### **Diverse Beam Search Decoding in Text Generation**

**Diverse Beam Search** is an extension of the standard beam search strategy aimed at generating more diverse sequences. While regular beam search tries to optimize for the highest probability sequences, **diverse beam search** ensures that the beams are not too similar to each other, allowing for more variety in the generated outputs.

### **Key Parameters for Diverse Beam Search:**
1. **`num_beams`**: Specifies the number of beams (hypotheses) to track during generation.
2. **`num_beam_groups`**: Controls how many groups of beams there should be. Each group of beams will explore a different part of the probability space.
3. **`diversity_penalty`**: Ensures that outputs from different groups are distinct by penalizing beams that are too similar to other beams in the same group. This encourages diversity among the generated sequences.

By dividing the total number of beams into groups, each group explores a different part of the output space, leading to a set of diverse sequences. The diversity penalty helps to ensure that even within a group, the beams are not too similar, leading to more varied output.

### **How It Works:**
- **Beam Search**: Used within each group to find the best sequences.
- **Diversity Penalty**: Penalizes sequences that are too similar to others, encouraging the model to generate diverse outputs.
- **Beam Groups**: Divides the search space into several groups, each of which performs beam search independently.

### **Example Code for Diverse Beam Search Decoding:**

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Define the checkpoint and the input prompt
checkpoint = "google/pegasus-xsum"
prompt = (
    "The Permaculture Design Principles are a set of universal design principles "
    "that can be applied to any location, climate and culture, and they allow us to design "
    "the most efficient and sustainable human habitation and food production systems. "
    "Permaculture is a design system that encompasses a wide variety of disciplines, such "
    "as ecology, landscape design, environmental science and energy conservation, and the "
    "Permaculture design principles are drawn from these various disciplines. Each individual "
    "design principle itself embodies a complete conceptual framework based on sound "
    "scientific principles. When we bring all these separate  principles together, we can "
    "create a design system that both looks at whole systems, the parts that these systems "
    "consist of, and how those parts interact with each other to create a complex, dynamic, "
    "living system. Each design principle serves as a tool that allows us to integrate all "
    "the separate parts of a design, referred to as elements, into a functional, synergistic, "
    "whole system, where the elements harmoniously interact and work together in the most "
    "efficient way possible."
)

# Tokenize the input prompt
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt")

# Load the model
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)

# Generate the output using diverse beam search decoding
outputs = model.generate(**inputs, num_beams=5, num_beam_groups=5, max_new_tokens=30, diversity_penalty=1.0)

# Decode the generated output
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```

### **Explanation of Parameters:**
- **`num_beams=5`**: We are using 5 beams to track the top 5 hypotheses.
- **`num_beam_groups=5`**: The 5 beams are divided into 5 groups, each exploring a different part of the output space.
- **`diversity_penalty=1.0`**: A penalty is applied to beams that are too similar, encouraging more diverse outputs.
- **`max_new_tokens=30`**: The generation will stop after generating 30 new tokens.

### **Generated Output:**
```plaintext
The Design Principles are a set of universal design principles that can be applied to any location, climate and
culture, and they allow us to design the most efficient and sustainable human habitation and food production systems.
```

### **Advantages of Diverse Beam Search:**
- **Increased Diversity**: The diversity penalty ensures that different beams produce different outputs, which reduces redundancy and enhances creativity in text generation.
- **Better Coverage**: By using multiple beam groups, the model can cover a broader range of possible outputs.
- **High-Quality Output**: Even though the outputs are diverse, beam search ensures that they are still coherent and high-quality.

### **When to Use Diverse Beam Search:**
- **Text Generation with Diversity**: When you want the model to generate multiple distinct outputs from a given prompt without repeating similar sentences.
- **Creative Writing**: In tasks that require a variety of creative outputs, such as story generation, where different sequences should be explored.
- **Machine Translation**: In translation tasks where the model might offer multiple valid translations and you want to ensure diversity among them.

### **Conclusion:**
Diverse beam search provides a more flexible decoding strategy that allows for generating multiple distinct outputs while ensuring that each generated sequence is of high quality. By adjusting the number of beams, beam groups, and diversity penalties, you can fine-tune the level of diversity and coherence in the generated text.