### Configure and Use EvolutionDirector

Source: https://context7.com/lucidrains/transformer-directed-evolution/llms.txt

Shows how to instantiate an EvolutionDirector transformer and use it to guide the genetic algorithm's evolution process.

```python
import torch
from transformer_directed_evolution import ToyGeneticAlgorithmEnv, EvolutionDirector

# Create the genetic algorithm environment
env = ToyGeneticAlgorithmEnv(
    goal='Hello World',
    population_size=50,
    max_steps=200
)

# Create the evolution director with transformer configuration
director = EvolutionDirector(
    dim_genome=env.gene_length,          # Input dimension matches gene length
    transformer=dict(                     # x-transformers Encoder config
        dim=64,                           # Model dimension
        depth=2,                          # Number of transformer layers
        attn_dim_head=64,                 # Attention head dimension
        heads=4,                          # Number of attention heads
    ),
    mutation_rate_bins=25,               # Discrete bins for mutation rate
    max_mutation_rate=0.2,               # Maximum allowed mutation rate
    critic_fitness_weight=1.0,           # Weight for fitness in critic
    critic_diversity_weight=0.5,         # Weight for diversity in critic
)

# Run genetic algorithm WITH the trained director
trials = 5
results_with_director, fitness_with = env.run(
    num_trials=trials,
    director=director,
    pass_fitness_to_director=True,  # Include fitness info in director input
    display=False
)

# Run WITHOUT director for comparison
env.reset()
results_without_director, fitness_without = env.run(num_trials=trials)

print(f"With Director - Avg generations: {results_with_director.float().mean():.1f}")
print(f"Without Director - Avg generations: {results_without_director.float().mean():.1f}")
```

--------------------------------

### Initialize and Run ToyGeneticAlgorithmEnv

Source: https://context7.com/lucidrains/transformer-directed-evolution/llms.txt

Demonstrates setting up a genetic algorithm environment to evolve strings towards a target and executing the evolution process.

```python
import torch
from transformer_directed_evolution import ToyGeneticAlgorithmEnv

# Create a genetic algorithm environment with custom parameters
env = ToyGeneticAlgorithmEnv(
    goal='Attention is all you need',  # Target string to evolve towards
    population_size=100,                # Number of individuals in population
    mutation_rate=0.05,                 # Probability of gene mutation
    frac_fittest_survive=0.25,          # Top 25% survive each generation
    frac_tournament=0.25,               # Tournament selection fraction
    display=False,                      # Print population each generation
    max_steps=500                       # Maximum generations before timeout
)

# Run the genetic algorithm without any external direction
num_trials = 10
generations_completed, max_fitnesses = env.run(num_trials=num_trials)

print(f"Average generations to converge: {generations_completed.float().mean():.1f}")
print(f"Max fitness achieved: {max_fitnesses.max():.4f}")

# Access environment properties
print(f"Gene length: {env.gene_length}")
print(f"Population diversity: {env.diversity:.4f}")
print(f"Current generation: {env.generation.item()}")

# Manually reset and step through the environment
env.reset()
done, fitnesses = env.forward(display=True)  # Single generation step
```

--------------------------------

### Calculate Generalized Advantage Estimation (GAE)

Source: https://context7.com/lucidrains/transformer-directed-evolution/llms.txt

Computes advantages for PPO training using simulated trajectory data. Supports GPU acceleration via associative scans. Ensure rewards, values, and masks are provided as PyTorch tensors.

```python
import torch
from transformer_directed_evolution.evolution_director import calc_generalized_advantage_estimate

# Simulated trajectory data from environment rollout
sequence_length = 100

rewards = torch.randn(sequence_length)      # Rewards at each timestep
values = torch.randn(sequence_length)       # Value estimates from critic
masks = torch.ones(sequence_length)         # Episode continuation masks (0 at episode end)

# Calculate GAE advantages
advantages = calc_generalized_advantage_estimate(
    rewards=rewards,
    values=values,
    masks=masks,
    gamma=0.99,              # Discount factor
    lam=0.95,                # GAE lambda for bias-variance tradeoff
    use_accelerated=None     # Auto-detect GPU for accelerated scan
)

print(f"Advantages shape: {advantages.shape}")
print(f"Advantage mean: {advantages.mean():.4f}")
print(f"Advantage std: {advantages.std():.4f}")

# GPU example with explicit acceleration
if torch.cuda.is_available():
    rewards_gpu = rewards.cuda()
    values_gpu = values.cuda()
    masks_gpu = masks.cuda()

    advantages_gpu = calc_generalized_advantage_estimate(
        rewards_gpu, values_gpu, masks_gpu,
        use_accelerated=True  # Force accelerated associative scan
    )
    print(f"GPU advantages computed: {advantages_gpu.device}")
```

