


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=0	):


  '''simple docstring'''

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

    lowerCamelCase :					Union[str, Any]    =				new_item.replace("out_layers.0"      ,						"norm2"	)
    lowerCamelCase :					List[str]    =				new_item.replace("out_layers.3"      ,						"conv2"	)

    lowerCamelCase :					Dict    =				new_item.replace("emb_layers.1"      ,						"time_emb_proj"	)
    lowerCamelCase :					Optional[int]    =				new_item.replace("skip_connection"      ,						"conv_shortcut"	)

    lowerCamelCase :					Optional[int]    =				shave_segments(SCREAMING_SNAKE_CASE_      ,						n_shave_prefix_segments=SCREAMING_SNAKE_CASE_	)

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

  return mapping






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=0	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				[]
  for old_item in old_list:
    lowerCamelCase :					List[str]    =				old_item

    lowerCamelCase :					Tuple    =				new_item.replace("norm.weight"      ,						"group_norm.weight"	)
    lowerCamelCase :					int    =				new_item.replace("norm.bias"      ,						"group_norm.bias"	)

    lowerCamelCase :					Optional[Any]    =				new_item.replace("proj_out.weight"      ,						"proj_attn.weight"	)
    lowerCamelCase :					Optional[int]    =				new_item.replace("proj_out.bias"      ,						"proj_attn.bias"	)

    lowerCamelCase :					List[Any]    =				shave_segments(SCREAMING_SNAKE_CASE_      ,						n_shave_prefix_segments=SCREAMING_SNAKE_CASE_	)

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

  return mapping






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=None      ,						SCREAMING_SNAKE_CASE_=None      ,						SCREAMING_SNAKE_CASE_=None	):


  '''simple docstring'''

  assert isinstance(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	), "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():
      lowerCamelCase :					List[str]    =				old_checkpoint[path]
      lowerCamelCase :					List[str]    =				old_tensor.shape[0] // 3

      lowerCamelCase :					Optional[int]    =				(-1, channels) if len(old_tensor.shape	) == 3 else (-1)

      lowerCamelCase :					List[Any]    =				old_tensor.shape[0] // config["num_head_channels"] // 3

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

      lowerCamelCase :					str    =				query.reshape(SCREAMING_SNAKE_CASE_	)
      lowerCamelCase :					Dict    =				key.reshape(SCREAMING_SNAKE_CASE_	)
      lowerCamelCase :					List[str]    =				value.reshape(SCREAMING_SNAKE_CASE_	)

  for path in paths:
    lowerCamelCase :					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

    # Global renaming happens here
    lowerCamelCase :					List[str]    =				new_path.replace("middle_block.0"      ,						"mid_block.resnets.0"	)
    lowerCamelCase :					List[str]    =				new_path.replace("middle_block.1"      ,						"mid_block.attentions.0"	)
    lowerCamelCase :					Union[str, Any]    =				new_path.replace("middle_block.2"      ,						"mid_block.resnets.1"	)

    if additional_replacements is not None:
      for replacement in additional_replacements:
        lowerCamelCase :					Any    =				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:
      lowerCamelCase :					List[Any]    =				old_checkpoint[path["old"]][:, :, 0]
    else:
      lowerCamelCase :					List[str]    =				old_checkpoint[path["old"]]






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				{}

  lowerCamelCase :					List[Any]    =				checkpoint["time_embed.0.weight"]
  lowerCamelCase :					List[Any]    =				checkpoint["time_embed.0.bias"]
  lowerCamelCase :					List[Any]    =				checkpoint["time_embed.2.weight"]
  lowerCamelCase :					Optional[int]    =				checkpoint["time_embed.2.bias"]

  lowerCamelCase :					Optional[Any]    =				checkpoint["input_blocks.0.0.weight"]
  lowerCamelCase :					List[Any]    =				checkpoint["input_blocks.0.0.bias"]

  lowerCamelCase :					List[str]    =				checkpoint["out.0.weight"]
  lowerCamelCase :					List[str]    =				checkpoint["out.0.bias"]
  lowerCamelCase :					Tuple    =				checkpoint["out.2.weight"]
  lowerCamelCase :					Union[str, Any]    =				checkpoint["out.2.bias"]

  # Retrieves the keys for the input blocks only
  lowerCamelCase :					List[str]    =				len({".".join(layer.split("."	)[:2]	) for layer in checkpoint if "input_blocks" in layer}	)
  lowerCamelCase :					Dict    =				{
      layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
      for layer_id in range(SCREAMING_SNAKE_CASE_	)
  }

