





# 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      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'microsoft/speecht5_tts'
     __magic_name__   =			(
         '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.'
     )
     __magic_name__   =			'text_reader'
     __magic_name__   =			SpeechTaProcessor
     __magic_name__   =			SpeechTaForTextToSpeech
     __magic_name__   =			SpeechTaHifiGan

     __magic_name__   =			['text']
     __magic_name__   =			['audio']




     def        lowerCAmelCase__		(    self							):
         if self.post_processor is None:
             _A     =							'microsoft/speecht5_hifigan'
         super().setup()




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=None							):
         _A     =							self.pre_processor(text=snake_case_				,     return_tensors='pt'				,     truncation=snake_case_							)

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

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

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




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         with torch.no_grad():
             return self.model.generate_speech(**snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         with torch.no_grad():
             return self.post_processor(snake_case_							).cpu().detach()









'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







       lowercase__      =					[int(A			) for i in ip_va_address.split('''.'''			) if i.isdigit()]
       return len(A			) == 4 and all(0 <= int(A			) <= 254 for octet in octets			)


if __name__ == "__main__":
 lowerCamelCase				:  Optional[Any]    		=      input().strip()
 lowerCamelCase				:  Union[str, Any]    		=      'valid' if is_ip_va_address_valid(ip) else 'invalid'
 print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




