"""
Validation Engine Module

This module provides comprehensive validation capabilities for the Semantica
framework, enabling rule-based and constraint-based validation of knowledge graphs.

Key Features:
    - Rule-based validation
    - Constraint-based validation
    - Custom validation rules
    - Validation result reporting

Main Classes:
    - ValidationEngine: Main validation engine
    - RuleValidator: Rule-based validation
    - ConstraintValidator: Constraint-based validation

Example Usage:
    >>> from semantica.kg_qa import ValidationEngine
    >>> engine = ValidationEngine()
    >>> result = engine.validate(knowledge_graph, rules=[rule1, rule2])
    >>> engine.add_rule(custom_rule)

Author: Semantica Contributors
License: MIT
"""

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

from ..utils.exceptions import ValidationError
from ..utils.logging import get_logger
from ..utils.progress_tracker import get_progress_tracker


@dataclass
class ValidationResult:
    """
    Validation result dataclass.
    
    This dataclass represents the result of a validation operation, containing
    validation status, errors, warnings, and optional metadata.
    
    Attributes:
        valid: Whether the validation passed (True if no errors)
        errors: List of error messages (critical validation failures)
        warnings: List of warning messages (non-critical issues)
        metadata: Additional validation metadata dictionary
    """
    
    valid: bool
    errors: List[str] = field(default_factory=list)
    warnings: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)


class ValidationEngine:
    """
    Validation engine.
    
    This class provides rule-based validation capabilities for knowledge graphs,
    enabling custom validation rules and constraint checking.
    
    Features:
        - Custom validation rules
        - Rule management (add, remove)
        - Validation result reporting
        - Error and warning collection
    
    Example Usage:
        >>> engine = ValidationEngine()
        >>> engine.add_rule(custom_validation_rule)
        >>> result = engine.validate(knowledge_graph)
        >>> if not result.valid:
        ...     print(f"Errors: {result.errors}")
    """
    
    def __init__(self, **kwargs):
        """
        Initialize validation engine.
        
        Sets up the engine with configuration and initializes rule storage.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("validation_engine")
        self.config = kwargs
        self.rules: List[Callable] = []
        
        # Initialize progress tracker
        self.progress_tracker = get_progress_tracker()
        
        self.logger.debug("Validation engine initialized")
    
    def validate(
        self,
        knowledge_graph: Any,
        rules: Optional[List[Callable]] = None
    ) -> ValidationResult:
        """
        Validate knowledge graph.
        
        This method validates a knowledge graph against a list of validation
        rules. Rules can be provided as arguments or use the engine's stored
        rules. Each rule should return a dict with "error" and/or "warning"
        keys, or raise an exception.
        
        Args:
            knowledge_graph: Knowledge graph instance to validate
            rules: Optional list of validation rule functions (if None, uses
                  stored rules). Each rule should accept the knowledge graph
                  as argument and return a dict or raise an exception.
            
        Returns:
            ValidationResult: Validation result containing:
                - valid: True if no errors, False otherwise
                - errors: List of error messages
                - warnings: List of warning messages
                - metadata: Additional validation metadata
        """
            rules_to_use = rules or self.rules
            errors = []
            warnings = []
            
            self.progress_tracker.update_tracking(tracking_id, message=f"Validating with {len(rules_to_use)} rule(s)...")
            for rule in rules_to_use:
                try:
                    result = rule(knowledge_graph)
                    if isinstance(result, dict):
                        if result.get("error"):
                            errors.append(result["error"])
                        if result.get("warning"):
                            warnings.append(result["warning"])
                except Exception as e:
                    self.logger.error(f"Validation rule error: {e}")
                    errors.append(f"Validation rule failed: {e}")
            
            result = ValidationResult(
                valid=len(errors) == 0,
                errors=errors,
                warnings=warnings
            )
            self.progress_tracker.stop_tracking(tracking_id, status="completed",
                                               message=f"Validation complete: {len(errors)} errors, {len(warnings)} warnings")
            return result
            
        except Exception as e:
            self.progress_tracker.stop_tracking(tracking_id, status="failed", message=str(e))
            raise
    
    def add_rule(self, rule: Callable) -> None:
        """
        Add validation rule.
        
        This method adds a validation rule function to the engine's rule list.
        The rule will be used in subsequent validate() calls.
        
        Args:
            rule: Validation rule function (should accept knowledge graph and
                 return dict with "error"/"warning" keys or raise exception)
        """
        self.rules.append(rule)
    
    def remove_rule(self, rule: Callable) -> None:
        """
        Remove validation rule.
        
