
import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
      from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileViTImageProcessor



class 		UpperCamelCase__  (       __lowercase    ):
   def       lowerCAmelCase			(self :					Any   ):
    __a :  str						=  self.config_class(**self.inputs_dict   )
    self.parent.assertTrue(hasattr(snake_case_					,				'''hidden_sizes'''   )   )
    self.parent.assertTrue(hasattr(snake_case_					,				'''neck_hidden_sizes'''   )   )
    self.parent.assertTrue(hasattr(snake_case_					,				'''num_attention_heads'''   )   )





class 		UpperCamelCase__  :
   def __init__(self :					Any					,				snake_case_ :					Optional[int]					,				snake_case_ :					Tuple=1_3					,				snake_case_ :					List[Any]=3_2					,				snake_case_ :					List[str]=2					,				snake_case_ :					Optional[int]=3					,				snake_case_ :					List[Any]=6_4_0					,				snake_case_ :					int=4					,				snake_case_ :					Any="silu"					,				snake_case_ :					List[Any]=3					,				snake_case_ :					Optional[int]=3_2					,				snake_case_ :					List[str]=0.1					,				snake_case_ :					List[str]=0.1					,				snake_case_ :					Tuple=0.1					,				snake_case_ :					Optional[Any]=0.02					,				snake_case_ :					Union[str, Any]=True					,				snake_case_ :					int=True					,				snake_case_ :					List[Any]=1_0					,				snake_case_ :					Union[str, Any]=None					,				):
    __a :  Dict						=  parent
    __a :  Union[str, Any]						=  batch_size
    __a :  List[str]						=  image_size
    __a :  Union[str, Any]						=  patch_size
    __a :  Tuple						=  num_channels
    __a :  Union[str, Any]						=  last_hidden_size
    __a :  Optional[Any]						=  num_attention_heads
    __a :  Union[str, Any]						=  hidden_act
    __a :  Union[str, Any]						=  conv_kernel_size
    __a :  List[Any]						=  output_stride
    __a :  Tuple						=  hidden_dropout_prob
    __a :  str						=  attention_probs_dropout_prob
    __a :  Any						=  classifier_dropout_prob
    __a :  str						=  use_labels
    __a :  List[Any]						=  is_training
    __a :  Any						=  num_labels
    __a :  Any						=  initializer_range
    __a :  Optional[Any]						=  scope
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Tuple						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

    __a :  Union[str, Any]						=  None
    __a :  List[str]						=  None
    if self.use_labels:
     __a :  Any						=  ids_tensor([self.batch_size]					,				self.num_labels   )
     __a :  Dict						=  ids_tensor([self.batch_size, self.image_size, self.image_size]					,				self.num_labels   )

    __a :  Dict						=  self.get_config()

    return config, pixel_values, labels, pixel_labels
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    return MobileViTConfig(
        image_size=self.image_size					,				patch_size=self.patch_size					,				num_channels=self.num_channels					,				num_attention_heads=self.num_attention_heads					,				hidden_act=self.hidden_act					,				conv_kernel_size=self.conv_kernel_size					,				output_stride=self.output_stride					,				hidden_dropout_prob=self.hidden_dropout_prob					,				attention_probs_dropout_prob=self.attention_probs_dropout_prob					,				classifier_dropout_prob=self.classifier_dropout_prob					,				initializer_range=self.initializer_range					,				)
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					List[Any]					,				snake_case_ :					List[Any]   ):
    __a :  Optional[int]						=  MobileViTModel(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Any						=  model(snake_case_   )
    self.parent.assertEqual(
        result.last_hidden_state.shape					,				(
            self.batch_size,
            self.last_hidden_size,
            self.image_size // self.output_stride,
            self.image_size // self.output_stride,
        )					,				)
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					str					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					List[str]					,				snake_case_ :					str   ):
    __a :  Any						=  self.num_labels
    __a :  Tuple						=  MobileViTForImageClassification(snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[str]						=  model(snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)   )
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					str					,				snake_case_ :					str					,				snake_case_ :					Tuple					,				snake_case_ :					int   ):
    __a :  List[str]						=  self.num_labels
    __a :  Union[str, Any]						=  MobileViTForSemanticSegmentation(snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  int						=  model(snake_case_   )
    self.parent.assertEqual(
        result.logits.shape					,				(
            self.batch_size,
            self.num_labels,
            self.image_size // self.output_stride,
            self.image_size // self.output_stride,
        )					,				)
    __a :  List[Any]						=  model(snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(
        result.logits.shape					,				(
            self.batch_size,
            self.num_labels,
            self.image_size // self.output_stride,
            self.image_size // self.output_stride,
        )					,				)







   def       lowerCAmelCase			(self :					Dict   ):
    __a :  Union[str, Any]						=  self.prepare_config_and_inputs()
    __a				,       __a				,       __a				,       __a :  Dict						=  config_and_inputs
    __a :  Optional[int]						=  {'''pixel_values''': pixel_values}
    return config, inputs_dict





