
from typing import TYPE_CHECKING

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


__a         =   {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a         =   [
						    'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RobertaPreLayerNormForCausalLM',
						    'RobertaPreLayerNormForMaskedLM',
						    'RobertaPreLayerNormForMultipleChoice',
						    'RobertaPreLayerNormForQuestionAnswering',
						    'RobertaPreLayerNormForSequenceClassification',
						    'RobertaPreLayerNormForTokenClassification',
						    'RobertaPreLayerNormModel',
						    'RobertaPreLayerNormPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a         =   [
						    'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRobertaPreLayerNormForCausalLM',
						    'TFRobertaPreLayerNormForMaskedLM',
						    'TFRobertaPreLayerNormForMultipleChoice',
						    'TFRobertaPreLayerNormForQuestionAnswering',
						    'TFRobertaPreLayerNormForSequenceClassification',
						    'TFRobertaPreLayerNormForTokenClassification',
						    'TFRobertaPreLayerNormMainLayer',
						    'TFRobertaPreLayerNormModel',
						    'TFRobertaPreLayerNormPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a         =   [
						    'FlaxRobertaPreLayerNormForCausalLM',
						    'FlaxRobertaPreLayerNormForMaskedLM',
						    'FlaxRobertaPreLayerNormForMultipleChoice',
						    'FlaxRobertaPreLayerNormForQuestionAnswering',
						    'FlaxRobertaPreLayerNormForSequenceClassification',
						    'FlaxRobertaPreLayerNormForTokenClassification',
						    'FlaxRobertaPreLayerNormModel',
						    'FlaxRobertaPreLayerNormPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_roberta_prelayernorm import (
						    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    RobertaPreLayerNormConfig,
						    RobertaPreLayerNormOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roberta_prelayernorm import (
												    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RobertaPreLayerNormForCausalLM,
												    RobertaPreLayerNormForMaskedLM,
												    RobertaPreLayerNormForMultipleChoice,
												    RobertaPreLayerNormForQuestionAnswering,
												    RobertaPreLayerNormForSequenceClassification,
												    RobertaPreLayerNormForTokenClassification,
												    RobertaPreLayerNormModel,
												    RobertaPreLayerNormPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roberta_prelayernorm import (
												    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRobertaPreLayerNormForCausalLM,
												    TFRobertaPreLayerNormForMaskedLM,
												    TFRobertaPreLayerNormForMultipleChoice,
												    TFRobertaPreLayerNormForQuestionAnswering,
												    TFRobertaPreLayerNormForSequenceClassification,
												    TFRobertaPreLayerNormForTokenClassification,
												    TFRobertaPreLayerNormMainLayer,
												    TFRobertaPreLayerNormModel,
												    TFRobertaPreLayerNormPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roberta_prelayernorm import (
												    FlaxRobertaPreLayerNormForCausalLM,
												    FlaxRobertaPreLayerNormForMaskedLM,
												    FlaxRobertaPreLayerNormForMultipleChoice,
												    FlaxRobertaPreLayerNormForQuestionAnswering,
												    FlaxRobertaPreLayerNormForSequenceClassification,
												    FlaxRobertaPreLayerNormForTokenClassification,
												    FlaxRobertaPreLayerNormModel,
												    FlaxRobertaPreLayerNormPreTrainedModel,
												)

else:
						import sys

						__a         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""
import random

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      List[Any]						, __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		a[left_index]
   _lowerCamelCase	:					Dict   =		left_index + 1
   for j in range(left_index + 1						, __snake_case   ):
      if a[j] < pivot:
         _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		a[i], a[j]
         i += 1
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		a[i - 1], a[left_index]
   return i - 1

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]						, __snake_case							:      List[str]   ):




   """simple docstring"""







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

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		input("""Enter numbers separated by a comma:\n"""   ).strip()
   _lowerCamelCase	:					int   =		[int(__snake_case   ) for item in user_input.split(""","""   )]

   quick_sort_random(__snake_case						, 0						, len(__snake_case   )   )

   print(__snake_case   )


if __name__ == "__main__":
 main()

