

import argparse

import torch

from diffusers import UNetaDConditionModel







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[Any]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      List[str]=None   , SCREAMING_SNAKE_CASE		:      int=None   , SCREAMING_SNAKE_CASE		:      Any=None		)   ->   str:

 """simple docstring"""






 assert isinstance(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		), "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():
   __lowerCAmelCase:       List[str]            =				old_checkpoint[path]
   __lowerCAmelCase:       Tuple            =				old_tensor.shape[0] // 3

   __lowerCAmelCase:       int            =				(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

   __lowerCAmelCase:       Tuple            =				old_tensor.shape[0] // config['num_head_channels'] // 3

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

   __lowerCAmelCase:       Optional[int]            =				query.reshape(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       str            =				key.reshape(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       List[Any]            =				value.reshape(SCREAMING_SNAKE_CASE		)

 for path in paths:
  __lowerCAmelCase:       Optional[int]            =				path['new']

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
  __lowerCAmelCase:       List[str]            =				old_checkpoint[path['old']]
  __lowerCAmelCase:       str            =				['proj_attn.weight']
  __lowerCAmelCase:       Any            =				['proj_out.weight', 'proj_in.weight']
  if any(k in new_path for k in names		):
   __lowerCAmelCase:       Any            =				weight[:, :, 0]
  elif any(k in new_path for k in names_a		) and len(weight.shape		) > 2 and ".attentions." not in new_path:
   __lowerCAmelCase:       Tuple            =				weight[:, :, 0]
  else:
   __lowerCAmelCase:       Tuple            =				weight







def _a			(	SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      List[Any]=0		)   ->   List[Any]:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				[]
 for old_item in old_list:
  __lowerCAmelCase:       Any            =				old_item

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

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

  #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

 return mapping







def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      List[str]=1		)   ->   str:

 """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			(	SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      Any=0		)   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				[]
 for old_item in old_list:
  mapping.append({'old': old_item, 'new': old_item}		)

 return mapping







def _a			(	SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      Any=0		)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				[]
 for old_item in old_list:
  __lowerCAmelCase:       Tuple            =				old_item.replace('in_layers.0'   , 'norm1'		)
  __lowerCAmelCase:       List[Any]            =				new_item.replace('in_layers.2'   , 'conv1'		)

  __lowerCAmelCase:       int            =				new_item.replace('out_layers.0'   , 'norm2'		)
  __lowerCAmelCase:       Optional[int]            =				new_item.replace('out_layers.3'   , 'conv2'		)

  __lowerCAmelCase:       List[Any]            =				new_item.replace('emb_layers.1'   , 'time_emb_proj'		)
  __lowerCAmelCase:       Dict            =				new_item.replace('skip_connection'   , 'conv_shortcut'		)

  __lowerCAmelCase:       Dict            =				shave_segments(SCREAMING_SNAKE_CASE   , n_shave_prefix_segments=SCREAMING_SNAKE_CASE		)

  if "temopral_conv" not in old_item:
   mapping.append({'old': old_item, 'new': new_item}		)

 return mapping







def _a			(	SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      Dict=None   , SCREAMING_SNAKE_CASE		:      Tuple=False		)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase:       Any            =				{}
 __lowerCAmelCase:       Dict            =				list(checkpoint.keys()		)

 __lowerCAmelCase:       Optional[Any]            =				'model.diffusion_model.'

 # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
 if sum(k.startswith('model_ema'		) for k in keys		) > 1_00 and extract_ema:
  print(f'''Checkpoint {path} has both EMA and non-EMA weights.'''		)
  print(
      'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
      ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'		)
  for key in keys:
   if key.startswith('model.diffusion_model'		):
    __lowerCAmelCase:       List[Any]            =				'model_ema.' + ''.join(key.split('.'		)[1:]		)
    __lowerCAmelCase:       Tuple            =				checkpoint.pop(SCREAMING_SNAKE_CASE		)
 else:
  if sum(k.startswith('model_ema'		) for k in keys		) > 1_00:
   print(
       'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
       ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'		)

  for key in keys:
   __lowerCAmelCase:       int            =				checkpoint.pop(SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       Optional[int]            =				{}

 __lowerCAmelCase:       Any            =				unet_state_dict['time_embed.0.weight']
 __lowerCAmelCase:       Optional[int]            =				unet_state_dict['time_embed.0.bias']
 __lowerCAmelCase:       Optional[Any]            =				unet_state_dict['time_embed.2.weight']
 __lowerCAmelCase:       List[Any]            =				unet_state_dict['time_embed.2.bias']

 if config["class_embed_type"] is None:
  # No parameters to port
  ...
 elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  __lowerCAmelCase:       List[Any]            =				unet_state_dict['label_emb.0.0.weight']
  __lowerCAmelCase:       List[str]            =				unet_state_dict['label_emb.0.0.bias']
  __lowerCAmelCase:       int            =				unet_state_dict['label_emb.0.2.weight']
  __lowerCAmelCase:       Union[str, Any]            =				unet_state_dict['label_emb.0.2.bias']
 else:
  raise NotImplementedError(f'''Not implemented `class_embed_type`: {config['class_embed_type']}'''		)

