import numpy as np
from typing import Optional

from .watermarker import WatermarkerBase


class DummyWatermarker(WatermarkerBase):
    """
    A dummy watermarker for testing and demonstration purposes.
    
    This watermarker doesn't actually embed or extract real watermarks,
    but serves as a placeholder implementation for testing the framework.
    """
    
    def apply_watermark(self, wav: np.ndarray, watermark: Optional[np.ndarray] = None, 
                       sample_rate: int = 44100, **kwargs) -> np.ndarray:
        """
        Simulates applying a watermark by simply rounding the audio signal.
        
        Args:
            wav: Input audio signal as numpy array
            watermark: Ignored in this implementation
            sample_rate: Ignored in this implementation
            **kwargs: Additional ignored parameters
            
        Returns:
            The input audio with minimal modification (rounded to 5 decimal places)
        """
        return wav.round(5)

    def get_watermark(self, watermarked_wav: np.ndarray, sample_rate: int = 44100,
                     watermark_length: Optional[int] = None, **kwargs) -> np.ndarray:
        """
        Simulates extracting a watermark by returning random data.
        
        Args:
            watermarked_wav: Watermarked audio signal as numpy array
            sample_rate: Ignored in this implementation
            watermark_length: Length of the dummy watermark to generate
            **kwargs: Additional ignored parameters
            
        Returns:
            A random binary watermark of specified length or default 32 bits
        """
        length = watermark_length if watermark_length is not None else 32
        return np.random.randint(0, 2, size=length).astype(np.float32)