


import argparse
import json

import torch

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







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					str=1			)  ->   Union[str, 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 						_lowercase       (     __lowerCamelCase			:					Any		,__lowerCamelCase			:					str=0			)  ->   List[Any]:





     '''simple docstring'''





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

          UpperCamelCase__							:  Any								=     shave_segments(_UpperCAmelCase		,n_shave_prefix_segments=_UpperCAmelCase			)

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

     return mapping







def 						_lowercase       (     __lowerCamelCase			:					Union[str, Any]		,__lowerCamelCase			:					Optional[int]=0		,__lowerCamelCase			:					Union[str, Any]=False			)  ->   Dict:





     '''simple docstring'''





     UpperCamelCase__							:  List[str]								=     []
     for old_item in old_list:
          UpperCamelCase__							:  str								=     old_item

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

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

          UpperCamelCase__							:  List[Any]								=     shave_segments(_UpperCAmelCase		,n_shave_prefix_segments=_UpperCAmelCase			)
          mapping.append({'''old''': old_item, '''new''': new_item}			)

     return mapping







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Dict		,__lowerCamelCase			:					Tuple=None		,__lowerCamelCase			:					Tuple=None		,__lowerCamelCase			:					Dict=None			)  ->   Any:





     '''simple docstring'''





     assert isinstance(_UpperCAmelCase		,_UpperCAmelCase			), "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__							:  Optional[int]								=     old_checkpoint[path]
               UpperCamelCase__							:  Any								=     old_tensor.shape[0] // 3

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

               UpperCamelCase__							:  List[str]								=     old_tensor.shape[0] // config.get('''num_head_channels'''		,1			) // 3

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

               UpperCamelCase__							:  Any								=     query.reshape(_UpperCAmelCase			).squeeze()
               UpperCamelCase__							:  Any								=     key.reshape(_UpperCAmelCase			).squeeze()
               UpperCamelCase__							:  Optional[int]								=     value.reshape(_UpperCAmelCase			).squeeze()

     for path in paths:
          UpperCamelCase__							:  List[Any]								=     path['''new''']

          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          UpperCamelCase__							:  Union[str, Any]								=     new_path.replace('''down.'''		,'''down_blocks.'''			)
          UpperCamelCase__							:  Optional[int]								=     new_path.replace('''up.'''		,'''up_blocks.'''			)

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

          if "attentions" in new_path:
               UpperCamelCase__							:  Any								=     old_checkpoint[path['''old''']].squeeze()
          else:
               UpperCamelCase__							:  Union[str, Any]								=     old_checkpoint[path['''old''']]







def 						_lowercase       (     __lowerCamelCase			:					Optional[Any]		,__lowerCamelCase			:					int			)  ->   Union[str, Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Tuple								=     {}

     UpperCamelCase__							:  Any								=     checkpoint['''temb.dense.0.weight''']
     UpperCamelCase__							:  Tuple								=     checkpoint['''temb.dense.0.bias''']
     UpperCamelCase__							:  Tuple								=     checkpoint['''temb.dense.1.weight''']
     UpperCamelCase__							:  List[Any]								=     checkpoint['''temb.dense.1.bias''']

     UpperCamelCase__							:  Tuple								=     checkpoint['''norm_out.weight''']
     UpperCamelCase__							:  Optional[Any]								=     checkpoint['''norm_out.bias''']

     UpperCamelCase__							:  List[str]								=     checkpoint['''conv_in.weight''']
     UpperCamelCase__							:  Any								=     checkpoint['''conv_in.bias''']
     UpperCamelCase__							:  str								=     checkpoint['''conv_out.weight''']
     UpperCamelCase__							:  Union[str, Any]								=     checkpoint['''conv_out.bias''']

     UpperCamelCase__							:  Optional[Any]								=     len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''down''' in layer}			)
     UpperCamelCase__							:  Union[str, Any]								=     {
         layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(_UpperCAmelCase			)
     }

     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(_UpperCAmelCase			)}

     for i in range(_UpperCAmelCase			):
          UpperCamelCase__							:  List[str]								=     (i - 1) // (config['''layers_per_block'''] + 1)

          if any('''downsample''' in layer for layer in down_blocks[i]			):
               UpperCamelCase__							:  Any								=     checkpoint[
                   F'down.{i}.downsample.op.weight'
               ]
               UpperCamelCase__							:  Dict								=     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__							:  List[Any]								=     len(
                   {'''.'''.join(shave_segments(_UpperCAmelCase		,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(_UpperCAmelCase			)
               }

               if num_blocks > 0:
                    for j in range(config['''layers_per_block''']			):
                         UpperCamelCase__							:  Dict								=     renew_resnet_paths(blocks[j]			)
                         assign_to_checkpoint(_UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase			)

          if any('''attn''' in layer for layer in down_blocks[i]			):
               UpperCamelCase__							:  Optional[int]								=     len(
                   {'''.'''.join(shave_segments(_UpperCAmelCase		,2			).split('''.'''			)[:2]			) for layer in down_blocks[i] if '''attn''' in layer}			)
               UpperCamelCase__							:  List[Any]								=     {
                   layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                   for layer_id in range(_UpperCAmelCase			)
               }

               if num_attn > 0:
                    for j in range(config['''layers_per_block''']			):
                         UpperCamelCase__							:  int								=     renew_attention_paths(attns[j]			)
                         assign_to_checkpoint(_UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,config=_UpperCAmelCase			)

