code: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=1			)						->							List[Any]:
  
  
  
  
         '''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__   (    UpperCAmelCase_    ,     UpperCAmelCase_=0			)						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[Any]           =			[]
         for old_item in old_list:
                _lowercase					:				int           =			old_item
                _lowercase					:				Union[str, Any]           =			new_item.replace('''block.'''    ,     '''resnets.'''			)
                _lowercase					:				Optional[int]           =			new_item.replace('''conv_shorcut'''    ,     '''conv1'''			)
                _lowercase					:				int           =			new_item.replace('''in_shortcut'''    ,     '''conv_shortcut'''			)
                _lowercase					:				List[Any]           =			new_item.replace('''temb_proj'''    ,     '''time_emb_proj'''			)
  
                _lowercase					:				Tuple           =			shave_segments(lowercase__    ,     n_shave_prefix_segments=lowercase__			)
  
                mapping.append({'''old''': old_item, '''new''': new_item}			)
  
         return mapping
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=0    ,     UpperCAmelCase_=False			)						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Union[str, Any]           =			[]
         for old_item in old_list:
                _lowercase					:				List[str]           =			old_item
  
                # In `model.mid`, the layer is called `attn`.
                if not in_mid:
                       _lowercase					:				List[str]           =			new_item.replace('''attn'''    ,     '''attentions'''			)
                _lowercase					:				int           =			new_item.replace('''.k.'''    ,     '''.key.'''			)
                _lowercase					:				Tuple           =			new_item.replace('''.v.'''    ,     '''.value.'''			)
                _lowercase					:				int           =			new_item.replace('''.q.'''    ,     '''.query.'''			)
  
                _lowercase					:				Optional[Any]           =			new_item.replace('''proj_out'''    ,     '''proj_attn'''			)
                _lowercase					:				int           =			new_item.replace('''norm'''    ,     '''group_norm'''			)
  
                _lowercase					:				Union[str, Any]           =			shave_segments(lowercase__    ,     n_shave_prefix_segments=lowercase__			)
                mapping.append({'''old''': old_item, '''new''': new_item}			)
  
         return mapping
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=None			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         assert isinstance(lowercase__    ,     lowercase__			), "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():
                       _lowercase					:				Any           =			old_checkpoint[path]
                       _lowercase					:				List[Any]           =			old_tensor.shape[0] // 3
  
                       _lowercase					:				Union[str, Any]           =			(-1, channels) if len(old_tensor.shape			) == 3 else (-1)
  
                       _lowercase					:				Optional[Any]           =			old_tensor.shape[0] // config.get('''num_head_channels'''    ,     1			) // 3
  
                       _lowercase					:				str           =			old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
                       _lowercase       ,	_lowercase       ,	_lowercase					:				Any           =			old_tensor.split(channels // num_heads    ,     dim=1			)
  
                       _lowercase					:				Optional[Any]           =			query.reshape(lowercase__			).squeeze()
                       _lowercase					:				List[str]           =			key.reshape(lowercase__			).squeeze()
                       _lowercase					:				Union[str, Any]           =			value.reshape(lowercase__			).squeeze()
  
         for path in paths:
                _lowercase					:				Optional[int]           =			path['''new''']
  
                if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                       continue
  
                _lowercase					:				Dict           =			new_path.replace('''down.'''    ,     '''down_blocks.'''			)
                _lowercase					:				Optional[Any]           =			new_path.replace('''up.'''    ,     '''up_blocks.'''			)
  
                if additional_replacements is not None:
                       for replacement in additional_replacements:
                              _lowercase					:				Optional[int]           =			new_path.replace(replacement['''old''']    ,     replacement['''new''']			)
  
                if "attentions" in new_path:
                       _lowercase					:				Dict           =			old_checkpoint[path['''old''']].squeeze()
                else:
                       _lowercase					:				Optional[Any]           =			old_checkpoint[path['''old''']]
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				str           =			{}
  
