## Change Summary

This means `pydantic/__init__.py` is now lazy about all imports using `__getattr__`.

Import performance changes:
### Before

```
code/pydantic 0 ➤ pdm run python -m timeit -n 1 -r 1 "import pydantic"
1 loop, best of 1: 29.7 msec per loop
```

```
code/pydantic 0 ➤ pdm run python -m timeit -n 1 -r 1 "from pydantic import BaseModel"
1 loop, best of 1: 30.4 msec per loop
```

### After

```
code/pydantic 0 ➤ pdm run python -m timeit -n 1 -r 1 "import pydantic"
1 loop, best of 1: 4.8 msec per loop
```

```
(env3112) code/pydantic 0 ➤ pdm run python -m timeit -n 1 -r 1 "from pydantic import BaseModel"           
1 loop, best of 1: 18.7 msec per loop
```

Tuna output:

<img width="1421" alt="image" src="https://github.com/pydantic/pydantic/assets/4039449/0a57434e-fdda-4709-8793-d96c0a0ee762">

## Related issue number

As promised in https://github.com/pydantic/pydantic/pull/7423#issuecomment-1733361629.

## Checklist

* [x] The pull request title is a good summary of the changes - it will be used in the changelog
* [ ] Unit tests for the changes exist
* [x] Tests pass on CI
* [ ] Documentation reflects the changes where applicable
* [x] My PR is ready to review, **please add a comment including the phrase "please review" to assign reviewers**


Selected Reviewer: @sydney-runkle
Use `__getattr__` for all package imports, improve import time. ## Change Summary

This means `pydantic/__init__.py` is now lazy about all imports using `__getattr__`.

Import performance changes:
### Before

```
code/pydantic 0 ➤ pdm run python -m timeit -n 1 -r 1 "import pydantic"
1 loop, best of 1: 29.7 msec per loop
```

```
code/pydantic 0 ➤ pdm run python -m timeit -n 1 -r 1 "from pydantic import BaseModel"
1 loop, best of 1: 30.4 msec per loop
```

### After

```
code/pydantic 0 ➤ pdm run python -m timeit -n 1 -r 1 "import pydantic"
1 loop, best of 1: 4.8 msec per loop
```

```
(env3112) code/pydantic 0 ➤ pdm run python -m timeit -n 1 -r 1 "from pydantic import BaseModel"           
1 loop, best of 1: 18.7 msec per loop
```

Tuna output:

<img width="1421" alt="image" src="https://github.com/pydantic/pydantic/assets/4039449/0a57434e-fdda-4709-8793-d96c0a0ee762">

## Related issue number

As promised in https://github.com/pydantic/pydantic/pull/7423#issuecomment-1733361629.

## Checklist

* [x] The pull request title is a good summary of the changes - it will be used in the changelog
* [ ] Unit tests for the changes exist
* [x] Tests pass on CI
* [ ] Documentation reflects the changes where applicable
* [x] My PR is ready to review, **please add a comment including the phrase "please review" to assign reviewers**


Selected Reviewer: @sydney-runkle
"""Decorator for validating function calls."""
from __future__ import annotations as _annotations

from typing import TYPE_CHECKING, Any, Callable, TypeVar, overload

from ._internal import _validate_call

__all__ = ('validate_call',)

if TYPE_CHECKING:
    from .config import ConfigDict

    AnyCallableT = TypeVar('AnyCallableT', bound=Callable[..., Any])


@overload
def validate_call(
    *, config: ConfigDict | None = None, validate_return: bool = False
) -> Callable[[AnyCallableT], AnyCallableT]:
    ...


@overload
def validate_call(__func: AnyCallableT) -> AnyCallableT:
    ...


def validate_call(
    __func: AnyCallableT | None = None,
    *,
    config: ConfigDict | None = None,
    validate_return: bool = False,
) -> AnyCallableT | Callable[[AnyCallableT], AnyCallableT]:
    """Usage docs: https://docs.pydantic.dev/2.4/concepts/validation_decorator/

    Returns a decorated wrapper around the function that validates the arguments and, optionally, the return value.

    Usage may be either as a plain decorator `@validate_call` or with arguments `@validate_call(...)`.

    Args:
        __func: The function to be decorated.
        config: The configuration dictionary.
        validate_return: Whether to validate the return value.

    Returns:
        The decorated function.
    """

    def validate(function: AnyCallableT) -> AnyCallableT:
        if isinstance(function, (classmethod, staticmethod)):
            name = type(function).__name__
            raise TypeError(f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)')
        return _validate_call.ValidateCallWrapper(function, config, validate_return)  # type: ignore

    if __func:
        return validate(__func)
    else:
        return validate

"""Decorator for validating function calls."""
from __future__ import annotations as _annotations

from typing import TYPE_CHECKING, Any, Callable, TypeVar, overload

from ._internal import _validate_call

__all__ = ('validate_call',)

if TYPE_CHECKING:
    from .config import ConfigDict

    AnyCallableT = TypeVar('AnyCallableT', bound=Callable[..., Any])


@overload
def validate_call(
    *, config: ConfigDict | None = None, validate_return: bool = False
) -> Callable[[AnyCallableT], AnyCallableT]:
    ...


@overload
def validate_call(__func: AnyCallableT) -> AnyCallableT:
    ...


def validate_call(
    __func: AnyCallableT | None = None,
    *,
    config: ConfigDict | None = None,
    validate_return: bool = False,
) -> AnyCallableT | Callable[[AnyCallableT], AnyCallableT]:
    """Usage docs: https://docs.pydantic.dev/2.4/concepts/validation_decorator/

    Returns a decorated wrapper around the function that validates the arguments and, optionally, the return value.

    Usage may be either as a plain decorator `@validate_call` or with arguments `@validate_call(...)`.

    Args:
        __func: The function to be decorated.
        config: The configuration dictionary.
        validate_return: Whether to validate the return value.

    Returns:
        The decorated function.
    """

    def validate(function: AnyCallableT) -> AnyCallableT:
        if isinstance(function, (classmethod, staticmethod)):
            name = type(function).__name__
            raise TypeError(f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)')
        return _validate_call.ValidateCallWrapper(function, config, validate_return)  # type: ignore

    if __func:
        return validate(__func)
    else:
        return validate
