






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor







class 				UpperCAmelCase_  (		unittest.TestCase		):




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=7	,						_lowerCAmelCase=3	,						_lowerCAmelCase=30	,						_lowerCAmelCase=400	,						_lowerCAmelCase=True	,						_lowerCAmelCase=None	,						_lowerCAmelCase=True	,						_lowerCAmelCase=[0.5, 0.5, 0.5]	,						_lowerCAmelCase=[0.5, 0.5, 0.5]	,						_lowerCAmelCase=True	,						_lowerCAmelCase=1 / 255	,						_lowerCAmelCase=True	,						):
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          UpperCAmelCase__      :  Any				      =			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
          UpperCAmelCase__      :  Optional[Any]				      =			parent
          UpperCAmelCase__      :  Optional[Any]				      =			batch_size
          UpperCAmelCase__      :  Optional[int]				      =			num_channels
          UpperCAmelCase__      :  Union[str, Any]				      =			min_resolution
          UpperCAmelCase__      :  str				      =			max_resolution
          UpperCAmelCase__      :  Union[str, Any]				      =			do_resize
          UpperCAmelCase__      :  List[Any]				      =			size
          UpperCAmelCase__      :  Tuple				      =			do_normalize
          UpperCAmelCase__      :  str				      =			image_mean
          UpperCAmelCase__      :  Any				      =			image_std
          UpperCAmelCase__      :  Union[str, Any]				      =			do_rescale
          UpperCAmelCase__      :  Any				      =			rescale_factor
          UpperCAmelCase__      :  Union[str, Any]				      =			do_pad




     def   __UpperCAmelCase  (				self		):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
          }




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=False		):
          if not batched:
               UpperCAmelCase__      :  Optional[int]				      =			image_inputs[0]
               if isinstance(_lowerCAmelCase	,						Image.Image		):
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Any				      =			image.size
               else:
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			image.shape[1], image.shape[2]
               if w < h:
                    UpperCAmelCase__      :  Union[str, Any]				      =			int(self.size["""shortest_edge"""] * h / w		)
                    UpperCAmelCase__      :  Union[str, Any]				      =			self.size["""shortest_edge"""]
               elif w > h:
                    UpperCAmelCase__      :  List[str]				      =			self.size["""shortest_edge"""]
                    UpperCAmelCase__      :  int				      =			int(self.size["""shortest_edge"""] * w / h		)
               else:
                    UpperCAmelCase__      :  Tuple				      =			self.size["""shortest_edge"""]
                    UpperCAmelCase__      :  List[Any]				      =			self.size["""shortest_edge"""]

          else:
               UpperCAmelCase__      :  int				      =			[]
               for image in image_inputs:
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  List[str]				      =			self.get_expected_values([image]		)
                    expected_values.append((expected_height, expected_width)		)
               UpperCAmelCase__      :  Dict				      =			max(_lowerCAmelCase	,						key=lambda _lowerCAmelCase		: item[0]		)[0]
               UpperCAmelCase__      :  Optional[int]				      =			max(_lowerCAmelCase	,						key=lambda _lowerCAmelCase		: item[1]		)[1]

          return expected_height, expected_width







@require_torch
@require_vision
class 				UpperCAmelCase_  (		__lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  DeformableDetrImageProcessor if is_vision_available() else None




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			DeformableDetrImageProcessingTester(self		)




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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""image_mean"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""image_std"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_normalize"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_resize"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_rescale"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_pad"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""size"""		)		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			self.image_processing_class.from_dict(self.image_processor_dict		)
          self.assertEqual(image_processor.size	,						{"""shortest_edge""": 18, """longest_edge""": 1333}		)
          self.assertEqual(image_processor.do_pad	,						_lowerCAmelCase		)

          UpperCAmelCase__      :  List[str]				      =			self.image_processing_class.from_dict(
              self.image_processor_dict	,						size=42	,						max_size=84	,						pad_and_return_pixel_mask=_lowerCAmelCase		)
          self.assertEqual(image_processor.size	,						{"""shortest_edge""": 42, """longest_edge""": 84}		)
          self.assertEqual(image_processor.do_pad	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          pass




     def   __UpperCAmelCase  (				self		):
          # Initialize image_processing
          UpperCAmelCase__      :  Optional[Any]				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          UpperCAmelCase__      :  int				      =			prepare_image_inputs(self.image_processor_tester	,						equal_resolution=_lowerCAmelCase		)
          for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase	,						Image.Image		)

          # Test not batched input
          UpperCAmelCase__      :  Tuple				      =			image_processing(image_inputs[0]	,						return_tensors="""pt"""		).pixel_values

          UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			self.image_processor_tester.get_expected_values(_lowerCAmelCase		)

          self.assertEqual(
              encoded_images.shape	,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,						)

