### Installation of lazyregistry

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Commands to install the lazyregistry library using popular Python package managers.

```bash
pip install lazyregistry
uv add "lazyregistry"
```

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

### Quick Start with Registry

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Demonstrates how to initialize a Registry and register objects using both lazy import strings and direct object references.

```python
from lazyregistry import Registry

registry = Registry(name="plugins")

# Register by import string (lazy - imported on access)
registry["json"] = "json:dumps"

# Register by instance (immediate - already imported)
import pickle
registry["pickle"] = pickle.dumps

# Import happens here
serializer = registry["json"]
```

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

### Plugin System with Decorator Registration in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Provides an example of building an extensible plugin architecture using decorator-based registration with lazy loading. A global plugin registry is defined to manage plugins, enabling dynamic loading and management of extensions.

```python
from lazyregistry import Registry

# Create a global plugin registry
PLUGINS: Registry[str, type] = Registry(name="plugins")

```

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

### Plugin System Implementation

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Shows how to create an extensible plugin architecture using a decorator-based registration pattern with the Registry class.

```python
from lazyregistry import Registry

PLUGINS = Registry(name="plugins")

def plugin(name: str):
    def decorator(cls):
        PLUGINS[name] = cls
        return cls
    return decorator

@plugin("uppercase")
class UppercasePlugin:
    def process(self, text: str) -> str:
        return text.upper()
```

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

### Optimizing Imports with LazyRegistry

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Compares traditional eager imports with the lazy approach provided by the library, demonstrating how to defer heavy module loading until the moment of access.

```python
from lazyregistry import Registry

registry = Registry(name="components")
registry.register("a", "heavy_module_1:ClassA")

# ClassA is only imported here
component = registry["a"]
```

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

### Registry and Namespace Usage

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Demonstrates basic dictionary-style registration and the use of Namespaces to organize multiple registries. It supports both immediate object assignment and lazy-loading via import strings.

```python
from lazyregistry import Registry, NAMESPACE

# Basic Registry
registry = Registry()
registry["key"] = "module:object"  # Lazy
registry["key2"] = actual_object    # Immediate

# Namespace usage
NAMESPACE["models"]["bert"] = "transformers:BertModel"
model = NAMESPACE["models"]["bert"]
```

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

### Configuring LazyImportDict

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Shows how to customize the behavior of the base LazyImportDict class. Users can toggle auto-import string conversion and eager loading settings.

```python
from lazyregistry.registry import LazyImportDict

registry = LazyImportDict()
registry.auto_import_strings = True
registry.eager_load = False

registry["key"] = "module:object"
```

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

### Run Test Suite with Coverage

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Command to execute the project test suite using uv and pytest, including coverage reporting to ensure code quality.

```bash
uv run pytest tests/ --cov=lazyregistry --cov-report=term-missing
```

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

### Pretrained Model Registration

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Illustrates using PretrainedMixin and AutoRegistry to manage models. It supports decorator-based registration, direct assignment, and bulk updates for automatic type detection from configuration files.

```python
from lazyregistry.pretrained import PretrainedMixin, AutoRegistry

class AutoModel(AutoRegistry):
    registry = NAMESPACE["models"]
    config_class = PretrainedConfig
    type_key = "model_type"

@AutoModel.register_module("bert")
class BertModel(BaseModel):
    config_class = BertConfig

AutoModel.registry["gpt2"] = GPT2Model
loaded = AutoModel.from_pretrained("./path")
```

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

### Update Registry with String References

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Demonstrates how to register models using string references to defer actual module imports until the registry is accessed. This pattern helps in avoiding heavy dependency loading at startup.

```python
AutoModel.registry.update({
    "bert": "mypackage.bert:BertModel",
    "gpt2": "mypackage.gpt2:GPT2Model",
})
```

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

### Combining with lazy-loader

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Shows how to integrate lazy-loader with lazyregistry to achieve full package-level lazy imports, maintaining IDE support via TYPE_CHECKING blocks.

```python
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from .auto import AutoModel
else:
    import lazy_loader as lazy
    __getattr__, __dir__, __all__ = lazy.attach(
        __name__,
        submod_attrs={"auto": ["AutoModel"]}
    )
```

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

### Using ImportString for Lazy Loading

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Demonstrates the use of the ImportString class to represent an import path that is only resolved when the load() method is called.

```python
from lazyregistry import ImportString

# Create an import string
import_str = ImportString("json:dumps")

# Load the object when needed
func = import_str.load()
func({"key": "value"})
```

