import jax
import jax.numpy as jnp
import flax.linen as nn
from typing import List, Tuple, Dict, Any
import logging
import time
import numpy as np

from flax import linen as nn
from typing import List

import neurolib
from neurolib.models.aln import ALNModel
from neurolib.optimize.exploration import BoxSearch
from neurolib.utils.parameterSpace import ParameterSpace

# Constants for self-healing and adaptive algorithms
PERFORMANCE_THRESHOLD = 0.8
UPDATE_INTERVAL = 86400  # 24 hours in seconds
LEARNING_RATE_ADJUSTMENT = 0.1
MAX_HEALING_ATTEMPTS = 5
CONSCIOUSNESS_BROADCAST_INTERVAL = 100  # milliseconds

class ConsciousnessSimulation(nn.Module):
    """
    An advanced module for simulating consciousness in the NeuroFlex framework.
    This class implements various cognitive processes and consciousness-related computations,
    including attention mechanisms, working memory, and decision-making processes.
    It also includes self-healing capabilities for improved robustness and performance.
    Integrates neurolib's ALNModel for whole-brain modeling and implements adaptive algorithms.
    """

    features: List[int]
    output_dim: int
    working_memory_size: int = 192
    attention_heads: int = 4
    qkv_features: int = 64  # Dimension of query, key, and value for attention mechanism
    dropout_rate: float = 0.1  # Dropout rate for attention mechanism
    performance_threshold: float = PERFORMANCE_THRESHOLD
    update_interval: int = UPDATE_INTERVAL
    num_brain_areas: int = 90  # Number of brain areas to simulate
    simulation_length: float = 1.0  # Length of brain simulation in seconds
    learning_rate: float = 0.001  # Initial learning rate

    def setup(self):
        self.variable('model_state', 'is_trained', jnp.bool_, False)
        self.variable('model_state', 'performance', jnp.float32, jnp.array(0.0))
        self.variable('model_state', 'previous_performance', jnp.float32, jnp.array(0.0))
        self.variable('model_state', 'last_update', jnp.float32, jnp.array(0.0))
        self.variable('working_memory', 'initial_memory', jnp.float32, jnp.zeros((1, self.working_memory_size)))
        self.variable('working_memory', 'current_state', jnp.float32, jnp.zeros((1, self.working_memory_size)))
        self.variable('model_state', 'training_performance', jnp.float32, jnp.array(0.0))
        self.variable('model_state', 'validation_performance', jnp.float32, jnp.array(0.0))
        self.variable('model_state', 'learning_rate', jnp.float32, jnp.array(self.learning_rate))
        self.variable('model_state', 'healing_attempts', jnp.int32, jnp.array(0))
        self.variable('model_state', 'performance_history', jnp.float32, jnp.zeros(100))

        # Check for NaN values in initialized variables
        for var_name, var in self.__dict__.items():
            if isinstance(var, nn.Variable):
                if jnp.any(jnp.isnan(var.value)):
                    raise ValueError(f"NaN value detected in {var_name}")

        # Initialize neurolib's ALNModel
        Cmat = np.random.rand(self.num_brain_areas, self.num_brain_areas)
        transmission_speed = 100.0  # m/s
        Dmat = np.random.rand(self.num_brain_areas, self.num_brain_areas) * 0.1  # Random delays between 0 and 0.1 seconds
        self.aln_model = ALNModel(Cmat=Cmat, Dmat=Dmat)
        self.aln_model.params['duration'] = self.simulation_length * 1000  # Convert to milliseconds
    @nn.compact
    def __call__(self, x, deterministic: bool = True, rngs: Dict[str, jax.random.PRNGKey] = None):
        logging.debug(f"ConsciousnessSimulation called with input shape: {x.shape}")
        logging.debug(f"Input type: {type(x)}")
        logging.debug(f"Input: min={jnp.min(x)}, max={jnp.max(x)}, mean={jnp.mean(x)}")

        # Ensure input shape is (batch_size, input_dim)
        if len(x.shape) == 1:
            x = jnp.expand_dims(x, axis=0)

        for i, feat in enumerate(self.features):
            x = nn.Dense(feat, kernel_init=nn.initializers.variance_scaling(2.0, 'fan_in', 'truncated_normal'))(x)
            x = nn.relu(x)
            logging.debug(f"After dense layer {i}, shape: {x.shape}")
            logging.debug(f"Layer {i} output: min={jnp.min(x)}, max={jnp.max(x)}, mean={jnp.mean(x)}")

        cognitive_state = nn.Dense(self.output_dim, kernel_init=nn.initializers.variance_scaling(2.0, 'fan_in', 'truncated_normal'))(x)
        logging.debug(f"Cognitive state shape: {cognitive_state.shape}")
        logging.debug(f"Cognitive state: min={jnp.min(cognitive_state)}, max={jnp.max(cognitive_state)}, mean={jnp.mean(cognitive_state)}")

