wry 0.4.0

Why Repeat Yourself? - Define your CLI once with Pydantic models

wry - Why Repeat Yourself? CLI

wry (Why Repeat Yourself?) is a Python library that combines the power of Pydantic models with Click CLI framework, enabling you to define your CLI arguments and options in one place using type annotations. Following the DRY (Don't Repeat Yourself) principle, it eliminates the repetition of defining arguments, types, and validation rules separately.

Features

Core Features

• 🎯 Single Source of Truth: Define your CLI structure using Pydantic models with type annotations
• 🔍 Type Safety: Full type checking and validation using Pydantic
• 🌍 Multiple Input Sources: Automatically handles CLI arguments, environment variables, and config files
• 📊 Value Source Tracking: Know whether each config value came from CLI, env, config file, or defaults
• 🎨 Auto-Generated CLI: Automatically generates Click options and arguments from your Pydantic models
• 📝 Rich Help Text: Auto-generated help includes type information, constraints, and defaults
• 🔧 Validation: Leverage Pydantic's validation system with helpful error messages
• 🌳 Environment Variable Support: Automatic env var discovery with customizable prefixes
• 📁 Config File Support: Load configuration from JSON files with proper precedence

Installation

pip install wry

Quick Start

The simplest way to use wry is with AutoWryModel, which automatically generates CLI options for all fields:

import click
from pydantic import Field
from wry import AutoWryModel

class AppArgs(AutoWryModel):
    """Configuration for my app."""

    name: str = Field(description="Your name")
    age: int = Field(default=25, ge=0, le=120, description="Your age")
    verbose: bool = Field(default=False, description="Verbose output")

@click.command()
@AppArgs.generate_click_parameters()
def main(**kwargs: Any):
    """My simple CLI application."""
    # Create the model instance from kwargs
    config = AppArgs(**kwargs)
    click.echo(f"Hello {config.name}, you are {config.age} years old!")

if __name__ == "__main__":
    main()

See comprehensive examples:

• examples/autowrymodel_comprehensive.py - All AutoWryModel features including aliases
• examples/wrymodel_comprehensive.py - WryModel with source tracking
• examples/multimodel_comprehensive.py - Multi-model usage

Run it:

$ python app.py --name Alice --age 30 --verbose
Hello Alice, you are 30 years old!
Name was provided via: ValueSource.CLI

# Also supports environment variables
$ export WRY_NAME=Bob
$ python app.py --age 35
Hello Bob, you are 35 years old!

Value Source Tracking

wry tracks where each configuration value came from, supporting all four sources:

• DEFAULT: Values from model field defaults
• ENV: Values from environment variables
• JSON: Values from configuration files (via --config)
• CLI: Values from command-line arguments

Basic Usage (No Source Tracking)

@click.command()
@AppArgs.generate_click_parameters()
def main(**kwargs: Any):
    # Simple instantiation - no source tracking
    config = AppArgs(**kwargs)
    # Works fine, but config.source.* will always show CLI

Full Source Tracking (Recommended)

To enable accurate source tracking, use @click.pass_context and from_click_context():

@click.command()
@AppArgs.generate_click_parameters()
@click.pass_context
def main(ctx: click.Context, **kwargs: Any):
    # Full source tracking with context
    config = AppArgs.from_click_context(ctx, **kwargs)

    # Check individual field sources
    print(config.source.name)     # ValueSource.CLI
    print(config.source.age)      # ValueSource.ENV
    print(config.source.verbose)  # ValueSource.DEFAULT

    # Get summary of all sources
    summary = config.get_sources_summary()
    # {
    #     ValueSource.CLI: ['name'],
    #     ValueSource.ENV: ['age'],
    #     ValueSource.JSON: ['timeout'],
    #     ValueSource.DEFAULT: ['verbose']
    # }

Comprehensive Example

See examples/source_tracking_comprehensive.py for a complete example showing all four sources working together. Run it with:

# With defaults only
python examples/source_tracking_comprehensive.py

# With environment variables
export MYAPP_TIMEOUT=120
export MYAPP_DEBUG=true
python examples/source_tracking_comprehensive.py

# Mix all sources (CLI > ENV > JSON > DEFAULT)
export MYAPP_TIMEOUT=120
python examples/source_tracking_comprehensive.py --config examples/sample_config.json --port 3000

Output shows source for each field:

host         = json-server.com      [from JSON]
port         = 3000                 [from CLI]    ← CLI overrides JSON
debug        = True                 [from ENV]
timeout      = 120                  [from ENV]
log_level    = DEBUG                [from JSON]

Configuration Precedence

Values are resolved in the following order (highest to lowest priority):

