






"""simple docstring"""



from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
						from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




"""simple docstring"""




from __future__ import annotations

_a      :		List[str]           =   10





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list[int]	)       ->      list[int]:
  _lowerCAmelCase						:       Optional[int]  			=     1
  _lowerCAmelCase						:       Union[str, Any]  			=     max(_lowerCamelCase	)
  while placement <= max_digit:
    # declare and initialize empty buckets
    _lowerCAmelCase						:       list[list]  			=     [[] for _ in range(_lowerCamelCase	)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      _lowerCAmelCase						:       Tuple  			=     int((i / placement) % RADIX	)
      buckets[tmp].append(_lowerCamelCase	)
    # put each buckets' contents into list_of_ints
    _lowerCAmelCase						:       List[str]  			=     0
    for b in range(_lowerCamelCase	):
      for i in buckets[b]:
        _lowerCAmelCase						:       Any  			=     i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
     import doctest

     doctest.testmod()




