





from math import log

from scipy.constants import Boltzmann, physical_constants

_a						=   300  # TEMPERATURE (unit = K)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,)       ->   float:







	'''simple docstring'''







	if donor_conc <= 0:
		raise ValueError('''Donor concentration should be positive'''							)
	elif acceptor_conc <= 0:
		raise ValueError('''Acceptor concentration should be positive'''							)
	elif intrinsic_conc <= 0:
		raise ValueError('''Intrinsic concentration should be positive'''							)
	elif donor_conc <= intrinsic_conc:
		raise ValueError(
		    '''Donor concentration should be greater than intrinsic concentration'''							)
	elif acceptor_conc <= intrinsic_conc:
		raise ValueError(
		    '''Acceptor concentration should be greater than intrinsic concentration'''							)
	else:
		return (
		    Boltzmann
		    * T
		    * log((donor_conc * acceptor_conc) / intrinsic_conc**2							)
		    / physical_constants["electron volt"][0]
		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()










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






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""ClapFeatureExtractor"""
			lowerCAmelCase_							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase							)





			def __call__(					self ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''sampling_rate''' ,						__lowerCAmelCase							)

				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:
					lowerCamelCase__              =	self.tokenizer(__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

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

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





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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



