# Copyright (c) 2022 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 copy

import paddle
from paddle import _C_ops
from paddle.base import core
from paddle.base.data_feeder import check_variable_and_dtype
from paddle.base.framework import EagerParamBase
from paddle.base.layer_helper import LayerHelper
from paddle.framework import in_dynamic_or_pir_mode

__all__ = []


# TODO(qili93): remove this op after custom op and custom device
# integrated and then move this op along with its code to plugin.
def _npu_identity(x, format=-1):
    """

    This OP takes in the Tensor :attr:`x` and change it to output with
    aclFormat with int value. This API is only used for Ascend NPU.

    Args:
        x(Tensor): An input N-D Tensor with data type bool, float16,
                   float32, float64, int32, int64, int16, int8, uint8.
        format(int): Storage data format of the output in aclFormat,
                     default value is -1.

    Returns:
        Tensor: A Tensor with acl storage format on Ascend NPU.

    Examples:
        .. code-block:: pycon

            >>> # doctest: +REQUIRES(env:NPU)
            >>> import paddle
            >>> paddle.device.set_device('npu')

            >>> x = paddle.ones(shape=[6])
            >>> y = paddle.incubate._npu_identity(x, 3)  # ACL_FORMAT_NC1HWC0 = 3
            >>> print(y.shape)
            paddle.Size([1, 1, 1, 1, 16])
    """
    if in_dynamic_or_pir_mode():
        return _C_ops.npu_identity(x, format)
    else:
        check_variable_and_dtype(
            x,
            'x',
            [
                'bool',
                'int8',
                'uint8',
                'int16',
                'int32',
                'int64',
                'float16',
                'float32',
                'float64',
            ],
            'npu_identity',
        )

        helper = LayerHelper('npu_identity', **locals())
        out = helper.create_variable_for_type_inference(
            dtype=x.dtype, stop_gradient=x.stop_gradient
        )
        helper.append_op(
            type='npu_identity',
            inputs={'x': [x]},
            outputs={'out': [out]},
            attrs={'format': format},
        )
        return out


def _load_reload_impl(src_tensor, func):
    """
    Helper to create a new destination tensor and call 'func(dst, src)'
    which is either offload or reload.
    """
    if isinstance(src_tensor, EagerParamBase):
        state = copy.deepcopy(src_tensor.__dict__)
        new_param = EagerParamBase(src_tensor.shape, src_tensor.dtype, **state)
        task = func(new_param, src_tensor)
        return new_param, task
    elif isinstance(src_tensor, paddle.Tensor):
        new_varbase = core.eager.Tensor()
        task = func(new_varbase, src_tensor)
        return new_varbase, task


def create_async_load():
    """
    Constructs a new AsyncLoad object.
    It is used to load/reload data asynchronously on GPU.
    """
    custom_devices = paddle.device.get_all_custom_device_type()
    if paddle.is_compiled_with_xpu():
        return core.XpuAsyncLoad()
    elif any(
        paddle.is_compiled_with_custom_device(dev) for dev in custom_devices
    ):
        return None
    else:  # default is GPU or CUDA
        return core.AsyncLoad()


def create_xpu_async_load():
    """
    Constructs a new AsyncLoad object.
    It is used to load/reload data asynchronously on XPU.
    """
    return core.XpuAsyncLoad()


class _NoopAsyncTask:
    """A dummy Task for sync‐fallback on XPU."""

    def is_completed(self):
        return True

    def cpu_wait(self):
        pass

    def xpu_wait(self):
        pass


def async_offload(src_tensor, async_load):
    """
    Loads the source tensor into the destination tensor asynchronously.

    Args:
        src_tensor (EagerParamBase|paddle.Tensor): The source tensor.
        async_load (core.AsyncLoad): The AsyncLoad object.

    Returns:
        tuple: A tuple containing two elements:
         - dest_tensor (EagerParamBase|paddle.Tensor): The destination tensor.
         - task (Task): The task that loads the source tensor into the destination tensor.
    """
    is_xpu_tensor = (
        paddle.is_compiled_with_xpu()
        and hasattr(src_tensor, "place")
        and src_tensor.place.is_xpu_place()
    )

    # async_offload does not support custom device now
    custom_devices = paddle.device.get_all_custom_device_type()
    is_custom_tensor = (
        any(
            paddle.is_compiled_with_custom_device(dev) for dev in custom_devices
        )
        and hasattr(src_tensor, "place")
        and src_tensor.place.custom_device_type() in custom_devices
    )

    if is_xpu_tensor or is_custom_tensor:
        # sync fallback
        host_tensor = src_tensor.cpu()
        out = paddle.to_tensor(host_tensor.numpy(), place=paddle.CPUPlace())
        return out, _NoopAsyncTask()

    return _load_reload_impl(src_tensor, async_load.offload)


def async_reload(src_tensor, async_load):
    """
    Reloads the source tensor into the destination tensor asynchronously.

    Args:
        src_tensor (EagerParamBase|paddle.Tensor): The source tensor.
        async_load (core.AsyncLoad): The AsyncLoad object.

    Returns:
        tuple: A tuple containing two elements:
         - dest_tensor (EagerParamBase|paddle.Tensor): The destination tensor.
         - task (Task): The task that reloads the source tensor into the destination tensor.
    """

    if (
        paddle.is_compiled_with_xpu()
        and hasattr(src_tensor, "place")
        and src_tensor.place.is_cpu_place()
    ):
        arr = src_tensor.numpy()
        xpu = paddle.to_tensor(arr, place=paddle.XPUPlace(0))
        return xpu, _NoopAsyncTask()

    return _load_reload_impl(src_tensor, async_load.reload)


def async_offload_with_offset(
    src_tensor, dst_tensor, src_offset, dst_offset, offload_size, async_loader
):
    """
    Offloading the source tensor into the destination tensor asynchronously with offset and size customized.

    Args:
        src_tensor (EagerParamBase|paddle.Tensor): The source tensor.
        dst_tensor (EagerParamBase|paddle.Tensor): The destination tensor.
        src_offset (int): The element offset of the source tensor.
        dst_offset (int): The element offset of the destination tensor.
        offload_size (int): The size of the data to be loaded.
        async_loader (core.AsyncLoad): The AsyncLoad object.

    Returns:
        task (Task): The task that operates partial offloading.
    """
    assert len(src_tensor.shape) <= 1, "Only support 1-D tensor"
    assert len(dst_tensor.shape) <= 1, "Only support 1-D tensor"
    assert src_tensor.dtype == dst_tensor.dtype, "Only support same dtype"

    return async_loader.offload_with_offset(
        dst_tensor, src_tensor, dst_offset, src_offset, offload_size
    )


def enable_activation_offload(model, enable=True, retry_times=1):
    """
    Enable activation offload
    """
    if enable:
        paddle.set_flags({"FLAGS_offload_retry_times": retry_times})
        paddle.core.register_offload_callback()
        paddle.core.set_skip_offload_callback_tensors(model.parameters())
    else:
        paddle.set_flags({"FLAGS_offload_retry_times": -1})
        paddle.core.clear_offload_callback()
        paddle.core.set_skip_offload_callback_tensors([])