from __future__ import annotations

from collections import defaultdict

from emmet.core.eos import EOSDoc

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


class EOSRester(BaseRester):
    suffix = "materials/eos"
    document_model = EOSDoc  # type: ignore
    primary_key = "material_id"

    def search(
        self,
        material_ids: str | list[str] | None = None,
        energies: tuple[float, float] | None = None,
        volumes: tuple[float, float] | None = None,
        num_chunks: int | None = None,
        chunk_size: int = 1000,
        all_fields: bool = True,
        fields: list[str] | None = None,
    ) -> list[EOSDoc] | list[dict]:
        """Query equations of state docs using a variety of search criteria.

        Arguments:
            material_ids (str, List[str]): Search for equation of states associated with the specified Material IDs
            energies (Tuple[float,float]): Minimum and maximum energy in eV/atom to consider for EOS plot range.
            volumes (Tuple[float,float]): Minimum and maximum volume in A³/atom to consider for EOS plot range.
            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 EOSDoc to return data for.
                Default is material_id only if all_fields is False.

        Returns:
            ([EOSDoc], [dict]) List of equations of state docs or dictionaries.
        """
        query_params = defaultdict(dict)  # type: dict

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

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

        if volumes:
            query_params.update({"volumes_min": volumes[0], "volumes_max": volumes[1]})

        if energies:
            query_params.update(
                {"energies_min": energies[0], "energies_max": energies[1]}
            )

        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,
        )