         _lowercase					:				List[Any]           =			checkpoint['''temb.dense.0.weight''']
         _lowercase					:				Optional[Any]           =			checkpoint['''temb.dense.0.bias''']
         _lowercase					:				List[str]           =			checkpoint['''temb.dense.1.weight''']
         _lowercase					:				Union[str, Any]           =			checkpoint['''temb.dense.1.bias''']
  
         _lowercase					:				Union[str, Any]           =			checkpoint['''norm_out.weight''']
         _lowercase					:				str           =			checkpoint['''norm_out.bias''']
  
         _lowercase					:				List[Any]           =			checkpoint['''conv_in.weight''']
         _lowercase					:				Union[str, Any]           =			checkpoint['''conv_in.bias''']
         _lowercase					:				Tuple           =			checkpoint['''conv_out.weight''']
         _lowercase					:				List[Any]           =			checkpoint['''conv_out.bias''']
  
         _lowercase					:				int           =			len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''down''' in layer}			)
         _lowercase					:				Union[str, Any]           =			{
             layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(lowercase__			)
         }
  
         _lowercase					:				int           =			len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''up''' in layer}			)
         _lowercase					:				Any           =			{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(lowercase__			)}
  
         for i in range(lowercase__			):
                _lowercase					:				int           =			(i - 1) // (config['''layers_per_block'''] + 1)
  
                if any('''downsample''' in layer for layer in down_blocks[i]			):
                       _lowercase					:				Any           =			checkpoint[
                           F'down.{i}.downsample.op.weight'
                       ]
                       _lowercase					:				Optional[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]			):
                       _lowercase					:				str           =			len(
                           {'''.'''.join(shave_segments(lowercase__    ,     2			).split('''.'''			)[:2]			) for layer in down_blocks[i] if '''block''' in layer}			)
                       _lowercase					:				int           =			{
                           layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                           for layer_id in range(lowercase__			)
                       }
  
                       if num_blocks > 0:
                              for j in range(config['''layers_per_block''']			):
                                     _lowercase					:				Optional[int]           =			renew_resnet_paths(blocks[j]			)
                                     assign_to_checkpoint(lowercase__    ,     lowercase__    ,     lowercase__			)
  
                if any('''attn''' in layer for layer in down_blocks[i]			):
                       _lowercase					:				List[Any]           =			len(
                           {'''.'''.join(shave_segments(lowercase__    ,     2			).split('''.'''			)[:2]			) for layer in down_blocks[i] if '''attn''' in layer}			)
                       _lowercase					:				Any           =			{
                           layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                           for layer_id in range(lowercase__			)
                       }
  
                       if num_attn > 0:
                              for j in range(config['''layers_per_block''']			):
                                     _lowercase					:				Any           =			renew_attention_paths(attns[j]			)
                                     assign_to_checkpoint(lowercase__    ,     lowercase__    ,     lowercase__    ,     config=lowercase__			)
  
         _lowercase					:				str           =			[key for key in checkpoint if '''mid.block_1''' in key]
         _lowercase					:				List[str]           =			[key for key in checkpoint if '''mid.block_2''' in key]
         _lowercase					:				Dict           =			[key for key in checkpoint if '''mid.attn_1''' in key]
  
         # Mid new 2
         _lowercase					:				Optional[Any]           =			renew_resnet_paths(lowercase__			)
         assign_to_checkpoint(
             lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]    ,     )
  
         _lowercase					:				Tuple           =			renew_resnet_paths(lowercase__			)
         assign_to_checkpoint(
             lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]    ,     )
  
         _lowercase					:				Tuple           =			renew_attention_paths(lowercase__    ,     in_mid=lowercase__			)
         assign_to_checkpoint(
             lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]    ,     )
  
         for i in range(lowercase__			):
                _lowercase					:				str           =			num_up_blocks - 1 - i
  
                if any('''upsample''' in layer for layer in up_blocks[i]			):
                       _lowercase					:				List[Any]           =			checkpoint[
                           F'up.{i}.upsample.conv.weight'
                       ]
                       _lowercase					:				Optional[int]           =			checkpoint[F'up.{i}.upsample.conv.bias']
  