          # Test batched
          UpperCAmelCase__      ,		UpperCAmelCase__      :  Union[str, Any]				      =			self.image_processor_tester.get_expected_values(_lowerCAmelCase	,						batched=_lowerCAmelCase		)

          UpperCAmelCase__      :  Optional[int]				      =			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,
                  expected_height,
                  expected_width,
              )	,						)




     def   __UpperCAmelCase  (				self		):
          # Initialize image_processing
          UpperCAmelCase__      :  Optional[Any]				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          UpperCAmelCase__      :  Optional[Any]				      =			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
          UpperCAmelCase__      :  Dict				      =			image_processing(image_inputs[0]	,						return_tensors="""pt"""		).pixel_values

          UpperCAmelCase__      ,		UpperCAmelCase__      :  int				      =			self.image_processor_tester.get_expected_values(_lowerCAmelCase		)

          self.assertEqual(
              encoded_images.shape	,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,						)

          # Test batched
          UpperCAmelCase__      :  Optional[int]				      =			image_processing(_lowerCAmelCase	,						return_tensors="""pt"""		).pixel_values

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[Any]				      =			self.image_processor_tester.get_expected_values(_lowerCAmelCase	,						batched=_lowerCAmelCase		)

          self.assertEqual(
              encoded_images.shape	,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,						)




     def   __UpperCAmelCase  (				self		):
          # Initialize image_processing
          UpperCAmelCase__      :  str				      =			self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          UpperCAmelCase__      :  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
          UpperCAmelCase__      :  Optional[int]				      =			image_processing(image_inputs[0]	,						return_tensors="""pt"""		).pixel_values

          UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[Any]				      =			self.image_processor_tester.get_expected_values(_lowerCAmelCase		)

          self.assertEqual(
              encoded_images.shape	,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,						)

          # Test batched
          UpperCAmelCase__      :  str				      =			image_processing(_lowerCAmelCase	,						return_tensors="""pt"""		).pixel_values

          UpperCAmelCase__      ,		UpperCAmelCase__      :  Dict				      =			self.image_processor_tester.get_expected_values(_lowerCAmelCase	,						batched=_lowerCAmelCase		)

          self.assertEqual(
              encoded_images.shape	,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,						)




     @slow
     def   __UpperCAmelCase  (				self		):
          # prepare image and target
          UpperCAmelCase__      :  Optional[Any]				      =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
          with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""	,						"""r"""		) as f:
               UpperCAmelCase__      :  Any				      =			json.loads(f.read()		)

          UpperCAmelCase__      :  List[Any]				      =			{"""image_id""": 39769, """annotations""": target}

          # encode them
          UpperCAmelCase__      :  Tuple				      =			DeformableDetrImageProcessor()
          UpperCAmelCase__      :  Tuple				      =			image_processing(images=_lowerCAmelCase	,						annotations=_lowerCAmelCase	,						return_tensors="""pt"""		)

          # verify pixel values
          UpperCAmelCase__      :  List[str]				      =			torch.Size([1, 3, 800, 1066]		)
          self.assertEqual(encoding["""pixel_values"""].shape	,						_lowerCAmelCase		)

          UpperCAmelCase__      :  Optional[Any]				      =			torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
          self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]	,						_lowerCAmelCase	,						atol=1e-4		)		)

          # verify area
          UpperCAmelCase__      :  Dict				      =			torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	,						_lowerCAmelCase		)		)
          # verify boxes
          UpperCAmelCase__      :  List[Any]				      =			torch.Size([6, 4]		)
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  Any				      =			torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,						_lowerCAmelCase	,						atol=1e-3		)		)
          # verify image_id
          UpperCAmelCase__      :  List[Any]				      =			torch.tensor([39769]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,						_lowerCAmelCase		)		)
          # verify is_crowd
          UpperCAmelCase__      :  Optional[int]				      =			torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,						_lowerCAmelCase		)		)
          # verify class_labels
          UpperCAmelCase__      :  Optional[Any]				      =			torch.tensor([75, 75, 63, 65, 17, 17]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,						_lowerCAmelCase		)		)
          # verify orig_size
          UpperCAmelCase__      :  Optional[int]				      =			torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,						_lowerCAmelCase		)		)
          # verify size
          UpperCAmelCase__      :  Optional[int]				      =			torch.tensor([800, 1066]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,						_lowerCAmelCase		)		)




     @slow
     def   __UpperCAmelCase  (				self		):
          # prepare image, target and masks_path
          UpperCAmelCase__      :  Union[str, Any]				      =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
          with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""	,						"""r"""		) as f:
               UpperCAmelCase__      :  Tuple				      =			json.loads(f.read()		)

          UpperCAmelCase__      :  int				      =			{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

          UpperCAmelCase__      :  Union[str, Any]				      =			pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""		)