1. CLI arguments
2. Environment variables
3. Config file values
4. Default values

Environment Variables

wry automatically generates environment variable names from field names:

# Set environment variables
export WRY_NAME="Alice"
export WRY_AGE=25

# These will be picked up automatically
python myapp.py --verbose

View supported environment variables:

python myapp.py --show-env-vars

Config Files

Load configuration from JSON files:

python myapp.py --config settings.json

Where settings.json contains:

{
    "name": "Bob",
    "age": 35,
    "verbose": true
}

Advanced Usage

Multi-Model Commands

Use multiple Pydantic models in a single command:

from typing import Annotated
import click
from wry import WryModel, AutoOption, multi_model, create_models

class ServerConfig(WryModel):
    host: Annotated[str, AutoOption] = "localhost"
    port: Annotated[int, AutoOption] = 8080

class DatabaseArgs(WryModel):
    db_url: Annotated[str, AutoOption] = "sqlite:///app.db"
    pool_size: Annotated[int, AutoOption] = 5

@click.command()
@multi_model(ServerConfig, DatabaseConfig)
@click.pass_context
def serve(ctx: click.Context, **kwargs: Any):
    # Create model instances
    configs = create_models(ctx, kwargs, ServerConfig, DatabaseConfig)
    server = configs[ServerConfig]
    database = configs[DatabaseConfig]

    print(f"Starting server at {server.host}:{server.port}")
    print(f"Database: {database.db_url} (pool size: {database.pool_size})")

AutoWryModel - Zero Configuration

Automatically generate options for all fields:

import click
from wry import AutoWryModel
from pydantic import Field

class QuickConfig(AutoWryModel):
    """All fields automatically become CLI options!"""

    name: str = Field(description="Your name")
    age: int = Field(default=30, ge=0, le=120)
    email: str = Field(description="Your email")

@click.command()
@QuickConfig.generate_click_parameters()
def quickstart(config: QuickConfig):
    print(f"Hello {config.name}!")

Direct Configuration Creation

Create configs without decorators:

from wry import WryModel

class Config(WryModel):
    name: str = "default"
    verbose: bool = False

# Create with source tracking
config = Config.create_with_sources(
    name="Alice",  # Will be tracked as programmatic source
    verbose=True
)

# Or from Click context (in a command)
config = Config.from_click_context(ctx, **kwargs)

Advanced Features

Multi-Model Commands

Use multiple configuration models in a single command:

from wry import WryModel, multi_model, create_models

class DatabaseArgs(WryModel):
    host: str = Field(default="localhost")
    port: int = Field(default=5432)

class AppArgs(WryModel):
    debug: bool = Field(default=False)
    workers: int = Field(default=4)

@click.command()
@multi_model(DatabaseConfig, AppArgs)
@click.pass_context
def main(ctx: click.Context, **kwargs: Any):
    # Automatically splits kwargs between models
    configs = create_models(ctx, kwargs, DatabaseConfig, AppArgs)

    db_config = configs[DatabaseConfig]
    app_config = configs[AppArgs]

    click.echo(f"Connecting to {db_config.host}:{db_config.port}")
    click.echo(f"Running with {app_config.workers} workers")

Strict Mode (Default)

By default, generate_click_parameters runs in strict mode to prevent common mistakes:

@click.command()
@Config.generate_click_parameters()  # strict=True by default
@Config.generate_click_parameters()  # ERROR: Duplicate decorator detected!
def main(**kwargs: Any):
    pass

To allow multiple decorators (not recommended):

@Config.generate_click_parameters(strict=False)

Manual Field Control

For more control over CLI generation, use the traditional WryModel with annotations:

from typing import Annotated
from wry import WryModel, AutoOption, AutoArgument

class Config(WryModel):
    # Environment variable prefix
    env_prefix = "MYAPP_"

    # Required positional argument
    input_file: Annotated[str, AutoArgument] = Field(
        description="Input file path"
    )

    # Optional flag with short option
    verbose: Annotated[bool, AutoOption] = Field(
        default=False,
        description="Enable verbose output"
    )

    # Option with validation
    timeout: Annotated[int, AutoOption] = Field(
        default=30,
        ge=1,
        le=300,
        description="Timeout in seconds"
    )

Using Pydantic Aliases for Custom CLI Names

New in v0.3.2+: Pydantic field aliases automatically control the generated CLI option names and environment variable names!

