import jax
import jax.numpy as jnp
from jax import grad, jit, vmap
from typing import Dict, List, Tuple
from .advanced_thinking import CDSTDP
import jax.tree_util
from abc import ABC, abstractmethod
import time
import logging

def create_sensory_modules(key: jax.random.PRNGKey):
    keys = jax.random.split(key, 4)
    return {
        "vision": jax.random.normal(keys[1], (100,)),
        "audition": jax.random.normal(keys[2], (100,)),
        "touch": jax.random.normal(keys[3], (100,)),
    }

@jax.jit
def integrate_inputs(inputs: Dict[str, jnp.ndarray]) -> jnp.ndarray:
    integrated = jnp.concatenate(list(inputs.values()))
    return jax.nn.relu(integrated).astype(jnp.float32)

@jax.jit
def update_modules(modules: Dict[str, jnp.ndarray], inputs: Dict[str, jnp.ndarray],
                   consciousness_state: jnp.ndarray, feedback: jnp.ndarray,
                   learning_rate: float) -> Dict[str, jnp.ndarray]:
    def update_module(module, input_data):
        delta = jnp.outer(input_data, consciousness_state) * feedback
        return module + learning_rate * delta
    return jax.tree_util.tree_map(update_module, modules, inputs)

def create_consciousness(key: jax.random.PRNGKey):
    return jax.random.normal(key, (100,))

@jax.jit
def process_consciousness(module: jnp.ndarray, integrated_input: jnp.ndarray) -> jnp.ndarray:
    reshaped_input = jnp.reshape(integrated_input, (-1, module.shape[-1]))
    consciousness_state = jnp.add(module, jnp.sum(reshaped_input, axis=0))
    return jax.nn.sigmoid(consciousness_state)

def create_feedback_mechanism(key: jax.random.PRNGKey):
    return jax.random.normal(key, (100,))

@jax.jit
def apply_feedback(mechanism: jnp.ndarray, consciousness_state: jnp.ndarray) -> jnp.ndarray:
    # Apply complex non-linear transformation to consciousness_state with increased scaling
    transformed_state = jax.nn.tanh(consciousness_state * 3000.0) + jax.nn.sigmoid(consciousness_state * 2000.0)
    transformed_state += jax.nn.elu(consciousness_state * 2500.0) + jax.nn.gelu(consciousness_state * 1800.0)
    transformed_state += jax.nn.softplus(consciousness_state * 2200.0)  # Additional non-linear transformation

    # Introduce chaotic element using logistic map with increased complexity
    r = 3.9999  # Chaos parameter (closer to 4 for increased chaos)
    chaotic_element = r * transformed_state * (1 - transformed_state)
    chaotic_element = jax.nn.tanh(chaotic_element * 15.0) + jax.nn.sigmoid(chaotic_element * 12.0)
    chaotic_element += jax.nn.elu(chaotic_element * 10.0)  # Additional chaotic component

    # Multi-scale approach using jax.lax.reduce_window with smaller window size
    def avg_pool(x, window_size):
        return jax.lax.reduce_window(x, 0.0, jax.lax.add, (window_size,), (1,), 'VALID') / window_size

    coarse_grained = avg_pool(transformed_state, 2)  # Reduced window size for finer granularity
    fine_grained = transformed_state - jnp.repeat(coarse_grained, 2)[:transformed_state.shape[0]]

    # Ensure coarse_grained and fine_grained have the same length as transformed_state
    coarse_grained = jnp.repeat(coarse_grained, 2)[:transformed_state.shape[0]]
    fine_grained = fine_grained[:transformed_state.shape[0]]

    # Ensure mechanism has the same shape as consciousness_state for proper broadcasting
    mechanism = jnp.broadcast_to(mechanism, consciousness_state.shape)