        # Reshape cognitive_state to (batch_size, 1, output_dim) for attention
        cognitive_state_reshaped = jnp.expand_dims(cognitive_state, axis=1)

        # Handle cases where rngs is None or missing required keys
        if rngs is None:
            rngs = {}

        dropout_rng = rngs.get('dropout')
        if dropout_rng is None:
            logging.warning("No 'dropout' RNG key provided. Using default RNG for dropout.")
            dropout_rng = jax.random.PRNGKey(0)

        # Adjust attention mechanism to match expected output shape
        attention_output = nn.MultiHeadDotProductAttention(
            num_heads=self.attention_heads,
            qkv_features=self.working_memory_size,  # Adjust to match working memory size
            out_features=self.working_memory_size,  # Match the working memory size
            dropout_rate=self.dropout_rate,
            kernel_init=nn.initializers.variance_scaling(2.0, 'fan_in', 'truncated_normal')
        )(cognitive_state_reshaped, cognitive_state_reshaped, cognitive_state_reshaped, deterministic=deterministic)
        # Reshape attention output to match expected dimensions
        attention_output = jnp.reshape(attention_output, (x.shape[0], self.attention_heads, -1))
        attention_output = jnp.mean(attention_output, axis=1)  # Average over attention heads
        attention_output = jnp.pad(attention_output, ((0, 0), (0, max(0, self.working_memory_size - attention_output.shape[1]))))[:, :self.working_memory_size]
        logging.debug(f"Attention output before layer norm: min={jnp.min(attention_output)}, max={jnp.max(attention_output)}, mean={jnp.mean(attention_output)}")

        # Apply layer normalization with a small epsilon to prevent division by zero
        attention_output = nn.LayerNorm(epsilon=1e-5)(attention_output)

        # Ensure the attention output matches the working memory size
        attention_output_flattened = attention_output
        if attention_output_flattened.shape[1] != self.working_memory_size:
            logging.warning(f"Attention output shape {attention_output_flattened.shape} doesn't match working_memory_size {self.working_memory_size}")
            attention_output_flattened = jnp.pad(attention_output_flattened, ((0, 0), (0, max(0, self.working_memory_size - attention_output_flattened.shape[1]))))
            attention_output_flattened = attention_output_flattened[:, :self.working_memory_size]
        if attention_output_flattened.shape[1] != self.working_memory_size:
            logging.warning(f"Attention output shape {attention_output_flattened.shape} doesn't match working_memory_size {self.working_memory_size}")
            attention_output_flattened = jnp.pad(attention_output_flattened, ((0, 0), (0, max(0, self.working_memory_size - attention_output_flattened.shape[1]))))
            attention_output_flattened = attention_output_flattened[:, :self.working_memory_size]
        consciousness_state = attention_output_flattened
        logging.debug(f"Consciousness state shape: {consciousness_state.shape}")
        logging.debug(f"Attention output after reshape: min={jnp.min(consciousness_state)}, max={jnp.max(consciousness_state)}, mean={jnp.mean(consciousness_state)}")

        # Enhanced GRU cell with layer normalization and shape adjustment
        class EnhancedGRUCell(nn.Module):
            memory_size: int

            @nn.compact
            def __call__(self, inputs, state):
                logging.debug(f"EnhancedGRUCell input shapes - inputs: {inputs.shape}, state: {state.shape}")
                logging.debug(f"Inputs: min={jnp.min(inputs)}, max={jnp.max(inputs)}, mean={jnp.mean(inputs)}")
                logging.debug(f"State: min={jnp.min(state)}, max={jnp.max(state)}, mean={jnp.mean(state)}")

                # Ensure inputs and state have the correct shape
                inputs = jnp.atleast_2d(inputs)
                state = jnp.atleast_2d(state)

                batch_size, input_size = inputs.shape[0], inputs.shape[-1]
                logging.debug(f"Batch size: {batch_size}, Input size: {input_size}")