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

### Pretrained Model Registration

Source: https://github.com/milkclouds/lazyregistry/blob/main/README.md

Illustrates the HuggingFace-style save/load pattern using AutoRegistry and PretrainedMixin for managing model configurations and lazy loading.

```python
from lazyregistry import NAMESPACE
from lazyregistry.pretrained import AutoRegistry, PretrainedConfig, PretrainedMixin

class BertConfig(PretrainedConfig):
    model_type: str = "bert"
    hidden_size: int = 768

class BaseModel(PretrainedMixin):
    config_class = PretrainedConfig

class AutoModel(AutoRegistry):
    registry = NAMESPACE["models"]
    config_class = PretrainedConfig
    type_key = "model_type"

@AutoModel.register_module("bert")
class BertModel(BaseModel):
    config_class = BertConfig

# Register directly
AutoModel.registry["gpt2"] = "transformers:GPT2Model"
```

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

### Registry with Lazy Import Support in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Illustrates the usage of the Registry class for managing components with automatic lazy import. Strings assigned to the registry are converted to ImportString and loaded on demand, while direct object assignments are loaded immediately. Supports eager loading and standard dictionary operations.

```python
from lazyregistry import Registry

# Create a named registry
serializers = Registry(name="serializers")

# Register by import string (lazy - imported on first access)serializers["json"] = "json:dumps"
serializers["base64_encode"] = "base64:b64encode"

# Register direct objects (immediate - no lazy loading)
import pickle
serializers["pickle"] = pickle.dumps

# Access triggers lazy import for string-registered items
json_dumps = serializers["json"]  # json module imported here
result = json_dumps({"name": "test"})
print(result)  # '{"name": "test"}'

# Enable eager loading for critical components
serializers.eager_load = True
serializers["yaml"] = "yaml:safe_dump"  # Imported immediately

# Dict-style operations work as expected
serializers.update({
    "toml_parse": "tomllib:loads",
    "csv_write": "csv:writer"
})

print(list(serializers.keys()))  # ['json', 'base64_encode', 'pickle', 'yaml', 'toml_parse', 'csv_write']
```

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

### Namespace for Organizing Registries in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Demonstrates the use of the Namespace class for organizing multiple isolated registries. Registries within a namespace are created on-demand when accessed, providing a structured way to manage different sets of components.

```python
from lazyregistry import NAMESPACE, Namespace

# Use the global NAMESPACE singleton
NAMESPACE["models"]["bert"] = "transformers:BertModel"
NAMESPACE["models"]["gpt2"] = "transformers:GPT2Model"
NAMESPACE["tokenizers"]["wordpiece"] = "tokenizers:Tokenizer"

# Each registry is independent
print(list(NAMESPACE["models"].keys()))      # ['bert', 'gpt2']
print(list(NAMESPACE["tokenizers"].keys()))  # ['wordpiece']

# Create custom namespaces for isolation
my_namespace = Namespace()
my_namespace["plugins"]["processor"] = "myapp.plugins:Processor"
my_namespace["handlers"]["api"] = "myapp.handlers:APIHandler"

# Access items through the namespace
Processor = my_namespace["plugins"]["processor"]
```

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

### Save and Load Models with AutoRegistry

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Demonstrates how to save a model configuration and state to a directory and reload it using AutoModel. This pattern relies on the presence of a config.json file to infer the model type.

```python
config = BertConfig(hidden_size=1024)
model = BertModel(config=config)

with tempfile.TemporaryDirectory() as tmpdir:
    model.save_pretrained(tmpdir)

    # AutoModel.from_pretrained detects type from config.json
    loaded = AutoModel.from_pretrained(tmpdir)
    print(f"Auto-detected type: {type(loaded).__name__}")
    print(f"Config: {loaded.config}")
```

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

### Plugin Manager for Executing Registered Plugins in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Implements a `PluginManager` class with static methods to execute registered plugins. `execute` runs a single plugin, while `pipeline` chains multiple plugins. `available` returns a sorted list of registered plugin names. It relies on the `PLUGINS` dictionary populated by the `@plugin` decorator.

```python
class PluginManager:
    @staticmethod
    def execute(plugin_name: str, text: str) -> str:
        plugin_class = PLUGINS[plugin_name]
        return plugin_class().process(text)

    @staticmethod
    def pipeline(text: str, *plugin_names: str) -> str:
        result = text
        for name in plugin_names:
            result = PluginManager.execute(name, result)
        return result

    @staticmethod
    def available() -> list[str]:
        return sorted(PLUGINS.keys())

# Usage examples:
print(PluginManager.available())  # ['capitalize', 'reverse', 'uppercase']
print(PluginManager.execute("uppercase", "hello"))  # 'HELLO'
print(PluginManager.pipeline("hello", "uppercase", "reverse"))  # 'OLLEH'
```

