






import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def 		_a					(   a				:Tuple			,							a				:Union[str, Any]=1				)							->    int:







 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					(   a				:Union[str, Any]			,							a				:str=0				)							->    str:
 a							=   []
 for old_item in old_list:
  a							=   old_item
  a							=   new_item.replace('''block.'''			,							'''resnets.'''				)
  a							=   new_item.replace('''conv_shorcut'''			,							'''conv1'''				)
  a							=   new_item.replace('''in_shortcut'''			,							'''conv_shortcut'''				)
  a							=   new_item.replace('''temb_proj'''			,							'''time_emb_proj'''				)

  a							=   shave_segments(a			,							n_shave_prefix_segments=a				)

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

 return mapping
def 		_a					(   a				:List[str]			,							a				:Tuple=0			,							a				:Union[str, Any]=False				)							->    Optional[Any]:
 a							=   []
 for old_item in old_list:
  a							=   old_item

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

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

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

 return mapping
def 		_a					(   a				:Optional[int]			,							a				:Optional[int]			,							a				:List[str]			,							a				:Tuple=None			,							a				:int=None			,							a				:int=None				)							->    Dict:
 assert isinstance(a			,							a				), "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():
   a							=   old_checkpoint[path]
   a							=   old_tensor.shape[0] // 3

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

   a							=   old_tensor.shape[0] // config.get('''num_head_channels'''			,							1				) // 3

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

   a							=   query.reshape(a				).squeeze()
   a							=   key.reshape(a				).squeeze()
   a							=   value.reshape(a				).squeeze()

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

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

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

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

  if "attentions" in new_path:
   a							=   old_checkpoint[path['''old''']].squeeze()
  else:
   a							=   old_checkpoint[path['''old''']]
def 		_a					(   a				:Optional[int]			,							a				:Union[str, Any]				)							->    Optional[Any]:
 a							=   {}

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

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

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

 a							=   len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in checkpoint if '''down''' in layer}				)
 a							=   {
     layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a				)
 }

 a							=   len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in checkpoint if '''up''' in layer}				)
 a							=   {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(a				)}

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

  if any('''downsample''' in layer for layer in down_blocks[i]				):
   a							=   checkpoint[
       F"""down.{i}.downsample.op.weight"""
   ]
   a							=   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]				):
   a							=   len(
       {'''.'''.join(shave_segments(a			,							2				).split('''.'''				)[:2]				) for layer in down_blocks[i] if '''block''' in layer}				)
   a							=   {
       layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
       for layer_id in range(a				)
   }

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

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

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

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

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

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

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

 for i in range(a				):
  a							=   num_up_blocks - 1 - i

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

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

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

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

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

 a							=   {k.replace('''mid_new_2'''			,							'''mid_block'''				): v for k, v in new_checkpoint.items()}
 return new_checkpoint
def 		_a					(   a				:Union[str, Any]			,							a				:Any				)							->    Dict:
 a							=   {}

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

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

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

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

 a							=   len({'''.'''.join(layer.split('''.'''				)[:3]				) for layer in checkpoint if '''down''' in layer}				)
 a							=   {
     layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a				)
 }

 a							=   len({'''.'''.join(layer.split('''.'''				)[:3]				) for layer in checkpoint if '''up''' in layer}				)
 a							=   {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(a				)}

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

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

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

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

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

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

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

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

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

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

 for i in range(a				):
  a							=   num_up_blocks - 1 - i

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

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

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

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

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

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

 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())

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

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

     if config["_class_name"] == "VQModel":
          UpperCAmelCase__            =	VQModel(**config)
          model.load_state_dict(converted_checkpoint)
          model.save_pretrained(args.dump_path)
     elif config["_class_name"] == "AutoencoderKL":
          UpperCAmelCase__            =	AutoencoderKL(**config)
          model.load_state_dict(converted_checkpoint)
          model.save_pretrained(args.dump_path)
     else:
          UpperCAmelCase__            =	UNetaDModel(**config)
          model.load_state_dict(converted_checkpoint)

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

          UpperCAmelCase__            =	DDPMPipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)










def 		_a					(   a				:list				)							->    list:
 for i, _ in enumerate(a				):
  if (i % 2 == 1) == (nums[i - 1] > nums[i]):
   a						,  a							=   nums[i], nums[i - 1]

 return nums


if __name__ == "__main__":
     print("Enter the array elements:")
     UpperCAmelCase__            =	list(map(int, input().split()))
     print("The unsorted array is:")
     print(array)
     print("Array after Wiggle sort:")
     print(wiggle_sort(array))


