import argparse

import torch

from diffusers import UNetaDConditionModel
def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase=None       ,							lowercase=None       ,							lowercase=None				):
    assert isinstance(lowercase       ,							lowercase				), "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[Any]         =  old_checkpoint[path]
            lowerCAmelCase_:       List[Any]         =  old_tensor.shape[0] // 3

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

            lowerCAmelCase_:       Optional[Any]         =  old_tensor.shape[0] // config["num_head_channels"] // 3

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

            lowerCAmelCase_:       Optional[Any]         =  query.reshape(lowercase				)
            lowerCAmelCase_:       int         =  key.reshape(lowercase				)
            lowerCAmelCase_:       Any         =  value.reshape(lowercase				)

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

        # proj_attn.weight has to be converted from conv 1D to linear
        lowerCAmelCase_:       str         =  old_checkpoint[path["old"]]
        lowerCAmelCase_:       Dict         =  ["proj_attn.weight"]
        lowerCAmelCase_:       Union[str, Any]         =  ["proj_out.weight", "proj_in.weight"]
        if any(k in new_path for k in names				):
            lowerCAmelCase_:       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:
            lowerCAmelCase_:       Optional[int]         =  weight[:, :, 0]
        else:
            lowerCAmelCase_:       Union[str, Any]         =  weight
def 		snake_case__  (		lowercase       ,							lowercase=0				):
    lowerCAmelCase_:       List[str]         =  []
    for old_item in old_list:
        lowerCAmelCase_:       Optional[Any]         =  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				):
    lowerCAmelCase_:       Optional[int]         =  []
    for old_item in old_list:
        mapping.append({"old": old_item, "new": old_item}				)

    return mapping
def 		snake_case__  (		lowercase       ,							lowercase=0				):
    lowerCAmelCase_:       Union[str, Any]         =  []
    for old_item in old_list:
        lowerCAmelCase_:       Tuple         =  old_item.replace("in_layers.0"       ,							"norm1"				)
        lowerCAmelCase_:       Dict         =  new_item.replace("in_layers.2"       ,							"conv1"				)

        lowerCAmelCase_:       Optional[int]         =  new_item.replace("out_layers.0"       ,							"norm2"				)
        lowerCAmelCase_:       List[str]         =  new_item.replace("out_layers.3"       ,							"conv2"				)

        lowerCAmelCase_:       List[Any]         =  new_item.replace("emb_layers.1"       ,							"time_emb_proj"				)
        lowerCAmelCase_:       Optional[int]         =  new_item.replace("skip_connection"       ,							"conv_shortcut"				)

        lowerCAmelCase_:       Tuple         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)

        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				):
    lowerCAmelCase_:       Tuple         =  {}
    lowerCAmelCase_:       Any         =  list(checkpoint.keys()				)

    lowerCAmelCase_:       str         =  "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"				):
                lowerCAmelCase_:       List[str]         =  "model_ema." + "".join(key.split("."				)[1:]				)
                lowerCAmelCase_:       Tuple         =  checkpoint.pop(lowercase				)
    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:
            lowerCAmelCase_:       str         =  checkpoint.pop(lowercase				)

    lowerCAmelCase_:       Optional[Any]         =  {}

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

    lowerCAmelCase_:       Optional[Any]         =  unet_state_dict["input_blocks.0.0.weight"]
    lowerCAmelCase_:       Tuple         =  unet_state_dict["input_blocks.0.0.bias"]

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

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

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

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

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

    for i in range(1       ,							lowercase				):
        lowerCAmelCase_:       Optional[int]         =  (i - 1) // (config["layers_per_block"] + 1)
        lowerCAmelCase_:       Tuple         =  (i - 1) % (config["layers_per_block"] + 1)

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

        lowerCAmelCase_:       List[str]         =  renew_resnet_paths(lowercase				)
        lowerCAmelCase_:       int         =  {"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
        assign_to_checkpoint(
            lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)

        lowerCAmelCase_:       Tuple         =  [key for key in resnets if "temopral_conv" in key]
        lowerCAmelCase_:       Dict         =  renew_temp_conv_paths(lowercase				)
        lowerCAmelCase_:       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(
            lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)

