


"""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, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=7	,					lowerCamelCase__=3	,					lowerCamelCase__=18	,					lowerCamelCase__=30	,					lowerCamelCase__=400	,					lowerCamelCase__=True	,					lowerCamelCase__=None	,					lowerCamelCase__=0.8_75	,					lowerCamelCase__=True	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      size if size is not None else {"shortest_edge": 20}
								snake_case__  :						List[Any]		=      parent
								snake_case__  :						Dict		=      batch_size
								snake_case__  :						Any		=      num_channels
								snake_case__  :						Optional[int]		=      image_size
								snake_case__  :						int		=      min_resolution
								snake_case__  :						Tuple		=      max_resolution
								snake_case__  :						int		=      do_resize
								snake_case__  :						Union[str, Any]		=      size
								snake_case__  :						Tuple		=      crop_pct
								snake_case__  :						int		=      do_normalize
								snake_case__  :						str		=      image_mean
								snake_case__  :						Union[str, Any]		=      image_std







							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "crop_pct": self.crop_pct,
								}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  ConvNextImageProcessor if is_vision_available() else None




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      ConvNextImageProcessingTester(self)




							@property
							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_resize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"size"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"crop_pct"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_normalize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_mean"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_std"))




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 20})

								snake_case__  :						List[Any]		=      self.image_processing_class.from_dict(self.image_processor_dict	,					size=42)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 42})




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								snake_case__  :						Union[str, Any]		=      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__  :						List[Any]		=      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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						Union[str, Any]		=      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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								snake_case__  :						int		=      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__  :						Optional[int]		=      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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						Dict		=      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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)







							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								snake_case__  :						Any		=      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__  :						Union[str, Any]		=      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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						List[Any]		=      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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)







"""simple docstring"""

from __future__ import annotations

from queue import Queue
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		a ^= b
		b ^= a
		a ^= b
		return a, b
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       1
		while (1 << j) < max_node:
				for i in range(1,		max_node + 1     ):
						lowerCamelCase	:     Tuple            =       parent[j - 1][parent[j - 1][i]]
				j += 1
		return parent
def 						UpperCAmelCase   (						a_,		a_,		a_,		a_     ):





		'''simple docstring'''


		if level[u] < level[v]:
				lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       swap(a_,		a_     )
		# making depth of u same as depth of v
		for i in range(18,		-1,		-1     ):
				if level[u] - (1 << i) >= level[v]:
						lowerCamelCase	:     int            =       parent[i][u]
    # at the same depth if u==v that mean lca is found
		if u == v:
				return u
		# moving both nodes upwards till lca in found
		for i in range(18,		-1,		-1     ):
				if parent[i][u] not in [0, parent[i][v]]:
						lowerCamelCase      ,       lowerCamelCase	:     str            =       parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
		return parent[0][u]
def 						UpperCAmelCase   (						a_,		a_,		a_,		a_,		a_ = 1,		):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       0
		lowerCamelCase	:     Queue[int]            =       Queue(maxsize=a_     )
		q.put(a_     )
		while q.qsize() != 0:
				lowerCamelCase	:     Tuple            =       q.get()
				for v in graph[u]:
						if level[v] == -1:
								lowerCamelCase	:     Optional[Any]            =       level[u] + 1
								q.put(a_     )
								lowerCamelCase	:     str            =       u
		return level, parent
def 						UpperCAmelCase   (						):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       13
		# initializing with 0
		lowerCamelCase	:     Dict            =       [[0 for _ in range(max_node + 10     )] for _ in range(20     )]
		# initializing with -1 which means every node is unvisited
		lowerCamelCase	:     List[Any]            =       [-1 for _ in range(max_node + 10     )]
		lowerCamelCase	:     dict[int, list[int]]            =       {
		    1: [2, 3, 4],
		    2: [5],
		    3: [6, 7],
		    4: [8],
		    5: [9, 10],
		    6: [11],
		    7: [],
		    8: [12, 13],
		    9: [],
		    10: [],
		    11: [],
		    12: [],
		    13: [],
		}
		lowerCamelCase      ,       lowerCamelCase	:     Dict            =       breadth_first_search(a_,		a_,		a_,		a_,		1     )
		lowerCamelCase	:     List[Any]            =       create_sparse(a_,		a_     )
		print('LCA of node 1 and 3 is: ',		lowest_common_ancestor(1,		3,		a_,		a_     )     )
		print('LCA of node 5 and 6 is: ',		lowest_common_ancestor(5,		6,		a_,		a_     )     )
		print('LCA of node 7 and 11 is: ',		lowest_common_ancestor(7,		11,		a_,		a_     )     )
		print('LCA of node 6 and 7 is: ',		lowest_common_ancestor(6,		7,		a_,		a_     )     )
		print('LCA of node 4 and 12 is: ',		lowest_common_ancestor(4,		12,		a_,		a_     )     )
		print('LCA of node 8 and 8 is: ',		lowest_common_ancestor(8,		8,		a_,		a_     )     )


if __name__ == "__main__":
					main()






