"""Gold layer implementation for consumption-ready, optimized data.

This module contains the GoldLayer class which will be fully implemented in MR3.
The Gold layer is responsible for optimization, organization, and export-ready data
preparation.
"""

from __future__ import annotations

import warnings
from collections.abc import Callable
from datetime import datetime
from pathlib import Path
from typing import Any

from importobot.medallion.base_layers import BaseMedallionLayer
from importobot.medallion.interfaces.data_models import (
    DataLineage,
    DataQualityMetrics,
    LayerMetadata,
    ProcessingResult,
)
from importobot.medallion.interfaces.enums import ProcessingStatus
from importobot.medallion.interfaces.records import BronzeRecord, RecordMetadata
from importobot.medallion.placeholder_base import PlaceholderMixin
from importobot.services.optimization_service import (
    OptimizationOutcome,
    OptimizationService,
)
from importobot.utils.optimization import OptimizerConfig
from importobot.utils.validation_models import (
    QualitySeverity,
    ValidationResult,
)


class GoldLayer(BaseMedallionLayer, PlaceholderMixin):
    """Gold layer for consumption-ready, optimized data.

    The Gold layer implements optimization, organization, and export-ready data
    preparation.
    This is a placeholder implementation that will be completed in MR3.

    Future implementation will include:
    - OptimizedConverter for performance-tuned Robot Framework generation
    - SuiteOrganizer for intelligent test grouping and dependency resolution
    - LibraryOptimizer for minimal, conflict-free library imports
    - Multiple output formats beyond Robot Framework (TestNG, pytest)
    - Conversion analytics and quality reporting dashboard
    - Integration with existing GenericSuggestionEngine
    - Execution feasibility validation and performance optimization
    """

    def __init__(
        self,
        storage_path: Path | None = None,
        optimization_service: OptimizationService | None = None,
    ) -> None:
        """Initialize the Gold layer."""
        super().__init__("gold", storage_path)
        self._optimization_service = optimization_service or OptimizationService()
        warnings.warn(
            "GoldLayer is currently a placeholder implementation; "
            "APIs may change without notice.",
            UserWarning,
            stacklevel=2,
        )

    def ingest(self, data: Any, metadata: LayerMetadata) -> ProcessingResult:
        """Ingest and optimize data into the Gold layer.

        This is a placeholder implementation that will be completed in MR3.
        Future implementation will include data optimization, organization,
        and export-ready preparation for multiple output formats.

        Args:
            data: Curated data from Silver layer
            metadata: Layer metadata for tracking

        Returns:
            ProcessingResult indicating pending implementation
        """
        # Placeholder implementation - will be completed in MR3
        # pylint: disable=duplicate-code
        start_time = datetime.now()

        optimization_preview = self._run_optimization_preview(data, metadata)

        return ProcessingResult(
            status=ProcessingStatus.PENDING,
            processed_count=0,
            success_count=0,
            error_count=0,
            warning_count=0,
            skipped_count=1,
            processing_time_ms=0.0,
            start_timestamp=start_time,
            metadata=metadata,
            quality_metrics=DataQualityMetrics(),
            errors=["Gold layer implementation pending MR3"],
            details=self._build_placeholder_details(optimization_preview),
        )

    def validate(self, data: Any) -> ValidationResult:
        """Validate data for Gold layer processing.

        This is a placeholder implementation that will be completed in MR3.
        Future implementation will include execution feasibility validation,
        performance optimization checks, and export readiness verification.

        Args:
            data: Data to validate

        Returns:
            ValidationResult indicating pending implementation
        """
        # pylint: disable=duplicate-code
        return ValidationResult(
            is_valid=False,
            severity=QualitySeverity.INFO,
            error_count=0,
            warning_count=1,
            issues=["Gold layer validation pending MR3"],
        )

    def ingest_with_detection(
        self, data: dict[str, Any], source_info: dict[str, Any]
    ) -> BronzeRecord:
        """Process data with format detection (to be implemented in MR3)."""
        raise self._not_implemented_error("ingest_with_detection", "MR3")

    def get_record_metadata(self, record_id: str) -> RecordMetadata | None:
        """Retrieve record metadata (to be implemented in MR3)."""
        return self._placeholder_record_metadata(record_id)

    def get_record_lineage(self, record_id: str) -> DataLineage | None:
        """Retrieve record lineage information (to be implemented in MR3)."""
        return self._placeholder_record_lineage(record_id)

    def validate_bronze_data(self, data: dict[str, Any]) -> dict[str, Any]:
        """Validate bronze data for gold layer processing (to be implemented in MR3)."""
        return self._placeholder_validate_bronze_data(data, "Gold", "MR3")

    def get_bronze_records(
        self,
        filter_criteria: dict[str, Any] | None = None,
        limit: int | None = None,
    ) -> list[BronzeRecord]:
        """Retrieve bronze records for gold processing (to be implemented in MR3)."""
        return self._placeholder_get_bronze_records(filter_criteria, limit)

    # Preview integration -------------------------------------------------
    def _run_optimization_preview(
        self,
        data: Any,
        metadata: LayerMetadata,
    ) -> OptimizationOutcome | None:
        """Trigger a lightweight optimization preview when configured.

