"""simple docstring"""


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=1					)				->      Any:
     if n_shave_prefix_segments >= 0:
          return ".".join(path.split('.'					)[n_shave_prefix_segments:]					)
     else:
          return ".".join(path.split('.'					)[:n_shave_prefix_segments]					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=0					)				->      Optional[Any]:
     snake_case_					       =					[]
     for old_item in old_list:
          snake_case_					       =					old_item.replace('in_layers.0'						,					'norm1'					)
          snake_case_					       =					new_item.replace('in_layers.2'						,					'conv1'					)

          snake_case_					       =					new_item.replace('out_layers.0'						,					'norm2'					)
          snake_case_					       =					new_item.replace('out_layers.3'						,					'conv2'					)

          snake_case_					       =					new_item.replace('emb_layers.1'						,					'time_emb_proj'					)
          snake_case_					       =					new_item.replace('skip_connection'						,					'conv_shortcut'					)

          snake_case_					       =					shave_segments(UpperCAmelCase						,					n_shave_prefix_segments=UpperCAmelCase					)

          mapping.append({'old': old_item, 'new': new_item}					)

     return mapping







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=0					)				->      List[Any]:
     snake_case_					       =					[]
     for old_item in old_list:
          snake_case_					       =					old_item

          snake_case_					       =					new_item.replace('norm.weight'						,					'group_norm.weight'					)
          snake_case_					       =					new_item.replace('norm.bias'						,					'group_norm.bias'					)

          snake_case_					       =					new_item.replace('proj_out.weight'						,					'proj_attn.weight'					)
          snake_case_					       =					new_item.replace('proj_out.bias'						,					'proj_attn.bias'					)

          snake_case_					       =					shave_segments(UpperCAmelCase						,					n_shave_prefix_segments=UpperCAmelCase					)

          mapping.append({'old': old_item, 'new': new_item}					)

     return mapping







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					UpperCAmelCase=None					)				->      Any:
     assert isinstance(UpperCAmelCase						,					UpperCAmelCase					), "Paths should be a list of dicts containing 'old' and 'new' keys."

     # Splits the attention layers into three variables.
     if attention_paths_to_split is not None:
          for path, path_map in attention_paths_to_split.items():
               snake_case_					       =					old_checkpoint[path]
               snake_case_					       =					old_tensor.shape[0] // 3

               snake_case_					       =					(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

               snake_case_					       =					old_tensor.shape[0] // config['num_head_channels'] // 3

               snake_case_					       =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
               snake_case_       ,	snake_case_       ,	snake_case_					       =					old_tensor.split(channels // num_heads						,					dim=1					)

               snake_case_					       =					query.reshape(UpperCAmelCase					)
               snake_case_					       =					key.reshape(UpperCAmelCase					)
               snake_case_					       =					value.reshape(UpperCAmelCase					)

     for path in paths:
          snake_case_					       =					path['new']

          # These have already been assigned
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          # Global renaming happens here
          snake_case_					       =					new_path.replace('middle_block.0'						,					'mid_block.resnets.0'					)
          snake_case_					       =					new_path.replace('middle_block.1'						,					'mid_block.attentions.0'					)
          snake_case_					       =					new_path.replace('middle_block.2'						,					'mid_block.resnets.1'					)

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    snake_case_					       =					new_path.replace(replacement['old']						,					replacement['new']					)

        # proj_attn.weight has to be converted from conv 1D to linear
          if "proj_attn.weight" in new_path:
               snake_case_					       =					old_checkpoint[path['old']][:, :, 0]
          else:
               snake_case_					       =					old_checkpoint[path['old']]







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      Optional[Any]:
     snake_case_					       =					{}

     snake_case_					       =					checkpoint['time_embed.0.weight']
     snake_case_					       =					checkpoint['time_embed.0.bias']
     snake_case_					       =					checkpoint['time_embed.2.weight']
     snake_case_					       =					checkpoint['time_embed.2.bias']

     snake_case_					       =					checkpoint['input_blocks.0.0.weight']
     snake_case_					       =					checkpoint['input_blocks.0.0.bias']

     snake_case_					       =					checkpoint['out.0.weight']
     snake_case_					       =					checkpoint['out.0.bias']
     snake_case_					       =					checkpoint['out.2.weight']
     snake_case_					       =					checkpoint['out.2.bias']

     # Retrieves the keys for the input blocks only
     snake_case_					       =					len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'input_blocks' in layer}					)
     snake_case_					       =					{
         layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
         for layer_id in range(UpperCAmelCase					)
     }

