#
# 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 os
import pickle
from typing import List

import numpy as np

from rl_coach.core_types import ObservationType
from rl_coach.filters.observation.observation_filter import ObservationFilter
from rl_coach.spaces import ObservationSpace
from rl_coach.utilities.shared_running_stats import NumpySharedRunningStats, NumpySharedRunningStats


class ObservationNormalizationFilter(ObservationFilter):
    """
    Normalizes the observation values with a running mean and standard deviation of
    all the observations seen so far. The normalization is performed element-wise. Additionally, when working with
    multiple workers, the statistics used for the normalization operation are accumulated over all the workers.
    """
    def __init__(self, clip_min: float=-5.0, clip_max: float=5.0, name='observation_stats'):
        """
        :param clip_min: The minimum value to allow after normalizing the observation
        :param clip_max: The maximum value to allow after normalizing the observation
        """
        super().__init__()
        self.clip_min = clip_min
        self.clip_max = clip_max
        self.running_observation_stats = None
        self.name = name
        self.supports_batching = True
        self.observation_space = None

    def set_device(self, device, memory_backend_params=None, mode='numpy') -> None:
        """
        An optional function that allows the filter to get the device if it is required to use tensorflow ops
        :param device: the device to use
        :memory_backend_params: if not None, holds params for a memory backend for sharing data (e.g. Redis)
        :param mode: the arithmetic module to use {'tf' | 'numpy'}
        :return: None
        """
        if mode == 'tf':
            from rl_coach.architectures.tensorflow_components.shared_variables import TFSharedRunningStats
            self.running_observation_stats = TFSharedRunningStats(device, name=self.name, create_ops=False,
                                                            pubsub_params=memory_backend_params)
        elif mode == 'numpy':
            self.running_observation_stats = NumpySharedRunningStats(name=self.name,
                                                                     pubsub_params=memory_backend_params)

    def set_session(self, sess) -> None:
        """
        An optional function that allows the filter to get the session if it is required to use tensorflow ops
        :param sess: the session
        :return: None
        """
        self.running_observation_stats.set_session(sess)

    def filter(self, observations: List[ObservationType], update_internal_state: bool=True) -> ObservationType:
        observations = np.array(observations)
        if update_internal_state:
            self.running_observation_stats.push(observations)
            self.last_mean = self.running_observation_stats.mean
            self.last_stdev = self.running_observation_stats.std

        return self.running_observation_stats.normalize(observations)

    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
        self.running_observation_stats.set_params(shape=input_observation_space.shape,
                                                  clip_values=(self.clip_min, self.clip_max))
        return input_observation_space

    def save_state_to_checkpoint(self, checkpoint_dir: str, checkpoint_prefix: str):
        self.running_observation_stats.save_state_to_checkpoint(checkpoint_dir, checkpoint_prefix)

    def restore_state_from_checkpoint(self, checkpoint_dir: str, checkpoint_prefix: str):
        self.running_observation_stats.restore_state_from_checkpoint(checkpoint_dir, checkpoint_prefix)
 
numpy.array