        The upcoming Gold layer implementation will feed real objectives and
        parameter spaces into this method. Today it only runs when callers pass
        `conversion_optimization` metadata, keeping existing behaviour unchanged.
        """
        optimization_settings = metadata.custom_metadata.get("conversion_optimization")
        if not optimization_settings or not optimization_settings.get("enabled"):
            return None

        scenario_name = optimization_settings.get(
            "scenario_name",
            f"gold-optimization-{metadata.session_id or metadata.user_id}",
        )

        objective = self._build_default_conversion_objective(
            data, optimization_settings
        )
        initial_parameters = self._default_initial_parameters(optimization_settings)
        parameter_bounds = self._default_parameter_bounds(optimization_settings)

        self._optimization_service.register_scenario(
            scenario_name,
            objective_function=objective,
            initial_parameters=initial_parameters,
            parameter_bounds=parameter_bounds,
            algorithm=optimization_settings.get("algorithm"),
            metadata={
                "source": "GoldLayer.ingest",
                "preview": True,
                "requested_by": metadata.user_id,
            },
        )

        max_iterations = optimization_settings.get("preview_max_iterations", 25)
        gradient_config = OptimizerConfig(
            max_iterations=max_iterations,
            tolerance=optimization_settings.get("tolerance", 1e-4),
            adaptive_learning=True,
        )

        outcome = self._optimization_service.execute(
            scenario_name,
            gradient_config=gradient_config,
        )
        return outcome

    @staticmethod
    def _build_placeholder_details(
        optimization_preview: OptimizationOutcome | None,
    ) -> dict[str, Any]:
        details: dict[str, Any] = {}
        if optimization_preview:
            details["optimization_preview"] = {
                "algorithm": optimization_preview.algorithm,
                "score": optimization_preview.score,
                "parameters": optimization_preview.parameters,
                "metadata": optimization_preview.details.get("metadata", {}),
            }
        return details

    def _build_default_conversion_objective(
        self,
        data: Any,
        settings: dict[str, Any],
    ) -> Callable[[dict[str, float]], float]:
        """Construct a placeholder objective for upcoming optimization tasks."""
        target_quality = settings.get("target_quality_score", 0.92)
        baseline_quality = settings.get("baseline_quality_score", 0.75)
        baseline_latency = settings.get("baseline_latency_ms", 650.0)
        target_latency = settings.get("target_latency_ms", 400.0)
        suite_complexity = settings.get(
            "suite_complexity",
            self._estimate_suite_complexity(data),
        )

        def objective(parameters: dict[str, float]) -> float:
            quality_weight = parameters.get("quality_weight", 1.0)
            latency_weight = parameters.get("latency_weight", 0.5)

            projected_quality = baseline_quality * (1.0 + 0.12 * quality_weight)
            quality_penalty = (projected_quality - target_quality) ** 2

            projected_latency = baseline_latency / max(0.2, 1.0 + latency_weight)
            projected_latency += suite_complexity * 2.5
            latency_penalty = (
                (projected_latency - target_latency) / max(target_latency, 1.0)
            ) ** 2

            regularization = 0.01 * (quality_weight**2 + latency_weight**2)
            return float(quality_penalty + latency_penalty + regularization)

        return objective

    @staticmethod
    def _estimate_suite_complexity(data: Any) -> float:
        if isinstance(data, dict):
            for key in ("test_cases", "tests", "cases"):
                value = data.get(key)
                if isinstance(value, list):
                    return float(len(value))
        if isinstance(data, list):
            return float(len(data))
        return 1.0

    @staticmethod
    def _default_initial_parameters(settings: dict[str, Any]) -> dict[str, float]:
        return {
            "quality_weight": float(settings.get("initial_quality_weight", 1.0)),
            "latency_weight": float(settings.get("initial_latency_weight", 0.5)),
        }

    @staticmethod
    def _default_parameter_bounds(
        settings: dict[str, Any],
    ) -> dict[str, tuple[float, float]]:
        parameter_bounds = settings.get("parameter_bounds")
        if isinstance(parameter_bounds, dict):
            normalized_bounds = {}
            for key, value in parameter_bounds.items():
                if isinstance(value, list | tuple) and len(value) == 2:
                    normalized_bounds[key] = (float(value[0]), float(value[1]))
            if normalized_bounds:
                return normalized_bounds

        return {
            "quality_weight": (0.0, 5.0),
            "latency_weight": (0.0, 3.0),
        }


__all__ = ["GoldLayer"]