 __lowerCAmelCase:       List[Any]            =				unet_state_dict['input_blocks.0.0.weight']
 __lowerCAmelCase:       str            =				unet_state_dict['input_blocks.0.0.bias']

 __lowerCAmelCase:       Union[str, Any]            =				[v for v in unet_state_dict if v.startswith('input_blocks.0.1'		)]
 __lowerCAmelCase:       Optional[int]            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       List[Any]            =				{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
 assign_to_checkpoint(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       str            =				unet_state_dict['out.0.weight']
 __lowerCAmelCase:       Optional[Any]            =				unet_state_dict['out.0.bias']
 __lowerCAmelCase:       Optional[Any]            =				unet_state_dict['out.2.weight']
 __lowerCAmelCase:       Any            =				unet_state_dict['out.2.bias']

 # Retrieves the keys for the input blocks only
 __lowerCAmelCase:       List[Any]            =				len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'input_blocks' in layer}		)
 __lowerCAmelCase:       Any            =				{
     layer_id: [key for key in unet_state_dict if f'''input_blocks.{layer_id}''' in key]
     for layer_id in range(SCREAMING_SNAKE_CASE		)
 }

 # Retrieves the keys for the middle blocks only
 __lowerCAmelCase:       Optional[Any]            =				len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'middle_block' in layer}		)
 __lowerCAmelCase:       List[Any]            =				{
     layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
     for layer_id in range(SCREAMING_SNAKE_CASE		)
 }

 # Retrieves the keys for the output blocks only
 __lowerCAmelCase:       Union[str, Any]            =				len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'output_blocks' in layer}		)
 __lowerCAmelCase:       Optional[Any]            =				{
     layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
     for layer_id in range(SCREAMING_SNAKE_CASE		)
 }

 for i in range(1   , SCREAMING_SNAKE_CASE		):
  __lowerCAmelCase:       List[str]            =				(i - 1) // (config['layers_per_block'] + 1)
  __lowerCAmelCase:       Optional[Any]            =				(i - 1) % (config['layers_per_block'] + 1)

  __lowerCAmelCase:       str            =				[
      key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key and f'''input_blocks.{i}.0.op''' not in key
  ]
  __lowerCAmelCase:       Union[str, Any]            =				[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
  __lowerCAmelCase:       List[str]            =				[key for key in input_blocks[i] if f'''input_blocks.{i}.2''' in key]

  if f'''input_blocks.{i}.op.weight''' in unet_state_dict:
   __lowerCAmelCase:       Any            =				unet_state_dict.pop(
       f'''input_blocks.{i}.op.weight'''		)
   __lowerCAmelCase:       Dict            =				unet_state_dict.pop(
       f'''input_blocks.{i}.op.bias'''		)

  __lowerCAmelCase:       Dict            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       Union[str, Any]            =				{'old': f'''input_blocks.{i}.0''', 'new': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
  assign_to_checkpoint(
      SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

  __lowerCAmelCase:       List[Any]            =				[key for key in resnets if 'temopral_conv' in key]
  __lowerCAmelCase:       Dict            =				renew_temp_conv_paths(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       Optional[int]            =				{
      'old': f'''input_blocks.{i}.0.temopral_conv''',
      'new': f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
  }
  assign_to_checkpoint(
      SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

  if len(SCREAMING_SNAKE_CASE		):
   __lowerCAmelCase:       Tuple            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       List[str]            =				{'old': f'''input_blocks.{i}.1''', 'new': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

  if len(SCREAMING_SNAKE_CASE		):
   __lowerCAmelCase:       Any            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       Any            =				{
       'old': f'''input_blocks.{i}.2''',
       'new': f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
   }
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       List[Any]            =				middle_blocks[0]
 __lowerCAmelCase:       List[Any]            =				[key for key in resnet_a if 'temopral_conv' in key]
 __lowerCAmelCase:       int            =				middle_blocks[1]
 __lowerCAmelCase:       Dict            =				middle_blocks[2]
 __lowerCAmelCase:       str            =				middle_blocks[3]
 __lowerCAmelCase:       List[Any]            =				[key for key in resnet_a if 'temopral_conv' in key]

 __lowerCAmelCase:       Optional[int]            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[Any]            =				{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]		)

 __lowerCAmelCase:       Tuple            =				renew_temp_conv_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       List[str]            =				{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]		)

 __lowerCAmelCase:       Dict            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       str            =				{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]		)

