from __future__ import annotations

import json
from collections import Counter, defaultdict
from datetime import UTC, datetime, timedelta
from pathlib import Path
from typing import Any

from slop_farmer.config import DashboardDataOptions
from slop_farmer.data.parquet_io import read_json, read_parquet_rows
from slop_farmer.data.snapshot_paths import (
    ResolvedAnalysisReportPath,
    resolve_default_dashboard_analysis_report,
)
from slop_farmer.data.snapshot_source import resolve_snapshot_source_dir


def run_dashboard_data(options: DashboardDataOptions) -> Path:
    snapshot_dir = _resolve_snapshot_dir(options)
    manifest = _read_optional_json(snapshot_dir / "manifest.json")
    issues = read_parquet_rows(snapshot_dir / "issues.parquet")
    pull_requests = read_parquet_rows(snapshot_dir / "pull_requests.parquet")
    analysis_path = _resolve_analysis_input(snapshot_dir, options.analysis_input)
    analysis = _read_optional_json(analysis_path.path) if analysis_path is not None else {}
    contributor_report = _read_optional_json(
        options.contributors_input or snapshot_dir / "new-contributors-report.json"
    )
    pr_scope_report = _read_optional_json(
        options.pr_scope_input or snapshot_dir / "pr-scope-clusters.json"
    )

    repo = (
        manifest.get("repo")
        or (pull_requests[0]["repo"] if pull_requests else None)
        or (issues[0]["repo"] if issues else None)
        or ""
    )
    snapshot_id = manifest.get("snapshot_id") or snapshot_dir.name
    reference_time = _reference_time(snapshot_id, pull_requests)
    cutoff = reference_time - timedelta(days=options.window_days)

    issue_map = {int(row["number"]): row for row in issues if row.get("number") is not None}
    pr_map = {int(row["number"]): row for row in pull_requests if row.get("number") is not None}
    recent_pull_requests = []
    for row in pull_requests:
        created_at = _coerce_datetime(row.get("created_at"))
        if created_at is not None and created_at >= cutoff:
            recent_pull_requests.append(row)
    recent_pull_requests.sort(key=lambda row: row.get("created_at") or "", reverse=True)
    recent_numbers = {
        int(row["number"]) for row in recent_pull_requests if row.get("number") is not None
    }

    clusters, memberships = _cluster_rows(analysis, issue_map, pr_map, recent_numbers)
    pr_scope_clusters = _pr_scope_cluster_rows(pr_scope_report, pr_map, recent_numbers)
    contributors = _contributor_rows(contributor_report, recent_pull_requests, memberships)
    prs = _pr_rows(recent_pull_requests, memberships)

    summary = {
        "repo": repo,
        "snapshot_id": snapshot_id,
        "generated_at": datetime.now(tz=UTC)
        .replace(microsecond=0)
        .isoformat()
        .replace("+00:00", "Z"),
        "window_days": options.window_days,
        "reference_time": reference_time.isoformat().replace("+00:00", "Z"),
        "pr_count": len(prs),
        "open_pr_count": sum(1 for row in prs if row["state"] == "open"),
        "merged_pr_count": sum(1 for row in prs if row["merged"]),
        "cluster_count": len(clusters),
        "clustered_pr_count": sum(1 for row in prs if row["cluster_id"]),
        "contributor_count": len(contributors),
        "analysis_available": bool(analysis),
        "analysis_source": None if analysis_path is None else analysis_path.source,
        "analysis_variant": None if analysis_path is None else analysis_path.variant,
        "analysis_snapshot_id": (
            None
            if analysis_path is None
            else (
                analysis_path.snapshot_id
                or (
                    str(analysis.get("snapshot_id")).strip()
                    if analysis.get("snapshot_id") is not None
                    else None
                )
            )
        ),
        "analysis_id": None if analysis_path is None else analysis_path.analysis_id,
        "contributors_available": bool(contributor_report),
        "pr_scope_available": bool(pr_scope_report),
        "pr_scope_cluster_count": len(pr_scope_clusters),
    }

