




'''simple docstring'''





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				int							=   "ClapFeatureExtractor"
			lowerCAmelCase_					:				str							=   ("RobertaTokenizer", "RobertaTokenizerFast")




			def __init__( self    ,							a__    ,							a__							)    ->    Optional[int]:

										'''simple docstring'''




										super().__init__(a__    ,							a__							)




			def __call__( self    ,							a__=None    ,							a__=None    ,							a__=None    ,							**a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				kwargs.pop("sampling_rate"    ,							a__							)

										if text is None and audios is None:
																	raise ValueError("You have to specify either text or audios. Both cannot be none."							)

										if text is not None:
																	snake_case_      =				self.tokenizer(a__    ,							return_tensors=a__    ,							**a__							)

										if audios is not None:
																	snake_case_      =				self.feature_extractor(
																	    a__    ,							sampling_rate=a__    ,							return_tensors=a__    ,							**a__							)

										if text is not None and audios is not None:
																	snake_case_      =				audio_features.input_features
																	return encoding
										elif text is not None:
																	return encoding
										else:
																	return BatchEncoding(data=dict(**a__							)    ,							tensor_type=a__							)




			def        lowerCAmelCase__    ( self    ,							*a__    ,							**a__							)    ->    Dict:

										'''simple docstring'''




										return self.tokenizer.batch_decode(*a__    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							*a__    ,							**a__							)    ->    Optional[int]:

										'''simple docstring'''




										return self.tokenizer.decode(*a__    ,							**a__							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										snake_case_      =				self.tokenizer.model_input_names
										snake_case_      =				self.feature_extractor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)









'''simple docstring'''





from __future__ import annotations

import numpy as np


def 					UpperCamelCase_(     snake_case							:	np.ndarray					):

							'''simple docstring'''






							snake_case_ ,				snake_case_      =				np.shape(snake_case					)
							if rows != columns:
														snake_case_      =				(
														    "'table' has to be of square shaped array but got a "
														    f'{rows}x{columns} array:\n{table}'
														)
														raise ValueError(snake_case					)

							snake_case_      =				np.zeros((rows, columns)					)
							snake_case_      =				np.zeros((rows, columns)					)
							for i in range(snake_case					):
														for j in range(snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					if upper[j][j] == 0:
																												raise ArithmeticError("No LU decomposition exists"					)
																					snake_case_      =				(table[i][j] - total) / upper[j][j]
														snake_case_      =				1
														for j in range(snake_case							,    snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					snake_case_      =				table[i][j] - total
							return lower, upper


if __name__ == "__main__":
		import doctest

		doctest.testmod()




