

from typing import TYPE_CHECKING

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


_lowerCamelCase							:  List[Any]     =				{
    'configuration_flava': [
        'FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'FlavaConfig',
        'FlavaImageCodebookConfig',
        'FlavaImageConfig',
        'FlavaMultimodalConfig',
        'FlavaTextConfig',
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase							:  str     =				['FlavaFeatureExtractor']
     _lowerCamelCase							:  Any     =				['FlavaImageProcessor']
     _lowerCamelCase							:  str     =				['FlavaProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase							:  str     =				[
         'FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FlavaForPreTraining',
         'FlavaImageCodebook',
         'FlavaImageModel',
         'FlavaModel',
         'FlavaMultimodalModel',
         'FlavaPreTrainedModel',
         'FlavaTextModel',
     ]

if TYPE_CHECKING:
     from .configuration_flava import (
         FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
         FlavaConfig,
         FlavaImageCodebookConfig,
         FlavaImageConfig,
         FlavaMultimodalConfig,
         FlavaTextConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_flava import FlavaFeatureExtractor
          from .image_processing_flava import FlavaImageProcessor
          from .processing_flava import FlavaProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flava import (
              FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlavaForPreTraining,
              FlavaImageCodebook,
              FlavaImageModel,
              FlavaModel,
              FlavaMultimodalModel,
              FlavaPreTrainedModel,
              FlavaTextModel,
          )

else:
     import sys

     _lowerCamelCase							:  Optional[Any]     =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













"""simple docstring"""







from __future__ import annotations





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):

					# Base Case
					if counter == 0 or capacity == 0:
										return 0

					# If weight of the nth item is more than Knapsack of capacity,
					#   then this item cannot be included in the optimal solution,
					# else return the maximum of two cases:
					#   (1) nth item included
					#   (2) not included
					if weights[counter - 1] > capacity:
										return knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
					else:
										a__ 							=	capacity - weights[counter - 1]
										a__ 							=	values[counter - 1] + knapsack(
										    lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										a__ 							=	knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										return max(lowerCamelCase_			,  lowerCamelCase_)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