        This method removes a validation rule function from the engine's rule list.
        
        Args:
            rule: Validation rule function to remove
        """
        if rule in self.rules:
            self.rules.remove(rule)


class RuleValidator:
    """
    Rule-based validation engine.
    
    This class provides rule-based validation capabilities, enabling validation
    against specific rule strings or identifiers.
    
    Features:
        - Single rule validation
        - Multiple rule validation
        - Rule parsing and execution (planned)
    
    Example Usage:
        >>> validator = RuleValidator()
        >>> result = validator.validate_rule(knowledge_graph, "rule_name")
        >>> results = validator.validate_all_rules(knowledge_graph, ["rule1", "rule2"])
    """
    
    def __init__(self, **kwargs):
        """
        Initialize rule validator.
        
        Sets up the validator with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("rule_validator")
        self.config = kwargs
        
        self.logger.debug("Rule validator initialized")
    
    def validate_rule(
        self,
        knowledge_graph: Any,
        rule: str
    ) -> ValidationResult:
        """
        Validate against a specific rule.
        
        This method validates a knowledge graph against a specific rule string
        or identifier. Currently returns a placeholder result. In practice,
        this would parse and execute the rule.
        
        Args:
            knowledge_graph: Knowledge graph instance
            rule: Rule string or identifier
            
        Returns:
            ValidationResult: Validation result (currently placeholder)
        """
        # In practice, this would parse and execute the rule
        # For now, return a placeholder
        return ValidationResult(valid=True)
    
    def validate_all_rules(
        self,
        knowledge_graph: Any,
        rules: List[str]
    ) -> Dict[str, ValidationResult]:
        """
        Validate against multiple rules.
        
        This method validates a knowledge graph against multiple rules and
        returns a dictionary mapping each rule name to its validation result.
        
        Args:
            knowledge_graph: Knowledge graph instance
            rules: List of rule strings or identifiers
            
        Returns:
            dict: Dictionary mapping rule names to ValidationResult objects
        """
        results = {}
        for rule in rules:
            results[rule] = self.validate_rule(knowledge_graph, rule)
        
        return results


class ConstraintValidator:
    """
    Constraint-based validation engine.
    
    This class provides constraint-based validation capabilities, enabling
    validation against schema constraints such as required properties, domain
    and range constraints for relationships.
    
    Features:
        - Entity constraint validation
        - Relationship constraint validation
        - Domain and range validation
    
    Example Usage:
        >>> validator = ConstraintValidator()
        >>> result = validator.validate_constraints(knowledge_graph, constraints)
    """
    
    def __init__(self, **kwargs):
        """
        Initialize constraint validator.
        
        Sets up the validator with configuration options.
        
        Args:
            **kwargs: Configuration options (currently unused)
        """
        self.logger = get_logger("constraint_validator")
        self.config = kwargs
        
        self.logger.debug("Constraint validator initialized")
    
    def validate_constraints(
        self,
        knowledge_graph: Any,
        constraints: Dict[str, Any]
    ) -> ValidationResult:
        """
        Validate against constraints.
        
        This method validates a knowledge graph against schema constraints,
        checking entity constraints (required properties) and relationship
        constraints (domain and range).
        
        Args:
            knowledge_graph: Knowledge graph instance
            constraints: Constraints dictionary containing:
                - entities: Dictionary mapping entity types to constraint dicts
                          with "required_props" list
                - relationships: Dictionary mapping relationship types to
                                constraint dicts with "domain" and "range"
            
        Returns:
            ValidationResult: Validation result with errors and warnings
        """
        errors = []
        warnings = []
        
        # Validate entity constraints
        entity_constraints = constraints.get("entities", {})
        for entity_type, constraint in entity_constraints.items():
            required_props = constraint.get("required_props", [])
            
            # Check if entities of this type have required properties
            # This is simplified - in practice would query the graph
            if required_props:
                warnings.append(f"Entity type {entity_type} requires properties: {required_props}")
        
        # Validate relationship constraints
        rel_constraints = constraints.get("relationships", {})
        for rel_type, constraint in rel_constraints.items():
            domain = constraint.get("domain")
            range_val = constraint.get("range")
            
            if domain and range_val:
                # Check domain and range constraints
                pass  # Would validate in practice
        
        return ValidationResult(
            valid=len(errors) == 0,
            errors=errors,
            warnings=warnings
        )

