# Copyright 2017-2020 The GPflow Contributors. 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 typing import Callable, Iterable, List, Optional, Sequence, Tuple, TypeVar, Union

import numpy as np
import scipy.optimize
import tensorflow as tf
from scipy.optimize import OptimizeResult

__all__ = ["Scipy"]

Variables = Iterable[tf.Variable]  # deprecated
StepCallback = Callable[[int, Sequence[tf.Variable], Sequence[tf.Tensor]], None]
LossClosure = Callable[[], tf.Tensor]


class Scipy:
    def minimize(
        self,
        closure: LossClosure,
        variables: Sequence[tf.Variable],
        method: Optional[str] = "L-BFGS-B",
        step_callback: Optional[StepCallback] = None,
        compile: bool = True,
        **scipy_kwargs,
    ) -> OptimizeResult:
        """
        Minimize is a wrapper around the `scipy.optimize.minimize` function
        handling the packing and unpacking of a list of shaped variables on the
        TensorFlow side vs. the flat numpy array required on the Scipy side.

        Args:
            closure: A closure that re-evaluates the model, returning the loss
                to be minimized.
            variables: The list (tuple) of variables to be optimized
                (typically `model.trainable_variables`)
            method: The type of solver to use in SciPy. Defaults to "L-BFGS-B".
            step_callback: If not None, a callable that gets called once after
                each optimisation step. The callable is passed the arguments
                `step`, `variables`, and `values`. `step` is the optimisation
                step counter, `variables` is the list of trainable variables as
                above, and `values` is the corresponding list of tensors of
                matching shape that contains their value at this optimisation
                step.
            compile: If True, wraps the evaluation function (the passed `closure`
                as well as its gradient computation) inside a `tf.function()`,
                which will improve optimization speed in most cases.

            scipy_kwargs: Arguments passed through to `scipy.optimize.minimize`
                Note that Scipy's minimize() takes a `callback` argument, but
                you probably want to use our wrapper and pass in `step_callback`.

        Returns:
            The optimization result represented as a Scipy ``OptimizeResult``
            object. See the Scipy documentation for description of attributes.
        """
        if not callable(closure):
            raise TypeError(
                "The 'closure' argument is expected to be a callable object."
            )  # pragma: no cover
        variables = tuple(variables)
        if not all(isinstance(v, tf.Variable) for v in variables):
            raise TypeError(
                "The 'variables' argument is expected to only contain tf.Variable instances (use model.trainable_variables, not model.trainable_parameters)"
            )  # pragma: no cover
        initial_params = self.initial_parameters(variables)

        func = self.eval_func(closure, variables, compile=compile)
        if step_callback is not None:
            if "callback" in scipy_kwargs:
                raise ValueError("Callback passed both via `step_callback` and `callback`")

            callback = self.callback_func(variables, step_callback)
            scipy_kwargs.update(dict(callback=callback))

        return scipy.optimize.minimize(
            func, initial_params, jac=True, method=method, **scipy_kwargs
        )

    @classmethod
    def initial_parameters(cls, variables: Sequence[tf.Variable]) -> tf.Tensor:
        return cls.pack_tensors(variables)

    @classmethod
    def eval_func(
        cls, closure: LossClosure, variables: Sequence[tf.Variable], compile: bool = True
    ) -> Callable[[np.ndarray], Tuple[np.ndarray, np.ndarray]]:
        def _tf_eval(x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
            values = cls.unpack_tensors(variables, x)
            cls.assign_tensors(variables, values)

            loss, grads = _compute_loss_and_gradients(closure, variables)
            return loss, cls.pack_tensors(grads)

        if compile:
            _tf_eval = tf.function(_tf_eval)

        def _eval(x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
            loss, grad = _tf_eval(tf.convert_to_tensor(x))
            return loss.numpy().astype(np.float64), grad.numpy().astype(np.float64)

        return _eval

    @classmethod
    def callback_func(
        cls, variables: Sequence[tf.Variable], step_callback: StepCallback
    ) -> Callable[[np.ndarray], None]:
        step = 0  # type: int

        def _callback(x: np.ndarray) -> None:
            nonlocal step
            values = cls.unpack_tensors(variables, x)
            step_callback(step, variables, values)
            step += 1

        return _callback

    @staticmethod
    def pack_tensors(tensors: Sequence[Union[tf.Tensor, tf.Variable]]) -> tf.Tensor:
        flats = [tf.reshape(tensor, (-1,)) for tensor in tensors]
        tensors_vector = tf.concat(flats, axis=0)
        return tensors_vector

    @staticmethod
    def unpack_tensors(
        to_tensors: Sequence[Union[tf.Tensor, tf.Variable]], from_vector: tf.Tensor
    ) -> List[tf.Tensor]:
        s = 0
        values = []
        for target_tensor in to_tensors:
            shape = tf.shape(target_tensor)
            dtype = target_tensor.dtype
            tensor_size = tf.reduce_prod(shape)
            tensor_vector = from_vector[s : s + tensor_size]
            tensor = tf.reshape(tf.cast(tensor_vector, dtype), shape)
            values.append(tensor)
            s += tensor_size
        return values

    @staticmethod
    def assign_tensors(to_tensors: Sequence[tf.Variable], values: Sequence[tf.Tensor]) -> None:
        if len(to_tensors) != len(values):
            raise ValueError("to_tensors and values should have same length")
        for target, value in zip(to_tensors, values):
            target.assign(value)


def _compute_loss_and_gradients(
    loss_closure: LossClosure, variables: Sequence[tf.Variable]
) -> Tuple[tf.Tensor, Sequence[tf.Tensor]]:
    with tf.GradientTape(watch_accessed_variables=False) as tape:
        tape.watch(variables)
        loss = loss_closure()
    grads = tape.gradient(loss, variables)
    return loss, grads