# 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 contextlib
import paddle
from paddle.static import gradients
from ..fluid import framework
from ..fluid.dygraph import grad
from ..tensor.creation import assign
from ..tensor import reshape, zeros_like, to_tensor
from .utils import _tensors, _stack_tensor_or_return_none, _replace_none_with_zero_tensor


@contextlib.contextmanager
def gradient_scope(*var_lists, create_graph=False, allow_unused=False):
    def grad_fn(ys, xs, v=None, create_graph=create_graph):
        if v is not None:
            assert len(ys) == len(v), (
                f'The argument {v} is expected to be of the same size as the output. '
                f'Here the output is {ys}, and `v` is {v}.')
        if allow_unused:
            ys = [
                to_tensor(
                    [0.0], stop_gradient=False) if y is None else y for y in ys
            ]
        return grad(
            ys, xs, v, create_graph=create_graph, allow_unused=allow_unused)

    def return_fn(out):
        if isinstance(out, paddle.Tensor):
            if not create_graph:
                out = out.detach()
            return out
        if isinstance(out, list):
            return list(return_fn(x) for x in out)
        elif isinstance(out, tuple):
            return tuple(return_fn(x) for x in out)
        else:
            assert out is None
            return out

    def process(vl):
        if vl is None:
            return None
        out = []
        # If v is treated as constant in the outer scope, its gradient is guaranteed
        # not to be taken beyond this scope. Within this scope, however, v's gradient
        # may be computed. We only need to detach v in this case.
        # Otherwise, v's gradient is valid, and is subject to update beyond this scope.
        # In this case we must not confuse the gradient in the outer scope with the
        # inner one's. Moreover, we need to make sure that the result from the inner
        # scope can flow back to the outer scope. This can be satisfied by extending
        # the original variable with a duplication operation v1 = v so that v still
        # maintains the complete lineage.
        for v in vl:
            if v is None:
                out.append(v)
                continue
            if create_graph and not v.stop_gradient:
                v = assign(v)
            else:
                v = v.detach()
                v.stop_gradient = False
            out.append(v)
        return out

    try:
        var_lists = [process(vl) for vl in var_lists]
        bundle = var_lists + [grad_fn, return_fn]
        yield bundle
    finally:
        pass


@framework.dygraph_only
def vjp(func, inputs, v=None, create_graph=False, allow_unused=False):
    r"""Computes the Vector-Jacobian product, a functional form of
    reverse mode automatic differentiation.

    Args:
        func(Callable): `func` takes as input a tensor or a list/tuple
            of tensors and returns a tensor or a list/tuple of tensors.
        inputs(list[Tensor]|tuple[Tensor]|Tensor): used as positional
            arguments to evaluate `func`. `inputs` is accepted as one
            tensor or a list of tensors.
        v(list[Tensor]|tuple[Tensor]|Tensor|None, optional): the
            cotangent vector invovled in the VJP computation. `v` matches
            the size and shape of `func`'s output. Default value is None
            and in this case is equivalent to all ones the same size
            of `func`'s output.
        create_graph(bool, optional): if `True`, gradients can be
            evaluated on the results. If `False`, taking gradients on
            the results is invalid. Default value is False.
        allow_unused(bool, optional): In case that some Tensors of
            `inputs` do not contribute to the computation of the output.
            If `allow_unused` is False, an error will be raised,
            Otherwise, the gradients of the said inputs are returned
            None. Default value is False.

    Returns:
        output(tuple):
            func_out(list[Tensor]|tuple[Tensor]|Tensor): the output of
                `func(inputs)`
            vjp(list[Tensor]): the pullback results of `v` on `func`

    Examples:
      .. code-block:: python

        def func(x):
          return paddle.matmul(x, x)

        x = paddle.ones(shape=[2, 2], dtype='float32')
        output, inputs_grad = vjp(func, x)
        print(inputs_grad)
        # [Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
        #        [[4., 4.],
        #         [4., 4.]])]

        v = paddle.to_tensor([[1.0, 0.0], [0.0, 0.0]])
        output, inputs_grad = vjp(func, x, v)
        print(inputs_grad)
        # [Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
        #        [[2., 1.],
        #         [1., 0.]])]

        output, inputs_grad = vjp(func, x, v, create_graph=True)
        print(inputs_grad)
        # [Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
        #        [[2., 1.],
        #         [1., 0.]])]

        y = paddle.ones(shape=[2, 2], dtype='float32')
        def func_unused(x, y):
          return paddle.matmul(x, x)

        output, inputs_grad = vjp(func, [x, y], v)
        # ValueError: (InvalidArgument) The 1-th input does not appear in the backward graph. 
        # Please check the input variable or set allow_unused=True to get None result.
        # [Hint: Expected allow_unused_ == true, but received allow_unused_:0 != true:1.]     

