
"""simple docstring"""

def      lowercase__    (       lowerCAmelCase					:       int			)			->   list[int]:






    """simple docstring"""




    if length <= 0 or not isinstance(lowerCAmelCase			,					lowerCAmelCase			):
        raise ValueError('Length must be a positive integer.'			)
    return [n * (2 * n - 1) for n in range(lowerCAmelCase			)]


if __name__ == "__main__":
   print(hexagonal_numbers(length=5))
   print(hexagonal_numbers(length=10))





"""simple docstring"""

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_            =    {
    '''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_            =    ['''SpeechT5Tokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''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_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



