

'''simple docstring'''




from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


_lowerCAmelCase         =						logging.get_logger(__name__)

# General docstring
_lowerCAmelCase         =						'ResNetConfig'

# Base docstring
_lowerCAmelCase         =						'microsoft/resnet-50'
_lowerCAmelCase         =						[1, 2048, 7, 7]

# Image classification docstring
_lowerCAmelCase         =						'microsoft/resnet-50'
_lowerCAmelCase         =						'tiger cat'

_lowerCAmelCase         =						[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class   _SCREAMING_SNAKE_CASE   (   nn.Module     ):



 def __init__(   self		:   int			,				a__		:   int			,				a__		:   int			,				a__		:   int = 3			,				a__		:   int = 1			,				a__		:   str = "relu"      ):
      super().__init__()
      __magic_name__             =    nn.Convad(
          _lowercase			,				_lowercase			,				kernel_size=_lowercase			,				stride=_lowercase			,				padding=kernel_size // 2			,				bias=_lowercase      )
      __magic_name__             =    nn.BatchNormad(_lowercase      )
      __magic_name__             =    ACTaFN[activation] if activation is not None else nn.Identity()



 def 			snake_case__  (   self		:   str			,				a__		:   Tensor      ):
      __magic_name__             =    self.convolution(_lowercase      )
      __magic_name__             =    self.normalization(_lowercase      )
      __magic_name__             =    self.activation(_lowercase      )
      return hidden_state






class   _SCREAMING_SNAKE_CASE   (   nn.Module     ):



 def __init__(   self		:   List[str]			,				a__		:   ResNetConfig      ):
      super().__init__()
      __magic_name__             =    ResNetConvLayer(
          config.num_channels			,				config.embedding_size			,				kernel_size=7			,				stride=2			,				activation=config.hidden_act      )
      __magic_name__             =    nn.MaxPoolad(kernel_size=3			,				stride=2			,				padding=1      )
      __magic_name__             =    config.num_channels



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Tensor      ):
      __magic_name__             =    pixel_values.shape[1]
      if num_channels != self.num_channels:
           raise ValueError(
               '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''      )
      __magic_name__             =    self.embedder(_lowercase      )
      __magic_name__             =    self.pooler(_lowercase      )
      return embedding






class   _SCREAMING_SNAKE_CASE   (   nn.Module     ):



 def __init__(   self		:   Optional[int]			,				a__		:   int			,				a__		:   int			,				a__		:   int = 2      ):
      super().__init__()
      __magic_name__             =    nn.Convad(_lowercase			,				_lowercase			,				kernel_size=1			,				stride=_lowercase			,				bias=_lowercase      )
      __magic_name__             =    nn.BatchNormad(_lowercase      )



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Tensor      ):
      __magic_name__             =    self.convolution(_lowercase      )
      __magic_name__             =    self.normalization(_lowercase      )
      return hidden_state






class   _SCREAMING_SNAKE_CASE   (   nn.Module     ):



 def __init__(   self		:   Any			,				a__		:   int			,				a__		:   int			,				a__		:   int = 1			,				a__		:   str = "relu"      ):
      super().__init__()
      __magic_name__             =    in_channels != out_channels or stride != 1
      __magic_name__             =    (
          ResNetShortCut(_lowercase			,				_lowercase			,				stride=_lowercase      ) if should_apply_shortcut else nn.Identity()
      )
      __magic_name__             =    nn.Sequential(
          ResNetConvLayer(_lowercase			,				_lowercase			,				stride=_lowercase      )			,				ResNetConvLayer(_lowercase			,				_lowercase			,				activation=_lowercase      )			,				)
      __magic_name__             =    ACTaFN[activation]



