#
# 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 copy
from enum import Enum
from typing import Union

import numpy as np

from rl_coach.agents.agent import Agent
from rl_coach.architectures.head_parameters import MeasurementsPredictionHeadParameters
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters, \
     MiddlewareScheme
from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
from rl_coach.exploration_policies.e_greedy import EGreedyParameters
from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
from rl_coach.memories.memory import MemoryGranularity
from rl_coach.spaces import SpacesDefinition, VectorObservationSpace


class HandlingTargetsAfterEpisodeEnd(Enum):
    LastStep = 0
    NAN = 1


class DFPNetworkParameters(NetworkParameters):
    def __init__(self):
        super().__init__()
        self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='leaky_relu'),
                                            'measurements': InputEmbedderParameters(activation_function='leaky_relu'),
                                            'goal': InputEmbedderParameters(activation_function='leaky_relu')}

        self.input_embedders_parameters['observation'].scheme = [
            Conv2d(32, 8, 4),
            Conv2d(64, 4, 2),
            Conv2d(64, 3, 1),
            Dense(512),
        ]

        self.input_embedders_parameters['measurements'].scheme = [
            Dense(128),
            Dense(128),
            Dense(128),
        ]

        self.input_embedders_parameters['goal'].scheme = [
            Dense(128),
            Dense(128),
            Dense(128),
        ]

        self.middleware_parameters = FCMiddlewareParameters(activation_function='leaky_relu',
                                                            scheme=MiddlewareScheme.Empty)
        self.heads_parameters = [MeasurementsPredictionHeadParameters(activation_function='leaky_relu')]
        self.async_training = False
        self.batch_size = 64
        self.adam_optimizer_beta1 = 0.95


class DFPMemoryParameters(EpisodicExperienceReplayParameters):
    def __init__(self):
        self.max_size = (MemoryGranularity.Transitions, 20000)
        self.shared_memory = True
        super().__init__()


class DFPAlgorithmParameters(AlgorithmParameters):
    def __init__(self):
        super().__init__()
        self.num_predicted_steps_ahead = 6
        self.goal_vector = [1.0, 1.0]
        self.future_measurements_weights = [0.5, 0.5, 1.0]
        self.use_accumulated_reward_as_measurement = False
        self.handling_targets_after_episode_end = HandlingTargetsAfterEpisodeEnd.NAN
        self.scale_measurements_targets = {}
        self.num_consecutive_playing_steps = EnvironmentSteps(8)


class DFPAgentParameters(AgentParameters):
    def __init__(self):
        super().__init__(algorithm=DFPAlgorithmParameters(),
                         exploration=EGreedyParameters(),
                         memory=DFPMemoryParameters(),
                         networks={"main": DFPNetworkParameters()})

    @property
    def path(self):
        return 'rl_coach.agents.dfp_agent:DFPAgent'


# Direct Future Prediction Agent - http://vladlen.info/papers/learning-to-act.pdf
class DFPAgent(Agent):
    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
        super().__init__(agent_parameters, parent)
        self.current_goal = self.ap.algorithm.goal_vector
        self.target_measurements_scale_factors = None

    def learn_from_batch(self, batch):
        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()

        network_inputs = batch.states(network_keys)
        network_inputs['goal'] = np.repeat(np.expand_dims(self.current_goal, 0),
                                           self.ap.network_wrappers['main'].batch_size, axis=0)

        # get the current outputs of the network
        targets = self.networks['main'].online_network.predict(network_inputs)

        # change the targets for the taken actions
        for i in range(self.ap.network_wrappers['main'].batch_size):
            targets[i, batch.actions()[i]] = batch[i].info['future_measurements'].flatten()

        result = self.networks['main'].train_and_sync_networks(network_inputs, targets)
        total_loss, losses, unclipped_grads = result[:3]

        return total_loss, losses, unclipped_grads

    def choose_action(self, curr_state):
        if self.exploration_policy.requires_action_values():
            # predict the future measurements
            tf_input_state = self.prepare_batch_for_inference(curr_state, 'main')
            tf_input_state['goal'] = np.expand_dims(self.current_goal, 0)
            measurements_future_prediction = self.networks['main'].online_network.predict(tf_input_state)[0]
            action_values = np.zeros(len(self.spaces.action.actions))
            num_steps_used_for_objective = len(self.ap.algorithm.future_measurements_weights)