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

        if len(lowercase				):
            lowerCAmelCase_:       Tuple         =  renew_attention_paths(lowercase				)
            lowerCAmelCase_:       Union[str, Any]         =  {
                "old": F'''input_blocks.{i}.2''',
                "new": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
            }
            assign_to_checkpoint(
                lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)

    lowerCAmelCase_:       List[Any]         =  middle_blocks[0]
    lowerCAmelCase_:       Any         =  [key for key in resnet_a if "temopral_conv" in key]
    lowerCAmelCase_:       int         =  middle_blocks[1]
    lowerCAmelCase_:       str         =  middle_blocks[2]
    lowerCAmelCase_:       List[Any]         =  middle_blocks[3]
    lowerCAmelCase_:       Dict         =  [key for key in resnet_a if "temopral_conv" in key]

    lowerCAmelCase_:       Tuple         =  renew_resnet_paths(lowercase				)
    lowerCAmelCase_:       Tuple         =  {"old": "middle_block.0", "new": "mid_block.resnets.0"}
    assign_to_checkpoint(
        lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase       ,							additional_replacements=[meta_path]				)

    lowerCAmelCase_:       Optional[int]         =  renew_temp_conv_paths(lowercase				)
    lowerCAmelCase_:       List[str]         =  {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
    assign_to_checkpoint(
        lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase       ,							additional_replacements=[meta_path]				)

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

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

    lowerCAmelCase_:       Optional[int]         =  renew_attention_paths(lowercase				)
    lowerCAmelCase_:       Union[str, Any]         =  {"old": "middle_block.1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(
        lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)

    lowerCAmelCase_:       List[str]         =  renew_attention_paths(lowercase				)
    lowerCAmelCase_:       Union[str, Any]         =  {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
    assign_to_checkpoint(
        lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)

    for i in range(lowercase				):
        lowerCAmelCase_:       int         =  i // (config["layers_per_block"] + 1)
        lowerCAmelCase_:       Dict         =  i % (config["layers_per_block"] + 1)
        lowerCAmelCase_:       Dict         =  [shave_segments(lowercase       ,							2				) for name in output_blocks[i]]
        lowerCAmelCase_:       Union[str, Any]         =  {}

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

        if len(lowercase				) > 1:
            lowerCAmelCase_:       Tuple         =  [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
            lowerCAmelCase_:       List[Any]         =  [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
            lowerCAmelCase_:       Any         =  [key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

            lowerCAmelCase_:       str         =  renew_resnet_paths(lowercase				)
            lowerCAmelCase_:       Tuple         =  renew_resnet_paths(lowercase				)

            lowerCAmelCase_:       str         =  {"old": F'''output_blocks.{i}.0''', "new": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
            assign_to_checkpoint(
                lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)

            lowerCAmelCase_:       List[str]         =  [key for key in resnets if "temopral_conv" in key]
            lowerCAmelCase_:       List[str]         =  renew_temp_conv_paths(lowercase				)
            lowerCAmelCase_:       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(
                lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)

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

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

            if len(lowercase				):
                lowerCAmelCase_:       int         =  renew_attention_paths(lowercase				)
                lowerCAmelCase_:       Tuple         =  {
                    "old": F'''output_blocks.{i}.1''',
                    "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                }
                assign_to_checkpoint(
                    lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[meta_path]       ,							config=lowercase				)

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

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

    return new_checkpoint


if __name__ == "__main__":
 a     :			str					    =						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     :			List[str]					    =						parser.parse_args()

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

 a     :			Tuple					    =						convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

 a     :			Any					    =						set(unet.state_dict().keys()) - set(converted_ckpt.keys())
 a     :			List[str]					    =						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 --
def 		snake_case__  (		lowercase = 998001				):

   # fetches the next number
   for number in range(n - 1       ,							9999       ,							-1				):
      lowerCAmelCase_:       str         =  str(lowercase				)

      # checks whether 'str_number' is a palindrome.
      if str_number == str_number[::-1]:
         lowerCAmelCase_:       List[str]         =  999

         # if 'number' is a product of two 3-digit numbers
         # then number is the answer otherwise fetch next number.
         while divisor != 99:
            if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
               return number
            divisor -= 1
   raise ValueError("That number is larger than our acceptable range."				)


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