    output_dir = options.output_dir.resolve()
    output_dir.mkdir(parents=True, exist_ok=True)
    _write_json(summary, output_dir / "summary.json")
    _write_json(clusters, output_dir / "clusters.json")
    _write_json(pr_scope_clusters, output_dir / "pr_scope_clusters.json")
    _write_json(prs, output_dir / "prs.json")
    _write_json(contributors, output_dir / "contributors.json")
    return output_dir


def _resolve_snapshot_dir(options: DashboardDataOptions) -> Path:
    snapshots_root = (
        options.snapshot_root.resolve()
        if options.snapshot_root is not None
        else (Path("data") / "snapshots").resolve()
    )
    return resolve_snapshot_source_dir(
        snapshot_dir=options.snapshot_dir,
        local_snapshots_root=snapshots_root,
        hf_repo_id=options.hf_repo_id,
        hf_revision=options.hf_revision,
        hf_materialize_dir=options.hf_materialize_dir,
        hf_output_dir=snapshots_root.parent,
    )


def _resolve_analysis_input(
    snapshot_dir: Path, override_path: Path | None
) -> ResolvedAnalysisReportPath | None:
    if override_path is not None:
        resolved = override_path.resolve()
        if not resolved.exists():
            raise FileNotFoundError(f"Dashboard analysis input not found: {resolved}")
        return ResolvedAnalysisReportPath(
            path=resolved,
            variant=_analysis_variant_for_path(resolved),
            source="override",
        )
    return resolve_default_dashboard_analysis_report(snapshot_dir)


def _read_optional_json(path: Path) -> dict[str, Any]:
    if path.exists():
        return read_json(path)
    return {}


def _write_json(payload: Any, path: Path) -> None:
    path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")


def _reference_time(snapshot_id: str, pull_requests: list[dict[str, Any]]) -> datetime:
    parsed = _parse_snapshot_id(snapshot_id)
    if parsed is not None:
        return parsed
    timestamps = [
        timestamp
        for row in pull_requests
        for timestamp in (
            _coerce_datetime(row.get("updated_at")),
            _coerce_datetime(row.get("created_at")),
        )
        if timestamp is not None
    ]
    if timestamps:
        return max(timestamps)
    return datetime.now(tz=UTC)


def _parse_snapshot_id(value: str) -> datetime | None:
    try:
        return datetime.strptime(value, "%Y%m%dT%H%M%SZ").replace(tzinfo=UTC)
    except ValueError:
        return None


def _coerce_datetime(value: Any) -> datetime | None:
    if not value or not isinstance(value, str):
        return None
    try:
        return datetime.fromisoformat(value.replace("Z", "+00:00"))
    except ValueError:
        return None


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


def _excerpt(value: Any, limit: int = 240) -> str | None:
    if not value or not isinstance(value, str):
        return None
    compact = " ".join(value.split())
    if len(compact) <= limit:
        return compact
    return compact[: limit - 1].rstrip() + "…"


def _analysis_variant_for_path(path: Path) -> str:
    if path.name == "analysis-report-hybrid.json":
        return "hybrid"
    if path.name == "analysis-report.json":
        return "deterministic"
    return "override"


