





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

import numpy
from matplotlib import pyplot

__A       : Optional[int]						    = 0.2_5 + 0.0j
__A       : Tuple						    = -0.4 + 0.6j
__A       : str						    = -0.1 + 0.651j
__A       : Optional[int]						    = -2.0
__A       : List[str]						    = 56
__A       : List[Any]						    = 2.0
__A       : Union[str, Any]						    = 666





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Dict:




    """simple docstring"""




    return numpy.exp(UpperCamelCase__							) + c_parameter





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   str:




    """simple docstring"""




    return z_values * z_values + c_parameter





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    UpperCamelCase__    =       numpy.linspace(-window_size       ,    UpperCamelCase__       ,    UpperCamelCase__							)
    UpperCamelCase__    =       x.reshape((nb_pixels, 1)							)
    UpperCamelCase__    =       numpy.linspace(-window_size       ,    UpperCamelCase__       ,    UpperCamelCase__							)
    UpperCamelCase__    =       y.reshape((1, nb_pixels)							)
    return x + 1.0j * y





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE = None       ,    )       ->   List[Any]:




    """simple docstring"""




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





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    )       ->   str:




    """simple docstring"""




    UpperCamelCase__    =       (abs(UpperCamelCase__							)).transpose()
    UpperCamelCase__    =       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(					)       ->   List[Any]:




    """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       : Optional[int]						    = prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __A       : Optional[Any]						    = 24
 __A       : Optional[int]						    = 2 * abs(c_cauliflower) + 1
 __A       : Optional[int]						    = 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       : Union[str, Any]						    = 64
 __A       : Optional[int]						    = 2 * abs(c_polynomial_a) + 1
 __A       : 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)

 __A       : Optional[Any]						    = 161
 __A       : Optional[Any]						    = 2 * abs(c_polynomial_a) + 1
 __A       : Union[str, 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       : Optional[int]						    = 12
 __A       : Optional[Any]						    = 1_0_0_0_0.0
 __A       : List[str]						    = 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)












from sklearn.metrics import accuracy_score

import datasets


__A       : Union[str, Any]						    = "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


__A       : List[Any]						    = "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


__A       : List[str]						    = "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION  )
class     lowerCamelCase(	datasets.Metric  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         return datasets.MetricInfo(
             description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'       )       ),
                     'references': datasets.Sequence(datasets.Value('int32'       )       ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'       ),
                     'references': datasets.Value('int32'       ),
                 }       )							,    reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_=True							,    snake_case_=None       ):
         return {
             "accuracy": float(
                 accuracy_score(snake_case_							,    snake_case_							,    normalize=snake_case_							,    sample_weight=snake_case_       )       )
         }