        output, inputs_grad = vjp(func, [x, y], v, allow_unused=True)
        print(inputs_grad)
        # [Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
        #        [[2., 1.],
        #         [1., 0.]]), None]
    """
    xs = _tensors(inputs, "inputs")
    if v is not None:
        v = _tensors(v, "v")

    with gradient_scope(
            xs, v, create_graph=create_graph,
            allow_unused=allow_unused) as [xs, v, grad_fn, return_fn]:
        outputs = func(*xs)
        ys = _tensors(outputs, "outputs")
        grads = grad_fn(ys, xs, v)
        outputs, grads = return_fn(outputs), return_fn(grads)

    return outputs, grads


@framework.dygraph_only
def jvp(func, inputs, v=None, create_graph=False, allow_unused=False):
    r"""
    Computes the Jacobian-Vector product for a function at the given
    inputs and a vector in the tangent space induced by the inputs.

    .. note::
        **This API is ONLY available in imperative mode.**

    Args:
        func(Callable): `func` takes as input a tensor or a list/tuple
            of tensors and returns a tensor or a list/tuple of tensors.
        inputs(list[Tensor]|tuple[Tensor]|Tensor): used as positional
            arguments to evaluate `func`. `inputs` is accepted as one
            tensor or a list/tuple of tensors.
        v(list[Tensor]|tuple[Tensor]|Tensor|None, optional): the
            tangent vector invovled in the JVP computation. `v` matches
            the size and shape of `inputs`. `v` is Optional if `func`
            returns a single tensor. Default value is None and in this
            case is equivalent to all ones the same size of `inputs`.
        create_graph(bool, optional): if `True`, gradients can
            be evaluated on the results. If `False`, taking gradients
            on the results is invalid. Default value is False.
        allow_unused(bool, optional): In case that some Tensors of
            `inputs` do not contribute to the computation of the output.
            If `allow_unused` is False, an error will be raised,
            Otherwise, the gradients of the said inputs are returned
            None. Default value is False.

    Returns:
        output(tuple):
            func_out(list[Tensor]|tuple[Tensor]|Tensor): the output of
                `func(inputs)`
            jvp(list[Tensor]): the pullback results of `v` on `func`

    Examples:
    .. code-block:: python

        def func(x):
          return paddle.matmul(x, x)

        x = paddle.ones(shape=[2, 2], dtype='float32')

        output, inputs_grad = jvp(func, x)
        print(inputs_grad)
        # [Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
        #        [[2., 2.],
        #         [2., 2.]])]

        v = paddle.to_tensor([[1.0, 0.0], [0.0, 0.0]])
        output, inputs_grad = vjp(func, x, v)
        print(inputs_grad)
        # [Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
        #        [[1., 1.],
        #         [0., 0.]])]

    """
    xs = _tensors(inputs, "inputs")
    if v is not None:
        v = _tensors(v, "v")

    with gradient_scope(
            xs, v, create_graph=create_graph,
            allow_unused=allow_unused) as [xs, v, grad_fn, return_fn]:
        outputs = func(*xs)
        ys = _tensors(outputs, "outputs")
        ys_grad = [zeros_like(y) for y in ys]
        xs_grad = grad_fn(ys, xs, ys_grad, create_graph=True)
        ys_grad = grad_fn(xs_grad, ys_grad, v)
        outputs, ys_grad = return_fn(outputs), return_fn(ys_grad)

    return outputs, ys_grad


@framework.dygraph_only
def jacobian(func, inputs, create_graph=False, allow_unused=False):
    ''' 
    .. note::
        **This API is ONLY available in the imperative mode.**

    This function computes the Jacobian matrix of `func` with respect to `inputs`.

    Parameters:
        func (function): a Python function that takes a Tensor or a Tensor
            list/tuple as inputs and returns a Tensor or a Tensor tuple.
        inputs (Tensor|list(Tensor)|tuple(Tensor)): the input Tensor or 
            Tensor list/tuple of the function ``func``.
        create_graph (bool, optional): whether to create the gradient graphs
            of the computing process. When it is True, higher order derivatives
            are supported to compute; when it is False, the gradient graphs of
            the computing process would be discarded. Defaults to ``False``.
        allow_unused (bool, optional): whether to raise error or return None if
            some Tensors of `inputs` are unreachable in the graph. Error would
            be raised if allow_unused=False, and None would be returned as
            their gradients if allow_unused=True. Default False.
    Returns:
        Jacobian (Tensor or nested tuple of Tensors): if function ``func``
        takes a Tensor as inputs and returns a Tensor as outputs, Jacobian
        will be a single Tensor containing the Jacobian matrix for the
        linearized inputs and outputs. If one of the inputs and outputs is
        a Tensor, and another is a Tensor list/tuple, then the Jacobian will
        be a tuple of Tensors. If both of inputs and outputs are Tensor
        list/tuple, then the Jacobian will be a tuple of tuple of Tensors
        where ``Jacobian[i][j]`` will contain the Jacobian matrix of the
        linearized ``i``th output and ``j``th input and will have same
        dtype and device as the corresponding input. ``Jacobian[i][j]`` will
        have as size ``m * n``, where ``m`` and ``n`` denote the numbers of
        elements of ``i``th output and ``j``th input respectively.


