import tensorflow as tf
from typing import List, Tuple, Optional
import time
import logging

class TensorFlowConvolutions(tf.keras.layers.Layer):
    def __init__(self, filters: List[int], kernel_sizes: List[Tuple[int, int]], strides: List[Tuple[int, int]],
                 paddings: List[str], activation: str = 'relu'):
        super(TensorFlowConvolutions, self).__init__()
        self.conv_layers = []
        for f, k, s, p in zip(filters, kernel_sizes, strides, paddings):
            self.conv_layers.append(tf.keras.layers.Conv2D(filters=f, kernel_size=k, strides=s, padding=p, activation=activation))
        self.flatten = tf.keras.layers.Flatten()

    def call(self, inputs):
        x = inputs
        for layer in self.conv_layers:
            x = layer(x)
        return self.flatten(x)

def create_conv_model(input_shape: Tuple[int, ...], filters: List[int], kernel_sizes: List[Tuple[int, int]],
                      strides: List[Tuple[int, int]], paddings: List[str], output_dim: int) -> tf.keras.Model:
    inputs = tf.keras.Input(shape=input_shape)
    conv_layers = TensorFlowConvolutions(filters, kernel_sizes, strides, paddings)
    x = conv_layers(inputs)
    outputs = tf.keras.layers.Dense(output_dim)(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

class AdaptiveOptimizer(tf.keras.optimizers.Optimizer):
    def __init__(self, learning_rate=0.001, name="AdaptiveOptimizer", **kwargs):
        super(AdaptiveOptimizer, self).__init__(name, **kwargs)
        self._lr = learning_rate
        self._lr_t = None

    def _create_slots(self, var_list):
        for var in var_list:
            self.add_slot(var, "m")

    def _resource_apply_dense(self, grad, var, apply_state=None):
        var_dtype = var.dtype.base_dtype
        lr_t = self._decayed_lr(var_dtype)
        m = self.get_slot(var, "m")
        m_t = m.assign(0.9 * m + 0.1 * grad)
        var_update = var.assign_sub(lr_t * m_t)
        return tf.group(*[var_update, m_t])

    def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
        var_dtype = var.dtype.base_dtype
        lr_t = self._decayed_lr(var_dtype)
        m = self.get_slot(var, "m")
        m_t = m.assign(0.9 * m)
        m_t = m_t.scatter_add(tf.IndexedSlices(0.1 * grad, indices))
        var_update = var.assign_sub(lr_t * m_t)
        return tf.group(*[var_update, m_t])

    def get_config(self):
        config = super(AdaptiveOptimizer, self).get_config()
        config.update({"learning_rate": self._serialize_hyperparameter("learning_rate")})
        return config

def train_conv_model(model: tf.keras.Model, x_train: tf.Tensor, y_train: tf.Tensor,
                     epochs: int = 10, batch_size: int = 32,
                     validation_data: Optional[Tuple[tf.Tensor, tf.Tensor]] = None) -> dict:
    optimizer = AdaptiveOptimizer()
    loss_fn = tf.keras.losses.MeanSquaredError()
    train_loss = tf.keras.metrics.Mean(name='train_loss')
    val_loss = tf.keras.metrics.Mean(name='val_loss')
    history = {'train_loss': [], 'val_loss': []}

    @tf.function
    def train_step(x, y):
        with tf.GradientTape() as tape:
            predictions = model(x, training=True)
            loss = loss_fn(y, predictions)
        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
        train_loss(loss)

    @tf.function
    def test_step(x, y):
        predictions = model(x, training=False)
        loss = loss_fn(y, predictions)
        val_loss(loss)

    for epoch in range(epochs):
        train_loss.reset_states()
        val_loss.reset_states()

        for i in range(0, len(x_train), batch_size):
            batch_x = x_train[i:i+batch_size]
            batch_y = y_train[i:i+batch_size]
            train_step(batch_x, batch_y)

        if validation_data:
            val_x, val_y = validation_data
            test_step(val_x, val_y)

        history['train_loss'].append(train_loss.result().numpy())
        if validation_data:
            history['val_loss'].append(val_loss.result().numpy())

        # Self-healing mechanism
        if epoch > 0 and history['train_loss'][-1] > history['train_loss'][-2]:
            logging.info(f"Performance degradation detected at epoch {epoch}. Adjusting learning rate.")
            optimizer.learning_rate.assign(optimizer.learning_rate * 0.9)

    return history

def conv_predict(model: tf.keras.Model, x: tf.Tensor) -> tf.Tensor:
    return model(x, training=False)

# Self-healing mechanism
def self_heal(model: tf.keras.Model, x_train: tf.Tensor, y_train: tf.Tensor):
    logging.info("Initiating self-healing process...")

    # Reinitialize weights of the last layer
    for layer in model.layers[-1:]:
        if hasattr(layer, 'kernel_initializer') and hasattr(layer, 'bias_initializer'):
            new_weights = layer.kernel_initializer(shape=layer.kernel.shape)
            new_bias = layer.bias_initializer(shape=layer.bias.shape)
            layer.set_weights([new_weights, new_bias])

    # Perform a quick retraining
    train_conv_model(model, x_train, y_train, epochs=5, batch_size=32)

    logging.info("Self-healing process completed.")

# Performance monitoring
def monitor_performance(model: tf.keras.Model, x_test: tf.Tensor, y_test: tf.Tensor, threshold: float = 0.8):
    test_loss = tf.keras.losses.MeanSquaredError()
    predictions = model(x_test, training=False)
    current_performance = 1 - test_loss(y_test, predictions).numpy()  # Assuming lower loss is better

    if current_performance < threshold:
        logging.warning(f"Model performance ({current_performance:.4f}) is below threshold ({threshold}).")
        return False
    return True
