



'''simple docstring'''





import argparse

import torch

from diffusers import UNetaDConditionModel







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__=None      ,					lowercase__=None      ,					lowercase__=None		):
  assert isinstance(__A      ,					__A		), "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():
      UpperCAmelCase__			:				Union[str, Any]												=       old_checkpoint[path]
      UpperCAmelCase__			:				str												=       old_tensor.shape[0] // 3

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

      UpperCAmelCase__			:				Union[str, Any]												=       old_tensor.shape[0] // config["num_head_channels"] // 3

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

      UpperCAmelCase__			:				int												=       query.reshape(__A		)
      UpperCAmelCase__			:				Optional[Any]												=       key.reshape(__A		)
      UpperCAmelCase__			:				Any												=       value.reshape(__A		)

  for path in paths:
    UpperCAmelCase__			:				List[str]												=       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:
        UpperCAmelCase__			:				Optional[Any]												=       new_path.replace(replacement["old"]      ,					replacement["new"]		)

            # proj_attn.weight has to be converted from conv 1D to linear
    UpperCAmelCase__			:				str												=       old_checkpoint[path["old"]]
    UpperCAmelCase__			:				List[Any]												=       ["proj_attn.weight"]
    UpperCAmelCase__			:				str												=       ["proj_out.weight", "proj_in.weight"]
    if any(k in new_path for k in names		):
      UpperCAmelCase__			:				List[str]												=       weight[:, :, 0]
    elif any(k in new_path for k in names_a		) and len(weight.shape		) > 2 and ".attentions." not in new_path:
      UpperCAmelCase__			:				str												=       weight[:, :, 0]
    else:
      UpperCAmelCase__			:				Optional[int]												=       weight







def snake_case_    (				lowercase__      ,					lowercase__=0		):
  UpperCAmelCase__			:				Union[str, Any]												=       []
  for old_item in old_list:
    UpperCAmelCase__			:				Dict												=       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 snake_case_    (				lowercase__      ,					lowercase__=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 snake_case_    (				lowercase__      ,					lowercase__=0		):
  UpperCAmelCase__			:				Dict												=       []
  for old_item in old_list:
    mapping.append({"old": old_item, "new": old_item}		)

  return mapping







def snake_case_    (				lowercase__      ,					lowercase__=0		):
  UpperCAmelCase__			:				List[str]												=       []
  for old_item in old_list:
    UpperCAmelCase__			:				str												=       old_item.replace("in_layers.0"      ,					"norm1"		)
    UpperCAmelCase__			:				Optional[int]												=       new_item.replace("in_layers.2"      ,					"conv1"		)

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

    UpperCAmelCase__			:				Dict												=       new_item.replace("emb_layers.1"      ,					"time_emb_proj"		)
    UpperCAmelCase__			:				int												=       new_item.replace("skip_connection"      ,					"conv_shortcut"		)

    UpperCAmelCase__			:				Optional[int]												=       shave_segments(__A      ,					n_shave_prefix_segments=__A		)

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

  return mapping







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__=None      ,					lowercase__=False		):
  UpperCAmelCase__			:				List[Any]												=       {}
  UpperCAmelCase__			:				int												=       list(checkpoint.keys()		)

  UpperCAmelCase__			:				List[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		) > 1_0_0 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"		):
        UpperCAmelCase__			:				Union[str, Any]												=       "model_ema." + "".join(key.split("."		)[1:]		)
        UpperCAmelCase__			:				Optional[Any]												=       checkpoint.pop(__A		)
  else:
    if sum(k.startswith("model_ema"		) for k in keys		) > 1_0_0:
      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:
      UpperCAmelCase__			:				Optional[int]												=       checkpoint.pop(__A		)

  UpperCAmelCase__			:				Optional[Any]												=       {}

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

  UpperCAmelCase__			:				List[Any]												=       unet_state_dict["input_blocks.0.0.weight"]
  UpperCAmelCase__			:				List[Any]												=       unet_state_dict["input_blocks.0.0.bias"]

  UpperCAmelCase__			:				Union[str, Any]												=       [v for v in unet_state_dict if v.startswith("input_blocks.0.1"		)]
  UpperCAmelCase__			:				str												=       renew_attention_paths(__A		)
  UpperCAmelCase__			:				Dict												=       {"old": "input_blocks.0.1", "new": "transformer_in"}
  assign_to_checkpoint(__A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

  UpperCAmelCase__			:				List[Any]												=       unet_state_dict["out.0.weight"]
  UpperCAmelCase__			:				List[str]												=       unet_state_dict["out.0.bias"]
  UpperCAmelCase__			:				List[str]												=       unet_state_dict["out.2.weight"]
  UpperCAmelCase__			:				List[Any]												=       unet_state_dict["out.2.bias"]

  # Retrieves the keys for the input blocks only
  UpperCAmelCase__			:				str												=       len({".".join(layer.split("."		)[:2]		) for layer in unet_state_dict if "input_blocks" in layer}		)
  UpperCAmelCase__			:				int												=       {
      layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
      for layer_id in range(__A		)
  }