--------------------------------

### Calculate PPO Loss Functions

Source: https://context7.com/lucidrains/transformer-directed-evolution/llms.txt

Computes actor and critic losses for training the director using Proximal Policy Optimization objectives.

```python
import torch
from transformer_directed_evolution import EvolutionDirector

director = EvolutionDirector(
    dim_genome=10,
    transformer=dict(dim=32, depth=1, heads=2, attn_dim_head=16),
    critic_fitness_weight=1.0,
    critic_diversity_weight=0.5
)

# Example training data (typically collected from environment rollouts)
batch_size = 32
num_actions = 25  # mutation_rate_bins

# Simulated training tensors
logits = torch.randn(batch_size, num_actions)
old_log_probs = torch.randn(batch_size, 1)
actions = torch.randint(0, num_actions, (batch_size, 1))
advantages = torch.randn(2, batch_size)  # [fitness_advantage, diversity_advantage]
values = torch.randn(batch_size)
rewards = torch.randn(batch_size)

# Calculate actor loss (PPO clipped surrogate objective)
actor_loss = director.actor_loss(
    logits=logits,
    old_log_probs=old_log_probs,
    actions=actions,
    advantages=advantages,
    eps_clip=0.2,           # PPO clip parameter
    entropy_weight=0.01,    # Entropy bonus for exploration
    norm_eps=1e-5           # Layer norm epsilon
)

# Calculate critic loss (value function MSE)
critic_loss = director.critic_loss(
    advantages=advantages,
    values=values,
    rewards=rewards
)

print(f"Actor loss: {actor_loss.item():.4f}")
print(f"Critic loss: {critic_loss.item():.4f}")

# Combined loss for optimization
total_loss = actor_loss + 0.5 * critic_loss
total_loss.backward()
```

--------------------------------

### Interact with Environment via Generator

Source: https://context7.com/lucidrains/transformer-directed-evolution/llms.txt

Utilizes a Python generator interface for step-by-step control over the genetic algorithm evolution process.

```python
import torch
from transformer_directed_evolution import ToyGeneticAlgorithmEnv, EvolutionDirector

env = ToyGeneticAlgorithmEnv(goal='Evolution', population_size=30, max_steps=100)
director = EvolutionDirector(
    dim_genome=env.gene_length,
    transformer=dict(dim=32, depth=1, heads=2, attn_dim_head=16)
)

# Create generator for step-by-step control
env.reset()
gen = env.to_environment_generator()

# Get initial state
state, parent_ids, fitnesses = next(gen)
print(f"Initial population shape: {state.shape}")

# Step through evolution with director guidance
done = False
generation = 0
while not done and generation < 50:
    # Get director's recommendations
    with torch.no_grad():
        actions = director(state, parent_ids, fitnesses=fitnesses)

    # Add display flag and send actions to generator
    actions['display'] = (generation % 10 == 0)  # Display every 10 generations

    # Advance one generation
    state, parent_ids, fitnesses, diversity, done = gen.send(actions)

    print(f"Gen {generation}: Max fitness={fitnesses.max():.4f}, Diversity={diversity:.4f}")
    generation += 1

print(f"Evolution completed at generation {env.generation.item()}")
```

--------------------------------

### Perform Forward Pass with EvolutionDirector

Source: https://context7.com/lucidrains/transformer-directed-evolution/llms.txt

Processes gene pools and parent selection to generate crossover masks and mutation rates for environment steps.

```python
import torch
from transformer_directed_evolution import ToyGeneticAlgorithmEnv, EvolutionDirector

env = ToyGeneticAlgorithmEnv(goal='Test', population_size=20)
director = EvolutionDirector(
    dim_genome=env.gene_length,
    transformer=dict(dim=32, depth=1, heads=2, attn_dim_head=16)
)

# Get current state from environment
env.reset()
done, fitnesses = env.forward()  # Initialize environment

genome_pool = env.gene_pool      # Shape: (population_size, gene_length)
parent_ids = env.parent_ids      # Shape: (num_children, 2)

# Get director's recommended actions
actions = director(
    genome_pool=genome_pool,
    parent_ids=parent_ids,
    fitnesses=fitnesses,                 # Optional: include fitness information
    pred_selection_operator=False,       # Whether to predict selection mask
    natural_selection_size=None          # Size if using selection operator
)

print(f"Crossover mask shape: {actions['crossover_mask'].shape}")
print(f"Mutation rate: {actions['mutation_rate'].item():.4f}")

# Apply director's actions to environment step
done, new_fitnesses = env.forward(
    crossover_mask=actions['crossover_mask'],
    mutation_rate=actions['mutation_rate'],
    mutation_strength=0.5,               # Strength of mutations when applied
    display=False
)
```

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