






from typing import TYPE_CHECKING

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


_a      	=  {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  ['''ConvNextFeatureExtractor''']
		_a      	=  ['''ConvNextImageProcessor''']

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

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''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      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)



class  lowercase				:




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    name
											snake_case_							=    value
											snake_case_							=    weight




				def __repr__(					self       ):
											return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''




				def     a							(					self       ):
											return self.value




				def     a							(					self       ):
											return self.name




				def     a							(					self       ):
											return self.weight




				def     a							(					self       ):
											return self.value / self.weight





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    []
							for i in range(len(UpperCamelCase__      )      ):
														menu.append(Things(name[i]   ,							value[i]   ,							weight[i]      )      )
							return menu





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    sorted(UpperCamelCase__   ,							key=UpperCamelCase__   ,							reverse=UpperCamelCase__      )
							snake_case_							=    []
							snake_case_				,    snake_case_							=    0.0, 0.0
							for i in range(len(UpperCamelCase__      )      ):
														if (total_cost + items_copy[i].get_weight()) <= max_cost:
																					result.append(items_copy[i]      )
																					total_cost += items_copy[i].get_weight()
																					total_value += items_copy[i].get_value()
							return (result, total_value)





def 					__lowerCamelCase	( ):







							'''simple docstring'''








if __name__ == "__main__":
					import doctest

					doctest.testmod()