  # Retrieves the keys for the middle blocks only
  UpperCAmelCase__			:				Union[str, Any]												=       len({".".join(layer.split("."		)[:2]		) for layer in unet_state_dict if "middle_block" in layer}		)
  UpperCAmelCase__			:				Dict												=       {
      layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
      for layer_id in range(__A		)
  }

  # Retrieves the keys for the output blocks only
  UpperCAmelCase__			:				str												=       len({".".join(layer.split("."		)[:2]		) for layer in unet_state_dict if "output_blocks" in layer}		)
  UpperCAmelCase__			:				Tuple												=       {
      layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
      for layer_id in range(__A		)
  }

  for i in range(1      ,					__A		):
    UpperCAmelCase__			:				List[str]												=       (i - 1) // (config["layers_per_block"] + 1)
    UpperCAmelCase__			:				Union[str, Any]												=       (i - 1) % (config["layers_per_block"] + 1)

    UpperCAmelCase__			:				Dict												=       [
        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
    ]
    UpperCAmelCase__			:				Dict												=       [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
    UpperCAmelCase__			:				str												=       [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:
      UpperCAmelCase__			:				str												=       unet_state_dict.pop(
          F"""input_blocks.{i}.op.weight"""		)
      UpperCAmelCase__			:				int												=       unet_state_dict.pop(
          F"""input_blocks.{i}.op.bias"""		)

    UpperCAmelCase__			:				Optional[Any]												=       renew_resnet_paths(__A		)
    UpperCAmelCase__			:				List[Any]												=       {"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    assign_to_checkpoint(
        __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

    UpperCAmelCase__			:				Optional[int]												=       [key for key in resnets if "temopral_conv" in key]
    UpperCAmelCase__			:				Optional[Any]												=       renew_temp_conv_paths(__A		)
    UpperCAmelCase__			:				List[Any]												=       {
        "old": F"""input_blocks.{i}.0.temopral_conv""",
        "new": F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
    }
    assign_to_checkpoint(
        __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

    if len(__A		):
      UpperCAmelCase__			:				List[str]												=       renew_attention_paths(__A		)
      UpperCAmelCase__			:				Union[str, Any]												=       {"old": F"""input_blocks.{i}.1""", "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
      assign_to_checkpoint(
          __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

    if len(__A		):
      UpperCAmelCase__			:				str												=       renew_attention_paths(__A		)
      UpperCAmelCase__			:				str												=       {
          "old": F"""input_blocks.{i}.2""",
          "new": F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

  UpperCAmelCase__			:				Dict												=       middle_blocks[0]
  UpperCAmelCase__			:				Tuple												=       [key for key in resnet_a if "temopral_conv" in key]
  UpperCAmelCase__			:				str												=       middle_blocks[1]
  UpperCAmelCase__			:				Tuple												=       middle_blocks[2]
  UpperCAmelCase__			:				Union[str, Any]												=       middle_blocks[3]
  UpperCAmelCase__			:				Any												=       [key for key in resnet_a if "temopral_conv" in key]

  UpperCAmelCase__			:				Optional[Any]												=       renew_resnet_paths(__A		)
  UpperCAmelCase__			:				Optional[int]												=       {"old": "middle_block.0", "new": "mid_block.resnets.0"}
  assign_to_checkpoint(
      __A      ,					__A      ,					__A      ,					config=__A      ,					additional_replacements=[meta_path]		)

  UpperCAmelCase__			:				Union[str, Any]												=       renew_temp_conv_paths(__A		)
  UpperCAmelCase__			:				List[str]												=       {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
  assign_to_checkpoint(
      __A      ,					__A      ,					__A      ,					config=__A      ,					additional_replacements=[meta_path]		)

  UpperCAmelCase__			:				Optional[Any]												=       renew_resnet_paths(__A		)
  UpperCAmelCase__			:				List[Any]												=       {"old": "middle_block.3", "new": "mid_block.resnets.1"}
  assign_to_checkpoint(
      __A      ,					__A      ,					__A      ,					config=__A      ,					additional_replacements=[meta_path]		)

  UpperCAmelCase__			:				Optional[Any]												=       renew_temp_conv_paths(__A		)
  UpperCAmelCase__			:				Optional[Any]												=       {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
  assign_to_checkpoint(
      __A      ,					__A      ,					__A      ,					config=__A      ,					additional_replacements=[meta_path]		)

  UpperCAmelCase__			:				str												=       renew_attention_paths(__A		)
  UpperCAmelCase__			:				List[str]												=       {"old": "middle_block.1", "new": "mid_block.attentions.0"}
  assign_to_checkpoint(
      __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