     # Retrieves the keys for the middle blocks only
     snake_case_					       =					len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'middle_block' in layer}					)
     snake_case_					       =					{
         layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
         for layer_id in range(UpperCAmelCase					)
     }

     # Retrieves the keys for the output blocks only
     snake_case_					       =					len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'output_blocks' in layer}					)
     snake_case_					       =					{
         layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
         for layer_id in range(UpperCAmelCase					)
     }

     for i in range(1						,					UpperCAmelCase					):
          snake_case_					       =					(i - 1) // (config['num_res_blocks'] + 1)
          snake_case_					       =					(i - 1) % (config['num_res_blocks'] + 1)

          snake_case_					       =					[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
          snake_case_					       =					[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

          if f'input_blocks.{i}.0.op.weight' in checkpoint:
               snake_case_					       =					checkpoint[
                   f'input_blocks.{i}.0.op.weight'
               ]
               snake_case_					       =					checkpoint[
                   f'input_blocks.{i}.0.op.bias'
               ]
               continue

          snake_case_					       =					renew_resnet_paths(UpperCAmelCase					)
          snake_case_					       =					{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
          snake_case_					       =					{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
          assign_to_checkpoint(
              UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					additional_replacements=[meta_path, resnet_op]						,					config=UpperCAmelCase					)

          if len(UpperCAmelCase					):
               snake_case_					       =					renew_attention_paths(UpperCAmelCase					)
               snake_case_					       =					{
                   'old': f'input_blocks.{i}.1',
                   'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
               }
               snake_case_					       =					{
                   f'input_blocks.{i}.1.qkv.bias': {
                       'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                       'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                       'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                   },
                   f'input_blocks.{i}.1.qkv.weight': {
                       'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                       'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                       'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                   },
               }
               assign_to_checkpoint(
                   UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					additional_replacements=[meta_path]						,					attention_paths_to_split=UpperCAmelCase						,					config=UpperCAmelCase						,					)

     snake_case_					       =					middle_blocks[0]
     snake_case_					       =					middle_blocks[1]
     snake_case_					       =					middle_blocks[2]

     snake_case_					       =					renew_resnet_paths(UpperCAmelCase					)
     assign_to_checkpoint(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					config=UpperCAmelCase					)

     snake_case_					       =					renew_resnet_paths(UpperCAmelCase					)
     assign_to_checkpoint(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					config=UpperCAmelCase					)

     snake_case_					       =					renew_attention_paths(UpperCAmelCase					)
     snake_case_					       =					{
         'middle_block.1.qkv.bias': {
             'key': 'mid_block.attentions.0.key.bias',
             'query': 'mid_block.attentions.0.query.bias',
             'value': 'mid_block.attentions.0.value.bias',
         },
         'middle_block.1.qkv.weight': {
             'key': 'mid_block.attentions.0.key.weight',
             'query': 'mid_block.attentions.0.query.weight',
             'value': 'mid_block.attentions.0.value.weight',
         },
     }
     assign_to_checkpoint(
         UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					attention_paths_to_split=UpperCAmelCase						,					config=UpperCAmelCase					)

     for i in range(UpperCAmelCase					):
          snake_case_					       =					i // (config['num_res_blocks'] + 1)
          snake_case_					       =					i % (config['num_res_blocks'] + 1)
          snake_case_					       =					[shave_segments(UpperCAmelCase						,					2					) for name in output_blocks[i]]
          snake_case_					       =					{}

          for layer in output_block_layers:
               snake_case_       ,	snake_case_					       =					layer.split('.'					)[0], shave_segments(UpperCAmelCase						,					1					)
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(UpperCAmelCase					)
               else:
                    snake_case_					       =					[layer_name]

          if len(UpperCAmelCase					) > 1:
               snake_case_					       =					[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
               snake_case_					       =					[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

               snake_case_					       =					renew_resnet_paths(UpperCAmelCase					)
               snake_case_					       =					renew_resnet_paths(UpperCAmelCase					)

               snake_case_					       =					{'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
               assign_to_checkpoint(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					additional_replacements=[meta_path]						,					config=UpperCAmelCase					)

               if ["conv.weight", "conv.bias"] in output_block_list.values():
                    snake_case_					       =					list(output_block_list.values()					).index(['conv.weight', 'conv.bias']					)
                    snake_case_					       =					checkpoint[
                        f'output_blocks.{i}.{index}.conv.weight'
                    ]
                    snake_case_					       =					checkpoint[
                        f'output_blocks.{i}.{index}.conv.bias'
                    ]

