import os
import time

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

import tensorflow as tf

tf.get_logger().setLevel("ERROR")
tf.keras.utils.set_random_seed(2026)

AUTOTUNE = tf.data.AUTOTUNE
EXAMPLES = 256
BATCH_SIZE = 32
EPOCHS = 3
IMAGE_SHAPE = (128, 128, 3)
TARGET_SIZE = (96, 96)

source_image = tf.random.uniform(
    IMAGE_SHAPE,
    minval=0,
    maxval=256,
    dtype=tf.int32,
    seed=7,
)
encoded_image = tf.io.encode_jpeg(tf.cast(source_image, tf.uint8)).numpy()
encoded_images = tf.constant([encoded_image] * EXAMPLES)
labels = tf.range(EXAMPLES, dtype=tf.int32) % 2

def decode_and_resize(image_bytes, label):
    image = tf.io.decode_jpeg(image_bytes, channels=3)
    image = tf.image.resize(image, TARGET_SIZE)
    image = tf.cast(image, tf.float32) / 255.0
    return image, label