

'''simple docstring'''
from collections.abc import Iterable
from typing import Any







class 							A__   :





  def __init__(		self				:     List[Any]							, _a				:     int | None = None			)     ->   Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =value
       _SCREAMING_SNAKE_CASE							   =None  # Added in order to delete a node easier
       _SCREAMING_SNAKE_CASE							   =None
       _SCREAMING_SNAKE_CASE							   =None





  def __repr__(		self				:     List[Any]			)     ->   str:





       '''simple docstring'''






       from pprint import pformat

       if self.left is None and self.right is None:
            return str(self.value			)
       return pformat({f"{self.value}": (self.left, self.right)}							, indent=1			)







class 							A__   :





  def __init__(		self				:     Optional[Any]							, _a				:     Node | None = None			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =root





  def __str__(		self				:     List[Any]			)     ->   str:





       '''simple docstring'''






       return str(self.root			)





  def 						A  (		self				:     List[Any]							, _a				:     Node							, _a				:     Node | None			)     ->   None:





       '''simple docstring'''






       if new_children is not None:  # reset its kids
            _SCREAMING_SNAKE_CASE							   =node.parent
       if node.parent is not None:  # reset its parent
            if self.is_right(_a			):  # If it is the right children
                 _SCREAMING_SNAKE_CASE							   =new_children
            else:
                 _SCREAMING_SNAKE_CASE							   =new_children
       else:
            _SCREAMING_SNAKE_CASE							   =new_children





  def 						A  (		self				:     Optional[int]							, _a				:     Node			)     ->   bool:





       '''simple docstring'''






       if node.parent and node.parent.right:
            return node == node.parent.right
       return False





  def 						A  (		self				:     List[Any]			)     ->   bool:





       '''simple docstring'''






       return self.root is None





  def 						A  (		self				:     Union[str, Any]							, _a				:     Dict			)     ->   None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =Node(_a			)  # create a new Node
       if self.empty():  # if Tree is empty
            _SCREAMING_SNAKE_CASE							   =new_node  # set its root
       else:  # Tree is not empty
            _SCREAMING_SNAKE_CASE							   =self.root  # from root
            if parent_node is None:
                 return
            while True:  # While we don't get to a leaf
                 if value < parent_node.value:  # We go left
                      if parent_node.left is None:
                           _SCREAMING_SNAKE_CASE							   =new_node  # We insert the new node in a leaf
                           break
                      else:
                           _SCREAMING_SNAKE_CASE							   =parent_node.left
                 else:
                      if parent_node.right is None:
                           _SCREAMING_SNAKE_CASE							   =new_node
                           break
                      else:
                           _SCREAMING_SNAKE_CASE							   =parent_node.right
            _SCREAMING_SNAKE_CASE							   =parent_node





  def 						A  (		self				:     str							, *_a				:     str			)     ->   None:





       '''simple docstring'''






       for value in values:
            self.__insert(_a			)





  def 						A  (		self				:     int							, _a				:     Optional[Any]			)     ->   Node | None:





       '''simple docstring'''






       if self.empty():
            raise IndexError('Warning: Tree is empty! please use another.'			)
       else:
            _SCREAMING_SNAKE_CASE							   =self.root
            # use lazy evaluation here to avoid NoneType Attribute error
            while node is not None and node.value is not value:
                 _SCREAMING_SNAKE_CASE							   =node.left if value < node.value else node.right
            return node





  def 						A  (		self				:     Tuple							, _a				:     Node | None = None			)     ->   Node | None:





       '''simple docstring'''






       if node is None:
            if self.root is None:
                 return None
            _SCREAMING_SNAKE_CASE							   =self.root

       if not self.empty():
            while node.right is not None:
                 _SCREAMING_SNAKE_CASE							   =node.right
       return node





  def 						A  (		self				:     Optional[int]							, _a				:     Node | None = None			)     ->   Node | None:





       '''simple docstring'''






       if node is None:
            _SCREAMING_SNAKE_CASE							   =self.root
       if self.root is None:
            return None
       if not self.empty():
            _SCREAMING_SNAKE_CASE							   =self.root
            while node.left is not None:
                 _SCREAMING_SNAKE_CASE							   =node.left
       return node





  def 						A  (		self				:     Union[str, Any]							, _a				:     int			)     ->   None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.search(_a			)  # Look for the node with that label
       if node is not None:
            if node.left is None and node.right is None:  # If it has no children
                 self.__reassign_nodes(_a							, _a			)
            elif node.left is None:  # Has only right children
                 self.__reassign_nodes(_a							, node.right			)
            elif node.right is None:  # Has only left children
                 self.__reassign_nodes(_a							, node.left			)
            else:
                 _SCREAMING_SNAKE_CASE							   =self.get_max(
                     node.left			)  # Gets the max value of the left branch
                 self.remove(tmp_node.value			)  # type: ignore
                 _SCREAMING_SNAKE_CASE							   =(
                     tmp_node.value  # type: ignore
                 )  # Assigns the value to the node to delete and keep tree structure





  def 						A  (		self				:     List[Any]							, _a				:     Node | None			)     ->   Iterable:





