"""simple docstring"""
class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = ""		,    lowerCAmelCase = False    ):






										"""simple docstring"""

										snake_case								=			{}

										# A node will be a leaf if the tree contains its word
										snake_case								=			is_leaf

										snake_case								=			prefix

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			0
										for q, w in zip(self.prefix		,    lowerCAmelCase    ):
																if q != w:
																						break

																x += 1

										return self.prefix[:x], self.prefix[x:], word[x:]

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										for word in words:
																self.insert(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if self.prefix == word:
																snake_case								=			True

										# Case 2: The node has no edges that have a prefix to the word
										# Solution: We create an edge from the current node to a new one
										# containing the word
										elif word[0] not in self.nodes:
																snake_case								=			RadixNode(prefix=lowerCAmelCase		,    is_leaf=lowerCAmelCase    )

										else:
																snake_case								=			self.nodes[word[0]]
																snake_case					,snake_case					,snake_case								=			incoming_node.match(
																    lowerCAmelCase    )

																# Case 3: The node prefix is equal to the matching
																# Solution: We insert remaining word on the next node
																if remaining_prefix == "":
																						self.nodes[matching_string[0]].insert(lowerCAmelCase    )

																# Case 4: The word is greater equal to the matching
																# Solution: Create a node in between both nodes, change
																# prefixes and add the new node for the remaining word
																else:
																						snake_case								=			remaining_prefix

																						snake_case								=			self.nodes[matching_string[0]]
																						snake_case								=			RadixNode(lowerCAmelCase		,    lowerCAmelCase    )
																						snake_case								=			aux_node

																						if remaining_word == "":
																												snake_case								=			True
																						else:
																												self.nodes[matching_string[0]].insert(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.nodes.get(word[0]		,    lowerCAmelCase    )
										if not incoming_node:
																return False
										else:
																snake_case					,snake_case					,snake_case								=			incoming_node.match(
																    lowerCAmelCase    )
																# If there is remaining prefix, the word can't be on the tree
																if remaining_prefix != "":
																						return False
																# This applies when the word and the prefix are equal
																elif remaining_word == "":
																						return incoming_node.is_leaf
																# We have word remaining so we check the next node
																else:
																						return incoming_node.find(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.nodes.get(word[0]		,    lowerCAmelCase    )
										if not incoming_node:
																return False
										else:
																snake_case					,snake_case					,snake_case								=			incoming_node.match(
																    lowerCAmelCase    )
																# If there is remaining prefix, the word can't be on the tree
																if remaining_prefix != "":
																						return False
																# We have word remaining so we check the next node
																elif remaining_word != "":
																						return incoming_node.delete(lowerCAmelCase    )
																else:
																						# If it is not a leaf, we don't have to delete
																						if not incoming_node.is_leaf:
																												return False
																						else:
																												# We delete the nodes if no edges go from it
																												if len(incoming_node.nodes    ) == 0:
																																		del self.nodes[word[0]]
																																		# We merge the current node with its only child
																																		if len(self.nodes    ) == 1 and not self.is_leaf:
																																								snake_case								=			list(self.nodes.values()    )[0]
																																								snake_case								=			merging_node.is_leaf
																																								self.prefix += merging_node.prefix
																																								snake_case								=			merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																												elif len(incoming_node.nodes    ) > 1:
																																		snake_case								=			False
																												# If there is 1 edge, we merge it with its child
																												else:
																																		snake_case								=			list(incoming_node.nodes.values()    )[0]
																																		snake_case								=			merging_node.is_leaf
																																		incoming_node.prefix += merging_node.prefix
																																		snake_case								=			merging_node.nodes

																												return True

				def 		snake_case (				self		,    lowerCAmelCase = 0    ):






										"""simple docstring"""

										if self.prefix != "":
																print('-' * height		,    self.prefix		,    '  (leaf)' if self.is_leaf else ''    )

										for value in self.nodes.values():
																value.print_tree(height + 1    )





def    lowerCAmelCase__    (			)					->				bool:





						"""simple docstring"""






						snake_case								=			'banana bananas bandana band apple all beast'.split()
						snake_case								=			RadixNode()
						root.insert_many(_UpperCamelCase					)

						assert all(root.find(_UpperCamelCase					) for word in words					)
						assert not root.find('bandanas'					)
						assert not root.find('apps'					)
						root.delete('all'					)
						assert not root.find('all'					)
						root.delete('banana'					)
						assert not root.find('banana'					)
						assert root.find('bananas'					)

						return True





def    lowerCAmelCase__    (			)					->				None:





						"""simple docstring"""






						assert test_trie()





def    lowerCAmelCase__    (			)					->				None:





						"""simple docstring"""






						snake_case								=			RadixNode()
						snake_case								=			'banana bananas bandanas bandana band apple all beast'.split()
						root.insert_many(_UpperCamelCase					)

						print('Words:' ,			_UpperCamelCase					)
						print('Tree:'					)
						root.print_tree()


if __name__ == "__main__":
					main()



"""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=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    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]		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			num_channels
										snake_case								=			image_size
										snake_case								=			min_resolution
										snake_case								=			max_resolution
										snake_case								=			do_resize
										snake_case								=			size if size is not None else {'height': 18, 'width': 20}
										snake_case								=			do_thumbnail
										snake_case								=			do_align_axis
										snake_case								=			do_pad
										snake_case								=			do_normalize
										snake_case								=			image_mean
										snake_case								=			image_std

				def 		snake_case (				self    ):






										"""simple docstring"""

										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_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						DonutImageProcessor if is_vision_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			DonutImageProcessingTester(self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_resize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'size'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_thumbnail'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_align_long_axis'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_pad'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_normalize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_mean'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_std'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,    {'height': 18, 'width': 20}    )

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

										# Previous config had dimensions in (width, height) order
										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=(42, 84)    )
										self.assertEqual(image_processor.size		,    {'height': 84, 'width': 42}    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PIL images
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    Image.Image    )

										# Test not batched input
										snake_case								=			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
										snake_case								=			image_processing(lowerCAmelCase		,    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    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase		,    numpify=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    np.ndarray    )

										# Test not batched input
										snake_case								=			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
										snake_case								=			image_processing(lowerCAmelCase		,    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    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase		,    torchify=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    torch.Tensor    )

										# Test not batched input
										snake_case								=			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
										snake_case								=			image_processing(lowerCAmelCase		,    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'],
										    )		,    )


