

'''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        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							'microsoft/speecht5_tts'
   _SCREAMING_SNAKE_CASE   :					Optional[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.'
   )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							'text_reader'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							SpeechTaProcessor
   _SCREAMING_SNAKE_CASE   :					Dict     	=							SpeechTaForTextToSpeech
   _SCREAMING_SNAKE_CASE   :					int     	=							SpeechTaHifiGan

   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							['text']
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							['audio']


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if self.post_processor is None:
         _lowercase      :				Optional[Any]      					=       "microsoft/speecht5_hifigan"
      super().setup()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """simple docstring"""





      _lowercase      :				Any      					=       self.pre_processor(text=_UpperCamelCase      ,    return_tensors="pt"      ,    truncation=_UpperCamelCase  )

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

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

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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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









'''simple docstring'''
from timeit import timeit







def  _A		(     snake_case					)      ->     int:
   if number < 0:
      raise ValueError("the value of input must not be negative"					)
   _lowercase      :				Union[str, Any]      					=       0
   while number:
      number &= number - 1
      result += 1
   return result







def  _A		(     snake_case					)      ->     int:
   if number < 0:
      raise ValueError("the value of input must not be negative"					)
   _lowercase      :				int      					=       0
   while number:
      if number % 2 == 1:
         result += 1
      number >>= 1
   return result







def  _A		(     )      ->     None:

   def do_benchmark(snake_case					) -> None:
      _lowercase      :				Optional[int]      					=       "import __main__ as z"
      print(F'''Benchmark when {number = }:'''					)
      print(F'''{get_set_bits_count_using_modulo_operator(snake_case					) = }'''					)
      _lowercase      :				int      					=       timeit("z.get_set_bits_count_using_modulo_operator(25)"		,							setup=snake_case					)
      print(F'''timeit() runs in {timing} seconds'''					)
      print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(snake_case					) = }'''					)
      _lowercase      :				Optional[int]      					=       timeit(
          "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"		,							setup=snake_case		,							)
      print(F'''timeit() runs in {timing} seconds'''					)

   for number in (25, 37, 58, 0):
      do_benchmark(snake_case					)
      print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()






