import os
import time
import random
import torch
from torch import nn
import numpy as np
import scipy as sp
# import plotly.graph_objects as go
# import plotly.express as ex

from utils.checkpoints import load_model
from models.base import AEEncoder, VariationalEncoder, Decoder
from models.upscaler import Upscaler



# class PyramidLevel(nn.Module):
    
#     def __init__(self, config):
#         super(PyramidLevel, self).__init__()
#         self.latent_dim = config['latent_dim']
#         self.size = config['resolution']

#         self.encoder: VariationalEncoder = VariationalEncoder(config['encoder_block_settings'], config['input_features'],  kernel_size=config['kernel_size'])
#         self.decoder: Decoder = Decoder(config['decoder_block_settings'], config['kernel_size'])

#         self.upscale_enhancer = None
#         if config['upscale_enhancer'] != '':
#             k_size = config['upscale_enhancer_kernel_size']
#             self.upscale_enhancer = Upscaler(config['resolution'], kernel_size=k_size)
#             load_model(self.upscale_enhancer, config['upscale_enhancer'])
#             for param in self.upscale_enhancer.parameters(): param.requires_grad = False

#         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 = torch.normal(shape=(uv_mask.shape[0], self.latent_dim))
#         else:
#             eps = noise

#         z = torch.concat([eps, z_cond], dim=-1)

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

#         return x


#     def reparameterize(self, mean, logvar, training, fix_noise=False):
#         if training == True:
#             if not fix_noise:
#                 eps = torch.normal(mean=0.0, std=1.0, size=mean.shape)
#             else:
#                 eps = torch.normal(mean=0.0, std=1.0, size=mean.shape)
#                 eps = torch.tile(eps, [mean.shape[0],1])
#             out = eps * torch.exp(logvar * .5) + mean
#         else:
#             out = torch.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 = torch.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], dim=1)
#         mean, logvar = self.encoder(x, training=training)
#         z = self.reparameterize(mean, logvar, training)

#         z = torch.concat([z, z_cond], dim=-1)
#         x = self.decoder(z, training=training)

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

#         x = x * uv_mask[:,None,:,:]

#         # 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 PyramidLevel(nn.Module):
    
    def __init__(self, config):
        super(PyramidLevel, self).__init__()
        self.size = config['resolution']

        self.encoder: VariationalEncoder = VariationalEncoder(config['encoder_block_settings'], input_features=config['input_features'],  kernel_size=config['kernel_size'])
        self.decoder: Decoder = Decoder(config['decoder_block_settings'], config['kernel_size'])

        self.upscale_enhancer = None
        if config['upscale_enhancer'] != '':
            k_size = config['kernel_size']
            self.upscale_enhancer = Upscaler(config['resolution'], kernel_size=k_size).to('cuda')
            load_model(self.upscale_enhancer, config['upscale_enhancer'])
            for param in self.upscale_enhancer.parameters(): param.requires_grad = False


    def enhance(self, low, mask):
        x = self.upscale_enhancer([low, mask])
        return x


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

        z = torch.concat([eps, z_cond], dim=-1)

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

        return x


    def reparameterize(self, mean, logvar, fix_noise=False):
        if self.training == True:
            if not fix_noise:
                eps = torch.normal(mean=0.0, std=1.0, size=mean.shape).to(mean.device)
            else:
                eps = torch.normal(mean=0.0, std=1.0, size=mean.shape).to(mean.device)
                eps = torch.tile(eps, [mean.shape[0],1])
            out = eps * torch.exp(logvar * .5) + mean
        else:
            out = torch.exp(logvar * .5) + mean
        return out


    def forward(self, inputs, z_cond):
        uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs

        x = torch.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], dim=1)
        mean, logvar = self.encoder(x)
        z = self.reparameterize(mean, logvar)
        z = torch.reshape(z, (mean.shape[0], -1, 8, 4))

        z = torch.concat([z, z_cond], dim=1)
        x = self.decoder(z)

        x = x * uv_mask[:,None,:,:]

        # 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