

from typing import TYPE_CHECKING

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


__lowercase				      =						{
    """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:
      __lowercase				      =						["""SpeechT5Tokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				      =						[
          """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

      __lowercase				      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







'''simple docstring'''



def A ():
						for n in range(1					,      1000000						):
												yield n * (n + 1) // 2


def A (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					1
						_lowerCAmelCase    		=					2
						while i * i <= n:
												_lowerCAmelCase    		=					0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												divisors_count *= multiplicity + 1
												i += 1
						if n > 1:
												divisors_count *= 2
						return divisors_count


def A ():
						return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase						) > 500						)


if __name__ == "__main__":
						print(solution())