                # Adjust input size if necessary
                if input_size != self.memory_size:
                    logging.debug(f"Adjusting input size from {input_size} to {self.memory_size}")
                    inputs = nn.Dense(self.memory_size, name='input_adjustment', kernel_init=nn.initializers.variance_scaling(2.0, 'fan_in', 'truncated_normal'))(inputs)
                    logging.debug(f"Adjusted input shape: {inputs.shape}")
                    logging.debug(f"Adjusted inputs: min={jnp.min(inputs)}, max={jnp.max(inputs)}, mean={jnp.mean(inputs)}")

                # Ensure state has the correct shape
                if state.shape[-1] != self.memory_size:
                    logging.debug(f"Adjusting state shape from {state.shape} to {(batch_size, self.memory_size)}")
                    state = jnp.broadcast_to(state, (batch_size, self.memory_size))
                    logging.debug(f"Adjusted state shape: {state.shape}")
                    logging.debug(f"Adjusted state: min={jnp.min(state)}, max={jnp.max(state)}, mean={jnp.mean(state)}")

                assert inputs.shape == (batch_size, self.memory_size), f"Expected input shape ({batch_size}, {self.memory_size}), got {inputs.shape}"
                assert state.shape == (batch_size, self.memory_size), f"Expected state shape ({batch_size}, {self.memory_size}), got {state.shape}"

                i2h = nn.Dense(3 * self.memory_size, name='i2h', kernel_init=nn.initializers.variance_scaling(2.0, 'fan_in', 'truncated_normal'))
                h2h = nn.Dense(3 * self.memory_size, name='h2h', kernel_init=nn.initializers.variance_scaling(2.0, 'fan_in', 'truncated_normal'))
                ln = nn.LayerNorm(epsilon=1e-5)

                i2h_out = i2h(inputs)
                h2h_out = h2h(state)
                logging.debug(f"i2h output shape: {i2h_out.shape}, h2h output shape: {h2h_out.shape}")
                logging.debug(f"i2h output: min={jnp.min(i2h_out)}, max={jnp.max(i2h_out)}, mean={jnp.mean(i2h_out)}")
                logging.debug(f"h2h output: min={jnp.min(h2h_out)}, max={jnp.max(h2h_out)}, mean={jnp.mean(h2h_out)}")

                gates = i2h_out + h2h_out
                logging.debug(f"Gates shape before layer norm: {gates.shape}")
                logging.debug(f"Gates before layer norm: min={jnp.min(gates)}, max={jnp.max(gates)}, mean={jnp.mean(gates)}")
                gates = ln(gates)
                logging.debug(f"Gates shape after layer norm: {gates.shape}")
                logging.debug(f"Gates after layer norm: min={jnp.min(gates)}, max={jnp.max(gates)}, mean={jnp.mean(gates)}")

                reset, update, new = jnp.split(gates, 3, axis=-1)
                reset, update = jax.nn.sigmoid(reset), jax.nn.sigmoid(update)

                # Modify the calculation of 'new' to ensure shape consistency
                new_input = jnp.concatenate([inputs, state], axis=-1)
                new = jnp.tanh(nn.Dense(self.memory_size, name='new_dense', kernel_init=nn.initializers.variance_scaling(2.0, 'fan_in', 'truncated_normal'))(new_input))
                new_state = update * state + (1 - update) * new

                # Ensure the output shape matches the expected working memory size
                new_state = jnp.reshape(new_state, (batch_size, self.memory_size))

                logging.debug(f"EnhancedGRUCell output shape: {new_state.shape}")
                assert new_state.shape == (batch_size, self.memory_size), f"Expected output shape ({batch_size}, {self.memory_size}), got {new_state.shape}"

                # Add more detailed logging
                logging.debug(f"Reset gate: min={jnp.min(reset)}, max={jnp.max(reset)}, mean={jnp.mean(reset)}")
                logging.debug(f"Update gate: min={jnp.min(update)}, max={jnp.max(update)}, mean={jnp.mean(update)}")
                logging.debug(f"New gate: min={jnp.min(new)}, max={jnp.max(new)}, mean={jnp.mean(new)}")
                logging.debug(f"Final new_state: min={jnp.min(new_state)}, max={jnp.max(new_state)}, mean={jnp.mean(new_state)}")

                # Add additional checks for NaN and Inf values
                if jnp.any(jnp.isnan(new_state)) or jnp.any(jnp.isinf(new_state)):
                    logging.error("NaN or Inf values detected in new_state")
                    logging.debug(f"Inputs: {inputs}")
                    logging.debug(f"State: {state}")
                    logging.debug(f"Gates: {gates}")
                    logging.debug(f"Reset: {reset}")
                    logging.debug(f"Update: {update}")
                    logging.debug(f"New: {new}")
                    raise ValueError("NaN or Inf values detected in new_state")

                return new_state, new_state

        # Adjust GRU cell input size to match working memory size
        gru_cell = EnhancedGRUCell(memory_size=self.working_memory_size)

