from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import logging
import typing

from builtins import object
from typing import \
    Any, List, Optional, Text, Dict, Callable

import inspect
import copy
from rasa_core.featurizers import \
    MaxHistoryTrackerFeaturizer, BinarySingleStateFeaturizer

if typing.TYPE_CHECKING:
    from rasa_core.domain import Domain
    from rasa_core.featurizers import TrackerFeaturizer
    from rasa_core.trackers import DialogueStateTracker
    from rasa_core.training.data import DialogueTrainingData

logger = logging.getLogger(__name__)


class Policy(object):
    SUPPORTS_ONLINE_TRAINING = False
    MAX_HISTORY_DEFAULT = 5

    @classmethod
    def _standard_featurizer(cls):
        return MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(),
                                           cls.MAX_HISTORY_DEFAULT)

    @classmethod
    def _create_featurizer(cls, featurizer=None):
        return copy.deepcopy(featurizer) \
               if featurizer else cls._standard_featurizer()

    def __init__(self, featurizer=None):
        # type: (Optional[TrackerFeaturizer]) -> None
        self.__featurizer = self._create_featurizer(featurizer)

    @property
    def featurizer(self):
        return self.__featurizer

    @staticmethod
    def _get_valid_params(func, **kwargs):
        # type: (Callable, **Any) -> Dict
        # filter out kwargs that cannot be passed to func
        try:
            # python 3.x is used
            valid_keys = inspect.signature(func).parameters.keys()
        except AttributeError:
            # python 2.x is used
            valid_keys = inspect.getargspec(func).args

        params = {key: kwargs.get(key)
                  for key in valid_keys if kwargs.get(key)}
        ignored_params = {key: kwargs.get(key)
                          for key in kwargs.keys()
                          if not params.get(key)}

        # TODO change that
        if isinstance(params['batch_size'], list):
            params['batch_size'] = params['batch_size'][0]

        logger.debug("Parameters ignored by `model.fit(...)`: {}"
                     "".format(ignored_params))
        return params

    def featurize_for_training(
            self,
            trackers,  # type: List[DialogueStateTracker]
            domain,  # type: Domain
            **kwargs  # type: **Any
    ):
        # type: (...) -> DialogueTrainingData
        """Transform training trackers into a vector representation.
        The trackers, consisting of multiple turns, will be transformed
        into a float vector which can be used by a ML model."""

        training_data = self.featurizer.featurize_trackers(trackers,
                                                           domain)

        max_training_samples = kwargs.get('max_training_samples')
        if max_training_samples is not None:
            logger.debug("Limit training data to {} training samples."
                         "".format(max_training_samples))
            training_data.limit_training_data_to(max_training_samples)

        return training_data

    def train(self,
              training_trackers,  # type: List[DialogueStateTracker]
              domain,  # type: Domain
              **kwargs  # type: **Any
              ):
        # type: (...) -> None
        """Trains the policy on given training trackers."""

        raise NotImplementedError("Policy must have the capacity "
                                  "to train.")

    def continue_training(self, training_trackers, domain, **kwargs):
        # type: (List[DialogueStateTracker], Domain, **Any) -> None
        """Continues training an already trained policy.

        This doesn't need to be supported by every policy. If it is supported,
        the policy can be used for online training and the implementation for
        the continued training should be put into this function."""

        pass

    def predict_action_probabilities(self, tracker, domain):
        # type: (DialogueStateTracker, Domain) -> List[float]
        """Predicts the next action the bot should take
        after seeing the tracker.

        Returns the list of probabilities for the next actions"""

        raise NotImplementedError("Policy must have the capacity "
                                  "to predict.")

    def persist(self, path):
        # type: (Text) -> None
        """Persists the policy to a storage."""
        raise NotImplementedError("Policy must have the capacity "
                                  "to persist itself.")

    @classmethod
    def load(cls, path):
        # type: (Text) -> Policy
        """Loads a policy from the storage.
            Needs to load its featurizer"""
        raise NotImplementedError("Policy must have the capacity "
                                  "to load itself.")

@@ -2041,16 +2041,45 @@
      if 
+params.get('batch_size') and 
 isinstan
@@ -2102,16 +2102,67 @@
 _size'%5D,
+%0A                                                  
  list):%0A

fix read batch_size as a list