


'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""Wav2Vec2FeatureExtractor"""
  a_  					=							"""AutoTokenizer"""






  def __init__(							self :       str	,_a :       str	,_a :       Any ):

        '''simple docstring'''




        super().__init__(_a	,_a )
        A_						:  List[str]          =				self.feature_extractor
        A_						:  Dict          =				False






  @classmethod
  def        _a							(							cls :       Any	,_a :       List[str]	,**_a :       Optional[int] ):

        '''simple docstring'''




        try:
              return super().from_pretrained(_a	,**_a )
        except OSError:
              warnings.warn(
                  f'Loading a tokenizer inside {cls.__name__} from a config that does not'
                  """ include a `tokenizer_class` attribute is deprecated and will be """
                  """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
                  """ attribute to either your `config.json` or `tokenizer_config.json` """
                  """file to suppress this warning: """	,_a	,)

              A_						:  List[str]          =				WavaVecaFeatureExtractor.from_pretrained(_a	,**_a )
              A_						:  Any          =				WavaVecaCTCTokenizer.from_pretrained(_a	,**_a )

              return cls(feature_extractor=_a	,tokenizer=_a )






  def __call__(							self :       int	,*_a :       List[Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




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

        if "raw_speech" in kwargs:
              warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
              A_						:  str          =				kwargs.pop("""raw_speech""" )
        else:
              A_						:  List[Any]          =				kwargs.pop("""audio"""	,_a )
        A_						:  Optional[Any]          =				kwargs.pop("""sampling_rate"""	,_a )
        A_						:  List[str]          =				kwargs.pop("""text"""	,_a )
        if len(_a ) > 0:
              A_						:  List[Any]          =				args[0]
              A_						:  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:
              A_						:  Union[str, Any]          =				self.feature_extractor(_a	,*_a	,sampling_rate=_a	,**_a )
        if text is not None:
              A_						:  Dict          =				self.tokenizer(_a	,**_a )

        if text is None:
              return inputs
        elif audio is None:
              return encodings
        else:
              A_						:  Dict          =				encodings["""input_ids"""]
              return inputs






  def        _a							(							self :       Optional[int]	,*_a :       Union[str, Any]	,**_a :       List[Any] ):

        '''simple docstring'''




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

        A_						:  Optional[int]          =				kwargs.pop("""input_features"""	,_a )
        A_						:  Union[str, Any]          =				kwargs.pop("""labels"""	,_a )
        if len(_a ) > 0:
              A_						:  int          =				args[0]
              A_						:  Tuple          =				args[1:]

        if input_features is not None:
              A_						:  List[str]          =				self.feature_extractor.pad(_a	,*_a	,**_a )
        if labels is not None:
              A_						:  Tuple          =				self.tokenizer.pad(_a	,**_a )

        if labels is None:
              return input_features
        elif input_features is None:
              return labels
        else:
              A_						:  str          =				labels["""input_ids"""]
              return input_features






  def        _a							(							self :       Optional[int]	,*_a :       Tuple	,**_a :       Dict ):

        '''simple docstring'''




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






  def        _a							(							self :       Union[str, Any]	,*_a :       List[str]	,**_a :       Optional[Any] ):

        '''simple docstring'''




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






  @contextmanager
  def        _a							(							self :       Union[str, 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.""" )
        A_						:  int          =				True
        A_						:  List[str]          =				self.tokenizer
        yield
        A_						:  Optional[int]          =				self.feature_extractor
        A_						:  Optional[Any]          =				False




'''simple docstring'''


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




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      complex							,						lowerCamelCase  :      str = "x"							,						lowerCamelCase  :      float = 10**-10							,						lowerCamelCase  :      int = 1							,						):
      A_						:  int          =				symbols(lowerCamelCase)
      A_						:  List[Any]          =				lambdify(lowerCamelCase							,						lowerCamelCase)
      A_						:  List[str]          =				lambdify(lowerCamelCase							,						diff(lowerCamelCase							,						lowerCamelCase))

      A_						:  str          =				starting_point

      while True:
            if diff_function(lowerCamelCase) != 0:
                  A_						:  int          =				prev_guess - multiplicity * func(lowerCamelCase) / diff_function(
                      lowerCamelCase)
            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

            A_						:  Union[str, 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', 10, precision=0.0_0_5)}""",
     )

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