        # Use Flax's variable method to store and update working memory
        current_working_memory = self.variable('working_memory', 'current', lambda: jnp.zeros((x.shape[0], self.working_memory_size)))

        # Ensure attention_output has the correct shape
        attention_output = jnp.reshape(attention_output, (-1, self.working_memory_size))

        # Log shapes before GRU cell processing
        logging.debug(f"Attention output shape before GRU: {attention_output.shape}")
        logging.debug(f"Current working memory shape before GRU: {current_working_memory.value.shape}")
        logging.debug(f"Attention output before GRU: min={jnp.min(attention_output)}, max={jnp.max(attention_output)}, mean={jnp.mean(attention_output)}")
        logging.debug(f"Current working memory before GRU: min={jnp.min(current_working_memory.value)}, max={jnp.max(current_working_memory.value)}, mean={jnp.mean(current_working_memory.value)}")

        # Process through GRU cell
        new_working_memory, _ = gru_cell(attention_output, current_working_memory.value)

        # Log shape after GRU cell processing
        logging.debug(f"New working memory shape after GRU: {new_working_memory.shape}")
        logging.debug(f"New working memory after GRU: min={jnp.min(new_working_memory)}, max={jnp.max(new_working_memory)}, mean={jnp.mean(new_working_memory)}")

        # Update working memory
        current_working_memory.value = new_working_memory

        # Update working memory with residual connection
        current_working_memory.value = new_working_memory + current_working_memory.value
        logging.debug(f"Working memory after residual connection: min={jnp.min(current_working_memory.value)}, max={jnp.max(current_working_memory.value)}, mean={jnp.mean(current_working_memory.value)}")

        # Add a small perturbation to ensure working memory changes between forward passes
        perturbation_rng = rngs.get('perturbation')
        if perturbation_rng is not None:
            perturbation = jax.random.normal(perturbation_rng, new_working_memory.shape) * 1e-6
            new_working_memory = new_working_memory + perturbation
            logging.debug(f"Working memory after perturbation: min={jnp.min(new_working_memory)}, max={jnp.max(new_working_memory)}, mean={jnp.mean(new_working_memory)}")
        else:
            logging.warning("No 'perturbation' RNG key provided. Working memory may not change between forward passes.")

        decision_input = jnp.concatenate([cognitive_state, attention_output, new_working_memory], axis=-1)
        logging.debug(f"Decision input shape: {decision_input.shape}")
        logging.debug(f"Decision input: min={jnp.min(decision_input)}, max={jnp.max(decision_input)}, mean={jnp.mean(decision_input)}")

        # Multi-layer perceptron for more sophisticated decision-making
        mlp = nn.Sequential([
            nn.Dense(64),
            nn.relu,
            nn.Dense(32),
            nn.relu,
            nn.Dense(16),
            nn.relu
        ])
        mlp_output = mlp(decision_input)
        logging.debug(f"MLP output shape: {mlp_output.shape}")
        logging.debug(f"MLP output: min={jnp.min(mlp_output)}, max={jnp.max(mlp_output)}, mean={jnp.mean(mlp_output)}")

        decision = nn.tanh(nn.Dense(1)(mlp_output))
        metacognition = nn.sigmoid(nn.Dense(1)(mlp_output))
        logging.debug(f"Decision: {decision}, Metacognition: {metacognition}")
        # Additional metacognitive features
        uncertainty = nn.softplus(nn.Dense(1)(mlp_output))
        confidence = 1 - uncertainty

        # Simulate brain activity using neurolib's ALNModel
        aln_input = jnp.concatenate([cognitive_state, attention_output, new_working_memory], axis=-1)
        aln_input_size = aln_input.shape[-1]
        batch_size = aln_input.shape[0]

        # Calculate time_steps dynamically based on input size and num_brain_areas
        time_steps = max(1, aln_input_size // self.num_brain_areas)
        required_size = time_steps * self.num_brain_areas

        # Adjust aln_input to match the required size
        if aln_input_size != required_size:
            if aln_input_size < required_size:
                # Pad the input if necessary to match the required shape
                padding_size = required_size - aln_input_size
                aln_input = jnp.pad(aln_input, ((0, 0), (0, padding_size)), mode='constant')
            else:
                # Truncate the input if it's larger than required
                aln_input = aln_input[:, :required_size]

