





# 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							(						__lowercase    ):
      _snake_case				  ='''microsoft/speecht5_tts'''
      _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.'''
      )
      _snake_case				  ='''text_reader'''
      _snake_case				  =SpeechTaProcessor
      _snake_case				  =SpeechTaForTextToSpeech
      _snake_case				  =SpeechTaHifiGan

      _snake_case				  =['''text''']
      _snake_case				  =['''audio''']



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Tuple:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    Any=None							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =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_           =load_dataset("Matthijs/cmu-arctic-xvectors"		,	split="validation"							)
            UpperCAmelCase_           =torch.tensor(embeddings_dataset[7305]["xvector"]							).unsqueeze(0							)

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



      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    Dict							)    ->     Union[str, Any]:
         '''simple docstring'''

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






      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    Optional[Any]							)    ->     Optional[Any]:
         '''simple docstring'''

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











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    int		)  ->int:



    '''simple docstring'''







    return int((input_a, input_a).count(0		) == 0		)







def 						__lowercase	(  )  ->None:



    '''simple docstring'''







    assert and_gate(0							,0		) == 0
    assert and_gate(0							,1		) == 0
    assert and_gate(1							,0		) == 0
    assert and_gate(1							,1		) == 1


if __name__ == "__main__":
       test_and_gate()
       print(and_gate(1, 0))
       print(and_gate(0, 0))
       print(and_gate(0, 1))
       print(and_gate(1, 1))




