



'''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 _UpperCamelCase		(     _snake_case		):





				'''simple docstring'''





				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  :  List[str]):







											'''simple docstring'''

											if self.post_processor is None:
																		__lowercase       	='microsoft/speecht5_hifigan'
											super().setup()

				def 				__lowerCamelCase     (							self  :  Any  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  List[str]=None):







											'''simple docstring'''

											__lowercase       	=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.')

																		__lowercase       	=load_dataset('Matthijs/cmu-arctic-xvectors'  ,       split='validation')
																		__lowercase       	=torch.tensor(embeddings_dataset[7_3_0_5]['xvector']).unsqueeze(0)

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

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  Optional[int]):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Any  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											with torch.no_grad():
																		return self.post_processor(_lowerCAmelCase).cpu().detach()








'''simple docstring'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if not isinstance(_lowerCAmelCase  ,   _lowerCAmelCase					):
														raise TypeError('only integers accepted as input'					)
							else:
														__lowercase       	=str(abs(_lowerCAmelCase					)					)
														__lowercase       	=[list(_lowerCAmelCase					) for char in range(len(_lowerCAmelCase					)					)]
														for index in range(len(_lowerCAmelCase					)					):
																					num_transpositions[index].pop(_lowerCAmelCase					)
														return max(
														    int(''.join(list(_lowerCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
		__import__("""doctest""").testmod()



