




'''simple docstring'''





import argparse
import json

import torch

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


def 					UpperCamelCase						(	snake_case						,  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 					UpperCamelCase						(	snake_case						,  snake_case=0 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[]
   for old_item in old_list:
      SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			old_item
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			new_item.replace('''block.'''						,  '''resnets.''' )
      SCREAMING_SNAKE_CASE__      :					Any							    =			new_item.replace('''conv_shorcut'''						,  '''conv1''' )
      SCREAMING_SNAKE_CASE__      :					Dict							    =			new_item.replace('''in_shortcut'''						,  '''conv_shortcut''' )
      SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			new_item.replace('''temb_proj'''						,  '''time_emb_proj''' )

      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			shave_segments(snake_case						,  n_shave_prefix_segments=snake_case )

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

   return mapping


def 					UpperCamelCase						(	snake_case						,  snake_case=0						,  snake_case=False ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					int							    =			[]
   for old_item in old_list:
      SCREAMING_SNAKE_CASE__      :					Dict							    =			old_item

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

      SCREAMING_SNAKE_CASE__      :					Any							    =			new_item.replace('''proj_out'''						,  '''proj_attn''' )
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			new_item.replace('''norm'''						,  '''group_norm''' )

      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			shave_segments(snake_case						,  n_shave_prefix_segments=snake_case )
      mapping.append({'''old''': old_item, '''new''': new_item} )

   return mapping


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case=None						,  snake_case=None						,  snake_case=None ):




   '''simple docstring'''

   assert isinstance(snake_case						,  snake_case ), "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():
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			old_checkpoint[path]
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			old_tensor.shape[0] // 3

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

         SCREAMING_SNAKE_CASE__      :					Dict							    =			old_tensor.shape[0] // config.get('''num_head_channels'''						,  1 ) // 3

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

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			query.reshape(snake_case ).squeeze()
         SCREAMING_SNAKE_CASE__      :					Any							    =			key.reshape(snake_case ).squeeze()
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			value.reshape(snake_case ).squeeze()

   for path in paths:
      SCREAMING_SNAKE_CASE__      :					Dict							    =			path['''new''']

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

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

      if additional_replacements is not None:
         for replacement in additional_replacements:
            SCREAMING_SNAKE_CASE__      :					int							    =			new_path.replace(replacement['''old''']						,  replacement['''new'''] )

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


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Tuple							    =			{}

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			checkpoint['''temb.dense.0.weight''']
   SCREAMING_SNAKE_CASE__      :					str							    =			checkpoint['''temb.dense.0.bias''']
   SCREAMING_SNAKE_CASE__      :					str							    =			checkpoint['''temb.dense.1.weight''']
   SCREAMING_SNAKE_CASE__      :					str							    =			checkpoint['''temb.dense.1.bias''']

   SCREAMING_SNAKE_CASE__      :					int							    =			checkpoint['''norm_out.weight''']
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			checkpoint['''norm_out.bias''']

   SCREAMING_SNAKE_CASE__      :					Any							    =			checkpoint['''conv_in.weight''']
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			checkpoint['''conv_in.bias''']
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			checkpoint['''conv_out.weight''']
   SCREAMING_SNAKE_CASE__      :					Dict							    =			checkpoint['''conv_out.bias''']

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

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''up''' in layer} )
   SCREAMING_SNAKE_CASE__      :					str							    =			{layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(snake_case )}

   for i in range(snake_case ):
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			(i - 1) // (config['''layers_per_block'''] + 1)

      if any('''downsample''' in layer for layer in down_blocks[i] ):
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			checkpoint[
             f"""down.{i}.downsample.op.weight"""
         ]
         SCREAMING_SNAKE_CASE__      :					int							    =			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] ):
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			len(
             {'''.'''.join(shave_segments(snake_case						,  2 ).split('''.''' )[:2] ) for layer in down_blocks[i] if '''block''' in layer} )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{
             layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
             for layer_id in range(snake_case )
         }

         if num_blocks > 0:
            for j in range(config['''layers_per_block'''] ):
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			renew_resnet_paths(blocks[j] )
               assign_to_checkpoint(snake_case						,  snake_case						,  snake_case )

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

         if num_attn > 0:
            for j in range(config['''layers_per_block'''] ):
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			renew_attention_paths(attns[j] )
               assign_to_checkpoint(snake_case						,  snake_case						,  snake_case						,  config=snake_case )

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			[key for key in checkpoint if '''mid.block_1''' in key]
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[key for key in checkpoint if '''mid.block_2''' in key]
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			[key for key in checkpoint if '''mid.attn_1''' in key]

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

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			renew_resnet_paths(snake_case )
   assign_to_checkpoint(
       snake_case						,  snake_case						,  snake_case						,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]						,  )

   SCREAMING_SNAKE_CASE__      :					str							    =			renew_attention_paths(snake_case						,  in_mid=snake_case )
   assign_to_checkpoint(
       snake_case						,  snake_case						,  snake_case						,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]						,  )

   for i in range(snake_case ):
      SCREAMING_SNAKE_CASE__      :					int							    =			num_up_blocks - 1 - i

