


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel







def   A   (		A__		,		A__=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   (		A__		,		A__=0 ):
     UpperCAmelCase						:				Any		  =			[]
     for old_item in old_list:
          UpperCAmelCase						:				Optional[int]		  =			old_item
          UpperCAmelCase						:				str		  =			new_item.replace("block."		,		"resnets." )
          UpperCAmelCase						:				Any		  =			new_item.replace("conv_shorcut"		,		"conv1" )
          UpperCAmelCase						:				List[Any]		  =			new_item.replace("in_shortcut"		,		"conv_shortcut" )
          UpperCAmelCase						:				Tuple		  =			new_item.replace("temb_proj"		,		"time_emb_proj" )

          UpperCAmelCase						:				str		  =			shave_segments(A__		,		n_shave_prefix_segments=A__ )

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

     return mapping







def   A   (		A__		,		A__=0		,		A__=False ):
     UpperCAmelCase						:				int		  =			[]
     for old_item in old_list:
          UpperCAmelCase						:				Optional[int]		  =			old_item

          # In `model.mid`, the layer is called `attn`.
          if not in_mid:
               UpperCAmelCase						:				Dict		  =			new_item.replace("attn"		,		"attentions" )
          UpperCAmelCase						:				Optional[Any]		  =			new_item.replace(".k."		,		".key." )
          UpperCAmelCase						:				Any		  =			new_item.replace(".v."		,		".value." )
          UpperCAmelCase						:				int		  =			new_item.replace(".q."		,		".query." )

          UpperCAmelCase						:				Dict		  =			new_item.replace("proj_out"		,		"proj_attn" )
          UpperCAmelCase						:				Tuple		  =			new_item.replace("norm"		,		"group_norm" )

          UpperCAmelCase						:				Any		  =			shave_segments(A__		,		n_shave_prefix_segments=A__ )
          mapping.append({"old": old_item, "new": new_item} )

     return mapping







def   A   (		A__		,		A__		,		A__		,		A__=None		,		A__=None		,		A__=None ):
     assert isinstance(A__		,		A__ ), "Paths should be a list of dicts containing 'old' and 'new' keys."

     if attention_paths_to_split is not None:
          if config is None:
               raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'." )

          for path, path_map in attention_paths_to_split.items():
               UpperCAmelCase						:				Any		  =			old_checkpoint[path]
               UpperCAmelCase						:				Union[str, Any]		  =			old_tensor.shape[0] // 3

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

               UpperCAmelCase						:				str		  =			old_tensor.shape[0] // config.get("num_head_channels"		,		1 ) // 3

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

               UpperCAmelCase						:				Optional[Any]		  =			query.reshape(A__ ).squeeze()
               UpperCAmelCase						:				Union[str, Any]		  =			key.reshape(A__ ).squeeze()
               UpperCAmelCase						:				Optional[Any]		  =			value.reshape(A__ ).squeeze()

     for path in paths:
          UpperCAmelCase						:				Optional[Any]		  =			path["new"]

          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          UpperCAmelCase						:				List[Any]		  =			new_path.replace("down."		,		"down_blocks." )
          UpperCAmelCase						:				Dict		  =			new_path.replace("up."		,		"up_blocks." )

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    UpperCAmelCase						:				Any		  =			new_path.replace(replacement["old"]		,		replacement["new"] )

          if "attentions" in new_path:
               UpperCAmelCase						:				str		  =			old_checkpoint[path["old"]].squeeze()
          else:
               UpperCAmelCase						:				Optional[Any]		  =			old_checkpoint[path["old"]]







def   A   (		A__		,		A__ ):
     UpperCAmelCase						:				Dict		  =			{}

     UpperCAmelCase						:				Optional[Any]		  =			checkpoint["temb.dense.0.weight"]
     UpperCAmelCase						:				Tuple		  =			checkpoint["temb.dense.0.bias"]
     UpperCAmelCase						:				Tuple		  =			checkpoint["temb.dense.1.weight"]
     UpperCAmelCase						:				Any		  =			checkpoint["temb.dense.1.bias"]

     UpperCAmelCase						:				List[Any]		  =			checkpoint["norm_out.weight"]
     UpperCAmelCase						:				Any		  =			checkpoint["norm_out.bias"]

     UpperCAmelCase						:				List[str]		  =			checkpoint["conv_in.weight"]
     UpperCAmelCase						:				Dict		  =			checkpoint["conv_in.bias"]
     UpperCAmelCase						:				Union[str, Any]		  =			checkpoint["conv_out.weight"]
     UpperCAmelCase						:				Any		  =			checkpoint["conv_out.bias"]

