from tensorflow.keras.optimizers import Optimizer
from tensorflow.keras import backend as K


class AccumOptimizer(Optimizer):
    """Optimizer
    Inheriting Optimizer class, wrapping the original optimizer
    to achieve a new corresponding optimizer of gradient accumulation.
    # Arguments
        optimizer: an instance of keras optimizer (supporting
                    all keras optimizers currently available);
        steps_per_update: the steps of gradient accumulation
    # Returns
        a new keras optimizer.
    """
    def __init__(self, optimizer, steps_per_update=1, **kwargs):
        super(AccumOptimizer, self).__init__(**kwargs)
        self.optimizer = optimizer
        with K.name_scope(self.__class__.__name__):
            self.steps_per_update = steps_per_update
            self.iterations = K.variable(0, dtype='int64', name='iterations')
            self.cond = K.equal(self.iterations % self.steps_per_update, 0)
            self.lr = self.optimizer.lr
            self.optimizer.lr = K.switch(self.cond, self.optimizer.lr, 0.)
            for attr in ['momentum', 'rho', 'beta_1', 'beta_2']:
                if hasattr(self.optimizer, attr):
                    value = getattr(self.optimizer, attr)
                    setattr(self, attr, value)
                    setattr(self.optimizer, attr, K.switch(self.cond, value, 1 - 1e-7))
            for attr in self.optimizer.get_config():
                if not hasattr(self, attr):
                    value = getattr(self.optimizer, attr)
                    setattr(self, attr, value)
            # Cover the original get_gradients method with accumulative gradients.
            def get_gradients(loss, params):
                return [ag / self.steps_per_update for ag in self.accum_grads]
            self.optimizer.get_gradients = get_gradients
    def get_updates(self, loss, params):
        self.updates = [
            K.update_add(self.iterations, 1),
            K.update_add(self.optimizer.iterations, K.cast(self.cond, 'int64')),
        ]
        # gradient accumulation
        self.accum_grads = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        grads = self.get_gradients(loss, params)
        for g, ag in zip(grads, self.accum_grads):
            self.updates.append(K.update(ag, K.switch(self.cond, g, ag + g)))
        # inheriting updates of original optimizer
        self.updates.extend(self.optimizer.get_updates(loss, params)[1:])
        self.weights.extend(self.optimizer.weights)
        return self.updates
    def get_config(self):
        iterations = K.eval(self.iterations)
        K.set_value(self.iterations, 0)
        config = self.optimizer.get_config()
        K.set_value(self.iterations, iterations)
        return config