     UpperCamelCase__							:  List[Any]								=     [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__							:  int								=     [key for key in checkpoint if '''mid.attn_1''' in key]

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

     UpperCamelCase__							:  Dict								=     renew_resnet_paths(_UpperCAmelCase			)
     assign_to_checkpoint(
         _UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]		,)

     UpperCamelCase__							:  Dict								=     renew_attention_paths(_UpperCAmelCase		,in_mid=_UpperCAmelCase			)
     assign_to_checkpoint(
         _UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]		,)

     for i in range(_UpperCAmelCase			):
          UpperCamelCase__							:  Optional[Any]								=     num_up_blocks - 1 - i

          if any('''upsample''' in layer for layer in up_blocks[i]			):
               UpperCamelCase__							:  Optional[Any]								=     checkpoint[
                   F'up.{i}.upsample.conv.weight'
               ]
               UpperCamelCase__							:  Dict								=     checkpoint[F'up.{i}.upsample.conv.bias']

          if any('''block''' in layer for layer in up_blocks[i]			):
               UpperCamelCase__							:  Dict								=     len(
                   {'''.'''.join(shave_segments(_UpperCAmelCase		,2			).split('''.'''			)[:2]			) for layer in up_blocks[i] if '''block''' in layer}			)
               UpperCamelCase__							:  int								=     {
                   layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(_UpperCAmelCase			)
               }

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

          if any('''attn''' in layer for layer in up_blocks[i]			):
               UpperCamelCase__							:  int								=     len(
                   {'''.'''.join(shave_segments(_UpperCAmelCase		,2			).split('''.'''			)[:2]			) for layer in up_blocks[i] if '''attn''' in layer}			)
               UpperCamelCase__							:  List[Any]								=     {
                   layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(_UpperCAmelCase			)
               }

               if num_attn > 0:
                    for j in range(config['''layers_per_block'''] + 1			):
                         UpperCamelCase__							:  str								=     {'''old''': F'up_blocks.{i}', '''new''': F'up_blocks.{block_id}'}
                         UpperCamelCase__							:  Dict								=     renew_attention_paths(attns[j]			)
                         assign_to_checkpoint(_UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,additional_replacements=[replace_indices]			)

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







def 						_lowercase       (     __lowerCamelCase			:					Union[str, Any]		,__lowerCamelCase			:					Tuple			)  ->   str:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[int]								=     {}

     UpperCamelCase__							:  str								=     checkpoint['''encoder.norm_out.weight''']
     UpperCamelCase__							:  Optional[Any]								=     checkpoint['''encoder.norm_out.bias''']

     UpperCamelCase__							:  int								=     checkpoint['''encoder.conv_in.weight''']
     UpperCamelCase__							:  str								=     checkpoint['''encoder.conv_in.bias''']
     UpperCamelCase__							:  str								=     checkpoint['''encoder.conv_out.weight''']
     UpperCamelCase__							:  Optional[Any]								=     checkpoint['''encoder.conv_out.bias''']

     UpperCamelCase__							:  List[str]								=     checkpoint['''decoder.norm_out.weight''']
     UpperCamelCase__							:  str								=     checkpoint['''decoder.norm_out.bias''']

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

     UpperCamelCase__							:  int								=     len({'''.'''.join(layer.split('''.'''			)[:3]			) for layer in checkpoint if '''down''' in layer}			)
     UpperCamelCase__							:  Any								=     {
         layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(_UpperCAmelCase			)
     }

     UpperCamelCase__							:  Tuple								=     len({'''.'''.join(layer.split('''.'''			)[:3]			) for layer in checkpoint if '''up''' in layer}			)
     UpperCamelCase__							:  Tuple								=     {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(_UpperCAmelCase			)}

     for i in range(_UpperCAmelCase			):
          UpperCamelCase__							:  Any								=     (i - 1) // (config['''layers_per_block'''] + 1)

          if any('''downsample''' in layer for layer in down_blocks[i]			):
               UpperCamelCase__							:  Tuple								=     checkpoint[
                   F'encoder.down.{i}.downsample.conv.weight'
               ]
               UpperCamelCase__							:  Optional[Any]								=     checkpoint[
                   F'encoder.down.{i}.downsample.conv.bias'
               ]

          if any('''block''' in layer for layer in down_blocks[i]			):
               UpperCamelCase__							:  List[str]								=     len(
                   {'''.'''.join(shave_segments(_UpperCAmelCase		,3			).split('''.'''			)[:3]			) for layer in down_blocks[i] if '''block''' in layer}			)
               UpperCamelCase__							:  List[Any]								=     {
                   layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                   for layer_id in range(_UpperCAmelCase			)
               }

