# MIT License

# Copyright (c) 2020 <NAME>

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Subclass of BOMLInnerGradTrad to implement the LL optimization strategy for `Meta-Feature-Based`methods.

"""
from collections import OrderedDict

import tensorflow as tf

from boml.lower_iter import BOMLInnerGradTrad
from boml.utils import add_list


class BOMLInnerGradSimple(BOMLInnerGradTrad):
    def __init__(
        self,
        update_op,
        dynamics,
        objective,
        inner_param_tensor=None,
        outer_param_tensor=[],
        model_param_tensor=[],
    ):
        """

        :param update_op: the operation to perform gradient-based optimization
        :param dynamics: the iterative formats of dynamical system
        :param objective: inner objectives
        :param inner_param_tensor: tensor of inner parameters for BP
        :param outer_param_tensor: tensor of outer parameters for BP
        :param model_param_tensor: tensor of model parameters ,such as t-layer or Warp-layer
        """
        self._inner_param_fast_tensor = inner_param_tensor
        self._outer_param_tensor = outer_param_tensor
        self._model_param_tensor = model_param_tensor
        super().__init__(update_op=update_op, dynamics=dynamics, objective=objective)

    @staticmethod
    def compute_gradients(
        boml_opt,
        loss_inner,
        loss_outer=None,
        param_dict=OrderedDict(),
        var_list=None,
        **inner_kargs
    ):
        """
        :param boml_opt: instance of modified optimizers in the `optimizer` module
        :param loss_inner: Lower-Level objectives
        :param loss_outer: Upper-Level objectives
        :param param_dict: dictionary of general parameters for different algorithms
        :param var_list: the list of parameters in the base-learner
        :param inner_kargs: optional arguments for tensorflow optimizers, like global_step, gate_gradients
        :return: initialized instance of inner_grad for UL optimization
        """
        minimize_kargs = {
            inner_arg: inner_kargs[inner_arg]
            for inner_arg in set(inner_kargs.keys()) - set(param_dict.keys())
        }

        assert loss_inner is not None, "argument:inner_objective must be initialized"
        update_op, dynamics = boml_opt.minimize(loss_inner, var_list, *minimize_kargs)
        fast_param, outer_grad, model_grad = BOMLInnerGradSimple.bml_inner_grad_trad(
            loss_inner=loss_inner, param_dict=param_dict, var_list=var_list
        )

        return BOMLInnerGradSimple(
            update_op=update_op,
            dynamics=dynamics,
            objective=loss_inner,
            inner_param_tensor=fast_param,
            outer_param_tensor=outer_grad,
            model_param_tensor=model_grad,
        )

    @staticmethod
    def bml_inner_grad_trad(loss_inner, param_dict=OrderedDict(), var_list=[]):
        """

        :param loss_inner: LL objectives
        :param param_dict: dictionary of general parameters for different algorithms
        :param var_list: the list of parameters in the base-learner
        :return:
        """
        assert len(var_list) > 0, "no task_specific variables to optimizer"
        task_model = param_dict["experiment"].model
        task_param = task_model.task_parameter
        outer_param_grad = []
        model_param_grad = []
        grads = tf.gradients(loss_inner, list(task_param.values()))
        if param_dict["use_warp"]:
            outer_param_loss = param_dict["outer_loss_func"](
                pred=task_model.re_forward(param_dict["experiment"].x_).out,
                label=param_dict["experiment"].y_
            )
            model_param_loss = param_dict["model_loss_func"](
                pred=task_model.re_forward(param_dict["experiment"].x_).out,
                label=param_dict["experiment"].y_
            )
            outer_param_grad = tf.gradients(outer_param_loss, list(var_list))
            model_param_grad = tf.gradients(
                model_param_loss, list(task_model.model_param_dict.values())
            )

        if param_dict["first_order"]:
            grads_dict = dict(
                zip(
                    task_model.task_parameter.keys(),
                    [tf.stop_gradient(grad) for grad in grads],
                )
            )
        else:
            grads_dict = dict(zip(task_model.task_parameter.keys(), grads))

        task_model.task_parameter = OrderedDict(
            zip(
                task_param.keys(),
                [
                    task_param[key] - param_dict["learning_rate"] * grads_dict[key]
                    for key in task_param.keys()
                ],
            )
        )

        for _ in range(param_dict["T"] - 1):
            task_model = task_model.re_forward(param_dict["experiment"].x)
            task_param = task_model.task_parameter

            iter_loss = param_dict["loss_func"](
                pred=task_model.out, label=param_dict["experiment"].y
            )

