


'''simple docstring'''


from __future__ import annotations


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if len(SCREAMING_SNAKE_CASE							) == 0:
          return []
     __A  ,__A	:       Tuple      =							min(SCREAMING_SNAKE_CASE							), max(SCREAMING_SNAKE_CASE							)
     __A	:       Dict      =							int(max_value - min_value							) + 1
     __A	:       list[list]      =							[[] for _ in range(SCREAMING_SNAKE_CASE							)]

     for i in my_list:
          buckets[int(i - min_value							)].append(SCREAMING_SNAKE_CASE							)

     return [v for bucket in buckets for v in sorted(SCREAMING_SNAKE_CASE							)]


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
      assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

      _UpperCamelCase 							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

