
import warnings

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

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = ['image_processor', 'tokenizer']
			__UpperCamelCase      :						Any					      = 'CLIPImageProcessor'
			__UpperCamelCase      :						Optional[int]					      = ('CLIPTokenizer', 'CLIPTokenizerFast')

			def __init__(       self :       Union[str, Any]						,	snake_case_ :       str=None						,	snake_case_ :       Union[str, Any]=None						,	**snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							Optional[int]	=						None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""						,	UpperCAmelCase__						,	)
											UpperCamelCase_:							List[Any]	=						kwargs.pop("""feature_extractor"""      )

							UpperCamelCase_:							Optional[Any]	=						image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""      )
							if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""      )

							super().__init__(UpperCAmelCase__						,	UpperCAmelCase__      )

			def __call__(       self :       Any						,	snake_case_ :       Dict=None						,	snake_case_ :       Union[str, Any]=None						,	snake_case_ :       int=None						,	**snake_case_ :       Union[str, Any]      ):

							if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

							if text is not None:
											UpperCamelCase_:							int	=						self.tokenizer(UpperCAmelCase__						,	return_tensors=UpperCAmelCase__						,	**UpperCAmelCase__      )

							if images is not None:
											UpperCamelCase_:							Dict	=						self.image_processor(UpperCAmelCase__						,	return_tensors=UpperCAmelCase__						,	**UpperCAmelCase__      )

							if text is not None and images is not None:
											UpperCamelCase_:							Tuple	=						image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**UpperCAmelCase__      )						,	tensor_type=UpperCAmelCase__      )

			def       lowerCAmelCase__	(       self :       List[str]						,	*snake_case_ :       Dict						,	**snake_case_ :       int      ):
							return self.tokenizer.batch_decode(*UpperCAmelCase__						,	**UpperCAmelCase__      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	*snake_case_ :       Optional[Any]						,	**snake_case_ :       str      ):
							return self.tokenizer.decode(*UpperCAmelCase__						,	**UpperCAmelCase__      )

			@property
			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.tokenizer.model_input_names
							UpperCamelCase_:							Tuple	=						self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

			@property
			def       lowerCAmelCase__	(       self :       Tuple      ):
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,	UpperCAmelCase__						,	)
							return self.image_processor_class




			@property
			def       lowerCAmelCase__	(       self :       Any      ):
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,	UpperCAmelCase__						,	)
							return self.image_processor






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







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = "x"     ,    lowerCamelCase = 10**-10     ,    lowerCamelCase = 1     ,    )  ->	complex:
				UpperCamelCase_:							Optional[Any]	=						symbols(lowerCamelCase						)
				UpperCamelCase_:							int	=						lambdify(lowerCamelCase     ,    lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						lambdify(lowerCamelCase     ,    diff(lowerCamelCase     ,    lowerCamelCase						)						)

				UpperCamelCase_:							Tuple	=						starting_point

				while True:
								if diff_function(lowerCamelCase						) != 0:
												UpperCamelCase_:							List[Any]	=						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

								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', 10, precision=0.005)}""",
							)

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






