import jax
import jax.numpy as jnp
from jax import jit
import flax.linen as nn
from flax.training import train_state
import optax
import numpy as np
import gym
from typing import Sequence, Callable, Optional, Tuple, List, Dict
from alphafold.common import residue_constants
from alphafold.data import templates, pipeline
import logging
import scipy.signal as signal
import pywt
import shap
from quantum_nn_module import QuantumNeuralNetwork
from ldm.models.diffusion.ddpm import DDPM
from vae import VAE
import pyhmmer
from art.attacks.evasion import FastGradientMethod
from art.estimators.classification import JAXClassifier, PyTorchClassifier
import lale
from lale import operators as lale_ops
import torch
from .generative_ai import GenerativeAIFramework, create_generative_ai_framework
import os
import sentencepiece
from .inception_module import InceptionModule, MultiScaleProcessing
from .rl_module import RLAgent, RLEnvironment, train_rl_agent, create_train_state, select_action
from .alphafold_integration import AlphaFoldIntegration
from .bci_module import BCISignalProcessor
from .quantum_module import QuantumCircuit
from .bci_module import BCIProcessor
from .cognitive_module import CognitiveLayer
from .consciousness_module import ConsciousnessModule

class Tokenizer:
    def __init__(self, model_path: Optional[str]):
        assert os.path.isfile(model_path), model_path
        self.sp_model = sentencepiece.SentencePieceProcessor()
        self.sp_model.Load(model_path)
        self.n_words: int = self.sp_model.GetPieceSize()
        self.bos_id: int = self.sp_model.bos_id()
        self.eos_id: int = self.sp_model.eos_id()
        self.pad_id: int = self.sp_model.pad_id()

    def encode(self, s: str, bos: bool = True, eos: bool = False) -> List[int]:
        assert isinstance(s, str)
        t = self.sp_model.EncodeAsIds(s)
        if bos:
            t = [self.bos_id] + t
        if eos:
            t = t + [self.eos_id]
        return t

    def decode(self, t: List[int]) -> str:
        return self.sp_model.DecodeIds(t)

try:
    import detectron2
    from detectron2.engine import DefaultPredictor
    from detectron2.config import get_cfg
    from detectron2.utils.visualizer import Visualizer
    from detectron2.data import MetadataCatalog
    DETECTRON2_AVAILABLE = True
except ImportError as e:
    DETECTRON2_AVAILABLE = False
    logging.error(f"Detectron2 import error: {str(e)}. Some features will be disabled.")
except Exception as e:
    DETECTRON2_AVAILABLE = False
    logging.error(f"Unexpected error importing Detectron2: {str(e)}. Some features will be disabled.")

from .neuroflex_nn import NeuroFlexNN

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

class NeuroFlex(NeuroFlexNN):
    features: Sequence[int]
    activation: Callable = nn.relu
    dropout_rate: float = 0.5
    fairness_constraint: float = 0.1
    use_cnn: bool = False
    use_rnn: bool = False
    use_lstm: bool = False
    use_gan: bool = False
    conv_dim: int = 2
    use_rl: bool = False
    output_dim: Optional[int] = None
    rnn_hidden_size: int = 64
    lstm_hidden_size: int = 64
    use_bci: bool = False
    bci_channels: int = 64
    bci_sampling_rate: int = 1000
    wireless_latency: float = 0.01
    bci_signal_processing: str = 'fft'
    bci_noise_reduction: bool = False
    use_n1_implant: bool = False
    n1_electrode_count: int = 1024
    ui_feedback_delay: float = 0.05
    consciousness_sim: bool = False
    use_xla: bool = False
    use_dnn: bool = False
    use_shap: bool = False
    use_ddpm: bool = False
    ddpm_timesteps: int = 1000
    ddpm_beta_schedule: str = "linear"
    use_vae: bool = False
    vae_latent_dim: int = 32
    vae_hidden_dim: int = 256
    use_art: bool = False
    art_attack_type: str = 'fgsm'
    art_epsilon: float = 0.3
    use_lale: bool = False
    lale_operator: Optional[str] = None
    use_detectron2: bool = False
    detectron2_config: Optional[str] = None
    detectron2_weights: Optional[str] = None
    use_generative_ai: bool = False
    generative_ai_features: Tuple[int, ...] = (64, 32)
    generative_ai_output_dim: int = 10
    generative_ai_learning_rate: float = 1e-3
    use_tokenizer: bool = False
    tokenizer_model_path: Optional[str] = None
    tokenizer_vocab_size: int = 50000
    tokenizer_max_length: int = 512
    use_inception: bool = False
    inception_modules: int = 3
    inception_channels: Sequence[int] = (64, 96, 128)
    inception_reduction_factor: float = 0.7
    multi_scale_processing: bool = False
    multi_scale_levels: int = 3
    multi_scale_features: Sequence[int] = (32, 64, 128)
    action_dim: Optional[int] = None
    rl_learning_rate: float = 1e-3
    rl_num_episodes: int = 1000
    use_alphafold: bool = False
    alphafold_max_recycling: int = 3
    use_quantum: bool = False
    quantum_num_qubits: int = 4
    quantum_num_layers: int = 2
    advanced_bci: bool = False
    advanced_bci_feature_extraction: str = 'wavelet'
    cognitive_architecture: bool = False
    cognitive_architecture_layers: int = 3
    consciousness_sim_complexity: int = 5
    python_version: str = '3.9'

    def setup(self):
        if self.use_ddpm:
            self.ddpm = DDPM(
                unet_config={},  # Add appropriate UNet config
                timesteps=self.ddpm_timesteps,
                beta_schedule=self.ddpm_beta_schedule,
            )
        if self.use_vae:
            # Determine input shape based on the first feature dimension
            input_shape = (self.features[0],) if isinstance(self.features[0], int) else self.features[0]
            self.vae = VAE(
                latent_dim=self.vae_latent_dim,
                hidden_dim=self.vae_hidden_dim,
                input_shape=input_shape
            )
        if self.use_cnn:
            self.Conv = nn.Conv if self.conv_dim == 2 else nn.Conv3D
        if self.use_inception:
            self.inception_layers = [InceptionModule(channels=self.inception_channels,
                                                     reduction_factor=self.inception_reduction_factor)
                                     for _ in range(self.inception_modules)]
        if self.multi_scale_processing:
            self.multi_scale_layers = [nn.Dense(feat) for feat in self.multi_scale_features]
        if self.use_rnn:
            self.rnn = nn.RNN(nn.LSTMCell(self.rnn_hidden_size))
        if self.use_lstm:
            self.lstm = nn.scan(nn.LSTMCell(self.lstm_hidden_size),
                                variable_broadcast="params",
                                split_rngs={"params": False})
        if self.use_lale:
            self.lale_pipeline = self.setup_lale_pipeline()
        if self.use_detectron2:
            self.setup_detectron2()
        if self.use_rl:
            if self.action_dim is None:
                raise ValueError("action_dim must be specified when use_rl is True")
            self.rl_agent = RLAgent(features=self.features[:-1], action_dim=self.action_dim)
            self.rl_env = RLEnvironment("CartPole-v1")  # Replace with appropriate environment

