"""
Framework Plugin Template for Genesis-Flow

This template provides a starting point for creating new ML framework plugins.
Copy this file and customize it for your specific framework.

Example: Creating a TensorFlow plugin
1. Copy this file to tensorflow_plugin.py
2. Replace "MyFramework" with "TensorFlow"
3. Update metadata with TensorFlow-specific information
4. Implement TensorFlow-specific methods
5. Register in entry points or built-in plugins
"""

import logging
from typing import Dict, Callable

from mlflow.plugins.base import FrameworkPlugin, PluginMetadata, PluginType

logger = logging.getLogger(__name__)

class MyFrameworkPlugin(FrameworkPlugin):
    """
    Framework plugin template for Genesis-Flow.
    
    Replace "MyFramework" with your framework name throughout this file.
    """
    
    @classmethod
    def get_default_metadata(cls) -> PluginMetadata:
        """
        Get default metadata for the framework plugin.
        
        Customize all fields below for your framework.
        """
        return PluginMetadata(
            name="myframework",  # Change to your framework name (lowercase)
            version="1.0.0",     # Your plugin version
            description="My Framework integration for Genesis-Flow",  # Framework description
            author="Your Name or Organization",  # Plugin author
            plugin_type=PluginType.FRAMEWORK,   # Keep as FRAMEWORK
            dependencies=["myframework"],        # Required Python packages
            optional_dependencies=[              # Optional packages that enhance functionality
                "numpy", 
                "scipy",
                # Add framework-specific optional dependencies
            ],
            min_genesis_flow_version="1.0.0",   # Minimum Genesis-Flow version
            max_genesis_flow_version=None,      # Maximum version (None for no limit)
            homepage="https://myframework.org", # Framework homepage
            documentation="https://docs.myframework.org/mlflow",  # MLflow integration docs
            license="Apache 2.0",               # Framework license
            tags=[                               # Descriptive tags
                "machine-learning", 
                "myframework",
                # Add framework-specific tags like "deep-learning", "nlp", etc.
            ],
        )
    
    def get_module_path(self) -> str:
        """
        Get the module path for framework integration.
        
        This should match the MLflow integration module for your framework.
        Example: "mlflow.tensorflow" for TensorFlow
        """
        return "mlflow.myframework"  # Change to your framework module
    
    def get_autolog_functions(self) -> Dict[str, Callable]:
        """
        Get autologging functions provided by this framework.
        
        Returns dictionary mapping function names to callables.
        """
        try:
            # Import your framework's MLflow integration
            from mlflow.myframework import autolog
            
            return {
                "autolog": autolog,
                # Add other autolog-related functions if available
            }
            
        except ImportError:
            logger.warning("MyFramework autolog not available")
            return {}
    
    def get_save_functions(self) -> Dict[str, Callable]:
        """
        Get model saving functions provided by this framework.
        
        Returns dictionary mapping function names to callables.
        """
        try:
            # Import your framework's MLflow integration
            from mlflow.myframework import log_model, save_model
            
            return {
                "log_model": log_model,
                "save_model": save_model,
                # Add other save-related functions if available
            }
            
        except ImportError:
            logger.warning("MyFramework save functions not available")
            return {}
    
    def get_load_functions(self) -> Dict[str, Callable]:
        """
        Get model loading functions provided by this framework.
        
        Returns dictionary mapping function names to callables.
        """
        try:
            # Import your framework's MLflow integration
            from mlflow.myframework import load_model
            
            return {
                "load_model": load_model,
                # Add other load-related functions if available
            }
            
        except ImportError:
            logger.warning("MyFramework load functions not available")
            return {}
    
    def enable(self) -> bool:
        """
        Enable the framework plugin with enhanced functionality.
        
        Add framework-specific setup and configuration here.
        """
        try:
            # Call parent enable method
            if not super().enable():
                return False
            
            # Add framework-specific setup
            self._setup_framework_environment()
            
            self._logger.info("MyFramework plugin enabled with full functionality")
            return True
            
        except Exception as e:
            self._logger.error(f"Failed to enable MyFramework plugin: {e}")
            return False
    
    def _setup_framework_environment(self):
        """
        Setup framework-specific environment and configurations.
        
