


import argparse

import torch

from diffusers import UNetaDConditionModel






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase=None ,     __lowerCamelCase=None ,     __lowerCamelCase=None       )		->List[str]:


 '''simple docstring'''
 assert isinstance(__lowerCamelCase ,     __lowerCamelCase       ), "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():
   snake_case   : Optional[int]	  =      old_checkpoint[path]
   snake_case   : int	  =      old_tensor.shape[0] // 3

   snake_case   : str	  =      (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

   snake_case   : List[Any]	  =      old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

   snake_case   : Optional[Any]	  =      query.reshape(__lowerCamelCase       )
   snake_case   : Optional[int]	  =      key.reshape(__lowerCamelCase       )
   snake_case   : List[Any]	  =      value.reshape(__lowerCamelCase       )

 for path in paths:
  snake_case   : Optional[Any]	  =      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:
    snake_case   : Dict	  =      new_path.replace(replacement['''old'''] ,     replacement['''new''']       )

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






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase=0       )		->int:


 '''simple docstring'''
 snake_case   : Optional[int]	  =      []
 for old_item in old_list:
  snake_case   : int	  =      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	(   __lowerCamelCase ,     __lowerCamelCase=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	(   __lowerCamelCase ,     __lowerCamelCase=0       )		->List[str]:


 '''simple docstring'''
 snake_case   : Dict	  =      []
 for old_item in old_list:
  mapping.append({'''old''': old_item, '''new''': old_item}       )

 return mapping






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase=0       )		->Dict:


 '''simple docstring'''
 snake_case   : Optional[Any]	  =      []
 for old_item in old_list:
  snake_case   : str	  =      old_item.replace('''in_layers.0''' ,     '''norm1'''       )
  snake_case   : List[str]	  =      new_item.replace('''in_layers.2''' ,     '''conv1'''       )

  snake_case   : str	  =      new_item.replace('''out_layers.0''' ,     '''norm2'''       )
  snake_case   : List[str]	  =      new_item.replace('''out_layers.3''' ,     '''conv2'''       )

  snake_case   : Tuple	  =      new_item.replace('''emb_layers.1''' ,     '''time_emb_proj'''       )
  snake_case   : List[str]	  =      new_item.replace('''skip_connection''' ,     '''conv_shortcut'''       )

  snake_case   : Dict	  =      shave_segments(__lowerCamelCase ,     n_shave_prefix_segments=__lowerCamelCase       )

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

 return mapping






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase=None ,     __lowerCamelCase=False       )		->Optional[Any]:


 '''simple docstring'''
 snake_case   : str	  =      {}
 snake_case   : int	  =      list(checkpoint.keys()       )

 snake_case   : 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       ) > 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'''       ):
    snake_case   : int	  =      '''model_ema.''' + ''''''.join(key.split('''.'''       )[1:]       )
    snake_case   : int	  =      checkpoint.pop(__lowerCamelCase       )
 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:
   snake_case   : int	  =      checkpoint.pop(__lowerCamelCase       )

 snake_case   : List[str]	  =      {}

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

 snake_case   : Union[str, Any]	  =      unet_state_dict['''input_blocks.0.0.weight''']
 snake_case   : int	  =      unet_state_dict['''input_blocks.0.0.bias''']

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

 snake_case   : Dict	  =      unet_state_dict['''out.0.weight''']
 snake_case   : int	  =      unet_state_dict['''out.0.bias''']
 snake_case   : Tuple	  =      unet_state_dict['''out.2.weight''']
 snake_case   : Any	  =      unet_state_dict['''out.2.bias''']

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

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

 # Retrieves the keys for the output blocks only
 snake_case   : Tuple	  =      len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in unet_state_dict if '''output_blocks''' in layer}       )
 snake_case   : Optional[int]	  =      {
     layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
     for layer_id in range(__lowerCamelCase       )
 }

 for i in range(1 ,     __lowerCamelCase       ):
  snake_case   : Any	  =      (i - 1) // (config['''layers_per_block'''] + 1)
  snake_case   : Optional[Any]	  =      (i - 1) % (config['''layers_per_block'''] + 1)

  snake_case   : 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
  ]
  snake_case   : str	  =      [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
  snake_case   : int	  =      [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:
   snake_case   : Optional[Any]	  =      unet_state_dict.pop(
       f'''input_blocks.{i}.op.weight'''       )
   snake_case   : List[str]	  =      unet_state_dict.pop(
       f'''input_blocks.{i}.op.bias'''       )

  snake_case   : Optional[Any]	  =      renew_resnet_paths(__lowerCamelCase       )
  snake_case   : Tuple	  =      {'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
  assign_to_checkpoint(
      __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     additional_replacements=[meta_path] ,     config=__lowerCamelCase       )

  snake_case   : Tuple	  =      [key for key in resnets if '''temopral_conv''' in key]
  snake_case   : Optional[int]	  =      renew_temp_conv_paths(__lowerCamelCase       )
  snake_case   : str	  =      {
      '''old''': f'''input_blocks.{i}.0.temopral_conv''',
      '''new''': f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
  }
  assign_to_checkpoint(
      __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     additional_replacements=[meta_path] ,     config=__lowerCamelCase       )

  if len(__lowerCamelCase       ):
   snake_case   : Optional[Any]	  =      renew_attention_paths(__lowerCamelCase       )
   snake_case   : List[Any]	  =      {'''old''': f'''input_blocks.{i}.1''', '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
   assign_to_checkpoint(
       __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     additional_replacements=[meta_path] ,     config=__lowerCamelCase       )

