





"""simple docstring"""





import argparse
import json

import torch

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




def 		lowerCAmelCase_     (  lowercase_       :							Optional[int]			,				lowercase_       :							List[Any]=1		):

 '''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 		lowerCAmelCase_     (  lowercase_       :							Dict			,				lowercase_       :							str=0		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[]
 for old_item in old_list:
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	old_item
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	new_item.replace('''block.'''			,				'''resnets.'''		)
  __SCREAMING_SNAKE_CASE			:      Dict 							=	new_item.replace('''conv_shorcut'''			,				'''conv1'''		)
  __SCREAMING_SNAKE_CASE			:      str 							=	new_item.replace('''in_shortcut'''			,				'''conv_shortcut'''		)
  __SCREAMING_SNAKE_CASE			:      int 							=	new_item.replace('''temb_proj'''			,				'''time_emb_proj'''		)

  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	shave_segments(lowercase_			,				n_shave_prefix_segments=lowercase_		)

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

 return mapping




def 		lowerCAmelCase_     (  lowercase_       :							Optional[int]			,				lowercase_       :							List[Any]=0			,				lowercase_       :							Optional[int]=False		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	[]
 for old_item in old_list:
  __SCREAMING_SNAKE_CASE			:      str 							=	old_item

  # In `model.mid`, the layer is called `attn`.
  if not in_mid:
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	new_item.replace('''attn'''			,				'''attentions'''		)
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	new_item.replace('''.k.'''			,				'''.key.'''		)
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	new_item.replace('''.v.'''			,				'''.value.'''		)
  __SCREAMING_SNAKE_CASE			:      Any 							=	new_item.replace('''.q.'''			,				'''.query.'''		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	new_item.replace('''proj_out'''			,				'''proj_attn'''		)
  __SCREAMING_SNAKE_CASE			:      Any 							=	new_item.replace('''norm'''			,				'''group_norm'''		)

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

 return mapping




def 		lowerCAmelCase_     (  lowercase_       :							List[str]			,				lowercase_       :							int			,				lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]=None			,				lowercase_       :							Any=None			,				lowercase_       :							List[Any]=None		):

 '''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():
   __SCREAMING_SNAKE_CASE			:      List[Any] 							=	old_checkpoint[path]
   __SCREAMING_SNAKE_CASE			:      int 							=	old_tensor.shape[0] // 3

   __SCREAMING_SNAKE_CASE			:      List[Any] 							=	(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

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

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

   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	query.reshape(lowercase_		).squeeze()
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	key.reshape(lowercase_		).squeeze()
   __SCREAMING_SNAKE_CASE			:      Any 							=	value.reshape(lowercase_		).squeeze()

 for path in paths:
  __SCREAMING_SNAKE_CASE			:      int 							=	path['''new''']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  __SCREAMING_SNAKE_CASE			:      List[str] 							=	new_path.replace('''down.'''			,				'''down_blocks.'''		)
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	new_path.replace('''up.'''			,				'''up_blocks.'''		)

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

  if "attentions" in new_path:
   __SCREAMING_SNAKE_CASE			:      Dict 							=	old_checkpoint[path['''old''']].squeeze()
  else:
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	old_checkpoint[path['''old''']]




def 		lowerCAmelCase_     (  lowercase_       :							int			,				lowercase_       :							Optional[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	{}

 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''temb.dense.0.weight''']
 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''temb.dense.0.bias''']
 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''temb.dense.1.weight''']
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint['''temb.dense.1.bias''']

 __SCREAMING_SNAKE_CASE			:      Dict 							=	checkpoint['''norm_out.weight''']
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	checkpoint['''norm_out.bias''']

 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''conv_in.weight''']
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	checkpoint['''conv_in.bias''']
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	checkpoint['''conv_out.weight''']
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	checkpoint['''conv_out.bias''']

 __SCREAMING_SNAKE_CASE			:      List[str] 							=	len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''down''' in layer}		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase_		)
 }