This allows you to have concise Python field names while exposing descriptive CLI options:

from pydantic import Field
from wry import AutoWryModel

class DatabaseConfig(AutoWryModel):
    env_prefix = "DB_"

    # Concise Python field name: db_url
    # Alias controls CLI option: --database-url
    # Environment variable: DB_DATABASE_URL
    db_url: str = Field(
        alias="database_url",
        default="sqlite:///app.db",
        description="Database connection URL"
    )

    pool_size: int = Field(
        alias="connection_pool_size",
        default=5,
        description="Maximum connection pool size"
    )

How it works:

• Python field: db_url (concise, easy to type)
• CLI option: --database-url (descriptive, user-friendly)
• Environment variable: DB_DATABASE_URL (consistent with CLI)
• JSON config: Accepts both db_url and database_url

Requirements:

• None! WryModel automatically sets validate_by_name=True and validate_by_alias=True
  • This tells Pydantic to accept both field names and aliases
  • No need to configure anything - it just works!
• Aliases automatically control option names, env var names, and help text

Full support (v0.3.2+):

• ✅ Aliases automatically control auto-generated option names
• ✅ Environment variables use alias names (consistent with CLI)
• ✅ Source tracking works correctly
• ✅ JSON config accepts both field names and aliases

Why this feature exists:

Before v0.3.2, if you wanted custom CLI option names, you had to use explicit click.option() decorators for every field. The alias feature eliminates this boilerplate for the common case where you just want different names.

For advanced use cases (short options, custom Click types):

You can still combine aliases with explicit click.option() decorators:

class Config(AutoWryModel):
    # Explicit click.option for short option support
    verbose: Annotated[int, click.option("-v", "--verbose", count=True)] = Field(default=0)

See examples/autowrymodel_comprehensive.py for examples of explicit Click decorators.

See also:

• examples/autowrymodel_comprehensive.py - Complete AutoWryModel example with aliases
• examples/wrymodel_comprehensive.py - WryModel with aliases and source tracking

Development

Prerequisites

• Python 3.10+
• Git with SSH key configured for signing

Setup

# Clone the repository
git clone [e-mail]:tahouse/wry.git
cd wry

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install package in development mode with dev dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests with coverage
pytest

# Run with coverage report
pytest --cov=wry --cov-report=html

# Run specific test
pytest tests/test_core.py::TestWryModel::test_basic_model_creation

# Run all checks (recommended before pushing)
./check.sh

Testing Across Python Versions

wry supports Python 3.10, 3.11, and 3.12. To ensure compatibility:

# Test with all available Python versions locally
./scripts/test_all_versions.sh

# Test in CI-like environment using Docker
./scripts/test_ci_locally.sh

# Run GitHub Actions locally with act (requires act to be installed)
./scripts/test_with_act.sh

Code Quality

This project uses pre-commit hooks to ensure code quality:

• ruff: Linting and code formatting
• mypy: Type checking (pinned to >=1.17.1)
• pytest: Tests with 90% coverage requirement
• bandit: Security checks
• safety: Dependency vulnerability scanning

Pre-commit will run automatically on git commit. To run manually:

pre-commit run --all-files

Version Compatibility

To ensure consistent behavior between local development and CI:

• pydantic: >=2.9.2 (for proper type inference)
• mypy: >=1.17.1 (for accurate type checking)
• Python: 3.10+ (we test against 3.10, 3.11, and 3.12)

Install the exact versions used in CI:

pip install -e ".[dev]" --upgrade

Coverage Requirements

This project enforces 90% code coverage. To check coverage locally:

pytest --cov=wry --cov-report=term-missing --cov-fail-under=90

Release Process

This project uses Git tags and GitHub Actions for releases. Only maintainers can create releases.

Creating a Release

1. Ensure all changes are committed and pushed to main

2. Create and push a signed tag:

   git tag -s v0.1.0 -m "Release version 0.1.0"
   git push origin v0.1.0
   

3. The CI/CD pipeline will automatically:

   • Run all tests
   • Build source and wheel distributions
   • Upload to PyPI
   • Create a GitHub release
   • Sign artifacts with Sigstore

Versioning

This project follows Semantic Versioning:

• MAJOR version for incompatible API changes
• MINOR version for new functionality (backwards compatible)
• PATCH version for backwards compatible bug fixes

Version numbers are managed by setuptools-scm and derived from git tags.

Development Releases

Every push to main creates a development release on PyPI:

pip install --pre wry  # Install latest dev version

Architecture

Code Organization

The wry codebase is organized into focused modules:

Main Package:

• wry/__init__.py: Package exports and version handling
• wry/click_integration.py: Click-specific decorators and parameter generation
• wry/multi_model.py: Support for multiple models in single commands
• wry/auto_model.py: Zero-configuration model with automatic option generation

Core Subpackage (wry/core/):

• model.py: Core WryModel implementation with value tracking
• sources.py: Value source definitions and tracking
• accessors.py: Property accessors for field metadata
• field_utils.py: Field constraint extraction and utilities
• env_utils.py: Environment variable handling

Design Principles

1. WRY (Why Repeat Yourself?): Define CLI structure once using Pydantic models
2. Type Safety: Leverage Python's type system for validation and IDE support
3. Explicit is Better: Users must opt-in to features like source tracking via @click.pass_context
4. Composability: Mix and match models, decorators, and configurations
5. Source Tracking: Always know where configuration values came from

Contributing

We welcome contributions! Please follow these guidelines to ensure a smooth process.