    Examples 1:
        .. code-block:: python

            import paddle

            def func(x):
                return paddle.matmul(x, x)

            x = paddle.ones(shape=[2, 2], dtype='float32')
            x.stop_gradient = False
            jacobian = paddle.autograd.jacobian(func, x)
            print(jacobian)
            # Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[2., 1., 1., 0.],
            #         [1., 2., 0., 1.],
            #         [1., 0., 2., 1.],
            #         [0., 1., 1., 2.]])

    Examples 2:
        .. code-block:: python

            import paddle

            def func(x, y):
                return paddle.matmul(x, y)

            x = paddle.ones(shape=[2, 2], dtype='float32')
            y = paddle.ones(shape=[2, 2], dtype='float32') * 2
            x.stop_gradient = False
            y.stop_gradient = False
            jacobian = paddle.autograd.jacobian(func, [x, y], create_graph=True)
            print(jacobian)
            # (Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
            #        [[2., 2., 0., 0.],
            #         [2., 2., 0., 0.],
            #         [0., 0., 2., 2.],
            #         [0., 0., 2., 2.]]), 
            #  Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
            #        [[1., 0., 1., 0.],
            #         [0., 1., 0., 1.],
            #         [1., 0., 1., 0.],
            #         [0., 1., 0., 1.]]))

    Examples 3:
        .. code-block:: python

            import paddle

            def func(x, y):
                return paddle.matmul(x, y), x * x

            x = paddle.ones(shape=[2, 2], dtype='float32')
            y = paddle.ones(shape=[2, 2], dtype='float32') * 2
            x.stop_gradient = False
            y.stop_gradient = False
            jacobian = paddle.autograd.jacobian(func, [x, y], allow_unused=True)
            print(jacobian)
            # ((Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[2., 2., 0., 0.],
            #         [2., 2., 0., 0.],
            #         [0., 0., 2., 2.],
            #         [0., 0., 2., 2.]]),
            #   Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[1., 0., 1., 0.],
            #         [0., 1., 0., 1.],
            #         [1., 0., 1., 0.],
            #         [0., 1., 0., 1.]])),
            #  (Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[2., 0., 0., 0.],
            #         [0., 2., 0., 0.],
            #         [0., 0., 2., 0.],
            #         [0., 0., 0., 2.]]), None))

    '''
    inputs = _tensors(inputs, "inputs")
    outputs = _tensors(func(*inputs), "outputs")
    fin_size = len(inputs)
    fout_size = len(outputs)
    flat_outputs = tuple(reshape(output, shape=[-1]) for output in outputs)
    jacobian = tuple()
    for i, flat_output in enumerate(flat_outputs):
        jac_i = list([] for _ in range(fin_size))
        for k in range(len(flat_output)):
            row_k = grad(
                flat_output[k],
                inputs,
                create_graph=create_graph,
                retain_graph=True,
                allow_unused=allow_unused)
            for j in range(fin_size):
                jac_i[j].append(
                    reshape(
                        row_k[j], shape=[-1])
                    if isinstance(row_k[j], paddle.Tensor) else None)
        jacobian += (tuple(
            _stack_tensor_or_return_none(jac_i_j) for jac_i_j in jac_i), )
    if fin_size == 1 and fout_size == 1:
        return jacobian[0][0]
    elif fin_size == 1 and fout_size != 1:
        return tuple(jacobian[i][0] for i in range(fout_size))
    elif fin_size != 1 and fout_size == 1:
        return jacobian[0]
    else:
        return jacobian


@framework.dygraph_only
def batch_jacobian(func, inputs, create_graph=False, allow_unused=False):
    ''' 
    .. note::
        **This API is ONLY available in the imperative mode.**

    This function computes the batch Jacobian matrix of `func` with respect to `inputs`.
    Noted that the first dimension of inputs is batch size.

