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# The ASF licenses this file to You under the Apache License, Version 2.0
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"""
Generate 'Supported pandas APIs' documentation file
"""
import warnings
from distutils.version import LooseVersion
from enum import Enum, unique
from inspect import getmembers, isclass, isfunction, signature
from typing import Any, Callable, Dict, List, NamedTuple, Set, TextIO, Tuple

import pyspark.pandas as ps
import pyspark.pandas.groupby as psg
import pyspark.pandas.window as psw
from pyspark.pandas.exceptions import PandasNotImplementedError

import pandas as pd
import pandas.core.groupby as pdg
import pandas.core.window as pdw

MAX_MISSING_PARAMS_SIZE = 5
COMMON_PARAMETER_SET = {
    "kwargs",
    "args",
    "cls",
}  # These are not counted as missing parameters.
MODULE_GROUP_MATCH = [(pd, ps), (pdw, psw), (pdg, psg)]

RST_HEADER = """
=====================
Supported pandas API
=====================

.. currentmodule:: pyspark.pandas

The following table shows the pandas APIs that implemented or non-implemented from pandas API on
Spark. Some pandas API do not implement full parameters, so the third column shows missing
parameters for each API.

* 'Y' in the second column means it's implemented including its whole parameter.
* 'N' means it's not implemented yet.
* 'P' means it's partially implemented with the missing of some parameters.

All API in the list below computes the data with distributed execution except the ones that require
the local execution by design. For example, `DataFrame.to_numpy() <https://spark.apache.org/docs/
latest/api/python/reference/pyspark.pandas/api/pyspark.pandas.DataFrame.to_numpy.html>`__
requires to collect the data to the driver side.

If there is non-implemented pandas API or parameter you want, you can create an `Apache Spark
JIRA <https://issues.apache.org/jira/projects/SPARK/summary>`__ to request or to contribute by
your own.

The API list is updated based on the pandas 2.0.0 pre-release.

"""


@unique
class Implemented(Enum):
    IMPLEMENTED = "Y"
    NOT_IMPLEMENTED = "N"
    PARTIALLY_IMPLEMENTED = "P"


class SupportedStatus(NamedTuple):
    """
    Defines a supported status for specific pandas API
    """

    implemented: str
    missing: str


def generate_supported_api(output_rst_file_path: str) -> None:
    """
    Generate supported APIs status dictionary.

    Parameters
    ----------
    output_rst_file_path : str
        The path to the document file in RST format.

    Write supported APIs documentation.
    """
    pandas_latest_version = "1.5.3"
    if LooseVersion(pd.__version__) != LooseVersion(pandas_latest_version):
        msg = (
            "Warning: Latest version of pandas (%s) is required to generate the documentation; "
            "however, your version was %s" % (pandas_latest_version, pd.__version__)
        )
        warnings.warn(msg, UserWarning)
        raise ImportError(msg)

    all_supported_status: Dict[Tuple[str, str], Dict[str, SupportedStatus]] = {}
    for pd_module_group, ps_module_group in MODULE_GROUP_MATCH:
        pd_modules = _get_pd_modules(pd_module_group)
        _update_all_supported_status(
            all_supported_status, pd_modules, pd_module_group, ps_module_group
        )
    _write_rst(output_rst_file_path, all_supported_status)


def _create_supported_by_module(
    module_name: str, pd_module_group: Any, ps_module_group: Any
) -> Dict[str, SupportedStatus]:
    """
    Retrieves supported status of pandas module

