import collections
import contextlib
import ctypes
import warnings
from typing import Any, Dict, Union, Tuple

import torch
from . import is_initialized, _get_device_index, _lazy_init
from ._utils import _dummy_type

from ._memory_viz import segments as _segments, memory as _memory

from torch.types import Device
from torch import _C

__all__ = ["caching_allocator_alloc", "caching_allocator_delete", "set_per_process_memory_fraction",
           "empty_cache", "memory_stats", "memory_stats_as_nested_dict", "reset_accumulated_memory_stats",
           "reset_peak_memory_stats", "reset_max_memory_allocated", "reset_max_memory_cached",
           "memory_allocated", "max_memory_allocated", "memory_reserved", "max_memory_reserved",
           "memory_cached", "max_memory_cached", "memory_snapshot", "memory_summary", "list_gpu_processes",
           "mem_get_info", "get_allocator_backend", "CUDAPluggableAllocator", "change_current_allocator"]


if not hasattr(torch._C, '_cuda_CUDAAllocator'):
    # Define dummy base classes
    torch._C.__dict__['_cuda_CUDAAllocator'] = _dummy_type('_cuda_CUDAAllocator')


def _host_allocator():
    _lazy_init()
    return torch._C._cuda_cudaHostAllocator()


@contextlib.contextmanager
def _free_mutex():
    torch._C._cuda_lock_mutex()
    try:
        yield
    finally:
        torch._C._cuda_unlock_mutex()


def caching_allocator_alloc(size, device: Union[Device, int] = None, stream=None):
    r"""Performs a memory allocation using the CUDA memory allocator.

    Memory is allocated for a given device and a stream, this
    function is intended to be used for interoperability with other
    frameworks. Allocated memory is released through
    :func:`~torch.cuda.caching_allocator_delete`.

    Args:
        size (int): number of bytes to be allocated.
        device (torch.device or int, optional): selected device. If it is
            ``None`` the default CUDA device is used.
        stream (torch.cuda.Stream or int, optional): selected stream. If is ``None`` then
            the default stream for the selected device is used.

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    if device is None:
        device = torch.cuda.current_device()
    device = _get_device_index(device)
    if stream is None:
        stream = torch.cuda.current_stream(device)
    if isinstance(stream, torch.cuda.streams.Stream):
        stream = stream.cuda_stream
    if not isinstance(stream, int):
        raise TypeError('Invalid type for stream argument, must be '
                        '`torch.cuda.Stream` or `int` representing a pointer '
                        'to a existing stream')
    with torch.cuda.device(device):
        return torch._C._cuda_cudaCachingAllocator_raw_alloc(size, stream)


def caching_allocator_delete(mem_ptr):
    r"""Deletes memory allocated using the CUDA memory allocator.

    Memory allocated with :func:`~torch.cuda.caching_allocator_alloc`.
    is freed here. The associated device and stream are tracked inside
    the allocator.

    Args:
        mem_ptr (int): memory address to be freed by the allocator.

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    torch._C._cuda_cudaCachingAllocator_raw_delete(mem_ptr)


def set_per_process_memory_fraction(fraction, device: Union[Device, int] = None) -> None:
    r"""Set memory fraction for a process.
    The fraction is used to limit an caching allocator to allocated memory on a CUDA device.
    The allowed value equals the total visible memory multiplied fraction.
    If trying to allocate more than the allowed value in a process, will raise an out of
    memory error in allocator.

    Args:
        fraction(float): Range: 0~1. Allowed memory equals total_memory * fraction.
        device (torch.device or int, optional): selected device. If it is
            ``None`` the default CUDA device is used.
    .. note::
        In general, the total available free memory is less than the total capacity.
    """
    _lazy_init()
    if device is None:
        device = torch.cuda.current_device()
    device = _get_device_index(device)
    if not isinstance(fraction, float):
        raise TypeError('Invalid type for fraction argument, must be `float`')
    if fraction < 0 or fraction > 1:
        raise ValueError('Invalid fraction value: {}. '
                         'Allowed range: 0~1'.format(fraction))

    torch._C._cuda_setMemoryFraction(fraction, device)


def empty_cache() -> None:
    r"""Releases all unoccupied cached memory currently held by the caching
    allocator so that those can be used in other GPU application and visible in
    `nvidia-smi`.

