from __future__ import annotations

import json
import os
from pathlib import Path
from typing import Any

from pydantic import BaseModel

from slop_farmer.config import AnalysisOptions
from slop_farmer.data.parquet_io import read_json
from slop_farmer.reports.analysis import LLM_PROVIDER_ENV_VARS, run_analysis
from slop_farmer.reports.canonical_duplicate_pr import (
    SnapshotBundle,
    load_snapshot_bundle,
    select_ranked_duplicate_pr_cluster,
    select_ranked_duplicate_pr_clusters,
)

DEFAULT_DUPLICATE_PR_MODEL = "gpt-5.4-mini?service_tier=flex"
HYBRID_REPORT_FILENAME = "analysis-report-hybrid.json"


class DuplicatePrClusterMergeabilityResponse(BaseModel):
    accept: bool
    confidence: float
    reason: str


def ensure_hybrid_report(
    *,
    report_path: Path | None,
    snapshot_dir: Path | None,
    model: str = DEFAULT_DUPLICATE_PR_MODEL,
) -> Path:
    resolved_report, resolved_snapshot_dir = _resolve_duplicate_pr_inputs(
        report_path=report_path,
        snapshot_dir=snapshot_dir,
    )
    if resolved_report is not None and _report_has_llm_enrichment(resolved_report):
        return resolved_report

    cached_hybrid_report = resolved_snapshot_dir / HYBRID_REPORT_FILENAME
    if cached_hybrid_report.exists() and _report_has_llm_enrichment(cached_hybrid_report):
        return cached_hybrid_report.resolve()

    assert_hybrid_analysis_prerequisites()
    output_path = cached_hybrid_report.resolve()
    generated_report = run_analysis(
        AnalysisOptions(
            snapshot_dir=resolved_snapshot_dir,
            output_dir=resolved_snapshot_dir.parent,
            output=output_path,
            hf_repo_id=None,
            hf_revision=None,
            hf_materialize_dir=None,
            ranking_backend="hybrid",
            model=model,
            max_clusters=10,
        )
    ).resolve()
    if not _report_has_llm_enrichment(generated_report):
        raise RuntimeError(
            f"Hybrid analysis for {resolved_snapshot_dir} completed without LLM enrichment. "
            "Install the optional fast-agent dependency, configure a provider API key, and retry."
        )
    return generated_report


def assert_hybrid_analysis_prerequisites() -> None:
    problems: list[str] = []
    try:
        import fast_agent  # noqa: F401
    except Exception:
        problems.append(
            "Install `slop-farmer[llm]` or `fast-agent-mcp` so hybrid duplicate-PR gating can run."
        )

    if not any(bool(os.environ.get(name)) for name in LLM_PROVIDER_ENV_VARS):
        problems.append(
            "Set one of OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, or DEEPSEEK_API_KEY."
        )

    if problems:
        raise RuntimeError(
            "Hybrid duplicate-PR analysis prerequisites are missing. " + " ".join(problems)
        )


def load_duplicate_pr_bundle(
    *,
    report_path: Path | None,
    snapshot_dir: Path | None,
    model: str = DEFAULT_DUPLICATE_PR_MODEL,
) -> SnapshotBundle:
    hybrid_report_path = ensure_hybrid_report(
        report_path=report_path,
        snapshot_dir=snapshot_dir,
        model=model,
    )
    return load_snapshot_bundle(hybrid_report_path)


def list_mergeable_duplicate_pr_clusters(
    *,
    report_path: Path | None,
    snapshot_dir: Path | None,
    limit: int | None,
    model: str = DEFAULT_DUPLICATE_PR_MODEL,
) -> list[dict[str, Any]]:
    if limit is not None and limit < 1:
        raise ValueError("--limit must be at least 1")

    bundle = load_duplicate_pr_bundle(
        report_path=report_path,
        snapshot_dir=snapshot_dir,
        model=model,
    )
    assert_hybrid_analysis_prerequisites()

