#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
from pathlib import Path
from typing import Any

import numpy as np
import pandas as pd
import torch

from bgc_retrieval.artifacts import create_run_directory, sha256_file, write_json_immutable
from bgc_retrieval.baselines import cosine_scores, pfam_jaccard_scores
from bgc_retrieval.checkpoints import load_checkpoint
from bgc_retrieval.config import load_config
from bgc_retrieval.data import BGCEmbeddingDataset
from bgc_retrieval.evaluation import evaluate_retrieval
from bgc_retrieval.model import ModelConfig
from bgc_retrieval.reporting import write_paper_outputs
from bgc_retrieval.residual import (
    ResidualGeneWeightingEncoder,
    RetrievalScoreCache,
    encode_residual_components,
    select_validation_weights,
    validation_grid,
)
from bgc_retrieval.splits import load_split
from bgc_retrieval.training import choose_device


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--config", default="configs/residual_griseus.yaml")
    parser.add_argument("--checkpoint", action="append", required=True)
    parser.add_argument("--run-id", required=True)
    parser.add_argument("--split", default="data/manifests/silver_split.csv")
    args = parser.parse_args()

    config = load_config(args.config)
    values = config.values
    if values["scope"]["organism"] != "Streptomyces griseus":
        raise ValueError("Residual evaluation is locked to Streptomyces griseus")
    split_path = Path(args.split).resolve()
    assignments = load_split(split_path)
    atlas_path = config.resolve_path("data", "atlas_csv")
    embeddings_path = config.resolve_path("data", "embeddings_h5")
    model_config = ModelConfig.from_dict(values["model"])
    dataset = BGCEmbeddingDataset(
        embeddings_path, atlas_path, assignments, model_config.esm_dimension
    )
    device = choose_device()

    raw_embeddings: dict[str, torch.Tensor] | None = None
    learned_models: list[dict[str, torch.Tensor]] = []
    for checkpoint in args.checkpoint:
        model = ResidualGeneWeightingEncoder(model_config)
        load_checkpoint(checkpoint, model, split_path)
        model.to(device)
        raw, learned = encode_residual_components(
            model,
            dataset,
            device,
            int(values["training"]["num_workers"]),
        )
        if raw_embeddings is None:
            raw_embeddings = raw
        learned_models.append(learned)
    if raw_embeddings is None:
        raise RuntimeError("No residual checkpoints were loaded")

    atlas = pd.read_csv(atlas_path, usecols=["bgc_id", "pfam_ids"])
    pfam_sets = {
        str(row.bgc_id): set(str(row.pfam_ids).split(";"))
        if pd.notna(row.pfam_ids)
        else set()
        for row in atlas.itertuples(index=False)
    }
    evaluation = values["evaluation"]
    residual = values["residual"]
    evaluation_seed = int(evaluation["seed"])
    alphas = [float(value) for value in residual["alpha_grid"]]
    betas = [float(value) for value in residual["pfam_beta_grid"]]

    validation_cache = RetrievalScoreCache(raw_embeddings, learned_models, pfam_sets)
    validation = validation_grid(
        assignments,
        validation_cache,
        alphas,
        betas,
        int(evaluation["reference_size"]),
        int(evaluation["query_draws"]),
        evaluation_seed,
        evaluation["recall_at"],
        evaluation["ndcg_at"],
    )
    metric = str(evaluation["primary_metric"])
    residual_alpha, _ = select_validation_weights(validation, metric, "residual_")
    hybrid_alpha, hybrid_beta = select_validation_weights(validation, metric, "hybrid_")
    if hybrid_beta is None:
        raise RuntimeError("Hybrid validation did not select beta")

    test_cache = RetrievalScoreCache(raw_embeddings, learned_models, pfam_sets)

    def raw_score(candidates: list[str], references: list[str]) -> dict[str, float]:
        return cosine_scores(candidates, references, raw_embeddings, "mean")

    def learned_score(candidates: list[str], references: list[str]) -> dict[str, float]:
        _, learned, _ = test_cache.components(candidates, references)
        return learned

    def pfam_score(candidates: list[str], references: list[str]) -> dict[str, float]:
        return pfam_jaccard_scores(candidates, references, pfam_sets, "max")

    methods: dict[str, Any] = {
        "pfam_jaccard_max": pfam_score,
        "raw_esm_mean": raw_score,
        "weighted_gene_esm": learned_score,
        f"residual_validation_alpha_{residual_alpha:g}": (
            lambda candidates, references: test_cache.residual(
                candidates, references, residual_alpha
            )
        ),
        f"residual_pfam_validation_a{hybrid_alpha:g}_b{hybrid_beta:g}": (
            lambda candidates, references: test_cache.hybrid(
                candidates, references, hybrid_alpha, hybrid_beta
            )
        ),
    }
    test_results = evaluate_retrieval(
        assignments,
        "test",
        methods,
        int(evaluation["reference_size"]),
        int(evaluation["query_draws"]),
        evaluation_seed,
        evaluation["recall_at"],
        evaluation["ndcg_at"],
    )

    run_dir = create_run_directory(
        config.resolve_path("project", "run_root"),
        args.run_id,
    )
    metadata = {
        "schema_version": 1,
        "organism_scope": values["scope"]["organism"],
        "task_scope": values["scope"]["task"],
        "analysis_status": "post_hoc_redesign_pilot",
        "selected_residual_alpha": residual_alpha,
        "selected_hybrid_alpha": hybrid_alpha,
        "selected_pfam_beta": hybrid_beta,
        "weights_selected_on": "validation_only",
        "checkpoint_sha256": {
            str(path): sha256_file(path) for path in args.checkpoint
        },
        "split_sha256": sha256_file(split_path),
        "label_tier": "silver",
        "publication_eligible": False,
        "scope_warning": (
            "The atlas provenance states Streptomyces griseus, but the atlas table "
            "does not contain a machine-verifiable species column."
        ),
    }
    write_paper_outputs(
        run_dir,
        test_results,
        [metric, "mrr", "map", "ndcg@50", "tie_fraction"],
        int(evaluation["bootstrap_samples"]),
        float(evaluation["confidence_level"]),
        evaluation_seed,
        metadata,
    )
    validation.to_csv(run_dir / "validation_weight_search.csv", index=False)
    write_json_immutable(run_dir / "config.json", values)
    print(json.dumps(metadata, indent=2, sort_keys=True))


if __name__ == "__main__":
    main()