    .. note::
        :func:`~torch.cuda.empty_cache` doesn't increase the amount of GPU
        memory available for PyTorch. However, it may help reduce fragmentation
        of GPU memory in certain cases. See :ref:`cuda-memory-management` for
        more details about GPU memory management.
    """
    if is_initialized():
        torch._C._cuda_emptyCache()


def memory_stats(device: Union[Device, int] = None) -> Dict[str, Any]:
    r"""Returns a dictionary of CUDA memory allocator statistics for a
    given device.

    The return value of this function is a dictionary of statistics, each of
    which is a non-negative integer.

    Core statistics:

    - ``"allocated.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      number of allocation requests received by the memory allocator.
    - ``"allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      amount of allocated memory.
    - ``"segment.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      number of reserved segments from ``cudaMalloc()``.
    - ``"reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      amount of reserved memory.
    - ``"active.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      number of active memory blocks.
    - ``"active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      amount of active memory.
    - ``"inactive_split.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      number of inactive, non-releasable memory blocks.
    - ``"inactive_split_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      amount of inactive, non-releasable memory.

    For these core statistics, values are broken down as follows.

    Pool type:

    - ``all``: combined statistics across all memory pools.
    - ``large_pool``: statistics for the large allocation pool
      (as of October 2019, for size >= 1MB allocations).
    - ``small_pool``: statistics for the small allocation pool
      (as of October 2019, for size < 1MB allocations).

    Metric type:

    - ``current``: current value of this metric.
    - ``peak``: maximum value of this metric.
    - ``allocated``: historical total increase in this metric.
    - ``freed``: historical total decrease in this metric.

    In addition to the core statistics, we also provide some simple event
    counters:

    - ``"num_alloc_retries"``: number of failed ``cudaMalloc`` calls that
      result in a cache flush and retry.
    - ``"num_ooms"``: number of out-of-memory errors thrown.

    The caching allocator can be configured via ENV to not split blocks larger than a
    defined size (see Memory Management section of the Cuda Semantics documentation).
    This helps avoid memory fragmentation but may have a performance
    penalty. Additional outputs to assist with tuning and evaluating impact:

    - ``"max_split_size"``: blocks above this size will not be split.
    - ``"oversize_allocations.{current,peak,allocated,freed}"``:
      number of over-size allocation requests received by the memory allocator.
    - ``"oversize_segments.{current,peak,allocated,freed}"``:
      number of over-size reserved segments from ``cudaMalloc()``.

    The caching allocator can be configured via ENV to round memory allocations in order
    to reduce fragmentation. Sometimes the overhead from rounding can be higher than
    the fragmentation it helps reduce. The following stat can be used to check if
    rounding adds too much overhed:

    - ``"requested_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
      memory requested by client code, compare this with allocated_bytes to check if
      allocation rounding adds too much overhead.

    Args:
        device (torch.device or int, optional): selected device. Returns
            statistics for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.

    .. note::
        With :ref:`backend:cudaMallocAsync<cuda-memory-envvars>`, some stats are not
        meaningful, and are always reported as zero.
    """
    result = []

    def _recurse_add_to_result(prefix, obj):
        if isinstance(obj, dict):
            if len(prefix) > 0:
                prefix += "."
            for k, v in obj.items():
                _recurse_add_to_result(prefix + k, v)
        else:
            result.append((prefix, obj))

    stats = memory_stats_as_nested_dict(device=device)
    _recurse_add_to_result("", stats)
    result.sort()

    return collections.OrderedDict(result)


def memory_stats_as_nested_dict(device: Union[Device, int] = None) -> Dict[str, Any]:
    r"""Returns the result of :func:`~torch.cuda.memory_stats` as a nested dictionary."""
    if not is_initialized():
        return {}
    device = _get_device_index(device, optional=True)
    return torch._C._cuda_memoryStats(device)


def reset_accumulated_memory_stats(device: Union[Device, int] = None) -> None:
    r"""Resets the "accumulated" (historical) stats tracked by the CUDA memory allocator.

