


import argparse

import torch

from diffusers import UNetaDConditionModel

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







 """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():
   UpperCamelCase__       :     Any 							= old_checkpoint[path]
   UpperCamelCase__       :     int 							= old_tensor.shape[0] // 3

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

   UpperCamelCase__       :     int 							= old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

   UpperCamelCase__       :     List[Any] 							= query.reshape(SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     int 							= key.reshape(SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     Tuple 							= value.reshape(SCREAMING_SNAKE_CASE  )

 for path in paths:
  UpperCamelCase__       :     List[str] 							= 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:
    UpperCamelCase__       :     Union[str, Any] 							= new_path.replace(replacement['''old''']     ,			replacement['''new''']  )

        # proj_attn.weight has to be converted from conv 1D to linear
  UpperCamelCase__       :     Tuple 							= old_checkpoint[path['''old''']]
  UpperCamelCase__       :     List[Any] 							= ['''proj_attn.weight''']
  UpperCamelCase__       :     Optional[int] 							= ['''proj_out.weight''', '''proj_in.weight''']
  if any(k in new_path for k in names  ):
   UpperCamelCase__       :     Optional[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:
   UpperCamelCase__       :     int 							= weight[:, :, 0]
  else:
   UpperCamelCase__       :     Optional[int] 							= weight

def       _a	(					SCREAMING_SNAKE_CASE						:					List[str]     ,			SCREAMING_SNAKE_CASE						:					int=0  ):







 """simple docstring"""



 UpperCamelCase__       :     List[str] 							= []
 for old_item in old_list:
  UpperCamelCase__       :     Tuple 							= 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						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					Dict=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       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]=0  ):







 """simple docstring"""



 UpperCamelCase__       :     Optional[int] 							= []
 for old_item in old_list:
  mapping.append({'''old''': old_item, '''new''': old_item}  )

 return mapping

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







 """simple docstring"""



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

  UpperCamelCase__       :     Optional[Any] 							= new_item.replace('''out_layers.0'''     ,			'''norm2'''  )
  UpperCamelCase__       :     List[Any] 							= new_item.replace('''out_layers.3'''     ,			'''conv2'''  )

  UpperCamelCase__       :     Optional[int] 							= new_item.replace('''emb_layers.1'''     ,			'''time_emb_proj'''  )
  UpperCamelCase__       :     Optional[Any] 							= new_item.replace('''skip_connection'''     ,			'''conv_shortcut'''  )

  UpperCamelCase__       :     Union[str, Any] 							= 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						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					Union[str, Any]=None     ,			SCREAMING_SNAKE_CASE						:					Any=False  ):







 """simple docstring"""



 UpperCamelCase__       :     Optional[int] 							= {}
 UpperCamelCase__       :     List[Any] 							= list(checkpoint.keys()  )

 UpperCamelCase__       :     List[str] 							= '''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  ) > 100 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'''  ):
    UpperCamelCase__       :     Union[str, Any] 							= '''model_ema.''' + ''''''.join(key.split('''.'''  )[1:]  )
    UpperCamelCase__       :     Dict 							= checkpoint.pop(SCREAMING_SNAKE_CASE  )
 else:
  if sum(k.startswith('''model_ema'''  ) for k in keys  ) > 100:
   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:
   UpperCamelCase__       :     int 							= checkpoint.pop(SCREAMING_SNAKE_CASE  )

 UpperCamelCase__       :     Any 							= {}

 UpperCamelCase__       :     Any 							= unet_state_dict['''time_embed.0.weight''']
 UpperCamelCase__       :     List[Any] 							= unet_state_dict['''time_embed.0.bias''']
 UpperCamelCase__       :     Optional[int] 							= unet_state_dict['''time_embed.2.weight''']
 UpperCamelCase__       :     List[str] 							= 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":
  UpperCamelCase__       :     List[Any] 							= unet_state_dict['''label_emb.0.0.weight''']
  UpperCamelCase__       :     str 							= unet_state_dict['''label_emb.0.0.bias''']
  UpperCamelCase__       :     Optional[int] 							= unet_state_dict['''label_emb.0.2.weight''']
  UpperCamelCase__       :     Optional[int] 							= unet_state_dict['''label_emb.0.2.bias''']
 else:
  raise NotImplementedError(F"Not implemented `class_embed_type`: {config['class_embed_type']}"  )