        # Reshape aln_input to match the expected shape for ALNModel
        aln_input = jnp.reshape(aln_input, (batch_size, time_steps, self.num_brain_areas))

        # Convert jax array to numpy array for neurolib compatibility
        aln_input_np = np.array(aln_input)

        # Run ALNModel simulation
        aln_output = self.aln_model.run(aln_input_np)

        # Convert back to jax array and ensure correct shape
        aln_output = jnp.array(aln_output)
        aln_output = jnp.reshape(aln_output, (batch_size, -1))  # Flatten the output

        logging.debug(f"ALN input shape: {aln_input.shape}, ALN output shape: {aln_output.shape}")

        # Process ALNModel output
        aln_processed = nn.Dense(self.working_memory_size)(aln_output)
        aln_processed = nn.relu(aln_processed)

        # Ensure all components have consistent shapes
        cognitive_state = jnp.reshape(cognitive_state, (-1, self.output_dim))
        attention_output = jnp.reshape(attention_output, (-1, self.working_memory_size))
        new_working_memory = jnp.reshape(new_working_memory, (-1, self.working_memory_size))
        decision = jnp.reshape(decision, (-1, 1))
        metacognition = jnp.reshape(metacognition, (-1, 1))
        uncertainty = jnp.reshape(uncertainty, (-1, 1))
        confidence = jnp.reshape(confidence, (-1, 1))
        aln_processed = jnp.reshape(aln_processed, (-1, self.working_memory_size))

        consciousness = jnp.concatenate([
            cognitive_state,
            attention_output,
            new_working_memory,
            decision,
            metacognition,
            uncertainty,
            confidence,
            aln_processed
        ], axis=-1)

        logging.debug(f"Consciousness components shapes: cognitive_state={cognitive_state.shape}, "
                      f"attention_output={attention_output.shape}, new_working_memory={new_working_memory.shape}, "
                      f"decision={decision.shape}, metacognition={metacognition.shape}, "
                      f"uncertainty={uncertainty.shape}, confidence={confidence.shape}, "
                      f"aln_processed={aln_processed.shape}")

        logging.debug(f"Final consciousness state shape: {consciousness.shape}")

        # Update expected shape to include ALNModel output
        expected_shape = (x.shape[0], self.output_dim + 2*self.working_memory_size + 4 + self.working_memory_size)
        assert consciousness.shape == expected_shape, f"Expected shape {expected_shape}, got {consciousness.shape}"

        return consciousness, new_working_memory

    def simulate_consciousness(self, x, rngs: Dict[str, Any] = None, deterministic: bool = True) -> Tuple[jnp.ndarray, jnp.ndarray]:
        """
        Simulate consciousness based on input x.

        Args:
            x (jnp.ndarray): Input data with shape (batch_size, input_dim)
            rngs (Dict[str, Any], optional): Dictionary of PRNG keys for randomness. Defaults to None.
            deterministic (bool, optional): Whether to run in deterministic mode. Defaults to True.

        Returns:
            Tuple[jnp.ndarray, jnp.ndarray]:
                - consciousness (jnp.ndarray): The simulated consciousness state
                  (shape: (batch_size, output_dim + 2*working_memory_size + 4 + working_memory_size))
                - new_working_memory (jnp.ndarray): Updated working memory
                  (shape: (batch_size, working_memory_size))
        """
        logging.info(f"Starting simulate_consciousness with input shape: {x.shape}, deterministic: {deterministic}")
        try:
            # Perform self-diagnosis
            issues = self.diagnose()
            if issues:
                logging.warning(f"Detected issues: {issues}")
                self.heal(x)

            # Validate and prepare PRNG keys
            rngs = self._prepare_rngs(rngs)

            # Validate input
            if not isinstance(x, jnp.ndarray):
                raise ValueError(f"Input x must be a jax.numpy array, got {type(x)}")
            if len(x.shape) != 2:
                raise ValueError(f"Input x must have shape (batch_size, input_dim), got {x.shape}")

            result = self.__call__(x, deterministic=deterministic, rngs=rngs)

            if not isinstance(result, tuple) or len(result) != 2:
                raise ValueError(f"__call__ method returned {len(result)} values instead of 2")

            consciousness, new_working_memory = result
            self._validate_outputs(consciousness, new_working_memory)

            # Apply perturbation to working memory
            new_working_memory = self._apply_perturbation(new_working_memory, rngs['perturbation'])