 __SCREAMING_SNAKE_CASE			:      str 							=	len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''up''' in layer}		)
 __SCREAMING_SNAKE_CASE			:      str 							=	{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_		):
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	(i - 1) // (config['''layers_per_block'''] + 1)

  if any('''downsample''' in layer for layer in down_blocks[i]		):
   __SCREAMING_SNAKE_CASE			:      Dict 							=	checkpoint[
       F'''down.{i}.downsample.op.weight'''
   ]
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	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]		):
   __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				2		).split('''.'''		)[:2]		) for layer in down_blocks[i] if '''block''' in layer}		)
   __SCREAMING_SNAKE_CASE			:      str 							=	{
       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''']		):
     __SCREAMING_SNAKE_CASE			:      Tuple 							=	renew_resnet_paths(blocks[j]		)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_		)

  if any('''attn''' in layer for layer in down_blocks[i]		):
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				2		).split('''.'''		)[:2]		) for layer in down_blocks[i] if '''attn''' in layer}		)
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	{
       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''']		):
     __SCREAMING_SNAKE_CASE			:      str 							=	renew_attention_paths(attns[j]		)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_			,				config=lowercase_		)

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	[key for key in checkpoint if '''mid.block_1''' in key]
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	[key for key in checkpoint if '''mid.block_2''' in key]
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	[key for key in checkpoint if '''mid.attn_1''' in key]

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

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

 __SCREAMING_SNAKE_CASE			:      Any 							=	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_		):
  __SCREAMING_SNAKE_CASE			:      str 							=	num_up_blocks - 1 - i

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

  if any('''block''' in layer for layer in up_blocks[i]		):
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				2		).split('''.'''		)[:2]		) for layer in up_blocks[i] if '''block''' in layer}		)
   __SCREAMING_SNAKE_CASE			:      Any 							=	{
       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		):
     __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	{'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     __SCREAMING_SNAKE_CASE			:      Dict 							=	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]		):
   __SCREAMING_SNAKE_CASE			:      str 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				2		).split('''.'''		)[:2]		) for layer in up_blocks[i] if '''attn''' in layer}		)
   __SCREAMING_SNAKE_CASE			:      Any 							=	{
       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		):
     __SCREAMING_SNAKE_CASE			:      Tuple 							=	{'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     __SCREAMING_SNAKE_CASE			:      List[Any] 							=	renew_attention_paths(attns[j]		)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[replace_indices]		)

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




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	{}

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint['''encoder.norm_out.weight''']
 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''encoder.norm_out.bias''']

 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	checkpoint['''encoder.conv_in.weight''']
 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''encoder.conv_in.bias''']
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	checkpoint['''encoder.conv_out.weight''']
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	checkpoint['''encoder.conv_out.bias''']

 __SCREAMING_SNAKE_CASE			:      Any 							=	checkpoint['''decoder.norm_out.weight''']
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint['''decoder.norm_out.bias''']

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	checkpoint['''decoder.conv_in.weight''']
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	checkpoint['''decoder.conv_in.bias''']
 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''decoder.conv_out.weight''']
 __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint['''decoder.conv_out.bias''']

 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	len({'''.'''.join(layer.split('''.'''		)[:3]		) for layer in checkpoint if '''down''' in layer}		)
 __SCREAMING_SNAKE_CASE			:      int 							=	{
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase_		)
 }

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	len({'''.'''.join(layer.split('''.'''		)[:3]		) for layer in checkpoint if '''up''' in layer}		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{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_		):
  __SCREAMING_SNAKE_CASE			:      int 							=	(i - 1) // (config['''layers_per_block'''] + 1)

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

  if any('''block''' in layer for layer in down_blocks[i]		):
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				3		).split('''.'''		)[:3]		) for layer in down_blocks[i] if '''block''' in layer}		)
   __SCREAMING_SNAKE_CASE			:      str 							=	{
       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''']		):
     __SCREAMING_SNAKE_CASE			:      Any 							=	renew_resnet_paths(blocks[j]		)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_		)

  if any('''attn''' in layer for layer in down_blocks[i]		):
   __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				3		).split('''.'''		)[:3]		) for layer in down_blocks[i] if '''attn''' in layer}		)
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	{
       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''']		):
     __SCREAMING_SNAKE_CASE			:      Tuple 							=	renew_attention_paths(attns[j]		)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_			,				config=lowercase_		)

 __SCREAMING_SNAKE_CASE			:      Any 							=	[key for key in checkpoint if '''mid.block_1''' in key]
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	[key for key in checkpoint if '''mid.block_2''' in key]
 __SCREAMING_SNAKE_CASE			:      Any 							=	[key for key in checkpoint if '''mid.attn_1''' in key]

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

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	renew_resnet_paths(lowercase_		)
 assign_to_checkpoint(
     lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]			,				)

 __SCREAMING_SNAKE_CASE			:      List[str] 							=	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_		):
  __SCREAMING_SNAKE_CASE			:      str 							=	num_up_blocks - 1 - i