    See :func:`~torch.cuda.memory_stats` for details. Accumulated stats correspond to
    the `"allocated"` and `"freed"` keys in each individual stat dict, as well as
    `"num_alloc_retries"` and `"num_ooms"`.

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

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    device = _get_device_index(device, optional=True)
    return torch._C._cuda_resetAccumulatedMemoryStats(device)


def reset_peak_memory_stats(device: Union[Device, int] = None) -> None:
    r"""Resets the "peak" stats tracked by the CUDA memory allocator.

    See :func:`~torch.cuda.memory_stats` for details. Peak stats correspond to the
    `"peak"` key in each individual stat dict.

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

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    device = _get_device_index(device, optional=True)
    return torch._C._cuda_resetPeakMemoryStats(device)


def reset_max_memory_allocated(device: Union[Device, int] = None) -> None:
    r"""Resets the starting point in tracking maximum GPU memory occupied by
    tensors for a given device.

    See :func:`~torch.cuda.max_memory_allocated` for details.

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

    .. warning::
        This function now calls :func:`~torch.cuda.reset_peak_memory_stats`, which resets
        /all/ peak memory stats.

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    warnings.warn(
        "torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, "
        "which resets /all/ peak memory stats.",
        FutureWarning)
    return reset_peak_memory_stats(device=device)


def reset_max_memory_cached(device: Union[Device, int] = None) -> None:
    r"""Resets the starting point in tracking maximum GPU memory managed by the
    caching allocator for a given device.

    See :func:`~torch.cuda.max_memory_cached` for details.

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

    .. warning::
        This function now calls :func:`~torch.cuda.reset_peak_memory_stats`, which resets
        /all/ peak memory stats.

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    warnings.warn(
        "torch.cuda.reset_max_memory_cached now calls torch.cuda.reset_peak_memory_stats, "
        "which resets /all/ peak memory stats.",
        FutureWarning)
    return reset_peak_memory_stats(device=device)


def memory_allocated(device: Union[Device, int] = None) -> int:
    r"""Returns the current GPU memory occupied by tensors in bytes for a given
    device.

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

    .. note::
        This is likely less than the amount shown in `nvidia-smi` since some
        unused memory can be held by the caching allocator and some context
        needs to be created on GPU. See :ref:`cuda-memory-management` for more
        details about GPU memory management.
    """
    return memory_stats(device=device).get("allocated_bytes.all.current", 0)


def max_memory_allocated(device: Union[Device, int] = None) -> int:
    r"""Returns the maximum GPU memory occupied by tensors in bytes for a given
    device.

    By default, this returns the peak allocated memory since the beginning of
    this program. :func:`~torch.cuda.reset_peak_memory_stats` can be used to
    reset the starting point in tracking this metric. For example, these two
    functions can measure the peak allocated memory usage of each iteration in a
    training loop.

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

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    return memory_stats(device=device).get("allocated_bytes.all.peak", 0)


def memory_reserved(device: Union[Device, int] = None) -> int:
    r"""Returns the current GPU memory managed by the caching allocator in bytes
    for a given device.

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

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    return memory_stats(device=device).get("reserved_bytes.all.current", 0)


def max_memory_reserved(device: Union[Device, int] = None) -> int:
    r"""Returns the maximum GPU memory managed by the caching allocator in bytes
    for a given device.

    By default, this returns the peak cached memory since the beginning of this
    program. :func:`~torch.cuda.reset_peak_memory_stats` can be used to reset
    the starting point in tracking this metric. For example, these two functions
    can measure the peak cached memory amount of each iteration in a training
    loop.