@require_torch
class 		UpperCamelCase__  (       __lowercase    ,__lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      (
       (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE      :							int	       =      (
       {
           "feature-extraction": MobileViTModel,
           "image-classification": MobileViTForImageClassification,
           "image-segmentation": MobileViTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   _SCREAMING_SNAKE_CASE      :							str	       =      False
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      False
   _SCREAMING_SNAKE_CASE      :							str	       =      False
   _SCREAMING_SNAKE_CASE      :							str	       =      False
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  List[Any]						=  MobileViTModelTester(self   )
    __a :  Dict						=  MobileViTConfigTester(self					,				config_class=snake_case_					,				has_text_modality=snake_case_   )
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    self.config_tester.run_common_tests()
   @unittest.skip(reason='''MobileViT does not use inputs_embeds'''   )
   def       lowerCAmelCase			(self :					int   ):
    pass
   @unittest.skip(reason='''MobileViT does not support input and output embeddings'''   )
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    pass
   @unittest.skip(reason='''MobileViT does not output attentions'''   )
   def       lowerCAmelCase			(self :					Tuple   ):
    pass
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a				,       __a :  int						=  self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __a :  int						=  model_class(snake_case_   )
     __a :  Any						=  inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __a :  Optional[int]						=  [*signature.parameters.keys()]

     __a :  Optional[Any]						=  ['''pixel_values''']
     self.assertListEqual(arg_names[:1]					,				snake_case_   )
   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
   def       lowerCAmelCase			(self :					Any   ):
    pass
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Dict						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*snake_case_   )
   def       lowerCAmelCase			(self :					Tuple   ):
    def check_hidden_states_output(snake_case_ :					Optional[Any]					,				snake_case_ :					List[str]					,				snake_case_ :					int   ):
     __a :  Optional[int]						=  model_class(snake_case_   )
     model.to(snake_case_   )
     model.eval()

     with torch.no_grad():
      __a :  Optional[int]						=  model(**self._prepare_for_class(snake_case_					,				snake_case_   )   )

     __a :  int						=  outputs.hidden_states

     __a :  Optional[int]						=  5
     self.assertEqual(len(snake_case_   )					,				snake_case_   )

     # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
     # with the width and height being successively divided by 2.
     __a :  int						=  2
     for i in range(len(snake_case_   )   ):
      self.assertListEqual(
          list(hidden_states[i].shape[-2:]   )					,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]					,				)
      divisor *= 2

     self.assertEqual(self.model_tester.output_stride					,				divisor // 2   )

    __a				,       __a :  str						=  self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __a :  Union[str, Any]						=  True
     check_hidden_states_output(snake_case_					,				snake_case_					,				snake_case_   )

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

     check_hidden_states_output(snake_case_					,				snake_case_					,				snake_case_   )
   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  str						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_   )







   @slow
   def       lowerCAmelCase			(self :					List[str]   ):
    for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __a :  Tuple						=  MobileViTModel.from_pretrained(snake_case_   )
     self.assertIsNotNone(snake_case_   )





def    __UpperCamelCase     (				):
 __a :  Dict						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
 return image



@require_torch
@require_vision
class 		UpperCamelCase__  (       unittest.TestCase    ):
   @cached_property
   def       lowerCAmelCase			(self :					Dict   ):
    return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''   ) if is_vision_available() else None
   @slow
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Dict						=  MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''   ).to(snake_case_   )

    __a :  List[Any]						=  self.default_image_processor
    __a :  Optional[Any]						=  prepare_img()
    __a :  List[str]						=  image_processor(images=snake_case_					,				return_tensors='''pt'''   ).to(snake_case_   )

    # forward pass
    with torch.no_grad():
     __a :  List[str]						=  model(**snake_case_   )

    # verify the logits
    __a :  Tuple						=  torch.Size((1, 1_0_0_0)   )
    self.assertEqual(outputs.logits.shape					,				snake_case_   )