        # Initialize GenerativeAIFramework
        if self.use_generative_ai:
            self.generative_ai_framework = create_generative_ai_framework(
                features=self.generative_ai_features,
                output_dim=self.generative_ai_output_dim
            )
            self.generative_ai_state = self.generative_ai_framework.init_model(
                jax.random.PRNGKey(0),
                (1,) + self.generative_ai_features[0:1]  # Assuming first feature is input dimension
            )

        # Initialize Tokenizer
        if self.use_tokenizer:
            assert os.path.isfile(self.tokenizer_model_path), f"Tokenizer model not found at {self.tokenizer_model_path}"
            self.tokenizer = Tokenizer(self.tokenizer_model_path)

        # Initialize multi-scale processing
        self.multi_scale_layers = [nn.Dense(feat) for feat in self.multi_scale_features]

        # Initialize AlphaFold integration
        if self.use_alphafold:
            self.alphafold_integration = AlphaFoldIntegration()
            self.alphafold_integration.setup_model({'max_recycling': self.alphafold_max_recycling})

        # Initialize Quantum Neural Network
        if self.use_quantum:
            self.quantum_nn = QuantumNeuralNetwork(num_qubits=self.quantum_num_qubits, num_layers=self.quantum_num_layers)

        # Initialize advanced BCI functionality
        if self.use_bci:
            self.bci_processor = BCIProcessor(
                channels=self.bci_channels,
                sampling_rate=self.bci_sampling_rate,
                noise_reduction=self.bci_noise_reduction,
                feature_extraction=self.advanced_bci_feature_extraction if self.advanced_bci else 'fft'
            )

        # Initialize cognitive architecture
        if self.cognitive_architecture:
            self.cognitive_layers = [CognitiveLayer(size=64) for _ in range(self.cognitive_architecture_layers)]

        # Set up consciousness simulation
        if self.consciousness_sim:
            self.consciousness_module = ConsciousnessModule(complexity=self.consciousness_sim_complexity)

        # Ensure compatibility with specified Python version
        self.ensure_python_compatibility()

    def setup_detectron2(self):
        if not DETECTRON2_AVAILABLE:
            logging.warning("Detectron2 is not available. Some features will be disabled.")
            self.detectron2_predictor = None
            return

        try:
            cfg = get_cfg()
            if self.detectron2_config:
                cfg.merge_from_file(self.detectron2_config)
            else:
                logging.info("No custom Detectron2 config provided. Using default configuration.")

            cfg.MODEL.WEIGHTS = self.detectron2_weights if self.detectron2_weights else "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"
            cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
            self.detectron2_predictor = DefaultPredictor(cfg)
            logging.info("Detectron2 setup completed successfully.")
        except Exception as e:
            logging.error(f"Error setting up Detectron2: {str(e)}")
            self.detectron2_predictor = None

    def setup_lale_pipeline(self):
        # Define a basic Lale pipeline
        from lale import operators as lale_ops
        from lale.lib.sklearn import LogisticRegression, RandomForestClassifier

        # Create a choice between LogisticRegression and RandomForestClassifier
        classifier = lale_ops.make_choice(
            LogisticRegression, RandomForestClassifier
        )

        # Create a pipeline with the classifier
        pipeline = classifier

        # If a specific Lale operator is specified, use it instead
        if self.lale_operator:
            pipeline = getattr(lale_ops, self.lale_operator)()

        return pipeline

    @nn.compact
    def __call__(self, x, training: bool = False, sensitive_attribute: jnp.ndarray = None):
        original_shape = x.shape

        # Tokenizer processing
        if self.use_tokenizer:
            if isinstance(x, str):
                x = jnp.array(self.tokenizer.encode(x))
            elif isinstance(x, jnp.ndarray) and x.ndim == 2:
                x = jnp.apply_along_axis(lambda s: jnp.array(self.tokenizer.encode(s.decode('utf-8'))), 1, x)

        # BCI and N1 implant signal processing
        if self.use_bci:
            x = self.bci_signal_processing(x)

        # Advanced cognitive architecture processing
        if self.cognitive_architecture:
            x = self.cognitive_architecture_block(x)

        # Inception-inspired multi-scale processing
        if self.use_inception:
            x = self.inception_block(x)
        elif self.use_cnn:
            x = self.cnn_block(x)

        # VAE processing
        if self.use_vae:
            try:
                rng = self.make_rng('vae')
                x_flat = x.reshape((x.shape[0], -1))  # Flatten input
                x_recon, mean, logvar = self.vae(x_flat, rng)
                x = x_recon.reshape(x.shape)  # Reshape back to original shape

                # Compute VAE loss for potential use in training
                vae_loss = self.vae.loss_function(x_recon, x_flat, mean, logvar)
                self.vae_loss = vae_loss  # Store for later use if needed
            except Exception as e:
                logging.error(f"Error in VAE processing: {str(e)}")
                # Fallback to original input if VAE fails
                pass

        # Detectron2 processing
        if self.use_detectron2 and DETECTRON2_AVAILABLE:
            try:
                x = self.detectron2_block(x, training)
            except Exception as e:
                logging.error(f"Error in Detectron2 processing: {str(e)}")
                # Fallback to original input if Detectron2 processing fails
                pass
        elif self.use_detectron2 and not DETECTRON2_AVAILABLE:
            logging.warning("Detectron2 processing requested but not available. Skipping.")