                if any('''block''' in layer for layer in up_blocks[i]			):
                       _lowercase					:				Dict           =			len(
                           {'''.'''.join(shave_segments(lowercase__    ,     2			).split('''.'''			)[:2]			) for layer in up_blocks[i] if '''block''' in layer}			)
                       _lowercase					:				int           =			{
                           layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(lowercase__			)
                       }
  
                       if num_blocks > 0:
                              for j in range(config['''layers_per_block'''] + 1			):
                                     _lowercase					:				List[Any]           =			{'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
                                     _lowercase					:				List[str]           =			renew_resnet_paths(blocks[j]			)
                                     assign_to_checkpoint(lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[replace_indices]			)
  
                if any('''attn''' in layer for layer in up_blocks[i]			):
                       _lowercase					:				Optional[Any]           =			len(
                           {'''.'''.join(shave_segments(lowercase__    ,     2			).split('''.'''			)[:2]			) for layer in up_blocks[i] if '''attn''' in layer}			)
                       _lowercase					:				Any           =			{
                           layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(lowercase__			)
                       }
  
                       if num_attn > 0:
                              for j in range(config['''layers_per_block'''] + 1			):
                                     _lowercase					:				int           =			{'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
                                     _lowercase					:				Optional[Any]           =			renew_attention_paths(attns[j]			)
                                     assign_to_checkpoint(lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[replace_indices]			)
  
         _lowercase					:				int           =			{k.replace('''mid_new_2'''    ,     '''mid_block'''			): v for k, v in new_checkpoint.items()}
         return new_checkpoint
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				int           =			{}
  
         _lowercase					:				List[str]           =			checkpoint['''encoder.norm_out.weight''']
         _lowercase					:				Any           =			checkpoint['''encoder.norm_out.bias''']
  
         _lowercase					:				int           =			checkpoint['''encoder.conv_in.weight''']
         _lowercase					:				str           =			checkpoint['''encoder.conv_in.bias''']
         _lowercase					:				Union[str, Any]           =			checkpoint['''encoder.conv_out.weight''']
         _lowercase					:				List[str]           =			checkpoint['''encoder.conv_out.bias''']
  
         _lowercase					:				List[Any]           =			checkpoint['''decoder.norm_out.weight''']
         _lowercase					:				str           =			checkpoint['''decoder.norm_out.bias''']
  
         _lowercase					:				Dict           =			checkpoint['''decoder.conv_in.weight''']
         _lowercase					:				str           =			checkpoint['''decoder.conv_in.bias''']
         _lowercase					:				Optional[Any]           =			checkpoint['''decoder.conv_out.weight''']
         _lowercase					:				Optional[int]           =			checkpoint['''decoder.conv_out.bias''']
  
         _lowercase					:				Dict           =			len({'''.'''.join(layer.split('''.'''			)[:3]			) for layer in checkpoint if '''down''' in layer}			)
         _lowercase					:				List[Any]           =			{
             layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(lowercase__			)
         }
  
         _lowercase					:				List[str]           =			len({'''.'''.join(layer.split('''.'''			)[:3]			) for layer in checkpoint if '''up''' in layer}			)
         _lowercase					:				Optional[int]           =			{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(lowercase__			)}
  
         for i in range(lowercase__			):
                _lowercase					:				List[Any]           =			(i - 1) // (config['''layers_per_block'''] + 1)
  
                if any('''downsample''' in layer for layer in down_blocks[i]			):
                       _lowercase					:				Optional[int]           =			checkpoint[
                           F'encoder.down.{i}.downsample.conv.weight'
                       ]
                       _lowercase					:				Optional[int]           =			checkpoint[
                           F'encoder.down.{i}.downsample.conv.bias'
                       ]
  
                if any('''block''' in layer for layer in down_blocks[i]			):
                       _lowercase					:				List[Any]           =			len(
                           {'''.'''.join(shave_segments(lowercase__    ,     3			).split('''.'''			)[:3]			) for layer in down_blocks[i] if '''block''' in layer}			)
                       _lowercase					:				List[str]           =			{
                           layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                           for layer_id in range(lowercase__			)
                       }
  
