

'''simple docstring'''







from typing import TYPE_CHECKING

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


__lowerCamelCase									=     {
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''GPTNeoForCausalLM''',
          '''GPTNeoForQuestionAnswering''',
          '''GPTNeoForSequenceClassification''',
          '''GPTNeoForTokenClassification''',
          '''GPTNeoModel''',
          '''GPTNeoPreTrainedModel''',
          '''load_tf_weights_in_gpt_neo''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''FlaxGPTNeoForCausalLM''',
          '''FlaxGPTNeoModel''',
          '''FlaxGPTNeoPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_neo import (
                GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTNeoForCausalLM,
                GPTNeoForQuestionAnswering,
                GPTNeoForSequenceClassification,
                GPTNeoForTokenClassification,
                GPTNeoModel,
                GPTNeoPreTrainedModel,
                load_tf_weights_in_gpt_neo,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
      import sys

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



'''simple docstring'''







from bisect import bisect
from itertools import accumulate
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       A_						       =   sorted(zip(UpperCAmelCase__,							UpperCAmelCase__					),							key=lambda UpperCAmelCase__					: x[0] / x[1],							reverse=UpperCAmelCase__					)
       A_       ,      A_						       =   [i[0] for i in r], [i[1] for i in r]
       A_						       =   list(accumulate(UpperCAmelCase__					)					)
       A_						       =   bisect(UpperCAmelCase__,							UpperCAmelCase__					)
       return (
           0
           if k == 0
           else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]					)
       )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