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

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    return memory_stats(device=device).get("reserved_bytes.all.peak", 0)


def memory_cached(device: Union[Device, int] = None) -> int:
    r"""Deprecated; see :func:`~torch.cuda.memory_reserved`."""
    warnings.warn(
        "torch.cuda.memory_cached has been renamed to torch.cuda.memory_reserved",
        FutureWarning)
    return memory_reserved(device=device)


def max_memory_cached(device: Union[Device, int] = None) -> int:
    r"""Deprecated; see :func:`~torch.cuda.max_memory_reserved`."""
    warnings.warn(
        "torch.cuda.max_memory_cached has been renamed to torch.cuda.max_memory_reserved",
        FutureWarning)
    return max_memory_reserved(device=device)


def memory_snapshot():
    r"""Returns a snapshot of the CUDA memory allocator state across all devices.

    Interpreting the output of this function requires familiarity with the
    memory allocator internals.

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    return torch._C._cuda_memorySnapshot()['segments']


def memory_summary(device: Union[Device, int] = None, abbreviated: bool = False) -> str:
    r"""Returns a human-readable printout of the current memory allocator
    statistics for a given device.

    This can be useful to display periodically during training, or when
    handling out-of-memory exceptions.

    Args:
        device (torch.device or int, optional): selected device. Returns
            printout for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).
        abbreviated (bool, optional): whether to return an abbreviated summary
            (default: False).

    .. note::
        See :ref:`cuda-memory-management` for more details about GPU memory
        management.
    """
    device = _get_device_index(device, optional=True)
    stats = memory_stats(device=device)

    def _format_size(sz, pref_sz):
        prefixes = ["B  ", "KiB", "MiB", "GiB", "TiB", "PiB"]
        prefix = prefixes[0]
        for new_prefix in prefixes[1:]:
            if pref_sz < 768 * 1024:
                break
            prefix = new_prefix
            sz //= 1024
            pref_sz /= 1024
        return "{:6d} {}".format(sz, prefix)

    def _format_count(cnt, pref_cnt):
        prefixes = [" ", "K", "M"]
        prefix = prefixes[0]
        for new_prefix in prefixes[1:]:
            if pref_cnt < 750 * 1000:
                break
            prefix = new_prefix
            cnt //= 1000
            pref_cnt /= 1000
        return "{:7d} {} ".format(cnt, prefix)

    metrics_to_display = [
        ("allocated_bytes", "Allocated memory", _format_size),
        ("active_bytes", "Active memory", _format_size),
        ("requested_bytes", "Requested memory", _format_size),
        ("reserved_bytes", "GPU reserved memory", _format_size),
        ("inactive_split_bytes", "Non-releasable memory", _format_size),
        ("allocation", "Allocations", _format_count),
        ("active", "Active allocs", _format_count),
        ("segment", "GPU reserved segments", _format_count),
        ("inactive_split", "Non-releasable allocs", _format_count),
    ]

    lines = []
    lines.append("=" * 75)
    lines.append(" {_:16} PyTorch CUDA memory summary, device ID {device:<17d} ")
    lines.append("-" * 75)
    lines.append("  {_:9} CUDA OOMs: {num_ooms:<12d} | {_:6} cudaMalloc retries: {num_alloc_retries:<8d}  ")
    lines.append("=" * 75)
    lines.append("        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  ")

    for metric_key, metric_name, formatter in metrics_to_display:
        lines.append("-" * 75)
        submetrics = [("all", metric_name)]
        if not abbreviated:
            submetrics.append(("large_pool", "      from large pool"))
            submetrics.append(("small_pool", "      from small pool"))

        current_prefval, peak_prefval, allocated_prefval, freed_prefval = None, None, None, None

        for submetric_key, submetric_name in submetrics:
            prefix = metric_key + "." + submetric_key + "."