                       if num_blocks > 0:
                              for j in range(config['''layers_per_block''']			):
                                     _lowercase					:				Dict           =			renew_resnet_paths(blocks[j]			)
                                     assign_to_checkpoint(lowercase__    ,     lowercase__    ,     lowercase__			)
  
                if any('''attn''' in layer for layer in down_blocks[i]			):
                       _lowercase					:				Union[str, Any]           =			len(
                           {'''.'''.join(shave_segments(lowercase__    ,     3			).split('''.'''			)[:3]			) for layer in down_blocks[i] if '''attn''' in layer}			)
                       _lowercase					:				Any           =			{
                           layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                           for layer_id in range(lowercase__			)
                       }
  
                       if num_attn > 0:
                              for j in range(config['''layers_per_block''']			):
                                     _lowercase					:				str           =			renew_attention_paths(attns[j]			)
                                     assign_to_checkpoint(lowercase__    ,     lowercase__    ,     lowercase__    ,     config=lowercase__			)
  
         _lowercase					:				Optional[Any]           =			[key for key in checkpoint if '''mid.block_1''' in key]
         _lowercase					:				str           =			[key for key in checkpoint if '''mid.block_2''' in key]
         _lowercase					:				int           =			[key for key in checkpoint if '''mid.attn_1''' in key]
  
         # Mid new 2
         _lowercase					:				Any           =			renew_resnet_paths(lowercase__			)
         assign_to_checkpoint(
             lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]    ,     )
  
         _lowercase					:				Union[str, Any]           =			renew_resnet_paths(lowercase__			)
         assign_to_checkpoint(
             lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]    ,     )
  
         _lowercase					:				Union[str, Any]           =			renew_attention_paths(lowercase__    ,     in_mid=lowercase__			)
         assign_to_checkpoint(
             lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]    ,     )
  
         for i in range(lowercase__			):
                _lowercase					:				Tuple           =			num_up_blocks - 1 - i
  
                if any('''upsample''' in layer for layer in up_blocks[i]			):
                       _lowercase					:				List[Any]           =			checkpoint[
                           F'decoder.up.{i}.upsample.conv.weight'
                       ]
                       _lowercase					:				Union[str, Any]           =			checkpoint[
                           F'decoder.up.{i}.upsample.conv.bias'
                       ]
  
                if any('''block''' in layer for layer in up_blocks[i]			):
                       _lowercase					:				List[str]           =			len(
                           {'''.'''.join(shave_segments(lowercase__    ,     3			).split('''.'''			)[:3]			) for layer in up_blocks[i] if '''block''' in layer}			)
                       _lowercase					:				List[str]           =			{
                           layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(lowercase__			)
                       }
  
                       if num_blocks > 0:
                              for j in range(config['''layers_per_block'''] + 1			):
                                     _lowercase					:				Union[str, Any]           =			{'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
                                     _lowercase					:				Any           =			renew_resnet_paths(blocks[j]			)
                                     assign_to_checkpoint(lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[replace_indices]			)
  
                if any('''attn''' in layer for layer in up_blocks[i]			):
                       _lowercase					:				Optional[int]           =			len(
                           {'''.'''.join(shave_segments(lowercase__    ,     3			).split('''.'''			)[:3]			) for layer in up_blocks[i] if '''attn''' in layer}			)
                       _lowercase					:				Union[str, Any]           =			{
                           layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(lowercase__			)
                       }
  
                       if num_attn > 0:
                              for j in range(config['''layers_per_block'''] + 1			):
                                     _lowercase					:				Optional[int]           =			{'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
                                     _lowercase					:				int           =			renew_attention_paths(attns[j]			)
                                     assign_to_checkpoint(lowercase__    ,     lowercase__    ,     lowercase__    ,     additional_replacements=[replace_indices]			)
  