    Parameters:
        func (function): a Python function that takes a Tensor or a Tensor
            list/tuple as inputs(the first dimension is batch size) and 
            returns a Tensor or a Tensor tuple.
        inputs (Tensor|list(Tensor)|tuple(Tensor)): the input Tensor or 
            Tensor list/tuple of the function ``func``, Noted that
            the first dimension of inputs is batch size.
        create_graph (bool, optional): whether to create the gradient graphs
            of the computing process. When it is True, higher order derivatives
            are supported to compute; when it is False, the gradient graphs of
            the computing process would be discarded. Defaults to ``False``.
        allow_unused (bool, optional): whether to raise error or return None if
            some Tensors of `inputs` are unreachable in the graph. Error would
            be raised if allow_unused=False, and None would be returned as
            their gradients if allow_unused=True. Default False.
    Returns:
        Jacobian (Tensor or nested tuple of Tensors): if function ``func``
        takes a Tensor as inputs and returns a Tensor as outputs, Jacobian
        will be a single Tensor containing the Jacobian matrix for the
        linearized inputs and outputs. If one of the inputs and outputs is
        a Tensor, and another is a Tensor list/tuple, then the Jacobian will
        be a tuple of Tensors. If both of inputs and outputs are Tensor
        list/tuple, then the Jacobian will be a tuple of tuple of Tensors.
        Noted that the first dimension of inputs is batch size.
        
        For example,
        the inputs shape and outputs shape of function ``func` is [batch_size, num] 
        and [batch_size, num] respectively, then the Jacobian will be a Tensor with
        a shape of [num, batch_size * num], where ``Jacobian[i][j]`` will contain 
        the Jacobian matrix of the ``i``th column output and the ``j``th input and 
        will have same dtype and device as the corresponding input.
        Other situations can be deduced by analogy.

    Examples 1:
        .. code-block:: python

            import paddle

            x = paddle.ones(shape=(4, 2), dtype='float64')
            weight = paddle.ones(shape=(2, 4), dtype='float64')
            y = paddle.ones(shape=(4, 2), dtype='float64')

            def func(x):
                return paddle.matmul(paddle.matmul(x, weight), y)

            x.stop_gradient = False
            batch_jacobian = paddle.autograd.batch_jacobian(func, x)
            print(batch_jacobian)
            # Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #      [[4., 4., 4., 4., 4., 4., 4., 4.],
            #       [4., 4., 4., 4., 4., 4., 4., 4.]])

    Examples 2:
        .. code-block:: python

            import paddle

            x = paddle.ones(shape=(4, 2), dtype='float64')
            weight = paddle.ones(shape=(2, 4), dtype='float64')
            y = paddle.ones(shape=(4, 2), dtype='float64')

            def func(x):
                return paddle.matmul(paddle.matmul(x, weight), y), x * x

            x.stop_gradient = False
            batch_jacobian = paddle.autograd.batch_jacobian(func, x) 
            print(batch_jacobian)    
            # (Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #       [[4., 4., 4., 4., 4., 4., 4., 4.],
            #        [4., 4., 4., 4., 4., 4., 4., 4.]]), Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #       [[2., 0., 2., 0., 2., 0., 2., 0.],
            #        [0., 2., 0., 2., 0., 2., 0., 2.]]))

    Examples 3:
        .. code-block:: python

            import paddle

            x = paddle.ones(shape=(4, 2), dtype='float64')
            weight = paddle.ones(shape=(2, 4), dtype='float64')
            y = paddle.ones(shape=(4, 2), dtype='float64')

            def func(x, y):
                return x * y

            x.stop_gradient = False
            y.stop_gradient = False
            batch_jacobian = paddle.autograd.batch_jacobian(func, [x, y])
            print(batch_jacobian)
            # (Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #       [[1., 0., 1., 0., 1., 0., 1., 0.],
            #        [0., 1., 0., 1., 0., 1., 0., 1.]]), Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #       [[1., 0., 1., 0., 1., 0., 1., 0.],
            #        [0., 1., 0., 1., 0., 1., 0., 1.]]))
   
    '''
    inputs = _tensors(inputs, "inputs")
    outputs = _tensors(func(*inputs), "outputs")
    batch_size = inputs[0].shape[0]
    for input in inputs:
        assert input.shape[
            0] == batch_size, "The first dimension of input should equals to the same batch size!"
    for output in outputs:
        assert output.shape[
            0] == batch_size, "The first dimension of output should equals to the same batch size!"
    fin_size = len(inputs)
    fout_size = len(outputs)
    flat_outputs = tuple(
        reshape(
            output, shape=[batch_size, -1]) for output in outputs)
    jacobian = tuple()
    for i, flat_output in enumerate(flat_outputs):
        jac_i = list([] for _ in range(fin_size))
        for k in range(flat_output.shape[1]):
            row_k = grad(
                flat_output[:, k],
                inputs,
                create_graph=create_graph,
                retain_graph=True,
                allow_unused=allow_unused)
            for j in range(fin_size):
                jac_i[j].append(
                    reshape(
                        row_k[j], shape=[-1])
                    if isinstance(row_k[j], paddle.Tensor) else None)
        jacobian += (tuple(
            _stack_tensor_or_return_none(jac_i_j) for jac_i_j in jac_i), )
    if fin_size == 1 and fout_size == 1:
        return jacobian[0][0]
    elif fin_size == 1 and fout_size != 1:
        return tuple(jacobian[i][0] for i in range(fout_size))
    elif fin_size != 1 and fout_size == 1:
        return jacobian[0]
    else:
        return jacobian