    Parameters
    ----------
    module_name : str
        Class name that exists in the path of the module.
    pd_module_group : Any
        Specific path of importable pandas module.
    ps_module_group: Any
        Specific path of importable pyspark.pandas module.
    """
    pd_module = getattr(pd_module_group, module_name) if module_name else pd_module_group
    try:
        ps_module = getattr(ps_module_group, module_name) if module_name else ps_module_group
    except (AttributeError, PandasNotImplementedError):
        # module not implemented
        return {}

    pd_funcs = dict(
        [
            m
            for m in getmembers(pd_module, isfunction)
            if not m[0].startswith("_") and m[0] in pd_module.__dict__
        ]
    )
    if not pd_funcs:
        return {}

    ps_funcs = dict(
        [
            m
            for m in getmembers(ps_module, isfunction)
            if not m[0].startswith("_") and m[0] in ps_module.__dict__
        ]
    )

    return _organize_by_implementation_status(
        module_name, pd_funcs, ps_funcs, pd_module_group, ps_module_group
    )


def _organize_by_implementation_status(
    module_name: str,
    pd_funcs: Dict[str, Callable],
    ps_funcs: Dict[str, Callable],
    pd_module_group: Any,
    ps_module_group: Any,
) -> Dict[str, SupportedStatus]:
    """
    Check the implementation status and parameters of both modules.

    Parameters
    ----------
    module_name : str
        Class name that exists in the path of the module.
    pd_funcs: Dict[str, Callable]
        function name and function object mapping of pandas module.
    ps_funcs: Dict[str, Callable]
        function name and function object mapping of pyspark.pandas module.
    pd_module_group : Any
        Specific path of importable pandas module.
    ps_module_group: Any
        Specific path of importable pyspark.pandas module.
    """
    pd_dict = {}
    for pd_func_name, pd_func in pd_funcs.items():
        ps_func = ps_funcs.get(pd_func_name)
        if ps_func:
            missing_set = (
                set(signature(pd_func).parameters)
                - set(signature(ps_func).parameters)
                - COMMON_PARAMETER_SET
            )
            if missing_set:
                # partially implemented
                pd_dict[pd_func_name] = SupportedStatus(
                    implemented=Implemented.PARTIALLY_IMPLEMENTED.value,
                    missing=_transform_missing(
                        module_name,
                        pd_func_name,
                        missing_set,
                        pd_module_group.__name__,
                        ps_module_group.__name__,
                    ),
                )
            else:
                # implemented including it's whole parameter
                pd_dict[pd_func_name] = SupportedStatus(
                    implemented=Implemented.IMPLEMENTED.value, missing=""
                )
        else:
            # not implemented yet
            pd_dict[pd_func_name] = SupportedStatus(
                implemented=Implemented.NOT_IMPLEMENTED.value, missing=""
            )
    return pd_dict


def _transform_missing(
    module_name: str,
    pd_func_name: str,
    missing_set: Set[str],
    pd_module_path: str,
    ps_module_path: str,
) -> str:
    """
    Transform missing parameters into table information string.

    Parameters
    ----------
    module_name : str
        Class name that exists in the path of the module.
    pd_func_name : str
        Name of pandas API.
    missing_set : Set[str]
        A set of parameters not yet implemented.
    pd_module_path : str
        Path string of pandas module.
    ps_module_path : str
        Path string of pyspark.pandas module.

    Examples
    --------
    >>> _transform_missing("DataFrame", "add", {"axis", "fill_value", "level"},
    ...                     "pandas.DataFrame", "pyspark.pandas.DataFrame")
    '``axis`` , ``fill_value`` , ``level``'
    """
    missing_str = " , ".join("``%s``" % x for x in sorted(missing_set)[:MAX_MISSING_PARAMS_SIZE])
    if len(missing_set) > MAX_MISSING_PARAMS_SIZE:
        module_dot_func = "%s.%s" % (module_name, pd_func_name) if module_name else pd_func_name
        additional_str = (
            " and more. See the "
            + "`%s.%s " % (pd_module_path, module_dot_func)
            + "<https://pandas.pydata.org/docs/reference/api/"
            + "%s.%s.html>`__ and " % (pd_module_path, module_dot_func)
            + "`%s.%s " % (ps_module_path, module_dot_func)
            + "<https://spark.apache.org/docs/latest/api/python/reference/pyspark.pandas/api/"
            + "%s.%s.html>`__ for detail." % (ps_module_path, module_dot_func)
        )
        missing_str += additional_str
    return missing_str


def _get_pd_modules(pd_module_group: Any) -> List[str]:
    """
    Returns sorted pandas member list from pandas module path.

