






from __future__ import annotations
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  list(range(len(lowerCamelCase_)))
      UpperCamelCase					       =  [v / w for v, w in zip(lowerCamelCase_       ,							lowerCamelCase_)]
      index.sort(key=lambda _UpperCamelCase: ratio[i]       ,							reverse=lowerCamelCase_)

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

      return max_value, fractions


if __name__ == "__main__":
       import doctest

       doctest.testmod()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__      :     Optional[int]       =					{
    'configuration_clap': [
        'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ClapAudioConfig',
        'ClapConfig',
        'ClapTextConfig',
    ],
    'processing_clap': ['ClapProcessor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__      :     str       =					[
							    'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ClapModel',
							    'ClapPreTrainedModel',
							    'ClapTextModel',
							    'ClapTextModelWithProjection',
							    'ClapAudioModel',
							    'ClapAudioModelWithProjection',
							]
							A__      :     List[Any]       =					['ClapFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_clap import (
							    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
							    ClapAudioConfig,
							    ClapConfig,
							    ClapTextConfig,
							)
							from .processing_clap import ClapProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clap import ClapFeatureExtractor
														from .modeling_clap import (
														    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ClapAudioModel,
														    ClapAudioModelWithProjection,
														    ClapModel,
														    ClapPreTrainedModel,
														    ClapTextModel,
														    ClapTextModelWithProjection,
														)


else:
							import sys

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




