"""
KG Quality Assessor Module

This module provides the main quality assessment coordination for the Semantica
framework, integrating all quality assurance components to provide comprehensive
quality assessment and reporting.

Key Features:
    - Overall quality assessment
    - Quality report generation
    - Quality issue identification
    - Consistency checking
    - Completeness validation

Main Classes:
    - KGQualityAssessor: Main quality assessment coordinator
    - ConsistencyChecker: Consistency validation engine
    - CompletenessValidator: Completeness validation engine

Example Usage:
    >>> from semantica.kg_qa import KGQualityAssessor
    >>> assessor = KGQualityAssessor()
    >>> score = assessor.assess_overall_quality(knowledge_graph)
    >>> report = assessor.generate_quality_report(knowledge_graph, schema)
    >>> issues = assessor.identify_quality_issues(knowledge_graph, schema)

Author: Semantica Contributors
License: MIT
"""

from typing import Any, Dict, List, Optional

from ..utils.logging import get_logger
from ..utils.progress_tracker import get_progress_tracker
from .quality_metrics import QualityMetrics, CompletenessMetrics, ConsistencyMetrics
from .validation_engine import ValidationEngine
from .reporting import QualityReporter, QualityReport


class KGQualityAssessor:
    """
    Knowledge Graph Quality Assessor.
    
    This class serves as the main coordinator for knowledge graph quality
    assessment, integrating quality metrics, validation, and reporting
    components to provide comprehensive quality analysis.
    
    Features:
        - Overall quality score calculation
        - Comprehensive quality report generation
        - Quality issue identification
        - Integration with all QA components
    
    Example Usage:
        >>> assessor = KGQualityAssessor()
        >>> score = assessor.assess_overall_quality(knowledge_graph)
        >>> report = assessor.generate_quality_report(knowledge_graph, schema)
        >>> issues = assessor.identify_quality_issues(knowledge_graph, schema)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize KG quality assessor.
        
        Sets up the assessor with all quality assurance components including
        quality metrics, completeness metrics, consistency metrics, validation
        engine, and quality reporter.
        
        Args:
            **kwargs: Configuration options passed to all components
        """
        self.logger = get_logger("kg_quality_assessor")
        self.config = kwargs
        
        # Initialize components
        self.quality_metrics = QualityMetrics(**kwargs)
        self.completeness_metrics = CompletenessMetrics(**kwargs)
        self.consistency_metrics = ConsistencyMetrics(**kwargs)
        self.validation_engine = ValidationEngine(**kwargs)
        self.quality_reporter = QualityReporter(**kwargs)
        
        # Initialize progress tracker
        self.progress_tracker = get_progress_tracker()
        
        self.logger.debug("KG quality assessor initialized")
    
    def assess_overall_quality(
        self,
        knowledge_graph: Any
    ) -> float:
        """
        Assess overall quality of knowledge graph.
        
        This method calculates an overall quality score for the knowledge
        graph by aggregating various quality metrics (completeness, consistency,
        etc.) into a single score.
        
        Args:
            knowledge_graph: Knowledge graph instance (object with entities
                           and relationships, or dict with "entities" and
                           "relationships" keys)
            
        Returns:
            float: Overall quality score between 0.0 and 1.0 (higher is better)
        """
        # Track quality assessment
        tracking_id = self.progress_tracker.start_tracking(
            file=None,
            module="kg_qa",
            submodule="KGQualityAssessor",
            message="Assessing overall quality"
        )
        
        try:
            self.logger.info("Assessing overall quality")
            
            self.progress_tracker.update_tracking(tracking_id, message="Calculating quality metrics...")
            # Calculate metrics
            overall_score = self.quality_metrics.calculate_overall_score(knowledge_graph)
            
            self.progress_tracker.stop_tracking(tracking_id, status="completed",
                                               message=f"Overall quality score: {overall_score:.2f}")
            return overall_score
            
        except Exception as e:
            self.progress_tracker.stop_tracking(tracking_id, status="failed", message=str(e))
            raise
    
    def generate_quality_report(
        self,
        knowledge_graph: Any,
        schema: Optional[Dict[str, Any]] = None
    ) -> QualityReport:
        """
        Generate comprehensive quality report.
        