                    # Clear attentions as they have been attributed above.
                    if len(UpperCAmelCase					) == 2:
                         snake_case_					       =					[]

               if len(UpperCAmelCase					):
                    snake_case_					       =					renew_attention_paths(UpperCAmelCase					)
                    snake_case_					       =					{
                        'old': f'output_blocks.{i}.1',
                        'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                    }
                    snake_case_					       =					{
                        f'output_blocks.{i}.1.qkv.bias': {
                            'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                            'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                            'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                        },
                        f'output_blocks.{i}.1.qkv.weight': {
                            'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                            'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                            'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                        },
                    }
                    assign_to_checkpoint(
                        UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					additional_replacements=[meta_path]						,					attention_paths_to_split=to_split if any('qkv' in key for key in attentions					) else None						,					config=UpperCAmelCase						,					)
          else:
               snake_case_					       =					renew_resnet_paths(UpperCAmelCase						,					n_shave_prefix_segments=1					)
               for path in resnet_0_paths:
                    snake_case_					       =					'.'.join(['output_blocks', str(UpperCAmelCase					), path['old']]					)
                    snake_case_					       =					'.'.join(['up_blocks', str(UpperCAmelCase					), 'resnets', str(UpperCAmelCase					), path['new']]					)

                    snake_case_					       =					checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
    __UpperCamelCase									=			argparse.ArgumentParser()

    parser.add_argument(
        '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
    )

    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the architecture.''',
    )

    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

    __UpperCamelCase									=			parser.parse_args()

    __UpperCamelCase									=			torch.load(args.checkpoint_path)

    with open(args.config_file) as f:
        __UpperCamelCase									=			json.loads(f.read())

    __UpperCamelCase									=			convert_ldm_checkpoint(checkpoint, config)

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

    __UpperCamelCase									=			UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)

    try:
        __UpperCamelCase									=			DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
        __UpperCamelCase									=			VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

        __UpperCamelCase									=			LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
        pipe.save_pretrained(args.dump_path)
    except:  # noqa: E722
        model.save_pretrained(args.dump_path)



from __future__ import annotations

from typing import Any



class 		UpperCamelCase__  :
   def __init__(self :					Union[str, Any]					,				snake_case_ :					int   ):
    __a :  Dict						=  num_of_nodes
    __a :  list[list[int]]						=  []
    __a :  dict[int, int]						=  {}
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					int   ):
    self.m_edges.append([u_node, v_node, weight]   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					int   ):

    if self.m_component[u_node] == u_node:
     return u_node
    return self.find_component(self.m_component[u_node]   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					int   ):

    if self.m_component[u_node] != u_node:
     for k in self.m_component:
      __a :  Optional[int]						=  self.find_component(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					list[int]					,				snake_case_ :					int					,				snake_case_ :					int   ):

    if component_size[u_node] <= component_size[v_node]:
     __a :  List[str]						=  v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(snake_case_   )

    elif component_size[u_node] >= component_size[v_node]:
     __a :  Optional[int]						=  self.find_component(snake_case_   )
     component_size[u_node] += component_size[v_node]
     self.set_component(snake_case_   )







   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  str						=  []
    __a :  int						=  0

    __a :  list[Any]						=  [-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes   ):
     self.m_component.update({node: node}   )
     component_size.append(1   )

    __a :  Union[str, Any]						=  self.m_num_of_nodes

    while num_of_components > 1:
     for edge in self.m_edges:
      __a				,       __a				,       __a :  Optional[Any]						=  edge

      __a :  List[str]						=  self.m_component[u]
      __a :  List[Any]						=  self.m_component[v]

      if u_component != v_component:

       for component in (u_component, v_component):
        if (
            minimum_weight_edge[component] == -1
            or minimum_weight_edge[component][2] > w
        ):
         __a :  str						=  [u, v, w]

     for edge in minimum_weight_edge:
      if isinstance(snake_case_					,				snake_case_   ):
       __a				,       __a				,       __a :  str						=  edge

       __a :  Any						=  self.m_component[u]
       __a :  Any						=  self.m_component[v]

       if u_component != v_component:
        mst_weight += w
        self.union(snake_case_					,				snake_case_					,				snake_case_   )
        print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"   )
        num_of_components -= 1

     __a :  Optional[int]						=  [-1] * self.m_num_of_nodes
    print(f"The total weight of the minimal spanning tree is: {mst_weight}"   )





def    __UpperCamelCase     (				):
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()
