

import warnings
from contextlib import contextmanager

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






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Union[str, Any]     					=				'''Wav2Vec2FeatureExtractor'''
	_lowercase		:   Any     					=				'''AutoTokenizer'''




	def __init__(     self						:     List[str]					,      __A						:     List[Any]					,      __A						:     Optional[Any]							):
		super().__init__(__A					,      __A							)
		__A :			Optional[Any]		    =			self.feature_extractor
		__A :			str		    =			False




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Dict					,      __A						:     List[str]					,      **__A						:     Optional[Any]							):
		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 :			Any		    =			WavaVecaFeatureExtractor.from_pretrained(__A					,      **__A							)
			__A :			List[str]		    =			WavaVecaCTCTokenizer.from_pretrained(__A					,      **__A							)

			return cls(feature_extractor=__A					,      tokenizer=__A							)




	def __call__(     self						:     Optional[int]					,      *__A						:     Optional[Any]					,      **__A						:     List[str]							):
		# For backward compatibility
		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 :			Any		    =			kwargs.pop("""raw_speech"""							)
		else:
			__A :			List[str]		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			int		    =			kwargs.pop("""sampling_rate"""					,      __A							)
		__A :			Tuple		    =			kwargs.pop("""text"""					,      __A							)
		if len(__A							) > 0:
			__A :			Union[str, Any]		    =			args[0]
			__A :			Optional[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 :			Tuple		    =			self.feature_extractor(__A					,      *__A					,      sampling_rate=__A					,      **__A							)
		if text is not None:
			__A :			Any		    =			self.tokenizer(__A					,      **__A							)

		if text is None:
			return inputs
		elif audio is None:
			return encodings
		else:
			__A :			List[str]		    =			encodings["""input_ids"""]
			return inputs




	def        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     int					,      **__A						:     Tuple							):
		# For backward compatibility
		if self._in_target_context_manager:
			return self.current_processor.pad(*__A					,      **__A							)

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

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

		if labels is None:
			return input_features
		elif input_features is None:
			return labels
		else:
			__A :			Optional[Any]		    =			labels["""input_ids"""]
			return input_features




	def        lowerCAmelCase_       (     self						:     List[Any]					,      *__A						:     List[Any]					,      **__A						:     List[str]							):
		return self.tokenizer.batch_decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      *__A						:     Dict					,      **__A						:     Optional[int]							):
		return self.tokenizer.decode(*__A					,      **__A							)




	@contextmanager
	def        lowerCAmelCase_       (     self						:     str							):
		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 :			Dict		    =			True
		__A :			List[str]		    =			self.tokenizer
		yield
		__A :			Tuple		    =			self.feature_extractor
		__A :			int		    =			False








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   complex			,a__    :   str = "x"			,a__    :   float = 10**-10			,a__    :   int = 1			,)					->				complex:
	__A :			Tuple		    =			symbols(a__       )
	__A :			List[str]		    =			lambdify(a__			,a__       )
	__A :			Any		    =			lambdify(a__			,diff(a__			,a__       )       )

	__A :			Dict		    =			starting_point

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

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





