


from maths.prime_factors import prime_factors
def     __lowerCamelCase						(			UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_			,  UpperCAmelCase_						):
            a						:Optional[int]										=	F'''Input value of [number={number}] must be an integer'''
            raise TypeError(UpperCAmelCase_						)
      if number < 1:
            raise ValueError('''Input must be a positive integer'''						)
      return -1 if len(prime_factors(UpperCAmelCase_						)						) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()









# 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 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'microsoft/speecht5_tts'
 SCREAMING_SNAKE_CASE__				=	(
     '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__				=	'text_reader'
 SCREAMING_SNAKE_CASE__				=	SpeechTaProcessor
 SCREAMING_SNAKE_CASE__				=	SpeechTaForTextToSpeech
 SCREAMING_SNAKE_CASE__				=	SpeechTaHifiGan

 SCREAMING_SNAKE_CASE__				=	['text']
 SCREAMING_SNAKE_CASE__				=	['audio']




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.post_processor is None:
             a						:List[Any]										=	'''microsoft/speecht5_hifigan'''
       super().setup()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       a						: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.'''   )

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       with torch.no_grad():
             return self.model.generate_speech(**_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       with torch.no_grad():
             return self.post_processor(_lowerCamelCase   ).cpu().detach()





