


'''simple docstring'''




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def      _a		(lowercase__ :      Any       ,      lowercase__ :      Any=1	) -> int:






     """simple docstring"""







     if n_shave_prefix_segments >= 0:
          return ".".join(path.split('.'	)[n_shave_prefix_segments:]	)
     else:
          return ".".join(path.split('.'	)[:n_shave_prefix_segments]	)





def      _a		(lowercase__ :      Dict       ,      lowercase__ :      Tuple=0	) -> str:






     """simple docstring"""
     __snake_case    			=	[]
     for old_item in old_list:
          __snake_case    			=	old_item
          __snake_case    			=	new_item.replace('block.'       ,      'resnets.'	)
          __snake_case    			=	new_item.replace('conv_shorcut'       ,      'conv1'	)
          __snake_case    			=	new_item.replace('in_shortcut'       ,      'conv_shortcut'	)
          __snake_case    			=	new_item.replace('temb_proj'       ,      'time_emb_proj'	)

          __snake_case    			=	shave_segments(lowercase__       ,      n_shave_prefix_segments=lowercase__	)

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

     return mapping





def      _a		(lowercase__ :      Tuple       ,      lowercase__ :      List[str]=0       ,      lowercase__ :      Optional[Any]=False	) -> Optional[Any]:






     """simple docstring"""
     __snake_case    			=	[]
     for old_item in old_list:
          __snake_case    			=	old_item

          # In `model.mid`, the layer is called `attn`.
          if not in_mid:
               __snake_case    			=	new_item.replace('attn'       ,      'attentions'	)
          __snake_case    			=	new_item.replace('.k.'       ,      '.key.'	)
          __snake_case    			=	new_item.replace('.v.'       ,      '.value.'	)
          __snake_case    			=	new_item.replace('.q.'       ,      '.query.'	)

          __snake_case    			=	new_item.replace('proj_out'       ,      'proj_attn'	)
          __snake_case    			=	new_item.replace('norm'       ,      'group_norm'	)

          __snake_case    			=	shave_segments(lowercase__       ,      n_shave_prefix_segments=lowercase__	)
          mapping.append({'old': old_item, 'new': new_item}	)

     return mapping





def      _a		(lowercase__ :      Optional[Any]       ,      lowercase__ :      int       ,      lowercase__ :      Union[str, Any]       ,      lowercase__ :      int=None       ,      lowercase__ :      str=None       ,      lowercase__ :      Optional[int]=None	) -> Dict:






     """simple docstring"""
     assert isinstance(lowercase__       ,      lowercase__	), "Paths should be a list of dicts containing 'old' and 'new' keys."

     if attention_paths_to_split is not None:
          if config is None:
               raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'	)

          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.get('num_head_channels'       ,      1	) // 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(lowercase__	).squeeze()
               __snake_case    			=	key.reshape(lowercase__	).squeeze()
               __snake_case    			=	value.reshape(lowercase__	).squeeze()

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

          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          __snake_case    			=	new_path.replace('down.'       ,      'down_blocks.'	)
          __snake_case    			=	new_path.replace('up.'       ,      'up_blocks.'	)

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

          if "attentions" in new_path:
               __snake_case    			=	old_checkpoint[path['old']].squeeze()
          else:
               __snake_case    			=	old_checkpoint[path['old']]





def      _a		(lowercase__ :      List[Any]       ,      lowercase__ :      Tuple	) -> Optional[Any]:






     """simple docstring"""
     __snake_case    			=	{}

     __snake_case    			=	checkpoint['temb.dense.0.weight']
     __snake_case    			=	checkpoint['temb.dense.0.bias']
     __snake_case    			=	checkpoint['temb.dense.1.weight']
     __snake_case    			=	checkpoint['temb.dense.1.bias']

     __snake_case    			=	checkpoint['norm_out.weight']
     __snake_case    			=	checkpoint['norm_out.bias']

     __snake_case    			=	checkpoint['conv_in.weight']
     __snake_case    			=	checkpoint['conv_in.bias']
     __snake_case    			=	checkpoint['conv_out.weight']
     __snake_case    			=	checkpoint['conv_out.bias']

     __snake_case    			=	len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'down' in layer}	)
     __snake_case    			=	{
         layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowercase__	)
     }

     __snake_case    			=	len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'up' in layer}	)
     __snake_case    			=	{layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowercase__	)}

     for i in range(lowercase__	):
          __snake_case    			=	(i - 1) // (config['layers_per_block'] + 1)

          if any('downsample' in layer for layer in down_blocks[i]	):
               __snake_case    			=	checkpoint[
                   f'down.{i}.downsample.op.weight'
               ]
               __snake_case    			=	checkpoint[f'down.{i}.downsample.op.bias']
          #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
          #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

          if any('block' in layer for layer in down_blocks[i]	):
               __snake_case    			=	len(
                   {'.'.join(shave_segments(lowercase__       ,      2	).split('.'	)[:2]	) for layer in down_blocks[i] if 'block' in layer}	)
               __snake_case    			=	{
                   layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                   for layer_id in range(lowercase__	)
               }

               if num_blocks > 0:
                    for j in range(config['layers_per_block']	):
                         __snake_case    			=	renew_resnet_paths(blocks[j]	)
                         assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__	)

          if any('attn' in layer for layer in down_blocks[i]	):
               __snake_case    			=	len(
                   {'.'.join(shave_segments(lowercase__       ,      2	).split('.'	)[:2]	) for layer in down_blocks[i] if 'attn' in layer}	)
               __snake_case    			=	{
                   layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                   for layer_id in range(lowercase__	)
               }

               if num_attn > 0:
                    for j in range(config['layers_per_block']	):
                         __snake_case    			=	renew_attention_paths(attns[j]	)
                         assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__	)

