


'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					global f  # a global dp table for knapsack
					if f[i][j] < 0:
										if j < wt[i - 1]:
															__a      :	List[str]    =  mf_knapsack(i - 1						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										else:
															__a      :	Tuple    =  max(
															    mf_knapsack(i - 1						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )						,				mf_knapsack(i - 1						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				j - wt[i - 1]       ) + val[i - 1]						,				)
										__a      :	Any    =  val
					return f[i][j]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       str       ):
					__a      :	int    =  [[0] * (w + 1) for _ in range(n + 1       )]

					for i in range(1						,				n + 1       ):
										for w_ in range(1						,				w + 1       ):
															if wt[i - 1] <= w_:
																				__a      :	str    =  max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]						,				dp[i - 1][w_]       )
															else:
																				__a      :	Union[str, Any]    =  dp[i - 1][w_]

					return dp[n][w_], dp






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       list						,				_SCREAMING_SNAKE_CASE				:       list       ):
					if not (isinstance(_SCREAMING_SNAKE_CASE						,				(list, tuple)       ) and isinstance(_SCREAMING_SNAKE_CASE						,				(list, tuple)       )):
										raise ValueError(
										    'Both the weights and values vectors must be either lists or tuples'       )

					__a      :	Any    =  len(_SCREAMING_SNAKE_CASE       )
					if num_items != len(_SCREAMING_SNAKE_CASE       ):
										__a      :	int    =  (
										    'The number of weights must be the same as the number of values.\n'
										    F"""But got {num_items} weights and {len(_SCREAMING_SNAKE_CASE       )} values"""
										)
										raise ValueError(_SCREAMING_SNAKE_CASE       )
					for i in range(_SCREAMING_SNAKE_CASE       ):
										if not isinstance(wt[i]						,				_SCREAMING_SNAKE_CASE       ):
															__a      :	Any    =  (
															    'All weights must be integers but got weight of '
															    F"""type {type(wt[i]       )} at index {i}"""
															)
															raise TypeError(_SCREAMING_SNAKE_CASE       )

					__a   ,    __a      :	List[str]    =  knapsack(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					__a      :	set    =  set()
					_construct_solution(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )

					return optimal_val, example_optional_set






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       list						,				_SCREAMING_SNAKE_CASE				:       list						,				_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       set       ):
					# for the current item i at a maximum weight j to be part of an optimal subset,
					# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
					# where i - 1 means considering only the previous items at the given maximum weight
					if i > 0 and j > 0:
										if dp[i - 1][j] == dp[i][j]:
															_construct_solution(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				i - 1						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										else:
															optimal_set.add(_SCREAMING_SNAKE_CASE       )
															_construct_solution(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				i - 1						,				j - wt[i - 1]						,				_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	__lowercase     :						Tuple	=     [3, 2, 4, 4]
	__lowercase     :						Any	=     [4, 3, 2, 3]
	__lowercase     :						Optional[Any]	=     4
	__lowercase     :						List[Any]	=     6
	__lowercase     :						List[Any]	=     [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
	__lowercase	,				__lowercase     :						List[str]	=     knapsack(w, wt, val, n)
	print(optimal_solution)
	print(mf_knapsack(n, wt, val, w))  # switched the n and w

	# testing the dynamic programming problem with example
	# the optimal subset for the above example are items 3 and 4
	__lowercase	,				__lowercase     :						Union[str, Any]	=     knapsack_with_example_solution(w, wt, val)
	assert optimal_solution == 8
	assert optimal_subset == {3, 4}
	print('optimal_value = ', optimal_solution)
	print('An optimal subset corresponding to the optimal value', optimal_subset)







'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_feature_extractor()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  ClapProcessor.from_pretrained(self.tmpdirname							)

						self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer.get_vocab()							)
						self.assertIsInstance(processor.tokenizer						,    __a							)

						self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  ClapProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

						self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()							)
						self.assertIsInstance(processor.tokenizer						,    __a							)

						self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  processor(audios=__a						,    return_tensors='np'							)

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1E-2							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Union[str, Any]    =  'This is a test string'

						__a      :	Union[str, Any]    =  processor(text=__a							)

						__a      :	Tuple    =  tokenizer(__a							)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

						__a      :	List[str]    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_feature_extractor()
						__a      :	Optional[int]    =  self.get_tokenizer()

						__a      :	int    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						self.assertListEqual(
						    processor.model_input_names[2:]						,    feature_extractor.model_input_names						,    msg='`processor` and `feature_extractor` model input names do not match'						,    )