     UpperCAmelCase						:				List[Any]		  =			len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "down" in layer} )
     UpperCAmelCase						:				Optional[Any]		  =			{
         layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(A__ )
     }

     UpperCAmelCase						:				Optional[int]		  =			len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "up" in layer} )
     UpperCAmelCase						:				int		  =			{layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(A__ )}

     for i in range(A__ ):
          UpperCAmelCase						:				Tuple		  =			(i - 1) // (config["layers_per_block"] + 1)

          if any("downsample" in layer for layer in down_blocks[i] ):
               UpperCAmelCase						:				List[str]		  =			checkpoint[
                   f'down.{i}.downsample.op.weight'
               ]
               UpperCAmelCase						:				List[Any]		  =			checkpoint[f'down.{i}.downsample.op.bias']
          #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
          #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

          if any("block" in layer for layer in down_blocks[i] ):
               UpperCAmelCase						:				Tuple		  =			len(
                   {".".join(shave_segments(A__		,		2 ).split("." )[:2] ) for layer in down_blocks[i] if "block" in layer} )
               UpperCAmelCase						:				Dict		  =			{
                   layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                   for layer_id in range(A__ )
               }

               if num_blocks > 0:
                    for j in range(config["layers_per_block"] ):
                         UpperCAmelCase						:				List[str]		  =			renew_resnet_paths(blocks[j] )
                         assign_to_checkpoint(A__		,		A__		,		A__ )

          if any("attn" in layer for layer in down_blocks[i] ):
               UpperCAmelCase						:				Dict		  =			len(
                   {".".join(shave_segments(A__		,		2 ).split("." )[:2] ) for layer in down_blocks[i] if "attn" in layer} )
               UpperCAmelCase						:				List[str]		  =			{
                   layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                   for layer_id in range(A__ )
               }

               if num_attn > 0:
                    for j in range(config["layers_per_block"] ):
                         UpperCAmelCase						:				Optional[int]		  =			renew_attention_paths(attns[j] )
                         assign_to_checkpoint(A__		,		A__		,		A__		,		config=A__ )

     UpperCAmelCase						:				List[str]		  =			[key for key in checkpoint if "mid.block_1" in key]
     UpperCAmelCase						:				Tuple		  =			[key for key in checkpoint if "mid.block_2" in key]
     UpperCAmelCase						:				List[str]		  =			[key for key in checkpoint if "mid.attn_1" in key]

     # Mid new 2
     UpperCAmelCase						:				Union[str, Any]		  =			renew_resnet_paths(A__ )
     assign_to_checkpoint(
         A__		,		A__		,		A__		,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]		,		)

     UpperCAmelCase						:				Any		  =			renew_resnet_paths(A__ )
     assign_to_checkpoint(
         A__		,		A__		,		A__		,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]		,		)

     UpperCAmelCase						:				int		  =			renew_attention_paths(A__		,		in_mid=A__ )
     assign_to_checkpoint(
         A__		,		A__		,		A__		,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]		,		)

     for i in range(A__ ):
          UpperCAmelCase						:				List[str]		  =			num_up_blocks - 1 - i

          if any("upsample" in layer for layer in up_blocks[i] ):
               UpperCAmelCase						:				Optional[int]		  =			checkpoint[
                   f'up.{i}.upsample.conv.weight'
               ]
               UpperCAmelCase						:				Any		  =			checkpoint[f'up.{i}.upsample.conv.bias']

          if any("block" in layer for layer in up_blocks[i] ):
               UpperCAmelCase						:				Tuple		  =			len(
                   {".".join(shave_segments(A__		,		2 ).split("." )[:2] ) for layer in up_blocks[i] if "block" in layer} )
               UpperCAmelCase						:				List[str]		  =			{
                   layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(A__ )
               }

               if num_blocks > 0:
                    for j in range(config["layers_per_block"] + 1 ):
                         UpperCAmelCase						:				List[Any]		  =			{"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
                         UpperCAmelCase						:				int		  =			renew_resnet_paths(blocks[j] )
                         assign_to_checkpoint(A__		,		A__		,		A__		,		additional_replacements=[replace_indices] )

          if any("attn" in layer for layer in up_blocks[i] ):
               UpperCAmelCase						:				Tuple		  =			len(
                   {".".join(shave_segments(A__		,		2 ).split("." )[:2] ) for layer in up_blocks[i] if "attn" in layer} )
               UpperCAmelCase						:				Union[str, Any]		  =			{
                   layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(A__ )
               }

               if num_attn > 0:
                    for j in range(config["layers_per_block"] + 1 ):
                         UpperCAmelCase						:				List[Any]		  =			{"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
                         UpperCAmelCase						:				List[str]		  =			renew_attention_paths(attns[j] )
                         assign_to_checkpoint(A__		,		A__		,		A__		,		additional_replacements=[replace_indices] )