        Customize this method for your framework's initialization needs.
        """
        try:
            # Import your framework
            import myframework  # Replace with your framework import
            
            # Log framework version and capabilities
            self._logger.info(f"MyFramework version: {myframework.__version__}")
            
            # Check for GPU availability if relevant
            if hasattr(myframework, 'is_gpu_available'):
                gpu_available = myframework.is_gpu_available()
                self._logger.info(f"GPU available: {gpu_available}")
            
            # Setup hooks for automatic logging
            self.register_hook("model_save", self._on_model_save)
            self.register_hook("model_load", self._on_model_load)
            self.register_hook("training_start", self._on_training_start)
            self.register_hook("training_end", self._on_training_end)
            
            # Framework-specific configuration
            self._configure_framework_settings()
            
        except Exception as e:
            self._logger.warning(f"MyFramework environment setup warning: {e}")
    
    def _configure_framework_settings(self):
        """Configure framework-specific settings for optimal MLflow integration."""
        try:
            # Add framework-specific configuration here
            # Example: Setting logging levels, performance options, etc.
            pass
            
        except Exception as e:
            self._logger.warning(f"MyFramework configuration warning: {e}")
    
    def _on_model_save(self, model, *args, **kwargs):
        """
        Hook called when a model is saved.
        
        Customize this to add framework-specific model save processing.
        """
        try:
            # Check if it's your framework's model type
            import myframework
            
            if isinstance(model, myframework.BaseModel):  # Replace with your model base class
                self._logger.debug("MyFramework model save detected")
                
                # Add custom processing here
                # Example: Log model architecture, parameters, etc.
                self._log_model_metadata(model)
                
        except Exception as e:
            self._logger.warning(f"MyFramework model save hook error: {e}")
    
    def _on_model_load(self, model_path, *args, **kwargs):
        """
        Hook called when a model is loaded.
        
        Customize this to add framework-specific model load processing.
        """
        try:
            self._logger.debug(f"MyFramework model load detected: {model_path}")
            
            # Add custom processing here
            # Example: Validate model, setup runtime environment, etc.
            
        except Exception as e:
            self._logger.warning(f"MyFramework model load hook error: {e}")
    
    def _on_training_start(self, *args, **kwargs):
        """Hook called when training starts."""
        try:
            self._logger.debug("MyFramework training start detected")
            
            # Add custom processing here
            # Example: Setup training monitoring, resource tracking, etc.
            
        except Exception as e:
            self._logger.warning(f"MyFramework training start hook error: {e}")
    
    def _on_training_end(self, *args, **kwargs):
        """Hook called when training ends."""
        try:
            self._logger.debug("MyFramework training end detected")
            
            # Add custom processing here
            # Example: Log final metrics, cleanup resources, etc.
            
        except Exception as e:
            self._logger.warning(f"MyFramework training end hook error: {e}")
    
    def _log_model_metadata(self, model):
        """Log additional metadata about the model."""
        try:
            # Example: Log model-specific information
            # This is where you'd add framework-specific metadata logging
            pass
            
        except Exception as e:
            self._logger.warning(f"MyFramework model metadata logging error: {e}")
    
    def check_dependencies(self) -> bool:
        """
        Enhanced dependency checking for the framework.
        
        Customize this to add framework-specific dependency validation.
        """
        # Check basic dependencies first
        if not super().check_dependencies():
            return False
        
        try:
            # Import and validate your framework
            import myframework  # Replace with your framework import
            
            # Check minimum framework version
            from packaging.version import Version
            min_version = "1.0.0"  # Set your minimum version requirement
            
            if Version(myframework.__version__) < Version(min_version):
                self._logger.error(f"MyFramework {min_version}+ required, found {myframework.__version__}")
                return False
            
            # Add framework-specific validation
            if not self._validate_framework_environment():
                return False
            
            self._logger.debug(f"MyFramework {myframework.__version__} dependency satisfied")
            return True
            
        except ImportError:
            self._logger.error("MyFramework not installed")
            return False
        except Exception as e:
            self._logger.error(f"MyFramework dependency check failed: {e}")
            return False
    
    def _validate_framework_environment(self) -> bool:
        """
        Validate framework-specific environment requirements.
        
        Add custom validation logic here.
        """
        try:
            # Add framework-specific validation
            # Example: Check for required system libraries, GPU drivers, etc.
            
            # Example validation:
            # import myframework
            # if not myframework.is_properly_configured():
            #     self._logger.error("MyFramework is not properly configured")
            #     return False
            
            return True
            
        except Exception as e:
            self._logger.error(f"MyFramework environment validation failed: {e}")
            return False

# Example usage and registration:

# 1. To use this plugin directly:
# metadata = MyFrameworkPlugin.get_default_metadata()
# plugin = MyFrameworkPlugin(metadata)
# plugin.enable()

# 2. To register in built-in plugins, add to mlflow/plugins/builtin/__init__.py:
# from mlflow.plugins.builtin.myframework_plugin import MyFrameworkPlugin
# BUILTIN_PLUGINS["myframework"] = MyFrameworkPlugin

# 3. To register via entry points, add to setup.py:
# entry_points={
#     "genesis_flow.frameworks": [
#         "myframework = mypackage.myframework_plugin:MyFrameworkPlugin",
#     ],
# }