'''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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[str]      							=   '''microsoft/speecht5_tts'''
      UpperCAmelCase_      :      List[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.'''
      )
      UpperCAmelCase_      :      Optional[Any]      							=   '''text_reader'''
      UpperCAmelCase_      :      Optional[int]      							=   SpeechTaProcessor
      UpperCAmelCase_      :      Tuple      							=   SpeechTaForTextToSpeech
      UpperCAmelCase_      :      Optional[int]      							=   SpeechTaHifiGan

      UpperCAmelCase_      :      str      							=   ['''text''']
      UpperCAmelCase_      :      str      							=   ['''audio''']




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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  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.'''							)

            UpperCAmelCase__							:				Optional[int]   				=  load_dataset('''Matthijs/cmu-arctic-xvectors'''					,split='''validation'''							)
            UpperCAmelCase__							:				Dict   				=  torch.tensor(embeddings_dataset[7305]['''xvector''']							).unsqueeze(0							)

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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[str]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



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





'''simple docstring'''

import math







def __UpperCamelCase(   _A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   return math.pow(_A  ,			2    ) - a







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   return 2 * x







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  2.0

   while start <= a:
      UpperCAmelCase__							:				int   				=  math.pow(_A  ,			2    )

   return start







def __UpperCamelCase(   _A					:				float  ,			_A					:				int = 99_99  ,			_A					:				float = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1    ):
   '''simple docstring'''

   if a < 0:
      raise ValueError('''math domain error'''    )

   UpperCAmelCase__							:				Any   				=  get_initial_point(_A    )

   for _ in range(_A    ):
      UpperCAmelCase__							:				Optional[Any]   				=  value
      UpperCAmelCase__							:				Tuple   				=  value - fx(_A  ,			_A    ) / fx_derivative(_A    )
      if abs(prev_value - value    ) < tolerance:
         return value

   return value


if __name__ == "__main__":
  from doctest import testmod

  testmod()




