from __future__ import annotations

from collections import Counter
from dataclasses import asdict, dataclass
from pathlib import Path
import json
from typing import Any

import yaml

from . import generation
from . import profiles
from .bench import (
    Infospace,
    KnowledgeArtifact,
    export_mermaid,
    load_infospace,
    relationship_summary,
    run_collection_checks,
)
from .validation import structural_checks


REPO_ROOT = Path(__file__).resolve().parents[2]
DEFAULT_INFOSPACE_ROOT = REPO_ROOT / "infospace"

REVIEW_KIT_COMPONENTS = {
    "manifest": "agent/review-kit/review-kit.yaml",
    "workflow": "agent/review-kit/review-workflow.yaml",
    "scorecard": "agent/review-kit/scorecard.yaml",
    "model_selection_guide": "agent/review-kit/model-selection-guide.yaml",
    "schema": "schemas/alignment-review.schema.yaml",
}
ALIGNMENT_TEMPLATE_PATH = "agent/templates/consumer-alignment-workplan.template.md"


class CanonServiceError(Exception):
    def __init__(
        self,
        code: str,
        message: str,
        details: dict[str, Any] | None = None,
    ) -> None:
        super().__init__(message)
        self.code = code
        self.message = message
        self.details = details or {}

    def to_dict(self) -> dict[str, Any]:
        return {
            "ok": False,
            "error": {
                "code": self.code,
                "message": self.message,
                "details": self.details,
            },
        }


@dataclass(frozen=True)
class CanonContext:
    repo_root: Path
    infospace_root: Path
    infospace: Infospace


def load_context(root: Path | str | None = None) -> CanonContext:
    infospace_root = Path(root) if root else DEFAULT_INFOSPACE_ROOT
    try:
        infospace = load_infospace(infospace_root)
    except Exception as exc:
        raise CanonServiceError(
            "infospace_load_failed",
            f"Unable to load infospace at {infospace_root}",
            {"root": str(infospace_root), "reason": str(exc)},
        ) from exc
    return CanonContext(
        repo_root=REPO_ROOT,
        infospace_root=infospace_root,
        infospace=infospace,
    )


def inspect_canon(root: Path | str | None = None) -> dict[str, Any]:
    context = load_context(root)
    artifacts = context.infospace.artifacts
    kinds = Counter(artifact.kind for artifact in artifacts)
    return {
        "ok": True,
        "repo": {
            "slug": "info-tech-canon",
            "root": str(context.repo_root),
        },
        "infospace": {
            "slug": context.infospace.config.slug,
            "name": context.infospace.config.name,
            "root": str(context.infospace_root),
            "artifact_count": len(artifacts),
            "kinds": dict(sorted(kinds.items())),
        },
        "service": {
            "package": "info_tech_canon",
            "contract": "cli-json-api",
        },
    }


def list_artifacts(
    root: Path | str | None = None,
    *,
    kind: str | None = None,
) -> dict[str, Any]:
    context = load_context(root)
    artifacts = [
        _artifact_to_dict(artifact, context.infospace_root)
        for artifact in context.infospace.artifacts
        if kind is None or artifact.kind == kind
    ]
    return {
        "ok": True,
        "count": len(artifacts),
        "artifacts": artifacts,
    }


def list_models(root: Path | str | None = None) -> dict[str, Any]:
    return list_artifacts(root, kind="model")


def list_standards(root: Path | str | None = None) -> dict[str, Any]:
    return list_artifacts(root, kind="standard")


def review_kit(root: Path | str | None = None) -> dict[str, Any]:
    context = load_context(root)
    components = {
        name: {
            "path": relative,
            "content": _read_yaml_component(context.infospace_root, relative),
        }
        for name, relative in REVIEW_KIT_COMPONENTS.items()
    }
    template = alignment_template(root)
    return {
        "ok": True,
        "review_kit": components["manifest"]["content"],
        "components": components,
        "template": {
            "path": template["path"],
            "content": template["content"],
        },
    }


def alignment_template(root: Path | str | None = None) -> dict[str, Any]:
    context = load_context(root)
    path = context.infospace_root / ALIGNMENT_TEMPLATE_PATH
    try:
        content = path.read_text(encoding="utf-8")
    except FileNotFoundError as exc:
        raise CanonServiceError(
            "missing_alignment_template",
            "Consumer alignment workplan template not found.",
            {"path": str(path)},
        ) from exc
    return {
        "ok": True,
        "path": ALIGNMENT_TEMPLATE_PATH,
        "content": content,
    }


def validate_canon(root: Path | str | None = None) -> dict[str, Any]:
    context = load_context(root)
    errors: list[dict[str, Any]] = []
    warnings: list[dict[str, Any]] = []

    artifact_ids = {artifact.id for artifact in context.infospace.artifacts}
    for artifact in context.infospace.artifacts:
        artifact_path = context.infospace_root / artifact.path
        if not artifact_path.is_file():
            errors.append(
                {
                    "code": "missing_artifact_path",
                    "artifact_id": artifact.id,
                    "path": artifact.path,
                }
            )
        for relationship in artifact.relationships:
            target = relationship.get("target")
            if target not in artifact_ids:
                errors.append(
                    {
                        "code": "missing_relationship_target",
                        "artifact_id": artifact.id,
                        "target": target,
                    }
                )

    for discipline in context.infospace.config.disciplines:
        discipline_path = context.infospace_root / discipline.path
        if not discipline_path.is_file():
            errors.append(
                {
                    "code": "missing_discipline_path",
                    "discipline": discipline.name,
                    "path": discipline.path,
                }
            )

