


"""simple docstring"""







import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class  __A							(			unittest.TestCase     ):






 """simple docstring"""




 @cached_property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''		)


 @cached_property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=self.tokenizer_ab
     a											=''' I am a small frog.'''
     a											=tok([src_text]					,       padding=lowercase__					,       truncation=lowercase__		)['''input_ids''']
     a											=tok.batch_decode(lowercase__					,       skip_special_tokens=lowercase__					,       clean_up_tokenization_spaces=lowercase__		)[0]
     assert src_text == decoded


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=self.rust_tokenizer_ab
     a											=''' I am a small frog.'''
     a											=tok([src_text]					,       padding=lowercase__					,       truncation=lowercase__		)['''input_ids''']
     a											=tok.batch_decode(lowercase__					,       skip_special_tokens=lowercase__					,       clean_up_tokenization_spaces=lowercase__		)[0]
     assert src_text == decoded


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     assert self.tokenizer_ab.add_prefix_space
     assert self.tokenizer_ab([''' Sam''', '''Sam''']		).input_ids == [[5502, 2], [5502, 2]]





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     assert self.rust_tokenizer_ab.add_prefix_space
     assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']		).input_ids == [[5502, 2], [5502, 2]]






'''simple docstring'''

from __future__ import annotations




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      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:
            _snake_case		:						List[str]         =  capacity - weights[counter - 1]
            _snake_case		:						Tuple         =  values[counter - 1] + knapsack(
                lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            _snake_case		:						Any         =  knapsack(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            return max(lowerCAmelCase_      ,   lowerCAmelCase_						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