    # Compute feedback with increased sensitivity and variability
    feedback_coarse = jax.nn.sigmoid(coarse_grained * 60) * mechanism  # Increased scaling
    feedback_fine = jax.nn.tanh(fine_grained * 60) * mechanism  # Increased scaling

    # Combine feedback_coarse and feedback_fine with enhanced non-linear mixing
    feedback = jax.nn.sigmoid(feedback_coarse + feedback_fine) * jax.nn.tanh(feedback_coarse * feedback_fine)
    feedback += jax.nn.elu(feedback_coarse - feedback_fine)  # Additional mixing term

    # Mixture of experts with increased number of experts and complexity
    num_experts = 48  # Increased number of experts
    expert_outputs = jnp.stack([jax.nn.sigmoid(feedback * (i + 1) * 2.0) for i in range(num_experts)])  # Increased scaling

    expert_weights = jax.nn.softmax(jnp.sum(feedback) * 8 * jnp.ones(num_experts))  # Increased temperature
    combined_output = jnp.sum(expert_outputs * expert_weights[:, None], axis=0)

    # Dynamic normalization with adjusted epsilon and enhanced non-linear scaling
    mean, var = jnp.mean(combined_output), jnp.var(combined_output)
    normalized_output = jax.nn.tanh((combined_output - mean) / jnp.sqrt(var + 1e-12) * 6.0)  # Increased scaling, smaller epsilon

    # Combine normalized output with chaotic element
    output = normalized_output + chaotic_element * 0.6  # Increased chaotic influence

    # Add frequency-based modulation with increased effect and complexity
    frequencies = jnp.linspace(0, 80, num=output.shape[0])  # Increased frequency range
    modulation = jnp.sin(frequencies * jnp.pi * jnp.mean(jnp.abs(output))) * jnp.cos(frequencies * 0.9)
    output = output * (1 + modulation * 1.2)  # Increased modulation effect

    # Apply final non-linear transformation to amplify small differences
    output = jax.nn.tanh(output * jnp.exp(jnp.abs(output)) * 300.0)  # Increased scaling factor

    # Introduce additional non-linearity with a mixture of activations
    output = jax.nn.sigmoid(output * 40) * 1.8 - jax.nn.relu(output * 30) * 0.4 + jax.nn.elu(output * 25) * 0.3
    output += jax.nn.softplus(output * 20) * 0.2  # Additional activation in the mixture

    # Add residual connection to preserve small changes with increased weight
    output = output + consciousness_state * 0.15

    # Introduce a small amount of noise to further increase sensitivity
    noise = jax.random.normal(jax.random.PRNGKey(0), output.shape) * 1e-5
    output += noise

    # Final normalization to ensure output is within [-1, 1]
    output = jnp.clip(output, -1, 1)

    # Ensure the output has the same shape as the input consciousness_state
    output = jnp.reshape(output, consciousness_state.shape)

    return output

from abc import ABC, abstractmethod

class CognitiveComponent(ABC):
    @abstractmethod
    def process(self, inputs):
        pass

class SensoryProcessing(CognitiveComponent):
    def __init__(self, config: Dict):
        prng_key = config['prng_key']
        keys = jax.random.split(prng_key, 3)
        self.modules = {
            "vision": jax.random.normal(keys[0], (100,)),
            "audition": jax.random.normal(keys[1], (100,)),
            "touch": jax.random.normal(keys[2], (100,)),
        }

    def process(self, inputs: Dict[str, jnp.ndarray]) -> jnp.ndarray:
        return jax.lax.stop_gradient(integrate_inputs(inputs))

class Consciousness(CognitiveComponent):
    def __init__(self, config: Dict):
        prng_key = config['prng_key']
        self.state = jnp.asarray(create_consciousness(prng_key))

    def process(self, integrated_input: jnp.ndarray) -> jnp.ndarray:
        return jnp.asarray(process_consciousness(self.state, integrated_input))

