import keras
from keras.callbacks import Callback
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model, load_model
from keras import optimizers
import math
import random
import numpy as np
import matplotlib.pyplot as plt


class Adam_dlr(optimizers.Optimizer):

    """Adam optimizer.
    Default parameters follow those provided in the original paper.
    # Arguments
        split_1: split layer 1
        split_2: split layer 2
        lr: float >= 0. List of Learning rates. [Early layers, Middle layers, Final Layers]
        beta_1: float, 0 < beta < 1. Generally close to 1.
        beta_2: float, 0 < beta < 1. Generally close to 1.
        epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`.
        decay: float >= 0. Learning rate decay over each update.
        amsgrad: boolean. Whether to apply the AMSGrad variant of this
            algorithm from the paper "On the Convergence of Adam and
            Beyond".
    # References
        - modifying source code of Adam (https://github.com/keras-team/keras/blob/master/keras/optimizers.py#L436)
        - [Adam - A Method for Stochastic Optimization](
           https://arxiv.org/abs/1412.6980v8)
        - [On the Convergence of Adam and Beyond](
           https://openreview.net/forum?id=ryQu7f-RZ)
    """

    def __init__(self, split_1, split_2, lr=[1e-7, 1e-4, 1e-2], beta_1=0.9, beta_2=0.999,
                 epsilon=None, decay=0., amsgrad=False, **kwargs):
        super(Adam_dlr, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.iterations = K.variable(0, dtype='int64', name='iterations')
            self.lr = K.variable(lr, name='lr')
            self.beta_1 = K.variable(beta_1, name='beta_1')
            self.beta_2 = K.variable(beta_2, name='beta_2')
            self.decay = K.variable(decay, name='decay')
            # Extracting name of the split layers
            self.split_1 = split_1.weights[0].name
            self.split_2 = split_2.weights[0].name
        if epsilon is None:
            epsilon = K.epsilon()
        self.epsilon = epsilon
        self.initial_decay = decay
        self.amsgrad = amsgrad

    @keras.optimizers.interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
                                                      K.dtype(self.decay))))

        t = K.cast(self.iterations, K.floatx()) + 1
        lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
                     (1. - K.pow(self.beta_1, t)))

        ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        if self.amsgrad:
            vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        else:
            vhats = [K.zeros(1) for _ in params]
        self.weights = [self.iterations] + ms + vs + vhats
        
        # Setting lr of the initial layers
        lr_grp = lr_t[0]
        for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
            
            # Updating lr when the split layer is encountered
            if p.name == self.split_1:
                lr_grp = lr_t[1]
            if p.name == self.split_2:
                lr_grp = lr_t[2]
                
            m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
            if self.amsgrad:
                vhat_t = K.maximum(vhat, v_t)
                p_t = p - lr_grp * m_t / (K.sqrt(vhat_t) + self.epsilon) # Using updated lr
                self.updates.append(K.update(vhat, vhat_t))
            else:
                p_t = p - lr_grp * m_t / (K.sqrt(v_t) + self.epsilon)

            self.updates.append(K.update(m, m_t))
            self.updates.append(K.update(v, v_t))
            new_p = p_t

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(K.update(p, new_p))
        return self.updates

    def get_config(self):
        print('Optimizer LR: ', K.get_value(self.lr))
#         print()
        config = {
                  'lr': (K.get_value(self.lr)),
                  'beta_1': float(K.get_value(self.beta_1)),
                  'beta_2': float(K.get_value(self.beta_2)),
                  'decay': float(K.get_value(self.decay)),
                  'epsilon': self.epsilon,
                  'amsgrad': self.amsgrad}
        base_config = super(Adam_dlr, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))
    
