# video_compression_model.py

import cv2
import numpy as np
import tensorflow as tf

from global_train import LOGGER

PRESET_SPEED_CATEGORIES = ["ultrafast", "superfast", "veryfast", "faster", "fast", "medium", "slow", "slower", "veryslow"]
NUM_PRESET_SPEEDS = len(PRESET_SPEED_CATEGORIES)
NUM_CHANNELS = 3
WIDTH = 640
HEIGHT = 360

#from tensorflow.keras.mixed_precision import Policy

#policy = Policy('mixed_float16')
#tf.keras.mixed_precision.set_global_policy(policy)



class VideoCompressionModel(tf.keras.Model):
    def __init__(self):
        super(VideoCompressionModel, self).__init__()
        LOGGER.debug("Initializing VideoCompressionModel.")

        # Add an additional channel for the histogram features
        input_shape_with_histogram = (HEIGHT, WIDTH, 2)  # 1 channel for edges, 1 for histogram

        self.encoder = tf.keras.Sequential([
            tf.keras.layers.InputLayer(input_shape=input_shape_with_histogram),
            tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same'),
            tf.keras.layers.MaxPooling2D((2, 2), padding='same'),
            tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same'),
            tf.keras.layers.MaxPooling2D((2, 2), padding='same')
        ])

        self.decoder = tf.keras.Sequential([
            tf.keras.layers.Conv2DTranspose(32, (3, 3), activation='relu', padding='same'),
            tf.keras.layers.UpSampling2D((2, 2)),
            tf.keras.layers.Conv2DTranspose(64, (3, 3), activation='relu', padding='same'),
            tf.keras.layers.UpSampling2D((2, 2)),
            tf.keras.layers.Conv2DTranspose(1, (3, 3), activation='sigmoid', padding='same')
        ])

    def call(self, inputs):
        encoded = self.encoder(inputs)
        decoded = self.decoder(encoded)
        return decoded