





'''simple docstring'''

import argparse

import torch

from diffusers import UNetaDConditionModel




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_=None      ,   lowerCAmelCase_=None      ,   lowerCAmelCase_=None						):





      """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		:						str         =  old_checkpoint[path]
                  _snake_case		:						Dict         =  old_tensor.shape[0] // 3

                  _snake_case		:						Tuple         =  (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

                  _snake_case		:						Dict         =  old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

                  _snake_case		:						List[Any]         =  query.reshape(lowerCAmelCase_						)
                  _snake_case		:						List[Any]         =  key.reshape(lowerCAmelCase_						)
                  _snake_case		:						Optional[Any]         =  value.reshape(lowerCAmelCase_						)

      for path in paths:
            _snake_case		:						Tuple         =  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		:						Optional[int]         =  old_checkpoint[path['''old''']]
            _snake_case		:						Tuple         =  ['''proj_attn.weight''']
            _snake_case		:						List[Any]         =  ['''proj_out.weight''', '''proj_in.weight''']
            if any(k in new_path for k in names						):
                  _snake_case		:						int         =  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		:						Optional[Any]         =  weight[:, :, 0]
            else:
                  _snake_case		:						Optional[Any]         =  weight




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_=0						):





      """simple docstring"""



      _snake_case		:						Optional[Any]         =  []
      for old_item in old_list:
            _snake_case		:						List[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     (				lowerCAmelCase_      ,   lowerCAmelCase_=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     (				lowerCAmelCase_      ,   lowerCAmelCase_=0						):





      """simple docstring"""



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

      return mapping




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_=0						):





      """simple docstring"""



      _snake_case		:						Optional[int]         =  []
      for old_item in old_list:
            _snake_case		:						Union[str, Any]         =  old_item.replace('''in_layers.0'''      ,   '''norm1'''						)
            _snake_case		:						int         =  new_item.replace('''in_layers.2'''      ,   '''conv1'''						)

            _snake_case		:						Union[str, Any]         =  new_item.replace('''out_layers.0'''      ,   '''norm2'''						)
            _snake_case		:						int         =  new_item.replace('''out_layers.3'''      ,   '''conv2'''						)

            _snake_case		:						Union[str, Any]         =  new_item.replace('''emb_layers.1'''      ,   '''time_emb_proj'''						)
            _snake_case		:						List[str]         =  new_item.replace('''skip_connection'''      ,   '''conv_shortcut'''						)

            _snake_case		:						List[str]         =  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  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_=None      ,   lowerCAmelCase_=False						):





      """simple docstring"""



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

      _snake_case		:						int         =  '''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		:						Union[str, Any]         =  '''model_ema.''' + ''''''.join(key.split('''.'''						)[1:]						)
                        _snake_case		:						Dict         =  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		:						Union[str, Any]         =  checkpoint.pop(lowerCAmelCase_						)

      _snake_case		:						Tuple         =  {}

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

      _snake_case		:						Tuple         =  unet_state_dict['''input_blocks.0.0.weight''']
      _snake_case		:						int         =  unet_state_dict['''input_blocks.0.0.bias''']

      _snake_case		:						List[Any]         =  [v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''						)]
      _snake_case		:						Optional[int]         =  renew_attention_paths(lowerCAmelCase_						)
      _snake_case		:						Optional[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		:						Tuple         =  unet_state_dict['''out.0.bias''']
      _snake_case		:						Tuple         =  unet_state_dict['''out.2.weight''']
      _snake_case		:						List[str]         =  unet_state_dict['''out.2.bias''']

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

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

            _snake_case		:						Tuple         =  renew_resnet_paths(lowerCAmelCase_						)
            _snake_case		:						Optional[int]         =  {'''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		:						Any         =  [key for key in resnets if '''temopral_conv''' in key]
            _snake_case		:						Optional[int]         =  renew_temp_conv_paths(lowerCAmelCase_						)
            _snake_case		:						Dict         =  {
                '''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		:						Dict         =  renew_attention_paths(lowerCAmelCase_						)
                  _snake_case		:						Union[str, 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		:						Dict         =  {
                      '''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		:						str         =  middle_blocks[0]
      _snake_case		:						List[Any]         =  [key for key in resnet_a if '''temopral_conv''' in key]
      _snake_case		:						List[str]         =  middle_blocks[1]
      _snake_case		:						List[Any]         =  middle_blocks[2]
      _snake_case		:						Optional[Any]         =  middle_blocks[3]
      _snake_case		:						Tuple         =  [key for key in resnet_a if '''temopral_conv''' in key]

      _snake_case		:						str         =  renew_resnet_paths(lowerCAmelCase_						)
      _snake_case		:						List[Any]         =  {'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
      assign_to_checkpoint(
          lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   config=lowerCAmelCase_      ,   additional_replacements=[meta_path]						)

