






"""simple docstring"""
class      UpperCamelCase_			(__A	):
	pass







class      UpperCamelCase_			(__A	):
	pass







class      UpperCamelCase_			:





	def __init__( self   :					List[str]		)		->     Tuple:
				UpperCAmelCase_	:					int     	=   [
				    [],
				    [],
				    [],
				]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int		)		->     None:
				try:
							if len(self.queues[priority]		) >= 100:
										raise OverflowError("Maximum queue size is 100"		)
							self.queues[priority].append(lowerCAmelCase_		)
				except IndexError:
							raise ValueError("Valid priorities are 0, 1, and 2"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				for queue in self.queues:
							if queue:
										return queue.pop(0		)
				raise UnderFlowError("All queues are empty"		)





	def __str__( self   :					int		)		->     str:
				return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues		)		)







class      UpperCamelCase_			:





	def __init__( self   :					int		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Tuple     	=   []





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int		)		->     None:
				if len(self.queue		) == 100:
							raise OverFlowError("Maximum queue size is 100"		)
				self.queue.append(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				if not self.queue:
							raise UnderFlowError("The queue is empty"		)
				else:
							UpperCAmelCase_	:					Union[str, Any]     	=   min(self.queue		)
							self.queue.remove(lowerCAmelCase_		)
							return data





	def __str__( self   :					Dict		)		->     str:
				return str(self.queue		)





def 							snake_case (  ):
			UpperCAmelCase_	:					Dict     	=   FixedPriorityQueue()
			fpq.enqueue(0      ,10		)
			fpq.enqueue(1      ,70		)
			fpq.enqueue(0      ,1_00		)
			fpq.enqueue(2      ,1		)
			fpq.enqueue(2      ,5		)
			fpq.enqueue(1      ,7		)
			fpq.enqueue(2      ,4		)
			fpq.enqueue(1      ,64		)
			fpq.enqueue(0      ,1_28		)
			print(A__		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(A__		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)

def 							snake_case (  ):
			UpperCAmelCase_	:					Dict     	=   ElementPriorityQueue()
			epq.enqueue(10		)
			epq.enqueue(70		)
			epq.enqueue(1_00		)
			epq.enqueue(1		)
			epq.enqueue(5		)
			epq.enqueue(7		)
			epq.enqueue(4		)
			epq.enqueue(64		)
			epq.enqueue(1_28		)
			print(A__		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(A__		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)


if __name__ == "__main__":
			fixed_priority_queue()
			element_priority_queue()







from typing import TYPE_CHECKING

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


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

