import numpy as np

import torch

import torch.nn as nn

class ActiveCodecAI(nn.Module):

"""

An AI-driven codec that adapts its latent representation

based on the 'activity' or complexity of the input data.

"""

def __init__(self, input_dim=1024, latent_dim=128):

super(ActiveCodecAI, self).__init__()

# Encoder: Compressing input to latent space

self.encoder = nn.Sequential(

nn.Linear(input_dim, 512),

nn.ReLU(),

nn.Linear(512, latent_dim)

)

# Decoder: Reconstructing input from latent space

self.decoder = nn.Sequential(

nn.Linear(latent_dim, 512),

nn.ReLU(),

nn.Linear(512, input_dim),

nn.Sigmoid()

)

def compute_activity(self, x):

"""

Determines the 'complexity' of the signal.

Higher variance = higher activity = need for more bits.

"""

return torch.var(x)

def forward(self, x, activity_threshold=0.05):

# 1. Active Assessment

activity = self.compute_activity(x)

# 2. Encode

latent = self.encoder(x)

# 3. Dynamic Quantization (Simplified)

# If activity is low, we 'prune' or zero out low-energy neurons

# to save bandwidth.

if activity < activity_threshold:

latent = torch.round(latent * 5) / 5 # Aggressive quantization

# 4. Decode

reconstruction = self.decoder(latent)

return reconstruction, activity

# --- Implementation Example ---

codec = ActiveCodecAI()

input_signal = torch.rand(1, 1024) # Simulated signal frame

output, complexity = codec(input_signal)

print(f"Signal Complexity (Activity): {complexity.item():.4f}")

print(f"Reconstruction Shape: {output.shape}")

import torch

import torch.nn as nn

import torch.distributions as dist

class EntropyBottleneck(nn.Module):

"""

Active Bitrate Control: This module models the probability distribution

of the latent space to estimate 'bit-cost' in real-time.

"""

def __init__(self, channels):

super().__init__()

self.gaussian_params = nn.Parameter(torch.zeros(1, channels, 1, 1))

def forward(self, x):

# Active likelihood estimation for entropy coding

sigma = torch.exp(self.gaussian_params)

dist_model = dist.Normal(0, sigma)

likelihoods = dist_model.log_prob(x).exp()

return x, likelihoods

class AdvancedActiveCodec(nn.Module):

def __init__(self, in_channels=3, latent_channels=192):

super().__init__()

# Encoder (Analysis Transform) - HD Quality focus

self.encoder = nn.Sequential(

nn.Conv2d(in_channels, latent_channels, 5, stride=2, padding=2),

nn.GDN(latent_channels), # Generalized Divisive Normalization

nn.Conv2d(latent_channels, latent_channels, 5, stride=2, padding=2),

nn.GDN(latent_channels)

)

# Hyper-Encoder (The 'Active' Intelligence)

# It analyzes the latent space to predict its entropy

self.hyper_encoder = nn.Sequential(

nn.Conv2d(latent_channels, latent_channels, 3, stride=1, padding=1),

nn.ReLU(),

nn.Conv2d(latent_channels, latent_channels, 3, stride=2, padding=1)

)

self.entropy_bottleneck = EntropyBottleneck(latent_channels)

# Decoder (Synthesis Transform)

self.decoder = nn.Sequential(

nn.ConvTranspose2d(latent_channels, latent_channels, 5, stride=2, padding=2, output_padding=1),

nn.IGDN(latent_channels),

nn.ConvTranspose2d(latent_channels, in_channels, 5, stride=2, padding=2, output_padding=1)

)

def forward(self, x):

y = self.encoder(x)

z = self.hyper_encoder(y)

# Active Bottlenecking

y_hat, likelihoods = self.entropy_bottleneck(y)

reconstruction = self.decoder(y_hat)

return reconstruction, likelihoods

# --- Scientific Reasoning Implementation ---

# To implement this on your dev profile (https://github.com/gilbertalgordo/dev)

# you would integrate a Rate-Distortion Loss:

# Loss = Lambda * Distortion(x, x_hat) + Rate(likelihoods)