






"""simple docstring"""







import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor


class    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__=7	,  snake_case__=3	,  snake_case__=18	,  snake_case__=30	,  snake_case__=400	,  snake_case__=True	,  snake_case__=None	,  snake_case__=True	,  snake_case__=None	,  snake_case__=True	,  snake_case__=[0.5, 0.5, 0.5]	,  snake_case__=[0.5, 0.5, 0.5]	,  snake_case__=False	,  ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	size if size is not None else {"height": 20, "width": 20}
												lowercase__    :				int=	crop_size if crop_size is not None else {"height": 18, "width": 18}
												lowercase__    :				List[Any]=	parent
												lowercase__    :				Union[str, Any]=	batch_size
												lowercase__    :				int=	num_channels
												lowercase__    :				str=	image_size
												lowercase__    :				Optional[Any]=	min_resolution
												lowercase__    :				Dict=	max_resolution
												lowercase__    :				str=	do_resize
												lowercase__    :				List[Any]=	size
												lowercase__    :				Any=	do_center_crop
												lowercase__    :				Optional[Any]=	crop_size
												lowercase__    :				Any=	do_normalize
												lowercase__    :				Any=	image_mean
												lowercase__    :				int=	image_std
												lowercase__    :				int=	do_reduce_labels




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_reduce_labels": self.do_reduce_labels,
												}





def 						lowercase__()    ->Optional[Any]:




						"""simple docstring"""



						lowercase__    :				str=	load_dataset("hf-internal-testing/fixtures_ade20k" ,		split="test"						)

						lowercase__    :				int=	Image.open(dataset[0]["file"]						)
						lowercase__    :				Tuple=	Image.open(dataset[1]["file"]						)

						return image, map





def 						lowercase__()    ->str:




						"""simple docstring"""



						lowercase__    :				str=	load_dataset("hf-internal-testing/fixtures_ade20k" ,		split="test"						)

						lowercase__    :				Optional[int]=	Image.open(ds[0]["file"]						)
						lowercase__    :				int=	Image.open(ds[1]["file"]						)
						lowercase__    :				Dict=	Image.open(ds[2]["file"]						)
						lowercase__    :				str=	Image.open(ds[3]["file"]						)

						return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= BeitImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				int=	BeitImageProcessingTester(self      )




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(snake_case__	,  "do_resize"      )      )
												self.assertTrue(hasattr(snake_case__	,  "size"      )      )
												self.assertTrue(hasattr(snake_case__	,  "do_center_crop"      )      )
												self.assertTrue(hasattr(snake_case__	,  "center_crop"      )      )
												self.assertTrue(hasattr(snake_case__	,  "do_normalize"      )      )
												self.assertTrue(hasattr(snake_case__	,  "image_mean"      )      )
												self.assertTrue(hasattr(snake_case__	,  "image_std"      )      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,  {"height": 20, "width": 20}      )
												self.assertEqual(image_processor.crop_size	,  {"height": 18, "width": 18}      )
												self.assertEqual(image_processor.do_reduce_labels	,  snake_case__      )

												lowercase__    :				str=	self.image_processing_class.from_dict(
												    self.image_processor_dict	,  size=42	,  crop_size=84	,  reduce_labels=snake_case__      )
												self.assertEqual(image_processor.size	,  {"height": 42, "width": 42}      )
												self.assertEqual(image_processor.crop_size	,  {"height": 84, "width": 84}      )
												self.assertEqual(image_processor.do_reduce_labels	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				List[str]=	self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												lowercase__    :				List[Any]=	prepare_image_inputs(self.image_processor_tester	,  equal_resolution=snake_case__      )
												for image in image_inputs:
																		self.assertIsInstance(snake_case__	,  Image.Image      )

												# Test not batched input
												lowercase__    :				Dict=	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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )

												# Test batched
												lowercase__    :				Optional[Any]=	image_processing(snake_case__	,  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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				Union[str, Any]=	self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												lowercase__    :				Optional[Any]=	prepare_image_inputs(self.image_processor_tester	,  equal_resolution=snake_case__	,  numpify=snake_case__      )
												for image in image_inputs:
																		self.assertIsInstance(snake_case__	,  np.ndarray      )

												# Test not batched input
												lowercase__    :				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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )

												# Test batched
												lowercase__    :				Dict=	image_processing(snake_case__	,  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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				Tuple=	self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												lowercase__    :				str=	prepare_image_inputs(self.image_processor_tester	,  equal_resolution=snake_case__	,  torchify=snake_case__      )
												for image in image_inputs:
																		self.assertIsInstance(snake_case__	,  torch.Tensor      )

												# Test not batched input
												lowercase__    :				str=	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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )

												# Test batched
												lowercase__    :				Dict=	image_processing(snake_case__	,  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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				List[str]=	self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												lowercase__    :				Optional[int]=	prepare_image_inputs(self.image_processor_tester	,  equal_resolution=snake_case__	,  torchify=snake_case__      )
												lowercase__    :				Optional[int]=	[]
												for image in image_inputs:
																		self.assertIsInstance(snake_case__	,  torch.Tensor      )
																		maps.append(torch.zeros(image.shape[-2:]      ).long()      )

												# Test not batched input
												lowercase__    :				Optional[Any]=	image_processing(image_inputs[0]	,  maps[0]	,  return_tensors="pt"      )
												self.assertEqual(
												    encoding["pixel_values"].shape	,  (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )
												self.assertEqual(
												    encoding["labels"].shape	,  (
												        1,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )
												self.assertEqual(encoding["labels"].dtype	,  torch.long      )
												self.assertTrue(encoding["labels"].min().item() >= 0      )
												self.assertTrue(encoding["labels"].max().item() <= 255      )

												# Test batched
												lowercase__    :				Any=	image_processing(snake_case__	,  snake_case__	,  return_tensors="pt"      )
												self.assertEqual(
												    encoding["pixel_values"].shape	,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )
												self.assertEqual(
												    encoding["labels"].shape	,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )
												self.assertEqual(encoding["labels"].dtype	,  torch.long      )
												self.assertTrue(encoding["labels"].min().item() >= 0      )
												self.assertTrue(encoding["labels"].max().item() <= 255      )

												# Test not batched input (PIL images)
												lowercase__,     lowercase__    :				Any=	prepare_semantic_single_inputs()

												lowercase__    :				Any=	image_processing(snake_case__	,  snake_case__	,  return_tensors="pt"      )
												self.assertEqual(
												    encoding["pixel_values"].shape	,  (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )
												self.assertEqual(
												    encoding["labels"].shape	,  (
												        1,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )
												self.assertEqual(encoding["labels"].dtype	,  torch.long      )
												self.assertTrue(encoding["labels"].min().item() >= 0      )
												self.assertTrue(encoding["labels"].max().item() <= 255      )

												# Test batched input (PIL images)
												lowercase__,     lowercase__    :				List[str]=	prepare_semantic_batch_inputs()

												lowercase__    :				Tuple=	image_processing(snake_case__	,  snake_case__	,  return_tensors="pt"      )
												self.assertEqual(
												    encoding["pixel_values"].shape	,  (
												        2,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )
												self.assertEqual(
												    encoding["labels"].shape	,  (
												        2,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )	,  )
												self.assertEqual(encoding["labels"].dtype	,  torch.long      )
												self.assertTrue(encoding["labels"].min().item() >= 0      )
												self.assertTrue(encoding["labels"].max().item() <= 255      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				int=	self.image_processing_class(**self.image_processor_dict      )

												# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
												lowercase__,     lowercase__    :				int=	prepare_semantic_single_inputs()
												lowercase__    :				Union[str, Any]=	image_processing(snake_case__	,  snake_case__	,  return_tensors="pt"      )
												self.assertTrue(encoding["labels"].min().item() >= 0      )
												self.assertTrue(encoding["labels"].max().item() <= 150      )

												lowercase__    :				Any=	True
												lowercase__    :				Optional[Any]=	image_processing(snake_case__	,  snake_case__	,  return_tensors="pt"      )
												self.assertTrue(encoding["labels"].min().item() >= 0      )
												self.assertTrue(encoding["labels"].max().item() <= 255      )














"""simple docstring"""







from __future__ import annotations


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	data
												lowercase__    :				Node | None=	None
												lowercase__    :				Node | None=	None





def 						lowercase__(A						)    ->None:  # In Order traversal of the tree




						"""simple docstring"""



						if tree:
												display(tree.left						)
												print(tree.data						)
												display(tree.right						)





def 						lowercase__(A						)    ->int:




						"""simple docstring"""



						return 1 + max(depth_of_tree(tree.left						) ,		depth_of_tree(tree.right						)						) if tree else 0





def 						lowercase__(A						)    ->bool:




						"""simple docstring"""



						if not tree:
												return True
						if tree.left and tree.right:
												return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
						else:
												return not tree.left and not tree.right





def 						lowercase__()    ->None:  # Main function for testing.




						"""simple docstring"""



						lowercase__    :				Tuple=	Node(1						)
						lowercase__    :				Optional[int]=	Node(2						)
						lowercase__    :				List[str]=	Node(3						)
						lowercase__    :				Tuple=	Node(4						)
						lowercase__    :				Optional[int]=	Node(5						)
						lowercase__    :				Any=	Node(6						)
						lowercase__    :				Optional[Any]=	Node(7						)
						lowercase__    :				Optional[int]=	Node(8						)
						lowercase__    :				List[str]=	Node(9						)

						print(is_full_binary_tree(A						)						)
						print(depth_of_tree(A						)						)
						print("Tree is: "						)
						display(A						)


if __name__ == "__main__":
			main()






