"""simple docstring"""


import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
 from datasets import load_dataset






class    a__    (   _a	):
   snake_case_							=      "microsoft/speecht5_tts"
   snake_case_							=      (
       "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
       "text to read (in English) and returns a waveform object containing the sound."
   )
   snake_case_							=      "text_reader"
   snake_case_							=      SpeechTaProcessor
   snake_case_							=      SpeechTaForTextToSpeech
   snake_case_							=      SpeechTaHifiGan

   snake_case_							=      ["text"]
   snake_case_							=      ["audio"]


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if self.post_processor is None:
             lowercase__				 =							"microsoft/speecht5_hifigan"
        super().setup()


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase=None  ):
        '''simple docstring'''



        lowercase__				 =							self.pre_processor(text=_UpperCAmelCase,				return_tensors="pt",				truncation=_UpperCAmelCase  )

        if speaker_embeddings is None:
             if not is_datasets_available():
                  raise ImportError("Datasets needs to be installed if not passing speaker embeddings."  )

             lowercase__				 =							load_dataset("Matthijs/cmu-arctic-xvectors",				split="validation"  )
             lowercase__				 =							torch.tensor(embeddings_dataset[7305]["xvector"]  ).unsqueeze(0  )

        return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        with torch.no_grad():
             return self.model.generate_speech(**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        with torch.no_grad():
             return self.post_processor(_UpperCAmelCase  ).cpu().detach()
"""simple docstring"""


import itertools
import math







def 					__a   (		A      ):






     '''simple docstring'''



     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(A      ) + 1      )    ,				6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							2
     while True:
          if is_prime(A      ):
               yield num
          num += 1







def 					__a   (		A = 1_00_01      ):






     '''simple docstring'''



     return next(itertools.islice(prime_generator()    ,				nth - 1    ,				A      )      )


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




