
"""simple docstring"""


import warnings

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




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			str									=						['image_processor', 'tokenizer']
				a__:			Tuple									=						'AutoImageProcessor'
				a__:			Optional[Any]									=						'AutoTokenizer'




				def __init__(							self       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	**UpperCAmelCase      ):
							lowerCamelCase_ 		=     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       ,	)
										lowerCamelCase_ 		=     kwargs.pop('''feature_extractor'''      )

							lowerCamelCase_ 		=     image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError('''You have to specify an image_processor.'''      )
							if tokenizer is None:
										raise ValueError('''You have to specify a tokenizer.'''      )

							super().__init__(UpperCAmelCase       ,	UpperCAmelCase      )
							lowerCamelCase_ 		=     self.image_processor




				def __call__(							self       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	**UpperCAmelCase      ):

							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:
										lowerCamelCase_ 		=     self.tokenizer(UpperCAmelCase       ,	return_tensors=UpperCAmelCase       ,	**UpperCAmelCase      )

							if images is not None:
										lowerCamelCase_ 		=     self.image_processor(UpperCAmelCase       ,	return_tensors=UpperCAmelCase       ,	**UpperCAmelCase      )

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




				def 					UpperCAmelCase__	(							self       ,	*UpperCAmelCase       ,	**UpperCAmelCase      ):
							return self.tokenizer.batch_decode(*UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	*UpperCAmelCase       ,	**UpperCAmelCase      ):
							return self.tokenizer.decode(*UpperCAmelCase       ,	**UpperCAmelCase      )




				@property
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.tokenizer.model_input_names
							lowerCamelCase_ 		=     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




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





"""simple docstring"""


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

import numpy
from matplotlib import pyplot

A_						       =				0.25 + 0.0J
A_						       =				-0.4 + 0.6J
A_						       =				-0.1 + 0.651J
A_						       =				-2.0
A_						       =				56
A_						       =				2.0
A_						       =				666






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return numpy.exp(lowerCAmelCase__	) + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return z_values * z_values + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     x.reshape((nb_pixels, 1)	)
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     y.reshape((1, nb_pixels)	)
			return x + 1.0J * y






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__ = None  ,):
			lowerCamelCase_ 		=     z_a.astype('''complex64'''	)
			for _ in range(lowerCAmelCase__	):
						lowerCamelCase_ 		=     eval_function(lowerCAmelCase__  ,lowerCAmelCase__	)
						if infinity is not None:
									numpy.nan_to_num(lowerCAmelCase__  ,copy=lowerCAmelCase__  ,nan=lowerCAmelCase__	)
									lowerCamelCase_ 		=     infinity
			return z_n






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,):
			lowerCamelCase_ 		=     (abs(lowerCAmelCase__	)).transpose()
			lowerCamelCase_ 		=     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    lowercase	(  ):
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in multiply'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''invalid value encountered in multiply'''  ,)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in absolute'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in exp'''	)


if __name__ == "__main__":
						A_						       =				prepare_grid(window_size, nb_pixels)

						ignore_overflow_warnings()  # See file header for explanations

						A_						       =				24
						A_						       =				2 * abs(c_cauliflower) + 1
						A_						       =				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)

						A_						       =				64
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				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)

						A_						       =				161
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				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)

						A_						       =				12
						A_						       =				10000.0
						A_						       =				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)



