import tensorflow as tf

from stylegan2.utils import lerp
from stylegan2.layers.mapping_block import Mapping
from stylegan2.layers.synthesis_block import Synthesis


class Generator(tf.keras.Model):
    def __init__(self, g_params, **kwargs):
        super(Generator, self).__init__(**kwargs)

        self.z_dim = g_params['z_dim']
        self.w_dim = g_params['w_dim']
        self.labels_dim = g_params['labels_dim']
        self.n_mapping = g_params['n_mapping']
        self.resolutions = g_params['resolutions']
        self.featuremaps = g_params['featuremaps']
        self.w_ema_decay = 0.995
        self.style_mixing_prob = 0.9
        self.zero_body = tf.ones((4,3,512,256)) * tf.transpose(g_params['zero_body'], [2,0,1])[tf.newaxis,:,:,:]


        self.n_broadcast = len(self.resolutions) * 2
        self.mixing_layer_indices = tf.range(self.n_broadcast, dtype=tf.int32)[tf.newaxis, :, tf.newaxis]

        self.g_mapping = Mapping(self.w_dim, self.labels_dim, self.n_mapping, name='g_mapping')
        self.broadcast = tf.keras.layers.Lambda(lambda x: tf.tile(x[:, tf.newaxis], [1, self.n_broadcast, 1]))
        self.synthesis = Synthesis(self.resolutions, self.featuremaps, name='g_synthesis')

    def build(self, input_shape):
        # w_avg
        self.w_avg = tf.Variable(tf.zeros(shape=[self.w_dim], dtype=tf.dtypes.float32), name='w_avg', trainable=False,
                                 synchronization=tf.VariableSynchronization.ON_READ,
                                 aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)

    @tf.function
    def set_as_moving_average_of(self, src_net):
        beta, beta_nontrainable = 0.99, 0.0

        for cw, sw in zip(self.weights, src_net.weights):
            assert sw.shape == cw.shape
            # print('{} <=> {}'.format(cw.name, sw.name))

            if 'w_avg' in cw.name:
                cw.assign(lerp(sw, cw, beta_nontrainable))
            else:
                cw.assign(lerp(sw, cw, beta))
        return

    def update_moving_average_of_w(self, w_broadcasted):
        # compute average of current w
        batch_avg = tf.reduce_mean(w_broadcasted[:, 0], axis=0)

        # compute moving average of w and update(assign) w_avg
        self.w_avg.assign(lerp(batch_avg, self.w_avg, self.w_ema_decay))
        return

    def style_mixing_regularization(self, latents1, conditions, w_broadcasted1):
        # get another w and broadcast it
        latents2 = tf.random.normal(shape=tf.shape(latents1), dtype=tf.dtypes.float32)
        dlatents2 = self.g_mapping([latents2, conditions])
        w_broadcasted2 = self.broadcast(dlatents2)

        # find mixing limit index
        # mixing_cutoff_index = tf.cond(
        #     pred=tf.less(tf.random.uniform([], 0.0, 1.0), self.style_mixing_prob),
        #     true_fn=lambda: tf.random.uniform([], 1, self.n_broadcast, dtype=tf.dtypes.int32),
        #     false_fn=lambda: tf.constant(self.n_broadcast, dtype=tf.dtypes.int32))
        if tf.random.uniform([], 0.0, 1.0) < self.style_mixing_prob:
            mixing_cutoff_index = tf.random.uniform([], 1, self.n_broadcast, dtype=tf.dtypes.int32)
        else:
            mixing_cutoff_index = tf.constant(self.n_broadcast, dtype=tf.dtypes.int32)

        # mix it
        mixed_w_broadcasted = tf.where(
            condition=tf.broadcast_to(self.mixing_layer_indices < mixing_cutoff_index, tf.shape(w_broadcasted1)),
            x=w_broadcasted1,
            y=w_broadcasted2)
        return mixed_w_broadcasted

    def truncation_trick(self, w_broadcasted, truncation_psi, truncation_cutoff=None):
        ones = tf.ones_like(self.mixing_layer_indices, dtype=tf.float32)
        tpsi = ones * truncation_psi
        if truncation_cutoff is None:
            truncation_coefs = tpsi
        else:
            indices = tf.range(self.n_broadcast)
            truncation_coefs = tf.where(condition=tf.less(indices, truncation_cutoff), x=tpsi, y=ones)

        truncated_w_broadcasted = lerp(self.w_avg, w_broadcasted, truncation_coefs)
        return truncated_w_broadcasted

    def call(self, inputs, ret_w_broadcasted=False, truncation_psi=1.0, truncation_cutoff=None, training=None, mask=None):
        latents, conditions = inputs

        dlatents = self.g_mapping([latents, conditions])
        w_broadcasted = self.broadcast(dlatents)

        if training:
            self.update_moving_average_of_w(w_broadcasted)
            w_broadcasted = self.style_mixing_regularization(latents, conditions, w_broadcasted)

        if not training:
            w_broadcasted = self.truncation_trick(w_broadcasted, truncation_psi, truncation_cutoff)

        image_out = self.synthesis(w_broadcasted)
        # image_out = image_out + conditions[2]
        image_out = image_out + self.zero_body[:image_out.shape[0]]
        image_out = image_out * conditions[3][:,tf.newaxis,:,:]

        if ret_w_broadcasted:
            return image_out, w_broadcasted
        else:
            return image_out

    def compute_output_shape(self, input_shape):
        return input_shape[0][0], 3, self.resolutions[-1], self.resolutions[-1]