# 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_  ):
							lowercase__    :							Any										=				"microsoft/speecht5_tts"
							lowercase__    :							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."
							)
							lowercase__    :							str										=				"text_reader"
							lowercase__    :							List[str]										=				SpeechTaProcessor
							lowercase__    :							List[str]										=				SpeechTaForTextToSpeech
							lowercase__    :							List[Any]										=				SpeechTaHifiGan

							lowercase__    :							Optional[Any]										=				["text"]
							lowercase__    :							Optional[Any]										=				["audio"]




							def 						__snake_case(    self :       List[str]   )       ->					str:



												'''simple docstring'''

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




							def 						__snake_case(    self :       Optional[int]						,				_UpperCamelCase :       int						,				_UpperCamelCase :       Union[str, Any]=None   )       ->					List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.pre_processor(text=a__						,				return_tensors="pt"						,				truncation=a__   )

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

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

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




							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       Any   )       ->					Any:



												'''simple docstring'''

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





							def 						__snake_case(    self :       Dict						,				_UpperCamelCase :       str   )       ->					Optional[int]:



												'''simple docstring'''

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












def 	UpperCamelCase	(							snake_case__  : int	):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    0
     while num > 0:
          digit_sum += num % 10
          num //= 10
     return digit_sum






def 	UpperCamelCase	(							snake_case__  : int = 100	):




     '''simple docstring'''






     __snake_case      :Tuple 							=    1
     __snake_case      :Dict 							=    2

     for i in range(2						,max_n + 1	):
          __snake_case      :Optional[int] 							=    pre_numerator
          __snake_case      :List[Any] 							=    2 * i // 3 if i % 3 == 0 else 1
          __snake_case      :Dict 							=    cur_numerator
          __snake_case      :Optional[Any] 							=    e_cont * pre_numerator + temp

     return sum_digits(snake_case__	)


if __name__ == "__main__":
     print(f'''{solution() = }''')


