import os
import time
import argparse
import wandb
from cv2 import drawFrameAxes
import numpy as np
import tensorflow as tf
from custom_utils.custom_utils import UV2Mesh, maskFacesBatch
from tensorboardX import SummaryWriter

from utils import str_to_bool
from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
from load_models import load_generator, load_discriminator
from dataset import DatasetSimple
from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss
# from losses import collision_penalty as collision_penalty
from losses import collision_loss as collision_penalty
from convnext.models.convnext_tf import ConvNeXt
from stylegan2_ae.generator import Generator


os.environ["CUDA_VISIBLE_DEVICES"] = "3"
run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='ae_1_2', notes='convnext + stylegan generator')
artifact = wandb.Artifact('code', type='code')
artifact.add_file('auto_encoder.py')
artifact.add_file('stylegan2_ae/layers/mapping_block.py')
run.log_artifact(artifact)

uv2mesh = UV2Mesh(res=256)



def filter_resolutions_featuremaps(resolutions, featuremaps, res):
    index = resolutions.index(res)
    filtered_resolutions = resolutions[:index + 1]
    filtered_featuremaps = featuremaps[:index + 1]
    return filtered_resolutions, filtered_featuremaps


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

        resolutions = [4, 8, 16, 32, 64, 128, 256, 512, 1024]
        g_featuremaps = [256, 256, 256, 256, 256, 128, 64, 32, 16]
        train_resolutions, train_g_featuremaps = filter_resolutions_featuremaps(resolutions, g_featuremaps, 512)
        g_params = {
            'z_dim': 256,
            'w_dim': 256,
            'labels_dim': -1,
            'n_mapping': 8,
            'resolutions': train_resolutions,
            'featuremaps': train_g_featuremaps,
        }

        # test_latent = tf.ones((1, g_params['z_dim']), dtype=tf.float32)
        self.decoder = Generator(g_params)
        # _ = generator(test_latent)


        self.encoder = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 256, g_params['z_dim']], include_top=False)
        self.pooling = tf.keras.layers.GlobalAveragePooling2D()


    def call(self, inputs, training=None):
        x, uv_mask = inputs

        x = tf.transpose(x, [0,2,3,1])
        x = self.encoder(x, training=training)
        x = self.pooling(x)

        x = self.decoder(x, training=training)
        x = x * uv_mask[:,tf.newaxis,:,:]

        return x


@tf.function
def train_step(batch):
    posed_uv, conditions,_, mesh_body_posed, mesh_mask = batch
    uv_mask = conditions[3]

    with tf.GradientTape() as tape:

        pred = model([posed_uv, uv_mask])

        mesh_pred = uv2mesh(pred)
        mesh_orig = uv2mesh(posed_uv)

        e_loss = 0.0#edge_loss(mesh_pred, mesh_orig, mesh_mask)
        c_loss = 0.0#collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
        l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(posed_uv - pred)), axis=1) * uv_mask)
        loss = l1_loss + e_loss + c_loss

    metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mesh_mask[:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(mesh_mask)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric }
        


@tf.function
def valid_step(batch):
    posed_uv, conditions,_, mesh_body_posed, mesh_mask = batch
    uv_mask = conditions[3]

    pred = model([posed_uv, uv_mask])
    mesh_pred = uv2mesh(pred)
    mesh_orig = uv2mesh(posed_uv)

    e_loss = 0.0#edge_loss(mesh_pred, mesh_orig, mesh_mask)
    c_loss = 0.0#collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
    l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(posed_uv - pred)), axis=1) * uv_mask)
    loss = l1_loss + e_loss + c_loss

    metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mesh_mask[:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(mesh_mask)
    return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric }


if __name__ == '__main__':
    allow_memory_growth()
    
    
    name = 'test-9'
    dataset_train = DatasetSimple('/home/workspace/data/dataset/stylegan_new/train').cache().batch(8).repeat()
    dataset_valid = DatasetSimple('/home/workspace/data/dataset/stylegan_new/valid').cache().batch(8).repeat()

    model = AutoEncoder()
    # optimizer = tf.optimizers.Adam(learning_rate=0.002, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
    optimizer = tf.optimizers.Adam()
    folder = 'models/ae_logs/'
    train_summary_writer = tf.summary.create_file_writer(folder + name + '/train')
    valid_summary_writer = tf.summary.create_file_writer(folder + name + '/valid')
    mesh_writer = SummaryWriter(folder + name + '/mesh')

    step = 0
    for batch in dataset_train:
        train_loss = train_step(batch)

        for key in train_loss.keys():
            with train_summary_writer.as_default():
                tf.summary.scalar(key, train_loss[key], step=step)

        data_valid = iter(dataset_valid).get_next()
        valid_loss = valid_step(data_valid)

        for key in valid_loss.keys():
            with valid_summary_writer.as_default():
                tf.summary.scalar(key, valid_loss[key], step=step)

        if step % 250 == 0:
            pred = model([data_valid[0], data_valid[1][3]])
            
            mesh_pred = uv2mesh(pred)
            mesh_orig = uv2mesh(data_valid[0])
            
            orig_img = tf.transpose(data_valid[0], [0,2,3,1])
            pred_img = tf.transpose(pred, [0,2,3,1])
            img = tf.concat((orig_img, pred_img), axis=2)
            with train_summary_writer.as_default():
                tf.summary.image('images', img, step=step)

            F = np.load("custom_utils/smpl_faces.npy")
            garment, faces = maskFacesBatch(mesh_pred.numpy(), F, data_valid[4].numpy().astype(bool))
            gt, _ = maskFacesBatch(mesh_orig.numpy(), F, data_valid[4].numpy().astype(bool))
            
            for i in range(len(garment)):
                mesh_writer.add_mesh('garment_pred_' + str(i), vertices=garment[i][np.newaxis,:,:], faces=faces[i][np.newaxis,:,:], global_step=step)
                mesh_writer.add_mesh('garment_gt_' + str(i), vertices=gt[i][np.newaxis,:,:], faces=faces[i][np.newaxis,:,:], global_step=step)

        print(step)

        step += 1