
"""simple docstring"""




import argparse

import torch

from diffusers import UNetaDConditionModel



def 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							List[Any]=None							,     lowerCAmelCase						:							Tuple=None							,     lowerCAmelCase						:							Dict=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():
               UpperCAmelCase__				:			str							=    old_checkpoint[path]
               UpperCAmelCase__				:			Dict							=    old_tensor.shape[0] // 3

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

               UpperCAmelCase__				:			List[str]							=    old_tensor.shape[0] // config["num_head_channels"] // 3

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

               UpperCAmelCase__				:			List[str]							=    query.reshape(lowerCAmelCase		)
               UpperCAmelCase__				:			Tuple							=    key.reshape(lowerCAmelCase		)
               UpperCAmelCase__				:			Dict							=    value.reshape(lowerCAmelCase		)

     for path in paths:
          UpperCAmelCase__				:			Optional[int]							=    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__				:			List[str]							=    old_checkpoint[path["old"]]
          UpperCAmelCase__				:			Dict							=    ["proj_attn.weight"]
          UpperCAmelCase__				:			List[str]							=    ["proj_out.weight", "proj_in.weight"]
          if any(k in new_path for k in names		):
               UpperCAmelCase__				:			Tuple							=    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__				:			Tuple							=    weight[:, :, 0]
          else:
               UpperCAmelCase__				:			int							=    weight



def 				a__			(					lowerCAmelCase						:							List[Any]							,     lowerCAmelCase						:							Dict=0		):






     '''simple docstring'''
     UpperCAmelCase__				:			Any							=    []
     for old_item in old_list:
          UpperCAmelCase__				:			Tuple							=    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						:							List[Any]							,     lowerCAmelCase						:							Optional[int]=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						:							str							,     lowerCAmelCase						:							List[str]=0		):






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

     return mapping



def 				a__			(					lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							List[str]=0		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    []
     for old_item in old_list:
          UpperCAmelCase__				:			List[Any]							=    old_item.replace("in_layers.0"							,     "norm1"		)
          UpperCAmelCase__				:			List[str]							=    new_item.replace("in_layers.2"							,     "conv1"		)

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

          UpperCAmelCase__				:			Optional[Any]							=    new_item.replace("emb_layers.1"							,     "time_emb_proj"		)
          UpperCAmelCase__				:			Tuple							=    new_item.replace("skip_connection"							,     "conv_shortcut"		)

          UpperCAmelCase__				:			List[Any]							=    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						:							int							,     lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Tuple=None							,     lowerCAmelCase						:							Dict=False		):






     '''simple docstring'''
     UpperCAmelCase__				:			str							=    {}
     UpperCAmelCase__				:			Optional[Any]							=    list(checkpoint.keys()		)

     UpperCAmelCase__				:			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		) > 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"		):
                    UpperCAmelCase__				:			Union[str, Any]							=    "model_ema." + "".join(key.split("."		)[1:]		)
                    UpperCAmelCase__				:			Union[str, Any]							=    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:
               UpperCAmelCase__				:			Union[str, Any]							=    checkpoint.pop(lowerCAmelCase		)

     UpperCAmelCase__				:			List[Any]							=    {}

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

     UpperCAmelCase__				:			int							=    unet_state_dict["input_blocks.0.0.weight"]
     UpperCAmelCase__				:			str							=    unet_state_dict["input_blocks.0.0.bias"]

     UpperCAmelCase__				:			Tuple							=    [v for v in unet_state_dict if v.startswith("input_blocks.0.1"		)]
     UpperCAmelCase__				:			Optional[Any]							=    renew_attention_paths(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    {"old": "input_blocks.0.1", "new": "transformer_in"}
     assign_to_checkpoint(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     additional_replacements=[meta_path]							,     config=lowerCAmelCase		)

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

     # Retrieves the keys for the input blocks only
     UpperCAmelCase__				:			Optional[int]							=    len({".".join(layer.split("."		)[:2]		) for layer in unet_state_dict if "input_blocks" in layer}		)
     UpperCAmelCase__				:			Union[str, 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
     UpperCAmelCase__				:			List[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(lowerCAmelCase		)
     }

     # Retrieves the keys for the output blocks only
     UpperCAmelCase__				:			Optional[int]							=    len({".".join(layer.split("."		)[:2]		) for layer in unet_state_dict if "output_blocks" in layer}		)
     UpperCAmelCase__				:			List[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		):
          UpperCAmelCase__				:			Union[str, Any]							=    (i - 1) // (config["layers_per_block"] + 1)
          UpperCAmelCase__				:			Union[str, Any]							=    (i - 1) % (config["layers_per_block"] + 1)

          UpperCAmelCase__				:			int							=    [
              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__				:			List[Any]							=    [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]
          UpperCAmelCase__				:			Optional[int]							=    [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__				:			Union[str, Any]							=    unet_state_dict.pop(
                   F"input_blocks.{i}.op.weight"		)
               UpperCAmelCase__				:			Any							=    unet_state_dict.pop(
                   F"input_blocks.{i}.op.bias"		)

