"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase      : int       ={
    'configuration_markuplm': ['MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarkupLMConfig'],
    'feature_extraction_markuplm': ['MarkupLMFeatureExtractor'],
    'processing_markuplm': ['MarkupLMProcessor'],
    'tokenization_markuplm': ['MarkupLMTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase      : List[Any]       =['MarkupLMTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase      : List[Any]       =[
           'MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MarkupLMForQuestionAnswering',
           'MarkupLMForSequenceClassification',
           'MarkupLMForTokenClassification',
           'MarkupLMModel',
           'MarkupLMPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
       from .feature_extraction_markuplm import MarkupLMFeatureExtractor
       from .processing_markuplm import MarkupLMProcessor
       from .tokenization_markuplm import MarkupLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_markuplm_fast import MarkupLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_markuplm import (
                  MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MarkupLMForQuestionAnswering,
                  MarkupLMForSequenceClassification,
                  MarkupLMForTokenClassification,
                  MarkupLMModel,
                  MarkupLMPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase      : Tuple       =_LazyModule(__name__, globals()['''__file__'''], _import_structure)











"""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()