  UpperCAmelCase__			:				Tuple												=       renew_attention_paths(__A		)
  UpperCAmelCase__			:				Any												=       {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
  assign_to_checkpoint(
      __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

  for i in range(__A		):
    UpperCAmelCase__			:				str												=       i // (config["layers_per_block"] + 1)
    UpperCAmelCase__			:				Tuple												=       i % (config["layers_per_block"] + 1)
    UpperCAmelCase__			:				Optional[int]												=       [shave_segments(__A      ,					2		) for name in output_blocks[i]]
    UpperCAmelCase__			:				int												=       {}

    for layer in output_block_layers:
      UpperCAmelCase__					, UpperCAmelCase__			:				Optional[int]												=       layer.split("."		)[0], shave_segments(__A      ,					1		)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(__A		)
      else:
        UpperCAmelCase__			:				Dict												=       [layer_name]

    if len(__A		) > 1:
      UpperCAmelCase__			:				Any												=       [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
      UpperCAmelCase__			:				Union[str, Any]												=       [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
      UpperCAmelCase__			:				int												=       [key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

      UpperCAmelCase__			:				str												=       renew_resnet_paths(__A		)
      UpperCAmelCase__			:				Union[str, Any]												=       renew_resnet_paths(__A		)

      UpperCAmelCase__			:				List[Any]												=       {"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(
          __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

      UpperCAmelCase__			:				List[str]												=       [key for key in resnets if "temopral_conv" in key]
      UpperCAmelCase__			:				Tuple												=       renew_temp_conv_paths(__A		)
      UpperCAmelCase__			:				Optional[Any]												=       {
          "old": F"""output_blocks.{i}.0.temopral_conv""",
          "new": F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

      UpperCAmelCase__			:				str												=       {k: sorted(__A		) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        UpperCAmelCase__			:				List[Any]												=       list(output_block_list.values()		).index(["conv.bias", "conv.weight"]		)
        UpperCAmelCase__			:				Union[str, Any]												=       unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.weight"""
        ]
        UpperCAmelCase__			:				List[Any]												=       unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.bias"""
        ]

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

      if len(__A		):
        UpperCAmelCase__			:				Optional[int]												=       renew_attention_paths(__A		)
        UpperCAmelCase__			:				str												=       {
            "old": F"""output_blocks.{i}.1""",
            "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
        }
        assign_to_checkpoint(
            __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)

      if len(__A		):
        UpperCAmelCase__			:				Any												=       renew_attention_paths(__A		)
        UpperCAmelCase__			:				List[Any]												=       {
            "old": F"""output_blocks.{i}.2""",
            "new": F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
        }
        assign_to_checkpoint(
            __A      ,					__A      ,					__A      ,					additional_replacements=[meta_path]      ,					config=__A		)
    else:
      UpperCAmelCase__			:				Any												=       renew_resnet_paths(__A      ,					n_shave_prefix_segments=1		)
      for path in resnet_0_paths:
        UpperCAmelCase__			:				Union[str, Any]												=       ".".join(["output_blocks", str(__A		), path["old"]]		)
        UpperCAmelCase__			:				List[Any]												=       ".".join(["up_blocks", str(__A		), "resnets", str(__A		), path["new"]]		)
        UpperCAmelCase__			:				Union[str, Any]												=       unet_state_dict[old_path]

      UpperCAmelCase__			:				List[Any]												=       [l for l in output_block_layers if "temopral_conv" in l]
      for path in temopral_conv_paths:
        UpperCAmelCase__			:				Dict												=       path.split("temopral_conv."		)[-1]
        UpperCAmelCase__			:				List[Any]												=       ".".join(["output_blocks", str(__A		), str(__A		), "temopral_conv", pruned_path]		)
        UpperCAmelCase__			:				str												=       ".".join(["up_blocks", str(__A		), "temp_convs", str(__A		), pruned_path]		)
        UpperCAmelCase__			:				int												=       unet_state_dict[old_path]

  return new_checkpoint


if __name__ == "__main__":
      SCREAMING_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("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      SCREAMING_SNAKE_CASE    =					parser.parse_args()

      SCREAMING_SNAKE_CASE    =					torch.load(args.checkpoint_path, map_location="""cpu""")
      SCREAMING_SNAKE_CASE    =					UNetaDConditionModel()

      SCREAMING_SNAKE_CASE    =					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

      SCREAMING_SNAKE_CASE    =					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
      SCREAMING_SNAKE_CASE    =					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 snake_case_    (				lowercase__      ,					lowercase__ = 1_3		):
  UpperCAmelCase__			:				Optional[Any]												=       ""
  for c in s:
    if "A" <= c <= "Z":
      out += chr(ord("A"		) + (ord(lowercase__		) - ord("A"		) + n) % 2_6		)
    elif "a" <= c <= "z":
      out += chr(ord("a"		) + (ord(lowercase__		) - ord("a"		) + n) % 2_6		)
    else:
      out += c
  return out







def snake_case_    (				):
  UpperCAmelCase__			:				Dict												=       input("Enter message: "		)

  UpperCAmelCase__			:				Optional[int]												=       dencrypt(lowercase__      ,					1_3		)
  print("Encryption:"      ,					lowercase__		)

  UpperCAmelCase__			:				Union[str, Any]												=       dencrypt(lowercase__      ,					1_3		)
  print("Decryption: "      ,					lowercase__		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



