"""
Automated Fixes Module

This module provides automated fixing capabilities for the Semantica framework,
enabling automatic resolution of common knowledge graph quality issues.

Key Features:
    - Duplicate entity and relationship fixing
    - Inconsistency resolution
    - Missing property completion
    - Conflicting property merging
    - Conflict and disagreement resolution

Main Classes:
    - AutomatedFixer: Main automated fixing engine
    - AutoMerger: Automatic merging of duplicates and conflicts
    - AutoResolver: Automatic conflict and inconsistency resolution

Example Usage:
    >>> from semantica.kg_qa import AutomatedFixer
    >>> fixer = AutomatedFixer()
    >>> result = fixer.fix_duplicates(knowledge_graph)
    >>> result = fixer.fix_missing_properties(knowledge_graph, schema)

Author: Semantica Contributors
License: MIT
"""

from typing import Any, Dict, List, Optional
from dataclasses import dataclass

from ..utils.logging import get_logger
from ..utils.progress_tracker import get_progress_tracker
from .quality_metrics import QualityMetrics


@dataclass
class FixResult:
    """
    Fix result dataclass.
    
    This dataclass represents the result of an automated fix operation,
    containing success status, number of fixes applied, errors encountered,
    and additional metadata.
    
    Attributes:
        success: Whether the fix operation was successful
        fixed_count: Number of issues fixed
        errors: List of error messages encountered during fixing
        metadata: Additional metadata about the fix operation
    """
    
    success: bool
    fixed_count: int
    errors: List[str]
    metadata: Dict[str, Any]


class AutomatedFixer:
    """
    Automated fixing engine.
    
    This class provides automated fixing capabilities for common knowledge
    graph quality issues, including duplicates, inconsistencies, and
    missing properties.
    
    Features:
        - Duplicate entity fixing
        - Inconsistency resolution
        - Missing property completion
        - Integration with quality metrics
    
    Example Usage:
        >>> fixer = AutomatedFixer()
        >>> result = fixer.fix_duplicates(knowledge_graph)
        >>> if result.success:
        ...     print(f"Fixed {result.fixed_count} issues")
    """
    
    def __init__(self, **kwargs):
        """
        Initialize automated fixer.
        
        Sets up the fixer with configuration and quality metrics calculator.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("automated_fixer")
        self.config = kwargs
        self.quality_metrics = QualityMetrics()
        
        # Initialize progress tracker
        self.progress_tracker = get_progress_tracker()
        
        self.logger.debug("Automated fixer initialized")
    
    def fix_duplicates(
        self,
        knowledge_graph: Any
    ) -> FixResult:
        """
        Fix duplicate entities.
        
        This method identifies and fixes duplicate entities in the knowledge
        graph. In practice, this would use the deduplication module to detect
        and merge duplicates.
        
        Args:
            knowledge_graph: Knowledge graph instance (object with entities
                           and relationships, or dict with "entities" and
                           "relationships" keys)
            
        Returns:
            FixResult: Fix result containing:
                - success: Whether fixing was successful
                - fixed_count: Number of duplicates fixed
                - errors: List of error messages
                - metadata: Additional fix metadata
        """
        # Track duplicate fixing
        tracking_id = self.progress_tracker.start_tracking(
            file=None,
            module="kg_qa",
            submodule="AutomatedFixer",
            message="Fixing duplicate entities"
        )
        
        try:
            self.logger.info("Fixing duplicate entities")
            
            self.progress_tracker.update_tracking(tracking_id, message="Detecting duplicates...")
            # In practice, this would use deduplication module
            # For now, return placeholder
            result = FixResult(
                success=True,
                fixed_count=0,
                errors=[],
                metadata={}
            )
            self.progress_tracker.stop_tracking(tracking_id, status="completed",
                                               message=f"Fixed {result.fixed_count} duplicate(s)")
            return result
            
        except Exception as e:
            self.progress_tracker.stop_tracking(tracking_id, status="failed", message=str(e))
            raise
    
    def fix_inconsistencies(
        self,
        knowledge_graph: Any
    ) -> FixResult:
        """
        Fix inconsistencies.
        
        This method identifies and fixes logical inconsistencies in the
        knowledge graph, such as conflicting property values or contradictory
        relationships.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            FixResult: Fix result with success status and fix count
        """
        self.logger.info("Fixing inconsistencies")
        
        # In practice, this would resolve logical inconsistencies
        return FixResult(
            success=True,
            fixed_count=0,
            errors=[],
            metadata={}
        )
    
    def fix_missing_properties(
        self,
        knowledge_graph: Any,
        schema: Dict[str, Any]
    ) -> FixResult:
        """
        Fix missing required properties.
        
        This method identifies entities with missing required properties
        (as defined in the schema) and attempts to fix them by adding
        default values or inferring values from context.
        