    Parameters
    ----------
    pd_module_group : Any
        Specific path of importable pandas module.
    """
    return sorted([m[0] for m in getmembers(pd_module_group, isclass) if not m[0].startswith("_")])


def _update_all_supported_status(
    all_supported_status: Dict[Tuple[str, str], Dict[str, SupportedStatus]],
    pd_modules: List[str],
    pd_module_group: Any,
    ps_module_group: Any,
) -> None:
    """
    Updates supported status across multiple module paths.

    Parameters
    ----------
    all_supported_status: Dict[Tuple[str, str], Dict[str, SupportedStatus]]
        Data that stores the supported status across multiple module paths.
    pd_modules: List[str]
        Name list of pandas modules.
    pd_module_group : Any
        Specific path of importable pandas module.
    ps_module_group: Any
        Specific path of importable pyspark.pandas module.
    """
    pd_modules += [""]  # for General Function APIs
    for module_name in pd_modules:
        supported_status = _create_supported_by_module(
            module_name, pd_module_group, ps_module_group
        )
        if supported_status:
            all_supported_status[(module_name, ps_module_group.__name__)] = supported_status


def _write_table(
    module_name: str,
    module_path: str,
    supported_status: Dict[str, SupportedStatus],
    w_fd: TextIO,
) -> None:
    """
    Write table by using Sphinx list-table directive.
    """
    lines = []
    if module_name:
        lines.append(module_name)
    else:
        lines.append("General Function")
    lines.append(" API\n")
    lines.append("-" * 100)
    lines.append("\n")
    lines.append(".. currentmodule:: %s" % module_path)
    if module_name:
        lines.append(".%s\n" % module_name)
    else:
        lines.append("\n")
    lines.append("\n")
    lines.append(".. list-table::\n")
    lines.append("    :header-rows: 1\n")
    lines.append("\n")
    lines.append("    * - API\n")
    lines.append("      - Implemented\n")
    lines.append("      - Missing parameters\n")
    for func_str, status in supported_status.items():
        func_str = _escape_func_str(func_str)
        if status.implemented == Implemented.NOT_IMPLEMENTED.value:
            lines.append("    * - %s\n" % func_str)
        else:
            lines.append("    * - :func:`%s`\n" % func_str)
        lines.append("      - %s\n" % status.implemented)
        lines.append("      - \n") if not status.missing else lines.append(
            "      - %s\n" % status.missing
        )
    w_fd.writelines(lines)


def _escape_func_str(func_str: str) -> str:
    """
    Transforms which affecting rst data format.
    """
    # TODO: Take into account that this function can create links incorrectly
    # We can create alias links or links to parent methods
    if func_str.endswith("_"):
        return func_str[:-1] + "\_"  # noqa: W605
    else:
        return func_str


def _write_rst(
    output_rst_file_path: str,
    all_supported_status: Dict[Tuple[str, str], Dict[str, SupportedStatus]],
) -> None:
    """
    Writes the documentation to the target file path.
    """
    with open(output_rst_file_path, "w") as w_fd:
        w_fd.write(RST_HEADER)
        for module_info, supported_status in all_supported_status.items():
            module, module_path = module_info
            if supported_status:
                _write_table(module, module_path, supported_status, w_fd)
                w_fd.write("\n")


def _test() -> None:
    import doctest
    import sys

    import pyspark.pandas.supported_api_gen

    globs = pyspark.pandas.supported_api_gen.__dict__.copy()
    (failure_count, test_count) = doctest.testmod(pyspark.pandas.supported_api_gen, globs=globs)
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()