       '''simple docstring'''






       if node is not None:
            yield node  # Preorder Traversal
            yield from self.preorder_traverse(node.left			)
            yield from self.preorder_traverse(node.right			)





  def 						A  (		self				:     str							, _a				:     Dict=None			)     ->   Any:





       '''simple docstring'''






       if traversal_function is None:
            return self.preorder_traverse(self.root			)
       else:
            return traversal_function(self.root			)





  def 						A  (		self				:     Tuple							, _a				:     list							, _a				:     Node | None			)     ->   None:





       '''simple docstring'''






       if node:
            self.inorder(_a							, node.left			)
            arr.append(node.value			)
            self.inorder(_a							, node.right			)





  def 						A  (		self				:     str							, _a				:     int							, _a				:     Node			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =[]
       self.inorder(_a							, _a			)  # append all values to list using inorder traversal
       return arr[k - 1]


def 	_lowerCAmelCase (						_UpperCamelCase						:					Node | None    )				->      list[Node]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[]
     if curr_node is not None:
          _SCREAMING_SNAKE_CASE							   =postorder(curr_node.left    ) + postorder(curr_node.right    ) + [curr_node]
     return node_list


def 	_lowerCAmelCase (						)				->      None:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =(8, 3, 6, 1, 10, 14, 13, 4, 7)
     _SCREAMING_SNAKE_CASE							   =BinarySearchTree()
     for i in testlist:
          t.insert(_UpperCamelCase    )

     # Prints all the elements of the list in order traversal
     print(_UpperCamelCase    )

     if t.search(6    ) is not None:
          print('The value 6 exists'    )
     else:
          print('The value 6 doesn\'t exist'    )

     if t.search(-1    ) is not None:
          print('The value -1 exists'    )
     else:
          print('The value -1 doesn\'t exist'    )

     if not t.empty():
          print('Max Value: '							,			t.get_max().value    )  # type: ignore
          print('Min Value: '							,			t.get_min().value    )  # type: ignore

     for i in testlist:
          t.remove(_UpperCamelCase    )
          print(_UpperCamelCase    )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)



'''simple docstring'''
import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)


def 	_lowerCAmelCase (						_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					List[Any]    )				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =checkpoint

     _SCREAMING_SNAKE_CASE							   ={}

     _SCREAMING_SNAKE_CASE							   =vae_state_dict['encoder.conv_in.weight']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['encoder.conv_in.bias']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['encoder.conv_out.weight']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['encoder.conv_out.bias']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['encoder.norm_out.weight']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['encoder.norm_out.bias']

     _SCREAMING_SNAKE_CASE							   =vae_state_dict['decoder.conv_in.weight']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['decoder.conv_in.bias']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['decoder.conv_out.weight']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['decoder.conv_out.bias']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['decoder.norm_out.weight']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['decoder.norm_out.bias']

     _SCREAMING_SNAKE_CASE							   =vae_state_dict['quant_conv.weight']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['quant_conv.bias']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['post_quant_conv.weight']
     _SCREAMING_SNAKE_CASE							   =vae_state_dict['post_quant_conv.bias']

     # Retrieves the keys for the encoder down blocks only
     _SCREAMING_SNAKE_CASE							   =len({'.'.join(layer.split('.'    )[:3]    ) for layer in vae_state_dict if 'encoder.down' in layer}    )
     _SCREAMING_SNAKE_CASE							   ={
         layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(_UpperCamelCase    )
     }

     # Retrieves the keys for the decoder up blocks only
     _SCREAMING_SNAKE_CASE							   =len({'.'.join(layer.split('.'    )[:3]    ) for layer in vae_state_dict if 'decoder.up' in layer}    )
     _SCREAMING_SNAKE_CASE							   ={
         layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(_UpperCamelCase    )
     }

     for i in range(_UpperCamelCase    ):
          _SCREAMING_SNAKE_CASE							   =[key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]

          if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
               _SCREAMING_SNAKE_CASE							   =vae_state_dict.pop(
                   f"encoder.down.{i}.downsample.conv.weight"    )
               _SCREAMING_SNAKE_CASE							   =vae_state_dict.pop(
                   f"encoder.down.{i}.downsample.conv.bias"    )

