



'''simple docstring'''






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

import numpy
from matplotlib import pyplot

A_				:					str							=      0.25 + 0.0J
A_				:					Dict							=      -0.4 + 0.6J
A_				:					Dict							=      -0.1 + 0.651J
A_				:					List[Any]							=      -2.0
A_				:					Any							=      5_6
A_				:					str							=      2.0
A_				:					int							=      6_6_6




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						numpy.ndarray:





       '''simple docstring'''


       return numpy.exp(lowerCAmelCase_						) + c_parameter




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						numpy.ndarray:





       '''simple docstring'''


       return z_values * z_values + c_parameter




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						numpy.ndarray:





       '''simple docstring'''


       _UpperCAmelCase    :		str     	=					numpy.linspace(-window_size			,  lowerCAmelCase_			,  lowerCAmelCase_						)
       _UpperCAmelCase    :		Dict     	=					x.reshape((nb_pixels, 1)						)
       _UpperCAmelCase    :		int     	=					numpy.linspace(-window_size			,  lowerCAmelCase_			,  lowerCAmelCase_						)
       _UpperCAmelCase    :		Union[str, Any]     	=					y.reshape((1, nb_pixels)						)
       return x + 1.0j * y




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  )->						numpy.ndarray:





       '''simple docstring'''


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




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )->						None:





       '''simple docstring'''


       _UpperCAmelCase    :		Any     	=					(abs(lowerCAmelCase_						)).transpose()
       _UpperCAmelCase    :		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        snake_case_   (	)->						None:





       '''simple docstring'''


       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_				:					List[Any]							=      prepare_grid(window_size, nb_pixels)

   ignore_overflow_warnings()  # See file header for explanations

   A_				:					str							=      2_4
   A_				:					Tuple							=      2 * abs(c_cauliflower) + 1
   A_				:					List[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_				:					Union[str, Any]							=      6_4
   A_				:					Union[str, Any]							=      2 * abs(c_polynomial_a) + 1
   A_				:					Dict							=      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							=      1_6_1
   A_				:					Optional[int]							=      2 * abs(c_polynomial_a) + 1
   A_				:					Dict							=      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_2
   A_				:					List[str]							=      1_0000.0
   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)








'''simple docstring'''






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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor



class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def __init__(				self    ,a_    ,a_=7    ,a_=3    ,a_=18    ,a_=30    ,a_=400    ,a_=True    ,a_=None    ,a_=True    ,a_=False    ,a_=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,a_=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,a_=True    ,)						->  Optional[int]:
        _UpperCAmelCase    :		Dict     	=					size if size is not None else {"""height""": 20, """width""": 20}
        _UpperCAmelCase    :		Dict     	=					parent
        _UpperCAmelCase    :		Tuple     	=					batch_size
        _UpperCAmelCase    :		Optional[int]     	=					num_channels
        _UpperCAmelCase    :		int     	=					image_size
        _UpperCAmelCase    :		List[Any]     	=					min_resolution
        _UpperCAmelCase    :		str     	=					max_resolution
        _UpperCAmelCase    :		Tuple     	=					do_resize
        _UpperCAmelCase    :		Union[str, Any]     	=					size
        _UpperCAmelCase    :		int     	=					do_normalize
        _UpperCAmelCase    :		List[str]     	=					image_mean
        _UpperCAmelCase    :		Any     	=					image_std
        _UpperCAmelCase    :		Union[str, Any]     	=					do_pad
        _UpperCAmelCase    :		Tuple     	=					do_convert_rgb







 def _snake_case					(				self				)						->  List[str]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }







 def _snake_case					(				self    ,a_=False    ,a_=False    ,a_=False				)						->  Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               _UpperCAmelCase    :		Union[str, Any]     	=					[]
               for i in range(self.batch_size				):
                      image_inputs.append(
                          np.random.randint(
                              255    ,size=(self.num_channels, self.max_resolution, self.max_resolution)    ,dtype=np.uinta				)				)
        else:
               _UpperCAmelCase    :		Optional[int]     	=					[]
               for i in range(self.batch_size				):
                      _UpperCAmelCase	,_UpperCAmelCase    :		int     	=					np.random.choice(np.arange(self.min_resolution    ,self.max_resolution				)    ,2				)
                      image_inputs.append(np.random.randint(255    ,size=(self.num_channels, width, height)    ,dtype=np.uinta				)				)

        if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               _UpperCAmelCase    :		Any     	=					[Image.fromarray(np.moveaxis(a_    ,0    ,-1				)				) for x in image_inputs]

        if torchify:
               _UpperCAmelCase    :		Optional[int]     	=					[torch.from_numpy(a_				) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class 				lowercase			(    _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    BlipImageProcessor if is_vision_available() else None







 def _snake_case					(				self				)						->  List[Any]:
        _UpperCAmelCase    :		Dict     	=					BlipImageProcessingTester(self				)







 @property
 def _snake_case					(				self				)						->  str:
        return self.image_processor_tester.prepare_image_processor_dict()







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		List[str]     	=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(a_    ,"""do_resize"""				)				)
        self.assertTrue(hasattr(a_    ,"""size"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_normalize"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_mean"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_std"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_convert_rgb"""				)				)







 def _snake_case					(				self				)						->  Any:
        pass







 def _snake_case					(				self				)						->  Union[str, Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		List[str]     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        _UpperCAmelCase    :		str     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,Image.Image				)

        # Test not batched input
        _UpperCAmelCase    :		List[str]     	=					image_processor(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    :		List[str]     	=					image_processor(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 _snake_case					(				self				)						->  Optional[Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		str     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        _UpperCAmelCase    :		str     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_    ,numpify=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,np.ndarray				)

        # Test not batched input
        _UpperCAmelCase    :		Dict     	=					image_processor(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    :		int     	=					image_processor(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 _snake_case					(				self				)						->  List[str]:
        # Initialize image_processor
        _UpperCAmelCase    :		Dict     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        _UpperCAmelCase    :		Union[str, Any]     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_    ,torchify=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,torch.Tensor				)

        # Test not batched input
        _UpperCAmelCase    :		str     	=					image_processor(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    :		Optional[Any]     	=					image_processor(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"""],
            )    ,)







@require_torch
@require_vision
class 				lowercase			(    _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    BlipImageProcessor if is_vision_available() else None







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					BlipImageProcessingTester(self    ,num_channels=4				)
        _UpperCAmelCase    :		List[Any]     	=					3







 @property
 def _snake_case					(				self				)						->  int:
        return self.image_processor_tester.prepare_image_processor_dict()







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(a_    ,"""do_resize"""				)				)
        self.assertTrue(hasattr(a_    ,"""size"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_normalize"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_mean"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_std"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_convert_rgb"""				)				)







 def _snake_case					(				self				)						->  Union[str, Any]:
        pass







 def _snake_case					(				self				)						->  List[Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		Any     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        _UpperCAmelCase    :		Optional[Any]     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,Image.Image				)

        # Test not batched input
        _UpperCAmelCase    :		List[Any]     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		Dict     	=					image_processor(a_    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)