     UpperCAmelCase						:				Optional[int]		  =			{k.replace("mid_new_2"		,		"mid_block" ): v for k, v in new_checkpoint.items()}
     return new_checkpoint







def   A   (		A__		,		A__ ):
     UpperCAmelCase						:				List[Any]		  =			{}

     UpperCAmelCase						:				str		  =			checkpoint["encoder.norm_out.weight"]
     UpperCAmelCase						:				Dict		  =			checkpoint["encoder.norm_out.bias"]

     UpperCAmelCase						:				Union[str, Any]		  =			checkpoint["encoder.conv_in.weight"]
     UpperCAmelCase						:				List[str]		  =			checkpoint["encoder.conv_in.bias"]
     UpperCAmelCase						:				List[Any]		  =			checkpoint["encoder.conv_out.weight"]
     UpperCAmelCase						:				Dict		  =			checkpoint["encoder.conv_out.bias"]

     UpperCAmelCase						:				int		  =			checkpoint["decoder.norm_out.weight"]
     UpperCAmelCase						:				Optional[Any]		  =			checkpoint["decoder.norm_out.bias"]

     UpperCAmelCase						:				Tuple		  =			checkpoint["decoder.conv_in.weight"]
     UpperCAmelCase						:				Tuple		  =			checkpoint["decoder.conv_in.bias"]
     UpperCAmelCase						:				int		  =			checkpoint["decoder.conv_out.weight"]
     UpperCAmelCase						:				str		  =			checkpoint["decoder.conv_out.bias"]

     UpperCAmelCase						:				List[Any]		  =			len({".".join(layer.split("." )[:3] ) for layer in checkpoint if "down" in layer} )
     UpperCAmelCase						:				Optional[Any]		  =			{
         layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(A__ )
     }

     UpperCAmelCase						:				Optional[Any]		  =			len({".".join(layer.split("." )[:3] ) for layer in checkpoint if "up" in layer} )
     UpperCAmelCase						:				List[str]		  =			{layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(A__ )}

     for i in range(A__ ):
          UpperCAmelCase						:				Union[str, Any]		  =			(i - 1) // (config["layers_per_block"] + 1)

          if any("downsample" in layer for layer in down_blocks[i] ):
               UpperCAmelCase						:				Any		  =			checkpoint[
                   f'encoder.down.{i}.downsample.conv.weight'
               ]
               UpperCAmelCase						:				Optional[int]		  =			checkpoint[
                   f'encoder.down.{i}.downsample.conv.bias'
               ]

          if any("block" in layer for layer in down_blocks[i] ):
               UpperCAmelCase						:				Optional[Any]		  =			len(
                   {".".join(shave_segments(A__		,		3 ).split("." )[:3] ) for layer in down_blocks[i] if "block" in layer} )
               UpperCAmelCase						:				Tuple		  =			{
                   layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                   for layer_id in range(A__ )
               }

               if num_blocks > 0:
                    for j in range(config["layers_per_block"] ):
                         UpperCAmelCase						:				Optional[Any]		  =			renew_resnet_paths(blocks[j] )
                         assign_to_checkpoint(A__		,		A__		,		A__ )

          if any("attn" in layer for layer in down_blocks[i] ):
               UpperCAmelCase						:				List[str]		  =			len(
                   {".".join(shave_segments(A__		,		3 ).split("." )[:3] ) for layer in down_blocks[i] if "attn" in layer} )
               UpperCAmelCase						:				Tuple		  =			{
                   layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                   for layer_id in range(A__ )
               }

               if num_attn > 0:
                    for j in range(config["layers_per_block"] ):
                         UpperCAmelCase						:				Union[str, Any]		  =			renew_attention_paths(attns[j] )
                         assign_to_checkpoint(A__		,		A__		,		A__		,		config=A__ )

     UpperCAmelCase						:				Optional[int]		  =			[key for key in checkpoint if "mid.block_1" in key]
     UpperCAmelCase						:				Optional[int]		  =			[key for key in checkpoint if "mid.block_2" in key]
     UpperCAmelCase						:				Optional[int]		  =			[key for key in checkpoint if "mid.attn_1" in key]

     # Mid new 2
     UpperCAmelCase						:				List[Any]		  =			renew_resnet_paths(A__ )
     assign_to_checkpoint(
         A__		,		A__		,		A__		,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]		,		)

     UpperCAmelCase						:				Optional[int]		  =			renew_resnet_paths(A__ )
     assign_to_checkpoint(
         A__		,		A__		,		A__		,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]		,		)

