






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      "MCTCTFeatureExtractor"
	UpperCamelCase__								=      "AutoTokenizer"

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			self.feature_extractor
							_UpperCAmelCase									=			False

	def __call__(							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

							if "raw_speech" in kwargs:
													warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'    )
													_UpperCAmelCase									=			kwargs.pop('raw_speech'    )
							else:
													_UpperCAmelCase									=			kwargs.pop('audio'       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			kwargs.pop('sampling_rate'       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			kwargs.pop('text'       ,    UpperCAmelCase    )
							if len(UpperCAmelCase    ) > 0:
													_UpperCAmelCase									=			args[0]
													_UpperCAmelCase									=			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									=			self.feature_extractor(UpperCAmelCase       ,    *UpperCAmelCase       ,    sampling_rate=UpperCAmelCase       ,    **UpperCAmelCase    )
							if text is not None:
													_UpperCAmelCase									=			self.tokenizer(UpperCAmelCase       ,    **UpperCAmelCase    )

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

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

							_UpperCAmelCase									=			kwargs.pop('input_features'       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			kwargs.pop('labels'       ,    UpperCAmelCase    )
							if len(UpperCAmelCase    ) > 0:
													_UpperCAmelCase									=			args[0]
													_UpperCAmelCase									=			args[1:]

							if input_features is not None:
													_UpperCAmelCase									=			self.feature_extractor.pad(UpperCAmelCase       ,    *UpperCAmelCase       ,    **UpperCAmelCase    )
							if labels is not None:
													_UpperCAmelCase									=			self.tokenizer.pad(UpperCAmelCase       ,    **UpperCAmelCase    )

							if labels is None:
													return input_features
							elif input_features is None:
													return labels
							else:
													_UpperCAmelCase									=			labels['input_ids']
													return input_features

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	@contextmanager
	def        UpperCamelCase    (							self    ):
							"""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									=			True
							_UpperCAmelCase									=			self.tokenizer
							yield
							_UpperCAmelCase									=			self.feature_extractor
							_UpperCAmelCase									=			False







'''simple docstring'''




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






def        _lowerCAmelCase			(  __snake_case  : str ,							__snake_case  : complex ,							__snake_case  : str = "x" ,							__snake_case  : float = 10**-10 ,							__snake_case  : int = 1 ,							)     ->     complex:
     __A						: int      =       symbols(__snake_case			)
     __A						: Tuple      =       lambdify(__snake_case ,							__snake_case			)
     __A						: Any      =       lambdify(__snake_case ,							diff(__snake_case ,							__snake_case			)			)

     __A						: str      =       starting_point

     while True:
          if diff_function(__snake_case			) != 0:
               __A						: Optional[Any]      =       prev_guess - multiplicity * func(__snake_case			) / diff_function(
                   __snake_case			)
          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						: Dict      =       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=0.005)}""",
  )

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