






'''simple docstring'''






from __future__ import annotations




def        _SCREAMING_SNAKE_CASE  (				A  :       list[int]  ,		A  :       list[int]  ,		A  :       int  )    -> tuple[float, list[float]]:







   """simple docstring"""
   __snake_case				:      Any     							= list(range(len(A  )  )  )
   __snake_case				:      Optional[int]     							= [v / w for v, w in zip(A  ,		A  )]
   index.sort(key=lambda A  : ratio[i]  ,		reverse=A  )

   __snake_case				:      float     							= 0
   __snake_case				:      list[float]     							= [0] * len(A  )
   for i in index:
      if weight[i] <= capacity:
         __snake_case				:      Union[str, Any]     							= 1
         max_value += value[i]
         capacity -= weight[i]
      else:
         __snake_case				:      Optional[Any]     							= capacity / weight[i]
         max_value += value[i] * capacity / weight[i]
         break

   return max_value, fractions


if __name__ == "__main__":
      import doctest

      doctest.testmod()







'''simple docstring'''






from typing import TYPE_CHECKING

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


__A    	=   {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BioGptForCausalLM''',
          '''BioGptForTokenClassification''',
          '''BioGptForSequenceClassification''',
          '''BioGptModel''',
          '''BioGptPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)