         _lowercase					:				Tuple           =			{k.replace('''mid_new_2'''    ,     '''mid_block'''			): v for k, v in new_checkpoint.items()}
         _lowercase					:				Union[str, Any]           =			checkpoint['''quant_conv.weight''']
         _lowercase					:				List[Any]           =			checkpoint['''quant_conv.bias''']
         if "quantize.embedding.weight" in checkpoint:
                _lowercase					:				List[Any]           =			checkpoint['''quantize.embedding.weight''']
         _lowercase					:				int           =			checkpoint['''post_quant_conv.weight''']
         _lowercase					:				str           =			checkpoint['''post_quant_conv.bias''']
  
         return new_checkpoint
  
  
  if __name__ == "__main__":
     UpperCamelCase__				       =			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.')
  
     UpperCamelCase__				       =			parser.parse_args()
     UpperCamelCase__				       =			torch.load(args.checkpoint_path)
  
     with open(args.config_file) as f:
        UpperCamelCase__				       =			json.loads(f.read())
  
     # unet case
     UpperCamelCase__				       =			{key.split('.')[0] for key in checkpoint.keys()}
     if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
        UpperCamelCase__				       =			convert_vq_autoenc_checkpoint(checkpoint, config)
     else:
        UpperCamelCase__				       =			convert_ddpm_checkpoint(checkpoint, config)
  
     if "ddpm" in config:
        del config["ddpm"]
  
     if config["_class_name"] == "VQModel":
        UpperCamelCase__				       =			VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
     elif config["_class_name"] == "AutoencoderKL":
        UpperCamelCase__				       =			AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
     else:
        UpperCamelCase__				       =			UNetaDModel(**config)
        model.load_state_dict(converted_checkpoint)
  
        UpperCamelCase__				       =			DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
        UpperCamelCase__				       =			DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)
style_context: 
  
  
  import os
  from pathlib import Path
  from typing import Dict, List
  
  import fire
  import torch
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from transformers.utils.logging import get_logger
  
  
  UpperCamelCase__				       =			get_logger(__name__)
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[str]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if text.startswith(UpperCAmelCase_			):
                return text[len(UpperCAmelCase_			) :]
         return text  # or whatever
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         return {remove_prefix(UpperCAmelCase_    ,     '''model.'''			): v for k, v in sd.items()}
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[int]           =			{}
         for k in state_dicts[0].keys():
                _lowercase					:				Tuple           =			[sd[k] for sd in state_dicts]
                _lowercase					:				Union[str, Any]           =			sum(UpperCAmelCase_			) / len(UpperCAmelCase_			)
                assert isinstance(UpperCAmelCase_    ,     torch.Tensor			)
                _lowercase					:				Union[str, Any]           =			new_t
         return new_sd
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							None:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[str]           =			AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_			)
         if os.path.isfile(UpperCAmelCase_			):
                _lowercase					:				Optional[Any]           =			[pl_ckpt_path]
         else:
                assert os.path.isdir(UpperCAmelCase_			)
                _lowercase					:				List[str]           =			list(Path(UpperCAmelCase_			).glob('''*.ckpt'''			)			)
                assert ckpt_files, F'could not find any ckpt files inside the {pl_ckpt_path} directory'
  
         if len(UpperCAmelCase_			) > 1:
                logger.info(F'averaging the weights of {ckpt_files}'			)
  
         _lowercase					:				List[Any]           =			[sanitize(torch.load(UpperCAmelCase_    ,     map_location='''cpu'''			)['''state_dict''']			) for x in ckpt_files]
         _lowercase					:				Optional[Any]           =			average_state_dicts(UpperCAmelCase_			)
  
         _lowercase       ,	_lowercase					:				str           =			hf_model.load_state_dict(UpperCAmelCase_    ,     strict=UpperCAmelCase_			)
         assert not missing, F'missing keys: {missing}'
         hf_model.save_pretrained(UpperCAmelCase_			)
         try:
                _lowercase					:				Union[str, Any]           =			AutoTokenizer.from_pretrained(UpperCAmelCase_			)
                tok.save_pretrained(UpperCAmelCase_			)
         except Exception:
                pass
                # dont copy tokenizer if cant
  
  
  if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)