






from __future__ import annotations

_SCREAMING_SNAKE_CASE   =  10





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


if __name__ == "__main__":
    import doctest

    doctest.testmod()










from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[List[PIL.Image.Image], np.ndarray]
       __lowerCAmelCase    :		Optional[List[bool]]


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