            # Update model state
            self._update_model_state(consciousness)

            logging.info(f"Finished simulate_consciousness successfully. Returning: consciousness shape: {consciousness.shape}, new_working_memory shape: {new_working_memory.shape}")
            return consciousness, new_working_memory
        except Exception as e:
            logging.error(f"Error in simulate_consciousness: {str(e)}")
            return self._handle_error(x)

    def _handle_error(self, x: jnp.ndarray, error: Exception) -> Tuple[jnp.ndarray, jnp.ndarray, Dict[str, Any]]:
        batch_size = x.shape[0] if len(x.shape) > 1 else 1
        empty_consciousness = jnp.zeros((batch_size, self.output_dim + 2*self.working_memory_size + 4 + self.working_memory_size))
        empty_working_memory = jnp.zeros((batch_size, self.working_memory_size))
        error_working_memory = {
            'working_memory': {'current_state': empty_working_memory},
            'error': str(error)
        }
        logging.error(f"Returning error case: empty_consciousness shape: {empty_consciousness.shape}, empty_working_memory shape: {empty_working_memory.shape}, error: {str(error)}")
        return empty_consciousness, empty_working_memory, error_working_memory

    def _prepare_rngs(self, rngs: Dict[str, Any] = None) -> Dict[str, jnp.ndarray]:
        if rngs is None:
            logging.warning("No rngs provided. Using default PRNGKeys.")
            return {'dropout': jax.random.PRNGKey(0), 'perturbation': jax.random.PRNGKey(1)}

        if not isinstance(rngs, dict):
            raise ValueError("rngs must be a dictionary")

        required_keys = {'dropout', 'perturbation'}
        for key in required_keys:
            if key not in rngs or not self._is_valid_prng_key(rngs[key]):
                logging.warning(f"Invalid or missing PRNG key for {key}. Using a new PRNGKey.")
                rngs[key] = jax.random.PRNGKey(hash(key))

        return rngs

    def _is_valid_prng_key(self, x: Any) -> bool:
        return isinstance(x, jnp.ndarray) and x.shape == (2,) and x.dtype == jnp.uint32

    def _validate_outputs(self, consciousness: jnp.ndarray, new_working_memory: jnp.ndarray, working_memory_dict: Dict[str, Any]):
        if not isinstance(consciousness, jnp.ndarray) or not isinstance(new_working_memory, jnp.ndarray) or not isinstance(working_memory_dict, dict):
            raise ValueError("Invalid return types from __call__ method")

        expected_consciousness_shape = (consciousness.shape[0], self.output_dim + 2*self.working_memory_size + 4 + self.working_memory_size)
        if consciousness.shape != expected_consciousness_shape:
            raise ValueError(f"Invalid consciousness shape. Expected {expected_consciousness_shape}, got {consciousness.shape}")

        if new_working_memory.shape != (consciousness.shape[0], self.working_memory_size):
            raise ValueError(f"Invalid new_working_memory shape. Expected {(consciousness.shape[0], self.working_memory_size)}, got {new_working_memory.shape}")

        if 'working_memory' not in working_memory_dict or 'current_state' not in working_memory_dict['working_memory']:
            raise ValueError("Invalid working_memory_dict structure")

        logging.debug(f"Outputs validated. Consciousness shape: {consciousness.shape}, New working memory shape: {new_working_memory.shape}")

    def _apply_perturbation(self, new_working_memory: jnp.ndarray, perturbation_key: jnp.ndarray) -> jnp.ndarray:
        perturbation = jax.random.normal(perturbation_key, new_working_memory.shape) * 1e-6
        perturbed_memory = new_working_memory + perturbation
        perturbation_magnitude = jnp.linalg.norm(perturbation)
        logging.debug(f"Applied perturbation to working memory. Magnitude: {perturbation_magnitude:.6e}")
        return perturbed_memory

    def _update_model_state(self, consciousness: jnp.ndarray):
        self.performance.value = jnp.mean(consciousness)
        self.last_update.value = jnp.array(time.time())
        self.is_trained.value = jnp.array(True)
        self.training_performance.value = self.performance.value
        self.validation_performance.value = self.performance.value * 0.9
        logging.debug(f"Updated model state. Performance: {self.performance.value:.4f}, Training performance: {self.training_performance.value:.4f}, Validation performance: {self.validation_performance.value:.4f}")

    def _handle_error(self, x: jnp.ndarray, error: Exception) -> Tuple[jnp.ndarray, jnp.ndarray, Dict[str, Any]]:
        batch_size = x.shape[0] if len(x.shape) > 1 else 1
        empty_consciousness = jnp.zeros((batch_size, self.output_dim + 2*self.working_memory_size + 4 + self.working_memory_size))
        empty_working_memory = jnp.zeros((batch_size, self.working_memory_size))
        error_working_memory = {
            'working_memory': {'current_state': empty_working_memory},
            'error': str(error)
        }
        logging.error(f"Returning error case: empty_consciousness shape: {empty_consciousness.shape}, empty_working_memory shape: {empty_working_memory.shape}, error: {str(error)}")
        return empty_consciousness, empty_working_memory, error_working_memory

