






import random

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						__magic_name__						,__magic_name__						,__magic_name__      :       List[str]       						= [], [], []
						for element in data:
												if element < pivot:
																		less.append(_A			)
												elif element > pivot:
																		greater.append(_A			)
												else:
																		equal.append(_A			)
						return less, equal, greater

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						if index >= len(_A			) or index < 0:
												return None

						__magic_name__      :       Optional[int]       						= items[random.randint(0,   len(_A			) - 1			)]
						__magic_name__      :       Optional[int]       						= 0
						__magic_name__						,__magic_name__						,__magic_name__      :       int       						= _partition(_A,   _A			)
						__magic_name__      :       Dict       						= len(_A			)
						__magic_name__      :       Dict       						= len(_A			)

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








from typing import TYPE_CHECKING

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


__magic_name__:					Tuple							=     {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					Dict							=     [
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					int							=     [
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		__magic_name__:					Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
