import torch
import onnxruntime


class DeviceManager(object):
    _instance = None
    forceTensor: bool = False

    @classmethod
    def get_instance(cls):
        if cls._instance is None:
            cls._instance = cls()
        return cls._instance

    def __init__(self):
        self.gpu_num = torch.cuda.device_count()
        self.mps_enabled: bool = (
            getattr(torch.backends, "mps", None) is not None
            and torch.backends.mps.is_available()
        )

    def getDevice(self, id: int):
        if id < 0 or (self.gpu_num == 0 and self.mps_enabled is False):
            dev = torch.device("cpu")
        elif self.mps_enabled:
            dev = torch.device("mps")
        else:
            dev = torch.device("cuda", index=id)
        return dev

    def getOnnxExecutionProvider(self, gpu: int):
        availableProviders = onnxruntime.get_available_providers()
        devNum = torch.cuda.device_count()
        if gpu >= 0 and "CUDAExecutionProvider" in availableProviders and devNum > 0:
            return ["CUDAExecutionProvider"], [{"device_id": gpu}]
        elif gpu >= 0 and "DmlExecutionProvider" in availableProviders:
            return ["DmlExecutionProvider"], [{}]
        else:
            return ["CPUExecutionProvider"], [
                {
                    "intra_op_num_threads": 8,
                    "execution_mode": onnxruntime.ExecutionMode.ORT_PARALLEL,
                    "inter_op_num_threads": 8,
                }
            ]

    def setForceTensor(self, forceTensor: bool):
        self.forceTensor = forceTensor

    def halfPrecisionAvailable(self, id: int):
        if self.gpu_num == 0:
            return False
        if id < 0:
            return False
        if self.forceTensor:
            return False

        try:
            gpuName = torch.cuda.get_device_name(id).upper()
            if (
                ("16" in gpuName and "V100" not in gpuName)
                or "P40" in gpuName.upper()
                or "1070" in gpuName
                or "1080" in gpuName
            ):
                return False
        except Exception as e:
            print(e)
            return False

        cap = torch.cuda.get_device_capability(id)
        if cap[0] < 7:  # コンピューティング機能が7以上の場合half precisionが使えるとされている（が例外がある？T500とか）
            return False

        return True

    def getDeviceMemory(self, id: int):
        try:
            return torch.cuda.get_device_properties(id).total_memory
            # except Exception as e:
        except:
            # print(e)
            return 0