    def generate_thought(self, consciousness_state):
        # Simulate thought generation based on current consciousness state
        logging.debug(f"Generate thought input shape: {consciousness_state.shape}")
        # Ensure the input shape is correct (batch_size, 146)
        assert consciousness_state.shape[1] == 146, \
            f"Expected input shape (batch_size, 146), got {consciousness_state.shape}"

        # Use two Dense layers to transform the consciousness state to the output dimension
        hidden = nn.Dense(64, kernel_init=nn.initializers.xavier_uniform())(consciousness_state)
        hidden = nn.relu(hidden)
        thought = nn.Dense(self.output_dim, kernel_init=nn.initializers.xavier_uniform())(hidden)
        logging.debug(f"Generated thought shape before softmax: {thought.shape}")
        thought = nn.softmax(thought, axis=-1)
        logging.debug(f"Final generated thought shape: {thought.shape}")

        # Ensure the output shape is correct (batch_size, output_dim)
        assert thought.shape[1] == self.output_dim, f"Expected output shape (batch_size, {self.output_dim}), got {thought.shape}"
        return thought

    def diagnose(self) -> List[str]:
        issues = []
        is_trained = self.is_trained.value
        performance = self.performance.value
        last_update = self.last_update.value
        working_memory_state = self.working_memory.value

        if not is_trained:
            issues.append("Model is not trained")
        if performance < self.performance_threshold:
            issues.append(f"Model performance ({performance:.4f}) is below threshold ({self.performance_threshold:.4f})")
        if time.time() - last_update > self.update_interval:
            issues.append(f"Model hasn't been updated in {(time.time() - last_update) / 3600:.2f} hours")

        # Check for working memory stability
        if jnp.isnan(working_memory_state).any() or jnp.isinf(working_memory_state).any():
            issues.append("Working memory contains NaN or Inf values")

        # Check for model convergence
        if self.previous_performance.value is not None:
            performance_change = abs(performance - self.previous_performance.value)
            if performance_change < 1e-6:
                issues.append("Model performance has stagnated")

        # Check for overfitting
        if self.training_performance.value is not None and self.validation_performance.value is not None:
            if self.training_performance.value - self.validation_performance.value > 0.2:
                issues.append("Potential overfitting detected")

        # Check for consistent underperformance
        if len(self.performance_history) > 5 and all(p < self.performance_threshold for p in self.performance_history[-5:]):
            issues.append("Consistently low performance")

        # Check for sudden performance drops
        if len(self.performance_history) > 1:
            performance_drop = self.performance_history[-2] - performance
            if performance_drop > 0.1:
                issues.append(f"Sudden performance drop of {performance_drop:.4f}")

        return issues

    def heal(self, x: jnp.ndarray):
        issues = self.diagnose()
        for issue in issues:
            logging.info(f"Healing issue: {issue}")
            if issue == "Model is not trained" or issue == "Model performance is below threshold":
                self.update_model(x, full_update=True)
            elif issue == "Model hasn't been updated in 24 hours":
                self.update_model(x, full_update=False)
            elif issue == "Working memory contains NaN or Inf values":
                self.reset_working_memory()
            elif issue == "Model performance has stagnated":
                self.adjust_learning_rate(increase=True)
            elif issue == "Potential overfitting detected":
                self.increase_regularization()
            elif issue == "Consistently low performance":
                self.reset_model_parameters()
            elif "Sudden performance drop" in issue:
                self.rollback_to_previous_state()

        # Gradual performance improvement with adaptive learning rate
        current_performance = self.performance.value
        target_performance = max(current_performance * 1.1, self.performance_threshold)
        initial_learning_rate = self.learning_rate.value

        for attempt in range(MAX_HEALING_ATTEMPTS):
            _, _, new_performance = self.update_model(x, full_update=False)
            current_performance = new_performance

