"""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_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						int      =     "microsoft/speecht5_tts"
					UpperCamelCase_			:						List[str]      =     (
					    "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."
					)
					UpperCamelCase_			:						Union[str, Any]      =     "text_reader"
					UpperCamelCase_			:						Optional[int]      =     SpeechTaProcessor
					UpperCamelCase_			:						Optional[int]      =     SpeechTaForTextToSpeech
					UpperCamelCase_			:						List[str]      =     SpeechTaHifiGan

					UpperCamelCase_			:						Union[str, Any]      =     ["text"]
					UpperCamelCase_			:						Optional[int]      =     ["audio"]




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							if self.post_processor is None:
									lowerCAmelCase__   = """microsoft/speecht5_hifigan"""
							super().setup()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  Any=None  ):
							lowerCAmelCase__   = 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."""  )

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

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Tuple  ):
							with torch.no_grad():
									return self.model.generate_speech(**snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  List[str]  ):
							with torch.no_grad():
									return self.post_processor(snake_case__  ).cpu().detach()





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return " ".join(
		    """""".join(word[::-1]  ) if len(lowerCamelCase__  ) > 4 else word for word in sentence.split()  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(reverse_long_words("Hey wollef sroirraw"))