    __a :  Optional[Any]						=  torch.tensor([-1.9364, -1.2327, -0.4653]   ).to(snake_case_   )

    self.assertTrue(torch.allclose(outputs.logits[0, :3]					,				snake_case_					,				atol=1E-4   )   )
   @slow
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Union[str, Any]						=  MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )
    __a :  Union[str, Any]						=  model.to(snake_case_   )

    __a :  Tuple						=  MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )

    __a :  List[str]						=  prepare_img()
    __a :  Tuple						=  image_processor(images=snake_case_					,				return_tensors='''pt'''   ).to(snake_case_   )

    # forward pass
    with torch.no_grad():
     __a :  str						=  model(**snake_case_   )
    __a :  Optional[int]						=  outputs.logits

    # verify the logits
    __a :  Any						=  torch.Size((1, 2_1, 3_2, 3_2)   )
    self.assertEqual(logits.shape					,				snake_case_   )

    __a :  List[str]						=  torch.tensor(
        [
            [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
            [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
            [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
        ]					,				device=snake_case_					,				)

    self.assertTrue(torch.allclose(logits[0, :3, :3, :3]					,				snake_case_					,				atol=1E-4   )   )







   @slow
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Union[str, Any]						=  MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )
    __a :  Optional[Any]						=  model.to(snake_case_   )

    __a :  Union[str, Any]						=  MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )

    __a :  Tuple						=  prepare_img()
    __a :  Any						=  image_processor(images=snake_case_					,				return_tensors='''pt'''   ).to(snake_case_   )

    # forward pass
    with torch.no_grad():
     __a :  Tuple						=  model(**snake_case_   )

    __a :  Dict						=  outputs.logits.detach().cpu()

    __a :  Optional[int]						=  image_processor.post_process_semantic_segmentation(outputs=snake_case_					,				target_sizes=[(5_0, 6_0)]   )
    __a :  Dict						=  torch.Size((5_0, 6_0)   )
    self.assertEqual(segmentation[0].shape					,				snake_case_   )

    __a :  Tuple						=  image_processor.post_process_semantic_segmentation(outputs=snake_case_   )
    __a :  List[str]						=  torch.Size((3_2, 3_2)   )
    self.assertEqual(segmentation[0].shape					,				snake_case_   )


from __future__ import annotations

import math
import random
from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Optional[Any]   ):
    __a :  list[Any]						=  []
    __a :  int						=  0
    __a :  int						=  0
   def       lowerCAmelCase			(self :					Optional[int]   ):
    return self.head == self.tail
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Any   ):
    self.data.append(snake_case_   )
    __a :  str						=  self.tail + 1
   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  int						=  self.data[self.head]
    __a :  Union[str, Any]						=  self.head + 1
    return ret
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    return self.tail - self.head







   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    print(self.data   )
    print('''**************'''   )
    print(self.data[self.head : self.tail]   )





class 		UpperCamelCase__  :
   def __init__(self :					List[str]					,				snake_case_ :					Any   ):
    __a :  List[str]						=  data
    __a :  MyNode | None						=  None
    __a :  MyNode | None						=  None
    __a :  int						=  1
   def       lowerCAmelCase			(self :					int   ):
    return self.data
   def       lowerCAmelCase			(self :					Dict   ):
    return self.left
   def       lowerCAmelCase			(self :					int   ):
    return self.right
   def       lowerCAmelCase			(self :					int   ):
    return self.height
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					Any   ):
    __a :  Tuple						=  data
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					MyNode | None   ):
    __a :  Any						=  node
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					MyNode | None   ):
    __a :  List[str]						=  node







   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					int   ):
    __a :  Union[str, Any]						=  height





def    __UpperCamelCase     (				lowerCAmelCase__							:				MyNode | None  ):
 if node is None:
  return 0
 return node.get_height()




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ):
 if a > b:
  return a
 return b




def    __UpperCamelCase     (				lowerCAmelCase__							:				MyNode  ):
 print('''left rotation node:'''  ,   node.get_data()  )
 __a :  str						=  node.get_left()
 assert ret is not None
 node.set_left(ret.get_right()  )
 ret.set_right(lowerCAmelCase__  )
 __a :  List[Any]						=  my_max(get_height(node.get_right()  )  ,   get_height(node.get_left()  )  ) + 1
 node.set_height(lowerCAmelCase__  )
 __a :  Union[str, Any]						=  my_max(get_height(ret.get_right()  )  ,   get_height(ret.get_left()  )  ) + 1
 ret.set_height(lowerCAmelCase__  )
 return ret