class FeedbackMechanism(CognitiveComponent):
    def __init__(self, config: Dict):
        prng_key = config['prng_key']
        self.mechanism = jnp.asarray(create_feedback_mechanism(prng_key))

    def process(self, consciousness_state: jnp.ndarray) -> jnp.ndarray:
        return jnp.asarray(apply_feedback(self.mechanism, consciousness_state))

class CognitiveArchitecture:
    """
    A modular 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.
        sensory_processing (SensoryProcessing): The sensory processing component.
        consciousness (Consciousness): The consciousness component.
        feedback_mechanism (FeedbackMechanism): The feedback mechanism component.
        performance (float): Current performance metric of the architecture.
        last_update_time (float): Timestamp of the last update.
    """

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

        Args:
            config (Dict): Configuration parameters for the cognitive architecture.
        """
        def convert_to_jax_array(x):
            if isinstance(x, dict):
                return jax.tree_util.tree_map(convert_to_jax_array, x)
            if isinstance(x, (int, float)):
                return jnp.array(x, dtype=jnp.float32)
            return x

        # Ensure 'seed' is an integer and create a PRNG key
        seed = int(config.get('seed', 0))
        config['seed'] = seed
        config['prng_key'] = jax.random.PRNGKey(seed)

        self.config = jax.tree_util.tree_map(convert_to_jax_array, config)

        self.sensory_processing = SensoryProcessing(self.config)
        self.consciousness = Consciousness(self.config)
        self.feedback_mechanism = FeedbackMechanism(self.config)

        self.performance = 0.0
        self.last_update_time = time.time()
        self.performance_history = []
        self.performance_threshold = 0.8
        self.update_interval = 3600  # 1 hour in seconds
        self.performance_history_size = 100  # New attribute
        self.learning_rate = 0.001  # New attribute for adaptive learning rate

    @staticmethod
    @jax.jit
    def agi_prototype_module(input_data: jnp.ndarray, weights1: jnp.ndarray, weights2: jnp.ndarray) -> jnp.ndarray:
        """
        A prototype module for Artificial General Intelligence (AGI) concepts.

        This method implements a simple multi-layer perceptron as a placeholder
        for more advanced AGI concepts. It processes the input through two dense
        layers with non-linear activations and outputs a probability distribution.

        Args:
            input_data (jnp.ndarray): Input data for the AGI module, shape (..., input_dim).
            weights1 (jnp.ndarray): Weights for the first layer, shape (input_dim, hidden_dim).
            weights2 (jnp.ndarray): Weights for the second layer, shape (hidden_dim, output_dim).

        Returns:
            jnp.ndarray: Processed output from the AGI module, shape (..., output_dim).
        """
        # Scale weights to introduce more variability
        scaled_weights1 = weights1 * jax.random.uniform(jax.random.PRNGKey(0), weights1.shape, minval=0.5, maxval=1.5)
        scaled_weights2 = weights2 * jax.random.uniform(jax.random.PRNGKey(1), weights2.shape, minval=0.5, maxval=1.5)

        # Add non-linearity and standardization for better differentiation
        x = jax.nn.relu(jnp.einsum('...i,ij->...j', input_data, scaled_weights1))
        x = jax.nn.standardize(x, axis=-1)  # Standardization instead of normalization
        x = jax.nn.tanh(jnp.einsum('...i,ij->...j', x, scaled_weights2))  # Change to tanh for more non-linearity
        return jax.nn.softmax(x, axis=-1)

    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, and applies feedback
        using the modular components. It also includes self-healing mechanisms.

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

        Returns:
            Tuple[jnp.ndarray, jnp.ndarray]:
                Updated consciousness state and feedback.