    checks = run_collection_checks(context.infospace.artifacts)
    threshold_errors = _evaluate_thresholds(
        checks.metrics,
        context.infospace.config.viability,
    )
    errors.extend(threshold_errors)
    structural = structural_checks(context)
    errors.extend(structural["errors"])
    warnings.extend(structural["warnings"])

    return {
        "ok": not errors,
        "errors": errors,
        "warnings": warnings,
        "metrics": checks.metrics,
        "details": checks.details,
    }


def write_validation_report(
    destination: Path | str,
    root: Path | str | None = None,
) -> dict[str, Any]:
    payload = validate_canon(root)
    path = Path(destination)
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")
    payload["report_path"] = str(path)
    return payload


def artifact_graph(
    root: Path | str | None = None,
    *,
    output_format: str = "json",
) -> dict[str, Any]:
    context = load_context(root)
    summary = relationship_summary(context.infospace.artifacts)
    if output_format == "mermaid":
        return {"ok": True, "format": "mermaid", "graph": export_mermaid(summary)}
    if output_format != "json":
        raise CanonServiceError(
            "unsupported_graph_format",
            f"Unsupported graph format: {output_format}",
            {"supported": ["json", "mermaid"]},
        )
    return {
        "ok": True,
        "format": "json",
        "graph": {
            "node_count": summary.node_count,
            "edge_count": summary.edge_count,
            "nodes": summary.nodes,
            "edges": [asdict(edge) for edge in summary.edges],
            "relationship_types": summary.relationship_types,
        },
    }


def profile_inspect(
    profile: str,
    root: Path | str | None = None,
) -> dict[str, Any]:
    context = load_context(root)
    profile_path = context.infospace_root / "profiles" / profile / "profile.yaml"
    if not profile_path.is_file():
        raise CanonServiceError(
            "missing_profile",
            f"Profile not found: {profile}",
            {"profile": profile, "path": str(profile_path)},
        )
    try:
        return profiles.inspect_profile(context, profile)
    except ValueError as exc:
        raise CanonServiceError(
            "invalid_profile",
            f"Profile must be a YAML mapping: {profile}",
            {"profile": profile, "path": str(profile_path)},
        ) from exc


def profile_validate(
    profile: str,
    root: Path | str | None = None,
) -> dict[str, Any]:
    context = load_context(root)
    profile_path = context.infospace_root / "profiles" / profile / "profile.yaml"
    if not profile_path.is_file():
        raise CanonServiceError(
            "missing_profile",
            f"Profile not found: {profile}",
            {"profile": profile, "path": str(profile_path)},
        )
    return profiles.validate_profile(context, profile)


def profile_graph(
    profile: str,
    root: Path | str | None = None,
    *,
    output_format: str = "json",
) -> dict[str, Any]:
    context = load_context(root)
    profile_path = context.infospace_root / "profiles" / profile / "profile.yaml"
    if not profile_path.is_file():
        raise CanonServiceError(
            "missing_profile",
            f"Profile not found: {profile}",
            {"profile": profile, "path": str(profile_path)},
        )
    try:
        return profiles.profile_graph(context, profile, output_format=output_format)
    except ValueError as exc:
        raise CanonServiceError(
            "unsupported_graph_format",
            str(exc),
            {"supported": ["json", "mermaid"]},
        ) from exc


def generate_indexes(root: Path | str | None = None) -> dict[str, Any]:
    return generation.generate_indexes(load_context(root))


def generate_tree(root: Path | str | None = None) -> dict[str, Any]:
    return generation.generate_tree(load_context(root))


def generate_agent_briefs(root: Path | str | None = None) -> dict[str, Any]:
    return generation.generate_agent_briefs(load_context(root))


def list_views(root: Path | str | None = None) -> dict[str, Any]:
    return generation.list_generated_views(load_context(root))


def read_view(name: str, root: Path | str | None = None) -> dict[str, Any]:
    try:
        return generation.read_generated_view(load_context(root), name)
    except FileNotFoundError as exc:
        raise CanonServiceError(
            "missing_view",
            f"View not found: {name}",
            {"view": name},
        ) from exc
    except ValueError as exc:
        raise CanonServiceError(
            "invalid_view_name",
            str(exc),
            {"view": name},
        ) from exc


def _read_yaml_component(infospace_root: Path, relative: str) -> Any:
    path = infospace_root / relative
    try:
        with path.open("r", encoding="utf-8") as handle:
            return yaml.safe_load(handle) or {}
    except FileNotFoundError as exc:
        raise CanonServiceError(
            "missing_review_kit_component",
            f"Review kit component not found: {relative}",
            {"path": str(path)},
        ) from exc
    except yaml.YAMLError as exc:
        raise CanonServiceError(
            "invalid_review_kit_component",
            f"Review kit component is not valid YAML: {relative}",
            {"path": str(path), "reason": str(exc)},
        ) from exc


def _artifact_to_dict(
    artifact: KnowledgeArtifact,
    infospace_root: Path,
) -> dict[str, Any]:
    data = artifact.to_dict()
    data["exists"] = (infospace_root / artifact.path).is_file()
    return data


def _evaluate_thresholds(
    metrics: dict[str, float],
    thresholds: dict[str, Any],
) -> list[dict[str, Any]]:
    errors: list[dict[str, Any]] = []
    for metric, threshold in thresholds.items():
        value = metrics.get(metric)
        if value is None:
            continue
        min_value = getattr(threshold, "min", None)
        max_value = getattr(threshold, "max", None)
        if min_value is not None and value < min_value:
            errors.append(
                {
                    "code": "metric_below_threshold",
                    "metric": metric,
                    "value": value,
                    "min": min_value,
                }
            )
        if max_value is not None and value > max_value:
            errors.append(
                {
                    "code": "metric_above_threshold",
                    "metric": metric,
                    "value": value,
                    "max": max_value,
                }
            )
    return errors