     UpperCAmelCase						:				Dict		  =			renew_attention_paths(A__		,		in_mid=A__ )
     assign_to_checkpoint(
         A__		,		A__		,		A__		,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]		,		)

     for i in range(A__ ):
          UpperCAmelCase						:				int		  =			num_up_blocks - 1 - i

          if any("upsample" in layer for layer in up_blocks[i] ):
               UpperCAmelCase						:				Any		  =			checkpoint[
                   f'decoder.up.{i}.upsample.conv.weight'
               ]
               UpperCAmelCase						:				Optional[int]		  =			checkpoint[
                   f'decoder.up.{i}.upsample.conv.bias'
               ]

          if any("block" in layer for layer in up_blocks[i] ):
               UpperCAmelCase						:				List[Any]		  =			len(
                   {".".join(shave_segments(A__		,		3 ).split("." )[:3] ) for layer in up_blocks[i] if "block" in layer} )
               UpperCAmelCase						:				str		  =			{
                   layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(A__ )
               }

               if num_blocks > 0:
                    for j in range(config["layers_per_block"] + 1 ):
                         UpperCAmelCase						:				Optional[int]		  =			{"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
                         UpperCAmelCase						:				Any		  =			renew_resnet_paths(blocks[j] )
                         assign_to_checkpoint(A__		,		A__		,		A__		,		additional_replacements=[replace_indices] )

          if any("attn" in layer for layer in up_blocks[i] ):
               UpperCAmelCase						:				int		  =			len(
                   {".".join(shave_segments(A__		,		3 ).split("." )[:3] ) for layer in up_blocks[i] if "attn" in layer} )
               UpperCAmelCase						:				str		  =			{
                   layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(A__ )
               }

               if num_attn > 0:
                    for j in range(config["layers_per_block"] + 1 ):
                         UpperCAmelCase						:				Tuple		  =			{"old": f'up_blocks.{i}', "new": f'up_blocks.{block_id}'}
                         UpperCAmelCase						:				Union[str, Any]		  =			renew_attention_paths(attns[j] )
                         assign_to_checkpoint(A__		,		A__		,		A__		,		additional_replacements=[replace_indices] )

     UpperCAmelCase						:				Union[str, Any]		  =			{k.replace("mid_new_2"		,		"mid_block" ): v for k, v in new_checkpoint.items()}
     UpperCAmelCase						:				Any		  =			checkpoint["quant_conv.weight"]
     UpperCAmelCase						:				Optional[int]		  =			checkpoint["quant_conv.bias"]
     if "quantize.embedding.weight" in checkpoint:
          UpperCAmelCase						:				Union[str, Any]		  =			checkpoint["quantize.embedding.weight"]
     UpperCAmelCase						:				str		  =			checkpoint["post_quant_conv.weight"]
     UpperCAmelCase						:				Dict		  =			checkpoint["post_quant_conv.bias"]

     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())

      # unet case
      _snake_case  = {key.split(".")[0] for key in checkpoint.keys()}
      if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
            _snake_case  = convert_vq_autoenc_checkpoint(checkpoint, config)
      else:
            _snake_case  = convert_ddpm_checkpoint(checkpoint, config)

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

      if config["_class_name"] == "VQModel":
            _snake_case  = VQModel(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      elif config["_class_name"] == "AutoencoderKL":
            _snake_case  = AutoencoderKL(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      else:
            _snake_case  = UNetaDModel(**config)
            model.load_state_dict(converted_checkpoint)

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

            _snake_case  = DDPMPipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)







import math







def   A   (		A__ ):
     UpperCAmelCase						:				List[str]		  =			math.log(2		,		1_0 ) * number
     UpperCAmelCase						:				int		  =			round((log_number - int(A__ ))		,		1_5 )

     return difference







def   A   (		A__ = 6_7_8_9_1_0 ):
     UpperCAmelCase						:				str		  =			9_0
     UpperCAmelCase						:				Any		  =			0

     UpperCAmelCase						:				Dict		  =			math.log(1.23		,		1_0 )
     UpperCAmelCase						:				Optional[Any]		  =			math.log(1.24		,		1_0 )
     UpperCAmelCase						:				Tuple		  =			0

     while position < number:
          UpperCAmelCase						:				Tuple		  =			log_difference(A__ )

          if difference >= upper_limit:
               power_iterator += 9_3

          elif difference < lower_limit:
               power_iterator += 1_9_6

          else:
               UpperCAmelCase						:				Any		  =			power_iterator
               power_iterator += 1_9_6
               position += 1

     return previous_power


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      print(f'''{solution() = }''')