        Raises:
            ValueError: If input shapes are incompatible with the architecture or inputs are not JAX arrays.
        """
        try:
            validated_inputs = self._validate_inputs(inputs)
            integrated_sensory_data = self.sensory_processing.process(validated_inputs)
            integrated_sensory_data = jax.lax.stop_gradient(integrated_sensory_data)

            consciousness_state = self.consciousness.process(integrated_sensory_data)
            feedback = self.feedback_mechanism.process(consciousness_state)

            self._update_performance(consciousness_state, feedback)
            self._self_heal()

            return consciousness_state, feedback
        except (ValueError, TypeError) as e:
            raise ValueError(f"Error in update_architecture: {str(e)}") from e

    def _validate_inputs(self, inputs: Dict[str, jnp.ndarray]) -> Dict[str, jnp.ndarray]:
        """
        Validate and preprocess input data.

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

        Returns:
            Dict[str, jnp.ndarray]: Validated and preprocessed inputs.

        Raises:
            ValueError: If inputs are incompatible or have incorrect shapes.
        """
        expected_shape = (100,)

        def ensure_jax_array(x):
            return jnp.asarray(x, dtype=jnp.float32) if not isinstance(x, jnp.ndarray) else x

        def check_shape(x, name):
            if x.shape != expected_shape:
                raise ValueError(f"Input '{name}' has shape {x.shape}, expected {expected_shape}")
            return x

        try:
            return {name: check_shape(ensure_jax_array(value), name)
                    for name, value in inputs.items()}
        except Exception as e:
            raise ValueError(f"Invalid inputs: {str(e)}. Ensure all inputs are compatible JAX arrays with shape {expected_shape}.") from e

    def _update_performance(self, consciousness_state: jnp.ndarray, feedback: jnp.ndarray):
        """
        Update the performance metric of the cognitive architecture.

        Args:
            consciousness_state (jnp.ndarray): The current consciousness state.
            feedback (jnp.ndarray): The current feedback.
        """
        # This is a simple performance metric. You may want to implement a more sophisticated one.
        self.performance = jnp.mean(jnp.abs(consciousness_state - feedback))
        self.last_update_time = time.time()
        self.performance_history.append(self.performance)
        if len(self.performance_history) > self.performance_history_size:
            self.performance_history.pop(0)

    def _self_heal(self):
        """
        Implement self-healing mechanisms based on the current performance and last update time.
        """
        current_time = time.time()
        if self.performance < self.performance_threshold or (current_time - self.last_update_time) > self.update_interval:
            logging.info("Self-healing mechanism activated.")
            if self.performance < self.performance_threshold:
                # Implement adaptive learning rate adjustment
                self.learning_rate *= 1.1  # Increase learning rate by 10%
                logging.info(f"Adjusted learning rate to {self.learning_rate}")

            # Reinitialize components with potentially improved parameters
            self.sensory_processing = SensoryProcessing(self.config)
            self.consciousness = Consciousness(self.config)
            self.feedback_mechanism = FeedbackMechanism(self.config)

            # Perform additional training or optimization here
            # ...

            logging.info("Components reinitialized and additional optimization performed.")
            self.last_update_time = current_time

    def diagnose(self) -> List[str]:
        """
        Diagnose the current state of the cognitive architecture.

        Returns:
            List[str]: A list of diagnosed issues.
        """
        issues = []
        if self.performance < self.performance_threshold:
            issues.append("Low performance")
        if (time.time() - self.last_update_time) > self.update_interval:
            issues.append("Long time since last update")
        if len(self.performance_history) > 5 and all(p < self.performance_threshold for p in self.performance_history[-5:]):
            issues.append("Consistently low performance")
        return issues

    def adjust_learning_rate(self):
        """
        Adjust the learning rate based on recent performance history.
        """
        if len(self.performance_history) >= 2:
            if self.performance_history[-1] > self.performance_history[-2]:
                self.learning_rate *= 1.05  # Increase learning rate if performance is improving
            else:
                self.learning_rate *= 0.95  # Decrease learning rate if performance is declining
        self.learning_rate = max(min(self.learning_rate, 0.1), 1e-5)  # Keep learning rate within reasonable bounds