 __lowerCAmelCase:       Union[str, Any]            =				renew_temp_conv_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Dict            =				{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]		)

 __lowerCAmelCase:       List[Any]            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Union[str, Any]            =				{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       Tuple            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Tuple            =				{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

 for i in range(SCREAMING_SNAKE_CASE		):
  __lowerCAmelCase:       int            =				i // (config['layers_per_block'] + 1)
  __lowerCAmelCase:       str            =				i % (config['layers_per_block'] + 1)
  __lowerCAmelCase:       str            =				[shave_segments(SCREAMING_SNAKE_CASE   , 2		) for name in output_blocks[i]]
  __lowerCAmelCase:       Optional[Any]            =				{}

  for layer in output_block_layers:
   __lowerCAmelCase     ,							__lowerCAmelCase:       int            =				layer.split('.'		)[0], shave_segments(SCREAMING_SNAKE_CASE   , 1		)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(SCREAMING_SNAKE_CASE		)
   else:
    __lowerCAmelCase:       Any            =				[layer_name]

  if len(SCREAMING_SNAKE_CASE		) > 1:
   __lowerCAmelCase:       Optional[int]            =				[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
   __lowerCAmelCase:       str            =				[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
   __lowerCAmelCase:       str            =				[key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

   __lowerCAmelCase:       List[str]            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       str            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)

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

   __lowerCAmelCase:       Union[str, Any]            =				[key for key in resnets if 'temopral_conv' in key]
   __lowerCAmelCase:       Dict            =				renew_temp_conv_paths(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       Dict            =				{
       'old': f'''output_blocks.{i}.0.temopral_conv''',
       'new': f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
   }
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

   __lowerCAmelCase:       Dict            =				{k: sorted(SCREAMING_SNAKE_CASE		) for k, v in output_block_list.items()}
   if ["conv.bias", "conv.weight"] in output_block_list.values():
    __lowerCAmelCase:       List[Any]            =				list(output_block_list.values()		).index(['conv.bias', 'conv.weight']		)
    __lowerCAmelCase:       List[str]            =				unet_state_dict[
        f'''output_blocks.{i}.{index}.conv.weight'''
    ]
    __lowerCAmelCase:       Tuple            =				unet_state_dict[
        f'''output_blocks.{i}.{index}.conv.bias'''
    ]

    # Clear attentions as they have been attributed above.
    if len(SCREAMING_SNAKE_CASE		) == 2:
     __lowerCAmelCase:       Any            =				[]

   if len(SCREAMING_SNAKE_CASE		):
    __lowerCAmelCase:       List[str]            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
    __lowerCAmelCase:       Tuple            =				{
        'old': f'''output_blocks.{i}.1''',
        'new': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
    }
    assign_to_checkpoint(
        SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

   if len(SCREAMING_SNAKE_CASE		):
    __lowerCAmelCase:       int            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
    __lowerCAmelCase:       int            =				{
        'old': f'''output_blocks.{i}.2''',
        'new': f'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
    }
    assign_to_checkpoint(
        SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)
  else:
   __lowerCAmelCase:       Dict            =				renew_resnet_paths(SCREAMING_SNAKE_CASE   , n_shave_prefix_segments=1		)
   for path in resnet_0_paths:
    __lowerCAmelCase:       Dict            =				'.'.join(['output_blocks', str(SCREAMING_SNAKE_CASE		), path['old']]		)
    __lowerCAmelCase:       Dict            =				'.'.join(['up_blocks', str(SCREAMING_SNAKE_CASE		), 'resnets', str(SCREAMING_SNAKE_CASE		), path['new']]		)
    __lowerCAmelCase:       int            =				unet_state_dict[old_path]

   __lowerCAmelCase:       str            =				[l for l in output_block_layers if 'temopral_conv' in l]
   for path in temopral_conv_paths:
    __lowerCAmelCase:       Optional[Any]            =				path.split('temopral_conv.'		)[-1]
    __lowerCAmelCase:       str            =				'.'.join(['output_blocks', str(SCREAMING_SNAKE_CASE		), str(SCREAMING_SNAKE_CASE		), 'temopral_conv', pruned_path]		)
    __lowerCAmelCase:       List[str]            =				'.'.join(['up_blocks', str(SCREAMING_SNAKE_CASE		), 'temp_convs', str(SCREAMING_SNAKE_CASE		), pruned_path]		)
    __lowerCAmelCase:       Any            =				unet_state_dict[old_path]

 return new_checkpoint


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

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

  _a			=       torch.load(args.checkpoint_path, map_location='''cpu''')
  _a			=       UNetaDConditionModel()

  _a			=       convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

  _a			=       set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  _a			=       set(converted_ckpt.keys()) - set(unet.state_dict().keys())

  assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

  # load state_dict
  unet.load_state_dict(converted_ckpt)

  unet.save_pretrained(args.dump_path)

