



from typing import TYPE_CHECKING

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


__lowerCamelCase								=				{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase								=				[
        """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DonutSwinModel""",
        """DonutSwinPreTrainedModel""",
    ]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase								=				["""DonutFeatureExtractor"""]
    __lowerCamelCase								=				["""DonutImageProcessor"""]


if TYPE_CHECKING:
    from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
    from .processing_donut import DonutProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_donut_swin import (
            DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            DonutSwinModel,
            DonutSwinPreTrainedModel,
        )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_donut import DonutFeatureExtractor
        from .image_processing_donut import DonutImageProcessor

else:
    import sys

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




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	int:

  # 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(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
  else:
    _A          =      capacity - weights[counter - 1]
    _A          =      values[counter - 1] + knapsack(
        __snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    _A          =      knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    return max(__snake_case  ,			__snake_case							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()