import jax
import jax.numpy as jnp
from jax import grad, jit, vmap
from typing import Tuple, Dict, Any, Sequence, Union
from flax import linen as nn
from flax.training import train_state
import optax
from jax.typing import ArrayLike

Shape = Sequence[Union[int, Any]]

class CDSTDP:
    def __init__(self, learning_rate: float = 0.01, time_window: float = 20.0):
        self.learning_rate = learning_rate
        self.time_window = time_window

    @staticmethod
    @jit
    def stdp_window(delta_t: jnp.ndarray) -> jnp.ndarray:
        """STDP learning window function."""
        return jnp.where(delta_t >= 0,
                         jnp.exp(-delta_t / 20.0),
                         -0.5 * jnp.exp(delta_t / 20.0))

    @staticmethod
    @jit
    def consciousness_coefficient(synaptic_activity: jnp.ndarray) -> jnp.ndarray:
        """Calculate consciousness coefficient based on synaptic activity."""
        return jnp.tanh(synaptic_activity)

    @jit
    def update_weights(self, weights: jnp.ndarray, pre_spikes: jnp.ndarray, post_spikes: jnp.ndarray, synaptic_activity: jnp.ndarray) -> jnp.ndarray:
        """Update synaptic weights based on CD-STDP."""
        def weight_update(w, pre, post, activity):
            delta_t = post - pre
            stdp = self.stdp_window(delta_t)
            cc = self.consciousness_coefficient(activity)
            return w + self.learning_rate * stdp * cc

        return vmap(weight_update)(weights, pre_spikes, post_spikes, synaptic_activity)

class NeuroFlexNN(nn.Module):
    features: Tuple[int, ...]
    use_cnn: bool = False
    use_rl: bool = False
    output_dim: int = 10
    dtype: Any = jnp.float32  # Explicitly set default dtype to jnp.float32

    def setup(self):
        if self.use_cnn:
            self.conv1 = nn.Conv(features=32, kernel_size=(3, 3), strides=(1, 1), padding='SAME', dtype=self.dtype, name='conv1')
            self.conv2 = nn.Conv(features=64, kernel_size=(3, 3), strides=(1, 1), padding='SAME', dtype=self.dtype, name='conv2')

        self.dense_layers = [nn.Dense(feat, dtype=self.dtype) for feat in self.features[:-1]]
        self.final_dense = nn.Dense(self.features[-1], dtype=self.dtype)

    def __call__(self, x):
        print(f"Input shape: {x.shape}")
        # Ensure input has a batch dimension
        if len(x.shape) == 1:
            x = x[None, ...]

        if self.use_cnn:
            # Adjust input shape for CNN if necessary
            if len(x.shape) == 2:
                x = x.reshape((x.shape[0], 1, 1, x.shape[1]))
            elif len(x.shape) == 3:
                x = x.reshape((x.shape[0], x.shape[1], x.shape[2], 1))
            x = self.cnn_block(x)
            print(f"CNN output shape: {x.shape}")
            # Flatten the CNN output
            x = x.reshape((x.shape[0], -1))
            print(f"Flattened CNN output shape: {x.shape}")
        else:
            # Flatten the input for dense layers if not using CNN
            x = x.reshape((x.shape[0], -1))
            print(f"Flattened input shape: {x.shape}")

        for i, dense in enumerate(self.dense_layers):
            x = dense(x)
            x = nn.relu(x)
            print(f"Dense layer {i+1} output shape: {x.shape}")
        x = self.final_dense(x)
        print(f"Final output shape: {x.shape}")
        return x

    def cnn_block(self, x):
        x = self.conv1(x)
        x = nn.relu(x)
        x = nn.avg_pool(x, window_shape=(2,) * (x.ndim - 2), strides=(2,) * (x.ndim - 2))

        x = self.conv2(x)
        x = nn.relu(x)
        x = nn.avg_pool(x, window_shape=(2,) * (x.ndim - 2), strides=(2,) * (x.ndim - 2))

        return x

    def select_action(self, x):
        logits = self(x)
        return jnp.argmax(logits, axis=-1)

    def simulate_consciousness(self, x):
        """
        Simulate consciousness by applying a non-linear transformation to the network output.
        This is a simplified representation and does not capture the full complexity of consciousness.
        """
        # Apply a sigmoid activation to compress the output to (0, 1)
        consciousness = jax.nn.sigmoid(x)