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

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

 snake_case   : Dict	  =      renew_resnet_paths(__lowerCamelCase       )
 snake_case   : str	  =      {'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
 assign_to_checkpoint(
     __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     config=__lowerCamelCase ,     additional_replacements=[meta_path]       )

 snake_case   : Optional[int]	  =      renew_temp_conv_paths(__lowerCamelCase       )
 snake_case   : Optional[int]	  =      {'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
 assign_to_checkpoint(
     __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     config=__lowerCamelCase ,     additional_replacements=[meta_path]       )

 snake_case   : Dict	  =      renew_resnet_paths(__lowerCamelCase       )
 snake_case   : Optional[Any]	  =      {'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
 assign_to_checkpoint(
     __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     config=__lowerCamelCase ,     additional_replacements=[meta_path]       )

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

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

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

 for i in range(__lowerCamelCase       ):
  snake_case   : Tuple	  =      i // (config['''layers_per_block'''] + 1)
  snake_case   : Dict	  =      i % (config['''layers_per_block'''] + 1)
  snake_case   : Optional[int]	  =      [shave_segments(__lowerCamelCase ,     2       ) for name in output_blocks[i]]
  snake_case   : Dict	  =      {}

  for layer in output_block_layers:
   snake_case  ,					snake_case   : List[str]	  =      layer.split('''.'''       )[0], shave_segments(__lowerCamelCase ,     1       )
   if layer_id in output_block_list:
    output_block_list[layer_id].append(__lowerCamelCase       )
   else:
    snake_case   : str	  =      [layer_name]

  if len(__lowerCamelCase       ) > 1:
   snake_case   : Union[str, Any]	  =      [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
   snake_case   : Any	  =      [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
   snake_case   : Optional[int]	  =      [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

   snake_case   : Tuple	  =      renew_resnet_paths(__lowerCamelCase       )
   snake_case   : List[str]	  =      renew_resnet_paths(__lowerCamelCase       )

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

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

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

    # Clear attentions as they have been attributed above.
    if len(__lowerCamelCase       ) == 2:
     snake_case   : Dict	  =      []

   if len(__lowerCamelCase       ):
    snake_case   : str	  =      renew_attention_paths(__lowerCamelCase       )
    snake_case   : Optional[Any]	  =      {
        '''old''': f'''output_blocks.{i}.1''',
        '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
    }
    assign_to_checkpoint(
        __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     additional_replacements=[meta_path] ,     config=__lowerCamelCase       )

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

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

 return new_checkpoint


if __name__ == "__main__":
  a				:							Optional[Any]					=			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				:							Union[str, Any]					=			parser.parse_args()

  a				:							List[Any]					=			torch.load(args.checkpoint_path, map_location='cpu')
  a				:							int					=			UNetaDConditionModel()

  a				:							Any					=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

  a				:							List[str]					=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  a				:							str					=			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 --










import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : List[str]	  =      ArgumentParser(
     description=(
         '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
     )       )

 # Optional arguments for the launch helper
 parser.add_argument('''--num_cores''' ,     type=__lowerCamelCase ,     default=1 ,     help='''Number of TPU cores to use (1 or 8).'''       )

 # positional
 parser.add_argument(
     '''training_script''' ,     type=__lowerCamelCase ,     help=(
         '''The full path to the single TPU training '''
         '''program/script to be launched in parallel, '''
         '''followed by all the arguments for the '''
         '''training script'''
     ) ,     )

 # rest from the training program
 parser.add_argument('''training_script_args''' ,     nargs=__lowerCamelCase       )

 return parser.parse_args()






def    _UpperCAmelCase	(   )		->Optional[int]:


 '''simple docstring'''
 snake_case   : Optional[Any]	  =      parse_args()

 # Import training_script as a module.
 snake_case   : str	  =      Path(args.training_script       )
 sys.path.append(str(script_fpath.parent.resolve()       )       )
 snake_case   : str	  =      script_fpath.stem
 snake_case   : Optional[int]	  =      importlib.import_module(__lowerCamelCase       )

 # Patch sys.argv
 snake_case   : int	  =      [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores       )]

 xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores       )


if __name__ == "__main__":
  main()






