# 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.

from __future__ import annotations

import logging
import typing
from typing import TYPE_CHECKING, TypeVar

import paddle
from paddle.base import backward, core, framework
from paddle.base.core import prim_config
from paddle.incubate.autograd import primx, utils

if TYPE_CHECKING:
    from collections.abc import Sequence

    from paddle import Tensor
    from paddle.base.framework import Block

    _TensorOrTensorsT = TypeVar("_TensorOrTensorsT", Tensor, Sequence[Tensor])


@framework.static_only
def forward_grad(
    outputs: _TensorOrTensorsT,
    inputs: _TensorOrTensorsT,
    grad_inputs: _TensorOrTensorsT | None = None,
) -> _TensorOrTensorsT:
    """Forward mode of automatic differentiation.

    Note:
        **ONLY available in the static graph mode and primitive operators.**

    Args:
        outputs(Tensor|Sequence[Tensor]): The output tensor or tensors.
        inputs(Tensor|Sequence[Tensor]): The input tensor or tensors.
        grad_inputs(Tensor|Sequence[Tensor]): Optional, the gradient Tensor or
            Tensors of inputs which has the same shape with inputs, Defaults to
            None, in this case is equivalent to all ones.

    Returns:
        grad_outputs(Tensor|Sequence[Tensor]): The gradients for outputs.

    Examples:

        .. code-block:: python

            >>> # doctest: +SKIP('Transform NOT has linearize')
            >>> import numpy as np
            >>> import paddle

            >>> paddle.enable_static()
            >>> paddle.incubate.autograd.enable_prim()

            >>> startup_program = paddle.static.Program()
            >>> main_program = paddle.static.Program()

            >>> with paddle.static.program_guard(main_program, startup_program):
            ...     x = paddle.static.data('x', shape=[1], dtype='float32')
            ...     y = x * x
            ...     y_grad = paddle.incubate.autograd.forward_grad(y, x)
            ...     paddle.incubate.autograd.prim2orig()
            ...
            >>> exe = paddle.static.Executor()
            >>> exe.run(startup_program)
            >>> y_grad = exe.run(main_program, feed={'x': np.array([2.]).astype('float32')}, fetch_list=[y_grad])
            >>> print(y_grad)
            [array([4.], dtype=float32)]

            >>> paddle.incubate.autograd.disable_prim()
            >>> paddle.disable_static()
    """
    if not utils.prim_enabled():
        raise RuntimeError(
            'forward_grad must be running on primitive'
            'operators, use enable_prim to turn it on.'
        )

    if not isinstance(
        outputs, (framework.Variable, typing.Sequence, paddle.pir.Value)
    ):
        raise TypeError(
            f'Expected outputs is Tensor|Sequence[Tensor], '
            f'but got {type(outputs)}.'
        )

    if not isinstance(
        inputs, (framework.Variable, typing.Sequence, paddle.pir.Value)
    ):
        raise TypeError(
            f'Expected inputs is Tensor|Sequence[Tensor], '
            f'but got {type(inputs)}.'
        )

    ys, xs, xs_dot = (
        utils.as_tensors(outputs),
        utils.as_tensors(inputs),
        utils.as_tensors(grad_inputs),
    )

    block = framework.default_main_program().current_block()
    if any(x.block != block for x in xs + ys):
        raise RuntimeError(
            'Variable in inputs and targets should exist in current block of '
            'main program.'
        )

    primx.orig2prim(block)
    ad = primx.Transform(ys[0].block)
    _, ys_dot = ad.linearize(xs, ys, xs_dot)

    return (
        ys_dot[0]
        if isinstance(outputs, (framework.Variable, paddle.pir.Value))
        else ys_dot
    )


@framework.static_only
def grad(
    outputs: _TensorOrTensorsT,
    inputs: _TensorOrTensorsT,
    grad_outputs: _TensorOrTensorsT | None = None,
) -> _TensorOrTensorsT:
    """Reverse mode of automatic differentiation.

    Note:
        **ONLY available in the static graph mode and primitive operators**

    Args:
        outputs(Tensor|Sequence[Tensor]): The output Tensor or Tensors.
        inputs(Tensor|Sequence[Tensor]): The input Tensor or Tensors.
        grad_outputs(Tensor|Sequence[Tensor]): Optional, the gradient Tensor or
            Tensors of outputs which has the same shape with outputs, Defaults
            to None, in this case is equivalent to all ones.

    Returns:
        grad_inputs(Tensor|Tensors): The gradients for inputs.

    Examples:

        .. code-block:: python

            >>> # doctest: +SKIP('Transform NOT has linearize')
            >>> import numpy as np
            >>> import paddle

            >>> paddle.enable_static()
            >>> paddle.incubate.autograd.enable_prim()

            >>> startup_program = paddle.static.Program()
            >>> main_program = paddle.static.Program()
            >>> with paddle.static.program_guard(main_program, startup_program):
            ...     x = paddle.static.data('x', shape=[1], dtype='float32')
            ...     x.stop_gradients = False
            ...     y = x * x
            ...     x_grad = paddle.incubate.autograd.grad(y, x)
            ...     paddle.incubate.autograd.prim2orig()
            ...
            >>> exe = paddle.static.Executor()
            >>> exe.run(startup_program)
            >>> x_grad = exe.run(main_program, feed={'x': np.array([2.]).astype('float32')}, fetch_list=[x_grad])
            >>> print(x_grad)
            [array([4.], dtype=float32)]

