from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase	: Optional[Any]    		=  {
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: int    		=  [
					    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Data2VecAudioForAudioFrameClassification',
					    'Data2VecAudioForCTC',
					    'Data2VecAudioForSequenceClassification',
					    'Data2VecAudioForXVector',
					    'Data2VecAudioModel',
					    'Data2VecAudioPreTrainedModel',
					]
					__UpperCamelCase	: Optional[int]    		=  [
					    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Data2VecTextForCausalLM',
					    'Data2VecTextForMaskedLM',
					    'Data2VecTextForMultipleChoice',
					    'Data2VecTextForQuestionAnswering',
					    'Data2VecTextForSequenceClassification',
					    'Data2VecTextForTokenClassification',
					    'Data2VecTextModel',
					    'Data2VecTextPreTrainedModel',
					]
					__UpperCamelCase	: Any    		=  [
					    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Data2VecVisionForImageClassification',
					    'Data2VecVisionForMaskedImageModeling',
					    'Data2VecVisionForSemanticSegmentation',
					    'Data2VecVisionModel',
					    'Data2VecVisionPreTrainedModel',
					]

if is_tf_available():
					__UpperCamelCase	: List[str]    		=  [
					    'TFData2VecVisionForImageClassification',
					    'TFData2VecVisionForSemanticSegmentation',
					    'TFData2VecVisionModel',
					    'TFData2VecVisionPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
					from .configuration_dataavec_text import (
					    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecTextConfig,
					    DataaVecTextOnnxConfig,
					)
					from .configuration_dataavec_vision import (
					    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecVisionConfig,
					    DataaVecVisionOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dataavec_audio import (
										    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecAudioForAudioFrameClassification,
										    DataaVecAudioForCTC,
										    DataaVecAudioForSequenceClassification,
										    DataaVecAudioForXVector,
										    DataaVecAudioModel,
										    DataaVecAudioPreTrainedModel,
										)
										from .modeling_dataavec_text import (
										    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecTextForCausalLM,
										    DataaVecTextForMaskedLM,
										    DataaVecTextForMultipleChoice,
										    DataaVecTextForQuestionAnswering,
										    DataaVecTextForSequenceClassification,
										    DataaVecTextForTokenClassification,
										    DataaVecTextModel,
										    DataaVecTextPreTrainedModel,
										)
										from .modeling_dataavec_vision import (
										    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecVisionForImageClassification,
										    DataaVecVisionForMaskedImageModeling,
										    DataaVecVisionForSemanticSegmentation,
										    DataaVecVisionModel,
										    DataaVecVisionPreTrainedModel,
										)
					if is_tf_available():
										from .modeling_tf_dataavec_vision import (
										    TFDataaVecVisionForImageClassification,
										    TFDataaVecVisionForSemanticSegmentation,
										    TFDataaVecVisionModel,
										    TFDataaVecVisionPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase	: Optional[int]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import random
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							[], [], []
						for element in data:
												if element < pivot:
																		less.append(_lowercase		)
												elif element > pivot:
																		greater.append(_lowercase		)
												else:
																		equal.append(_lowercase		)
						return less, equal, greater



def    A			(					_lowercase	,						_lowercase		):
						# index = len(items) // 2 when trying to find the median
						#   (value of index when items is sorted)

						# invalid input
						if index >= len(_lowercase		) or index < 0:
												return None

						SCREAMING_SNAKE_CASE							:					Dict           =							items[random.randint(0	,						len(_lowercase		) - 1		)]
						SCREAMING_SNAKE_CASE							:					int           =							0
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							_partition(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(_lowercase		)

						# index is the pivot
						if m <= index < m + count:
												return pivot
						# must be in smaller
						elif m > index:
												return quick_select(_lowercase	,						_lowercase		)
						# must be in larger
						else:
												return quick_select(_lowercase	,						index - (m + count)		)





