




# 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					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''microsoft/speecht5_tts'''
			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.'''
			)
			snake_case_  						=			'''text_reader'''
			snake_case_  						=			SpeechTaProcessor
			snake_case_  						=			SpeechTaForTextToSpeech
			snake_case_  						=			SpeechTaHifiGan

			snake_case_  						=			['''text''']
			snake_case_  						=			['''audio''']


			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							if self.post_processor is None:
											__lowerCamelCase									=       'microsoft/speecht5_hifigan'
							super().setup()


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__=None				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       self.pre_processor(text=lowerCamelCase__     ,		return_tensors='pt'     ,		truncation=lowerCamelCase__				)

							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[7_305]['xvector']				).unsqueeze(0				)

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


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							with torch.no_grad():
											return self.model.generate_speech(**lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							with torch.no_grad():
											return self.post_processor(lowerCamelCase__				).cpu().detach()








from string import ascii_uppercase

__A								=       {str(ord(c) - 55): c for c in ascii_uppercase}







def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: int )	->			str:





				"""simple docstring"""


				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('int() can\'t convert non-string with explicit base' )
				if num < 0:
								raise ValueError('parameter must be positive int' )
				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('\'str\' object cannot be interpreted as an integer' )
				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('\'float\' object cannot be interpreted as an integer' )
				if base in (0, 1):
								raise ValueError('base must be >= 2' )
				if base > 36:
								raise ValueError('base must be <= 36' )
				__lowerCamelCase									=       ''
				__lowerCamelCase									=       0
				__lowerCamelCase									=       0
				while div != 1:
								__lowerCamelCase			,		__lowerCamelCase									=       divmod(UpperCamelCase__					,	UpperCamelCase__ )
								if base >= 11 and 9 < mod < 36:
												__lowerCamelCase									=       ALPHABET_VALUES[str(UpperCamelCase__ )]
								else:
												__lowerCamelCase									=       str(UpperCamelCase__ )
								new_value += actual_value
								__lowerCamelCase									=       num // base
								__lowerCamelCase									=       div
								if div == 0:
												return str(new_value[::-1] )
								elif div == 1:
												new_value += str(UpperCamelCase__ )
												return str(new_value[::-1] )

				return new_value[::-1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					for base in range(2, 37):
										for num in range(10_00):
															assert int(decimal_to_any(num, base), base) == num, (
															    num,
															    base,
															    decimal_to_any(num, base),
															    int(decimal_to_any(num, base), base),
															)