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]      ):
      __magic_name__             =    hidden_state
      __magic_name__             =    self.layer(_lowercase      )
      __magic_name__             =    self.shortcut(_lowercase      )
      hidden_state += residual
      __magic_name__             =    self.activation(_lowercase      )
      return hidden_state






class   _SCREAMING_SNAKE_CASE   (   nn.Module     ):



 def __init__(   self		:   str			,				a__		:   int			,				a__		:   int			,				a__		:   int = 1			,				a__		:   str = "relu"			,				a__		:   int = 4      ):
      super().__init__()
      __magic_name__             =    in_channels != out_channels or stride != 1
      __magic_name__             =    out_channels // reduction
      __magic_name__             =    (
          ResNetShortCut(_lowercase			,				_lowercase			,				stride=_lowercase      ) if should_apply_shortcut else nn.Identity()
      )
      __magic_name__             =    nn.Sequential(
          ResNetConvLayer(_lowercase			,				_lowercase			,				kernel_size=1      )			,				ResNetConvLayer(_lowercase			,				_lowercase			,				stride=_lowercase      )			,				ResNetConvLayer(_lowercase			,				_lowercase			,				kernel_size=1			,				activation=_lowercase      )			,				)
      __magic_name__             =    ACTaFN[activation]



 def 			snake_case__  (   self		:   Dict			,				a__		:   Union[str, Any]      ):
      __magic_name__             =    hidden_state
      __magic_name__             =    self.layer(_lowercase      )
      __magic_name__             =    self.shortcut(_lowercase      )
      hidden_state += residual
      __magic_name__             =    self.activation(_lowercase      )
      return hidden_state






class   _SCREAMING_SNAKE_CASE   (   nn.Module     ):



 def __init__(   self		:   Union[str, Any]			,				a__		:   ResNetConfig			,				a__		:   int			,				a__		:   int			,				a__		:   int = 2			,				a__		:   int = 2			,				):
      super().__init__()

      __magic_name__             =    ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

      __magic_name__             =    nn.Sequential(
          # downsampling is done in the first layer with stride of 2
          layer(_lowercase			,				_lowercase			,				stride=_lowercase			,				activation=config.hidden_act      )			,				*[layer(_lowercase			,				_lowercase			,				activation=config.hidden_act      ) for _ in range(depth - 1      )]			,				)



 def 			snake_case__  (   self		:   List[str]			,				a__		:   Tensor      ):
      __magic_name__             =    input
      for layer in self.layers:
           __magic_name__             =    layer(_lowercase      )
      return hidden_state






class   _SCREAMING_SNAKE_CASE   (   nn.Module     ):



 def __init__(   self		:   Any			,				a__		:   ResNetConfig      ):
      super().__init__()
      __magic_name__             =    nn.ModuleList([]      )
      # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
      self.stages.append(
          ResNetStage(
              _lowercase			,				config.embedding_size			,				config.hidden_sizes[0]			,				stride=2 if config.downsample_in_first_stage else 1			,				depth=config.depths[0]			,				)      )
      __magic_name__             =    zip(config.hidden_sizes			,				config.hidden_sizes[1:]      )
      for (in_channels, out_channels), depth in zip(_lowercase			,				config.depths[1:]      ):
           self.stages.append(ResNetStage(_lowercase			,				_lowercase			,				_lowercase			,				depth=_lowercase      )      )



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Tensor			,				a__		:   bool = False			,				a__		:   bool = True      ):
      __magic_name__             =    () if output_hidden_states else None

      for stage_module in self.stages:
           if output_hidden_states:
                __magic_name__             =    hidden_states + (hidden_state,)

