





"""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 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




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

					__lowerCAmelCase  =  ['''text''']
					__lowerCAmelCase  =  ['''audio''']







					def  _lowerCamelCase		(						self     ):
									if self.post_processor is None:
													__a :						List[str]			       =  '''microsoft/speecht5_hifigan'''
									super().setup()







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=None     ):
									__a :						Union[str, Any]			       =  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 :						Any			       =  load_dataset('''Matthijs/cmu-arctic-xvectors'''	,			split='''validation'''     )
													__a :						Union[str, Any]			       =  torch.tensor(embeddings_dataset[7305]['''xvector''']     ).unsqueeze(0     )

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







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									with torch.no_grad():
													return self.model.generate_speech(**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									with torch.no_grad():
													return self.post_processor(_UpperCAmelCase     ).cpu().detach()






"""simple docstring"""






def     __A      (			a_						:int = 2_00)		->		int:
				__a :						int			       =  [1, 2, 5, 10, 20, 50, 1_00, 2_00]
				__a :						List[Any]			       =  [0] * (pence + 1)
				__a :						Tuple			       =  1  # base case: 1 way to make 0 pence

				for coin in coins:
								for i in range(a_   ,       pence + 1   ,       1):
												number_of_ways[i] += number_of_ways[i - coin]
				return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73_682