        This method generates a comprehensive quality report including overall
        quality score, completeness score, consistency score, identified issues,
        and improvement recommendations.
        
        Args:
            knowledge_graph: Knowledge graph instance
            schema: Optional schema definition for validation (if provided,
                   enables completeness checking against schema constraints)
            
        Returns:
            QualityReport: Comprehensive quality report containing:
                - timestamp: Report generation timestamp
                - overall_score: Overall quality score
                - completeness_score: Completeness score
                - consistency_score: Consistency score
                - issues: List of identified quality issues
                - recommendations: List of improvement recommendations
                - metadata: Additional report metadata
        """
        self.logger.info("Generating quality report")
        
        # Calculate metrics
        overall_score = self.quality_metrics.calculate_overall_score(knowledge_graph)
        
        # Get entities and relationships (simplified - in practice would query graph)
        entities = getattr(knowledge_graph, "entities", [])
        relationships = getattr(knowledge_graph, "relationships", [])
        
        completeness_score = 0.0
        if schema and entities:
            completeness_score = self.completeness_metrics.calculate_entity_completeness(
                entities,
                schema
            )
        
        consistency_score = self.consistency_metrics.calculate_logical_consistency(
            knowledge_graph
        )
        
        quality_metrics = {
            "overall": overall_score,
            "completeness": completeness_score,
            "consistency": consistency_score
        }
        
        # Generate report
        report = self.quality_reporter.generate_report(
            knowledge_graph,
            quality_metrics
        )
        
        return report
    
    def identify_quality_issues(
        self,
        knowledge_graph: Any,
        schema: Optional[Dict[str, Any]] = None
    ) -> List[Dict[str, Any]]:
        """
        Identify quality issues in knowledge graph.
        
        This method identifies and returns all quality issues found in the
        knowledge graph, including completeness issues, consistency issues,
        and other quality problems.
        
        Args:
            knowledge_graph: Knowledge graph instance
            schema: Optional schema for validation
            
        Returns:
            list: List of quality issue dictionaries, each containing:
                - id: Issue identifier
                - type: Issue type (e.g., "completeness", "consistency")
                - severity: Issue severity ("low", "medium", "high")
                - description: Issue description
                - entity_id: Related entity ID (if applicable)
                - relationship_id: Related relationship ID (if applicable)
        """
        self.logger.info("Identifying quality issues")
        
        # Generate report to get issues
        report = self.generate_quality_report(knowledge_graph, schema)
        
        # Convert issues to dictionaries
        issues = [
            {
                "id": issue.id,
                "type": issue.type,
                "severity": issue.severity,
                "description": issue.description,
                "entity_id": issue.entity_id,
                "relationship_id": issue.relationship_id
            }
            for issue in report.issues
        ]
        
        return issues


class ConsistencyChecker:
    """
    Consistency checking engine.
    
    This class provides consistency checking capabilities for knowledge graphs,
    validating logical, temporal, and hierarchical consistency.
    
    Features:
        - Logical consistency checking
        - Temporal consistency checking
        - Hierarchical consistency checking
    
    Example Usage:
        >>> checker = ConsistencyChecker()
        >>> is_logical = checker.check_logical_consistency(knowledge_graph)
        >>> is_temporal = checker.check_temporal_consistency(knowledge_graph)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize consistency checker.
        
        Sets up the checker with consistency metrics calculator.
        
        Args:
            **kwargs: Configuration options passed to ConsistencyMetrics
        """
        self.logger = get_logger("consistency_checker")
        self.consistency_metrics = ConsistencyMetrics(**kwargs)
        self.config = kwargs
        
        self.logger.debug("Consistency checker initialized")
    
    def check_logical_consistency(
        self,
        knowledge_graph: Any
    ) -> bool:
        """
        Check logical consistency.
        