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

### Lazy Loading with ImportString in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Demonstrates how to use ImportString for deferred loading of Python objects. ImportString represents an import path and only performs the actual import when its load() method is called, allowing for graceful handling of import errors.

```python
from lazyregistry import ImportString

# Create import strings for deferred loading
json_dumps = ImportString("json:dumps")
pickle_loads = ImportString("pickle:loads")

# Nothing is imported yet - load() triggers the actual import
dumps_func = json_dumps.load()
loads_func = pickle_loads.load()

# Use the loaded functions
result = dumps_func({"key": "value", "count": 42})
print(result)  # '{"key": "value", "count": 42}'

# Handle import errors gracefully
try:
    bad_import = ImportString("nonexistent_module:function")
    bad_import.load()
except ImportError as e:
    print(f"Import failed: {e}")  # ImportFailedError with details
```

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

### Pydantic-based Configuration for Pretrained Models in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Demonstrates defining model-specific configurations using `PretrainedConfig`, a Pydantic base class. `BertConfig` and `GPT2Config` inherit from it, defining their unique parameters like `model_type`, `hidden_size`, etc. It shows how to create, serialize to JSON, and deserialize from JSON with type validation.

```python
from lazyregistry.pretrained import PretrainedConfig

class BertConfig(PretrainedConfig):
    model_type: str = "bert"
    hidden_size: int = 768
    num_layers: int = 12
    attention_heads: int = 12

class GPT2Config(PretrainedConfig):
    model_type: str = "gpt2"
    hidden_size: int = 768
    num_layers: int = 12
    vocab_size: int = 50257

# Create and serialize configs
config = BertConfig(hidden_size=1024, num_layers=24)
print(config.model_dump_json(indent=2))

# Validate and load from JSON
json_str = '{"model_type": "bert", "hidden_size": 512, "num_layers": 6, "attention_heads": 8}'
loaded_config = BertConfig.model_validate_json(json_str)
print(loaded_config.hidden_size)
```

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

### Handle Import Failures in Lazy Registries

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Shows how to catch specific import errors when accessing lazily registered components. It demonstrates the use of ImportFailedError and the broader LazyRegistryError hierarchy.

```python
registry = Registry(name="test")
registry["bad"] = "nonexistent_module:SomeClass"

try:
    _ = registry["bad"]
except ImportFailedError as e:
    print(f"Import failed: {e}")
    print(f"Original error: {e.__cause__}")

try:
    _ = registry["bad"]
except LazyRegistryError as e:
    print(f"Registry error: {e}")
```

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

### Mixin for Saving and Loading Pretrained Models in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Introduces `PretrainedMixin`, a class providing `save_pretrained` and `from_pretrained` methods. Models inheriting from this mixin can serialize their configuration (defined by `config_class`) to a JSON file and be reloaded from a directory. It facilitates model persistence and sharing.

```python
from pathlib import Path
import tempfile
from lazyregistry.pretrained import PretrainedConfig, PretrainedMixin

class MyModelConfig(PretrainedConfig):
    model_type: str = "my_model"
    hidden_size: int = 256
    dropout: float = 0.1

class MyModel(PretrainedMixin):
    config_class = MyModelConfig

    def __init__(self, *args, config, **kwargs):
        super().__init__(*args, config=config, **kwargs)
        self.hidden_size = config.hidden_size

# Create and save a model
config = MyModelConfig(hidden_size=512, dropout=0.2)
model = MyModel(config=config)

with tempfile.TemporaryDirectory() as tmpdir:
    model.save_pretrained(tmpdir)
    print(f"Saved config to: {tmpdir}/config.json")

    config_path = Path(tmpdir) / "config.json"
    print(config_path.read_text())

    loaded = MyModel.from_pretrained(tmpdir)
    print(f"Loaded hidden_size: {loaded.config.hidden_size}")
    print(f"Loaded dropout: {loaded.config.dropout}")
```

