



"""simple docstring"""







from __future__ import annotations

from random import randrange


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		if len(__UpperCAmelCase      ) < 2:
				return collection
		_lowercase				: List[Any]     =   randrange(len(__UpperCAmelCase      )      )  # Use random element as pivot
		_lowercase				: int     =   collection[pivot_index]
		_lowercase				: list[int]     =   []  # All elements greater than pivot
		_lowercase				: list[int]     =   []  # All elements less than or equal to pivot

		for element in collection[:pivot_index]:
				(greater if element > pivot else lesser).append(__UpperCAmelCase      )

		for element in collection[pivot_index + 1 :]:
				(greater if element > pivot else lesser).append(__UpperCAmelCase      )

		return [*quick_sort(__UpperCAmelCase      ), pivot, *quick_sort(__UpperCAmelCase      )]


if __name__ == "__main__":
		UpperCAmelCase:  Optional[Any]			     =				input("""Enter numbers separated by a comma:\n""").strip()
		UpperCAmelCase:  Tuple			     =				[int(item) for item in user_input.split(""",""")]
		print(quick_sort(unsorted))










"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case	:						Any					=					{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					[
		    'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'UniSpeechSatForAudioFrameClassification',
		    'UniSpeechSatForCTC',
		    'UniSpeechSatForPreTraining',
		    'UniSpeechSatForSequenceClassification',
		    'UniSpeechSatForXVector',
		    'UniSpeechSatModel',
		    'UniSpeechSatPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_snake_case	:						Union[str, Any]					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