        # AlphaFold processing
        if self.use_alphafold:
            try:
                x = self.alphafold_block(x)
            except Exception as e:
                logging.error(f"Error in AlphaFold processing: {str(e)}")
                # Fallback to original input if AlphaFold processing fails
                pass

        # Quantum Neural Network processing
        if self.use_quantum:
            try:
                x = self.quantum_block(x)
            except Exception as e:
                logging.error(f"Error in Quantum Neural Network processing: {str(e)}")
                # Fallback to original input if Quantum processing fails
                pass

        # Ensure input is 3D for RNN/LSTM: (batch_size, sequence_length, features)
        if self.use_rnn or self.use_lstm:
            if len(x.shape) == 2:
                # Reshape 2D input to 3D: (batch_size, 1, features)
                x = x.reshape(x.shape[0], 1, -1)
            elif len(x.shape) > 3:
                # Reshape higher dimensional input to 3D
                x = x.reshape(x.shape[0], -1, x.shape[-1])

        if self.use_rnn:
            x = self.rnn_block(x)

        if self.use_lstm:
            x = self.lstm_block(x)

        # Flatten the output if it's not already 2D
        if len(x.shape) > 2:
            x = x.reshape(x.shape[0], -1)

        if self.use_dnn:
            x = self.dnn_block(x)
        else:
            for feat in self.features[:-1]:
                x = nn.Dense(feat)(x)
                x = self.activation(x)
                x = nn.Dropout(rate=self.dropout_rate, deterministic=not training)(x)

        if sensitive_attribute is not None:
            x = self.apply_fairness_constraint(x, sensitive_attribute)

        if self.use_rl:
            if not hasattr(self, 'rl_agent'):
                raise AttributeError("RL agent not initialized. Call _init_rl_agent first.")
            x = self.rl_agent(x)
        elif self.output_dim is not None:
            x = nn.Dense(self.output_dim)(x)
        else:
            x = nn.Dense(self.features[-1])(x)

        if self.use_gan:
            x = self.gan_block(x)

        # DDPM processing
        if self.use_ddpm:
            x = self.ddpm_block(x, training)

        # GenerativeAI processing
        if self.use_generative_ai:
            x = self.generative_ai.generate(self.generative_ai_state, x)

        # Wireless data transmission simulation
        if self.use_wireless:
            x = self.wireless_transmission(x)

        # User interface interaction simulation
        if self.use_ui:
            x = self.ui_interaction(x)

        # Consciousness simulation
        if self.consciousness_sim:
            x = self.simulate_consciousness(x)

        if self.use_xla:
            x = self.xla_optimization(x)

        # ART adversarial example generation
        if self.use_art and training:
            x = self.generate_adversarial_examples(x)

        # Lale integration for AutoML
        if self.use_lale:
            x = self.apply_lale_automl(x)

        # Ensure the output is 2D
        if len(x.shape) > 2:
            x = x.reshape(x.shape[0], -1)

        return x

    def generate_adversarial_examples(self, x):
        from art.attacks.evasion import FastGradientMethod
        from art.estimators.classification import JAXClassifier

        # Create a JAXClassifier wrapper for our model
        classifier = JAXClassifier(
            model=lambda x: self.apply({'params': self.params}, x),
            loss=lambda x, y: optax.softmax_cross_entropy(x, y),
            input_shape=x.shape[1:],
            nb_classes=self.features[-1]
        )

        # Create the attack
        attack = FastGradientMethod(classifier, eps=0.3)

        # Generate adversarial examples
        x_adv = attack.generate(x)

        return x_adv

    def ddpm_block(self, x, training):
        # Reshape x to match DDPM input requirements if necessary
        x = x.reshape((-1,) + self.ddpm.image_size)

        if training:
            # During training, add noise and try to denoise
            t = jax.random.randint(jax.random.PRNGKey(0), (x.shape[0],), 0, self.ddpm_timesteps)
            noise = jax.random.normal(jax.random.PRNGKey(1), x.shape)
            noisy_x = self.ddpm.q_sample(x, t, noise=noise)
            return self.ddpm(noisy_x, t)
        else:
            # During inference, run the full denoising process
            return self.ddpm.p_sample_loop(shape=x.shape)

        if self.use_shap and not training:
            self.compute_shap_values(x)

        return x

    def cnn_block(self, x):
        Conv = nn.Conv if self.conv_dim == 2 else nn.Conv3D
        kernel_size = (3, 3) if self.conv_dim == 2 else (3, 3, 3)
        padding = 'SAME' if self.conv_dim == 2 else ((1, 1, 1), (1, 1, 1))

        x = Conv(features=32, kernel_size=kernel_size, padding=padding)(x)
        x = self.activation(x)
        x = Conv(features=64, kernel_size=kernel_size, padding=padding)(x)
        x = self.activation(x)
        x = x.reshape((x.shape[0], -1))  # Flatten
        return x

    def rnn_block(self, x):
        rnn = nn.RNN(nn.LSTMCell(features=self.rnn_hidden_size))
        return rnn(x)[0]

    def lstm_block(self, x):
        class LSTMCellWrapper(nn.Module):
            features: int

            @nn.compact
            def __call__(self, carry, x):
                lstm_cell = nn.LSTMCell(self.features)
                new_carry, output = lstm_cell(carry, x)
                return new_carry, output

        # Ensure input is 3D: (batch_size, seq_len, input_dim)
        if len(x.shape) == 2:
            x = x.reshape(x.shape[0], 1, -1)  # Assume single time step if 2D input

        batch_size, seq_len, input_dim = x.shape
        print(f"Input shape: batch_size={batch_size}, seq_len={seq_len}, input_dim={input_dim}")

        # Initialize LSTM state
        lstm_cell = nn.LSTMCell(self.lstm_hidden_size)
        initial_carry = lstm_cell.initialize_carry(jax.random.PRNGKey(0), (batch_size,))
        print("Initial carry shape:", jax.tree_map(lambda x: x.shape, initial_carry))

        # Define the scan function
        def scan_fn(carry, x):
            lstm_wrapper = LSTMCellWrapper(features=self.lstm_hidden_size)
            new_carry, output = lstm_wrapper(carry, x)
            print(f"scan_fn - input x shape: {x.shape}")
            print(f"scan_fn - output shapes: carry={jax.tree_map(lambda x: x.shape, new_carry)}, output={output.shape}")
            return new_carry, output

