"""Create recovery_planner
Generated by Phi-Octopus Eden
2025-11-07 04:00:20.211004
"""

```python
from typing import Dict, Any

class RecoveryPlanner:
    """
    A class for creating a limited error recovery planner.
    
    This planner is designed to handle errors by providing a fallback strategy 
    when an error occurs during execution of a task. It keeps track of the 
    state before and after each task and allows for rollback or retry based 
    on the error encountered.

    Attributes:
        initial_state (Dict[str, Any]): The initial state before any tasks are executed.
        current_state (Dict[str, Any]): The current state during task execution.
        fallback_strategies (Dict[str, Callable[[Any], Any]]): A dictionary of fallback strategies for different errors.

    Methods:
        add_fallback_strategy: Adds a fallback strategy to the planner.
        execute_task: Executes a task and handles any errors by applying fallbacks if available.
    """

    def __init__(self):
        self.initial_state = {}
        self.current_state = {}
        self.fallback_strategies = {}

    def add_fallback_strategy(self, error_type: type, strategy: Callable[[Any], Any]) -> None:
        """
        Adds a fallback strategy for handling errors of the specified type.

        Args:
            error_type (type): The type of error to handle.
            strategy (Callable[[Any], Any]): A function that takes the current state and returns the new state after applying the fallback.
        """
        self.fallback_strategies[error_type] = strategy

    def execute_task(self, task: Callable[..., Any]) -> Any:
        """
        Executes a given task while handling potential errors using the registered fallback strategies.

        Args:
            task (Callable[..., Any]): The task to be executed which might raise an error.

        Returns:
            Any: The result of the task execution after applying any necessary fallbacks.
        
        Raises:
            Exception: If no suitable fallback strategy is available for the encountered error.
        """
        try:
            self.current_state = self.initial_state.copy()  # Copy initial state before executing
            return task()
        except Exception as e:
            if isinstance(e, tuple(self.fallback_strategies.keys())) and callable(fallback := self.fallback_strategies[type(e)]):
                print(f"Error occurred: {e}. Applying fallback strategy...")
                try:
                    new_state = fallback(self.current_state)
                    self.current_state.update(new_state)  # Apply changes to the current state
                    return self.current_state  # Return updated state as the result of recovery
                except Exception:
                    raise Exception("Failed to apply fallback strategy") from e

        raise e


# Example usage:

def example_task() -> Dict[str, Any]:
    """
    An example task that may fail and needs error recovery.
    """
    import random
    
    # Simulate a task with a 30% chance of failing
    if random.random() < 0.3:
        raise ValueError("Example task failed.")
    
    return {"task_result": "success"}


def fallback_on_value_error(state: Dict[str, Any]) -> Dict[str, Any]:
    """
    Fallback strategy for ValueErrors.
    """
    state["task_result"] = "fallback applied"
    return state


# Initialize recovery planner
recovery_planner = RecoveryPlanner()

# Register fallback strategies
recovery_planner.add_fallback_strategy(ValueError, fallback_on_value_error)

# Execute the task with potential errors and recoveries
result = recovery_planner.execute_task(example_task)
print(result)
```

This Python script defines a `RecoveryPlanner` class that can be used to manage error recovery in tasks. It includes an example usage section where a task is defined that has a 30% chance of raising a `ValueError`, and a fallback strategy for handling this type of error is provided. The `execute_task` method handles the execution of the task and applies the appropriate fallback if ne