     __snake_case    			=	[key for key in checkpoint if 'mid.block_1' in key]
     __snake_case    			=	[key for key in checkpoint if 'mid.block_2' in key]
     __snake_case    			=	[key for key in checkpoint if 'mid.attn_1' in key]

     # Mid new 2
     __snake_case    			=	renew_resnet_paths(lowercase__	)
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,      )

     __snake_case    			=	renew_resnet_paths(lowercase__	)
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,      )

     __snake_case    			=	renew_attention_paths(lowercase__       ,      in_mid=lowercase__	)
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,      )

     for i in range(lowercase__	):
          __snake_case    			=	num_up_blocks - 1 - i

          if any('upsample' in layer for layer in up_blocks[i]	):
               __snake_case    			=	checkpoint[
                   f'up.{i}.upsample.conv.weight'
               ]
               __snake_case    			=	checkpoint[f'up.{i}.upsample.conv.bias']

          if any('block' in layer for layer in up_blocks[i]	):
               __snake_case    			=	len(
                   {'.'.join(shave_segments(lowercase__       ,      2	).split('.'	)[:2]	) for layer in up_blocks[i] if 'block' in layer}	)
               __snake_case    			=	{
                   layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowercase__	)
               }

               if num_blocks > 0:
                    for j in range(config['layers_per_block'] + 1	):
                         __snake_case    			=	{'old': f'up_blocks.{i}', 'new': f'up_blocks.{block_id}'}
                         __snake_case    			=	renew_resnet_paths(blocks[j]	)
                         assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[replace_indices]	)

          if any('attn' in layer for layer in up_blocks[i]	):
               __snake_case    			=	len(
                   {'.'.join(shave_segments(lowercase__       ,      2	).split('.'	)[:2]	) for layer in up_blocks[i] if 'attn' in layer}	)
               __snake_case    			=	{
                   layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowercase__	)
               }

               if num_attn > 0:
                    for j in range(config['layers_per_block'] + 1	):
                         __snake_case    			=	{'old': f'up_blocks.{i}', 'new': f'up_blocks.{block_id}'}
                         __snake_case    			=	renew_attention_paths(attns[j]	)
                         assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[replace_indices]	)

     __snake_case    			=	{k.replace('mid_new_2'       ,      'mid_block'	): v for k, v in new_checkpoint.items()}
     return new_checkpoint





def      _a		(lowercase__ :      str       ,      lowercase__ :      str	) -> Dict:






     """simple docstring"""
     __snake_case    			=	{}

     __snake_case    			=	checkpoint['encoder.norm_out.weight']
     __snake_case    			=	checkpoint['encoder.norm_out.bias']

     __snake_case    			=	checkpoint['encoder.conv_in.weight']
     __snake_case    			=	checkpoint['encoder.conv_in.bias']
     __snake_case    			=	checkpoint['encoder.conv_out.weight']
     __snake_case    			=	checkpoint['encoder.conv_out.bias']

     __snake_case    			=	checkpoint['decoder.norm_out.weight']
     __snake_case    			=	checkpoint['decoder.norm_out.bias']

     __snake_case    			=	checkpoint['decoder.conv_in.weight']
     __snake_case    			=	checkpoint['decoder.conv_in.bias']
     __snake_case    			=	checkpoint['decoder.conv_out.weight']
     __snake_case    			=	checkpoint['decoder.conv_out.bias']

     __snake_case    			=	len({'.'.join(layer.split('.'	)[:3]	) for layer in checkpoint if 'down' in layer}	)
     __snake_case    			=	{
         layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowercase__	)
     }

     __snake_case    			=	len({'.'.join(layer.split('.'	)[:3]	) for layer in checkpoint if 'up' in layer}	)
     __snake_case    			=	{layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowercase__	)}

     for i in range(lowercase__	):
          __snake_case    			=	(i - 1) // (config['layers_per_block'] + 1)

          if any('downsample' in layer for layer in down_blocks[i]	):
               __snake_case    			=	checkpoint[
                   f'encoder.down.{i}.downsample.conv.weight'
               ]
               __snake_case    			=	checkpoint[
                   f'encoder.down.{i}.downsample.conv.bias'
               ]

          if any('block' in layer for layer in down_blocks[i]	):
               __snake_case    			=	len(
                   {'.'.join(shave_segments(lowercase__       ,      3	).split('.'	)[:3]	) for layer in down_blocks[i] if 'block' in layer}	)
               __snake_case    			=	{
                   layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                   for layer_id in range(lowercase__	)
               }

               if num_blocks > 0:
                    for j in range(config['layers_per_block']	):
                         __snake_case    			=	renew_resnet_paths(blocks[j]	)
                         assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__	)

          if any('attn' in layer for layer in down_blocks[i]	):
               __snake_case    			=	len(
                   {'.'.join(shave_segments(lowercase__       ,      3	).split('.'	)[:3]	) for layer in down_blocks[i] if 'attn' in layer}	)
               __snake_case    			=	{
                   layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                   for layer_id in range(lowercase__	)
               }

               if num_attn > 0:
                    for j in range(config['layers_per_block']	):
                         __snake_case    			=	renew_attention_paths(attns[j]	)
                         assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__	)

