code: from __future__ import annotations
  
  from queue import Queue
  
  
  
  
  
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->tuple[int, int]:
  
    '''simple docstring'''
  
  
  
  
    a ^= b
    b ^= a
    a ^= b
    return a, b
  
  
  
  
  
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->list[list[int]]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     1
    while (1 << j) < max_node:
      for i in range(1 ,				max_node + 1					):
        a__     		=     parent[j - 1][parent[j - 1][i]]
      j += 1
    return parent
  
  
  
  
  
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->int:
  
    '''simple docstring'''
  
  
  
  
    if level[u] < level[v]:
      a__		,a__     		=     swap(lowerCamelCase__ ,				lowerCamelCase__					)
    # making depth of u same as depth of v
    for i in range(18 ,				-1 ,				-1					):
      if level[u] - (1 << i) >= level[v]:
        a__     		=     parent[i][u]
      # at the same depth if u==v that mean lca is found
    if u == v:
      return u
    # moving both nodes upwards till lca in found
    for i in range(18 ,				-1 ,				-1					):
      if parent[i][u] not in [0, parent[i][v]]:
        a__		,a__     		=     parent[i][u], parent[i][v]
      # returning longest common ancestor of u,v
    return parent[0][u]
  
  
  
  
  
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ = 1 ,				)				->tuple[list[int], list[list[int]]]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     0
    a__     		=     Queue(maxsize=lowerCamelCase__					)
    q.put(lowerCamelCase__					)
    while q.qsize() != 0:
      a__     		=     q.get()
      for v in graph[u]:
        if level[v] == -1:
          a__     		=     level[u] + 1
          q.put(lowerCamelCase__					)
          a__     		=     u
    return level, parent
  
  
  
  
  
  def 				__lowercase	(						)				->None:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     13
    # initializing with 0
    a__     		=     [[0 for _ in range(max_node + 10					)] for _ in range(20					)]
    # initializing with -1 which means every node is unvisited
    a__     		=     [-1 for _ in range(max_node + 10					)]
    a__     		=     {
        1: [2, 3, 4],
        2: [5],
        3: [6, 7],
        4: [8],
        5: [9, 10],
        6: [11],
        7: [],
        8: [12, 13],
        9: [],
        10: [],
        11: [],
        12: [],
        13: [],
    }
    a__		,a__     		=     breadth_first_search(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				1					)
    a__     		=     create_sparse(lowerCamelCase__ ,				lowerCamelCase__					)
    print("LCA of node 1 and 3 is: " ,				lowest_common_ancestor(1 ,				3 ,				lowerCamelCase__ ,				lowerCamelCase__					)					)
    print("LCA of node 5 and 6 is: " ,				lowest_common_ancestor(5 ,				6 ,				lowerCamelCase__ ,				lowerCamelCase__					)					)
    print("LCA of node 7 and 11 is: " ,				lowest_common_ancestor(7 ,				11 ,				lowerCamelCase__ ,				lowerCamelCase__					)					)
    print("LCA of node 6 and 7 is: " ,				lowest_common_ancestor(6 ,				7 ,				lowerCamelCase__ ,				lowerCamelCase__					)					)
    print("LCA of node 4 and 12 is: " ,				lowest_common_ancestor(4 ,				12 ,				lowerCamelCase__ ,				lowerCamelCase__					)					)
    print("LCA of node 8 and 8 is: " ,				lowest_common_ancestor(8 ,				8 ,				lowerCamelCase__ ,				lowerCamelCase__					)					)
  
  
  if __name__ == "__main__":
       main()
  
  
style_context: import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->List[str]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     UNetaDModel(**lowerCamelCase__					)
    a__     		=     checkpoint["all_modules.0.W"].data
    a__     		=     checkpoint["all_modules.0.W"].data
    a__     		=     checkpoint["all_modules.1.weight"].data
    a__     		=     checkpoint["all_modules.1.bias"].data
  
    a__     		=     checkpoint["all_modules.2.weight"].data
    a__     		=     checkpoint["all_modules.2.bias"].data
  
    a__     		=     checkpoint["all_modules.3.weight"].data
    a__     		=     checkpoint["all_modules.3.bias"].data
  
    a__     		=     checkpoint[list(checkpoint.keys()					)[-4]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-3]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-2]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-1]].data
  
    a__     		=     4
  
    def set_attention_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
    def set_resnet_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
  
      if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
        a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
        a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
    for i, block in enumerate(new_model_architecture.downsample_blocks					):
      a__     		=     hasattr(lowerCamelCase__ ,				"attentions"					)
      for j in range(len(block.resnets					)					):
        set_resnet_weights(block.resnets[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
        if has_attentions:
          set_attention_weights(block.attentions[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
          module_index += 1
  
      if hasattr(lowerCamelCase__ ,				"downsamplers"					) and block.downsamplers is not None:
        set_resnet_weights(block.resnet_down ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
        a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
        module_index += 1
  
    set_resnet_weights(new_model_architecture.mid_block.resnets[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
    set_attention_weights(new_model_architecture.mid_block.attentions[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
    set_resnet_weights(new_model_architecture.mid_block.resnets[1] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
  
    for i, block in enumerate(new_model_architecture.up_blocks					):
      a__     		=     hasattr(lowerCamelCase__ ,				"attentions"					)
      for j in range(len(block.resnets					)					):
        set_resnet_weights(block.resnets[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
      if has_attentions:
        set_attention_weights(
            block.attentions[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)  # why can there only be a single attention layer for up?
        module_index += 1
  
      if hasattr(lowerCamelCase__ ,				"resnet_up"					) and block.resnet_up is not None:
        a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
        module_index += 1
        a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
        module_index += 1
        set_resnet_weights(block.resnet_up ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
  
    a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
    a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
    module_index += 1
    a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
    a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
  
    return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    argparse.ArgumentParser()
  
       parser.add_argument(
           """--checkpoint_path""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
           type=str,
           required=False,
           help="""Path to the checkpoint to convert.""",
       )
  
       parser.add_argument(
           """--config_file""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
           type=str,
           required=False,
           help="""The config json file corresponding to the architecture.""",
       )
  
       parser.add_argument(
           """--dump_path""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
           type=str,
           required=False,
           help="""Path to the output model.""",
       )
  
       _SCREAMING_SNAKE_CASE   						=    parser.parse_args()
  
       _SCREAMING_SNAKE_CASE   						=    torch.load(args.checkpoint_path, map_location="""cpu""")
  
       with open(args.config_file) as f:
            _SCREAMING_SNAKE_CASE   						=    json.loads(f.read())
  
       _SCREAMING_SNAKE_CASE   						=    convert_ncsnpp_checkpoint(
           checkpoint,
           config,
       )
  
       if "sde" in config:
            del config["sde"]
  
       _SCREAMING_SNAKE_CASE   						=    UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)
  
       try:
            _SCREAMING_SNAKE_CASE   						=    ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  
            _SCREAMING_SNAKE_CASE   						=    ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
            model.save_pretrained(args.dump_path)
  
  