


import argparse

import torch

from diffusers import UNetaDConditionModel
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case=None	,		__snake_case=None	,		__snake_case=None		)	->     int:







 """simple docstring"""


 assert isinstance(UpperCamelCase_	,		UpperCamelCase_		), "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():
   _lowercase				 =old_checkpoint[path]
   _lowercase				 =old_tensor.shape[0] // 3

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

   _lowercase				 =old_tensor.shape[0] // config['num_head_channels'] // 3

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

   _lowercase				 =query.reshape(UpperCamelCase_		)
   _lowercase				 =key.reshape(UpperCamelCase_		)
   _lowercase				 =value.reshape(UpperCamelCase_		)

 for path in paths:
  _lowercase				 =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:
    _lowercase				 =new_path.replace(replacement['''old''']	,		replacement['''new''']		)

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







 """simple docstring"""


 _lowercase				 =[]
 for old_item in old_list:
  _lowercase				 =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 							UpperCAmelCase_   (				__snake_case	,		__snake_case=1		)	->     Optional[Any]:







 """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 							UpperCAmelCase_   (				__snake_case	,		__snake_case=0		)	->     Optional[int]:







 """simple docstring"""


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

 return mapping
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case=0		)	->     Any:







 """simple docstring"""


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

  _lowercase				 =new_item.replace('''out_layers.0'''	,		'''norm2'''		)
  _lowercase				 =new_item.replace('''out_layers.3'''	,		'''conv2'''		)

  _lowercase				 =new_item.replace('''emb_layers.1'''	,		'''time_emb_proj'''		)
  _lowercase				 =new_item.replace('''skip_connection'''	,		'''conv_shortcut'''		)

  _lowercase				 =shave_segments(UpperCamelCase_	,		n_shave_prefix_segments=UpperCamelCase_		)

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

 return mapping
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case=None	,		__snake_case=False		)	->     Tuple:







 """simple docstring"""


 _lowercase				 ={}
 _lowercase				 =list(checkpoint.keys()		)

 _lowercase				 ='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'''		):
    _lowercase				 ='model_ema.' + ''.join(key.split('''.'''		)[1:]		)
    _lowercase				 =checkpoint.pop(UpperCamelCase_		)
 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:
   _lowercase				 =checkpoint.pop(UpperCamelCase_		)

 _lowercase				 ={}

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

 _lowercase				 =unet_state_dict['input_blocks.0.0.weight']
 _lowercase				 =unet_state_dict['input_blocks.0.0.bias']

 _lowercase				 =[v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''		)]
 _lowercase				 =renew_attention_paths(UpperCamelCase_		)
 _lowercase				 ={'old': 'input_blocks.0.1', 'new': 'transformer_in'}
 assign_to_checkpoint(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

 _lowercase				 =unet_state_dict['out.0.weight']
 _lowercase				 =unet_state_dict['out.0.bias']
 _lowercase				 =unet_state_dict['out.2.weight']
 _lowercase				 =unet_state_dict['out.2.bias']

 # Retrieves the keys for the input blocks only
 _lowercase				 =len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in unet_state_dict if '''input_blocks''' in layer}		)
 _lowercase				 ={
     layer_id: [key for key in unet_state_dict if F"input_blocks.{layer_id}" in key]
     for layer_id in range(UpperCamelCase_		)
 }

 # Retrieves the keys for the middle blocks only
 _lowercase				 =len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in unet_state_dict if '''middle_block''' in layer}		)
 _lowercase				 ={
     layer_id: [key for key in unet_state_dict if F"middle_block.{layer_id}" in key]
     for layer_id in range(UpperCamelCase_		)
 }

 # Retrieves the keys for the output blocks only
 _lowercase				 =len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in unet_state_dict if '''output_blocks''' in layer}		)
 _lowercase				 ={
     layer_id: [key for key in unet_state_dict if F"output_blocks.{layer_id}" in key]
     for layer_id in range(UpperCamelCase_		)
 }

 for i in range(1	,		UpperCamelCase_		):
  _lowercase				 =(i - 1) // (config['layers_per_block'] + 1)
  _lowercase				 =(i - 1) % (config['layers_per_block'] + 1)

  _lowercase				 =[
      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
  ]
  _lowercase				 =[key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]
  _lowercase				 =[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:
   _lowercase				 =unet_state_dict.pop(
       F"input_blocks.{i}.op.weight"		)
   _lowercase				 =unet_state_dict.pop(
       F"input_blocks.{i}.op.bias"		)

  _lowercase				 =renew_resnet_paths(UpperCamelCase_		)
  _lowercase				 ={'old': F"input_blocks.{i}.0", 'new': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
  assign_to_checkpoint(
      UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

