





'''simple docstring'''

import random





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[str]    )						->Optional[int]:
    _SCREAMING_SNAKE_CASE									=			a[left_index]
    _SCREAMING_SNAKE_CASE									=			left_index + 1
    for j in range(left_index + 1					,       __lowerCamelCase    ):
        if a[j] < pivot:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			a[i], a[j]
            i += 1
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			a[i - 1], a[left_index]
    return i - 1





def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    int    )						->str:
    if left < right:
        _SCREAMING_SNAKE_CASE									=			random.randint(__lowerCamelCase					,       right - 1    )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        _SCREAMING_SNAKE_CASE									=			partition(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
        quick_sort_random(
            __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            __lowerCamelCase					,       pivot_index + 1					,       __lowerCamelCase    )  # recursive quicksort to the right of the pivot point





def lowerCamelCase						(     )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			input("""Enter numbers separated by a comma:\n"""    ).strip()
    _SCREAMING_SNAKE_CASE									=			[int(__lowerCamelCase    ) for item in user_input.split(""","""    )]

    quick_sort_random(__lowerCamelCase					,       0					,       len(__lowerCamelCase    )    )

    print(__lowerCamelCase    )


if __name__ == "__main__":
      main()










'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline



