



'''simple docstring'''






from typing import TYPE_CHECKING

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


UpperCamelCase__:   Any								=			{
    "configuration_bert": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertConfig", "BertOnnxConfig"],
    "tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   List[str]								=			["BertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Optional[Any]								=			[
						    "BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BertForMaskedLM",
						    "BertForMultipleChoice",
						    "BertForNextSentencePrediction",
						    "BertForPreTraining",
						    "BertForQuestionAnswering",
						    "BertForSequenceClassification",
						    "BertForTokenClassification",
						    "BertLayer",
						    "BertLMHeadModel",
						    "BertModel",
						    "BertPreTrainedModel",
						    "load_tf_weights_in_bert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   List[str]								=			[
						    "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFBertEmbeddings",
						    "TFBertForMaskedLM",
						    "TFBertForMultipleChoice",
						    "TFBertForNextSentencePrediction",
						    "TFBertForPreTraining",
						    "TFBertForQuestionAnswering",
						    "TFBertForSequenceClassification",
						    "TFBertForTokenClassification",
						    "TFBertLMHeadModel",
						    "TFBertMainLayer",
						    "TFBertModel",
						    "TFBertPreTrainedModel",
						]
try:
						if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Any								=			["TFBertTokenizer"]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Any								=			[
						    "FlaxBertForCausalLM",
						    "FlaxBertForMaskedLM",
						    "FlaxBertForMultipleChoice",
						    "FlaxBertForNextSentencePrediction",
						    "FlaxBertForPreTraining",
						    "FlaxBertForQuestionAnswering",
						    "FlaxBertForSequenceClassification",
						    "FlaxBertForTokenClassification",
						    "FlaxBertModel",
						    "FlaxBertPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
						from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bert_fast import BertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bert import (
												    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BertForMaskedLM,
												    BertForMultipleChoice,
												    BertForNextSentencePrediction,
												    BertForPreTraining,
												    BertForQuestionAnswering,
												    BertForSequenceClassification,
												    BertForTokenClassification,
												    BertLayer,
												    BertLMHeadModel,
												    BertModel,
												    BertPreTrainedModel,
												    load_tf_weights_in_bert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bert import (
												    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFBertEmbeddings,
												    TFBertForMaskedLM,
												    TFBertForMultipleChoice,
												    TFBertForNextSentencePrediction,
												    TFBertForPreTraining,
												    TFBertForQuestionAnswering,
												    TFBertForSequenceClassification,
												    TFBertForTokenClassification,
												    TFBertLMHeadModel,
												    TFBertMainLayer,
												    TFBertModel,
												    TFBertPreTrainedModel,
												)

						try:
												if not is_tensorflow_text_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bert_tf import TFBertTokenizer

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bert import (
												    FlaxBertForCausalLM,
												    FlaxBertForMaskedLM,
												    FlaxBertForMultipleChoice,
												    FlaxBertForNextSentencePrediction,
												    FlaxBertForPreTraining,
												    FlaxBertForQuestionAnswering,
												    FlaxBertForSequenceClassification,
												    FlaxBertForTokenClassification,
												    FlaxBertModel,
												    FlaxBertPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__:   Any								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










import random






def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					a					     =   a[left_index]
					a					     =   left_index + 1
					for j in range(left_index + 1 ,     A       ):
										if a[j] < pivot:
															a     ,				a					     =   a[i], a[j]
															i += 1
					a     ,				a					     =   a[i - 1], a[left_index]
					return i - 1






def     a(      A       :    List[Any] ,     A       :    List[Any] ,     A       :    Union[str, Any]       )		->       List[Any]:
					"""simple docstring"""





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






def     a(      )		->       Any:
					"""simple docstring"""





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

					quick_sort_random(A ,     0 ,     len(A       )       )

					print(A       )


if __name__ == "__main__":
				main()