          _SCREAMING_SNAKE_CASE							   =renew_vae_resnet_paths(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   ={'old': f"down.{i}.block", 'new': f"down_blocks.{i}.resnets"}
          assign_to_checkpoint(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			additional_replacements=[meta_path]							,			config=_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =[key for key in vae_state_dict if 'encoder.mid.block' in key]
     _SCREAMING_SNAKE_CASE							   =2
     for i in range(1							,			num_mid_res_blocks + 1    ):
          _SCREAMING_SNAKE_CASE							   =[key for key in mid_resnets if f"encoder.mid.block_{i}" in key]

          _SCREAMING_SNAKE_CASE							   =renew_vae_resnet_paths(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   ={'old': f"mid.block_{i}", 'new': f"mid_block.resnets.{i - 1}"}
          assign_to_checkpoint(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			additional_replacements=[meta_path]							,			config=_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =[key for key in vae_state_dict if 'encoder.mid.attn' in key]
     _SCREAMING_SNAKE_CASE							   =renew_vae_attention_paths(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   ={'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
     assign_to_checkpoint(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			additional_replacements=[meta_path]							,			config=_UpperCamelCase    )
     conv_attn_to_linear(_UpperCamelCase    )

     for i in range(_UpperCamelCase    ):
          _SCREAMING_SNAKE_CASE							   =num_up_blocks - 1 - i
          _SCREAMING_SNAKE_CASE							   =[
              key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
          ]

          if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
               _SCREAMING_SNAKE_CASE							   =vae_state_dict[
                   f"decoder.up.{block_id}.upsample.conv.weight"
               ]
               _SCREAMING_SNAKE_CASE							   =vae_state_dict[
                   f"decoder.up.{block_id}.upsample.conv.bias"
               ]

          _SCREAMING_SNAKE_CASE							   =renew_vae_resnet_paths(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   ={'old': f"up.{block_id}.block", 'new': f"up_blocks.{i}.resnets"}
          assign_to_checkpoint(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			additional_replacements=[meta_path]							,			config=_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =[key for key in vae_state_dict if 'decoder.mid.block' in key]
     _SCREAMING_SNAKE_CASE							   =2
     for i in range(1							,			num_mid_res_blocks + 1    ):
          _SCREAMING_SNAKE_CASE							   =[key for key in mid_resnets if f"decoder.mid.block_{i}" in key]

          _SCREAMING_SNAKE_CASE							   =renew_vae_resnet_paths(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   ={'old': f"mid.block_{i}", 'new': f"mid_block.resnets.{i - 1}"}
          assign_to_checkpoint(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			additional_replacements=[meta_path]							,			config=_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =[key for key in vae_state_dict if 'decoder.mid.attn' in key]
     _SCREAMING_SNAKE_CASE							   =renew_vae_attention_paths(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   ={'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
     assign_to_checkpoint(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			additional_replacements=[meta_path]							,			config=_UpperCamelCase    )
     conv_attn_to_linear(_UpperCamelCase    )
     return new_checkpoint


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					str							,			)				->      List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =requests.get(
         ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'    )
     _SCREAMING_SNAKE_CASE							   =io.BytesIO(r.content    )

     _SCREAMING_SNAKE_CASE							   =OmegaConf.load(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =5_12
     _SCREAMING_SNAKE_CASE							   ='cuda' if torch.cuda.is_available() else 'cpu'
     if checkpoint_path.endswith('safetensors'    ):
          from safetensors import safe_open

          _SCREAMING_SNAKE_CASE							   ={}
          with safe_open(_UpperCamelCase							,			framework='pt'							,			device='cpu'    ) as f:
               for key in f.keys():
                    _SCREAMING_SNAKE_CASE							   =f.get_tensor(_UpperCamelCase    )
     else:
          _SCREAMING_SNAKE_CASE							   =torch.load(_UpperCamelCase							,			map_location=_UpperCamelCase    )['state_dict']

     # Convert the VAE model.
     _SCREAMING_SNAKE_CASE							   =create_vae_diffusers_config(_UpperCamelCase							,			image_size=_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =custom_convert_ldm_vae_checkpoint(_UpperCamelCase							,			_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =AutoencoderKL(**_UpperCamelCase    )
     vae.load_state_dict(_UpperCamelCase    )
     vae.save_pretrained(_UpperCamelCase    )


if __name__ == "__main__":
     lowerCamelCase			:				Union[str, Any] 			=					argparse.ArgumentParser()

     parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
     parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")

     lowerCamelCase			:				List[str] 			=					parser.parse_args()

     vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