            >>> paddle.incubate.autograd.disable_prim()
            >>> paddle.disable_static()
    """
    if not utils.prim_enabled():
        grad_inputs = backward.gradients(outputs, inputs, grad_outputs)
        # backward.gradients returns a list though the inputs is a single Tensor.
        # The follow code snippet fixes the problem by return the first element
        # of grad_inputs when the inputs is a single Tensor.
        if (
            isinstance(inputs, (framework.Variable, paddle.pir.Value))
            and isinstance(grad_inputs, typing.Sequence)
            and len(grad_inputs) > 0
        ):
            return grad_inputs[0]
        else:
            return grad_inputs

    if not isinstance(
        outputs, (framework.Variable, typing.Sequence, paddle.pir.Value)
    ):
        raise TypeError(
            f'Expected outputs is Tensor|Sequence[Tensor], '
            f'but got {type(outputs)}.'
        )

    if not isinstance(
        inputs, (framework.Variable, typing.Sequence, paddle.pir.Value)
    ):
        raise TypeError(
            f'Expected inputs is Tensor|Sequence[Tensor], '
            f'but got {type(inputs)}.'
        )

    ys, xs, ys_bar = (
        utils.as_tensors(outputs),
        utils.as_tensors(inputs),
        utils.as_tensors(grad_outputs),
    )
    block = framework.default_main_program().current_block()
    if any((x is not None and x.block != block) for x in xs + ys):
        raise RuntimeError(
            'Variable in inputs and outputs should be None or in current block of main program'
        )

    # TODO(Tongxin) without any prior knowledge about whether the program
    # is completely lowered to primitive ops, it's mandatory to run the lowering
    # pass once and again. This is obviously inefficient and needs to be
    # optimized.
    primx.orig2prim(block)
    ad = primx.Transform(block)
    xs_dot, ys_dot = ad.linearize(xs, ys)
    if any(var is None for var in ys_dot):
        raise RuntimeError(
            'Grads cannot be computed. The given outputs does not depend on inputs'
        )
    ys_bar, xs_bar = ad.transpose(ys_dot, xs_dot, ys_bar)

    # remove xs_dot and their constructor ops
    op_indexes = []
    for var in xs_dot:
        if var is not None:
            op_index = block.ops.index(var.op)
            if op_index < 0:
                raise ValueError(
                    f'op_index should be greater than or equal to 0, but op_index={op_index}.'
                )
            op_indexes.append(op_index)

    ad.erase_ops(sorted(op_indexes))
    ad.erase_dots(xs_dot)

    return (
        xs_bar[0]
        if isinstance(inputs, (framework.Variable, paddle.pir.Value))
        else xs_bar
    )


@framework.static_only
def to_prim(
    blocks: Block | Sequence[Block],
    blacklist: set[str] | frozenset[str] = frozenset(),
    whitelist: set[str] | frozenset[str] = frozenset(),
    start_idx: int = -1,
    backward_length: int = -1,
) -> None:
    """Search nonbasic ops which have be registered composite rules and replace them with primitive ops.
    The operators in blacklist will be excluded from program when lowering into primitives, and only the
    operators in whitelist will be lowering. The priority of blacklist is higher than whitelist, it means
    an operator both in blacklist and whitelist will not be lowering.

    The finally set that will be lowering is:
        (blocks.ops & ops have decomposite rule & whitelist) - blacklist

    Args:
        blacklist(frozenset): The Operators that will be exclude when lowering into primitives.
        whitelist(frozenset): Only the operators in whitelist will be lowering into primitives.
        start_idx(int): If start_idx exceeds -1, ops[start_idx:] will be processed. Default: -1.
        backward_length(int): If backward_length exceeds -1, ops[:-backward_length] will be processed. Default: -1.
    """
    if not core._is_fwd_prim_enabled():
        return
    if isinstance(blocks, paddle.base.framework.Block):
        logging.info("Atomize composite op to primitive ops begin.")
        main_program = blocks.program
    elif isinstance(blocks, typing.Sequence):
        for item in blocks:
            if not isinstance(item, paddle.base.framework.Block):
                raise TypeError(
                    f"Expect block or sequence of blocks, but sequence contains {type(item)}."
                )
        main_program = blocks[0].program
    else:
        raise TypeError(
            f"Expect block or sequence of blocks, but got {type(blocks)}."
        )
    if not isinstance(blacklist, (set, frozenset)):
        raise TypeError(
            f'Expected type of blacklist is set|frozenset, but got {type(blacklist)}.'
        )
    if not isinstance(whitelist, (set, frozenset)):
        raise TypeError(
            f'Expected type of whitelist is set|frozenset, but got {type(whitelist)}.'
        )

    blacklist = prim_config["forward_blacklist"] | blacklist

    with framework.program_guard(main_program):
        logging.info("Lowering composite forward ops begin...")

        if len(blacklist) > 0 and len(whitelist) > 0:
            filter_ = lambda x: x.type in whitelist and x.type not in blacklist
        elif len(blacklist) > 0 and len(whitelist) == 0:
            filter_ = lambda x: x.type not in blacklist
        elif len(blacklist) == 0 and len(whitelist) > 0:
            filter_ = lambda x: x.type in whitelist
        else:
            filter_ = lambda x: True
        primx._lower_composite(
            blocks,
            filter_,
            start_idx=start_idx,
            backward_length=backward_length,
        )
        replace_ops = prim_config["composite_ops_record"]
        logging.info(f"Lowering composite forward ops finish: {replace_ops}")