"""
Dynamic API Request Handler for COGNEXA ML Service

Provides:
- Flexible request validation with fallback defaults
- Automatic field coercion and type conversion
- Better error messages
- Graceful schema validation

Version: 1.0.0
"""

import logging
from typing import Dict, Any, List, Optional, Type, TypeVar, Union
from functools import wraps
from datetime import datetime
import json

T = TypeVar('T')
logger = logging.getLogger(__name__)


class ValidationError(Exception):
    """Validation error with detailed information"""
    def __init__(self, field: str, value: Any, expected_type: str, message: str = None):
        self.field = field
        self.value = value
        self.expected_type = expected_type
        self.message = message or f"Field '{field}' failed validation"
        super().__init__(self.message)


class FlexibleRequestHandler:
    """Handles flexible request validation with fallbacks"""
    
    @staticmethod
    def coerce_value(value: Any, target_type: Type[T], field_name: str = "field") -> T:
        """Coerce value to target type with intelligent fallbacks"""
        
        if value is None:
            return None
        
        try:
            # String to number
            if target_type in [int, float]:
                if isinstance(value, str):
                    value = float(value)
                    if target_type is int:
                        value = int(value)
                return target_type(value)
            
            # String to bool
            elif target_type is bool:
                if isinstance(value, str):
                    return value.lower() in ['true', '1', 'yes', 'on']
                return bool(value)
            
            # Dict preservation
            elif target_type is dict:
                if isinstance(value, dict):
                    return value
                elif isinstance(value, str):
                    try:
                        return json.loads(value)
                    except:
                        return {}
                return {}
            
            # List preservation
            elif target_type is list:
                if isinstance(value, list):
                    return value
                elif isinstance(value, str):
                    try:
                        return json.loads(value)
                    except:
                        return [value]
                return [value] if value else []
            
            # Direct conversion
            else:
                return target_type(value)
                
        except (ValueError, TypeError) as e:
            logger.warning(f"Could not coerce {field_name}={value} to {target_type.__name__}: {e}")
            return None
    
    @staticmethod
    def validate_dict(data: Dict[str, Any], schema: Dict[str, tuple]) -> Dict[str, Any]:
        """
        Validate and coerce dictionary against schema.
        
        Schema format:
        {
            'field_name': (required, type, default_value),
            'name': (True, str, None),  # required string
            'age': (False, int, 18),    # optional int, default 18
        }
        """
        result = {}
        errors = []
        
        for field_name, (required, field_type, default) in schema.items():
            value = data.get(field_name, default)
            
            # Check required
            if required and value is None:
                errors.append(f"Required field '{field_name}' is missing")
                continue
            
            # Coerce type
            if value is not None:
                coerced = FlexibleRequestHandler.coerce_value(value, field_type, field_name)
                if coerced is None and value is not None:
                    # Try using default
                    coerced = default
                    logger.warning(f"Field {field_name}: coercion failed, using default: {default}")
                result[field_name] = coerced
            else:
                result[field_name] = default
        
        return result, errors


class DynamicRequestValidator:
    """Validates and coerces API requests dynamically"""
    
    # Define schemas for common request types
    PERSONALITY_ANALYSIS_SCHEMA = {
        'responses': (True, dict, {}),  # required dict
        'response_scale_max': (False, int, 5),  # optional int, default 5
    }
    
    PRODUCTIVITY_FORECAST_SCHEMA = {
        'user_id': (True, str, None),
        'historical_data': (False, list, []),
        'forecast_days': (False, int, 7),
        'personality': (False, dict, None),
    }
    
    TASK_PREDICTION_SCHEMA = {
        'title': (True, str, None),
        'description': (False, str, ""),
        'category': (False, str, "PERSONAL"),
        'priority': (False, str, "MEDIUM"),
        'complexity': (False, int, 3),
        'estimated_duration': (False, int, 60),
        'due_date': (False, str, None),
        'personality': (False, dict, None),
    }
    
    PERSONALITY_RESPONSE_SCHEMA = {
        'responses': (True, dict, {}),
        'response_scale_max': (False, int, 5),
    }
    
    ENGAGEMENT_PREDICTION_SCHEMA = {
        'user_id': (True, str, None),
        'task_id': (True, str, None),
        'task': (False, dict, {}),
        'user_profile': (False, dict, {}),
        'recent_interactions': (False, list, []),
        'similar_tasks': (False, list, []),
    }
    
    @classmethod
    def validate_personality_analysis(cls, data: Dict) -> tuple[Dict, List[str]]:
        """Validate personality analysis request"""
        return FlexibleRequestHandler.validate_dict(data, cls.PERSONALITY_ANALYSIS_SCHEMA)
    
    @classmethod
    def validate_productivity_forecast(cls, data: Dict) -> tuple[Dict, List[str]]:
        """Validate productivity forecast request"""
        return FlexibleRequestHandler.validate_dict(data, cls.PRODUCTIVITY_FORECAST_SCHEMA)
    
    @classmethod
    def validate_task_prediction(cls, data: Dict) -> tuple[Dict, List[str]]:
        """Validate task prediction request"""
        return FlexibleRequestHandler.validate_dict(data, cls.TASK_PREDICTION_SCHEMA)
    
    @classmethod
    def validate_personality_responses(cls, data: Dict) -> tuple[Dict, List[str]]:
        """Validate personality responses request"""
        return FlexibleRequestHandler.validate_dict(data, cls.PERSONALITY_RESPONSE_SCHEMA)
    
    @classmethod
    def validate_engagement_prediction(cls, data: Dict) -> tuple[Dict, List[str]]:
        """Validate engagement prediction request"""
        return FlexibleRequestHandler.validate_dict(data, cls.ENGAGEMENT_PREDICTION_SCHEMA)


def flexible_endpoint(endpoint_name: str = None):
    """
    Decorator for API endpoints that validates and coerces requests.
    