          # encode them
          UpperCAmelCase__      :  str				      =			DeformableDetrImageProcessor(format="""coco_panoptic"""		)
          UpperCAmelCase__      :  Optional[Any]				      =			image_processing(images=_lowerCAmelCase	,						annotations=_lowerCAmelCase	,						masks_path=_lowerCAmelCase	,						return_tensors="""pt"""		)

          # verify pixel values
          UpperCAmelCase__      :  Dict				      =			torch.Size([1, 3, 800, 1066]		)
          self.assertEqual(encoding["""pixel_values"""].shape	,						_lowerCAmelCase		)

          UpperCAmelCase__      :  str				      =			torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
          self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]	,						_lowerCAmelCase	,						atol=1e-4		)		)

          # verify area
          UpperCAmelCase__      :  Tuple				      =			torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]	,						_lowerCAmelCase		)		)
          # verify boxes
          UpperCAmelCase__      :  Optional[int]				      =			torch.Size([6, 4]		)
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  Any				      =			torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,						_lowerCAmelCase	,						atol=1e-3		)		)
          # verify image_id
          UpperCAmelCase__      :  Tuple				      =			torch.tensor([39769]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,						_lowerCAmelCase		)		)
          # verify is_crowd
          UpperCAmelCase__      :  int				      =			torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,						_lowerCAmelCase		)		)
          # verify class_labels
          UpperCAmelCase__      :  Optional[int]				      =			torch.tensor([17, 17, 63, 75, 75, 93]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,						_lowerCAmelCase		)		)
          # verify masks
          UpperCAmelCase__      :  List[Any]				      =			822873
          self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()	,						_lowerCAmelCase		)
          # verify orig_size
          UpperCAmelCase__      :  str				      =			torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,						_lowerCAmelCase		)		)
          # verify size
          UpperCAmelCase__      :  Optional[int]				      =			torch.tensor([800, 1066]		)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,						_lowerCAmelCase		)		)










'''simple docstring'''
from __future__ import annotations

import math
import random
from typing import Any

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self    ):

  """simple docstring"""







  __A	:		list[Any]	  =      []
  __A	:		int	  =      0
  __A	:		int	  =      0


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.head == self.tail


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.data.append(__lowercase    )
  __A	:		Any	  =      self.tail + 1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.data[self.head]
  __A	:		Union[str, Any]	  =      self.head + 1
  return ret


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.tail - self.head







 def 			snake_case__ (   self    ):

  """simple docstring"""







  print(self.data    )
  print('**************'    )
  print(self.data[self.head : self.tail]    )


class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      data
  __A	:		MyNode | None	  =      None
  __A	:		MyNode | None	  =      None
  __A	:		int	  =      1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.data


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.left


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.right


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.height


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      data


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      node


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      node







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      height


def       _lowercase							(					UpperCamelCase__							:       MyNode | None      ):
 if node is None:
  return 0
 return node.get_height()


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int      ):
 if a > b:
  return a
 return b


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 print('left rotation node:',					node.get_data()      )
 __A	:		Optional[Any]	  =      node.get_left()
 assert ret is not None
 node.set_left(ret.get_right()      )
 ret.set_right(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 __A	:		Optional[Any]	  =      my_max(get_height(ret.get_right()      ),					get_height(ret.get_left()      )      ) + 1
 ret.set_height(UpperCamelCase__      )
 return ret


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 print('right rotation node:',					node.get_data()      )
 __A	:		Optional[Any]	  =      node.get_right()
 assert ret is not None
 node.set_right(ret.get_left()      )
 ret.set_left(UpperCamelCase__      )
 __A	:		List[str]	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 __A	:		Any	  =      my_max(get_height(ret.get_right()      ),					get_height(ret.get_left()      )      ) + 1
 ret.set_height(UpperCamelCase__      )
 return ret


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 __A	:		Dict	  =      node.get_left()
 assert left_child is not None
 node.set_left(left_rotation(UpperCamelCase__      )      )
 return right_rotation(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 __A	:		Tuple	  =      node.get_right()
 assert right_child is not None
 node.set_right(right_rotation(UpperCamelCase__      )      )
 return left_rotation(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       MyNode | None,					UpperCamelCase__							:       Any      ):
 if node is None:
  return MyNode(UpperCamelCase__      )
 if data < node.get_data():
  node.set_left(insert_node(node.get_left(),					UpperCamelCase__      )      )
  if (
      get_height(node.get_left()      ) - get_height(node.get_right()      ) == 2
  ):  # an unbalance detected
   __A	:		Union[str, Any]	  =      node.get_left()
   assert left_child is not None
   if (
       data < left_child.get_data()
   ):  # new node is the left child of the left child
    __A	:		Optional[Any]	  =      right_rotation(UpperCamelCase__      )
   else:
    __A	:		Any	  =      lr_rotation(UpperCamelCase__      )
 else:
  node.set_right(insert_node(node.get_right(),					UpperCamelCase__      )      )
  if get_height(node.get_right()      ) - get_height(node.get_left()      ) == 2:
   __A	:		Union[str, Any]	  =      node.get_right()
   assert right_child is not None
   if data < right_child.get_data():
    __A	:		int	  =      rl_rotation(UpperCamelCase__      )
   else:
    __A	:		Optional[Any]	  =      left_rotation(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 return node


