# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

import torch


def get_max_cuda_memory(device: Optional[torch.device] = None) -> int:
    """Returns the maximum GPU memory occupied by tensors in megabytes (MB) for
    a given device. By default, this returns the peak allocated memory since
    the beginning of this program.

    Args:
        device (torch.device, optional): selected device. Returns
            statistic for the current device, given by
            :func:`~torch.cuda.current_device`, if ``device`` is None.
            Defaults to None.

    Returns:
        int: The maximum GPU memory occupied by tensors in megabytes
        for a given device.
    """
    mem = torch.cuda.max_memory_allocated(device=device)
    mem_mb = torch.tensor([int(mem) // (1024 * 1024)],
                          dtype=torch.int,
                          device=device)
    torch.cuda.reset_peak_memory_stats()
    return int(mem_mb.item())


def is_cuda_available() -> bool:
    """Returns True if cuda devices exist."""
    return torch.cuda.is_available()


def is_npu_available() -> bool:
    """Returns True if Ascend PyTorch and npu devices exist."""
    try:
        import torch_npu  # noqa: F401
    except Exception:
        return False
    return hasattr(torch, 'npu') and torch.npu.is_available()


def is_mlu_available() -> bool:
    """Returns True if Cambricon PyTorch and mlu devices exist."""
    return hasattr(torch, 'is_mlu_available') and torch.is_mlu_available()


def is_mps_available() -> bool:
    """Return True if mps devices exist.

    It's specialized for mac m1 chips and require torch version 1.12 or higher.
    """
    return hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()


def get_device() -> str:
    """Returns the currently existing device type.

    Returns:
        str: cuda | npu | mlu | mps | cpu.
    """
    if is_npu_available():
        return 'npu'
    elif is_cuda_available():
        return 'cuda'
    elif is_mlu_available():
        return 'mlu'
    elif is_mps_available():
        return 'mps'
    else:
        return 'cpu'

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

import torch


def get_max_cuda_memory(device: Optional[torch.device] = None) -> int:
    """Returns the maximum GPU memory occupied by tensors in megabytes (MB) for
    a given device. By default, this returns the peak allocated memory since
    the beginning of this program.

    Args:
        device (torch.device, optional): selected device. Returns
            statistic for the current device, given by
            :func:`~torch.cuda.current_device`, if ``device`` is None.
            Defaults to None.

    Returns:
        int: The maximum GPU memory occupied by tensors in megabytes
        for a given device.
    """
    mem = torch.cuda.max_memory_allocated(device=device)
    mem_mb = torch.tensor([int(mem) // (1024 * 1024)],
                          dtype=torch.int,
                          device=device)
    torch.cuda.reset_peak_memory_stats()
    return int(mem_mb.item())


def is_cuda_available() -> bool:
    """Returns True if cuda devices exist."""
    return torch.cuda.is_available()


def is_mlu_available() -> bool:
    """Returns True if Cambricon PyTorch and mlu devices exist."""
    return hasattr(torch, 'is_mlu_available') and torch.is_mlu_available()


def is_mps_available() -> bool:
    """Return True if mps devices exist.

    It's specialized for mac m1 chips and require torch version 1.12 or higher.
    """
    return hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()


def get_device() -> str:
    """Returns the currently existing device type.

    Returns:
        str: cuda | mlu | mps | cpu.
    """
    if is_cuda_available():
        return 'cuda'
    elif is_mlu_available():
        return 'mlu'
    elif is_mps_available():
        return 'mps'
    else:
        return 'cpu'
