



"""simple docstring"""
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class 					a	(  nn.Module       ):



  def __init__(						self :					Any       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int=0.0       ,		lowerCAmelCase :					Optional[int] = None       ,		lowerCAmelCase :					str = "geglu"       ,		lowerCAmelCase :					Optional[int] = None       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					str = "layer_norm"       ,		lowerCAmelCase :					bool = False       ,		)     ->					Union[str, Any]:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: Tuple     			=only_cross_attention

     SCREAMING_SNAKE_CASE_: List[Any]     			=(num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
     SCREAMING_SNAKE_CASE_: Any     			=(num_embeds_ada_norm is not None) and norm_type == "ada_norm"

     if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
        raise ValueError(
            f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
            f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''		)

     # Define 3 blocks. Each block has its own normalization layer.
     # 1. Self-Attn
     if self.use_ada_layer_norm:
        SCREAMING_SNAKE_CASE_: str     			=AdaLayerNorm(__lowerCamelCase       ,		__lowerCamelCase		)
     elif self.use_ada_layer_norm_zero:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=AdaLayerNormZero(__lowerCamelCase       ,		__lowerCamelCase		)
     else:
        SCREAMING_SNAKE_CASE_: Tuple     			=nn.LayerNorm(__lowerCamelCase       ,		elementwise_affine=__lowerCamelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=Attention(
         query_dim=__lowerCamelCase       ,		heads=__lowerCamelCase       ,		dim_head=__lowerCamelCase       ,		dropout=__lowerCamelCase       ,		bias=__lowerCamelCase       ,		cross_attention_dim=cross_attention_dim if only_cross_attention else None       ,		upcast_attention=__lowerCamelCase       ,		)

     # 2. Cross-Attn
     if cross_attention_dim is not None or double_self_attention:
        # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
        # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
        # the second cross attention block.
        SCREAMING_SNAKE_CASE_: Any     			=(
            AdaLayerNorm(__lowerCamelCase       ,		__lowerCamelCase		)
            if self.use_ada_layer_norm
            else nn.LayerNorm(__lowerCamelCase       ,		elementwise_affine=__lowerCamelCase		)
        )
        SCREAMING_SNAKE_CASE_: int     			=Attention(
            query_dim=__lowerCamelCase       ,		cross_attention_dim=cross_attention_dim if not double_self_attention else None       ,		heads=__lowerCamelCase       ,		dim_head=__lowerCamelCase       ,		dropout=__lowerCamelCase       ,		bias=__lowerCamelCase       ,		upcast_attention=__lowerCamelCase       ,		)  # is self-attn if encoder_hidden_states is none
     else:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=None
        SCREAMING_SNAKE_CASE_: str     			=None

     # 3. Feed-forward
     SCREAMING_SNAKE_CASE_: Dict     			=nn.LayerNorm(__lowerCamelCase       ,		elementwise_affine=__lowerCamelCase		)
     SCREAMING_SNAKE_CASE_: int     			=FeedForward(__lowerCamelCase       ,		dropout=__lowerCamelCase       ,		activation_fn=__lowerCamelCase       ,		final_dropout=__lowerCamelCase		)

     # let chunk size default to None
     SCREAMING_SNAKE_CASE_: str     			=None
     SCREAMING_SNAKE_CASE_: Any     			=0



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					int		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=chunk_size
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=dim





  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					Optional[torch.FloatTensor] = None       ,		lowerCAmelCase :					Optional[torch.FloatTensor] = None       ,		lowerCAmelCase :					Optional[torch.FloatTensor] = None       ,		lowerCAmelCase :					Optional[torch.LongTensor] = None       ,		lowerCAmelCase :					Dict[str, Any] = None       ,		lowerCAmelCase :					Optional[torch.LongTensor] = None       ,		)     ->					str:


     '''simple docstring'''




     if self.use_ada_layer_norm:
        SCREAMING_SNAKE_CASE_: Tuple     			=self.norma(__lowerCamelCase       ,		__lowerCamelCase		)
     elif self.use_ada_layer_norm_zero:
        SCREAMING_SNAKE_CASE_: Dict     			=self.norma(
            __lowerCamelCase       ,		__lowerCamelCase       ,		__lowerCamelCase       ,		hidden_dtype=hidden_states.dtype		)
     else:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.norma(__lowerCamelCase		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=cross_attention_kwargs if cross_attention_kwargs is not None else {}

     SCREAMING_SNAKE_CASE_: Dict     			=self.attna(
         __lowerCamelCase       ,		encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None       ,		attention_mask=__lowerCamelCase       ,		**__lowerCamelCase       ,		)
     if self.use_ada_layer_norm_zero:
        SCREAMING_SNAKE_CASE_: Dict     			=gate_msa.unsqueeze(1		) * attn_output
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=attn_output + hidden_states

     # 2. Cross-Attention
     if self.attna is not None:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=(
            self.norma(__lowerCamelCase       ,		__lowerCamelCase		) if self.use_ada_layer_norm else self.norma(__lowerCamelCase		)
        )

        SCREAMING_SNAKE_CASE_: int     			=self.attna(
            __lowerCamelCase       ,		encoder_hidden_states=__lowerCamelCase       ,		attention_mask=__lowerCamelCase       ,		**__lowerCamelCase       ,		)
        SCREAMING_SNAKE_CASE_: List[Any]     			=attn_output + hidden_states

     # 3. Feed-forward
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.norma(__lowerCamelCase		)

     if self.use_ada_layer_norm_zero:
        SCREAMING_SNAKE_CASE_: Dict     			=norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

     if self._chunk_size is not None:
        # "feed_forward_chunk_size" can be used to save memory
        if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
           raise ValueError(
               f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''		)

        SCREAMING_SNAKE_CASE_: str     			=norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
        SCREAMING_SNAKE_CASE_: Dict     			=torch.cat(
            [self.ff(__lowerCamelCase		) for hid_slice in norm_hidden_states.chunk(__lowerCamelCase       ,		dim=self._chunk_dim		)]       ,		dim=self._chunk_dim       ,		)
     else:
        SCREAMING_SNAKE_CASE_: List[str]     			=self.ff(__lowerCamelCase		)

     if self.use_ada_layer_norm_zero:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=gate_mlp.unsqueeze(1		) * ff_output

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ff_output + hidden_states

     return hidden_states







class 					a	(  nn.Module       ):



  def __init__(						self :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Optional[int] = None       ,		lowerCAmelCase :					int = 4       ,		lowerCAmelCase :					float = 0.0       ,		lowerCAmelCase :					str = "geglu"       ,		lowerCAmelCase :					bool = False       ,		)     ->					Dict:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=int(dim * mult		)
     SCREAMING_SNAKE_CASE_: Tuple     			=dim_out if dim_out is not None else dim

     if activation_fn == "gelu":
        SCREAMING_SNAKE_CASE_: List[str]     			=GELU(__lowerCamelCase       ,		__lowerCamelCase		)
     if activation_fn == "gelu-approximate":
        SCREAMING_SNAKE_CASE_: str     			=GELU(__lowerCamelCase       ,		__lowerCamelCase       ,		approximate="""tanh"""		)
     elif activation_fn == "geglu":
        SCREAMING_SNAKE_CASE_: Any     			=GEGLU(__lowerCamelCase       ,		__lowerCamelCase		)
     elif activation_fn == "geglu-approximate":
        SCREAMING_SNAKE_CASE_: Optional[int]     			=ApproximateGELU(__lowerCamelCase       ,		__lowerCamelCase		)

     SCREAMING_SNAKE_CASE_: Any     			=nn.ModuleList([]		)
     # project in
     self.net.append(__lowerCamelCase		)
     # project dropout
     self.net.append(nn.Dropout(__lowerCamelCase		)		)
     # project out
     self.net.append(nn.Linear(__lowerCamelCase       ,		__lowerCamelCase		)		)
     # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
     if final_dropout:
        self.net.append(nn.Dropout(__lowerCamelCase		)		)





  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					Optional[int]		)     ->					List[str]:


     '''simple docstring'''




     for module in self.net:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=module(__lowerCamelCase		)
     return hidden_states







class 					a	(  nn.Module       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					str = "none"		)     ->					Optional[Any]:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[Any]     			=nn.Linear(__lowerCamelCase       ,		__lowerCamelCase		)
     SCREAMING_SNAKE_CASE_: int     			=approximate



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					str		)     ->					List[str]:


     '''simple docstring'''




     if gate.device.type != "mps":
        return F.gelu(__lowerCamelCase       ,		approximate=self.approximate		)
     # mps: gelu is not implemented for float16
     return F.gelu(gate.to(dtype=torch.floataa		)       ,		approximate=self.approximate		).to(dtype=gate.dtype		)