def _cluster_rows(
    analysis: dict[str, Any],
    issue_map: dict[int, dict[str, Any]],
    pr_map: dict[int, dict[str, Any]],
    recent_numbers: set[int],
) -> tuple[list[dict[str, Any]], dict[int, list[dict[str, str]]]]:
    rows: list[dict[str, Any]] = []
    memberships: dict[int, list[dict[str, str]]] = defaultdict(list)
    for cluster in analysis.get("meta_bugs", []):
        pr_numbers = [_coerce_int(value) for value in cluster.get("pr_numbers", [])]
        pr_numbers = [value for value in pr_numbers if value is not None]
        recent_pr_numbers = [number for number in pr_numbers if number in recent_numbers]
        outside_window_pr_numbers = [
            number for number in pr_numbers if number not in recent_numbers
        ]
        if not recent_pr_numbers:
            continue
        canonical_pr_number = _coerce_int(cluster.get("canonical_pr_number"))
        canonical_issue_number = _coerce_int(cluster.get("canonical_issue_number"))
        cluster_id = str(cluster.get("cluster_id") or f"cluster-{recent_pr_numbers[0]}")
        title = _cluster_title(
            cluster, issue_map, pr_map, canonical_issue_number, canonical_pr_number
        )
        recent_authors = sorted(
            {
                str(pr_map[number].get("author_login"))
                for number in recent_pr_numbers
                if number in pr_map and pr_map[number].get("author_login")
            }
        )
        last_activity_at = max(
            (
                pr_map[number].get("updated_at") or pr_map[number].get("created_at")
                for number in recent_pr_numbers
                if number in pr_map
            ),
            default=None,
        )
        row = {
            "cluster_id": cluster_id,
            "title": title,
            "summary": cluster.get("summary"),
            "status": cluster.get("status"),
            "confidence": cluster.get("confidence"),
            "canonical_issue_number": canonical_issue_number,
            "canonical_pr_number": canonical_pr_number,
            "issue_numbers": [
                _coerce_int(value)
                for value in cluster.get("issue_numbers", [])
                if _coerce_int(value) is not None
            ],
            "pr_numbers": pr_numbers,
            "recent_pr_numbers": recent_pr_numbers,
            "pr_count": len(pr_numbers),
            "recent_pr_count": len(recent_pr_numbers),
            "outside_window_prs": [
                _pr_member_stub(number, pr_map.get(number, {}))
                for number in outside_window_pr_numbers
            ],
            "authors": recent_authors,
            "last_activity_at": last_activity_at,
            "evidence_types": list(cluster.get("evidence_types", [])),
            "pr_similarity": _cluster_similarity_map(cluster, canonical_pr_number),
            "pairwise_similarity": _cluster_pairwise_similarity(cluster),
            "github_url": _cluster_github_url(
                issue_map, pr_map, canonical_issue_number, canonical_pr_number
            ),
        }
        rows.append(row)
        for number in recent_pr_numbers:
            role = "canonical" if canonical_pr_number == number else "member"
            memberships[number].append({"cluster_id": cluster_id, "role": role})
    rows.sort(
        key=lambda row: (
            -int(row["recent_pr_count"]),
            -int(row["pr_count"]),
            -(float(row["confidence"]) if row["confidence"] is not None else 0.0),
            row["last_activity_at"] or "",
        ),
        reverse=False,
    )
    return rows, memberships


def _cluster_title(
    cluster: dict[str, Any],
    issue_map: dict[int, dict[str, Any]],
    pr_map: dict[int, dict[str, Any]],
    canonical_issue_number: int | None,
    canonical_pr_number: int | None,
) -> str:
    if canonical_issue_number is not None and canonical_issue_number in issue_map:
        return str(
            issue_map[canonical_issue_number].get("title") or f"Issue #{canonical_issue_number}"
        )
    if canonical_pr_number is not None and canonical_pr_number in pr_map:
        return str(pr_map[canonical_pr_number].get("title") or f"PR #{canonical_pr_number}")
    summary = cluster.get("summary")
    if summary:
        return str(summary)
    cluster_id = cluster.get("cluster_id") or "cluster"
    return str(cluster_id)


def _cluster_github_url(
    issue_map: dict[int, dict[str, Any]],
    pr_map: dict[int, dict[str, Any]],
    canonical_issue_number: int | None,
    canonical_pr_number: int | None,
) -> str | None:
    if canonical_issue_number is not None and canonical_issue_number in issue_map:
        return issue_map[canonical_issue_number].get("html_url")
    if canonical_pr_number is not None and canonical_pr_number in pr_map:
        return pr_map[canonical_pr_number].get("html_url")
    return None


def _cluster_similarity_map(
    cluster: dict[str, Any], canonical_pr_number: int | None
) -> dict[str, dict[str, float]]:
    if canonical_pr_number is None:
        return {}
    scores: dict[str, dict[str, float]] = {}
    for comparison in cluster.get("pr_comparisons", []):
        left = _coerce_int(comparison.get("left_pr_number"))
        right = _coerce_int(comparison.get("right_pr_number"))
        if left != canonical_pr_number and right != canonical_pr_number:
            continue
        other = right if left == canonical_pr_number else left
        if other is None:
            continue
        scores[str(other)] = {
            "patch_similarity": float(comparison.get("patch_similarity") or 0.0),
            "code_similarity": float(comparison.get("code_similarity") or 0.0),
            "size_similarity": float(comparison.get("size_similarity") or 0.0),
            "file_overlap": float(comparison.get("file_overlap") or 0.0),
            "area_overlap": float(comparison.get("area_overlap") or 0.0),
        }
    return scores


