

from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFCvtForImageClassification, TFCvtModel
			from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Any	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.config_class(**self.inputs_dict	)
				self.parent.assertTrue(hasattr(a      ,      "embed_dim"	)	)
				self.parent.assertTrue(hasattr(a      ,      "num_heads"	)	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict      ,      a							: int      ,      a							: List[str]=13      ,      a							: Optional[int]=64      ,      a							: List[str]=3      ,      a							: Dict=[16, 48, 96]      ,      a							: List[Any]=[1, 3, 6]      ,      a							: int=[1, 2, 10]      ,      a							: List[str]=[7, 3, 3]      ,      a							: List[Any]=[4, 2, 2]      ,      a							: Tuple=[2, 1, 1]      ,      a							: Union[str, Any]=[2, 2, 2]      ,      a							: List[str]=[False, False, True]      ,      a							: int=[0.0, 0.0, 0.0]      ,      a							: Dict=0.02      ,      a							: List[Any]=1e-12      ,      a							: Tuple=True      ,      a							: Optional[int]=True      ,      a							: Union[str, Any]=2      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       parent
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       patch_sizes
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       patch_stride
				SCREAMING_SNAKE_CASE   :      str				     =       patch_padding
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       is_training
				SCREAMING_SNAKE_CASE   :      Dict				     =       use_labels
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_labels
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Any				     =       embed_dim
				SCREAMING_SNAKE_CASE   :      str				     =       num_heads
				SCREAMING_SNAKE_CASE   :      Any				     =       stride_kv
				SCREAMING_SNAKE_CASE   :      Tuple				     =       depth
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       cls_token
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       attention_drop_rate
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Tuple				     =       layer_norm_eps
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				SCREAMING_SNAKE_CASE   :      Any				     =       None
				if self.use_labels:
					# create a random int32 tensor of given shape
					SCREAMING_SNAKE_CASE   :      str				     =       ids_tensor([self.batch_size]      ,      self.num_labels	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_config()
				return config, pixel_values, labels
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[int]:

				"""simple docstring"""

				return CvtConfig(
				    image_size=self.image_size      ,      num_labels=self.num_labels      ,      num_channels=self.num_channels      ,      embed_dim=self.embed_dim      ,      num_heads=self.num_heads      ,      patch_sizes=self.patch_sizes      ,      patch_padding=self.patch_padding      ,      patch_stride=self.patch_stride      ,      stride_kv=self.stride_kv      ,      depth=self.depth      ,      cls_token=self.cls_token      ,      attention_drop_rate=self.attention_drop_rate      ,      initializer_range=self.initializer_range      ,      )
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: str      ,      a							: List[str]      ,      a							: str	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       TFCvtModel(config=a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       model(a      ,      training=a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (self.image_size, self.image_size)
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       image_size[0], image_size[1]
				for i in range(len(self.depth	)	):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.embed_dim[-1], height, width)	)
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: int      ,      a							: List[Any]      ,      a							: List[str]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.num_labels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       TFCvtForImageClassification(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model(a      ,      labels=a      ,      training=a	)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)	)






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      str				     =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class _UpperCamelCase							(							__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
			lowerCamelCase__           =(
			    {'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFCvtModelTester(self	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFCvtConfigTester(self      ,      config_class=a      ,      has_text_modality=a      ,      hidden_size=37	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				self.config_tester.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()
			@unittest.skip(reason="Cvt does not output attentions"	)
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="Cvt does not use inputs_embeds"	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="Cvt does not support input and output embeddings"	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Dict:

				"""simple docstring"""

				pass
			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0      ,      reason="TF does not support backprop for grouped convolutions on CPU."      ,      )
			def 							__UpperCamelCase (			self							: int	)							->       Tuple:

				"""simple docstring"""

				super().test_dataset_conversion()
			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0      ,      reason="TF does not support backprop for grouped convolutions on CPU."      ,      )
			@slow
			def 							__UpperCamelCase (			self							: List[str]	)							->       str:

				"""simple docstring"""

				super().test_keras_fit()
			@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.keras.mixed_precision.Policy("mixed_float16"	)
				tf.keras.mixed_precision.set_global_policy(a	)
				super().test_keras_fit()
				tf.keras.mixed_precision.set_global_policy("float32"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       inspect.signature(model.call	)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE   :      Tuple				     =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       str:

				"""simple docstring"""

				def check_hidden_states_output(a							: List[str]      ,      a							: Union[str, Any]      ,      a							: List[str]	):
					SCREAMING_SNAKE_CASE   :      Tuple				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(**self._prepare_for_class(a      ,      a	)	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       outputs.hidden_states

					SCREAMING_SNAKE_CASE   :      int				     =       len(self.model_tester.depth	)
					self.assertEqual(len(a	)      ,      a	)

					# verify the first hidden states (first block)
					self.assertListEqual(
					    list(hidden_states[0].shape[-3:]	)      ,      [
					        self.model_tester.embed_dim[0],
					        self.model_tester.image_size // 4,
					        self.model_tester.image_size // 4,
					    ]      ,      )

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       True
					check_hidden_states_output(a      ,      a      ,      a	)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE   :      str				     =       True

					check_hidden_states_output(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a	)






			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[int]:

				"""simple docstring"""

				for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       TFCvtModel.from_pretrained(a	)
					self.assertIsNotNone(a	)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_tf
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Tuple	)							->       Tuple:

				"""simple docstring"""

				return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)






