"""Core design pipeline: context building, execution, output formatting."""

import re
import tempfile
from pathlib import Path

import gradio as gr
import pandas as pd
import spaces
import torch

from app_config import WEIGHTS_DIR
from constraints import _build_pos_constraint_df, _validate_design_inputs
from ensemble import _generate_protpardelle_ensemble, _setup_user_ensemble_dir
from file_utils import _copy_uploaded_files, _get_file_path, _sanitize_download_stem, _write_zip_from_paths
from models import get_model
from self_consistency import _run_self_consistency

# ZeroGPU quota-aware retry: request the max duration first, and if the
# scheduler returns a quota error (which is free — no GPU time consumed),
# parse the remaining seconds and retry with that exact amount.
_MAX_GPU_DURATION = 120  # Per-call max; daily quota is 210s but per-call cap is lower
_gpu_duration_override: int | None = None


def _dynamic_gpu_duration(*args, **kwargs) -> int:
    """Return the current GPU duration for @spaces.GPU scheduling."""
    return _gpu_duration_override if _gpu_duration_override is not None else _MAX_GPU_DURATION


def _parse_quota_left(error: Exception) -> int | None:
    """Extract remaining GPU seconds from a ZeroGPU quota error message.

    Returns the number of seconds left, or None if not a recoverable quota error.
    """
    message = getattr(error, 'message', None)
    if not isinstance(message, str):
        return None
    match = re.search(r'(\d+)s left\)', message)
    return int(match.group(1)) if match else None


def _build_design_context(
    pdb_paths: list[str],
    ensemble_mode: str,
    tmpdir: Path,
    num_protpardelle_conformers: int,
    fixed_pos_seq: str,
    fixed_pos_scn: str,
    fixed_pos_override_seq: str,
    pos_restrict_aatype: str,
    symmetry_pos: str,
) -> tuple[list[str] | dict[str, list[str]], pd.DataFrame | None]:
    pdb_key = Path(pdb_paths[0]).stem
    pos_constraint_df = _build_pos_constraint_df(
        pdb_key=pdb_key,
        fixed_pos_seq=fixed_pos_seq,
        fixed_pos_scn=fixed_pos_scn,
        fixed_pos_override_seq=fixed_pos_override_seq,
        pos_restrict_aatype=pos_restrict_aatype,
        symmetry_pos=symmetry_pos,
    )
    if ensemble_mode == "none":
        return pdb_paths, pos_constraint_df

    if ensemble_mode == "synthetic":
        design_inputs = _generate_protpardelle_ensemble(
            pdb_path=pdb_paths[0],
            num_conformers=num_protpardelle_conformers,
            out_dir=tmpdir,
            weights_dir=WEIGHTS_DIR,
        )
    else:
        design_inputs = _setup_user_ensemble_dir(pdb_paths=pdb_paths)

    if pos_constraint_df is not None:
        from caliby import make_ensemble_constraints

        row = pos_constraint_df.iloc[0]
        cols = {col: row[col] for col in pos_constraint_df.columns if col != "pdb_key"}
        pos_constraint_df = make_ensemble_constraints({pdb_key: cols}, design_inputs)

    return design_inputs, pos_constraint_df


def _format_outputs(outputs: dict) -> tuple[pd.DataFrame, str, list[str]]:
    out_pdb_list = outputs["out_pdb"]
    df = pd.DataFrame(
        {
            "Sample": [Path(out_pdb).stem for out_pdb in out_pdb_list],
            "Sequence": outputs["seq"],
            "Energy (U)": outputs["U"],
        }
    )
    fasta_lines = []
    for i, (eid, seq) in enumerate(zip(outputs["example_id"], outputs["seq"])):
        fasta_lines.append(f">{eid}_sample{i}")
        fasta_lines.append(seq)
    fasta_text = "\n".join(fasta_lines)
    return df, fasta_text, out_pdb_list


@spaces.GPU(duration=_dynamic_gpu_duration)
def _design_sequences_gpu(
    pdb_files: list | None,
    ensemble_mode: str,
    model_variant: str,
    num_seqs: int,
    omit_aas: list[str] | None,
    temperature: float,
    fixed_pos_seq: str,
    fixed_pos_scn: str,
    fixed_pos_override_seq: str,
    pos_restrict_aatype: str,
    symmetry_pos: str,
    num_protpardelle_conformers: int,
    run_af2_eval: bool = False,
):
    validation_error = _validate_design_inputs(pdb_files, ensemble_mode)
    if validation_error:
        return pd.DataFrame(), validation_error, None, None, {}, {}

    device = "cuda" if torch.cuda.is_available() else "cpu"
    torch.set_grad_enabled(False)
    download_stem = _sanitize_download_stem(_get_file_path(pdb_files[0]).stem)