def _cluster_pairwise_similarity(cluster: dict[str, Any]) -> list[dict[str, Any]]:
    rows: list[dict[str, Any]] = []
    for comparison in cluster.get("pr_comparisons", []):
        left = _coerce_int(comparison.get("left_pr_number"))
        right = _coerce_int(comparison.get("right_pr_number"))
        if left is None or right is None:
            continue
        rows.append(
            {
                "left_pr_number": left,
                "right_pr_number": right,
                "patch_similarity": float(comparison.get("patch_similarity") or 0.0),
                "code_similarity": float(comparison.get("code_similarity") or 0.0),
                "size_similarity": float(comparison.get("size_similarity") or 0.0),
                "file_overlap": float(comparison.get("file_overlap") or 0.0),
                "area_overlap": float(comparison.get("area_overlap") or 0.0),
            }
        )
    return rows


def _pr_scope_cluster_rows(
    pr_scope_report: dict[str, Any],
    pr_map: dict[int, dict[str, Any]],
    recent_numbers: set[int],
) -> list[dict[str, Any]]:
    rows: list[dict[str, Any]] = []
    for cluster in pr_scope_report.get("pr_scope_clusters", []):
        pr_numbers = [_coerce_int(value) for value in cluster.get("pr_numbers", [])]
        pr_numbers = [value for value in pr_numbers if value is not None]
        recent_pr_numbers = [number for number in pr_numbers if number in recent_numbers]
        outside_window_pr_numbers = [
            number for number in pr_numbers if number not in recent_numbers
        ]
        if not recent_pr_numbers:
            continue
        representative_pr_number = _coerce_int(cluster.get("representative_pr_number"))
        recent_authors = sorted(
            {
                str(pr_map[number].get("author_login"))
                for number in recent_pr_numbers
                if number in pr_map and pr_map[number].get("author_login")
            }
        )
        last_activity_at = max(
            (
                pr_map[number].get("updated_at") or pr_map[number].get("created_at")
                for number in recent_pr_numbers
                if number in pr_map
            ),
            default=None,
        )
        representative = pr_map.get(representative_pr_number or -1, {})
        rows.append(
            {
                "kind": "pr_scope",
                "cluster_id": str(cluster.get("cluster_id") or f"pr-scope-{recent_pr_numbers[0]}"),
                "title": _pr_scope_title(cluster, pr_map, representative_pr_number),
                "summary": cluster.get("summary"),
                "representative_pr_number": representative_pr_number,
                "representative_title": representative.get("title"),
                "representative_url": representative.get("html_url"),
                "pr_numbers": pr_numbers,
                "recent_pr_numbers": recent_pr_numbers,
                "pr_count": len(pr_numbers),
                "recent_pr_count": len(recent_pr_numbers),
                "outside_window_prs": [
                    _pr_member_stub(number, pr_map.get(number, {}))
                    for number in outside_window_pr_numbers
                ],
                "authors": recent_authors,
                "last_activity_at": last_activity_at,
                "average_similarity": float(cluster.get("average_similarity") or 0.0),
                "shared_filenames": list(cluster.get("shared_filenames") or []),
                "shared_directories": list(cluster.get("shared_directories") or []),
                "pairwise": _pr_scope_pairwise_rows(cluster),
            }
        )
    rows.sort(
        key=lambda row: (
            -int(row["recent_pr_count"]),
            -int(row["pr_count"]),
            -(float(row["average_similarity"]) if row["average_similarity"] is not None else 0.0),
            row["last_activity_at"] or "",
            str(row["cluster_id"]),
        )
    )
    return rows


def _pr_scope_title(
    cluster: dict[str, Any],
    pr_map: dict[int, dict[str, Any]],
    representative_pr_number: int | None,
) -> str:
    if representative_pr_number is not None and representative_pr_number in pr_map:
        title = pr_map[representative_pr_number].get("title")
        if title:
            return f"Scope: {title}"
    shared_filenames = [str(value) for value in (cluster.get("shared_filenames") or []) if value]
    if shared_filenames:
        return f"Scope: {shared_filenames[0]}"
    shared_directories = [
        str(value) for value in (cluster.get("shared_directories") or []) if value
    ]
    if shared_directories:
        return f"Scope: {shared_directories[0]}"
    summary = cluster.get("summary")
    if summary:
        return str(summary)
    return str(cluster.get("cluster_id") or "pr-scope")