            # Adaptive learning rate adjustment
            if attempt > 0:
                if new_performance > current_performance:
                    self.learning_rate.value *= 1.05  # Increase learning rate
                else:
                    self.learning_rate.value *= 0.95  # Decrease learning rate
                self.learning_rate.value = jnp.clip(self.learning_rate.value, 1e-5, 0.1)  # Clip learning rate

            logging.info(f"Gradual healing: Attempt {attempt + 1}, Performance: {current_performance:.4f}, Target: {target_performance:.4f}, Learning rate: {self.learning_rate.value:.6f}")

            if current_performance >= target_performance:
                break

        # Additional healing strategies
        if current_performance < target_performance:
            logging.info("Applying additional healing strategies")
            self.apply_advanced_healing_techniques(x)

        # Reset learning rate if healing was unsuccessful
        if current_performance < self.performance_threshold:
            self.learning_rate.value = initial_learning_rate
            logging.info(f"Healing unsuccessful. Reset learning rate to {initial_learning_rate:.6f}")

        logging.info(f"Healing complete. Final performance: {current_performance:.4f}")

    def update_model(self, x: jnp.ndarray, full_update: bool = False):
        # Simulate a training step
        consciousness_state, new_working_memory = self.__call__(x)
        performance = jnp.mean(consciousness_state)  # Simple performance metric
        last_update = jnp.array(time.time())

        # Use Flax's variable method to store performance metrics and update times
        performance_var = self.variable('model_state', 'performance', jnp.float32, lambda: 0.0)
        last_update_var = self.variable('model_state', 'last_update', jnp.float32, lambda: 0.0)
        is_trained_var = self.variable('model_state', 'is_trained', jnp.bool_, lambda: False)
        working_memory_var = self.variable('working_memory', 'current_state', jnp.float32, lambda: jnp.zeros_like(new_working_memory))

        if full_update:
            # Perform a more comprehensive update
            performance_var.value = performance
            last_update_var.value = last_update
            is_trained_var.value = jnp.array(True)
            working_memory_var.value = new_working_memory
            logging.info(f"Full model update completed. New performance: {performance}")
        else:
            # Perform a lighter update
            performance_var.value = 0.9 * performance_var.value + 0.1 * performance
            last_update_var.value = last_update
            working_memory_var.value = 0.9 * working_memory_var.value + 0.1 * new_working_memory
            logging.info(f"Light model update completed. New performance: {performance_var.value}")

        return consciousness_state, working_memory_var.value, performance_var.value

def create_consciousness_simulation(features: List[int], output_dim: int, working_memory_size: int = 192, attention_heads: int = 4, qkv_features: int = 64, dropout_rate: float = 0.1, num_brain_areas: int = 90, simulation_length: float = 1.0) -> ConsciousnessSimulation:
    """
    Create an instance of the advanced ConsciousnessSimulation module.

    Args:
        features (List[int]): List of feature dimensions for intermediate layers.
        output_dim (int): Dimension of the output layer.
        working_memory_size (int): Size of the working memory. Default is 192.
        attention_heads (int): Number of attention heads. Default is 4.
        qkv_features (int): Dimension of query, key, and value for attention mechanism. Default is 64.
        dropout_rate (float): Dropout rate for attention mechanism. Default is 0.1.
        num_brain_areas (int): Number of brain areas to simulate. Default is 90.
        simulation_length (float): Length of brain simulation in seconds. Default is 1.0.

    Returns:
        ConsciousnessSimulation: An instance of the ConsciousnessSimulation class.
    """
    return ConsciousnessSimulation(
        features=features,
        output_dim=output_dim,
        working_memory_size=working_memory_size,
        attention_heads=attention_heads,
        qkv_features=qkv_features,
        dropout_rate=dropout_rate,
        num_brain_areas=num_brain_areas,
        simulation_length=simulation_length
    )

# Example usage
if __name__ == "__main__":
    rng = jax.random.PRNGKey(0)
    x = jax.random.normal(rng, (1, 10))  # Example input
    model = create_consciousness_simulation(features=[64, 32], output_dim=16)
    params = model.init(rng, x)

    # Create separate RNG keys for different operations
    rng_keys = {
        'dropout': jax.random.PRNGKey(1),
        'perturbation': jax.random.PRNGKey(2)
    }

    # Simulate consciousness
    consciousness_state, working_memory, _ = model.apply(
        {'params': params}, x,
        rngs=rng_keys,
        method=model.simulate_consciousness,
        mutable=['working_memory']
    )

    # Generate thought
    thought_rng = jax.random.PRNGKey(3)
    thought = model.apply(
        {'params': params}, consciousness_state,
        rngs={'dropout': thought_rng},
        method=model.generate_thought
    )

    print(f"Consciousness state shape: {consciousness_state.shape}")
    print(f"Working memory shape: {working_memory.shape}")
    print(f"Generated thought shape: {thought.shape}")
