


"""simple docstring"""







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


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				int   ="ClapFeatureExtractor"
       a  :				Dict   =("RobertaTokenizer", "RobertaTokenizerFast")

       def __init__(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         super().__init__(snake_case__		,     snake_case__						)

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






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							kwargs.pop("sampling_rate"		,     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:
           lowerCAmelCase     :     int								=							self.tokenizer(snake_case__		,     return_tensors=snake_case__		,     **snake_case__						)

         if audios is not None:
           lowerCAmelCase     :     Tuple								=							self.feature_extractor(
               snake_case__		,     sampling_rate=snake_case__		,     return_tensors=snake_case__		,     **snake_case__						)

         if text is not None and audios is not None:
           lowerCAmelCase     :     Union[str, Any]								=							audio_features.input_features
           return encoding
         elif text is not None:
           return encoding
         else:
           return BatchEncoding(data=dict(**snake_case__						)		,     tensor_type=snake_case__						)

       def        lowercase__							(						self		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





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

       def        lowercase__							(						self		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





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







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.tokenizer.model_input_names
         lowerCAmelCase     :     Optional[int]								=							self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names						)						)









"""simple docstring"""






def      lowerCamelCase       (     _UpperCamelCase		:      float       ,			_UpperCamelCase		:      float       ,			_UpperCamelCase		:      int							)		->       float:

							'''simple docstring'''



							if principal <= 0:
														raise Exception("""Principal borrowed must be > 0"""							)
							if rate_per_annum < 0:
														raise Exception("""Rate of interest must be >= 0"""							)
							if years_to_repay <= 0 or not isinstance(_UpperCamelCase       ,			_UpperCamelCase							):
														raise Exception("""Years to repay must be an integer > 0"""							)

							# Yearly rate is divided by 12 to get monthly rate
							__UpperCAmelCase   :						List[Any]      							=					rate_per_annum / 1_2

							# Years to repay is multiplied by 12 to get number of payments as payment is monthly
							__UpperCAmelCase   :						Optional[int]      							=					years_to_repay * 1_2

							return (
							    principal
							    * rate_per_month
							    * (1 + rate_per_month) ** number_of_payments
							    / ((1 + rate_per_month) ** number_of_payments - 1)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