     __snake_case    			=	[key for key in checkpoint if 'mid.block_1' in key]
     __snake_case    			=	[key for key in checkpoint if 'mid.block_2' in key]
     __snake_case    			=	[key for key in checkpoint if 'mid.attn_1' in key]

     # Mid new 2
     __snake_case    			=	renew_resnet_paths(lowercase__	)
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,      )

     __snake_case    			=	renew_resnet_paths(lowercase__	)
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,      )

     __snake_case    			=	renew_attention_paths(lowercase__       ,      in_mid=lowercase__	)
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,      )

     for i in range(lowercase__	):
          __snake_case    			=	num_up_blocks - 1 - i

          if any('upsample' in layer for layer in up_blocks[i]	):
               __snake_case    			=	checkpoint[
                   f'decoder.up.{i}.upsample.conv.weight'
               ]
               __snake_case    			=	checkpoint[
                   f'decoder.up.{i}.upsample.conv.bias'
               ]

          if any('block' in layer for layer in up_blocks[i]	):
               __snake_case    			=	len(
                   {'.'.join(shave_segments(lowercase__       ,      3	).split('.'	)[:3]	) for layer in up_blocks[i] if 'block' in layer}	)
               __snake_case    			=	{
                   layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowercase__	)
               }

               if num_blocks > 0:
                    for j in range(config['layers_per_block'] + 1	):
                         __snake_case    			=	{'old': f'up_blocks.{i}', 'new': f'up_blocks.{block_id}'}
                         __snake_case    			=	renew_resnet_paths(blocks[j]	)
                         assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[replace_indices]	)

          if any('attn' in layer for layer in up_blocks[i]	):
               __snake_case    			=	len(
                   {'.'.join(shave_segments(lowercase__       ,      3	).split('.'	)[:3]	) for layer in up_blocks[i] if 'attn' in layer}	)
               __snake_case    			=	{
                   layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowercase__	)
               }

               if num_attn > 0:
                    for j in range(config['layers_per_block'] + 1	):
                         __snake_case    			=	{'old': f'up_blocks.{i}', 'new': f'up_blocks.{block_id}'}
                         __snake_case    			=	renew_attention_paths(attns[j]	)
                         assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[replace_indices]	)

     __snake_case    			=	{k.replace('mid_new_2'       ,      'mid_block'	): v for k, v in new_checkpoint.items()}
     __snake_case    			=	checkpoint['quant_conv.weight']
     __snake_case    			=	checkpoint['quant_conv.bias']
     if "quantize.embedding.weight" in checkpoint:
          __snake_case    			=	checkpoint['quantize.embedding.weight']
     __snake_case    			=	checkpoint['post_quant_conv.weight']
     __snake_case    			=	checkpoint['post_quant_conv.bias']

     return new_checkpoint


if __name__ == "__main__":
       _a      :      List[str]       				=      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.")

       _a      :      List[Any]       				=      parser.parse_args()
       _a      :      Optional[Any]       				=      torch.load(args.checkpoint_path)

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

       # unet case
       _a      :      List[str]       				=      {key.split(".")[0] for key in checkpoint.keys()}
       if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
              _a      :      Dict       				=      convert_vq_autoenc_checkpoint(checkpoint, config)
       else:
              _a      :      Optional[int]       				=      convert_ddpm_checkpoint(checkpoint, config)

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

       if config["_class_name"] == "VQModel":
              _a      :      List[str]       				=      VQModel(**config)
              model.load_state_dict(converted_checkpoint)
              model.save_pretrained(args.dump_path)
       elif config["_class_name"] == "AutoencoderKL":
              _a      :      Optional[int]       				=      AutoencoderKL(**config)
              model.load_state_dict(converted_checkpoint)
              model.save_pretrained(args.dump_path)
       else:
              _a      :      List[str]       				=      UNetaDModel(**config)
              model.load_state_dict(converted_checkpoint)

              _a      :      Optional[Any]       				=      DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

              _a      :      List[Any]       				=      DDPMPipeline(unet=model, scheduler=scheduler)
              pipe.save_pretrained(args.dump_path)








'''simple docstring'''




def      _a		(lowercase__ :      int	) -> int:






     """simple docstring"""
     return 1 if input_a == 0 else 0





def      _a		() -> None:






     """simple docstring"""
     assert not_gate(0	) == 1
     assert not_gate(1	) == 0


if __name__ == "__main__":
       print(not_gate(0))
       print(not_gate(1))




