





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     (      __UpperCamelCase				):


  '''simple docstring'''
  a_				:							int          ="microsoft/speecht5_tts"
  a_				:							List[str]          =(
      "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."
  )
  a_				:							Union[str, Any]          ="text_reader"
  a_				:							Optional[int]          =SpeechTaProcessor
  a_				:							Optional[int]          =SpeechTaForTextToSpeech
  a_				:							List[str]          =SpeechTaHifiGan

  a_				:							Union[str, Any]          =["text"]
  a_				:							Optional[int]          =["audio"]







  def UpperCamelCase_			(   self   :       str				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       Optional[Any]   ,					UpperCamelCase   :       Union[str, Any]   ,					UpperCamelCase   :       Any=None				):


    '''simple docstring'''





    _snake_case :					List[Any]								=						self.pre_processor(text=snake_case__   ,					return_tensors='pt'   ,					truncation=snake_case__				)

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

      _snake_case :					Optional[Any]								=						load_dataset('Matthijs/cmu-arctic-xvectors'   ,					split='validation'				)
      _snake_case :					Optional[int]								=						torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

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







  def UpperCamelCase_			(   self   :       List[Any]   ,					UpperCamelCase   :       Tuple				):


    '''simple docstring'''





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







  def UpperCamelCase_			(   self   :       Union[str, Any]   ,					UpperCamelCase   :       List[str]				):


    '''simple docstring'''





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





"""simple docstring"""




from __future__ import annotations

from typing import Any

class    a_   :




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  int = 6  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							self.create_linked_list(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  int  ):
							lowerCAmelCase__   = Node()
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							for _ in range(1	,    snake_case__  ):
									lowerCAmelCase__   = Node()
									lowerCAmelCase__   = current_node
									lowerCAmelCase__   = previous_node
									lowerCAmelCase__   = current_node
							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = previous_node




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return (
							    self.front == self.rear
							    and self.front is not None
							    and self.front.data is None
							)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							return self.front.data if self.front else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Any  ):
							if self.rear is None:
									return

							self.check_is_full()
							if not self.is_empty():
									lowerCAmelCase__   = self.rear.next
							if self.rear:
									lowerCAmelCase__   = data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							if self.rear is None or self.front is None:
									return None
							if self.front == self.rear:
									lowerCAmelCase__   = self.front.data
									lowerCAmelCase__   = None
									return data

							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = old_front.next
							lowerCAmelCase__   = old_front.data
							lowerCAmelCase__   = None
							return data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							if self.is_empty():
									raise Exception("""Empty Queue"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							if self.rear and self.rear.next == self.front:
									raise Exception("""Full Queue"""  )

class    a_   :




					def __init__(						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None


if __name__ == "__main__":
			import doctest

			doctest.testmod()