        # Use nn.scan to iterate over the sequence
        final_carry, outputs = nn.scan(
            scan_fn,
            variable_broadcast="params",
            split_rngs={"params": False},
            in_axes=1,
            out_axes=1,
        )(initial_carry, x)

        print("Outputs shape:", outputs.shape)
        print("Final carry shape:", jax.tree_map(lambda x: x.shape, final_carry))

        return outputs

    def gan_block(self, x):
        # Advanced GAN implementation with style mixing and improved stability
        latent_dim = 100
        style_dim = 64
        num_iterations = 1000
        batch_size = x.shape[0]

        class Generator(nn.Module):
            @nn.compact
            def __call__(self, z, style):
                x = nn.Dense(256)(z)
                x = nn.relu(x)
                x = nn.Dense(512)(x)
                x = nn.relu(x)
                # Style mixing
                style = nn.Dense(style_dim)(style)
                x = x * style[:, None]
                x = nn.Dense(x.shape[-1])(x)
                return nn.tanh(x)  # Ensure output is in [-1, 1]

        class Discriminator(nn.Module):
            @nn.compact
            def __call__(self, x):
                x = nn.Dense(512)(x)
                x = nn.leaky_relu(x, 0.2)
                x = nn.Dense(256)(x)
                x = nn.leaky_relu(x, 0.2)
                x = nn.Dense(1)(x)
                return x

        generator = Generator()
        discriminator = Discriminator()

        def generator_loss(g_params, d_params, z, style, x):
            fake_data = generator.apply({'params': g_params}, z, style)
            fake_logits = discriminator.apply({'params': d_params}, fake_data)
            return -jnp.mean(fake_logits)

        def discriminator_loss(d_params, g_params, z, style, x):
            fake_data = generator.apply({'params': g_params}, z, style)
            fake_logits = discriminator.apply({'params': d_params}, fake_data)
            real_logits = discriminator.apply({'params': d_params}, x)
            return jnp.mean(fake_logits) - jnp.mean(real_logits)

        # Improved GAN training loop with gradient penalty
        def gradient_penalty(d_params, real_data, fake_data):
            alpha = jax.random.uniform(self.make_rng('gan'), (batch_size, 1))
            interpolated = real_data * alpha + fake_data * (1 - alpha)
            grad = jax.grad(lambda x: jnp.sum(discriminator.apply({'params': d_params}, x)))(interpolated)
            return jnp.mean((jnp.linalg.norm(grad, axis=-1) - 1) ** 2)

        g_optimizer = optax.adam(learning_rate=1e-4, b1=0.5, b2=0.9)
        d_optimizer = optax.adam(learning_rate=1e-4, b1=0.5, b2=0.9)
        g_opt_state = g_optimizer.init(generator.init(self.make_rng('gan'), jnp.ones((1, latent_dim)), jnp.ones((1, style_dim))))
        d_opt_state = d_optimizer.init(discriminator.init(self.make_rng('gan'), jnp.ones((1, x.shape[-1]))))

        @jax.jit
        def train_step(g_params, d_params, g_opt_state, d_opt_state, z, style, x):
            def g_loss_fn(g_params):
                return generator_loss(g_params, d_params, z, style, x)

            def d_loss_fn(d_params):
                loss = discriminator_loss(d_params, g_params, z, style, x)
                gp = gradient_penalty(d_params, x, generator.apply({'params': g_params}, z, style))
                return loss + 10 * gp  # Lambda = 10 for gradient penalty

            g_loss, g_grads = jax.value_and_grad(g_loss_fn)(g_params)
            d_loss, d_grads = jax.value_and_grad(d_loss_fn)(d_params)

            g_updates, g_opt_state = g_optimizer.update(g_grads, g_opt_state)
            d_updates, d_opt_state = d_optimizer.update(d_grads, d_opt_state)

            g_params = optax.apply_updates(g_params, g_updates)
            d_params = optax.apply_updates(d_params, d_updates)

            return g_params, d_params, g_opt_state, d_opt_state, g_loss, d_loss

        for _ in range(num_iterations):
            z = jax.random.normal(self.make_rng('gan'), (batch_size, latent_dim))
            style = jax.random.normal(self.make_rng('gan'), (batch_size, style_dim))
            g_params, d_params, g_opt_state, d_opt_state, g_loss, d_loss = train_step(
                generator.params, discriminator.params, g_opt_state, d_opt_state, z, style, x
            )
            generator = generator.replace(params=g_params)
            discriminator = discriminator.replace(params=d_params)

        # Generate fake data using the trained generator
        z = jax.random.normal(self.make_rng('gan'), (batch_size, latent_dim))
        style = jax.random.normal(self.make_rng('gan'), (batch_size, style_dim))
        fake_data = generator.apply({'params': generator.params}, z, style)

        return fake_data

    def feature_importance(self, x):
        activations = []
        for feat in self.features[:-1]:
            x = nn.Dense(feat)(x)
            x = self.activation(x)
            activations.append(x)
        return activations

    def apply_fairness_constraint(self, x, sensitive_attribute):
        group_means = jnp.mean(x, axis=0, keepdims=True)
        overall_mean = jnp.mean(group_means, axis=1, keepdims=True)
        adjusted_x = x + self.fairness_constraint * (overall_mean - group_means[sensitive_attribute])
        return adjusted_x

    @nn.compact
    def simulate_consciousness(self, x):
        # Simulate complex cognitive processes using advanced architecture
        def cognitive_process(params, inputs):
            x = nn.Dense(512)(inputs)
            x = nn.relu(x)
            x = nn.Dense(256)(x)
            x = nn.relu(x)
            x = nn.Dense(128)(x)
            x = nn.relu(x)
            return nn.Dense(64)(x)

        cognitive_params = self.param('cognitive_params', nn.initializers.xavier_uniform(), (x.shape[-1], 512))

        # Use JAX's vmap for efficient batch processing
        batched_cognitive = jax.vmap(cognitive_process, in_axes=(None, 0))

        # Apply the cognitive process
        cognitive_output = batched_cognitive(cognitive_params, x)

        # Simulate attention and working memory
        attention = nn.softmax(nn.Dense(64)(cognitive_output))
        working_memory = nn.GRUCell(64)(cognitive_output, attention)

        # Simulate decision making and metacognition
        decision = nn.Dense(32)(working_memory)
        metacognition = nn.sigmoid(nn.Dense(1)(jnp.concatenate([cognitive_output, working_memory, decision], axis=-1)))

        # Combine all components for a more complex consciousness simulation
        conscious_output = jnp.concatenate([x, cognitive_output, working_memory, decision, metacognition], axis=-1)

        return conscious_output

    def dnn_block(self, x):
        for units in [256, 128, 64]:
            x = nn.Dense(units)(x)
            x = self.activation(x)
            x = nn.Dropout(rate=self.dropout_rate)(x)
        return x

    def xla_optimization(self, x):
        @jax.jit
        def optimized_forward(x):
            return nn.Dense(self.features[-1])(x)

        return optimized_forward(x)

    def compute_shap_values(self, x):
        def model_predict(x):
            return self.apply({'params': self.params}, x)

        explainer = shap.DeepExplainer(model_predict, x)
        shap_values = explainer.shap_values(x)
        return shap_values