    mergeable_clusters: list[dict[str, Any]] = []
    for candidate in select_ranked_duplicate_pr_clusters(bundle):
        gate_result = assess_duplicate_pr_cluster_mergeability(bundle, candidate, model=model)
        if not gate_result.accept:
            continue
        mergeable_clusters.append(
            {
                **candidate,
                "repo": bundle.repo,
                "snapshot_id": bundle.snapshot_id,
                "report_path": str(bundle.report_path),
                "mergeability_confidence": round(float(gate_result.confidence), 3),
                "mergeability_reason": gate_result.reason,
            }
        )
        if limit is not None and len(mergeable_clusters) >= limit:
            break
    return mergeable_clusters


def select_mergeable_duplicate_pr_cluster(
    bundle: SnapshotBundle,
    *,
    cluster_id: str | None,
    model: str = DEFAULT_DUPLICATE_PR_MODEL,
) -> dict[str, Any]:
    assert_hybrid_analysis_prerequisites()
    if cluster_id is not None:
        candidate = select_ranked_duplicate_pr_cluster(bundle, cluster_id=cluster_id)
        gate_result = assess_duplicate_pr_cluster_mergeability(bundle, candidate, model=model)
        if not gate_result.accept:
            raise ValueError(
                f"Cluster {cluster_id} did not pass the mergeability gate: {gate_result.reason}"
            )
        return {
            **candidate,
            "mergeability_confidence": round(float(gate_result.confidence), 3),
            "mergeability_reason": gate_result.reason,
        }

    for candidate in select_ranked_duplicate_pr_clusters(bundle):
        gate_result = assess_duplicate_pr_cluster_mergeability(bundle, candidate, model=model)
        if gate_result.accept:
            return {
                **candidate,
                "mergeability_confidence": round(float(gate_result.confidence), 3),
                "mergeability_reason": gate_result.reason,
            }
    raise ValueError("No duplicate PR cluster passed the mergeability gate.")


def assess_duplicate_pr_cluster_mergeability(
    bundle: SnapshotBundle,
    candidate: dict[str, Any],
    *,
    model: str = DEFAULT_DUPLICATE_PR_MODEL,
) -> DuplicatePrClusterMergeabilityResponse:
    packet = _duplicate_pr_cluster_packet(bundle, candidate)
    result = _run_duplicate_pr_cluster_gate(packet, model=model)
    if result is None:
        raise RuntimeError("Hybrid duplicate-PR mergeability gate failed to return a result.")
    return result


def _resolve_duplicate_pr_inputs(
    *,
    report_path: Path | None,
    snapshot_dir: Path | None,
) -> tuple[Path | None, Path]:
    if (report_path is None) == (snapshot_dir is None):
        raise ValueError("Provide exactly one of --report or --snapshot-dir.")
    if report_path is not None:
        resolved_report = report_path.resolve()
        return resolved_report, resolved_report.parent.resolve()
    assert snapshot_dir is not None
    return None, snapshot_dir.resolve()


def _report_has_llm_enrichment(report_path: Path) -> bool:
    if not report_path.exists():
        return False
    try:
        payload = read_json(report_path)
    except Exception:
        return False
    return bool(payload.get("llm_enrichment"))


def _duplicate_pr_cluster_packet(
    bundle: SnapshotBundle, candidate: dict[str, Any]
) -> dict[str, Any]:
    pr_rows = {
        int(row["number"]): row for row in bundle.pull_requests if row.get("number") is not None
    }
    issue_rows = {int(row["number"]): row for row in bundle.issues if row.get("number") is not None}

