



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=7				,       lowerCamelCase__=3				,       lowerCamelCase__=1_8				,       lowerCamelCase__=3_0				,       lowerCamelCase__=4_0_0				,       lowerCamelCase__=True				,       lowerCamelCase__=None				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=[0.5, 0.5, 0.5]				,       lowerCamelCase__=[0.5, 0.5, 0.5]				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				min_resolution
									_lowerCamelCase        =				max_resolution
									_lowerCamelCase        =				do_resize
									_lowerCamelCase        =				size if size is not None else {'''height''': 1_8, '''width''': 2_0}
									_lowerCamelCase        =				do_thumbnail
									_lowerCamelCase        =				do_align_axis
									_lowerCamelCase        =				do_pad
									_lowerCamelCase        =				do_normalize
									_lowerCamelCase        =				image_mean
									_lowerCamelCase        =				image_std





					def 			snake_case__      (    self   ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_thumbnail": self.do_thumbnail,
									    "do_align_long_axis": self.do_align_axis,
									    "do_pad": self.do_pad,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}


@require_torch
@require_vision
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Dict										= DonutImageProcessor if is_vision_available() else None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				DonutImageProcessingTester(self   )

					@property
					def 			snake_case__      (    self   ):
									return self.image_processor_tester.prepare_image_processor_dict()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(UpperCamelCase__				,       '''do_resize'''   )   )
									self.assertTrue(hasattr(UpperCamelCase__				,       '''size'''   )   )
									self.assertTrue(hasattr(UpperCamelCase__				,       '''do_thumbnail'''   )   )
									self.assertTrue(hasattr(UpperCamelCase__				,       '''do_align_long_axis'''   )   )
									self.assertTrue(hasattr(UpperCamelCase__				,       '''do_pad'''   )   )
									self.assertTrue(hasattr(UpperCamelCase__				,       '''do_normalize'''   )   )
									self.assertTrue(hasattr(UpperCamelCase__				,       '''image_mean'''   )   )
									self.assertTrue(hasattr(UpperCamelCase__				,       '''image_std'''   )   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processing_class.from_dict(self.image_processor_dict   )
									self.assertEqual(image_processor.size				,       {'''height''': 1_8, '''width''': 2_0}   )

									_lowerCamelCase        =				self.image_processing_class.from_dict(self.image_processor_dict				,       size=4_2   )
									self.assertEqual(image_processor.size				,       {'''height''': 4_2, '''width''': 4_2}   )

									# Previous config had dimensions in (width, height) order
									_lowerCamelCase        =				self.image_processing_class.from_dict(self.image_processor_dict				,       size=(4_2, 8_4)   )
									self.assertEqual(image_processor.size				,       {'''height''': 8_4, '''width''': 4_2}   )

					def 			snake_case__      (    self   ):
									pass

					@is_flaky()
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__				,       Image.Image   )

									# Test not batched input
									_lowerCamelCase        =				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
									_lowerCamelCase        =				image_processing(UpperCamelCase__				,       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'''],
									    )				,       )

					@is_flaky()
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__				,       numpify=UpperCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__				,       np.ndarray   )

									# Test not batched input
									_lowerCamelCase        =				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
									_lowerCamelCase        =				image_processing(UpperCamelCase__				,       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'''],
									    )				,       )





					@is_flaky()
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__				,       torchify=UpperCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__				,       torch.Tensor   )

									# Test not batched input
									_lowerCamelCase        =				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
									_lowerCamelCase        =				image_processing(UpperCamelCase__				,       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'''],
									    )				,       )





"""simple docstring"""
from __future__ import annotations

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

# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE  :						List[str]    							=      numpy.array([0, 0])
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      numpy.array([0.5, 0.866_0254])
__SCREAMING_SNAKE_CASE  :						Tuple    							=      numpy.array([1, 0])
__SCREAMING_SNAKE_CASE  :						List[Any]    							=      [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]						,   lowercase_     :							int							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				initial_vectors
				for _ in range(lowercase_							):
								_lowerCamelCase        =				iteration_step(lowercase_							)
				return vectors



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				[]
				for i, start_vector in enumerate(vectors[:-1]							):
								_lowerCamelCase        =				vectors[i + 1]
								new_vectors.append(lowercase_							)
								_lowerCamelCase        =				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        lowerCAmelCase_(   lowercase_     :							numpy.ndarray						,   lowercase_     :							float							)	->       numpy.ndarray:
				_lowerCamelCase        =				numpy.radians(lowercase_							)
				_lowerCamelCase  ,     _lowerCamelCase        =				numpy.cos(lowercase_							), numpy.sin(lowercase_							)
				_lowerCamelCase        =				numpy.array(((c, -s), (s, c))							)
				return numpy.dot(lowercase_						,   lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       None:
				_lowerCamelCase        =				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()
				_lowerCamelCase  ,     _lowerCamelCase        =				zip(*lowercase_							)
				plt.plot(lowercase_						,   lowercase_							)
				plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__SCREAMING_SNAKE_CASE  :						str    							=      iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)
