





"""simple docstring"""


from __future__ import annotations

from queue import Queue





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					int		)->		tuple[int, int]:
       a ^= b
       b ^= a
       a ^= b
       return a, b





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					list[list[int]]		)->		list[list[int]]:
       _lowerCamelCase    	=					1
       while (1 << j) < max_node:
              for i in range(1						,  max_node + 1		):
                     _lowerCamelCase    	=					parent[j - 1][parent[j - 1][i]]
              j += 1
       return parent





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					int						,  snake_case   :					list[int]						,  snake_case   :					list[list[int]]		)->		int:
       # u must be deeper in the tree than v
       if level[u] < level[v]:
              _lowerCamelCase	,	_lowerCamelCase    	=					swap(snake_case						,  snake_case		)
       # making depth of u same as depth of v
       for i in range(18						,  -1						,  -1		):
              if level[u] - (1 << i) >= level[v]:
                     _lowerCamelCase    	=					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]]:
                     _lowerCamelCase	,	_lowerCamelCase    	=					parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
       return parent[0][u]





def SCREAMING_SNAKE_CASE_   (       snake_case   :					list[int]						,  snake_case   :					list[list[int]]						,  snake_case   :					int						,  snake_case   :					dict[int, list[int]]						,  snake_case   :					int = 1						,  )->		tuple[list[int], list[list[int]]]:
       _lowerCamelCase    	=					0
       _lowerCamelCase    	=					Queue(maxsize=snake_case		)
       q.put(snake_case		)
       while q.qsize() != 0:
              _lowerCamelCase    	=					q.get()
              for v in graph[u]:
                     if level[v] == -1:
                            _lowerCamelCase    	=					level[u] + 1
                            q.put(snake_case		)
                            _lowerCamelCase    	=					u
       return level, parent





def SCREAMING_SNAKE_CASE_   (       )->		None:
       _lowerCamelCase    	=					13
       # initializing with 0
       _lowerCamelCase    	=					[[0 for _ in range(max_node + 10		)] for _ in range(20		)]
       # initializing with -1 which means every node is unvisited
       _lowerCamelCase    	=					[-1 for _ in range(max_node + 10		)]
       _lowerCamelCase    	=					{
           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: [],
       }
       _lowerCamelCase	,	_lowerCamelCase    	=					breadth_first_search(snake_case						,  snake_case						,  snake_case						,  snake_case						,  1		)
       _lowerCamelCase    	=					create_sparse(snake_case						,  snake_case		)
       print('LCA of node 1 and 3 is: '						,  lowest_common_ancestor(1						,  3						,  snake_case						,  snake_case		)		)
       print('LCA of node 5 and 6 is: '						,  lowest_common_ancestor(5						,  6						,  snake_case						,  snake_case		)		)
       print('LCA of node 7 and 11 is: '						,  lowest_common_ancestor(7						,  11						,  snake_case						,  snake_case		)		)
       print('LCA of node 6 and 7 is: '						,  lowest_common_ancestor(6						,  7						,  snake_case						,  snake_case		)		)
       print('LCA of node 4 and 12 is: '						,  lowest_common_ancestor(4						,  12						,  snake_case						,  snake_case		)		)
       print('LCA of node 8 and 8 is: '						,  lowest_common_ancestor(8						,  8						,  snake_case						,  snake_case		)		)


if __name__ == "__main__":
      main()













"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Tuple						,  snake_case   :					List[Any]		)->		str:
       _lowerCamelCase    	=					UNetaDModel(**snake_case		)
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.2.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.2.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.3.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.3.bias'].data

       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-4]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-3]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-2]].data
       _lowerCamelCase    	=					checkpoint[list(checkpoint.keys()		)[-1]].data

       _lowerCamelCase    	=					4

       def set_attention_weights(snake_case   :					List[Any]						,  snake_case   :					Any						,  snake_case   :					List[Any]		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

       def set_resnet_weights(snake_case   :					Tuple						,  snake_case   :					Optional[int]						,  snake_case   :					Tuple		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
              _lowerCamelCase    	=					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:
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

       for i, block in enumerate(new_model_architecture.downsample_blocks		):
              _lowerCamelCase    	=					hasattr(snake_case						,  'attentions'		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]						,  snake_case						,  snake_case		)
                     module_index += 1
                     if has_attentions:
                            set_attention_weights(block.attentions[j]						,  snake_case						,  snake_case		)
                            module_index += 1

              if hasattr(snake_case						,  'downsamplers'		) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down						,  snake_case						,  snake_case		)
                     module_index += 1
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.Conv_0.bias'].data
                     module_index += 1

       set_resnet_weights(new_model_architecture.mid_block.resnets[0]						,  snake_case						,  snake_case		)
       module_index += 1
       set_attention_weights(new_model_architecture.mid_block.attentions[0]						,  snake_case						,  snake_case		)
       module_index += 1
       set_resnet_weights(new_model_architecture.mid_block.resnets[1]						,  snake_case						,  snake_case		)
       module_index += 1

       for i, block in enumerate(new_model_architecture.up_blocks		):
              _lowerCamelCase    	=					hasattr(snake_case						,  'attentions'		)
              for j in range(len(block.resnets		)		):
                     set_resnet_weights(block.resnets[j]						,  snake_case						,  snake_case		)
                     module_index += 1
              if has_attentions:
                     set_attention_weights(
                         block.attentions[0]						,  snake_case						,  snake_case		)  # why can there only be a single attention layer for up?
                     module_index += 1

              if hasattr(snake_case						,  'resnet_up'		) and block.resnet_up is not None:
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up						,  snake_case						,  snake_case		)
                     module_index += 1

       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
       module_index += 1
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
      A_     :      Any								=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.""",
      )

      A_     :      int								=parser.parse_args()

      A_     :      Dict								=torch.load(args.checkpoint_path, map_location="""cpu""")

      with open(args.config_file) as f:
            A_     :      List[str]								=json.loads(f.read())

      A_     :      str								=convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

      if "sde" in config:
            del config["sde"]

      A_     :      str								=UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            A_     :      List[Any]								=ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

            A_     :      Optional[Any]								=ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)