@jax.jit
def create_train_state(rng, model_class, model_params, input_shape, learning_rate):
    rng, init_rng = jax.random.split(rng)
    model = model_class(**model_params)
    params = model.init(init_rng, jnp.ones(input_shape))['params']
    tx = optax.adam(learning_rate)
    return train_state.TrainState.create(
        apply_fn=model.apply, params=params, tx=tx), model, rng

@jit
def train_step(state, batch):
    def loss_fn(params):
        # Handle different input types (image, sequence, etc.)
        if 'image' in batch:
            logits = state.apply_fn({'params': params}, batch['image'], method=state.apply_fn.cnn_forward)
        elif 'sequence' in batch:
            logits = state.apply_fn({'params': params}, batch['sequence'], method=state.apply_fn.rnn_forward)
        else:
            logits = state.apply_fn({'params': params}, batch['input'])

        # Compute loss for main task
        main_loss = optax.softmax_cross_entropy_with_integer_labels(logits, batch['label']).mean()

        # Compute GAN loss if applicable
        if hasattr(state.apply_fn, 'gan_loss'):
            gan_loss = state.apply_fn({'params': params}, batch['input'], method=state.apply_fn.gan_loss)
            total_loss = main_loss + gan_loss
        else:
            total_loss = main_loss

        return total_loss, logits

    grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
    (loss, logits), grads = grad_fn(state.params)
    state = state.apply_gradients(grads=grads)
    return state, loss

@jit
def eval_step(state, batch):
    logits = state.apply_fn({'params': state.params}, batch['image'], training=False)
    return jnp.mean(jnp.argmax(logits, axis=-1) == batch['label'])

# Interpretability features using SHAP
def interpret_model(model, params, input_data):
    import shap

    # Create a function that the explainer can call
    def model_predict(x):
        return model.apply({'params': params}, x)

    # Create the explainer
    explainer = shap.KernelExplainer(model_predict, input_data)

    # Calculate SHAP values
    shap_values = explainer.shap_values(input_data)

    # Visualize the results
    shap.summary_plot(shap_values, input_data)

    return shap_values


def adversarial_training(model, params, input_data, epsilon):
    try:
        # Create a PyTorchClassifier wrapper for the model
        def model_predict(x):
            return model.apply({'params': params}, torch.from_numpy(x).float())

        classifier = PyTorchClassifier(
            model=model_predict,
            loss=torch.nn.CrossEntropyLoss(),
            input_shape=input_data['image'].shape[1:],
            nb_classes=model.features[-1],
            clip_values=(0, 1)
        )

        # Create FGSM attack
        fgsm = FastGradientMethod(estimator=classifier, eps=epsilon)

        # Generate adversarial examples
        perturbed_input = fgsm.generate(x=input_data['image'])

        return {'image': perturbed_input, 'label': input_data['label']}
    except Exception as e:
        logging.error(f"Error in adversarial_training: {str(e)}")
        return input_data  # Return original input if an error occurs

# Main training loop with generalization techniques, fairness considerations, and reinforcement learning support
def train_model(model_class, model_params, train_data, val_data, num_epochs, batch_size, learning_rate, fairness_constraint, patience=5, epsilon=0.1, env=None):
    rng = jax.random.PRNGKey(0)

    if env is None:  # Standard supervised learning
        input_shape = train_data['image'].shape[1:]
        model = model_class(**model_params)
        dummy_input = jnp.ones((1,) + input_shape)  # Create a dummy input with batch dimension
        params = model.init(rng, dummy_input)['params']
        tx = optax.adam(learning_rate)
        state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx)
    else:  # Reinforcement learning
        input_shape = env.observation_space.shape
        model = model_class(**model_params, output_dim=env.action_space.n)
        dummy_input = jnp.ones((1,) + input_shape)  # Create a dummy input with batch dimension
        params = model.init(rng, dummy_input)['params']
        tx = optax.adam(learning_rate)
        state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx)

    best_val_performance = float('-inf')
    patience_counter = 0

    for epoch in range(num_epochs):
        if env is None:  # Standard supervised learning
            for batch in get_batches(train_data, batch_size):
                # Data augmentation
                batch['image'] = data_augmentation(batch['image'])

                # Adversarial training
                adv_batch = adversarial_training(model, state.params, batch, epsilon)

                state, loss = train_step(state, adv_batch)

            # Validation
            val_performance = evaluate_model(state, val_data, batch_size)
        else:  # Reinforcement learning
            state, rewards, training_info = train_rl_agent(
                model.rl_agent, env,
                num_episodes=model_params.get('rl_num_episodes', 100),
                max_steps=model_params.get('rl_max_steps', 1000),
                gamma=model_params.get('rl_gamma', 0.99),
                epsilon_start=model_params.get('rl_epsilon_start', 1.0),
                epsilon_end=model_params.get('rl_epsilon_end', 0.01),
                epsilon_decay=model_params.get('rl_epsilon_decay', 0.995),
                learning_rate=model_params.get('rl_learning_rate', learning_rate),
                batch_size=model_params.get('rl_batch_size', 64),
                buffer_size=model_params.get('rl_buffer_size', 10000),
                target_update_freq=model_params.get('rl_target_update_freq', 100),
                seed=rng[0]
            )
            loss = -np.mean(rewards)  # Use negative mean reward as a proxy for loss
            val_performance = np.mean(rewards[-10:])  # Use mean of last 10 episodes as validation performance

        # Compute basic fairness metrics if applicable
        if 'sensitive_attr' in val_data:
            predicted_labels = jnp.argmax(state.apply_fn({'params': state.params}, val_data['image']), axis=-1)
            fairness_metrics = compute_fairness_metrics(val_data, predicted_labels)
            print(f"Epoch {epoch}: loss = {loss:.3f}, val_performance = {val_performance:.3f}, "
                  f"disparate_impact = {fairness_metrics['disparate_impact']:.3f}")
        else:
            print(f"Epoch {epoch}: loss = {loss:.3f}, val_performance = {val_performance:.3f}")