			@slow
			def 							__UpperCamelCase (			self							: Dict	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.default_image_processor
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       prepare_img()
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_processor(images=a      ,      return_tensors="tf"	)

				# forward pass
				SCREAMING_SNAKE_CASE   :      List[str]				     =       model(**a	)

				# verify the logits
				SCREAMING_SNAKE_CASE   :      Dict				     =       tf.TensorShape((1, 1000)	)
				self.assertEqual(outputs.logits.shape      ,      a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       tf.constant([0.9285, 0.9015, -0.3150]	)
				self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()      ,      a      ,      atol=1e-4	)	)


from __future__ import annotations

import math
import random
from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Union[str, Any]	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      list[Any]				     =       []
				SCREAMING_SNAKE_CASE   :      int				     =       0
				SCREAMING_SNAKE_CASE   :      int				     =       0
			def 							__UpperCamelCase (			self							: List[Any]	)							->       bool:

				"""simple docstring"""

				return self.head == self.tail
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				self.data.append(a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.tail + 1
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.data[self.head]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.head + 1
				return ret
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				return self.tail - self.head






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       None:

				"""simple docstring"""

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






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[str]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       data
				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
				SCREAMING_SNAKE_CASE   :      int				     =       1
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				return self.data
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       MyNode | None:

				"""simple docstring"""

				return self.left
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       MyNode | None:

				"""simple docstring"""

				return self.right
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				return self.height
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       data
			def 							__UpperCamelCase (			self							: Any      ,      a							: MyNode | None	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       node
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: MyNode | None	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       node






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       height

def  lowerCamelCase__ (					_a):
	if node is None:
		return 0
	return node.get_height()

def  lowerCamelCase__ (					_a							,			_a):
	if a > b:
		return a
	return b

def  lowerCamelCase__ (					_a):
	print("left rotation node:"							,			node.get_data())
	SCREAMING_SNAKE_CASE   :      List[str]				     =       node.get_left()
	assert ret is not None
	node.set_left(ret.get_right())
	ret.set_right(_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       my_max(get_height(ret.get_right())							,			get_height(ret.get_left())) + 1
	ret.set_height(_a)
	return ret

def  lowerCamelCase__ (					_a):
	print("right rotation node:"							,			node.get_data())
	SCREAMING_SNAKE_CASE   :      Dict				     =       node.get_right()
	assert ret is not None
	node.set_right(ret.get_left())
	ret.set_left(_a)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       my_max(get_height(ret.get_right())							,			get_height(ret.get_left())) + 1
	ret.set_height(_a)
	return ret

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       node.get_left()
	assert left_child is not None
	node.set_left(left_rotation(_a))
	return right_rotation(_a)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      int				     =       node.get_right()
	assert right_child is not None
	node.set_right(right_rotation(_a))
	return left_rotation(_a)

def  lowerCamelCase__ (					_a							,			_a):
	if node is None:
		return MyNode(_a)
	if data < node.get_data():
		node.set_left(insert_node(node.get_left()							,			_a))
		if (
		    get_height(node.get_left()) - get_height(node.get_right()) == 2
		):  # an unbalance detected
			SCREAMING_SNAKE_CASE   :      List[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
				SCREAMING_SNAKE_CASE   :      Tuple				     =       right_rotation(_a)
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       lr_rotation(_a)
	else:
		node.set_right(insert_node(node.get_right()							,			_a))
		if get_height(node.get_right()) - get_height(node.get_left()) == 2:
			SCREAMING_SNAKE_CASE   :      Any				     =       node.get_right()
			assert right_child is not None
			if data < right_child.get_data():
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       rl_rotation(_a)
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       left_rotation(_a)
	SCREAMING_SNAKE_CASE   :      str				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	return node

def  lowerCamelCase__ (					_a):
	while True:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       root.get_right()
		if right_child is None:
			break
		SCREAMING_SNAKE_CASE   :      str				     =       right_child
	return root.get_data()

def  lowerCamelCase__ (					_a):
	while True:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       root.get_left()
		if left_child is None:
			break
		SCREAMING_SNAKE_CASE   :      List[str]				     =       left_child
	return root.get_data()

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       root.get_left()
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       root.get_right()
	if root.get_data() == data:
		if left_child is not None and right_child is not None:
			SCREAMING_SNAKE_CASE   :      Any				     =       get_left_most(_a)
			root.set_data(_a)
			root.set_right(del_node(_a							,			_a))
		elif left_child is not None:
			SCREAMING_SNAKE_CASE   :      Dict				     =       left_child
		elif right_child is not None:
			SCREAMING_SNAKE_CASE   :      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(_a							,			_a))
	else:  # root.get_data() < data
		if right_child is None:
			return root
		else:
			root.set_right(del_node(_a							,			_a))

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






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: str	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				return get_height(self.root	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				print("insert:" + str(a	)	)
				SCREAMING_SNAKE_CASE   :      Any				     =       insert_node(self.root      ,      a	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				print("delete:" + str(a	)	)
				if self.root is None:
					print("Tree is empty!"	)
					return
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       del_node(self.root      ,      a	)






			def __str__(			self							: Optional[int]      ,      )							->       str:  # a level traversale, gives a more intuitive look on the tree

				"""simple docstring"""

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

def  lowerCamelCase__ (					):
	import doctest

	doctest.testmod()


if __name__ == "__main__":
			_test()
			a_						     =    AVLtree()
			a_						     =    list(range(10))
			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))