



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 			__lowerCAmelCase  (						__UpperCAmelCase ):
 lowerCAmelCase__							       =	'''microsoft/speecht5_tts'''
 lowerCAmelCase__							       =	(
     '''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.'''
 )
 lowerCAmelCase__							       =	'''text_reader'''
 lowerCAmelCase__							       =	SpeechTaProcessor
 lowerCAmelCase__							       =	SpeechTaForTextToSpeech
 lowerCAmelCase__							       =	SpeechTaHifiGan

 lowerCAmelCase__							       =	['''text''']
 lowerCAmelCase__							       =	['''audio''']




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    if self.post_processor is None:
       __lowerCamelCase    				=     """microsoft/speecht5_hifigan"""
    super().setup()




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=None					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.pre_processor(text=lowerCamelCase__						,					return_tensors='''pt'''						,					truncation=lowerCamelCase__					)

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

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

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




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    with torch.no_grad():
       return self.model.generate_speech(**lowerCamelCase__					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    with torch.no_grad():
       return self.post_processor(lowerCamelCase__					).cpu().detach()










"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 assert column_title.isupper()
 lowercase__       :       int				=						0
 lowercase__       :       Optional[int]				=						len(lowerCamelCase__  ) - 1
 lowercase__       :       int				=						0

 while index >= 0:
  lowercase__       :       List[str]				=						(ord(column_title[index]  ) - 64) * pow(26       ,		lowerCamelCase__  )
  answer += value
  power += 1
  index -= 1

 return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()