






"""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_							(      _UpperCamelCase  ):
				UpperCAmelCase__        =				'''microsoft/speecht5_tts'''
				UpperCAmelCase__        =				(
				    '''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.'''
				)
				UpperCAmelCase__        =				'''text_reader'''
				UpperCAmelCase__        =				SpeechTaProcessor
				UpperCAmelCase__        =				SpeechTaForTextToSpeech
				UpperCAmelCase__        =				SpeechTaHifiGan

				UpperCAmelCase__        =				['''text''']
				UpperCAmelCase__        =				['''audio''']







				def     _snake_case					(							self							:						str	)				->					Dict:
										if self.post_processor is None:
																__magic_name__            =     '''microsoft/speecht5_hifigan'''
										super().setup()







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						int=None	)				->					Dict:
										__magic_name__            =     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."	)

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

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







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						int	)				->					Union[str, Any]:
										with torch.no_grad():
																return self.model.generate_speech(**_UpperCAmelCase	)







				def     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						List[str]	)				->					Optional[int]:
										with torch.no_grad():
																return self.post_processor(_UpperCAmelCase	).cpu().detach()











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:str   ):

						'''simple docstring'''






						return "".join(chr(ord(__lowerCamelCase   ) - 3_2   ) if "a" <= char <= "z" else char for char in word   )


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

			testmod()



