from __future__ import absolute_import

import numpy as np
from keras import backend as K

from .callbacks import Print
from .grad_modifiers import get
from .utils import utils


_PRINT_CALLBACK = Print()


def _identity(x):
    return x


class Optimizer(object):

    def __init__(self, input_tensor, losses, input_range=(0, 255), wrt_tensor=None, norm_grads=True):
        """Creates an optimizer that minimizes weighted loss function.

        Args:
            input_tensor: An input tensor of shape: `(samples, channels, image_dims...)` if `image_data_format=
                channels_first` or `(samples, image_dims..., channels)` if `image_data_format=channels_last`.
            losses: List of ([Loss](vis.losses#Loss), weight) tuples.
            input_range: Specifies the input range as a `(min, max)` tuple. This is used to rescale the
                final optimized input to the given range. (Default value=(0, 255))
            wrt_tensor: Short for, with respect to. This instructs the optimizer that the aggregate loss from `losses`
                should be minimized with respect to `wrt_tensor`.
                `wrt_tensor` can be any tensor that is part of the model graph. Default value is set to None
                which means that loss will simply be minimized with respect to `input_tensor`.
            norm_grads: True to normalize gradients. Normalization avoids very small or large gradients and ensures
                a smooth gradient gradient descent process. If you want the actual gradient
                (for example, visualizing attention), set this to false.
        """
        self.input_tensor = input_tensor
        self.input_range = input_range
        self.loss_names = []
        self.loss_functions = []
        self.wrt_tensor = self.input_tensor if wrt_tensor is None else wrt_tensor

        overall_loss = None
        for loss, weight in losses:
            # Perf optimization. Don't build loss function with 0 weight.
            if weight != 0:
                loss_fn = weight * loss.build_loss()
                overall_loss = loss_fn if overall_loss is None else overall_loss + loss_fn
                self.loss_names.append(loss.name)
                self.loss_functions.append(loss_fn)

        # Compute gradient of overall with respect to `wrt` tensor.
        grads = K.gradients(overall_loss, self.wrt_tensor)[0]
        if norm_grads:
            grads = grads / (K.sqrt(K.mean(K.square(grads))) + K.epsilon())

        # The main function to compute various quantities in optimization loop.
        self.compute_fn = K.function([self.input_tensor, K.learning_phase()],
                                     self.loss_functions + [overall_loss, grads, self.wrt_tensor])

    def _rmsprop(self, grads, cache=None, decay_rate=0.95):
        """Uses RMSProp to compute step from gradients.

        Args:
            grads: numpy array of gradients.
            cache: numpy array of same shape as `grads` as RMSProp cache
            decay_rate: How fast to decay cache

        Returns:
            A tuple of
                step: numpy array of the same shape as `grads` giving the step.
                    Note that this does not yet take the learning rate into account.
                cache: Updated RMSProp cache.
        """
        if cache is None:
            cache = np.zeros_like(grads)
        cache = decay_rate * cache + (1 - decay_rate) * grads ** 2
        step = -grads / np.sqrt(cache + K.epsilon())
        return step, cache

    def _get_seed_input(self, seed_input):
        """Creates a random `seed_input` if None. Otherwise:
            - Ensures batch_size dim on provided `seed_input`.
            - Shuffle axis according to expected `image_data_format`.
        """
        desired_shape = (1, ) + K.int_shape(self.input_tensor)[1:]
        if seed_input is None:
            return utils.random_array(desired_shape, mean=np.mean(self.input_range),
                                      std=0.05 * (self.input_range[1] - self.input_range[0]))

        # Add batch dim if needed.
        if len(seed_input.shape) != len(desired_shape):
            seed_input = np.expand_dims(seed_input, 0)

        # Only possible if channel idx is out of place.
        if seed_input.shape != desired_shape:
            seed_input = np.moveaxis(seed_input, -1, 1)
        return seed_input.astype(K.floatx())

    def minimize(self, seed_input=None, max_iter=200,
                 input_modifiers=None, grad_modifier=None,
                 callbacks=None, verbose=True):
        """Performs gradient descent on the input image with respect to defined losses.

        Args:
            seed_input: An N-dim numpy array of shape: `(samples, channels, image_dims...)` if `image_data_format=
                channels_first` or `(samples, image_dims..., channels)` if `image_data_format=channels_last`.
                Seeded with random noise if set to None. (Default value = None)
            max_iter: The maximum number of gradient descent iterations. (Default value = 200)
            input_modifiers: A list of [InputModifier](vis.input_modifiers#inputmodifier) instances specifying
                how to make `pre` and `post` changes to the optimized input during the optimization process.
                `pre` is applied in list order while `post` is applied in reverse order. For example,
                `input_modifiers = [f, g]` means that `pre_input = g(f(inp))` and `post_input = f(g(inp))`
            grad_modifier: gradient modifier to use. See [grad_modifiers](vis.grad_modifiers.md). If you don't
                specify anything, gradients are unchanged. (Default value = None)
            callbacks: A list of [OptimizerCallback](vis.callbacks#optimizercallback) instances to trigger.
            verbose: Logs individual losses at the end of every gradient descent iteration.
                Very useful to estimate loss weight factor(s). (Default value = True)

        Returns:
            The tuple of `(optimized input, grads with respect to wrt, wrt_value)` after gradient descent iterations.
        """
        seed_input = self._get_seed_input(seed_input)
        input_modifiers = input_modifiers or []
        grad_modifier = _identity if grad_modifier is None else get(grad_modifier)

        callbacks = callbacks or []
        if verbose:
            callbacks.append(_PRINT_CALLBACK)

        cache = None
        best_loss = float('inf')
        best_input = None

        grads = None
        wrt_value = None

        for i in range(max_iter):
            # Apply modifiers `pre` step
            for modifier in input_modifiers:
                seed_input = modifier.pre(seed_input)

            # 0 learning phase for 'test'
            computed_values = self.compute_fn([seed_input, 0])
            losses = computed_values[:len(self.loss_names)]
            named_losses = list(zip(self.loss_names, losses))
            overall_loss, grads, wrt_value = computed_values[len(self.loss_names):]

            # TODO: theano grads shape is inconsistent for some reason. Patch for now and investigate later.
            if grads.shape != wrt_value.shape:
                grads = np.reshape(grads, wrt_value.shape)

            # Apply grad modifier.
            grads = grad_modifier(grads)

            # Trigger callbacks
            for c in callbacks:
                c.callback(i, named_losses, overall_loss, grads, wrt_value)

            # Gradient descent update.
            # It only makes sense to do this if wrt_tensor is input_tensor. Otherwise shapes wont match for the update.
            if self.wrt_tensor is self.input_tensor:
                step, cache = self._rmsprop(grads, cache)
                seed_input += step

            # Apply modifiers `post` step
            for modifier in reversed(input_modifiers):
                seed_input = modifier.post(seed_input)

            if overall_loss < best_loss:
                best_loss = overall_loss.copy()
                best_input = seed_input.copy()

        # Trigger on_end
        for c in callbacks:
            c.on_end()

        return utils.deprocess_input(best_input[0], self.input_range), grads, wrt_value

@@ -2423,62 +2423,28 @@
 s = 
-grads / (K.sqrt(K.mean(K.square(grads))) + K.epsilon()
+K.l2_normalize(grads
 )%0A%0A 

use keras l2_normalize