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

          if any('''attn''' in layer for layer in down_blocks[i]			):
               UpperCamelCase__							:  Optional[int]								=     len(
                   {'''.'''.join(shave_segments(_UpperCAmelCase		,3			).split('''.'''			)[:3]			) for layer in down_blocks[i] if '''attn''' in layer}			)
               UpperCamelCase__							:  Union[str, Any]								=     {
                   layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                   for layer_id in range(_UpperCAmelCase			)
               }

               if num_attn > 0:
                    for j in range(config['''layers_per_block''']			):
                         UpperCamelCase__							:  Dict								=     renew_attention_paths(attns[j]			)
                         assign_to_checkpoint(_UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,config=_UpperCAmelCase			)

     UpperCamelCase__							:  Optional[int]								=     [key for key in checkpoint if '''mid.block_1''' in key]
     UpperCamelCase__							:  Union[str, Any]								=     [key for key in checkpoint if '''mid.block_2''' in key]
     UpperCamelCase__							:  str								=     [key for key in checkpoint if '''mid.attn_1''' in key]

     # Mid new 2
     UpperCamelCase__							:  Optional[int]								=     renew_resnet_paths(_UpperCAmelCase			)
     assign_to_checkpoint(
         _UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]		,)

     UpperCamelCase__							:  Optional[Any]								=     renew_resnet_paths(_UpperCAmelCase			)
     assign_to_checkpoint(
         _UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]		,)

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

     for i in range(_UpperCAmelCase			):
          UpperCamelCase__							:  Union[str, Any]								=     num_up_blocks - 1 - i

          if any('''upsample''' in layer for layer in up_blocks[i]			):
               UpperCamelCase__							:  Union[str, Any]								=     checkpoint[
                   F'decoder.up.{i}.upsample.conv.weight'
               ]
               UpperCamelCase__							:  Any								=     checkpoint[
                   F'decoder.up.{i}.upsample.conv.bias'
               ]

          if any('''block''' in layer for layer in up_blocks[i]			):
               UpperCamelCase__							:  Tuple								=     len(
                   {'''.'''.join(shave_segments(_UpperCAmelCase		,3			).split('''.'''			)[:3]			) for layer in up_blocks[i] if '''block''' in layer}			)
               UpperCamelCase__							:  List[Any]								=     {
                   layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(_UpperCAmelCase			)
               }

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

          if any('''attn''' in layer for layer in up_blocks[i]			):
               UpperCamelCase__							:  Dict								=     len(
                   {'''.'''.join(shave_segments(_UpperCAmelCase		,3			).split('''.'''			)[:3]			) for layer in up_blocks[i] if '''attn''' in layer}			)
               UpperCamelCase__							:  Any								=     {
                   layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(_UpperCAmelCase			)
               }

               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(_UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,additional_replacements=[replace_indices]			)

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

     return new_checkpoint


if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE :					Tuple			=	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.""")

   _SCREAMING_SNAKE_CASE :					Union[str, Any]			=	parser.parse_args()
   _SCREAMING_SNAKE_CASE :					List[Any]			=	torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      _SCREAMING_SNAKE_CASE :					Optional[int]			=	json.loads(f.read())

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

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

   if config["_class_name"] == "VQModel":
      _SCREAMING_SNAKE_CASE :					int			=	VQModel(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
      _SCREAMING_SNAKE_CASE :					Optional[Any]			=	AutoencoderKL(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   else:
      _SCREAMING_SNAKE_CASE :					str			=	UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

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

      _SCREAMING_SNAKE_CASE :					Tuple			=	DDPMPipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)





import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


_SCREAMING_SNAKE_CASE :					Optional[int]			=	2 << 20  # 2mb
_SCREAMING_SNAKE_CASE :					Optional[Any]			=	get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def 	__lowercase(     self					:   List[str]   )  -> List[Any]:
           UpperCamelCase__							:  int								=     self._options.get_all_options().get('''runner'''   )
           return runner in [None, "DirectRunner", "PortableRunner"]







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[str]=False			)  ->   Optional[int]:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     FileSystems
     if fs.exists(__lowerCamelCase			):
          if force_upload:
               logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'			)
          else:
               logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'			)
               return
     with fs.create(__lowerCamelCase			) as remote_file:
          with open(__lowerCamelCase		,'''rb'''			) as local_file:
               UpperCamelCase__							:  List[Any]								=     local_file.read(__lowerCamelCase			)
               while chunk:
                    remote_file.write(__lowerCamelCase			)
                    UpperCamelCase__							:  Union[str, Any]								=     local_file.read(__lowerCamelCase			)







def 						_lowercase       (     __lowerCamelCase			:					Any		,__lowerCamelCase			:					int		,__lowerCamelCase			:					str=False			)  ->   Tuple:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[int]								=     FileSystems
     if os.path.exists(__lowerCamelCase			):
          if force_download:
               logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'			)
          else:
               logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'			)
               return
     with fs.open(__lowerCamelCase			) as remote_file:
          with open(__lowerCamelCase		,'''wb'''			) as local_file:
               UpperCamelCase__							:  Any								=     remote_file.read(__lowerCamelCase			)
               while chunk:
                    local_file.write(__lowerCamelCase			)
                    UpperCamelCase__							:  Dict								=     remote_file.read(__lowerCamelCase			)




