

"""simple docstring"""




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	:       Tuple        =						-0.4 + 0.6J
__A	:       List[Any]        =						-0.1 + 0.651J
__A	:       Union[str, Any]        =						-2.0
__A	:       Dict        =						56
__A	:       List[Any]        =						2.0
__A	:       Optional[Any]        =						6_66
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return numpy.exp(lowercase__ ) + c_parameter
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return z_values * z_values + c_parameter
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   numpy.linspace(-window_size     ,				lowercase__     ,				lowercase__ )
					A     =   x.reshape((nb_pixels, 1) )
					A     =   numpy.linspace(-window_size     ,				lowercase__     ,				lowercase__ )
					A     =   y.reshape((1, nb_pixels) )
					return x + 1.0J * y
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ = None     ,				):
					"""simple docstring"""
					A     =   z_a.astype("complex64" )
					for _ in range(lowercase__ ):
										A     =   eval_function(lowercase__     ,				lowercase__ )
										if infinity is not None:
															numpy.nan_to_num(lowercase__     ,				copy=lowercase__     ,				nan=lowercase__ )
															A     =   infinity
					return z_n
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				):
					"""simple docstring"""
					A     =   (abs(lowercase__ )).transpose()
					A     =   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		(		):
					"""simple docstring"""
					warnings.filterwarnings(
					    "ignore"     ,				category=lowercase__     ,				message="overflow encountered in multiply" )
					warnings.filterwarnings(
					    "ignore"     ,				category=lowercase__     ,				message="invalid value encountered in multiply"     ,				)
					warnings.filterwarnings(
					    "ignore"     ,				category=lowercase__     ,				message="overflow encountered in absolute" )
					warnings.filterwarnings(
					    "ignore"     ,				category=lowercase__     ,				message="overflow encountered in exp" )


if __name__ == "__main__":
	__A	:       Optional[Any]        =						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	:       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	:       Optional[Any]        =						64
	__A	:       List[str]        =						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	:       Any        =						1_61
	__A	:       Union[str, Any]        =						2 * abs(c_polynomial_a) + 1
	__A	:       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)

	__A	:       str        =						12
	__A	:       List[str]        =						1_00_00.0
	__A	:       Dict        =						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)









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import EfficientNetImageProcessor






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			int:
         __UpperCamelCase       				=size if size is not None else {'height': 18, 'width': 18}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[Any]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






@require_torch
@require_vision
class        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    EfficientNetImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=EfficientNetImageProcessorTester(self       )






    @property
    def _a   (     self       )    ->			Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'height': 18, 'width': 18}       )

         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict   ,  size=42       )
         self.assertEqual(image_processor.size   ,  {'height': 42, 'width': 42}       )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  Image.Image       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  numpify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  np.ndarray       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			Dict:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  torchify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  torch.Tensor       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )





