



"""simple docstring"""
def        lowerCAmelCase_(   lowercase_     :							Optional[int]						,   lowercase_     :							str						,   lowercase_     :							Any						,   lowercase_     :							int							)	->       List[str]:
				global f  # a global dp table for knapsack
				if f[i][j] < 0:
								if j < wt[i - 1]:
												_lowerCamelCase        =				mf_knapsack(i - 1						,   lowercase_						,   lowercase_						,   lowercase_							)
								else:
												_lowerCamelCase        =				max(
												    mf_knapsack(i - 1						,   lowercase_						,   lowercase_						,   lowercase_							)						,   mf_knapsack(i - 1						,   lowercase_						,   lowercase_						,   j - wt[i - 1]							) + val[i - 1]						,   )
								_lowerCamelCase        =				val
				return f[i][j]



def        lowerCAmelCase_(   lowercase_     :							Tuple						,   lowercase_     :							List[str]						,   lowercase_     :							Tuple						,   lowercase_     :							Dict							)	->       Optional[int]:
				_lowerCamelCase        =				[[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_:
																_lowerCamelCase        =				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]						,   dp[i - 1][w_]							)
												else:
																_lowerCamelCase        =				dp[i - 1][w_]

				return dp[n][w_], dp



def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							list						,   lowercase_     :							list							)	->       Tuple:
				if not (isinstance(lowercase_						,   (list, tuple)							) and isinstance(lowercase_						,   (list, tuple)							)):
								raise ValueError(
								    '''Both the weights and values vectors must be either lists or tuples'''							)

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

				_lowerCamelCase  ,     _lowerCamelCase        =				knapsack(lowercase_						,   lowercase_						,   lowercase_						,   lowercase_							)
				_lowerCamelCase        =				set()
				_construct_solution(lowercase_						,   lowercase_						,   lowercase_						,   lowercase_						,   lowercase_							)

				return optimal_val, example_optional_set



def        lowerCAmelCase_(   lowercase_     :							list						,   lowercase_     :							list						,   lowercase_     :							int						,   lowercase_     :							int						,   lowercase_     :							set							)	->       Optional[Any]:
				# 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(lowercase_						,   lowercase_						,   i - 1						,   lowercase_						,   lowercase_							)
								else:
												optimal_set.add(lowercase_							)
												_construct_solution(lowercase_						,   lowercase_						,   i - 1						,   j - wt[i - 1]						,   lowercase_							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      [3, 2, 4, 4]
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      [4, 3, 2, 3]
			__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      4
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      6
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						int    							=      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
			__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						int    							=      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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Tuple    							=      ['''SpeechT5Tokenizer''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Any    							=      [
			    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SpeechT5ForSpeechToText''',
			    '''SpeechT5ForSpeechToSpeech''',
			    '''SpeechT5ForTextToSpeech''',
			    '''SpeechT5Model''',
			    '''SpeechT5PreTrainedModel''',
			    '''SpeechT5HifiGan''',
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						int    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