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

  if any('''block''' in layer for layer in up_blocks[i]		):
   __SCREAMING_SNAKE_CASE			:      Any 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				3		).split('''.'''		)[:3]		) for layer in up_blocks[i] if '''block''' in layer}		)
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	{
       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		):
     __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     __SCREAMING_SNAKE_CASE			:      Tuple 							=	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]		):
   __SCREAMING_SNAKE_CASE			:      Any 							=	len(
       {'''.'''.join(shave_segments(lowercase_			,				3		).split('''.'''		)[:3]		) for layer in up_blocks[i] if '''attn''' in layer}		)
   __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	{
       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		):
     __SCREAMING_SNAKE_CASE			:      List[str] 							=	{'''old''': F'''up_blocks.{i}''', '''new''': F'''up_blocks.{block_id}'''}
     __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	renew_attention_paths(attns[j]		)
     assign_to_checkpoint(lowercase_			,				lowercase_			,				lowercase_			,				additional_replacements=[replace_indices]		)

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

 return new_checkpoint


if __name__ == "__main__":
    _lowerCamelCase  					=     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.''')

    _lowerCamelCase  					=     parser.parse_args()
    _lowerCamelCase  					=     torch.load(args.checkpoint_path)

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

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

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

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

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

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








"""simple docstring"""





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Union[str, Any]=False			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Dict="data2vec."		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''backbone.''' if is_semantic else ''''''

 __SCREAMING_SNAKE_CASE			:      Any 							=	[]
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
         (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
         (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('''mask_token''', F'''{hf_prefix}embeddings.mask_token'''),
          (
              '''rel_pos_bias.relative_position_bias_table''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
          ),
          (
              '''rel_pos_bias.relative_position_index''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
          ),
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
          ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
          ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
          ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''fc_norm.weight''', F'''{hf_prefix}pooler.layernorm.weight'''),
          ('''fc_norm.bias''', F'''{hf_prefix}pooler.layernorm.bias'''),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]		)

 return rename_keys




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Union[str, Any]			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Any=False			,				lowercase_       :							Dict="data2vec_vision."		):

 '''simple docstring'''






 for i in range(config.num_hidden_layers		):
  __SCREAMING_SNAKE_CASE			:      int 							=	'''backbone.''' if is_semantic else ''''''
  # queries, keys and values
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	in_proj_weight[
      : config.hidden_size, :
  ]
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	q_bias
  __SCREAMING_SNAKE_CASE			:      str 							=	in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	in_proj_weight[
      -config.hidden_size :, :
  ]
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
  __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	gamma_a
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   __SCREAMING_SNAKE_CASE			:      Dict 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
   __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

   __SCREAMING_SNAKE_CASE			:      List[str] 							=	table
   __SCREAMING_SNAKE_CASE			:      Any 							=	index




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[str] 							=	argparse.ArgumentParser(
     '''Convert Data2VecVision to HF for image classification and pretraining'''			,				add_help=lowercase_		)
 parser.add_argument('''--hf_checkpoint_name'''			,				type=lowercase_		)
 parser.add_argument('''--input_size'''			,				default=224			,				type=lowercase_			,				help='''images input size'''		)
 parser.add_argument('''--beit_checkpoint'''			,				default=''''''			,				help='''beit checkpoint'''		)

 return parser.parse_args()




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






 def load_state_dict(lowercase_       :							Optional[Any]			,				lowercase_       :							int			,				lowercase_       :							Union[str, Any]=""			,				lowercase_       :							Optional[int]="relative_position_index"		):
  __SCREAMING_SNAKE_CASE			:      Any 							=	[]
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  # copy state_dict so _load_from_state_dict can modify it
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	getattr(lowercase_			,				'''_metadata'''			,				lowercase_		)
  __SCREAMING_SNAKE_CASE			:      str 							=	state_dict.copy()
  if metadata is not None:
   __SCREAMING_SNAKE_CASE			:      int 							=	metadata

  def load(lowercase_       :							Any			,				lowercase_       :							Optional[Any]=""		):
   __SCREAMING_SNAKE_CASE			:      Dict 							=	{} if metadata is None else metadata.get(prefix[:-1]			,				{}		)
   module._load_from_state_dict(
       lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_		)
   for name, child in module._modules.items():
    if child is not None:
     load(lowercase_			,				prefix + name + '''.'''		)

  load(lowercase_			,				prefix=lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  for key in missing_keys:
   __SCREAMING_SNAKE_CASE			:      Dict 							=	True
   for ignore_key in ignore_missing.split('''|'''		):
    if ignore_key in key:
     __SCREAMING_SNAKE_CASE			:      Dict 							=	False
     break
   if keep_flag:
    warn_missing_keys.append(lowercase_		)
   else:
    ignore_missing_keys.append(lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Tuple 							=	warn_missing_keys

  if len(lowercase_		) > 0:
   print(
       '''Weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print(
       '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''\n'''.join(lowercase_		)		)