           __magic_name__             =    stage_module(_lowercase      )

      if output_hidden_states:
           __magic_name__             =    hidden_states + (hidden_state,)

      if not return_dict:
           return tuple(v for v in [hidden_state, hidden_states] if v is not None      )

      return BaseModelOutputWithNoAttention(
          last_hidden_state=_lowercase			,				hidden_states=_lowercase			,				)






class   _SCREAMING_SNAKE_CASE   (   _lowerCAmelCase     ):
 __SCREAMING_SNAKE_CASE					:List[str]      	=					ResNetConfig
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					"resnet"
 __SCREAMING_SNAKE_CASE					:str      	=					"pixel_values"
 __SCREAMING_SNAKE_CASE					:Tuple      	=					True



 def 			snake_case__  (   self		:   Any			,				a__		:   Optional[int]      ):
      if isinstance(_lowercase			,				nn.Convad      ):
           nn.init.kaiming_normal_(module.weight			,				mode='''fan_out'''			,				nonlinearity='''relu'''      )
      elif isinstance(_lowercase			,				(nn.BatchNormad, nn.GroupNorm)      ):
           nn.init.constant_(module.weight			,				1      )
           nn.init.constant_(module.bias			,				0      )



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Dict			,				a__		:   int=False      ):
      if isinstance(_lowercase			,				_lowercase      ):
           __magic_name__             =    value


_lowerCAmelCase         =						r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

_lowerCAmelCase         =						r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""		,_lowerCAmelCase		,)
class   _SCREAMING_SNAKE_CASE   (   _lowerCAmelCase     ):



 def __init__(   self		:   str			,				a__		:   Dict      ):
      super().__init__(_lowercase      )
      __magic_name__             =    config
      __magic_name__             =    ResNetEmbeddings(_lowercase      )
      __magic_name__             =    ResNetEncoder(_lowercase      )
      __magic_name__             =    nn.AdaptiveAvgPoolad((1, 1)      )
      # Initialize weights and apply final processing
      self.post_init()



 @add_start_docstrings_to_model_forward(_lowercase      )
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC			,				output_type=_lowercase			,				config_class=_CONFIG_FOR_DOC			,				modality='''vision'''			,				expected_output=_EXPECTED_OUTPUT_SHAPE			,				)
 def 			snake_case__  (   self		:   str			,				a__		:   Tensor			,				a__		:   Optional[bool] = None			,				a__		:   Optional[bool] = None      ):
      __magic_name__             =    (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      __magic_name__             =    return_dict if return_dict is not None else self.config.use_return_dict

      __magic_name__             =    self.embedder(_lowercase      )

      __magic_name__             =    self.encoder(
          _lowercase			,				output_hidden_states=_lowercase			,				return_dict=_lowercase      )

      __magic_name__             =    encoder_outputs[0]

      __magic_name__             =    self.pooler(_lowercase      )

      if not return_dict:
           return (last_hidden_state, pooled_output) + encoder_outputs[1:]

      return BaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=_lowercase			,				pooler_output=_lowercase			,				hidden_states=encoder_outputs.hidden_states			,				)






@add_start_docstrings(
    """\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    """		,_lowerCAmelCase		,)
class   _SCREAMING_SNAKE_CASE   (   _lowerCAmelCase     ):



 def __init__(   self		:   Union[str, Any]			,				a__		:   Tuple      ):
      super().__init__(_lowercase      )
      __magic_name__             =    config.num_labels
      __magic_name__             =    ResNetModel(_lowercase      )
      # classification head
      __magic_name__             =    nn.Sequential(
          nn.Flatten()			,				nn.Linear(config.hidden_sizes[-1]			,				config.num_labels      ) if config.num_labels > 0 else nn.Identity()			,				)
      # initialize weights and apply final processing
      self.post_init()



 @add_start_docstrings_to_model_forward(_lowercase      )
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT			,				output_type=_lowercase			,				config_class=_CONFIG_FOR_DOC			,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,				)
 def 			snake_case__  (   self		:   int			,				a__		:   Optional[torch.FloatTensor] = None			,				a__		:   Optional[torch.LongTensor] = None			,				a__		:   Optional[bool] = None			,				a__		:   Optional[bool] = None			,				):
      __magic_name__             =    return_dict if return_dict is not None else self.config.use_return_dict

      __magic_name__             =    self.resnet(_lowercase			,				output_hidden_states=_lowercase			,				return_dict=_lowercase      )