 UpperCamelCase__       :     Tuple 							= unet_state_dict['''input_blocks.0.0.weight''']
 UpperCamelCase__       :     Any 							= unet_state_dict['''input_blocks.0.0.bias''']

 UpperCamelCase__       :     List[str] 							= [v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''  )]
 UpperCamelCase__       :     str 							= renew_attention_paths(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     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  )

 UpperCamelCase__       :     Optional[int] 							= unet_state_dict['''out.0.weight''']
 UpperCamelCase__       :     Optional[int] 							= unet_state_dict['''out.0.bias''']
 UpperCamelCase__       :     Any 							= unet_state_dict['''out.2.weight''']
 UpperCamelCase__       :     Optional[int] 							= unet_state_dict['''out.2.bias''']

 # Retrieves the keys for the input blocks only
 UpperCamelCase__       :     List[str] 							= len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in unet_state_dict if '''input_blocks''' in layer}  )
 UpperCamelCase__       :     Optional[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
 UpperCamelCase__       :     str 							= len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in unet_state_dict if '''middle_block''' in layer}  )
 UpperCamelCase__       :     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
 UpperCamelCase__       :     int 							= len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in unet_state_dict if '''output_blocks''' in layer}  )
 UpperCamelCase__       :     List[str] 							= {
     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  ):
  UpperCamelCase__       :     Optional[Any] 							= (i - 1) // (config['''layers_per_block'''] + 1)
  UpperCamelCase__       :     Dict 							= (i - 1) % (config['''layers_per_block'''] + 1)

  UpperCamelCase__       :     Tuple 							= [
      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
  ]
  UpperCamelCase__       :     Tuple 							= [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]
  UpperCamelCase__       :     Tuple 							= [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:
   UpperCamelCase__       :     Any 							= unet_state_dict.pop(
       F"input_blocks.{i}.op.weight"  )
   UpperCamelCase__       :     Union[str, Any] 							= unet_state_dict.pop(
       F"input_blocks.{i}.op.bias"  )

  UpperCamelCase__       :     List[str] 							= renew_resnet_paths(SCREAMING_SNAKE_CASE  )
  UpperCamelCase__       :     List[str] 							= {'''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  )

  UpperCamelCase__       :     Union[str, Any] 							= [key for key in resnets if '''temopral_conv''' in key]
  UpperCamelCase__       :     int 							= renew_temp_conv_paths(SCREAMING_SNAKE_CASE  )
  UpperCamelCase__       :     Dict 							= {
      '''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  ):
   UpperCamelCase__       :     int 							= renew_attention_paths(SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     Optional[int] 							= {'''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  ):
   UpperCamelCase__       :     str 							= renew_attention_paths(SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     Optional[int] 							= {
       '''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  )

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

 UpperCamelCase__       :     Optional[int] 							= renew_resnet_paths(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     int 							= {'''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]  )

 UpperCamelCase__       :     Optional[int] 							= renew_temp_conv_paths(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     Any 							= {'''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]  )

 UpperCamelCase__       :     str 							= renew_resnet_paths(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     Tuple 							= {'''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]  )

 UpperCamelCase__       :     int 							= renew_temp_conv_paths(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     List[str] 							= {'''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]  )

 UpperCamelCase__       :     Dict 							= renew_attention_paths(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     str 							= {'''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  )

 UpperCamelCase__       :     Tuple 							= renew_attention_paths(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     Union[str, Any] 							= {'''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  ):
  UpperCamelCase__       :     Union[str, Any] 							= i // (config['''layers_per_block'''] + 1)
  UpperCamelCase__       :     List[str] 							= i % (config['''layers_per_block'''] + 1)
  UpperCamelCase__       :     Tuple 							= [shave_segments(SCREAMING_SNAKE_CASE     ,			2  ) for name in output_blocks[i]]
  UpperCamelCase__       :     Tuple 							= {}

  for layer in output_block_layers:
   UpperCamelCase__							,					UpperCamelCase__       :     str 							= 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:
    UpperCamelCase__       :     int 							= [layer_name]

  if len(SCREAMING_SNAKE_CASE  ) > 1:
   UpperCamelCase__       :     Union[str, Any] 							= [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
   UpperCamelCase__       :     Any 							= [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]
   UpperCamelCase__       :     str 							= [key for key in output_blocks[i] if F"output_blocks.{i}.2" in key]

   UpperCamelCase__       :     List[str] 							= renew_resnet_paths(SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     Optional[int] 							= renew_resnet_paths(SCREAMING_SNAKE_CASE  )