def _pr_scope_pairwise_rows(cluster: dict[str, Any]) -> list[dict[str, Any]]:
    rows: list[dict[str, Any]] = []
    for comparison in cluster.get("pairwise", []):
        left = _coerce_int(comparison.get("left_pr_number"))
        right = _coerce_int(comparison.get("right_pr_number"))
        if left is None or right is None:
            continue
        rows.append(
            {
                "left_pr_number": left,
                "right_pr_number": right,
                "similarity": float(comparison.get("similarity") or 0.0),
                "content_similarity": float(comparison.get("content_similarity") or 0.0),
                "size_similarity": float(comparison.get("size_similarity") or 0.0),
                "breadth_similarity": float(comparison.get("breadth_similarity") or 0.0),
                "concentration_similarity": float(
                    comparison.get("concentration_similarity") or 0.0
                ),
                "shared_filenames": list(comparison.get("shared_filenames") or []),
                "shared_directories": list(comparison.get("shared_directories") or []),
            }
        )
    return rows


def _pr_member_stub(number: int, row: dict[str, Any]) -> dict[str, Any]:
    html_url = row.get("html_url")
    return {
        "number": number,
        "title": row.get("title"),
        "author": row.get("author_login"),
        "state": row.get("state"),
        "merged": bool(row.get("merged")),
        "draft": bool(row.get("draft")),
        "created_at": row.get("created_at"),
        "updated_at": row.get("updated_at"),
        "changed_files": _coerce_int(row.get("changed_files")),
        "additions": _coerce_int(row.get("additions")),
        "deletions": _coerce_int(row.get("deletions")),
        "html_url": html_url,
        "files_url": f"{html_url}/files" if html_url else None,
    }


def _pr_rows(
    pull_requests: list[dict[str, Any]],
    memberships: dict[int, list[dict[str, str]]],
) -> list[dict[str, Any]]:
    rows = []
    for row in pull_requests:
        number = _coerce_int(row.get("number"))
        if number is None:
            continue
        cluster_memberships = memberships.get(number, [])
        primary_membership = cluster_memberships[0] if cluster_memberships else None
        html_url = row.get("html_url")
        rows.append(
            {
                "number": number,
                "title": row.get("title"),
                "author": row.get("author_login"),
                "state": row.get("state"),
                "author_association": row.get("author_association"),
                "merged": bool(row.get("merged")),
                "draft": bool(row.get("draft")),
                "created_at": row.get("created_at"),
                "updated_at": row.get("updated_at"),
                "changed_files": _coerce_int(row.get("changed_files")),
                "additions": _coerce_int(row.get("additions")),
                "deletions": _coerce_int(row.get("deletions")),
                "comments_count": _coerce_int(row.get("comments_count")),
                "review_comments_count": _coerce_int(row.get("review_comments_count")),
                "labels": list(row.get("labels") or []),
                "body_excerpt": _excerpt(row.get("body")),
                "cluster_id": primary_membership["cluster_id"] if primary_membership else None,
                "cluster_role": primary_membership["role"] if primary_membership else None,
                "cluster_ids": [membership["cluster_id"] for membership in cluster_memberships],
                "html_url": html_url,
                "files_url": f"{html_url}/files" if html_url else None,
                "conversation_url": html_url,
            }
        )
    return rows