  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					str		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.proj(__lowerCamelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.gelu(__lowerCamelCase		)
     return hidden_states







class 					a	(  nn.Module       ):



  def __init__(						self :					List[str]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: str     			=nn.Linear(__lowerCamelCase       ,		dim_out * 2		)



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					Union[str, Any]		)     ->					Optional[int]:


     '''simple docstring'''




     if gate.device.type != "mps":
        return F.gelu(__lowerCamelCase		)
     # mps: gelu is not implemented for float16
     return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)





  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					Tuple		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.proj(__lowerCamelCase		).chunk(2       ,		dim=-1		)
     return hidden_states * self.gelu(__lowerCamelCase		)







class 					a	(  nn.Module       ):



  def __init__(						self :					Tuple       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int		)     ->					Union[str, Any]:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=nn.Linear(__lowerCamelCase       ,		__lowerCamelCase		)





  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Union[str, Any]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=self.proj(__lowerCamelCase		)
     return x * torch.sigmoid(1.7_0_2 * x		)







class 					a	(  nn.Module       ):



  def __init__(						self :					List[str]       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					Optional[int]		)     ->					Union[str, Any]:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=nn.Embedding(__lowerCamelCase       ,		__lowerCamelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=nn.SiLU()
     SCREAMING_SNAKE_CASE_: Any     			=nn.Linear(__lowerCamelCase       ,		embedding_dim * 2		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=nn.LayerNorm(__lowerCamelCase       ,		elementwise_affine=__lowerCamelCase		)





  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					Any		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.linear(self.silu(self.emb(__lowerCamelCase		)		)		)
     SCREAMING_SNAKE_CASE_: str     			=torch.chunk(__lowerCamelCase       ,		2		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.norm(__lowerCamelCase		) * (1 + scale) + shift
     return x







class 					a	(  nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					Union[str, Any]		)     ->					Optional[int]:


     '''simple docstring'''




     super().__init__()

     SCREAMING_SNAKE_CASE_: Dict     			=CombinedTimestepLabelEmbeddings(__lowerCamelCase       ,		__lowerCamelCase		)

     SCREAMING_SNAKE_CASE_: Dict     			=nn.SiLU()
     SCREAMING_SNAKE_CASE_: Any     			=nn.Linear(__lowerCamelCase       ,		6 * embedding_dim       ,		bias=__lowerCamelCase		)
     SCREAMING_SNAKE_CASE_: Tuple     			=nn.LayerNorm(__lowerCamelCase       ,		elementwise_affine=__lowerCamelCase       ,		eps=1E-6		)





  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					Dict=None		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.linear(self.silu(self.emb(__lowerCamelCase       ,		__lowerCamelCase       ,		hidden_dtype=__lowerCamelCase		)		)		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=emb.chunk(6       ,		dim=1		)
     SCREAMING_SNAKE_CASE_: int     			=self.norm(__lowerCamelCase		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
     return x, gate_msa, shift_mlp, scale_mlp, gate_mlp







class 					a	(  nn.Module       ):



  def __init__(						self :					str       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Optional[str] = None       ,		lowerCAmelCase :					float = 1E-5		)     ->					Dict:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: Dict     			=num_groups
     SCREAMING_SNAKE_CASE_: List[str]     			=eps

     if act_fn is None:
        SCREAMING_SNAKE_CASE_: List[str]     			=None
     else:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=get_activation(__lowerCamelCase		)

     SCREAMING_SNAKE_CASE_: List[Any]     			=nn.Linear(__lowerCamelCase       ,		out_dim * 2		)





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					int		)     ->					str:


     '''simple docstring'''




     if self.act:
        SCREAMING_SNAKE_CASE_: int     			=self.act(__lowerCamelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.linear(__lowerCamelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=emb[:, :, None, None]
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=emb.chunk(2       ,		dim=1		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=F.group_norm(__lowerCamelCase       ,		self.num_groups       ,		eps=self.eps		)
     SCREAMING_SNAKE_CASE_: Any     			=x * (1 + scale) + shift
     return x








"""simple docstring"""
from __future__ import annotations

import math
import random
from typing import Any
class 					a	:



  def __init__(						self :					str		)     ->					None:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					bool:


     '''simple docstring'''




     return self.head == self.tail



  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					Any		)     ->					None:


