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"""
utils.py - Utility functions for the project.
"""
import logging
import re
from pathlib import Path
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
level=logging.INFO,
)
import torch
from natsort import natsorted
def validate_pytorch2(torch_version: str = None):
torch_version = torch.__version__ if torch_version is None else torch_version
pattern = r"^2\.\d+(\.\d+)*"
return True if re.match(pattern, torch_version) else False
def truncate_word_count(text, max_words=512):
"""
truncate_word_count - a helper function for the gradio module
Parameters
----------
text : str, required, the text to be processed
max_words : int, optional, the maximum number of words, default=512
Returns
-------
dict, the text and whether it was truncated
"""
# split on whitespace with regex
words = re.split(r"\s+", text)
processed = {}
if len(words) > max_words:
processed["was_truncated"] = True
processed["truncated_text"] = " ".join(words[:max_words])
else:
processed["was_truncated"] = False
processed["truncated_text"] = text
return processed
def load_examples(src):
"""
load_examples - a helper function for the gradio module to load examples
Returns:
list of str, the examples
"""
src = Path(src)
src.mkdir(exist_ok=True)
examples = [f for f in src.glob("*.txt")]
examples = natsorted(examples)
# load the examples into a list
text_examples = []
for example in examples:
with open(example, "r") as f:
text = f.read()
text_examples.append([text, "large", 2, 512, 0.7, 3.5, 3])
return text_examples
def load_example_filenames(example_path: str or Path):
"""
load_example_filenames - a helper function for the gradio module to load examples
Returns:
dict, the examples (filename:full path)
"""
example_path = Path(example_path)
# load the examples into a list
examples = {f.name: f for f in example_path.glob("*.txt")}
return examples
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