# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle
from paddle.fluid import core
from paddle.fluid.wrapped_decorator import signature_safe_contextmanager

from .streams import Stream  # noqa: F401
from .streams import Event  # noqa: F401

__all__ = [
    'Stream',
    'Event',
    'current_stream',
    'synchronize',
    'device_count',
    'empty_cache',
    'stream_guard',
]


def current_stream(device=None):
    '''
    Return the current CUDA stream by the device.

    Parameters:
        device(paddle.CUDAPlace()|int, optional): The device or the ID of the device which want to get stream from. 
        If device is None, the device is the current device. Default: None.
    
    Returns:
        CUDAStream: the stream to the device.
    
    Examples:
        .. code-block:: python

            # required: gpu
            import paddle

            s1 = paddle.device.cuda.current_stream()

            s2 = paddle.device.cuda.current_stream(0)

            s3 = paddle.device.cuda.current_stream(paddle.CUDAPlace(0))

    '''

    device_id = -1

    if device is not None:
        if isinstance(device, int):
            device_id = device
        elif isinstance(device, core.CUDAPlace):
            device_id = device.get_device_id()
        else:
            raise ValueError("device type must be int or paddle.CUDAPlace")

    return core._get_current_stream(device_id)


def synchronize(device=None):
    '''
    Wait for the compute on the given CUDA device to finish.

    Parameters:
        device(paddle.CUDAPlace()|int, optional): The device or the ID of the device.
        If device is None, the device is the current device. Default: None.
    
    Examples:
        .. code-block:: python

            # required: gpu
            import paddle

            paddle.device.cuda.synchronize()
            paddle.device.cuda.synchronize(0)
            paddle.device.cuda.synchronize(paddle.CUDAPlace(0))

    '''

    device_id = -1

    if device is not None:
        if isinstance(device, int):
            device_id = device
        elif isinstance(device, core.CUDAPlace):
            device_id = device.get_device_id()
        else:
            raise ValueError("device type must be int or paddle.CUDAPlace")

    return core._device_synchronize(device_id)


def device_count():
    '''
    Return the number of GPUs available.
    
    Returns:
        int: the number of GPUs available.

    Examples:
        .. code-block:: python

            import paddle

            paddle.device.cuda.device_count()

    '''

    num_gpus = core.get_cuda_device_count() if hasattr(
        core, 'get_cuda_device_count') else 0

    return num_gpus


def empty_cache():
    '''
    Releases idle cached memory held by the allocator so that those can be used in other GPU
    application and visible in `nvidia-smi`. In most cases you don't need to use this function,
    Paddle does not release the memory back to the OS when you remove Tensors on the GPU,
    Because it keeps gpu memory in a pool so that next allocations can be done much faster.

    Examples:
        .. code-block:: python

            import paddle

            # required: gpu
            paddle.set_device("gpu")
            tensor = paddle.randn([512, 512, 512], "float")
            del tensor
            paddle.device.cuda.empty_cache()
    '''

    if core.is_compiled_with_cuda():
        core.cuda_empty_cache()


def _set_current_stream(stream):
    '''
    Set the current stream.

    Parameters:
        stream(paddle.device.cuda.Stream): The selected stream.

    Returns:
        CUDAStream: The previous stream.

    '''

    if not isinstance(stream, paddle.device.cuda.Stream):
        raise TypeError("stream type should be paddle.device.cuda.Stream")

    cur_stream = current_stream()
    if id(stream) == id(cur_stream):
        return stream
    return core._set_current_stream(stream)


@signature_safe_contextmanager
def stream_guard(stream):
    '''
    **Notes**:
        **This API only supports dygraph mode currently.**

    A context manager that specifies the current stream context by the given stream.

    Parameters:
        stream(paddle.device.cuda.Stream): the selected stream. If stream is None, just yield. The default value is None.

    Examples:
        .. code-block:: python

            # required: gpu
            import paddle

            s = paddle.device.cuda.Stream()
            data1 = paddle.ones(shape=[20])
            data2 = paddle.ones(shape=[20])
            with paddle.device.cuda.stream_guard(s):
                data3 = data1 + data2

    '''

    if stream is not None and not isinstance(stream, paddle.device.cuda.Stream):
        raise TypeError("stream type should be paddle.device.cuda.Stream")

    cur_stream = current_stream()
    if stream is None or id(stream) == id(cur_stream):
        yield
    else:
        pre_stream = _set_current_stream(stream)
        try:
            yield
        finally:
            stream = _set_current_stream(pre_stream)