


"""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 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       Dict									=    '''microsoft/speecht5_tts'''
   _a   :       Optional[int]									=    (
       '''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   :       Tuple									=    '''text_reader'''
   _a   :       Union[str, Any]									=    SpeechTaProcessor
   _a   :       int									=    SpeechTaForTextToSpeech
   _a   :       Any									=    SpeechTaHifiGan

   _a   :       str									=    ['''text''']
   _a   :       Union[str, Any]									=    ['''audio''']


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    if self.post_processor is None:
     lowercase__       :       Optional[int]				=						"""microsoft/speecht5_hifigan"""
    super().setup()


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=None						)      ->							List[str]:
    lowercase__       :       Any				=						self.pre_processor(text=lowerCamelCase__ ,     return_tensors="""pt""" ,     truncation=lowerCamelCase__						)

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

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

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							Any:
    with torch.no_grad():
     return self.model.generate_speech(**lowerCamelCase__						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    with torch.no_grad():
     return self.post_processor(lowerCamelCase__						).cpu().detach()



"""simple docstring"""


import random


def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       List[Any]				=						a[left_index]
 lowercase__       :       List[Any]				=						left_index + 1
 for j in range(left_index + 1       ,		lowerCamelCase__  ):
  if a[j] < pivot:
   lowercase__	,       lowercase__       :       Dict				=						a[i], a[j]
   i += 1
 lowercase__	,       lowercase__       :       Any				=						a[i - 1], a[left_index]
 return i - 1


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]  ):
 if left < right:
  lowercase__       :       str				=						random.randint(lowerCamelCase__       ,		right - 1  )
  lowercase__	,       lowercase__       :       Any				=						(
      a[left],
      a[pivot],
  )  # switches the pivot with the left most bound
  lowercase__       :       str				=						partition(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
  quick_sort_random(
      lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )  # recursive quicksort to the left of the pivot point
  quick_sort_random(
      lowerCamelCase__       ,		pivot_index + 1       ,		lowerCamelCase__  )  # recursive quicksort to the right of the pivot point


def    _lowerCamelCase   (     ):
 lowercase__       :       List[Any]				=						input("""Enter numbers separated by a comma:\n"""  ).strip()
 lowercase__       :       int				=						[int(lowerCamelCase__  ) for item in user_input.split(""","""  )]

 quick_sort_random(lowerCamelCase__       ,		0       ,		len(lowerCamelCase__  )  )

 print(lowerCamelCase__  )


if __name__ == "__main__":
   main()