@framework.dygraph_only
def batch_hessian(func, inputs, create_graph=False, allow_unused=False):
    ''' 
    .. note::
        **This API is ONLY available in the imperative mode.**

    This function computes the batch Hessian matrix of `func` with respect to `inputs`.
    Noted that the first dimension of inputs is batch size.

    Parameters:
        func (function): a Python function that takes a Tensor or a Tensor
            list/tuple as inputs(the first dimension is batch size) and
            returns a Tensor with shape [batch_size, 1].
        inputs (Tensor|list(Tensor)|tuple(Tensor)): the input Tensor or 
            Tensor list/tuple of the function ``func``.
            Noted that the first dimension of inputs is batch size.
        create_graph (bool, optional): whether to create the gradient graphs
            of the computing process. When it is True, higher order derivatives
            are supported to compute; when it is False, the gradient graphs of
            the computing process would be discarded. Defaults to ``False``.
        allow_unused (bool, optional): whether to raise error or return None if
            some Tensors of `inputs` are unreachable in the graph. Error would
            be raised if allow_unused=False, and None would be returned as
            their gradients if allow_unused=True. Default False.
    Returns:
        Hessian (Tensor or a tuple of tuple of Tensors): if function ``func``
        takes a Tensor as ``inputs``, Hessian will be a single Tensor containing
        the Hessian matrix for the linearized ``inputs`` Tensor. If function
        ``func`` takes a Tensor list/tuple as ``inputs``, then the Hessian will
        be a tuple of tuple of Tensors. Noted that the first dimension of inputs 
        is batch size and the execution step is to obtain the result of the 
        first order differentiation, and then differentiate the batch input.

        For example,
        the inputs shape and outputs shape of function ``func` is [batch_size, num] 
        and [batch_size, 1] respectively, then the batched Hessian will be a Tensor with
        a shape of [num, batch_size * num].
        
        Why the final shape in this case is that?
        because batch_hessian will create a inner func(the wrapper of paddle.grad() func)
        to computes the sum of gradients of `outputs` with respect to each `inputs`,
        this inner func will get the first order differentiation and shape is [batch_size, num], 
        then call batch_jacobian to compute jacobian between the first order differentiation
        and the origin inputs. The final result ``Hessian[i][j]`` will contain the Jacobian 
        matrix of the ``i``th column output(Noted that this output means the first order 
        differentiation) and the ``j``th input and will have same dtype and device as the 
        corresponding input. Other situations can be deduced by analogy.
    

    Examples 1:
        .. code-block:: python

            import paddle

            x = paddle.ones(shape=(4, 2), dtype='float64')
            weight = paddle.ones(shape=(2, 4), dtype='float64')
            y = paddle.ones(shape=(4, 2), dtype='float64')

            def func(x):
                return paddle.matmul(x * x, weight)[:, 0:1]
            
           
            x.stop_gradient = False
            batch_hessian = paddle.autograd.batch_hessian(func, x)
            print(batch_hessian)
            # Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #      [[2., 0., 2., 0., 2., 0., 2., 0.],
            #       [0., 2., 0., 2., 0., 2., 0., 2.]])

    Examples 2:
        .. code-block:: python

            import paddle

            x = paddle.ones(shape=(4, 2), dtype='float64')
            weight = paddle.ones(shape=(2, 4), dtype='float64')
            y = paddle.ones(shape=(4, 2), dtype='float64')

            def func(x, y):
                return paddle.matmul(x * x * y * y, weight)[:, 0:1]
            
            x.stop_gradient = False
            y.stop_gradient = False
            batch_hessian = paddle.autograd.batch_hessian(func, [x, y])
            print(batch_hessian)
            # ((Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #        [[2., 0., 2., 0., 2., 0., 2., 0.],
            #         [0., 2., 0., 2., 0., 2., 0., 2.]]), 
            #   Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #        [[4., 0., 4., 0., 4., 0., 4., 0.],
            #         [0., 4., 0., 4., 0., 4., 0., 4.]])), 
            #  (Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #        [[4., 0., 4., 0., 4., 0., 4., 0.],
            #         [0., 4., 0., 4., 0., 4., 0., 4.]]), 
            #   Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #        [[2., 0., 2., 0., 2., 0., 2., 0.],
            #         [0., 2., 0., 2., 0., 2., 0., 2.]])))
            