  _lowercase				 =[key for key in resnets if 'temopral_conv' in key]
  _lowercase				 =renew_temp_conv_paths(UpperCamelCase_		)
  _lowercase				 ={
      'old': F"input_blocks.{i}.0.temopral_conv",
      'new': F"down_blocks.{block_id}.temp_convs.{layer_in_block_id}",
  }
  assign_to_checkpoint(
      UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

  if len(UpperCamelCase_		):
   _lowercase				 =renew_attention_paths(UpperCamelCase_		)
   _lowercase				 ={'old': F"input_blocks.{i}.1", 'new': F"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
   assign_to_checkpoint(
       UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

  if len(UpperCamelCase_		):
   _lowercase				 =renew_attention_paths(UpperCamelCase_		)
   _lowercase				 ={
       'old': F"input_blocks.{i}.2",
       'new': F"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
   }
   assign_to_checkpoint(
       UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

 _lowercase				 =middle_blocks[0]
 _lowercase				 =[key for key in resnet_a if 'temopral_conv' in key]
 _lowercase				 =middle_blocks[1]
 _lowercase				 =middle_blocks[2]
 _lowercase				 =middle_blocks[3]
 _lowercase				 =[key for key in resnet_a if 'temopral_conv' in key]

 _lowercase				 =renew_resnet_paths(UpperCamelCase_		)
 _lowercase				 ={'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
 assign_to_checkpoint(
     UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		config=UpperCamelCase_	,		additional_replacements=[meta_path]		)

 _lowercase				 =renew_temp_conv_paths(UpperCamelCase_		)
 _lowercase				 ={'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
 assign_to_checkpoint(
     UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		config=UpperCamelCase_	,		additional_replacements=[meta_path]		)

 _lowercase				 =renew_resnet_paths(UpperCamelCase_		)
 _lowercase				 ={'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
 assign_to_checkpoint(
     UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		config=UpperCamelCase_	,		additional_replacements=[meta_path]		)

 _lowercase				 =renew_temp_conv_paths(UpperCamelCase_		)
 _lowercase				 ={'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
 assign_to_checkpoint(
     UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		config=UpperCamelCase_	,		additional_replacements=[meta_path]		)

 _lowercase				 =renew_attention_paths(UpperCamelCase_		)
 _lowercase				 ={'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
 assign_to_checkpoint(
     UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

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

 for i in range(UpperCamelCase_		):
  _lowercase				 =i // (config['layers_per_block'] + 1)
  _lowercase				 =i % (config['layers_per_block'] + 1)
  _lowercase				 =[shave_segments(UpperCamelCase_	,		2		) for name in output_blocks[i]]
  _lowercase				 ={}

  for layer in output_block_layers:
   _lowercase				 =layer.split('''.'''		)[0], shave_segments(UpperCamelCase_	,		1		)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(UpperCamelCase_		)
   else:
    _lowercase				 =[layer_name]

  if len(UpperCamelCase_		) > 1:
   _lowercase				 =[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
   _lowercase				 =[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]
   _lowercase				 =[key for key in output_blocks[i] if F"output_blocks.{i}.2" in key]

   _lowercase				 =renew_resnet_paths(UpperCamelCase_		)
   _lowercase				 =renew_resnet_paths(UpperCamelCase_		)

   _lowercase				 ={'old': F"output_blocks.{i}.0", 'new': F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
   assign_to_checkpoint(
       UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

   _lowercase				 =[key for key in resnets if 'temopral_conv' in key]
   _lowercase				 =renew_temp_conv_paths(UpperCamelCase_		)
   _lowercase				 ={
       'old': F"output_blocks.{i}.0.temopral_conv",
       'new': F"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
   }
   assign_to_checkpoint(
       UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

   _lowercase				 ={k: sorted(UpperCamelCase_		) for k, v in output_block_list.items()}
   if ["conv.bias", "conv.weight"] in output_block_list.values():
    _lowercase				 =list(output_block_list.values()		).index(['''conv.bias''', '''conv.weight''']		)
    _lowercase				 =unet_state_dict[
        F"output_blocks.{i}.{index}.conv.weight"
    ]
    _lowercase				 =unet_state_dict[
        F"output_blocks.{i}.{index}.conv.bias"
    ]

    # Clear attentions as they have been attributed above.
    if len(UpperCamelCase_		) == 2:
     _lowercase				 =[]

   if len(UpperCamelCase_		):
    _lowercase				 =renew_attention_paths(UpperCamelCase_		)
    _lowercase				 ={
        'old': F"output_blocks.{i}.1",
        'new': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
    }
    assign_to_checkpoint(
        UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_	,		additional_replacements=[meta_path]	,		config=UpperCamelCase_		)

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

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

 return new_checkpoint


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

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

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

  UpperCAmelCase__        =				convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

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









"""simple docstring"""


def 					a			(  UpperCamelCase_				:					float		)	->		int:
 return int(UpperCamelCase_		) if x - int(UpperCamelCase_		) <= 0 else int(UpperCamelCase_		) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()





