
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A_						       =				logging.get_logger(__name__)


if is_vision_available():
						import PIL




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			int									=						['pixel_values']




				def __init__(							self       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	UpperCAmelCase = PILImageResampling.BICUBIC       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	UpperCAmelCase = True       ,	UpperCAmelCase = 1 / 255       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = True       ,	**UpperCAmelCase       ,	):
							super().__init__(**UpperCAmelCase      )
							lowerCamelCase_ 		=     size if size is not None else {'''shortest_edge''': 224}
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase       ,	default_to_square=UpperCAmelCase      )
							lowerCamelCase_ 		=     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )

							lowerCamelCase_ 		=     do_resize
							lowerCamelCase_ 		=     size
							lowerCamelCase_ 		=     resample
							lowerCamelCase_ 		=     do_center_crop
							lowerCamelCase_ 		=     crop_size
							lowerCamelCase_ 		=     do_rescale
							lowerCamelCase_ 		=     rescale_factor
							lowerCamelCase_ 		=     do_normalize
							lowerCamelCase_ 		=     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							lowerCamelCase_ 		=     image_std if image_std is not None else OPENAI_CLIP_STD
							lowerCamelCase_ 		=     do_convert_rgb




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = PILImageResampling.BICUBIC       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase       ,	default_to_square=UpperCAmelCase      )
							lowerCamelCase_ 		=     get_resize_output_image_size(
							    UpperCAmelCase       ,	size=(size['''height'''], size['''width'''])       ,	default_to_square=UpperCAmelCase      )
							return resize(UpperCAmelCase       ,	size=UpperCAmelCase       ,	resample=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )
							return center_crop(UpperCAmelCase       ,	size=(size['''height'''], size['''width'''])       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							return rescale(UpperCAmelCase       ,	scale=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							return normalize(UpperCAmelCase       ,	mean=UpperCAmelCase       ,	std=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = ChannelDimension.FIRST       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_ 		=     size if size is not None else self.size
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase       ,	default_to_square=UpperCAmelCase      )
							lowerCamelCase_ 		=     resample if resample is not None else self.resample
							lowerCamelCase_ 		=     do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase_ 		=     crop_size if crop_size is not None else self.crop_size
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )
							lowerCamelCase_ 		=     do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_ 		=     rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_ 		=     do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_ 		=     image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_ 		=     image_std if image_std is not None else self.image_std
							lowerCamelCase_ 		=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							lowerCamelCase_ 		=     make_list_of_images(UpperCAmelCase      )

							if not valid_images(UpperCAmelCase      ):
										raise ValueError(
										    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
										    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

							if do_resize and size is None:
										raise ValueError('''Size must be specified if do_resize is True.'''      )

							if do_center_crop and crop_size is None:
										raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

							if do_rescale and rescale_factor is None:
										raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

							if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
										lowerCamelCase_ 		=     [convert_to_rgb(UpperCAmelCase      ) for image in images]

							# All transformations expect numpy arrays.
							lowerCamelCase_ 		=     [to_numpy_array(UpperCAmelCase      ) for image in images]

							if do_resize:
										lowerCamelCase_ 		=     [self.resize(image=UpperCAmelCase       ,	size=UpperCAmelCase       ,	resample=UpperCAmelCase      ) for image in images]

							if do_center_crop:
										lowerCamelCase_ 		=     [self.center_crop(image=UpperCAmelCase       ,	size=UpperCAmelCase      ) for image in images]

							if do_rescale:
										lowerCamelCase_ 		=     [self.rescale(image=UpperCAmelCase       ,	scale=UpperCAmelCase      ) for image in images]

							if do_normalize:
										lowerCamelCase_ 		=     [self.normalize(image=UpperCAmelCase       ,	mean=UpperCAmelCase       ,	std=UpperCAmelCase      ) for image in images]

							lowerCamelCase_ 		=     [to_channel_dimension_format(UpperCAmelCase       ,	UpperCAmelCase      ) for image in images]

							lowerCamelCase_ 		=     {'''pixel_values''': images}
							return BatchFeature(data=UpperCAmelCase       ,	tensor_type=UpperCAmelCase      )





"""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)



