




'''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    __lowercase						(  __magic_name__ ):
   _a							=      """microsoft/speecht5_tts"""
   _a							=      (
       """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."""
   )
   _a							=      """text_reader"""
   _a							=      SpeechTaProcessor
   _a							=      SpeechTaForTextToSpeech
   _a							=      SpeechTaHifiGan

   _a							=      ["""text"""]
   _a							=      ["""audio"""]







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     if self.post_processor is None:
       __a			=					'microsoft/speecht5_hifigan'
     super().setup()







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    Any:
     __a			=					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.'      )

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

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    str:
     with torch.no_grad():
       return self.model.generate_speech(**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     with torch.no_grad():
       return self.post_processor(UpperCamelCase      ).cpu().detach()









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int			):
  return int((input_a, input_a).count(0			) != 0			)


def 						SCREAMING_SNAKE_CASE       (      ):
  assert nand_gate(0				,					0			) == 1
  assert nand_gate(0				,					1			) == 1
  assert nand_gate(1				,					0			) == 1
  assert nand_gate(1				,					1			) == 0


if __name__ == "__main__":
  print(nand_gate(0, 0))
  print(nand_gate(0, 1))
  print(nand_gate(1, 0))
  print(nand_gate(1, 1))