            current = stats[prefix + "current"]
            peak = stats[prefix + "peak"]
            allocated = stats[prefix + "allocated"]
            freed = stats[prefix + "freed"]

            if current_prefval is None:
                current_prefval = current
                peak_prefval = peak
                allocated_prefval = allocated
                freed_prefval = freed

            lines.append(" {:<21} | {} | {} | {} | {} ".format(
                submetric_name,
                formatter(current, current_prefval),
                formatter(peak, peak_prefval),
                formatter(allocated, allocated_prefval),
                formatter(freed, freed_prefval)),
            )

    metrics_to_display = [
        ("oversize_allocations", "Oversize allocations", _format_count),
        ("oversize_segments", "Oversize GPU segments", _format_count),
    ]

    for metric_key, metric_name, formatter in metrics_to_display:
        lines.append("-" * 75)

        prefix = metric_key + "."

        current = stats[prefix + "current"]
        peak = stats[prefix + "peak"]
        allocated = stats[prefix + "allocated"]
        freed = stats[prefix + "freed"]

        lines.append(" {:<21} | {} | {} | {} | {} ".format(
            metric_name,
            formatter(current, current),
            formatter(peak, peak),
            formatter(allocated, allocated),
            formatter(freed, freed)),
        )

    lines.append("=" * 75)

    fmt_dict = {"_": "", "device": device}
    for k, v in stats.items():
        fmt_dict[k.replace(".", "-")] = v
    return "|" + "|\n|".join(lines).format(**fmt_dict) + "|\n"


def list_gpu_processes(device: Union[Device, int] = None) -> str:
    r"""Returns a human-readable printout of the running processes
    and their GPU memory use for a given device.

    This can be useful to display periodically during training, or when
    handling out-of-memory exceptions.

    Args:
        device (torch.device or int, optional): selected device. Returns
            printout for the current device, given by :func:`~torch.cuda.current_device`,
            if :attr:`device` is ``None`` (default).
    """

    try:
        import pynvml  # type: ignore[import]
    except ModuleNotFoundError:
        return("pynvml module not found, please install pynvml")
    from pynvml import NVMLError_DriverNotLoaded
    try:
        pynvml.nvmlInit()
    except NVMLError_DriverNotLoaded:
        return ("cuda driver can't be loaded, is cuda enabled?")
    device = _get_device_index(device, optional=True)
    handle = pynvml.nvmlDeviceGetHandleByIndex(device)
    procs = pynvml.nvmlDeviceGetComputeRunningProcesses(handle)
    lines = []
    lines.append(f"GPU:{device}")
    if len(procs) == 0:
        lines.append("no processes are running")
    for p in procs:
        mem = p.usedGpuMemory / (1024 * 1024)
        lines.append(f"process {p.pid:>10d} uses {mem:>12.3f} MB GPU memory")
    return "\n".join(lines)

def mem_get_info(device: Union[Device, int] = None) -> Tuple[int, int]:
    r"""Returns the global free and total GPU memory occupied for a given
    device using cudaMemGetInfo.

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

    .. note::
        See :ref:`cuda-memory-management` for more
        details about GPU memory management.
    """
    if device is None:
        device = torch.cuda.current_device()
    device = _get_device_index(device)
    return torch.cuda.cudart().cudaMemGetInfo(device)

def _record_memory_history(enabled: bool, record_context=True,
                           trace_alloc_max_entries=1,
                           trace_alloc_record_context=False, device: Union[Device, int] = None,
                           _enable_expensive_cpp=False):
    """Enables recording of Python stack traces to be associated with memory
    allocations, so you can tell what allocated any piece of memory in
    :func:`torch.memory_snapshot`.

    The Python trace collection is fast (2us per trace), so you may consider
    enabling this on production jobs if you anticipate ever having to debug
    memory issues.

