



'''simple docstring'''




import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

UpperCamelCase__	:     Any				  =			0.2_5 + 0.0J
UpperCamelCase__	:     List[Any]				  =			-0.4 + 0.6J
UpperCamelCase__	:     Optional[Any]				  =			-0.1 + 0.651J
UpperCamelCase__	:     Any				  =			-2.0
UpperCamelCase__	:     Tuple				  =			56
UpperCamelCase__	:     Union[str, Any]				  =			2.0
UpperCamelCase__	:     Optional[int]				  =			6_66


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       int ):
		return numpy.exp(snake_case__ ) + c_parameter


def     lowerCAmelCase_       (     _lowerCamelCase:       Tuple	,				_lowerCamelCase:       Optional[Any] ):
		return z_values * z_values + c_parameter


def     lowerCAmelCase_       (     _lowerCamelCase:       Dict	,				_lowerCamelCase:       Optional[int] ):
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		numpy.linspace(-window_size	,				snake_case__	,				snake_case__ )
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		x.reshape((nb_pixels, 1) )
		__SCREAMING_SNAKE_CASE	:		Dict   	=		numpy.linspace(-window_size	,				snake_case__	,				snake_case__ )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		y.reshape((1, nb_pixels) )
		return x + 1.0J * y


def     lowerCAmelCase_       (     _lowerCamelCase:       Optional[int]	,				_lowerCamelCase:       str	,				_lowerCamelCase:       List[str]	,				_lowerCamelCase:       List[Any]	,				_lowerCamelCase:       List[str] = None	,				):
		__SCREAMING_SNAKE_CASE	:		Any   	=		z_a.astype("""complex64""" )
		for _ in range(snake_case__ ):
				__SCREAMING_SNAKE_CASE	:		str   	=		eval_function(snake_case__	,				snake_case__ )
				if infinity is not None:
						numpy.nan_to_num(snake_case__	,				copy=snake_case__	,				nan=snake_case__ )
						__SCREAMING_SNAKE_CASE	:		Dict   	=		infinity
		return z_n


def     lowerCAmelCase_       (     _lowerCamelCase:       List[str]	,				_lowerCamelCase:       Tuple	,				_lowerCamelCase:       Optional[Any]	,				_lowerCamelCase:       Union[str, Any]	,				):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		(abs(snake_case__ )).transpose()
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		abs_z_final[::-1, :]
		pyplot.matshow(abs_z_final < escape_radius )
		pyplot.title(F"Julia set of ${function_label}$, $c={function_params}$" )
		pyplot.show()


def     lowerCAmelCase_       (     ):
		warnings.filterwarnings(
		    """ignore"""	,				category=snake_case__	,				message="""overflow encountered in multiply""" )
		warnings.filterwarnings(
		    """ignore"""	,				category=snake_case__	,				message="""invalid value encountered in multiply"""	,				)
		warnings.filterwarnings(
		    """ignore"""	,				category=snake_case__	,				message="""overflow encountered in absolute""" )
		warnings.filterwarnings(
		    """ignore"""	,				category=snake_case__	,				message="""overflow encountered in exp""" )


if __name__ == "__main__":
						UpperCamelCase__	:     List[str]				  =			prepare_grid(window_size, nb_pixels)

						ignore_overflow_warnings()  # See file header for explanations

						UpperCamelCase__	:     str				  =			24
						UpperCamelCase__	:     Optional[int]				  =			2 * abs(c_cauliflower) + 1
						UpperCamelCase__	:     Dict				  =			iterate_function(
						    eval_quadratic_polynomial,
						    c_cauliflower,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results('''z^2+c''', c_cauliflower, escape_radius, z_final)

						UpperCamelCase__	:     Any				  =			64
						UpperCamelCase__	:     str				  =			2 * abs(c_polynomial_a) + 1
						UpperCamelCase__	:     Optional[Any]				  =			iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results('''z^2+c''', c_polynomial_a, escape_radius, z_final)

						UpperCamelCase__	:     Union[str, Any]				  =			1_61
						UpperCamelCase__	:     Dict				  =			2 * abs(c_polynomial_a) + 1
						UpperCamelCase__	:     int				  =			iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results('''z^2+c''', c_polynomial_a, escape_radius, z_final)

						UpperCamelCase__	:     str				  =			12
						UpperCamelCase__	:     Optional[Any]				  =			1_00_00.0
						UpperCamelCase__	:     List[Any]				  =			iterate_function(
						    eval_exponential,
						    c_exponential,
						    nb_iterations,
						    z_a + 2,
						    infinity=1.0E10,
						)
						show_results('''e^z+c''', c_exponential, escape_radius, z_final)




'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		['''image_processor''', '''tokenizer''']
			_A				:		Union[str, Any]         =		'''AutoImageProcessor'''
			_A				:		Optional[Any]         =		'''AutoTokenizer'''

			def __init__(							self					:			Tuple							,					lowerCAmelCase__					:			Tuple=None							,					lowerCAmelCase__					:			Union[str, Any]=None							,					**lowerCAmelCase__					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""							,					lowerCAmelCase__							,					)
							__SCREAMING_SNAKE_CASE	:		str   	=		kwargs.pop("""feature_extractor"""						)

					__SCREAMING_SNAKE_CASE	:		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__(lowerCAmelCase__							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		Dict   	=		False

			def __call__(							self					:			List[str]							,					*lowerCAmelCase__					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		kwargs.pop("""images"""							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		kwargs.pop("""text"""							,					lowerCAmelCase__						)
					if len(lowerCAmelCase__						) > 0:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		args[0]
							__SCREAMING_SNAKE_CASE	:		int   	=		args[1:]

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

					if images is not None:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor(lowerCAmelCase__							,					*lowerCAmelCase__							,					**lowerCAmelCase__						)
					if text is not None:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.tokenizer(lowerCAmelCase__							,					**lowerCAmelCase__						)

					if text is None:
							return inputs
					elif images is None:
							return encodings
					else:
							__SCREAMING_SNAKE_CASE	:		int   	=		encodings["""input_ids"""]
							return inputs

			def UpperCamelCase__ (							self					:			Any							,					*lowerCAmelCase__					:			Any							,					**lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






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

			def UpperCamelCase__ (							self					:			int							,					*lowerCAmelCase__					:			str							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






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

			@contextmanager
			def UpperCamelCase__ (							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 images inputs, or in a separate call."""						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		True
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.tokenizer
					yield
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		False

			@property
			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,					lowerCAmelCase__							,					)
					return self.image_processor_class



			@property
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,					lowerCAmelCase__							,					)
					return self.image_processor