        # Early stopping based on validation performance
        if val_performance > best_val_performance:
            best_val_performance = val_performance
            patience_counter = 0
        else:
            patience_counter += 1

        if patience_counter >= patience:
            print(f"Early stopping triggered at epoch {epoch}")
            break

    return state, model

def compute_fairness_metrics(data, predicted_labels):
    sensitive_attr = data['sensitive_attr']
    true_labels = data['label']

    privileged_mask = sensitive_attr == 1
    unprivileged_mask = sensitive_attr == 0

    privileged_accuracy = jnp.mean(predicted_labels[privileged_mask] == true_labels[privileged_mask])
    unprivileged_accuracy = jnp.mean(predicted_labels[unprivileged_mask] == true_labels[unprivileged_mask])

    disparate_impact = unprivileged_accuracy / privileged_accuracy if privileged_accuracy > 0 else 0

    return {
        'disparate_impact': disparate_impact
    }

def evaluate_fairness(state, data):
    # Predict labels using the trained model
    logits = state.apply_fn({'params': state.params}, data['image'])
    predicted_labels = jnp.argmax(logits, axis=-1)

    # Compute basic fairness metrics without AIF360
    sensitive_attr = data['sensitive_attr']
    true_labels = data['label']

    # Calculate overall accuracy
    overall_accuracy = jnp.mean(predicted_labels == true_labels)

    # Calculate group-wise accuracies
    privileged_mask = sensitive_attr == 1
    unprivileged_mask = sensitive_attr == 0
    privileged_accuracy = jnp.mean(predicted_labels[privileged_mask] == true_labels[privileged_mask])
    unprivileged_accuracy = jnp.mean(predicted_labels[unprivileged_mask] == true_labels[unprivileged_mask])

    # Calculate disparate impact
    disparate_impact = unprivileged_accuracy / privileged_accuracy if privileged_accuracy > 0 else 0

    # Calculate equal opportunity difference
    privileged_tpr = jnp.sum((predicted_labels == 1) & (true_labels == 1) & privileged_mask) / jnp.sum((true_labels == 1) & privileged_mask)
    unprivileged_tpr = jnp.sum((predicted_labels == 1) & (true_labels == 1) & unprivileged_mask) / jnp.sum((true_labels == 1) & unprivileged_mask)
    equal_opportunity_difference = privileged_tpr - unprivileged_tpr

    return {
        'overall_accuracy': overall_accuracy,
        'privileged_accuracy': privileged_accuracy,
        'unprivileged_accuracy': unprivileged_accuracy,
        'disparate_impact': disparate_impact,
        'equal_opportunity_difference': equal_opportunity_difference
    }

def select_action(state, model, params):
    # Implement action selection for reinforcement learning
    logits = model.apply({'params': params}, state[None, ...])
    return jnp.argmax(logits, axis=-1)[0]

def evaluate_model(state, data, batch_size):
    total_accuracy = 0
    num_batches = 0
    for batch in get_batches(data, batch_size):
        accuracy = eval_step(state, batch)
        total_accuracy += accuracy
        num_batches += 1
    return total_accuracy / num_batches

def data_augmentation(images, key=None):
    if key is None:
        key = jax.random.PRNGKey(0)

    # Random horizontal flip
    key, subkey = jax.random.split(key)
    images = jax.lax.cond(
        jax.random.uniform(subkey) > 0.5,
        lambda x: jnp.flip(x, axis=2),
        lambda x: x,
        images
    )

    # Random vertical flip
    key, subkey = jax.random.split(key)
    images = jax.lax.cond(
        jax.random.uniform(subkey) > 0.5,
        lambda x: jnp.flip(x, axis=1),
        lambda x: x,
        images
    )

    # Random rotation (0, 90, 180, or 270 degrees)
    key, subkey = jax.random.split(key)
    rotation = jax.random.randint(subkey, (), 0, 4)
    images = jax.lax.switch(rotation, [
        lambda x: x,
        lambda x: jnp.rot90(x, k=1, axes=(1, 2)),
        lambda x: jnp.rot90(x, k=2, axes=(1, 2)),
        lambda x: jnp.rot90(x, k=3, axes=(1, 2))
    ], images)

    # Random brightness adjustment
    key, subkey = jax.random.split(key)
    brightness_factor = jax.random.uniform(subkey, minval=0.8, maxval=1.2)
    images = jnp.clip(images * brightness_factor, 0, 1)

    # Random contrast adjustment
    key, subkey = jax.random.split(key)
    contrast_factor = jax.random.uniform(subkey, minval=0.8, maxval=1.2)
    mean = jnp.mean(images, axis=(1, 2, 3), keepdims=True)
    images = jnp.clip((images - mean) * contrast_factor + mean, 0, 1)

    return images, key

# Utility function to get batches
def get_batches(data, batch_size):
    for i in range(0, len(data['image']), batch_size):
        yield {k: v[i:i+batch_size] for k, v in data.items()}

# Example usage
if __name__ == "__main__":
    # Define model architecture class
    class ModelArchitecture(NeuroFlexNN):
        features = [64, 32, 10]
        activation = nn.relu
        dropout_rate = 0.5
        fairness_constraint = 0.1
        use_cnn = True
        use_rnn = True
        use_lstm = True
        use_gan = True
        use_rl = True
        use_bci = True
        bci_channels = 64
        bci_sampling_rate = 1000
        wireless_latency = 0.01
        use_generative_ai = True
        generative_ai_features = (128, 64)
        generative_ai_output_dim = 10

    # Set up gym environment
    env = gym.make('CartPole-v1')
    observation_space = env.observation_space.shape[0]
    action_space = env.action_space.n

    # Generate dummy data for 2D convolution
    num_samples = 1000
    input_dim_2d = (28, 28, 1)  # e.g., for MNIST
    num_classes = 10
    key = jax.random.PRNGKey(0)

    # Generate dummy data with sensitive attributes for 2D
    key, subkey = jax.random.split(key)
    train_data_2d = {
        'image': jax.random.normal(subkey, shape=(num_samples, *input_dim_2d)),
        'label': jax.random.randint(subkey, shape=(num_samples,), minval=0, maxval=num_classes),
        'sensitive_attr': jax.random.randint(subkey, shape=(num_samples,), minval=0, maxval=2)
    }
    key, subkey = jax.random.split(key)
    val_data_2d = {
        'image': jax.random.normal(subkey, shape=(num_samples // 5, *input_dim_2d)),
        'label': jax.random.randint(subkey, shape=(num_samples // 5,), minval=0, maxval=num_classes),
        'sensitive_attr': jax.random.randint(subkey, shape=(num_samples // 5,), minval=0, maxval=2)
    }