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

### Implement Custom State Persistence with PretrainedMixin

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Extends PretrainedMixin to handle custom model state, such as vocabulary files, alongside standard configuration. This allows for complex objects to be serialized and deserialized using the save_pretrained and from_pretrained interface.

```python
class Tokenizer(PretrainedMixin):
    config_class = TokenizerConfig

    def save_pretrained(self, save_directory: PathLike) -> None:
        super().save_pretrained(save_directory)
        save_path = Path(save_directory)
        vocab_file = save_path / "vocab.txt"
        sorted_vocab = sorted(self.vocab.items(), key=lambda x: x[1])
        vocab_file.write_text("\n".join(word for word, _ in sorted_vocab))

    @classmethod
    def from_pretrained(cls, pretrained_path: PathLike, **kwargs: Any):
        config_file = Path(pretrained_path) / cls.config_filename
        config = cls.config_class.model_validate_json(config_file.read_text())
        vocab_file = Path(pretrained_path) / "vocab.txt"
        vocab = {}
        if vocab_file.exists():
            words = vocab_file.read_text().strip().split("\n")
            vocab = {word: idx for idx, word in enumerate(words)}
        return cls(config=config, vocab=vocab, **kwargs)
```

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

### Custom Lazy Import Dictionary with LazyImportDict in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Shows how to create custom registry implementations using LazyImportDict. This class provides fine-grained control over lazy import behavior, including options for automatically importing strings and deferring loading until access.

```python
from lazyregistry.registry import LazyImportDict

# Create a custom lazy dict
components = LazyImportDict()

# Configure behavior
components.auto_import_strings = True   # Auto-convert strings to ImportString (default)
components.eager_load = False           # Defer loading until access (default)

# Register components
components["encoder"] = "json:JSONEncoder"
components["decoder"] = "json:JSONDecoder"

# Access triggers import
EncoderClass = components["encoder"]
encoder = EncoderClass()
print(encoder.encode({"a": 1}))  # '{"a": 1}'

# Disable auto-conversion for raw strings
raw_dict = LazyImportDict()
raw_dict.auto_import_strings = False
raw_dict["key"] = "json:dumps"  # Stored as plain string, not ImportString
print(raw_dict["key"])  # 'json:dumps' (string, not function)
```

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

### Auto-loader Registry for Dynamic Model Instantiation in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Presents `AutoRegistry`, a registry that automatically detects and instantiates model classes based on their `model_type` from configuration files. It supports multiple registration methods: decorator (`@AutoModel.register_module`), direct assignment, and bulk updates. This enables flexible loading of different model architectures.

```python
import tempfile
from lazyregistry import NAMESPACE
from lazyregistry.pretrained import AutoRegistry, PretrainedConfig, PretrainedMixin

class BertConfig(PretrainedConfig):
    model_type: str = "bert"
    hidden_size: int = 768

class GPT2Config(PretrainedConfig):
    model_type: str = "gpt2"
    hidden_size: int = 768

class BaseModel(PretrainedMixin):
    config_class = PretrainedConfig

class AutoModel(AutoRegistry):
    registry = NAMESPACE["models"]
    config_class = PretrainedConfig
    type_key = "model_type"

@AutoModel.register_module("bert")
class BertModel(BaseModel):
    config_class = BertConfig

@AutoModel.register_module("gpt2")
class GPT2Model(BaseModel):
    config_class = GPT2Config

# Example usage (assuming models are saved and loaded):
# config = BertConfig(hidden_size=1024)
# model = AutoModel.from_pretrained(config)
# print(isinstance(model, BertModel)) # True
```

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

### Plugin Registration Decorator Factory in Python

Source: https://context7.com/milkclouds/lazyregistry/llms.txt

Defines a decorator factory `plugin` for registering classes as plugins. It stores the decorated class in a global `PLUGINS` dictionary, keyed by a provided name. This enables a simple plugin system where classes can be registered and later accessed by their registered name.

```python
PLUGINS = {}

def plugin(name: str):
    def decorator(cls: type) -> type:
        PLUGINS[name] = cls
        return cls
    return decorator

@plugin("uppercase")
class UppercasePlugin:
    def process(self, text: str) -> str:
        return text.upper()

@plugin("reverse")
class ReversePlugin:
    def process(self, text: str) -> str:
        return text[::-1]

@plugin("capitalize")
class CapitalizePlugin:
    def process(self, text: str) -> str:
        return text.capitalize()
```

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