





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__( __SCREAMING_SNAKE_CASE    ):






       '''simple docstring'''


       __snake_case 			=    'microsoft/speecht5_tts'
       __snake_case 			=    (
           '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 			=    'text_reader'
       __snake_case 			=    SpeechTaProcessor
       __snake_case 			=    SpeechTaForTextToSpeech
       __snake_case 			=    SpeechTaHifiGan

       __snake_case 			=    ['text']
       __snake_case 			=    ['audio']




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:




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




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=None							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							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."							)

                 _SCREAMING_SNAKE_CASE       :	Dict						    =							load_dataset("Matthijs/cmu-arctic-xvectors"    ,						split="validation"							)
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.tensor(embeddings_dataset[7_3_0_5]["xvector"]							).unsqueeze(0							)

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




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Union[str, Any]:




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







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Any:




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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LiltForQuestionAnswering',
           'LiltForSequenceClassification',
           'LiltForTokenClassification',
           'LiltModel',
           'LiltPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

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