    .. warning:
        The :attr:`_enable_expensive_cpp` arguments lets you enable also
        collecting C++ stack traces.  This collection is VERY SLOW and should
        only be used if you are debugging framework problems on a minified
        example.  In principle, it should be possible to implement fast C++
        stack trace collection; file an issue with us if you need it.
    """
    with torch.cuda.device(device):
        _C._cuda_recordMemoryHistory(enabled, record_context, _enable_expensive_cpp,
                                     trace_alloc_max_entries, trace_alloc_record_context)

def _snapshot(device: Union[Device, int] = None):
    with torch.cuda.device(device):
        return _C._cuda_memorySnapshot()

def _save_segment_usage(filename='output.svg', snapshot=None):
    if snapshot is None:
        snapshot = _snapshot()
    with open(filename, 'w') as f:
        f.write(_segments(snapshot))

def _save_memory_usage(filename='output.svg', snapshot=None):
    if snapshot is None:
        snapshot = _snapshot()
    with open(filename, 'w') as f:
        f.write(_memory(snapshot))

def _set_allocator_settings(env: str):
    return torch._C._cuda_cudaCachingAllocator_set_allocator_settings(env)

def get_allocator_backend() -> str:
    r"""Returns a string describing the active allocator backend as set by
    ``PYTORCH_CUDA_ALLOC_CONF``. Currently available backends are
    ``native`` (PyTorch's native caching allocator) and `cudaMallocAsync``
    (CUDA's built-in asynchronous allocator).

    .. note::
        See :ref:`cuda-memory-management` for details on choosing the allocator backend.
    """
    return torch._C._cuda_getAllocatorBackend()

class _CUDAAllocator:
    r"""Wrapper over internal CUDA memory allocators.
    """
    def __init__(self, allocator: torch._C._cuda_CUDAAllocator):
        self._allocator = allocator

    def allocator(self):
        return self._allocator


class CUDAPluggableAllocator(_CUDAAllocator):
    r"""CUDA memory allocator loaded from a so file.

    Memory allocators are compiled in .so files and loaded dynamically using ctypes.
    To change the active allocator use the :func:`torch.memory.cuda.change_current_allocator`
    function.

    Args:
        path_to_so_file(str): Path in the filesystem to the `.so` file containing
            the allocator functions
        alloc_fn_name(str): Name of the function to perform the memory allocation
            in the so file. The signature must be:
            void* alloc_fn_name(ssize_t size, int device, cudaStream_t stream);
        free_fn_name(str): Name of the function to perform the memory release
            in the so file. The signature must be:
            void free_fn_name(void* ptr, size_t size, cudaStream_t stream);

    .. warning::
        This is currently supported only in unix OSs

    .. note::
        See :ref:`cuda-memory-management` for details on creating and using a custom allocator
    """
    def __init__(self, path_to_so_file: str, alloc_fn_name: str, free_fn_name: str):
        allocator = ctypes.CDLL(path_to_so_file)
        alloc_fn = ctypes.cast(getattr(allocator, alloc_fn_name), ctypes.c_void_p).value
        free_fn = ctypes.cast(getattr(allocator, free_fn_name), ctypes.c_void_p).value
        assert alloc_fn is not None
        assert free_fn is not None
        self._allocator = torch._C._cuda_customAllocator(alloc_fn, free_fn)


def change_current_allocator(allocator: _CUDAAllocator) -> None:
    r"""Changes the currently used memory allocator to be the one provided.
    If the current allocator has already been used/initialized, this function will error.


    Args:
        allocator (torch.cuda.memory._CUDAAllocator): allocator to be set as the active one.
    .. note::
        See :ref:`cuda-memory-management` for details on creating and using a custom allocator
    """
    torch._C._cuda_changeCurrentAllocator(allocator.allocator())


def _get_current_allocator() -> _CUDAAllocator:
    r"""Returns the allocator being currently used.

    .. note::
        See :ref:`cuda-memory-management` for details on creating and using a custom allocator
    """
    return _CUDAAllocator(torch._C._cuda_getAllocator())
