




"""simple docstring"""

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)









from __future__ import annotations





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				list(range(len(a_)))
							snake_case_           =				[v / w for v, w in zip(a_				,	a_)]
							index.sort(key=lambda a_: ratio[i]				,	reverse=a_)

							snake_case_           =				0
							snake_case_           =				[0] * len(a_)
							for i in index:
														if weight[i] <= capacity:
																					snake_case_           =				1
																					max_value += value[i]
																					capacity -= weight[i]
														else:
																					snake_case_           =				capacity / weight[i]
																					max_value += value[i] * capacity / weight[i]
																					break

							return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()





