




'''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			(					_UpperCamelCase							):
       lowercase      :       Tuple 		="microsoft/speecht5_tts"
       lowercase      :       Optional[Any] 		=(
           "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."
       )
       lowercase      :       Optional[Any] 		="text_reader"
       lowercase      :       List[str] 		=SpeechTaProcessor
       lowercase      :       List[str] 		=SpeechTaForTextToSpeech
       lowercase      :       List[Any] 		=SpeechTaHifiGan

       lowercase      :       str 		=["text"]
       lowercase      :       List[Any] 		=["audio"]






       def 	lowercase__	(				self    ):



              """simple docstring"""







              if self.post_processor is None:
                     lowerCamelCase_				="microsoft/speecht5_hifigan"
              super().setup()






       def 	lowercase__	(				self,						lowerCAmelCase,						lowerCAmelCase=None    ):



              """simple docstring"""







              lowerCamelCase_				=self.pre_processor(text=a_,						return_tensors='''pt''',						truncation=a_    )

              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[7_305]['''xvector''']    ).unsqueeze(0    )

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






       def 	lowercase__	(				self,						lowerCAmelCase    ):



              """simple docstring"""







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






       def 	lowercase__	(				self,						lowerCAmelCase    ):



              """simple docstring"""







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











"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase						=			{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						=			[
		    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InformerForPrediction''',
		    '''InformerModel''',
		    '''InformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_informer import (
				    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InformerForPrediction,
				    InformerModel,
				    InformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)