"""
 Copyright 2018 Johns Hopkins University  (Author: Jesus Villalba)
 Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
"""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from six.moves import xrange


from abc import ABCMeta, abstractmethod
import os
import json
from copy import deepcopy

import numpy as np
import h5py

from .hyp_defs import float_save, float_cpu

class HypModel(object):
    __metaclass__ = ABCMeta

    def __init__(self, name=None, **kwargs):
        self.name = name
        self._is_init = False

        
    def copy(self):
        return deepcopy(self)


    @property
    def is_init(self):
        return self._is_init


    def init_to_false(self):
        self._is_init = False
        
    
    @abstractmethod
    def initialize(self):
        pass

    
    @abstractmethod
    def fit(self, x, sample_weights=None, x_val=None, sample_weights_val=None):
        pass


    @abstractmethod
    def fit_generator(self, x, x_val=None):
        pass

    
    @abstractmethod
    def save(self, file_path):
        file_dir = os.path.dirname(file_path)
        if not(os.path.isdir(file_dir)):
            os.makedirs(file_dir, exist_ok=True)
        with h5py.File(file_path,'w') as f:
            config=self.to_json()
            f.create_dataset('config', data=np.array(config, dtype='S'))
            self.save_params(f)

        
    @abstractmethod
    def save_params(self, f):
        assert True, 'save_params method not defined for %s' % (self.__class__.__name__)

    
    def _save_params_from_dict(self, f, params, dtypes=None):
        if dtypes is None:
            dtypes = dict((k, float_save()) for k in params)

        if self.name is None:
            prefix = ''
        else:
            prefix = self.name + '/'
        for k, v in params.items():
            if v is None:
                continue
            if not isinstance(v, np.ndarray):
                v = np.asarray(v)
            p_name = prefix + k
            f.create_dataset(p_name, data=v.astype(dtypes[k], copy=False))
                         

    @classmethod
    def load_config(cls, file_path):
        try:
            with h5py.File(file_path,'r') as f:
                json_str = str(np.asarray(f['config']).astype('U'))
                return  cls.load_config_from_json(json_str)
        except:
            with open(file_path,'r') as f:
                return cls.load_config_from_json(f.read())


            
    @classmethod
    def load(cls, file_path):
        with h5py.File(file_path,'r') as f:
            json_str = str(np.asarray(f['config']).astype('U'))
            config = cls.load_config_from_json(json_str)
            return cls.load_params(f, config)

        
    @classmethod
    def load_params(cls, f, config):
        return cls(name=config['name'])

    
    @staticmethod
    def _load_params_to_dict(f, name, params, dtypes=None):
        if dtypes is None:
            dtypes = dict((k, float_cpu()) for k in params)
        if name is None:
            prefix = ''
        else:
            prefix = name + '/'

        param_dict = {}
        for k in params:
            p_name = prefix + k
            param_dict[k] = np.asarray(f[p_name], dtype=dtypes[k])
        return param_dict

    
    @abstractmethod
    def get_config(self):
        config = {
            'class_name': self.__class__.__name__,
            'name': self.name}
        return config

    
    def to_json(self, **kwargs):
        # Piece of code borrowed from keras
        def get_json_type(obj):
            # if obj is any numpy type
            if type(obj).__module__ == np.__name__:
                return obj.item()

            # if obj is a python 'type'
            if type(obj).__name__ == type.__name__:
                return obj.__name__
            
            raise TypeError('Not JSON Serializable:', obj)

        config=self.get_config()
        return json.dumps(config, default=get_json_type, **kwargs)


    @staticmethod
    def load_config_from_json(json_str):
        return json.loads(json_str)