    gr.Info("Loading model...")
    model = get_model(model_variant, device)

    with tempfile.TemporaryDirectory() as tmpdir:
        tmpdir = Path(tmpdir)
        pdb_paths = _copy_uploaded_files(pdb_files, tmpdir)
        input_pdb_data = {Path(p).stem: Path(p).read_text() for p in pdb_paths}

        out_dir = tmpdir / "outputs"
        out_dir.mkdir(parents=True, exist_ok=True)

        if ensemble_mode == "synthetic":
            gr.Info("Generating conformer ensemble...")
        elif ensemble_mode == "user":
            gr.Info("Preparing uploaded ensemble...")
        design_inputs, pos_constraint_df = _build_design_context(
            pdb_paths=pdb_paths,
            ensemble_mode=ensemble_mode,
            tmpdir=tmpdir,
            num_protpardelle_conformers=num_protpardelle_conformers,
            fixed_pos_seq=fixed_pos_seq,
            fixed_pos_scn=fixed_pos_scn,
            fixed_pos_override_seq=fixed_pos_override_seq,
            pos_restrict_aatype=pos_restrict_aatype,
            symmetry_pos=symmetry_pos,
        )

        gr.Info("Designing sequences...")
        sample_kwargs = dict(
            out_dir=str(out_dir),
            num_seqs_per_pdb=num_seqs,
            omit_aas=omit_aas if omit_aas else None,
            temperature=temperature,
            num_workers=0,
            pos_constraint_df=pos_constraint_df,
        )
        if ensemble_mode == "none":
            outputs = model.sample(design_inputs, **sample_kwargs)
        else:
            outputs = model.ensemble_sample(design_inputs, **sample_kwargs)

        df, fasta_text, out_pdb_list = _format_outputs(outputs)

        sc_zip_path = None
        af2_pdb_data = {}
        if run_af2_eval:
            gr.Info("Running AF2 self-consistency evaluation...")
            sc_zip_path, af2_pdb_data = _run_self_consistency(model, df, out_pdb_list, out_dir, download_stem)

        out_zip_path = _write_zip_from_paths(out_pdb_list, download_stem, "_designs.zip")
        return df, fasta_text, out_zip_path, sc_zip_path, af2_pdb_data, input_pdb_data


def design_sequences(
    pdb_files: list | None,
    ensemble_mode: str,
    model_variant: str,
    num_seqs: int,
    omit_aas: list[str] | None,
    temperature: float,
    fixed_pos_seq: str,
    fixed_pos_scn: str,
    fixed_pos_override_seq: str,
    pos_restrict_aatype: str,
    symmetry_pos: str,
    num_protpardelle_conformers: int,
    run_af2_eval: bool = False,
):
    """Run sequence design with ZeroGPU quota-aware retry.

    Requests the max GPU duration first. If the scheduler returns a quota
    error (free — no GPU time consumed), parses the remaining seconds and
    retries with that exact amount to maximize GPU utilization.
    """
    global _gpu_duration_override

    _gpu_duration_override = None
    try:
        return _design_sequences_gpu(
            pdb_files=pdb_files,
            ensemble_mode=ensemble_mode,
            model_variant=model_variant,
            num_seqs=num_seqs,
            omit_aas=omit_aas,
            temperature=temperature,
            fixed_pos_seq=fixed_pos_seq,
            fixed_pos_scn=fixed_pos_scn,
            fixed_pos_override_seq=fixed_pos_override_seq,
            pos_restrict_aatype=pos_restrict_aatype,
            symmetry_pos=symmetry_pos,
            num_protpardelle_conformers=num_protpardelle_conformers,
            run_af2_eval=run_af2_eval,
        )
    except gr.Error as e:
        remaining = _parse_quota_left(e)
        print(f"[ZeroGPU retry] Caught gr.Error, parsed remaining={remaining}, message={getattr(e, 'message', str(e))}")
        if remaining is None or remaining <= 0:
            raise
        gr.Info(f"GPU quota: {remaining}s remaining, retrying with exact quota")
        _gpu_duration_override = remaining - 1
        try:
            return _design_sequences_gpu(
                pdb_files=pdb_files,
                ensemble_mode=ensemble_mode,
                model_variant=model_variant,
                num_seqs=num_seqs,
                omit_aas=omit_aas,
                temperature=temperature,
                fixed_pos_seq=fixed_pos_seq,
                fixed_pos_scn=fixed_pos_scn,
                fixed_pos_override_seq=fixed_pos_override_seq,
                pos_restrict_aatype=pos_restrict_aatype,
                symmetry_pos=symmetry_pos,
                num_protpardelle_conformers=num_protpardelle_conformers,
                run_af2_eval=run_af2_eval,
            )
        finally:
            _gpu_duration_override = None