Useful util method for generated text from LLM.py

import random
import textwrap
import torch  # You missed importing the torch module.
device = 'cuda' if torch.cuda.is_available() else 'cpu'

import textwrap

def text_wrapper(text, width=90):
    # Wraps the input text to the specified width

    # Check for null, empty, or non-string input
    if not text or not isinstance(text, str):
        return text

    # Validate and handle invalid width
    if not isinstance(width, int) or width <= 0:
        raise ValueError("Width must be a positive integer.")

    wrapped_lines = []

    # splits the input text into a list of lines based on newline chars
    # To ensures that existing paragraph or line breaks in
    # the input text are preserved.
    for line in text.split('\n'):
        # Preserve indentation: i.e. detects and preserves any
        # leading whitespace (indentation) in each line,
        # applying it to wrapped lines as well.
        leading_spaces = len(line) - len(line.lstrip(' '))
        indentation = ' ' * leading_spaces

        # Wrap each line and add indentation to each subsequent line
        wrapped_line = textwrap.fill(line, width=width,
                                     subsequent_indent=indentation)
        # textwrap.fill() wraps each line to the specified width.
        # If a line is longer than width, it's broken into shorter lines,
        # ensuring that no line exceeds the specified width.
        # Words are not broken across lines; instead,
        # the breaking occurs at whitespace.
        wrapped_lines.append(wrapped_line)

    # Finally, rejoin the individually wrapped lines into a single string,
    # separating them with newline characters.
    # This maintains the paragraph structure of the original text.
    return '\n'.join(wrapped_lines)