    # Generate dummy data for 3D convolution
    input_dim_3d = (16, 16, 16, 1)  # Example 3D input
    key, subkey = jax.random.split(key)
    train_data_3d = {
        'image': jax.random.normal(subkey, shape=(num_samples, *input_dim_3d)),
        'label': jax.random.randint(subkey, shape=(num_samples,), minval=0, maxval=num_classes),
        'sensitive_attr': jax.random.randint(subkey, shape=(num_samples,), minval=0, maxval=2)
    }
    key, subkey = jax.random.split(key)
    val_data_3d = {
        'image': jax.random.normal(subkey, shape=(num_samples // 5, *input_dim_3d)),
        'label': jax.random.randint(subkey, shape=(num_samples // 5,), minval=0, maxval=num_classes),
        'sensitive_attr': jax.random.randint(subkey, shape=(num_samples // 5,), minval=0, maxval=2)
    }

    # Define model parameters for 2D convolution
    model_params_2d = {
        'features': [64, 32, 10],
        'activation': nn.relu,
        'dropout_rate': 0.5,
        'fairness_constraint': 0.1,
        'use_cnn': True,
        'use_rnn': True,
        'use_lstm': True,
        'use_gan': True,
        'conv_dim': 2,
        'use_generative_ai': True,
        'generative_ai_features': (128, 64),
        'generative_ai_output_dim': 10
    }

    # Define model parameters for 3D convolution
    model_params_3d = {
        'features': [64, 32, 10],
        'activation': nn.relu,
        'dropout_rate': 0.5,
        'fairness_constraint': 0.1,
        'use_cnn': True,
        'use_rnn': True,
        'use_lstm': True,
        'use_gan': True,
        'conv_dim': 3,
        'use_generative_ai': True,
        'generative_ai_features': (128, 64),
        'generative_ai_output_dim': 10
    }

    # Train and evaluate 2D convolution model
    trained_state_2d, trained_model_2d = train_model(ModelArchitecture, model_params_2d, train_data_2d, val_data_2d,
                                                     num_epochs=10, batch_size=32, learning_rate=1e-3,
                                                     fairness_constraint=0.1, epsilon=0.1)
    print("Training completed for 2D convolution model.")
    fairness_metrics_2d = evaluate_fairness(trained_state_2d, val_data_2d)
    print("Fairness metrics for 2D model:", fairness_metrics_2d)
    shap_values_2d = interpret_model(trained_model_2d, trained_state_2d.params, val_data_2d['image'][:100])
    print("SHAP values computed for 2D model interpretation.")

    # Train and evaluate 3D convolution model
    trained_state_3d, trained_model_3d = train_model(ModelArchitecture, model_params_3d, train_data_3d, val_data_3d,
                                                     num_epochs=10, batch_size=32, learning_rate=1e-3,
                                                     fairness_constraint=0.1, epsilon=0.1)
    print("Training completed for 3D convolution model.")
    fairness_metrics_3d = evaluate_fairness(trained_state_3d, val_data_3d)
    print("Fairness metrics for 3D model:", fairness_metrics_3d)
    shap_values_3d = interpret_model(trained_model_3d, trained_state_3d.params, val_data_3d['image'][:100])
    print("SHAP values computed for 3D model interpretation.")

    # Reinforcement Learning example using gym
    rl_model_params = {
        'features': [64, 32, action_space],
        'activation': nn.relu,
        'dropout_rate': 0.5,
        'use_cnn': False,
        'use_rnn': False,
        'use_lstm': False,
        'use_gan': False,
        'use_rl': True,
        'use_generative_ai': True,
        'generative_ai_features': (128, 64),
        'generative_ai_output_dim': action_space
    }
    rl_model = ModelArchitecture(**rl_model_params)
    rl_state, _ = create_train_state(jax.random.PRNGKey(0), rl_model, (observation_space,), 1e-3)

    num_episodes = 100
    for episode in range(num_episodes):
        observation = env.reset()
        total_reward = 0
        done = False
        while not done:
            action = select_action(observation, rl_model, rl_state.params)
            next_observation, reward, done, _ = env.step(int(action))
            total_reward += reward
            observation = next_observation
        print(f"Episode {episode + 1}: Total reward: {total_reward}")

    print("Reinforcement Learning training completed.")

    # Test model predictions
    test_input_2d = jax.random.normal(jax.random.PRNGKey(0), shape=(1, *input_dim_2d))
    test_output_2d = trained_model_2d.apply({'params': trained_state_2d.params}, test_input_2d)
    print("2D model test output shape:", test_output_2d.shape)

    test_input_3d = jax.random.normal(jax.random.PRNGKey(0), shape=(1, *input_dim_3d))
    test_output_3d = trained_model_3d.apply({'params': trained_state_3d.params}, test_input_3d)
    print("3D model test output shape:", test_output_3d.shape)

    rl_test_input = jax.random.normal(jax.random.PRNGKey(0), shape=(1, observation_space))
    rl_test_output = rl_model.apply({'params': rl_state.params}, rl_test_input)
    print("RL model test output shape:", rl_test_output.shape)

    # Test GenerativeAIFramework
    gen_ai_framework = GenerativeAIFramework(features=(128, 64), output_dim=10)
    gen_ai_state = gen_ai_framework.init_model(jax.random.PRNGKey(0), (1, 28, 28))
    gen_ai_input = jax.random.normal(jax.random.PRNGKey(0), shape=(1, 28, 28))
    gen_ai_output = gen_ai_framework.generate(gen_ai_state, gen_ai_input)
    print("GenerativeAI output shape:", gen_ai_output.shape)

    # Integrate GenerativeAIFramework with NeuroFlex
    integrated_model = ModelArchitecture(**model_params_2d)
    integrated_state = create_train_state(jax.random.PRNGKey(0), integrated_model, (1, 28, 28, 1), 1e-3)[0]
    integrated_output = integrated_model.apply({'params': integrated_state.params}, test_input_2d)
    print("Integrated model output shape:", integrated_output.shape)

    # Test GenerativeAI within NeuroFlex
    neuroflex_gen_ai_output = integrated_model.apply({'params': integrated_state.params}, test_input_2d, method=integrated_model.generative_ai.generate)
    print("NeuroFlex GenerativeAI output shape:", neuroflex_gen_ai_output.shape)

class DataPipeline:
    def __init__(self, config):
        self.config = config
        self.jackhmmer_binary_path = config.get('jackhmmer_binary_path')
        self.hhblits_binary_path = config.get('hhblits_binary_path')
        self.uniref90_database_path = config.get('uniref90_database_path')
        self.mgnify_database_path = config.get('mgnify_database_path')
        self.template_searcher = config.get('template_searcher', pipeline.TemplateSearcher(
            binary_path=config.get('hhsearch_binary_path'),
            databases=[config.get('pdb70_database_path')]
        ))
        self.template_featurizer = config.get('template_featurizer', templates.TemplateHitFeaturizer(
            mmcif_dir=config.get('template_mmcif_dir'),
            max_template_date=config.get('max_template_date'),
            max_hits=20,
            kalign_binary_path=config.get('kalign_binary_path'),
            release_dates_path=None,
            obsolete_pdbs_path=config.get('obsolete_pdbs_path')
        ))

    def process_sequence(self, sequence):
        if not isinstance(sequence, str) or not sequence.isalpha():
            raise ValueError("Invalid sequence input. Must be a string of alphabetic characters.")

