func_wrapper.py is a Python module designed to streamline the integration of Hugging Face Transformers into your natural language processing (NLP) projects. It provides a set of input and output conversion wrappers to simplify the process of passing data between your custom functions and Transformers' data structures.

Input Conversion Wrappers:

inputs_to_transformers_tensors: This wrapper converts input data (text, tensors, etc.) into Transformers-compatible data structures. It is particularly useful when your custom functions expect diverse input types.
Output Conversion Wrappers:

outputs_to_pytorch_tensors: After your custom function returns data, this wrapper ensures that the output data is converted into PyTorch tensors or other appropriate formats.
Usage:

Import func_wrapper.py into your project.
Initialize a Hugging Face Transformers model and tokenizer.
Wrap your custom function with to_transformers_tensors_and_back. This wrapped function can now accept and return Transformers-compatible data.
Here's a simple example of how to use func_wrapper.py:

import torch
from transformers import BertForSequenceClassification, BertTokenizer
from iivy.functional.frontends.transformers.func_wrapper import to_transformers_tensors_and_back

# Initialize the model and tokenizer
model_name = "bert-base-uncased"
model = BertForSequenceClassification.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)

# Wrap your custom function using the conversion wrappers
wrapped_function = to_transformers_tensors_and_back(your_function, model, tokenizer)

# Prepare sample input data
sample_input_text = "This is a sample input text."
sample_input_tensor = torch.rand((3, 3))

# Call your wrapped function with the sample input data
output = wrapped_function(sample_input_text, sample_input_tensor)

# The output is automatically converted to PyTorch tensors
print(output)

Please note that func_wrapper.py is still in development, and further enhancements and refinements are expected. Your feedback and contributions to improve its functionality are welcome.

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