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 while True:
  __A	:		str	  =      root.get_right()
  if right_child is None:
   break
  __A	:		Union[str, Any]	  =      right_child
 return root.get_data()


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 while True:
  __A	:		Union[str, Any]	  =      root.get_left()
  if left_child is None:
   break
  __A	:		int	  =      left_child
 return root.get_data()


def       _lowercase							(					UpperCamelCase__							:       MyNode,					UpperCamelCase__							:       Any      ):
 __A	:		List[Any]	  =      root.get_left()
 __A	:		int	  =      root.get_right()
 if root.get_data() == data:
  if left_child is not None and right_child is not None:
   __A	:		List[Any]	  =      get_left_most(UpperCamelCase__      )
   root.set_data(UpperCamelCase__      )
   root.set_right(del_node(UpperCamelCase__,					UpperCamelCase__      )      )
  elif left_child is not None:
   __A	:		Tuple	  =      left_child
  elif right_child is not None:
   __A	:		str	  =      right_child
  else:
   return None
 elif root.get_data() > data:
  if left_child is None:
   print('No such data'      )
   return root
  else:
   root.set_left(del_node(UpperCamelCase__,					UpperCamelCase__      )      )
 else:  # root.get_data() < data
  if right_child is None:
   return root
  else:
   root.set_right(del_node(UpperCamelCase__,					UpperCamelCase__      )      )

 if get_height(UpperCamelCase__      ) - get_height(UpperCamelCase__      ) == 2:
  assert right_child is not None
  if get_height(right_child.get_right()      ) > get_height(right_child.get_left()      ):
   __A	:		List[str]	  =      left_rotation(UpperCamelCase__      )
  else:
   __A	:		Union[str, Any]	  =      rl_rotation(UpperCamelCase__      )
 elif get_height(UpperCamelCase__      ) - get_height(UpperCamelCase__      ) == -2:
  assert left_child is not None
  if get_height(left_child.get_left()      ) > get_height(left_child.get_right()      ):
   __A	:		str	  =      right_rotation(UpperCamelCase__      )
  else:
   __A	:		List[Any]	  =      lr_rotation(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(root.get_right()      ),					get_height(root.get_left()      )      ) + 1
 root.set_height(UpperCamelCase__      )
 return root

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self    ):

  """simple docstring"""







  __A	:		MyNode | None	  =      None


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return get_height(self.root    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  print('insert:' + str(__lowercase    )    )
  __A	:		Optional[int]	  =      insert_node(self.root   ,   __lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  print('delete:' + str(__lowercase    )    )
  if self.root is None:
   print('Tree is empty!'    )
   return
  __A	:		List[Any]	  =      del_node(self.root   ,   __lowercase    )







 def __str__(   self   ,   ):  # a level traversale, gives a more intuitive look on the tree

  """simple docstring"""







  __A	:		Union[str, Any]	  =      ''
  __A	:		Optional[Any]	  =      MyQueue()
  q.push(self.root    )
  __A	:		Tuple	  =      self.get_height()
  if layer == 0:
   return output
  __A	:		List[Any]	  =      0
  while not q.is_empty():
   __A	:		Union[str, Any]	  =      q.pop()
   __A	:		Any	  =      ' ' * int(math.pow(2   ,   layer - 1    )    )
   output += space
   if node is None:
    output += "*"
    q.push(__lowercase    )
    q.push(__lowercase    )
   else:
    output += str(node.get_data()    )
    q.push(node.get_left()    )
    q.push(node.get_right()    )
   output += space
   __A	:		Optional[int]	  =      cnt + 1
   for i in range(100    ):
    if cnt == math.pow(2   ,   __lowercase    ) - 1:
     __A	:		Optional[int]	  =      layer - 1
     if layer == 0:
      output += "\n*************************************"
      return output
     output += "\n"
     break
  output += "\n*************************************"
  return output


def       _lowercase							(					):
 import doctest

 doctest.testmod()


if __name__ == "__main__":
 _test()
 UpperCAmelCase_			:  Optional[Any]										=   AVLtree()
 UpperCAmelCase_			:  Dict										=   list(range(1_0))
 random.shuffle(lst)
 for i in lst:
  t.insert(i)
  print(str(t))
 random.shuffle(lst)
 for i in lst:
  t.del_node(i)
  print(str(t))






