'''simple docstring'''







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




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Optional[Any]     		= 'ClapFeatureExtractor'
						A_			:							List[str]     		= ('RobertaTokenizer', 'RobertaTokenizerFast')




						def __init__(    self :       List[Any]							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       int      )     ->				Tuple:
													super().__init__(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_      )




						def __call__(    self :       List[str]							,							UpperCamelCase_ :       Dict=None							,							UpperCamelCase_ :       Optional[Any]=None							,							UpperCamelCase_ :       Optional[Any]=None							,							**UpperCamelCase_ :       int      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = kwargs.pop('sampling_rate'							,							SCREAMING_SNAKE_CASE_      )

													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:
																				SCREAMING_SNAKE_CASE__     :str						    = self.tokenizer(SCREAMING_SNAKE_CASE_							,							return_tensors=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_      )

													if audios is not None:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = self.feature_extractor(
																				    SCREAMING_SNAKE_CASE_							,							sampling_rate=SCREAMING_SNAKE_CASE_							,							return_tensors=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_      )

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




						def      __lowerCamelCase       (    self :       str							,							*UpperCamelCase_ :       Optional[Any]							,							**UpperCamelCase_ :       str      )     ->				Tuple:
													return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_      )




						def      __lowerCamelCase       (    self :       List[Any]							,							*UpperCamelCase_ :       Any							,							**UpperCamelCase_ :       Optional[int]      )     ->				List[str]:
													return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_      )




						@property
						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.tokenizer.model_input_names
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.feature_extractor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )








from __future__ import annotations

from math import pi, sqrt




def 							a		(				snake_case__:   float		,  snake_case__:   float   ):


				'''simple docstring'''

				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative'''   )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative'''   )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance   )))   ),
								)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