    Examples 3:
        .. code-block:: python

            import paddle

            x = paddle.ones(shape=(4, 2), dtype='float64')
            weight = paddle.ones(shape=(2, 4), dtype='float64')
            y = paddle.ones(shape=(4, 2), dtype='float64')
            
            def func(x, y):
                return paddle.matmul(x * x, weight)[:, 0:1]

            x.stop_gradient = False
            y.stop_gradient = False
            batch_hessian = paddle.autograd.batch_hessian(func, [x, y], allow_unused=True)
            print(batch_hessian)
            # ((Tensor(shape=[2, 8], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #        [[2., 0., 2., 0., 2., 0., 2., 0.],
            #         [0., 2., 0., 2., 0., 2., 0., 2.]]), None), (None, None))

    '''
    inputs = _tensors(inputs, "inputs")
    outputs = func(*inputs)
    batch_size = inputs[0].shape[0]
    for input in inputs:
        assert input.shape[
            0] == batch_size, "The first dimension of input should equals to the same batch size!"
    assert isinstance(outputs, paddle.Tensor) and outputs.shape == [
        batch_size, 1
    ], "The function to compute batched Hessian matrix should return a Tensor of shape [batch_size, 1]"

    def jac_func(*ins):
        grad_inputs = grad(
            outputs,
            ins,
            create_graph=True,
            retain_graph=True,
            allow_unused=allow_unused)
        return tuple(
            _replace_none_with_zero_tensor(grad_inputs[i], inputs[i])
            for i in range(len(inputs)))

    return batch_jacobian(
        jac_func, inputs, create_graph=create_graph, allow_unused=allow_unused)


@framework.dygraph_only
def hessian(func, inputs, create_graph=False, allow_unused=False):
    ''' 
    .. note::
        **This API is ONLY available in the imperative mode.**

    This function computes the Hessian matrix of `func` with respect to `inputs`.

    Parameters:
        func (function): a Python function that takes a Tensor or a Tensor
            list/tuple as inputs and returns a Tensor with a single element.
        inputs (Tensor|list(Tensor)|tuple(Tensor)): the input Tensor or 
            Tensor list/tuple of the function ``func``.
        create_graph (bool, optional): whether to create the gradient graphs
            of the computing process. When it is True, higher order derivatives
            are supported to compute; when it is False, the gradient graphs of
            the computing process would be discarded. Defaults to ``False``.
        allow_unused (bool, optional): whether to raise error or return None if
            some Tensors of `inputs` are unreachable in the graph. Error would
            be raised if allow_unused=False, and None would be returned as
            their gradients if allow_unused=True. Default False.
    Returns:
        Hessian (Tensor or a tuple of tuple of Tensors): if function ``func``
        takes a Tensor as ``inputs``, Hessian will be a single Tensor containing
        the Hessian matrix for the linearized ``inputs`` Tensor. If function
        ``func`` takes a Tensor list/tuple as ``inputs``, then the Hessian will
        be a tuple of tuple of Tensors where ``Hessian[i][j]`` will contain the
        Hessian matrix of the ``i``th input and ``j``th input with size ``m * n``.
        Here ``m`` and ``n`` denote the number of elements of the ``i`` th input
        and the ``j`` th input respectively.

    Examples 1:
        .. code-block:: python

            import paddle

            def func(x):
                return paddle.sum(paddle.matmul(x, x))
            
            x = paddle.ones(shape=[2, 2], dtype='float32')
            x.stop_gradient = False
            hessian = paddle.autograd.hessian(func, x)
            print(hessian)
            # Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[2., 1., 1., 0.],
            #         [1., 0., 2., 1.],
            #         [1., 2., 0., 1.],
            #         [0., 1., 1., 2.]])

    Examples 2:
        .. code-block:: python

            import paddle

            def func(x, y):
                return paddle.sum(paddle.matmul(x, y))
            
            x = paddle.ones(shape=[2, 2], dtype='float32')
            y = paddle.ones(shape=[2, 2], dtype='float32')
            x.stop_gradient = False
            y.stop_gradient = False
            hessian = paddle.autograd.hessian(func, [x, y])
            print(hessian)
            # ((Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[0., 0., 0., 0.],
            #         [0., 0., 0., 0.],
            #         [0., 0., 0., 0.],
            #         [0., 0., 0., 0.]]),
            #   Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[1., 1., 0., 0.],
            #         [0., 0., 1., 1.],
            #         [1., 1., 0., 0.],
            #         [0., 0., 1., 1.]])),
            #  (Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[1., 0., 1., 0.],
            #         [1., 0., 1., 0.],
            #         [0., 1., 0., 1.],
            #         [0., 1., 0., 1.]]),
            #   Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[0., 0., 0., 0.],
            #         [0., 0., 0., 0.],
            #         [0., 0., 0., 0.],
            #         [0., 0., 0., 0.]])))

