"""Query module for for Protenix prediction endpoint."""

from __future__ import annotations

from io import StringIO
from itertools import chain
from pathlib import Path
from typing import Any

from folding_studio_data_models import FoldingModel
from pydantic import BaseModel, Field

from folding_studio.query import Query
from folding_studio.utils.fasta import validate_fasta
from folding_studio.utils.path_helpers import validate_path


class ProtenixParameters(BaseModel):
    """Protenix inference parameters."""

    seeds: str = Field(alias="seed", default="0", coerce_numbers_to_str=True)
    use_msa_server: bool = True


class ProtenixQuery(Query):
    """Protenix model query."""

    MODEL = FoldingModel.PROTENIX

    def __init__(
        self,
        fasta_files: dict[str, Any],
        query_name: str,
        parameters: ProtenixParameters = ProtenixParameters(),
    ):
        if not fasta_files:
            raise ValueError("FASTA files dictionary cannot be empty.")

        self.fasta_files = fasta_files
        self.query_name = query_name
        self._parameters = parameters

    @classmethod
    def from_protein_sequence(
        cls, sequence: str, query_name: str | None = None, **kwargs
    ) -> ProtenixQuery:
        """Initialize a ProtenixQuery from a str protein sequence.

        Args:
            sequence (str): The protein sequence in string format.
            query_name (str | None, optional): User-defined query name. Defaults to None.
            seed (int, optional): Random seed. Defaults to 0.
            use_msa_server (bool, optional): Use the MSA server for inference. Defaults to False.

        Returns:
            ProtenixQuery: An instance of ProtenixQuery with the sequence stored as a FASTA file.
        """
        record = validate_fasta(StringIO(sequence))

        query_name = (
            query_name
            if query_name is not None
            else record.description.split("|", maxsplit=1)[0]  # first tag
        )
        return cls(
            fasta_files={query_name: sequence},
            query_name=query_name,
            parameters=ProtenixParameters(**kwargs),
        )

    @classmethod
    def from_file(
        cls: ProtenixQuery, path: str | Path, query_name: str | None = None, **kwargs
    ) -> ProtenixQuery:
        """Initialize a ProtenixQuery instance from a file.

        Supported file format are:
            - FASTA

        Args:
            path (str | Path): Path of the FASTA file.
            query_name (str | None, optional): User-defined query name. Defaults to None.
            seed (int, optional): Random seed. Defaults to 0.
            use_msa_server (bool, optional): Use the MSA server for inference. Defaults to False.

        Returns:
            ProtenixQuery
        """
        path = validate_path(path, is_file=True, file_suffix=(".fasta", ".fa"))
        query_name = query_name or path.stem
        return cls(
            fasta_files={path.stem: validate_fasta(path, str_output=True)},
            query_name=query_name,
            parameters=ProtenixParameters(**kwargs),
        )

    @classmethod
    def from_directory(
        cls: ProtenixQuery, path: str | Path, query_name: str | None = None, **kwargs
    ) -> ProtenixQuery:
        """Initialize a ProtenixQuery instance from a directory.

        Supported file format in directory are:
            - FASTA

        Args:
            path (str | Path): Path to a directory of FASTA files.
            query_name (str | None, optional): User-defined query name. Defaults to None.
            seed (int, optional): Random seed. Defaults to 0.
            use_msa_server (bool, optional): Use the MSA server for inference. Defaults to False.

        Raises:
            ValueError: If no FASTA file are present in the directory.

        Returns:
            ProtenixQuery
        """
        path = validate_path(path, is_dir=True)
        fasta_files = {}
        for file in chain(path.glob("*.fasta"), path.glob("*.fa")):
            fasta_files[file.stem] = validate_fasta(file, str_output=True)
        if not fasta_files:
            raise ValueError(f"No FASTA files found in directory '{path}'.")
        query_name = query_name or path.name
        return cls(
            fasta_files=fasta_files,
            query_name=query_name,
            parameters=ProtenixParameters(**kwargs),
        )

    @property
    def payload(self) -> dict[str, Any]:
        """Payload to send to the prediction API endpoint."""
        return {
            "fasta_files": self.fasta_files,
            "use_msa_server": self.parameters.use_msa_server,
            "seeds": self.parameters.seeds,
        }

    @property
    def parameters(self) -> ProtenixParameters:
        """Parameters of the query."""
        return self._parameters