    pull_request_packets: list[dict[str, Any]] = []
    for pr_number in candidate["source_pr_numbers"]:
        pull_request = pr_rows.get(int(pr_number))
        if pull_request is None:
            continue
        files = [
            row
            for row in bundle.pr_files
            if _coerce_int(row.get("pull_request_number")) == int(pr_number)
        ]
        diff_row = next(
            (
                row
                for row in bundle.pr_diffs
                if _coerce_int(row.get("pull_request_number")) == int(pr_number)
            ),
            None,
        )
        comments = [
            row
            for row in bundle.comments
            if row.get("parent_kind") == "pull_request"
            and _coerce_int(row.get("parent_number")) == int(pr_number)
        ]
        reviews = [
            row
            for row in bundle.reviews
            if _coerce_int(row.get("pull_request_number")) == int(pr_number)
        ]
        review_comments = [
            row
            for row in bundle.review_comments
            if _coerce_int(row.get("pull_request_number")) == int(pr_number)
        ]
        pull_request_packets.append(
            {
                "number": int(pr_number),
                "title": pull_request.get("title"),
                "body_excerpt": _excerpt(pull_request.get("body"), 600),
                "filenames": sorted(
                    {str(row.get("filename")) for row in files if row.get("filename")}
                )[:20],
                "diff_preview": _excerpt((diff_row or {}).get("diff"), 900),
                "discussion_comments": [
                    _excerpt(row.get("body"), 180) for row in comments[:2] if row.get("body")
                ],
                "reviews": [
                    {
                        "state": row.get("state"),
                        "body_excerpt": _excerpt(row.get("body"), 180),
                    }
                    for row in reviews[:2]
                ],
                "review_comments": [
                    {
                        "path": row.get("path"),
                        "body_excerpt": _excerpt(row.get("body"), 180),
                    }
                    for row in review_comments[:2]
                ],
            }
        )

    target_issue_packet: dict[str, Any] | None = None
    target_issue_number = _coerce_int(candidate.get("target_issue_number"))
    if target_issue_number is not None and target_issue_number in issue_rows:
        issue = issue_rows[target_issue_number]
        issue_comments = [
            row
            for row in bundle.comments
            if row.get("parent_kind") == "issue"
            and _coerce_int(row.get("parent_number")) == target_issue_number
        ]
        target_issue_packet = {
            "number": target_issue_number,
            "title": issue.get("title"),
            "body_excerpt": _excerpt(issue.get("body"), 500),
            "comments": [
                _excerpt(row.get("body"), 180) for row in issue_comments[:2] if row.get("body")
            ],
        }

    return {
        "repo": bundle.repo,
        "snapshot_id": bundle.snapshot_id,
        "cluster_id": candidate["cluster_id"],
        "summary": candidate.get("summary"),
        "canonical_issue_number": _coerce_int(candidate.get("canonical_issue_number")),
        "canonical_pr_number": _coerce_int(candidate.get("canonical_pr_number")),
        "target_issue": target_issue_packet,
        "source_pr_numbers": candidate["source_pr_numbers"],
        "pull_requests": pull_request_packets,
    }


def _run_duplicate_pr_cluster_gate(
    packet: dict[str, Any],
    *,
    model: str,
) -> DuplicatePrClusterMergeabilityResponse | None:
    try:
        from fast_agent import FastAgent
    except Exception:
        return None

    instruction = (
        "You decide whether a cluster of open GitHub pull requests should be synthesized into one "
        "canonical pull request. Accept only when the PRs appear to implement the same concrete "
        "code-path fix and one small patch could replace them. Reject when the root cause, scope, "
        "or implementation strategy diverges, or when the overlap is only docs/tests/chatter."
    )
    fast = FastAgent("slop-farmer-duplicate-pr-mergeability")

    @fast.agent(name="mergeability_gate", instruction=instruction, model=model, use_history=False)
    async def mergeability_gate_stub() -> None:
        return None

    prompt = json.dumps(packet, indent=2, sort_keys=True)
    try:
        import asyncio

        async def _run() -> DuplicatePrClusterMergeabilityResponse | None:
            async with fast.run() as agent:
                result, _ = await agent.mergeability_gate.structured(
                    prompt,
                    DuplicatePrClusterMergeabilityResponse,
                )
                return result

        return asyncio.run(_run())
    except Exception:
        return None


def _excerpt(value: Any, limit: int) -> str | None:
    text = str(value or "").strip()
    if not text:
        return None
    if len(text) <= limit:
        return text
    return text[: limit - 1].rstrip() + "…"


def _coerce_int(value: Any) -> int | None:
    if value is None:
        return None
    try:
        return int(value)
    except (TypeError, ValueError):
        return None