"""simple docstring"""






import math



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5       ,    int(math.sqrt(__UpperCAmelCase ) + 1 )       ,    6 ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 1_0_0_0_1 )  ->  int:

   try:
      lowercase__:   Dict					=      int(__UpperCAmelCase )
   except (TypeError, ValueError):
      raise TypeError('''Parameter nth must be int or castable to int.''' ) from None
   if nth <= 0:
      raise ValueError('''Parameter nth must be greater than or equal to one.''' )
   lowercase__:   list[int]					=      []
   lowercase__:   Dict					=      2
   while len(__UpperCAmelCase ) < nth:
      if is_prime(__UpperCAmelCase ):
         primes.append(__UpperCAmelCase )
         num += 1
      else:
         num += 1
   return primes[len(__UpperCAmelCase ) - 1]


if __name__ == "__main__":
       print(f'''{solution() = }''')

"""simple docstring"""






from typing import TYPE_CHECKING

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


__A			   = {
    "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:
       __A			   = ["SpeechT5Tokenizer"]

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

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