






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(  __lowerCamelCase   ):
					snake_case_		:				Tuple										=	"""microsoft/speecht5_tts"""
					snake_case_		:				Dict										=	(
					    """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_		:				Optional[int]										=	"""text_reader"""
					snake_case_		:				str										=	SpeechTaProcessor
					snake_case_		:				int										=	SpeechTaForTextToSpeech
					snake_case_		:				Optional[Any]										=	SpeechTaHifiGan

					snake_case_		:				Tuple										=	["""text"""]
					snake_case_		:				List[str]										=	["""audio"""]


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Tuple:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		int=None						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		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."						)

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

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	List[str]:




											'''simple docstring'''

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	List[Any]:




											'''simple docstring'''

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









def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						__snake_case				=		0
						while n > 0:
												res += n % 10
												n //= 10
						return res





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						return n if n < 10 else n % 10 + sum_of_digits(n // 10 )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase ) -> None:
												__snake_case				=		F'''{func.__name__}({value})'''
												__snake_case				=		timeit(F'''__main__.{call}'''      ,	setup="import __main__" )
												print(F'''{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds''' )

						for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
												for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
																		benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase )
												print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





