code: import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      UNetaDModel(**lowercase_				)
      lowerCAmelCase				:			Optional[int]    			=      checkpoint['all_modules.0.W'].data
      lowerCAmelCase				:			List[Any]    			=      checkpoint['all_modules.0.W'].data
      lowerCAmelCase				:			str    			=      checkpoint['all_modules.1.weight'].data
      lowerCAmelCase				:			List[Any]    			=      checkpoint['all_modules.1.bias'].data
  
      lowerCAmelCase				:			str    			=      checkpoint['all_modules.2.weight'].data
      lowerCAmelCase				:			Optional[Any]    			=      checkpoint['all_modules.2.bias'].data
  
      lowerCAmelCase				:			Any    			=      checkpoint['all_modules.3.weight'].data
      lowerCAmelCase				:			Optional[Any]    			=      checkpoint['all_modules.3.bias'].data
  
      lowerCAmelCase				:			Optional[int]    			=      checkpoint[list(checkpoint.keys()				)[-4]].data
      lowerCAmelCase				:			Any    			=      checkpoint[list(checkpoint.keys()				)[-3]].data
      lowerCAmelCase				:			Optional[Any]    			=      checkpoint[list(checkpoint.keys()				)[-2]].data
      lowerCAmelCase				:			List[str]    			=      checkpoint[list(checkpoint.keys()				)[-1]].data
  
      lowerCAmelCase				:			Any    			=      4
  
      def set_attention_weights(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				):
          lowerCAmelCase				:			Optional[Any]    			=      old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
          lowerCAmelCase				:			Tuple    			=      old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
          lowerCAmelCase				:			Union[str, Any]    			=      old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
          lowerCAmelCase				:			Any    			=      old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
          lowerCAmelCase				:			List[str]    			=      old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
          lowerCAmelCase				:			Any    			=      old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
          lowerCAmelCase				:			Optional[Any]    			=      old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
          lowerCAmelCase				:			int    			=      old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
          lowerCAmelCase				:			int    			=      old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
          lowerCAmelCase				:			Tuple    			=      old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
      def set_resnet_weights(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				):
          lowerCAmelCase				:			List[Any]    			=      old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
          lowerCAmelCase				:			List[str]    			=      old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
          lowerCAmelCase				:			Any    			=      old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
          lowerCAmelCase				:			Optional[int]    			=      old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
          lowerCAmelCase				:			Optional[int]    			=      old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
          lowerCAmelCase				:			Any    			=      old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
          lowerCAmelCase				:			Union[str, Any]    			=      old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
          lowerCAmelCase				:			Union[str, Any]    			=      old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
          lowerCAmelCase				:			Any    			=      old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
          lowerCAmelCase				:			Optional[Any]    			=      old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
  
          if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
              lowerCAmelCase				:			int    			=      old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
              lowerCAmelCase				:			Dict    			=      old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
      for i, block in enumerate(new_model_architecture.downsample_blocks				):
          lowerCAmelCase				:			List[str]    			=      hasattr(lowercase_,      'attentions'				)
          for j in range(len(block.resnets				)				):
              set_resnet_weights(block.resnets[j],      lowercase_,      lowercase_				)
              module_index += 1
              if has_attentions:
                  set_attention_weights(block.attentions[j],      lowercase_,      lowercase_				)
                  module_index += 1
  
          if hasattr(lowercase_,      'downsamplers'				) and block.downsamplers is not None:
              set_resnet_weights(block.resnet_down,      lowercase_,      lowercase_				)
              module_index += 1
              lowerCAmelCase				:			str    			=      checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
              lowerCAmelCase				:			Optional[int]    			=      checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
              module_index += 1
  
      set_resnet_weights(new_model_architecture.mid_block.resnets[0],      lowercase_,      lowercase_				)
      module_index += 1
      set_attention_weights(new_model_architecture.mid_block.attentions[0],      lowercase_,      lowercase_				)
      module_index += 1
      set_resnet_weights(new_model_architecture.mid_block.resnets[1],      lowercase_,      lowercase_				)
      module_index += 1
  
      for i, block in enumerate(new_model_architecture.up_blocks				):
          lowerCAmelCase				:			Optional[Any]    			=      hasattr(lowercase_,      'attentions'				)
          for j in range(len(block.resnets				)				):
              set_resnet_weights(block.resnets[j],      lowercase_,      lowercase_				)
              module_index += 1
          if has_attentions:
              set_attention_weights(
                  block.attentions[0],      lowercase_,      lowercase_				)  # why can there only be a single attention layer for up?
              module_index += 1
  
