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# %% [markdown]
# # Why Instance Registries?
#
# Some components need configuration that can't easily be passed at instantiation time. For example, scorers often need:
# - A configured `chat_target` for LLM-based scoring
# - Specific prompt templates
# - Other dependencies
#
# Instance registries let initializers register fully-configured instances that are ready to use.

# %% [markdown]
# ## Listing Available Instances
#
# Use `instances.get_names()` to see registered instances, or `instances.list_metadata()` for details.

# %%
from pyrit.prompt_target import OpenAIChatTarget
from pyrit.registry import ScorerRegistry
from pyrit.score import SelfAskRefusalScorer
from pyrit.setup import IN_MEMORY, initialize_pyrit_async

await initialize_pyrit_async(memory_db_type=IN_MEMORY)  # type: ignore

# Get the registry singleton
registry = ScorerRegistry.get_registry_singleton()

# Register a scorer instance for demonstration
chat_target = OpenAIChatTarget()
refusal_scorer = SelfAskRefusalScorer(chat_target=chat_target)
registry.instances.register(refusal_scorer)

# List what's available
names = registry.instances.get_names()
print(f"Registered scorers: {names}")

# %% [markdown]
# ## Getting an Instance
#
# Use `instances.get()` to retrieve a pre-configured instance by name. The instance is ready to use immediately.

# %%
# Get the first registered scorer
if names:
    scorer_name = names[0]
    scorer = registry.instances.get(scorer_name)
    print(f"Retrieved scorer: {scorer}")
    print(f"Scorer type: {type(scorer).__name__}")

# %% [markdown]
# ## Inspecting Metadata
#
# Scorer metadata includes the scorer type and identifier for tracking.

# %%
from pyrit.output import output_scorer_async

# Get metadata for all registered scorers
metadata = registry.instances.list_metadata()
for item in metadata:
    print(f"\n{item.unique_name}:")
    print(f"  Class: {item.class_name}")
    print(f"  Type: {item.params.get('scorer_type', 'unknown')}")

    await output_scorer_async(scorer_identifier=item)

# %% [markdown]
# ## Filtering
#
# Use `list_metadata()` with `include_filters` and `exclude_filters` dictionaries to filter scorers by any metadata property. `include_filters` requires ALL criteria to match (AND logic). `exclude_filters` excludes items matching ANY criteria. Filters use exact match for simple types and membership check for list types.

# %%
# Filter by scorer_type (based on isinstance check against TrueFalseScorer/FloatScaleScorer)
true_false_scorers = registry.instances.list_metadata(include_filters={"scorer_type": "true_false"})
print(f"True/False scorers: {[m.unique_name for m in true_false_scorers]}")

# Filter by class_name
refusal_scorers = registry.instances.list_metadata(include_filters={"class_name": "SelfAskRefusalScorer"})
print(f"Refusal scorers: {[m.unique_name for m in refusal_scorers]}")

# Combine multiple filters (AND logic)
specific_scorers = registry.instances.list_metadata(
    include_filters={"scorer_type": "true_false", "class_name": "SelfAskRefusalScorer"}
)
print(f"True/False refusal scorers: {[m.unique_name for m in specific_scorers]}")

# %% [markdown]
# ## Using Target Initializer
#
# You can optionally use the `TargetInitializer` to automatically configure and register targets that use commonly used environment variables (from `.env_example`). This initializer does not strictly require any environment variables - it simply registers whatever endpoints are available.

# %%
from pyrit.registry import TargetRegistry
from pyrit.setup import initialize_pyrit_async
from pyrit.setup.initializers import TargetInitializer

# Using built-in initializer
await initialize_pyrit_async(  # type: ignore
    memory_db_type="InMemory", initializers=[TargetInitializer()]
)

# Get the registry singleton
registry = TargetRegistry.get_registry_singleton()
# List registered targets
target_names = registry.instances.get_names()
print(f"Registered targets after initialization: {target_names}")