


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE=1   )->    Union[str, 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 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE=0   )->    Dict:




      """simple docstring"""

      snake_case_			= []
      for old_item in old_list:
            snake_case_			= old_item
            snake_case_			= new_item.replace('''block.'''   ,   '''resnets.'''   )
            snake_case_			= new_item.replace('''conv_shorcut'''   ,   '''conv1'''   )
            snake_case_			= new_item.replace('''in_shortcut'''   ,   '''conv_shortcut'''   )
            snake_case_			= new_item.replace('''temb_proj'''   ,   '''time_emb_proj'''   )

            snake_case_			= shave_segments(SCREAMING_SNAKE_CASE   ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE   )

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

      return mapping






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE=0   ,   SCREAMING_SNAKE_CASE=False   )->    Dict:




      """simple docstring"""

      snake_case_			= []
      for old_item in old_list:
            snake_case_			= old_item

            # In `model.mid`, the layer is called `attn`.
            if not in_mid:
                  snake_case_			= new_item.replace('''attn'''   ,   '''attentions'''   )
            snake_case_			= new_item.replace('''.k.'''   ,   '''.key.'''   )
            snake_case_			= new_item.replace('''.v.'''   ,   '''.value.'''   )
            snake_case_			= new_item.replace('''.q.'''   ,   '''.query.'''   )

            snake_case_			= new_item.replace('''proj_out'''   ,   '''proj_attn'''   )
            snake_case_			= new_item.replace('''norm'''   ,   '''group_norm'''   )

            snake_case_			= shave_segments(SCREAMING_SNAKE_CASE   ,   n_shave_prefix_segments=SCREAMING_SNAKE_CASE   )
            mapping.append({'''old''': old_item, '''new''': new_item}   )

      return mapping






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE=None   ,   SCREAMING_SNAKE_CASE=None   ,   SCREAMING_SNAKE_CASE=None   )->    Optional[int]:




      """simple docstring"""

      assert isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ), "Paths should be a list of dicts containing 'old' and 'new' keys."

      if attention_paths_to_split is not None:
            if config is None:
                  raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'''   )

            for path, path_map in attention_paths_to_split.items():
                  snake_case_			= old_checkpoint[path]
                  snake_case_			= old_tensor.shape[0] // 3

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

                  snake_case_			= old_tensor.shape[0] // config.get('''num_head_channels'''   ,   1   ) // 3

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

                  snake_case_			= query.reshape(SCREAMING_SNAKE_CASE   ).squeeze()
                  snake_case_			= key.reshape(SCREAMING_SNAKE_CASE   ).squeeze()
                  snake_case_			= value.reshape(SCREAMING_SNAKE_CASE   ).squeeze()

      for path in paths:
            snake_case_			= path['''new''']

            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            snake_case_			= new_path.replace('''down.'''   ,   '''down_blocks.'''   )
            snake_case_			= new_path.replace('''up.'''   ,   '''up_blocks.'''   )

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

            if "attentions" in new_path:
                  snake_case_			= old_checkpoint[path['''old''']].squeeze()
            else:
                  snake_case_			= old_checkpoint[path['''old''']]






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Tuple:




      """simple docstring"""

      snake_case_			= {}

      snake_case_			= checkpoint['''temb.dense.0.weight''']
      snake_case_			= checkpoint['''temb.dense.0.bias''']
      snake_case_			= checkpoint['''temb.dense.1.weight''']
      snake_case_			= checkpoint['''temb.dense.1.bias''']

      snake_case_			= checkpoint['''norm_out.weight''']
      snake_case_			= checkpoint['''norm_out.bias''']

      snake_case_			= checkpoint['''conv_in.weight''']
      snake_case_			= checkpoint['''conv_in.bias''']
      snake_case_			= checkpoint['''conv_out.weight''']
      snake_case_			= checkpoint['''conv_out.bias''']

      snake_case_			= len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''down''' in layer}   )
      snake_case_			= {
          layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE   )
      }

      snake_case_			= len({'''.'''.join(layer.split('''.'''   )[:2]   ) for layer in checkpoint if '''up''' in layer}   )
      snake_case_			= {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE   )}

      for i in range(SCREAMING_SNAKE_CASE   ):
            snake_case_			= (i - 1) // (config['''layers_per_block'''] + 1)

            if any('''downsample''' in layer for layer in down_blocks[i]   ):
                  snake_case_			= checkpoint[
                      f'''down.{i}.downsample.op.weight'''
                  ]
                  snake_case_			= checkpoint[f'''down.{i}.downsample.op.bias''']
            #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
            #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

            if any('''block''' in layer for layer in down_blocks[i]   ):
                  snake_case_			= len(
                      {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE   ,   2   ).split('''.'''   )[:2]   ) for layer in down_blocks[i] if '''block''' in layer}   )
                  snake_case_			= {
                      layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
                      for layer_id in range(SCREAMING_SNAKE_CASE   )
                  }

                  if num_blocks > 0:
                        for j in range(config['''layers_per_block''']   ):
                              snake_case_			= renew_resnet_paths(blocks[j]   )
                              assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

