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# this file be licensed under the Apache-2.0 license or a
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# Modifications Copyright OpenSearch Contributors. See
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import collections.abc as collections_abc
from typing import Any, Optional

from .utils import DslBase


def SF(name_or_sf: Any, **params: Any) -> Any:  # pylint: disable=invalid-name
    # {"script_score": {"script": "_score"}, "filter": {}}
    if isinstance(name_or_sf, collections_abc.Mapping):
        if params:
            raise ValueError("SF() cannot accept parameters when passing in a dict.")
        kwargs = {}
        sf = name_or_sf.copy()  # type: ignore
        for k in ScoreFunction._param_defs:
            if k in name_or_sf:
                kwargs[k] = sf.pop(k)

        # not sf, so just filter+weight, which used to be boost factor
        if not sf:
            name = "boost_factor"
        # {'FUNCTION': {...}}
        elif len(sf) == 1:
            name, params = sf.popitem()
        else:
            raise ValueError("SF() got an unexpected fields in the dictionary: %r" % sf)

        # boost factor special case, see https://github.com/elastic/elasticsearch/issues/6343
        if not isinstance(params, collections_abc.Mapping):
            params = {"value": params}

        # mix known params (from _param_defs) and from inside the function
        kwargs.update(params)
        return ScoreFunction.get_dsl_class(name)(**kwargs)

    # ScriptScore(script="_score", filter=Q())
    if isinstance(name_or_sf, ScoreFunction):
        if params:
            raise ValueError(
                "SF() cannot accept parameters when passing in a ScoreFunction object."
            )
        return name_or_sf

    # "script_score", script="_score", filter=Q()
    return ScoreFunction.get_dsl_class(name_or_sf)(**params)


class ScoreFunction(DslBase):
    _type_name: str = "score_function"
    _type_shortcut = staticmethod(SF)
    _param_defs = {
        "query": {"type": "query"},
        "filter": {"type": "query"},
        "weight": {},
    }
    name: Optional[str] = None

    def to_dict(self) -> Any:
        d = super(ScoreFunction, self).to_dict()
        # filter and query dicts should be at the same level as us
        for k in self._param_defs:
            if k in d[self.name]:
                d[k] = d[self.name].pop(k)
        return d


class ScriptScore(ScoreFunction):
    name = "script_score"


class BoostFactor(ScoreFunction):
    name = "boost_factor"

    def to_dict(self) -> Any:
        d = super(BoostFactor, self).to_dict()
        if "value" in d[self.name]:
            d[self.name] = d[self.name].pop("value")
        else:
            del d[self.name]
        return d


class RandomScore(ScoreFunction):
    name = "random_score"


class FieldValueFactor(ScoreFunction):
    name = "field_value_factor"


class Linear(ScoreFunction):
    name = "linear"


class Gauss(ScoreFunction):
    name = "gauss"


class Exp(ScoreFunction):
    name = "exp"