parser.add_argument("--colab", action="store_true", help="Launch in colab")
        parser.add_argument("--noparallel", action="store_true", help="Disable parallel processing")
        parser.add_argument("--noautoopen", action="store_true", help="Do not open in browser automatically")
        
        if len(sys.argv) > 1 and sys.argv[0].endswith("colab_kernel_launcher.py"):
            args = parser.parse_known_args(sys.argv[1:])[0]
        else:
            args = parser.parse_args()

        args.port = args.port if 0 <= args.port <= 65535 else 7865

        return args.pycmd, args.port, args.colab, args.noparallel, args.noautoopen

    @staticmethod
    def has_mps() -> bool:
        if not torch.backends.mps.is_available():
            return False
        try:
            torch.zeros(1).to(torch.device("mps"))
            return True
        except Exception:
            return False

    def device_config(self) -> tuple:
        if torch.cuda.is_available():
            i_device = int(self.device.split(":")[-1])
            self.gpu_name = torch.cuda.get_device_name(i_device)
            if ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) or "P40" in self.gpu_name.upper() or "1060" in self.gpu_name or "1070" in self.gpu_name or "1080" in self.gpu_name:
                print("Found GPU", self.gpu_name, ", force to fp32")
                self.is_half = False
            else:
                print("Found GPU", self.gpu_name)
            self.gpu_mem = int(torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024 + 0.4)
        elif self.has_mps():
            print("No supported Nvidia GPU found, use MPS instead")
            self.device = "mps"
            self.is_half = False
        else:
            print("No supported Nvidia GPU found, use CPU instead")
            self.device = "cpu"
            self.is_half = False

        if self.n_cpu == 0:
            self.n_cpu = cpu_count()

        if self.is_half:
            x_pad, x_query, x_center, x_max = 3, 10, 60, 65
        else:
            x_pad, x_query, x_center, x_max = 1, 6, 38, 41

        if self.gpu_mem is not None and self.gpu_mem <= 4:
            x_pad, x_query, x_center, x_max = 1, 5, 30, 32

        return x_pad, x_query, x_center, x_max

if __name__ == "__main__":
    config = Config()
    print(config.__dict__)