

"""simple docstring"""


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





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				int       				=  """ClapFeatureExtractor"""
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  ("""RobertaTokenizer""", """RobertaTokenizerFast""")

				def __init__(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Tuple)->       Optional[int]:



						'''simple docstring'''


						super().__init__(UpperCamelCase__					,  UpperCamelCase__)

				def __call__(     self							:    int					,  UpperCamelCase__							:    Dict=None					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    Dict=None					,  **UpperCamelCase__							:    List[str])->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      kwargs.pop("sampling_rate"					,  UpperCamelCase__)

						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:	Any   				=      self.tokenizer(UpperCamelCase__					,  return_tensors=UpperCamelCase__					,  **UpperCamelCase__)

						if audios is not None:
								__lowerCAmelCase:	Optional[int]   				=      self.feature_extractor(
								    UpperCamelCase__					,  sampling_rate=UpperCamelCase__					,  return_tensors=UpperCamelCase__					,  **UpperCamelCase__)

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

				def 							lowercase_					(     self							:    Union[str, Any]					,  *UpperCamelCase__							:    Tuple					,  **UpperCamelCase__							:    Tuple)->       int:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    str					,  *UpperCamelCase__							:    int					,  **UpperCamelCase__							:    Optional[int])->       List[str]:



						'''simple docstring'''


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




				@property
				def 							lowercase_					(     self							:    Any)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.tokenizer.model_input_names
						__lowerCAmelCase:	Any   				=      self.feature_extractor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))





"""simple docstring"""


from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 1_0**-1_0			)     ->     float:
		__lowerCAmelCase:	Union[str, Any]   				=      a
		while True:
				__lowerCAmelCase:	Optional[int]   				=      Decimal(__SCREAMING_SNAKE_CASE			) - (
				    Decimal(eval(__SCREAMING_SNAKE_CASE			)			) / Decimal(eval(str(diff(__SCREAMING_SNAKE_CASE			)			)			)			)  # noqa: S307
				)
				# This number dictates the accuracy of the answer
				if abs(eval(__SCREAMING_SNAKE_CASE			)			) < precision:  # noqa: S307
						return float(__SCREAMING_SNAKE_CASE			)


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
			# Find root of polynomial
			print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
			# Find Square Root of 5
			print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
			# Exponential Roots
			print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')