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

      if any('''block''' in layer for layer in up_blocks[i] ):
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			len(
             {'''.'''.join(shave_segments(snake_case						,  2 ).split('''.''' )[:2] ) for layer in up_blocks[i] if '''block''' in layer} )
         SCREAMING_SNAKE_CASE__      :					Dict							    =			{
             layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(snake_case )
         }

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

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

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

   SCREAMING_SNAKE_CASE__      :					int							    =			{k.replace('''mid_new_2'''						,  '''mid_block''' ): v for k, v in new_checkpoint.items()}
   return new_checkpoint


def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{}

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			checkpoint['''encoder.norm_out.weight''']
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			checkpoint['''encoder.norm_out.bias''']

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			checkpoint['''encoder.conv_in.weight''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			checkpoint['''encoder.conv_in.bias''']
   SCREAMING_SNAKE_CASE__      :					str							    =			checkpoint['''encoder.conv_out.weight''']
   SCREAMING_SNAKE_CASE__      :					str							    =			checkpoint['''encoder.conv_out.bias''']

   SCREAMING_SNAKE_CASE__      :					Dict							    =			checkpoint['''decoder.norm_out.weight''']
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			checkpoint['''decoder.norm_out.bias''']

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			checkpoint['''decoder.conv_in.weight''']
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			checkpoint['''decoder.conv_in.bias''']
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			checkpoint['''decoder.conv_out.weight''']
   SCREAMING_SNAKE_CASE__      :					str							    =			checkpoint['''decoder.conv_out.bias''']

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

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in checkpoint if '''up''' in layer} )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			{layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(snake_case )}

   for i in range(snake_case ):
      SCREAMING_SNAKE_CASE__      :					str							    =			(i - 1) // (config['''layers_per_block'''] + 1)

      if any('''downsample''' in layer for layer in down_blocks[i] ):
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			checkpoint[
             f"""encoder.down.{i}.downsample.conv.weight"""
         ]
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			checkpoint[
             f"""encoder.down.{i}.downsample.conv.bias"""
         ]

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

         if num_blocks > 0:
            for j in range(config['''layers_per_block'''] ):
               SCREAMING_SNAKE_CASE__      :					List[str]							    =			renew_resnet_paths(blocks[j] )
               assign_to_checkpoint(snake_case						,  snake_case						,  snake_case )

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

         if num_attn > 0:
            for j in range(config['''layers_per_block'''] ):
               SCREAMING_SNAKE_CASE__      :					Any							    =			renew_attention_paths(attns[j] )
               assign_to_checkpoint(snake_case						,  snake_case						,  snake_case						,  config=snake_case )

   SCREAMING_SNAKE_CASE__      :					Any							    =			[key for key in checkpoint if '''mid.block_1''' in key]
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[key for key in checkpoint if '''mid.block_2''' in key]
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[key for key in checkpoint if '''mid.attn_1''' in key]

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

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			renew_resnet_paths(snake_case )
   assign_to_checkpoint(
       snake_case						,  snake_case						,  snake_case						,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]						,  )

   SCREAMING_SNAKE_CASE__      :					int							    =			renew_attention_paths(snake_case						,  in_mid=snake_case )
   assign_to_checkpoint(
       snake_case						,  snake_case						,  snake_case						,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]						,  )

   for i in range(snake_case ):
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			num_up_blocks - 1 - i

      if any('''upsample''' in layer for layer in up_blocks[i] ):
         SCREAMING_SNAKE_CASE__      :					str							    =			checkpoint[
             f"""decoder.up.{i}.upsample.conv.weight"""
         ]
         SCREAMING_SNAKE_CASE__      :					Dict							    =			checkpoint[
             f"""decoder.up.{i}.upsample.conv.bias"""
         ]

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

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

      if any('''attn''' in layer for layer in up_blocks[i] ):
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			len(
             {'''.'''.join(shave_segments(snake_case						,  3 ).split('''.''' )[:3] ) for layer in up_blocks[i] if '''attn''' in layer} )
         SCREAMING_SNAKE_CASE__      :					Dict							    =			{
             layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(snake_case )
         }

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

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

   return new_checkpoint


if __name__ == "__main__":
       lowercase__								=						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.''')

       lowercase__								=						parser.parse_args()
       lowercase__								=						torch.load(args.checkpoint_path)

       with open(args.config_file) as f:
              lowercase__								=						json.loads(f.read())

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

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

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

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

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







'''simple docstring'''





import math
from decimal import Decimal, getcontext


def 					UpperCamelCase						(	snake_case = 4_00_00_00 ):




   '''simple docstring'''

   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			int(snake_case )
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.''' )
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			1_00
   SCREAMING_SNAKE_CASE__      :					int							    =			(Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )

   SCREAMING_SNAKE_CASE__      :					int							    =			(math.floor(math.log(n * (phi + 2)						,  snake_case ) - 1 ) // 3) * 3 + 2
   SCREAMING_SNAKE_CASE__      :					int							    =			Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
   SCREAMING_SNAKE_CASE__      :					Any							    =			num // 2
   return int(snake_case )


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

