#
# Copyright (c) 2017 Intel Corporation
#
# 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 numpy as np

from agents.agent import Agent
from architectures.network_wrapper import NetworkWrapper
from utils import RunPhase, Signal


class ValueOptimizationAgent(Agent):
    def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0, create_target_network=True):
        Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
        self.main_network = NetworkWrapper(tuning_parameters, create_target_network, self.has_global, 'main',
                                           self.replicated_device, self.worker_device)
        self.networks.append(self.main_network)
        self.q_values = Signal("Q")
        self.signals.append(self.q_values)

        self.reset_game(do_not_reset_env=True)

    # Algorithms for which q_values are calculated from predictions will override this function
    def get_q_values(self, prediction):
        return prediction

    def get_prediction(self, curr_state):
        return self.main_network.online_network.predict(self.tf_input_state(curr_state))

    def _validate_action(self, policy, action):
        if np.array(action).shape != ():
            raise ValueError((
                'The exploration_policy {} returned a vector of actions '
                'instead of a single action. ValueOptimizationAgents '
                'require exploration policies which return a single action.'
            ).format(policy.__class__.__name__))

    def choose_action(self, curr_state, phase=RunPhase.TRAIN):
        prediction = self.get_prediction(curr_state)
        actions_q_values = self.get_q_values(prediction)

        # choose action according to the exploration policy and the current phase (evaluating or training the agent)
        if phase == RunPhase.TRAIN:
            exploration_policy = self.exploration_policy
        else:
            exploration_policy = self.evaluation_exploration_policy

        action = exploration_policy.get_action(actions_q_values)
        self._validate_action(exploration_policy, action)

        # this is for bootstrapped dqn
        if type(actions_q_values) == list and len(actions_q_values) > 0:
            actions_q_values = actions_q_values[self.exploration_policy.selected_head]
        actions_q_values = actions_q_values.squeeze()

        # store the q values statistics for logging
        self.q_values.add_sample(actions_q_values)

        # store information for plotting interactively (actual plotting is done in agent)
        if self.tp.visualization.plot_action_values_online:
            for idx, action_name in enumerate(self.env.actions_description):
                self.episode_running_info[action_name].append(actions_q_values[idx])

        action_value = {"action_value": actions_q_values[action], "max_action_value": np.max(actions_q_values)}
        return action, action_value