            if param_dict["use_warp"]:
                outer_param_loss = param_dict["outer_loss_func"](
                    pred=task_model.re_forward(param_dict["experiment"].x_).out,
                    label=param_dict["experiment"].y_,
                )
                model_param_loss = param_dict["model_loss_func"](
                    pred=task_model.re_forward(param_dict["experiment"].x_).out,
                    label=param_dict["experiment"].y_,
                )
                outer_param_grad = add_list(
                    outer_param_grad, tf.gradients(outer_param_loss, list(var_list))
                )
                model_param_grad = add_list(
                    model_param_grad,
                    tf.gradients(
                        model_param_loss, list(task_model.model_param_dict.values())
                    ),
                )

            grads = tf.gradients(iter_loss, list(task_param.values()))

            if param_dict["first_order"]:
                grads_dict = dict(
                    zip(
                        task_model.task_parameter.keys(),
                        [tf.stop_gradient(grad) for grad in grads],
                    )
                )
            else:
                grads_dict = dict(zip(task_model.task_parameter.keys(), grads))

            task_model.task_parameter = OrderedDict(
                zip(
                    task_param.keys(),
                    [
                        task_param[key] - param_dict["learning_rate"] * grads_dict[key]
                        for key in task_param.keys()
                    ],
                )
            )
        param_dict["experiment"].model = task_model

        return task_model.task_parameter, outer_param_grad, model_param_grad

    @property
    def outer_param_tensor(self):
        """
        :return: temporary weights dictionary used in maml and fomaml for back propagation
        """
        assert (
            self._outer_param_tensor is not None
        ), "temporary weights dictionary must be initialized before being called"
        return self._outer_param_tensor

    @property
    def model_param_tensor(self):
        """
        :return: temporary weights dictionary used in maml and fomaml for back propagation
        """
        assert (
            self._model_param_tensor is not None
        ), "temporary weights dictionary must be initialized before being called"
        return self._model_param_tensor

# MIT License

# Copyright (c) 2020 <NAME>

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Subclass of BOMLInnerGradTrad to implement the LL optimization strategy for `Meta-Feature-Based`methods.

"""
from collections import OrderedDict

import tensorflow as tf

from boml.lower_iter import BOMLInnerGradTrad
from boml.utils import add_list


class BOMLInnerGradSimple(BOMLInnerGradTrad):
    def __init__(
        self,
        update_op,
        dynamics,
        objective,
        inner_param_tensor=None,
        outer_param_tensor=[],
        model_param_tensor=[],
    ):
        """

        :param update_op: the operation to perform gradient-based optimization
        :param dynamics: the iterative formats of dynamical system
        :param objective: inner objectives
        :param inner_param_tensor: tensor of inner parameters for BP
        :param outer_param_tensor: tensor of outer parameters for BP
        :param model_param_tensor: tensor of model parameters ,such as t-layer or Warp-layer
        """
        self._inner_param_fast_tensor = inner_param_tensor
        self._outer_param_tensor = outer_param_tensor
        self._model_param_tensor = model_param_tensor
        super().__init__(update_op=update_op, dynamics=dynamics, objective=objective)

    @staticmethod
    def compute_gradients(
        boml_opt,
        loss_inner,
        loss_outer=None,
        param_dict=OrderedDict(),
        var_list=None,
        **inner_kargs
    ):
        """
        :param boml_opt: instance of modified optimizers in the `optimizer` module
        :param loss_inner: Lower-Level objectives
        :param loss_outer: Upper-Level objectives
        :param param_dict: dictionary of general parameters for different algorithms
        :param var_list: the list of parameters in the base-learner
        :param inner_kargs: optional arguments for tensorflow optimizers, like global_step, gate_gradients
        :return: initialized instance of inner_grad for UL optimization
        """
        minimize_kargs = {
            inner_arg: inner_kargs[inner_arg]
            for inner_arg in set(inner_kargs.keys()) - set(param_dict.keys())
        }

        assert loss_inner is not None, "argument:inner_objective must be initialized"
        update_op, dynamics = boml_opt.minimize(loss_inner, var_list, *minimize_kargs)
        fast_param, outer_grad, model_grad = BOMLInnerGradSimple.bml_inner_grad_trad(
            loss_inner=loss_inner, param_dict=param_dict, var_list=var_list
        )

        return BOMLInnerGradSimple(
            update_op=update_op,
            dynamics=dynamics,
            objective=loss_inner,
            inner_param_tensor=fast_param,
            outer_param_tensor=outer_grad,
            model_param_tensor=model_grad,
        )

    @staticmethod
    def bml_inner_grad_trad(loss_inner, param_dict=OrderedDict(), var_list=[]):
        """