    Examples 3:
        .. code-block:: python

            import paddle

            def func(x, y):
                return paddle.sum(paddle.matmul(x, x))
            
            x = paddle.ones(shape=[2, 2], dtype='float32')
            y = paddle.ones(shape=[2, 2], dtype='float32')
            x.stop_gradient = False
            y.stop_gradient = False
            hessian = paddle.autograd.hessian(func, [x, y], allow_unused=True)
            print(hessian)
            # ((Tensor(shape=[4, 4], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[2., 1., 1., 0.],
            #         [1., 0., 2., 1.],
            #         [1., 2., 0., 1.],
            #         [0., 1., 1., 2.]]), None), (None, None))

    '''
    inputs = _tensors(inputs, "inputs")
    outputs = func(*inputs)
    assert isinstance(outputs, paddle.Tensor) and outputs.shape == [
        1
    ], "The function to compute Hessian matrix should return a Tensor with a single element"

    def jac_func(*ins):
        grad_inputs = grad(
            outputs,
            ins,
            create_graph=True,
            retain_graph=True,
            allow_unused=allow_unused)
        return tuple(
            _replace_none_with_zero_tensor(grad_inputs[i], inputs[i])
            for i in range(len(inputs)))

    return jacobian(
        jac_func, inputs, create_graph=create_graph, allow_unused=allow_unused)


@framework.dygraph_only
def vhp(func, inputs, v=None, create_graph=False, allow_unused=False):
    ''' 
    .. note::
        **This API is ONLY available in the imperative mode.**

    This function computes the product between a vector ``v`` and the
    Hessian matrix of `func` with respect to `inputs`.

    Parameters:
        func (function): a Python function that takes a Tensor or a Tensor
            list/tuple as inputs and returns a Tensor with a single element.
        inputs (Tensor|list(Tensor)|tuple(Tensor)): the input Tensor or 
            Tensor list/tuple of the function ``func``.
        v (Tensor|list(Tensor)|tuple(Tensor)|None, optional): the vector used
            to compute vector hessian product. ``v`` should have same shape
            and dtype with ``inputs``. If ``v`` is None, it will be set as
            Tensor|list(Tensor) with all elements 1. Defaults to "None".
        create_graph (bool, optional): whether to create the gradient graphs
            of the computing process. When it is True, higher order derivatives
            are supported to compute; when it is False, the gradient graphs of
            the computing process would be discarded. Defaults to ``False``.
        allow_unused (bool, optional): whether to raise error or return None if
            some Tensors of `inputs` are unreachable in the graph. Error would
            be raised if allow_unused=False, and None would be returned as
            their gradients if allow_unused=True. Default False.
    Returns:
        output (tuple): tuple with:
            func_output (Tensor): output of ``func(inputs)``
            vhp (list(Tensor)): result of the vector hessian product
            with the same shape and dtype as the inputs.
    Examples 1:
        .. code-block:: python
            import paddle
            def func(x):
                return paddle.sum(paddle.matmul(x, x))
            
            x = paddle.ones(shape=[2, 2], dtype='float32')
            x.stop_gradient = False
            vx = paddle.ones(shape=[2, 2], dtype='float32') * 2
            vhp_rslt = paddle.autograd.vhp(func, x, v=vx)
            print(vhp_rslt)
            # (Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
            #        [8.]),
            #  Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[8., 8.],
            #         [8., 8.]]))

    Examples 2:
        .. code-block:: python
            import paddle
            def func(x):
                return paddle.sum(paddle.matmul(x, x))
            
            x = paddle.ones(shape=[2, 2], dtype='float32')
            x.stop_gradient = False
            vhp_rslt = paddle.autograd.vhp(func, x)
            print(vhp_rslt)
            # (Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
            #        [8.]),
            #  Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[4., 4.],
            #         [4., 4.]]))

