


'''simple docstring'''


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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'''







  a_  					=							"""microsoft/speecht5_tts"""
  a_  					=							(
      """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_  					=							"""text_reader"""
  a_  					=							SpeechTaProcessor
  a_  					=							SpeechTaForTextToSpeech
  a_  					=							SpeechTaHifiGan

  a_  					=							["""text"""]
  a_  					=							["""audio"""]






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




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






  def        _a							(							self :       Any	,_a :       Optional[int]	,_a :       List[str]=None ):

        '''simple docstring'''




        A_						:  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.""" )

              A_						:  Dict          =				load_dataset("""Matthijs/cmu-arctic-xvectors"""	,split="""validation""" )
              A_						:  Union[str, Any]          =				torch.tensor(embeddings_dataset[7305]["""xvector"""] ).unsqueeze(0 )

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






  def        _a							(							self :       Any	,_a :       List[str] ):

        '''simple docstring'''




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






  def        _a							(							self :       int	,_a :       List[str] ):

        '''simple docstring'''




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




'''simple docstring'''


from __future__ import annotations




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]):
                  if adj not in explored:
                        stack.append(lowerCamelCase)
      return explored


__magic_name__			=      {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, 'A'))