  # -- finish converting the unet --








from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any






class 							A_			:
     _lowercase      :					Any       =     ('_front', '_back', '_len')






     @dataclass
     class 							A_			:
          _lowercase      :					Any       =     None
          _lowercase      :					Deque._Node | None       =     None
          _lowercase      :					Deque._Node | None       =     None







     class 							A_			:
          _lowercase      :					Optional[Any]       =     ('_cur',)


          def __init__(							self      :		Any      ,  UpperCAmelCase      :		Deque._Node | None )							->     None:
           __lowerCAmelCase:       int            =				cur


          def __iter__(							self      :		int )							->     Deque._Iterator:
           return self


          def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Any:
           if self._cur is None:
            # finished iterating
            raise StopIteration
           __lowerCAmelCase:       Optional[Any]            =				self._cur.val
           __lowerCAmelCase:       List[Any]            =				self._cur.next_node

           return val







     def __init__(							self      :		Any      ,  UpperCAmelCase      :		Iterable[Any] | None = None )							->     None:
      __lowerCAmelCase:       Any            =				None
      __lowerCAmelCase:       Any            =				None
      __lowerCAmelCase:       int            =				0

      if iterable is not None:
       # append every value to the deque
       for val in iterable:
        self.append(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Any )							->     None:
      __lowerCAmelCase:       Union[str, Any]            =				self._Node(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
      if self.is_empty():
       # front = back
       __lowerCAmelCase:       int            =				node
       __lowerCAmelCase:       List[Any]            =				1
      else:
       # connect nodes
       __lowerCAmelCase:       Optional[Any]            =				node
       __lowerCAmelCase:       str            =				self._back
       __lowerCAmelCase:       Optional[int]            =				node  # assign new back to the new node

       self._len += 1

       # make sure there were no errors
       assert not self.is_empty(), "Error on appending value."


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Any )							->     None:
      __lowerCAmelCase:       int            =				self._Node(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
      if self.is_empty():
       # front = back
       __lowerCAmelCase:       Union[str, Any]            =				node
       __lowerCAmelCase:       str            =				1
      else:
       # connect nodes
       __lowerCAmelCase:       Dict            =				self._front
       __lowerCAmelCase:       Optional[int]            =				node
       __lowerCAmelCase:       Any            =				node  # assign new front to the new node

       self._len += 1

       # make sure there were no errors
       assert not self.is_empty(), "Error on appending value."


     def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  UpperCAmelCase      :		Iterable[Any] )							->     None:
      for val in iterable:
       self.append(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Iterable[Any] )							->     None:
      for val in iterable:
       self.appendleft(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Dict )							->     Any:
      assert not self.is_empty(), "Deque is empty."

      __lowerCAmelCase:       Optional[int]            =				self._back
      __lowerCAmelCase:       Optional[Any]            =				self._back.prev_node  # set new back
      # drop the last node - python will deallocate memory automatically
      __lowerCAmelCase:       Optional[int]            =				None

      self._len -= 1

      return topop.val


     def   UpperCAmelCase  (							self      :		Dict )							->     Any:
      assert not self.is_empty(), "Deque is empty."

      __lowerCAmelCase:       Optional[Any]            =				self._front
      __lowerCAmelCase:       Optional[Any]            =				self._front.next_node  # set new front and drop the first node
      __lowerCAmelCase:       int            =				None

      self._len -= 1

      return topop.val


     def   UpperCAmelCase  (							self      :		Dict )							->     bool:
      return self._front is None


     def __len__(							self      :		List[Any] )							->     int:
      return self._len


     def __eq__(							self      :		Union[str, Any]      ,  UpperCAmelCase      :		object )							->     bool:

      if not isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       return NotImplemented

      __lowerCAmelCase:       List[Any]            =				self._front
      __lowerCAmelCase:       Dict            =				other._front

      # if the length of the dequeues are not the same, they are not equal
      if len(self ) != len(UpperCAmelCase ):
       return False

      while me is not None and oth is not None:
       # compare every value
       if me.val != oth.val:
        return False
       __lowerCAmelCase:       Optional[Any]            =				me.next_node
       __lowerCAmelCase:       Optional[Any]            =				oth.next_node

      return True


     def __iter__(							self      :		Tuple )							->     Deque._Iterator:
      return Deque._Iterator(self._front )


     def __repr__(							self      :		Optional[Any] )							->     str:
      __lowerCAmelCase:       Union[str, Any]            =				[]
      __lowerCAmelCase:       Optional[int]            =				self._front
      while aux is not None:
       # append the values in a list to display
       values_list.append(aux.val )
       __lowerCAmelCase:       Any            =				aux.next_node

      return F'''[{', '.join(repr(UpperCAmelCase ) for val in values_list )}]'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()





