import jax
import jax.numpy as jnp
from jax import grad, jit, vmap
from typing import Dict, List, Tuple
from .advanced_thinking import CDSTDP

class CognitiveArchitecture:
    """
    A cognitive architecture that simulates various aspects of cognition including
    sensory processing, consciousness, and feedback mechanisms.

    This class integrates multiple cognitive processes to create a simplified model
    of a cognitive system, utilizing concepts from neuroscience and cognitive science.

    Attributes:
        config (Dict): Configuration parameters for the cognitive architecture.
        cdstdp (CDSTDP): An instance of the Consciousness-Driven Spike-Timing-Dependent Plasticity model.
        sensory_modules (Dict): A dictionary of sensory processing modules.
        consciousness_module (jnp.ndarray): A representation of the consciousness module.
        feedback_mechanism (jnp.ndarray): A representation of the feedback mechanism.
    """

    def __init__(self, config: Dict):
        """
        Initialize the CognitiveArchitecture with the given configuration.

        Args:
            config (Dict): Configuration parameters for the cognitive architecture.
        """
        self.config = config
        self.cdstdp = CDSTDP()
        self.sensory_modules = self._initialize_sensory_modules()
        self.consciousness_module = self._initialize_consciousness_module()
        self.feedback_mechanism = self._initialize_feedback_mechanism()

    def _initialize_sensory_modules(self) -> Dict:
        """
        Initialize the sensory processing modules.

        Returns:
            Dict: A dictionary of sensory modules with random initial states.
        """
        return {
            "vision": jax.random.normal(jax.random.PRNGKey(0), (100,)),
            "audition": jax.random.normal(jax.random.PRNGKey(1), (100,)),
            "touch": jax.random.normal(jax.random.PRNGKey(2), (100,)),
        }

    def _initialize_consciousness_module(self) -> jnp.ndarray:
        """
        Initialize the consciousness module.

        Returns:
            jnp.ndarray: A random initial state for the consciousness module.
        """
        return jax.random.normal(jax.random.PRNGKey(3), (100,))

    def _initialize_feedback_mechanism(self) -> jnp.ndarray:
        """
        Initialize the feedback mechanism.

        Returns:
            jnp.ndarray: A random initial state for the feedback mechanism.
        """
        return jax.random.normal(jax.random.PRNGKey(4), (100,))

    @jit
    def integrate_sensory_inputs(self, inputs: Dict[str, jnp.ndarray]) -> jnp.ndarray:
        """
        Integrate multi-modal sensory inputs.

        Args:
            inputs (Dict[str, jnp.ndarray]): A dictionary of sensory inputs.

        Returns:
            jnp.ndarray: The integrated sensory input.
        """
        integrated = jnp.concatenate([inputs[key] for key in self.sensory_modules.keys()])
        return jax.nn.relu(integrated)

    @jit
    def process_consciousness(self, integrated_input: jnp.ndarray) -> jnp.ndarray:
        """
        Process the integrated input through the consciousness module.

        Args:
            integrated_input (jnp.ndarray): The integrated sensory input.

        Returns:
            jnp.ndarray: The processed consciousness state.
        """
        consciousness_state = self.consciousness_module + integrated_input
        return jax.nn.sigmoid(consciousness_state)

    @jit
    def apply_feedback(self, consciousness_state: jnp.ndarray) -> jnp.ndarray:
        """
        Apply feedback based on the current consciousness state.

        Args:
            consciousness_state (jnp.ndarray): The current consciousness state.

        Returns:
            jnp.ndarray: The feedback signal.
        """
        feedback = jnp.dot(self.feedback_mechanism, consciousness_state)
        return jax.nn.tanh(feedback)

    def update_architecture(self, inputs: Dict[str, jnp.ndarray]) -> Tuple[jnp.ndarray, jnp.ndarray]:
        """
        Update the cognitive architecture based on new inputs.

        This method integrates sensory inputs, processes consciousness, applies feedback,
        and updates the sensory modules using CD-STDP.

        Args:
            inputs (Dict[str, jnp.ndarray]): A dictionary of new sensory inputs.

        Returns:
            Tuple[jnp.ndarray, jnp.ndarray]: The updated consciousness state and feedback.
        """
        integrated = self.integrate_sensory_inputs(inputs)
        consciousness_state = self.process_consciousness(integrated)
        feedback = self.apply_feedback(consciousness_state)

        # Update weights using CD-STDP
        for key in self.sensory_modules:
            self.sensory_modules[key] = self.cdstdp.update_weights(
                self.sensory_modules[key],
                inputs[key],
                consciousness_state,
                feedback
            )

        return consciousness_state, feedback

    def agi_prototype_module(self, input_data: jnp.ndarray) -> jnp.ndarray:
        """
        A prototype module for Artificial General Intelligence (AGI) concepts.

        This method serves as a placeholder for implementing more advanced AGI concepts.

        Args:
            input_data (jnp.ndarray): Input data for the AGI module.

        Returns:
            jnp.ndarray: Processed output from the AGI module.
        """
        return jax.nn.softmax(input_data)

def test_cognitive_architecture():
    config = {"learning_rate": 0.01}
    cog_arch = CognitiveArchitecture(config)

    # Generate sample inputs
    inputs = {
        "vision": jax.random.normal(jax.random.PRNGKey(5), (100,)),
        "audition": jax.random.normal(jax.random.PRNGKey(6), (100,)),
        "touch": jax.random.normal(jax.random.PRNGKey(7), (100,)),
    }

    consciousness_state, feedback = cog_arch.update_architecture(inputs)
    print("Consciousness State:", consciousness_state[:5])
    print("Feedback:", feedback[:5])

if __name__ == "__main__":
    test_cognitive_architecture()