        Args:
            knowledge_graph: Knowledge graph instance
            schema: Schema definition containing required property constraints
            
        Returns:
            FixResult: Fix result with number of properties added
        """
        self.logger.info("Fixing missing properties")
        
        fixed_count = 0
        errors = []
        
        # In practice, this would:
        # 1. Find entities with missing required properties
        # 2. Add default values or infer values
        # 3. Update the knowledge graph
        
        return FixResult(
            success=len(errors) == 0,
            fixed_count=fixed_count,
            errors=errors,
            metadata={}
        )


class AutoMerger:
    """
    Automatic merging engine.
    
    This class provides automatic merging capabilities for duplicate entities,
    relationships, and conflicting properties in knowledge graphs.
    
    Features:
        - Duplicate entity merging
        - Duplicate relationship merging
        - Conflicting property resolution
    
    Example Usage:
        >>> merger = AutoMerger()
        >>> result = merger.merge_duplicate_entities(knowledge_graph)
        >>> result = merger.merge_conflicting_properties(knowledge_graph)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize auto merger.
        
        Sets up the merger with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("auto_merger")
        self.config = kwargs
        
        self.logger.debug("Auto merger initialized")
    
    def merge_duplicate_entities(
        self,
        knowledge_graph: Any
    ) -> FixResult:
        """
        Merge duplicate entities.
        
        This method identifies duplicate entities and merges them into
        single entities, combining properties and updating relationships.
        In practice, this would use the deduplication module.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            FixResult: Merge result with number of entities merged
        """
        self.logger.info("Merging duplicate entities")
        
        # In practice, this would:
        # 1. Identify duplicate entities
        # 2. Merge properties
        # 3. Update relationships
        # 4. Remove duplicates
        
        return FixResult(
            success=True,
            fixed_count=0,
            errors=[],
            metadata={}
        )
    
    def merge_duplicate_relationships(
        self,
        knowledge_graph: Any
    ) -> FixResult:
        """
        Merge duplicate relationships.
        
        This method identifies duplicate relationships (same source, target,
        and type) and merges them, combining properties and metadata.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            FixResult: Merge result with number of relationships merged
        """
        self.logger.info("Merging duplicate relationships")
        
        return FixResult(
            success=True,
            fixed_count=0,
            errors=[],
            metadata={}
        )
    
    def merge_conflicting_properties(
        self,
        knowledge_graph: Any
    ) -> FixResult:
        """
        Merge conflicting properties.
        
        This method identifies entities with conflicting property values
        (same property with different values) and resolves conflicts using
        configurable strategies (e.g., highest confidence, most recent).
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            FixResult: Merge result with number of conflicts resolved
        """
        self.logger.info("Merging conflicting properties")
        
        return FixResult(
            success=True,
            fixed_count=0,
            errors=[],
            metadata={}
        )


class AutoResolver:
    """
    Automatic resolution engine.
    
    This class provides automatic resolution capabilities for conflicts,
    disagreements, and inconsistencies in knowledge graphs.
    
    Features:
        - Conflict resolution
        - Disagreement resolution
        - Inconsistency resolution
    
    Example Usage:
        >>> resolver = AutoResolver()
        >>> result = resolver.resolve_conflicts(knowledge_graph)
        >>> result = resolver.resolve_inconsistencies(knowledge_graph)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize auto resolver.
        
        Sets up the resolver with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("auto_resolver")
        self.config = kwargs
        
        self.logger.debug("Auto resolver initialized")
    
    def resolve_conflicts(
        self,
        knowledge_graph: Any
    ) -> FixResult:
        """
        Resolve conflicts.
        
        This method identifies and resolves conflicts in the knowledge graph,
        such as conflicting property values or contradictory relationships.
        In practice, this would use the conflict resolution module.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            FixResult: Resolution result with number of conflicts resolved
        """
        self.logger.info("Resolving conflicts")
        
        return FixResult(
            success=True,
            fixed_count=0,
            errors=[],
            metadata={}
        )
    
    def resolve_disagreements(
        self,
        knowledge_graph: Any
    ) -> FixResult:
        """
        Resolve disagreements.
        
        This method identifies and resolves disagreements between different
        sources or versions of the same information in the knowledge graph.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            FixResult: Resolution result with number of disagreements resolved
        """
        self.logger.info("Resolving disagreements")
        
        return FixResult(
            success=True,
            fixed_count=0,
            errors=[],
            metadata={}
        )
    
    def resolve_inconsistencies(
        self,
        knowledge_graph: Any
    ) -> FixResult:
        """
        Resolve inconsistencies.
        
        This method identifies and resolves logical inconsistencies in the
        knowledge graph, such as circular dependencies or contradictory
        hierarchical relationships.
        
        Args:
            knowledge_graph: Knowledge graph instance
            
        Returns:
            FixResult: Resolution result with number of inconsistencies resolved
        """
        self.logger.info("Resolving inconsistencies")
        
        return FixResult(
            success=True,
            fixed_count=0,
            errors=[],
            metadata={}
        )

