readme: # deep-todo
  
  Wondering what to do? Not anymore!
  
  Generate arbitrary todo's.
  
  Source: <https://colab.research.google.com/drive/1PlKLrGHaCuvWCKNC4fmQEMElF-iRec9f?usp=sharing>
  
  The todo's come from a random selection of (public) repositories I had on my computer.
  
  
  ### Sample
  
  A bunch of todo's:
  
  ```
  ----------------------------------------------------------------------------------------------------
  0: TODO: should we check the other edges?/
  1: TODO: add more information here.
  2: TODO: We could also add more general functions in this case to avoid/
  3: TODO: It seems strange to have the same constructor when the base set of/
  4: TODO: This implementation should be simplified, as it's too complex to handle the/
  5: TODO: we should be able to relax the intrinsic if not
  6: TODO: Make sure this doesn't go through the next generation of plugins.  It would be better if this was
  7: TODO: There is always a small number of errors when we have this type/
  8: TODO: Add support for 't' values (not 't') for all the constant types/
  9: TODO: Check that we use loglef_cxx in the loop*
  10: TODO: Support double or double values./
  11: TODO: Add tests that verify that this function does not work for all targets/
  12: TODO: we'd expect the result to be identical to the same value in terms of
  13: TODO: We are not using a new type for 'w' as it does not denote 'y' yet, so we could/
  14: TODO: if we had to find a way to extract the source file directly, we would/
  15: TODO: this should fold into a flat array that would be/
  16: TODO: Check if we can make it work with the correct address./
  17: TODO: support v2i with V2R4+
  18: TODO: Can a fast-math-flags check be generalized to all types of data? */
  19: TODO: Add support for other type-specific VOPs.
  ```
  
  Generated by:
  
  ```
  tf.random.set_seed(0)
  
  sample_outputs = model.generate(
      input_ids,
      do_sample=True, 
      max_length=40, 
      top_k=50, 
      top_p=0.95, 
      num_return_sequences=20
  )
  
  print("Output:\\
  " + 100 * '-')
  for i, sample_output in enumerate(sample_outputs):
    m = tokenizer.decode(sample_output, skip_special_tokens=True)
    m = m.split("TODO")[1].strip()
    print("{}: TODO{}".format(i, m))
  ```
  
  
  ## TODO
  
  - [ ] Fixup the data; it seems to contain multiple todo's per line
  - [ ] Preprocess the data in a better way
  - [ ] Download github and train it on everything