      _snake_case		:						List[Any]         =  renew_temp_conv_paths(lowerCAmelCase_						)
      _snake_case		:						str         =  {'''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		:						Tuple         =  renew_resnet_paths(lowerCAmelCase_						)
      _snake_case		:						Tuple         =  {'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
      assign_to_checkpoint(
          lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   config=lowerCAmelCase_      ,   additional_replacements=[meta_path]						)

      _snake_case		:						Union[str, Any]         =  renew_temp_conv_paths(lowerCAmelCase_						)
      _snake_case		:						Dict         =  {'''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		:						Dict         =  renew_attention_paths(lowerCAmelCase_						)
      _snake_case		:						Optional[Any]         =  {'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
      assign_to_checkpoint(
          lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   additional_replacements=[meta_path]      ,   config=lowerCAmelCase_						)

      _snake_case		:						Optional[int]         =  renew_attention_paths(lowerCAmelCase_						)
      _snake_case		:						Dict         =  {'''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		:						List[str]         =  i % (config['''layers_per_block'''] + 1)
            _snake_case		:						Optional[Any]         =  [shave_segments(lowerCAmelCase_      ,   2						) for name in output_blocks[i]]
            _snake_case		:						List[Any]         =  {}

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

            if len(lowerCAmelCase_						) > 1:
                  _snake_case		:						Tuple         =  [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
                  _snake_case		:						List[str]         =  [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
                  _snake_case		:						Optional[Any]         =  [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

                  _snake_case		:						int         =  renew_resnet_paths(lowerCAmelCase_						)
                  _snake_case		:						Optional[int]         =  renew_resnet_paths(lowerCAmelCase_						)

                  _snake_case		:						Optional[Any]         =  {'''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		:						List[str]         =  [key for key in resnets if '''temopral_conv''' in key]
                  _snake_case		:						Tuple         =  renew_temp_conv_paths(lowerCAmelCase_						)
                  _snake_case		:						Tuple         =  {
                      '''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		:						Dict         =  {k: sorted(lowerCAmelCase_						) for k, v in output_block_list.items()}
                  if ["conv.bias", "conv.weight"] in output_block_list.values():
                        _snake_case		:						List[str]         =  list(output_block_list.values()						).index(['''conv.bias''', '''conv.weight''']						)
                        _snake_case		:						Optional[Any]         =  unet_state_dict[
                            f'''output_blocks.{i}.{index}.conv.weight'''
                        ]
                        _snake_case		:						Union[str, Any]         =  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		:						Dict         =  renew_attention_paths(lowerCAmelCase_						)
                        _snake_case		:						Union[str, 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		:						int         =  renew_attention_paths(lowerCAmelCase_						)
                        _snake_case		:						Dict         =  {
                            '''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		:						str         =  renew_resnet_paths(lowerCAmelCase_      ,   n_shave_prefix_segments=1						)
                  for path in resnet_0_paths:
                        _snake_case		:						Optional[Any]         =  '''.'''.join(['''output_blocks''', str(lowerCAmelCase_						), path['''old''']]						)
                        _snake_case		:						int         =  '''.'''.join(['''up_blocks''', str(lowerCAmelCase_						), '''resnets''', str(lowerCAmelCase_						), path['''new''']]						)
                        _snake_case		:						Tuple         =  unet_state_dict[old_path]

                  _snake_case		:						Union[str, Any]         =  [l for l in output_block_layers if '''temopral_conv''' in l]
                  for path in temopral_conv_paths:
                        _snake_case		:						List[Any]         =  path.split('''temopral_conv.'''						)[-1]
                        _snake_case		:						Dict         =  '''.'''.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		:						int         =  unet_state_dict[old_path]

      return new_checkpoint


if __name__ == "__main__":
   UpperCAmelCase       :		Dict		=				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       :		List[str]		=				parser.parse_args()

   UpperCAmelCase       :		str		=				torch.load(args.checkpoint_path, map_location='cpu')
   UpperCAmelCase       :		Tuple		=				UNetaDConditionModel()

   UpperCAmelCase       :		List[str]		=				convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

   UpperCAmelCase       :		Optional[Any]		=				set(unet.state_dict().keys()) - set(converted_ckpt.keys())
   UpperCAmelCase       :		Union[str, Any]		=				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     (				lowerCAmelCase_ = 4_000_000						):





      """simple docstring"""



      _snake_case		:						Optional[int]         =  1
      _snake_case		:						Tuple         =  2
      _snake_case		:						Optional[Any]         =  0
      while j <= n:
            if j % 2 == 0:
                  total += j
            _snake_case  ,				_snake_case		:						str         =  j, i + j

      return total


if __name__ == "__main__":
   print(F"""{solution() = }""")