    Examples 3:
        .. code-block:: python
            import paddle
            def func(x, y):
                return paddle.sum(paddle.matmul(x, x))
            
            x = paddle.ones(shape=[2, 2], dtype='float32')
            x.stop_gradient = False
            y = paddle.ones(shape=[2, 2], dtype='float32')
            y.stop_gradient = False
            vx = paddle.ones(shape=[2, 2], dtype='float32') * 2
            vy = paddle.ones(shape=[2, 2], dtype='float32') * 3
            vhp_rslt = paddle.autograd.vhp(func, [x, y], v=[vx, vy], allow_unused=True)
            print(vhp_rslt)
            # (Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
            #        [8.]),
            # [Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #        [[8., 8.],
            #         [8., 8.]]), None])
    '''
    xs = _tensors(inputs, "inputs")
    if v is not None:
        v = _tensors(v, "v")

    with gradient_scope(
            xs, v, create_graph=create_graph,
            allow_unused=allow_unused) as [xs, v, grad_fn, return_fn]:
        outputs = func(*xs)
        ys = _tensors(outputs, "outputs")
        assert len(ys) == 1 and isinstance(
            ys[0], paddle.Tensor
        ) and ys[0].shape == [
            1
        ], "The function to compute vhp should return a Tensor with a single element"
        jac = grad_fn(ys, xs, create_graph=True)
        vhp = grad_fn(jac, xs, v)
        outputs, vhp = return_fn(outputs), return_fn(vhp)
    return outputs, vhp


class Jacobian(object):
    r"""
    Object that represents the Jacobian matrix of a muli-input multi-output 
    function.

    The Jacobian values are lazily evaluated if accessed through indices.
    In contrast, slicing access would trigger evaluating the full matrix
    if it's not already computed.

    Examples:
        .. code-block:: python
            import paddle        
            import numpy as np

            def func(xs):
                x, y = xs
                return paddle.matmul(x, y)
            
            main = fluid.Program()
            startup = fluid.Program()
            with fluid.program_guard(main, startup):
                x = paddle.static.data(name='x', shape=[2, 2], dtype='float32')
                JJ = paddle.autograd.functional.Jacobian(func, [x, x])
                nrow, ncol = JJ.shape()
                full_jacobian = JJ[:]
            place = fluid.CUDAPlace(0)
            exe = fluid.Executor(place)
            exe.run(startup)

            feeds = {'x': np.array([[2., 2.], [2., 1.]]).astype('float32')}
            jacobian = exe.run(main, feed=feeds, fetch_list=[full_jacobian])[0]
            print(jacobian)
            # [[4. 2. 2. 0. 4. 2. 2. 0.]
            #  [2. 3. 0. 2. 2. 3. 0. 2.]
            #  [2. 0. 3. 2. 2. 0. 3. 2.]
            #  [0. 2. 2. 2. 0. 2. 2. 2.]]
    """

    def __init__(self, func, inputs, batch=False):
        r"""Constructing a Jacobian matrix.

        Parameters:
            func (Callable): a Python function that takes as input a Tensor
                or a Tensor list and outputs a Tensor or a Tensor list.
            inputs (Tensor|list[Tensor]): a Tensor or a list of Tensors as
                `func`'s input.
            batch (bool):  if True the 0'th axis is considered the batch
                dimension, both on input and output.
        """

        def enable_grads(inputs):
            if isinstance(inputs, (list, tuple)):
                for x in inputs:
                    x.stop_gradient = False
            else:
                assert isinstance(inputs, paddle.fluid.framework.Variable), (
                    f"Expecting {inputs} to be paddle.fluid.framework.Variable,"
                    f" however it's found to be a(n) {type(inputs)}.")
                inputs.stop_gradient = False
            return inputs

        self.batch = batch
        self.xs = enable_grads(inputs)
        ys = func(inputs)
        if not isinstance(ys, list):
            ys = [ys]
        self.y = self.flatten_all(ys)
        self.ydim = self.y.shape[-1]
        self.xdim = self.flatten_all(inputs).shape[-1]
        self.bdim = self.y.shape[0]
        self.jacobian = {}

    def flatten(self, x):
        to = [x.shape[0], -1] if self.batch else [-1]
        return x.reshape(to)

    def flatten_all(self, xs):
        return paddle.concat([self.flatten(x) for x in xs], axis=-1)

    def shape(self):
        return (self.ydim, self.xdim)

    def __getitem__(self, tup):
        if hasattr(tup, '__iter__'):
            i, j = tup
        else:
            i, j = tup, None

        if isinstance(i, slice):
            slicing = True
        else:
            slicing = False

        if slicing:
            if 'full' not in self.jacobian:
                rows = [
                    self.flatten_all(gradients(self.y[..., i], self.xs))
                    for i in range(self.ydim)
                ]
                self.jacobian['full'] = paddle.stack(rows)
            return self.jacobian['full'][i]

        assert 0 <= i < self.ydim, f"Jacobian index i={i} is not valid."
        assert (j is None) or (
            0 <= j < self.xdim), f"Jacobian index j={j} is not valid."
        if 'full' in self.jacobian:
            JJ = self.jacobian['full']
        else:
            JJ = self.jacobian
            if i not in self.jacobian:
                self.jacobian[i] = self.flatten_all(
                    gradients(self.y[..., i], self.xs))

        if j is None:
            return JJ[i]
        else:
            return JJ[i][..., j]