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




def    __UpperCamelCase     (				lowerCAmelCase__							:				MyNode  ):
 __a :  Union[str, Any]						=  node.get_left()
 assert left_child is not None
 node.set_left(left_rotation(lowerCAmelCase__  )  )
 return right_rotation(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				MyNode  ):
 __a :  Optional[int]						=  node.get_right()
 assert right_child is not None
 node.set_right(right_rotation(lowerCAmelCase__  )  )
 return left_rotation(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				MyNode | None  ,   lowerCAmelCase__							:				Any  ):
 if node is None:
  return MyNode(lowerCAmelCase__  )
 if data < node.get_data():
  node.set_left(insert_node(node.get_left()  ,   lowerCAmelCase__  )  )
  if (
      get_height(node.get_left()  ) - get_height(node.get_right()  ) == 2
  ):  # an unbalance detected
   __a :  Tuple						=  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 :  str						=  right_rotation(lowerCAmelCase__  )
   else:
    __a :  Dict						=  lr_rotation(lowerCAmelCase__  )
 else:
  node.set_right(insert_node(node.get_right()  ,   lowerCAmelCase__  )  )
  if get_height(node.get_right()  ) - get_height(node.get_left()  ) == 2:
   __a :  Dict						=  node.get_right()
   assert right_child is not None
   if data < right_child.get_data():
    __a :  str						=  rl_rotation(lowerCAmelCase__  )
   else:
    __a :  Tuple						=  left_rotation(lowerCAmelCase__  )
 __a :  Any						=  my_max(get_height(node.get_right()  )  ,   get_height(node.get_left()  )  ) + 1
 node.set_height(lowerCAmelCase__  )
 return node




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




def    __UpperCamelCase     (				lowerCAmelCase__							:				MyNode  ):
 while True:
  __a :  Optional[int]						=  root.get_left()
  if left_child is None:
   break
  __a :  int						=  left_child
 return root.get_data()




def    __UpperCamelCase     (				lowerCAmelCase__							:				MyNode  ,   lowerCAmelCase__							:				Any  ):
 __a :  Optional[Any]						=  root.get_left()
 __a :  List[str]						=  root.get_right()
 if root.get_data() == data:
  if left_child is not None and right_child is not None:
   __a :  str						=  get_left_most(lowerCAmelCase__  )
   root.set_data(lowerCAmelCase__  )
   root.set_right(del_node(lowerCAmelCase__  ,   lowerCAmelCase__  )  )
  elif left_child is not None:
   __a :  int						=  left_child
  elif right_child is not None:
   __a :  List[Any]						=  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(lowerCAmelCase__  ,   lowerCAmelCase__  )  )
 else:  # root.get_data() < data
  if right_child is None:
   return root
  else:
   root.set_right(del_node(lowerCAmelCase__  ,   lowerCAmelCase__  )  )

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



class 		UpperCamelCase__  :
   def __init__(self :					Optional[Any]   ):
    __a :  MyNode | None						=  None
   def       lowerCAmelCase			(self :					List[Any]   ):
    return get_height(self.root   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Any   ):
    print('''insert:''' + str(snake_case_   )   )
    __a :  List[Any]						=  insert_node(self.root					,				snake_case_   )
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Any   ):
    print('''delete:''' + str(snake_case_   )   )
    if self.root is None:
     print('''Tree is empty!'''   )
     return
    __a :  Union[str, Any]						=  del_node(self.root					,				snake_case_   )







   def __str__(self :					List[str]					,				):  # a level traversale, gives a more intuitive look on the tree
    __a :  Union[str, Any]						=  ''''''
    __a :  int						=  MyQueue()
    q.push(self.root   )
    __a :  List[str]						=  self.get_height()
    if layer == 0:
     return output
    __a :  List[Any]						=  0
    while not q.is_empty():
     __a :  List[str]						=  q.pop()
     __a :  Optional[int]						=  ''' ''' * int(math.pow(2					,				layer - 1   )   )
     output += space
     if node is None:
      output += "*"
      q.push(snake_case_   )
      q.push(snake_case_   )
     else:
      output += str(node.get_data()   )
      q.push(node.get_left()   )
      q.push(node.get_right()   )
     output += space
     __a :  int						=  cnt + 1
     for i in range(1_0_0   ):
      if cnt == math.pow(2					,				snake_case_   ) - 1:
       __a :  str						=  layer - 1
       if layer == 0:
        output += "\n*************************************"
        return output
       output += "\n"
       break
    output += "\n*************************************"
    return output





def    __UpperCamelCase     (				):
 import doctest

 doctest.testmod()


if __name__ == "__main__":
      _test()
      lowercase__       =AVLtree()
      lowercase__       =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))
