code: 
  
  from __future__ import annotations
  
  from random import randrange
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					if len(UpperCAmelCase_    ) < 2:
  										return collection
  					lowercase__					:     Tuple   = 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__":
  							snake_case_    						=				input('''Enter numbers separated by a comma:\n''').strip()
  							snake_case_    						=				[int(item) for item in user_input.split(''',''')]
  							print(quick_sort(unsorted))
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  