   UpperCamelCase__       :     Tuple 							= {'''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  )

   UpperCamelCase__       :     str 							= [key for key in resnets if '''temopral_conv''' in key]
   UpperCamelCase__       :     List[str] 							= renew_temp_conv_paths(SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     Any 							= {
       '''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  )

   UpperCamelCase__       :     Optional[int] 							= {k: sorted(SCREAMING_SNAKE_CASE  ) for k, v in output_block_list.items()}
   if ["conv.bias", "conv.weight"] in output_block_list.values():
    UpperCamelCase__       :     int 							= list(output_block_list.values()  ).index(['''conv.bias''', '''conv.weight''']  )
    UpperCamelCase__       :     Dict 							= unet_state_dict[
        F"output_blocks.{i}.{index}.conv.weight"
    ]
    UpperCamelCase__       :     Union[str, Any] 							= unet_state_dict[
        F"output_blocks.{i}.{index}.conv.bias"
    ]

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

   if len(SCREAMING_SNAKE_CASE  ):
    UpperCamelCase__       :     Tuple 							= renew_attention_paths(SCREAMING_SNAKE_CASE  )
    UpperCamelCase__       :     List[Any] 							= {
        '''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  ):
    UpperCamelCase__       :     List[Any] 							= renew_attention_paths(SCREAMING_SNAKE_CASE  )
    UpperCamelCase__       :     Optional[Any] 							= {
        '''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:
   UpperCamelCase__       :     Tuple 							= renew_resnet_paths(SCREAMING_SNAKE_CASE     ,			n_shave_prefix_segments=1  )
   for path in resnet_0_paths:
    UpperCamelCase__       :     Dict 							= '''.'''.join(['''output_blocks''', str(SCREAMING_SNAKE_CASE  ), path['''old''']]  )
    UpperCamelCase__       :     Optional[int] 							= '''.'''.join(['''up_blocks''', str(SCREAMING_SNAKE_CASE  ), '''resnets''', str(SCREAMING_SNAKE_CASE  ), path['''new''']]  )
    UpperCamelCase__       :     List[Any] 							= unet_state_dict[old_path]

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

 return new_checkpoint


if __name__ == "__main__":
       __UpperCamelCase							:							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("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
       __UpperCamelCase							:							Optional[Any]    							=					parser.parse_args()

       __UpperCamelCase							:							int    							=					torch.load(args.checkpoint_path, map_location="cpu")
       __UpperCamelCase							:							int    							=					UNetaDConditionModel()

       __UpperCamelCase							:							Optional[int]    							=					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       __UpperCamelCase							:							str    							=					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       __UpperCamelCase							:							Optional[int]    							=					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 hashlib import mda, shaaaa

__UpperCamelCase							:							Dict    							=					(shaaaa, mda)

class    __magic_name__						:


     def __init__(   self				:  Optional[Any]				,						lowerCamelCase__				:  int = 8      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= 0b0
      UpperCamelCase__       :     Optional[int] 							= size


     def   UpperCAmelCase__						(   self				:  Tuple				,						lowerCamelCase__				:  str      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= self.hash_(lowerCamelCase__      )
      self.bitarray |= h


     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  str      )	->   bool:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= self.hash_(lowerCamelCase__      )
      return (h & self.bitarray) == h


     def __contains__(   self				:  Any				,						lowerCamelCase__				:  str      )	->   bool:





      '''simple docstring'''






      return self.exists(lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  int      )	->   str:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= bin(lowerCamelCase__      )[2:]
      return res.zfill(self.size      )


     @property
     def   UpperCAmelCase__						(   self				:  Tuple      )	->   str:





      '''simple docstring'''






      return self.format_bin(self.bitarray      )


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  str      )	->   int:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= 0b0
      for func in HASH_FUNCTIONS:
       UpperCamelCase__       :     Optional[Any] 							= (
           int.from_bytes(func(value.encode()      ).digest()				,						'''little'''      ) % self.size
       )
       res |= 2**position
      return res


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  str      )	->   str:





      '''simple docstring'''






      return self.format_bin(self.hash_(lowerCamelCase__      )      )


     @property
     def   UpperCAmelCase__						(   self				:  str      )	->   float:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= bin(self.bitarray      ).count('''1'''      )
      return (n_ones / self.size) ** len(lowerCamelCase__      )


