code: 
  
  import warnings
  from collections.abc import Callable
  from typing import Any
  
  import numpy
  from matplotlib import pyplot
  
  __A   :     List[Any]      =			0.25 + 0.0j
  __A   :     Any      =			-0.4 + 0.6j
  __A   :     List[str]      =			-0.1 + 0.6_5_1j
  __A   :     int      =			-2.0
  __A   :     Optional[int]      =			56
  __A   :     Optional[int]      =			2.0
  __A   :     Dict      =			666
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> numpy.ndarray:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      return numpy.exp(_UpperCAmelCase				) + c_parameter
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> numpy.ndarray:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      return z_values * z_values + c_parameter
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> numpy.ndarray:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Union[str, Any]    			=      numpy.linspace(-window_size,      _UpperCAmelCase,      _UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      x.reshape((nb_pixels, 1)				)
      lowerCAmelCase				:			Tuple    			=      numpy.linspace(-window_size,      _UpperCAmelCase,      _UpperCAmelCase				)
      lowerCAmelCase				:			int    			=      y.reshape((1, nb_pixels)				)
      return x + 1.0j * y
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase = None,      )					-> numpy.ndarray:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      z_a.astype('complex64'				)
      for _ in range(_UpperCAmelCase				):
          lowerCAmelCase				:			List[Any]    			=      eval_function(_UpperCAmelCase,      _UpperCAmelCase				)
          if infinity is not None:
              numpy.nan_to_num(_UpperCAmelCase,      copy=_UpperCAmelCase,      nan=_UpperCAmelCase				)
              lowerCAmelCase				:			List[str]    			=      infinity
      return z_n
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> None:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      (abs(_UpperCAmelCase				)).transpose()
      lowerCAmelCase				:			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 		SCREAMING_SNAKE_CASE__				(							)					-> None:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      warnings.filterwarnings(
          'ignore',      category=_UpperCAmelCase,      message='overflow encountered in multiply'				)
      warnings.filterwarnings(
          'ignore',      category=_UpperCAmelCase,      message='invalid value encountered in multiply',      )
      warnings.filterwarnings(
          'ignore',      category=_UpperCAmelCase,      message='overflow encountered in absolute'				)
      warnings.filterwarnings(
          'ignore',      category=_UpperCAmelCase,      message='overflow encountered in exp'				)
  
  
  if __name__ == "__main__":
   __A   :     List[Any]      =			prepare_grid(window_size, nb_pixels)
  
   ignore_overflow_warnings()  # See file header for explanations
  
   __A   :     Tuple      =			24
   __A   :     Union[str, Any]      =			2 * abs(c_cauliflower) + 1
   __A   :     Union[str, Any]      =			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   :     Dict      =			64
   __A   :     List[str]      =			2 * abs(c_polynomial_a) + 1
   __A   :     List[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)
  
   __A   :     List[Any]      =			161
   __A   :     List[str]      =			2 * abs(c_polynomial_a) + 1
   __A   :     Tuple      =			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   :     Tuple      =			12
   __A   :     List[str]      =			1_00_00.0
   __A   :     Optional[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)
  
  
  
  
  
  
style_context: 
  
  __A   :     List[Any]      =			'''
  # Installation de Transformers
  ! pip install transformers datasets
  # Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
  # ! pip install git+https://github.com/huggingface/transformers.git
  '''
  
  __A   :     Dict      =			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
  __A   :     Tuple      =			{
      '''{processor_class}''': '''FakeProcessorClass''',
      '''{model_class}''': '''FakeModelClass''',
      '''{object_class}''': '''FakeObjectClass''',
  }
  
  
  
  
  
  