code: 
  
  
  import numpy as np
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						List[Any]="utf-8"       ,							UpperCamelCase__  :						Optional[Any]="surrogatepass"		):
         _A   : Dict       					=				bin(int.from_bytes(text.encode(UpperCamelCase__       ,							UpperCamelCase__		)       ,							"big"		)		)[2:]
         return bits.zfill(8 * ((len(UpperCamelCase__		) + 7) // 8)		)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Tuple       ,							UpperCamelCase__  :						List[Any]="utf-8"       ,							UpperCamelCase__  :						Tuple="surrogatepass"		):
         _A   : int       					=				int(UpperCamelCase__       ,							2		)
         return n.to_bytes((n.bit_length() + 7) // 8       ,							"big"		).decode(UpperCamelCase__       ,							UpperCamelCase__		) or "\0"
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]		):
         if size_par + len(UpperCamelCase__		) <= 2**size_par - (len(UpperCamelCase__		) - 1):
                raise ValueError("size of parity don't match with size of data"		)
  
         _A   : int       					=				[]
         _A   : str       					=				[]
         _A   : int       					=				[bin(UpperCamelCase__		)[2:] for x in range(1       ,							size_par + len(UpperCamelCase__		) + 1		)]
  
         # sorted information data for the size of the output data
         _A   : Tuple       					=				[]
         # data position template + parity
         _A   : Any       					=				[]
         # parity bit counter
         _A   : Optional[Any]       					=				0
         # counter position of data bits
         _A   : Dict       					=				0
  
         for x in range(1       ,							size_par + len(UpperCamelCase__		) + 1		):
                # Performs a template of bit positions - who should be given,
                # and who should be parity
                if qtd_bp < size_par:
                       if (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
                              data_out_gab.append("P"		)
                              _A   : int       					=				qtd_bp + 1
                       else:
                              data_out_gab.append("D"		)
                else:
                       data_out_gab.append("D"		)
  
                # Sorts the data to the new output size
                if data_out_gab[-1] == "D":
                       data_ord.append(data[cont_data]		)
                       cont_data += 1
                else:
                       data_ord.append(UpperCamelCase__		)
  
      # Calculates parity
         _A   : str       					=				0  # parity bit counter
         for bp in range(1       ,							size_par + 1		):
                # Bit counter one for a given parity
                _A   : Any       					=				0
                # counter to control the loop reading
                _A   : Optional[Any]       					=				0
                for x in data_ord:
                       if x is not None:
                              try:
                                     _A   : Optional[int]       					=				(bin_pos[cont_loop])[-1 * (bp)]
                              except IndexError:
                                     _A   : Optional[Any]       					=				"0"
                              if aux == "1" and x == "1":
                                     cont_bo += 1
                       cont_loop += 1
                parity.append(cont_bo % 2		)
  
                qtd_bp += 1
  
         # Mount the message
         _A   : Any       					=				0  # parity bit counter
         for x in range(0       ,							size_par + len(UpperCamelCase__		)		):
                if data_ord[x] is None:
                       data_out.append(str(parity[cont_bp]		)		)
                       cont_bp += 1
                else:
                       data_out.append(data_ord[x]		)
  
         return data_out
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						str		):
         _A   : Optional[Any]       					=				[]
         # Parity bit counter
         _A   : List[Any]       					=				0
         # Counter p data bit reading
         _A   : int       					=				0
         # list of parity received
         _A   : List[str]       					=				[]
         _A   : Optional[Any]       					=				[]
  
         for x in range(1       ,							len(UpperCamelCase__		) + 1		):
                # Performs a template of bit positions - who should be given,
                #  and who should be parity
                if qtd_bp < size_par and (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
                       data_out_gab.append("P"		)
                       _A   : Optional[Any]       					=				qtd_bp + 1
                else:
                       data_out_gab.append("D"		)
  
                # Sorts the data to the new output size
                if data_out_gab[-1] == "D":
                       data_output.append(data[cont_data]		)
                else:
                       parity_received.append(data[cont_data]		)
                cont_data += 1
  
         # -----------calculates the parity with the data
         _A   : Union[str, Any]       					=				[]
         _A   : List[Any]       					=				[]
         _A   : Dict       					=				[bin(UpperCamelCase__		)[2:] for x in range(1       ,							size_par + len(UpperCamelCase__		) + 1		)]
  
         #  sorted information data for the size of the output data
         _A   : Union[str, Any]       					=				[]
         # Data position feedback + parity
         _A   : Union[str, Any]       					=				[]
         # Parity bit counter
         _A   : Dict       					=				0
         # Counter p data bit reading
         _A   : Union[str, Any]       					=				0
  
         for x in range(1       ,							size_par + len(UpperCamelCase__		) + 1		):
                # Performs a template position of bits - who should be given,
                # and who should be parity
                if qtd_bp < size_par and (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
                       data_out_gab.append("P"		)
                       _A   : Union[str, Any]       					=				qtd_bp + 1
                else:
                       data_out_gab.append("D"		)
  
                # Sorts the data to the new output size
                if data_out_gab[-1] == "D":
                       data_ord.append(data_output[cont_data]		)
                       cont_data += 1
                else:
                       data_ord.append(UpperCamelCase__		)
  
      # Calculates parity
         _A   : int       					=				0  # parity bit counter
         for bp in range(1       ,							size_par + 1		):
                # Bit counter one for a certain parity
                _A   : Dict       					=				0
                # Counter to control loop reading
                _A   : Dict       					=				0
                for x in data_ord:
                       if x is not None:
                              try:
                                     _A   : Optional[int]       					=				(bin_pos[cont_loop])[-1 * (bp)]
                              except IndexError:
                                     _A   : Union[str, Any]       					=				"0"
                              if aux == "1" and x == "1":
                                     cont_bo += 1
                       cont_loop += 1
                parity.append(str(cont_bo % 2		)		)
  
                qtd_bp += 1
  
         # Mount the message
         _A   : List[str]       					=				0  # Parity bit counter
         for x in range(0       ,							size_par + len(UpperCamelCase__		)		):
                if data_ord[x] is None:
                       data_out.append(str(parity[cont_bp]		)		)
                       cont_bp += 1
                else:
                       data_out.append(data_ord[x]		)
  
         _A   : Tuple       					=				parity_received == parity
         return data_output, ack
  
style_context: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
         _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
         _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
         _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data
  
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
         _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
         _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data
  
         _A   : str       					=				4
  
         def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
                _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
                _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
                _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
                _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
                _A   : Union[str, 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:
                       _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                       _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks		):
                _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       if has_attentions:
                              set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                              module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                       set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                       _A   : 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]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
  
         for i, block in enumerate(new_model_architecture.up_blocks		):
                _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                if has_attentions:
                       set_attention_weights(
                           block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                       module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                       _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
  
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
         module_index += 1
         _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				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.',
      )
  
      lowerCAmelCase__   	=				parser.parse_args()
  
      lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')
  
      with open(args.config_file) as f:
          lowerCAmelCase__   	=				json.loads(f.read())
  
      lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )
  
      if "sde" in config:
          del config["sde"]
  
      lowerCAmelCase__   	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      try:
          lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
          lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
          model.save_pretrained(args.dump_path)
  