





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def _A       (   __magic_name__  , __magic_name__=1	):
   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       (   __magic_name__  , __magic_name__=0	):
   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	)

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

   return mapping


def _A       (   __magic_name__  , __magic_name__=0	):
   lowercase__			       =  []
   for old_item in old_list:
      lowercase__			       =  old_item

      lowercase__			       =  new_item.replace("norm.weight"  , "group_norm.weight"	)
      lowercase__			       =  new_item.replace("norm.bias"  , "group_norm.bias"	)

      lowercase__			       =  new_item.replace("proj_out.weight"  , "proj_attn.weight"	)
      lowercase__			       =  new_item.replace("proj_out.bias"  , "proj_attn.bias"	)

      lowercase__			       =  shave_segments(_UpperCAmelCase  , n_shave_prefix_segments=_UpperCAmelCase	)

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

   return mapping


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__=None  , __magic_name__=None  , __magic_name__=None	):
   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

      # Global renaming happens here
      lowercase__			       =  new_path.replace("middle_block.0"  , "mid_block.resnets.0"	)
      lowercase__			       =  new_path.replace("middle_block.1"  , "mid_block.attentions.0"	)
      lowercase__			       =  new_path.replace("middle_block.2"  , "mid_block.resnets.1"	)

      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
      if "proj_attn.weight" in new_path:
         lowercase__			       =  old_checkpoint[path["old"]][:, :, 0]
      else:
         lowercase__			       =  old_checkpoint[path["old"]]


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  {}

   lowercase__			       =  checkpoint["time_embed.0.weight"]
   lowercase__			       =  checkpoint["time_embed.0.bias"]
   lowercase__			       =  checkpoint["time_embed.2.weight"]
   lowercase__			       =  checkpoint["time_embed.2.bias"]

   lowercase__			       =  checkpoint["input_blocks.0.0.weight"]
   lowercase__			       =  checkpoint["input_blocks.0.0.bias"]

   lowercase__			       =  checkpoint["out.0.weight"]
   lowercase__			       =  checkpoint["out.0.bias"]
   lowercase__			       =  checkpoint["out.2.weight"]
   lowercase__			       =  checkpoint["out.2.bias"]

   # Retrieves the keys for the input blocks only
   lowercase__			       =  len({".".join(layer.split("."	)[:2]	) for layer in checkpoint if "input_blocks" in layer}	)
   lowercase__			       =  {
       layer_id: [key for key in checkpoint 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 checkpoint if "middle_block" in layer}	)
   lowercase__			       =  {
       layer_id: [key for key in checkpoint 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 checkpoint if "output_blocks" in layer}	)
   lowercase__			       =  {
       layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
       for layer_id in range(_UpperCAmelCase	)
   }

   for i in range(1  , _UpperCAmelCase	):
      lowercase__			       =  (i - 1) // (config["num_res_blocks"] + 1)
      lowercase__			       =  (i - 1) % (config["num_res_blocks"] + 1)

      lowercase__			       =  [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
      lowercase__			       =  [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

      if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
         lowercase__			       =  checkpoint[
             f'''input_blocks.{i}.0.op.weight'''
         ]
         lowercase__			       =  checkpoint[
             f'''input_blocks.{i}.0.op.bias'''
         ]
         continue

      lowercase__			       =  renew_resnet_paths(_UpperCAmelCase	)
      lowercase__			       =  {"old": f'''input_blocks.{i}.0''', "new": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
      lowercase__			       =  {"old": "resnets.2.op", "new": "downsamplers.0.op"}
      assign_to_checkpoint(
          _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , additional_replacements=[meta_path, resnet_op]  , 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}''',
         }
         lowercase__			       =  {
             f'''input_blocks.{i}.1.qkv.bias''': {
                 "key": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                 "query": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                 "value": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
             },
             f'''input_blocks.{i}.1.qkv.weight''': {
                 "key": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                 "query": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                 "value": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
             },
         }
         assign_to_checkpoint(
             _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , additional_replacements=[meta_path]  , attention_paths_to_split=_UpperCAmelCase  , config=_UpperCAmelCase  , )

   lowercase__			       =  middle_blocks[0]
   lowercase__			       =  middle_blocks[1]
   lowercase__			       =  middle_blocks[2]

   lowercase__			       =  renew_resnet_paths(_UpperCAmelCase	)
   assign_to_checkpoint(_UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , config=_UpperCAmelCase	)

   lowercase__			       =  renew_resnet_paths(_UpperCAmelCase	)
   assign_to_checkpoint(_UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , config=_UpperCAmelCase	)

   lowercase__			       =  renew_attention_paths(_UpperCAmelCase	)
   lowercase__			       =  {
       "middle_block.1.qkv.bias": {
           "key": "mid_block.attentions.0.key.bias",
           "query": "mid_block.attentions.0.query.bias",
           "value": "mid_block.attentions.0.value.bias",
       },
       "middle_block.1.qkv.weight": {
           "key": "mid_block.attentions.0.key.weight",
           "query": "mid_block.attentions.0.query.weight",
           "value": "mid_block.attentions.0.value.weight",
       },
   }
   assign_to_checkpoint(
       _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , attention_paths_to_split=_UpperCAmelCase  , config=_UpperCAmelCase	)

   for i in range(_UpperCAmelCase	):
      lowercase__			       =  i // (config["num_res_blocks"] + 1)
      lowercase__			       =  i % (config["num_res_blocks"] + 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__			       =  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	)

         if ["conv.weight", "conv.bias"] in output_block_list.values():
            lowercase__			       =  list(output_block_list.values()	).index(["conv.weight", "conv.bias"]	)
            lowercase__			       =  checkpoint[
                f'''output_blocks.{i}.{index}.conv.weight'''
            ]
            lowercase__			       =  checkpoint[
                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}''',
            }
            lowercase__			       =  {
                f'''output_blocks.{i}.1.qkv.bias''': {
                    "key": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                    "query": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                    "value": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
                },
                f'''output_blocks.{i}.1.qkv.weight''': {
                    "key": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                    "query": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                    "value": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
                },
            }
            assign_to_checkpoint(
                _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , additional_replacements=[meta_path]  , attention_paths_to_split=to_split if any("qkv" in key for key in attentions	) else None  , 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__			       =  checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
  _snake_case           =			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.""")

  _snake_case           =			parser.parse_args()

  _snake_case           =			torch.load(args.checkpoint_path)

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

  _snake_case           =			convert_ldm_checkpoint(checkpoint, config)

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

  _snake_case           =			UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    _snake_case           =			DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
    _snake_case           =			VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

    _snake_case           =			LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)













from itertools import count


def _A       (   __magic_name__ = 50	):
   lowercase__			       =  [1] * min_block_length

   for n in count(__magic_name__	):
      fill_count_functions.append(1	)

      for block_length in range(__magic_name__  , n + 1	):
         for block_start in range(n - block_length	):
            fill_count_functions[n] += fill_count_functions[
                n - block_start - block_length - 1
            ]

         fill_count_functions[n] += 1

      if fill_count_functions[n] > 100_0000:
         break

   return n


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