def _contributor_rows(
    contributor_report: dict[str, Any],
    pull_requests: list[dict[str, Any]],
    memberships: dict[int, list[dict[str, str]]],
) -> list[dict[str, Any]]:
    recent_pr_counts = Counter(
        str(row.get("author_login")) for row in pull_requests if row.get("author_login")
    )
    recent_associations = _recent_repo_associations(pull_requests)
    recent_cluster_counts = Counter(
        str(row.get("author_login"))
        for row in pull_requests
        if row.get("author_login")
        for _membership in memberships.get(_coerce_int(row.get("number")) or -1, [])
    )
    report_rows = contributor_report.get("contributors", [])
    if not report_rows:
        rows = [
            {
                "author": author,
                "name": None,
                "profile_url": f"https://github.com/{author}",
                "repo_pull_requests_url": None,
                "repo_issues_url": None,
                "snapshot_pr_count": count,
                "snapshot_issue_count": 0,
                "recent_pr_count": count,
                "cluster_count": recent_cluster_counts.get(author, 0),
                "repo_association": recent_associations.get(author),
                "new_to_repo": None,
                "first_seen_in_snapshot": None,
                "report_reason": None,
                "known_contributor": _is_known_repo_association(recent_associations.get(author)),
                "follow_through_score": None,
                "breadth_score": None,
                "automation_risk_signal": None,
                "heuristic_note": None,
                "account_age_days": None,
                "quality_score": None,
                "public_pr_count_42d": None,
                "public_repo_count_42d": None,
            }
            for author, count in recent_pr_counts.items()
        ]
        rows.sort(key=lambda row: (-int(row["recent_pr_count"]), row["author"]))
        return rows

    rows = []
    for contributor in report_rows:
        author = contributor.get("author_login")
        if not author:
            continue
        recent_pr_count = recent_pr_counts.get(str(author), 0)
        if recent_pr_count == 0 and not contributor.get("snapshot_pr_count"):
            continue
        rows.append(
            {
                "author": author,
                "name": contributor.get("name"),
                "profile_url": contributor.get("profile_url"),
                "repo_pull_requests_url": contributor.get("repo_pull_requests_url"),
                "repo_issues_url": contributor.get("repo_issues_url"),
                "snapshot_pr_count": _coerce_int(contributor.get("snapshot_pr_count")) or 0,
                "snapshot_issue_count": _coerce_int(contributor.get("snapshot_issue_count")) or 0,
                "recent_pr_count": recent_pr_count,
                "cluster_count": recent_cluster_counts.get(str(author), 0),
                "repo_association": contributor.get("repo_association")
                or recent_associations.get(str(author)),
                "new_to_repo": contributor.get("new_to_repo"),
                "first_seen_in_snapshot": contributor.get("first_seen_in_snapshot"),
                "report_reason": contributor.get("report_reason"),
                "known_contributor": _known_contributor(contributor),
                "follow_through_score": contributor.get("follow_through_score"),
                "breadth_score": contributor.get("breadth_score"),
                "automation_risk_signal": contributor.get("automation_risk_signal"),
                "heuristic_note": contributor.get("heuristic_note"),
                "account_age_days": _coerce_int(contributor.get("account_age_days")),
                "quality_score": None,
                "public_pr_count_42d": _coerce_int(
                    (contributor.get("activity") or {}).get("visible_authored_pr_count")
                ),
                "public_repo_count_42d": _coerce_int(
                    (contributor.get("activity") or {}).get("distinct_repos_with_authored_prs")
                ),
            }
        )
    rows.sort(
        key=lambda row: (
            -int(row["recent_pr_count"]),
            -int(row["snapshot_pr_count"]),
            -int(row["cluster_count"]),
            str(row["author"]),
        )
    )
    return rows


def _known_contributor(contributor: dict[str, Any]) -> bool:
    return _is_known_repo_association(contributor.get("repo_association"))


def _recent_repo_associations(pull_requests: list[dict[str, Any]]) -> dict[str, str | None]:
    grouped: dict[str, set[str]] = defaultdict(set)
    for row in pull_requests:
        login = str(row.get("author_login") or "").strip()
        association = str(row.get("author_association") or "").strip()
        if not login or not association:
            continue
        grouped[login].add(association)
    return {login: _select_repo_association(sorted(values)) for login, values in grouped.items()}


def _select_repo_association(values: list[str]) -> str | None:
    if not values:
        return None
    priority = {
        "OWNER": 70,
        "MEMBER": 60,
        "COLLABORATOR": 50,
        "CONTRIBUTOR": 40,
        "FIRST_TIME_CONTRIBUTOR": 30,
        "FIRST_TIMER": 20,
        "NONE": 10,
    }
    return max(values, key=lambda value: (priority.get(value, 0), value))


def _is_known_repo_association(value: Any) -> bool:
    return str(value or "") in {"OWNER", "MEMBER", "COLLABORATOR"}