          if hasattr(lowercase_,      'resnet_up'				) and block.resnet_up is not None:
              lowerCAmelCase				:			int    			=      checkpoint[f"all_modules.{module_index}.weight"].data
              lowerCAmelCase				:			Dict    			=      checkpoint[f"all_modules.{module_index}.bias"].data
              module_index += 1
              lowerCAmelCase				:			Tuple    			=      checkpoint[f"all_modules.{module_index}.weight"].data
              lowerCAmelCase				:			str    			=      checkpoint[f"all_modules.{module_index}.bias"].data
              module_index += 1
              set_resnet_weights(block.resnet_up,      lowercase_,      lowercase_				)
              module_index += 1
  
      lowerCAmelCase				:			int    			=      checkpoint[f"all_modules.{module_index}.weight"].data
      lowerCAmelCase				:			int    			=      checkpoint[f"all_modules.{module_index}.bias"].data
      module_index += 1
      lowerCAmelCase				:			Tuple    			=      checkpoint[f"all_modules.{module_index}.weight"].data
      lowerCAmelCase				:			List[str]    			=      checkpoint[f"all_modules.{module_index}.bias"].data
  
      return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
   __A   :     int      =			argparse.ArgumentParser()
  
   parser.add_argument(
       '''--checkpoint_path''',
       default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
       type=str,
       required=False,
       help='''Path to the checkpoint to convert.''',
   )
  
   parser.add_argument(
       '''--config_file''',
       default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
       type=str,
       required=False,
       help='''The config json file corresponding to the architecture.''',
   )
  
   parser.add_argument(
       '''--dump_path''',
       default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
       type=str,
       required=False,
       help='''Path to the output model.''',
   )
  
   __A   :     Union[str, Any]      =			parser.parse_args()
  
   __A   :     Union[str, Any]      =			torch.load(args.checkpoint_path, map_location='''cpu''')
  
   with open(args.config_file) as f:
    __A   :     Union[str, Any]      =			json.loads(f.read())
  
   __A   :     Optional[int]      =			convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )
  
   if "sde" in config:
    del config["sde"]
  
   __A   :     Any      =			UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)
  
   try:
    __A   :     List[str]      =			ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  
    __A   :     int      =			ScoreSdeVePipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
    model.save_pretrained(args.dump_path)
  
  
  
  
  
  
style_context: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      [
          [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
      lowerCAmelCase				:			str    			=      [
          ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
  
      for i in range(1,      len_source_seq + 1				):
          lowerCAmelCase				:			int    			=      i * delete_cost
          lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"
  
      for i in range(1,      len_destination_seq + 1				):
          lowerCAmelCase				:			Tuple    			=      i * insert_cost
          lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"
  
      for i in range(1,      len_source_seq + 1				):
          for j in range(1,      len_destination_seq + 1				):
              if source_seq[i - 1] == destination_seq[j - 1]:
                  lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                  lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
              else:
                  lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)
  
              if costs[i - 1][j] + delete_cost < costs[i][j]:
                  lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"
  
              if costs[i][j - 1] + insert_cost < costs[i][j]:
                  lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                  lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"
  
      return costs, ops
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if i == 0 and j == 0:
          return []
      else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
              lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
          elif ops[i][j][0] == "D":
              lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
              seq.append(ops[i][j]				)
              return seq
          else:
              lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
  
  
  if __name__ == "__main__":
   __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)
  
   __A   :     Optional[int]      =			len(operations)
   __A   :     Optional[Any]      =			len(operations[0])
   __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)
  
   __A   :     int      =			list('''Python''')
   __A   :     Tuple      =			0
   __A   :     Tuple      =			0
  
   with open('''min_cost.txt''', '''w''') as file:
    for op in sequence:
     print(''''''.join(string))
  
     if op[0] == "C":
      file.write('''%-16s''' % '''Copy %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost -= 1
     elif op[0] == "R":
      __A   :     Any      =			op[2]
  
      file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
      file.write('''\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 1
     elif op[0] == "D":
      string.pop(i)
  
      file.write('''%-16s''' % '''Delete %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
     else:
      string.insert(i, op[1])
  
      file.write('''%-16s''' % '''Insert %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
  
     i += 1
  
    print(''''''.join(string))
    print('''Cost: ''', cost)
  
    file.write('''\r\nMinimum cost: ''' + str(cost))
  
  
  
  
  
  