Development Setup

We use standard Python virtual environments and pip for dependency management:

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install package in development mode with all dependencies
pip install -e ".[dev]"

Note: All dependencies are specified in pyproject.toml. The [dev] extra includes testing, linting, and development tools.

Common Development Commands

# Install package in editable mode
pip install -e ".[dev]"

# Update dependencies
pip install -e ".[dev]" --upgrade

# Show installed packages
pip list

# Run tests
pytest

# Run with coverage
pytest --cov=wry

# Run linting
ruff check wry tests
ruff format wry tests

# Run type checking
mypy wry

Getting Started

1. Fork the repository on GitHub

2. Clone your fork locally:

   git clone [e-mail]:YOUR_USERNAME/wry.git
   cd wry
   

3. Add upstream remote:

   git remote add upstream [e-mail]:tahouse/wry.git
   

4. Create a feature branch:

   git checkout -b feature/your-feature-name
   

Development Workflow

1. Set up development environment:

   python -m venv venv       # Create virtual environment
   source venv/bin/activate  # Activate it
   pip install -e ".[dev]"   # Install with dev dependencies
   pre-commit install        # Set up git hooks
   

2. Make your changes:

   • Follow existing code style and patterns
   • Add/update tests for new functionality
   • Update documentation as needed
   • Add docstrings to all new functions/classes

3. Test your changes:

   # Run tests
   pytest
   
   # Check coverage (must be ≥90%)
   pytest --cov=wry --cov-report=term-missing
   
   # Run linting
   pre-commit run --all-files
   # Or run individual tools:
   ruff check wry tests
   mypy wry
   

4. Commit your changes:

   • Use Conventional Commits format
   • Examples:
     • feat: add support for YAML config files
     • fix: handle empty config files gracefully
     • docs: update examples for new API
     • test: add tests for edge cases
     • refactor: simplify value source tracking

Pull Request Guidelines

1. Update your branch:

   git fetch upstream
   git rebase upstream/main
   

2. Push to your fork:

   git push origin feature/your-feature-name
   

3. Create Pull Request:

   • Use a clear, descriptive title
   • Reference any related issues
   • Describe what changes you made and why
   • Include examples if applicable
   • Ensure all CI checks pass

Code Style

• Use type hints for all function arguments and return values
• Follow PEP 8 (enforced by ruff)
• Maximum line length: 88 characters (Black's default)
• Use descriptive variable names
• Add docstrings to all public functions/classes/modules

Testing Guidelines

• Write tests for all new functionality
• Maintain 100% code coverage
• Use pytest fixtures for common test setups
• Test both happy paths and edge cases
• Include tests for error conditions

Documentation

• Update README.md if adding new features
• Add/update docstrings
• Include usage examples in docstrings
• Update type hints

What We're Looking For

• Bug fixes: Always welcome!
• New features: Please open an issue first to discuss
• Documentation: Improvements always appreciated
• Tests: Additional test cases for edge conditions
• Performance: Optimizations with benchmarks
• Examples: More usage examples

Questions?

• Open an issue for bugs or feature requests
• Start a discussion for general questions
• Check existing issues/PRs before creating new ones

License

This project is licensed under the MIT License - see the LICENSE file for details.

Future Features

Automatic Model Instantiation (Proof of Concept)

We're exploring a cleaner API that would automatically instantiate models and pass them to your function. See examples/auto_instantiate_poc.py for a working proof of concept:

# Potential future syntax
@click.command()
@AppConfig.click_command()  # or @auto_instantiate(AppConfig)
def main(config: AppConfig):
    """The decorator would handle instantiation automatically."""
    click.echo(f"Hello {config.name}!")
    # Source tracking would work automatically too!

This would:

• Automatically handle @click.pass_context when needed for source tracking
• Instantiate the model and pass it with the correct parameter name
• Support multiple models in a single command
• Make the API more intuitive and similar to other libraries

If you're interested in this feature, please provide feedback!

Acknowledgments

• Built on top of Click and Pydantic
• Inspired by the DRY (Don't Repeat Yourself) principle We'd also like to acknowledgme pydanclick, which uses a similar clean syntax (no kwargs to command functions). The code for this feature will be independently written given that wry supports source tracking, constraint help text creation, instantiation from config files, and several other features not supported by pydanclick.