from __future__ import annotations

from dataclasses import dataclass
from datetime import UTC, datetime
from typing import Any

from app.core.errors import AppError
from app.models import Dataset


@dataclass(frozen=True)
class TemporalInterval:
    start: datetime | None
    end: datetime | None
    granularity: str | None

    @property
    def bounded(self) -> bool:
        return self.start is not None and self.end is not None

    def as_dict(self) -> dict[str, Any]:
        return {
            "start": self.start.isoformat() if self.start else None,
            "end": self.end.isoformat() if self.end else None,
            "granularity": self.granularity,
        }


class TemporalCompatibilityService:
    @staticmethod
    def ensure_detection_source_supported(dataset: Dataset) -> None:
        metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
        if metadata.get("supports_detection") is False:
            raise AppError(
                code="DETECTION_SOURCE_TEMPORALLY_UNSUPPORTED",
                message="The selected raster edition is not approved for the configured detection model",
                details={
                    "dataset_id": str(dataset.id),
                    "source_name": dataset.source_name,
                    "product_key": metadata.get("product_key"),
                    "observed_at": TemporalCompatibilityService._iso(dataset.observed_at),
                    "valid_from": TemporalCompatibilityService._iso(dataset.valid_from),
                    "valid_to": TemporalCompatibilityService._iso(dataset.valid_to),
                },
                status_code=422,
            )

    @staticmethod
    def assess_detection_qa(candidate: Dataset, reference: Dataset) -> dict[str, Any]:
        candidate_interval = TemporalCompatibilityService._interval(candidate)
        reference_interval = TemporalCompatibilityService._interval(reference)
        candidate_historical = TemporalCompatibilityService._is_historical_detection_source(candidate)

        if candidate_historical:
            if not reference_interval.bounded:
                TemporalCompatibilityService._raise_mismatch(
                    candidate,
                    reference,
                    candidate_interval,
                    reference_interval,
                    "Historical imagery requires a reference dataset with an explicit compatible validity period.",
                )
            if not TemporalCompatibilityService._overlaps(candidate_interval, reference_interval):
                TemporalCompatibilityService._raise_mismatch(
                    candidate,
                    reference,
                    candidate_interval,
                    reference_interval,
                    "The historical imagery and reference dataset validity periods do not overlap.",
                )

        if (
            candidate_interval.bounded
            and reference_interval.bounded
            and not TemporalCompatibilityService._overlaps(candidate_interval, reference_interval)
        ):
            TemporalCompatibilityService._raise_mismatch(
                candidate,
                reference,
                candidate_interval,
                reference_interval,
                "The candidate and reference dataset validity periods do not overlap.",
            )

        return {
            "status": "compatible",
            "policy": "explicit_interval_overlap_for_historical_sources",
            "candidate_dataset_id": str(candidate.id),
            "reference_dataset_id": str(reference.id),
            "candidate_historical": candidate_historical,
            "candidate_interval": candidate_interval.as_dict(),
            "reference_interval": reference_interval.as_dict(),
        }

    @staticmethod
    def _is_historical_detection_source(dataset: Dataset) -> bool:
        metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
        if metadata.get("supports_detection") is False:
            return True
        product_key = str(metadata.get("product_key") or "").strip().lower()
        return dataset.source_name == "digitaal_vlaanderen_orthophoto" and product_key not in {"", "most_recent"}

    @staticmethod
    def _interval(dataset: Dataset) -> TemporalInterval:
        start = TemporalCompatibilityService._utc(dataset.valid_from or dataset.observed_at)
        end = TemporalCompatibilityService._utc(dataset.valid_to)
        granularity = dataset.temporal_granularity

        if start is not None and end is None and granularity == "year":
            end = datetime(start.year, 12, 31, 23, 59, 59, tzinfo=UTC)
        elif start is not None and end is None and granularity == "day":
            end = start.replace(hour=23, minute=59, second=59, microsecond=999999)

        return TemporalInterval(start=start, end=end, granularity=granularity)

    @staticmethod
    def _overlaps(left: TemporalInterval, right: TemporalInterval) -> bool:
        if not left.bounded or not right.bounded:
            return True
        return left.start <= right.end and right.start <= left.end

    @staticmethod
    def _raise_mismatch(
        candidate: Dataset,
        reference: Dataset,
        candidate_interval: TemporalInterval,
        reference_interval: TemporalInterval,
        message: str,
    ) -> None:
        raise AppError(
            code="DETECTION_QA_TEMPORAL_MISMATCH",
            message=message,
            details={
                "candidate_dataset_id": str(candidate.id),
                "reference_dataset_id": str(reference.id),
                "candidate_interval": candidate_interval.as_dict(),
                "reference_interval": reference_interval.as_dict(),
            },
            status_code=422,
        )

    @staticmethod
    def _utc(value: datetime | None) -> datetime | None:
        if value is None:
            return None
        if value.tzinfo is None:
            return value.replace(tzinfo=UTC)
        return value.astimezone(UTC)

    @staticmethod
    def _iso(value: datetime | None) -> str | None:
        normalized = TemporalCompatibilityService._utc(value)
        return normalized.isoformat() if normalized else None