          UpperCAmelCase__				:			Union[str, Any]							=    renew_resnet_paths(lowerCAmelCase		)
          UpperCAmelCase__				:			List[Any]							=    {"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		)

          UpperCAmelCase__				:			Dict							=    [key for key in resnets if "temopral_conv" in key]
          UpperCAmelCase__				:			Union[str, Any]							=    renew_temp_conv_paths(lowerCAmelCase		)
          UpperCAmelCase__				:			Optional[Any]							=    {
              "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		):
               UpperCAmelCase__				:			List[Any]							=    renew_attention_paths(lowerCAmelCase		)
               UpperCAmelCase__				:			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		):
               UpperCAmelCase__				:			Tuple							=    renew_attention_paths(lowerCAmelCase		)
               UpperCAmelCase__				:			Any							=    {
                   "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		)

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

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

     UpperCAmelCase__				:			Tuple							=    renew_temp_conv_paths(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
     assign_to_checkpoint(
         lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     config=lowerCAmelCase							,     additional_replacements=[meta_path]		)

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

     UpperCAmelCase__				:			List[Any]							=    renew_temp_conv_paths(lowerCAmelCase		)
     UpperCAmelCase__				:			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]		)

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

     UpperCAmelCase__				:			int							=    renew_attention_paths(lowerCAmelCase		)
     UpperCAmelCase__				:			Tuple							=    {"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		):
          UpperCAmelCase__				:			int							=    i // (config["layers_per_block"] + 1)
          UpperCAmelCase__				:			Union[str, Any]							=    i % (config["layers_per_block"] + 1)
          UpperCAmelCase__				:			Any							=    [shave_segments(lowerCAmelCase							,     2		) for name in output_blocks[i]]
          UpperCAmelCase__				:			Any							=    {}

          for layer in output_block_layers:
               UpperCAmelCase__				,						UpperCAmelCase__				:			Union[str, Any]							=    layer.split("."		)[0], shave_segments(lowerCAmelCase							,     1		)
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(lowerCAmelCase		)
               else:
                    UpperCAmelCase__				:			str							=    [layer_name]

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

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

               UpperCAmelCase__				:			Optional[int]							=    {"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		)

               UpperCAmelCase__				:			str							=    [key for key in resnets if "temopral_conv" in key]
               UpperCAmelCase__				:			int							=    renew_temp_conv_paths(lowerCAmelCase		)
               UpperCAmelCase__				:			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		)

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

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

               if len(lowerCAmelCase		):
                    UpperCAmelCase__				:			str							=    renew_attention_paths(lowerCAmelCase		)
                    UpperCAmelCase__				:			int							=    {
                        "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		):
                    UpperCAmelCase__				:			Tuple							=    renew_attention_paths(lowerCAmelCase		)
                    UpperCAmelCase__				:			int							=    {
                        "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:
               UpperCAmelCase__				:			Optional[Any]							=    renew_resnet_paths(lowerCAmelCase							,     n_shave_prefix_segments=1		)
               for path in resnet_0_paths:
                    UpperCAmelCase__				:			Any							=    ".".join(["output_blocks", str(lowerCAmelCase		), path["old"]]		)
                    UpperCAmelCase__				:			Union[str, Any]							=    ".".join(["up_blocks", str(lowerCAmelCase		), "resnets", str(lowerCAmelCase		), path["new"]]		)
                    UpperCAmelCase__				:			int							=    unet_state_dict[old_path]

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

     return new_checkpoint


if __name__ == "__main__":
    A__   :						Tuple 							=     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.""")
    A__   :						Optional[Any] 							=     parser.parse_args()

    A__   :						Any 							=     torch.load(args.checkpoint_path, map_location="""cpu""")
    A__   :						Union[str, Any] 							=     UNetaDConditionModel()

    A__   :						int 							=     convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

    A__   :						int 							=     set(unet.state_dict().keys()) - set(converted_ckpt.keys())
    A__   :						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						:							str							,     lowerCAmelCase						:							int = 13		):






     '''simple docstring'''
     UpperCAmelCase__				:			Any							=    ""
     for c in s:
          if "A" <= c <= "Z":
               out += chr(ord("A"		) + (ord(lowerCAmelCase		) - ord("A"		) + n) % 26		)
          elif "a" <= c <= "z":
               out += chr(ord("a"		) + (ord(lowerCAmelCase		) - ord("a"		) + n) % 26		)
          else:
               out += c
     return out



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[int]							=    input("Enter message: "		)

     UpperCAmelCase__				:			Optional[Any]							=    dencrypt(lowerCAmelCase							,     13		)
     print("Encryption:"							,     lowerCAmelCase		)

     UpperCAmelCase__				:			List[str]							=    dencrypt(lowerCAmelCase							,     13		)
     print("Decryption: "							,     lowerCAmelCase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