            # calculate the score of each action by multiplying it's future measurements with the goal vector
            for action_idx in range(len(self.spaces.action.actions)):
                action_measurements = measurements_future_prediction[action_idx]
                action_measurements = np.reshape(action_measurements,
                                                 (self.ap.algorithm.num_predicted_steps_ahead,
                                                  self.spaces.state['measurements'].shape[0]))
                future_steps_values = np.dot(action_measurements, self.current_goal)
                action_values[action_idx] = np.dot(future_steps_values[-num_steps_used_for_objective:],
                                                   self.ap.algorithm.future_measurements_weights)
        else:
            action_values = None

        # choose action according to the exploration policy and the current phase (evaluating or training the agent)
        action = self.exploration_policy.get_action(action_values)

        if action_values is not None:
            action_values = action_values.squeeze()
            action_info = ActionInfo(action=action, action_value=action_values[action])
        else:
            action_info = ActionInfo(action=action)

        return action_info

    def set_environment_parameters(self, spaces: SpacesDefinition):
        self.spaces = copy.deepcopy(spaces)
        self.spaces.goal = VectorObservationSpace(shape=self.spaces.state['measurements'].shape,
                                                  measurements_names=
                                                  self.spaces.state['measurements'].measurements_names)

        # if the user has filled some scale values, check that he got the names right
        if set(self.spaces.state['measurements'].measurements_names).intersection(
                self.ap.algorithm.scale_measurements_targets.keys()) !=\
                set(self.ap.algorithm.scale_measurements_targets.keys()):
            raise ValueError("Some of the keys in parameter scale_measurements_targets ({})  are not defined in "
                             "the measurements space {}".format(self.ap.algorithm.scale_measurements_targets.keys(),
                                                                self.spaces.state['measurements'].measurements_names))

        super().set_environment_parameters(self.spaces)

        # the below is done after calling the base class method, as it might add accumulated reward as a measurement

        # fill out the missing measurements scale factors
        for measurement_name in self.spaces.state['measurements'].measurements_names:
            if measurement_name not in self.ap.algorithm.scale_measurements_targets:
                self.ap.algorithm.scale_measurements_targets[measurement_name] = 1

        self.target_measurements_scale_factors = \
            np.array([self.ap.algorithm.scale_measurements_targets[measurement_name] for measurement_name in
                      self.spaces.state['measurements'].measurements_names])

    def handle_episode_ended(self):
        last_episode = self.current_episode_buffer
        if self.phase in [RunPhase.TRAIN, RunPhase.HEATUP] and last_episode:
            self._update_measurements_targets(last_episode,
                                              self.ap.algorithm.num_predicted_steps_ahead)
        super().handle_episode_ended()

    def _update_measurements_targets(self, episode, num_steps):
        if 'measurements' not in episode.transitions[0].state or episode.transitions[0].state['measurements'] == []:
            raise ValueError("Measurements are not present in the transitions of the last episode played. ")
        measurements_size = self.spaces.state['measurements'].shape[0]
        for transition_idx, transition in enumerate(episode.transitions):
            transition.info['future_measurements'] = np.zeros((num_steps, measurements_size))
            for step in range(num_steps):
                offset_idx = transition_idx + 2 ** step

                if offset_idx >= episode.length():
                    if self.ap.algorithm.handling_targets_after_episode_end == HandlingTargetsAfterEpisodeEnd.NAN:
                        # the special MSE loss will ignore those entries so that the gradient will be 0 for these
                        transition.info['future_measurements'][step] = np.nan
                        continue

                    elif self.ap.algorithm.handling_targets_after_episode_end == HandlingTargetsAfterEpisodeEnd.LastStep:
                        offset_idx = - 1

                transition.info['future_measurements'][step] = \
                    self.target_measurements_scale_factors * \
                    (episode.transitions[offset_idx].state['measurements'] - transition.state['measurements'])
