Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.


TASK :
**Problem Statement:**

In today's data-driven world, the need to process and analyze diverse types of data—such as text, audio, image, and video—has led to the development of sophisticated tokenization techniques. Tokenization is the process of converting different forms of raw data into smaller, manageable pieces, or "tokens," that can be effectively handled by machine learning models. The challenge of creating a unified tokenization technique capable of handling multiple modalities (i.e., text, audio, image, and video data) is complex, yet essential for developing general-purpose models that can work across domains. 

Assume we have the following data types:

1. **Text Data:** Represented as a `list(str)`, where each string corresponds to a text entry, such as sentences or paragraphs.
2. **Audio Data:** Represented as a `list(np.array)`, where each NumPy array corresponds to a time series signal of an audio clip, containing amplitude values for each time step.
3. **Image Data:** Represented as a `list(np.array)` with dimensions `(b, c, h, w)` where `b` is the batch size, `c` is the number of color channels, `h` is the height of the image, and `w` is the width.
4. **Video Data:** Represented as a `list(np.array)` with dimensions `(b, c, d, h, w)` where `b` is the batch size, `c` is the number of color channels, `d` is the number of frames (or duration in time), `h` is the height, and `w` is the width of each frame.

Given these diverse data types, our task is to develop a **unique general tokenization technique** that can effectively handle the heterogeneous nature of text, audio, image, and video data. The key requirement is to ensure that this technique can map each type of data into a unified token space, which will allow models to process them as compatible input regardless of their original format.

### **1. Tokenization for Text Data:**
Tokenization of text is a well-established process that involves breaking down a string into smaller pieces such as words, subwords, or characters. Common approaches include:
- **Word-based tokenization** (e.g., splitting by spaces or punctuation),
- **Subword tokenization** (e.g., Byte-Pair Encoding (BPE) or WordPiece), and 
- **Character-based tokenization** (representing each character as a token).
The choice of text tokenization technique depends on the task at hand. Subword tokenization, for instance, is often more efficient for modern language models as it reduces the vocabulary size while still retaining the ability to represent rare words.

### **2. Tokenization for Audio Data:**
Audio data can be tokenized by converting the waveform into a sequence of feature vectors. Some popular approaches include:
- **Spectrogram tokenization:** First, the raw waveform is converted into a spectrogram using techniques like Short-Time Fourier Transform (STFT), Mel-Frequency Cepstral Coefficients (MFCC), or Mel-spectrograms. These transformations capture the frequency content over time, compressing the data while preserving important information.
- **Learned representations:** Recent models like Wav2Vec and HuBERT directly learn tokenizations by training on raw audio signals, producing a sequence of audio tokens that can be fed into models like Transformers.

### **3. Tokenization for Image Data:**
Tokenization for images typically involves:
- **Grid-based tokenization:** Splitting an image into smaller, fixed-size patches (e.g., 16x16) and representing each patch as a token. This is the approach used in Vision Transformers (ViTs), where each patch is linearly embedded and treated as a sequence of tokens.
- **Convolutional Feature Extraction:** Alternatively, convolutional layers can be used to extract high-level feature maps from the image, which are then flattened into a token sequence. These tokens represent local features in the image and can be processed by sequential models.

### **4. Tokenization for Video Data:**
Video data is highly complex as it includes both spatial and temporal information. Some tokenization techniques include:
- **Frame-based tokenization:** Treat each frame of the video as a sequence of image tokens (using techniques like image tokenization described above) and concatenate them over time.
- **Spatiotemporal tokenization:** This technique extracts both spatial and temporal features by processing video patches in both the spatial dimensions (height, width) and the temporal dimension (frames). Vision Transformers adapted for videos often use this approach.

### **Unified Tokenization Strategy:**
A unique tokenization technique must unify the representations across text, audio, image, and video. One promising direction is **embedding space unification**, where all modalities are tokenized into a shared latent space. This can be done using modality-specific encoders (e.g., Transformers or CNNs) that map raw data to embeddings, followed by a shared model that processes these embeddings. By ensuring that all data types are transformed into a consistent sequence of tokens, cross-modal understanding becomes possible, enabling models to learn from and relate across multiple modalities.

In summary, the development of a general tokenization technique requires carefully designed methods for each data type, combined with mechanisms to bring the tokenized data into a shared format for downstream tasks like classification, generation, or multimodal analysis.