


'''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  lowerCAmelCase				(				snake_case__							):
	_lowerCamelCase   :					Dict			      = """microsoft/speecht5_tts"""
	_lowerCamelCase   :					Tuple			      = (
	    """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   :					Union[str, Any]			      = """text_reader"""
	_lowerCamelCase   :					List[Any]			      = SpeechTaProcessor
	_lowerCamelCase   :					Optional[Any]			      = SpeechTaForTextToSpeech
	_lowerCamelCase   :					Tuple			      = SpeechTaHifiGan

	_lowerCamelCase   :					List[Any]			      = ["""text"""]
	_lowerCamelCase   :					Tuple			      = ["""audio"""]



	def 				lowercase     (					self    ):
		if self.post_processor is None:
			lowerCAmelCase					:    Dict      							= "microsoft/speecht5_hifigan"
		super().setup()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=None    ):
		lowerCAmelCase					:    Tuple      							= self.pre_processor(text=lowercase_ ,					return_tensors='pt' ,					truncation=lowercase_    )

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

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

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



	def 				lowercase     (					self ,					snake_case__    ):
		with torch.no_grad():
			return self.model.generate_speech(**lowercase_    )



	def 				lowercase     (					self ,					snake_case__    ):
		with torch.no_grad():
			return self.post_processor(lowercase_    ).cpu().detach()




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase  :		int		     =	{
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Tuple		     =	[
				    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'NezhaForNextSentencePrediction',
				    'NezhaForMaskedLM',
				    'NezhaForPreTraining',
				    'NezhaForMultipleChoice',
				    'NezhaForQuestionAnswering',
				    'NezhaForSequenceClassification',
				    'NezhaForTokenClassification',
				    'NezhaModel',
				    'NezhaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nezha import (
								    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NezhaForMaskedLM,
								    NezhaForMultipleChoice,
								    NezhaForNextSentencePrediction,
								    NezhaForPreTraining,
								    NezhaForQuestionAnswering,
								    NezhaForSequenceClassification,
								    NezhaForTokenClassification,
								    NezhaModel,
								    NezhaPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase  :		str		     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
