#072 gdtest_long_docs

"""Package with extensively documented functions."""

__version__ = "0.1.0"
__all__ = ["complex_transform", "detailed_validate", "full_process"]


def complex_transform(
    data: list,
    mode: str = "standard",
    threshold: float = 0.5,
    inplace: bool = False,
) -> list:
    """
    Apply a complex transformation to input data.

    This function performs a multi-step transformation on the input
    data. The transformation is controlled by the ``mode`` parameter,
    which can be ``"standard"``, ``"fast"``, or ``"precise"``.

    Parameters
    ----------
    data
        Input data as a list of numeric values. Each element must
        be a finite number (int or float). Empty lists are allowed
        and will return empty lists.
    mode
        Transformation mode. One of:

        - ``"standard"`` — balanced speed/accuracy (default)
        - ``"fast"`` — optimized for speed at cost of precision
        - ``"precise"`` — maximum accuracy, slower
    threshold
        Minimum value threshold. Values below this are filtered
        out before transformation. Must be non-negative.
    inplace
        If True, modify the input list in place. If False (default),
        return a new list.

    Returns
    -------
    list
        Transformed data. If ``inplace=True``, this is the same
        object as ``data``.

    Raises
    ------
    ValueError
        If ``mode`` is not one of the recognized values.
    TypeError
        If ``data`` contains non-numeric elements.

    Notes
    -----
    The transformation algorithm is based on the windowed moving
    average technique described in [1]_. For large datasets
    (>10,000 elements), the ``"fast"`` mode is recommended.

    The time complexity is O(n) for ``"fast"`` mode and O(n log n)
    for ``"precise"`` mode.

    Warnings
    --------
    Using ``inplace=True`` modifies the original data and cannot
    be undone. Always make a copy if you need the original data.

    Examples
    --------
    Basic usage with default parameters:

    >>> from gdtest_long_docs import complex_transform
    >>> complex_transform([1, 2, 3, 4, 5])
    [1, 2, 3, 4, 5]

    Using fast mode:

    >>> complex_transform([1, 2, 3], mode="fast")
    [1, 2, 3]

    With threshold filtering:

    >>> complex_transform([0.1, 0.5, 1.0], threshold=0.3)
    [0.5, 1.0]

    References
    ----------
    .. [1] Smith, J. (2020). "Data Transformation Techniques."
       Journal of Applied Computing, 15(3), 42-58.
    """
    return data


def detailed_validate(
    schema: dict,
    data: dict,
    strict: bool = True,
) -> dict:
    """
    Validate data against a schema with detailed error reporting.

    Performs comprehensive validation of ``data`` against the
    provided ``schema`` dictionary. Each key in the schema maps
    to a type or validator specification.

    Parameters
    ----------
    schema
        Validation schema. Keys are field names, values are type
        objects or callable validators. Example::

            schema = {
                "name": str,
                "age": int,
                "email": lambda x: "@" in x,
            }
    data
        Data dictionary to validate.
    strict
        If True (default), raise on first error. If False,
        collect all errors and return them.

    Returns
    -------
    dict
        Validation report with keys:

        - ``"valid"`` (bool) — overall result
        - ``"errors"`` (list) — list of error messages
        - ``"fields_checked"`` (int) — count of fields validated

    Raises
    ------
    ValueError
        If ``strict=True`` and validation fails.
    KeyError
        If schema references a field not present in data.

    Notes
    -----
    Schema validators receive the value and should return True
    for valid data or raise an exception/return False for invalid.

    Examples
    --------
    >>> detailed_validate({"name": str}, {"name": "Alice"})
    {'valid': True, 'errors': [], 'fields_checked': 1}
    """
    return {"valid": True, "errors": [], "fields_checked": len(schema)}


def full_process(
    items: list,
    pipeline: list = None,
    verbose: bool = False,
    max_workers: int = 1,
    timeout: float = 30.0,
) -> dict:
    """
    Process items through a configurable pipeline.

    Applies each stage in ``pipeline`` to the items sequentially
    (or in parallel if ``max_workers > 1``).

    Parameters
    ----------
    items
        List of items to process.
    pipeline
        List of callable stages. Each receives items and returns
        modified items. If None, uses a default pipeline.
    verbose
        If True, print progress information.
    max_workers
        Number of parallel workers. Use 1 for sequential.
    timeout
        Maximum processing time in seconds per stage.

    Returns
    -------
    dict
        Processing results with keys:

        - ``"items"`` — processed items
        - ``"stages_run"`` — number of stages executed
        - ``"elapsed"`` — total time in seconds

    Raises
    ------
    TimeoutError
        If any stage exceeds the timeout.
    RuntimeError
        If a pipeline stage fails.

    Notes
    -----
    Parallel processing uses a thread pool. For CPU-bound stages,
    consider using ``max_workers=1`` to avoid GIL contention.

    Examples
    --------
    >>> full_process([1, 2, 3])
    {'items': [1, 2, 3], 'stages_run': 0, 'elapsed': 0.0}

    >>> full_process([1, 2, 3], pipeline=[str], verbose=True)
    Processing stage 1/1...
    {'items': ['1', '2', '3'], 'stages_run': 1, 'elapsed': 0.0}
    """
    return {"items": items, "stages_run": 0, "elapsed": 0.0}