        :param loss_inner: LL objectives
        :param param_dict: dictionary of general parameters for different algorithms
        :param var_list: the list of parameters in the base-learner
        :return:
        """
        assert len(var_list) > 0, "no task_specific variables to optimizer"
        task_model = param_dict["experiment"].model
        task_param = task_model.task_parameter
        outer_param_grad = []
        model_param_grad = []
        grads = tf.gradients(loss_inner, list(task_param.values()))
        if param_dict["use_warp"]:
            outer_param_loss = param_dict["outer_loss_func"](
                pred=task_model.re_forward(param_dict["experiment"].x_).out,
                label=param_dict["experiment"].y_
            )
            model_param_loss = param_dict["model_loss_func"](
                pred=task_model.re_forward(param_dict["experiment"].x_).out,
                label=param_dict["experiment"].y_
            )
            outer_param_grad = tf.gradients(outer_param_loss, list(var_list))
            model_param_grad = tf.gradients(
                model_param_loss, list(task_model.model_param_dict.values())
            )

        if param_dict["first_order"]:
            grads_dict = dict(
                zip(
                    task_model.task_parameter.keys(),
                    [tf.stop_gradient(grad) for grad in grads],
                )
            )
        else:
            grads_dict = dict(zip(task_model.task_parameter.keys(), grads))

        task_model.task_parameter = OrderedDict(
            zip(
                task_param.keys(),
                [
                    task_param[key] - param_dict["learning_rate"] * grads_dict[key]
                    for key in task_param.keys()
                ],
            )
        )

        for _ in range(param_dict["T"] - 1):
            task_model = task_model.re_forward(param_dict["experiment"].x)
            task_param = task_model.task_parameter

            iter_loss = param_dict["loss_func"](
                pred=task_model.out, label=param_dict["experiment"].y
            )

            if param_dict["use_warp"]:
                outer_param_loss = param_dict["outer_loss_func"](
                    pred=task_model.re_forward(param_dict["experiment"].x_).out,
                    label=param_dict["experiment"].y_,
                )
                model_param_loss = param_dict["model_loss_func"](
                    pred=task_model.re_forward(param_dict["experiment"].x_).out,
                    label=param_dict["experiment"].y_,
                )
                outer_param_grad = add_list(
                    outer_param_grad, tf.gradients(outer_param_loss, list(var_list))
                )
                model_param_grad = add_list(
                    model_param_grad,
                    tf.gradients(
                        model_param_loss, list(task_model.model_param_dict.values())
                    ),
                )

            grads = tf.gradients(iter_loss, list(task_param.values()))

            if param_dict["first_order"]:
                grads_dict = dict(
                    zip(
                        task_model.task_parameter.keys(),
                        [tf.stop_gradient(grad) for grad in grads],
                    )
                )
            else:
                grads_dict = dict(zip(task_model.task_parameter.keys(), grads))

            task_model.task_parameter = OrderedDict(
                zip(
                    task_param.keys(),
                    [
                        task_param[key] - param_dict["learning_rate"] * grads_dict[key]
                        for key in task_param.keys()
                    ],
                )
            )
        param_dict["experiment"].model = task_model

        return task_model.task_parameter, outer_param_grad, model_param_grad

    @property
    def outer_param_tensor(self):
        """
        :return: temporary weights dictionary used in maml and fomaml for back propagation
        """
        assert (
            self._outer_param_tensor is not None
        ), "temporary weights dictionary must be initialized before being called"
        return self._outer_param_tensor

    @property
    def model_param_tensor(self):
        """
        :return: temporary weights dictionary used in maml and fomaml for back propagation
        """
        assert (
            self._model_param_tensor is not None
        ), "temporary weights dictionary must be initialized before being called"
        return self._model_param_tensor

# MIT License # Copyright (c) 2020 <NAME> # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. Subclass of BOMLInnerGradTrad to implement the LL optimization strategy for `Meta-Feature-Based`methods. :param update_op: the operation to perform gradient-based optimization         :param dynamics: the iterative formats of dynamical system         :param objective: inner objectives         :param inner_param_tensor: tensor of inner parameters for BP         :param outer_param_tensor: tensor of outer parameters for BP         :param model_param_tensor: tensor of model parameters ,such as t-layer or Warp-layer :param boml_opt: instance of modified optimizers in the `optimizer` module         :param loss_inner: Lower-Level objectives         :param loss_outer: Upper-Level objectives         :param param_dict: dictionary of general parameters for different algorithms         :param var_list: the list of parameters in the base-learner         :param inner_kargs: optional arguments for tensorflow optimizers, like global_step, gate_gradients         :return: initialized instance of inner_grad for UL optimization :param loss_inner: LL objectives         :param param_dict: dictionary of general parameters for different algorithms         :param var_list: the list of parameters in the base-learner         :return: :return: temporary weights dictionary used in maml and fomaml for back propagation :return: temporary weights dictionary used in maml and fomaml for back propagation