      __magic_name__             =    outputs.pooler_output if return_dict else outputs[1]

      __magic_name__             =    self.classifier(_lowercase      )

      __magic_name__             =    None

      if labels is not None:
           if self.config.problem_type is None:
                if self.num_labels == 1:
                     __magic_name__             =    '''regression'''
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                     __magic_name__             =    '''single_label_classification'''
                else:
                     __magic_name__             =    '''multi_label_classification'''
           if self.config.problem_type == "regression":
                __magic_name__             =    MSELoss()
                if self.num_labels == 1:
                     __magic_name__             =    loss_fct(logits.squeeze()			,				labels.squeeze()      )
                else:
                     __magic_name__             =    loss_fct(_lowercase			,				_lowercase      )
           elif self.config.problem_type == "single_label_classification":
                __magic_name__             =    CrossEntropyLoss()
                __magic_name__             =    loss_fct(logits.view(-1			,				self.num_labels      )			,				labels.view(-1      )      )
           elif self.config.problem_type == "multi_label_classification":
                __magic_name__             =    BCEWithLogitsLoss()
                __magic_name__             =    loss_fct(_lowercase			,				_lowercase      )

      if not return_dict:
           __magic_name__             =    (logits,) + outputs[2:]
           return (loss,) + output if loss is not None else output

      return ImageClassifierOutputWithNoAttention(loss=_lowercase			,				logits=_lowercase			,				hidden_states=outputs.hidden_states      )






@add_start_docstrings(
    """\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    """		,_lowerCAmelCase		,)
class   _SCREAMING_SNAKE_CASE   (   _lowerCAmelCase		,_lowerCAmelCase     ):



 def __init__(   self		:   Optional[int]			,				a__		:   int      ):
      super().__init__(_lowercase      )
      super()._init_backbone(_lowercase      )

      __magic_name__             =    [config.embedding_size] + config.hidden_sizes
      __magic_name__             =    ResNetEmbeddings(_lowercase      )
      __magic_name__             =    ResNetEncoder(_lowercase      )

      # initialize weights and apply final processing
      self.post_init()



 @add_start_docstrings_to_model_forward(_lowercase      )
 @replace_return_docstrings(output_type=_lowercase			,				config_class=_CONFIG_FOR_DOC      )
 def 			snake_case__  (   self		:   int			,				a__		:   Tensor			,				a__		:   Optional[bool] = None			,				a__		:   Optional[bool] = None      ):
      __magic_name__             =    return_dict if return_dict is not None else self.config.use_return_dict
      __magic_name__             =    (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )

      __magic_name__             =    self.embedder(_lowercase      )

      __magic_name__             =    self.encoder(_lowercase			,				output_hidden_states=_lowercase			,				return_dict=_lowercase      )

      __magic_name__             =    outputs.hidden_states

      __magic_name__             =    ()
      for idx, stage in enumerate(self.stage_names      ):
           if stage in self.out_features:
                feature_maps += (hidden_states[idx],)

      if not return_dict:
           __magic_name__             =    (feature_maps,)
           if output_hidden_states:
                output += (outputs.hidden_states,)
           return output

      return BackboneOutput(
          feature_maps=_lowercase			,				hidden_states=outputs.hidden_states if output_hidden_states else None			,				attentions=_lowercase			,				)









'''simple docstring'''




from __future__ import annotations

import math
import random
from typing import Any






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Union[str, Any]      ):
      __magic_name__						       =    []
      __magic_name__						       =    0
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   int      ):
      return self.head == self.tail



 def 			snake_case__  (   self		:   int			,				a__		:   Any      ):
      self.data.append(a__      )
      __magic_name__						       =    self.tail + 1



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.data[self.head]
      __magic_name__						       =    self.head + 1
      return ret



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.tail - self.head



 def 			snake_case__  (   self		:   List[Any]      ):
      print(self.data      )
      print('''**************'''      )
      print(self.data[self.head : self.tail]      )






