






# 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      (	lowercase_  ):







     '''simple docstring'''
     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 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			List[Any]=None				):


         """simple docstring"""



         __A         =	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."""				)

             __A         =	load_dataset("""Matthijs/cmu-arctic-xvectors""" ,	split="""validation"""				)
             __A         =	torch.tensor(embeddings_dataset[7_305]["""xvector"""]				).unsqueeze(0				)

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




     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			Optional[int]				):


         """simple docstring"""



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





     def 		lowerCAmelCase_     (				self       :			Optional[int] ,	UpperCamelCase_       :			Dict				):


         """simple docstring"""



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












__a      :       List[Any]				  =   8.3_1_4_4_5_9_8



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			float			,				__lowercase   :			float    )						->      float:





    """simple docstring"""

    if temperature < 0:
        raise Exception("""Temperature cannot be less than 0 K"""    )
    if molar_mass <= 0:
        raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""    )
    else:
        return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # example
  __a      :       int				  =   300
  __a      :       int				  =   28
  __a      :       int				  =   rms_speed_of_molecule(temperature, molar_mass)
  print(f"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")




