shutil.rmtree(site_path, ignore_errors=True)
    langs = [lang.name for lang in get_lang_paths() if lang.is_dir()]
    cpu_count = os.cpu_count() or 1
    process_pool_size = cpu_count * 4
    typer.echo(f"Using process pool size: {process_pool_size}")
    with Pool(process_pool_size) as p:
        p.map(build_lang, langs)


@app.command()
def update_languages() -> None:
    """

    end_datetime: datetime = Body(),
    process_after: timedelta = Body(),
    repeat_at: time | None = Body(default=None),
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,
        "end_datetime": end_datetime,
        "process_after": process_after,
        "repeat_at": repeat_at,

                async def process_fn(
                    fn: Callable[[], Coroutine[Any, Any, Any]],
                ) -> None:
                    result = await fn()
                    results.append(result)  # noqa: B023

                async with anyio.create_task_group() as tg:
                    for sub_value in value:
                        tg.start_soon(process_fn, sub_value.read)

    )
    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["uvicorn", "docs_src.separate_openapi_schemas.tutorial001:app"]
)
try:
    with sync_playwright() as playwright:
        run(playwright)
finally:

        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    )

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["uvicorn", "docs_src.separate_openapi_schemas.tutorial002:app"]
)
try:
    with sync_playwright() as playwright:
        run(playwright)
finally:

from the current build or not.

It uses the `_api/v2/api_packages.txt` file from the local wheel file.
The `_api/v2/api_packages.txt` file is created during the process of generating
TensorFlow API v2 init files and is stored in the wheel file after the build.

See README.md file for "how to run" instruction.
"""

import logging
import unittest
import pkg_resources

        UndefinedType as UndefinedType,  # noqa: F401
    )
    from pydantic.schema import (
        field_schema,
        get_flat_models_from_fields,
        get_model_name_map,
        model_process_schema,
    )
    from pydantic.schema import (  # type: ignore[no-redef]  # noqa: F401
        get_annotation_from_field_info as get_annotation_from_field_info,
    )