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[str]			,				a__		:   Any      ):
      __magic_name__						       =    data
      __magic_name__						       =    None
      __magic_name__						       =    None
      __magic_name__						       =    1



 def 			snake_case__  (   self		:   Optional[int]      ):
      return self.data



 def 			snake_case__  (   self		:   List[Any]      ):
      return self.left



 def 			snake_case__  (   self		:   Tuple      ):
      return self.right



 def 			snake_case__  (   self		:   Any      ):
      return self.height



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Any      ):
      __magic_name__						       =    data



 def 			snake_case__  (   self		:   int			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   Tuple			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   List[str]			,				a__		:   int      ):
      __magic_name__						       =    height




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     if node is None:
          return 0
     return node.get_height()




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     if a > b:
          return a
     return b




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''left rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_left()
     assert ret is not None
     node.set_left(ret.get_right()			)
     ret.set_right(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''right rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_right()
     assert ret is not None
     node.set_right(ret.get_left()			)
     ret.set_left(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_left()
     assert left_child is not None
     node.set_left(left_rotation(a			)			)
     return right_rotation(a			)




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_right()
     assert right_child is not None
     node.set_right(right_rotation(a			)			)
     return left_rotation(a			)




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     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
               __magic_name__						       =    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
                    __magic_name__						       =    right_rotation(a			)
               else:
                    __magic_name__						       =    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:
               __magic_name__						       =    node.get_right()
               assert right_child is not None
               if data < right_child.get_data():
                    __magic_name__						       =    rl_rotation(a			)
               else:
                    __magic_name__						       =    left_rotation(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     return node




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_right()
          if right_child is None:
               break
          __magic_name__						       =    right_child
     return root.get_data()




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_left()
          if left_child is None:
               break
          __magic_name__						       =    left_child
     return root.get_data()




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     __magic_name__						       =    root.get_left()
     __magic_name__						       =    root.get_right()
     if root.get_data() == data:
          if left_child is not None and right_child is not None:
               __magic_name__						       =    get_left_most(a			)
               root.set_data(a			)
               root.set_right(del_node(a		,						a			)			)
          elif left_child is not None:
               __magic_name__						       =    left_child
          elif right_child is not None:
               __magic_name__						       =    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()			):
               __magic_name__						       =    left_rotation(a			)
          else:
               __magic_name__						       =    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()			):
               __magic_name__						       =    right_rotation(a			)
          else:
               __magic_name__						       =    lr_rotation(a			)
     __magic_name__						       =    my_max(get_height(root.get_right()			)		,						get_height(root.get_left()			)			) + 1
     root.set_height(a			)
     return root






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]      ):
      __magic_name__						       =    None



 def 			snake_case__  (   self		:   List[Any]      ):
      return get_height(self.root      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Any      ):
      print('''insert:''' + str(a__      )      )
      __magic_name__						       =    insert_node(self.root			,				a__      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   Any      ):
      print('''delete:''' + str(a__      )      )
      if self.root is None:
           print('''Tree is empty!'''      )
           return
      __magic_name__						       =    del_node(self.root			,				a__      )



 def __str__(   self		:   Optional[Any]			,				):  # a level traversale, gives a more intuitive look on the tree
      __magic_name__						       =    ''''''
      __magic_name__						       =    MyQueue()
      q.push(self.root      )
      __magic_name__						       =    self.get_height()
      if layer == 0:
           return output
      __magic_name__						       =    0
      while not q.is_empty():
           __magic_name__						       =    q.pop()
           __magic_name__						       =    ''' ''' * 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
           __magic_name__						       =    cnt + 1
           for i in range(100      ):
                if cnt == math.pow(2			,				a__      ) - 1:
                     __magic_name__						       =    layer - 1
                     if layer == 0:
                          output += "\n*************************************"
                          return output
                     output += "\n"
                     break
      output += "\n*************************************"
      return output




def 						UpperCamelCase	(			)				->     None:






     '''simple docstring'''
     import doctest

     doctest.testmod()


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






