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

from custom_utils.custom_utils import load_model
from models.base import AEEncoder, VariationalEncoder, Decoder, VariationalEncoderNew, DecoderNew
from models.upscaler import UpscalerBasic, UpscalerBasicConv, UpscalerBasicNew


class PyramidLevel(tf.keras.Model):
    
    def __init__(self, config):
        super(PyramidLevel, self).__init__()
        self.latent_dim = config['latent_dim']
        self.size = config['resolution']

        self.encoder = VariationalEncoder(config['latent_dim'], config['encoder_depths'], config['encoder_dims'], kernel_size=config['kernel_size'])
        self.decoder = Decoder(config['decoder_input'], config['decoder_depths'], config['decoder_dims'], config['kernel_size'])

        self.upscale_enhancer = None
        if config['upscale_enhancer'] != '':
            k_size = config['upscale_enhancer_kernel_size'] if 'upscale_enhancer_kernel_size' in config.keys() else ((3 if config['resolution'] <= 64 else 5))
            self.upscale_enhancer = UpscalerBasicConv(config['resolution'], kernel_size=k_size)
            load_model(self.upscale_enhancer, config['upscale_enhancer'])
            self.upscale_enhancer.trainable = not config['upscale_enhancer_freeze']

        if config['avg_garment_path'] != '':
            self.avg_garment = np.load(config['avg_garment_path'])
        else:
            self.avg_garment = None


    def enhance(self, low, mask, training=None):
        x = self.upscale_enhancer([low, mask], training=training)
        return x


    def sample(self, z_cond, uv_mask, noise=None):
        if noise is None:
            eps = tf.random.normal(shape=(uv_mask.shape[0], self.latent_dim))
        else:
            eps = noise

        z = tf.concat([eps, z_cond], axis=-1)

        x = self.decoder(z)
        
        if self.avg_garment is not None:
            x = x + self.avg_garment[tf.newaxis,:,:,:]
        
        x = x * uv_mask[:,:,:,tf.newaxis]

        return x


    def reparameterize(self, mean, logvar, training, fix_noise=False):
        if training == True:
            if not fix_noise:
                eps = tf.random.normal(shape=mean.shape)
            else:
                eps = tf.random.normal(shape=(1, mean.shape[1]))
                eps = tf.tile(eps, [mean.shape[0],1])
            out = eps * tf.exp(logvar * .5) + mean
        else:
            out = tf.exp(logvar * .5) + mean
        return out


    def call(self, inputs, z_cond, training=None):
        uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs

        x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
        mean, logvar = self.encoder(x, training=training)
        z = self.reparameterize(mean, logvar, training)

        z = tf.concat([z, z_cond], axis=-1)
        x = self.decoder(z, training=training)

        if self.avg_garment is not None:
            x = x + self.avg_garment[tf.newaxis,:,:,:]

        x = x * uv_mask[:,:,:,tf.newaxis]

        # if self.upscale_enhancer is not None:
        #     pred_low = self.enhance(x, uv_mask, training=training)
        #     x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
        
        # x = x * uv_mask_high[:,:,:,tf.newaxis]

        return x, mean, logvar



class PyramidLevelNew(tf.keras.Model):
    
    def __init__(self, config):
        super(PyramidLevelNew, self).__init__()
        self.size = config['resolution']

        self.encoder = VariationalEncoderNew(config['encoder_depths'], config['encoder_dims'], kernel_size=config['kernel_size'], latent_dim=config['latent_dim'])
        self.decoder = DecoderNew(config['decoder_depths'], config['decoder_dims'], config['kernel_size'])

        self.upscale_enhancer = None
        if config['upscale_enhancer'] != '':
            self.upscale_enhancer = UpscalerBasicConv(config['resolution'], kernel_size=config['kernel_size'])
            load_model(self.upscale_enhancer, config['upscale_enhancer'])
            self.upscale_enhancer.trainable = not config['upscale_enhancer_freeze']

        if config['avg_garment_path'] != '':
            self.avg_garment = np.load(config['avg_garment_path'])
        else:
            self.avg_garment = None

        self.reshape = tf.keras.layers.Reshape((8, 4, config['latent_dim']))


    def enhance(self, low, mask, training=None):
        x = self.upscale_enhancer([low, mask], training=training)
        return x


    def sample(self, z_cond, uv_mask, noise=None):
        if noise is None:
            eps = tf.random.normal(shape=(uv_mask.shape[0], self.latent_dim))
        else:
            eps = noise

        z = tf.concat([eps, z_cond], axis=-1)

        x = self.decoder(z)
        
        if self.avg_garment is not None:
            x = x + self.avg_garment[tf.newaxis,:,:,:]
        
        x = x * uv_mask[:,:,:,tf.newaxis]

        return x


    def reparameterize(self, mean, logvar, training, fix_noise=False):
        if training == True:
            if not fix_noise:
                eps = tf.random.normal(shape=mean.shape)
            else:
                eps = tf.random.normal(shape=(1, mean.shape[1]))
                eps = tf.tile(eps, [mean.shape[0],1])
            out = eps * tf.exp(logvar * .5) + mean
        else:
            out = tf.exp(logvar * .5) + mean
        return out


    def call(self, inputs, z_cond, training=None):
        uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs

        x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
        mean, logvar = self.encoder(x, training=training)
        z = self.reparameterize(mean, logvar, training)
        z = self.reshape(z)

        z = tf.concat([z, z_cond], axis=-1)
        x = self.decoder(z, training=training)

        if self.avg_garment is not None:
            x = x + self.avg_garment[tf.newaxis,:,:,:]

        x = x * uv_mask[:,:,:,tf.newaxis]

        # if self.upscale_enhancer is not None:
        #     pred_low = self.enhance(x, uv_mask, training=training)
        #     x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
        
        # x = x * uv_mask_high[:,:,:,tf.newaxis]

        return x, mean, logvar