

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 convert_to_rgb, 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


if is_vision_available():
			import PIL


a__												=						logging.get_logger(__name__)

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       ["pixel_values"]


							def __init__(     self			,				_a = True			,				_a = None			,				_a = PILImageResampling.BICUBIC			,				_a = True			,				_a = 1 / 2_5_5			,				_a = True			,				_a = None			,				_a = None			,				_a = True			,				**_a			,				)       ->       None:
												super().__init__(**_a			)
												_a      :       str   =						size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
												_a      :       Union[str, Any]   =						get_size_dict(_a			,				default_to_square=_a			)

												_a      :       str   =						do_resize
												_a      :       str   =						size
												_a      :       Optional[int]   =						resample
												_a      :       Optional[int]   =						do_rescale
												_a      :       Tuple   =						rescale_factor
												_a      :       str   =						do_normalize
												_a      :       Optional[int]   =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												_a      :       Union[str, Any]   =						image_std if image_std is not None else OPENAI_CLIP_STD
												_a      :       int   =						do_convert_rgb


							def 							__lowercase							(     self			,				_a			,				_a			,				_a = PILImageResampling.BICUBIC			,				_a = None			,				**_a			,				)       ->       np.ndarray:
												_a      :       List[str]   =						get_size_dict(_a			,				default_to_square=_a			)
												if "height" not in size or "width" not in size:
																	raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""			)
												_a      :       int   =						(size['''height'''], size['''width'''])
												return resize(_a			,				size=_a			,				resample=_a			,				data_format=_a			,				**_a			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a = None			,				**_a			,				)       ->       Optional[int]:
												return rescale(_a			,				scale=_a			,				data_format=_a			,				**_a			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a = None			,				**_a			,				)       ->       np.ndarray:
												return normalize(_a			,				mean=_a			,				std=_a			,				data_format=_a			,				**_a			)




							def 							__lowercase							(     self			,				_a			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = ChannelDimension.FIRST			,				**_a			,				)       ->       PIL.Image.Image:
												_a      :       str   =						do_resize if do_resize is not None else self.do_resize
												_a      :       Optional[int]   =						resample if resample is not None else self.resample
												_a      :       Optional[Any]   =						do_rescale if do_rescale is not None else self.do_rescale
												_a      :       Any   =						rescale_factor if rescale_factor is not None else self.rescale_factor
												_a      :       Union[str, Any]   =						do_normalize if do_normalize is not None else self.do_normalize
												_a      :       Optional[Any]   =						image_mean if image_mean is not None else self.image_mean
												_a      :       Tuple   =						image_std if image_std is not None else self.image_std
												_a      :       List[Any]   =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												_a      :       List[Any]   =						size if size is not None else self.size
												_a      :       Optional[Any]   =						get_size_dict(_a			,				default_to_square=_a			)

												_a      :       List[Any]   =						make_list_of_images(_a			)

												if not valid_images(_a			):
																	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 or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize 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:
																	_a      :       List[str]   =						[convert_to_rgb(_a			) for image in images]

												# All transformations expect numpy arrays.
												_a      :       Dict   =						[to_numpy_array(_a			) for image in images]

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

												if do_rescale:
																	_a      :       List[str]   =						[self.rescale(image=_a			,				scale=_a			) for image in images]

												if do_normalize:
																	_a      :       List[Any]   =						[self.normalize(image=_a			,				mean=_a			,				std=_a			) for image in images]

												_a      :       Optional[int]   =						[to_channel_dimension_format(_a			,				_a			) for image in images]

												_a      :       Optional[Any]   =						BatchFeature(data={'''pixel_values''': images}			,				tensor_type=_a			)

												return encoded_outputs






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a__												=						numpy.array([0, 0])
a__												=						numpy.array([0.5, 0.8660254])
a__												=						numpy.array([1, 0])
a__												=						[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]						,__a :     int      )     ->	list[numpy.ndarray]:

					"""simple docstring"""

					_a      :       Tuple   =						initial_vectors
					for _ in range(__a      ):
										_a      :       int   =						iteration_step(__a      )
					return vectors



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]      )     ->	list[numpy.ndarray]:

					"""simple docstring"""

					_a      :       Tuple   =						[]
					for i, start_vector in enumerate(vectors[:-1]      ):
										_a      :       str   =						vectors[i + 1]
										new_vectors.append(__a      )
										_a      :       Optional[int]   =						end_vector - start_vector
										new_vectors.append(start_vector + difference_vector / 3      )
										new_vectors.append(
										    start_vector + difference_vector / 3 + rotate(difference_vector / 3						,60      )      )
										new_vectors.append(start_vector + difference_vector * 2 / 3      )
					new_vectors.append(vectors[-1]      )
					return new_vectors



def   __UpperCAmelCase					(      __a :     numpy.ndarray						,__a :     float      )     ->	numpy.ndarray:

					"""simple docstring"""

					_a      :       Tuple   =						numpy.radians(__a      )
					_a ,   _a      :       List[Any]   =						numpy.cos(__a      ), numpy.sin(__a      )
					_a      :       Dict   =						numpy.array(((c, -s), (s, c))      )
					return numpy.dot(__a						,__a      )



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]      )     ->	None:

					"""simple docstring"""

					_a      :       str   =						plt.gca()
					axes.set_aspect('''equal'''      )

					# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
					# y-coordinates as inputs, which are constructed from the vector-list using
					# zip()
					_a ,   _a      :       Optional[int]   =						zip(*__a      )
					plt.plot(__a						,__a      )
					plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			a__												=						iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)