    Usage:
        @flexible_endpoint("personality_analysis")
        async def my_endpoint(request: dict):
            ...
    """
    def decorator(func):
        @wraps(func)
        async def wrapper(request):
            try:
                # Convert request to dict
                if hasattr(request, 'dict'):
                    data = request.dict()
                else:
                    data = dict(request)
                
                # Validate based on endpoint
                validator = DynamicRequestValidator
                if endpoint_name == "personality_analysis":
                    validated, errors = validator.validate_personality_analysis(data)
                elif endpoint_name == "productivity_forecast":
                    validated, errors = validator.validate_productivity_forecast(data)
                elif endpoint_name == "task_prediction":
                    validated, errors = validator.validate_task_prediction(data)
                elif endpoint_name == "personality_responses":
                    validated, errors = validator.validate_personality_responses(data)
                elif endpoint_name == "engagement_prediction":
                    validated, errors = validator.validate_engagement_prediction(data)
                else:
                    validated, errors = data, []
                
                # Log warnings if there were validation issues
                if errors:
                    logger.warning(f"Validation issues for {endpoint_name}: {errors}")
                
                # Call function with validated data
                return await func(validated)
            
            except Exception as e:
                logger.error(f"Error in flexible_endpoint: {e}")
                raise
        
        return wrapper
    return decorator


class DynamicModelConfigurator:
    """Configures models dynamically based on input parameters"""
    
    @staticmethod
    def get_model_config(model_type: str, **kwargs) -> Dict[str, Any]:
        """Get dynamic model configuration"""
        
        base_config = {
            'verbose': kwargs.get('verbose', False),
            'random_seed': kwargs.get('random_seed', 42),
            'scale_features': kwargs.get('scale_features', True),
            'track_metrics': kwargs.get('track_metrics', True),
        }
        
        if model_type == 'task_completion':
            return {
                **base_config,
                'model_type': 'task_completion',
                'task_type': 'classification',
                'threshold': kwargs.get('threshold', 0.5),
                'min_confidence': kwargs.get('min_confidence', 0.3),
            }
        
        elif model_type == 'duration_prediction':
            return {
                **base_config,
                'model_type': 'duration_prediction',
                'task_type': 'regression',
                'confidence_interval': kwargs.get('confidence_interval', 0.95),
                'max_forecast_hours': kwargs.get('max_forecast_hours', 8),
            }
        
        elif model_type == 'stress_prediction':
            return {
                **base_config,
                'model_type': 'stress_prediction',
                'task_type': 'regression',
                'stress_scale_max': kwargs.get('stress_scale_max', 10),
                'alert_threshold': kwargs.get('alert_threshold', 7),
            }
        
        elif model_type == 'personality_clustering':
            return {
                **base_config,
                'model_type': 'personality_clustering',
                'task_type': 'clustering',
                'n_clusters': kwargs.get('n_clusters', 5),
                'personality_traits': kwargs.get('personality_traits', ['openness', 'conscientiousness', 'extraversion', 'agreeableness', 'neuroticism']),
            }
        
        elif model_type == 'engagement_prediction':
            return {
                **base_config,
                'model_type': 'engagement_prediction',
                'task_type': 'classification',
                'engagement_scale': kwargs.get('engagement_scale', 100),
                'positive_threshold': kwargs.get('positive_threshold', 50),
            }
        
        else:
            return base_config


class ParameterizedModelTrainer:
    """Train models with dynamic parameters"""
    
    @staticmethod
    def get_training_config(model_type: str, **kwargs) -> Dict[str, Any]:
        """Get dynamic training configuration"""
        
        base_config = {
            'epochs': kwargs.get('epochs', 100),
            'batch_size': kwargs.get('batch_size', 32),
            'learning_rate': kwargs.get('learning_rate', 0.01),
            'early_stopping_patience': kwargs.get('early_stopping_patience', 10),
            'validation_split': kwargs.get('validation_split', 0.2),
            'random_seed': kwargs.get('random_seed', 42),
        }
        
        if model_type == 'xgboost':
            return {
                **base_config,
                'max_depth': kwargs.get('max_depth', 6),
                'subsample': kwargs.get('subsample', 0.8),
                'colsample_bytree': kwargs.get('colsample_bytree', 0.8),
                'min_child_weight': kwargs.get('min_child_weight', 1),
                'gamma': kwargs.get('gamma', 0),
                'lambda': kwargs.get('lambda', 1),
            }
        
        elif model_type == 'random_forest':
            return {
                **base_config,
                'n_estimators': kwargs.get('n_estimators', 100),
                'max_depth': kwargs.get('max_depth', 15),
                'min_samples_split': kwargs.get('min_samples_split', 5),
                'min_samples_leaf': kwargs.get('min_samples_leaf', 2),
                'max_features': kwargs.get('max_features', 'sqrt'),
            }
        
        elif model_type == 'gradient_boosting':
            return {
                **base_config,
                'n_estimators': kwargs.get('n_estimators', 100),
                'learning_rate': kwargs.get('learning_rate', 0.01),
                'max_depth': kwargs.get('max_depth', 5),
                'min_samples_split': kwargs.get('min_samples_split', 5),
                'min_samples_leaf': kwargs.get('min_samples_leaf', 2),
            }
        
        else:
            return base_config