            if any('''attn''' in layer for layer in down_blocks[i]   ):
                  snake_case_			= len(
                      {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE   ,   2   ).split('''.'''   )[:2]   ) for layer in down_blocks[i] if '''attn''' in layer}   )
                  snake_case_			= {
                      layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
                      for layer_id in range(SCREAMING_SNAKE_CASE   )
                  }

                  if num_attn > 0:
                        for j in range(config['''layers_per_block''']   ):
                              snake_case_			= renew_attention_paths(attns[j]   )
                              assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   config=SCREAMING_SNAKE_CASE   )

      snake_case_			= [key for key in checkpoint if '''mid.block_1''' in key]
      snake_case_			= [key for key in checkpoint if '''mid.block_2''' in key]
      snake_case_			= [key for key in checkpoint if '''mid.attn_1''' in key]

      # Mid new 2
      snake_case_			= renew_resnet_paths(SCREAMING_SNAKE_CASE   )
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]   ,   )

      snake_case_			= renew_resnet_paths(SCREAMING_SNAKE_CASE   )
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]   ,   )

      snake_case_			= renew_attention_paths(SCREAMING_SNAKE_CASE   ,   in_mid=SCREAMING_SNAKE_CASE   )
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]   ,   )

      for i in range(SCREAMING_SNAKE_CASE   ):
            snake_case_			= num_up_blocks - 1 - i

            if any('''upsample''' in layer for layer in up_blocks[i]   ):
                  snake_case_			= checkpoint[
                      f'''up.{i}.upsample.conv.weight'''
                  ]
                  snake_case_			= checkpoint[f'''up.{i}.upsample.conv.bias''']

            if any('''block''' in layer for layer in up_blocks[i]   ):
                  snake_case_			= len(
                      {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE   ,   2   ).split('''.'''   )[:2]   ) for layer in up_blocks[i] if '''block''' in layer}   )
                  snake_case_			= {
                      layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE   )
                  }

                  if num_blocks > 0:
                        for j in range(config['''layers_per_block'''] + 1   ):
                              snake_case_			= {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
                              snake_case_			= renew_resnet_paths(blocks[j]   )
                              assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[replace_indices]   )

            if any('''attn''' in layer for layer in up_blocks[i]   ):
                  snake_case_			= len(
                      {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE   ,   2   ).split('''.'''   )[:2]   ) for layer in up_blocks[i] if '''attn''' in layer}   )
                  snake_case_			= {
                      layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE   )
                  }

                  if num_attn > 0:
                        for j in range(config['''layers_per_block'''] + 1   ):
                              snake_case_			= {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
                              snake_case_			= renew_attention_paths(attns[j]   )
                              assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[replace_indices]   )

      snake_case_			= {k.replace('''mid_new_2'''   ,   '''mid_block'''   ): v for k, v in new_checkpoint.items()}
      return new_checkpoint






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Optional[int]:




      """simple docstring"""

      snake_case_			= {}

      snake_case_			= checkpoint['''encoder.norm_out.weight''']
      snake_case_			= checkpoint['''encoder.norm_out.bias''']

      snake_case_			= checkpoint['''encoder.conv_in.weight''']
      snake_case_			= checkpoint['''encoder.conv_in.bias''']
      snake_case_			= checkpoint['''encoder.conv_out.weight''']
      snake_case_			= checkpoint['''encoder.conv_out.bias''']

      snake_case_			= checkpoint['''decoder.norm_out.weight''']
      snake_case_			= checkpoint['''decoder.norm_out.bias''']

      snake_case_			= checkpoint['''decoder.conv_in.weight''']
      snake_case_			= checkpoint['''decoder.conv_in.bias''']
      snake_case_			= checkpoint['''decoder.conv_out.weight''']
      snake_case_			= checkpoint['''decoder.conv_out.bias''']

      snake_case_			= len({'''.'''.join(layer.split('''.'''   )[:3]   ) for layer in checkpoint if '''down''' in layer}   )
      snake_case_			= {
          layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE   )
      }

      snake_case_			= len({'''.'''.join(layer.split('''.'''   )[:3]   ) for layer in checkpoint if '''up''' in layer}   )
      snake_case_			= {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE   )}

      for i in range(SCREAMING_SNAKE_CASE   ):
            snake_case_			= (i - 1) // (config['''layers_per_block'''] + 1)

            if any('''downsample''' in layer for layer in down_blocks[i]   ):
                  snake_case_			= checkpoint[
                      f'''encoder.down.{i}.downsample.conv.weight'''
                  ]
                  snake_case_			= checkpoint[
                      f'''encoder.down.{i}.downsample.conv.bias'''
                  ]

            if any('''block''' in layer for layer in down_blocks[i]   ):
                  snake_case_			= len(
                      {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE   ,   3   ).split('''.'''   )[:3]   ) for layer in down_blocks[i] if '''block''' in layer}   )
                  snake_case_			= {
                      layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
                      for layer_id in range(SCREAMING_SNAKE_CASE   )
                  }

