


"""simple docstring"""






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class 	lowerCamelCase__  (	__a							):







					"""simple docstring"""


					__a      			=				'''MCTCTFeatureExtractor'''
					__a      			=				'''AutoTokenizer'''






					def __init__(			self       :					List[Any]					,			UpperCamelCase       :					Union[str, Any]					,			UpperCamelCase       :					Dict    ):
												'''simple docstring'''






												super().__init__(_lowercase					,			_lowercase    )
												__UpperCAmelCase  :		Optional[int] 				=   self.feature_extractor
												__UpperCAmelCase  :		Optional[int] 				=   False






					def __call__(			self       :					Dict					,			*UpperCamelCase       :					List[str]					,			**UpperCamelCase       :					Union[str, Any]    ):
												'''simple docstring'''






												if self._in_target_context_manager:
																			return self.current_processor(*_lowercase					,			**_lowercase    )

												if "raw_speech" in kwargs:
																			warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""    )
																			__UpperCAmelCase  :		Optional[Any] 				=   kwargs.pop("""raw_speech"""    )
												else:
																			__UpperCAmelCase  :		List[str] 				=   kwargs.pop("""audio"""					,			_lowercase    )
												__UpperCAmelCase  :		str 				=   kwargs.pop("""sampling_rate"""					,			_lowercase    )
												__UpperCAmelCase  :		Union[str, Any] 				=   kwargs.pop("""text"""					,			_lowercase    )
												if len(_lowercase    ) > 0:
																			__UpperCAmelCase  :		str 				=   args[0]
																			__UpperCAmelCase  :		List[Any] 				=   args[1:]

												if audio is None and text is None:
																			raise ValueError("""You need to specify either an `audio` or `text` input to process."""    )

												if audio is not None:
																			__UpperCAmelCase  :		Union[str, Any] 				=   self.feature_extractor(_lowercase					,			*_lowercase					,			sampling_rate=_lowercase					,			**_lowercase    )
												if text is not None:
																			__UpperCAmelCase  :		Optional[int] 				=   self.tokenizer(_lowercase					,			**_lowercase    )

												if text is None:
																			return inputs
												elif audio is None:
																			return encodings
												else:
																			__UpperCAmelCase  :		Optional[Any] 				=   encodings['''input_ids''']
																			return inputs






					def lowerCamelCase__				(			self       :					str					,			*UpperCamelCase       :					int					,			**UpperCamelCase       :					Any    ):
												'''simple docstring'''






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






					def lowerCamelCase__				(			self       :					Optional[Any]					,			*UpperCamelCase       :					Optional[int]					,			**UpperCamelCase       :					str    ):
												'''simple docstring'''






												if self._in_target_context_manager:
																			return self.current_processor.pad(*_lowercase					,			**_lowercase    )

												__UpperCAmelCase  :		int 				=   kwargs.pop("""input_features"""					,			_lowercase    )
												__UpperCAmelCase  :		Dict 				=   kwargs.pop("""labels"""					,			_lowercase    )
												if len(_lowercase    ) > 0:
																			__UpperCAmelCase  :		Optional[int] 				=   args[0]
																			__UpperCAmelCase  :		Optional[Any] 				=   args[1:]

												if input_features is not None:
																			__UpperCAmelCase  :		List[Any] 				=   self.feature_extractor.pad(_lowercase					,			*_lowercase					,			**_lowercase    )
												if labels is not None:
																			__UpperCAmelCase  :		str 				=   self.tokenizer.pad(_lowercase					,			**_lowercase    )

												if labels is None:
																			return input_features
												elif input_features is None:
																			return labels
												else:
																			__UpperCAmelCase  :		Optional[Any] 				=   labels['''input_ids''']
																			return input_features






					def lowerCamelCase__				(			self       :					Optional[Any]					,			*UpperCamelCase       :					List[str]					,			**UpperCamelCase       :					int    ):
												'''simple docstring'''






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






					@contextmanager
					def lowerCamelCase__				(			self       :					Optional[Any]    ):
												'''simple docstring'''






												warnings.warn(
												    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
												    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
												    """your audio inputs, or in a separate call."""    )
												__UpperCAmelCase  :		Optional[int] 				=   True
												__UpperCAmelCase  :		List[str] 				=   self.tokenizer
												yield
												__UpperCAmelCase  :		List[str] 				=   self.feature_extractor
												__UpperCAmelCase  :		Tuple 				=   False

from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				complex	,       lowercase:				str = "x"	,       lowercase:				float = 10**-10	,       lowercase:				int = 1	,       )     ->					complex:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			symbols(lowercase		)
 _UpperCamelCase:  str    						=			lambdify(lowercase	,       lowercase		)
 _UpperCamelCase:  str    						=			lambdify(lowercase	,       diff(lowercase	,       lowercase		)		)

 _UpperCamelCase:  Optional[int]    						=			starting_point

 while True:
  if diff_function(lowercase		) != 0:
   _UpperCamelCase:  int    						=			prev_guess - multiplicity * func(lowercase		) / diff_function(
       lowercase		)
  else:
   raise ZeroDivisionError('''Could not find root'''		) from None

  # Precision is checked by comparing the difference of consecutive guesses
  if abs(next_guess - prev_guess		) < precision:
   return next_guess

  _UpperCamelCase:  Any    						=			next_guess


# 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
  # Find fourth Root of 5
  print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      f"""{newton_raphson('exp(x) - 1', 1_0, precision=0.0_0_5)}""",
  )

  # Find root of cos(x)
  print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")