        This method checks for logical inconsistencies in the knowledge graph,
        such as contradictory relationships or conflicting property values.
        Returns True if the consistency score is above the threshold (0.8).
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            bool: True if logically consistent (score >= 0.8), False otherwise
        """
        score = self.consistency_metrics.calculate_logical_consistency(knowledge_graph)
        return score >= 0.8
    
    def check_temporal_consistency(
        self,
        knowledge_graph: Any
    ) -> bool:
        """
        Check temporal consistency.
        
        This method checks for temporal inconsistencies in the knowledge graph,
        such as relationships with invalid time ranges or temporal contradictions.
        Returns True if the consistency score is above the threshold (0.8).
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            bool: True if temporally consistent (score >= 0.8), False otherwise
        """
        score = self.consistency_metrics.calculate_temporal_consistency(knowledge_graph)
        return score >= 0.8
    
    def check_hierarchical_consistency(
        self,
        knowledge_graph: Any
    ) -> bool:
        """
        Check hierarchical consistency.
        
        This method checks for hierarchical inconsistencies in the knowledge
        graph, such as circular inheritance or invalid parent-child relationships.
        Returns True if the consistency score is above the threshold (0.8).
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            bool: True if hierarchically consistent (score >= 0.8), False otherwise
        """
        score = self.consistency_metrics.calculate_hierarchical_consistency(knowledge_graph)
        return score >= 0.8


class CompletenessValidator:
    """
    Completeness validation engine.
    
    This class provides completeness validation capabilities for knowledge graphs,
    checking whether entities, relationships, and properties meet schema
    requirements.
    
    Features:
        - Entity completeness validation
        - Relationship completeness validation
        - Property completeness validation
    
    Example Usage:
        >>> validator = CompletenessValidator()
        >>> is_complete = validator.validate_entity_completeness(entities, schema)
        >>> is_rel_complete = validator.validate_relationship_completeness(relationships, schema)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize completeness validator.
        
        Sets up the validator with completeness metrics calculator.
        
        Args:
            **kwargs: Configuration options passed to CompletenessMetrics
        """
        self.logger = get_logger("completeness_validator")
        self.completeness_metrics = CompletenessMetrics(**kwargs)
        self.config = kwargs
        
        self.logger.debug("Completeness validator initialized")
    
    def validate_entity_completeness(
        self,
        entities: List[Dict[str, Any]],
        schema: Dict[str, Any]
    ) -> bool:
        """
        Validate entity completeness.
        
        This method validates whether entities have all required properties
        as defined in the schema. Returns True if the completeness score
        is above the threshold (0.8).
        
        Args:
            entities: List of entity dictionaries
            schema: Schema definition containing required property constraints
            
        Returns:
            bool: True if entities are complete (score >= 0.8), False otherwise
        """
        score = self.completeness_metrics.calculate_entity_completeness(entities, schema)
        return score >= 0.8
    
    def validate_relationship_completeness(
        self,
        relationships: List[Dict[str, Any]],
        schema: Dict[str, Any]
    ) -> bool:
        """
        Validate relationship completeness.
        
        This method validates whether relationships have all required properties
        as defined in the schema. Returns True if the completeness score
        is above the threshold (0.8).
        
        Args:
            relationships: List of relationship dictionaries
            schema: Schema definition containing relationship constraints
            
        Returns:
            bool: True if relationships are complete (score >= 0.8), False otherwise
        """
        score = self.completeness_metrics.calculate_relationship_completeness(
            relationships,
            schema
        )
        return score >= 0.8
    
    def validate_property_completeness(
        self,
        properties: Dict[str, Any],
        schema: Dict[str, Any]
    ) -> bool:
        """
        Validate property completeness.
        
        This method validates whether properties meet schema requirements
        for completeness. Returns True if the completeness score is above
        the threshold (0.8).
        
        Args:
            properties: Properties dictionary (mapping entity types to property dicts)
            schema: Schema definition containing property constraints
            
        Returns:
            bool: True if properties are complete (score >= 0.8), False otherwise
        """
        score = self.completeness_metrics.calculate_property_completeness(properties, schema)
        return score >= 0.8