        # Apply a threshold to simulate binary conscious/unconscious states
        threshold = 0.5
        conscious_state = jnp.where(consciousness > threshold, 1.0, 0.0)

        return conscious_state

def create_train_state(rng, model, dummy_input, learning_rate):
    params = model.init(rng, dummy_input)['params']
    tx = optax.adam(learning_rate)
    return train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx)

def data_augmentation(images, rng):
    """Apply random data augmentation to the input images."""
    def random_flip(image, key):
        flip_lr = jax.random.bernoulli(key, 0.5)
        return jnp.where(flip_lr, jnp.fliplr(image), image)

    def random_brightness(image, key):
        delta = jax.random.uniform(key, minval=-0.2, maxval=0.2)
        return jnp.clip(image + delta, 0, 1)

    def random_contrast(image, key):
        factor = jax.random.uniform(key, minval=0.8, maxval=1.2)
        mean = jnp.mean(image, axis=(0, 1), keepdims=True)
        return jnp.clip((image - mean) * factor + mean, 0, 1)

    keys = jax.random.split(rng, images.shape[0] * 3)
    keys = keys.reshape((3, images.shape[0], 2))

    augmented = images
    augmented = jax.vmap(random_flip)(augmented, keys[0])
    augmented = jax.vmap(random_brightness)(augmented, keys[1])
    augmented = jax.vmap(random_contrast)(augmented, keys[2])

    return augmented, keys[-1, -1]

def select_action(observation, model, params):
    # Ensure observation has a batch dimension
    if len(observation.shape) == 1:
        observation = observation[None, ...]
    logits = model.apply({'params': params}, observation)
    return jnp.argmax(logits, axis=-1)

def adversarial_training(model, params, input_data, epsilon):
    def loss_fn(x):
        logits = model.apply({'params': params}, x)
        return optax.softmax_cross_entropy_with_integer_labels(logits, input_data['label']).mean()

    grad_fn = jax.grad(loss_fn)
    grad = grad_fn(input_data['image'])
    perturbation = epsilon * jnp.sign(grad)
    perturbation = jnp.clip(perturbation, -epsilon, epsilon)  # Ensure perturbation magnitude is within epsilon
    perturbed_image = jnp.clip(input_data['image'] + perturbation, 0, 1)  # Ensure values are in [0, 1] range

    return {'image': perturbed_image, 'label': input_data['label']}

def test_cdstdp():
    """Simple test function for CD-STDP model."""
    cdstdp = CDSTDP()

    # Generate sample data
    key = jax.random.PRNGKey(0)
    weights = jax.random.normal(key, (10,))
    pre_spikes = jax.random.uniform(key, (10,)) * 100
    post_spikes = jax.random.uniform(key, (10,)) * 100
    synaptic_activity = jax.random.uniform(key, (10,))

    # Update weights
    new_weights = cdstdp.update_weights(weights, pre_spikes, post_spikes, synaptic_activity)

    print("Initial weights:", weights)
    print("Updated weights:", new_weights)

if __name__ == "__main__":
    test_cdstdp()

class NeuroFlex:
    def __init__(self, features, use_cnn=False, use_rl=False, output_dim=10):
        self.model = NeuroFlexNN(features=features, use_cnn=use_cnn, use_rl=use_rl, output_dim=output_dim)
        self.cdstdp = CDSTDP()

    def train(self, X, y, learning_rate=0.001, epochs=100):
        rng = jax.random.PRNGKey(0)
        state = create_train_state(rng, self.model, X[0], learning_rate)

        @jax.jit
        def train_step(state, batch_X, batch_y):
            def loss_fn(params):
                logits = self.model.apply({'params': params}, batch_X)
                return optax.softmax_cross_entropy_with_integer_labels(logits, batch_y).mean()

            loss, grads = jax.value_and_grad(loss_fn)(state.params)
            state = state.apply_gradients(grads=grads)
            return state, loss

        for epoch in range(epochs):
            state, loss = train_step(state, X, y)
            if epoch % 10 == 0:
                print(f"Epoch {epoch}, Loss: {loss}")

        return state

    def predict(self, X, params):
        return self.model.apply({'params': params}, X)

    def interpret(self, X, params):
        # Implement SHAP or other interpretability methods here
        pass

    def generate_adversarial(self, X, params, epsilon=0.1):
        return adversarial_training(self.model, params, {'image': X, 'label': None}, epsilon)

    def simulate_consciousness(self, X, params):
        output = self.predict(X, params)
        return self.model.simulate_consciousness(output)
