import logging
from typing import Dict, Any, List, Union
from modules.aicore.aicoreBase import BaseConnectorAi
from modules.datamodels.datamodelAi import AiModel, ModelCapabilitiesEnum, PriorityEnum, ProcessingModeEnum, OperationTypeEnum

# Configure logger
logger = logging.getLogger(__name__)

class AiInternal(BaseConnectorAi):
    """Internal connector for document processing, generation, and rendering."""
    
    def __init__(self):
        super().__init__()
        logger.info("Internal Connector initialized")
    
    def getConnectorType(self) -> str:
        """Get the connector type identifier."""
        return "internal"
    
    def getModels(self) -> List[AiModel]:
        """Get all available internal models."""
        return [
            AiModel(
                name="internal_extraction",
                displayName="Internal Document Extractor",
                connectorType="internal",
                maxTokens=0,  # Not token-based
                contextLength=0,
                costPer1kTokensInput=0.0,
                costPer1kTokensOutput=0.0,
                speedRating=8,
                qualityRating=8,
                capabilities=[ModelCapabilitiesEnum.CONTENT_EXTRACTION, ModelCapabilitiesEnum.TEXT_EXTRACTION],
                functionCall=self.extractDocument,
                priority=PriorityEnum.COST,
                processingMode=ProcessingModeEnum.BASIC,
                operationTypes=[OperationTypeEnum.EXTRACT],
                version="internal-extractor-v1",
                calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.001 + (bytesSent + bytesReceived) / (1024 * 1024) * 0.01
            ),
            AiModel(
                name="internal_generation",
                displayName="Internal Document Generator",
                connectorType="internal",
                maxTokens=0,  # Not token-based
                contextLength=0,
                costPer1kTokensInput=0.0,
                costPer1kTokensOutput=0.0,
                speedRating=7,
                qualityRating=8,
                capabilities=[ModelCapabilitiesEnum.TEXT_GENERATION, ModelCapabilitiesEnum.ANALYSIS],
                functionCall=self.generateDocument,
                priority=PriorityEnum.COST,
                processingMode=ProcessingModeEnum.BASIC,
                operationTypes=[OperationTypeEnum.GENERATE],
                version="internal-generator-v1",
                calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.002 + (bytesReceived / (1024 * 1024)) * 0.005
            ),
            AiModel(
                name="internal_rendering",
                displayName="Internal Document Renderer",
                connectorType="internal",
                maxTokens=0,  # Not token-based
                contextLength=0,
                costPer1kTokensInput=0.0,
                costPer1kTokensOutput=0.0,
                speedRating=6,
                qualityRating=9,
                capabilities=[ModelCapabilitiesEnum.TEXT_GENERATION, ModelCapabilitiesEnum.ANALYSIS],
                functionCall=self.renderDocument,
                priority=PriorityEnum.QUALITY,
                processingMode=ProcessingModeEnum.DETAILED,
                operationTypes=[OperationTypeEnum.GENERATE],
                version="internal-renderer-v1",
                calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: 0.003 + (bytesReceived / (1024 * 1024)) * 0.008
            )
        ]
    
    async def extractDocument(self, documentData: Union[str, bytes], extractionType: str = "basic") -> Dict[str, Any]:
        """
        Extract content from a document.
        
        Args:
            documentData: The document data to extract from
            extractionType: Type of extraction (basic, advanced, detailed)
            
        Returns:
            Dictionary with extraction results
        """
        try:
            logger.info(f"Starting document extraction with type: {extractionType}")
            
            # Simulate document extraction processing
            # In a real implementation, this would use actual document processing libraries
            
            if isinstance(documentData, bytes):
                content = documentData.decode('utf-8', errors='ignore')
            else:
                content = str(documentData)
            
            # Basic extraction logic
            extractedContent = {
                "text": content,
                "metadata": {
                    "extraction_type": extractionType,
                    "content_length": len(content),
                    "processing_time": 0.1  # Simulated
                }
            }
            
            logger.info(f"Document extraction completed successfully")
            return extractedContent
            
        except Exception as e:
            logger.error(f"Error during document extraction: {str(e)}")
            return {
                "error": str(e),
                "success": False
            }
    
    async def generateDocument(self, template: str, data: Dict[str, Any], format: str = "html") -> Dict[str, Any]:
        """
        Generate a document from a template and data.
        
        Args:
            template: The document template
            data: Data to populate the template
            format: Output format (html, pdf, docx, etc.)
            
        Returns:
            Dictionary with generated document
        """
        try:
            logger.info(f"Starting document generation with format: {format}")
            
            # Simulate document generation processing
            # In a real implementation, this would use actual templating engines
            
            # Basic template processing
            generatedContent = template
            for key, value in data.items():
                placeholder = f"{{{key}}}"
                generatedContent = generatedContent.replace(placeholder, str(value))
            
            result = {
                "content": generatedContent,
                "format": format,
                "metadata": {
                    "template_length": len(template),
                    "data_keys": list(data.keys()),
                    "processing_time": 0.2  # Simulated
                }
            }
            
            logger.info(f"Document generation completed successfully")
            return result
            
        except Exception as e:
            logger.error(f"Error during document generation: {str(e)}")
            return {
                "error": str(e),
                "success": False
            }
    
    async def renderDocument(self, content: str, targetFormat: str, options: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        Render a document to a specific format.
        
        Args:
            content: The content to render
            targetFormat: Target format (html, pdf, docx, etc.)
            options: Rendering options
            
        Returns:
            Dictionary with rendered document
        """
        try:
            logger.info(f"Starting document rendering to format: {targetFormat}")
            
            if options is None:
                options = {}
            
            # Simulate document rendering processing
            # In a real implementation, this would use actual rendering libraries
            
            # Basic rendering logic based on target format
            if targetFormat.lower() == "html":
                renderedContent = f"<html><body>{content}</body></html>"
            elif targetFormat.lower() == "pdf":
                # Simulate PDF rendering
                renderedContent = f"PDF_CONTENT_PLACEHOLDER: {content}"
            else:
                # Default to plain text
                renderedContent = content
            
            result = {
                "content": renderedContent,
                "format": targetFormat,
                "metadata": {
                    "input_length": len(content),
                    "output_length": len(renderedContent),
                    "processing_time": 0.3,  # Simulated
                    "options": options
                }
            }
            
            logger.info(f"Document rendering completed successfully")
            return result
            
        except Exception as e:
            logger.error(f"Error during document rendering: {str(e)}")
            return {
                "error": str(e),
                "success": False
            }
    
    async def _testConnection(self) -> bool:
        """
        Tests the internal processing capabilities.
        
        Returns:
            True if internal processing is working, False otherwise
        """
        try:
            # Test basic functionality
            testContent = "Test document content"
            result = await self.extractDocument(testContent)
            
            return result.get("success", True) and "error" not in result
            
        except Exception as e:
            logger.error(f"Internal connector test failed: {str(e)}")
            return False