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

  # Retrieves the keys for the output blocks only
  lowerCamelCase :					List[Any]    =				len({".".join(layer.split("."	)[:2]	) for layer in checkpoint if "output_blocks" in layer}	)
  lowerCamelCase :					Optional[Any]    =				{
      layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
      for layer_id in range(SCREAMING_SNAKE_CASE_	)
  }

  for i in range(1      ,						SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					Optional[Any]    =				(i - 1) // (config["num_res_blocks"] + 1)
    lowerCamelCase :					Tuple    =				(i - 1) % (config["num_res_blocks"] + 1)

    lowerCamelCase :					Optional[Any]    =				[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
    lowerCamelCase :					Any    =				[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:
      lowerCamelCase :					List[Any]    =				checkpoint[
          f"""input_blocks.{i}.0.op.weight"""
      ]
      lowerCamelCase :					Dict    =				checkpoint[
          f"""input_blocks.{i}.0.op.bias"""
      ]
      continue

    lowerCamelCase :					Optional[int]    =				renew_resnet_paths(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					List[str]    =				{"old": f"""input_blocks.{i}.0""", "new": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    lowerCamelCase :					Tuple    =				{"old": "resnets.2.op", "new": "downsamplers.0.op"}
    assign_to_checkpoint(
        SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						additional_replacements=[meta_path, resnet_op]      ,						config=SCREAMING_SNAKE_CASE_	)

    if len(SCREAMING_SNAKE_CASE_	):
      lowerCamelCase :					str    =				renew_attention_paths(SCREAMING_SNAKE_CASE_	)
      lowerCamelCase :					Union[str, Any]    =				{
          "old": f"""input_blocks.{i}.1""",
          "new": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
      }
      lowerCamelCase :					int    =				{
          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(
          SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						additional_replacements=[meta_path]      ,						attention_paths_to_split=SCREAMING_SNAKE_CASE_      ,						config=SCREAMING_SNAKE_CASE_      ,						)

  lowerCamelCase :					Dict    =				middle_blocks[0]
  lowerCamelCase :					Tuple    =				middle_blocks[1]
  lowerCamelCase :					List[str]    =				middle_blocks[2]

  lowerCamelCase :					Dict    =				renew_resnet_paths(SCREAMING_SNAKE_CASE_	)
  assign_to_checkpoint(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						config=SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[str]    =				renew_resnet_paths(SCREAMING_SNAKE_CASE_	)
  assign_to_checkpoint(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						config=SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Union[str, Any]    =				renew_attention_paths(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Union[str, Any]    =				{
      "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(
      SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						attention_paths_to_split=SCREAMING_SNAKE_CASE_      ,						config=SCREAMING_SNAKE_CASE_	)

  for i in range(SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					Optional[int]    =				i // (config["num_res_blocks"] + 1)
    lowerCamelCase :					Any    =				i % (config["num_res_blocks"] + 1)
    lowerCamelCase :					int    =				[shave_segments(SCREAMING_SNAKE_CASE_      ,						2	) for name in output_blocks[i]]
    lowerCamelCase :					Union[str, Any]    =				{}

    for layer in output_block_layers:
      lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				layer.split("."	)[0], shave_segments(SCREAMING_SNAKE_CASE_      ,						1	)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_	)
      else:
        lowerCamelCase :					Any    =				[layer_name]

    if len(SCREAMING_SNAKE_CASE_	) > 1:
      lowerCamelCase :					str    =				[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
      lowerCamelCase :					List[str]    =				[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

      lowerCamelCase :					Any    =				renew_resnet_paths(SCREAMING_SNAKE_CASE_	)
      lowerCamelCase :					int    =				renew_resnet_paths(SCREAMING_SNAKE_CASE_	)

      lowerCamelCase :					Optional[Any]    =				{"old": f"""output_blocks.{i}.0""", "new": f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						additional_replacements=[meta_path]      ,						config=SCREAMING_SNAKE_CASE_	)

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        lowerCamelCase :					Union[str, Any]    =				list(output_block_list.values()	).index(["conv.weight", "conv.bias"]	)
        lowerCamelCase :					Optional[int]    =				checkpoint[
            f"""output_blocks.{i}.{index}.conv.weight"""
        ]
        lowerCamelCase :					Any    =				checkpoint[
            f"""output_blocks.{i}.{index}.conv.bias"""
        ]

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

      if len(SCREAMING_SNAKE_CASE_	):
        lowerCamelCase :					Optional[int]    =				renew_attention_paths(SCREAMING_SNAKE_CASE_	)
        lowerCamelCase :					int    =				{
            "old": f"""output_blocks.{i}.1""",
            "new": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
        }
        lowerCamelCase :					Optional[Any]    =				{
            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(
            SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						additional_replacements=[meta_path]      ,						attention_paths_to_split=to_split if any("qkv" in key for key in attentions	) else None      ,						config=SCREAMING_SNAKE_CASE_      ,						)
    else:
      lowerCamelCase :					Union[str, Any]    =				renew_resnet_paths(SCREAMING_SNAKE_CASE_      ,						n_shave_prefix_segments=1	)
      for path in resnet_0_paths:
        lowerCamelCase :					Optional[int]    =				".".join(["output_blocks", str(SCREAMING_SNAKE_CASE_	), path["old"]]	)
        lowerCamelCase :					Union[str, Any]    =				".".join(["up_blocks", str(SCREAMING_SNAKE_CASE_	), "resnets", str(SCREAMING_SNAKE_CASE_	), path["new"]]	)

        lowerCamelCase :					str    =				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)







def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 4000000	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				[0, 1]
  lowerCamelCase :					Union[str, Any]    =				0
  while fib[i] <= n:
    fib.append(fib[i] + fib[i + 1]	)
    if fib[i + 2] > n:
      break
    i += 1
  lowerCamelCase :					Union[str, Any]    =				0
  for j in range(len(SCREAMING_SNAKE_CASE_	) - 1	):
    if fib[j] % 2 == 0:
      total += fib[j]

  return total


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