     '''simple docstring'''




     self.data.append(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=self.tail + 1



  def 	lowerCamelCase__       (						self :					int		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=self.data[self.head]
     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.head + 1
     return ret



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					int:


     '''simple docstring'''




     return self.tail - self.head





  def 	lowerCamelCase__       (						self :					str		)     ->					None:


     '''simple docstring'''




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







class 					a	:



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					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 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Any:


     '''simple docstring'''




     return self.data



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					MyNode | None:


     '''simple docstring'''




     return self.left



  def 	lowerCamelCase__       (						self :					Dict		)     ->					MyNode | None:


     '''simple docstring'''




     return self.right



  def 	lowerCamelCase__       (						self :					Any		)     ->					int:


     '''simple docstring'''




     return self.height



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Any		)     ->					None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=data



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					MyNode | None		)     ->					None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=node



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					MyNode | None		)     ->					None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=node





  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					int		)     ->					None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=height







def      __magic_name__				(							lowercase ):
   if node is None:
      return 0
   return node.get_height()





def      __magic_name__				(							lowercase							,			lowercase ):
   if a > b:
      return a
   return b





def      __magic_name__				(							lowercase ):
   print("""left rotation node:"""							,			node.get_data() )
   SCREAMING_SNAKE_CASE_: List[Any]     			=node.get_left()
   assert ret is not None
   node.set_left(ret.get_right() )
   ret.set_right(lowercase )
   SCREAMING_SNAKE_CASE_: Tuple     			=my_max(get_height(node.get_right() )							,			get_height(node.get_left() ) ) + 1
   node.set_height(lowercase )
   SCREAMING_SNAKE_CASE_: int     			=my_max(get_height(ret.get_right() )							,			get_height(ret.get_left() ) ) + 1
   ret.set_height(lowercase )
   return ret





def      __magic_name__				(							lowercase ):
   print("""right rotation node:"""							,			node.get_data() )
   SCREAMING_SNAKE_CASE_: List[Any]     			=node.get_right()
   assert ret is not None
   node.set_right(ret.get_left() )
   ret.set_left(lowercase )
   SCREAMING_SNAKE_CASE_: List[Any]     			=my_max(get_height(node.get_right() )							,			get_height(node.get_left() ) ) + 1
   node.set_height(lowercase )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=my_max(get_height(ret.get_right() )							,			get_height(ret.get_left() ) ) + 1
   ret.set_height(lowercase )
   return ret





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=node.get_left()
   assert left_child is not None
   node.set_left(left_rotation(lowercase ) )
   return right_rotation(lowercase )





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=node.get_right()
   assert right_child is not None
   node.set_right(right_rotation(lowercase ) )
   return left_rotation(lowercase )





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





def      __magic_name__				(							lowercase ):
   while True:
      SCREAMING_SNAKE_CASE_: Dict     			=root.get_right()
      if right_child is None:
         break
      SCREAMING_SNAKE_CASE_: str     			=right_child
   return root.get_data()





def      __magic_name__				(							lowercase ):
   while True:
      SCREAMING_SNAKE_CASE_: str     			=root.get_left()
      if left_child is None:
         break
      SCREAMING_SNAKE_CASE_: Dict     			=left_child
   return root.get_data()





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=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_: Union[str, Any]     			=get_left_most(lowercase )
         root.set_data(lowercase )
         root.set_right(del_node(lowercase							,			lowercase ) )
      elif left_child is not None:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=left_child
      elif right_child is not None:
         SCREAMING_SNAKE_CASE_: 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(lowercase							,			lowercase ) )
   else:  # root.get_data() < data
      if right_child is None:
         return root
      else:
         root.set_right(del_node(lowercase							,			lowercase ) )

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



  def __init__(						self :					int		)     ->					None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: MyNode | None     			=None



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					int:


     '''simple docstring'''




     return get_height(self.root		)



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					Any		)     ->					None:


     '''simple docstring'''




     print("""insert:""" + str(lowerCAmelCase		)		)
     SCREAMING_SNAKE_CASE_: Tuple     			=insert_node(self.root       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					Any		)     ->					None:


     '''simple docstring'''




     print("""delete:""" + str(lowerCAmelCase		)		)
     if self.root is None:
        print("""Tree is empty!"""		)
        return
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=del_node(self.root       ,		lowerCAmelCase		)





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


     '''simple docstring'''




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







def      __magic_name__				(							):
   import doctest

   doctest.testmod()


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



