






from typing import TYPE_CHECKING

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


lowerCamelCase         =	{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase         =	['''PerceiverFeatureExtractor''']
 lowerCamelCase         =	['''PerceiverImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase         =	[
     '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''PerceiverForImageClassificationConvProcessing''',
     '''PerceiverForImageClassificationFourier''',
     '''PerceiverForImageClassificationLearned''',
     '''PerceiverForMaskedLM''',
     '''PerceiverForMultimodalAutoencoding''',
     '''PerceiverForOpticalFlow''',
     '''PerceiverForSequenceClassification''',
     '''PerceiverLayer''',
     '''PerceiverModel''',
     '''PerceiverPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

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














from __future__ import annotations





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

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

     return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()






