import os
import time
import random
import tensorflow as tf
import numpy as np
# import plotly.graph_objects as go
# import plotly.express as ex

from custom_utils.custom_utils import load_model
from models.base import AEEncoder
from models.cvae_model import CVAE
from models.pyramid_level import PyramidLevel


class Pyramid(tf.keras.Model):
    
    def __init__(self, config):
        super(Pyramid, self).__init__()

        self.static_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
        if config['pyramid']['static_model'] != '':
            load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')

        self.body_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
        if config['pyramid']['body_model'] != '':
            load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')

        self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
        if config['pyramid']['curvature_model'] != '':
            load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')

        if config['pyramid']['freeze_encoders']:
            self.static_enc.trainable = False
            self.body_enc.trainable = False
            self.curv_enc.trainable = False

        self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
                                                 tf.keras.layers.Dense(256, activation='relu'),
                                                 tf.keras.layers.Dense(128, activation='relu'),
                                                 tf.keras.layers.Dense(config['pyramid']['latent_dim']),])

        self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae': config['cond_ae']} }) for i in range(len(config['pyramid']['levels']))]


    def encode_conds(self, uv_template, uv_body_posed, uv_curv):
        z_static = self.static_enc(uv_template)
        z_body = self.body_enc(uv_body_posed)
        z_curv = self.curv_enc(uv_curv)

        z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
        z_cond = self.cond_encoder(z_cond)

        return z_cond

    def call(self, inputs, training=None):
        uv_static, uv_body, uv_curv = inputs[-1][1], inputs[-1][2], inputs[-1][3]
        z_cond = self.encode_conds(uv_static, uv_body, uv_curv)

        out_levels = []
        pred, mean, logvar = self.levels[0](inputs[0], z_cond, training=training)
        out_levels.append([pred, mean, logvar])

        for i in range(1, len(self.levels)):
            pred_level, mean, logvar = self.levels[i](inputs[i], z_cond, training=training)
            pred = pred + pred_level
            out_levels.append([pred, mean, logvar])
        
        return out_levels