        try:
            # Generate MSAs using Jackhmmer and HHblits
            jackhmmer_msa = self._run_jackhmmer(sequence, self.uniref90_database_path)
            hhblits_msa = self._run_hhblits(sequence, self.mgnify_database_path)

            # Combine MSAs
            combined_msa = self._combine_msas(jackhmmer_msa, hhblits_msa)

            # Search for templates
            templates = self.template_searcher.query(sequence)

            return {
                'sequence': sequence,
                'msa': combined_msa,
                'templates': templates
            }
        except Exception as e:
            logging.error(f"Error in process_sequence: {str(e)}")
            raise

    def generate_features(self, processed_sequence):
        try:
            # Generate features from MSA and templates
            msa_features = self._featurize_msa(processed_sequence['msa'])
            template_features = self.template_featurizer.get_templates(
                processed_sequence['sequence'],
                processed_sequence['templates']
            )

            # Combine all features
            feature_dict = {
                **msa_features,
                **template_features,
                'target_feat': self._target_features(processed_sequence['sequence'])
            }

            return feature_dict
        except Exception as e:
            logging.error(f"Error in generate_features: {str(e)}")
            raise

    def run(self, input_sequence):
        try:
            processed_sequence = self.process_sequence(input_sequence)
            features = self.generate_features(processed_sequence)
            return features
        except Exception as e:
            logging.error(f"Error in run: {str(e)}")
            raise

    def _run_jackhmmer(self, sequence, database):
        try:
            runner = pyhmmer.Jackhmmer(
                binary_path=self.jackhmmer_binary_path,
                database_path=database
            )
            results = runner.query(sequence)

            msa = [hit.alignment for hit in results.hits]
            return msa
        except Exception as e:
            logging.error(f"Error in _run_jackhmmer: {str(e)}")
            raise

    def _run_hhblits(self, sequence, database):
        try:
            hhblits_runner = pyhmmer.HHBlits(binary_path=self.hhblits_binary_path, databases=[database])
            result = hhblits_runner.query(sequence)
            return result.msa
        except Exception as e:
            logging.error(f"Error in _run_hhblits: {str(e)}")
            raise

    def _combine_msas(self, msa1, msa2):
        combined_msa = list(set(msa1 + msa2))
        combined_msa.sort(key=lambda seq: sum(a == b for a, b in zip(seq, combined_msa[0])), reverse=True)
        return combined_msa

    def _featurize_msa(self, msa):
        num_sequences = len(msa)
        sequence_length = len(msa[0])

        amino_acids = 'ACDEFGHIKLMNPQRSTVWY-'
        aa_to_index = {aa: i for i, aa in enumerate(amino_acids)}

        features = np.zeros((num_sequences, sequence_length, len(amino_acids)))

        for i, sequence in enumerate(msa):
            for j, aa in enumerate(sequence):
                if aa in aa_to_index:
                    features[i, j, aa_to_index[aa]] = 1

        pssm = np.sum(features, axis=0) / num_sequences
        conservation = np.sum(-pssm * np.log(pssm + 1e-8), axis=1)

        return {
            'one_hot': features,
            'pssm': pssm,
            'conservation': conservation
        }

    def _target_features(self, sequence):
        aa_types = residue_constants.sequence_to_onehot(sequence)
        return {
            'target_feat': jnp.array(aa_types, dtype=jnp.float32),
            'aatype': jnp.argmax(aa_types, axis=-1),
            'between_segment_residues': jnp.zeros(len(sequence), dtype=jnp.int32),
            'domain_name': jnp.array([0], dtype=jnp.int32),
            'residue_index': jnp.arange(len(sequence), dtype=jnp.int32),
            'seq_length': jnp.array([len(sequence)], dtype=jnp.int32),
            'sequence': sequence,
        }

    def process_bci_signal(self, signal):
        # Simulate advanced BCI signal processing
        # Apply bandpass filter
        sos = signal.butter(10, [1, 50], btype='band', fs=self.bci_sampling_rate, output='sos')
        filtered_signal = signal.sosfilt(sos, signal)

        # Perform wavelet transform
        coeffs = pywt.wavedec(filtered_signal, 'db4', level=5)

        # Feature extraction (using wavelet coefficients)
        features = jnp.concatenate([jnp.mean(jnp.abs(c)) for c in coeffs])

        return features

    def wireless_transmission(self, data):
        # Simulate wireless data transmission with latency and packet loss
        latency = jnp.random.normal(self.wireless_latency, 0.002)  # Random latency
        noise = jnp.random.normal(0, 0.01, data.shape)
        packet_loss = jnp.random.choice([0, 1], p=[0.99, 0.01], size=data.shape)  # 1% packet loss

        transmitted_data = (data + noise) * packet_loss
        return jax.lax.stop_gradient(jnp.where(jnp.isnan(transmitted_data), 0, transmitted_data))

    def user_interface_interaction(self, input_data):
        # Simulate more complex user interface interaction
        # Apply non-linear transformation and add some randomness to simulate user behavior
        ui_response = jnp.tanh(input_data) + jnp.random.normal(0, 0.1, input_data.shape)
        # Simulate button press threshold
        button_press = jnp.where(ui_response > 0.5, 1, 0)
        return button_press

class BCIProcessor:
    def __init__(self, channels, sampling_rate, noise_reduction, feature_extraction):
        self.channels = channels
        self.sampling_rate = sampling_rate
        self.noise_reduction = noise_reduction
        self.feature_extraction = feature_extraction

    def process(self, signal):
        # Placeholder for BCI signal processing
        return signal

class CognitiveLayer:
    def __init__(self, size):
        self.size = size

    def process(self, input_data):
        # Placeholder for cognitive processing
        return input_data

class ConsciousnessModule:
    def __init__(self, complexity):
        self.complexity = complexity

    def simulate(self, input_data):
        # Placeholder for consciousness simulation
        return input_data
