


from __future__ import annotations

def 			lowerCAmelCase__					(     a__:						list[float]			)    ->						bool:


							'''simple docstring'''





							if len(a__			) < 2:
														raise ValueError('Monogons and Digons are not polygons in the Euclidean space'			)
							if any(i <= 0 for i in nums			):
														raise ValueError('All values must be greater than 0'			)
							_UpperCAmelCase											=     nums.copy()
							copy_nums.sort()
							return copy_nums[-1] < sum(copy_nums[:-1]			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




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__)
