


from __future__ import annotations

def 			lowerCAmelCase__					(     a__:						list[int]     ,    a__:						int			)    ->						list[list[int]]:


							'''simple docstring'''





							_UpperCAmelCase											=     []
							_UpperCAmelCase											=     []
							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     sum(a__			)
							create_state_space_tree(a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
							return result

def 			lowerCAmelCase__					(     a__:						list[int]     ,    a__:						int     ,    a__:						int     ,    a__:						list[int]     ,    a__:						list[list[int]]     ,    a__:						int     ,    )    ->						None:


							'''simple docstring'''





							if sum(a__			) > max_sum or (remaining_nums_sum + sum(a__			)) < max_sum:
														return
							if sum(a__			) == max_sum:
														result.append(a__			)
														return
							for index in range(a__     ,    len(a__			)			):
														create_state_space_tree(
														    a__     ,    a__     ,    index + 1     ,    [*path, nums[index]]     ,    a__     ,    remaining_nums_sum - nums[index]     ,    )
lowerCAmelCase__    :Optional[int]       =				[3, 3_4, 4, 1_2, 5, 2]
lowerCAmelCase__    :List[Any]       =				9
lowerCAmelCase__    :Any       =				generate_sum_of_subsets_soln(nums, max_sum)







print(*result)




from typing import TYPE_CHECKING

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


lowerCAmelCase__    :Tuple       =				{
    '''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:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''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

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
