from __future__ import annotations

from collections import defaultdict

from emmet.core.chemenv import (
    COORDINATION_GEOMETRIES,
    COORDINATION_GEOMETRIES_IUCR,
    COORDINATION_GEOMETRIES_IUPAC,
    COORDINATION_GEOMETRIES_NAMES,
    ChemEnvDoc,
)

from mp_api.client.core import BaseRester
from mp_api.client.core.utils import validate_ids


class ChemenvRester(BaseRester[ChemEnvDoc]):
    suffix = "materials/chemenv"
    document_model = ChemEnvDoc  # type: ignore
    primary_key = "material_id"

    def search(
        self,
        material_ids: str | list[str] | None = None,
        chemenv_iucr: COORDINATION_GEOMETRIES_IUCR
        | list[COORDINATION_GEOMETRIES_IUCR]
        | None = None,
        chemenv_iupac: COORDINATION_GEOMETRIES_IUPAC
        | list[COORDINATION_GEOMETRIES_IUPAC]
        | None = None,
        chemenv_name: COORDINATION_GEOMETRIES_NAMES
        | list[COORDINATION_GEOMETRIES_NAMES]
        | None = None,
        chemenv_symbol: COORDINATION_GEOMETRIES
        | list[COORDINATION_GEOMETRIES]
        | None = None,
        species: str | list[str] | None = None,
        elements: str | list[str] | None = None,
        exclude_elements: list[str] | None = None,
        csm: tuple[float, float] | None = None,
        density: tuple[float, float] | None = None,
        num_elements: tuple[int, int] | None = None,
        num_sites: tuple[int, int] | None = None,
        volume: tuple[float, float] | None = None,
        num_chunks: int | None = None,
        chunk_size: int = 1000,
        all_fields: bool = True,
        fields: list[str] | None = None,
    ) -> list[ChemEnvDoc] | list[dict]:
        """Query for chemical environment data.

        Arguments:
            material_ids (str, List[str]): Search forchemical environment associated with the specified Material IDs.
            chemenv_iucr (COORDINATION_GEOMETRIES_IUCR, List[COORDINATION_GEOMETRIES_IUCR]): Unique cationic species in
                IUCR format, e.g. "[3n]".
            chemenv_iupac (COORDINATION_GEOMETRIES_IUPAC, List[COORDINATION_GEOMETRIES_IUPAC]): Unique cationic species
                in IUPAC format, e.g., "T-4".
            chemenv_name (COORDINATION_GEOMETRIES_NAMES, List[COORDINATION_GEOMETRIES_NAMES]): Coordination environment
                descriptions in text form for unique cationic species, e.g. "Tetrahedron".
            chemenv_symbol (COORDINATION_GEOMETRIES, List[COORDINATION_GEOMETRIES]): Coordination environment
                descriptions as used in ChemEnv package for unique cationic species, e.g. "T:4".
            species (str, List[str]): Cationic species in the crystal structure, e.g. "Ti4+".
            elements (str, List[str]): Element names in the crystal structure, e.g., "Ti".
            exclude_elements (List[str]): A list of elements to exclude.
            csm (Tuple[float,float]): Minimum and maximum value of continuous symmetry measure to consider.
            density (Tuple[float,float]): Minimum and maximum density to consider.
            num_elements (Tuple[int,int]): Minimum and maximum number of elements to consider.
            num_sites (Tuple[int,int]): Minimum and maximum number of sites to consider.
            volume (Tuple[float,float]): Minimum and maximum volume to consider.
            num_chunks (int): Maximum number of chunks of data to yield. None will yield all possible.
            chunk_size (int): Number of data entries per chunk.
            all_fields (bool): Whether to return all fields in the document. Defaults to True.
            fields (List[str]): List of fields in ChemEnvDoc to return data for.

        Returns:
            ([ChemEnvDoc], [dict]) List of chemenv documents or dictionaries.
        """
        query_params = defaultdict(dict)  # type: dict

        if csm:
            query_params.update({"csm_min": csm[0], "csm_max": csm[1]})

        if volume:
            query_params.update({"volume_min": volume[0], "volume_max": volume[1]})

        if density:
            query_params.update({"density_min": density[0], "density_max": density[1]})

        if num_sites:
            query_params.update(
                {"nsites_min": num_sites[0], "nsites_max": num_sites[1]}
            )

        if elements:
            query_params.update({"elements": ",".join(elements)})

        if exclude_elements:
            query_params.update({"exclude_elements": ",".join(exclude_elements)})

        if num_elements:
            if isinstance(num_elements, int):
                num_elements = (num_elements, num_elements)
            query_params.update(
                {"nelements_min": num_elements[0], "nelements_max": num_elements[1]}
            )

        if material_ids:
            if isinstance(material_ids, str):
                material_ids = [material_ids]

            query_params.update({"material_ids": ",".join(validate_ids(material_ids))})

        chemenv_literals = {
            "chemenv_iucr": (chemenv_iucr, COORDINATION_GEOMETRIES_IUCR),
            "chemenv_iupac": (chemenv_iupac, COORDINATION_GEOMETRIES_IUPAC),
            "chemenv_name": (chemenv_name, COORDINATION_GEOMETRIES_NAMES),
            "chemenv_symbol": (chemenv_symbol, COORDINATION_GEOMETRIES),
        }

        for chemenv_var_name, (chemenv_var, literals) in chemenv_literals.items():
            if chemenv_var:
                t_types = {t if isinstance(t, str) else t.value for t in chemenv_var}
                valid_types = {*map(str, literals.__args__)}
                if invalid_types := t_types - valid_types:
                    raise ValueError(
                        f"Invalid type(s) passed for {chemenv_var_name}: {invalid_types}, valid types are: {valid_types}"
                    )

                query_params.update({chemenv_var_name: ",".join(t_types)})

        if species:
            if isinstance(species, str):
                species = [species]

            query_params.update({"species": ",".join(species)})

        query_params = {
            entry: query_params[entry]
            for entry in query_params
            if query_params[entry] is not None
        }

        return super()._search(
            num_chunks=num_chunks,
            chunk_size=chunk_size,
            all_fields=all_fields,
            fields=fields,
            **query_params,
        )