                  if num_blocks > 0:
                        for j in range(config['''layers_per_block''']   ):
                              snake_case_			= renew_resnet_paths(blocks[j]   )
                              assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

            if any('''attn''' in layer for layer in down_blocks[i]   ):
                  snake_case_			= len(
                      {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE   ,   3   ).split('''.'''   )[:3]   ) for layer in down_blocks[i] if '''attn''' in layer}   )
                  snake_case_			= {
                      layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
                      for layer_id in range(SCREAMING_SNAKE_CASE   )
                  }

                  if num_attn > 0:
                        for j in range(config['''layers_per_block''']   ):
                              snake_case_			= renew_attention_paths(attns[j]   )
                              assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   config=SCREAMING_SNAKE_CASE   )

      snake_case_			= [key for key in checkpoint if '''mid.block_1''' in key]
      snake_case_			= [key for key in checkpoint if '''mid.block_2''' in key]
      snake_case_			= [key for key in checkpoint if '''mid.attn_1''' in key]

      # Mid new 2
      snake_case_			= renew_resnet_paths(SCREAMING_SNAKE_CASE   )
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]   ,   )

      snake_case_			= renew_resnet_paths(SCREAMING_SNAKE_CASE   )
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]   ,   )

      snake_case_			= renew_attention_paths(SCREAMING_SNAKE_CASE   ,   in_mid=SCREAMING_SNAKE_CASE   )
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]   ,   )

      for i in range(SCREAMING_SNAKE_CASE   ):
            snake_case_			= num_up_blocks - 1 - i

            if any('''upsample''' in layer for layer in up_blocks[i]   ):
                  snake_case_			= checkpoint[
                      f'''decoder.up.{i}.upsample.conv.weight'''
                  ]
                  snake_case_			= checkpoint[
                      f'''decoder.up.{i}.upsample.conv.bias'''
                  ]

            if any('''block''' in layer for layer in up_blocks[i]   ):
                  snake_case_			= len(
                      {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE   ,   3   ).split('''.'''   )[:3]   ) for layer in up_blocks[i] if '''block''' in layer}   )
                  snake_case_			= {
                      layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE   )
                  }

                  if num_blocks > 0:
                        for j in range(config['''layers_per_block'''] + 1   ):
                              snake_case_			= {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
                              snake_case_			= renew_resnet_paths(blocks[j]   )
                              assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[replace_indices]   )

            if any('''attn''' in layer for layer in up_blocks[i]   ):
                  snake_case_			= len(
                      {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE   ,   3   ).split('''.'''   )[:3]   ) for layer in up_blocks[i] if '''attn''' in layer}   )
                  snake_case_			= {
                      layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE   )
                  }

                  if num_attn > 0:
                        for j in range(config['''layers_per_block'''] + 1   ):
                              snake_case_			= {'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
                              snake_case_			= renew_attention_paths(attns[j]   )
                              assign_to_checkpoint(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   additional_replacements=[replace_indices]   )

      snake_case_			= {k.replace('''mid_new_2'''   ,   '''mid_block'''   ): v for k, v in new_checkpoint.items()}
      snake_case_			= checkpoint['''quant_conv.weight''']
      snake_case_			= checkpoint['''quant_conv.bias''']
      if "quantize.embedding.weight" in checkpoint:
            snake_case_			= checkpoint['''quantize.embedding.weight''']
      snake_case_			= checkpoint['''post_quant_conv.weight''']
      snake_case_			= checkpoint['''post_quant_conv.bias''']

      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(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help="""The config json file corresponding to the architecture.""",
   )

   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)

   with open(args.config_file) as f:
      UpperCAmelCase     =       json.loads(f.read())

   # unet case
   UpperCAmelCase     =       {key.split(""".""")[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
      UpperCAmelCase     =       convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
      UpperCAmelCase     =       convert_ddpm_checkpoint(checkpoint, config)

   if "ddpm" in config:
      del config["ddpm"]

   if config["_class_name"] == "VQModel":
      UpperCAmelCase     =       VQModel(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
      UpperCAmelCase     =       AutoencoderKL(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   else:
      UpperCAmelCase     =       UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      UpperCAmelCase     =       DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

      UpperCAmelCase     =       DDPMPipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)



def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    list:




      """simple docstring"""

      snake_case_			= [True for i in range(n + 1   )]
      snake_case_			= [i - 1 for i in range(n + 1   )]
      snake_case_			= []
      for i in range(2   ,   n + 1   ):
            if is_prime[i]:
                  primes.append(SCREAMING_SNAKE_CASE   )
            for j in range(0   ,   len(SCREAMING_SNAKE_CASE   )   ):
                  if i * primes[j] >= n:
                        break
                  snake_case_			= False

                  if i % primes[j] == 0:
                        snake_case_			= totients[i] * primes[j]
                        break

                  snake_case_			= totients[i] * (primes[j] - 1)

      return totients


if __name__ == "__main__":
   import doctest

   doctest.testmod()