
from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE     :   Any							 =      {
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Optional[int]							 =      ['LayoutLMTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Any							 =      [
					    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'LayoutLMForMaskedLM',
					    'LayoutLMForSequenceClassification',
					    'LayoutLMForTokenClassification',
					    'LayoutLMForQuestionAnswering',
					    'LayoutLMModel',
					    'LayoutLMPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Dict							 =      [
					    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFLayoutLMForMaskedLM',
					    'TFLayoutLMForSequenceClassification',
					    'TFLayoutLMForTokenClassification',
					    'TFLayoutLMForQuestionAnswering',
					    'TFLayoutLMMainLayer',
					    'TFLayoutLMModel',
					    'TFLayoutLMPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
					from .tokenization_layoutlm import LayoutLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlm import (
										    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMForMaskedLM,
										    LayoutLMForQuestionAnswering,
										    LayoutLMForSequenceClassification,
										    LayoutLMForTokenClassification,
										    LayoutLMModel,
										    LayoutLMPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_layoutlm import (
										    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLayoutLMForMaskedLM,
										    TFLayoutLMForQuestionAnswering,
										    TFLayoutLMForSequenceClassification,
										    TFLayoutLMForTokenClassification,
										    TFLayoutLMMainLayer,
										    TFLayoutLMModel,
										    TFLayoutLMPreTrainedModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE     :   Dict							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) < 2:
		return collection
	lowercase_			 = randrange(len(__lowerCamelCase  )  )  # Use random element as pivot
	lowercase_			 = collection[pivot_index]
	lowercase_			 = []  # All elements greater than pivot
	lowercase_			 = []  # All elements less than or equal to pivot

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

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

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


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