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	{
     '''pretrained''': False,
     '''use_shared_rel_pos_bias''': True,
     '''use_abs_pos_emb''': False,
     '''init_values''': 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          '''num_classes''': 1000,
          '''use_mean_pooling''': True,
          '''init_scale''': 0.001,
          '''use_rel_pos_bias''': True,
      }		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	create_model(
     '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''			,				**lowercase_			,				)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	model.patch_embed.patch_size
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	(args.input_size // patch_size[0], args.input_size // patch_size[1])
 __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.load(args.beit_checkpoint			,				map_location='''cpu'''		)

 print(F'''Load ckpt from {args.beit_checkpoint}'''		)
 __SCREAMING_SNAKE_CASE			:      Any 							=	None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint[model_key]
   print(F'''Load state_dict by model_key = {model_key}'''		)
   break

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	list(checkpoint_model.keys()		)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(lowercase_		)

  if "relative_position_bias_table" in key:
   __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint_model[key]
   __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	rel_pos_bias.size()
   __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	model.state_dict()[key].size()
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(lowercase_			,				lowercase_			,				prefix=''''''		)

 return model




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	get_args()

 __SCREAMING_SNAKE_CASE			:      int 							=	'''ft1k''' in args.hf_checkpoint_name
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''large''' in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionConfig()
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      int 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False
  __SCREAMING_SNAKE_CASE			:      Dict 							=	True
  __SCREAMING_SNAKE_CASE			:      int 							=	1000

  __SCREAMING_SNAKE_CASE			:      Any 							=	'''huggingface/label-files'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	'''imagenet-1k-id2label.json'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	json.load(open(hf_hub_download(lowercase_			,				lowercase_			,				repo_type='''dataset'''		)			,				'''r'''		)		)
  __SCREAMING_SNAKE_CASE			:      Dict 							=	{int(lowercase_		): v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	idalabel
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	{v: k for k, v in idalabel.items()}
 else:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False

 if is_large:
  __SCREAMING_SNAKE_CASE			:      str 							=	1024
  __SCREAMING_SNAKE_CASE			:      Any 							=	4096
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	24
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	16

 # 2. Load Beit model
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	load_beit_model(lowercase_			,				lowercase_			,				lowercase_		)
 orig_model.eval()

 # 3. Forward Beit model
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	BeitImageProcessor(size=config.image_size			,				do_center_crop=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_processor(images=lowercase_			,				return_tensors='''pt'''		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	encoding['''pixel_values''']

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	orig_model(*lowercase_		)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionForImageClassification(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      Any 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''data2vec_vision.'''
 else:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionModel(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      str 							=	True
  __SCREAMING_SNAKE_CASE			:      Any 							=	''''''

 __SCREAMING_SNAKE_CASE			:      Dict 							=	create_rename_keys(lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	orig_model.state_dict()
 for src, dest in rename_keys:
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(lowercase_		)
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	val

 read_in_q_k_v(lowercase_			,				lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model.load_state_dict(lowercase_			,				strict=lowercase_		)
 print('''HF missing'''			,				lowercase_		)
 print('''HF unexpected_keys'''			,				lowercase_		)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hf_model(lowercase_		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.max(torch.abs(hf_output - orig_model_output		)		).item()

 print(F'''max_absolute_diff = {max_absolute_diff}'''		)
 __SCREAMING_SNAKE_CASE			:      str 							=	torch.allclose(lowercase_			,				lowercase_			,				atol=1E-3		)
 print('''Do both models output the same tensors?'''			,				'''🔥''' if success else '''💩'''		)
 if not success:
  raise Exception('''Something went wRoNg'''		)

 # 7. Save
 print(F'''Saving to {args.hf_checkpoint_name}'''		)
 hf_model.save_pretrained(args.hf_checkpoint_name		)
 image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
    main()
    # Run the following to convert checkpoints
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

