import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np
import json

# Example function to simulate data corruption in JSON structures
def introduce_noise(data, noise_level=0.1):
    noisy_data = data.copy()
    for key in list(data.keys()):
        if np.random.rand() < noise_level:
            if isinstance(data[key], dict):
                noisy_data[key] = introduce_noise(data[key], noise_level)
            else:
                del noisy_data[key]  # Simulating missing data
    return noisy_data

# Function to convert nested dictionaries to flat, vector-like structures
def flatten_dict(d, parent_key='', sep='_'):
    items = []
    for k, v in d.items():
        new_key = parent_key + sep + k if parent_key else k
        if isinstance(v, dict):
            items.extend(flatten_dict(v, new_key, sep=sep).items())
        else:
            items.append((new_key, v))
    return dict(items)

# Creating the model
def build_autoencoder(input_dim):
    input_layer = layers.Input(shape=(input_dim,))
    encoded = layers.Dense(128, activation='relu')(input_layer)
    encoded = layers.Dense(64, activation='relu')(encoded)
    decoded = layers.Dense(128, activation='relu')(encoded)
    decoded = layers.Dense(input_dim, activation='sigmoid')(decoded)
    
    autoencoder = models.Model(input_layer, decoded)
    encoder = models.Model(input_layer, encoded)
    
    autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
    return autoencoder, encoder

# Sample data
data_structure = {
    'copasi': {
        '_type': 'process',
        'address': 'local:!biosimulator_processes.processes.copasi_process.CopasiProcess',
        'config': {
            'model': {
                'model_source': 'biosimulator_processes/model_files/Caravagna2010.xml'
            }
        },
        'inputs': {
            'floating_species': ['floating_species_store'],
            'model_parameters': ['model_parameters_store'],
            'time': ['time_store'],
            'reactions': ['reactions_store']
        },
        'outputs': {
            'floating_species': ['floating_species_store'],
            'time': ['time_store'],
        }
    }
}

# Introducing noise
noisy_data = introduce_noise(data_structure, noise_level=0.2)
flattened_clean_data = flatten_dict(data_structure)
flattened_noisy_data = flatten_dict(noisy_data)

# Assume we have a way to create a numerical dataset from these (needs more specific implementation)
X_clean = np.array([list(flattened_clean_data.values())])  # placeholder
X_noisy = np.array([list(flattened_noisy_data.values())])  # placeholder

# Model
input_dim = X_clean.shape[1]
autoencoder, encoder = build_autoencoder(input_dim)

# Training (you would actually need many examples and iterations)
autoencoder.fit(X_noisy, X_clean, epochs=50)

# Predicting and denoising
predicted_clean_data = autoencoder.predict(X_noisy)